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Secretary

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Marshall Smith

Acting Assistant Secretary

National Center for Education Statistics

Pascal D. Forgione, Jr.

Commissioner

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iii

FOREWORD

The National Center for Education Statistics has a continuing interest in understanding new and

evolving analytic methods as well as thoroughly understanding the data we collect. During the course of its

participation in the International Reading Literacy Study, it became apparent that a number of researchers

from other countries shared common interests with the U.S. National Research team. Consequently, a

collaborative effort to further analyze specific portions of the data was undertaken with the intent of

learning more about both the particular issues as well as research methods. The papers presented here

were commissioned by NCES to promote the exchange of ideas among researchers; as such, they do not all

adhere to standard NCES practices. Because the views and the analytic methods represent a variety of

research traditions, we expect that they will provoke discussions, replications, replies, and refutations. If

so, the publication will have accomplished its task.

Jeanne E. Griffith

Associate Commissiooner

Data Development and Longitudinal Studies

Eugene H. Owen

Director

International Activities Program
iv
v

ACKNOWLEDGMENTS

This volume is the fourth to be released by the National Center for Education Statistics (NCES)

concerning the IEA Reading Literacy Study. The contributions to this present volume have cumulated the

efforts made nationally and internationally in connection with the study as a whole and the previous NCES

reports. The following contributions are of special note in connection with this volume.

The International Reading Literacy Study, which provided the basis for this report, was conducted

under the auspices of the International Association for the Evaluation of Educational Achievement (IEA).

The International Steering Committee, the International Coordinating Center, and the National Research

Coordinators of each of the participating countries developed the assessment instruments, assessment

procedures, and scaled scores used to report the results and oversaw the conduct of the study

internationally.

Within the United States, the research reported in this volume was initiated, encouraged, and

sponsored by the International Activities Group of the Data Development Division of NCES. Marilyn

Binkley was the U.S. National Research Coordinator and convener of the working group of National

Research Coordinators, whose deliberations gave rise to much of the research reported in this volume.

Overall direction for this activity was provided by Jeanne Griffith, Associate Commissioner for Data

Development throughout most of the life of the project, while Eugene Owen, Chief of the International

Activities Group, provided constant support and wise guidance.

A number of individuals contributed significantly to the various chapters of this report. Stephen

Roey and Marianne Winglee of Westat, Inc., provided considerable analytic and technical contributions to

chapters additional to those that they authored. Christina Yen and Paul Zador, also from Westat, also

provided considerable analytic and technical assistance on the project.

Stephen Norris and Linda Phillips of Memorial University, Newfoundland, Canada, provided expert

review and advice for the chapter Teaching Reading in the United States and Finland.

Strong and supportive overall technical review and guidance was provided by Susan Ahmed and

Mary Frase of NCES. Their efforts provided some measure of consistency and quality of technical

approach across this diverse set of manuscripts. They also provided detailed reviews of each manuscript

individually.

Each of the authors received funding support from his/her institution of affiliation. In addition,

Rainer Lehmann’s contribution was supported in part by a grant from the German Federal Ministry of

Education and Science. In no case are the views expressed in this report necessarily those of the

sponsoring institutions.

Expert technical editing was carried out by Carol Litman at Westat and Anita Wright of the

Educational Statistical Services Institute. Document preparation was the conducted by Gil Leigh and

Sylvie Warren of Westat.
vi
vii

TABLE OF CONTENTS

Page

Foreword ...................................................................................................... iii

Acknowledgments......................................................................................... v

Introduction .................................................................................................. 1

Section A Outcomes in Literacy Achievement............................................................. 3

Social Inequality, Social Segregation, and Their Relationship to Reading

Literacy in 22 Countries.............................................................................. 5

Stephen W. Raudenbush, Yuk Fai Cheong, and Randall P. Fotiu

1. Introduction...................................................................................... 5

2. The Anatomy of Social Inequality in Literacy.................................... 9

3. A Primary Analysis and a Series of Sensitivity Analyses................... 12

4. Data for the Primary Analysis........................................................... 12

5. Statistical Methodology .................................................................... 17

6. Results: Primary Analysis................................................................ 24

7. Sensitivity Analyses.......................................................................... 41

8. Final Conclusions............................................................................. 46

References........................................................................................ 50

Appendix: Empirical Bayes and Bayes Estimation Theory for

 Two-Level Models with Normal Errors........................................... 53

A Nine-Country Study: What Were the Differences Between the Lowand High-Performing Students in the IEA Reading Literacy Study?......... 63

Karin Taube and Jan Mejding

1. Method............................................................................................. 63

2. Results............................................................................................. 69

3. Conclusion ....................................................................................... 76

References........................................................................................ 100

Reading Literacy Among Immigrant Students in the United States and

the Former West Germany ......................................................................... 101

Rainer Lehmann

1. Introduction...................................................................................... 101

2. Definitions of “Immigrant Students” ................................................. 102

3. The Problem: Which Factors Affect the Achievement Gap Between

Immigrant and Nonimmigrant Students?............................................ 104

4. The Relative Social Position of Immigrant Students........................... 106

5. Differential Reading Achievement Between Immigrant Groups.......... 108

6. Determinants of the Achievement Gap Between Immigrant and

Nonimmigrant Students .................................................................... 110

7. Conclusion ....................................................................................... 112

References........................................................................................ 114
viii

TABLE OF CONTENTS (continued)

Page

Comparison of Reading Literacy Across Languages in Spanish Fourth

Graders ....................................................................................................... 115

Guillermo A. Gil, Keith Rust, and Marianne Winglee

1. Introduction...................................................................................... 115

2. Characteristics of the Spanish Languages.......................................... 116

3. Objectives of the Regional Study....................................................... 119

4. Samples of Schools and Students by Autonomous Community .......... 119

5. Reading Proficiency in Castilian and the Languages of the

Autonomous Communities................................................................ 121

6. Discussion........................................................................................ 133

References........................................................................................ 135

Section B Instructional Practices in Reading .............................................................. 137

Teaching Reading in the United States and Finland ................................... 139

Marilyn R. Binkley and Pirjo Linnakylä

1. Introduction...................................................................................... 139

2. Developing an Overview of Reading and Reading Instruction

Theories........................................................................................... 140

3. Exploring the Structure of Teachers’ Beliefs, Instructional

Activities, and Assessment Practices................................................. 150

4 Comparing Reading Instruction in the United States

and Finland ...................................................................................... 174

References........................................................................................ 177

A Nine-Country Study: How Do Teachers Teach Reading to 9-YearOlds? ........................................................................................................... 181

Emilie Barrier and Daniel Robin

1. Method............................................................................................. 181

2. Dispersion Factors............................................................................ 182

3. Typology of Teaching Practice.......................................................... 184

4. Conclusions...................................................................................... 188

References........................................................................................ 190
ix

TABLE OF CONTENTS (continued)

Page

Section C School Climate............................................................................................. 191

Consistencies in the Quality of School Life ................................................. 193

Trevor Williams and Stephen Roey

1. Affective Outcomes of Schooling ...................................................... 193

2. Quality of Life, Happiness, and Well-Being ...................................... 194

3. Quality of Life and Quality of School Life ........................................ 194

4. Domains of School Life .................................................................... 195

5. Models and Items ............................................................................. 196

6. Consistencies in the Quality of School Life........................................ 197

7. Statistically Consistent Latent Structures.......................................... 197

8. Conclusion ....................................................................................... 200

References........................................................................................ 201

Quality of School Life in the Finnish- and Swedish-Speaking Schools

in Finland..................................................................................................... 203

Pirjo Linnakylä and Viking Brunell

1. Context of the Study......................................................................... 203

2. Aims of the Article ........................................................................... 204

3. Defining the Quality of School Life................................................... 205

4. Method............................................................................................. 206

5. Results of the Assessment................................................................. 209

6. Conclusions...................................................................................... 219

References........................................................................................ 221

Analysis of the Williams and Batten Questionnaire on the Quality of

School Life in Spain..................................................................................... 223

Guillermo A. Gil

1. Introduction...................................................................................... 223

2. Williams and Batten’s Quality of School Life Scale........................... 223

3. Study of the Quality of School Life Scale in Spain ............................ 224

4. Discussion of Results and Conclusions.............................................. 238

References........................................................................................ 240
1

INTRODUCTION

The IEA International Reading Literacy Study was initiated in 1988 as a comparative study of the

reading literacy of 9- and 14-year-olds in the schools of 32 nations. The main assessments were

administered during the period October 1990 through April 1991, and the results were reported in 1992 and

thereafter in several international and national reports.

Over the several years that separate designing such a study from reporting about it, the principal

investigators within the various nations meet to discuss the form and content of the study as a whole and to

comment on the findings. During these meetings, they will often raise issues of importance to themselves as

researchers and more generally to education within their respective nations. Inevitably, informal groups

coalesce around common interests and problems. The papers in this volume arose in such a situation, and

since they contribute toward meeting one of the aims of the United States’ study—placing the U.S. results

in an international perspective—the National Center for Education Statistics provided support for the

development and publication of the analyses.

The nations of primary focus here are Denmark, Finland, France, the former West Germany, Italy,

Spain, Sweden, Switzerland, and the United States. The papers that emerged from the work of this group

of researchers address issues in three broad areas: factors related to variation in literacy outcomes, both

across and within countries; the teaching of reading; and the quality of life in schools. The papers in this

report are organized into three sections along these lines.

The first paper in the report presents an analytic approach that cuts across the group of countries

that participated in the Reading Literacy Study. In this paper, Raudenbush, Cheong, and Fotiu of

Michigan State University develop a statistical model and its application simultaneously to investigate the

effects on literacy of social inequality manifested within societies, schools, and individuals. They use data

from 22 countries to investigate these issues, focusing their conclusions on the place of the United States

among the nations in terms of the level of social inequality identified on these dimensions and its effects on

reading literacy. A second paper to focus on issues of inequality is by Taube and Mejding, the national

research coordinators for Sweden and Denmark, respectively. They explore the data from all nine nations

listed above in an attempt to answer one of the central issues in schooling—what it is that distinguishes

low-performing students from their high-performing age peers.

Lehmann’s paper on aspects of literacy outcomes is a comparison of the reading literacy of

immigrant students in the United States and in the former West Germany, as well as the source of

differences between immigrants and nonimmigrants. The focus is on immigrant students whose first

language is not the language of their adopted country (English and German, respectively). Lehmann finds

persistent differences arising from particular aspects of family and schooling—differences that are greater

in Germany than in the United States. The Gil, Rust, and Winglee analyses address questions about the

literacy level and its correlates of three regional languages among Spanish students. Through the use of

linear regression models, literacy in each region in the regional language is compared with literacy in the

national language, Castilian. In general, the authors find that proficiency in the national language is much

more strongly associated with student individual and family background characteristics measured in the

study than is proficiency in each of the regional languages.

The second section on instructional practices in reading contains two papers. The analyses by

Binkley and Linnakylä address the extent to which reading teachers, and the teaching of reading, varies

between the United States and Finland. They focus on the origins of these differences in the nature of the
2

reading theories teachers hold. The authors develop this theoretical basis in some detail and link it to

teachers’ beliefs and instructional practices. The paper by Barrier and Robin of France addresses the

extent to which the teaching of reading varies within and between all nine countries. They extend these

analyses to identify the dominant teaching styles in the various nations and the commonalties across

nations.

The focus of the final three papers is the quality of life in schools. While this matter almost certainly

has some bearing on reading literacy, the authors examine the subject as an educational issue in its own

right, taking advantage of the fact that a multi-item scale was included in the student questionnaire for 14-

year-olds. Williams and Roey explore the latent structure of the measure in eight nations, concluding from

their analyses that students in Denmark, Finland, France, Germany, Italy, Spain, Switzerland, and the

United States perceive the qualities of schooling in much the same way. It appears that some aspects of

schooling elicit the same kinds of affective responses in students no matter what the country. Linnakylä

and Brunell explore the quality of school life in each of Finland’s two school systems, one serving the

Finnish-speaking majority and the other catering to the Swedish-speaking minority, generally seen as the

socioeconomically advantaged group. The authors make comparisons between the two school systems,

with other Nordic countries, and with United States and German schools to arrive at the conclusion that

student dissatisfaction may be a problem in the Finnish-speaking school system. This finding is of

particular interest given the high achievement of Finnish students relative to those in the other participating

nations. Finally, Gil focuses on whether cultural differences between the four language groups in Spain are

reflected in different patterns of affective responses to schooling. His basic conclusion is that, in general,

all Spanish students have similar reactions to what is good and bad about life in schools.

In all, the nine papers provide an interesting view of issues regarding reading literacy that are of

concern to all nations engaged in the teaching of reading and in the amelioration of persistent inequalities in

literacy. These papers also provide a view of the statistical complexities that are required to mirror the

substantive complexities of cross-national analyses. Finally, they show the results of a mutual interest in

the affective outcomes of schooling, an interest that arose serendipitously during the course of collaboration

in this cross-national study of reading literacy.
3

SECTION A. OUTCOMES IN LITERACY ACHIEVEMENT

• Social Inequality, Social Segregation, and Their Relationship to Reading Literacy in 22

Countries

Stephen W. Raudenbush, Yuk Fai Cheong, and Randall P. Fotiu

• A Nine-Country Study: What Were the Differences Between the Low- and High-Performing

Students in the IEA Reading Literacy Study?

Karin Taube and Jan Mejding

• Reading Literacy Among Immigrant Students in the United States and the Former West

Germany

Rainer Lehmann

• Comparison of Reading Literacy Across Languages in Spanish Fourth Graders

Guillermo A. Gil, Keith Rust, and Marianne Winglee
Social Inequality, Social Segregation, and Their Relationship to

Reading Literacy in 22 Countries

Stephen W. Raudenbush, Yuk Fai Cheong, and Randall P. Fotiu

Michigan State University, USA

1. Introduction

1.1. Home and School Sources of Social Inequality in Literacy

Social inequality in educational achievement has been a subject of sustained debate and research in

the United States (cf., Ryan 1971; Jencks et al. 1972; Kenniston 1977; de Lone 1979; Grubb and Lazerson

1982; Kerckhoff 1993). Controversy has tended to focus on two institutions: the family and the school.

Home Environmental Effects. Families have long been known to vary substantially in their

capacities to provide educational environments that foster school readiness and reading literacy (Fraser

1959; Wolf 1968). Linguistic input from parents, the availability of books in the home, and early reading

experiences predict vocabulary development and early reading proficiency (Huttenlocher et al. 1991; Rutter

and Rutter 1993). Such differences in family environment are linked with social status indicators, including

income, parental occupation, and parental education (Coleman et al. 1966; Peaker 1971). Parents of high

social status are more likely than parents of low social status to have the resources and skills needed to

foster reading literacy in their children. Although debate persists about the use of social policy to modify

the unequal distribution of wealth or to intervene to provide enhanced educational opportunities for poor

children, there is little doubt that the social status of a child's family is linked to the home educational

environment and to educational achievement. This link between social background and achievement,

mediated by the home environment, is believed more pronounced for reading literacy than for mathematical

literacy (Bryk and Raudenbush 1988).

School Contributions to Social Inequality. Although few would deny that families vary in their

capacity to educate children, many commentators have emphasized the school as the key institution

translating inequalities of social origin into inequalities of educational and social attainment (Bowles and

Gintis 1976; Edmonds 1979; Rutter et al. 1979; Kerckhoff 1993). Children of diverse social status vary in

the schooling environments they experience. In the United States, for example, local control and funding of

schooling are linked to residential segregation by social status, apparently increasing the chances that

children of high-status families will attend schools with other "high-status" children while more

disadvantaged children will likely have relatively disadvantaged classmates. And many studies indicate that

the social composition of a school predicts that school's mean achievement, even after adjusting for the

effects of individual student background (see Willms' 1986 review). Thus, if two children with similar

family backgrounds attend schools of different social composition, the child attending the more advantaged

school can be expected to achieve more than the child attending the less advantaged school. This pervasive

effect has been labeled the "compositional" or "contextual" effect of social background (Firebaugh 1978;

Burstein 1980). What is less clear is the cause of this difference. The mean social background of a school is

undoubtedly correlated with a variety of ecological factors that might predict school learning, including, for

example, community expectations, peer influences, norms, resources, and effective practices. Bryk and

Thum (1989) were able to account for the substantial compositional effect of U.S. high schools by

identifying and specifying in their statistical model aspects of school climate and organization correlated

with school mean social class and predictive of achievement.1

 

1

Hauser (1970) has cautioned that inadequate specification of student-level predictors can inflate the estimate of the school compositional effect.

5
6

The Compositional Effects Model. The contributions of the individual and compositional effects

of socioeconomic status (SES) on achievement can be understood by referring to a regression model that

decomposes the effect of SES into its within- and between-school components:

where

Yij is the measure of educational achievement for student i in school j,

Xij is the indicator of SES for student i in school j,

_

Xj is the mean SES of school j,

_

X is the grand mean SES,

βb is the between-school effect of SES on achievement, and

βw is the within-school effect of SES on achievement.

Figure 1 depicts this relationship in a hypothetical society with three schools: school 1 is a lowSES school, school 2 an average-SES school, and school 3 a high-SES school. Within each school we see

a weak positive relationship between SES and achievement characterized by the within-school regression

slope βw. Ceter paribus, this slope represents the strength of association between the social status of the

child's home and that child's achievement. Specifically, βw is the expected difference in achievement

between two students who attend the same school but differ by one unit in SES. The between-school

regression relates the three school means on SES to the three schools' mean achievement and is

characterized by the larger regression slope, βb. The compositional effect of SES is then defined as the

discrepancy between the between-school and within-school effects, that is, βc = βb - βw. This is the expected

benefit of attending a school with a mean SES one unit higher than average, holding constant the SES of

the student.

One can also consider the overall association between SES and achievement, as indicated by the

regression coefficient βT. This is the slope of the regression line that would best fit the entire scatter of

points, ignoring the clustering of students within schools. For the example shown in Figure 1, βT is neither

as steep as the between-school slope, βb, nor as flat as the within-school slope, βw. In fact, the overall slope

βT is a weighted average of the other two:

where ηx

2

 is the proportion of variation of X that lies between schools (see review in Burstein 1980), that

is,

where SSbx and SSwx are the sums of squared deviations between and within schools, respectively, on the

SES indicator. The quantity ηx

2

 may be viewed as an index of segregation of schools with respect to X

E(Y | X )= + ( X - X )+ ( X - X ) ij ij a b w ij j bb j 1

b h b h b w

2

b x

2

T x = +(1 - )

2

SS +SS

SS =

bx wx

2 bx hx

3
Figure 1: Compositional effect model

School 1

School 2

School 3

X = SES

} }

}

Y=Achievement

βb

βw

βc

7

βc = βb – βw =“Compositional effect”

βT = η2 βb + (1–η2 ) β x w

x η2

 = SSbetween, x

SStotal, x =“Segregation index”

= βw + x η2 βc

x

Yij = β0j + βw (Xij – X.

j)

Y.

j = β0 + βb(X.j

 – X..)
8

(Willms and Paterson 1993). The more segregated the schools in a society, the more heavily dependent is

βT on the between-school slope, βb. 

This reasoning leads directly, then, to an expression for the overall effect of SES as a function of

the within-school effect and the compositional effect:

As Equation 4 shows, the overall effect of SES depends on the within-school effect, βw; the compositional

effect, βc; and the degree of segregation of schools by X, ηx

2

. This decomposition of the SES effect will

prove useful in cross-national comparisons of educational achievement.

1.2. Cross-National Studies of Educational Achievement and Inequality

As concern for improving educational effectiveness has intensified during recent years,

policymakers in many countries have demanded better information about the outcomes of schooling

(Willms 1992). Governments in a number of countries have recently developed performance indicators for

the purpose of monitoring levels of achievement, often with the aim of holding national, provincial, or local

officials accountable for the productivity of schools under their supervision (cf., Fitz-Gibbon 1991; Bosker

and Guldemond 1991; Wheeler, Raudenbush, and Pasigna 1992). The policy of using better information to

guide educational improvement poses, within each country, a series of methodological issues and dilemmas

discussed in detail by Willms (1992).

The policy environments impelling governments to monitor learning within each society have also

inspired a need to synthesize information about educational outcomes across societies. It is widely held

within many countries that improved educational achievement is a key to improved global competitiveness.

Thus, it is natural for policymakers within a country to inquire about the standing of their country relative

to other countries with respect to educational achievement, and a finding that one's country is faring poorly

in the educational competition becomes the occasion for intensified efforts to improve schooling within that

country.

Our purpose now is apply the logic of cross-national comparison to the study of social inequality in

reading literacy. Rather than asking how societies vary in mean literacy, we shall examine the extent to

which they vary in the social equality with which literacy is distributed. This work follows in the tradition

of Heyneman and Loxley (1983), who synthesized data from 29 countries with the aim of discovering the

extent to which countries vary in the socioeconomic inequality of educational outcomes. They found

socioeconomic status to be less important for predicting educational achievement in developing than in

developed countries. However, our purpose is to achieve a more fine-grained analysis by exploring the

within-school and between-school components of social inequality across 22 countries. The magnitudes of

these components have potentially different implications for theory and policy.

To modify the effect of individual social background on literacy (βw in model (1)), policymakers in

the United States have developed compensatory education programs such as Head Start and Chapter I. A

potentially different set of policy options may be relevant to modification of the school compositional effect

of social background (βb in model (1)).

First, social status compositional effects can occur only to the extent that educational organizations

are segregated with respect to social status. The more segregated are a society's schools with respect to

social status, the more potentially influential are social status compositional effects as determinants of

= + .

c

2

bT b w hx b

4
9

social inequality in achievement in a society. Hence, social status desegregation represents one option for

reducing such social inequality. McPherson and Willms (1986) reported that by moving from a selective to

a comprehensive secondary school system during the 1970s, Scotland reduced social class segregation and

increased the achievement of poor children, resulting in an overall increase in educational attainment across

all children.

The influence of school composition on social inequality depends, however, not only on segregation

by social status, but also on the distribution of resources as a function of school composition. Controversy

and litigation have arisen in a number of states in the United States concerning the causes and consequences

of educational funding differences between school districts varying in the social status of their students.

Holding constant the level of social status segregation in a nation's schools, it is reasonable to conjecture

that a large positive relationship between school social status composition and school resources will result

in a comparatively large compositional effect of social class. Of course, such school compositional effects

may arise for other reasons including, for example, peer interactions and normative environments that

condition parental expectations and pupil achievement motivation and may be related to social composition.

There are two reasons, then, to believe that social composition effects may be large in the United

States as compared to other middle- and high-income countries.

1. Between-school segregation by social status may be comparatively large. The local control

and funding of schooling encourages a degree of residential segregation, with wealthier

families moving into districts with favorable funding. To the extent parents understand the

importance of the school social composition effect, they have another incentive to compete

for housing in districts with favorable composition. Such incentives for residential

segregation by social status may be weaker in societies with centralized control and

funding of schools.

2. The local control and funding of schools leads to substantial variation in school resources

across U.S districts. Holding constant the level of between-school segregation across a set

of countries, this inequality in resources may lead to magnified social compositional effects

(see Equation 4). 

2. The Anatomy of Social Inequality in Literacy

Our analysis is based on the reasoning that the social inequality of educational outcomes (and

literacy achievement in particular) has four sources. These become clear if we use Equation 4 to define the

expected increase in achievement associated with a one standard deviation increase in X, our indicator of

SES:

Equation 5 shows how the overall level of social inequality in achievement depends upon four factors:

1. The overall degree of inequality in SES in the society, as indicated by Sx, the standard

deviation of SES in that society;

=( + )S .

 

Social Inequality in Achievement= S

c x

2

w x

T x

b h b

b

5
10

2. The degree of segregation of schools by SES, ηx

2

;

3. Within each school, the importance of student SES in predicting the outcome, βw; and

4. Holding student SES constant, the importance of the social composition of the school in

predicting achievement, βc. 

2

The first two sources are displayed in Figure 2. Overall inequality in SES varies on the vertical

axis and is measured by Sx, the standard deviation of the SES indicator within a given country. The degree

of SES segregation between schools varies on the horizontal axis and is operationalized by ηx

2

, the

proportion of variance in SES that lies between schools. The expected mean SES of the school attended by

a student of SES = Xij is approximately3

Thus, in a society with no segregation (ηx

2 = 0), the expected mean SES of the school attended by any

student is the grand mean of X, while in a society with complete segregation (h

2

x = 1.0 ), the expected

mean SES of the school attended by each student is the same as that student's SES. Thus, for any society, ηx

2

Sx

is the expected gap between the social composition of the typical school attended by a student who is one

standard deviation above average in SES and the typical school attended by a student who is of average

SES.

The upper right quadrant of Figure 2 gives the expected social composition gap for a society

characterized by high inequality in SES and high segregation. The lower right quadrant gives the gap for a

society with low inequality in SES but high segregation. The egalitarian ideal is found in the lower left

quadrantClow inequality, low segregation.

Associated with the four sources of social inequality in outcomes are different sets of social and

educational policy options. One might attempt to reduce the overall level of inequality in SES, that is,

reduce Sx by income redistribution, for example. One might reduce social status segregation between

schools as occurred, for example, in Scotland during the 1970s. Alternatively, one might attempt to reduce

achievement gaps between more and less advantaged students attending the same school (determined by βw)

by compensatory education policies or by early intervention into the home environment. And one may

attempt to reduce the impact on achievement, βc, of school segregation by equalizing school funding or

other resources or by intervening to improve the climate of schools attended disproportionately by socially

disadvantaged students.

Our analysis is not sufficiently fine-grained to evaluate the potential efficacy of such interventions.

Instead, we aim to explore the anatomy of social inequality in reading literacy in 22 countries to the extent

feasible given the limited data available and, by doing so, to clarify research issues and data quality

requirements that must be faced in future cross-national research focused on such inequality. We shall

examine not only the anatomy of social inequality in reading literacy within each country, but also the

 

2

Clearly, points 2 and 4 could be expanded to include effects of segregation between classrooms within schools and ability groups within classrooms.

For simplicity we restrict the current discussion to segregation between schools.

3

In fact, the regression coefficient for predicting Xj from Xij is J/(J-1)ηx

2

 where J is the number of schools.

E( X . | X )= X + ( X - X ). ij

2

j ij hx 6
11

Figure 2: Extent of social status (SES) variation and school segregation

Sx

Low High

Low High

Variation=Sx

Segregation= x η2

Sx x η2

 Sx

x η2

 Sx x η2

 Sx

x η2

 Sx Sx

Sx

Inequality in Outcome Index:

(βw + ηx

2 βc)Sx

within-school

effect of X on

outcome

Segregation

(by X)

Compositional

effect of X

on outcome

Overall Variation

in X

Predicted X .

j ≈X..+ (Xij–X..) x η2
12

mean level of reading literacy in each. This will enable exploration of whether equity is purchased only at

the price of excellence.

3. A Primary Analysis and a Series of Sensitivity Analyses

Data were collected as part of the IEA Reading Literacy Study. Unfortunately, the number of

variables measured on commensurate scales across all countries is quite limited, leaving statistical models

underspecified. To cope with this problem, we conducted a single primary analysis followed by a series of

sensitivity analyses seeking to assess the robustness of the findings from the primary analysis.

Our initial interest was at the primary school level, so our primary analysis uses the fourth grade

(Population A) data. Unfortunately, only one indicator of home environmentCnumber of books available at

homeCwas measured on a commensurate scale across a large number of countries in the fourth grade data.

 While certainly related to the social status of the child's family, this indicator is better viewed as an

indicator of home literacy resources. Our primary analysis examines the variation of this resource across

societies, the extent to which assignment to schools tends to segregate children on the basis of this resource,

the relationship between this resource and literacy within schools, and the compositional effect of attending

a school with other students who are more or less disadvantaged with respect to this resource.

While providing a first approximation to a portrait of home environmental inequality in literacy

across countries, the primary analysis, based only on one home resource indicator, undoubtedly

underspecifies the relationship between home background and literacy. Such underspecification may have a

variety of negative effects on the validity of the results, including the artifactual inflation of estimated

compositional effects (Hauser 1970; Hutchinson 1992). Therefore, we turned to a second data sourceCdata

from ninth graders (Population B)Cfor which a second home environmental indicator becomes available in

most countries participating in the IEA Reading Literacy Study: years of maternal education. A variety of

sensitivity analyses, using availability of books and maternal education separately and together, provided a

basis to assess the robustness of the Population A results.

Even on the basis of the primary analysis and a series of sensitivity analyses, however, the essential

conclusions of the study must be viewed as quite tentative. Our main purposes are to establish a

methodological framework for comparative research on social inequality in literacy and to establish new

data quality standards so that future international literacy surveys can provide better answers to these

questions.

Data and results for the primary analysis are considered in detail in the text of this paper. Results

of the secondary analyses are then summarized in the text with details about the data described in the

Appendix.

4. Data for the Primary Analysis

 

Twenty-seven countries of the 32 that participated in the Reading Literacy Study are

predominately high-income countries, mostly in Europe. Not all of these countries were used in the

analysis. Two low-income countries were excluded because their income levels were substantially different

from those of all other countries. (If there had been a larger number of low-income countries, they would

have been retained.) Two other countries were excluded because of apparent irregularities in test

administration. One other country was excluded because of insufficient data on key predictor variables.

Thus, the analytic sample included the 22 countries listed in Tables 1-7, which give descriptive statistics

for each country on each variable.
13

Within each country, schools were selected at random. Within most countries, one fourth grade4

classroom was selected at random, although some schools had only one fourth grade class. In a few

countries, two classrooms were selected per school. We view the design as a two-stage cluster sample

within each country having students clustered within classrooms/schools and classrooms/schools clustered

within countries. Note that because in most countries only one classroom was selected per school, we

cannot distinguish between school and classroom effects. Our analysis was conducted using classrooms as

the clusters. As mentioned in Section 5 of this paper, we view the countries as exchangeable (conditional

on the model at hand), so that we conceive of three levels of random variability in the data.

As indicated in Tables 1 through 7, the analytic sample includes 2,908 classrooms and 55,951

students, an average of 132 classrooms and 2,543 students per country and 19 students per classroom.

School and classroom variables include urban versus nonurban location, school size, class size, and

classroom mean availability of books in the home. These are described in Tables 1 through 4. Somewhat

more than half of the classrooms are urban (Table 1), though all classrooms in Singapore and 94 percent of

the classrooms in Hong Kong are urban. In contrast, fewer than half the classrooms in France, the

Netherlands, Portugal, and Switzerland are classified as urban. Average school enrollment (Table 2) is

416, with Singapore having exceptionally large schools (mean enrollment of 1,261) and France having the

smallest schools (mean of 112). Class sizes (Table 3) average 24 overall, with the largest classes found in

Singapore (mean of 37) and the smallest classes in Iceland (mean of just under 15). After transforming to

the logarithmic scale (described below), the overall mean of the classroom means of the number of books at

home was 4.31 (this corresponds to a figure of 73.4 books in the home, by taking the exponential). The

greatest mean was found in Sweden (4.94, equivalent to 138.8 books) and an extreme low mean was found

in Hong Kong (2.80, equivalent to 15.4 books).

Table 1. Distribution of urban versus rural location of Population A classrooms: IEA Reading

Literacy Study, 1991

Country Proportion urban Standard deviation Number of

classrooms

Entire sample .................................................64 .48 2,908

Belgium (French) ...........................................55 .50 113

Canada (British Columbia).............................88 .33 123

Finland...........................................................55 .50 67

France ............................................................35 .48 108

Germany East.................................................61 .49 82

Germany West................................................60 .49 89

Greece............................................................76 .43 141

Hong Kong .....................................................94 .23 124

Hungary..........................................................63 .49 135

Iceland............................................................59 .49 153

Ireland............................................................54 .50 114

Italy................................................................52 .50 105

Netherlands....................................................33 .47 77

New Zealand ..................................................81 .40 176

Norway...........................................................51 .50 158

Portugal..........................................................26 .44 124

Singapore ...................................................... 1.00 .00 206

Slovenia .........................................................65 .48 138

Spain..............................................................80 .40 232

Sweden...........................................................52 .50 118

Switzerland ....................................................35 .48 173

USA...............................................................80 .40 152

 

4

The grade selected actually varied across countries. Population A consists of the grade with the greatest number of students aged 9 at the time of the

assessment. This was grade 4 in the United States and several other countries, grade 3 in a number of other countries, and grade 5 in New Zealand.
14

Table 2. Distribution of school enrollment of Population A classrooms: IEA Reading Literacy

Study, 1991

Country Mean Standard deviation Number of

classrooms

Entire sample ................................................ 416.47 394.78 2,908

Belgium (French) .......................................... 241.31 137.37 113

Canada (British Columbia)............................ 327.46 147.09 123

Finland.......................................................... 271.58 145.99 67

France ........................................................... 111.99 85.42 108

Germany East................................................ 382.55 153.09 82

Germany West............................................... 293.12 146.69 89

Greece........................................................... 249.27 163.94 141

Hong Kong .................................................... 696.73 352.84 124

Hungary......................................................... 560.13 249.63 135

Iceland........................................................... 215.71 233.29 153

Ireland........................................................... 285.93 215.89 114

Italy............................................................... 470.38 277.16 105

Netherlands................................................... 178.13 78.97 77

New Zealand ................................................. 288.56 135.94 176

Norway.......................................................... 156.55 129.08 158

Portugal......................................................... 168.02 167.53 124

Singapore ...................................................... 1,261.22 489.50 206

Slovenia ........................................................ 701.03 365.86 138

Spain............................................................. 597.33 409.39 232

Sweden.......................................................... 233.75 161.37 118

Switzerland ................................................... 196.34 276.06 173

USA.............................................................. 505.93 306.60 152

Table 3. Distribution of class size of Population A classrooms: IEA Reading Literacy Study, 1991

Country Mean Standard deviation Number of

classrooms

Entire sample ................................................ 24.04 8.34 2,908

Belgium (French) .......................................... 20.21 4.47 113

Canada (British Columbia)............................ 23.33 3.19 123

Finland.......................................................... 24.48 5.08 67

France ........................................................... 21.34 6.03 108

Germany East................................................ 20.42 3.46 82

Germany West............................................... 22.20 4.31 89

Greece........................................................... 23.59 5.40 141

Hong Kong .................................................... 36.38 6.47 124

Hungary......................................................... 23.39 4.68 135

Iceland........................................................... 14.84 6.46 153

Ireland........................................................... 29.71 8.86 114

Italy............................................................... 16.36 5.17 105

Netherlands................................................... 24.29 5.75 77

New Zealand ................................................. 29.75 7.35 176

Norway.......................................................... 15.52 6.46 158

Portugal......................................................... 20.90 5.62 124

Singapore ...................................................... 36.83 5.22 206

Slovenia ........................................................ 24.65 3.91 138

Spain............................................................. 27.91 7.12 232

Sweden.......................................................... 19.98 4.07 118

Switzerland ................................................... 18.41 4.25 173

USA.............................................................. 23.94 5.55 152
15

Table 4. Distribution of number of books at home, with logarithmic transformation (classroom

means): IEA Reading Literacy Study, 1991

Country Mean Standard deviation Number of

classrooms

Entire sample ................................................ 4.31 .74 2,908

Belgium (French) .......................................... 4.54 .60 113

Canada (British Columbia)............................ 4.70 .46 123

Finland.......................................................... 4.62 .30 67

France ........................................................... 4.28 .57 108

Germany East................................................ 4.23 .50 82

Germany West............................................... 4.15 .42 89

Greece........................................................... 3.72 .65 141

Hong Kong .................................................... 2.80 .65 124

Hungary......................................................... 4.51 .49 135

Iceland........................................................... 4.86 .37 153

Ireland........................................................... 4.27 .58 114

Italy............................................................... 3.84 .58 105

Netherlands................................................... 4.69 .49 77

New Zealand ................................................. 4.58 .63 176

Norway.......................................................... 4.75 .50 158

Portugal......................................................... 3.39 .99 124

Singapore ...................................................... 3.89 .54 206

Slovenia ........................................................ 4.37 .41 138

Spain............................................................. 4.32 .58 232

Sweden.......................................................... 4.94 .35 118

Switzerland ................................................... 4.54 .55 173

USA.............................................................. 4.53 .54 152

Student-level variables include overall reading literacy, gender, and books in the home (Tables 5-

7). Overall reading literacy (Table 5) is the average of three test scores, each of which indicates proficiency

in reading a different type of text (narrative text, expository text, and documents). Country sample means

range from 484.06 in Portugal to 569.11 in Finland, with standard deviations ranging from 68.10 in Hong

Kong to 91.34 in Sweden. As one might expect, every country shows nearly equal proportions of males and

females (Table 6).

Availability of books at home (Table 7) was measured as a six-category variable, with categories

1 = none, 2 = 1-10, 3 = 11-50, 4 = 51-100, 5 = 101-200, 6 = more than 200. We initially coded these by

assigning the midpoint of each interval to that category (the highest category was set to 250). A preliminary

analysis indicated that within each country, the relationship between this indicator of book availability and

literacy had a positive linear component along with some evidence of negative curvature (i.e., a diminishing

positive effect of book availability). This result suggested a logarithmic transformation (after adding 1 to

avoid an argument of 0 for the log function). The resulting variable displayed an almost entirely linear

relationship to literacy in each country. Descriptive statistics (Table 7) indicate a mean of the new books

variable of 4.28, equivalent to about 71 books. Mean books ranged from 2.81 in Hong Kong (about 15

books) to 4.90 in Sweden (about 133 books). The standard deviation of this variable ranged from 0.84

(Sweden) to 1.56 (Hong Kong).

A number of variables we had hoped to use in the analysis were found unusable or unavailable.

These included a home possession score and a student possession score. These were measured on different

metrics in different countries with different types of possessions and commodities listed in different

countries and no attempt to equate the scales. The variable years of maternal education was unavailable for

Population A, though it was available for Population B. Our sensitivity analyses include analysis of

Population B data using maternal education in addition to books at home.
16

Table 5. Distribution of overall reading literacy of Population A students: IEA Reading Literacy

Study, 1991

Country Mean Standard deviation Number of students

Entire sample ................................................ 516.55 78.54 55,951

Belgium (French) .......................................... 510.37 72.91 1,924

Canada (British Columbia)............................ 504.87 74.93 2,035

Finland.......................................................... 569.11 69.37 1,377

France ........................................................... 533.54 69.49 1,460

Germany East................................................ 500.99 81.28 1,448

Germany West............................................... 511.93 81.07 1,621

Greece........................................................... 512.56 73.59 2,837

Hong Kong .................................................... 524.24 68.10 2,415

Hungary......................................................... 503.53 75.39 2,705

Iceland........................................................... 517.77 85.51 1,739

Ireland........................................................... 509.13 76.63 2,402

Italy............................................................... 538.45 77.30 1,476

Netherlands................................................... 487.02 71.93 1,341

New Zealand ................................................. 532.30 83.49 2,920

Norway.......................................................... 528.89 86.68 2,017

Portugal......................................................... 484.06 70.22 2,124

Singapore ...................................................... 513.48 72.01 7,286

Slovenia ........................................................ 500.09 77.92 3,200

Spain............................................................. 510.68 77.12 5,827

Sweden.......................................................... 538.87 91.34 2,084

Switzerland ................................................... 509.53 80.51 2,430

USA.............................................................. 546.25 74.30 3,283

Table 6. Distribution of gender of Population A students: IEA Reading Literacy Study, 1991

Country Proportion male Standard deviation Number of students

Entire sample .................................................51 .50 55,951

Belgium (French) ...........................................49 .50 1,924

Canada (British Columbia).............................52 .50 2,035

Finland...........................................................52 .50 1,377

France ............................................................49 .50 1,460

Germany East.................................................49 .50 1,448

Germany West................................................52 .50 1,621

Greece............................................................50 .50 2,837

Hong Kong .....................................................54 .50 2,415

Hungary..........................................................50 .50 2,705

Iceland............................................................51 .50 1,739

Ireland............................................................49 .50 2,402

Italy................................................................52 .50 1,476

Netherlands....................................................48 .50 1,341

New Zealand ..................................................52 .50 2,920

Norway...........................................................49 .50 2,017

Portugal..........................................................51 .50 2,124

Singapore .......................................................52 .50 7,286

Slovenia .........................................................51 .50 3,220

Spain..............................................................49 .50 5,827

Sweden...........................................................51 .50 2,084

Switzerland ....................................................52 .50 2,430

USA...............................................................50 .50 3,283
17

Table 7. Distribution of books at home, with logarithmic transformation for Population A students: 

IEA Reading Literacy Study, 1991

Country Mean Standard deviation Number of students

Entire sample ................................................ 4.28 1.28 55,951

Belgium (French) .......................................... 4.54 1.18 1,924

Canada (British Columbia)............................ 4.72 1.05 2,035

Finland.......................................................... 4.62 .89 1,377

France ........................................................... 4.28 1.20 1,460

Germany East................................................ 4.22 1.08 1,448

Germany West............................................... 4.16 1.19 1,621

Greece........................................................... 3.84 1.29 2,837

Hong Kong .................................................... 2.81 1.56 2,415

Hungary......................................................... 4.53 1.06 2,705

Iceland........................................................... 4.85 .86 1,739

Ireland........................................................... 4.31 1.20 2,402

Italy............................................................... 3.88 1.31 1,476

Netherlands................................................... 4.71 1.11 1,341

New Zealand ................................................. 4.62 1.15 2,920

Norway.......................................................... 4.80 .95 2,017

Portugal......................................................... 3.57 1.44 2,124

Singapore ...................................................... 3.92 1.39 7,286

Slovenia ........................................................ 4.39 1.11 3,200

Spain............................................................. 4.35 1.19 5,827

Sweden.......................................................... 4.90 .84 2,084

Switzerland ................................................... 4.60 1.05 2,430

USA.............................................................. 4.52 1.17 3,283

Histograms of all candidate variables and scatter plots between pairs of variables were examined

for each country's data. These analyses led to the exclusion of some variables as mentioned above and

informed choice of metric for those variables that remained. Small numbers of anomalous cases (at the

student level) were removed within several countries. These included students who achieved the minimum

on all three tests, likely indicating that they had not tried to respond to the test. Two countries having large

numbers of such cases and, as a result, displaying unexpectedly low mean overall literacy, were also

excluded.

5. Statistical Methodology

5.1. Overview

A number of inferential problems arise in synthesizing results from multiple countries. First,

within each country if data are collected via a two-stage cluster sampling procedure (as they were for this

study), it is important that model estimates appropriately reflect uncertainty associated with the clustering

of students in classrooms. Two common approaches are used to incorporate the clustering effect:

resampling approaches such as the bootstrap or jackknife; and model-based approaches. To cope with

effects of clustering, we have opted to use a hierarchical linear model (Raudenbush and Bryk 1986) within

each country. Effects of clusters are represented via random effects, the variance of which is incorporated

into standard error estimates for means. Although the resampling approaches might be viewed as more

robust than the model-based approach, the model-based approach extends better to the more complex

estimation tasks described below.

Second, an unknown degree of heterogeneity between countries will exist on mean literacy and

quantities like βw and βc. The magnitude of such heterogeneity is interesting in itself and also has
18

consequences for inferences about cross-national differences in outcomes. However, estimating the extent

of between-country heterogeneity is nontrivial statistically because each country's coefficients are estimated

with different precision. Thus, an iterative computational procedure is needed to estimate the betweencountry variance. More important, given a modest number of countries (n=22 in the analyses below), a

point estimate of the between-country variance will be imprecise. A confidence interval is needed, but large

sample confidence intervals based on the asymptotic normality of maximum likelihood estimators will often

be inappropriate in this small sample setting.

A more profound conceptual problem is in interpreting a measure of between-country variance. In

what sense are countries random? For example, the countries in the Reading Literacy Study volunteered

for the study, and so they cannot constitute a random sample.

To address the problems of estimating and interpreting between-country heterogeneity, we adopt a

Bayesian approach with estimation via Gibbs sampling (Fotiu 1989; Gelfand and Smith 1990; Seltzer

1993). In the Bayesian framework, the between-country variance represents the investigator's uncertainty

about the degree to which countries vary in their effects. Thus, we need not assume countries to have been

sampled randomly. We postulate a relatively noninformative prior distribution for the between-country

variance components. Then the posterior distribution of a variance gives us a range of plausible values of

the extent of between-country heterogeneity and, for each value, a degree of plausibility (technically the

posterior density).

Below we briefly illustrate the logic of our modeling procedure in the simple case of comparing

country means. We then extend the approach to a more general model that incorporates country differences

in social inequality and between-school segregation.

5.2. Testing Hypotheses About the Relationship Between Country Characteristics and Mean

Literacy

Though comparing country means is a plausible use of cross-national literacy data, many

researchers would seek to account for the variability among country means. For purposes of illustrating the

methodology, we consider the simple hypothesis that the level of development of a society, as indicated by

its gross national product (GNP), will be positively related to the level of reading literacy of its children.

Efficient estimation requires that the varying precision of the country means be taken into account via

weighted least squares (Seber 1978). However, the precision of the country mean (the inverse of its

variance) depends not only on the data within each country but also on the variance between countries. Let

bk denote the estimated mean outcome for country k and let βk denote the true mean. We may write

that is, ek is the error by which bk estimates βk and vk is thus the sampling variance of bk. However, the true

means βk, k = 1,...,K are themselves viewed as randomly varying about their predicted values. For

example, using GNP as a predictor, we have

b = +e , e N(0,v ) k bk k k _ k

(7)

= + (GNP ) +u , u N(0, ) bk g0 g1 k k k _ t

(8)
19

where uk is the unique effect associated with country k assumed normally distributed and τ is the betweencountry variance. Combining Equations 7 and 8, we have

Under the model of Equation 9 and with τ and vk known, the maximum likelihood estimator of the GNP

coefficient and its variance are given by weighted least squares with weights ωk = 1/(τ + vk) according to

the formulas

and

where

 When the data are balanced, the weights ωk are equal for every country, and Equation 10 reduces to

ordinary least squares, eliminating dependence of the coefficient estimate on τ. Moreover, Equation 11

simplifies and an exact t test becomes available, eliminating dependence of hypothesis testing on τ (see

Raudenbush 1992 for detailed applications in the balanced case).

However, when the data are unbalanced, which will generally be the case in international studies,

Equations 10 and 11 will depend upon τ via the dependence of the weights ωk on τ, and τ will not be

known.5 When τ is not known, the maximum likelihood estimates (mle's) of γ1 and its standard error are

Equations 10 and 11 with the mle of τ substituted in the construction of ωk. These mle's will be sensible

when τ is estimated with reasonable precision. However, this precision depends heavily upon the number of

countries, which will tend to be limited (K=22 in our case). When the precision is poor, Equation 11 will

underestimate the uncertainty associated with the mle of γ1.

Our strategy for coping with the small number of countries and the consequent limited precision of

the mle of τ is to employ a Bayesian estimation strategy. Using this approach, the posterior distribution of

γ1 gives a range of plausible values for that parameter and, associated with each value, its degree of

plausibility (posterior density). This posterior density fully incorporates the uncertainty about τ. An

important by-product of this analysis is a good approximation to the posterior density of τ itself, which

indicates the range of plausible degrees of heterogeneity in country means that remains after controlling for

the effects of GNP. 

 

5

The sampling variance vk can be precisely estimated and assumed known given the large amount of data typically gathered within countries in

international educational surveys.

b = + (GNP ) +u +e , u +e N(0, +v ). k g0 g1 k k k k k _ t k

(9)

( GNP - GNP )

( GNP - GNP )( b - b )

= 2

k k

k k k

1

w

w g

∑

∑

ˆ

(10)

( GNP - GNP )

1

Var( )= 2

k k

1

w

g

∑

ˆ

(11)

.

b

 and b=

* GNP GNP=

k

k k

k

k k

w

w

w

w

∑

∑

∑

∑
20

5.3. Studying Cross-National Differences in the Equity of the Literacy Distribution

The approach we have adopted for studying equity differences has the same structure as the model

for mean achievement described above. The model has the following elements:

! A two-level hierarchical model is first estimated for each country's data separately.

Estimation is via restricted maximum likelihood. The output for each country is a set of

regression coefficient estimates and their variance-covariance matrix. These separate

analyses are highly efficient because data are summarized within each classroom so that,

for each country, the effective sample size for the computations is the number of

classrooms rather than the number of students.

! A multivariate Bayes regression model is formulated to describe variation between

countries. The input data are the vectors of regression coefficients and associated variancecovariance matrices from the separate countries. The output is constituted by estimates of

the posterior densities of all quantities of interest.

! Bayesian computations are achieved via Gibbs sampling as described in detail in

Raudenbush, Cheong, and Fotiu (1994). This approach avoids the need for difficult

numerical integrations and produces an empirical representation of the relevant posterior

distributions.

The structure of the analytic model and assumptions are described in more detail in the next

section.

5.4. The Model

The choice of variables for the model at each of its levels was made after extensive exploratory

analysis of the data country by country. Many potentially relevant predictors were rejectedCbecause they

were clearly not measured on comparable metrics across countries, because of missing data, or because of

anomalous features of their distributions. As a result, the specification of the model is quite thin. For

example, our sole indicator for the social status of the students is the availability of books in the home.

While related to social status, this indicator better reflects the literacy environment of the home. Our sense

is that this variable is a better indicator of the social status composition of a classroom or country at the

aggregate level than of the child, implying that the estimate of compositional effects will be biased away

from zero (Hauser 1970).

Because the model is underspecified, substantive conclusions are made with extreme caution. Later

we consider a series of sensitivity analyses designed to check the credibility of these findings.

We formulate a within-country model having two levels. At level 1Cthe student levelCoverall

reading literacy is predicted by the logarithm of the number of books at home (log-books) and gender. This

model defines, for each classroom, three quantities of interest: a) the adjusted overall reading literacy mean

for the class; b) a regression coefficient indicating, for that class, the strength of association between logbooks and literacy; and c) a regression coefficient indicating, for that class, the gap in overall reading

literacy between males and females. These three quantities in essence define the distribution of literacy

within each class in terms of the average level of literacy and the equity of distribution of literacy with

respect to social status and gender. At level 2Cbetween classrooms within each countryCthese three

quantities become the outcome variables. We use the class mean of the variable log-books at home, school

size, the class size, and the urban versus rural location of the school to predict the classroom means. This
21

level-2 model defines a vector of regression coefficients for each country that become outcome variables at

the country level. Key country-level outcomes of interest6 are

The country's mean overall reading literacy;

! The effect of student-level log-books at home on overall reading; and

! The compositional effect of log-books.

Each of these is adjusted for the other variables in the model, including urban versus rural location,

school size, class size, log-books, school mean log-books, and gender. Variation in these outcomes across

countries is then studied by means of a multivariate Bayes regression model. We now turn to specification

of this model in detail.

Level-1 or Student-Level Model. Within each classroom j of country k, we formulate a model to

predict the overall reading literacy of fourth grade student i:

where

Yijk is the combined reading literacy outcome for child i in classroom j of country k;7

π0jk is the mean outcome for class j, country k (assuming books and gender are scaled

as deviations about their country means);

(books)ijk is the log of books at home for student ijk; so that

π1jk is the expected increase in literacy per unit increase in log-books for students

within classroom j of country k;

(gender)ijk is an indicator for males (1 = male; 0 = female) that has then been centered about

its country mean; so that

π2jk is the mean difference between males and females within classroom jk, adjusted

for the effect of books; and

eijk is a within-classroom random error assumed normally distributed with mean zero

and a country-specific within-classroom variance, that is, eijk ~ N(0, σk

2

).

 

6

See Raudenbush, Cheong, and Fotiu (1994) for a discussion of country differences in gender effects on literacy.

7

The outcome Yijk is the simple average of the narrative, expository, and document reading subtest scores for student ijk.

Y ijk =p0jk +p1jk (books )

ijk +p2jk (gender )ijk +eijk 12
22

Level-2 or Classroom-Level Model. The level-1 model defines three quantities (the π's) as

characterizing the distribution of overall reading literacy within each classroom. These now become the

outcomes in the level-2 model

where

β00k is the mean outcome for country k (all class-level predictors are expressed

as deviations from their country means);

(mean books)jk is the mean of log-books in the homes of students in class jk; so that

β01k is the compositional effect of log-books in the home within country k;

(class size)jk, (school size)jk, and (urban)jk are, respectively, the enrollment of the class,

the enrollment of the school, and an indicator for urban location, each 

deviated around their country means; so that 

β02k, β03k, and β04k are the associated regression coefficients within country k;

β10k and β20k, are the means of π1jk and π2jk respectively, across classrooms within

country k; and

u0jk, u1jk, and u2jk are random effects defined on classrooms within country k and are

assumed trivariate normal in distribution, that is

Restricted Maximum Likelihood Estimation Within Countries. Sufficient data were available to

permit estimation of all country level parameters in separate, within-country analyses. Specifically, the

computer package HLM3.0 of Bryk, Raudenbush, and Congdon (1992) was used to produce restricted

= +u

 

 

= +u ,

 

 

 + (school size ) + (urban ) +u

 

= + (mean books ) + (class size )

2jk 20k 2jk

1jk 10k 1jk

03k jk 04k jk 0jk

0jk 00k 01k jk 02k jk

p b

p b

b b

p b b b

13

14
23

maximum likelihood (REML) estimates of the variance-covariance components (σk

2

, τπk), where τπk is the

3-by-3 covariance matrix described in Equation 14. As described in Raudenbush (1988), inferences about

the regression coefficients (the βk's) are then based on their posterior means and variances given the REML

variance-covariance components.8

We define bk as the country-specific vector of estimates of the regression coefficients (the βk's) and

Vk as its covariance matrix. These summarize the results of estimation in country k and provide input into

the third level of the model, the between-country level.

Three β's are of particular interest in the between-country analysis: β00k (mean literacy); β01k (the

compositional effects of log-books); and β10k (the within-class effect of log-books); and β20k (the gender

gap). These are the latent outcomes to be synthesized in the Bayesian between-country analysis.

5.5. A Bayesian Synthesis of Results Across Countries

The two-level analyses based on each country's data produce the input for the between-country

synthesis. A new computing algorithm was needed to compute the posterior distributions using Gibbs

sampling, and this algorithm is described in detail by Raudenbush, Cheong, and Fotiu (1994). We

summarize the estimation method briefly below.

 

The Likelihood. Conditional on the true value of the regression coefficients, the estimates bk are

assumed normal, i.e.,

where bk is a vector of estimates from country k, βk = (β00k, β01k, β10k)

T

 is the corresponding vector of

parameters, and Vk is the variance-covariance matrix of the estimates bk. The dimensions of bk and Vk vary

according to the analytic task at hand.

An Exchangeable Prior for βk. Conditional on a set of known country-level predictors contained

in the matrix Wk, the parameters βk are assumed exchangeable. That is

Estimation via REML. It is possible to estimate Τ in Equation 16 via REML and then,

conditioning on this point estimate, to base inferences about γ on its posterior mean vector and covariance

matrix. The difficulty with this approach is that Τ will be estimated imprecisely based on only 22 countries,

and inferences about γ may be highly sensitive to this imprecision. The Bayesian approach via Gibbs

sampling, designed to overcome this problem, uses the REML estimates as starting values. We checked the

new Bayes results against the REML results, and they behaved as expected in comparison.

 

8

A vague prior is specified for the regression coefficients so that their posterior means are equivalent to generalized least squares estimates given the

REML variance-covariance estimates.

b | N( ,V ) k bk

_ bk k 15

=W +u , u N(0, ). bk kg k k _ Τ

16
24

Estimation via Bayes. We now formulate noninformative priors for β, γ, and Τ (Fotiu 1989) as

described in detail in the Appendix. Then the joint posterior density of the parameters is

where L(b|β,V,γ,Τ) is the likelihood of Equation 15, f(β|γ,Υ) is the exchangeable prior of Equation 16, and

p1 and p2 are noninformative priors described in Raudenbush, Cheong, and Fotiu (1994). Inferences about

the country-level regression coefficients, β, the between-country regression coefficients, γ, and the betweencountry variance-covariance matrix, Τ, are then based on their marginal posteriors:

Gibbs Sampling. Unfortunately, the integrals in Equation 18 are difficult to evaluate numerically,

as is the integral required to find the normalizing constant of Equation 17. Recently, Gibbs sampling

(Gelfand and Smith 1990) has become a popular approach to approximate such integrals. We refer the

interested reader to Fotiu (1989) for details; see Seltzer (1993) in the univariate case. We used the final

2,000 realizations from the Gibbs sampling process to approximate the marginal posteriors of the

parameters (Equation 18).

6. Results: Primary Analysis

Our interest focuses on a) comparing country means and b) studying country differences in the

equity of distribution of literacy with respect to home environmental inequality. However, as mentioned in

Section 3, all of the necessary information for these purposes was obtained by estimating within each

country the two-level model described by Equations 12 and 13. The key output from each country's

analysis is a vector of three estimates: of the parameters β00k (mean literacy); β01k (the compositional effect

of log-books); and β10k (the within-classroom effect of log-books).9 The uncertainty associated with

parameter estimates from each country is summarized by their variance-covariance matrices. These data

provided input to the Bayesian between-country analysis. Table 8 summarizes the marginal posterior

distributions of these three parameters (as defined in Equation 12) for each country.

6.1. Comparing Country Means

The first column of Table 8 gives the name of the country, and the second gives that country's

GNP. For convenience, the countries are listed in ascending rank order by GNP. Column three summarizes

the posterior distribution of β00k, that is, mean literacy, for each country, by listing the posterior mean and

standard deviation of the country mean. A moderate tendency for these posterior means to increase with

GNP is manifest.

 

9

These effects are adjusted for school size, class size, urban versus rural location, and student gender. However, the results presented below are

insensitive to this specification.

p( , , | b,V)=const.* L(b |V, , , )f( | , ) p ( ) p ( ) b g Τ b g Τ b g Τ 1 g 2 Τ

17

g ( | b,V)= p( , , |b,V) .

 

g ( | b,V)= p( , , |b,V)

 

g ( | b,V)= p( , , |b,V)

3

2

1

b g b g

g b g b

b b g g

Τ ∫ ∫ Τ ∂ ∂

∫ ∫ Τ ∂ ∂Τ

∫ ∫ Τ ∂ ∂Τ

18
25

Table 8. Posterior estimates for Population A students, by country: IEA Reading Literacy

Study, 1991

Country GNP per

capita

Mean literacy

β00k

School-level books

β01k

Student-level books

β10k

Gender effect

β20k

Mean Standard

deviation

Mean Standard

deviation

Mean Standard

deviation

Mean Standard

deviation

Hungary ......................... 2.46 501.85 2.62 24.26 5.82 14.49 1.26 -10.98 2.48

Portugal.......................... 3.65 481.71 2.98 14.08 3.66 9.02 1.17 -5.29 2.61

Greece............................ 4.80 509.63 3.45 15.98 5.77 9.12 1.07 -2.32 2.35

Slovenia ......................... 6.50 499.54 2.33 15.84 5.53 14.23 1.17 -13.67 2.32

Spain.............................. 7.74 507.45 1.95 24.52 3.44 11.09 0.87 -6.67 1.86

Ireland............................ 7.75 506.85 2.67 11.51 4.90 14.51 1.20 -14.33 2.73

Singapore ....................... 9.07 511.52 1.88 22.18 3.46 10.92 0.58 -9.61 1.43

Hong Kong..................... 9.22 523.59 2.87 13.16 4.57 5.53 0.88 -5.81 2.54

New Zealand.................. 10.00 529.71 2.32 24.90 3.97 16.24 1.42 -19.28 2.67

Germany East................. 11.30 501.58 3.50 14.50 6.20 13.46 1.86 -12.92 3.36

Italy................................ 13.33 536.89 4.55 5.73 7.29 11.87 1.36 -10.16 2.92

Belgium ......................... 14.49 510.92 2.59 25.11 4.52 11.27 1.35 -11.30 2.55

Netherlands.................... 14.52 487.13 3.31 18.53 6.56 11.39 1.74 -7.48 3.02

France ............................ 16.09 533.47 3.03 4.83 5.42 11.60 1.47 -7.75 2.92

Iceland ........................... 16.59 516.47 3.02 15.78 7.41 13.41 2.18 -17.05 3.65

Canada (B.C.) ................ 16.96 503.71 2.60 14.65 5.39 11.57 1.47 -10.97 2.70

Germany West................ 18.48 510.57 3.41 23.85 7.04 14.32 1.50 -10.55 3.05

Finland........................... 18.59 568.59 2.59 -0.43 8.41 10.57 1.94 -11.78 3.51

Sweden .......................... 19.30 538.41 2.82 18.77 7.50 12.72 1.88 -12.43 3.00

USA............................... 19.84 546.69 2.56 36.86 4.81 7.93 1.05 -8.10 2.19

Norway .......................... 19.99 528.47 2.61 8.84 5.34 13.98 1.84 -13.66 2.97

Switzerland .................... 27.50 506.96 2.50 -2.25 5.25 15.60 1.63 -7.40 2.94

Column Mean ............... 13.10 516.45 2.83 15.96 5.56 12.04 1.40 -10.43 2.72
26

Assessing Heterogeneity. How much do countries vary in their means relative to the variation

within countries (after controlling GNP)? For our data, the between-country residual variance in their

means will tend to be estimated with uncertainty despite the fact that each country has a substantial amount

of data. The precision of that variance estimate depends quite heavily on the number of countries providing

data. Figure 3 is a histogram that approximates the posterior distribution of the variance of the means, that

is, the posterior distribution of

This histogram is based on 2,000 sampled values of τβ00.

As the figure indicates, all plausible values of this variance are positive, implying clearly that the country

means are heterogeneous. The posterior mean of this between-country variance is 452.2. Recall that the

overall standard deviation of the outcome across all countries is 78.54 (Table 5). Thus, it appears that the

proportion of variance in the outcome that lies between countries is about 452.2/(452.2+78.542) = .068 so

that about 6.8 percent of the variance is between countries. However, as Figure 3 implies, values of τβ00 as

small as 200 and as large as 850 are plausible, implying that the percentage of variance lying between

countries could be as small as 3.1 percent or as large as 12.1 percent, giving some sense of the degree of

uncertainty about the extent to which literacy means vary across countries. Although no more than a

fraction of the variability in literacy lies between countries by any estimate, this does not imply that country

differences are trivial. As Table 8 indicates, it is common to find pairs of countries with posterior means

differing by more than half the overall standard deviation, a quite substantial effect size.

Figure 4 displays the posterior distribution of the regression coefficient γ001 relating GNP to mean

literacy. Our belief about the magnitude of this relationship does depend upon our opinion about the

variance between country means (see Equations 4 and 5 and the associated discussion). The posterior

distribution displayed in Figure 3 fully takes into account the uncertainty about this variance.

As Figure 4 indicates, the posterior probability distribution of γ001 is concentrated on values greater

than zero, implying the existence of a positive relationship between GNP and mean literacy. The posterior

mean is 1.42. Given the standard deviation of GNP of 6.38, we see that the posterior mean of γ001 = 1.42 is

equivalent to a standardized regression coefficient of 1.42*6.38/78.54 =.12. However, values of γ001 quite

near zero are plausible, and the posterior mean is twice the posterior standard deviation. Values as large as

3.0 are also plausible, implying that the standardized regression coefficient could be as small as 0 or as

large as .24. 

Figure 5 displays the posterior distributions of mean literacy as a function of GNP. Certain

countries that had appeared quite different from each other (e.g., Hungary and Norway) are achieving

about as expected given their GNP. However, the low performance of Portugal and the high performance of

Finland are not completely attributable to their substantial GNP differences, and the performance of

Switzerland, relative to its GNP, is notable.

6.2. Modeling Differences Between Countries in Social Inequality

Degree of Inequality and Segregation. Recall from Figure 2 that it is possible to locate countries

on a plot that indicates the degree of social inequality in a society and the extent to which that society's

schools (or classrooms) are socially segregated. Figure 6 provides such a plot for our data. Because the

measure of interest is access to books in the home (measured on a logarithmic metric), we refer to the

vertical axis as "home environmental inequality," literally, the standard deviation of our measure of access

b00k =t b00 Var( )

19
27

Figure 3: Posterior distribution of the variance of mean literacy

400

300

200

100

0

Std. Dev = 181.58

Mean = 452.2

N = 2000.00

Frequency

Var (β00k) = τβ00

150.0

250.0

350.0

450.0

550.0

650.0

750.0

850.0

950.0

1050.0

1150.0

1250.0

1350.0

1450.0

1550.0

1650.0
28

Figure 4: Posterior distribution of the regression coefficients

relating GNP with mean literacy

400

300

200

100

0

Std. Dev = .56

Mean = 1.42

N = 2,000.00

Frequency

GNP per capita coefficient= γ001

500

-1.00

-.50

0.00

50.0

1.00

1.50

2.00

2.50

3.00

3.00

3.50

4.00
Mean Literacy

0 10 20 

490580560540480500510520530550570

GNP per capita in thousands of dollars US

470

Portugal

Hungary

Slovenia

Greece

Spain

Ireland

Singapore

Hong Kong

New Zealand

Italy

France

Belgium

E. Germany

W. Germany

Iceland

Canada

Netherlands

USA

Sweden

Norway

Finland

Figure 5: Posterior distribution of mean fourth grade literacy; 98% Credibility Intervals by Country.

The regression line is 516.40 + 1.42 (GNP–13.10).

29
1.9

1.81.61.10.8

Inequality

Segregation Index

1.51.31.20.9

• Netherlands • Slovenia

• Canada

• E. Germany

• Hungary • Switzerland

• W. Germany

• Iceland

• Hong Kong

• Norway

• Singapore

• Belgium

• Greece

• Spain • Ireland

• New Zealand

• France

• Sweden

• Italy

• USA

• Finland

0.10 0.14 0.18 0.22 0.26 0.30 0.34 0.36 

1.71.41.0

0.12 0.16 0.20 0.24 0.28 0.32

Figure 6: Home environmental inequality and classroom segregation; fourth graders by country

30
31

to books in each country. And because the sampling designs in most countries involved students nested

within one classroom per school, we refer to the horizontal axis as the degree of segregation between

classrooms, literally, the proportion of variation in log-books that lies between classrooms.

We see from the figure that the "egalitarian ideal" (low inequality, low segregation) is most closely

approximated by those countries in the lower left quadrant, especially Finland and Sweden, and, to lesser

degrees, Iceland, Canada, Netherlands, and the former socialist countries Slovenia, East Germany, and

Hungary. West Germany is unusual in having a high degree of inequality but rather low segregation.

Portugal is moderately high on inequality and very high on segregation. A cluster of countries, including the

United States, is near the average on both dimensions.

An unfortunate feature of Figure 6 is its failure to represent uncertainty in the estimates, especially

the estimate of the segregation index. Figure 7 gives the posterior distributions of the segregation index for

each country.10 The countries are listed in order of GNP. Note that the 98 percent credibility interval for the

United States overlaps that of all other countries except Portugal (which has far higher segregation),

Slovenia, and Finland (both of which have lower segregation). Thus, the data do not support the hypothesis

that local funding and control of schooling lead to unusually high segregation. Rather, the degree of

segregation in the United States is very nearly typical for the countries at hand. 

Magnitude of Within-School and Compositional Effects of Log-books. Figures 8 and 9 display

the posterior distributions of the student-level effect and compositional effect of log-books in 22 countries.

Note that the United States exhibits a relatively large posterior mean for the compositional effect and a

comparatively modest individual effect. This result provides some evidence to support the hypothesis that

local control and funding of schools in the United States gives rise to especially large compositional effects.

Clearly, both the student-level and compositional effects, on average, are significantly positive

across countries as indicated by their posterior distributions (see summary Table 9), with the student level

effect having a posterior mean of 12.02 (posterior standard deviation = 0.81) and the compositional effect

having a posterior mean of 15.90 (standard deviation = 2.99). Moreover, these effects do vary significantly

from country to country as indicated by the posterior distributions of their variance components (Figures 10

and 11). The compositional effects are particularly highly variable.

Relationship Between Mean Literacy and Inequality in Literacy. An important policy question

is whether countries approximating an equal distribution of literacy are able to produce high mean literacy.

Figure 12 addresses this question, and a moderate negative relationship is manifest between inequality and

mean literacy (r = -.36); that is, countries with comparative equality tend also to have high mean literacy.

The clearest example is Finland, which exhibits the lowest degree of inequality and by far the highest mean

literacy. The negative relationship must be interpreted with care, because countries with low inequality tend

also to have high GNP (r = -.23 between inequality and GNP). However, the data suggest that reducing

inequality in literacy need not work against producing high mean literacy. 

 

10The sampling variance for each country's segregation index was computed via "parametric bootstrap" resampling (see Willms and Paterson 1993). If

log-books were normally distributed within a country, the segregation index would be distributed as 

t s c s c

t s c

h 2

J( n-1)

2

x

2

J -1

2

x x

2

J -1

2

2 x x

x

( n + ) +

( n + )

_

where n is the harmonic mean sample size. Two thousand replicates of this statistic were generated by substituting maximum likelihood estimates of σ

2

and τ into the above formula and generating the necessary chi-squared variates. The standard deviation of the resulting sampling distribution was then

treated as the standard error of the segregation index.
Segregation Index

The vertical line at 0.222 indicates the mean segregation index effect across countries

Figure 7: Posterior distribution of books segregation index effect;

 98% credibility intervals for fourth graders by country

Hungary

Portugal

Greece

Slovenia

Singapore

HongKong

NewZealand

E.Germany

Italy

Belgium

Netherlands

France

Iceland

Canada

W.Germany

Finland

Sweden

USA

Norway

Switzerland

Ireland

Spain0.05

0.070.050.100.120.140.160.180.200.220.240.270.280.300.320.340.360.380.400.420.440.460.480.500.520.54

32
Books Effect

The vertical line at 12.02 indicates the mean student-level books effect across countries

Figure 8: Posterior distribution of fourth grade student-level books effect;

98% credibility intervals by country

0 20 10 30

Hungary

Portugal

Greece

Slovenia

Singapore

HongKong

NewZealand

E.Germany

Italy

Belgium

Netherlands

France

Iceland

Canada

W.Germany

Finland

Sweden

USA

Norway

Switzerland

Ireland

Spain

33
The vertical line at 15.90 indicates the mean school-level books effect across countries

Books Effect

-40 -20 -10 0 10 30 50 -30 60 20 40

Figure 9: Posterior distribution of fourth grade school-level books effect;

98% credibility intervals by country

Hungary

Portugal

Greece

Slovenia

Singapore

HongKong

NewZealand

E.Germany

Italy

Belgium

Netherlands

France

Iceland

Canada

W.Germany

Finland

Sweden

USA

Norway

Switzerland

Ireland

Spain

34
36

Table 9. Between-country summaries: IEA Reading Literacy Study, 1991

Population A: 

Model 1 effects

Gamma Standard

error

Tau Tau as correlations

GNP SchoolbooksStudentbooksGender GNP SchoolbooksStudentbooksGender

Intercept............. 516.40 4.40

GNP................... 1.42 0.56 452.24 1.000

School-level

books ................. 15.90 2.99 -6.51 153.12 -0.025 1.000

Student-level

books ................. 12.02 0.81 -16.06 -7.07 11.68 -0.221 -0.167 1.000

Gender............... -10.39 1.37 -15.32 -2.70 -10.33 30.24 -0.131 -0.040 -0.550 1.000

Population B: 

Model 1 effects

Gamma Standard

error

Tau Tau as correlations

GNP SchoolbooksStudentbooksGender GNP SchoolbooksStudentbooksGender

Intercept............. 517.24 7.82

GNP................... 0.85 0.53 349.47 1.000

School-level

books ................. 35.67 5.45 -25.38 584.35 -0.056 1.000

Student-level

books ................. 13.76 1.72 34.78 -119.31 63.32 0.234 -0.620 1.000

Gender............... -2.99 1.38 -32.12 62.94 -34.32 32.50 -0.301 0.457 -0.757 1.000

Population B: 

Model 2 effects

Gamma Standard

error

Tau Tau as correlations

GNP Schoolmother's

education

StudentbooksMother's

education

GNP Schoolmother's

education

StudentbooksMother's

education

Intercept .............. 522.16 7.51

GNP .................... 0.64 0.48 290.23 1.000

School-level

mother's education

............................ 10.60 2.30 -8.55 110.51 -0.048 1.000

Student-level

books................... 12.22 1.42 31.83 -30.08 40.49 0.294 -0.450 1.000

Mother's

education ............. 2.52 0.44 13.23 -7.85 9.66 3.82 0.397 -0.382 0.777 1.000

Population B: 

Model 3 effects

Gamma Standard

error

Tau Tau as correlations

GNP SchoolbooksStudentbooksMother's

education

GNP SchoolbooksStudentbooksMother's

education

Intercept .............. 522.28 7.46

GNP .................... 0.63 0.47 293.58 1.000

School-level

books................... 34.20 5.71 -62.50 667.31 -0.141 1.000

Student-level

books...................

11.56 1.44 34.20 -96.94 40.95 0.312 -0.586 1.000

Mother's

education ............. 2.70 0.45 13.46 -37.26 10.76 4.11 0.387 -0.711 0.829 1.000
500

400

300

200

100

0

Std. Dev = 5.31

Mean = 11.7

N = 2,000.00

Frequency

4.0

8.0

12.0

16.0

20.0

24.0

28.0

32.0

36.0

40.0

44.0

48.0

52.0

56.0

Var(β10k) = τβ10

Figure 10: Posterior distribution of the variance

of the within-classroom effect of books

36
Std. Dev = 73.69

Mean = 153.1

N = 2,000.00

Frequency

Var(β01k) = τβ01

25.0

75.0

125.0

175.0

225.0

275.0

325.0

375.0

425.0

475.0

525.0

575.0

625.0

675.0

400

300

200

100

0

Figure 11: Posterior distribution of the variance

of the compositional effect of books

37
570

560550500480

Mean Literacy

Inequality in Literacy

540530520490

• Portugal

• Netherlands

• Slovenia

• Canada

•

 E. Germany

• Hungary

• Switzerland

• Iceland

• Hong Kong

• Norway

• Singapore • 

• Greece Belgium

•

 Spain

• Ireland

• New Zealand

• France

• Sweden

• Italy

• USA

• Finland

0 10 20 30 

510

Figure 12: Mean literacy and inequality in literacy; fourth graders by country

38
40

The relationship between home environmental inequality and literacy can alternatively be viewed

by comparing expected literacy levels for students who are comparatively advantaged or disadvantaged

relative to their compatriots. Figure 13 plots the expected literacy level for a student "high" in log-books

(vertical axis) and "low" in log-books (horizontal axis). The "high" student is one standard deviation above

the country mean, and the "low" student is one standard deviation below the country mean on log-books. 

The diagonal line indicates the "egalitarian ideal" of equal expected values for these two students. We see,

for example, that Finland is comparatively close to the diagonal line. The United States is about average in

its distance from the diagonal but has the second highest expected scores for both advantaged and

disadvantaged students. Thus, local control and funding of schools do not make the United States

unusually inegalitarian, and, in fact, disadvantaged students fare reasonably well in the United States,

though not nearly as well as advantaged students. The disadvantaged students in Finland appear to fare

better than even the advantaged students in any other country.

6.3. Conclusions from the Primary Analysis Based on the Population A Sample

The Population A data indicated quite substantial variation across countries in mean literacy with a

tendency for countries of higher GNP to have higher literacy. An analysis of the home environmental

inequality and classroom segregation based on such inequality revealed a set of countries that most closely

approximated the egalitarian ideal of low inequality and low segregation. These included several Northern

European countries and countries of the former socialist block. The United States was found to be quite

typical in terms of its level of inequality and segregation. However, inequality and segregation with respect

to a home environmental indicator may or may not translate into inequality in literacy. This depends on the

magnitude of the effect of variation within classrooms and the magnitude of the between-classroom

compositional effect.

We therefore computed a "literacy inequality index" that depends on a) the degree of inequality in

the home environmental measure of interest; b) the extent of classroom (or school) segregation based on

home environment; c) the magnitude of the within-classroom effect of home environment; and d) the

magnitude of the between-classroom compositional effect. The data provided some evidence for a negative

relationship between this index of variation and mean literacy.

Although the United States was found to have a relatively large compositional effect (meaning that

the classroom or school composition is highly related to a child's literacy outcomes), the United States did

not manifest an unusually high degree of overall literacy inequality. There are three reasons for this

seeming paradox. First, the United States exhibits a comparatively low degree of variation in home

environment. Second, the United States exhibits a low degree of segregation based on home environment.

Third, the within-school effect of home environment is comparatively small in the United States. In fact, in

the context of its overall high level of literacy, students who are disadvantaged in home literacy resources,

as indicated by log-books, score comparatively high on literacy as compared to similarly disadvantaged

students in other countries. Finally, the ideal of excellence (high mean literacy) and equality appears best

approximated by Finland, which has both the highest literacy level and the lowest degree of inequality of

any country in the sample.

All of these inferences are based on the Population A sample using only one indicator of home

environmental advantage: log of the number of books at home. Yet, it is known that compositional effects

can be amplified by underspecification of the student-level model (Hauser 1970; Hutchinson 1992).

Moreover, other dimensions of home environmental advantage may not behave as does log-books; and the

findings may vary if other grade levels are studied. For these reasons we turn to the Population B data,

which include maternal education as well as books at home.
580

570560520500

Advantaged Students

Disadvantaged Students

550540530510

• Portugal • Netherlands

• Slovenia • Canada • E. Germany

• Hungary • Switzerland

• W. Germany

• Iceland

• Hong Kong

• Norway

• Singapore

• Belgium

• Greece • Spain • Ireland

• New Zealand

• France

• Sweden

• Italy

• USA

455 465 475 485 495 505 515 525 535 

Figure 13: Expected literacy for advantaged and disadvantaged students; fourth graders by country

40
42

7. Sensitivity Analyses

Our analyses of Population B data are based on 58,559 students attending 3,167 schools in the

same 22 countries. These analyses were designed to respond to a series of questions arising from the

"primary" analyses.

7.1. Is the U.S. Compositional Effect an "Outlier"?

Hauser (1970) cautioned that compositional effects can arise as artifacts of poor measurement and

specification of what we call the level-1 model. Suppose that the within-group effect, βw, and the betweengroup effect, βb, (Equation 1) were equal, meaning that the compositional effect was null. However,

suppose that the home background indicator, Xij, was unreliable. The aggregated variable (the group mean

of X) would become increasingly reliable as the group sample size increases. This difference in reliabilities

would then lead to estimates of βw and βb having different degrees of bias, producing the basis for an

artifactual "compositional effect." Hutchinson (1992) conducted a simulation study showing exactly how

this process works depending on the reliability of X, the group sample size, and the proportion of variance

in X that lies between groups. Thus, a large compositional effect in a given country could arise because of

reliability differences at level 1 and level 2 on X that are peculiar to that country.

However, it could also be that the Population A results were simply idiosyncratic. Therefore, our

first analysis simply replicated the analysis of the data for this group. The posterior distributions of the

compositional effects for the 22 countries are displayed in Figure 14. Once again, the point estimate

(posterior mean) of the compositional effect in the United States exceeded that of other countries (though

the United States' 98 percent credibility interval overlaps with those of a number of other countries). The

point estimates of these effects across all countries correlated at r = .51 with those computed on the basis of

the analysis of grade 4 data. There is, then, some modest evidence of continuity across grade levels.

It could also be that the compositional effects are specific to the home environmental measure.

Here the Population B data are useful in providing a measure of years of maternal education in addition to

log-books at home. We specified a level-1 model in which both maternal education and log-books were

controlled, and the compositional effect of maternal education was estimated. Because both maternal

education and log-books contributed independently to prediction of literacy in most countries, this model is

arguably better specified than the model that controls only one home environmental indicator. The results

(Figure 15) again show the United States as having a large compositional effect, though its point estimate is

now exceeded by that of Singapore. These point estimates of compositional effects were quite highly related

(r = .79) to those displayed in Figure 14, which were based on log-books at level 2, indicating that this

effect is reasonably insensitive to choice of home environmental measure. 

To get a more fine-grained sense of the effect of adding a second home environmental indicator at

level 1, a model was then estimated in which maternal education and log-books were specified at level 1,

but now the mean of log-books was specified at level 2. Compositional effect estimates were very similar to

those displayed in Figure 14 (r = .99) with the average effect reduced by about one point. Thus, improved

specification of the level-1 model had little impact on the estimate of the compositional effect.
Hungary

Portugal

Greece

Slovenia

Singapore

HongKong

NewZealand

E.Germany

Italy

Belgium

Netherlands

France

Iceland

Canada

W.Germany

Finland

Sweden

USA

Norway

Switzerland

Ireland

Spain

Figure 14: Posterior distribution of ninth grade school-level books effect;

98% credibility intervals by country

Books Effect

The vertical line at 35.67 indicates the mean school-level books effect across countries

-50 60 -40 -20 -10 0 10 30 50 80 90 -30 20 40 70 100

42
Hungary

Portugal

Greece

Slovenia

Singapore

HongKong

NewZealand

E.Germany

Italy

Belgium

Netherlands

France

Iceland

Canada

W.Germany

Finland

Sweden

USA

Norway

Switzerland

Ireland

Spain

Education Effect

The vertical line at 10.60 indicates the mean school-level mother's education effect across countries

Figure 15: Posterior distribution of ninth grade school-level

mother's education effect; 98% credibility intervals by country

-10 0 20 50 10 30 40

43

1.0

0.90.50.3

Reliability at level 2

Reliability at level 1

0.80.70.60.4

• Canada

• USA

0.15 0.20 0.25 0.30 0.35 

• Netherlands

• France

• Iceland

• Finland

• Hong Kong

• New Zealand

• Belgium

• Singapore

• Norway

• Sweden

• W. Germany

• Switzerland

• Ireland

• E. Germany

• Italy

• Hun

Figure 16: Reliability of the books measure at level 1 and level 2; ninth graders by country

45
47

Although the unusually large compositional effect within the United States cannot be explained

away as an artifact of errors in variables at level 1, the results of the sensitivity analysis do suggest extreme

caution in interpreting these estimates. The magnitudes of these estimates do appear significantly inflated

by level-1 underspecification.

7.2. Are Cross-National Inferences About Home Environmental Inequality Sensitive to Grade

Level?

Recall that the overall inequality in literacy resulting from inequality in the home environment was

specified as resulting from four sources: the degree of inequality in home environmental resources, the

degree of segregation of classrooms by home environment, the effect within schools of home environment,

and the compositional effect. This achievement inequality index was plotted for the fourth grade data in

Figure 12. The same index was computed for grade 9, again using log-books at home as the indicator of

home environmental resources. This grade 9 index was found reasonably highly correlated with the grade 4

index, r = .64. Paralleling Figure 13 for fourth grade, Figure 17 plots expected literacy levels for students

advantaged and disadvantaged in home environmental resources as compared to their compatriots. Again, a

student classified as advantaged is one standard deviation above average, and a student classified as

disadvantaged is one standard deviation below average relative to other students in that country. A

comparison of plots for Population A and Population B shows obvious similarities (note the position of

Finland) and differences (see Portugal). The position of the United States appears somewhat less favorable

at ninth than at fourth grade.

8. Final Conclusions 

In this paper we have developed a conceptual framework and statistical methodology for studying

cross-national differences in mean literacy and social inequality in literacy. Overall social inequality in

literacy is viewed as arising from children's social inequalities of origin, social segregation, within-school

effects of inequality, and between-school effects of social composition. We applied this idea not to a generic

measure of social status, but rather to a measure of the educational resources available to children in 22

countries, specifically, the availability of books at home. This analysis was performed on large

representative samples of fourth and ninth graders supplemented by sensitivity analyses involving years of

maternal education at the ninth grade level. Conclusions involve the United States, the larger sample, and

methodology for cross-national research generally.

8.1. Conclusions Regarding the United States

1. It was hypothesized that because of its policies of local control and funding of schools and

the resulting residential social segregation, U.S. classrooms would be relatively highly

segregated on the basis of children's home educational environments. This hypothesis was

not supported: the United States was quite typical of the countries in regard to the degree

of segregation, with a few countries, especially Finland (with low segregation) and

Portugal (with high segregation) proving exceptional in this regard, for Population A.

2. For the same reasons, it was hypothesized that the United States would exhibit unusually

large compositional effects. This hypothesis found support for both grade levels. Although

the magnitude of the effect is quite uncertain given the artifactual inflation of the estimates,

the United States does appear exceptional in the salience of a classroom's social

composition for the literacy learning of its students.
580

570560520

Advantaged Students

Disadvantaged Students

550540530510

• Portugal

• Netherlands

• Slovenia

• Canada

• E. Germany

• Hungary

• Switzerland

• W. Germany

• Iceland

• Hong Kong

• Norway • Singapore

• Belgium

• Greece

• Spain

• Ireland

• New Zealand

• France

• Sweden

• Italy

• USA

470 480 490 500 510 520 530 

Figure 17: Expected literacy for advantaged and disadvantaged students; ninth graders by country

47
49

3. Nevertheless, inequalities in literacy arising from variations in home educational

environment do not appear exceptionally pronounced in the United States. The inequality

index for the United States was near the middle of the distribution, and, given the high

mean literacy of U.S. fourth graders, disadvantaged U.S. students fared comparatively

better than disadvantaged students from most other countries. The seeming paradoxCof

large compositional effects in the United States without high inequalityCcan be explained

by three factors: the comparatively moderate variation in home resources in the United

States, the comparatively moderate degree of classroom segregation, and the comparatively

moderate effect of home environment operating within classrooms.

8.2. Conclusions Regarding the Larger Sample

1. To label inequality "comparatively moderate" in the United States is not to endorse such

inequality. Countries vary significantly in the inequality index, and the data from some

countries clearly demonstrate the coexistence of high mean literacy and low inequality.

Finland is the most prominent example at both grade levels. There was no evidence at

either level that goals of excellence (high mean literacy) and equity (low inequality) are in

conflict.

2. Coefficients of stability across grade levels were moderately high for point estimates of

mean literacy (r = .59), inequality in literacy arising from home environment (r = .64),

compositional effects (r = .79), and within-school effects of log-books (r = .49). The

segregation index was less stable (r = .20).

3. Coefficients of stability across measures (using log-books versus using maternal education

with ninth grade data) were also moderately high: r = .79 for compositional effects, and r =

.68 for the segregation index.

8.3. Conclusions for Cross-National Research on Literacy

1. We recommend a concerted effort to construct, test, and administer a core of common

measures of home environment, socioeconomic status, and community and school context

across countries. Without this effort, it will be difficult to obtain meaningful estimates of

social segregation and social composition effects, undermining also our ability to study

social inequality in literacy cross-nationally. Moreover, estimates of other policy-relevant

effects, including effects of school and classroom resources, policies, and processes, will

be in question unless adequate control for social background and context are constructed.

2. Because of limited data, the current study made no attempt to assess effects of ethnicity

and linguistic background. The needed indicators will be country-specific because

particular ethnic and linguistic minorities exist only in single countries or small sets of

countries. Nevertheless, these effects can be studied using the methodology we have

presented here (see Mason et al. 1991 in the context of restricted maximum likelihood

(REML) using an approach that extends readily to our Bayesian approach).

3. We recommend that multiple classrooms per school be sampled whenever possible in

future cross-national surveys so that classroom and school variance, classroom and school

segregation, and composition effects may be estimated separately.
50

4. The Bayesian methodology for synthesizing results across countries, combined with

REML estimation within countries appears promising in a) accounting for uncertainty

within countries when there is a multistage cluster design, and b) accounting for

uncertainty arising from between-country heterogeneity. 

5. It appears feasible and useful to compare countries not only with respect to mean literacy

levels but also with respect to social inequality in literacy levels. Each country's index of

social inequality in literacy can be further "unpacked" into components reflecting withinschool social inequality in literacy and between-school social inequality in literacy, arising

from between-school social segregation.

6. It is then possible to compare the literacy levels of socially advantaged and disadvantaged

students across societies.
51

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Appendix

Empirical Bayes and Bayes Estimation Theory 

for Two-Level Models with Normal Errors

1. Introduction

Historically, the hierarchical linear model (HLM) has been developed and promoted from a

Bayesian perspective.1 More generally, Bayesian approaches to statistical problems have been studied

since Thomas R. Bayes's (1763) famous paper,2 but only recently have practical estimation techniques

been available to implement Bayesian statistical methods for many current applications. One

difficulty encountered with traditional implementation of Bayesian methods is the required integration

over one or more parameter spaces. Many applications of scientific interest have complicated,

multidimensional parameter spaces. Some of these integration problems can be solved with

sophisticated numerical analytic techniques, while others have been resistant to analytic solution.

Two estimation techniques, the EM algorithm developed by Dempster, Laird, and Rubin3 and

the Gibbs sampler introduced by Geman and Geman4 have been instrumental in making the Bayesian

approach to the HLM a practical alternative. The EM approach to HLM as first described by

Dempster, Rubin, and Tsutakawa5 can be viewed either as a strictly classical procedure or as

providing an approximation to the Bayesian posterior distribution. This approximation is known as

an empirical Bayes approach because the parameters of certain prior distributions are estimated from

the data rather than specified a priori. The empirical Bayes strategy we have adopted for withincountry analysis is described briefly in the next section. In Section 3, we discuss the Gibbs sampler

as an improved Bayes solution in the context HLM. The Gibbs sampler is a sampling-based algorithm

for calculating finite approximations to posterior distributions enabling one to incorporate more

information into the calculation of a posterior distribution and provide a better account of the

uncertainty associated with parameter estimation than is possible using EM. Fotiu,6 Gelfand and

Smith,7 and Seltzer8 provide more detailed treatments.

We note that the stage-1 analysis employs the empirical Bayes approach within each country.

The stage-2 analysis employs the Gibbs sampler to synthesize results from the several countries.

 

1D.V. Lindley, and A.F.M. Smith. Bayes Estimates for the Linear Model (with discussion). Journal

of the Royal Society, Series B, 34, 1-41, 1972.

2T.R. Bayes. An Essay Towards Solving a Problem in the Doctrine of Chances. Philosophical

Transactions of the Royal Society, 53, 370, 1763 (reprinted in Biometrika, 45, 293-315, 1958).

3A.P. Dempster, N.M. Laird, and D.B. Rubin. Maximum Likelihood from Incomplete Data Via the

EM Algorithm (with discussion). Journal of the Royal Statistical Society, Series B, 39, 1-38, 1977.

4

S. Geman, and D. Geman. Stochastic Relaxation, Gibbs Distributions and the Bayesian Restoration

of Images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6, 721-741, 1984.

5A.P. Dempster, D.B. Rubin, and R.K. Tsutakawa. Estimation in Covariance Components Models.

Journal of the American Statistical Association, 76, 341-353, 1981.

6R.P. Fotiu. A Comparison of the EM and Data Augmentation Algorithms on Simulated Small

Sample Hierarchical Data from Research on Education. Unpublished doctoral dissertation, East

Lansing, MI: Michigan State University, 1989.

7A.E. Gelfand, and A.F.M. Smith. Sampling-Based Approaches to Calculating Marginal Densities.

Journal of the American Statistical Association, 85, 398-409, 1990.

8M.H. Seltzer. Sensitivity Analysis for Fixed Effects in the Hierarchical Model: A Gibbs Sampling

Approach. Journal of Educational Statistics, 18(3), 207-235, 1993.

53
2. Empirical Bayes Estimation with the EM Algorithm

2.1 The Model

We now consider the two-level HLM and its assumptions for the empirical Bayes estimation

approach.9 The model is formulated in submodels: a level-1 model that describes variation within

clusters and a level-2 model that describes variation between clusters.

Level-1 Model. Within clusters such as classrooms, the outcome Y is viewed as

depending on characteristics of level-1 units according to the model 

where Y is a vector of outcomes, X is a matrix of known predictors, is a vector of unknown level-

(1)

1 regression coefficients describing the relationship between X and Y within the clusters, r is a vector

of level-1 random effects, and ∑ is a positive-definite level-1 covariance matrix. Assuming X to be

of full rank and known, one might estimate via generalized least squares, i.e., 

(2)

(3)

Typically, it is assumed that ∑ = σ

2

I in which case equation (2) reduces to ordinary least squares with

V = σ

2 (XTX)-1

.

Level-2 Model. Between clusters, the coefficients are viewed depending upon cluster

characteristics and random error according to the model

(4)

where W is a matrix of known cluster characteristics, is a vector of unknown level-2 regression

coefficients describing the relationship between W and between clusters, u is a vector of level-2

random effects, and T is a positive definite level-2 covariance matrix, having block diagonal structure

with J identical submatrices τ along the main diagonal, one submatrix for every cluster j = 1, 2,...,J,

i.e., T = subdiag (t). 

Combined Model. Substituting equation (4) into equation (1 ) gives the combined model

(5)

 

9We present the model in its "hierarchical form" as opposed to the more general mixed model form. Raudenbush (S.W. Raudenbush. Educational

Applications of Hierarchical Linear Models: A Review. Journal of Educational Statistics, 13,2,85-116, 1988) discusses the two forms of the

model. This clarifies the parallels with our application of Gibbs sampling, although the mixed model form is actually more general and will be

employed in the stage-1 analysis.

54
Premultiplying equation (5) by V XT∑

-1

 yields the equivalent model

(6)

showing that the marginal distribution of is N with = V + T. Thus, the generalized

least squares estimator of and its covariance matrix are given by 

(7)

and

(8)

Empirical Bayes Estimation. Following Dempster, Rubin, and Tsutakawa,10 we now

formulate a noninformative prior distribution for such that, a priori,

~ N (0,Γ), Γ

-1 → 0 (9)

Equation (9) assumes that the prior precision, Γ

-1, of our knowledge about the value of approaches

0. As a result, the specific value of the location parameter is inconsequential, and we have chosen

0 for convenience. Then the conditional posterior density of |Y , ,T is and the conditional

density of is N(b

*

,Db) where is given by equation (7), is given by equation (8), and

we have

(10)

and

(11)

where

(12)

We note that the conditional covariance between and , is

(13)

Empirical Bayes inferences about and are typically made by substituting maximum likelihood

(ML) estimates of ∑ and T in equations (7), (8), (10) and (11). Such inferences do not take into

account the uncertainty of the ML estimates.

 

10See footnote 5.

55
2.2 Covariance Estimation via EM

Suppose that, in addition to the data Y , the level-1 random effects r and the level-2 random

effects u were also observed. Then, with T = subdiag (t) and ∑ = s

2

I, ML estimators of the

covariance components t and s

2

 could be computed simply as

(14)

where J is the number of clusters, N is the number of level-1 units, uj is the jth subvector of u, and

rj is the jth subvector of r. Of course, the quantities u and r are not observed. However, given current

estimates of the covariance parameters, the sufficient statistics defined by equation (14) (termed

"complete-data sufficient statistics")11 can be estimated by their conditional expectations given the data

and these current parameter estimates. Thus, based on equations 10 to 13, and denoting current

estimates with the superscript "

p

", we have

(15)

where

(16)

Given an initial estimate of t and σ

2

, and therefore of the posterior distribution of (from

equations (7) and (8) and ß (from equations 10 to 13), the EM algorithm iteratively computes the

complete-data sufficient statistics (equation 15) and then uses these to compute new complete-data

ML estimators using equation (14). Equation (15) is called the "E" or "Expectation" step and

Equation (14) is called the "M" or "Maximization" step. Under quite mild conditions, each E-M cycle

increases the observed data likelihood 

(17)

until convergence to a maximum.

 

11See footnote 5.

56
At convergence, empirical Bayes estimates are based on

(18)

As mentioned, the empirical Bayes approach does not take into consideration the uncertainty of our

knowledge of the unknown variance-covariance components s

2

 and t.

3. Bayesian Estimation with the Gibbs Sampler

Bayes (Via Gibbs) Versus Empirical Bayes (Via EM). The Gibbs sampler is a special case

of the data-augmentation algorithm described by Tanner and Wong.12 These two approaches are

compared by Gelfand and Smith.13 A number of methodologies that offer solutions on a continuum

between the Gibbs sampler and the EM algorithm are discussed by Tanner.14 The essential difference

between empirical Bayes estimation via the EM algorithm and Bayesian estimation via the Gibbs

sampler applied to the HLM is that the Bayesian approach using the Gibbs sampler computes a

posterior distribution for the variance-covariance components in the model, rather than summarizing

this information into a point estimate as illustrated in equation 18. Hence, Bayesian inferences about

b, are based on

(19)

This Bayesian approach provides more information about the posterior distribution of a

model's parameters than is available with empirical Bayes because more elements of uncertainty are

accounted for explicitly.

The following assumptions for the Bayesian formulation are the same as those specified earlier

for the empirical Bayes approach, except that we now add prior distributions for the parameters s

2

and t. The variance parameter, s

2

, is assumed a priori to have an inverse chi-square distribution given

by

(20)

with the degrees of freedom parameter n0 and noncentrality parameter s0

2

. This prior distribution for

σ

2

 is considered noninformative in its contribution to the posterior distribution of s

2

 as n0 approaches

0. In addition, the variance-covariance matrix, t, is assumed a priori to have an inverse Wishart prior

distribution given by

t ~W-1(Y ,n) (21)

where Y is the precision matrix of the inverse Wishart distribution and n is the degrees of freedom

parameter. This prior distribution for n is assumed to be noninformative in its contribution to the

posterior distribution of t as the degrees of freedom parameter, n, approaches 0, and Y approaches

0.

 

12M.A. Tanner. Tools for Statistical Inference (New York: Springer-Verlag, 1992).

13See footnote 7.

14See footnote 12.

57
It is the assumptions concerning the model in conjunction with the data that determine the

joint distribution of all unknowns given by 

(22)

Two alternative expressions for this joint density15 are

(23)

with q1 = r1. Gibbs sampling exploits the fact that, although this joint density is not tractable, both

q3 and r2 are readily accessible (as shown below) so that it is simple to sample from those. Starting

from rough guesses at the values of and t, Gibbs works by sampling from r2 to obtain new values

of and s

2

. Knowing those values, it is easy to sample from q3, yielding new values of and t.

This process iterates as described in more detail in the next section. The goal is to obtain the joint

posterior densities of all unknowns, i.e., 

(24)

The marginal posteriors are readily derived from this joint posterior.

The Gibbs Sampler. The Gibbs sampler uses the data and distribution assumptions to generate

approximate posterior distributions by Monte Carlo sampling. Successive iterations move closer to

the true posterior distribution until stochastic convergence is achieved. After convergence, we can

collect a sufficiently large set of generated parameters from subsequent iterations as a finite

approximation to the true posterior distribution.

There are two basic steps to this algorithm. The first step is to calculate a current

approximation of a required posterior distribution. The second step is to sample from this distribution.

Initially, suppose the parameters and t from q3 in (23) and σ

2

 were observed. Then * and

could be calculated, where the asterisk (*) indicates an estimated posterior mean. Next, given * and

just calculated, can be sampled by Monte Carlo methods from the posterior distribution of

. With the knowledge of , an estimate of the central tendency of the conditional

distribution of s

2

 (given ) can be calculated and then s

2

 is sampled from its conditional distribution

given Y and (see below). The resulting parameter pair of and s

2

 approximates a sample from

r2 in (23).

In a similar manner, a sample of and t can be obtained. Given parameters and s

2

 from

r2 just realized along with t from the previous iteration, the posterior mean and variance of can

be calculated from the distribution of given ,s

2

, and t. Next, the location parameter for the

 

15C.N. Morris. Comment on article by Tanner and Wong. Journal of American Statistician, 82, 542-

543, 1987.

58
conditional density of (given and this new ) can be calculated and then t can be sampled from

its conditional density given and . This results in the parameter pair of and t approximating

a sample from q3 in (23).

To improve the approximations, the resulting sample from q3 is considered an intermediate

approximation of q3 and recycled back to calculate estimated conditional distributions and generate

a new sample to update r2. The new sample from r2 is used in the same manner to calculate

estimated conditional distributions and obtain a sample from q3. This iteration scheme is repeated

until convergence. Afterwards, m more iterations are completed and the parameter values from each

iteration are collected. If m is large, the mixture of the densities can be considered a finite

approximation to the joint posterior distribution given in (24).

One advantage of this algorithm is that not only are point estimates generated, but the results

also include finite approximations to the true joint posterior distribution. For example, the sampled

values for a parameter of interest can be sorted in order and then the a/2 percent tails of the

distribution can be easily determined. As a consequence, highest posterior densities can be easily

determined for both symmetric and nonsymmetric distributions.

Initial Values. It does not matter at what level parameter estimation begins. In the example

detailed next, we shall begin at the first level in the hierarchy to obtain values for and s

2

. Initial

values for , and s

2

 are required for the start of this algorithm, in addition to the ordinary least

squares (OLS) estimate of . Initial parameter estimates may be calculated by a variety of

techniques. Of course, better initial estimates will result in faster convergence. One strategy is to

use the empirical Bayes modal estimates of the posterior distributions as a starting point. 

Calculating and Sampling the First-Level Parameters b and s

2

. The sampling of the first-level

parameters requires the knowledge of and s

2

. The data Y are summarized in the OLS

estimator, and its sampling variance, V. The first iteration of the algorithm uses the initial values,

while subsequent iterations use the previous iteration's generated values.

The desired posterior density r2 can be rewritten as

(25)

Let s

2(i-1)

, , and t

(i-1) indicate the previous iteration's sample. New values for the 's can be

calculated as

(26)

A new set of j's can be sampled from the density of given , which is

N β where (i)

j

,D(β

(i 1)

j

) ,

(27)

59
First, the matrix is factored by the Cholesky method such that , where

is a lower triangular matrix. Next, the 's are sampled with the following equation:

(28)

where is a vector containing independent and identically distributed elements sampled from N(0,

1).

After generating new 's from (28), s

2*(i)

 can be calculated as follows:

(29)

where N = ∑nj. Alternatively, equation (29) can be expressed as 

(30)

to minimize computation and illustrate the partitioning of the sources of variation. The first part of

the expression enclosed in brackets computes the sum of the squared deviations of the ordinary least

squares prediction from the observed data vector Y . This expression can be computed once because

its value is constant across iterations of the algorithm. The second part of the expression adds the

variance as a function of the deviation of from the ith iteration's realization of the parameter .

It has been assumed that s

2

 has an inverse chi-square prior distribution with υ0 degrees of freedom.

The posterior distribution of s

2

 given the data and is 

(31)

For a noninformative prior, we can let υ0 approach 0 in its contribution to the posterior

distribution and (31) becomes

(32)

To sample s

2

, we generate a chi-square variate with N degrees of freedom, invert it, and substitute

it in (32) to obtain s

2(i)

. 

Now we have a sample from r2 of the parameter pair . These are passed on to

calculate and sample the second-level parameters in the HLM.

Calculating and Sampling the Second-Level Parameters and t. Given a pair of 

drawn from r2 we can calculate the posterior mean, and with t

(i-1) from the previous

iteration a sample is drawn from the posterior distribution of . In a similar manner, we can then

use our sampled to calculate a conditional distribution for t and then sample from it. The goal is

to achieve a realization of the parameter pair ( ) from q3. The distribution of q3 may be expressed

60
in the following form:

(33)

We note in passing that the calculation and sampling of and t does not directly depend on σ

2

.

A posterior sample of given and t is drawn from the normal distribution

(34)

The posterior mean value for can be calculated as:

(35)

A sample is drawn from equations 34 and 35 given and t

(i-1)

as follows. Let

(36)

The matrix A

(i) is then factored such that

(37)

where is a lower triangular Cholesky factor of A

(i). The matrix equation used to generate a new

is

(38)

The column vector x

(i) contains elements that are independently and identically distributed N(0, 1).

The new vector is used to update the posterior distribution of t. Let

(39)

Based on the noninformative inverse Wishart prior distribution with parameters Ψ and υ, the posterior

distribution of t given and is given by

t ~W-1(C(1)+Y , J+n) (40)

If we assume that the prior precision matrix Ψ approaches 0 and that the prior degrees of

freedom parameter υ approaches 0, we can sample τ from

(41)

61
To sample τ from equation (41), we find the Cholesky factor of the C

(i) such that C(i)

 =

D

(i)D(i)T, where D(i) is a lower triangular matrix. For E(i), a lower triangular matrix, define

(42)

If eij is an element of E where each e

2

jj element on the main diagonal is an independent chi-square

variable with J degrees of freedom and the elements below the diagonal are independently distributed

N(0, 1), then F

(i) will have an inverted Wishart distribution with J degrees of freedom.16

This concludes one complete iteration of the Gibbs sampler. To improve the approximation

to the posterior distribution of interest the new values for and t

(i) are passed to the next iteration

to generate new updated values for and s

2

. This process of calculation and sampling is continued

until convergence. After the algorithm has converged, a sequence of m further iterations are

performed. The parameter samples resulting from each iteration are collected. This sample of size

m of the model's parameters is considered a finite approximation to the true joint posterior

distribution.

3.1 The V-Known Modification

There are some situations when the dispersion matrix V can be estimated with enough

precision to be considered known. This may be a reasonable assumption when the sample size used

to compute V is sufficiently large, as in the case of the IEA Reading Literacy Study data from each

country. An advantage resulting when the V-known simplifying assumption is tenable is a reduction

in the algorithm's computational burden. In this situation, computing an estimate of V and sampling

from its posterior distribution every iteration is not required. Another advantage occurs frequently

in meta-analysis situations. Typically, access to the raw data is impossible and one is forced to work

with summary statistics. For the case where V is considered known, the only modification required

of the Gibbs sampler developed above is to skip the estimation and sampling of elements of V such

as s

2

. Otherwise the algorithm is the same.

 

16M.S. Bartlett. On the Theory of Statistical Regression. Proceedings of the Royal Statistical Society

of Edinburgh, 53, 260-283, 1933.

62
63

A Nine-Country Study: What Were the Differences Between the

Low- and High-Performing Students in the IEA Reading Literacy Study?

Karin Taube

University of Umea, Sweden

Jan Mejding

Danish Institute for Educational Research

While it is clear that differences in students’ reading ability can be found between countries (Elley

1992, 1994; Ross and Postlethwaite 1992; Linnakylä and Lundberg 1993), differences in reading ability

also exist within countries. The goal of the study described in this paper was to investigate whether withincountry differences in reading between low- and high-performing students share common traits across

countries. The traits in question are those that may originate either from the student, the home background,

the teaching and the resources at school, or the resources and reading behavior in the society.

The data analyzed here came from nine countries: Denmark, Finland, France, Germany, Italy,

Spain, Sweden, Switzerland, and the United States. The questions addressed are of the following kind: To

what extent were the factors of significant importance identical in relation to the students’ reading

performances in these countries? Was the pattern of important variables the same no matter the country?

1. Method

1.1 Students

Two populations in each country were included in the reading literacy study: Population A, the 9-

year-olds, and Population B, the 14-year-olds. The samples were picked to be representative of the

populations at these ages in the particular country.

1.2 Instruments

The students' reading abilities were measured by a reading test that included three different types of

written material: narrative passages, expository passages, and documents (maps, tables, charts, etc.) (Elley

1992). The tests were scored on an international scale with 500 as the international mean and 100 as one

standard deviation. In the current study, the total score based on the results in the three types of passages

has been used. Each student also answered a background questionnaire covering such areas as student

attitudes, reading behavior, and family circumstances.
64

1.3 Procedure

Three groups of students—high, medium, and low performing—were identified as a first step. The

medium group was designated as a reference group, and the three were characterized as follows:

• High-performing students: Those with a reading score > 1.3 standard deviations above the

mean;

• Middle-performing students: Those with a reading score ±0.13 standard deviations around

the mean; and

• Low-performing students: Those with a reading score >1.3 standard deviations below the

mean.

Given a normal distribution, these cutoff points would assign approximately the top 10 percent, the middle

10 percent, and the lowest 10 percent to the three respective performance groups.

Tables 1 and 2 show for each country the number of students at the two age levels, the grade level

tested, and the mean age of the students. The students in Denmark, Finland, Sweden, and Switzerland start

school at age 7, while students in France, Germany, Italy, Spain, and the United States start at age 6. This

is one of the reasons that different countries test students at different grade levels.

Table 1. Number of students participating in the IEA Reading Literacy Study, by country,

performance group, grade level, and total sample mean age: Population A

Sample

characteristic Denmark Finland France Germany Italy Spain Sweden Switzerland USA Total

IEA sample

size ............ 3,148 1,462 1,817 2,753 2,216 8,192 4,190 3,306 6,461 33,545

Selected

student group 891 419 543 831 657 2,440 1,280 985 1,931 9,977

Grade level

tested......... 3 3 4 3 4 4 3 3 4 -

Mean age ... 9.8 9.7 10.1 9.4 9.9 10.0 9.8 9.7 10.0 -

- Not applicable.

Table 2. Number of students participating in the IEA Reading Literacy Study, by country,

performance group, grade level, and total sample mean age: Population B

Sample

characteristic Denmark Finland France Germany Italy Spain Sweden Switzerland USA Total

IEA sample

size ............ 3,582 1,209 2,425 4,062 3,059 8,426 3,361 6,282 3,217 35,623

Selected

student group 1,049 361 693 1,223 918 2,509 1,024 1,852 943 10,572

Grade level

tested......... 8 8 9 8 8 8 8 8 9 -

Mean age ... 14.8 14.7 15.4 14.6 14.1 14.2 14.8 14.9 15.0 -

- Not applicable.
65

1.4 Variables

A number of background variables from the student questionnaire, assumed to be of importance in

relation to reading ability, were studied in both populations (Table 3).

The investigated variables in Population A were as follows:

Student characteristics:

age, gender, reading self-image, frequency reading books, frequency reading comics,

frequency reading magazines, frequency reading newspapers, frequency borrowing library

books.

Home characteristics:

number of books at home, a daily newspaper at home, number of meals per week,

frequency speaking the language of the reading test at home, hours watching TV,

frequency people at home read to student, frequency student reads to someone at home,

frequency student reads aloud at home.

The investigated variables in Population B were as follows:

Student characteristics:

age, gender, reading self-image, expected further education, frequency borrowing library

books.

Home characteristics:

parental education, number of books at home, a daily newspaper, number of meals per week,

frequency speaking the language of the reading test at home, hours watching TV, job or

duties, time spent on job.

School characteristics:

frequency reading at school

The Student Characteristics. Age is a somewhat complicated variable. For very young children it

is certainly important in relation to reading. For one, before a certain age, children cannot be expected to

be able to read at all. However, for teenagers differences in terms of reading performances connected to

differences in age tell another story. Students older than their classmates may be in those classes for

various reasons: Some may have started school very late. The biological maturation rate differs from

individual to individual, and in some countries it is possible to postpone starting school if the child is still

considered immature. Others may have been retained in grade because of long periods of absence or

because of their failure to meet the educational demands expected by the school system. In some cases,

students simply have their birthday in the very first part of the school year and, therefore, are older than

their classmates. The first mentioned situations could be expected to be connected to low reading

performances, while the latter probably is not.
66

Table 3. Variables used in the discriminant analyses: Populations A and B

Type of

variables

Var. names,

Pop A

Var. names,

Pop B

Variable labels Variable definitions

Student characteristics AGEY AGEY Very "young" student is at least 1.3 standard deviations

below the country mean =1;

else=0

AGEO AGEO Very "old" student is at least 1.3 standard deviations

above the country mean =1;

else=0

GENDER GENDER Boy or girl boy=0, girl=1

ASSRATE BSSRATE Reading self-image not very good=1, average=2, good=3, very

good=4

BSEDUCAR Expected further

education

0 year=1, 1 or 2 years=2, etc.

more than 10 years=7

ASBOOKF Freq reading books almost never=1, about once a month=2,

about once per week=3;

almost every day=4

ASCOMIF Freq read comics almost never=1, about once a month=2,

about once per week=3;

almost every day=4

ASMAGAF Freq read magazines almost never=1, about once a month=2,

about once per week=3;

almost every day=4

ASNEWSF Freq read newspapers almost never=1, about once a month=2,

about once per week=3;

almost every day=4

ASBORBO BSBORBO Freq borrow library

books

never=1, almost never=2, once a month=3,

once per week=4 etc.

Home characteristics PAREDQ Parental education number of years of education for parent with

the longest education=PARED

25% lowest PARED=1, 50% middle

PARED=2, 25% highest PARED=3

ASBOOKS BSBOOKS Number of books at

home

none=0, 1-10=2, 11-50=3, 51-100=4, 101-

200=5, >200=6

NEWS NEWS Newspaper at home

or not

daily newspaper=1, else=0

MEALS MEALS Meals per week 3 meals 7 days per week=1, else=0

ASUSLANR BSUSLANR Freq speak lang of the

reading test at home

never=1, almost never=2, sometimes=3,

almost always=4, always=5

ASTVR BSTVR Hours watching TV at

home

none=0, up to one hour=0.5, 1-2 = 1.5 ...etc

>5 hours=5.5

TVSQ* TVSQ* Hours watching TV at

home

ASTVR x ASTVR

BSTVR x BSVTVR

ASPRHTL People read to student

at home

never=1, 1 or 2 times a week=2,

3 or 4 times a week=3, almost every day=4

ASREATL Student read to

someone at home

never=1, 1 or 2 times a week=2,

3 or 4 times a week=3, almost every day=4

ASALOUF Read aloud at home never=1, <1 hour a week=2, 1-3 times a

week=3, nearly every day=4

JOBNONE Job or duties job=1, else=0

JOB Time with job >1 hour per day=1, else=0

School characteristics BSSREADD Frequency reading at

school

total time spent on school reading per week

*This variable was created because in some countries the relation between TV watching and the test score was not linear.

SOURCE: IEA Reading Literacy Study, 1991.
67

Students who are younger than their classmates may have parents who started them in school sooner

than they were meant to, and these students are likely to be good at reading. Young students may also be

those born in the last part of the school year. This may be a disadvantage during their first grades, but it

seems reasonable to assume that after 8 or 9 years in school this disadvantage has disappeared.

In this analysis we have focused on the two extreme age groups: AGEO is the older students more

than 1.3 standard deviations older than the country mean for that population; AGEY is the younger

students more than 1.3 standard deviations below the country mean.

Gender differences have given rise to some concern in the reading research literature. In most cases

girls outperform boys (Thorndike 1973; Downing 1972), but there are also examples of the opposite being

true (Preston 1962). Two explanations have mainly been brought up in connection with gender differences

and reading. One is based on the biological fact that some boys are less mature at school start than girls of

the same age, and as a result, these boys face difficulties when trying to learn how to read. The other is of

more social/psychological nature, focusing on role modeling: most primary school teachers are females,

which makes it easier for girls to identify positively with their teachers and accordingly to learn better.

Though age and gender are important student characteristics, we also have information on studentrelated variables that give us a more diversified picture. Low-performing students can be distinguished

from high-performing students in several ways. One of them is self-esteem (Taube 1988). Lowperforming students often have lost faith in their own abilities, and consequently they give up more easily

when confronted with demands on performance. Hence, if they perceive reading as difficult, they tend to

read less than their peers of same age and instead prefer other activities. The variables ASSRATE and

BSSRATE: how good are you at reading? and BSEDUCAR: how much further education do you expect

to have in the future? are indicators of the students’ perception of their own abilities in reading. The

variables ASBORBO and BSBORBO: how often do you borrow a book at the library? BSSREADD: how

often do you read at school? and ASBOOKF, ASCOMIF, ASMAGAF, and ASNEWSF: how frequently

do you read books, comics, magazines, and newspapers? tell us something about their reading activities,

while ASTVR and BSTVR: how much do you watch TV outside school? and TVSQ: the squared function

of (A/B)STVR, used because of a nonlinear relationship found in some countries, tell us about another

activity strongly competes with reading.

Although the frequency with which the student reads at school can also be thought of as a studentrelated variable, we have chosen to classify it as related more to the school level. The school, as an

organization, can leave more or less room for the students to read on their own during the school hours.

However, given the time to read at school, it is expected that the better students will engage more frequently

in this activity than the less able students.

The Home Characteristics. Finally, students’ home background is often seen to influence school

performance: the stronger the educational tradition in the family, the more readily reading material is

available in the home. The more often the language in which students receive education is also the

language spoken in their homes, the more likely it is that they will perform well at school in reading. A

number of variables in this study depict different aspects of home environmental support. PAREDQ:

number of years of education for the parent with the most education, ASBOOKS and BSBOOKS: amount

of books at home, NEWS: whether a daily newspaper is available at home or not, and ASUSLANR and

BSUSLANR: frequency with which the language taught in school is also spoken at home, are all examples

of such conditions that are believed either to promote or to obstruct school performance. The same goes

for the following three variables: MEALS: whether the student is used to having three regular daily meals
68

or not, JOBNONE: whether the student has a job or other family responsibilities, and JOB: if so, how

much time is then spent at that job.

The interesting point is to see whether the power of these variables that are generally accepted as

influential remains constant across the nine countries, or if different countries will show different patterns

within the functions that separate the high-performing student and the low-performing student from the

ordinary student.

1.5 Analysis Procedures

The discriminant analysis used in the current study consisted of both an interpretation and a

classification phase. The interpretation phase was a canonical discriminant analysis. It involved

• Deriving canonical discriminant functions;

• Establishing how many canonical discriminant functions to use;

• Examining the importance of the remaining functions; and

• Interpreting the meaning of the remaining functions for explaining differences between low-,

middle-, and high-performing 9- and 14-year-old students.

Canonical Discriminant Analyses. Canonical discriminant function analysis was used to study the

characteristic differences among the three performing groups. A canonical discriminant function is a linear

combination of discriminating variables and has the following mathematical form (Klecka 1980):

fkm= uo+u1X1km+u2X2km+....+upXpkm'

where

fkm= the value (score) on the canonical discriminant function for the case m in group k;

Xikm= the value on discriminating variable Xi for case m in group k; and

ui= coefficients to be estimated.

The coefficients for the first function are derived so that the group means on the function differ as much as

possible. The coefficients for the second function are derived in the same way. However, the values on the

second function are constrained not to be correlated with the values of the first function. The largest

number of functions it is possible to derive in this way equals to number of groups minus one. Each

dimension can be tested for statistical significance. Functions found to be insignificant can then be ignored.

Selection of Variables. In a first set of runs all the variables described above were used, resulting

in the so-called full model. To reduce the number of variables, we ran a stepwise discriminant analysis

with backward elimination. This works in much the same way as backward elimination in multiple

regression. The analysis starts by using all variables. At each step the least important variable will be
69

excluded until only those of significant importance for the discrimination between the groups are left—the

so-called reduced model.

When interpreting the results, levels of significance, percentage of correctly classified, etc., it should

be remembered that they pertain only to the chosen subsample of approximately 10 percent low-, medium-,

and high-performing students, respectively.

2. Results

2.1 Between-Country Analysis

To get an impression of how the three performance groups scored on the reading test within and

among countries, we plotted the mean results from the three groups according to their reading accuracy and

their reading speed. In this case reading accuracy was based on a scale that took into consideration only

the attempted items, and reading speed was the percentage of items attempted within the given time limit.

Figure 1 shows the results for the 9-year-old population. There are clear differences between the

performance groups within each country, and within the middle- and low-performing group, there are

differences among countries. It is noteworthy that in the low-performing group the mean completion rate

for the United States was as high as 95 percent, while in Denmark it was only 52 percent.

Figure 2 shows the results for the 14-year-old population. Again, we find clear within-country

differences, and, in the low-performing group, between-country differences as well.

2.2 Interpretation Phase

Stepwise canonical discriminant analysis was used to get a total model and a reduced model for each

country. Only the reduced models will be discussed here.

Using One or Two Functions. The order of importance of the functions is known since the

functions are derived in descending order of importance with the first function providing the greatest

discrimination. The second function contributes with the greatest power after the power of the first function

has been removed. In our case, the maximum number of unique functions would be two since we used three

groups of students. But are both the derived functions statistically and substantively significant? Even a

statistically significant function may be considered of less importance if it does not discriminate among the

three groups well enough. Although the first function always is the relatively most powerful one, it may

only show a weak relationship with the groups (Klecka 1980). The canonical correlation coefficients

summarize the relationship between the discriminant function and the groups. A high canonical correlation

coefficient, close to 1.0, shows a strong such relation. A comparison of how much of the total

discriminating power each of the functions contributes is also valuable in terms of evaluating their

importance.
70

Figure 1. Reading performance, by group and country: Population A

350

400

450

500

550

600

650

700

50 55 60 65 70 75 80 85 90 95 100

> 1.3 Stdv above country mean

+ 0.13 Stdv around country mean

> 1.3 Stdv below country mean

Students:

9 - Country Mean

Percent Items Reached

Reading speed

Denmark

Finland

France

Germany

Italy

Spain

Sweden

Switzerland

USA

Denmark

Finland

France

Germany

Italy

Spain

Sweden

Switzerland

USA

Denmark

Finland

France

Germany

Italy

Spain

Sweden

Switzerland USA
71

Figure 2. Reading performance, by group and country: Population B

350

400

450

500

550

600

650

700

75 80 85 90 95 100

> 1.3 Stdv above country mean

+ 0.13 Stdv around country mean

> 1.3 Stdv below country mean

Students:

9 - Country Mean

Percent Items Reached

Reading speed

Denmark

Finland

France

Italy

Spain

Switzerland

Denmark

Finland

France

Germany

Italy

Spain

Sweden

Switzerland

USA

Denmark

Finland France

Germany

Italy

Spain

Sweden

Switzerland

USA

Sweden

USA

Germany
72

Klecka (1980) recommends an examination of both the canonical correlation coefficients and the

relative percentage of the discriminating power. Tables 4 and 5 show the canonical correlation coefficients,

the percentage of the total discriminating power, and the statistical significance for the two functions in the

reduced models for Populations A and B in each of the nine countries.

Table 4. Canonical correlation coefficients, percentage of the total discriminating power, and

significance for the two canonical discriminant functions in the reduced models for the

nine countries: Population A

Country

Canonical

discriminant

function number

Canonical

correlation

coefficient

Percent of the total

discriminant

power

Pr>F

Denmark............................................. 1 0.71 97.8 0.0001

2 0.51 2.2 0.0171

Finland ............................................... 1 0.62 90.7 0.0001

2 0.24 9.3 0.0196

France................................................. 1 0.60 96.3 0.0001

2 0.14 3.7 0.0277

Germany ............................................. 1 0.64 93.2 0.0001

2 0.22 6.8 0.0130

Italy .................................................... 1 0.50 83.2 0.0001

2 0.25 16.9 0.0001

Spain .................................................. 1 0.63 95.8 0.0001

2 0.13 4.3 0.0001

Sweden ............................................... 1 0.66 97.4 0.0001

2 0.14 2.6 0.0095

Switzerland......................................... 1 0.71 94.0 0.0001

2 0.24 6.0 0.0001

USA.................................................... 1 0.55 97.6 0.0001

2 0.10 2.4 0.0383

SOURCE: IEA Reading Literacy Study, 1991.

As Table 4 shows, the canonical correlation for the first functions in Population A varied between

0.50 and 0.71. The relationship between the discriminant function and the groups seemed to be highest in

Denmark (0.71) and Switzerland (0.71) and lowest in Italy (0.50) and the United States (0.55).

The percentage of the total discriminating power for the first function in the nine countries varied

between 83.2 percent and 97.8 percent. Denmark (97.8 percent) and the United States (97.6 percent) had

the first function with the highest percentage and Italy (83.2 percent) the first function with the lowest

percentage of the total discriminating power. All the first functions were highly statistically significant.

In all cases, the second function showed a much lower canonical correlation with the groups (0.10-

0.25) and percentage of the total discriminating power (2.2 percent-16.9 percent). In relation to the other

countries, Italy seemed to be the one with a second function of most importance, while the second

discriminant functions for Denmark and the United States were of minor importance.
73

Table 5. Canonical correlation coefficients, percentage of the total discriminating power, and

significance for the two canonical discriminant functions in the reduced models for the

nine countries: Population B

Country

Canonical

discriminant

function number

Canonical

correlation

coefficient

Percent of the total

discriminant

power

Pr>F

Denmark............................................. 1 0.74 97.1 0.0001

2 0.19 2.9 0.0002

Finland ............................................... 1 0.66 99.3 0.0001

2 0.07 0.7 0.8829

France................................................. 1 0.70 98.9 0.0001

2 0.10 1.1 0.3444

Germany ............................................. 1 0.73 95.1 0.0001

2 0.23 4.9 0.0001

Italy .................................................... 1 0.67 98.2 0.0001

2 0.12 1.8 0.1312

Spain .................................................. 1 0.68 96.5 0.0001

2 0.18 3.5 0.0001

Sweden ............................................... 1 0.68 96.6 0.0001

2 0.17 3.4 0.0010

Switzerland......................................... 1 0.64 94.9 0.0001

2 0.19 5.1 0.0001

USA.................................................... 1 0.70 91.7 0.0001

2 0.28 8.2 0.0001

SOURCE: IEA Reading Literacy Study, 1991.

As is shown in Table 5, the canonical correlation for the first functions in Population B varied

between 0.64 and 0.74. The relationship between the discriminant function and the groups seemed to be

highest in Denmark (0.74) and Germany (0.73) and lowest in Switzerland (0.64).

The percentage of the total discriminating power for the functions varied between 91.7 percent and

99.3 percent, with Finland having the first function with the highest percentage of the total discriminating

power (99.3 percent) and the United States having the lowest (91.7 percent). All the first functions were

statistically significant. There seemed to be strong evidence for considering the first functions in all

countries as substantively meaningful with a fairly high level of utility in explaining group differences.

As expected the second functions showed much lower canonical correlation with the groups (0.07 to

0.28) and much lower percentages of total discriminating power (0.7 percent to 8.2 percent). In relation to

the other countries, the United States and Germany seemed to be the ones with the most important second

function, while the second discriminant functions for Finland, France, and Italy were not even significant.

For the latter countries, only the first functions will be used for further analyses.

2.3 The Most Important Structure Coefficients

A step towards understanding the meaning of the derived functions involves examination of the

relations between the individual background variables and the functions. This can be done using two kinds

of information: the standardized discriminant coefficients and the total structure coefficients.
74

The standardized discriminant coefficient tell us the relative importance of the individual

background variables in terms of contribution to the discriminant score. The total structure coefficients

show the correlation between each individual variable and the discriminant function. Klecka (1980)

suggests that the function in question should be "named" after including variables having the highest

structure coefficients, since these identify what kind of information in the function gives us most

discrimination between groups.

The use of standardized coefficients to evaluate the importance of a certain variable has a serious

limitation, and the total structure coefficients may be a better guide to the meaning of the canonical

discriminant functions (Klecka 1980). The limitation brought up by Klecka concerns the fact that two

highly correlated variables sharing almost the same discriminating information have to share their

contribution to the score. As a result, it may happen that their standardized coefficients are smaller than

would be the case if only one of the two variables was used.

Table 6 shows the most important structure coefficients for the first functions in Population A in all

the countries.

Table 6. The most important structure coefficients for the first functions: Population A

Country Var. 1 Coeff Var. 2 Coeff Var. 3 Coeff Var. 4 Coeff

Denmark......

Finland ........

France..........

Germany ......

Italy .............

Spain ...........

Sweden ........

Switzerland..

USA.............

ASSRATE

ASSRATE

ASSRATE

ASSRATE

ASSRATE

ASSRATE

ASSRATE

ASSRATE

ASSRATE

0.88

0.76

0.87

0.84

0.74

0.75

0.74

0.86

0.74

ASNEWSF

ASBOOKF

AGEO

ASBOOKS

AGEO

AGEO

ASNEWSF

ASBOOKF

TVSQ

0.49

0.41

 -0.53

0.63

 -0.49

 -0.55

0.51

0.49

 -0.42

ASCOMIF

AGEO

ASUSLANR

ASBOOKF

ASBOOKS

ASBOOKS

ASBOOKF

ASBOOKS

ASCOMIF

0.44

 -0.39

0.35

0.47

0.47

0.55

0.37

0.43

 -0.37

ASBOOKF

GENDER

MEALS

NEWS

ASCOMIF

ASBOOKF

ASREATL

ASUSLANR

MEALS

0.38

0.29

0.26

0.39

0.46

0.40

 -0.35

0.36

0.34

KEY: ASSRATE = reading self-image

NEWS = newspaper at home

ASBOOKF = freq. reading books;

AGEO = very "old";

ASBOOKS = number of books at home;

ASNEWSF = freq. reading newspapers;

TVSQ = hours watching TV at home;

ASCOMIF = freq. reading comics;

ASUSLANR = freq. speaking the language of the test at home;

ASREATL = student read to someone at home

MEALS = meals per week

GENDER = student gender

SOURCE: IEA Reading Literacy Study, 1991.
75

To summarize Population A:

• In all countries, reading self-image was the strongest discriminating variable.

• In Finland, France, Italy, and Spain being overaged was an important discriminating factor.

• The United States was the only country in which watching TV was a factor with strong

discriminating power.

• Number of books at home had substantial discriminating power in Germany, Spain, Italy, and

Switzerland.

• Frequency of book reading showed discriminating power in Switzerland, Finland, Germany,

Sweden, Denmark, and Spain.

• Frequency of reading comics was important in the United States, Denmark, and Italy. However, in

the United States, it was the low-performing students who most often read comics, while in Denmark

and Italy, this activity was done most often by the high-performing students.

• Denmark and Sweden were the only countries where frequency of reading newspapers showed

strong discriminating influence.

• Meals showed discriminating power in the United States and France.

Table 7 shows the most important structure coefficients for the first functions in Population B in all

the countries.

Table 7. The most important structure coefficients for the first function: Population B

Country Var. 1 Coeff Var. 2 Coeff Var. 3 Coeff Var. 4 Coeff

Denmark......

Finland ........

France..........

Germany ......

Italy .............

Spain ...........

Sweden ........

Switzerland..

USA.............

BSSRATE

BSSRATE

BSEDUCAR

BSBOOKS

BSEDUCAR

BSEDUCAR

BSSRATE

BSSRATE

BSSRATE

0.82

0.70

0.81

0.84

0.82

0.85

0.78

0.67

0.73

BSEDUCAR

BSEDUCAR

BSBOOKS

BSEDUCAR

BSSRATE

BSSRATE

BSEDUCAR

BSBOOKS

BSEDUCAR

0.62

0.61

0.68

0.68

0.75

0.70

0.67

0.67

0.60

BSBOOKS

BSBORBO

BSSRATE

BSSRATE

BSBOOKS

BSBOOKS

BSBOOKS

PAREDQ

BSBOOKS

0.61

0.52

0.64

0.53

0.61

0.69

0.61

0.61

0.59

PAREDQ

JOBNONE

AGEO

BSTVR

PAREDQ

AGEO

BSSREADD

BSEDUCAR

AGEO

0.54

0.43

-0.47

0.30

0.61

-0.52

0.43

0.49

-0.52

KEY: BSSRATE = reading self-image

BSEDUCAR = expected further education

BSBOOKS = number of books at home

BSBORBO = freq. borrowing library books

PAREDQ = parental education

JOBNONE = having a job or duties

AGEO = very "old";

BSTVR = hours watching TV at home

BSSREADD = freq. reading at school

SOURCE: IEA Reading Literacy Study, 1991.
76

To summarize Population B:

• Although the rank order of the variables varied between the countries, reading self-image,

expected further education, and number of books at home were the three most discriminating

variables in seven of the nine countries.

• In Denmark, Sweden, and the United States reading self-image, expected further education, and

number of books at home were the most significant discriminant variables.

• In Italy and Spain expected further education, reading self-image, and number of books at

home were most important variables, and in the order shown.

• Germany and France were similar since expected further education, number of books at home,

and reading self-image were their most discriminating variables. However, in Germany the most

discriminating variable was number of books at home, while in France it was expected further

education.

• In Finland reading self-image, expected further education, and frequency borrowing books

were the most important discriminant variables.

• In Switzerland reading self-image and number of books showed equal discriminant power

followed by parental education and expected further education.

3. Conclusion

As the term “reading self-image” suggests, the high- and low-performing students during the first

years of schooling soon become aware how well they are doing compared to other children. The developed

reading self-image probably will influence the students’ evaluation of their ability to succeed in further

education. Home background clearly is an important factor in this respect. The availability of reading

material at home (ASBOOKS, NEWS), the language of the home (ASUSLANR), and the regularity of

meals (MEALS) play a role in six of the nine Population A countries. In Population B, books in the home

(BSBOOKS) and parental education (PAREDQ) play a role in eight of the nine countries examined.

Student activities also differentiate between the high- and low-performing students: generally, the more

often the students are engaged in reading books, newspapers, and comics, the better they are at reading.

But some student activities may also count in the opposite direction, as the results from the United States

show: here extensive television viewing and comic reading are correlates of lower reading ability. Finally,

if the country allows for grade repetition for students that do not meet the academic requirements, then age

becomes a factor for the low-performing students—indicating that grade repetition in itself does not

eliminate reading problems.

As the students grow older, the influence of the home background factors seems to become even

stronger. Parental education, the availability of books at home, reading self-image, and the length of the

expected future education fit perfectly to our stereotypes of high- and low-performing students. However,

such student activities as borrowing books at the library and reading on one’s own at school may also

indicate high-performing students in some countries.
77

3.1 Classification Phase

The classification phase involves classifying the students into the low, medium, and high groups

according to the discriminant function. In this phase, each individual was classified into the group with the

highest score on a discriminant function. This function is determined by a measure of generalized squared

distance from the individual to the respective group centroids, so that the individual is classified into the

closest group. (Distance is measured in an n-dimensional space for n variables, and the group centroid is

the position of the group means of the variables in this space.)

The discriminant function used here is built directly on the discriminating variables. Classification

can also be based on the canonical discriminant functions from the canonical discriminant analysis.

However, "the final classifications will generally be identical" (Klecka 1980, 47).

The measure of interest in this phase is the percentage of correctly classified students. As the same

students were used both for derivation of the discriminant function and for classification, this is a biased

estimate of the probability of correct classification. It will tend to overestimate the power of the

classification procedure.

Table 8 shows the percentage of correctly classified students in Population A in each of the

performance groups and in total for each country with the countries ordered by the percent correctly

classified overall. Table 9 show the corresponding data for Population B.

Table 8. Percentage of correctly classified students, by country: Population A

Country

Percent correctly

classified as lowperforming students

Percent correctly

classified as middleperforming students

Percent correctly

classified as highperforming students

Percent correctly

classified

Denmark...............

Sweden .................

Finland .................

Switzerland...........

Germany ...............

Spain ....................

USA......................

Italy ......................

France...................

75.77

67.67

63.54

62.03

24.66

53.18

58.21

21.98

52.08

40.30

38.57

47.47

39.15

27.66

36.72

39.25

40.55

15.63

77.91

78.35

72.41

81.06

86.67

80.79

73.17

76.33

92.51

64.66

61.53

61.14

60.74

59.05

56.90

56.87

55.52

53.41

Mean .................... 53.24 36.14 79.91 58.87

SOURCE: IEA Reading Literacy Study, 1991.
78

Table 9. Percentage of correctly classified students, by country: Population B

Country

Percent correctly

classified as lowperforming students

Percent correctly

classified as

middle-performing

students

Percent correctly

classified as highperforming students

Percent correctly

classified

Finland .................

Denmark...............

Italy ......................

Germany ...............

USA......................

Spain ....................

France...................

Sweden .................

Switzerland...........

71.57

62.69

65.64

61.06

48.03

58.91

63.21

46.46

65.17

50.40

50.27

49.85

47.69

57.05

37.60

33.75

51.47

31.56

72.41

80.50

74.02

80.24

81.75

85.73

83.89

80.80

75.88

64.79

64.49

63.17

62.99

62.28

60.75

60.28

59.58

57.54

Mean .................... 60.30 45.52 79.47 61.76

SOURCE: IEA Reading Literacy Study, 1991.

As shown in Tables 8 and 9, in all countries and both populations, the highest performance group

had the highest percentage of correctly classified students. With a few exceptions (i.e., Italy and Germany

in Population A; the United States and Sweden in Population B), it was most difficult to classify students

correctly to the middle performance group. A comparison of the two populations revealed that it was

somewhat easier to classify students in Population B than in Population A, mainly because of a better

ability to classify the low- and middle-performing students in the former than the latter. The highest

percentage of correctly classified students in Population A were in the three Nordic countries, and in

Population B, in Finland, Denmark, and Italy. The lowest percentage of correctly classified students in

Population A were in France, and in Population B, in Switzerland.

3.2 Within-Country Analysis

Relations between the variables and the function. As mentioned earlier, an examination of the

relations between the individual background variables and the functions can be done using the standardized

discriminant coefficients and the total structure coefficients. Both coefficients will be listed in the tables

that follow: the standardized discriminant coefficient shows the relative importance of the individual

background variables in terms of contribution to the discriminant score, and the total structure coefficients

shows the correlation between each variable and the discriminant function.

Plotting centroids to understand the meaning. With only two discriminant functions the location of

the data cases can easily be plotted. However, if the number of cases is large, as in the current study, the

result will become a blur. One way of avoiding that outcome is to compute "centroids," which serve to

summarize the position of groups. The centroids of each group can then be plotted. In the following figures

the centroids for each performance group within each country and for both populations have been plotted to

illustrate the extent to which the functions discriminate between the groups.
79

3.2.1 Denmark

Population A

Relations between the variables and the function. Table 10 presents standardized discriminant

functions and the structure coefficients for the first and second functions in Denmark. As shown, nine

variables were kept in the reduced model for Denmark. The reading self-image (ASSRATE) and frequency

of reading different reading materials seem to be most important for the first function and gender for the

second function.

Table 10. Standardized discriminant and structure coefficients for the first and second functions in

Denmark: Population A

Function 1 "Self, Newsp. & Comics” Function 2 "Gender"

Variable Stand

coeff Variable Structcoeff Variable Standcoeff Variable Structcoeff

ASSRATE

ASREATL

ASNEWSF

AGEO

ASCOMIF

ASBOOKF

GENDER

ASBOOKS

ASMAGAF

0.99

-0.40

0.27

-0.25

0.20

0.17

0.15

0.12

0.07

ASSRATE

ASNEWSF

ASCOMIF

ASBOOKF

ASREATL

ASMAGAF

ASBOOKS

AGEO

GENDER

0.88

0.49

0.44

0.38

-0.34

0.30

0.24

-0.22

0.15

ASMAGAF

GENDER

ASCOMIF

ASNEWSF

ASREATL

AGEO

ASSRATE

ASBOOKS

ASBOOKF

0.72

0.56

-0.34

-0.28

0.25

-0.08

0.08

-0.06

-0.00

GENDER

ASMAGAF

ASREATL

ASCOMIF

AGEO

ASNEWSF

ASBOOKS

ASBOOKF

ASSRATE

0.65

0.57

0.29

-0.25

-0.17

-0.17

-0.15

0.14

0.03

SOURCE: IEA Reading Literacy Study, 1991.

“Self, Newsp. & Comics”

-2

-1

0

1

2

-2 -1 0 1 2

L

M

H

Denmark (9 variables)

Pop. A

Figure 3. Two-function plot of group

centroids for low- (L), middle- (M)

and high- (H) performing students

in Denmark for Population A

Using centroids to understand the meaning.

Figure 3 shows three very different groups for the first

function. The students in the high-performing group

have a better reading self-image and more often read

newspapers, comics, and books. For the second

function, the low- and high-performance groups were

fairly close and the middle group was apart. Gender

was the critical factor for the second function. There

was a higher percentage of females in the middle group

than in the other two. In the low-performing group,

there was a higher percentage of males as compared to

the other groups.

Population B

Relations between the variables and the function. Table 11 presents the standardized discriminant

coefficients and the structure coefficients for the first and second functions for Population B in Denmark.

As shown, as many as 11 variables could offer significant contributions to the reduced model for Denmark.

The three variables in the top for the first function were reading self-image (BSSRATE), expected further

education (BSEDUCAR), and number of books at home (BSBOOKS) and the function was labeled "Self,
80

Further Ed & Books.” The second function was more difficult to interpret. Obviously, gender, home

language, job outside school, and TV viewing were involved. However, gender dominated, and thus the

function was named after that variable.

Table 11. Standardized discriminant and structure coefficients for the first and second functions in

Denmark: Population B

Function 1 “Self, Further Ed. & Books” Function 2 “Gender”

Variable Stand

coeff Variable Structcoeff Variable Standcoeff Variable Structcoeff

BSSRATE

TVSQ

BSEDUCAR

BSBOOKS

PAREDQ

BSUSLANR

JOB

BSTVR

AGEO

GENDER

BSBORBO

0.84

-0.45

0.39

0.33

0.22

0.19

-0.16

0.16

-0.14

-0.08

0.07

BSSRATE

BSEDUCAR

BSBOOKS

PAREDQ

TVSQ

BSTVR

BSBORBO

BSUSLANR

AGEO

JOB

GENDER

0.82

0.62

0.61

0.54

-0.37

-0.35

0.28

0.26

-0.24

-0.21

0.08

TVSQ

BSTVR

GENDER

BSBORBO

JOB

BSEDUCAR

BSUSLANR

AGEO

PAREDQ

BSSRATE

BSBOOKS

1.78

-1.53

-0.52

0.42

-0.31

0.30

-0.27

0.18

0.14

-0.14

0.10

GENDER

BSUSLANR

JOB

TVSQ

BSBORBO

BSEDUCAR

AGEO

BSTVR

PAREDQ

BSBOOKS

BSSRATE

-0.45

-0.37

-0.35

0.34

0.34

0.27

0.24

0.23

0.18

-0.08

-0.01

 SOURCE: IEA Reading Literacy Study, 1991.

“Self, Aspir. & Books”

-2

-1

0

1

2

-2 -1 0 1 2

L

M

H

Denmark (11 variables)

Pop. B

Figure 4. Two-function plot of group

centroids for low- (L), middle- (M)

and high- (H) performing students

in Denmark for Population B

Using the centroids to understand the

meaning. Figure 4 shows the group centroids for the

two functions.The three performance groups are very

different on the first function. According to the

standardized group means for the most important

variables, the better readers have a better reading selfimage, expect more further education, and have more

books at home than the other two groups of students.

On the second function the group differences are

much smaller. However, gender stands out as the most

important variable for the function. The group means

for gender revealed that the highest percentage of

females can be found in the middle- performing group.

The highest percentage of males can be found in the low-performing group, while there is an almost

similar percentage of males and females in the high-performing group.
81

To summarize the data for Denmark:

• The three performance groups could be distinguished on the basis of 9 variables in Population

A and 11 variables in Population B.

• The most important discriminating variables in Population A were reading self-image,

frequency reading newspapers, and frequency reading comics; in Population B, reading selfimage, expected further education, and number of books at home.

• The canonical correlation for the first function was a little higher in Population B than in

Population A, while the percentage of the total discriminant power for the first function was

almost the same in the two populations.

• Denmark had the highest canonical correlation of all nine countries for the first discriminant

function in both populations, though sharing this status with Switzerland in Population A.

3.2.2 Finland

Population A

Relations between the variables and the function. Table 12 shows the standardized discriminant

coefficients and the structure coefficients for the first and second functions for Population A in Finland. As

shown, only 8 of 18 variables remained in the reduced models for Finland. Reading self-image (ASSRATE)

was the most important discriminant factor for the first function, and it was called "Self, Bookreading &

Age" after its most important structure coefficients. The most important variable for the second function

was frequency people read to the student at home (ASPRHTL), and the second function got its name from

that variable.

Table 12. Standardized discriminant and structure coefficients for the first and second functions in

Finland: Population A

Function 1 “Self, Bookreading & Age” Function 2 “People at home read”

Variable Stand

coeff Variable Structcoeff Variable Standcoeff Variable Structcoeff

ASSRATE

GENDER

AGEO

ASCOMIF

ASBOOKF

ASALOUF

ASPRHTL

AGEY

0.87

0.38

-0.38

0.37

0.33

-0.26

-0.24

-0.23

ASSRATE

ASBOOKF

AGEO

GENDER

ASCOMIF

ASALOUF

ASPRHTL

AGEY

0.76

0.41

-0.39

0.29

0.28

-0.23

-0.12

-0.05

ASPRHTL

ASCOMIF

AGEO

ASSRATE

AGEY

GENDER

ASALOUF

ASBOOKF

0.76

0.55

-0.23

-0.15

0.10

-0.08

0.05

-0.00

ASPRHTL

ASCOMIF

AGEO

ASALOUF

AGEY

GENDER

ASSRATE

ASBOOKF

0.80

0.64

-0.18

0.12

0.11

-0.05

-0.04

0.00

 SOURCE: IEA Reading Literacy Study, 1991.
82

“Self, Book reading & Age”

-2

-1

0 

1 

2 

-2 -1 0 1 2 

L 

M 

H 

Finland (8 variables) 

Pop. A

Figure 5. Two-function plot of group

centroids for low- (L), middle- (M)

and high- (H) performing students

in Finland for Population A

Using centroids to understand the meaning.

In Figure 5,the centroids for the low- (L), middle- (M)

and high- (H) performing groups on the two functions

for Population A in Finland are presented. As shown,

the group means for the three groups are clearly

distinguishable for the first function. The middle- and

high-performing students are somewhat closer to each

other in comparison with the low- performing group

on the first function. On the second function, the

middle-performing students seem to have a more

positive value than both the other groups. Thus, the

first and most important function discriminated among

the low-, middle- and high-performing groups on the

basis of eight variables, of which reading self-image,

frequency reading books, and age were the most

important.

An inspection of the total-sample standardized group means for these three important discriminating

variables showed that the students in the low-performing group had the lowest reading self-image, seldom

read books, and were older to a higher extent than students in the other groups. Furthermore, the highperforming 9-year-old students in Finland are quite the opposite. They have a better reading self-image,

read books more often, and are seldom overaged in comparison with the students in the low-performing

group. From the second function and the group means, it is obvious that people at home read most often to

the middle-performing students, seldom to the low-performing, and very seldom to the high-performing

students.

Population B

Relations between the variables and the function. Table 13 shows the standardized discriminant

coefficients and the structure coefficients for the first function for Population B in Finland. As a closer

look at the two types of coefficients in the table revealed, only two of the six variables left in the reduced

model—job and frequency watching TV (JOBNONE and TVSQ)—had lower rank when compared among

the structure coefficients than among the standardized coefficients. These two variables also had very weak

correlation both with each other and with the other variables in the table. Thus, the standardized

discriminant coefficients for these variables could not have yielded a higher rank than they "deserved"

because the variables were highly correlated with each other or with any of the other variables.

Among the remaining four variables in the reduced model, there were some moderate correlations, as

between frequency reading at school (BSSREADD) and frequency borrowing library books (BSBORBO)

(corr=.31) and between reading self-image (BSSRATE) and BSBORBO (corr=.30), which could have been

the reason for the lower positions of these variables among the standardized discriminant coefficients than

among the structure coefficients (Klecka 1980). A comparison of the latter coefficients showed that

BSSRATE, expected further education (BSEDUCAR), and BSBORBO had the highest correlation with

the discriminant function. Reading self-image and expected further education are conceptually connected. A

high self-image will often involve higher expectations and higher aspirations. Furthermore, since a good

reading ability is of crucial importance in connection with success in education, it seems reasonable to

assume that a student with a good reading ability expects many years of further education. This function is
83

labeled "Self, Aspir. and Borrow" after its three most important structure coefficients: reading self-image,

expected further education, and frequency borrowing books.

Table 13. Standardized discriminant and structure coefficients for the first function in Finland:

Population B

Function 1 Function 1 “Self, Aspir. & Borrow”

Variable Standard

Coefficients

Variable Structure

Coefficients

BSSRATE

JOBNONE

BSEDUCAR

BSBORBO

TVSQ

BSSREADD

0.62

0.59

0.45

0.37

-0.32

0.21

BSSRATE

BSEDUCAR

BSBORBO

JOBNONE

BSSREADD

TVSQ

0.70

0.61

0.52

0.43

0.42

-0.27

SOURCE: IEA Reading Literacy Study, 1991.

Using centroids to understand the meaning. Only the first function was significant in Finland. The

class means on the canonical variables for the high- (H), middle- (M) and low- (L) performing groups were

0.99, 0.08 and -1.21, respectively. Thus, the groups were clearly distinguishable, with the distance

between the high- and middle-performing groups somewhat smaller than between the middle- and the lowperforming groups. The high-performing group could be distinguished from the middle- and lowperforming students from their better reading self-image, longer expected further education, and a higher

frequency of borrowing books.

To summarize the data for Finland:

• The canonical correlation and the percentage of the total discriminant power for the first

function was higher in Population B than in Population A.

• In Population B, only the first function was significant.

• The three performing groups could be distinguished on the basis of eight variables in

Population A and six variables in Population B.

• The most important discriminating variables in Population A were reading self-image,

frequency of book reading, and age; and in Population B, reading self-image, expected further

education, and frequency borrowing books.
84

3.2.3 France

Population A

Relations between the variables and the function. Table 14 presents the standardized discriminant

coefficients and the structure coefficients for the first and the second functions in France. As shown, only 5

of the 18 variables were left in the reduced model. The first function was dominated by reading self-image

(ASSRATE) and age (AGEO), while the dominating variable for the second function was language at

home (ASUSLANR). The first function was named "Self & Age," and the second, "Language."

The second function concentrated on language at home. The high-performing students spoke the

language of the reading test more often and had a better reading self-image than the other groups. The lowperforming group consisted to a higher extent of students who were overaged.

Table 14. Standardized discriminant and structure coefficients for the first and second functions

in France: Population A

Function 1 “Self & Age” Function 2 “Language”

Variable Stand coeff Variable Struct coeff Variable Stand coeff Variable Struct coeff

ASSRATE

AGEO

ASUSLANR

ASREATL

MEALS

1.01

-0.37

0.30

-0.27

0.05

ASSRATE

AGEO

ASUSLANR

MEALS

ASREATL

0.87

-0.53

0.35

0.26

-0.23

ASUSLANR

AGEO

MEALS

ASSRATE

ASREATL

-0.71

0.48

0.42

0.38

-0.27

ASUSLANR

AGEO

MEALS

ASSRATE

ASREATL

-0.68

0.46

0.32

0.31

-0.17

SOURCE: IEA Reading Literacy Study, 1991.

“Self & Age”

-2

-1

0 

1 

2 

-2 -1 0 1 2 

L 

M 

H 

France (5 variables)

Pop. A

Figure 6. Two-function plot of group centroids

for low- (L), middle- (M), and high-

(H) performing students in France for

Population A

Using centroids to understand the

meaning. Figure 6 shows the centroids of the

group means on the canonical variables for low-,

middle-, and high-performing students in France.

For the first function the three group means are

clearly distinguishable. For the second function the

class means for the low- and the high-performing

groups are very close, while the middle group

seems to deviate from the others. Thus, the three

performance groups can be discriminated in terms

of reading self-image and age. The highperforming students have a much better reading

self-image than the low-performing group. The

low-performing students are older than the

students in the other groups.

Population B

Relations between the variables and the function. Table 15 presents the standardized discriminant

coefficients and the structure coefficients for the first function for Population B in France. As shown, the

rank order for the seven remaining variables in the reduced model was much the same for the standardized
85

coefficients and the structure coefficients. The variables BSEDUCAR, BSBOOKS, and BSSRATE were

relatively highly correlated (.34 to .37) and thus had higher total structure coefficients than standardized

discriminant coefficients. A possible name for this function would be "Aspir., Books & Self," indicating

the high importance of expected further education, number of books at home, and reading self-concept in

discriminating between different performing groups in France. Although not as important as the three most

important variables, age (AGEO) seemed to be of certain significance too. This fact may reflect the

relatively high retention rate in France, and consequently, the older students may not be the best readers.

Table 15. Standardized discriminant and structure coefficients for the first function in France:

Population B

Function 1 Function 1 “Aspir., Books and Self”

Variable Standard

Coefficients Variable StructureCoefficients

BSEDUCAR

BSBOOKS

BSSRATE

AGEO

JOB

BSUSLANR

BSTVR

0.70

0.42

0.41

-0.25

-0.20

0.18

-0.15

BSEDUCAR

BSBOOKS

BSSRATE

AGEO

BSUSLANR

JOB

BSTVR

0.81

0.68

0.64

-0.47

0.31

-0.31

-0.26

SOURCE: IEA Reading Literacy Study, 1991.

Using centroids to understand the meaning. The group means on the canonical variables for the

high-, middle-, and low-performing groups in France on the first and only significant discriminant function

were 1.04, 0.02, and -1.34, respectively, and, thus, they were highly discriminating.

To summarize the data for France:

• The canonical correlation and the percentage of the total discriminant power for the first

function in France was slightly higher in Population B than in Population A.

• Only the first function was significant in Population B.

• The three performance groups could be distinguished on the basis of five variables in

Population A and seven variables in Population B.

• The most important discriminating variables in Population A were reading self-image and

age; and in Population B, expected further education, number of books at home, and reading

self-image.

3.2.4 Germany

Population A

Relations between the variables and the function. Table 16 presents the standardized discriminant

coefficients and the structure coefficients for the first and second functions for Population A in Germany.

As shown, eight variables contributed significantly to the reduced model for Population A in Germany.
86

Reading self-image, number of books at home, and frequency of reading books were the variables with the

highest correlation with the first function. For the second function, number of books at home was the most

significant variable.

Table 16. Standardized discriminant and structure coefficients for the first and second functions

in Germany: Population A

Function 1 "Self & Books" Function 2 "Books"

Variable Stand

coeff Variable Structcoeff Variable Standcoeff Variable Structcoeff

ASSRATE

ASBOOKS

ASALOUF

AGEO

ASBOOKF

AGEY

NEWS

TVSQ

0.79

0.43

-0.26

-0.24

0.23

-0.22

0.20

-0.04

ASSRATE

ASBOOKS

ASBOOKF

NEWS

AGEO

AGEY

TVSQ

ASALOUF

0.84

0.63

0.47

0.39

0.35

-0.24

-0.19

-0.14

ASBOOKS

ASBOOKF

TVSQ

AGEO

ASSRATE

NEWS

AGEY

ASALOUF

0.79

-0.71

-0.39

0.35

-0.17

0.14

0.13

-0.07

ASBOOKS

ASBOOKF

TVSQ

AGEO

AGEY

ASALOUF

ASSRATE

NEWS

0.55

-0.46

-0.26

0.24

0.20

-0.17

-0.13

0.09

SOURCE: IEA Reading Literacy Study, 1991.

“Self & Books”

-2

-1

0 

1 

2 

-2 -1 0 1 2 

L 

M 

H 

Germany (8 variables) 

Pop. A

Figure 7. Two-function plot of group centroids

for low- (L), middle- (M), and high-

(H) performing students in Germany

for Population A

Using centroids to understand the meaning.

Figure 7 shows the centroids for the three

performance groups on the two functions. As

expected, the group centroids are well separated for

the first function and close for the second function.

An inspection of the standardized group means

shows a picture of the high-performing students as

students who have many books at home, good

reading self-image, read books frequently, and watch

TV less often than the students in the other groups.

Population B

Relations between the variables and the function. Table 17 presents the standardized discriminant

coefficients and the structure coefficients for the first and second functions for Population B in Germany.

As shown, 11 variables were able to contribute significantly to the reduced model for Germany. The most

important variables for the first function were number of books at home, expected further education and

reading self-image. The first function was labeled "Books & Aspir." The most important variable for the

second function was language at home.
87

Table 17. Standardized discriminant and structure coefficients for the first and second functions in

Germany: Population B

Function 1 “Books & Aspir.” Function 2 “Language”

Variable Stand

coeff Variable Structcoeff Variable Standcoeff Variable Structcoeff

BSBOOKS

BSEDUCAR

BSSRATE

BSTVR

TVSQ

BSUSLANR

AGEO

NEWS

JOB

GENDER

MEALS

0.70

0.53

0.32

0.30

-0.30

0.29

-0.21

0.16

-0.12

-0.11

0.08

BSBOOKS

BSEDUCAR

BSSRATE

BSUSLANR

AGEO

NEWS

MEAL

TVSQ

BSTVR

JOB

GENDER

0.84

0.68

0.53

0.48

-0.43

0.41

0.25

-0.22

-0.19

-0.19

-0.05

BSTVR

BSEDUCAR

TVSQ

BSUSLANR

BSBOOKS

MEALS

BSSRATE

AGEO

JOB

GENDER

NEWS

-0.68

0.61

0.58

-0.49

-0.37

0.36

0.24

0.24

-0.13

-0.12

-0.02

BSUSLANR

BSEDUCAR

AGEO

MEALS

BSSRATE

BSBOOKS

BSTVR

GENDER

NEWS

JOB

TVSQ

-0.56

0.53

0.35

0.35

0.21

-0.17

-0.14

-0.13

-0.12

-0.11

-0.04

SOURCE: IEA Reading Literacy Study, 1991.

“Books & Aspir.”

-2

-1

0 

1 

2 

-2 -1 0 1 2 

L 

M 

H 

Germany (11 variables)

Pop. B

Figure 8. Two-function plot of group

centroids for low- (L), middle-

(M), and high- (H) performing

students in Germany for

Population B

Using centroids to understand the meaning.

Figure 8 shows the centroids for the three performance

groups on the two functions. The groups were quite

distinct on the first function. For the second function the

middle group deviated somewhat, while the low- and

high-performing groups hold nearly the same positions.

According to the group means for the variables, the

group of high-performing students have many books at

home, expect longer further education, have a better

self-image, and speak the language of the reading test

more often at home in comparison with the two other

groups.

To summarize the data for Germany:

• The canonical correlation and the percentage of the total discriminant power for the first

function was higher in Population B than in Population A.

• The three performing groups could be distinguished on the basis of 8 variables in Population

A and 11 variables in Population B.

• The most important discriminating variables in Population A were reading self-image,

number of books at home, and frequency of bookreading; and in Population B, number of

books at home, expected further education, and reading self-image.
88

3.2.5 Italy

Population A

Relations between the variables and the function. Table 18 shows the standardized discriminant

coefficients and the structure coefficients for the first and the second functions for Population A in Italy. As

shown, there were eight variables in the reduced model for Italy in Population A. For the first function,

reading self-image, age, and number of books at home were the most important discriminant variables, and

these were used as the name for the function. Italy was the country with the most important second

function in Population A—people at home read to student (ASPRHTL)—and the table shows it as one of

the two most powerful of the variables included in the second function.

Table 18. Standardized discriminant and structure coefficients for the first and second functions

in Italy: Population A

Function 1 “Self, Age & Books” Function 2 “People at home read”

Variable Stand coeff Variable Struct coeff Variable Stand coeff Variable Struct coeff

ASSRATE

AGEO

ASBOOKS

ASNEWSF

TVSQ

ASCOMIF

ASMAGAF

ASPRHTL

0.62

-0.45

0.36

0.34

-0.32

0.24

-0.12

-.09

ASSRATE

AGEO

ASBOOKS

ASCOMIF

ASNEWSF

TVSQ

ASMAGAF

ASPRHTL

0.74

-0.49

0.47

0.46

0.37

-0.31

0.14

0.09

ASPRHTL

ASNEWSF

ASMAGAF

ASCOMIF

ASBOOKS

AGEO

ASSRATE

TVSQ

-0.69

0.64

0.36

-0.35

-0.17

0.14

0.11

0.00

ASPRHTL

ASNEWSF

ASMAGAF

ASCOMIF

AGEO

ASBOOKS

TVSQ

ASSRATE

-0.59

0.57

0.29

-0.27

0.20

-0.20

-0.03

0.01

SOURCE: IEA Reading Literacy Study, 1991.

“Self, Age & Books” 

-2

-1

0 

1 

2 

-2 -1 0 1 2 

L 

M 

H 

Italy (8 variables) 

Pop. A

Figure 9. Two-function plot of group

centroids for low- (L), middle- (M),

and high- (H) performing students

in Italy for Population A

Using centroids to understand the meaning.

Figure 9 shows the centroids for the high-, middle-,

and low-performing groups. For the first function,

the three group means are clearly distinguishable.

For the second function, the group means for the

low- and the high-performing groups are very close,

while the middle group shows a slightly more

negative value. Thus, the three performance groups

can be discriminated in terms of differing reading

self-image, age, and number of books at home. As

compared with the other groups, the high-performing

students had a better reading self-image, had more

books at home, and were seldom overaged. The

low-performing students were overaged to a higher

extent than the students in the other groups.

The second discriminant function showed that the variable, people at home read to student, had a

certain importance. The group means for the latter variable tells that people at home rarely read to low- and

high-performing students as opposed to the behavior of families of the middle-performing group.
89

Population B

Relations between the variables and the function. Table 19 presents the standardized discriminant

coefficients and the structure coefficients for the first function for Population B in Italy. As shown, 10

variables were kept in the reduced model. The four first variables, expected further education

(BSEDUCAR), reading self-image (BSSRATE), number of books at home (BSBOOKS), and parental

education (PAREDQ) had the same position in both rank orders of coefficients. They were also highly

correlated, and their importance became much more obvious when comparing the structure coefficients

than when comparing the standardized discriminant coefficients. To have responsibilities or work beside the

school work (JOB) showed a moderate importance. However, the discriminating power of expected further

education and reading self-image was much stronger. This function is labeled "Aspir. & Self.”

Table 19. Standardized discriminant and structure coefficients for the first function in Italy:

Population B

Function 1 “Aspir. & Self” Function 1 “Aspir & Self”

Variable Standard

Coefficients Variable StructureCoefficients

BSEDUCAR

BSSRATE

BSBOOKS

PAREDQ

BSBORBO

AGEY

JOB

NEWS

JOBNONE

MEALS

0.59

0.59

0.25

0.25

0.19

-0.17

-0.17

-0.14

-0.11

0.11

BSEDUCAR

BSSRATE

BSBOOKS

PAREDQ

JOB

BSBORBO

MEALS

NEWS

JOBNONE

AGEY

0.82

0.75

0.61

0.61

-0.34

0.29

0.16

0.14

0.12

-0.09

SOURCE: IEA Reading Literacy Study, 1991.

Using centroids to understand the meaning. The class means on the canonical variables for the

high-, middle-, and low-performing groups were 1.09, -0.06, and -1.18, respectively, and thus are clearly

distinguishable.

To summarize the data for Italy:

• The canonical correlation and the percentage of the total discriminant power for the first

function was higher in Population B than in Population A.

• In Population B only the first function was significant.

• The three performance groups could be distinguished on the basis of 8 variables in Population

A and 10 variables in Population B.

• The most important discriminating variables in Population A were reading self-image, age,

and number of books at home; and in Population B, expected further education, reading selfimage, number of books at home, and parental education.
90

3.2.6 Spain

Population A

Relations between the variables and the function. Table 20 shows the standardized discriminant

coefficients and the structure coefficients for the first and second functions in Population A in Spain. As

shown, 11 variables contributed significantly to the reduced model for Population A in Spain. Reading selfimage, age, and number of books at home were the variables with the highest correlation with the first

function, and age was the most important variable for the second function.

Table 20. Standardized discriminant and structure coefficients for the first and second functions in

Spain: Population A

Function 1 “Self, Age & Books” Function 2 “Age”

Variable Stand

coeff Variable Structcoeff Variable Standcoeff Variable Structcoeff

ASSRATE

TVSQ

ASTVR

AGEO

ASBOOKS

ASPRHTL

ASBOOKF

ASCOMIF

GENDER

ASNEWSF

ASMAGAF

0.69

-0.68

0.63

-0.46

0.38

-0.36

0.22

0.16

0.09

-0.05

-0.04

ASSRATE

AGEO

ASBOOKS

ASBOOKF

ASPRHTL

ASCOMIF

ASMAGAF

GENDER

ASNEWSF

TVSQ

ASTVR

0.75

-0.55

0.55

0.40

-0.39

0.29

0.08

0.07

0.07

-0.06

-0.01

AGEO

ASNEWSF

ASBOOKF

ASSRATE

ASCOMIF

ASPRHTL

TVSQ

GENDER

ASTVR

ASBOOKS

ASMAGAF

0.72

0.41

0.38

0.38

-0.29

-0.27

-0.23

-0.21

0.16

-0.13

-0.09

AGEO

ASNEWSF

ASSRATE

ASBOOKF

GENDER

ASPRHTL

ASBOOKS

ASCOMIF

TVSQ

ASMAGAF

ASTVR

0.64

0.42

0.34

0.31

-0.22

-0.21

-0.11

-0.03

-0.02

0.02

-0.01

SOURCE: IEA Reading Literacy Study, 1991.

“Self, Age & Books” 

-2

-1

0 

1 

2 

-2 -1 0 1 2 

L 

M 

H 

Spain (11 variables)

Pop. A

Figure 10. Two-function plot of group centroids

for low- (L), middle- (M), and high-

(H) performing students in Spain for

Population A

Using centroids to understand the meaning.

Figure 10 shows the centroids for the three

performance groups on the two functions. The

group centroids are well separated for the first

function and little separated for the second function.

The group means for the most important variables

show that the low-performing students in relation to

the other students are more often overaged.

Furthermore, the high-performing students had a

more positive reading self-image and more books at

home than the low- and middle-performing

students.
91

Population B

Relations between the variables and the function. Table 21 shows the standardized discriminant

coefficients and the structure coefficients for the first and second functions for Population B in Spain. As

shown, expected further education (BSEDUCAR), reading self-image (BSSRATE), and number of books

at home (BSBOOKS) dominate the first function, while age (AGEO) seems to be most important for the

second function in Spain. The first function was labeled "Aspir., Self & Books," and the second, "Age.”

Table 21. Standardized discriminant and structure coefficients for the first and second functions in

Spain: Population B

Function 1 “Aspir., Self & Books”” Function 2 “Age”

Variable Stand

coeff Variable Structcoeff Variable Standcoeff Variable Structcoeff

BSEDUCAR

BSSRATE

BSBOOKS

BSTVR

TVSQ

AGEO

JOBNONE

GENDER

BSSREADD

BSBORBO

0.68

0.52

0.43

0.30

-0.24

-0.21

0.15

0.05

-0.04

0.01

BSEDUCAR

BSSRATE

BSBOOKS

AGEO

JOBNONE

BSSREAD

BSBORBO

GENDER

TVSQ

BSTVR

0.85

0.70

0.69

-0.52

0.16

0.12

0.09

0.08

-0.05

-0.03

BSTVR

TVSQ

AGEO

BSBORBO

GENDER

BSSRATE

BSSREAD

BSBOOKS

JOBNONE

BSEDUCAR

-0.93

0.69

0.62

0.47

-0.35

0.32

-0.29

0.27

-0.07

-0.05

AGEO

BSBORBO

GENDER

BSTVR

BSSRATE

BSSREADD

TVSQ

BSBOOKS

BSEDUCAR

JOBNONE

0.56

0.45

-0.37

-0.27

0.26

-0.23

-0.19

0.19

-0.08

-0.05

SOURCE: IEA Reading Literacy Study, 1991.

“Aspir., Self & Books”

-2

-1

0 

1 

2 

-2 -1 0 1 2 

L 

M 

H 

Spain (10 variables)

Pop. B

Figure 11. Two-function plot of group

centroids for low- (L), middle- (M),

and high- (H) performing students

in Spain for Population A

Using centroids to understand the meaning.

The same pattern that has been shown for the other

countries is repeated here: obvious differences among

the three performance groups on the first function and

very small differences for the second function. The

standardized group means for the most important

variables showed the high-performing group to be

expecting longer further education, having a more

positive reading self-image and more books at home,

and not being overaged. For the low-performing group

the opposite was true, while the middle- performing

group had values in between the other two groups.
92

To summarize the data for Spain:

• The canonical correlation and the percentage of the total discriminant power for the first

function were higher in Population B than in Population A.

• The three performance groups could be distinguished on the basis of 11 variables in

Population A and 10 variables in Population B.

• The most important discriminating variables in Population A were reading self-image, age,

and number of books at home; and in Population B, expected further education, reading selfimage, and number of books in the home.

3.2.7 Sweden

Population A

Relations between the variables and the function. Table 22 presents the standardized discriminant

coefficients and the structure coefficients for the first and second functions in Population A. As shown, 12

variables remained in the reduced model. Reading self-image (ASSRATE), frequency reading newspapers

(ASNEWSF), and frequency reading books (ASBOOKF) were the variables with the highest correlation

with the first function, which was called "Self & Newspaper reading.” The second function, "Books," was

dominated by the variables number of books at home (ASBOOKS), gender (GENDER), and frequency

borrowing books (ASBORBO).

Table 22. Standardized discriminant and structure coefficients for the first and second functions

in Sweden: Population A

Function 1 “Self & Newspaper reading” Function 2 “Books”

Variable Stand

coeff Variable Structcoeff Variable Standcoeff Variable Structcoeff

ASSRATE

TVSQ

ASTVR

ASREATL

ASNEWSF

ASUSLANR

GENDER

ASCOMIF

ASBOOKF

ASPRHTL

ASBOOKS

ASBORBO

0.75

-0.62

0.43

-0.42

0.39

0.28

0.26

0.25

0.18

-0.15

0.12

-0.05

ASSRATE

ASNEWSF

ASBOOKF

ASREATL

ASCOMIF

ASUSLANR

ASBOOKS

ASPRHTL

GENDER

TVSQ

ASBORBO

ASTVR

0.74

0.51

0.37

-0.35

0.34

0.31

0.28

-0.23

0.22

-0.17

0.10

-0.10

ASTVR

TVSQ

ASBOOKS

GENDER

ASBORBO

ASBOOKF

ASREATL

ASCOMIF

ASNEWSF

ASSRATE

ASUSLANR

ASPRHTL

1.06

-1.01

-0.57

0.50

0.41

-0.35

0.35

0.28

-0.26

0.21

0.20

-0.06

ASBOOKS

GENDER

ASBORBO

ASREATL

ASNEWSF

ASCOMIF

ASBOOKF

ASUSLANR

TVSQ

ASSRATE

ASPRHTL

ASTVR

-0.53

0.41

0.36

0.26

-0.19

0.18

-0.14

0.13

-0.11

0.08

0.06

-0.04

SOURCE: IEA Reading Literacy Study, 1991.
93

“Self & Newspaper reading”

-2

-1

0

1

2

-2 -1 0 1 2

L

M

H

Sweden (12 variables)

Pop. A

Figure 12. Two-function plot of group

centroids for low- (L), middle-

(M), and high- (H) performing

students in Sweden for Population

A

Using the group centroids to understand the

meaning. Figure 12 shows the three group centroids

were, as expected, well separated on the first function

and rather close to each other on the second function.

The total-sample standardized group means indicate

that the high-performing students had a more positive

reading self-image, read the newspaper and books

more often, and had more books at home in

comparison with the other two groups. The lowperforming students seldom borrowed books but read

more often to someone at home in comparison with

the students in the other two groups. Furthermore,

there were relatively more females than males in the

high- and middle- performing groups, while the lowperforming group had a higher percentage of males.

Population B

Relations between the variables and the function. Table 23 shows the standardized discriminant

coefficients for the first and second functions in Population B in Sweden. As shown, 10 variables remained

in the reduced model for Population B. Reading self-image, expected further education, and number of

books at home were the most important variables for the first function, which was called "Self, Aspir. &

Books." The second function was dominated by the three factors age (AGEO), job (JOB), and TV

watching (TVSQ).

Table 23. Standardized discriminant and structure coefficients for the first and second functions in

Sweden: Population B

Function 1 “Self & Aspir. & Books” Function 2 “Age”

Variable Stand

coeff Variable Structcoeff Variable Standcoeff Variable Structcoeff

BSSRATE

BSEDUCAR

BSBOOKS

TVSQ

JOB

BSUSLANR

BSSREADD

AGEO

MEALS

BSBORBO

0.67

0.43

0.33

-0.27

-0.18

0.18

0.16

-0.15

0.11

0.10

BSSRATE

BSEDUCAR

BSBOOKS

BSSREADD

TVSQ

BSBORBO

MEALS

AGEO

BSUSLANR

JOB

0.78

0.67

0.61

0.43

-0.37

0.35

0.27

-0.25

0.22

-0.18

AGEO

TVSQ

MEALS

BSBORBO

BSBOOKS

JOB

BSEDUCAR

BSSREADD

BSSRATE

BSUSLANR

0.49

-0.41

0.39

0.37

-0.37

0.37

0.10

0.10

-0.09

-0.07

AGEO

JOB

TVSQ

MEALS

BSBORBO

BSBOOKS

BSUSLANR

BSSREADD

BSEDUCAR

BSSRATE

0.56

0.41

-0.39

0.37

0.37

-0.27

-0.19

0.17

0.15

0.01

SOURCE: IEA Reading Literacy Study, 1991.
94

“Self, Aspir. & Books”

-2

-1

0 

1 

2 

-2 -1 0 1 2 

L 

M 

H 

Sweden (10 variables) 

Pop. B

Figure 13. Two-function plot of group

centroids for low- (L), middle-

(M), and high- (H) performing

students in Sweden for

Population B

Using the centroids to understand the

meaning. Figure 13 shows the group centroids for the

first and second functions. The standardized class

means for the dominating variables reveals that the

high-performing students had a more positive reading

self-image, expected longer further education, and had

more books at home than the other students.

The low-performing students had a less

positive reading self-image, expected the least further

education, had the smallest number of books at home,

and watched TV for more hours than the students in

the other groups.

Furthermore, the high- and middle-performing

groups were at about the same level in terms of age

and having a job outside school, while the lowperforming students to a higher extent than the others

were overaged and had a job or duties outside school.

To summarize the data for Sweden:

• The canonical correlation for the first function were essentially the same in Populations A and

B.

• The percentage of the total discriminant power for the first function was almost the same in

Populations A and B.

• The three performance groups could be distinguished on the basis of 12 variables in

Population A and 10 variables in Population B.

• The most important discriminating variables in Population A were reading self-image,

frequency reading newspapers, and frequency reading books; and in Population B, reading

self-image, expected further education, and number of books at home.

3.2.8 Switzerland

Population A

Relations between the variables and the function. Table 24 presents the standardized discriminant

coefficients and the structure coefficients for the first and second functions in Population A. As shown,

reading self-image (ASSRATE), frequency reading books (ASBOOKF), and number of books at home

(ASBOOKS) were the most important variables for the first function, whereas frequency reading comics

(ASCOMIF) and age (AGEY) were the most significant for the second function.
95

Table 24. Standardized discriminant and structure coefficients for the first and second functions

in Switzerland: Population A

Function 1 “Self & Book reading” Function 2 “Comic reading”

Variable Stand

coeff Variable Structcoeff Variable Standcoeff Variable Structcoeff

ASSRATE

AGEO

ASBOOKF

ASBOOKS

ASALOUF

TVSQ

ASUSLANR

ASREATL

ASCOMIF

AGEY

ASMAGAF

0.98

-0.32

0.30

0.28

-0.24

-0.20

0.19

-0.17

0.15

-0.10

-0.05

ASSRATE

ASBOOKF

ASBOOKS

ASUSLANR

AGEO

TVSQ

ASCOMIF

ASMAGAF

ASALOUF

AGEY

ASREATL

0.86

0.49

0.43

0.36

-0.33

-0.31

0.30

0.28

-0.11

-0.04

0.00

ASCOMIF

ASMAGAF

ASSRATE

ASALOUF

ASBOOKF

AGEY

TVSQ

ASUSLANR

ASBOOKS

AGEO

ASREATL

0.61

-0.59

0.44

0.41

-0.33

0.32

0.29

0.23

-0.23

0.21

0.06

ASCOMIF

AGEY

ASALOUF

ASMAGAF

ASSRATE

TVSQ

ASREATL

AGEO

ASBOOKF

ASUSLANR

ASBOOKS

0.40

0.37

0.36

-0.33

0.25

0.22

0.20

0.19

-0.16

0.10

-0.10

SOURCE: IEA Reading Literacy Study, 1991.

“Self & Book reading” 

-2

-1

0 

1 

2 

-2 -1 0 1 2 

L 

M 

H 

Swiss (11 variables)

Pop. A

Figure 14. Two-function plot of group

centroids for low- (L), middle-

(M), and high- (H) performing

students in Switzerland for

Population A

Using the centroids to understand the meaning.

Figure 14 shows the group centroids for the first and

second functions in Population A. The figure shows a

big difference between the group means for the first

function and very small ones for the second function.

The standardized group means for the dominating

variables for the first function revealed that the good

readers have a more positive reading self-image, more

often read books, and have more books at home than the

students in the other two groups. The poor readers have

the lowest reading self-image, seldom read books, and

have the smallest number of books at home.

The standardized group means for the dominating

variables for the second function revealed that the lowperforming students read comics very seldom in

comparison with the middle- and high-performing

students, who were very similar to each other in terms of

comic reading.

Population B

Relations between the variables and the function. Table 25 presents the standardized discriminant

coefficients and the structure coefficients for the first and second functions in Population B. As shown,

reading self-image (BSSRATE) and number of books at home (BSBOOKS) are of similar importance for

the first function, followed by parental education (PAREDQ). For the second function, frequency speaking

the language of the test at home (BSUSLANR) and gender (GENDER) were the most significant variables.
96

Table 25. Standardized discriminant and structure coefficients for the first and second functions in

Switzerland: Population B

Function 1 “Self & Books” Function 2 “Language & Gender”

Variable Stand

coeff Variable Structcoeff Variable Standcoeff Variable Structcoeff

BSSRATE

PAREDQ

BSBOOKS

BSUSLANR

BSEDUCAR

JOB

TVSQ

NEWS

GENDER

AGEO

BSBORBO

0.58

0.36

0.36

0.27

0.23

-0.21

-0.20

0.13

-0.10

-0.09

0.07

BSSRATE

BSBOOKS

PAREDQ

BSEDUCAR

BSUSLANR

TVSQ

BSBORBO

NEWS

AGEO

JOB

GENDER

0.67

0.67

0.61

0.48

0.45

-0.39

0.35

0.32

-0.28

-0.27

0.04

GENDER

BSUSLANR

BSBORBO

BSEDUCAR

BSSRATE

NEWS

AGEO

JOB

TVSQ

BSBOOKS

PAREDQ

-0.60

-0.45

0.38

0.29

0.27

-0.27

0.27

0.24

-0.23

-0.11

-0.01

BSUSLANR

GENDER

NEWS

BSBORBO

AGEO

BSEDUCAR

BSSRATE

JOB

TVSQ

BSBOOKS

PAREDQ

-0.52

-0.51

-0.32

0.31

0.30

0.27

0.20

0.14

-0.09

-0.07

-0.02

SOURCE: IEA Reading Literacy Study, 1991.

“Self & Books”

-2

-1

0 

1 

2 

-2 -1 0 1 2 

L 

M 

H 

Swiss (11 variables)

Pop. B

Figure 15. Two-function plot of group

centroids for low- (L), middle- (M),

and high- (H) performing students

in Switzerland for Population B

Using the centroids to understand the meaning.

Figure 15 shows big difference between the group

means for the first function and very small ones for the

second function. The standardized group means for the

dominating variables for the first function revealed that

the group of good readers have the most positive reading

self-image, the highest number of books at home, and

the parents with the most education. The lowperforming students were very low on all these

variables.

The standardized group means for the most

important variables for the second function showed that

students in the low-performing group spoke the

language of the test at home less often than the students

in the other two groups, who actually were fairly similar

on that point. Furthermore, the low-performing and the

high-performing students included a higher percentage

of males than the middle-performing students.
97

To summarize the data for Switzerland:

• As opposed to what has been found out for all the other countries in the current study, the

canonical correlation for the first function was somewhat higher in Population A than in

Population B.

• The percentage of the total discriminant power for the first function was almost the same in

Populations A and B.

• The three performance groups could be distinguished on the basis of 11 variables in

Population A and 11 variables in Population B.

• The most important discriminating variables in Population A were reading self-image,

frequency of reading books, and number of books at home; and in Population B, reading selfimage, number of books at home, and parental education.

3.2.9 The United States

Population A

Relations between the variables and the function. Table 26 presents the standardized discriminant

coefficients and the structure coefficients for the first and second functions in Population A. As shown,

reading self-image (ASSRATE), frequency watching TV (TVSQ), and frequency reading comics

(ASCOMIF) were the most important variables for the first function. In the second function, read to the

students at home (ASPRHTI) and watching television (TVSQ) were the most significant variables.

Table 26. Standardized discriminant and structure coefficients for the first and second functions in

the United States: Population A

Function 1 “Self, TV & Comics” Function 2 “Read to students & TV”

Variable Stand

coeff Variable Structcoeff Variable Standcoeff Variable Structcoeff

ASSRATE

ASCOMIF

ASREATL

TVSQ

MEALS

NEWS

ASNEWSF

ASBORBO

ASPRHTL

0.78

-0.38

-0.33

-0.32

0.23

0.23

0.20

0.19

-0.10

ASSRATE

TVSQ

ASCOMIF

MEAL

NEWS

ASBORBO

ASNEWSF

ASREATL

ASPRHTL

0.74

-0.42

-0.37

0.34

0.33

0.27

0.27

-0.24

-0.06

ASPRHTL

TVSQ

ASREATL

ASBORBO

NEWS

MEALS

ASCOMIF

ASNEWSF

ASSRATE

0.59

0.57

-0.38

0.37

0.37

-0.25

-0.14

-0.06

0.02

ASPRHTL

TVSQ

NEWS

ASBORBO

MEALS

ASREATL

ASNEWSF

ASCOMIF

ASSRATE

0.52

0.51

0.41

0.32

-0.21

-0.18

-0.08

-0.06

0.06

SOURCE: IEA Reading Literacy Study, 1991.
98

-2

-1

0

1

2

-2 -1 0 1 2

L

M

H

"Self, TV, & Comics"

USA (9 variables)

Pop. A

Figure 16. Two-function plot of group

centroids for low- (L), middle-

(M), and high- (H) performing

students in the United States for

Population A

Using the centroids to understand the

meaning. Figure 16 shows the group centroids for

the first and second functions in Population A. The

figure shows again significant difference between the

group means for the first function, but very small

differences for the second function. The standardized

group means for the dominating variables for the first

function show that the high-performance group have a

more positive reading self-image, seldom read comics,

and watch less TV at home than the students in the

other two groups. The poor readers have the lowest

reading self-image, often read comics, and spend more

time in front of the TV at home.

Population B

Relations between the variables and the function. Table 27 presents the standardized discriminant

coefficients and the structure coefficients for the first and second functions in Population B. As shown, 11

variables remained in the reduced model for Population B. Reading self-image, expected further education,

and number of books at home were the most important variables for the first function, called "Self, Aspir.

& Books." The second function was dominated by the factors age (AGEO) and meals per week.

Table 27. Standardized discriminant and structure coefficients for the first and second functions in

the United States: Population B

Function 1 “Self, Aspir. & Books” Function 2 “Age”

Variable Stand

coeff Variable Structcoeff Variable Standcoeff Variable Structcoeff

BSSRATE

TVSQ

BSTVR

BSSREADD

AGEO

BSBOOKS

BSUSLANR

PAREDQ

JOB

BSEDUCAR

MEALS

0.62

-0.58

0.35

0.32

-0.32

0.30

0.27

0.23

-0.21

0.20

-0.03

BSSRATE

BSEDUCAR

BSBOOKS

AGEO

BSSREADD

PAREDQ

BSUSLANR

TVSQ

BSTVR

JOB

MEALS

0.73

0.60

0.59

-0.52

0.51

0.48

0.31

-0.30

-0.29

-0.23

0.13

BSTVR

AGEO

TVSQ

BSSRATE

MEALS

BSUSLANR

BSBOOKS

BSEDUCAR

PAREDQ

BSSREADD

JOB

-0.96

0.73

0.68

0.55

0.34

-0.29

-0.24

0.23

-0.13

0.05

0.04

AGEO

MEALS

BSSRATE

BSTVR

BSUSLANR

TVSQ

BSBOOKS

BSEDUCAR

BSSREADD

JOB

PAREDQ

0.51

0.42

0.39

-0.37

-0.35

-0.31

-0.13

0.10

0.07

0.06

-0.04

SOURCE: IEA Reading Literacy Study, 1991.
99

-2

-1

0

1

2

-2 -1 0 1 2

L

M

H

"Self, Aspir. & Books"

USA (11 variables)

Pop. B

Figure 17. Two-function plot of group

centroids for low- (L), middle- (M)

and high- (H) performing students

in USA for Population B

Using the centroids to understand the meaning.

Figure 17 shows the group centroids for the United

States for Population B. The figure shows large

difference between the group means for the first

function but very small differences between the highand the low-performing group for the second function.

The standardized group means for the most significant

variables for the first function show that the highperformance group have a more positive reading selfimage, expect more further education, have parents

with more education, and have more books at home

than the students in the other two groups. The poor

readers have the lowest reading self-image, tend to be

older than the rest, often watch TV, and often have job

responsibilities.

To summarize the data for the United States:

• The canonical correlation for the first function was higher in Population B than in Population

A.

• The percentage of the total discriminative power for the first function was somewhat higher in

Population A than in Population B.

• The three performance groups could be distinguished on the basis of 9 variables in Population

A and 11 variables in Population B.

• The most important discriminating variables in Population A were reading self-image,

frequency of watching TV, and frequency of reading comics; and in Population B, reading

self-image, expected further education, and number of books at home.
100

References

Downing, J. (1972). Comparative reading. New York: MacMillan.

Elley, W.B. (1992). How in the world do students read? Hamburg: The International Association for the

Evaluation of Educational Achievement.

Elley, W.B. (ed.). (1994). The IEA study of reading literacy: Achievement and instruction in thirty-two

school systems. Oxford: Pergamon/Elsevier.

Klecka, W.R. (1980). Discriminant analysis. Quantitative Applications in the Social Sciences. No. 19.

Newbury Park, CA: Sage Publications.

Linnakylä, P., and Lundberg, I. (1993). Teaching around the world. The Hague: The International

Association for the Evaluation of Educational Achievement.

Preston, R.C. (1962). Reading achievement of German and American children. School and Society, 90,

350-354.

Ross, K., and Postlethwaite, N. (1992). Effective schools in reading. The Hague: The International

Association for the Evaluation of Educational Achievement.

Taube, K. (1988). Reading acquisition and self-concept, Ph.D. Diss. Department of Psychology,

University of Umeå, Umeå, Sweden.

Thorndike, R.L. (1973). Reading comprehension education in fifteen countries. Uppsala, Sweden:

Almqvist & Wiksell.
Reading Literacy Among Immigrant Students in the United States 

and the Former West Germany

Rainer Lehmann

Humboldt University, Germany

1. Introduction

Virtually all industrialized countries have immigrant students in their schools, even if those

countries have not considered themselves open to permanent immigration. In general, under conditions

of uninhibited mobility, economic and/or political disparities between regions and countries will result in

migratory movements and, subsequently, in the establishment of sizable ethnic and language minorities

in the areas that appear to offer more favorable conditions. It is a fairly regular pattern of migration that

opportunity-seeking individuals, usually male, are soon followed by their families. Others find families

after immigration, and they often prefer partners from their home country or at least home culture.

Children from their marriages will then attend schools in the host country, for even if remigration

ultimately may occur, the schooling age of the children coincides with the economically active phase of

their parents' lives. It is typical of some minorities that intracultural marriage preferences are maintained

in the second and subsequent generations, so that the size of the minority may grow substantially even

if immigration policy is restricted. This is particularly true if birth rates within these groups are higher

than what is average in the host country.

Thus, immigration groups become part of a nation's history. They become involved in—and in

turn, actively influence—social institutions of all kinds, including schools. In spite of the general pattern

just outlined, however, conditions and effects may vary considerably between countries and between

immigrant groups within a given country. In attempting to compare and contrast the status of immigrant

groups across countries, it is not easy to take the variation between countries and between immigrant

groups within countries into account. In particular, the term "immigrant student" is not as clear as it

might appear at first. Consequently, insufficient attention has been paid in the IEA Reading Literacy

Study to deriving internationally comparable concepts. As a result, the present task of producing

comparative analyses for the United States and the former West Germany has been hampered by more

ambiguity than was anticipated. Nevertheless, we will attempt to reach as much comparability as possible

in order to investigate the social position and reading achievement of immigrant students in these two

educational systems to determine whether, and if so, why, disadvantages associated with an immigrant

status vary between the two countries.

The United States has a long tradition of immigration and may well owe its very existence to

such movements. Consequently, American schools can build upon a wealth of accumulated experience

when faced with the need to integrate the children of immigrant groups. For a number of reasons,

however, the flow of immigration has varied considerably, in terms of both origins and intensity. Obvious

indications of this are local and regional concentrations of certain ethnic groups and their relative growth

over time. For a discussion of the history of the education of language minority immigrant students in

the United States, see, for example, Hakuta (1986) and Crawford (1991).

In contrast to the United States, Germany has not officially declared itself open to immigration.

There has been, to be sure, a substantial influx of persons over the last three or four decades. Beginning

in the late 1950s, recruitment campaigns have attracted unskilled labor, mainly from Mediterranean

101
countries, most notably Turkey, to provide the booming West German economy with a much-needed

supplement to its work force. Although this practice was officially discontinued in 1973, a policy

including the right of "family reunification" has kept this group approximately stable in size, in spite of

other programs that awarded incentives to migrant workers returning to their home countries. A second

group of immigrants is composed of persons from Eastern Europe who can claim German ancestry and

who, for that reason, are awarded German citizenship immediately upon arrival. Some of these people

come from territories that were part of Germany until 1945, others from regional or local minorities in

Eastern and Southeastern Europe; in both groups, the use of standard German as a language has generally

not been part of their experience. A third group is made up of immigrants who have entered the country

under liberal procedures of reviewing requests for political asylum in Germany. Many of these persons,

mainly from Third World countries and, more recently, from war-affected areas in the Balkans, will

ultimately stay in the country, although in well over 90 percent of the cases the courts have decided that

the request for asylum was unjustified. Given that due process of law has often extended over many

years, repatriation is the exception, because concomitant social and economic hardships are pragmatically

acknowledged. A final group that might be of interest here is difficult to define. Inspection of the IEA

data revealed that there is a surprisingly large number of students who are German according to their

teachers' records but who at least occasionally speak some foreign language in their homes. In most

cases, these students will be from mixed marriages: they are German by virtue of the citizenship of at

least one of their parents, yet they appear to live in a bilingual home environment.

2. Definitions of "Immigrant Students"

There are several ways to define operationally the groups to be investigated here. It is important

to note that the adequacy of such definitions depends to a large degree on certain context factors, such

that formally equivalent definitions could even lead to incomparable results. In order to make this clear,

it may be instructive to mention some of the similarities and dissimilarities between immigrant groups

in the United States and in Germany.

The United States considers itself open to immigration, at least in principle, and is prepared under

certain conditions to award citizenship after a relatively short period of time. Since it cannot be assumed

that the children of recently arrived citizens are soon integrated into the majority culture (which is least

likely to happen in "closed" ethnic neighborhoods), nationality or citizenship are ruled out as defining

elements, because too few presumably disadvantaged students would be captured by the respective

operationalization. Self-ascribed ethnicity, in turn, would result in too broad a definition, considering, for

example, many extremely well-adapted Asian students whose ethnic background has remained a socially

relevant trait because of its visibility. Race, finally, is hardly a suitable criterion here, for even though it

may be associated with educational disadvantages in some definable minorities, the respective distinctions

have little to do with recent migratory movements.

In the specific American context, a language-based definition of the term "immigrant student"

seems to be the most appropriate of the various options available, given the relatively scarce information

on minority aspects in the IEA student questionnaires. In particular, those students whose first language

is not English are considered immigrant to the United States for the present purpose. This definition

assumes that potential disadvantages in educational biographies are primarily linked to differences

between the child's first language and the English-dominated educational system. It assumes further that

possible cultural biases against these students will be highly correlated with the language issue, and this

also implies that, other factors being equal, possible disadvantages of students from culturally or

102
ethnically definable subgroups tend to disappear once language assimilation has reached the parent

generation. It should be noted that the language used in the home is ignored in the present definition.

This is based on the observation that this variable has a much smaller effect on reading achievement than

the child's first language. Table 1 presents these findings for the two populations in the United States,

together with the percentages of students included in the above definition of immigrant students (9.2

percent of Population A students and 10.7 percent of Population B students).

Table 1. Students' immigrant status according to first language and language at home, and reading

scores: United States

Immigrant status/child's first language and

language in the home

Population A

9-year-olds

(N=6,248)

Population B

14-year-olds

(N=3,209)

Percent Reading score* Percent Reading score*

Nonimmigrant

English first language; use English at home . .

English first language; other language at home

74.9

15.9

554

538

76.8

12.5

544

534

Immigrant

Other first language; use English at home . . .

Other first language; other language at home .

1.2

8.0

518

526

1.7

9.0

515

501

*Equally weighted average of international domain scores for narrative, expository texts, and documents (cf. Elley 1993, p. 45f; 57).

SOURCE: IEA Reading Literacy Study, U.S. National Study data, 1991.

Using the German IEA data to determine immigrant students requires an indirect approach.

Fortunately, it is possible to do this in a way that closely parallels the approach taken with the U.S. data.

As in the United States, German citizenship is ruled out as a defining element because of its ambiguous

relationship with the immigrant status: the lack of German citizenship may or may not be related to a

migration having occurred in the last two generations, and conversely, the criterion of having German

citizenship would include many recent arrivals from Eastern Europe. In both cases,

language—presumably a major factor in putting immigrant students at a disadvantage—would be captured

only partially. There are, however, two variables in the German data set that provide the relevant

information incorporated into the definition for the American sample: teacher's indication of student

nationality and student indication of his/her mother tongue. These two variables coincide almost perfectly

(99.7 percent in both samples). It was therefore determined to use the teacher variable that leads to the

following definition: Those students who are judged as being non-German by their teachers are

considered immigrant to Germany. It should be reiterated that this definition corresponds almost perfectly

to the language-based definition used for the United States: only 0.3 percent of all students (or no more

than 3 percent of the immigrant students) would be categorized differently if the student-identified mother

tongue had been used in the definition.

103
It is also useful to consider the information pertaining to students from a mixed background, as

opposed to immigrants as defined above and German monolinguals. As shown in Table 2, the additional

use of a foreign language in the home does not put the students at a disadvantage if the mother tongue

is recognized by the teacher (and indicated by the student) as being German. If, however, the foreign

language in the home is truly the first language in the home and forms part of a syndrome often

characterized by an unstable situation in the migratory process and unfavorable social conditions, a very

noticeable achievement gap is observed. Some 13.6 percent of the Population A students and 11.7 percent

of the Population B students in the former West Germany are affected by this situation.

Table 2. Students' immigrant status according to teachers' judgments and the use of a foreign language

in the home, and reading scores: former West Germany

Immigrant status/foreign language in

the home

Population A

9-year-olds

(N=2,813)

Population B

14-year-olds

(N=4,208)

Percent Reading Score* Percent Reading Score*

Nonimmigrants

Only German in the home . . .

Also foreign language . . . . . . .

76.0

10.4

511

510

79.8

8.5

532

539

Immigrants 13.6 456 11.7 463

*Equally weighted average of international domain scores.

SOURCE: IEA Reading Literacy Study, West German National Study data, 1991.

3. The Problem: Which Factors Affect the Achievement Gap Between Immigrant and

Nonimmigrant Students?

The above discussion of the various options for defining immigrant status has already made

reference to some contextual factors that are apparently related to student reading achievement: The use

of language(s) in the home was demonstrated to be associated with differences in student performance on

the IEA reading test, and quite clearly, language use has to be seen in the wider perspective of the

migration process as a whole. Although Tables 1 and 2 present information on test performance in the

various subgroups, it may be helpful to show these findings in terms of the dichotomous, country-specific

definitions of immigrant student (Table 3).

104
Table 3. Mean reading achievement by immigrant status and population: United States and former

West Germany*

Country Population A Population B

United States

 Nonimmigrant students . . . . . . . . . . . . .

 Immigrant students . . . . . . . . . . . . . . . . .

551 (2)

525 (7)

(Standard deviation = 73.7)

543 (2)

503 (11)

(Standard deviation = 84.4)

Former West Germany

 Nonimmigrant students . . . . . . . . . . . . . .

 Immigrant students . . . . . . . . . . . . . . . . .

511 (3)

456 (7)

(Standard deviation = 82.9)

532 (3)

463 (6)

(Standard deviation = 77.4)

*Standard errors of the mean are given in parentheses. Standard errors were derived using estimated design effects of 5.5 (United

States, both populations), 2.7 (West Germany, Population A), and 3.5 (West Germany, Population B). Design effects were calculated

as given by Kish (1965).

SOURCE: IEA Reading Literacy Study, U.S. and West German National Study data, 1991.

The international scores were calibrated to achieve a population mean of 500 (averaged across all

countries participating in the IEA study) and a corresponding standard deviation of 100. With these scales

in mind, two important observations can be made from Table 3. First, the achievement gap between

immigrant and nonimmigrant students in the United States is substantially smaller than it is in former

West Germany. Second, the immigration gap appears to widen between populations in both educational

systems (although this trend is not statistically significant), but in spite of its tracking system for

secondary schools, the overall standard deviation of reading achievement decreases in West Germany.

The problem is, then, to ascertain why it is that in these two countries with similar levels of "human

development" (according to UNESCO), and with similar percentages of immigrant students in their

educational systems, such differences occur.

Obviously, there are great difficulties in providing a well-justified answer to this question

because, technically speaking, immigrant status is confounded with many other variables—few of which

are available for the present analysis. Mere descriptions of the special economic, social, legal, cultural,

and political context into which immigrant groups are embedded in each of the two countries would at

best be suggestive, rather than constituting a convincing explanation. The strategy adopted here is,

therefore, to use the available data to determine the relative impact of those factors actually measured in

an attempt to find evidence related to the differences between the two countries regarding their successful

integration of immigrant students.

This course of analysis is pursued in three broad steps. First, we will consider the extent to which

the home backgrounds between immigrant and nonimmigrant students differ in the United States and in

the former West Germany. It may well be that society as a whole, rather than the educational system, is

the primary source of the observed achievement gaps in that pronounced social disadvantages impose

higher demands on the schools' ability to compensate. Second, the results of these analyses are checked

against insights that may arise from the aggregate scores for language-defined subgroups of immigrants.

Finally, the educational systems' ability to compensate for specific immigration-related disadvantages can

then be analyzed by contrasting the influence of the immigrant status variable with that of known social

determinants of reading achievement.

105
4. The Relative Social Position of Immigrant Students

As was suggested in the introductory remarks, the population of immigrant students is quite

heterogeneous, both in the United States and in former West Germany, as far as their origins and

migratory backgrounds are concerned. Thus, it is not unlikely that their relative social positions will also

vary substantially, depending on the particular group to which they belong. Also, it will be useful for

subsequent interpretations to begin with a brief characterization of the groups to be investigated here.

In the case of the United States, it appeared appropriate to distinguish groups of students whose

mother tongue is not English: (1) the Spanish-speaking; (2) those with an Asian language as mother

tongue; (3) those with a European language other than English as mother tongue; and (4) those with

another language background. Table 4 contains the respective percentages for each group, as well as

measures of their relative position on key social variables.

Table 4. Indicators for the relative social position and the linguistic integration of four language groups

of immigrant students in the United States: Standardized distance of group centroids from the

total means (Population A)

Indicator English

(reference

group)

Spanish Asian European Other

Size of community . . . . . . . . . . . . . . . . . . . . . . . .

Socioeconomic status . . . . . . . . . . . . . . . . . . . . . .

Wealth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Advanced-skill orientation . . . . . . . . . . . . . . . . . . .

-.04

 .03

 .04

 .03

 .38

-.52

-.53

-.47

 .24

 .09

 .16

 .33

 .21

.19

-.04

 .05

 .65

-.27

-.47

-.49

Percent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90.8 5.3 0.7 1.6 1.6

Use of English in the home . . . . . . . . . . . . . . . . . - -.11 .02 .32 .05

Percent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . - 57.3 7.8 17.1 17.8

SOURCE: IEA Reading Literacy Study, U.S. National Study data, 1991.

These data reflect, in a quantitative way, some basic patterns of migratory movements into the

United States. First of all, migration is a predominantly metropolitan phenomenon. There is a clear

general overrepresentation of immigrant students in urban communities, most noticeably among members

of the Spanish and the Other groups. Secondly, there is a divergent pattern in terms of socioeconomic

status (parents' education, availability of books and encyclopedias, regularity of family meals), wealth

(primarily the existence of long-lasting consumer goods), and advanced-skill orientation (availability of

computers, calculators, typewriters, specialized literature in the home). In these terms, the Spanish and

the Other groups are markedly below the general averages, whereas Europeans and, above all, Asians

tend to rank higher than average. As far as the use of English in immigrant homes is concerned, the

differences between the various immigrant groups appear to suggest a close relationship to the history of

migratory waves. In the face of its generally precarious social position and its heavy concentration in

certain areas, the Spanish group has made the least progress in the process of substituting English for the

original mother tongue, whereas the European group is the most assimilated linguistically. A more

complete investigation, quite beyond the present scope, could perhaps reveal to what extent this is due

to the respective arrival times and to what extent it is related to opportunities for immigrants to establish

(or reinforce) cultural and linguistic communities sufficiently large to counteract linguistic assimilation.

106
If the latter is a significant factor and if the use of a non-English language in the home inhibits an easy

integration into the monolingual schools, then one would expect the larger and regionally more

concentrated groups to be associated with higher achievement gaps with respect to the majority-language

reading comprehension.

Table 5 contains some comparative data for the former West Germany. 

Table 5. Indicators for the relative social position and linguistic integration of five language groups of

immigrants in the former West Germany: Standardized distance of group centroids from the

total means (Population A)

Indicator

German

(reference

group)

Turkish Eastern

Europe Mediterranean English/French Other

Size of community . . . . . . . . .

Socioeconomic status . . . . . . .

Wealth . . . . . . . . . . . . . . . .

Advanced-skill orientation . . . .

-.09

 .07

 .06

 .07

 .53

-.73

-.40

-.55

 .51

-.38

-.61

-.46

 .58

-.30

-.32

-.22

 .87

-.08

 .56

 .42

 .60

-.44

-.34

-.42

Percent . . . . . . . . . . . . . . . . 86.4 4.5 3.7 2.8 0.4 2.2

Use of German in the home . - -.07 .10 .09 -.50 -.08

Percent . . . . . . . . . . . . . . . . - 33.6 27.4 21.0 2.7 15.3

SOURCE: IEA Reading Literacy Study, West German National Study data, University of Hamburg, 1991.

The German situation is similar to the American one in that the concentration of immigrants in

urban areas in also apparent, albeit in a still more pronounced fashion. This in itself would suggest that

there are less incentives for immigrant groups in Germany to actively seek social integration, since urban

areas provide the opportunities to maintain familiar cultural environments similar to those from the

original backgrounds. In addition, with the exception of the very small and clearly privileged AngloFrench group, the relative social position of immigrants in Germany is less favorable than it appears to

be in the United States. Only the Turks (including the Kurds), as the single largest group, are in a

situation more or less comparable to that of Spanish-speaking immigrants in the United States, but it is

worth noting that their disadvantages are less economically accentuated and more pronounced in terms

of socioeconomic status (as will be recalled, heavily related to educational background) and advancedskill orientation.

These differences can be summarized by defining a multiple-regression equation evaluated so that

differences between immigrant and nonimmigrant students are optimally predicted (equivalent to a

discriminant analysis, since in each instance only two groups are being compared). Table 6 presents the

standardized regression coefficients, the magnitudes of which indicate the unique relative import of each

of the predictors considered. In order to take student characteristics more fully into account, some key

student variables have been added to the model.

107
Table 6. Beta coefficients+

 from multiple regression analyses with immigrant (i.e., low) versus

nonimmigrant (i.e., high) status of students as dependent variable: Two populations from the

United States and former West Germany

Predictor

United States Former West Germany

Population A Population B Population A Population B

Size of community . . . . . . . . . . . . . . . . . . . . . . . .

Parents' SES . . . . . . . . . . . . . . . . . . . . . . . . . . .

Parents' advanced-skill orientation . . . . . . . . . . . . .

Type of school++ (high = private) . . . . . . . . . . . . . .

Student gender (high = female) . . . . . . . . . . . . . . .

Student age . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Student voluntary reading . . . . . . . . . . . . . . . . . . .

Student educational aspirations . . . . . . . . . . . . . . .

-.13

 .08

 .08

 .05

-.04

-.00

-.05

-

**

***

***

*

n.s.

n.s.

*

-.13

 .15

 .05

-.10

-.01

-.02

-.03

-.01

***

***

n.s.

***

n.s.

n.s.

n.s.

n.s.

-.20

 .10

 .11

-.07

 .00

-.17

 .00

-

***

**

***

***

n.s.

***

n.s.

-.11

 .15

 .07

 .02

-.01

-.18

-.01

 .11

***

***

**

n.s.

n.s.

***

n.s.

***

R

2

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .04 .06 .13 .12

+

Statistical significance levels are indicated, incorporating design effects as given in footnote of Table 3: not significant (n.s.); significant

at 10 percent level (*); significant at 5 percent level (**); significant at 1 percent level (***).

++For West Germany, Population B secondary school track, high denotes Academic.

SOURCE: IEA Reading Literacy Study, U.S. and West Germany National Study data, 1991.

Earlier interpretations are confirmed by these figures. The fact that the model discriminates better

between immigrants and nonimmigrants in both German samples (as indicated by R2) establishes on a

broader basis that background differences are greater there than in the United States. While one should

resist the temptation to interpret small differences between the two countries, three observations stand out.

First, the much greater influence of size of community in the German Population A as compared with

Population B reflects recent migratory movements: More recent immigrant waves with younger families

apparently have tended to arrive in urban centers, which can be substantiated by the disproportionately

larger numbers of Eastern European and Other students in Population A (data not shown here). Second,

the negative within-grade association between student age and nonimmigrant status, which is present in

the German data only, is a result of attempts to facilitate immigrant students' entry into the educational

system by special preparatory classes, compromises between educational attainment and (basically agedefined) grade membership, and—if other means fail—by grade repetition. Third, the mechanism of

allocating students to various tracks in secondary school, depending on their perceived aptitude, appears

to reinforce segregation tendencies that would, at this stage, be less visible if only subjective educational

aspirations were taken into account. 

In conclusion, it would seem highly plausible that the relatively high concentration of immigrant

students in some German localities, their underrepresentation in academic secondary schools, and their

unfavorable social position are related to their unsatisfactory progress through the educational system, as

compared with the experiences of immigrant students in the United States and all but one of the other

countries participating in the IEA Reading Literacy Study. In remains to be seen how much each of these

factors contributes to the observed achievement gaps.

5. Differential Reading Achievement Between Immigrant Groups

One approach to studying the contribution of the specific situations in which immigrant students

live is to look at differences between groups that are relatively homogeneous within but subject to

108
different impacts of tradition, culture, and environment. Table 7 lists the scores on the IEA reading

literacy test for the language groups in the United States. 

Table 7. Reading literacy scores by language group: Populations A and B, United States

Scale English

(reference group) Spanish Asian European Other

Population A

Narrative . . . . . . . . . .

Expository . . . . . . . . .

Documents . . . . . . . .

Total score . . . . . . . . .

557

541

554

551

523

511

508

514

591

569

561

574

541

530

537

536

533

521

526

527

Population B

Narrative . . . . . . . . . .

Expository . . . . . . . . .

Documents . . . . . . . .

Total score . . . . . . . . .

547

548

534

543

498

506

500

501

537

564

529

543

520

521

512

518

486

477

486

483

SOURCE: IEA Reading Literacy Study, U.S. National Study data, 1991.

As was predicted in the previous section, difficulties with reading comprehension are most

obvious among the students with Spanish as their mother tongue. Here, the most unfavorable conditions

in the social environment, or their comparatively low degree of linguistic assimilation, or the cumulative

effects of both appear to have operated against high test performance, and the tendency among Spanishspeaking immigrants to cluster in urban areas may have served to stabilize and reinforce these adverse

processes. The latter is not incompatible with the observation that urbanization is even stronger among

the students from the Other category, because these students appear to be generally less disadvantaged

in terms of the other predictors considered previously. The Asian group represents the other extreme.

These students, although very few in number in both samples, confirm the expectation of superior

performance, motivated not only by common lore, but also by their generally favorable backgrounds

(Table 4). In fact, both the relatively low performance of the Spanish-speaking students and the high

position of the Asian group appear to imply that the observed effects are due to long-term processes,

operating between at least two generations.

As a side observation, it may be interesting to note that in the older sample, the achievement

gaps, where they exist, are least pronounced in the documents component of the tests and most strongly

pronounced in the narrative domain. This suggests that linguistic proficiency and tacit cultural knowledge,

although captured only indirectly by these distinctions, are essential components of what has been

measured by the test and, at the same time, are likely to be less developed among immigrant students,

particularly if they come from comparatively intact language and culture subcommunities.

This last observation is also confirmed by the German data, as are the general patterns emerging

from the comparisons between different language groups (Table 8).

109
Table 8. Reading literacy scores by language group: Populations A and B, former West Germany

Scale German

(reference group) Turkish Eastern European Mediterranean English/French Other

Population A

Narrative . . . . . . . .

Expository . . . . . . .

Documents . . . . . .

Total score . . . . . . .

499

506

528

511

432

432

463

442

451

448

492

464

458

453

485

466

468

488

487

481

442

450

469

453

Population B

Narrative . . . . . . . .

Expository . . . . . . .

Documents . . . . . .

Total score . . . . . . .

525

531

540

532

438

448

457

448

444

456

484

461

459

470

493

474

528

536

553

539

450

465

476

464

SOURCE: IEA Reading Literacy Study, West German National Study data, University of Hamburg, 1991.

As was the case with respect to their relative social position, the Turkish (or Kurdish) students

are again in a relatively unfavorable situation concerning test performance. So the tentative explanations

posited above are also well in line with the test results observed in the German samples. In the special

case of the Turkish group, other factors, not considered so far, are also present in the data and may well

have increased the difficulties for these students to adapt themselves to the fundamentally monocultural

(i.e., not only monolingual) characteristics of the German educational system. The most obvious of these

factors is the Turks' Muslim religion which, for many of them, introduces elements of visible group

characteristics (e.g., religiously motivated clothing regulations) and tendencies to claim exemptions from

normal curricular (physical education, religious education) as well as extracurricular (e.g., class

excursions) activities. The present data are insufficient, however, to pursue these effects further.

That there are very distinct additional differences between the various immigrant groups can be

inferred from data not shown here. In most groups, the frequency of using German as a language in the

home increases over time since the first entry into Germany, but levels off after a period of some 3 or

4 years. Only in the case of immigrants from Eastern Europe, many of whom are legally considered

German citizens, does the process of linguistic assimilation continue unrestrictedly. It is likely that the

expectation to gain and strengthen an identity indistinguishable from the majority's self-definition plays

an important role here, and it may well be part of the explanation for this group attaining a state of

reading literacy on par with the Mediterranean group, which, on average, has been in the country for a

much longer period.

6. Determinants of the Achievement Gap Between Immigrant and Nonimmigrant Students

The previous analytical steps have shown that in both the United States and Germany, immigrant

status is clearly related to social disadvantages. These effects are significantly stronger in Germany as

a country with a short but intensified tradition of immigration that in 1991 (the year of testing) was

beginning to stretch the existing system of social and financial support for these groups to the point of

110
menacing its affordability. Given, then, that the educational disadvantages of immigrant students are so

closely interwoven with the empirically well-established adverse effects of social disadvantages, ways to

disentangle the unique contribution of the specific immigrant situation from more general social effects,

however closely these two are interrelated, should be sought.

Multiple regression analysis once again offers a technique to arrive at reasonable estimates of

how well an educational system succeeds in dealing with the specific disadvantages of students from

migrant families. In a fashion similar to the previous analyses, Table 9 presents the respective results.

Table 9. Beta coefficients+ from multiple regression analyses with reading achievement (international

total score) as dependent variable: Two populations from the United States and former West

Germany

Predictor

United States Former West Germany

Population A Population B Population A Population B

Size of community . . . . . . . . . . . . . . . . . . . . . . .

Parents' SES . . . . . . . . . . . . . . . . . . . . . . . . . .

Parents' advanced-skill orientation . . . . . . . . . . . .

Type of school (high = private)++. . . . . . . . . . . . .

Student gender (high = female) . . . . . . . . . . . . . .

Student age . . . . . . . . . . . . . . . . . . . . . . . . . . .

Student voluntary reading . . . . . . . . . . . . . . . . . .

Student educational aspirations . . . . . . . . . . . . . .

Immigrant status (high = nonimmigrant) . . . . . . .

-.06

 .17

 .17

 .08

 .04

-.14

 .03

-

.06

**

***

***

***

*

***

n.s.

**

-.06

 .18

 .09

 .04

 .04

-.18

-.01

 .22

 .09

*

***

***

n.s.

n.s.

***

n.s.

***

***

-.02

 .13

 .05

-.04

 .04

-.13

 .16

-

 .16

n.s

**

n.s.

n.s.

n.s.

***

***

***

 .02

 .10

-.04

-.05

-.02

-.10

 .06

 .55

.15

n.s.

***

n.s.

***

n.s.

***

***

***

***

R

2

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .14 .23 .13 .45

+

Statistical significance levels are indicated, incorporating design effects as given in the footnote to Table 3: not significant (n.s.);

significant at 10 percent level (*); significant at 5 percent level (**); significant at 1 percent level (***).

++For West Germany, secondary school track, high denotes Academic.

SOURCE: IEA Reading Literacy Study, U.S. and West German National Study data, 1991.

When looking at these data, it is interesting to note that the pattern of unique influences on

reading achievement (independent of underlying substantial covariances) varies between the two countries

investigated. In the United States, the parents' socioeconomic status and their orientation towards

advanced skills play a prominent role, surpassed only by the subjective indications of students'

educational aspirations. For Population B, in (West) Germany some of these effects appear to be

mediated by student affiliation to a given secondary school track, which in itself is a good measure of the

expected eventual educational attainment. Apart from this, the data show both commonalities and

dissimilarities between the two countries. Among the American schools, the well-known inner-city

problems are clearly demonstrated, whereas Germany appears to have a more homogeneous achievement

distribution once the influence of social variables is controlled for. The American data also show the

known relative superiority of private schools, which is not the case for Germany (but the number of

private schools in the sample is much too small to bear strong assertions as to this point). Gender effects

are present in both samples, but at the Population B level in Germany, the absolute superiority of females

in reading achievement is small and, as it seems, entirely mediated by the motivational factors of which

111
voluntary reading is an indicator. Incidentally, it was already apparent from Table 6 that in this respect

the immigrant students do not differ greatly from their nonimmigrant peers. The negative association of

student age with reading achievement is present in all four samples. It is, of course, not a causal

relationship in the strict sense, but a selection effect due to late school entry and grade repetition. Once

again, the fact that it is smaller for the German secondary school sample can be explained by the fact that

grade repetition is more frequent in the lower tracks of secondary school, and that it also often precedes

a transfer from a higher track to a lower one.

Bearing all that in mind, it is most remarkable that the residual effects for immigrant status are

still present in all data sets, but they are still substantially higher for Germany than for the United States.

Accordingly, the attempt to explain the achievement gap between immigrant and nonimmigrant students

on the basis of an underlying syndrome of social disadvantages has only been partially successful. Social

disadvantages—concentration in urban areas, lower affinity to education, lower standards of

living—contribute to the gap, but there are indications that other factors related to the immigrant status

add a very specific component to the unfavorable situation of immigrant students, and that this

component is more pervasive in Germany. 

7. Conclusion

At this point, the discussion of how best to understand—and subsequently alleviate—the situation

of immigrant students is usually characterized by the introduction of very broadly defined terms difficult

to handle empirically. References to specific historical and political circumstances, allegations as to the

effects of a bicultural identity on career planning, and hypotheses on possible interferences in the (almost)

simultaneous acquisition of a first and second language all carry a certain plausibility and are not totally

void of empirical evidence. At the present state of knowledge it would seem hopeless, however, to

develop these lines of thinking to a point where a convincing explanation for the quantitative differences

between the American and the German situations seems likely.

The above analyses have produced some results, however, that could guide further studies in the

search of a fuller understanding of the processes involved. The first of these observations concerns the

possible consequences of a distinctly bilingual socialization with the original language as the dominant

means of communication in the home and an exclusive use of the majority language in the school. While

the IEA data contain some information on the language use in the home, it is improbable that this factor

shows a very clear unique contribution to the explanation of reading achievement. The required more

advanced techniques, such as commonality analysis, are, unfortunately, not applicable here because of the

(proportionately) small number of immigrant students in the respective combined categories. It is highly

recommended that such research be conducted with appropriate designs in the future because of its

obvious implications. If it can be shown that slow linguistic assimilation in combination with related

factors is detrimental to the children's progress through the educational system, it may be advisable to

provide the parent generation with incentives to acquire a sufficient command of the majority language.

This is common practice in Sweden, for instance, where the right to reside in the country eventually

depends on passing a language examination.

Similar implications concern the potential effects of heavy local concentrations of immigrant

students. This aspect has two different facets, which have to be studied separately and dealt with at

different political levels. Clearly, residential patterns are not greatly influenced by educational planning,

but it is highly desirable to investigate their effects on student achievement. Again, the present data do

112
not permit carrying out the required analyses. Specialized studies designed to demonstrate the

interactions between residential patterns and other factors, however, could conceivably show the extent

to which closed neighborhoods affect educational development. Here, the second facet comes into play.

Since schools (especially primary schools) are often neighborhood schools, one would expect that the

resulting variation in the relative frequencies of immigrant students in classrooms is closely interacting

with environmental effects. It is known, of course, that mean achievement in classrooms with a high

density of immigrant students is relatively low. One would have to have very precise knowledge of the

"ecological effects" of such concentrations in schools, however, before imaginable countermeasures—such

as changes in the recruitment patterns of schools—are justifiable.

A final set of open questions concerns the specific influence of linguistic factors. There is a

rapidly growing body of research in this area motivated by the rather direct practical implications: Early

instruction in the mother tongue, bilingual education, or total immersion programs are options to be

considered, but it will be impossible to interpret their relative merits without reference to potentially

complex interactive processes with social factors involved. The IEA Reading Literacy Study tests were

not constructed to furnish, nor do they provide, the linguistic information to study the issues, nor do the

background questionnaires render sufficient information to pursue such aims. The available data do show

some of the questions, however, that must be addressed if the current disadvantages of immigrant students

are to be understood to a degree where rational decisions between existing policy options are possible.

113
References

Crawford, J. (1991). Bilingual education: History, politics, theory, and practice. Second

edition. Los Angeles, CA: Bilingual Educational Services, Inc.

Elley, W.B. (1993). The IEA Reading Literacy Study. The international report. Oxford:

Pergamon Press.

Hakuta, K. (1986). Mirror of language. The debate on bilingualism. New York: Basic Books.

Kish, L. (1965). Survey sampling. New York: Wiley.

114
115

Comparison of Reading Literacy Across Languages

in Spanish Fourth Graders

Guillermo A. Gil

Instituto Nacional de Calidad y Evaluación, Spain

Keith Rust, Marianne Winglee

Westat, Inc., USA

1. Introduction

This paper discusses the results of a comparative language study for fourth grade students among

regions (called autonomous communities) in Spain. The study of Spanish languages was conducted as part

of the IEA Reading Literacy Study in Spain, which consisted of three components:

1. An international component for comparisons across countries; that is, the relative

performance of Spanish students in comparison with students from other countries (see Elley

1992);

2. A Spanish component designed to study the proficiency levels of reading comprehension and

reading habits for Spain as a whole and to compare results among the country’s autonomous

communities; and

3. A language component to study and compare the reading proficiency in the different Spanish

languages.

The international and Spanish regional components of the study involved samples of students from

the whole of Spain. Students selected for these components were administered the IEA reading literacy test

in Castilian. The Spanish languages component, however, involved only three autonomous communities—

Catalonia, Galicia, and Valencia. Within these autonomous communities, an additional group of students

was selected, and these students were administered the reading literacy test in the language of the

community, which was Catalonian, Galician, or Valencian.

In the Spanish terminology, the terms “state,” “nation,” and “country” each mean the whole of

Spain, formed by 17 different autonomous communities that are denominated. Depending on the

circumstances, “nations,” “countries,” “principalities,” or “autonomous communities” are approximate

equivalents of the states that form the United States of America in relation to the Federal Union. Spain has

a semifederal political structure and has been, since 1978, in a general and continuous process of

decentralization of political powers, with a trend to becoming a state with a political federal structure. In

the rest of this article, “state” will be used to mean the whole of Spain and “autonomous communities”

refers to the various territories.

Section 2 of this paper briefly reviews the characteristics of the autonomous communities and

languages spoken in the different communities. Section 3 outlines the aims of the study and issues

addressed in the study, and Section 4 describes the sample design used to select schools and students and

the characteristics of students in the sample. Section 5 discusses the differences in students’ reading
116

literacy in Castilian and in the languages of the autonomous communities and examines the influence of

students’ characteristics on reading literacy. Section 6 discusses the results of this study.

2. Characteristics of the Spanish Languages

Several Spanish languages are used in Spain and around the world. Castilian, usually and

internationally known as Spanish, is the official language for the whole of Spain. It is also the Spanish

language used in the Philippines, most countries in South America, Central America, and Mexico, and by a

sizable Hispanic population in the United States. There are about 400 million people who speak Spanish,

and 330 million of them speak the Castilian Spanish as their first language.

The other Spanish languages are Catalonian, Galician, Basque, and Valencian. Each of these

languages is also the official language of its respective nation: the Catalonian nation, the Galician nation,

the Basque country, and the Valencian country. The Basque language (Euskera in Basque) was not

included in the study because of translation problems that arose with a substantive group of the reading

subtests. Figure 1 shows the locations of Catalonia, Galicia, and Valencia, and Table 1 provides some

demographic and economic data on these three communities.

Figure 1. Locations of the autonomous communities of Catalonia, Galicia, and Valencia
117

Table 1. Demographic and economic data for the antonomous communities of Catalonia, Galicia,

and Valencia: 1991

Population characteristic Catalonia Galicia Valencia Whole of Spain

Number of inhabitants..................................... 6,059,496 2,731,671 3,857,235 39,100,000

Per capita income1.......................................... 1,601,300 1,038,800 1,325,400 1,315,900

Percent of population in employment sector:

Agriculture............................................... 4% 3% 8% 10%

Industry.................................................... 35 26 35 30

Services.................................................... 61 71 57 60

Percent unemployed2...................................... 13 13 15 17

1Per capita income in pesetas.

2Percent unemployed in relation to the total active population.

SOURCE: Anuario El País, Spain, 1993.

The Spanish languages included in this study— Castilian (Spanish), Catalonian, Galician, and

Valencian—are all Latin-based romance languages. Because of the common root, the languages of the

autonomous communities are close to Castilian, although each is clearly a different language. Table 2

summarizes the relationship between Castilian and the languages of the autonomous regions and the

different governments’ efforts to promote the language.

Table 2. A comparison of the languages of autonomous communities relative to the Castilian

language (Spanish)

Characteristic Catalonian Galician Valencian

Linguistic and structural

features.............................. Close to Castilian;

Close to Portuguese and to

Castilian; Close to Castilian;

Clearly a different language Clearly a different language Sometimes considered a

dialect of Catalonian

Government's effort to

promote language .............. Strong Mild Moderate

Catalonian is spoken in Catalonia, an economically wealthy autonomous community in northeast

Spain, south of France and north of Valencia. Nearly half of the population of Catalonia are immigrants,

mainly from other parts of Spain. Catalonian has been the language of the upper bourgeoisie, the native

Catalonian upper and middle classes, and the home servants and peasantry for centuries. From the 1940s

to the 1970s, Catalonian was not widely and publicly used by the immigrant middle class, nor by the lower

class workers who were mainly recent immigrants. Since 1978, the government of Catalonia has strongly

promoted the use and the learning of the Catalonian language by all the population living in Catalonia

through a widely extended and government-supported school-based program of linguistic immersion and

other political measures.

In 1991, the time of data collection for the IEA Reading Literacy Study, the teaching at primary

levels was done under seven modalities of schools that offered different levels of immersion in Catalonian

and Castilian. In one extreme modality, there were schools that taught all subjects in Catalonian, except

for the Castilian language subject-matter. In the other extreme modality, schools taught all subjects in

Castilian, except for the Catalonian language subject-matter. By 1994, the seven modalities were to be
118

phased out, and the huge majority of schools in Catalonia were supposed to be teaching primarily in the

Catalonian language. Currently, most students at the end of compulsory education in Catalonia are

bilingual in Castilian and Catalonian.

Galician is spoken in Galicia, an autonomous community that is relatively poor in comparison with

the overall economic wealth of the state. Galicia is a region of Spain with a high rate of emigration and

almost no immigration. The Galician language has some similarities to the Portuguese language and some

similarities to Castilian and has traditionally been the usual home language, especially of the peasantry.

Valencian is spoken in Valencia, an economically wealthy, autonomous community in eastern Spain,

south of Catalonia and north of Murcia. The Valencian language is considered by most linguists and a

small segment of the population as a dialect of the Catalonian language. However, a substantial segment of

the population and the different Valencian political parties argue that early literature written in Valencian

preceded the Catalonian literature. Therefore, they consider Catalonian to be a product of the early

Valencian language and argue that Valencian is a different language. From a political point of view, there

is the fear that placing Valencian as a dialect of Catalonian may put Valencia in a situation of cultural and

political dependency on Catalonia. Officially, Valencian is considered a different language. The Valencian

language has been traditionally the language of the peasantry, and it has not been widely used by the middle

and upper classes.

Table 3 shows the results of a recent survey on the percentage of people who use the Castilian

language and the languages of the autonomous communities in which they live, people’s attitude about the

language that they believe should be used for education, and people’s preferred language for reading.

Table 3. Political and social status of the languages of autonomous communities in comparison to

the Castilian language (Spanish): Percentage of population with characteristic: 1994

Characteristic Catalonia Galicia Valencia

Currently understands and speaks the language of the

 community........................................................................ 73% 85% 54%

Currently has the language of the community as the main

 language ........................................................................... 50 56 34

Currently has Castilian as the main language...................... 49 40 65

Believes that compulsory teaching has to be done in Castilian 27 63 81

Believes that compulsory teaching has to be done in the

 language of the community ............................................... 62 23 8

Prefers Castilian for reading ............................................... 34 51 67

Prefers the language of the community for reading.............. 29 10 7

Note: “Not known” and “equal” answers are not included in the percentages.

SOURCE: Centro de Investigaciones Sociologicas, Spain, 1994.

In all three autonomous communities, the teaching and learning of Castilian and the language of the

respective autonomous community are compulsory in schools. The political importance of the languages of

the autonomous communities, however, has undergone some recent changes. Since the Constitution of

1978, the use of the language of the autonomous communities was promoted by their different

governments. The most vigorous promotion has been in Catalonia, less so in Galicia and Valencia.

Currently, most school children are bilingual in Castilian and the language of the autonomous community.

Catalonian, Galician, and Valencian are usually spoken in the parliaments of their autonomous

communities.
119

3. Objectives of the Regional Study

The situations in the three autonomous communities provide different socioeconomic and political

backgrounds for comparing factors that affect reading literacy in the different languages in Spain. Within

each community, factors that influence students’ reading proficiency in the language of the community and

in Castilian may be influenced by the social and political importance of the languages in the region.

The goal of the Spanish languages study, therefore, is to compare the reading proficiency of fourth

grade students tested in Castilian with that of students tested in Catalonian, Galician, and Valencian.

Students in grade 4 are typically 9 or 10 years of age. The specific issues addressed in the analysis are as

follows:

• Within Catalonia, Galicia, and Valencia:

- How do literacy scores in Castilian compare with scores in their own language?

- How do student characteristics affect literacy in Castilian? Do they affect literacy in

their own language in the same manner?

• Across the three autonomous communities, are there variations in students' literacy scores in

Castilian?

• Can the autonomous community differences, if there are any, be related to

- The economy and industry of the region,

- The linguistics and structural features of the languages,

- The political and social importance of the language in the autonomous communities?

4. Samples of Schools and Students by Autonomous Community

In Spain, as in other countries in the IEA Reading Literacy Study, students for the international

component of the study were selected using a stratified equal probability sampling scheme with

probabilities proportional to measures of size (i.e., number of students at grade level). In the first sampling

phase, the country was stratified into eight domains—Catalonia, Valencia, Galicia, Andalucia, Canary

Islands, Basque Country, M.E.C. (those communities administered by the Ministry of Education and

Science), and Navarra. Within each domain, representative samples of fourth grade schools were selected,

and one entire classroom of students was selected per school. For the Spanish state and Spanish languages

studies, a stratified unequal probability sampling scheme was used to secure representative samples per

community. The sample design resulted in approximately equal numbers of students per community in

spite of the differences in their size.

In Catalonia, Valencia, and Galicia, two samples of schools and classes of students were selected for

the Spanish state and language study. The objective was to have two comparable groups of students within

each autonomous community, one being tested in Castilian and another in the own language of the
120

community. Table 4 shows the number of schools and students in each region tested in the Castilian

language and in the language of the respective communities.

Table 4. Number of schools and students in language study

Autonomous Test Number of

community language Schools Students

Catalonia.............................................. Castilian

Catalonian

39

34

1,021

1,026

Galicia ................................................. Castilian

Galician

39

37

988

907

Valencia............................................... Castilian

Valencian

37

28

976

699

SOURCE: IEA Reading Literacy Study, Spanish National Study data, Spanish Ministry of Education and Science, 1991.

Given the sample design, the two samples of students within each autonomous community were

expected to have comparable characteristics. In general, we found this to be true with small differences due

to sampling error. In all three autonomous communities, the average age of students in both study groups

was about 10 years. The average number of books at home for both groups was between 50 and 100, and

both groups reported a similar number of items in home and student possessions. Also, both groups

reported spending an average of 2 hours per day watching TV and similar frequencies in voluntary reading

at home. The slight observed differences between test language groups in these characteristics are solely

due to sampling variations in the selection of schools. However, in comparing students’ literacy in the two

test languages, it is important to take into consideration the contribution of these factors. This is addressed

in the next section by including them in the regression models.

The characteristics that were different for the study groups were related to the language in which

they were tested: the frequency of using test language at home, and the frequency of being read to in the

test language. Tables 5 and 6 show the average scores on these characteristics. In Catalonia and Galicia,

students reported speaking Castilian more frequently than the language of the autonomous community. In

Valencia, however, both Castilian and Valencian were used with similar frequencies. Across the three

autonomous communities, the frequencies with which fourth grade students spoke Castilian were very

comparable (means of 3.8, 3.9, and 3.8 for the three communities, respectively).

Table 5. Average frequency1 in using different languages at home

Community

Language Catalonia Galicia Valencia

Castilian................................................................ 3.8 3.9 3.8

Language of the autonomous community............... 3.1 3.0 4.1

1Based on a 5-point scale where 1 = never, 5 = always.

SOURCE: IEA Reading Literacy Study, Spanish National Study data, Spanish Ministry of Education and Science, 1991.

A second difference between the study groups was the frequency with which people read to the

student in a test language at home (Table 6). In Catalonia and Galicia, students were being read to more

frequently in the language of the autonomous community than in Castilian. This difference, however, was

not found in Valencia.
121

Table 6. Average frequency1 of being read to at home, by language

Community

Language Catalonia Galicia Valencia

 Castilian ..................................................................... 1.5 1.5 1.7

 Language of the autonomous community .................... 2.4 2.8 1.9

1Based on a 4-point scale where 1 = never, 4 = daily.

SOURCE: IEA Reading Literacy Study, Spanish National Study data, Spanish Ministry of Education and Science, 1991.

5. Reading Proficiency in Castilian and the Languages of the Autonomous Communities

Table 7 shows the average reading proficiency scores for students tested in Castilian and for

students tested in the languages of the autonomous communities. In all three autonomous communities, the

scores for students tested in Castilian were higher than those for students tested in the language of the

community. In Catalonia, the mean reading proficiency score for students tested in Castilian was 516 and

the score for students tested in Catalonian was 484, a difference of 32 points between the two test groups.

In Valencia, the difference was 58 points, and in Galicia, the difference was 44 points. This means that in

all three communities, students are better at reading Castilian than the language of the community.

Table 7. Mean reading performance score in test language, by autonomous community

Test language Catalonia Galicia Valencia

 Castilian ............................................................... 516

(83)1

521

(77)

524

(82)

 Language of the autonomous community .............. 484

(112)

477

(116)

466

(112)

 Both...................................................................... 5002

(100)

5002

(100)

5002

(100)

1Numbers in parentheses are standard deviations.

2

Scores were scaled separately in each community and so cannot be used for comparisons across communities.

SOURCE: IEA Reading Literacy Study, Spanish National Study data, Spanish Ministry of Education and Science, 1991.

The scores shown in Table 7 were scaled using a three-parameter IRT model. A separate model was

developed for each autonomous community such that the mean score for both language test groups was set

at 500 and the standard deviation at 100. Since the scores for each region were standardized separately,

they cannot be used to compare student’s performance across communities.

Table 8 shows students' performances in Castilian scaled for state comparisons of all communities.

With this scale, the national average for all autonomous communities was set at 500. The communities

were listed in order of performance, and Catalonia ranked higher than both Valencia and Galicia. In

Catalonia, the average score was 513 points, 13 points above the state average. The performance of

children in Valencia and Galicia were very close to the state average, at 502 and 499, respectively. This

result indicates that even in view of the Catalonian government’s effort to promote the language of the

autonomous community, Catalonian students’ reading performance in Castilian is above the national

average.
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Table 8. Reading performance in Castilian, by autonomous community

Rank Autonomous community Mean Standard

deviation

1 Navarra ............................................................................... 523 99

2 Basque Country................................................................... 518 95

3 Catalonia............................................................................. 513 88

4 M.E.C.1............................................................................... 512 97

5 Valencia.............................................................................. 502 98

6 Galicia ................................................................................ 499 99

7 Andalucia............................................................................ 469 102

8 Canary Islands..................................................................... 463 106

All....................................................................................... 500 100

1 Territory administered by the Ministry of Education and Science.

SOURCE: IEA Reading Literacy Study, Spanish National Study data, Spanish Ministry of Education and Science, 1991.

To evaluate the effect of student characteristics on the reading literacy of the two languages in a

community, multivariable linear regression models were used (Neter, Wasserman, and Kutner 1985).

Separate models were specified for each autonomous community. The models were:

yi x x z x z x z = b0 + b1 1i + + b p pi + a 0 i + a 1 1i i + +a p pi i + ei. .. . . . (1)

where yi was the reading literacy performance of student i in the community, x pi was the pth student

characteristic for student i, and zi was 1 if student i was tested on the language of the autonomous

community, and 0 if tested on Castilian. The b ’s are the regression coefficients for the main effect

parameters, the a ’s are the coefficient for the interaction effects between the two languages used in the

community, and e i was the error term.

The statistical procedure SUDAAN (Shah et al. 1992) was used to compute the regression estimates

and conduct the hypotheses’ testing. In this study, the sampling procedure involved a multistage design in

which whole classes of students were selected within schools. When calculating the variance estimates for

the regression parameters, SUDAAN took into consideration the clustering of students within schools and

the stratifications used in the selection of schools.

Two hypotheses were tested with the model. One hypothesis tested was that the overall model fitted

for students tested in Castilian is the same as the model fitted for students tested in the other languages

( H 0 : a 0 = a 1 = a 2 = . . . = a p = 0 against an alternative H 1 : not so). A second analysis then assessed the

hypothesis that students’ differences in reading proficiency by language differed only in the mean, and not

in its relationship to other characteristics ( H 0 : a 1 = a 2 = . . . = a p = 0 against H 1 : not so). Both

hypotheses were rejected in all three communities suggesting that different equations should be used for the

two test language groups. Therefore, the fitted equations for the two language groups were:

Castilian language group: y i b b x b x = 0 + 1 1i + + p pi . .. , and

Community language group: y i (b a ) (b a ) x (b a )x = 0 + 0 + 1 + 1 1i + + p + p pi .. . (2)

where the b’s and a’s are estimates of b 

’

s and a ’s in equation (1).
123

Before presenting the results, it should be noted that students with extreme performance scores

(scores 3 standard deviations from the mean) were excluded from the model because they may have undue

influence on the parameter estimates. Table 9 shows the percentages of students excluded. In general, very

few students (ranging from 0 to 0.4 percent), scored higher than 800 points. The percentage who scored

below 200 was also small when the test language was Castilian, about 0.1 percent. However, when the test

language was the language of the autonomous community, a higher percentage of students scored below

reasonable levels of proficiency. In Catalonia, about 3.6 percent of the students tested in the Catalonian

language were completely improficient in the language. In Galicia and Valencia, the percentages were 7.3

and 7.6, respectively. The apparently lower percentage in Catalonia is probably due to the strength of the

linguistic policy of the Catalonian government promoting the language of Catalonia.

Table 9. Percentage of extreme performers in assessment, by community and language

Autonomous Test Percentage scoring

community language < 200 > 800

Catalonia.............................................. Castilian

Catalonian

0.1

3.6

0.1

0.4

Galicia ................................................. Castilian

Galician

0.1

7.3

0.0

0.0

Valencia............................................... Castilian

Valencian

0.1

7.6

0.1

0.0

SOURCE: IEA Reading Literacy Study, Spanish National Study data, Spanish Ministry of Education and Science, 1991.

Tables 10 through 12 show the regression equations estimated for the test language groups by

autonomous communities. The student characteristics that have a significant influence on reading literacy

in Castilian (at a =0.05 level for a two-tailed test) and characteristics that are significantly different

between the two test languages (significant interaction effects) are identified.

The results of the regression analyses showed that the student characteristics that affect reading

literacy in Castilian had limited effects on the reading literacy of the language of the autonomous

community. Student characteristics that had a significant effect on reading literacy in Castilian for all three

populations were age of student, number of books at home, and frequency of being read to. Other

characteristics were found to be significant in different communities. For example, in Catalonia hours of

TV watching was also found to be significant, although no TV effect was observed in the other two

autonomous communities. In Galicia, use of test language and frequency of voluntary reading were

significant. In Valencia, student possessions was a significant variable. The characteristic home

possessions was not significant in any of the three autonomous communities, probably because of a

relatively high correlation with the item number of books at home.
124

Table 10. Regression estimates of the effect of student characteristics on reading literacy in

Castilian and Catalonian: Catalonia

Student characteristic Castilian language (bi) Interaction with

language (ai)

Age ..................................................................................... -0.76 +1.08

Age2 ................................................................................... -0.32* +0.24**

Number of books at home .................................................... 14.24* -17.22**

Home possession items (1 unit on 2-point scale).................. 11.08 -1.04

Student possession items (1 unit on 2-point scale) ............... -3.19 +4.79

Infrequent user of test language (1=yes, 0 otherwise)........... 12.77 -8.31

Frequent user of test language (1=yes, 0 otherwise) ............. 6.64 +9.36

Hours watching TV ............................................................. -4.10* +3.17

Frequency of voluntary reading (1 unit on 4-point scale) ..... 2.06 -4.99

Frequency of being read to (1 unit on 4-point scale)............. -9.58* +11.88**

R2 ....................................................................................... 0.09

*Statistically significant effect on Castilian (0.05 level).

**Statistically significant interaction effect with language (0.05 level).

NOTE: Scale has standard deviation of 100.

SOURCE: IEA Reading Literacy Study, Spanish National Study data, Spanish Ministry of Education and Science, 1991.

Table 11. Regression estimates of the effect of student characteristics on reading literacy in

Castilian and Galician: Galicia

Student characteristic Castilian language (bi) Interaction with

language (ai)

Age ...................................................................................... -0.96* +0.52

Age2..................................................................................... -0.24* +0.24**

Number of books at home ..................................................... 12.33* -8.64**

Home possession items (1 unit on 2-point scale)................... 0.92 +1.44

Student possession items (1 unit on 2-point scale) ................ 1.39 +0.90

Infrequent user of test language (1=yes, 0 otherwise)............ -2.11 +1.87

Frequent user of test language (1=yes, 0 otherwise) .............. 18.54* -14.69

Hours watching TV .............................................................. -0.73 -2.52

Frequency of voluntary reading (1 unit on 4-point scale) ...... 7.21* 5.63

Frequency of being read to (1 unit on 4-point scale).............. -9.83* +9.24**

R2......................................................................................... 0.10

*Statistically significant effect on Castilian (0.05 level).

**Statistically significant interaction effect with language (0.05 level).

NOTE: Scale has standard deviation of 100.

SOURCE: IEA Reading Literacy Study, Spanish National Study data, Spanish Ministry of Education and Science, 1991.
125

Table 12. Regression estimates on the effect of student characteristics on reading literacy in

Castilian and Valencian: Valencia

Student characteristic Castilian language (bi) Interaction with

language (ai)

Age ..................................................................................... 0.00 +0.96

Age2 ................................................................................... -0.29* +0.20**

Number of books at home .................................................... 12.79* -5.40

Home possession items (1 unit on 2-point scale).................. 10.50 -13.33

Student possession items (1 unit on 2-point scale) ............... 15.63* -14.48

Infrequent user of test language (1=yes, 0 otherwise)........... 10.73 -7.68

Frequent user of test language (1=yes, 0 otherwise) ............. 11.87 -6.83

Hours watching TV ............................................................. 0.04 +0.14

Frequency of voluntary reading (1 unit on 4-point scale) ..... 2.43 -4.83

Frequency of being read to (1 unit on 4-point scale)............. -16.67* +18.55

R2 ....................................................................................... 0.16

*Statistically significant effect on Castilian (0.05 level).

**Statistically significant interaction effect with language (0.05 level).

NOTE: Scale has a standard deviation of 100.

SOURCE: IEA Reading Literacy Study, Spanish National Study data, Spanish Ministry of Education and Science, 1991.

In all three autonomous communities, significant interaction effects were found between the test

language and the student's age and frequency of being read to. In Catalonia and Galicia, significant

interaction effects between language and number of books at home also were observed.

To interpret the interaction effects, the following six figures plot the adjusted reading performance

for students in the two test language groups by the student characteristics age of student, frequency of

being read to, number of books at home, use of test language at home, TV watching, and frequency of

voluntary reading. The adjusted reading performance is calculated as € y = b ́x with certain x ’s in the

regression model set to specific values for evaluation; the remaining x ’s set to their mean values. The b ’s

are the estimated regression coefficients. These adjusted mean scores allowed us to examine the effects of

each student characteristic after holding constant the contribution of other characteristics in the regression

equation.

Effect of Age on Reading Literacy

Age of the student is expected to have a curvilinear effect on reading literacy performance because

the older children at a grade level are typically repeaters of the grade who tend to have lower performance.

For grade 4 in Spain, the mean age of students is about 10 years old, and most students who are 11 years

or older at this grade level (over 85 percent) are grade repeaters. To capture the curvilinear effects of age,

a second-order model was used in the regression analyses; that is, age was entered in the regression

equation as both a linear term ( x ) and a quadratic term ( x 2 ). The coefficients of the quadratic parameter

of age were significant in all three autonomous communities.

The adjusted means in Figure 2 show that the effect of age differed by the test language. For

literacy in Castilian, performance increased with age until about 10 years old (the average age for grade),

and declined thereafter. This trend was observed for all three autonomous communities. However, the

effect of age on the language of the autonomous communities was different. The trend appears much more
126

Figure 2. Reading performance by autonomous community, language, and age of student

Language: • Castilian

°

Language of autonomous community

Catalonia

460

500

540

9 10 11

Age (years)

Galicia

460

500

540

9 10 11

Age (years)

Valencia

460

500

540

9 10 11

Age (years)

SOURCE: IEA Reading Literacy Study, Spanish National Study data, Spanish Ministry of Education and Science, 1991.

Adjusted Mean

Reading Score

Adjusted Mean

Reading Score

Adjusted Mean

Reading Score
127

linear with age in each case, and generally speaking age does not have a strong relationship with reading

proficiency in the autonomous languages.

Effect of Number of Books at Home

The characteristic number of books at home had different effects by test language and by

autonomous communities. As shown in Figure 3, there was a positive association between reading literacy

in Castilian and the number of books in all three autonomous communities; the slope of improvement

appeared greatest in Catalonia. The number of books at home probably reflected the families’ interest in

reading, but also served as a measure of family possessions and family wealth. Therefore, the increasing

performance as the number of books increased probably reflects the effect of sociocultural class in the level

of achievement in reading.

The effects of number of books at home on the languages of the autonomous communities was less

intuitive. In Valencia, performance in the Valencian language appears to improve with increases in the

number of books. In Catalonia and Galicia, the number of books at home showed no significant

relationship to reading proficiency in the autonomous language. Unfortunately, the questionnaire did not

ask students the language in which the books at home were written, making it difficult to interpret these

results on any great depth.

Frequency of Being Read To

In each autonomous community, the characteristic frequency of being read to had different effects

on literacy in Castilian and literacy in the language of the community. The trends were comparable for the

three autonomous communities, however (Figure 4). For literacy in Castilian, students who were being

read to frequently had lower performance in Castilian than students who were rarely read to. The negative

association between being read to and reading literacy performance at this age level has also been found in

other countries, including the United States. A possible explanation is that by fourth grade, students who

are frequently read to are likely to have some reading dysfunction and therefore are low reading achievers.

The frequency of being read to, however, has no significant effect on the reading literacy in the languages

of the autonomous communities. Again, a limitation of this measurement was that students were not asked

to indicate the language used for reading.

Use of Test Language

The characteristic use of test language was entered in the regression equation as two dummy

variables: frequent user of test language and infrequent user of test language. The variable frequent user

of test language was set to 1 for students who responded "always" and "almost always" to the item about

the use of test language at home and 0 otherwise. The variable infrequent user of test language was set to

1 for students who responded "never" or "almost never," and 0 otherwise. Students who answered

"sometimes" had values of 0 on both variables.
128

Figure 3. Reading performance by autonomous community, language, and number of books at home

Language: • Castilian

°

Language of autonomous community

Catalonia

460

500

540

0 1-10 11-50 51-100 over 100

Number of books at home

Galicia

450

500

550

0 1-10 11-50 51-100 over 100

Number of books at home

Valencia

460

500

540

0 1-10 11-50 51-100 over 100

Number of books at home

SOURCE: IEA Reading Literacy Study, Spanish National Study data, Spanish Ministry of Education and Science, 1991.

Adjusted Mean

Reading Score

Adjusted Mean

Reading Score

Adjusted Mean

Reading Score
129

Figure 4. Reading performance by autonomous community, language, and frequency of being

read to

Language: • Castilian

°

Language of autonomous community

Catalonia

450

500

550

Rarely Daily

Frequency of being read to

Galicia

450

500

550

Rarely Daily

Frequency of being read to

Valencia

450

500

550

Rarely Daily

Frequency of being read to

SOURCE: IEA Reading Literacy Study, Spanish National Study data, Spanish Ministry of Education and Science, 1991.

Adjusted Mean

Reading Score

Adjusted Mean

Reading Score

Adjusted Mean

Reading Score
130

Figure 5. Reading performance by autonomous community, language, and use of test language at

home

Language: • Castilian

°

Language of autonomous community

Catalonia

460

500

540

Infrequent Occasional Frequent

Use of language at home

Galicia

460

500

540

Infrequent Occasional Frequent

Use of language at home

Valencia

460

500

540

Infrequent Occasional Frequent

Use of language at home

SOURCE: IEA Reading Literacy Study, Spanish National Study data, Spanish Ministry of Education and Science, 1991.

Adjusted Mean

Reading Score

Adjusted Mean

Reading Score

Adjusted Mean

Reading Score
131

These variables were used because in a bilingual community, frequent users of one language implies

infrequent users of the alternative language. In Catalonia, for example, when the test language was

Catalonian, frequent users of the test language were students from Catalonian-speaking families, and

infrequent users of the test language were students from Castilian-speaking families. When the test

language was Castilian, frequent users of the test language were students from Castilian-speaking families,

and infrequent users of test language were students from Catalonian-speaking families. Students who

sometimes used the test language may have been students from homes with mixed language backgrounds,

in which two or more languages were used.

The results showed that the frequency of use of the test language at home had limited relationship to

reading literacy (Figure 5). There was a positive association between frequent usage and literacy of

Castilian in Galicia, but no such association was found for the other autonomous communities and for the

other languages.

Hours of TV Watching

The effects of TV watching also differed by language and by region (Figure 6). In Catalonia, an

increase in the number of hours per day watching TV was associated with a negative performance in

Castilian, but no such effect was observed in the other autonomous communities. TV watching also had no

adverse effects on the literacy of the languages of the autonomous communities, although a slight (but not

statistically significant) negative trend is observed with the Galician language.

In order to understand the effect of TV watching, it was useful to examine people’s preference about

the language on television. Table 13 shows the percentage of the population that preferred watching

television in the two languages of each community. In Catalonia, the chance that people favored TV

watching in Castilian was about 1 in 4; the chance was about 1 in 3 in Galicia, and 1 in 2 in Valencia. In

contrast, the chances that people preferred the language of the autonomous communities were 1 in 3 in

Catalonia, 1 in 6 in Galicia, and 1 in 8 in Valencia. These rates of preference suggest that in Galicia and in

Valencia, an increase in the number of hours watching TV was probably associated with an increase in

exposure to the Castilian language. For Catalonia, the exposure to language through TV was relatively

mixed; not dominated by any one language. Taken together, the results suggest that when the increase in

TV watching was not associated with an increase in exposure to Castilian, TV watching had a negative

effect on reading literacy of the language.

Table 13. Preferred language on TV, by autonomous community

Autonomous community

Preferred language on TV Catalonia Galicia Valencia

Castilian................................................... 24% 34% 51%

Own language .......................................... 33% 17% 12%

Both ......................................................... 43% 48% 36%

NOTE: Percentages may not add to 100 due to rounding.

SOURCE: Centro de Investigaciones Sociologicas, Spain, 1994.
132

Figure 6. Reading performance by autonomous community, language, and hours per day watching

television

Language: • Castilian

°

Language of autonomous community

Catalonia

460

500

540

0 1 2 3 4 5

Hours per day watching television

Galicia

460

500

540

0 1 2 3 4 5

Hours per day watching television

Valencia

460

500

540

0 1 2 3 4 5

Hours per day watching television

SOURCE: IEA Reading Literacy Study, Spanish National Study data, Spanish Ministry of Education and Science, 1991.

Adjusted Mean

Reading Score

Adjusted Mean

Reading Score

Adjusted Mean

Reading Score
133

Frequency of Voluntary Reading

The characteristic frequency of voluntary reading had a positive effect on reading literacy of

Castilian in Galicia. No such effect was observed in the other autonomous communities (see Figure 7).

Frequency of reading also had no effect on the literacy of the languages of the autonomous communities.

6. Discussion

This study compared reading literacy in different Spanish languages in the autonomous communities

Catalonia, Galicia, and Valencia for fourth grade students. Two representative samples of students were

involved in each community. One sample was given a reading literacy test in Castilian, the Spanish

language spoken throughout the world, and a second sample was given the same test in the language of the

autonomous community. The results showed that student characteristics that typically affected literacy in

Castilian tended to have a limited effect on literacy of the languages of the communities. Literacy in the

languages of the autonomous communities may be more heavily influenced by the social and political status

of the languages and the linguistic policies of those communities.

In Spain, the languages of the autonomous communities may be regarded as minority languages that

face potential disuse over time. To ensure that the language stays in functional use by the people, the

governments of the autonomous communities have undertaken active measures to educate and promote

their own languages. Government support is especially relevant in Catalonia, where there is a high rate of

immigration. There are also strong incentives on the immigrants to learn Catalonian because of the high

social status of the language. Immigrants need to know Catalonian for social upgrading and for the

purpose of employment in the future. The need to promote the language of the community is less for

Galicia and Valencia because there is low immigration. In these communities, there is less danger that the

language of immigrants (almost exclusively Castilian) can become the main and dominant language,

resulting in the long term in a disuse of their own languages.

The language immersion programs in Catalonia appear to have been successful in promoting literacy

in the Catalonian language without unduly compromising literacy in Castilian. In Catalonia, relatively few

students are completely illiterate in the Catalonian language despite the high influx of immigrants who

speak Castilian. The situation in Catalonia suggests that literacy in a dominant language such as Castilian

is unlikely to be adversely affected by the introduction of a second language.

In summary, it seems unlikely that there are general rules regarding the performance of reading

when two languages are used together in a community. The achievement in reading literacy in each

language probably depends strongly on a number of factors:

• The different social statuses and political strengths of the languages;

• The real and functional use of a language—for oral and written communication, conversational

discourse, interchange of technical ideas, and other purposes;

• The situation in which the language is used—in communication media, school, work

environment, or political settings;

• The preferences of the people and their attitude about the relevance of a language; and

• The linguistic policy of the governments that can exert a different degree of pressure on the

population to study and learn a language.
134

Figure 7. Reading performance by autonomous community, language, and frequency of

voluntary reading

Language: • Castilian

°

Language of autonomous community

Catalonia

450

500

550

Rarely Daily

Frequency of voluntary reading

Galicia

450

500

550

Rarely Daily

Frequency of voluntary reading

Valencia

450

500

550

Rarely Daily

Frequency of voluntary reading

SOURCE: IEA Reading Literacy Study, Spanish National Study data, Spanish Ministry of Education and Science, 1991.

Adjusted Mean

Reading Score

Adjusted Mean

Reading Score

Adjusted Mean

Reading Score
135

References

Elley, W.B. (1992). How in the world do students read? The Hague: The International Association for

the Evaluation of Educational Achievement.

Neter, J., Wasserman, W., and Kutner, M.H. (1985). Applied linear statistical models. Second edition.

Homewood, IL: Irwin.

Shah, B.V., Barnwell, B.G., Hunt, P.N., and LaVange, L.M. (1992). SUDAAN User’s manual, Release

6.0. Research Triangle Park, NC: Research Triangle Institute.
137

SECTION B: INSTRUCTIONAL PRACTICES IN READING

• Teaching Reading in the United States and Finland

Marilyn R. Binkley and Pirjo Linnakylä

• A Nine-Country Study: How Do Teachers Teach Reading to 9-Year-Olds?

Emilie Barrier and Daniel Robin
139

Teaching Reading in the United States and Finland

Marilyn R. Binkley

National Center for Education Statistics, USA

Pirjo Linnakylä

University of Jyvaskyla, Finland

1. Introduction

It seems almost trite to draw attention to the importance of literacy in the world today. Clearly,

most national economies are based on the existence of a highly literate work force. All over the world,

from their very inception, schools have been given the task of developing the literacy abilities of their

students. As the school population grew and as compulsory schooling became widespread, the level of

literate functioning necessary has increased exponentially.

The recent IEA Reading Literacy Study gave researchers an opportunity to compare literacy

proficiency among nations and to consider other differences related to literacy, such as the organization of

instruction. In such a large-scale international study, one can find a wide spectrum of teaching traditions,

beliefs, values, and principles that guide school instruction. However, sometimes the educational, cultural,

and social environments of the participating countries are so dissimilar that the differences found in

instructional practice may only reflect the various prevailing stages of economic, social, or cultural

development. It is obvious that the instructional activities most useful at certain developmental stages of

educational evolution may not function at other stages of national development.

In this paper, we compare the instructional cultures of two of the highest achieving countries in the

IEA Reading Literacy Study—the United States and Finland. The focus is on two highly industrialized

nations that are very similar in a number of respects, but that also have differences. Both Finland and the

United States allocate a lot of resources to education in general and to teacher education specifically. Both

nations have populations that can be considered exceptionally literate. Both provide their students with

easy access to books and other texts in homes, nearby libraries, bookstores, and schools. Likewise, both

have lengthy teacher education programs and a strong academic tradition.

Cultural and educational differences, of course, also exist in such arenas as language and

orthography, literacy and teaching traditions, school organization and starting age, and principles of child

rearing at home and at school (cf. Elley 1992; Lundberg and Linnakylä 1992). Finland, as a Nordic

country, has educational roots in Scandinavian, German, and Baltic traditions that contrast with the AngloAmerican tradition predominant within the United States. Despite this difference in tradition, teachers from

both countries are likely to be familiar with the newer theories of reading and instruction, even though such

theories tend to originate primarily from research in North America.

There are also many common features of reading that pervade the entire world. For example, the

cognitive processes in reading comprehension are considered basically universal. Texts have many

common elements in their function, content, structure, and textual characteristics (Purves 1991). If this

were not so, an international reading assessment would not be possible.

In considering how reading is taught, one can begin by thinking about how teachers approach the

subject. Do reading teachers have an implicit theory that structures their thoughts, decisions, and actions?
140

Are there differences in theoretical approaches that reflect national cultures? Are there some common

universal traits characteristic of the act of reading itself, or are the instructional features nationally and

culturally constructed? These questions are the driving focus of this paper.

To examine the two teaching cultures, we used the empirical data gathered with the IEA Reading

Literacy Study Population A Teacher Questionnaire (teachers of the 9-year-old students). The

questionnaire was constructed from the perspective of an implicit general instructional model as held by

each of the National Research Coordinators in the IEA study. We, however, wanted to explore teachers'

instructional thinking as it explicitly related to reading theories. Thus, our main tasks were

• Develop an overview of reading theories;

• Explore the structure of the teachers' beliefs, activities, and assessment practices to provide a

tentative picture of teachers' implicit theories as they compare to existing reading theories;

• Describe and contrast the instructional features and their theoretical connections in two different

national teaching cultures;

• Compare the teachers' preferences regarding instructional beliefs and activities; and

• Explore the unity and diversity in the teaching of reading.

2. Developing an Overview of Reading and Reading Instruction Theories

2.1. The Relationship Between Reading and Reading Instruction Theory and More Generalized

Theories of Teaching and Learning

Although the prime purpose of this section is to develop an overview of reading theories and reading

instruction, it is important to note that theories of reading instruction are more than just theories of

reading—they also intersect with more general theories of instruction and learning. This is especially true

in the case of primary education, where the classroom teacher probably follows the same implicit theory

while teaching all or most subjects. As depicted in Figure 1, issues of cultural diversity and cognitive unity

also have a strong impact on how teachers organize their beliefs, activities, and assessments of student

learning. Given that the impact of these other aspects is likely to be consistent across all teaching within

the primary school classroom, the attributes of each theoretic stance in terms of reading instruction may

often be consistent with our understanding and theories of instruction in other specific academic disciplines.

2.2. The Process of Teaching

Based on the large body of research related to how instruction and learning takes place, it is possible

to construct a model of the teaching process. For example, according to some cognitive theories of

instruction, the process is seen as an activity involving teachers and students working together (Shulman

1986; Clark and Peterson 1986). This collaborative work involves both thinking and acting on the part of

all participants. Three significant attributes of each of the participants—capacities, actions, and

thoughts—serve as potential determinants of the teaching and learning that takes place in the classroom.1

 

1Capacities are defined as relatively stable characteristics of ability, actions compose the purposive activities of teachers or students, and thoughts are

the cognitions, emotions, beliefs, and intentions that guide the observable actions.
Figure 1: The structure of teaching reading in the United States and Finland

141

Teaching

Theories

Structures and Preferences of Beliefs

Instructional Activities

Assessment Aspects

Reading

Theories

Cognitive

Unity

Cultural

Diversity
142

Research focusing specifically on teachers' thoughts and actions indicates that there is a reciprocal

relationship between these attributes (Clark and Peterson 1986). Teachers' actions are to a great extent

guided by their knowledge and beliefs, which in turn are affected by the teachers' instructional actions and

students' behavior.

Research on the cultures of teaching focuses particularly on teachers' knowledge, values, and beliefs

for action (Feiman and Floden 1986). In the same vein, the ethnographic and process-tracing studies have

shown that teachers' instructional activities are in general guided by an implicit theory of action that has

been shown to be well integrated and internally consistent (Marland and Osborne 1990).

Research on classroom processes has led to the development of a model for studying the teaching

process (Bennett 1988; Yorke 1987). In brief, teachers' strategic classroom activities and actions are based

on their belief system. However, the enactment of teachers' and students' classroom activities serves as a

test of the implicit hypotheses of the teacher. If the students' actual outcomes differ from those the teacher

intended, then the teacher will revise or modify his/her own implicit belief system or even the strategic

planning and activities. This interactive model is depicted in Figure 2. If we assume that this model

provides a good picture of what really goes on within classrooms, schools, and nations, then we can use

this framework for comparing instructional systems across nations.

While constructing a frame for studying teachers' implicit or practical theories of reading instruction

within different national school systems, it is important to note that the research on teaching in general

accentuates the reciprocal nature of the relationship between teachers' instructional beliefs and purposive

activities. It is believed that the two reflect the main determinants of the teaching process. Consequently,

we hypothesize that there should be some consistency between teachers’ beliefs and the instructional

practices they frequently employ. Further, we hypothesize that this unity is based on definable theoretic

stances.

2.3. Common Features of Reading Theories

Because theories of reading have been promulgated for centuries, they are quite numerous. Logically

there are certain attributes common to all theories of reading. We have identified seven:

• Reading theories are evolutionary. No reading theory stands on its own; each draws upon

previous conceptualizations and modifies these to suit particular ends. Parallel with these

reading theories is a concurrent evolution of larger, more inclusive theories of thinking and

learning.

• Reading theories are partial. Given the complexity of reading, new insights into its nature are

constantly adding piece by piece to the extant knowledge.

• Each reading theory has a specific focus. For example, some theories concentrate on wordrecognition processes (Gough 1972, 1985; LaBerge and Samuels 1974, 1985; Stanovich 1991;

Rumelhart 1985), while others focus on comprehension, almost to the exclusion of letter-level

processes (Just and Carpenter 1985; Kintsch and van Dijk 1978).
Figure 2: A hypothetical model of the teaching process

Assessment Instructional

Activities

Strategic

Planning

Teacher's

Beliefs

143
144

• Reading theories with the same focal phenomenon take variant positions. For instance, within

the group of word recognition theories some emphasize strict linear processing (Gough 1972,

1985), while others emphasize the interaction of multilevel processes (e.g., Rumelhart 1985;

Stanovich 1991; LaBerge and Samuels 1974, 1985).

• Versions of each reading theory may range from moderate to extreme. For example, some

proponents of natural acquisition theories of reading argue that teaching children to read any

more than they are taught to speak is a cause of reading failure (Goelman, Oberg, and Smith

1984). Others maintain that while learning to read is natural, some instruction can promote

reading failure just as other instruction can help to prevent it (Applebee and Langer 1983).

• Reading theories are complementary, tending to be different rather than contradictory.

Because they concentrate on a different aspect of the same phenomenon, different theories tend

to contribute to an understanding of different aspects of reading. Taken together, they provide

a more comprehensive view than any one theory alone would.

• Not all reading theories look equally closely at reading. Word-recognition theories generally

tend to be local and microscopic in their examination of reading, while comprehension theories

tend to be more global and macroscopic.

The lack of a common focus makes it hard to contrast reading theories. However, there seem to be

certain positions regarding distinctive attributes of reading theories that have had salience at different

points in time. To classify reading theories, we have relied heavily on a categorization system originally

presented by Straw (1990) that distinguishes among five periods based on three criteria: locus of meaning,

nature of knowledge needed to be literate, and purpose of literacy. We have extended his descriptors by

focusing also on the attributes of theories of reading acquisition, instruction, and cognitive and affective

processes that would likely be associated with his periods. None of the reading theories fit neatly into one

and only one of these categories, nor into any one period of time. Given the evolutionary nature of reading

theories and instructional practice, remnants of earlier periods and conceptions of reading continue to hold

a very important place in both theory and practice.

2.4. The Progression of Reading Theories

The categorization system that we use divides conceptualizations of reading theories into four

periods. It combines Straw's first two periods, transmission and translation, into one that we label

"information transfer" consistent with the terminology put forth by Harste (1985), and it maintains the

remaining three: interaction, transaction, and social construction.2

2.4.1. Information Transfer

As defined by Straw, during his transmission period the meaning of text rests with the author, and

the knowledge (intention) incorporated into a text by an author is to be reproduced by the reader. This

 

2While Straw designates specific dates for each period, these designations are not important for our purposes. Rather, we are

more interested in the progression across time and the way in which these periods correspond to notions of acquisition,

instruction, and processes. Further, we believe that while many might disagree with his time periods, few would argue with

the progression.
145

conceptualization of reading supports conceptions of teaching and learning that cast the teacher as the

source of knowledge and the student as the recipient of that knowledge.

He contrasts this with the translation period, where meaning lies in the text, which is seen as

independent of its author. The reader is a decoder of text, not of authors' intentions. To decode text the

reader needs knowledge about reading and literature. Emphasis is placed on the entertainment value of text

as well as on the information found in it (Just and Carpenter 1980; Davis 1944; Gough 1972; LaBerge and

Samuels 1974).

From our perspective, we see the two as similar because in both instances the meaning of the text

rests outside of the reader, and in both cases the reader is expected to reproduce someone else's meaning

and knowledge as represented in the text. Consequently we have consolidated the two periods into one—

information transfer (according to Harste 1985).

In this period, which strongly represents a behavioristic view of thinking and learning, the cognitive

and affective processes are presented as separate domains (Bloom, Hastings, and Madaus 1971). The

period clearly emphasizes an information transfer from the text to the mind of the reader, and the affective

domain is clearly underestimated in the theoretical models (Mathewson 1985).

Consistent with that stance, one would expect stage models of reading acquisition that assume that

human development progresses through a series of qualitatively different stages that are hierarchically

ordered, and that higher stages cannot be reached without going through the ones below (Chall 1983;

Gough and Hillinger 1980; Mason 1980). While each of the theories associated with this period might

include a different number of stages of development, there is agreement that an understanding of the

alphabet is basic to reading acquisition (Juel 1991).

Given the notion of a hierarchy, it also seems reasonable to see reading acquisition as the accretion

of subskills or components that together make up reading (Barrett 1968; Gray 1960). In this view, reading

is seen as a collection of discrete skills, such as letter recognition, ability to make letter-sound

correspondences, word recognition, and sequencing ideas. Most basal reading series are structured upon

such a model of reading acquisition.

Reading instruction during this period is very prescriptive. A central tenet is that students must be

taught to use the single system of language properly. The definitions and rules of this system form the

basis for what is to be taught. Teaching phonics before children have a concept of reading is the epitome of

the prescriptive approach to reading literacy instruction. For instance, some theorists argue that first we

should teach the code, and only then allow children to read (Balmuth 1982; Flesch 1955).

Reading theories of this period compare human mental processes to the mechanical operation of a

computer. Discrete pieces of information that are taken in by the senses are processed by a series of

discrete steps. The output for one processor becomes the input for the next one in a linear series of steps

(Gough 1985). Variations in this view, which allow for information to be chunked into whole units

(LaBerge and Samuels 1985; Ruddell and Speaker 1985; Rumelhart 1985), mark the bridge to the next

period.
146

2.4.2. Interaction

During the interaction period, meaning resides with both readers and text. The theories of this period

assume that three sources of knowledge are needed by readers: authors, text, and experience. The good

reader is the one whose background knowledge fits the text. These theories also assume that meaning is

determinate. Reading is seen as a means whereby authors and readers can share knowledge and experience

(Frye 1957; Goodman 1970; Rumelhart 1977, 1985).

The interaction period stresses information processing. Reading is conceptualized as a series of

linguistic and cognitive steps. Although most of the models neglect affective elements (Gough 1985;

Rumelhart 1977, 1985; LaBerge and Samuels 1985), the later models by Ruddell and Speaker (1985)

acknowledge its importance as one of many elements in knowledge utilization.

This period marks the beginning of a shift in views regarding instruction and cognitive processing.

In contrast to the very prescriptive view previously held, we see the development of a psycholinguistic view

where language is perceived as an instrument and the vernacular speech children bring to school is seen as

an adequate base for learning to read. Spoken language is seen as the overt performance of underlying,

abstract abilities involving phonological, syntactic, and semantic components of linguistic competence. The

theme of building on those things the student already knows—linking the more formal language of school to

the informal vernacular and the more disciplined academic understandings to the experientially acquired

concepts already in place in the mind of the learner—fit the definition of interaction.

Learning to read is a matter of employing these components in the processing of meaning. Reading

is much more than recoding visual symbols into their spoken equivalents. It involves readers in using their

knowledge of oral language and their powers of conceptualization to derive meaning from print. The

reader's knowledge of language includes familiarity with the syntactic order of linguistic elements and the

semantic relationships among them. The reader's background experience with oral language is assumed to

be a critical factor in reading development.

Psycholinguistic approaches to reading instruction recognize the principle of continuity between

home and school in the young child's experience and language. Beginning readers encounter written

materials as a natural part of their language development and are encouraged to read them fluently in terms

of their own language and meanings, rather than precisely and accurately in terms of what appears on the

printed page, as is required in the prescriptive approach. The graphic symbols are only part of the

information that readers use; syntactic and semantic predictions supplement the visual display. These

sources of information are available from the child's own linguistic competence acquired in the preschool

years.

In the psycholinguistic view, language is a self-contained system to be acquired and refined by the

individual. Psycholinguists are primarily concerned with the individual reader and how that reader

establishes meaning for text. Of primary concern are the intrapersonal context, the background knowledge

and skills that the reader brings to the task of interpreting a text, and individual differences in knowledge

and skills.

Consistent with this psycholinguistic view, we see the development of schema-theoretic views where

individuals are believed to possess cognitive structures called schemata (Anderson and Pearson 1984).
147

These schemata consist of organized sets of concepts, and understanding a piece of text occurs when

stimuli from the text are fitted into one of these structures.3

2.4.3. Transaction

During the transaction period constructing meaning is considered to be a generative act in which the

reader's role is most prominent. The meaning of text is indeterminate and is constructed by readers while

reading. To do so, readers draw upon a variety of knowledge sources, including the text, knowledge of

language, and experience. In contrast to the first two periods, which are communicative, theories

associated with this period assume that reading is more than the reception or processing of information in

text. The reader generates meaning in response to text, and the purpose of reading is actualization

(Rosenblatt 1978; Tompkins 1980; Harste, Woodward, and Burke 1984; Straw 1990). Consequently, the

reader's affective attributes (e.g., the attitudes, motives, interests, and intentions the reader brings to the

setting) are actively engaged in text interpretation. Self-concept and self-regulation are crucial in terms of

the degree of control the reader is ready to take and how much courage and willingness the reader has to

interpret the text openly and individually (Harste 1985; McCombs and Whistler 1989).

This period is marked by the beginnings of a number of major shifts in the stances that theorists

take. The stage models that prevailed in the two prior periods begin to be challenged by another

conception—the nonstage model that assumes human development is continuous and reading does not

require qualitatively different abilities for children and adults. Therefore, what is required of a child to read

a piece of text is the same as what is required of the adult; the difference is that the adult has a broader

base of knowledge on which to draw in making an interpretation (Goodman and Goodman 1979; Harste,

Burke, and Woodward 1982; Smith 1973). In addition, the adult is likely to have a broader range of

intentions for the reading (Harste 1985).

Reading acquisition is no longer necessarily based on formal, well-structured, sequential instruction.

Theorists of this period maintain that reading acquisition is a natural activity analogous to learning to speak

one's native language. Children learn to speak naturally, without formal instruction when reared in the

context of other speakers of the language. Similarly, learning to read, just like learning to speak and to

walk, emerges early in life from children's experiences with spoken and written language (Goodman 1986;

Harste and Woodward 1989; Kastler, Rosen, and Hoffman 1987; Pearson 1985). Children learn to read

earlier in the context of more diverse oral language use (Snow and Perlman 1985), and through more active

engagement with written language (Cullinan 1989; Strickland and Morrow 1989; Sulzby 1985). Even

within this group of theorists, however, there is often the acknowledgment that children profit from help

(Ehri 1987; Goodman 1986; Harste and Woodward 1989).

As opposed to the subskill view that characterized earlier periods, we see the emergence of a holistic

view that maintains that reading is more than the sum of its parts and involves more than a collection of

skills (Goodman 1986; Harste and Woodward 1989). Every reading act, according to holistic theories,

requires the integration of skill, background knowledge, purpose and intention, and attitudes.

Consequently, the characteristics of the reader and the text cannot be analyzed separately, as

assumed by earlier reading theories. Reading emerges in the transaction between readers and text. In

contrast to earlier interactive models, which assume that the text and readers are separable entities, both

 

3

For example, a person's cognitive structure might contain a schema for DOG. The cognitive structure would consist of the

relationship of this concept both to more general concepts (canines, four-legged creatures, mammals), and to more particular

concepts (domestics, wild dogs, spaniels).
148

readers and texts are seen as aspects of a total event according to transactional theories of reading (Beach

and Hynds 1991).

During the transaction period, the psycholinguistic views expand to include a somewhat more

sociolinguistic position. From this perspective, language cannot be separated from its social context and

reading is viewed not only as a set of cognitive processes, but also as social and linguistic processes (Wells

1986). As social processes, reading is used to establish, structure, and maintain social relationships among

people. As linguistic processes, reading is used to communicate intentions and meanings between authors

and readers (Olson, Torrance, and Hildyard 1985).

Both the psycholinguistic and sociolinguistic theories of the period lead to experiential learning or

the achievement of linguistic abilities through engagement in language use. Children are encouraged and

allowed to learn to read by reading for purposes that are personally meaningful. School reading programs

provide opportunities for reflective appraisal of these communications (Moffett 1983). To this reflective

repertoire could be added powerful tools of appraisal in the form of sociolinguistic understandings about

such factors as the effects of certain kinds of audiences, situations, and purposes on meaning.

Learning to read is seen as a process of being socialized into the uses of written language. There is

renewed interest in the home as a setting in which some children become literate and from which schools

can learn how to establish settings that are more effective for general literacy teaching (Harste, Burke, and

Woodward 1982).

2.4.4. Social Construction

According to Straw (1990), in the newly emerging period called social construction, knowledge is

socially patterned and conditioned. The locus of meaning is in the social context, not with any person or

object. As in the transaction period, the focus is on the construction of meaning, not by a single author or

reader, but by the reader as a member of the larger social community (Vygotsky 1978; Hunt 1990; Hynds

1990). Social construction emphasizes the generation of meaning through social experience and interaction.

The reader constructs meaning by using many types and levels of knowledge that are socially and culturally

shared.

Social construction has its roots in pragmatism (Dewey 1938) and has been influenced by the ideas

of Vygotsky (1978) and cultural psychologists who have developed and modified his views (Bruner 1985;

1990; Cole 1985). Like pragmatists, social constructionists consider reading literacy and literacy learning

to be functional and social acts that are intentional and reflect shared knowledge, values, beliefs, and

expectations on the part of the reader. Students act not only as individuals, but they are also influenced by

their families, peers, teachers, the media, and the whole surrounding culture (McCarthey and Raphael

1989).

Cultural contents, myths, desires, intentions, commitments, and values, which are commonly shared

in the social community, are also expressed in the texts children read. This is important because our

culturally adapted way of life depends on shared meanings and shared concepts as well as on shared modes

of discourse for negotiating differences in meaning and interpretation (Bruner 1990, 13).

Three basic assumptions undergird theories of social construction. First, knowledge is constructed

by the interaction of the individual with the sociocultural environment. Second, higher mental functions
149

including reading are social and cultural by nature. And third, knowledgeable members of the culture can

assist others in learning (McCarthey and Raphael 1989, 21).

Reading, like other higher mental activities, requires voluntary self-regulation, conscious realization,

reflection, and the use of signs for mediation. Because such acts are social by nature, they depend on

communication across generations and between individuals. Consequently, reading acquisition begins in the

interaction of an individual with parents, siblings, peers, or teachers and the surrounding media

environment. The role of language and dialogue is critical, since it is through speech and social

communication that the learner acquires new abilities. Through a more experienced person modeling and

thinking aloud, students learn the role and functions of literacy within the culture and the different ways of

actualizing the meaning in the texts. The dialogue itself is not merely facilitative, but actually formative in

the development of the students' reflective and critical thinking in functional literacy (McCarthey and

Raphael 1989).

Within the context of social construction theories, learning is considered an internalization of social

interaction that occurs first between individuals and then within an individual. Internalization occurs in the

zone of proximal development, defined as "the distance between the actual development level as determined

by independent problem solving and the level of potential development as determined through problem

solving under adult guidance or in collaboration with more capable peers" (Vygotsky 1978, 87). In this

zone, learning awakens a variety of internal processes that are able to operate only when the child is

interacting with people in his/her environment. Once these processes are internalized, they become part of

the child's independent developmental achievement (Vygotsky 1978, 91).

In teaching reading, assisted instruction and student-teacher dialogue are emphasized. Assisted

instruction has been compared to the scaffolding provided by the structuring of tasks through instruction,

modeling, questioning, and feedback until the learner is able to function independently. The gradual release

of responsibility moves toward a period of joint responsibility and ends with the student assuming control

over learning (Pearson and Gallagher 1983).

The social construction period emphasizes the social nature of literacy practices in both society and

the school. Because learners who operate in different literacy environments differ with regard to their

reading experiences and their needs for functional literacy (Heath 1983), successful instruction is connected

with the learner's everyday life and is placed in a broad, challenging, and growing context that addresses

important needs of the reader both in and outside of school (Dewey 1938; Scheffler 1986).

Success or failure in reading may be due to cultural matches or mismatches between schools and

homes. Sometimes the problems may exemplify a cultural mismatch between students' and teachers' values

and intentions rather than a difference in ability (Anang 1982). Thus, during the social construction period,

teachers have a responsibility to base their instruction on the backgrounds their students bring to the

activity to be learned. Social construction assumes that the teacher has schema of the sociocultural

development of literacy and that those schema are actually put into use in the planning of educational

activities and in assessment (McCarthey and Raphael 1989).
150

2.4.5. Progression of Reading Theories Summarized

We arrive then at the definition of four dominant trends in reading theories:

• Information transfer, where the meaning of the text lies outside the reader who is expected to

reproduce it; where teaching is based on a prescriptive view of language; instruction is

hierarchical and subskill in nature; and processing is done in a linear fashion.

• Interaction, where the meaning of the text resides with both the text and the reader who is

expected to have some background knowledge that fits the text; and where we see an interaction

between the vernacular language of the student and the more formal language of school and

text.

• Transaction, where meaning is generated by the reader while reading; where a reader of any

age is expected to read in the same manner, albeit with differing levels of knowledge on which

to base an interpretation; and in which the reading act is clearly considered to be holistic in

nature and is tightly integrated into the socialization associated with active language use.

• Social construction, where knowledge is socially patterned and meaning is constructed through

social experience and interaction; in reading instruction knowledgeable members of the culture

play a prominent role in assisting in learning.

3. Exploring the Structure of Teachers' Beliefs, Instructional Activities, and Assessment Practices

3.1. Methodology

Within the IEA Reading Literacy Study, the teachers' beliefs and instructional and assessment

activities were accessed through three question blocks related to teachers' beliefs, the activities that they

have students do, and the things they test. Because of the difficulties in defining the total domain of

classroom activities, it is hard to be sure that these items represent a full or representative sample of

teachers' beliefs and instructional practices.

In most studies of this kind, and consistent with the way the data are gathered, researchers often

report on an item-by-item basis. Although we could review each of the instructional variables in this way,

we are not certain how it would help. To know that Finnish teachers do x more than American teachers do,

or that American teachers do y more than Finnish teachers does seem somewhat trivial. Alternatively,

researchers might define or derive a principal component and report on that in a comparative format. In

this instance, the researchers are assuming a common definition of the latent variable across national

contexts (see Lundberg and Linnakylä 1992).

For the purposes of this paper, however, we wished to ask a different question. We were more

concerned with the web of instructional practices—the combinations that work to form an instructional

network as they relate to a particular social milieu. Therefore, we considered it more useful to group these

items into more meaningful and coherent units for analysis.

Based on the research findings regarding the process of teaching, one would expect that these units

should, in principle, correspond to theories of reading—one hopes into those schools of thought we outlined

above. Therefore, we engaged in exploratory factor analyses to examine the latent structure of responses to
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these items in both countries’ data sets independent of the other. We looked at the data in this way because

we believed the items might have differing interpretations related to the variation in instructional and

cultural context.

3.1.1. Measurement Instruments

The measurement was focused on the teacher's beliefs, activities, and assessment approaches. Three

omnibus questions—what teachers believe about reading instruction, what they have students do, and what

they assess—included in the international instruments served as the basis of our inquiry.

What Teachers Believe. Teachers were asked to indicate their level of agreement with statements

about issues in reading instruction (Table 1). This question addresses teachers' beliefs about reading theory

and how instruction should be organized.
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Table 1. What teachers believe

Below you will find a number of statements about issues in reading instruction. Please state your degree of

agreement/disagreement with each statement by circling the appropriate number. (Circle one number on each line.)

Strongly

disagree

Disagree Not

certain

Agree Strongly

agree

a. When my students read to me, I expect them to read every

word accurately

1 2 3 4 5

b. Teachers should keep careful records of every student's

reading progress

1 2 3 4 5

c. Students should not be encouraged to read a word they don't

know

1 2 3 4 5

d. All students should enjoy reading 1 2 3 4 5

e. Most of what a student reads should be assessed 1 2 3 4 5

f. Every day students should be read to by the teacher from a

story book

1 2 3 4 5

g. Reading aloud by students to a class is a waste of time 1 2 3 4 5

h. Most students improve their reading best by extensive

reading on their own

1 2 3 4 5

i. Students should always understand why they are reading 1 2 3 4 5

j. Teachers should always groups students according to their

reading ability

1 2 3 4 5

k. 9-year-olds should not have access to books they will read in

the next school year

1 2 3 4 5

l. Class sets of graded reading material should be used as the

basis for the reading program

1 2 3 4 5

m. Students who can't understand what they read haven't been

taught proper comprehension skills

1 2 3 4 5

n. Every mistake a student makes in reading aloud should be

corrected at once

1 2 3 4 5

o. All students' comprehension assignments should be marked

carefully to provide them with feedback

1 2 3 4 5

p. Students should not start a new book until they have finished

the last

1 2 3 4 5

q. Parents should be actively encouraged to help their students

with reading

1 2 3 4 5

r. Students should learn most of their new words from lessons

designed to enhance their vocabulary

1 2 3 4 5

s. Reading learning materials should be carefully sequenced in

terms of language structures and vocabulary

1 2 3 4 5

t. Students should take a book home to read every day 1 2 3 4 5

u. Students should be encouraged to read texts they have written 1 2 3 4 5

v. Students should always understand what they are reading 1 2 3 4 5

w. Students should always choose their own books to read 1 2 3 4 5

x. A word recognition test is sufficient for assessing students'

reading levels

1 2 3 4 5

y. Teachers should carefully follow the sequence of the textbook 1 2 3 4 5

z. Students should undertake research projects to improve their

reading.

1 2 3 4 5

SOURCE: IEA Reading Literacy Study, Population A Teacher Questionnaire, 1991.
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What Teachers Have Students Do. Teachers were asked how frequently they have students do

certain reading activities (Table 2). This question looks at the kinds of assignments and activities teachers

expect students to complete.

Table 2. What teachers have students do

How often are your students typically involved in the following reading activities? (Circle one number per line only.)

Almost

never

About 1 or

2 times a

month

About 1 or

2 times a

week

Almost

every day

a. Learning letter-sound relationships and/or phonics 1 2 3 4

b. Word-attack skills (e.g., prediction) 1 2 3 4

c. Silent reading in class 1 2 3 4

d. Answering reading comprehension exercises in writing 1 2 3 4

e. Independent silent reading in the library 1 2 3 4

f. Listening to students reading aloud to a whole class 1 2 3 4

g. Listening to students reading aloud to small groups or pairs 1 2 3 4

h. Listening to teachers reading stories aloud 1 2 3 4

i. Discussion of books read by students 1 2 3 4

j. Learning new vocabulary words systematically (e.g., from lists) 1 2 3 4

k. Learning new vocabulary from texts 1 2 3 4

l. Learning library skills 1 2 3 4

m. Reading plays or dramas 1 2 3 4

n. Playing reading games (e.g., forming sentences from jumbled

words)

1 2 3 4

o. Dramatizing stories 1 2 3 4

p. Drawing in response to reading 1 2 3 4

q. Orally summarizing their reading 1 2 3 4

r. Relating experiences to reading 1 2 3 4

s. Reading other students' writing 1 2 3 4

t. Making predictions during reading 1 2 3 4

u. Diagramming story content 1 2 3 4

v. Looking for the theme or message 1 2 3 4

w. Making generalizations and inferences 1 2 3 4

x. Studying the style or structure of a text 1 2 3 4

y. Comparing pictures and stories 1 2 3 4

z. Student leading discussion about passage 1 2 3 4

aa. Reading in other subject areas 1 2 3 4

bb. Writing in response to reading 1 2 3 4

SOURCE: IEA Reading Literacy Study, Population A Teacher Questionnaire, 1991.
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What Teachers Assess. Teachers were asked how frequently they assessed certain aspects of

reading (Table 3).

Table 3. What teachers assess

How often do you assess these aspects of reading with all or most of your class? (Circle one number per line only.)

Never About

once a

year

About once

a term

About once

a month

About once a

week or more

a. Word recognition 1 2 3 4 5

b. Vocabulary 1 2 3 4 5

c. Text comprehension 1 2 3 4 5

d. Literary appreciation 1 2 3 4 5

e. Use of background knowledge 1 2 3 4 5

f. Sentence understanding 1 2 3 4 5

g. Phonic skills 1 2 3 4 5

h. Reading study skills 1 2 3 4 5

i. Amount of reading 1 2 3 4 5

j. Decoding 1 2 3 4 5

SOURCE: IEA Reading Literacy Study, Population A Teacher Questionnaire, 1991.

3.1.2. Sample

For the purposes of the Reading Literacy Study, it was decided to sample intact classes at the grade

level in which the modal age of the students was 9 years. As a result, in Finland, third grade classes were

sampled, while in the United States, fourth grade classes were chosen. The teacher of each participating

class was asked to fill out the teacher questionnaire. In Finland, 71 teachers of 1,552 students responded.

In contrast, in the United States 300 teachers of 6,729 students responded. Although the sample sizes

differ, national probability samples of classrooms were selected in both cases. This means that, in each

case, valid inferences can be made from the samples to the respective nation's teachers as a group.

In Finland, earlier studies have demonstrated relatively small differences between schools and

classrooms as compared with differences within schools and classrooms. In such a relativity homogeneous

educational system, the required sample size for making reliable national inferences is lower than in a

system with wide variations between schools such as in the United States. In addition, within the United

States, in schools where there was more than one fourth grade class, two classes were sampled, so that the

sample of 167 participating grade 4 schools gave rise to the sample of 300 teachers who responded. In

Finland, the 71 teachers were from 71 different schools. The two respective national samples are of

sufficient size to enable us to make reliable comparisons of teachers' instructional practices and beliefs in

reading. For more detail about the sample design used for the IEA Reading Literacy Study, see Elley

(1992).

3.1.3. Data Analysis

With each group of items and for each country separately, we engaged in exploratory factor

analyses to get at the latent structure of these items. As a general strategy, a principal factor solution was

obtained, and, in the first instance, factors with eigenvalues greater than one were rotated to an oblique

solution. In subsequent analyses, more or fewer factors were rotated until a solution was obtained that

exhibited good simple structure and whose factors could be assigned meaning consistent with the theory

and substance of reading and reading instruction.
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3.2. The American Findings

3.2.1. What American Teachers Believe

As seen in Figure 3, we defined two factors from this question block. The first factor, labeled

sequenced instruction, is characterized by sequencing, mastery of prior levels before moving on, accuracy,

and heavy teacher direction. Although never specifically stated, one might read into this factor a belief in

developmental stages that are carefully orchestrated by either the materials or the teacher. Sequence also

may be related to beliefs about the logic of the subject matter moving from simple to more complex.

Although a number of the items in this factor are not specifically unique to the period, the items

loading in this factor mostly characterize the theoretic stance underlying information transfer. "Accuracy"

is representative of reproduction of an author's or text's message or knowledge. The necessity for

correctness can easily be associated with a rule-driven or prescribed notion of language use. The controlled

movement across graded sets of materials can be related to the idea of a hierarchy and stages of

development. All of these attributes are characteristic of the information transfer period (Table 4).

In considering the distribution of teachers' responses to the items in this factor, the general picture

that emerges is that at a minimum, 60 percent of the teachers appear to disagree with beliefs that are

consistent with this factor. However, there are four items where this pattern is not as strong. Two items

are related to the use of sequenced materials in class. Here teachers seem to be more evenly divided in their

beliefs. Teachers also seem to be strongly supportive of providing feedback and monitoring student

progress.

In contrast, the second factor, extensive exposure to reading, is characterized by students’ active

involvement in frequent extended reading, both at school and at home. It focuses most on what the student

does. Here are elements of whole language approaches, with students being given a more central role in

constructing meaning. Similarly, there is mention of the integration of reading and writing, where students

are encouraged to read texts they themselves have written.

Although we see that all the items clustered in this factor need not be tied solely to a single

particular period, we find that the underlying theme of the items in this factor would appear to be most

closely associated with either the interaction or transaction periods. The movement between school and

home, and between reading and writing, represents an integration between a psycholinguistic or

sociolinguistic stance characteristic of these periods. These views are further developed by the statements

of enjoyment and extensive independent reading.

Teachers appear to support the beliefs espoused in this factor, with more than 74 percent agreeing

with all but one of the items. In that item, students should always understand what they are reading,

teachers seem to be permitting students a bit more latitude and perhaps are leaving more room for students

to be challenged by working at constructing meaning more interactively.

Table 4. American teachers’ belief factors related to reading theories

Reading Theories Agreement

Information

transfer

Interaction Transaction Social

construction

Sequenced instruction * -

Extensive exposure * * +

NOTE: The asterisk indicates that the factor strongly represents the period.

SOURCE: IEA Reading Literacy Study, U.S. National Study data, National Center for Education Statistics, 1991.

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3.2.2. What American Teachers Have Students Do

As described above, in this question teachers were asked how frequently they have students do

certain reading activities (Figure 4). Each of the three factors that emerge from this question reflects a

theoretic position related to reading instruction.

In factor one, schema-based activities, students focus on the organization and interrelated aspects

of text. They move back and forth from the detail to the overarching theme to make predictions and

generalizations. They use what they know from experience and about the structure of text.

The instructional activities in this factor closely mirror the definition of the interaction period. The

period focuses on reliance on background knowledge of the reader, which serves as a context for

understanding. In activities such as making predictions, relating experiences to reading, and looking for the

theme or message, students are calling forth the appropriate schemata for organizing the information

gathered from the text.

For all but two of the items included in this factor, over 70 percent of the teachers report frequently

having students do these things. In looking at the items, it is clear that they represent very common

practices associated with a directed reading lesson that have been suggested and included in teaching

manuals for years. With regard to the two remaining items—making generalizations and inferences, and

studying the style or structure of a text—if one believed in a hierarchy of skills these would likely be

considered beyond the range of a grade 4 student. Therefore, it is not surprising that fewer teachers

reported frequent use of these activities.

In factor two, integrated language arts activities, the emphasis is on bringing all communication

modes together. Students listen and discuss; they read and write as well as respond through other symbolic

modes (drama, art). The items grouped in this factor share an underlying theme that is closely tied to the

sociolinguistic theories characteristic of the transaction period, where reading and language more generally

are situated in a social context. The heavy reliance on discussion, dramatization, and writing of text seems

to indicate an emphasis on a more experiential approach to learning.

The social nature of the items in this factor suggest a possible precursor to the more current social

construction period. Social activities are strongly represented. However, within the context of the United

States, these items also describe very traditional activities historically occurring within other periods as

well. Given that items associated with the active construction of meaning do not appear in this factor, we

therefore more conservatively place it in the transaction period.

That there is a great deal of variability in the frequency with which teachers report using the

instructional activities in this group is to be expected given the nature of these items. Having students

dramatize stories or read plays or dramas is quite time consuming and possibly results in little added

benefit given the heavy time commitment they require. Even if the teacher was highly committed to this

type of approach, we would expect these kinds of differences among the items. However, in looking at

those items that teachers report using frequently, we note that they need not be associated only with this

type of program. Students are often asked to read aloud for diagnostic purposes. Students in any class

frequently write something in response to reading. And, it is not uncommon to have teachers in any subject

area draw students' attention to the accompanying pictures or diagrams and to make comparisons with the

text. Given the dispersion of response rates, one would be very hard pressed to make any statement about

teachers' commitment to this approach as a whole.

In the third factor, skills-based activities, the emphasis is on what is literally in the text. It is a very

bottom-up orientation focusing on letters, words, sentences, and text-based understanding. This factor

could most be associated with the information transfer period, where the teacher or the text organizes

tasks to be accomplished that become increasingly more difficult and call forth increasingly more complex

skills. The teachers surveyed seem to most frequently use the instructional activities

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included in this factor. That only 58.5 percent report frequently teaching letter-sound relationships is not

surprising, because these are teachers of grade 4 students who, in principle, should have moved beyond the

rudimentary forms of this particular type of activity. Similarly, playing reading games would also be most

likely to be associated with earlier grades—preschool, kindergarten, and first or second grade at the latest.

Because factor one, schema-based activities, involves drawing together background knowledge of

both content and text structure to construct meaning, this factor would be characteristic of the interactive

period. In contrast, the second factor, integrated language arts activities, includes activities that involve an

integration of symbolic forms, an interaction with the texts, and peers to go beyond the text. Although it is

probably most characteristic of the transaction period, it would also fit within the definition of the

interaction period. The third factor, skills-based activities, only includes items that focus on small units of

text—words, sentences. The very literal nature of these items places them in the information transfer

period (Table 5).

Table 5. American teachers’ activity factors related to reading theories

Reading Theories Agreement

Information

transfer

Interaction Transaction Social

construction

Schema-based activities * +

Integrated language arts * * -

Skills-based activities * +

NOTE: The asterisk indicates that the factor strongly represents the period.

SOURCE: IEA Reading Literacy Study, U.S. National Study data, National Center for Education Statistics, 1994.

3.2.3. What American Teachers Assess

Teachers were asked how frequently they assessed certain aspects of reading. According to the

factor analysis, teachers appear to emphasize three different things in their assessments (Figure 5). As seen

in factor one, contextualized reading, teachers are testing the entire process. The basics of decoding and

vocabulary are given less emphasis in this factor than relating reading to what the student knows. The

second factor, reading skills, focuses entirely on the basic subskills of reading–decoding, phonics. The

third factor, text-based understanding, maintains a heavy text-based, bottom-up orientation. Teachers are

focusing on what is specifically in the text.

One would be hard pressed to associate the assessment emphases with particular periods. Each has

somewhat overlapping elements. For example, word recognition and vocabulary are very closely related,

although the former is more strictly a decoding activity while the later includes some level of understanding.

It seems reasonable, however, to say that contextualized reading, due to its more inclusive nature, would

more likely be associated with either the interaction or transaction periods (Table 6). Reading skills

implies a more subskill approach and an analytic organization of instruction, which would require someone

outside the learner to structure. Consequently, a case could be made that this would best match the

information transfer period. The third factor, text-based understanding, given the progression from word

to sentence to text, appears to have elements of a hierarchy that are prevalent in both the information

transfer and interaction periods.

What is most striking about this group of factors and the distribution of teacher responses to the

items in each factor is that the teachers surveyed report frequently assessing everything, irrespective of the

content implied in the factor, and perhaps irrespective of what they might be teaching.

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Table 6. American teachers’ assessment factors related to reading theories

Reading Theories Agreement

Information

transfer

Interaction Transaction Social

construction

Reading skills * +

Text-based understanding * * +

Contextualized reading * * +

NOTE: The asterisk indicates that the factor strongly represents the period.

SOURCE: IEA Reading Literacy Study, National Center for Education Statistics, 1994.

3.2.4. Can We Identify an Implicit Theory of Reading and Instruction that Underlies the Way that

American Teachers Organize American Reading Instruction?

In principle, teachers might be expected to align their beliefs about instruction, what they have

students do, and what they assess according to a consistent theory of either reading or instruction. As noted

in the factors that emerged in the preceding sections, no clear and consistent theoretic stance emerged.

Instead, in each of the three sets of items we found factors that related to groups of theories. In addition,

across the questions there were factors that seemed to be related. In considering how they might fit

together, we placed each of the factors into a grid (Table 7).

Table 7. Relating American teachers' factors to reading theories

 Reading Theory Agreement

Information

transfer

Interaction Transaction Social

construction

What Sequenced instruction * -

teachers

believe Extensive exposure * * +

What Skills-based activities * +

teachers

have Schema-based activities * +

students

do Integrated language arts * * -

What Reading skills * +

teachers Text-based understanding * * +

test Contextualized reading * * +

SOURCE: IEA Reading Literacy Study, U.S. National Study data, National Center for Education Statistics, 1994.

The table serves as a summary of the theoretical placement of these factors. What we see is that

there is, in principle, the possibility for some consistency between teachers' beliefs, the activities they

assign, and what they test. For example, teachers who believe in sequenced instruction could have students

do activities that were skills based and might test reading skills knowledge and text-based understanding.

Teachers who believe in an extensive exposure to reading might emphasize integrated language arts

activities and test contextualized reading.

It is interesting to note that when one considers whether teachers support particular beliefs or

activities according to a consistent theoretic framework, we find that the teachers' beliefs are in conflict

with the activities they assign. For example, teachers disagree with belief statements related to information

transfer. However, they frequently assign activities and test behaviors associated with this theoretic stance.

Similarly, there is an inconsistency between beliefs and activities among teachers who express agreement

with beliefs related to transaction. Few frequently assign associated activities.

To test whether the theoretic consistency in fact occurred, we conducted a second order factor

analysis to identify broader second order constructs that might underlie the first order factors, thus
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reflecting a consistent implicit theory. This second order factor analysis resulted in three meaningful

factors—one that captures all testing and two that distinguish between two schools of thought in

instruction, as described below. Table 8 shows the second order factors.

Table 8. Theory and practice combined

Primary factor name Agreement

Second order factor 1 – Interaction/Transaction

Integrated language arts activities -

Schema-based activities +

 Second order factor 2 – Information Transfer

Sequenced instruction – teacher beliefs -

Skill-based activities +

Second order factor 3 – Assessment

Contextualized reading assessment +

Text-based understanding assessment +

Skills assessment +

NOTE: This table differs from the presentation in Reading Literacy in the United States: Technical Report. For the purposes of this paper and

comparison with Finland, fewer items were used and a new analysis was done.

SOURCE: IEA Reading Literacy Study, U.S. National Study data, National Center for Education Statistics, 1996.

Assessment Emphasis. Second order factor 3, the easiest factor to describe, brings all the questions

on assessment back together. Despite the fact that there are three possible emphases, assessment seems to

run together. An American teacher who tests a great deal is likely to test everything frequently. A number

of plausible explanations come to mind. It is much easier to test subskills where a correct answer is easily

definable. Perhaps available testing materials support this type of approach. Alternatively, a large number

of tests may be required by the district or the state. Often test scores serve as the basis of giving a term

grade. However, no conclusion regarding why this practice has come about can be drawn from the data

alone.

The other two factors that emerge are derived from the item blocks on beliefs and activities. One is

associated with reading and learning theories based on a notion of information transfer from the teacher or

the author to the student, and the other seems to be most related to notions of reading and learning as an

interaction or transaction between the teacher or author and the student.

Information Transfer Emphasis. Instruction and reading theories that can be grouped under the

heading of information transfer may be characterized as placing the meaning of the text outside the reader

who is expected to reproduce it (Straw 1990), organizing teaching according to a prescriptive view of

language (Balmuth 1982), providing instruction that is hierarchical and subskill in nature (Barrett 1968;

Gray 1960), and processing that is done in linear fashion (Gough 1985). The first order factors that

empirically fell into this category are strongly prescriptive and demand a high level of accuracy consistent

with a view of language usage that is correct and that is known by the teacher and the authors of texts and

materials.

What is interesting to note is that while the surveyed teachers' responses to the items on sequenced

instruction tended to disagree with this position, the teachers frequently reported using materials-directed

teaching practices that are consistent with this view.

Interaction/Transaction Emphasis. An interaction emphasis is characterized by a shared

responsibility for generating meaning between the text, the author, and the reader. In contrast, a

transaction emphasis may be characterized as having the meaning of the text generated by the reader while

reading. Those who ascribe to an interaction emphasis believe that the reader would come to the task with

the appropriate background knowledge necessary for understanding the intended meaning of the text. In

contrast, those who ascribe to a transaction emphasis believe that readers of all ages are expected to read in

the same manner, albeit with differing levels of knowledge on which they might base an interpretation. The
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act of reading is considered to be a holistic activity that is highly integrated into the socialization associated

with language use.

The first order factors in this construct most strongly represent the transaction period. For example,

the items subsumed under the factor integrated language arts stress the integration of communication

modes. This integration is highly consistent with the sociolinguistic theories characteristic of the period.

In contrast, the second factor, schema-based activities, includes items that could be characteristic of either

the interaction or transaction schools of thought. Schema theory began with the interaction period but still

remains a dominant force in later periods. Consequently, it seems reasonable to associate this second order

factor with both interaction and transaction.

In this second order factor there is an inconsistency that may be associated with the transition

between theoretic periods. Teachers agree with and frequently use activities related to schema theory. In

contrast, they are less supportive of activities related to an integrated language arts approach. This may

reflect the comparative newness of the intent of this approach as it relates to the construction of meaning.

Alternatively, this may also be a reaction to older conceptions of language arts programs.

3.2.5. What Conclusions Can We Draw About How American Teachers Organize Instruction?

These data provide us with a reasonable glimpse at the current state of American teachers'

instructional practices and beliefs. Although it is not easy to impose a systematic stance implied by the

theoretical periods after the fact, doing so does help inform our understanding of instructional practices.

Despite the fact that teachers may interpret the items in different ways, the factor analysis helps us to

unravel these differences more easily.

We find that teachers' beliefs do not seem to line up with the instructional activities they are likely to

assign. And, it is most evident that their assessment practices bear little relationship to their beliefs or

instructional activities. On the basis of this finding, we might conclude that teachers do not integrate their

instruction and assessment practices with their theoretic beliefs about reading.

Clearly there are other influences that may have had an even larger impact on instructional practice

but were not included in the model assumed by the items in the questionnaire. For example, teachers may

have been hampered in their attempts to teach in a manner consistent with a social constructionist point of

view due to lack of appropriate materials. If teachers have access only to basal reading series with older

copyright dates, the emphasis within the materials could preclude this emphasis. Alternatively, district- or

state-level assessment programs might necessitate an emphasis on subskills. If this were the case, teachers

might feel obliged to place a greater emphasis on these kinds of activities than they might otherwise be

inclined to do. These are only two possible explanations, and the questionnaire did not include items that

would allow us to systematically rule out these or other possible explanations.

Findings indicating an inconsistency between beliefs, activities, and assessments reinforce the

underlying principle motivating systemic reform, that is, all parts of the system must interact in a consistent

way simultaneously to bring about the most effective outcomes. However, what we see, even if only just

glimpse, is that there is comparatively little coherence between beliefs and practice consistent with any

theoretic stance.

At best these conclusions are tentative. First, the items were not specifically designed to measure

the implementation of the described theories. Second, the data are based on teacher self-reports and might

well be colored, at least to some degree, by notions of socially appropriate responses.
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3.3. The Finnish Findings

3.3.1. What Finnish Teachers Believe

Just like their American counterparts who took part in the IEA Reading Literacy Study, Finnish

teachers participating in the study were asked to identify their level of agreement with statements about

issues in reading instruction. In contrast to the more straightforward dichotomy related to reading theory

that seems to guide American teachers (see Figure 3), factor analysis of Finnish teachers' beliefs (Figure 6)

indicates that more than one principle may be guiding their instructional thinking. However, an overriding

principle related to general instructional guidelines plays a predominant role.4

In fact, the main structuring principle in teachers' thoughts may be directly tied to the control and

responsibility of learning (Pearson and Gallagher 1983). Four out of seven factors are organized around the

controlling element of instruction, i.e., teacher, student, or reading material. For example, the factors

teacher monitoring and teacher-directed correctness both indicate a heavy hand on the part of the teacher,

who in the first instance seems to constantly monitor, check, assess, and provide feedback to students, and

in the second, expects thorough understanding by the students resulting from managed instruction provided

by the professional teacher. Although the factor sequenced instruction focuses most on the organization of

reading materials and tests as well as on grouping students according to their ability, control is shared

between the materials and the teacher, who paces instruction appropriately. In the same vein, the factor

material-directed instruction also has features of external direction, giving more emphasis to the roles of

the materials and the outside expert who developed those materials than to the teacher.

In contrast to the factors discussed above, which placed control and responsibility for learning

outside of the learner, the factors enjoyment and interest, student-centered learning by reading, and

extensive exposure to reading emphasize the student's internal control of learning. The first factor appears

to focus on the affective domain of reading, the second and third on the learning context. These factors

seem to include motivational components as well (Roehler and Duffy 1991).

To place these factors within the framework of the reading theories developed earlier, we considered

how the attributes of control fit those theoretic stances. Teacher monitoring and teacher-directed

correctness reflect the information transfer period. The emphasis is on correct decoding and reproducing

the determinate meaning of the words and text. There appears to be an underlying assumption indicating

an explicit way to teach and to monitor correct comprehension. The teacher or the expert who designed the

materials is considered to be the source of knowledge. Reading is seen as a collection of subskills that have

to be taught properly and assessed frequently.

Similarly, the factors sequenced instruction and material-directed instruction reflect that a good

reader is one whose background knowledge and skills are consistent with the content and difficulty of the

text and, as such, can be placed in either the information transfer or interaction periods.

The factors enjoyment and interest, student-centered learning, and extensive exposure to reading

refer to views characteristic of the transactional and possibly the social construction periods. In these

instances the reader's personal motives and interests are considered significant in learning to read. In place

of formal, stage-based, sequential instruction, these factors stress a natural learning environment and the

reader's self-control. For example, student-centered learning refers to a social interaction through research

projects, and extensive exposure to reading could refer to the assumption that reading develops at its best

in a lifelike social environment. These particular aspects may be considered the precursors of a social

construction view.

 

4

 This emphasis may in part be explained by the content of the questionnaire.

166

Finnish teachers tended to agree with the items in the factor enjoyment and interest. On average, 75

percent of the teachers supported the views promulgated in these items, with slightly more than 85 percent

emphasizing that "all children should enjoy reading." Finnish teachers also supported the items in the

student-centered learning factor. On average, 65 percent of the teachers agreed with these items, with just

under 90 percent expressing strong agreement with the statement that "children should undertake research

projects to improve their reading."

On the other hand, Finnish teachers tended to disagree with the statements related to the other five

factors. On average, almost 90 percent of the teachers disagreed with the items associated with materialdirected instruction, 66 percent disagreed with items related to extensive exposure to reading, 51 percent

disagreed with teacher-directed correctness, 49 percent with teacher monitoring, and 48 percent with

sequenced instruction.

On the basis of these findings, we conclude that Finnish teachers appear to value the affective and

functional domains of reading instruction. We derive this conclusion based on the level of support for

enjoyment and interest and student-centered learning. Consequently, it appears that Finnish teachers

would most strongly support reading theories associated with the transaction period and would probably

adopt social construction views. Given that material-directed and teacher-directed were not favored, we

conclude that Finnish teachers do not openly value the theoretic stance underlying either the information

transfer or interaction views of reading. Table 9 serves as a summary of the relationship among the seven

factors that typify Finnish teachers' beliefs and periods in reading theory.

Table 9. Finnish teachers’ belief factors related to reading theories

Reading Theories Agreement

Information

transfer

Interaction Transaction Social

construction

Teacher monitoring * -

Teacher-directed correctness * -

Sequenced instruction * * -

Material-directed instruction * * -

Enjoyment and interest * (*) +

Student-centered learning * (*) +

Extensive exposure to reading * (*) -

NOTE: The asterisk indicates that the factor strongly represents the period. The asterisk in parentheses (*) indicates that the factor is not strongly

situated in the period, but elements of it are likely to be present.

SOURCE: IEA Reading Literacy Study, Finnish National Study data, University of Jyvaskyla, 1996.

3.3.2. What Finnish Teachers Have Students Do

Teachers were also asked how frequently their students participate in certain activities. The factor

analysis of Finnish teachers' instructional activities in the reading classroom reinforces the position outlined

above that there is not just one type of theory guiding the teachers' instructional activities (Figure 7).

Although the Finnish teachers organize instruction based on general theories of learning, the structure of

the classroom activities are more closely related to reading theories than to belief structure. This may be

partly a function of the content of the questionnaire.

The main principles structuring the activities appear to be the domains of reading, integration of

reading, and control or responsibility of learning. For example, the subareas or domains of reading are

highlighted in the factors literary comprehension, cooperative oral activities, independent silent reading,

practicing subskills, expanding comprehension, enlarging vocabulary, and listening activities. An

alternative configuration comes through in the factors integrating language arts, integrating literacy with

other subjects, expanding comprehension, cooperative oral activities, and listening activities. These

168

factors reflect an integration of communication and artistic modes or the integration of reading to other

school subjects. The factors independent silent reading and independent library work emphasize

students' own control and responsibility for learning, while the factor cooperative oral activities stresses

shared control of instruction.

From the perspective of reading theories, the factors may be associated with the full range of

theoretic paradigms outlined in Section 2 of this paper. The factors practicing subskills and enlarging

vocabulary are characteristic of the information transfer period because decoding, separate skills, and

literal understanding of words and text weigh heavily in these factors. The factors literary comprehension,

independent silent reading, independent library work, expanding comprehension, and listening activities

refer to features typical of the interaction period, where meaning is processed between the reader and the

text.

The transaction period is somewhat represented in the factors focusing on integration such as

integrating language arts, integrating literacy with other subject areas, and cooperative oral activities.

These factors stress generating meaning into other modes of communication or through artistic expression.

The students' own experience and open construction of meaning is emphasized in the actualization of

comprehension.

The content of the integration factors—integrating literacy with other subjects and cooperative oral

activities—may even include some features of the social construction view. At least some of the variables

included in these factors seem to allow for the possibility of constructing socially and culturally shared

meaning, while other variables indicate some socially controlled activity. Similarly, these integration

factors reflect some motivational elements that are related to cognition and action.

On the one hand, Finnish teachers appear to most frequently use the activities related to listening

activities (more than 80 percent of the teachers do three of the four associated activities frequently).

Depending on the particular item, between 51 and 94 percent of the teachers report frequent use of

activities related to independent silent reading, and more than half frequently use activities associated with

expanding comprehension. The Finnish teachers particularly stress extended reading, with 94 percent

frequently having students read silently in class and 84 percent frequently reading stories aloud to their

classes.

On the other hand, teachers reported rarely using activities related to the other seven factors. For

example, approximately 75 percent rarely had students participate in cooperative oral activities, 65 percent

rarely assigned activities related to enlarging vocabulary; and more than half rarely engaged students in

activities related to either literacy comprehension or independent library work.

This distribution of assigned activities leads to the conclusion that Finnish teachers prefer to

emphasize listening and independent silent reading while downplaying activities that emphasize students'

self-expression and active cooperation.

In considering how these factors relate to reading theory, it appears that Finnish teachers most

emphasize activities characteristic of the interaction view. And there were some signs of movement toward

activities consistent with a transaction view (i.e., expanding comprehension). Despite their expressed

strong belief in the theoretic stance associated with both transaction and social construction views, Finnish

teachers rarely used associated activities. Table 10 provides a summary of the relationship between the 10

factors that emerge from the Finnish teachers' responses to the activities items.
169

Table 10. Finnish teachers’ activity factors related to reading theories

Reading Theories Agreement

Information

transfer

Interaction Transaction Social

construction

Practicing subskills * -

Enlarging vocabulary * -

Listening activities * +

Independent silent reading * +

Independent library work * -

Literary comprehension * -

Expanding comprehension * +

Integrating language arts * -

Cooperative oral activities * (*) -

Integrating literacy w/other subjects * (*) -

NOTE: The asterisk indicates that the factor strongly represents the period. The asterisk in parentheses (*) indicates that the factor is not strongly

situated in this period, but elements of it are likely to be present.

SOURCE: IEA Reading Literacy Study, Finnish National Study data, University of Jyvaskyla, 1996.

3.3.3. What Finnish Teachers Assess

The factor analysis of Finnish teachers' assessment practices indicates that the main structuring

principle is the sequence of instruction. The first factor, diagnosing basic skills, has the highest loadings

on word recognition, phonics skills, and vocabulary—skills commonly associated with beginning reading

and instruction that takes place before actually beginning to read a selection. The factor monitoring text

comprehension stresses study skills, sentence understanding, and use of background knowledge—all

associated with comprehension development during instruction. The third factor, surveying reading

activity, in contrast to the first two focuses on the affective domain, with most emphasis on interests and

literary appreciation. This represents the outcome of reading instruction (Figure 8).

From the perspective of reading theories, these factors would be associated with a number of

periods. Diagnosing basic skills seems to be related to the information transfer period. Monitoring text

comprehension with high loadings in text features and reader's background knowledge could be associated

with the interaction or transaction period, especially if the reader's personal background experience is

considered significant in constructing text meaning. Surveying reading activity could be associated with

either the interaction or transaction period and even possibly the social construction period. Minimally

the variables amount of reading and literary appreciation could imply the readers' socially and culturally

shared preferences and values after instruction, indicating potential for developing into an association with

social construction themes.

The Finnish teachers' responses to the items in each of the different assessment factors indicate that

teachers focus on all three aspects of assessment. The greatest emphasis is placed on monitoring text

comprehension where, on average, 69 percent of the teachers report frequent assessment of sentence

understanding (90 percent) and text comprehension (87 percent). Because all aspects of assessment seem to

be utilized by Finnish teachers, it is hard to identify what their particular preference regarding assessment

practices might be. This is in stark contrast to their stance with regard to beliefs and instructional

pedagogy where only a few factors associated with specific periods were favored. These associations are

summarized in Table 11.

171

Table 11. Finnish teachers’ assessment factors related to reading theories

Reading theories Agreement

Information

transfer

Interaction Transaction Social

construction

Diagnosing basic skills * +

Monitoring text comprehension * * +

Surveying reading activity * * (*) +

NOTE: The asterisk indicates that the factor strongly represents the period. The asterisk in parentheses (*) indicates that the factor is not strongly

situated in this period, but elements of it are likely to be present.

SOURCE: IEA Reading Literacy Study, Finnish National Study data, University of Jyvaskyla, 1996.

3.3.4. Can We Identify an Implicit Theory of Reading and Instruction that Underlies the Way that

Finnish Teachers Organize Reading Instruction?

Similar to our interest in the alignment of American teacher beliefs, activities, and assessment

practices, we questioned how things compared in Finland. As was the case with the American data, the

factor structures of Finnish beliefs, activities, and assessment were not parallel or internally consistent.

The main principle structuring Finnish teachers' beliefs appeared to be the control of and responsibility for

learning. In structuring students' activities, the domains of reading and integration of activities were the

most significant organizers. For assessment practices, the main structure may best be described as the

sequence of instruction.

From the perspective of reading theories, the comparison of belief, activity, and assessment

structures suggests that the Finnish teachers' thinking reflects many different discrete reading theories,

which also vary in emphasis during different phases of instructional processes. The strongest implicit

theoretical organizers in Finnish reading instruction seem to be either the interaction or transaction views

(Table 12). In beliefs, three of the seven factors emphasize a position related to the transaction period,

while two relate to interaction. Five of the 10 activities factors were related to interaction and 3 to

transaction. In assessment, two of the three factors corresponded at least to some extent to the interaction

paradigm with two related to transaction. Despite the heavy emphasis on either interaction or transaction,

the behavioristic information transfer model was also evident in belief, activity, and assessment structures.

Table 12. Relating Finnish teachers’ factors to reading theories

Reading Theories Agreement

Information

transfer

Interaction Transaction Social

construction

Teacher monitoring * -

Teacher-directed correctness * -

What Sequenced instruction * * -

teachers Material-directed instruction * * -

believe Enjoyment and interest * +

Student-centered learning * (*) +

Extensive exposure to reading * (*) -

Practicing subskills * -

Enlarging vocabularies * -

Listening activities * +

What Independent silent reading * +

 teachers Independent library work * -

have Literary comprehension * -

students Expanding comprehension * +

do Integrating language arts * -

Cooperative oral activities * (*) -

Integrating literacy w/other subjects * (*) -

What Diagnosing basic skills * +

teachers Monitoring text comprehension * * +

test Surveying reading activity * * (*) +

NOTE: The asterisk indicates that the factor strongly represents the period. The asterisk in parentheses (*) indicates that the factor is not strongly

situated in this period, but elements are likely to be present.

SOURCE: IEA Reading Literacy Study, Finnish National Study data, University of Jyvaskyla, 1996.
172

Interestingly, a case could be made for the emergence of social construction views among Finnish

teachers. With some latitude in interpreting, two of the belief factors, two of the activity factors and one of

the assessment factors can be seen as precursors of that theoretic stance. This is particularly true if the

theories associated with social construction are considered to be a social extension of the transaction model

where reading reflects the intentions, attitudes, and values shared and promoted in the social and cultural

learning context. However, it is also possible that the teacher questionnaire did not provide adequate

representation of social construction in reading instruction.

The Finnish teachers fairly consistently held a negative view of information transfer theories. They

more often disagreed with items that would be so classified, and they also rarely favored activities

associated with the period. While the Finnish teachers did not subscribe to beliefs related to an interaction

theory of reading, they nonetheless reported frequent use of activities that would characterize that stance.

In contrast, the Finnish teachers espoused beliefs that typify the transaction period, but they rarely

assigned activities associated with that stance.

It would appear that while Finnish teachers hold beliefs that are quite progressive, their instructional

practices are less so. Finnish teachers believe that transaction and social construction views of reading—

motivation, enjoyment and interest, as well as a highly student-centered active participatory environment—

are important principles for the strategic planning of instruction. However, these principles do not seem to

guide the selection of instructional activities. When it comes to selecting instructional activities, the Finnish

teachers follow a more traditional model, one in which the student's role is more passive (the silent listener)

and more isolated or independent (the lone reader).

A second order factor analysis was conducted to explore the underlying common constructs of the

belief, activity, and assessment structures. As seen in Table 13, in Finland the second order analysis

resulted in eight factors and indicated that the principles related to control and integration of learning are

significant forces in instruction. Control and responsibility for learning is obvious in the factors sequenced

instruction, independent learning by reading, and cooperation. It is evident that integration plays an

important role in structuring the factors integrating subskills, assessing learning by reading, and

independent learning by reading. In addition, the reading domains also structure the factors as is the case

in oral activities, learning vocabulary, and expanding comprehension. An assessment emphasis is

indicated in one factor—assessing learning by reading—that loaded heavily with monitoring text

comprehension and surveying reading activity. However, the third assessment factor, diagnosing basic

skills, was not loaded in this second order factor, which seemed to emphasize assessment of comprehension

and learning by reading rather than diagnosing of basic skills.

When the second order factors are related to reading theories, the underlying theoretical approach

seems to be in the interaction view, which is indicated in most of the factors (integrating subskills,

assessing learning by reading, sequenced instruction, oral activities, and independent studying by

reading). The signs of the more progressive transaction and social construction periods could be seen in

the factors assessing learning by reading, independent studying by reading, cooperation, and expanding

comprehension. However, these factors also reflect other theoretical views.

In brief, the second order factor analysis reveals that Finnish teachers follow many different

practical theories, despite their emphasis on the interaction/information transfer stance. An

interaction/transaction view can also be traced, and there are even signs of a combination of diverse stances

indicative of either transaction or social construction views of reading.
173

Table 13. Theory and practice combined

Primary Factor Name Agreement

Second order factor 1 – Integrating subskills (information transfer/transaction)

Practicing subskills

-

Literary comprehension

-

Teacher-directed correctness

-

Integrating language arts

-

Diagnosing basic skills +

Integrating literacy with other subjects

-

Student-centered learning by reading +

Second order factor 2 – Assessing learning by reading (interaction/transaction)

Monitoring text comprehension +

Surveying reading activities +

Independent library work

-

Integrating literacy with other subjects

-

Second order factor 3 – Sequenced instruction (interaction/information transfer)

Sequenced instruction

-

Teacher monitoring

-

Independent library work

-

Teacher directed correctness

-

Independent silent reading +

Second order factor 4 - Oral activities (interaction)

Listening activities +

Extensive exposure to reading

-

Enjoyment and interest +

Cooperative oral activities

-

Student-centered learning by reading +

Monitoring text comprehension +

Second order factor 5 – Independent learning by reading (interaction/transaction)

Independent silent reading +

Material-directed instruction

-

Integrating literacy with other subjects

-

Extensive exposure to reading

-

Second order factor 6 – Learning vocabulary (information transfer)

Enlarging vocabulary

-

Enjoyment and interest +

Teacher monitoring

-

Extensive exposure to reading

-

Second order factor 7 – Cooperation (transaction)

Cooperative oral activities

-

Diagnosing basic skills +

Student-centered learning by reading +

Teacher-directed correctness

-

Second order factor 8 – Expanding comprehension (transaction)

Expanding comprehension +

Surveying reading activity +

SOURCE: IEA Reading Literacy Study, Finnish National Study data, University of Jyvaskyla, 1996.
174

3.3.5 What Conclusions Can We Draw About How Finnish Teachers Organize Instruction?

In ways that are similar to the findings about American teachers, we find that we do have a

preliminary picture of how Finnish teachers organize instruction. The Finnish teachers appear to believe

that the transaction view of reading—with an emphasis on motivation, enjoyment, and interest, as well as

student-centered strategies and extensive exposure to reading – is important when planning and organizing

instruction. However, these principles do not seem to translate into the instructional activities the teachers

frequently choose to use. Instead, the instructional activities of choice appear to follow the more traditional

views associated with the interaction period, where the student's role is that of a silent listener and a lonely

reader rather than that of an active and self-expressive language user or cooperative participant in a social

environment where meaning is generated based on shared values and views.

4. Comparing Reading Instruction in the United States and Finland

The most striking contrast between the underlying structures reflected in this survey of instruction in

the United States and Finland is the difference in the number of factors that seem to tie instruction together.

When the same rules for the identification of factors was applied, Finnish teachers' beliefs contained seven

groups while American teachers only had two. Similarly, Finnish teachers seemed to use activities in ways

that could be clustered into 10 factors, while Americans only segmented their activities into three clusters

(Table 14).

American teachers appear to organize both their beliefs and instructional activities in accordance

with the extensive and various programs that are readily available and form the mainstay of American

instruction. In contrast, the Finnish teachers appear to structure both their beliefs and teaching activities

into smaller units, separating teachers' and students' roles and responsibilities, sequencing instruction,

materials selection, domains of reading, phases of instruction, and motivation. This may be attributable to

the national curriculum, which specified the application of different methods flexibly in accordance with the

differences among students and their differing needs. This position also pervades the guidelines and

traditions of teacher training. Furthermore, Finnish teachers themselves choose the materials to be used

within the class from among the various published or self-made options available. Consequently their

decisions are made at a more microlevel and may result in greater diversity of texts within any one school

or across schools.

The most striking similarity appears to be related to the organization of assessment practices. Here

the teachers in both Finland and the United States seem to operate at the same level of generalization. Even

though the Finnish schools do not have a tradition of standardized tests, there is a similar prevalence of

models of testing that are comparable to the practices in America, where testing is an integral part of daily

and weekly instructional programs.

When we look beyond the surface differences associated with the number factors and focus on the

structuring principles underlying the belief factors of both the Finnish and American teachers, several

similarities are readily apparent. The contrast between an external teacher- or material-directed

instructional program and a more internal student-centered view are present in factor structures of both

countries. The first set of principles can be connected with the external teacher and text control typical of

the information transfer and interaction views. In contrast, the principles underlying the second set are

more characteristic of the transaction view of reading. Control and responsibility for learning are

significant discriminators in teachers' beliefs in both countries.

In quite a similar fashion, we note that both the Finnish and American teachers' preferences seem

parallel. Neither agrees with the belief statements associated with the information transfer or interaction

periods. Instead, the statements related to transaction or social construction are strongly preferred in both

cultures.
175

Table 14. Instruction factors in Finland and the United States

Finland United States

Factor Primary theory Agreement Factor Primary theory Agreement

Beliefs

Teacher monitoring Information transfer -

Teacher-directed

correctness

Information transfer - Sequenced instruction Information transfer/

interaction

-

Sequenced

Instruction

Information transfer -

Material-directed

instruction

Interaction -

Enjoyment and

interest

Transaction + Extensive exposure to

reading

Transaction +

Student-centered

learning

Transaction/social

construction

+

Extensive exposure

to reading

Transaction/social

construction

-

Activities

Practicing subskills Information transfer -

Enlarging

vocabulary

Information transfer - Skill-based activities Information transfer +

Listening activities Interaction +

Independent silent

reading

Interaction +

Independent library

work

Interaction - Schema-based

activities

Interaction +

Literary

comprehension

Interaction -

Expanding

comprehension

Interaction +

Integrating language

arts

Transaction - Integrated language

arts

Transaction -

Integrating literacy

w/other subjects

Transaction/social

construction

_

Cooperative oral

activities

Transaction/social

construction

-

Assessment

Diagnosing basic

skills

Information transfer + Reading skills Information transfer +

Monitoring text

comprehension

Interaction/

transaction

+ Text-based

understanding

Information

transfer/interaction

+

Surveying reading

activity

Interaction/

transaction

+ Contextualized

reading

Interaction/

transaction

+

SOURCE: IEA Reading Literacy Study, U.S. National Study data, National Center for Education Statistics, and Finnish National Study data,

University of Jyvaskyla, 1996.
176

Comparison of the activity factors reveals the same kind of unity and diversity. Both factor

structures are mainly organized according to the domains, subareas, or cognitive processes of reading—

skills, strategic activities, or integration with other language skills or even other subject areas. Despite the

similarity in organizing principle, the American teachers' activity structure is global in nature, taking a

large view of groups of instructional procedures, while the Finnish teachers are more specific in how they

group the purpose and intent of activities.

The Finnish teachers focus on aspects of control and responsibility for learning that in the Finnish

context may be teacher-directed, student-directed, or a shared responsibility. Furthermore, for Finnish

teachers the learning environment and especially the integration with other language skills are important

specifiers. For example, the Finns stress the role of listening, but this emphasis does not play a role in

structuring American instructional activities. This may be related to the Finnish tradition of extensive

practice of the auditive discrimination of phonemes, especially in the lower grades. Because the Finnish

language has a very regular grapheme-phoneme correspondence, practicing auditive discrimination obviates

the need for special spelling practice or memorization of spelling rules. The emphasis on listening may also

have beneficial motivational effects. Many Finnish studies have shown that the teachers' reading aloud has

both a strong motivational intention and effect. So, it is generally recommended both in the curriculum and

in the teachers' training.

In both teaching cultures, teachers' instructional beliefs clearly are more progressive than their

classroom activities (cf. Fishbein and Ajzen 1975; Lundberg and Linnakylä 1992). Both teacher groups

appreciated beliefs reflecting a transaction view of reading while most frequently using instructional

activities that reflected interaction model. Traditional attitudes on assessment were shared in both teaching

cultures.

It was especially difficult to interpret Finnish teachers' instructional factors from the perspective of

reading theory. This might be attributable to how the questionnaire was composed. It was not driven by

the perspective of theory. And fortunately, at least according to the newer theories, meaning is relative and

may vary across readers and cultures.

If the structuring principles of and the relations to reading theories are negotiated, it seems obvious

that in reading instruction the control of and responsibility for learning, as well as the variety of reading

domains and learning context and the integration of language and learning activities, are significant in

defining the theory of reading instruction. We mainly followed Straw's categorization of reading theories.

In his analysis, Straw (1990) used three perspectives for examination: locus of meaning, nature of

knowledge needed to be literate, and purpose of literacy. This system was an adequate representation of

the spectrum of reading theories. However, it was not quite as adequate when we consider theories of

instruction. The vantage point must be expanded to include the control and responsibility of learning as

well as the extent of integration in reading literacy.

Just like books, teaching to read books in different nations seems to be "...like mountain tops jutting

out of the sea with an underlying universal intellectual geography" (Bruner 1990, ix). It seems to us that

the unity of this intellectual geography is quite strong—as we saw when comparing the teaching cultures

of two distant countries with quite different school systems, with different teaching traditions, and with

very different languages. But even though the underlying intellectual basis was similar, there were also

some differences. These do not pose a problem, but rather a possibility—a possibility to explore many

new seas and to reach many new mountains.
177

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181

A Nine-Country Study: How Do Teachers Teach Reading to 9-Year-Olds?

Emilie Barrier and Daniel Robin

Centre International d’Etudes Pedagogiques, France

The IEA International Reading Literacy Study conducted in 1990-91 showed between-nation

differences in teachers' approaches to teaching reading (Elley 1992, 1994; Postlethwaite and Ross 1992;

Linnakylä and Lundberg 1993). But differences also existed within countries. The aim of the present study

is to determine the extent to which teaching practice within a given country is homogeneous, and to what

extent practice within one country is like that within another. Is it possible to say that teaching practice is

characteristic of cultural zones, as was shown to be the case for mathematics (Robin 1993; Robin and

Barrier 1991), or are they characteristic of a given educational system?

The data analyzed here come from nine countries: Denmark, Finland, France, Germany, Italy,

Spain, Sweden, Switzerland, and the United States. It comprises the answers given by the 1,803 teachers of

9-years-olds (Population A) surveyed in these countries.

1. Method

Two groups of pupils were targeted by the study on reading: 9-year-olds and 14-year-olds. The

sampled students were given reading tests and asked to complete a questionnaire on their backgrounds;

their teachers completed a questionnaire on their points of view, their teaching practices, and their

backgrounds. The teachers' geographical origins are shown in Table 1.

Table 1. Teachers’ geographical origin

Denmark Finland France Germany Italy Spain Sweden

SwitzerlandUnited

States

Number of teachers........ 209 71 136 149 154 324 234 227 299

SOURCE: IEA Reading Literacy Study, 1991.

Table 2 displays the teachers' responses about the teaching of reading and their own teaching

practices and methods. Taken together, these answers compose the "profile" of the teacher concerned. We

have chosen to compare these profiles by calculating the distance between them by means of a Principal

Component Analysis. This analysis makes it possible to bring out the principal factors accounting for the

dispersion among the profiles (Table 3).

Together with the Principal Component Analysis, a Hierarchical Cluster Analysis of the teachers

has been done. The distance measure chosen is Euclidean, and the criterion of aggregation is the

maximization of variance. Two teachers with the closest profiles are combined and replaced by their center

of gravity in a step-by-step fashion. A study of the histogram indicating the hierarchy makes it possible to

determine the pertinent number of clusters needed to describe the typology. It is possible to describe a

given cluster interpretation by its defining variables.
182

Table 2. Descriptive dimensions of teachers' strategies and methods

Dimension Number of items

Reading activities with the students........................................................................................ 28

Aims of reading instruction..................................................................................................... 12

Instructional strategies............................................................................................................ 13

Views with respect to issues in reading instruction.................................................................. 26

Methods to discover the students' needs in reading.................................................................. 9

Assessment of reading ............................................................................................................ 9

Assessment methods............................................................................................................... 6

SOURCE: IEA Reading Literacy Study, 1991.

Table 3. Eigenvalues of the factors of the Principal Component Analysis

Factor Eigenvalue Percent of inertia Cumulative percent of

inertia

1 12.98202 12.483 12.483

2 5.53273 5.320 17.803

3 3.99929 3.845 21.648

4 3.13399 3.013 24.661

SOURCE: IEA Reading Literacy Study, 1991.

The Principal Component Analysis and the Hierarchical Cluster Analysis enabled us to identify four

principal factors and a typology of teachers made up of 10 clusters.

2. Dispersion Factors

The Principal Component Analysis enables us to identify the main factors. The first four together

account for almost 25 percent of the inertia (variance) of the cloud (Table 3). If the latter had no structure

(hypersphere), the first four factors would account for less than 4 percent of the inertia (4 x 100 / 104).

Tables 4, 5, and 6 enable us to interpret the first three of the factors.

Table 4: Interpretation of Factor 1: 12.5 percent of the variance

Variable CTR

Positive end

Frequency of the assessment of vocabulary................................................................ 42

Frequency of the assessment of sentence understanding ............................................ 42

Frequency of the assessment of word recognition ..................................................... 40

Frequency of the assessment of the reading study skills............................................. 36

Frequency of the assessment of text comprehension .................................................. 35

Frequency of the assessment of phonic skills............................................................. 34

Negative end

No items

NOTE: CTR is the relative contribution of the variable to the factor structure.

SOURCE: IEA Reading Literacy Study, 1991.
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Table 5: Interpretation of Factor 2: 5.3 percent of the variance

Variable CTR

Positive end

When my pupils read to me, I expect them to read every word accurately ................................. 57

Every mistake a child makes in reading aloud should be corrected at once................................ 56

All children's comprehension assignments should be marked carefully to provide them

with feedback ..........................................................................................................................

48

Reading learning materials should be carefully sequenced in terms of language structures

and vocabulary.........................................................................................................................

47

Children should always understand what they are reading ....................................................... 36

Frequency of the assessment of vocabulary............................................................................... 32

Frequency of assessment of phonic skills ................................................................................ 28

Frequency of assessment of word recognition ........................................................................... 25

Negative end

Most of what a child reads should be assessed ......................................................................... 32

Activity: listening to teachers reading stories aloud ................................................................. 31

Instructional strategies: reading aloud to children.................................................................... 30

Activity : silent reading in class ............................................................................................... 26

Informal observation to discover the students' needs................................................................. 22

NOTE: CTR is the relative contribution of the variable to the factor structure.

SOURCE: IEA Reading Literacy Study, 1991.

Table 6 : Interpretation of Factor 3: 3.8 percent of the variance

Variable CTR

Positive end

Children should always understand what they are reading ....................................................... 38

Children should always understand why they are reading ......................................................... 29

When my pupils read to me, I expect them to read every word accurately ................................. 27

Every mistake a child makes in reading aloud should be corrected at once................................ 26

Negative end

Frequency of the assessment of literary appreciation ................................................................ 71

Frequency of the assessment of text comprehension ................................................................ 65

Frequency of the assessment of the use of background knowledge ........................................... 53

Frequency of the assessment of vocabulary .............................................................................. 48

Frequency of the assessment of decoding ................................................................................ 46

Frequency of the assessment of sentence understanding ........................................................... 42

Frequency of the assessment of word recognition ..................................................................... 40

NOTE: CTR is the relative contribution of the variable to the factor structure.

SOURCE: IEA Reading Literacy Study, 1991.

The first factor contrasts the Spanish and American teachers, who emphasize methods of

assessment, to the Finnish, German, and French teachers, who make textual understanding the basis of their

approach. The former give more importance to the elements of a document—vocabulary, words,

sentences—than to the document as a whole.

The second factor contrasts the Spanish and Swiss teachers to the Italian and American teachers.

The Spanish teachers tend to insist on reading that is perfectly correct from a formal point of view and on

an immediate assessment, whereas the others put greater emphasis on reading aloud by both teacher and

pupil, although they do not deny the importance of assessment. Assessment of elements is less important
184

than in the first case (in which vocabulary, knowledge of phonics, and word recognition are assessed

frequently). The second factor also involves the assessment of pupils but opposes two types of methods.

The third factor contrasts the Swedish, Danish, and, to a lesser extent, the Swiss teachers to the

Italian and Spanish teachers. It also contrasts the comprehension of the documents read, together with

correctness in reading, to understanding of the text, together with appreciation of its literary qualities, with

the exploitation of previously acquired knowledge. This second approach may also include the verification

of detail, such as the vocabulary.

Taken together, the factors that distinguish the teachers in this way put the emphasis on the contrast

between learning (and an assessment) based on a pointillist point of view and learning (and an assessment)

that is global, with more attention paid to understanding than to precision in reading.

From this initial analysis can be seen certain national tendencies that can be linked very generally

and approximately with the national averages of the pupils in Population A. The countries that place

greater emphasis on comprehension generally have better results, but this emphasis does not in itself imply

that little importance is given to precision in reading.

3. Typology of Teaching Practice

This analysis of the factors does not make clear the differences that can exist within countries. It

cannot be assumed that in each of these countries the teachers form a homogeneous group and use a single

method or approach to the teaching of reading. But it does allow comparisons between national tendencies

as it can be seen in Figure 1. One can see a grouping composed of Spain (SPA) and the United States

(USA), a group formed with Finland (FIN), France (FRA), Germany (GER), and another with Sweden

(SWE), Switzerland (SWI), and Denmark (DEN). Italy (ITA) is relatively isolated.

Figure 1 : Countries position on the first factorial plan

SPA

USA

ITA

SWE

DEN

SW

GER

FRA

FIN

Axis 1

Axis 2

 Assessement of

 text elements

 Pointillist Assessment

Holistic Assessment

Literary appreciation

Text comprehension

In order to indicate the extent to which the teachers as a whole form an unique group, a typology

that divides them into 10 groups has been defined. The teachers within the same cluster have more

characteristics in common than they have in common with the teachers in the other clusters. It is possible to
185

see which countries they come from and to check the extent to which practices within a country are the

same.

Figure 2 shows the resulting classification. The vertical lines between two clusters measure the

"distance" between these clusters. In each of the clusters the percentage of teachers from a given country is

indicated. Thus, cluster 1 (which includes 4.7 percent of all the teachers), is made up almost exclusively of

Swedish teachers, and 32 percent of the Swedish teachers are in this cluster. Most of the other teachers

from this country are in cluster 2 (44 percent), but there is also a minority in clusters 7 (7 percent) and 10

(9 percent). It is to be noted that clusters 1 and 2 are formed very early, and for this reason they form very

homogeneous groups. Cluster 1 contains mostly Swedish teachers, and in cluster 2 most are either Swedish

or American.

Table 7 indicates the percentage of teachers from each country to be found in each cluster. As

shown, most of the German teachers can be found in clusters 7 and 4, the Spaniards in 3 followed by 9, the

Americans in 10, 5, and 2, the vast majority of the Finns in 8, a majority of the French in 8 followed by 9,

the same number of Italians in 6 and in 3, the Swedes in 2 and 1, and the Swiss mainly in 7. It should also

be noted that some countries are not represented at all in certain clusters.

Table 7: Partition in 10 clusters: Percentage of teachers in each cluster, by country

Country Cluster 5

7.0 %

Cluster 6

4.0%

Cluster 3

15.9%

Cluster 1

4.7%

Cluster 7

15.1%

Cluster 8

8.7%

Cluster 2

11.6%

Cluster 9

11.0%

Cluster 10

10.1%

Cluster 4

11.9%

Denmark 0 0 2 0 23 2 7 4 2 58

Finland 0 0 6 0 1 82 3 0 3 6

France 2 0 8 1 6 48 6 19 4 6

Germany 1 1 8 1 38 2 2 12 3 33

Italy 6 40 39 0 1 1 2 6 4 1

Spain 3 3 51 0 3 0 0 31 8 1

Sweden 2 0 2 32 7 1 44 2 9 1

Switzerland 2 0 8 2 53 5 4 13 4 9

USA 31 0 3 0 3 3 21 1 35 2

SOURCE: IEA Reading Literacy Study, 1991.

Four wider groupings emerge from the typology as a whole: clusters 5 and 6, cluster 3, clusters 1,

7, 8, 2, and 4, and clusters 9 and 10. Each of these groups is characterized by certain general themes: the

development of a critical attitude for the first (clusters 5 and 6), constant assessment (cluster 3),

noncognitive aims for clusters 1, 7, 8, 2, and 4, and immediate understanding for clusters 9 and 10.

The Development of a Critical Attitude

Encouraging children to read, guided inference, and a critical attitude (cluster 5: 31 percent of

the Americans). This group consists almost entirely of Americans, with a small number of Italians. The

priority appears to be given to the attempt to encourage the pupils by every possible means (the reading of

stories by the teacher, the opportunity to take books home, the help of parents), together with the amount of

attention given to exact pronunciation and to monitoring progress. It is more important to develop a critical

attitude than to widen the pupil's horizons or his or her emotional development. This explains the search

for comparisons, generalizations, inferences, and messages, as well as the structure of the text. It also

explains the importance given to testing all aspects of the text, from decoding to the understanding of ideas.

The teachers in this group get the pupils to work on ideas, following the text as closely as possible, by

inference.
186

Figure 2. Resulting classification

DAN 58

USA 35 FRA 48

31 40

5 3 1 7 8 2 9 10 4

USA ITA

ITA 39

SPA 51

SWE

 32

DEN 23

 GER 38

FIN 82

SWI 53

SWE 44

USA 21

FRA 19

GER 12

SPA 31

SWI 13

GER 33

19

18

17

14

16

11

12

15

13

6

 Classification

 of teachers

Clusters

 Percentages 7.0 4.0 15.9 4.7 15.1 8.7 11.6 11.0 10.1 11.9
187

Change of language and critical attitude (cluster 6: 40 percent of the Italians). This cluster is

made up almost entirely of Italians, with few teachers from any other country included. As in the preceding

cluster, the development of a critical attitude is considered to be important, but emphasis is placed directly

on the techniques necessary to understand the text, and in particular on those that involve a change of

language, i.e., drawing, oral summaries, discussion, games, and scenarios. Much attention is paid to

correcting mistakes. The teachers in this group use various representations or images of the idea to develop

a critical attitude in their pupils. They base their approach on a living culture.

Constant Assessment

Oralization and assessment (cluster 3: 51 percent of the Spaniards, 39 percent of the Italians).

This cluster differs from the others because of the importance given to correctness in oral work and to

constant assessment, especially of vocabulary. Encouraging a love of reading or widening pupils' range of

choice are secondary considerations. Nevertheless, reading is the pretext for various activities such as

games, oral summaries, written commentaries, and identification of the themes or the plan. The parents are

also encouraged to help their children.

Noncognitive Aims for Reading

The pleasure of reading, above all (cluster 1: 32 percent of the Swedes). This cluster consists

almost entirely of Swedish teachers. It will be noted that it is very isolated on Figure 2, which indicates

that it is a particularly homogeneous group, and one that has a very different profile from the others.

The main aim of these teachers is to make their pupils like reading by appealing to their emotions.

They reject the idea of immediate and constant assessment and activities based on working out the meaning

or the vocabulary, but they do not emphasize understanding, either of the text or the sentences.

Understanding is less important than enjoyment.

Widening the pupils' horizons through a structured learning without any particular means

(cluster 7: 53 percent of the Swiss, 38 percent of the Germans, 23 percent of the Danes). This cluster

includes teachers who reject the idea of constantly assessing the pupils, but consider that children should

learn to read in a structured way and that the aim of teaching reading is to widen pupils' horizons, that is, to

enrich their emotional development through vocabulary. Answering questions concerning comprehension,

looking for the message, and making generalizations is given relatively little importance compared to their

principal preoccupation.

Development of pupils' research ability and rejection of oral skills (cluster 8: 82 percent of the

Finns, 48 percent of the French). To a certain extent, these teachers, like those in the preceding cluster,

accord little importance to the systematic assessment of the pupils, even though they are attentive to their

level. Developing the pupils' ability to research and study for themselves is considered to be fundamental.

Acquiring vocabulary is less important than personal reading. Reading aloud to the pupils is thought to be

a waste of time. What matters is the personal reaction to the text, individually rather than in a group

context.

Priority given to getting the pupils to read (cluster 2: 44 percent of the Swedes, 21 percent of the

Americans). The fundamental aim of the teachers in this group is to widen pupils' reading choice and to get

them to like reading. Various strategies are used: the reading of stories by the teacher, discussions,
188

compositions on the same theme, and the use of library. Parents are also encouraged to read to their

children and to participate in reading-related activities. On the other hand, little importance is given to

assessment, to pronunciation, to vocabulary, or even to comprehension.

Widening the Pupils' Horizons is the Only Aim

No particular encouragement is given to reading (cluster 4: 58 percent of the Danes, 33 percent

of the Germans). A third of the German teachers and a majority of Danes consider that widening the

pupils' horizons is fundamental. They give much less importance than their colleagues to correctness,

comprehension, and work based on the text. Paradoxically, reading does not seem to be important in itself

for these teachers.

Immediate Comprehension, with the Enjoyment of Reading not a Priority

Cluster 9: 31 percent of the Spaniards, 19 percent of the French, 13 percent of the Swiss,

12 percent of the Germans. The teachers in cluster 9—a third of the Spaniards and small numbers of the

French, Swiss, and German teachers—give great importance to correctness in reading and in progressing to

more and more difficult texts. Assessment is therefore constant. What is fundamental in their eyes is

developing textual comprehension, with comprehension being immediately apparent. In fact, they do not

work by generalizations or by inferences, the theme of a text is not identified, and there is no discussion of

the book. They do not try to develop literary appreciation. No importance is given to the enjoyment of

reading.

Cluster 10: 35 percent of the Americans. As in the preceding cluster, even though comprehension is

a priority, teachers are not very demanding in this respect, whereas monitoring pupils' progress by

assessment is considered to be important. The pupil has a personal activity (reading on his own) to do, but

he is not asked to do any work on the text, such as to make a plan or a summary. The teacher is very active

and very directive. Enjoyment of reading is not the main aim.

4. Conclusions

In most countries, it is clear that teachers do not make up a completely homogeneous group as far as

their approach to reading is concerned, even if certain tendencies are stronger in some countries. Only in

Finland do teachers make up such a group (82 percent of them are in cluster 8). For them, developing

pupils' ability to research questions is the main aim of their teaching.

The German teachers are mainly concerned with widening their pupils' horizons through reading,

and to this end use either highly structured teaching of the language (38 percent) or work of a literary

nature (33 percent). The Danes have a very similar approach (23 percent and 53 percent, respectively).

German and Danish teachers approach reading in a very similar way, their aims being cultural rather than

cognitive.

The Spaniards’ teaching is centered on monitoring procedures. Some give more importance to oral

work (51 percent), others to immediate comprehension (31 percent).
189

The approach of the Americans is the most varied. Some teachers are not very demanding, aiming

at immediate comprehension and not giving any particular importance to enjoyment (35 percent), others

strongly encourage pupils to read and develop a critical attitude (31 percent), while a third group is eclectic

in its approach (21 percent).

The French teachers are divided between two contrasting groups, one developing the ability to

research questions concerning the texts (48 percent), the other demanding immediate comprehension

without trying to get pupils to enjoy reading (19 percent).

Some of the Italians emphasize a living culture in which comprehension and oral work are most

important (40 percent), whereas others (39 percent) also give great importance to oral work, together with

constant assessment, particularly of the vocabulary.

The Swedish teachers consider that love of reading is the most important issue. Some try to achieve

it through encouragement (32 percent), others by the widening of the field of reading (44 percent).

The last group, the Swiss teachers, consider that it is essential that the teaching be structured. A

majority of them (53 percent) use reading as a means of widening pupils' horizons, others (13 percent) use

it to achieve immediate comprehension, closely monitored through assessment.

It is therefore possible to speak of national tendencies in the approach to reading that go beyond the

differences that may exist. Although we cannot speak of cultural zones, as was seen in the teaching of

mathematics, it does seem that most countries adopt consistent approaches within their educational

systems.
190

References

Elley, W.B. (1992). How in the world do students read? Hamburg: The International Association for

the Evaluation of Educational Achievement.

Elley, W.B. (ed.). (1994). The IEA study of reading literacy: Achievement and instruction in thirty-two

school systems. Oxford: Pergamon/Elsevier.

Linnakylä, P., and Lundberg, I. (1993). Teaching reading around the world. The Hague: The

International Association for the Evaluation of Educational Achievement.

Postlethwaite, T.N., and Ross, K.N. (1992). Effective schools in reading. Implications for educational

planners. The Hague: The International Association for the Evaluation of Educational

Achievement.

Robin, D. in L. Burstein (ed.). (1993). The IEA study of mathematics III: Student growth and classroom

processes. Oxford: Pergamon Press.

Robin, D., and Barrier, E. (1987). L’enseignement des mathématiques dans le contexte international.

Contribution à la réflexion. Revue Française de Pédagogie, 80, 5-15.
191

SECTION C: SCHOOL CLIMATE

• Consistencies in the Quality of School Life

Trevor Williams and Stephen Roey

• Quality of School Life in the Finnish- and Swedish-Speaking Schools in Finland

Pirjo Linnakylä and Viking Brunell

• Analysis of the Williams and Batten Questionnaire on the Quality of School Life in Spain

Guillermo A. Gil
193

Consistencies in the Quality of School Life

Trevor Williams and Stephen Roey

Westat, Inc., USA

Some schools are dull, depressing, even terrifying places, while others are lively, comfortable, and

reassuring ... such differences are enormously important... eliminating these differences ... would do

a great deal to make the quality of children's (and teacher's) lives more equal. Since children are in

school for a fifth of their lives, this would be a significant accomplishment.

This observation was made in connection with a study of inequality in American schools (Jencks et al.

1972, 256) at a time when most interest was directed at the cognitive outcomes of schooling—as it is now.

Nothing much has changed since that time; affective outcomes are recognized and their importance

acknowledged, but no one pays much attention to them.

Work on the affective outcomes of adults' lives—measures of quality of life, happiness, or wellbeing—is further advanced. The discussion that follows briefly examines the conceptualization and

development of a measure of the qualities of school life that draws on these more general “quality of life”

models, the results of applying this measure in the schools of eight nations, and the consistencies of students'

responses across these nations. More specifically, we consider whether this view of life in schools has

application outside the United States, in the schools of Denmark, Finland, France, Germany, Italy, Spain,

and Switzerland,1 and whether one could think of some universal dimensions of school life students take into

account in evaluating their life at school.

1. Affective Outcomes of Schooling

Much of what we know about the affective outcomes of schooling appears to have evolved as an

interest in the motivational components of achievement. The main thrust of this argument is that students

who are happier, more enthusiastic, and more engaged in life within schools are, all things equal, likely to

learn more and perform better on achievement tests. However, as Epstein and McPartland (1976, 15) point

out, there has been little systematic study of the aspects of school life that evoke these feelings. Despite a

general acceptance that such affective outcomes are important, they tend to be seen more as an (affective)

means to a (cognitive) end rather than an end in themselves.

The literature in question seems to see the affective outcomes of schooling in two main ways: as

school climate, the aggregate of individual values, beliefs, and behaviors into dimensions that characterize

the school or classroom; and attitudes to school, essentially feelings about school in general. Measures of

school climate consider schools as differing along some global dimension variously called climate, feel, or

tone (see Pace and Stern 1958; Halpin and Croft 1963; Stern 1970; and Moos 1978). Anderson and

Walberg (1974), Fraser (1980), and Anderson (1982) provide reviews of this literature.

 

1

 The data on which these analyses are based were obtained as part of the IEA International Reading Literacy Study in which some 30 nations took part. Consistent with the

purpose of this volume, the analyses are restricted to information on eight of these nations.
194

The interest in student attitudes has been more extensive. While some has been concerned with the

development of these attitudes (Dreeben 1968, for example), most are concerned with effects on achievement

and/or commitment to school. Some of the work takes a “mental health” approach—studies of neuroticism,

introversion-extroversion, anxiety, self-concept, and the like are common; the extensive use of Rotter's

(1966) internal-external control scale is a good example of the belief in attitudinal effects of this kind. The

interest of sociologists in alienation and related constructs in schools and students also reflects this

orientation; Stinchcombe (1964) and Otto and Featherman (1975) provide examples. Research into more

general attitudes on life in schools and classrooms is also reasonably well represented in the literature;

reviews by Jackson (1968) and Silberman (1971) capture the essence of this work.

The widely used Like-School scale created by IEA for use in their international comparative studies

was developed within the latter tradition. The scale, reported in Husen (1967, 121), consists of 11 items of

which the following are examples: I generally like my school-work; I find school interesting and

challenging. While there is no explicit theoretical basis for the scale, it seems to be measuring general

satisfaction with school and, in the context of these studies, is treated as a potential influence on

achievement.

2. Quality of Life, Happiness, and Well-Being

Other attempts to think about this affective aspect of schooling have used work on the quality of

(adult) life as a model2 (see Gurin, Veroff, and Feld 1960; Bradburn and Caplovitz 1969; Cantril l965;

Bradburn and Caplovitz 1969; Campbell, Converse, and Rodgers 1976; and Andrews and Withey 1976).

Gerson (1976) provides an overview of the theoretical perspectives adopted. Burt et al. (1978;1979)

summarize the underlying models used, capturing this summary in a general model involving four

dimensions: general affect—satisfaction with life as a whole; positive affect and negative affect—affect

based on more immediate experiences; and domain affect—satisfaction with specific domains of life such as

family, housing, neighborhood, education, leisure, the government, friendships, work, and so on.

3. Quality of Life and Quality of School Life

At least two measures of the quality of school life have their roots in models of this kind. Epstein and

McPartland (1976) link their work to this perspective developing three dimensions: general satisfaction with

school, commitment to classwork, and reaction to teachers. These seem to reflect, respectively, a measure

of general affect and measures of satisfaction with two domains of schooling.

Parallel work by Williams and Batten (Williams and Batten 1981; Williams 1984) was based

explicitly on the model proposed by Burt et al. (1978). These authors generalize the notions of general affect

and of positive and negative affect for life as whole to life in schools in a relatively straightforward way.

However, conceptualizing the nature of the domains of schooling that parallel those for life as a whole is less

straightforward.3 What is needed is some notion of the important aspects of schooling whose “quality” is

likely to generate feelings of well-being in students. This amounts to developing a model of schooling from

the perspective of students, something not addressed in any comprehensive way in the education literature. In

 

2

 Other work with similar concerns often identifies its focus as happiness or well-being rather than quality of life. However, since the focus is much the same in each case it

seems reasonable to use quality of life as a generic term in this instance.

3

 In the quality of life literature, education is one of the domains of life.
195

the light of this fact, an attempt was made to develop a definition of the domains of schooling from first

principles. The full argument is detailed in Williams and Batten (1981) and is summarized below.4

4. Domains of School Life

Feelings of well-being in any domain are derived from two sources: the level of consumption of

socially valued goods and services relative to socially prescribed norms, and the extent to which an individual

can control his/her own well-being (Burt et al. 1978, 367). In the present context, the problem faced was one

of defining the goods and services provided by schools and valued by students, goods and services to which

students had different degrees of access and over which they had different degrees of control. These

categories of goods and services define the domains of schooling in a way similar to that in which such

factors as occupation, income, and health define the domains of quality of life in general.

The nature of these categories of valued goods and services—the domains—were developed by

drawing on a theory of schooling that links social-structural and individual systems of action in schools. This

theory is developed in several papers (Spady and Mitchell 1977; Mitchell and Spady 1977; Mitchell 1977;

Spady 1979) and is used to support the existence of four domains of schooling: status, identity, adventure,

and opportunity. These authors draw on both structural-functional and symbolic-interaction theory to

construct a model of schooling that sees schools as action systems for integrating individual student

expectations for personal fulfillment with societal expectations for what schools are about. The socialstructural perspective is expressed as four broad expectations responsible for the creation and maintenance

of schools. Schools are expected to

• Facilitate and certify the achievement of technical competence;

• Encourage and enhance personal development;

• Generate and support social integration among individuals; and

• Nurture each student's sense of social responsibility.

And, in response to these societal expectations, schools have developed

• Certification structures, which certify the student's technical competence;

• Instruction structures, which facilitate personal development through learning;

• Socialization structures, which promote participation in the school's social system; and

• Supervision structures, which promote the learning of social norms and values.

From the student's perspective, certification processes, which embody performance standards, are only

attractive if they enable the student to qualify for desirable future opportunities—students evaluate their

schooling in terms of its perceived relevance for their future well-being. Instructional effectiveness is best

 

4

 Even though presented in summary form, the argument is still an extended one. In good part this comes about because of the need to develop this conceptualization from first

principles and because the basis for this development is somewhat unconventional and not well known.
196

realized when there is the experience of adventure in learning—high quality learning is that which is

intrinsically rewarding and generates self-motivation. The product of successful social integration is identity

formation, and students look at the quality of their school environment in terms of the extent to which it

provides for the development of self-awareness in interaction with others within the school. Similarly,

students see the quality of their school lives influenced by the extent to which schools encourage social

responsibility, which, in turn, is fostered when students are given the opportunity to achieve status in the

group.5

5. Models and Items

The end result of this process is a model of the quality of school life based on the following

dimensions: general affect, positive affect, negative affect, opportunity, adventure, identity, and status. In

these terms one can think of the quality of life of students reflecting, respectively, their overall well-being,

feelings of well-being and ill-being generated by recent events, and feelings of well-being generated by the

extent to which the schools they attend provide for the satisfaction of students' needs for opportunity,

adventure, identity, and status.

Items developed to tap general affect, positive affect, and negative affect were straightforward

adaptations of those found in traditional quality of life measures, with the exception that they referred to life

in school. The development of items to reflect the domains of schooling was, of necessity, undertaken from

first principles. However, this process was assisted by a more detailed elaboration of the opportunity,

adventure, identity, and status dimensions within the context of the original arguments of Spady and

Mitchell.6 This elaboration defined the scope of the domains, and hence the particular focus of the items,

more precisely.

Each of the items was written as an extension of the stem School is a place where.... and was

designed to be responded to on a 4-point agree-disagree scale. Both teachers and students found the

instrument to have high face validity in the sense that they saw it as addressing issues important in their lives

at school. Trial testing and item-analysis procedures designed to elicit the latent structure of the item pool

reduced the original pool of items to 29.7 These procedures are described in detail in Williams and Batten

(1981).

 

5

 This is not meant to imply that these are the only domains of schooling. These four aspects of schooling are a beginning and are consistent with a coherent argument about the

nature of schooling. We expect that further work on the development of this model would produce other domains of schooling important to students and, hence, to the feelings

of well-being that they express.

6

 See Williams and Batten (1981) for a more detailed development of the model that postulates some 20 domains in all, 5 separate constructs within each of the 4 domains

noted here.

7

 See Table 1 for wording of the items in question.
197

6. Consistencies in the Quality of School Life

This 29-item measure has been used a number of times with samples of high school students in

Australia and Canada and with university students8in the United States and Canada. In each instance, a

slightly modified version of the theoretical structure used to generate the items has been supported by the

data. The emergent structure to this measure of quality of school life differs in three ways from the model on

which it is based. First, we are unable to distinguish positive affect from general affect, though negative

affect retains its identity. Second, we were unable to find evidence of a latent variable we could identify as

adventure. Third, students tended to respond in much the same way to any item to do with teachers with the

result that it makes sense to talk about a teacher’s construct as part of this model. The end result is six

dimensions to this measure of the quality of school life: general affect, negative affect, opportunity,

teachers, identity, and status.

In one of their first attempts to extend their international comparative studies beyond cognitive

achievements, the IEA provided for the inclusion of these 29 items in student questionnaires administered in

1991 to national samples of 14-year-olds in the 30 nations taking part in the International Reading Literacy

Study. In so doing, they provided the opportunity to examine the consistencies of student responses across

nations rather less similar than are the United States, Canada, and Australia. Given fairly substantial cultural

differences between these nations and the differences between their school systems, one would not confidently

expect students to respond to these items in the same way. As a result we would not expect to find the same

six-construct latent structure to these data in each nation. However, if the same latent structure did emerge in

each nation, we would have grounds for claiming to have identified aspects of the quality of students' lives

that were universal rather than culture bound.

In examining this proposition we limited our attention to the eight nations previously identified.9 As a

relatively simple test of the notion that student responses had the same latent structure in each of these

nations, factor solutions10 were obtained separately for each data set and six factors11 rotated to an oblique

structure.12

7. Statistically Consistent Latent Structures

The results of these analyses are summarized in Table 1, which shows rotated factor loadings and

communalities for the eight nations. The presentation is compressed somewhat according to the following

conventions.

1. Only factor loadings equal to or greater than .413 are considered to give substantive definition

to factors.

 

8

Item wording was modified to suit the differences between universities and high schools.

9

 Comparisons of the United States with the seven other nations are the overall focus of the analyses reported in this volume for reasons set down in the

introductory section.

10 A principle axes solution with iterated communalities was used in each case.

11 Since the predicted latent structure was based on six constructs, we rotated six factors rather than use rule-of-thumb approaches like the eigenvalue-one criterion.

12 Oblimin rotation criteria were used.

13 Loadings are rounded to the first decimal place.
198

Table 1. Rotated factor loadings and communalities for items on quality of school life scale

Rotated factor loadings Communalities

DEN FIN FRA GER ITA SPA SWI USA DEN FIN FRA GER ITA SPA SWI USA

School is a place where. . . General Affect

I really like to go .6 .7 .8 .7 .7 .7 .7 .5 .5 .6 .7 .5 .5 .4 .6 .4

I feel happy .5 * * .6 .6 .4 .6 .4 .4 .4 .4 .5 .5 .4 .5 .4

I find that learning is a lot of fun .6 .5 .5 .7 .5 .6 .7 .8 .5 .5 .4 .6 .5 .5 .6 .7

I get enjoyment from being there .8 .6 .9 .9 .8 .8 .8 .9 .6 .6 .8 .7 .6 .7 .7 .7

I feel great .6 * S .5 * .4 .6 .4 .6 .4 .3 .4 .4 .4 .4 .5

Negative Affect

I feel lonely .7 .6 .6 .6 .7 .6 .6 .8 .5 .4 .4 .4 .5 .4 .4 .6

I feel restless .6 .5 .6 .7 .6 .7 .6 .6 .4 .3 .3 .4 .4 .4 .4 .4

I feel depressed .7 .7 .7 .7 .6 .7 .6 .7 .5 .5 .5 .4 .4 .5 .4 .5

I get upset .4 * .4 * * .7 * .6 .2 .1 .3 .2 .1 .4 .2 .3

Status

I feel important .4 .6 .6 .4 .5 * .5 .4 .3 .4 .4 .4 .3 .2 .3 .5

People have confidence in me * * .6 I * .6 .4 .4 .3 .4 .4 .4 .3 .4 .4 .5

People come to me for help .5 * .4 * * .4 .4 .5 .3 .3 .2 .3 .2 .2 .3 .4

I know that people think a lot of

 me .6 .8 .6 .6 .6 .5 .5 .8 .3 .6 .4 .4 .4 .2 .3 .5

People look up to me .7 .8 .5 .6 .7 .6 .5 .8 .4 .6 .3 .4 .5 .4 .2 .6

Identity

Mixing with other people helps

 me understand myself .7 .6 .7 .7 .6 .7 .6 .7 .4 .4 .4 .4 .4 .4 .5 .5

I learn to get along with other

 people .5 .4 .5 .7 .5 .5 .5 .6 .4 .3 .4 .4 .3 .4 .4 .5

I learn a lot about myself .6 .5 .6 .5 .6 .5 .7 .4 .5 .4 .5 .4 .4 .4 .5 .5

I get to know myself better .7 .5 .7 .5 .7 .7 .7 .4/G .5 .5 .6 .4 .5 .5 .6 .6

I have learned to accept other

 people as they are .4 * .4 .4 * * * * .2 .2 .2 .3 .2 .3 .3 .3

Teachers

Teachers are fair and just .6 .7 .6 .7 .7 .7 .7 .8 .4 .6 .4 .5 .5 .5 .5 .6

Teachers help me to do my best .5 .6 .6 .5 .6 .7 .6 .6 .3 .5 .4 .4 .5 .5 .5 .5

Teachers treat me fairly in class .8 .9 .7 .8 .8 .7 .8 .8 .6 .8 .5 .6 .6 .6 .6 .7

Teachers give me the marks I

 deserve .5 .4 .6 .5 .6 .6 .5 .5 .3 .4 .4 .3 .4 .4 .3 .4

Teachers listen to what I say .5 .6 .6 .4 .5 .7 .5 .5 .4 .5 .4 .3 .4 .5 .4 .5

Opportunity

I know how to cope with the

 work * * * * .4 * * * .1 .3 .1 .1 .3 .2 .2 .3

I know I can reach a satisfactory

 standard in my work .6 .6 .7 .6 .6 .7 .5 .7 .4 .3 .5 .4 .4 .5 .4 .5

I know the sorts of things I can

 do well .6 .6 .5 .5 .4 .6 .5 .8 .4 .4 .3 .4 .3 .4 .3 .6

I get satisfaction from the school

 work I do G G * * .5 G G G .5 .5 .3 .4 .4 .5 .4 .5

I know I can do well enough to

 be successful S .4 .6 .6 .6 .6 .4 .7 .4 .3 .4 .4 .4 .3 .3 .5

KEY: G= general affect; S = status; I = identity; * = variable has loading factor of less than .4 on any of the factors.

SOURCE: IEA Reading Literacy Study, 1991.
199

2. The normal 29x6 matrix of loadings is compressed into a 1x6 matrix. It may be easiest to

think of this in the following terms. The items in the 29x6 matrix for each nation are arrayed

construct by construct according to the a priori arguments: six items measuring general

affect, followed by four items measuring negative affect, followed by four 5-item groups

tapping status, identity, teachers, and opportunity. Assume that all loadings less than .4 are

shown as blank cells. The six columns are then overlaid to create a single composite column.

If good simple structure is achieved in the rotation, then one would have a column of 26

loadings greater than .4.

3. Where a variable fails to achieve a loading of .4 on any of the six factors, it is shown as an *.

The second postulated indicator of status for Denmark, for example, failed to achieve a

loading of .4 or better on any of the six factors.

4. Where an indicator achieved a loading of .4 or better, but on a factor other than the one

hypothesized, the factor on which it did load is indicated instead of the loading. Thus, in the

case of Denmark, the fifth and sixth hypothesized indicators of opportunity achieved loadings

of .4 or better on general affect (G) and status (S).

5. Where an indicator achieved loadings of .4 or better on more than one factor, it is indicated

by the loading and a pointer to the other factor—a situation that occurs once. In the case of

the United States, the fourth item thought to tap identity loads .4 on identity and at this value

or greater on general affect.

It may be helpful to illustrate the interpretation of these findings by examining the results for the

United States. In the full 29x6 matrix of factor loadings for the United States, six items defined a priori as

tapping general affect loaded on only one factor. Two other items also loaded (unexpectedly) on this

factor—the fourth item under identity, and the fourth item under opportunity. Given this, it seems

reasonable to think that general affect exists,14 and we would be justified in labeling this factor

accordingly.

In the case of the items hypothesized to tap negative affect, all four loaded on the one factor and on

no other, giving us reason to suggest that this may well be a dimension to the quality of (U.S.) students'

lives. The situation is analogous for the items defining, respectively, the status and teachers dimensions.

The five status items load on one factor only, and the five teachers items load on another, and on only that

one. There is justification then for thinking about these as legitimate dimensions to the quality of students'

lives. The situation for identity and opportunity suggests that there is less of a fit between hypotheses and

reality in this instance, though the fit is by no means bad. The fourth identity item loads on general affect

as well, and the fifth item does not achieve the criterion value on this or any other factor. Similarly, the first

item under opportunity fails to reach this same criterion on any factor. The fourth item does not load on

opportunity at all, but rather seems to be a measure of general affect.

By most standards this would be seen as a very positive result. The model developed a priori and

operationalized is supported by the data for the proposed latent structure. In the majority of cases, items

hypothesized to define one of the six constructs load on a single factor and on no other. The criteria for

simple structure are achieved, for the most part, in each of the eight nations, and items cluster to define

factors in the way predicted. Not too surprisingly there are a few items that do not work in the way

 

14In the sense that hypothetical constructs or unobservable variables can be thought of as existing.
200

expected. The first item in opportunity is an example, since it does not load on any factor except in Italy.

So, too, is the fourth item under opportunity, which seems to tap general affect rather than opportunity.

8. Conclusion

Overall, though, we are looking at latent structures that are remarkably consistent across nations.

This consistency is even more surprising if one considers that affective aspects of schooling are much more

likely to be culturally bound than cognitive dimensions. Our predictions at the outset were that what seemed

to be a fairly culturally specific model and instrument would present problems in translation at the outset

and, more importantly, would call forth different kinds of responses in different cultures. This seems not to

be the case overall. The data suggest that we may have identified dimensions of schooling that cross

national boundaries—aspects of life in school that are important to students everywhere and, to the extent

realized, influence student feelings of well-being.15

Students in Western European nations and the United States respond to life in schools in much the

same way. They can express the extent of their happiness with life in school, and they can also see a

negative side of schooling in general. At a less global level they consider their well-being in terms of the

extent to which the school provides them with the chance to develop notions of their own status vis a vis

that of their peers and teachers; provides the structures that facilitate social integration and the sense of

identity that grows from this experience; provides for harmonious and equitable interactions between

students and teachers; and provides the means by which student learning can be certified and in this way be

seen by others as a recognizable investment in the future.

Clearly one could do more with this model and measure of the quality of school life, and the

evidence presented here suggest that further effort would be warranted. More, and more refined, items

could be developed for the model as it stands to strengthen the six dimensions talked about here. The items

on positive and negative affect need to be more clearly defined as expressions of affect in the short term.

The opportunity dimension is a little weak. And it may be possible to operationalize the hypothesized

adventure dimension in such a way as to establish its identity as a separate construct. This would be

particularly relevant given the current interest in student motivation and engagement, or the lack thereof.

Further, almost certainly there are other domains of school life to which students respond. The

domains defined here were based on a particular model of what goes on in school, a model the authors

certainly do not claim to be all inclusive. Conceptualizing what these other domains might be is a

challenging task. It is probably a worthwhile task as well, since we seem to have been able to define and

measure some aspects of schooling important to students everywhere.

 

15 One might well object that this group of Western European countries and the United States are quite similar culturally and hence that these national similarities are not

too surprising. However, analyses conducted on all 30 nations but not reported here do not support this view. Although there is some variation between nations in the

patterns of loadings, it is much like we have seen here. In all, the same basic pattern emerges across all 30 nations.
201

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Quality of School Life in the Finnish- and Swedish-Speaking Schools in Finland

Pirjo Linnakylä and Viking Brunell

University of Jyvaskyla, Finland

1. Context of the Study

Finland is a bilingual country where 94 percent of the 5 million inhabitants speak Finnish and

about 6 percent speak Swedish as their mother tongue. Finland's constitution decrees that the cultural and

economic needs of Finnish- and Swedish-speaking populations are to be met on an equal basis. In

principle, all forms of social services should be available in Finnish and Swedish in all bilingual areas of

Finland.

Many countries where several languages are spoken have decided in favor of making the first

school language the same for all students. In Finland, students are offered a choice for the first school

language, and this language policy makes it possible for Finnish- and Swedish-speaking children to attend

separate schools. Furthermore, the Swedish-speaking population is entitled to education in Swedish at all

levels of the educational system from kindergarten to university. The Swedish schools have become the

real cornerstone for the existence of the Swedish-language culture in Finland. Mother tongue and literacy

instruction have an important position, especially in the early grades.

Language-minority students are often expected to have a lower performance level, particularly in

literacy, and this was supported by the data from most countries in the 1990-91 cross-national IEA

Reading Literacy Study. Children whose home language was different from the dominant language used

in schools showed lower literacy levels in both populations (9- and 14-year-olds) (Elley 1994). Finland,

however, has nearly avoided this problem and achieved an unusual level of literacy in both languages.

In the IEA study in Finland, the Finnish-speaking students at both age levels showed the highest reading

literacy levels in almost all domains. Likewise, in a parallel national study, the Swedish-speaking minority

students demonstrated an almost equal performance level (Brunell and Linnakylä 1994).

In Table 1, the scores of the Finnish-speaking students are compared with their peers attending

Swedish-speaking schools in Finland and also with those Swedish students who participated in the IEA

study in Sweden (cf. Brunell and Linnakylä 1994). As shown, the Swedish-speaking students in Finland

scored almost as high in reading literacy tests as their Finnish-speaking peers, who had the highest average

scores in both populations (Elley 1994). In Population A, the performance level of the Swedish-speaking

Finnish students came between that of Finnish-speaking Finns and Swedish students. In Population B,

the performance levels of the two Swedish-speaking populations were the same. In all, the mean

difference between reading literacy performance of the Finnish-speaking majority and the Swedishspeaking minority was rather marginal. Taking into consideration that the students of Sweden also

performed very well in the IEA study, scoring third in both age groups in the international comparison,

the performance level of Swedish-speaking minority students in Finland can be regarded exceptionally

high.

203
Table 1. Literacy scores of Finnish- and Swedish-speaking students in Finland and of Swedish

students in Sweden: Populations A and B

Literacy domain and population Mean percentage of correct answers

Population A

(9-year-olds)

Population B

(14-year-olds)

Documents

Finland: Finnish . . . . . . . 78 86

Finland: Swedish . . . . . . 78 82

Sweden . . . . . . . . . . . . . . 72 81

Expository

Finland: Finnish . . . . . . . 77 74

Finland: Swedish . . . . . . 73 71

Sweden . . . . . . . . . . . . . . 70 71

Narrative

Finland: Finnish . . . . . . . 79 79

Finland: Swedish . . . . . . 73 76

Sweden . . . . . . . . . . . . . . 72 77

Total

Finland: Finnish . . . . . . . 78 80

Finland: Swedish . . . . . . 75 77

Sweden . . . . . . . . . . . . . . 71 77

SOURCE: IEA Reading Literacy Study, Finnish National Study data and Swedish National Study data.

2. Aims of the Article

The purpose of this article is to explore how the quality of school life in general appears in the

two Finnish school systems, which seem to produce equally high cognitive achievement. Furthermore, the

aim was to examine whether the quality of school life in Finland resembles the Nordic school culture and

whether it has any correspondence to school life of Germany or the United States. The Finnish schools

in this study are compared to the other Nordic schools and the German and U.S. schools because of the

assumption that Finnish school system has partly followed the principles of the German and Baltic, later

the Scandinavian, and most recently the U.S. school systems (Iisalo 1991).

Earlier international assessments on the satisfaction with their schools expressed by Finnish

children and adolescents were not very flattering. In the 1970s, the IEA Six Subject Study, in which

Finnish-speaking students participated, did not provide a very positive picture of attitudes towards the

school and studying among Nordic students in general and Finnish students in particular. For example,

among 14-year-old students in the nations included in that study, overall satisfaction with their schools

was considerably lower in Finland, Sweden, and West Germany than in the United States, Hungary, and

Japan. Negative attitudes towards school were exceptionally common in Finland. Particularly striking in

these results was that Finnish students' relationship with the teachers indicated feeling of inferiority (low

self-esteem); that is, 73 percent of the students said that "they felt little" in front of the teacher (Husen

1973; Fägerlind and Munck 1981).

204
What is the situation in Finland in the 1990s? How do Finnish children enjoy school? How do

the Finnish-speaking majority schools and the Swedish-speaking minority schools differ in the quality of

school life? And how do Finnish schools compare with the other Nordic schools as well as with the

German and American schools? We use the extensive international data collected during the IEA Reading

Literacy Study (Elley 1994) and the national data collected simultaneously in Swedish-speaking schools

in Finland to address these questions.

3. Defining the Quality of School Life

When assessing schools, focus on cognitive achievements does not, of course, suffice. The

development of the affective domain has to be evaluated as well. In the affective domain, motivation to

learn has been most frequently assessed, since motivation is found crucial in energizing and directing

learning (Gage and Berliner 1988). It sustains our attention and maintains our effort. It may be reinforced

by extrinsic rewards or it may be a result of some intrinsic personal drive (Deci 1975). It seems to be

supported by a complex system of thought and feelings to understand the causes of our performance.

These attributions are related to subsequent behavior and also to our emotions (Weiner 1986). Motivation

is also viewed in connection with enjoyment, an optimal experience, which usually occurs when a person's

mind is stretched to its limits in a voluntary effort to accomplish something worthwhile (Csikszentmihalyi

1990).

Increasingly, interest has also been focused on the study of school ecology, school environment,

school climate and milieu, social relations in school, school culture, and the quality of school life (Fraser

1986; Csikszentmihalyi 1990).

The concept of the quality of school life has been derived from a more general concept, "the

quality of life" (Land and Spilerman 1975; Williams and Batten 1981; Csikszentmihalyi 1990). Quality

of life means the level and versatility of social life experienced by individual members of society. From

an individual's point of view, the quality of life is usually considered a general, holistic well-being or

enjoyment, i.e., how life as a whole is experienced at a certain moment in a given environment.

Evaluation is mainly focused on the positive—things that have brought happiness, pleasure, and

satisfaction—but also on negative experiences and feelings. Furthermore, experiences are evaluated in

some specific areas significant in the individual's life, such as family, friends, school, work, and leisure

(Gerson 1976; Williams and Batten 1981).

The "quality of school life" is defined in this study as students' general well-being and satisfaction

and their positive and negative experiences, particularly in activities typical of school life. According to

Williams and Batten (1981, 9) the typical activities and functions of school environment are as follows:

1. To facilitate and certify the achievement and technical competence valued in the society;

2. To encourage and enhance an individual's personal development;

3. To support individuals' socialization, social relations, and social integration; and

4. To nurture and guide an individual's social responsibility for his or her own actions and

for the groups to which he or she belongs.

205
To support these more general societal expectations, schools have created such structures and programs

that translate these expectations into actions within the school (Williams and Batten 1981). Societal

expectations can be met and schools structured successfully only if individuals approve and are attracted

to the outcomes and studying processes that they embody. As shown in Table 2, there are four major

areas of student motivational experiences corresponding to the four societal expectations and school

structures (Williams and Batten 1981).

Table 2. Domains of student experience of the quality of school life

Societal expectations School structures Student experiences

Technical competency Certification Opportunity

(security, adequacy)

Personal development Instruction Adventure

(adequacy, intimacy)

Social integration Socialization Identity

(intimacy, worth)

Social responsibility Supervision Status

(worth, security)

SOURCE: T. Williams and M. Batten, The Quality of School Life. ACER research monograph No. 12, 1981, p. 10.

From the student's point of view, obtaining competency is desirable if certification processes

offer a secure basis for the future and new opportunities for personal development and for functioning and

succeeding in society. From the perspective of individual growth, student experiences must include the

experience of adventure in learning, i.e., a joyful experience that makes learning intrinsically motivating.

The main motivating element of social integration is identity formation, the development of self-awareness

in a school class and in relation to the larger society. Acquiring of social responsibility is dependent upon

the student status and prestige in the group (Williams and Batten 1981, 10).

4. Method

4.1 Measuring the Quality of School Life

A questionnaire with 29 items originally constructed and refined by Williams and Batten (1981,

36) was used as the instrument for measuring the quality of school life. The items operationalize the

domains of the quality of school life mentioned above: general satisfaction, negative affect, and four

central domains of student experiences—opportunities for success, adventure of learning, and the

development of identity and status. Each domain was operationalized in five items with the exception of

negative affect, which had only four questions (Schleicher and Siniscalco 1991).

In exploratory analyses of refining the instrument, the adventure items—the joy and selfmotivation in learning—did not come out as a subscale of their own, but they were linked with opportunity questions. Therefore the adventure and opportunity items were combined when the questionnaire

was revised. Furthermore, the student-teacher relationship linked to socialization was assessed as an

independent domain, and even though it was not assumed to be an isolated factor in the original model,

it already emerged in Williams and Batten's exploratory analyses (1981) and later also in Ainley's (1986;

Ainley and Bourke 1989) empirical data as a distinctively essential domain in the quality of school life.

206
Table 3 shows the content of the items in the international questionnaire used in the IEA study,

their hypothetical domain, and for the Finnish-speaking schools, the structure of the empirical data based

on exploratory factor analysis (principal component with varimax rotation).

Students responded to the items by using the following scale: I definitely agree (4), mostly agree

(3), mostly disagree (2), and definitely disagree (1). The results of the assessment are described and

compared both variable by variable and factor by factor as the proportion of the students agreeing

(definitely agree and mostly agree) with the items. In the factor and regression analyses as well as in the

analysis of variance, the original ratings were used as an ordinal scale.

Factor analysis of the Finnish data indicated that the items of the questionnaire clearly clustered

according to the assumed factor structure. Consistent with the theoretical structure, the item variables

clustered into six factors, of which general satisfaction, teacher-student relations, student's status, and

social identity were the most distinct. The variables related to the domains of opportunity for success and

adventure and for developing oneself clustered into the same factor as in earlier analyses in Australia.

Furthermore, the teacher-student relationship also formed a distinct domain of its own. The only item that

did not behave as expected was the item "I feel happy": it did not fall into the factor of general

satisfaction but got a rather strong negative loading and clustered into the factor of negative affect. After

further analysis, the item variable was placed into the scale of negative affect and was reformulated as

"I do not feel happy" in order to avoid negative loading.

Factor analysis was applied to confirm the construct validity of the instrument. It seems reasonable

to argue for six construct dimensions within the theoretical context. Within the assessment of the quality

of school life, the results were thus analyzed according to the factor scales focusing on the following

domains: general satisfaction, negative affect, teacher-student relations, student's status in the school, social

identity, and the student's view of opportunity for succeeding in school.

Coefficient alpha, a measure of the reliability (internal consistency) of the whole instrument was

fairly high at .87. The six-factor solution explained 55 percent of the total variance. The alpha coefficients

measuring the reliability of the different domains based on the factors were as follows:

General satisfaction .83

Teacher-student relations .83

Student's status in school .78

Identity formation .71

Achievement and opportunity .67

Negative affect .60

207
Table 3. The items measuring the quality of school life classified according to their theoretical and

empirical factor (rotated principal component analysis) structure

Items Theoretical Empirical factor structure *

classification* 1 (G) 2 (T) 3 (S) 4 (I) 5 (A) 6 (N)

School is a place where...

I really like to go to school G .62

I get satisfaction from the school work I do G .62

I get enjoyment from being at school G .62

I find that learning is a lot of fun G .56

Teachers treat me fairly in class T .81

Teachers are fair and just T .68

Teachers listen to what I say T .61

Teachers help me to do my best T .58

Teachers give me the marks I deserve T .44

People look up to me S .74

I know that people think a lot of me S .71

I feel important S .55

People have confidence in me S .41

People come to me for help S .38

Mixing with other people helps

me understand myself I .60

I get to know myself better I .51

I learn to get along with other people I .47

I learn a lot about myself I .46

I have learnt to accept other people 

as they are I .33

I know the sorts of things I can do well A .57

I know I can reach a satisfactory

standard in my work A .54

I know I can do well enough to be

successful if I try A .45

I feel great A .40

I know how to cope with the work A .39

I feel depressed N .71

I feel lonely N .56

I feel restless N .51

I feel happy G -.38

I sometimes get nervous N .26

* G = general satisfaction

T = teacher-student relations

S = status in school

I = identity formation

A = achievement and opportunity

N = negative affect

SOURCE: IEA Reading Literacy Study, Finnish National Study data.

208
The coefficients indicate that the factors of general satisfaction and teacher relations are the most

internally consistent. The factor of negative affect proved the least internally consistent.

In the negative affect scale, the lowest loading (.26; in contrast to .52 in the original Australian

data) was in the item "I sometimes get nervous," which seemed to point to a translation problem. The item

was orginally expressed in English as "I sometimes get upset," which may have a slightly, or even more

than slightly, different meaning than in the Finnish item. The difference in item meaning may also cause

some unreliability in the comparative assessment of this item.

In the Swedish-speaking schools, the instrument differed to some extent. Three international items

were excluded and five national terms were included. Therefore, the factor structure of the data from the

Finnish-speaking schools is used as the basis in the following factor scales.

4.2. Who Participated in the Assessment?

The measurement took place in Finland in March 1991 in connection with the IEA Reading

Literacy Study. The parallel national study was conducted in Swedish-speaking schools in Finland at the

same time.

In the international study of the quality of school life the participating groups were those classes

where the majority of students were 14 years old. In Finland, these students were eighth graders in

comprehensive schools. Altogether, 1,379 eighth graders participated in this assessment in the Finnishspeaking schools. The Swedish-speaking schools were not sampled, but rather all schools and all eighth

graders (3,318) participated in the national study.

The findings in both Finnish- and Swedish-speaking schools are compared with those from the

other Nordic countries participating in the international assessment—Norway, Denmark, and Iceland.

Sweden did not participate in the assessment of the quality of school life. Among the countries compared

for this assesment, 24,732 students participated in the study (Table 4), including 14,772 students from the

four Nordic countries. The data were collected by the national research coordinators in the compared

countries under the supervision by the International Coordination Center at the University of Hamburg.

5. Results of the Assessment

The results of the assessment on the quality of school life are described and compared below both

by variables and by factor scales as the percentage of the students' agreement with the item statements

(definitely agreeing plus mostly agreeing).

209
Figure 1. Percentage of students in Finnish-speaking schools in Finland agreeing with the statement

GENERAL SATISFACTION

I really like to go to school.

I get satisfaction from the school work I do.

I get enjoyment from being at school.

I find that learning is a lot of fun.

TEACHER-STUDENT RELATIONS

Teachers treat me fairly in class.

Teachers are fair and just.

Teachers listen to what I say.

Teachers help me to do my best.

Teachers give me the marks I deserve.

STATUS IN SCHOOL

People look up to me.

I know people think a lot of me.

I feel important.

People have confidence in me.

People come to me for help.

IDENTITY FORMATION

Mixing with others helps me understand myself.

I get to know myself better.

I learn to get along with other people.

I learn a lot about myself.

I have learned to accept other people as they are.

ACHIEVEMENT AND OPPORTUNITY

I know the sorts of things I can do well.

I know I can reach a satisfactory standard.

I know I can do well enough if I try.

I feel great.

I know how to cope with the work.

NEGATIVE AFFECT

I feel depressed.

I feel lonely.

I feel restless.

I don't feel happy.

I sometimes get nervous.

0 20 40 60 80 100

SOURCE: IEA Reading Literacy Study, Finnish National Study data.
Table 4. Number of students participating in this comparison

Countries compared Number of students

 Finland

 Finnish-speaking schools 1,379

 Swedish-speaking schools (all schools, no sampling) 3,318

Total in Finland 4,697

Denmark 3,913

Iceland (all schools, no sampling) 3,855

Norway 2,307

Total in the Nordic countries 14,772

Germany/Western states 4,521

Germany/Eastern states 1,963

United States 3,476

Total in the combined countries 24,732

SOURCE: IEA Reading Literacy Study, Finnish National Study data.

5.1. How is the Quality of School Life in the Finnish-Speaking Majority Schools in Finland?

The results of students' self-assessment, as presented in Figure 1, show that Finnish 14-year-olds

experience the comprehensive school as a learning and growing environment that is partly inspiring and

partly depressing. However, general school satisfaction was clearly more common than was a negative

attitude towards school. Nearly half (49 percent) of the Finnish students reported that they enjoyed school

a lot. Another 49 percent said that they really liked to go to school. Nevertheless, few students (14

percent) agreed that they get satisfaction from the school work, even though 60 percent of the students

generally liked learning.

Negative attitudes towards school were nevertheless quite common. As many as 24 percent of the

students reported that they felt depressed at school, and even more (28 percent) felt restless. While 21

percent of the students were happy at school, loneliness was rarer (14 percent).

The students had a clear idea of how to achieve at school. Ninety-two percent of the students said

that they could succeed at school if they tried. They did not, however, all claim to have achieved success:

77 percent of the students stated that they achieved satisfactorily, and 74 percent reported that they did

well at school.

According to students, school was clearly experienced as a place for social growth. Ninety-one

percent thought that they had learned to get along with other people at school, and 81 percent stated that

they had learned to accept others the way they are.

210
The students also experienced their social status quite positively. Eighty-three percent of the

students said that other people had confidence in them, and 69 percent reported that others asked for their

help. Fifty-four percent felt important, 50 percent believed that people think a lot of them, and 47 percent

believed that people look up to them.

The students' relationship to the teachers was less positive than their sense of social status. Even

though 72 percent of the students thought that the teachers gave them the grades they deserved, only 49

percent of them stated that the teachers were fair and just. Furthermore, only 53 percent of the students

reported that the teachers listened to what they said, 56 percent stated that the teachers treated them fairly

in class, and 43 percent felt that the teachers helped them to do their best.

The results suggest that general well-being in Finnish schools is more common than general

dissatisfaction, although one-fourth of the students found school depressing. However, most Finnish

students seem to experience school as a place for many learning and achieving opportunities as well as

a place for social growth. Particularly, they learn to socialize with other students. Unfortunately, the

teacher-student relationship is not equally trustful, at least from the students' point of view.

5.2. How is the Quality of School Life in the Swedish-Speaking Minority Schools in Finland?

The general profile of the quality of school life in Swedish-speaking schools in Finland was quite

similar to the Finnish-speaking schools, as seen in Figure 2. The domains of social identity and

achievement and opportunity were rather similar, as was negative affect. Only the item "I sometimes get

upset," which may contain the translation error, showed significant dissimilarity.

Some differences in the profiles, however, could be found. The Swedish-speaking students

experienced their school life altogether more positively than their Finnish-speaking peers. General

satisfaction with school was more common (51 percent versus 43 percent) and negative affect clearly less

usual (22 percent versus 32 percent) in the Swedish-speaking schools than in the Finnish-speaking schools.

Particularly, learning was found to be more fun and school work more satisfying. 

The relationship between teachers and students was also more positive in the Swedish- than in

the Finnish-speaking schools. In particular, teachers were rated as more helpful (65 percent versus 43

percent). Furthermore, Swedish-speakers rated their status in school slightly more positively than their

Finnish-speaking peers. Feelings of importance (69 percent versus 54 percent) and the belief that others

have confidence in them were more common in Swedish-speaking schools.

In contrast, Finnish-speaking students' conceptions of potential achievement and opportunities at

school were more positive than Swedish-speaking students' views. In particular, Finnish students more

often felt great (74 percent versus 61 percent) and more of them believed that they could succeed if they

tried (91 percent versus 83 percent). Furthermore, Finnish-speaking students found the school more

influential on identity formation than did the Swedish-speaking students. Mixing with others seemed to

help the students in Finnish-speaking schools (81 percent) understand themselves more often than was the

case in the Swedish-speaking schools (62 percent).

The comparison between the Finnish- and Swedish-speaking quality profiles indicates significant

unity but also some diversity. Diversity, however, also can be found within the Swedish-speaking schools

if the profiles are drawn from the perspective of students with different language identity.

211
Figure 2. Percentage of students in Finland agreeing with the statement

GENERAL SATISFACTION

I really like to go to school.

I get satisfaction from the school work I do

I get enjoyment from being at school.

I find that learning is a lot of fun.

TEACHER-STUDENT RELATIONS

Teachers treat me fairly in class.

Teachers are fair and just.

Teachers listen to what I say.

Teachers help me to do my best.

Teachers give me the marks I deserve.

STATUS IN SCHOOL

People look up to me.

I know people think a lot of me.

I feel important.

People have confidence in me.

People come to me for help.

IDENTITY FORMATION

Mixing with others helps me understand myself.

I get to know myself better.

I learn to get along with other people.

I learn a lot about myself.

I have learned to accept other people.

ACHIEVEMENT AND OPPORTUNITY

I know the sorts of things I can do well.

I know I can reach a satisfactory standard.

I know I can do well enough if I try.

I feel great.

I know how to cope with the work.

NEGATIVE AFFECT

I feel depressed.

I feel lonely.

I feel restless.

I don't feel happy.

I sometimes get nervous.

0 20 40 60 80 100

Swedish-speaking

Finnish-speaking Schools

SOURCE: IEA Reading Literacy Study, Finnish National Study data.
5.3. Students' Language Identity and the Quality of School Life in the Swedish-Speaking Schools

In the Swedish-speaking schools in Finland, the students' home language may vary to great extent.

The home language may be Swedish or Finnish, or the home may be bilingual. Likewise, the students'

own language identity may vary. In this study, 62 percent of the students assessed themselves as Swedishspeakers, 36 percent thought they were bilingual, and 2.4 percent considered themselves as Finnishspeakers.

How did the students with different language identities experience the quality of school life in the

Swedish-speaking schools? The comparison of the quality profiles in different language groups (Figure

3) indicates that the students with monolingual Swedish or bilingual identity experience the quality of

school life more positively in almost all domains than do the students with the monolingual Finnish

background. The most striking distinctions between language groups were in the domains of general

school satisfaction, teacher-student relations, and achievement and opportunity. In negative affect,

however, the differences between language groups were not significant.

In brief, the Finnish-speaking students in the Swedish-speaking schools seem to enjoy school life

significantly less than do the monolingual Swedish-speakers and the bilingual students. This may be partly

caused by the language deficiency, which may evoke problems in social integration and also in teacherstudent interaction, both of which are strongly related to general school satisfaction (Linnakylä 1995).

5.4. Comparing Finnish Schools to the Other Nordic Schools

The students in the other Nordic countries participating in the study of the quality of school

life—Iceland, Norway, and Denmark—shared the Finnish students' views that resulted in data showing

general school satisfaction was stronger than general negative affect and that students had quite a distinct

view of how to work and succeed in school (Figure 4). Moreover, students in the compared countries

generally agreed that school was important in the growth of their social identity. However, there were

some differences in the Finnish and other Nordic schools, and the differences between the other Nordic

and Finnish schools were greater for the Finnish- than the Swedish-speaking schools in Finland.

The students in the other Nordic countries experienced their school life partly more and partly less

positively than their Finnish peers. General satisfaction and good relationship between students and

teachers were more common in the other Nordic countries than in Finland. Correspondingly, the negative

attitude towards school was less common in the other Nordic countries. In contrast, students' views of

potential achievement and opportunities at school were more positive in Finland than in the other Nordic

countries. Likewise, the students' social status in school and identity development were assessed more

positively in both types of Finnish schools than in the other Nordic schools.

The Swedish-speaking schools in Finland proved to some extent more similar to the other Nordic

schools than to the Finnish majority schools. In three domains—general school satisfaction, teacher-student

relations, and negative affect—the quality of school life in Swedish-speaking schools clearly resembled

the Nordic profile. However, in three other domains—social status, social identity, and achievement and

opportunity—the Swedish-speaking schools corresponded more closely to the Finnish-speaking schools.

212
Figure 3. Percentage of students in Finland agreeing with the statement, by students’ language

identity

GENERAL SATISFACTION

I really like to go to school ***

I get enjoyment from being at school ***

I find that learning is lot of fun ***

TEACHER-STUDENT RELATIONS

Teachers help me to do my best ***

Teachers are fair and just ***

Teachers listen to what I say **

Teachers give me the marks I deserve ***

STATUS IN SCHOOL

People look up to me **

I feel important ***

People have confidence in me *

People come to me for help ***

IDENTITY FORMATION

I learn to get along with other people **

I learn a lot about myself **

I have learned to accept other people **

ACHIEVEMENT AND OPPORTUNITY

I know I can reach a satisfactory standard ***

I know I can do well enough if I try ***

I feel great ***

I know how to cope with the work ***

0 20 40 60 80 100

Students' language identity

Finnish Bilingual Swedish

61.9% 35.7 % 2.4%

Significance levels: (comparing 

Finnish-speaking students with 

others):

* = .05

** = .01

*** = .001

SOURCE: IEA Reading Literacy Study, Finnish National Study data.
Figure 4. Percentage of students in Finland and other Nordic countries agreeing, by factor scale

0 20 40 60 80 10

Finland/Finnish Finland/Swedish Nordic countries (Iceland, Norway, Denmark)

TEACHER-STUDENT RELATIONS

43

51

54

61

64

47

85

82

74

GENERAL SATISFACTION

ACHIEVEMENT AND OPPORTUNITY

STATUS IN SCHOOL

 IDENTITY FORMATION

NEGATIVE AFFECT

Finland/Finnish

Finland/Swedish

Other Nordic countries

Finland/Finnish

Finland/Swedish

Other Nordic countries

55

64

65

78

75

67

Finland/Finnish

Finland/Swedish

Other Nordic countries

Finland/Finnish

Finland/Swedish

Other Nordic countries

Finland/Finnish

Finland/Swedish

Other Nordic countries

Finland/Finnish

Finland/Swedish

Other Nordic countries

32

22

20

SOURCE: IEA Reading Literacy Study, Finnish National Study data.
Particularly, developing social status in class was more strongly agreed upon among the Swedish-speaking

students than among their Finnish-speaking counterparts. Thus, the status domain distinctly deviated from

the Nordic profile.

The comparison of the findings suggest that the Swedish-speaking schools in Finland partly reflect

typical Finnish school culture with high achievement orientation and emphasis on students' social

integration and identity development. The Swedish-speaking schools, however, reflect the Nordic quality

of school life with more positive attitude toward school in general and a warmer relationship between

students and teachers.

5.5. Comparing Finnish Schools with German and U.S. Schools

The factor scales, as seen in Figure 5, indicate that school life in the countries compared here has

both similarities and differences. The students of all countries shared the view that general school

satisfaction was stronger than general negative affect, and that the students had quite a distinct picture of

how to work and succeed in school. Moreover, students in the compared countries generally agreed that

school was important in the growth of one's social identity.

However, there were some distinct differences in both general school satisfaction and different

domains of school life. In general satisfaction, the American and other Nordic students were clearly more

positive towards school life than Finnish students, particularly in the Finnish-speaking schools. However,

German students in both the Eastern and Western states were even less satisfied with their schools.

Accordingly, negative affect towards school was strongest in the Finnish-speaking schools in Finland and

in the German schools. Dissatisfaction was clearly weaker in the United States, in the other Nordic

countries, and in the Swedish-speaking schools in Finland.

The same profile favoring the U.S., Nordic, and the Swedish-speaking schools in Finland could

be found in the teacher-student-relationship. Among the compared countries, students' relationships to

their teachers were the most negative in Finnish-speaking schools in Finland and almost as negative in

Germany. The most positive was the relationship in the United States.

A different type of profile, however, was found in the domains of achievement and opportunity,

as well as in the domains of social identity and status in class. From the perspective of achievement and

opportunity, the Finnish schools, particularly the Finnish-speaking system, were regarded by students most

positively among compared school systems. In this area, Finnish students resembled Americans. A similar

profile was found in the assessment of social identity. The Americans and the Finns in the Finnishspeaking schools assessed their status in class most positively; the Germans, least positively. Almost the

same type of diversity also emerged in the domain of status in class. However, in this domain the U.S.

and the Swedish-speaking schools in Finland had the most positive attitude. The most negative attitude

was again found in Germany.

In all, Finnish students in both Finnish- and Swedish-speaking schools seemed to have some unity

and also some diversity in their assessment of the quality of school life. Differences seemed to reflect two

different school cultures—perhaps also different cultural codes—the more enjoyable Nordic and U.S.

school culture as well as the less enjoyable German school life.

Finnish-speaking students' assessment of the quality of school life resembled German students'

attitudes toward school both in general school satisfaction and negative affect, as well as in the domain

213
Figure 5. Percentage of students in Finland and all comparison countries agreeing with the statement,

by general factor scales

0 20 40 60 80 100

Finland/Finnish Finland/Swedish Nordic countries 1) Germany/W Germany/E USA

1) Iceland, Norway, Denmark

TEACHER-STUDENT RELATIONS

43

51

54

35

32

56

61

64

47

35

40

64

85

82

74

58

63

79

32

22

20

29

29

23

GENERAL SATISFACTION

ACHIEVEMENT AND OPPORTUNITY

STATUS IN SCHOOL

IDENTITY FORMATION

NEGATIVE AFFECT

Finland/Finnish

Finland/Swedish

Other Nordic countries

Germany/W

Germany/E

USA

55

64

65

59

60

68

78

75

67

59

68

76

Finland/Finnish

Finland/Swedish

Other Nordic countries

Germany/W

Germany/E

USA

Finland/Finnish

Finland/Swedish

Other Nordic countries

Germany/W

Germany/E

USA

Finland/Finnish

Finland/Swedish

Other Nordic countries

Germany/W

Germany/E

USA

Finland/Finnish

Finland/Swedish

Other Nordic countries

Germany/W

Germany/E

USA

Finland/Finnish

Finland/Swedish

Other Nordic countries

Germany/W

Germany/E

USA

SOURCE: IEA Reading Literacy Study, Finnish National Study data.
of teacher-student relations. In these domains, Swedish-speaking students' attitudes were more similar to

the views of the students in the other Nordic countries and in the United States. By contrast, the peer

relations, social identity, and social status in class were assessed in both Finnish school systems similarly

as in the United States and more positively than in the other Nordic countries or Germany. Likewise, the

importance of achievement and opportunity was seen more clearly in Finland in both school systems and

in the United States than in the other Nordic countries and Germany.

6. Conclusions

The results of the assessment on the quality of school life indicate many similarities as well as

some differences in the quality profiles in the Finnish- and Swedish-speaking schools in Finland. School

life was experienced as inspiring or depressing in almost the same domains in both Finnish school systems

and all compared countries. General school satisfaction was stronger than negative attitudes toward school.

Furthermore, the students had a fairly clear idea of how to work, achieve, and succeed in school, and they

also saw the importance of the school for the growth of their social identity.

Some diversity among Finnish school systems as well as among the compared countries, however,

appeared both in general profiles and within all quality domains. In Finnish school systems, diversity was

most obvious in teacher-student relations as well as in general school satisfaction and dissatisfaction. In

these respects the Swedish-speaking schools in Finland resembled the other Nordic and the U.S. schools.

The Finnish-speaking schools, however, corresponded in these domains to the German schools.

In the other domains—status in class, social identity, and achievement orientation—the Swedishand Finnish-speaking schools were more alike and resembled the U.S. rather than the Nordic or German

schools. In these domains, the German schools, particularly in Western states, had an exceptionally low

quality of school life.

In all, the findings suggest that the quality of school life in the Swedish-speaking schools in

Finland is more positive than in the Finnish-speaking schools. The culture of the Swedish-speaking

schools seems to have many characteristics similar to the other Nordic schools. Particularly, relations

between teachers and students proved warmer and more trustful in the Swedish-speaking school system

in the same manner as in the Nordic schools. In other studies it has been proved that the teacher-student

relationship is crucial to the general school satisfaction (Hoffman 1991; Linnakylä 1995). We also have

to keep in mind that the parents of the Swedish-speaking students have a higher educational background

and a better socioeconomic status; they are more often academically educated than the Finnish-speakers'

parents (Brunell and Linnakylä 1994). This may have an effect on the students' school motivation, status

in class, and quality of school life.

However, compared to the students in other countries, the Finnish students in both systems found

the school as a place for peer relations and for their own social growth. Moreover, the Finnish students

had a particularly clear idea of how to work and succeed in school. In this respect the Finnish schools

even exceeded the U.S. schools.

Dissatisfaction towards school, however, is still strong in Finland in the Finnish-speaking schools.

Even though the students find learning in general quite enjoyable and they seem to realize the relevance

of schooling and achieving in order to succeed, they still do not particularly like school nor especially the

kind of tasks it sets.

214
An important research task is to find the reasons for this relatively strong negativity in the

Finnish-speaking school culture. With a better insight into this phenomenon, we would be in a better

position to do something about this pattern of negativity and to create a more positive attitude towards

school, especially towards learning tasks, and to improve relationship between students and teachers. In

this respect, the Swedish-speaking schools and the other Nordic and U.S. schools could be used to model

some changes for the Finnish-speaking schools.

On the other hand, we must remember that the Finnish-speaking comprehensive school has done

rather well in achieving good academic learning standards (Elley 1994; Keeves 1992). Perhaps we have

made our Finnish-speaking schools into a demanding but depressing learning environment (cf. Lundberg

and Linnakylä 1992). Perhaps teacher-centeredness and external control are still so strong that our students

cannot see teachers as encouraging mentors. Maybe the German and Baltic traditions in the Finnishspeaking schools are stronger than in the Swedish-speaking schools, which have always had close

connections to the Scandinavian school culture (Iisalo 1991).

How could we develop our Finnish-speaking schools into more positive, cooperative, and

enjoyable environments for learning and growing without risking high achievement and good peer

relations between students? Could teaching methods that emphasize the students' own experiences and

interests, as well as cooperative learning and joint responsibility, be part of the solution? Could the

teacher be looking more for the students' strengths and talents rather than their weaknesses and mistakes?

Should we, accordingly, develop our assessment methods by favoring personal performance and portfolios

rather than normative tests? Should we break the tradition of school subjects and integrate our curriculum

more closely with the issues and problems of real life and students' own culture? There are many

questions, but there are also many opportunities to explore for those who develop our schools. How

actively these opportunities are tried out is, of course, up to each school and each individual teacher, but

it is also a challenge for comparative research, calling for broad-minded interdisciplanary effort and

cooperation with the Swedish-speaking, other Nordic, and U.S. schools.

Dissatisfaction can, of course, be fruitful and act as agent for change to create a new Finnish

school where academic achievement will still be excellent and where students as well as teachers will

experience joy in learning, working, and growing together.

215
References

Ainley, J. (1986). Influences on student views of the quality of school life. Paper presented in the

Conference of the Australian Association for Research in Education. Melbourne.

Ainley, J., and Bourke, S. (1989). Student views of primary schooling. Hawthorn, Vic.: ACER .

Brunell, V., and Linnakylä, P. (1994). Swedish speakers' literacy in the Finnish society. Journal of

Reading, 37(5), 368-375.

Csikszentmihalyi, M. (1990). Flow. The psychology of optimal experience. New York: Harper and Row.

Deci, E.L. (1975). Intrinsic motivation. New York: Plenum Press.

Elley, W. (1994). The IEA Study of Reading Literacy: Achievement and instruction in thirty-two school

systems. Oxford: Pergamon.

Fraser B.J. (1986). Classroom environment. London: Croom Helm.

Fägerlind, I., and Munck, I. (1981). Attitudes towards school and education in seven countries. Reports,

52. University of Stockholm. Institute of International Education. 

Gage, N.L., and Berliner, D.C. (1988). Educational psychology. Boston: Houghton Mifflin.

Gerson, E.M. (1976). On quality of life. American Sociological Review, 41, 793-806.

Hoffman, J.V. (1991). Teacher and school effects in learning to read. In R. Barr, M.L. Kamil, P.

Mosenthal, and P.D. Pearson (eds.). Handbook of reading research, Vol. II, 911-950, New York:

Longman. 

Husen, T. (1973). Svensk skola i internationell belysning. [Swedish school in international view].

Stockholm: Almqvist and Wiksell.

Iisalo, T. (1991). Kouluopetuksen vaiheita. [History of school teaching] Helsinki: Otava.

Keeves, J.P. (1992). Learning science in a changing world. Cross-national studies of science achievement:

1970 to 1984. The Hague: The International Association for the Evaluation of Educational

Achievement.

Land, K.C., and Spilerman, S. (eds.). (1975). Social indicator models. New York: Russell Sage

Foundation.

Linnakylä, P. (1993). Exploring the secret of Finnish reading literacy achievement. Scandinavian Journal

of Educational Research, 37(1), 63-74.

Linnakylä, P. (1995). Quality of school life in the Finnish comprehensive school. A comparative view.

Scandinavian Journal of Educational Research, 39(3). 

Lundberg, I., and Linnakylä, P. (1992). Teaching reading around the world. The Hague: The International

Association for the Evaluation of Educational Achievement.

216
Schleicher, A., and Siniscalco, M. (1991). The quality of school life. Unpublished manual. University of

Hamburg. International Coordination Center of the IEA Reading Literacy Study.

Weiner, B. (1986). An attributional theory of motivation and emotion. New York: Springer-Verlag.

Williams, T., and Batten, M. (1981). The quality of school life. ACER Research Monograph No. 12.

Hawthorn, Vic.: ACER.

217
Analysis of the Williams and Batten Questionnaire on the 

Quality of School Life in Spain

Guillermo A. Gil

Instituto Nacional de Calidad y Evaluación, Spain

1. Introduction

The aim of this paper is to examine the suitability of the Quality of School Life scale developed

by Williams and Batten (1981) for describing education in Spain. A number of analyses were performed

for the following purposes:

1. Examining the usefulness of the scale translated to Spanish with pupils engaged in the

eighth course of the General Basic Education (equivalent to eighth grade), with the view

to comparing the emergent factors and the assignment of the questionnaire's items to these

factors found by Williams and Batten in their study.

2. Ascertaining the equivalence of the measure when translated into the four languages of

Spain (Castilian—the predominant language, Catalonian, Galician, and Valencian). 

3. Estimating the reliability of the test as a whole and of the separate subscales.

4. Estimating the relationships between an overall measure of the Quality of School Life

scale and reading performance and other variables of interest. 

The data on which these analyses are based were collected through the IEA's International

Reading Literacy Study.

2. Williams and Batten's Quality of School Life Scale

The Williams and Batten Quality of School Life scale was developed initally in the early 1980s

by administering a questionnaire on two successive occasions using two different samples of Australian

pupils. When it was first administered, a version with 71 items was used; a reduced version with 42 items

was used the second time. Factor analysis was employed to identify the latent structure of those items

and to refine the item pool by eliminating those that made little contribution to the main clusters of items

identified. The analysis provided evidence for six theoretical constructs. It also showed the validity of

the several scales (defined as the correlation between the item composite and the associated constructs)

to be about .90. The authors concluded that their model, with slight modifications, fit the data adequately.

The final measure was based on items that identified six factors. 

The questionnaire is based on 29 questions to which the pupils reply in terms of a 4-point agreedisagree Likert scale. The questionnaire takes the form of an initial statement (School is a place where...),

a short paragraph of instructions, and finally the list of statements. 

The six underlying factors of the model, are as follows:

1. General Affect. Five items: (3) I really like to go; (21) I feel happy; (22) I find that

learning is a lot of fun; (23) I get enjoyment from being there; (28) I feel great.

223
3. Status. Five items: (5) I feel important; (8) People have confidence in me; (10) People

come to me for help; (13) I know that people think a lot of me; (15) People look up to

me.

4. Identity. Five items: (4) Mixing with other people helps me to understand myself; (6) I

learn to get along with other people; (19) I learn a lot about myself; (25) I get to know

myself better; (29) I have learned to accept other people as they are.

5. Teachers. Five items: (2) Teachers are fair and just; (7) Teachers help me do my best;

(9) Teachers treat me fairly in class; (14) Teachers give me the marks I deserve; (20)

Teachers listen to what I say.

6. Opportunity. Five items: (1) I know how to cope with the work; (17) I know I can reach

a satisfactory standard in my work; (18) I know the sorts of things I can do well; (24) I

get satisfaction from the school work I do; (26) I know I can do well enough to be

successful.

The final results obtained by Williams and Batten from the factor analysis are shown in Table 1.

The reliability of the scale was calculated using Heise and Bohnstead's Omega coefficient. The results

obtained for the subscales were .83 for general affect, .72 for negative affect, .86 for status, .74 for

identity, .79 for teachers, and .76 for opportunity.

3. Study of the Quality of School Life Scale in Spain

3.1 Sample

In implementing this study, the questionnaire on the Quality of School Life was administered to

eighth grade pupils aged 13-15. In the context of the IEA Reading Literacy Study, that population was

deemed appropriate for the study of reading achievement and for the analysis of the effects of the school,

family, and personal variables affecting it, since this is the final stage of basic education and nearly all

children of this age are still at school.

To select the sample, a nonproportional allocation technique was employed in those autonomous

communities that have responsibilities in the field of education, and a proportional technique was used

in the territory for which the Ministry of Education and Science is responsible. Accordingly, the sample

as a whole took the form of a mixed proportional/nonproportional sample design. In distributing the

sample, the reference features taken were the control of the school (state/private) and the size of the

community (over 20,000 inhabitants, between 20,000 and 100,000 inhabitants, and over 100,000

inhabitants). The distribution of the sample selected is as shown in Table 2.

In addition, the questionnaire was administered to samples of pupils from Spanish autonomous

communities having their own language—Catalonia, Valencia, and Galicia. Students in 34 schools were

tested in each of these autonomous communities for a total of 3,147 students tested in these vernacular

languages—1,099 in Catalonian, 936 in Galician, and 1,112 in Valencian.

224
Table 1. Factor analysis: Results of the analysis of the Williams and Batten questionnaire 

Factor Item Statements CA

FA h

2

*

GENERAL

 AFFECT

(G)

3 I really like to go .56 .46

21 I feel happy .58 .57

22 I find that learning is a lot of fun .56 .50 O

23 I get enjoyment from being there .68 .63

28 I feel great .63 .60

NEGATIVE

AFFECT

 (N)

11 I feel lonely .58 .39

12 I feel restless .43 .44 G

16 I feel depressed .70 .59

27 I get upset .52 .30

STATUS

(S)

5 I feel important .43 .42 G

 8 People have confidence in me .46 .38

10 People come to me for help .50 .35

13 I know that people think a lot of me .65 .56

15 People look up to me .71 .55 G

IDENTITY

(I)

4 Mixing with other people helps me to understand myself .54 .40

6 I learn to get along with other people .52 .39

19 I learn a lot about myself .58 .48

25 I get to know myself better .63 .54

29 I have learnt to accept other people as they are .44 .26

TEACHERS

(T)

2 Teachers are fair and just .67 .51

7 Teachers help me to do my best .54 .42

 9 Teachers treat me fairly in class .77 .65

14 Teachers give me the marks I deserve .60 .40

20 Teachers listen to what I say .61 .49

 

OPPORTUNITY

(O)

1 I know how to cope with the work .46 .30

17 I know I can reach a satisfactory standard in my work .67 .54

18 I know the sorts of things I can do well .48 .30

24 I get satisfaction from the school work I do .41 .40 G

26 I know I can do well enough to be successful .71 .58

CA FA = Factorial weight.

h

2

 = Communality.

* = Departures from simple structure; loadings greater than 0.4 on factors identified.

SOURCE: Williams, T., and Batten, M. (1981). The Quality of School Life. Hawthorn, Vic.: Australian Council

for Educational Research.

225
Table 2. Sample of the study: Eighth grade

 Autonomous communities Schools Students

 ANDALUSIA . . . . . . . . . . . . . . . . . . . . . 37 1,096

 CANARY ISLANDS . . . . . . . . . . . . . . . . 38 1,036

 CATALONIA . . . . . . . . . . . . . . . . . . . . . 39 1,121

 GALICIA . . . . . . . . . . . . . . . . . . . . . . . . 38 1,041

 BASQUE COUNTRY . . . . . . . . . . . . . . . 36 1,018

 VALENCIA . . . . . . . . . . . . . . . . . . . . . . 35 1,050

 MEC* . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 1,644

 NAVARRE . . . . . . . . . . . . . . . . . . . . . . . 40 1,024

 TOTAL . . . . . . . . . . . . . . . . . . . . . . . . . . 318 9,030

*Autonomous communities administered by the Ministry of Education and Science.

SOURCE: IEA Reading Literacy Study, Spanish National Study data, Spanish Ministry of Education and

Science, 1991.

3.2 Results

3.2.1 Underlying Factors in the Questionnaire

Principal Components Analysis was used to elicit the latent structure of these data in the Castilian

sample. Analogous analyses were undertaken using the data obtained from Catalonian, Galician, and

Valencian samples in order to ascertain the equivalence of the latent structures in the four different

languages. The eigenvalue-one criterion was used to select the numbers of factors to be rotated. An

oblimin rotation was used since the theoretical assumption was that the factors would be correlated.

From the total Castilian sample, six factors were identified (corresponding very closely with the

Williams and Batten factors shown in Table 1). After analyzing the content of each item allocated to each

factor, it was deemed advisable to rename the factors, since the terminology employed by Williams and

Batten did not seem to be wholly appropriate in Castilian. The idea underlying the sets of factor items

was expressed in Castilian in this way:

Original name in English New name in English New name in Castilian

GENERAL AFFECT POSITIVE FEELINGS SENTIMIENTOS POSITIVOS

NEGATIVE AFFECT NEGATIVE FEELINGS SENTIMIENTOS NEGATIVOS

STATUS SELF-ESTEEM AUTOESTIMA

IDENTITY LIVING TOGETHER CONVIVENCIA

TEACHERS RELATION WITH THE

TEACHERS

RELACIONES CON EL

PROFESORADO

OPPORTUNITY SELF-CONFIDENCE AUTOCONFIANZA

226
The results of the factor analysis for the sample in Castilian are shown in Table 3. Correlations

among factors ranged between -.36 and .32, thus giving support to the assumption that correlations among

factors do exist. With respect to the assignment of the items to the factors of the Williams and Batten

model, 27 of the 29 items loaded in the same way. In the case of the two items not corresponding to

expectations, item 1 showed a low factor loading, and item 24 did not load on the same factor with which

it was associated in the original model. In view of these results, it can be concluded in general terms that

the model of Williams and Batten is suitable, because the match between its factor structure and that

obtained through analysis of the scale in Castilian was confirmed. Nevertheless, it was deemed advisable

to place item 24 in the Positive Feelings factor rather than regarding it as part of the Self-Confidence

factor, and this was done for the subsequent analyses for this study. 

Subsequently, similar analyses were undertaken with samples of pupils responding to the

Catalonian, Galician, and Valencian translations of the questionnaire. The results obtained are reported

in Tables 4, 5, and 6 and reveal slight differences in the assignment of items to the factor structure as

compared with the test in Castilian. The origin of these differences might lie in the translation process,

which may have introduced some different nuances of meaning. It was therefore deemed advisable to

carry out a third series of factor analyses using the students from the autonomous communities of Galicia,

Catalonia, and Valencia tested in Castilian. The assignment of factors to questionnaire items as given by

the results of these factor analyses are shown in Tables 7, 8, and 9. The results of these additional

analyses are summarized in Tables 10 and 11 and are discussed taking the results obtained in the factor

analysis of the sample tested in Castilian as the point of reference, since the most reliable results are those

from that sample of 8,485 pupils. From these results it can be judged that the test possesses indicators for

the six factors found by Williams and Batten in the original version. In light of these analyses, it would

seem to be appropriate to define the Castilian version of Williams and Batten's Quality of School Life

scale as having 28 items, with item 24 being regarded as part of the Positive Feelings factor and item 1

being omitted.

• The factor corresponding to Negative Feelings (comprising items 11, 12, 16, and 27) and

the Relations with the Teachers factor (comprising items 2, 7, 9, 14, and 20) showed the

same factor structure in all samples—those tested in Castilian and those tested in the

native languages of Galicia, Catalonia, and Valencia (see Table 10). 

• The Self-Esteem factor (comprising items 5, 8, 10, 13, and 15) revealed an identical

factor structure for all the samples tested in Castilian and for the sample tested in

Valencian (see Table 11). 

• The Living Together factor (items 4, 6, 19, 25, and 29) showed an identical factor

structure for the general sample tested in Castilian and for the Galician and Valencian

samples tested in the Castilian language (see Table 11). 

• The Positive Feelings factor (items 3, 21, 22, 23, 24, and 28) showed an identical factor

structure in the general sample tested in Castilian, in the Valencian sample tested in

Valencian, and in the Galician sample tested in the Castilian language (see Tables 10 and

11).

• The Self-Confidence factor (items 17, 18 and 26) revealed an identical factor structure

for the sample tested in Castilian, the Catalonian sample tested in Catalonian, and the

Galician sample tested in the Castilian language (see Tables 10 and 11).

227
Table 3. Factor analysis: Sample tested in Castilian (N= 8,485)

Factor Item Statements CA

FA h

2

*

POSITIVE 

FEELINGS

(P)

3 I really like to go -.75 .57

21 I feel happy -.47 .50

22 I find that learning is a lot of fun -.69 .57

23 I get enjoyment from being there -.79 .70

24 I get satisfaction for the school work I do -.64 .60

28 I feel great -.44 .49

NEGATIVE

FEELINGS

(N)

 

11 I feel lonely .73 .56

12 I feel restless .75 .55

16 I feel depressed .78 .61

27 I feel upset .74 .58

SELFESTEEM

(SE)

5 I feel important .46 .34

 8 People have confidence in me .67 .53

10 People come to me for help .58 .38

13 I know that people think a lot of me .65 .44

15 People look up to me .69 .52

LIVING

TOGETHER

(L)

4 Mixing with other people helps me to understand -.81 .60

6 I learn to get along with other people -.70 .54

19 I learn a lot about myself -.62 .53

25 I get to know myself better -.68 .59

29 I have learnt to accept other people as they are -.40 .34

RELATIONS

WITH THE

TEACHERS

(T)

 

2 Teachers are fair and just .73 .57

7 Teachers help me to do my best .68 .58

 9 Teachers treat me fairly in class .74 .64

14 Teachers give me the marks I deserve .70 .53

20 Teachers listen to what I say .66 .53

SELFCONFIDENCE

(SC)

1 I know how to cope with the work .26 .24

17 I know I can reach a satisfactory standard in my

work .79 .63

18 I know the sorts of things I can do well .71 .58

26 I know I can do well enough to be successful .72 .55

CA FA = Factorial weight.

h

2

 = Communality.

* = Loading greater than 0.4 on second factor indicated.

SOURCE: IEA Reading Literacy Study, Spanish National Study data, Spanish Ministry of Education and

Science, 1991.

228
Table 4. Factor analysis: Catalonian sample tested in Catalonian (N=1,099)

Factor Item Statements CA

FA h

2

*

POSITIVE

FEELINGS

(P)

3 I really like to go -.85 .71

22 I think that learning is a lot of fun -.72 .59

23 I get enjoyment from being there -.89 .76

24 I get satisfaction from the school work I do -.46 .50

NEGATIVE

FEELINGS

(N)

11 I feel lonely .76 .61

12 I feel restless .69 .52

16 I feel depressed .83 .69

27 I feel upset .76 .63

SELF-ESTEEM

(SE)

5 I feel important .75 .54

13 I know that people think a lot of me .79 .59

15 People look up to me .61 .46

 LIVING

TOGETHER

(L)

4

Mixing with other people helps me to understand

myself -.68 .52

6 I learn to get along with other people -.72 .60

8 People have confidence in me -.45 .48

10 People come to me for help -.42 .53

RELATIONS

WITH

 THE

TEACHERS

(T)

2 Teachers are fair and just -.78 .64

7 Teachers help me to do my best -.62 .50

 9 Teachers treat me fairly in class -.83 .67

14 Teachers give me the marks I deserve -.64 .48

20 Teachers listen to what I say -.76 .60

 SELFCONFIDENCE

(SC)

1 I know how to cope with my work .36 .47

17 I know I can get a satisfactory standard in my work .81 .68

18 I know the sorts of things I can do well .63 .56

26 I know I can do well enough to be successful .79 .66

LIVING

TOGETHER

GENERAL

(LG)

19 I learn a lot about myself .41 .52 SE

21 I feel happy .45 .61

25 I get to know myself better .44 .51

28 I feel great .46 .60

29 I have learnt to accept other people as they are .49 .44

CA FA = Factorial weight.

h

2

 = Communality.

* = Loading greater than 0.4 on second factor indicated.

SOURCE: IEA Reading Literacy Study, Spanish National Study data, Spanish Ministry of Education and

Science, 1991.

229
Table 5. Factor analysis: Galician sample tested in Galician (N=936)

Factor Item Statements CA

FA h

2

*

POSITIVE

FEELINGS

(P)

3 I really like to go -.75 .53

22 I find that learning is a lot of fun -.72 .60

23 I get enjoyment for being there -.71 .67

24 I get satisfaction from the school work I do -.70 .55

25 I get to know my self better -.36 .47

NEGATIVE

FEELINGS

(N)

11 I feel lonely .73 .62

12 I feel restless .80 .64

16 I feel depressed .72 .56

27 I feel upset .69 .50

SELF-ESTEEM

 (SE)

 8 People have confidence in me -.54 .51

10 People come to me for help -.78 .62

13 I know that people think a lot of me -.50 .49

15 People look up to me -.56 .58

S

E

2

LIVING

TOGETHER

(L)

4 Mixing with other people help me to understand

myself .39 .40

6 I learn to get along with other people .44 .45

 21 I feel happy .38 .56

28 I feel great .45 .61

29 I have learnt to accept other people us they are .64 .52

RELATIONS

WITH

THE

TEACHERS

(T)

2 Teachers are fair and just -.74 .60

7 Teachers help me to do my best -.72 .59

 9 Teachers treat me fairly in class -.70 .58

14 Teachers give me the marks I deserve -.71 .57

20 Teachers listen to what I say -.68 .57

SELFCONFIDENCE

(SC)

1 I know how to cope with the work .62 .42 

17 I know I can reach a satisfactory standard in my

work .71 .51

18 I know the sorts of things I can do well .68 .55

19 I learn a lot about myself .62 .49

26 I know I can do well enough to be successful .58 .47

SELF-ESTEEM.2

(SE2)

 5 I feel important .79 .65

CA FA = Factorial weight.

h

2

 = Communality.

* = Loading greater than 0.4 on second factor indicated.

SOURCE: IEA Reading Literacy Study, Spanish National Study data, Spanish Ministry of Education and

Science, 1991.

230
Table 6. Factor analysis: Valencian sample tested in Valencian (N=1,112)

Factor Item Statements CA

FA h

2

*

POSITIVE

FEELINGS

(P)

3 I really like to go .75 .61

21 I feel happy .43 .50

22 I find that learning is a lot of fun .64 .56

23 I get enjoyment for being there .84 .73

24 I get satisfaction from the school work I do .74 .68

28 I feel great .43 .52

NEGATIVE

FEELINGS

(N)

11 I feel lonely .76 .57

12 I feel restless .75 .55

16 I feel depressed .74 .57

27 I feel upset .68 .52

SELFESTEEM

(SE)

5 I feel important .53 .41 P

8 People have confidence in me .57 .48

10 People come to me for help .59 .38

13 I know that people think a lot of me .56 .41

15 People look up to me .63 .46

LIVING

TOGETHER

(L)

4

Mixing with other people helps me to understand

myself -.71 .54

6 I learn to get along with other people -.62 .52

19 I learn a lot about myself -.59 .53

25 I get to know myself better -.73 .63

RELATIONS

WITH

THE

TEACHERS

(T)

2 Teachers are fair and just -.69 .55

7 Teachers help me do my best -.75 .60

 9 Teachers treat me fairly in class -.74 .63

14 Teachers give me the marks I deserve -.69 .53

20 Teachers listen to what I say -.69 .58

SELFCONFIDENCE

(SC)

1 I know how to cope with my work .34 .34 

17 I know I can reach a satisfactory standard in my

work .83 .67

18 I know the sort of things I can do well .64 .60

26 I know I can do well enough to be successful .67 .50

29 I have learnt to accept other people as they are .40 .39

CA FA = Factorial weight.

h

2

 = Communality.

* = Loading greater than 0.4 on second factor indicated.

SOURCE: IEA Reading Literacy Study, Spanish National Study data, Spanish Ministry of Education and

Science, 1991.

231
Table 7. Factor analysis: Catalonian sample tested in Castilian (N=1,121) 

Factor Item Statements CA

FA h

2

*

POSITIVE

FEELINGS

(P)

3 I really like to go -.77 .58

22 I find that learning is a lot of fun -.73 .61

23 I get enjoyment from being there -.78 .70

24 I get satisfaction from the school work I do -.68 .60

NEGATIVE

FEELINGS

(N)

11 I feel lonely .78 .64

12 I feel restless .78 .63

16 I feel depressed .73 .58

27 I feel upset .70 .61

SELF-ESTEEM

(SE)

5 I feel important .35 .46

 8 People have confidence in me .66 .52

10 People come to me for help .60 .46

13 I know that people think a lot of me .66 .49

15 People look up to me .73 .60

LIVING 

TOGETHER

(L)

4

Mixing with other people helps me to understand

myself -.77 .57

6 I learn to get along with other people -.64 .51

19 I learn a lot about myself -.58 .55

25 I get to know myself better -.69 .62

RELATIONS

WITH

THE

TEACHERS

(T)

2 Teachers are fair and just .67 .56

7 Teachers help me to do my best .70 .60

 9 Teachers treat me fairly in class .71 .66

14 Teachers give me the marks I deserve .67 .55

20 Teachers listen to what I say .62 .55

 SELFCONFIDENCE

(SC)

1 I know how to cope with my work .45 .35

17 I know I can reach a satisfactory standard in my

work .80 .66

18 I know the sorts of things I can do well .71 .59

26 I know I can do well enough to be successful .72 .54

LIVING

TOGETHER

GENERAL

(LG)

21 I feel happy -.57 .68

28 I feel great -.55 .66

29 I have learnt to accept other people as they are -.39 .43

CA FA = Factorial weight.

h

2

 = Communality.

* = Loading greater than 0.4 on second factor indicated.

SOURCE: IEA Reading Literacy Study, Spanish National Study data, Spanish Ministry of Education and

Science, 1991.

232
Table 8. Factor analysis: Galician sample tested in Castilian (N=1,041)

Factor Item Statements CA

FA h

2

*

POSITIVE

FEELINGS

(P)

3 I really like to go .69 .55

21 I feel happy .47 .57

22 I find that learning is a lot of fun .60 .54

23 I get enjoyment from being there .79 .72

24 I get satisfaction from the school work I do .68 .63

28 I feel great .41 .55

NEGATIVE

FEELINGS

(N)

11 I feel lonely .77 .64

12 I feel restless .74 .56

16 I feel depressed .74 .54

27 I feel upset .69 .51

SELFESTEEM

(SE)

5 I feel important .39 .39 P

 8 People have confidence in me .68 .53

10 People come to me for help .60 .37

13 I know that people think a lot of me .64 .46

15 People look up to me .66 .47

LIVING 

TOGETHER

(L)

4 Mixing with other people helps me to understand

myself -.71 .54

6 I learn to get along with other people -.69 .55

19 I learn a lot about myself -.59 .56

25 I get to know myself better -.72 .62

29 I have learnt to accept other people as they are -.34 .32

RELATIONS

WITH

THE

TEACHERS

(T)

2 Teachers are fair and just -.70 .54

7 Teachers help me to do my best -.69 .59

 9 Teachers treat me fairly in class -.72 .64

14 Teachers give me the marks I deserve -.70 .56

20 Teachers listen to what I say -.64 .57

 SELFCONFIDENCE

(SC)

1 I know how to cope with the work .17 .25 SE

17 I know I can reach a satisfactory standard in my

work .76 .62

18 I know the sorts of things I can do well .69 .57

26 I know I can do well enough to be successful .68 .52

CA FA = Factorial weight.

h

2

 = Communality.

* = Loading greater than 0.4 on second factor indicated.

SOURCE: IEA Reading Literacy Study, Spanish National Study data, Spanish Ministry of Education and

Science, 1991.

233
Table 9. Factor analysis: Valencian sample tested in Castilian (N=1,050)

Factor Item Statements CA

FA h

2

*

 POSITIVE

FEELINGS

(P)

3 I really like to go -.76 .59

22 I find that learning is a lot of fun -.65 .58

23 I get enjoyment from being there -.80 .72

24 I get satisfaction with the school work I do -.64 .64

NEGATIVE

FEELINGS

(N)

11 I feel lonely .77 .72

12 I feel restless .74 .56

16 I feel depressed .76 .62

27 I feel upset .70 .65

SELFESTEEM

(SE)

5 I feel important .56 .38 P

 8 People have confidence in me .68 .56

10 People come to me for help .55 .62

13 I know that people think a lot of me .67 .56

15 People look up to me .67 .62

LIVING

TOGETHER

(L)

4

Mixing with other people helps me to understand

myself .83 .66

6 I learn to get along with other people .69 .59

19 I learn a lot about myself .53 .52

25 I get to know myself better .58 .53

29 I have learnt to accept other people as they are .43 .41

RELATIONS

WITH

THE

TEACHERS

(R)

2 Teachers are fair and just -.75 .58

7 Teachers help me to do my best -.72 .61

 9 Teachers treat me fairly in class -.81 .71

14 Teachers give me the marks I deserve -.70 .52

20 Teachers listen to what I say -.70 .65

 SELFCONFIDENCE

(SC)

1 I know how to cope with my work .15 .30 

17 I know I can reach a satisfactory standard in my

work .50 .51

18 I know the sorts of things I can do well .45 .51

21 I feel happy .42 .62 P

26 I know I can do well enough to be successful .66 .55

28 I feel great .42 .55

CA FA = Factorial weight.

h

2

 = Communality.

* = Loading greater than 0.4 on second factor indicated.

SOURCE: IEA Reading Literacy Study, Spanish National Study data, Spanish Ministry of Education and

Science, 1991.

234
Table 10. Factor analysis: Samples tested in Castilian and samples tested in Catalonian, Galician,

and Valencian, by items associated with each subscale

Language Negative

Feelings

Relations

with the

Teachers

SelfEsteemLiving

Together

Living

Together,

General

Positive

Feelings

SelfConfiden

ce

N

Castilian . 11,12,16,2

7

2,7,9,

14,20

5,8,10,13,1

5

4,6,19,25,29 3,21,22,23,

24,28

17,18,26 8,485

Catalonian 11,12,16,2

7

2,7,9,14,

20

 5,13,15 4,6,8,10 19,21,25,28,

29

 3,22,23,24 

17,18,26 

1,099

Galician*11,12,16,2

7

2,7,9,14,

20

8,10,13,15 (4),6,(21),2

8,29

 3,22,23,24,(2

5)

1,17,18,1

9,

26

936

Valencian 11,12,16,2

7

2,7,9,14,

20

5,8,10,13,1

5

4,6,19,25 3,21,22,23,24

,

28

17,18,26,

29

1,112

*Item 5 was considered separately. This item had significance by itself (see Table 5).

NOTE: Figures in parentheses indicate items with loadings greater than 0.3 but less than 0.4.

SOURCE: IEA Reading Literacy Study, Spanish National Study data, Spanish Ministry of Education and

Science, 1991.

Table 11. Factor analysis: Samples tested in Castilian in Catalonia, Galicia, Valencia, and in the

other autonomous communities, by items associated with each subscale 

Region Negative

Feelings

Relations

with the

Teachers

SelfEsteemLiving

Together

Living

Together,

General

Positive 

Feelings

SelfConfidence N

Whole . . 11,12,16,2

7

2,7,9,

14,20

5,8,10,13,

15

4,6,19,25,

29

3,21,22,23,

24,28

17,18,26 8,485

Catalonia 11,12,16,2

7

2,7,9,14,

20

(5),8,10,13,

15

 4,6,19,25 

21,28,(29)

3,22,23,24 1,17,18,26 1,121

Galicia . . 11,12,16,2

7

2,7,9,14,

20

(5),8,10,13,

15

4,6,19,25,

(29)

 3,21,22,23,

24,28

17,18,26 1,041

Valencia . 11,12,16,2

7

2,7,9,14,

20

5,8,10,13,

15

4,6,19,25,

29

3,22,23,24 17,18,21,

26,28

1,050

NOTE: Figures in parentheses indicate items with loadings greater than 0.3 but less than 0.4.

SOURCE: IEA Reading Literacy Study, Spanish National Study data, Spanish Ministry of Education and

Science, 1991.

235
In relation to the samples tested in the vernacular languages of the autonomous communities, some

variations were found. For example, a variation in the sample tested in Catalonian was that items 8 and

10, usually associated with the Self-Esteem factor, were associated with the Living Together factor. Items

19, 21, 25, 28 and 29 formed a new factor, which was labeled "Living Together, General" (see Table 4).

A variation in the sample tested in Galician was that the item 5, usually incorporated to the Self-Esteem

factor, arose as an isolated item. Another variation in this sample was that items 21 and 28, associated

normally with the Positive Feelings factor, appeared under the Living Together factor. Similarly, item

19 became associated with the Self-Confidence factor instead of with the Living Together factor (see

Table 5). In the sample tested in Valencian, item 29, usually associated with the Living Together factor,

was associated with the Self-Confidence factor (see Table 6).

These variations in the results may be due in part to the process of translation, since they did not

arise when the same samples were tested in Castilian, nor did they arise in the general nationwide sample

tested in Castilian. It seems plausible, therefore, that these results can be explained in terms of problems

connected with shifts in the meaning of the items through translation.

Other variations also appeared in the analysis of the data for the samples of Catalonia, Galicia,

and Valencia tested in Castilian. In the Valencian sample, items 21 and 28, normally associated with

Positive Feelings, were instead associated with the Self-Confidence factor. This departure from

expectation would not appear to be particularly significant since it only arose in that sample. A second

departure from the general model arose in the Catalonian sample, where items 21, 28, and 29 formed a

group around the new factor, Living Together, General. 

It is interesting to note that, in general, most of the variation observed in the different analyses

relate to the items placed in the final part of the questionnaire, both for the populations tested in Castilian

and for the populations tested in the language of each of the autonomous communities.

3.2.2 Reliability

The reliability of the scale as a whole and of the various subscales was estimated, both for the

sample tested in Castilian and for the samples tested in the languages of Catalonia, Galicia, and Valencia.

The reliability of the scale for each sample was calculated through Cronbach's α coefficient. The

reliability for the national sample tested in Castilian was .85 for the scale as a whole and values lying

between .66 and .83 for the subscales. For the samples from the autonomous communities tested in

Galician, Catalonian, and Valencian, the reliabilities were estimated to lie between .87 and .83 for the

scale as a whole and between .62 and .85 for the subscales. It can be concluded then that the test was

highly reliable for the scale as a whole and adequate for the individual subscales.

On the basis of the results of the factor analysis, another estimate of reliability was carried out

without taking into account the effect of item 1, although eliminating this item did not affect the results.

The same reliability estimates were obtained for the national sample tested in Castilian in the calculations

based on 28 items as in those based on 29 items, except for the Self-Confidence factor for which the

reliability increased from .67 to .69. This result shows that item 1 can be eliminated from the test without

any loss of reliability. The results in terms of reliability rates, both for the national sample and for the

autonomous community samples, are shown in Tables 12 and 13.

236
Table 12. Reliability: National sample tested in Castilian 

Subscale α

COMPLETE SCALE .85

POSITIVE FEELINGS .83

NEGATIVE FEELINGS .77

SELF-ESTEEM .66

LIVING TOGETHER .77

RELATIONS WITH THE TEACHERS .83

SELF-CONFIDENCE .67

SOURCE: IEA Reading Literacy Study, Spanish National Study data, Spanish Ministry of Education and

Science, 1991.

Table 13. Reliability: Samples tested in Catalonian, Galician, and Valencian 

Subscale Galician Catalonian Valencian

COMPLETE SCALE .83 .84 .87

POSITIVE

FEELINGS .77 .81 .85

NEGATIVE

FEELINGS .72 .79 .73

SELF-ESTEEM .62 .68 .63

LIVING TOGETHER .68 .70 .77

RELATIONS WITH

THE TEACHERS .82 .79 .82

SELF-CONFIDENCE .67 .74 .73

SOURCE: IEA Reading Literacy Study, Spanish National Study data, Spanish Ministry of Education and

Science, 1991.

3.2.3 The Relationship Between Quality of School Life and Other Variables in the Educational Context

In order to exemplify some applications of the scale, the relationship between some variables in

the school environment and the factor weightings obtained by the pupils on the Quality of School Life

scale were estimated. For this purpose, a factor score derived from all 28 items of the final scale were

estimated. This factor score was calculated by multiplying the points given in each pupil's reply to each

item by its factor weighting in the first factor of the unrotated factor matrix. The scores were rescaled to

have a mean of zero. While the use of a single score such as this is useful on a summary measure, it is

also possible to use different factor scores for each one of the six obtained factors. Variables may show

different relationships across the six subscales. 

The variables analyzed were obtained from pupil or teacher questionnaires. For the purposes of

this section, one-way analysis of variance was used to calculate the significance of the differences

obtained, even though multivariate analyses would be more appropriate given the correlation existing

between many of these variables.

237
For example, the girls scored slightly higher than the boys in the Quality of School Life scale

(F=317,00; d.f.=1,8458; p≤.0000). Without taking other variables into account, it could be said that the

girls rate life in school more positively than do boys.

In the same way, in analyzing the variable "subsequent study expectations," which was evaluated

in the student questionnaire through the question, "For how many years more do you expect to go on

studying after you've finished this year?", we found that the Quality of School Life Scale score increased

in step with the number of years over which the pupils expected to carry on studying, there being a

significant relationship between these two variables (F=36,223; d.f.=6,8373; p≤.0000).

When attention was turned to the variable "time spent on homework," a uniform progression was

likewise noted; hence, in a seven-step scale (from "None" to "Over 5 hours"), the greater the time devoted

by the pupils to homework the higher the score on the Quality of School Life scale (F=31,47; d.f.=6,3658;

p≤.0000).

Studying the variable "time spent watching television outside school" also proved interesting in

that the less time the pupils spent watching television at home, the higher their scores in the scale

(F=10.86; d.f.=6.8436; p≤.0000). This makes sense when we consider the relationship between frequency

and quantity of homework and the time spent watching television.

In analyzing the variable "age of pupils," which ranged between 12 years 5 months and 16 years

9 months, three age bands were laid down, and it was shown that the pupils in the higher age band

obtained a lower score in the Quality of School Life scale. This result is largely a function of pupils

having had to repeat years, since 60.7 percent of pupils in the third band, the highest in terms of age, had

been obliged to repeat one school year, and 15.73 percent had repeated two school years. Naturally, these

variables were interrelated, since the score obtained in the Quality of School Life scale was also directly

linked to a pupil's status as a repeater (F=27.85;d.f.= 2.7954; p≤.0000).

Finally, the relationship between the scale score and some variables concerning the pupils' reading

performance were analyzed. In the IEA Reading Literacy Study, overall scores for pupils' reading

performance with different kinds of texts—narrative, expository, and documents—were estimated. It was

noted that the higher the pupils' scores in the Quality of School Life scale, the higher their scores in

understanding written narrative texts (F=5.56; d.f.=3.8481; p≤.0008). However, the relationship between

the score obtained in understanding expository texts and documents and the score obtained in the Quality

of School Life scale did not turn out to be statistically significant (F=2.33; d.f.=3.8481; p=0.0724 and

F=0.06; d.f.=3.8431; p=0.9784, respectively).

4. Discussion of Results and Conclusions

The Williams and Batten scale appears to be an appropriate instrument for estimating the quality

of life for pupils in a school context and for evaluating the relationship between significant factors in the

educational context. 

However, before any further development occurs, it would be advisable to pilot test and analyze

factorially the translated tests, since text translation problems may have been responsible in part for the

differences between the results obtained for the national sample tested in Castilian and those obtained for

the samples tested in Catalonian, Galician, and Valencian. It is also possible that some cultural

differences may underlie some of the differences found, and this possibility needs further examination.

238
Second, the Quality of Life Questionnaire is a measure in which, in the questionnaire's current

form, the factor of the pupils' forgetting about the context could result. The test is headed by the statement

"School is a place where ...," after which come six lines of instructions, and finally a list with the 29 items

of the test. The heading is not repeated at any stage, and so when the student is answering the last items

of the test, he or she may no longer remember whether the item is referring to life at school or to his or

her personal life in general.

The recasting of certain items to include the header statement as a reminder would be advisable,

since it has emerged from this study that some of the items worded in a general way, with no explicit

reference to school, are those that depart most from expectation (items 4, 5, 6, 8, 10, 11, 12, 13, 15, 16,

19, 21, 25, 27, 28 and 29), while those worded in more specific terms related to the school life reveal

greater correspondence with the original model (items 1, 2, 3, 7, 9, 14, 17, 18, 20, 22, 23, 24 and 26).

Within the group of general items, there is a subgroup concerning negative feelings that behaves in the

same way in all the studies examined (items 11, 12, 16, and 17).

239
Reference

Williams, T., and Batten, M. (1981). The quality of school life. Hawthorn, Victoria: Australian

Council for Educational Research.

240