Head Start FACES 2000:

A Whole-Child Perspective on Program Performance 

May 2003 

Prepared for: 

Louisa B.Tarullo, Ed.D.

Child Outcomes Research and Evaluation

Office of Planning, Research and Evaluation

Administration for Children and Families

U.S. Department of Health and Human Services

Washington, DC

Prepared by: 

Westat 

Nicholas Zill, Ph.D., Project Director

Gary Resnick, Ph.D., Associate Project Director

Kwang Kim, Ed.D., Senior Research Analyst

Kevin O’Donnell, M.P.P., Research Analyst

Alberto Sorongon, Research Associate

Xtria 

Ruth Hubbell McKey, Ph.D., Co-Project Director

Shefali Pai-Samant, Ph.D., Senior Research Analyst

Cheryl Clark, M.Ed., Senior Research Analyst

The CDM Group, Inc 

Robert O’Brien, Ph.D., Co-Project Director

Mary Ann D’Elio, M.S., Senior Research Analyst

This report was prepared for the Administration for Children and Families, U.S. Department of Health and Human Services 

(DHHS) under contract HHS-105-96-1912, Head Start Quality Research Consortium’s Performance Measure Center. 
Acknowledgments

This report would not have been possible without the commitment 

and cooperation of the staff and families at the Head Start programs 

in both the 1997 and 2000 cohorts of FACES. Staff responded 

thoughtfully to interviews, welcomed us into their classrooms, and 

helped us to arrange interviews with families and assessments with 

children. We are also grateful to the Head Start families who 

patiently met with us and provided insights into their time with Head 

Start and beyond. The report also benefited from the advice and 

comments of the members of the Head Start Quality Research 

Centers Consortium, who served as technical advisers to the study 

and provided valuable feedback on earlier drafts of this report. 
 

 

HEAD START FACES 2000

A Whole-Child Perspective on Program Performance 

Table of Contents 

EXECUTIVE SUMMARY ......................................................................................................i

INTRODUCTION ................................................................................................................1

A. The Head Start Program Performance Measures Initiative ............................................................1

B. Design and Methodology ....................................................................................................3

C. Related Research Initiatives ................................................................................................4

D. Overview of Report..........................................................................................................5

CHAPTER I Cognitive Gains Made by Head Start Children and Their Achievement in 

Kindergarten ..................................................................................................7

Research Questions..................................................................................................................7

Findings ....... ........................................................................................................................7

A. Emergent Literacy and Mathematics Skills of Head Start Children Compared With Those

of the General Population of Preschoolers ..............................................................................8

B. Change in Knowledge and Skills Over the Head StartYear............................................................8

C. Emergent Literacy Gains of Language-Minority Children ..........................................................14

D. Growth of Knowledge and Skills in Kindergarten ....................................................................15

Conclusions ... ......................................................................................................................18

CHAPTER II Social Skills and Problem Behavior of Head Start Children and Their 

Adjustment to Kindergarten..........................................................................21

Research Questions ..............................................................................................................21

Findings ..............................................................................................................................22

A. Cooperative Classroom Behavior of Head Start Children............................................................22

B. Problem Behavior of Head Start Children ..............................................................................24

C. Behavior and Adjustment in Kindergarten ..............................................................................29
 

 

 

Head Start FACES 2000 Table of Contents 

CHAPTER III Relationship Between Curricula and Family, Program, and Classroom 

Characteristics ............................................................................................33

Research Questions ................................................................................................................33

Methods ..... ........................................................................................................................33

Findings ..............................................................................................................................34

A. Head Start Teachers Report Using a Specific Curriculum, Receiving Training, and

Having Access to Their Curriculum ......................................................................................34

B. Relationship Between Curricula and Family, Program, and Classroom Characteristics ........................35

Implications . ........................................................................................................................41

CHAPTER IV Understanding Quality in Head Start Classrooms ..........................................43

Research Questions ................................................................................................................43

A. What Quality Means and How We Appraise It ........................................................................43

Findings ..............................................................................................................................46

B. The Quality of Most Head Start Classrooms Continues To Be Good ..............................................46

C. Teacher Qualifications and Experience Are Related to Classroom Quality ......................................51

D. Teacher Backgrounds and Experience Are Related to Their Attitudes and Knowledge ........................52

E. Teacher Attitudes and Knowledge Are Related to Classroom Quality in Head Start ..........................53

F. Teacher and Program-Level Factors Explain Significant Variations in Classroom Quality ....................54

G. Summary ....................................................................................................................57

Key Findings ........................................................................................................................58

CHAPTER V Relationship of Program and Classroom Characteristics to Children’s 

Cognitive Gains and Social Development in Head Start ....................................61

Research Questions ................................................................................................................61

Conceptual Framework ..........................................................................................................63 

Analysis Method ....................................................................................................................65

Findings ..............................................................................................................................66

A. Use of an Integrated Curriculum ........................................................................................66

B. Higher Teacher Salary Levels ..............................................................................................68

C. Teachers With Bachelor’s or Associate’s Degrees ......................................................................69

D. Full-Day Versus Part-Day Classes ........................................................................................72

E. More Frequent Parental Reading to Children ..........................................................................72
 

 

Table of Contents Head Start FACES 2000 

F. Classroom Quality Indicators Not Found to Relate to Gains........................................................76

Summary and Discussion..........................................................................................................79

CHAPTER VI Relationship of Family and Parental Characteristics to Children’s Cognitive 

and Social Development in Head Start ..........................................................91

Research Questions ................................................................................................................91

Findings ..............................................................................................................................92

A. Prevalence of Family and Parental Risk Factors and Their Relationship to Children’s Outcomes ..........92

B. Family Involvement and Its Relationship to School Readiness ......................................................97

C. Head Start’s Protective Role for Families and Children ..........................................................100

Summary .... ......................................................................................................................101

CHAPTER VII Predictive Validity of Cognitive and Behavioral Measures: Relationships 

Within and Across Cognitive and Social Developmental Domains ..................107

Research Questions ..............................................................................................................107

Findings ...... ......................................................................................................................107

A. Children’s Scores on the FACES Instruments at the End of Head Start Predict

Kindergarten Outcomes ..................................................................................................107

B. FACES Behavioral Ratings at the End of Head Start Predict Children’s Social Competence

in Kindergarten ..........................................................................................................113

C. Behavior Ratings at the End of Head Start Predict Reading Skills and General Knowledge at the

End of Kindergarten ....................................................................................................117

D. Head Start Fall to Spring Gain Scores From the FACES Battery Predict Kindergarten Outcomes ........119

E. Head Start Fall to Spring Gain Scores From the Behavior Ratings Predict Social Competence

in Kindergarten ............................................................................................................122

F. Head Start Fall to Spring Gain Scores From the Behavior Ratings Predict Reading Skills and General 

Knowledge at the End of Kindergarten ..............................................................................122

G. Head Start Fall to Spring Gain Scores From the Behavior Ratings Contribute to the Prediction of

Reading and General Knowledge at the End of Kindergarten ..................................................124

Conclusions . ......................................................................................................................126

CUMULATIVE REFERENCES ..............................................................................................129

APPENDIX ......................................................................................................................133

HEAD START FACES 2000

A Whole-Child Perspective on Program Performance 

List of Figures 

INTRODUCTION 

Figure I.1 Head Start Program Performance Measures Conceptual Framework ........................................2

CHAPTER I 

Figure 1.1 Most Children Entering Head Start Have Academic Skills Below National Norms ......................9

Figure 1.2 Head Start Students Show Gains in Vocabulary and Early Writing Skills During ProgramYear ......10

Figure 1.3 Vocabulary Standard Scores of Children in Fall and Spring of Head StartYear:

FACES 1997 Versus FACES 2000 ................................................................................10

Figure 1.4 Early Writing Standard Scores of Children in Fall and Spring of Head StartYear:

FACES 1997 Versus FACES 2000 ................................................................................11

Figure 1.5 Letter Identification Standard Scores of Children in Fall and Spring of Head StartYear:

FACES 1997 Versus FACES 2000 ................................................................................12

Figure 1.6 Early Math Standard Scores of Children in Fall and Spring of Head StartYear:

FACES 1997 Versus FACES 2000 ................................................................................13

Figure 1.7 Children who Enter Head Start With Lower Skills Show Larger Gains in Comparison

to National Norms ................................................................................................14

Figure 1.8 English-Language Vocabulary Skills of Head Start Children: Spanish-Speaking

Language-Minority, Language-Majority, and Combined Population ......................................16

Figure 1.9 Letter-Identification in English by Head Start Children: Spanish-Speaking

Language Minority, Language-Majority, and Combined Population ......................................16

Figure 1.10 Vocabulary and Letter Identification Skills in English and in Spanish of Head Start

Children From Spanish-Speaking Language-Minority Families ............................................17

Figure 1.11 Head Start Graduates Show Further Progress Toward National Norms in Kindergarten ..............18

CHAPTER II 

Figure 2.1 Head Start Students Show Gains in Cooperative Classroom Behavior During ProgramYear ........23

Figure 2.2 Head Start Students Show Significant Reductions in Hyperactive Behavior During

Program Year ........................................................................................................24
Head Start FACES 2000 List of Figures 

Figure 2.3 Head Start Students Showed Similar Patterns of Problem Behavior in 2000-2001 and

1997-1998 Program Years ........................................................................................25

Figure 2.4 Head Start Students who Enter With High Levels of Problem Behavior Show Improvement

During Program Year ..............................................................................................26

Figure 2.5 Parents of Head Start Children Report Reductions in Problem Behavior During Program Year ....28

Figure 2.6 Parents Report Greater Improvement in Head Start Students who Enter With High

Levels of Problem Behavior ......................................................................................29

CHAPTER III 

Figure 3.1 The Majority of Teachers Used Either Creative Curriculum or High/Scope ............................34

Figure 3.2 Teachers Liked Their Respective Curricula for a Variety of Reasons ......................................35

Figure 3.3 Most Teachers From Northeast, Midwest, and West Used Creative Curriculum or

High/Scope While Majority of Teachers From the South Used Other Curricula ......................36

Figure 3.4 The Classrooms of Teachers Who Used Creative Curriculum or High/Scope had

Significantly Higher Average ECERS-R Total Scores and ECERS-R Language Scores

Than Classrooms of Teachers Who Used Other Curricula ..................................................37

Figure 3.5 The Classrooms of Teachers who Used Creative Curriculum and High/Scope had Significantly 

Higher Average Quality Composite Scores Than Classrooms Using Other Curricula ................38

Figure 3.6 Classrooms That Used Creative Curriculum and High/Scope had Significantly Higher Average

ECERS-R Total Scores and ECERS-R Language Scores Than Classrooms That Used All Other

Curricula. Classrooms That Used Widely Available Curricula Had Significantly Higher Average

ECERS-R Language Scores Than Classrooms That Used All Other Curricula ..........................39

Figure 3.7 Classrooms That Used Creative Curriculum and High/Scope had Significantly Higher Average 

Quality Factor Scores Than Classrooms That Used All Other Curricula. There Were no Significant

Differences in the Average Quality Composite Scores Between Classrooms That Used Widely 

Available Curricula and All Other Curricula ..................................................................40

CHAPTER IV 

Figure 4.1 Classroom Quality in Head Start Compared With Other Preschool and Child Care Settings........47

Figure 4.2 Distribution of Classrooms on ECERS and ECERS-R, Fall 1997 and 2000 ............................47

Figure 4.3 Teacher Beliefs Are Correlated With Classroom Quality, Fall 2000 ......................................53

Figure 4.4 A Model of Program- and Classroom-Level Factors Explaining Quality in Head Start................55

CHAPTER V 

Figure 5.1 Analytical Model of Multilevel Factors Predicting to Classroom Quality and Children’s 

Achievement and Gains in the Head Start Year................................................................64
List of Figures Head Start FACES 2000 

Figure 5.2 Children in Head Start Program Using High/Scope Curriculum Show Greater Gains

in Letter Recognition Skills ......................................................................................68

Figure 5.3 Children in Head Start Programs With Higher Teacher Salaries Show Larger Gains in

Cooperative Classroom Behavior ................................................................................70

Figure 5.4 Children in Head Start Programs With Higher Teacher Salaries Show Larger Declines

in Hyperactive Behavior ..........................................................................................70

Figure 5.5 Children in Head Start Classes Taught by Teachers With Bachelor’s or Associate’s Degrees

Show Gains in Early Writing Skills ..............................................................................71

Figure 5.6 Head Start Children in Full-Day Classes Show Larger Gains in Letter Recognition Skills

Than Those in Part-Day Classes..................................................................................73

Figure 5.7 Children in Full-Day Head Start Classes Show Greater Gains in Early Writing Skills ................74

Figure 5.8 Head Start Children Whose Parents Read to Them More Often Show Larger Vocabulary Gains ....75

Figure 5.9 Children in Head Start Classes With Higher, Moderate, and Lower Quality Language

Activities Show Parallel Gains in Vocabulary Knowledge....................................................77

Figure 5.10 Children in Head Start Classes With Higher Child-Adult Ratios Show Larger Gains in Letter 

Recognition..........................................................................................................78

CHAPTER VII 

Figure 7.1 Correlations and Standardized Multiple Regression Coefficients Between Reading Scale Scores

at End of Kindergarten Year and FACES Assessment Scale Scores at End of Head Start Year........108

Figure 7.2 Correlations and Standardized Multiple Regression Coefficients Between General Knowledge

Scale Scores at End of Kindergarten Year and FACES Assessment Scale Scores at End of Head

Start Year ..........................................................................................................109

Figure 7.3 Correlations and Standardized Multiple Regression Coefficients Between Reading Scale Scores

at End of Kindergarten Year and FACES Assessment Scale Scores at End of Head Start Year

-Norm-Referenced Tests Only..................................................................................110

Figure 7.4 Correlations and Standardized Multiple Regression Coefficients Between General Knowledge

Scale Scores at End of Kindergarten Year and FACES Assessment Scale Scores at End of Head

Start Year-Norm-Referenced Tests Only ......................................................................111

Figure 7.5 Correlations and Standardized Multiple Regression Coefficients Between Reading Scale

Scores at End of Kindergarten Year and FACES Assessment Scale Scores at End of Head Start

Year-Criterion-Referenced Tests Only ........................................................................111

Figure 7.6 Correlations and Standardized Multiple Regression Coefficients Between General

Knowledge Scale Scores at End of Kindergarten Year and FACES Assessment Scale Scores at

End of Head Start Year-Criterion-Referenced Tests Only..................................................112

Figure 7.7 Correlation Coefficients Between Parent and Teacher Ratings of Children’s Social

Competence at the End of Head Start and Teacher Reported Ratings of Cooperative

Classroom Behavior at the End of Kindergarten ............................................................113
Head Start FACES 2000 List of Figures 

Figure 7.8 Correlations and Significant Standardized Multiple Regression Coefficients Between Teacher

Ratings of Cooperative Classroom Behavior at End of Kindergarten Year and Parent and

Teacher Behavior Ratings at End of Head Start Year........................................................114

Figure 7.9 Correlations and Significant Standardized Multiple Regression Coefficients Between Teacher

Ratings of Total Problem Behavior at End of Kindergarten Year and Parent and Teacher

Behavior Ratings at End of Head Start Year ..................................................................114

Figure 7.10 Correlation Coefficients Between Parent and Teacher Ratings of Children’s Social

Competence at the End of Head Start and Reading Scale Scores at End of Kindergarten ..........115

Figure 7.11 Correlations and Significant Standardized Multiple Regression Coefficients Between

Reading Scale Scores at End of Kindergarten Year and Parent and Teacher Behavior Ratings

at End of Head Start Year........................................................................................116

Figure 7.12 Correlation Coefficients Between Parent and Teacher Ratings of Children’s Social Competencies

at the End of Head Start and General Knowledge Scale Scores at End of Kindergarten ............117

Figure 7.13 Correlations and Significant Standardized Multiple Regression Coefficients Between General 

Knowledge Scale Scores at End of Kindergarten Year and Parent and Teacher Behavior Ratings

at End of Head Start Year........................................................................................118

Figure 7.14 Correlations and Standardized Multiple Regression Coefficients Between Reading Scale Scores

at End of Kindergarten Year and FACES Assessment Scale Gain Scores During Head Start Year ..119

Figure 7.15 Correlations and Standardized Multiple Regression Coefficients Between General Knowledge

Scale Scores at End of Kindergarten Year and FACES Assessment Scale Gain Scores During

Head Start Year ....................................................................................................121

Figure 7.16 Correlations and Standardized Multiple Regression Coefficients Between Teacher Ratings of 

Cooperative Classroom Behavior at End of Kindergarten Year and Parent and Teacher Behavior 

Ratings Gain Scores During Head Start Year ................................................................123

Figure 7.17 Correlations and Standardized Multiple Regression Coefficients Between Teacher Ratings

of Problem Behavior at End of Kindergarten Year and Parent and Teacher Behavior Ratings Gain

Scores During Head Start Year..................................................................................123

Figure 7.18 Correlations and Odds-Ratio Estimates Between Teachers’ Decisions at End of

Kindergarten Year to Assign Child to Repeat Kindergarten and FACES Assessment Scale

Scores at End of Head Start Year ..............................................................................125

Figure 7.19 Correlations and Odds-Ratio Estimates Between Teachers’ Decisions at End of Kindergarten

Year to Assign Child to Repeat Kindergarten and Parent and Teacher Behavior Ratings at End of 

Head Start Year ....................................................................................................126
HEAD START FACES 2000

A Whole-Child Perspective on Program Performance 

List of Tables 

CHAPTER III 

Table 3.1 The Majority of Urban Teachers Used Either Creative Curriculum or High/Scope While the 

Majority of Rural Teachers Used Creative Curriculum......................................................36

Table 3.2 Type of Curricula and Classroom Quality Scores ............................................................38

CHAPTER IV 

Table 4.1 Comparison of Lead Teacher Backgrounds, FACES Fall 1997 and Fall 2000 ............................50

Table 4.2 Summary of Center- and Classroom-Level Factors Predicting Classroom Quality, Fall 2000........56

CHAPTER V 

Table 5.1 Three-Level Regression Models of Assessment Scale Scores of Head Start Children in 

Fall and Spring of ProgramYear, and Fall-Spring Gains, 2000-2001 ..................................83-84

Table 5.2 Three-Level Regression Models of Assessment Scale Scores of Head Start Children in 

Fall and Spring of ProgramYear, and Fall-Spring Gains, 2000-2001 ..................................85-86

Table 5.3 Three-Level Regression Models of Behavior Rating Scores of Head Start Children in

Fall and Spring of ProgramYear, and Fall-Spring Gains, 2000-2001 ..................................87-88

Table 5.4 Three-Level Models of Behavior Rating Scores of Head Start Children in Fall and Spring

of ProgramYear, and Fall-Spring Gains, 2000-2001 ....................................................89-90

CHAPTER VI 

Table 6.1 Significant Head Start Moderators of Negative Outcomes Associated With Risk Factors......101-102

Table 6.2 Significant Adjusted Odds Ratios and 95% Confidence Intervals for Maternal Depression,

Exposure to Violence, Domestic Violence, Alcohol Use, and Involvement With the Criminal 

Justice System ....................................................................................................105

HEAD START FACES 2000

A Whole-Child Perspective on Program Performance 

Executive Summary 

In 1997, Head Start launched the Family and Child 

Experiences Survey (FACES), a study of a national 

random sample of Head Start programs designed to 

answer critical questions about child outcomes and 

program quality. In 2000, FACES began data collection 

on a new national cohort—FACES 2000—and plans 

are underway for a third cohort. Now, longitudinal 

data on successive, scientifically representative samples 

of children, families, teachers, classrooms, and 

programs are available. 

In both studies, children entered Head Start at a great 

disadvantage to other children, as evidenced by the 

children’s initial scores on standardized assessments of 

cognitive skills. Findings from both cohorts of FACES 

show that the gap between Head Start children and the 

general population of preschool-age children narrows 

during the Head Start year on key components of 

school readiness. This is true to a greater extent in the 

2000-2001 program year. However, despite the gains 

they make, Head Start children enter kindergarten still 

substantially below national averages on such 

assessments. 

Children made significant gains during the Head Start 

year relative to national norms, most notably in the 

areas of vocabulary knowledge and early writing skills. 

In the areas of letter recognition and knowledge of 

book and print conventions, children in 2000-2001 

made significantly greater gains than Head Start 

children in 1997-1998. Gains in vocabulary and early 

writing were similar to those in 1997-1998. In both 

cohorts, children who entered Head Start with lower 

skill levels made greater gains than those who entered 

with higher skill levels. This finding may be related in 

part to the tendency of scores to move closer to the 

population mean over successive assessments. 

In the domain of social and emotional development, 

children also showed growth in social skills and 

reduction in hyperactive behavior during the Head 

Start year. Children with high levels of shy, aggressive, 

or hyperactive behaviors (scoring in the top quarter) 

showed significant reduction of these behaviors. 

FACES 2000 also found that Head Start classrooms 

continue to be of good quality across a wide variety of 

indicators. In 2000-2001 there was an increased 

percentage of new teachers with advanced degrees 

entering Head Start, compared to 1997-1998. Head 

Start teachers in FACES 2000 were more likely to be 

younger, new to teaching Head Start, and entering 

with higher educational levels including graduate 

degrees. They were also more likely to be trained in 

Early Childhood Education and to be members of a 

professional organization. Results also show that Head 

Start teachers with higher levels of educational 

attainment, and with more years of teaching 

experience overall, were more likely to have 

knowledge and positive attitudes about early childhood 

education practices, which subsequently influenced 

classroom quality. Thus, teacher attitudes and 

knowledge mediate the relationship between teacher 

education and classroom quality. 

i 
Head Start FACES 2000 Executive Summary 

The vast majority of programs use a specific 

curriculum, as mandated by the Head Start Program 

Performance Standards. Several program factors are 

related to child outcomes, including higher teacher 

salaries and use of an integrated curriculum.Teachers’ 

educational credentials are linked to greater gains in 

early writing skills. In addition, provision of preschool 

services for a longer period each day is linked to 

greater cognitive gains. 

FACES 2000 presents information on family and 

parent characteristics important to any investigation of 

school readiness. For example, when parents read 

more frequently to their children, their children had 

higher scores on early literacy assessments. In 

addition, when families engaged their children in 

weekly and monthly educational activities, there was a 

positive correlation with positive child behaviors and 

emergent literacy. Head Start families face numerous 

risks and challenges that are related to children’s wellbeing. When Head Start parents were more depressed, 

their children had lower scores on a variety of 

cognitive measures. Positive correlations were also 

found between increased exposure to neighborhood 

violence and reports of child problem behavior. Parent 

involvement in Head Start was positively correlated 

with a number of positive cognitive and social 

outcomes. Importantly, Head Start involvement by 

parents moderated the negative effects of violence, 

depression, and other risk factors on children’s 

cognitive and social-emotional status. 

The Study 

FACES describes the characteristics, experiences, and 

outcomes for children and families served by Head 

Start. It also explores the relationships among family 

and program characteristics and outcomes. In 1997, 

the FACES design included a nationally representative 

sample of 3,200 children and their families in 40 

programs. The FACES 2000 sample includes 2,800 

children and their families in 43 different Head Start 

programs across the nation.The current report focuses 

on the FACES 2000 sample, as well as kindergarten 

follow-up data from the FACES 1997 sample. 

Each cohort of FACES employs a nationally stratified 

sample of Head Start programs, centers, classrooms, 

children, and parents. FACES 2000 features four 

phases of data collection and follows 3- and 4-year-old 

children from program entry, through one or two 

years of program experience, with follow-up in the 

spring of kindergarten. The FACES 2000 battery has 

four main components: the direct child assessment, 

parent interview, teacher and staff interviews, and 

classroom observations. Although there is no nonHead Start comparison group in FACES, the use of 

assessment measures with national norms permits 

comparisons between the skills of children in the 

sample and children of the same ages in the norming 

samples. Child outcomes can be compared with 

national averages on a range of standardized 

assessments with a mean of 100, and standard deviation 

of 15. 

Study Findings 

FACES provides information about the knowledge and 

skills that children have when they enter the Head Start 

program and the gains they make during the Head Start 

year and the first year of elementary school. It also 

describes the quality of Head Start classrooms, and 

factors that help explain variations in quality across 

Head Start classrooms. In addition, FACES 2000 data 

provide insight into the relationship of program and 

classroom characteristics to children’s outcomes, as 

well as the relationship of family and parental 

characteristics to children’s outcomes. 

Head Start Children’s Cognitive and SocialEmotional Development 

• Most children entered Head Start at a great 

disadvantage, with early literacy and math skills 

substantially below national averages. The typical 

ii 
Executive Summary Head Start FACES 2000 

Head Start child was found to enter at about the 16th 

percentile in vocabulary and early writing skills, at 

about the 31st percentile in letter recognition, and at 

about the 21st percentile in early math, when 

compared to the full spectrum of American children 

in the same age range. 

• There was considerable diversity in skill levels 

among Head Start children, however. The highest 

quarter of Head Start children were at or above the 

national average (50th percentile) in early language 

and number skills, while the lowest quarter of 

children ranked on average in the lowest 2 percent of 

all U.S. preschoolers in these areas. 

• As in 1997-1998, the gap between Head Start 

children and other preschool-age children narrowed 

during the Head Start year, especially with respect to 

vocabulary knowledge and early writing skills. 

Despite these gains, Head Start children still trail in 

these measures compared to national averages. 

• Head Start children showed greater progress in letter 

recognition skills than they had in 1997-1998, but 

they still did not reach national averages in this area. 

Although the children made progress in early math 

skills, they did not make gains toward national 

averages in this domain. 

• In 2000, Head Start children entered the program 

knowing about 4 letters of the alphabet, and left the 

program knowing about 9 letters on average, close to 

the congressional mandate of being able to name at 

least 10 letters. 

• Children who entered the program with lower levels 

of knowledge and skill showed larger gains during 

the program year, yet still lagged considerably behind 

national averages. Children who started with higher 

assessment scores in the fall wound up with higher 

scores in the spring, but showed less dramatic gains. 

The finding of greater gains for children who entered 

with lower scores may be related in part to the 

tendency of scores to move closer to the population 

mean over successive assessments. 

• Spanish-speaking children in Head Start showed 

significant gains in English vocabulary skills without 

declines in their Spanish vocabulary skills. They did 

not gain in letter recognition skills. 

• Based on follow-up of the 1997-1998 cohort, Head 

Start graduates showed further progress toward 

national averages during kindergarten. Gains of 

between a third to more than half a standard 

deviation were observed in vocabulary, early math, 

and early writing skills during kindergarten. Most 

Head Start graduates could identify most or all of the 

letters of the alphabet by the end of kindergarten and 

more than half could recognize beginning sounds of 

words. Nevertheless, Head Start graduates remained 

behind their more advantaged peers in early 

achievement. 

• The size of gains that children made while in Head 

Start were predictive of their achievement levels by 

the end of kindergarten. 

• Children showed growth in social skills and 

reduction in hyperactive behavior during the Head 

Start year. According to teacher report, the average 

score of Head Start children on a cooperative 

classroom behavior rating scale increased 

significantly from fall to spring. In addition, the 

average score of Head Start children on a hyperactive 

behavior rating scale decreased significantly during 

the program year. 

• Children with high levels (scoring in the top quarter) 

of shy, aggressive, or hyperactive behavior showed 

significant reductions in these problem behaviors in 

Head Start. 

• Behavior in Head Start is predictive of adjustment 

and performance in early elementary school. 

Cooperative classroom behavior ratings and problem 

behavior ratings by Head Start teachers of children at 

the end of Head Start were predictive of behavioral 

adjustment ratings by kindergarten teachers in the 

spring of the kindergarten year. In addition, children 

iii 
Head Start FACES 2000 Executive Summary 

who received higher cooperative behavior ratings and 

lower problem behavior ratings from Head Start 

teachers scored better on cognitive assessments at 

the end of kindergarten, even when their test scores 

in Head Start were taken into account. 

Head Start Programs’ Use of Curricula 

• The great majority of Head Start programs use a 

curriculum, as mandated by the Head Start Program 

Performance Standards. A wide variety of curricula 

are used, with a majority of programs selecting an 

integrated curriculum such as Creative Curriculum 

or High/Scope. 

• There is a relationship between program 

characteristics (region, urban-rural, characteristics 

of children and families) and the type of curriculum 

used. 

• There is a relationship between curricula and 

classroom quality, which may reflect the influences of 

other factors (such as the resources available to 

programs for purchasing and training in specific 

curricula), or may demonstrate the effect of certain 

curricula on quality. 

Quality in Head Start Classrooms 

• Head Start quality has been observed to be 

consistently good over time, using a variety of 

indicators including child-adult ratio, teacher-child 

interactions, and classroom activities and materials. 

Few classrooms scored below minimal quality. In 

fact, FACES shows that Head Start has a better, more 

limited range of quality than that seen in child care 

centers and preschools in several other national 

studies. 

• More Head Start teachers in 2000-2001 had 

obtained a graduate school degree compared to 

1997-1998. However, Head Start teachers have 

lower teaching qualifications on average than prekindergarten teachers in public elementary schools. 

• Head Start teachers in 2000 are also younger, 

compared with those in 1997-1998, and more of 

them have been teaching in Head Start for two years 

or less. These newer teachers are also the ones most 

likely to have a graduate school degree. 

• More teachers in 2000 studied Early Childhood 

Education or Child Development for their highest 

degree, and more teachers belong to a national 

professional association for early childhood 

educators, compared with those in 1997-1998. 

• Teacher backgrounds, qualifications, and experience 

are related to their attitudes and knowledge of early 

childhood development practices. Teachers with 

higher scores for positive attitudes and knowledge 

about early childhood education practices were more 

likely to have higher levels of educational attainment, 

have some graduate school education or higher, have 

more total years teaching, and belong to an early 

childhood education association. 

• Classrooms with higher levels of quality have 

teachers with higher levels of education, experience, 

and positive attitudes and knowledge about early 

childhood education practice. 

• Teacher education and attitudes are linked to 

classroom quality. The relationship between teacher 

education and classroom quality is explained by 

teacher’s attitudes and knowledge of early childhood 

education practice.Teachers who are more educated 

have more positive attitudes and knowledge, which 

translates into higher levels of classroom quality. 

• Variations in the quality of Head Start classrooms 

may be explained by characteristics of the families 

and children they serve, by the curriculum used in 

the program, and by teacher attitudes and knowledge 

about early childhood education practice. Head Start 

programs that provide for a common integrative 

curriculum across classrooms and that pay their 

teachers better have sufficient resources available to 

iv 
Executive Summary Head Start FACES 2000 

positively influence classroom quality through the 

quality of teachers hired, their experience, and 

attitudes and knowledge. 

Relationship of Program and Classroom 

Characteristics to Children’s Outcomes 

• Higher teacher salaries are linked to greater gains in 

several cognitive and social-emotional areas, 

including letter identification, oral communication 

of basic social information, and cooperative 

classroom behavior. Children in programs with 

higher teacher salaries also showed greater 

improvement in hyperactive problem behavior 

during the Head Start year. 

• Use of an integrated curriculum is linked to greater 

gains in several cognitive and social-emotional areas. 

Specifically, children in Head Start programs using 

High/Scope showed larger fall-spring gains in letter 

identification and cooperative classroom behaviors 

than children in programs using other curricula. 

Children in programs using High/Scope also showed 

greater improvement in total behavior problems and 

hyperactive problem behavior. 

• Teachers’ educational credentials are linked to 

greater gains in early writing skills. Children taught 

by Head Start teachers with bachelor’s degrees or 

associate’s degrees showed gains toward national 

averages in an assessment of early writing skills, 

whereas children taught by teachers with lesser 

credentials merely held their own against national 

norms. 

• Provision of preschool services for a longer period 

each day is linked to greater cognitive gains. 

Children in full-day classes in Head Start showed 

larger fall-spring gains in letter recognition and early 

writing skills than did children in part-day classes. 

• There is indirect evidence that encouraging parents 

to engage in more educational activities with their 

children at home is linked to greater cognitive gains. 

Children whose parents report reading to them 

every day show larger fall-spring gains in vocabulary 

knowledge and letter recognition skills than children 

whose parents report reading once or twice or less 

frequently per week. 

• Within the generally good quality range of Head 

Start classrooms, variation in quality as measured by 

the ECERS-R Language scale or the Caregiver 

Interaction Scale is not associated with differences in 

fall-spring achievement gains across classes. 

• Within the narrow range of group size in Head Start, 

variation in child:adult ratios is not associated with 

or is negatively associated with differences in fallspring achievement gains across classes. 

Relationship of Family and Parental Characteristics 

to Children’s Outcomes 

• Almost 90 percent of Head Start families manifested 

at least one of a set of six selected socioeconomic risk 

factors. About one fifth of the families had four or 

more risk factors. Children in these families had 

lower parent ratings on emergent literacy and higher 

teacher and parent ratings of problem behavior. In 

the assessments, these children scored lower on 

design copying, color naming, one-to-one counting, 

book knowledge, vocabulary, early math, early 

writing, letter identification, social awareness, 

comprehension, and print concepts. 

• Twenty-five percent of the parents were classified as 

moderately or severely depressed. Parents who were 

more depressed reported that their children had 

more problem behaviors and fewer positive social 

behaviors. Their children also had lower scores on 

one-to-one counting, creativity, and early math 

assessments, after controlling for parent education, 

income, and other demographic factors. 

• More than one fifth of the parents had witnessed 

violent crime. Five percent were victims of violent 

crime in the neighborhood, while a similar 

v 
Head Start FACES 2000 Executive Summary 

percentage were victims of violence in their homes. 

Parents reported that almost 10 percent of the 

children witnessed domestic violence during the 

previous year. Parents reported that less than 2 

percent of the children had been victims of violent 

crime or victims of domestic violence. Positive 

correlations were found between increased exposure 

to violence and reports of child problem behavior 

and maternal depression, after controlling for parent 

education, income, and other demographic factors. 

• Almost one fifth of the parents reported that 

someone in their household had been arrested and 

charged with a crime. Children in these families 

were more than three times more likely to have been 

a witness to either a violent crime or domestic 

violence in the past year. These children were also 

more than three times more likely to have been a 

victim of domestic violence or violent crime. These 

children had lower vocabulary scores, and were 

reported by both parents and teachers to be more 

aggressive and have more overall problem behaviors. 

• Families engaged their children in a number of 

weekly and monthly educational activities. The 

number of activities was positively correlated with 

positive child behaviors and emergent literacy and 

negatively correlated with problem behaviors, after 

controlling for the number of times a child was read 

to in the past week, parent education, income, and 

other demographic factors. In particular, the weekly 

activities had positive correlations with scores on 

color naming and vocabulary. Monthly activities 

were positively correlated with the social awareness, 

color naming, one-to-one counting, book 

knowledge, and print concepts assessments. 

• More than two thirds of parents had attended parentteacher conferences, observed in their children’s 

classrooms for at least 30 minutes, or met with a 

Head Start staff member in their homes. Parental 

involvement in Head Start was positively correlated 

with parental reports of positive social behavior and 

higher emergent literacy skills and negatively 

correlated with aggressive and overall problem 

behavior, after controlling for parent education, 

employment, and other demographic factors. 

Children with more involved parents scored higher 

on vocabulary, book knowledge, early writing, early 

math, and letter identification tasks. 

• Preliminary findings suggest that Head Start may play 

a role in protecting children from the negative 

outcomes associated with family risk factors, 

including maternal depression, exposure to violence, 

alcohol use, and involvement in the criminal justice 

system. Parent involvement in Head Start, parent 

reports that they and their children had positive 

experiences at Head Start, and parent satisfaction 

with the program significantly moderated negative 

relationships between risk factors and child 

behavioral and cognitive outcomes, controlling for 

parent education, income, and employment; child 

age, gender, and race; and the overall family activity 

level with the child. 

Predictive Validity of the FACES Cognitive and 

Behavioral Measures 

• Children’s scores on FACES assessments at the end of 

Head Start, as well as the gains they make during the 

Head Start year, strongly predict their performance 

at the end of kindergarten. As an indicator of preliteracy skills, the cognitive measures show strong 

associations with reading ability at the end of the 

kindergarten year. As an indicator of school 

adjustment and social competence, the behavior 

ratings demonstrate ability to predict kindergarten 

behaviors that promote learning and those that 

impede learning. 

• The vocabulary and early literacy instruments used 

in FACES tap different types of abilities (“inside-out” 

or decoding skills as well as “outside-in” or 

comprehension skills) that are important for 

children’s future reading proficiency and academic 

achievement. 

vi 
Executive Summary Head Start FACES 2000 

• Scores from the FACES instruments and the FACES 

behavior ratings both contribute to the prediction of 

the teacher’s practical decision of whether a child 

repeats kindergarten or is promoted to first grade. 

• The multi-measure and multi-method approach to 

the measurement of children’s abilities provides a 

variety of information sources that significantly 

contribute to the prediction of kindergarten 

outcomes. 

vii 
viii
HEAD START FACES 2000

A Whole-Child Perspective on Program Performance 

Introduction 

As part of the Head Start Program Performance 

Measures Initiative, Head Start launched the Family 

and Child Experiences Survey (FACES) in Fall 1997. 

With a nationally representative sample of 3,200 

children and their families in 40 programs, FACES 

describes the characteristics, experiences, and 

outcomes for children and families served by Head 

Start, as well as explores the relationships among 

family and program characteristics and outcomes. In 

fall 2000, Head Start began data collection on a new 

national cohort of FACES, called FACES 2000. The 

FACES 2000 sample includes 2,800 children and their 

families in 43 different Head Start programs across the 

nation. 

FACES provides critical information for the Head Start 

program on important aspects of outcomes, quality, 

and practices beyond the aggregated, administrative 

data previously collected. Through the ongoing, 

longitudinal FACES study, Head Start can examine key 

facets of program quality and children’s school 

readiness on successive, scientifically representative 

samples of children, families, teachers, classrooms, and 

programs. While these data are crucial for decisionmaking at the national level, there are important 

limitations on the questions they can answer. They do 

not provide information on every child in each 

program, nor do they provide information on or 

comparisons to children recruited but not served by 

Head Start. Those questions are being answered via the 

Head Start National Reporting System and the Head 

Start Impact Study, respectively. 

This introductory chapter describes the history and 

conceptual framework of FACES and the Head Start 

Program Performance Measures Initiative. In addition, 

it also discusses the research design and methodology 

of FACES, as well as the central information FACES 

provides in the context of related research endeavors. 

The chapter closes with an overview of the report. 

A. The Head Start Program 

Performance Measures Initiative 

The Head Start Program Performance Measures, 

launched in 1995, were developed in accordance with 

the recommendations of the 1993 Advisory 

Committee on Head Start Quality and Expansion, the 

mandate of Section 641A (b) of the Head Start Act (42 

USC 9831 et seq.) as reauthorized in 1994 and the 

Government Performance and Results Act 

(GPRA)(Public Law 103-62). 

The Head Start Act defines Program Performance 

Measures as “methods and procedures for measuring, 

annually and over longer periods, the quality and 

effectiveness of programs operated by Head Start 

agencies” that will be used to identify strengths and 

weaknesses in the Head Start program—both 

nationally and by region—and identify programmatic 

areas requiring additional training and technical 

assistance. 

Conceptual Framework 

In 1996-1997, a conceptual framework for the 

Program Performance Measures was developed and 

1
Head Start FACES 2000 Introduction 

Figure I.1. Head Start Program Performance Measures Conceptual Framework 

CHILD'S 

SCHOOL 

READINESS 

1 

ENHANCE 

children's 

growth and 

development. 

2 

STRENGTHEN 

families as the

 primary nurturers 

of their children. 

3 

PROVIDE 

children with 

educational, health, 

and nutritional services. 

4 

LINK 

children and 

families to needed 

community services. 

5 

ENSURE 

well-managed programs that involve parents in decision-making 

Outcomes 

Processes 

the measures were finalized.1 The conceptual 

framework unifies and organizes the Program 

Performance Measures to display the linkages between 

process and outcome measures for Head Start children 

and families. (See Figure I.1 for the graphical 

representation of the framework.) The framework is 

based on the ultimate goal of Head Start, which is to 

promote the school readiness of children. 

Head Start has adopted the “whole child” view of 

school readiness that was recommended by the Goal 

One Technical Planning Group of the National 

Education Goals Panel (Goal One Technical Planning 

Group, 1991, 1993). The panel defined school 

readiness as a multi-faceted phenomenon comprising 

five developmental domains that are important to the 

child’s readiness for school: physical well-being and 

motor development, social and emotional 

development, approaches to learning, language usage 

and emerging literacy, and cognition and general 

knowledge. Each of these domains is represented in 

the battery of measures FACES uses to assess how well 

the national sample of Head Start programs is 

performing. It takes into account the interrelatedness 

of cognitive, emotional, and social development; 

physical and mental health; and nutritional needs. 

School readiness is depicted at the top of the pyramid, 

with five objectives supporting it: 

1 See Head Start FACES: Longitudinal findings on program performance. Third progress report (ACYF, 2001) for a list of the 24 

Program Performance Measures. 

2 
Introduction Head Start FACES 2000 

• Objective 1. Enhance children’s healthy growth 

and development. 

• Objective 2. Strengthen families as the primary 

nurturers of their children. 

• Objective 3. Provide children with educational, 

health, and nutritional services. 

• Objective 4. Link children and families to needed 

community services. 

• Objective 5. Ensure well-managed programs that 

involve parents in decision-making. 

Each of these objectives is critical to helping children 

of low-income families attain their full potential. They 

also represent the cornerstones of the Head Start 

program. Objectives 1 and 2 represent outcomes or 

results that the program is designed to produce. 

Achieving both of these objectives is critical to the 

ultimate success of Head Start. As parent involvement 

and family support are key tenets of Head Start, both 

child and family-oriented outcome measures are 

included here. Objectives 3, 4, and 5 comprise the 

lower tiers of the pyramid and contain the process 

measures that are the basis for the attainment of 

Objectives 1 and 2 and the ultimate goal of enhancing 

children’s school readiness. An important aspect of the 

pyramid is the strong empirical connection between 

the provision of quality services (process measures) 

and improvements in child development (outcome 

measures). 

B. Design and Methodology 

Each cohort of FACES employs a nationally 

representative sample of Head Start programs, 

centers, classrooms, children, and parents.2 The 

2 See the Appendix for details on sample design and response rates. 

sample is stratified by three variables: region of the 

country (northeast, midwest, south, or west); 

urbanicity (urban versus rural); and percentage of 

minority families in the program (50 percent or more 

versus less than 50 percent). The first cohort of FACES 

had six phases of data collection, and followed 3- and 

4-year-old Head Start children from entry into Head 

Start, through one or two years of program 

participation, with follow-up in spring of kindergarten 

and spring of first grade (ACYF, 2001, 1998).3 FACES 

2000 features four phases of data collection and follows 

3- and 4-year-old Head Start children from program 

entry through spring of kindergarten. For the current 

report, complete kindergarten data are not yet 

available. However, the report includes information on 

kindergarten performance from the FACES 1997 

cohort. 

Survey Measures and Instruments 

The FACES 2000 battery has four main components: 

the child assessment, parent interview, teacher and 

staff interviews, and classroom observations. The child 

outcomes include the major components of school 

readiness, and are collected through direct child 

assessments and rating scales completed by parents and 

teachers. Parent interviews are conducted with the 

primary caregiver of the Head Start child, and tap 

parenting behaviors, the socioeconomic characteristics 

of the family, and parental health and well-being. 

Interviews are administered to classroom teachers, 

center directors, program directors, and component 

coordinators to collect data on staff experience, 

education, and training as well as knowledge and 

beliefs about child development, and educational 

activities with children and parents. Classroom 

observations collect data on both the structure of the 

classroom and classroom processes, such as teacher3 The initial cohort of FACES also included an embedded case study of a longitudinal sample of 120 randomly selected families 

from the larger FACES sample; see A Descriptive Study of Head Start Families: FACES Technical Report I, (ACYF, 2002). 

3 
Head Start FACES 2000 Introduction 

child interactions.This battery has remained largely the 

same since 1997, with some minor revisions based on 

field experiences and newly released versions of 

instruments.4 

C. Related Research Initiatives 

ACF considers FACES—and the ongoing information 

it provides about program quality and its relationship 

to child outcomes—a critical component of its effort 

to study Head Start. Indeed, policymakers relied on 

data from FACES during Head Start’s 1998 

Reauthorization, as Head Start officials were able to 

report to Congressional leaders on the quality of Head 

Start programs and the knowledge and skills of Head 

Start children as they completed the program. The 

data and experiences from FACES assisted the 1999 

Advisory Committee on Head Start Research and 

Evaluation as it deliberated the design of a national 

impact study of Head Start as mandated by Congress. 

In addition, data from FACES have been widely 

disseminated within the Head Start community to 

assist with continuous efforts of program improvement 

and have guided training and technical assistance 

efforts. 

The purpose and design of FACES complement related 

ongoing research initiatives funded by ACF and other 

agencies interested in the well-being of children. 

The National Head Start Impact Study is a longitudinal 

study that involves a sample of approximately 5,000 3­

and 4-year old children across an estimated 75 

nationally representative grantee/delegate agency 

groups. The Impact Study was congressionally 

mandated in the Head Start reauthorization of 1998. 

The Impact Study takes place only in communities 

where there are more eligible children and families 

than can be served by the program. This study has two 

primary goals. The first is to determine on a national 

basis how Head Start affects the school readiness of 

children participating in the program as compared to 

eligible children not enrolled in Head Start. The 

second goal of the study is to determine under which 

conditions Head Start works best and for which 

children. The children participating have been 

randomly assigned to either a treatment group (which 

receives Head Start services) or a comparison group 

(which does not receive Head Start services). 

The Head Start Bureau is also conducting a field test of 

the National Reporting System on Child Outcomes 

(NRS). When fully implemented, the child outcomes 

information will assess the progress of approximately 

500,000 4- and 5-year-old children in Head Start. It 

will produce a national outcomes report of children’s 

ability and progress on the presidentially and 

congressionally mandated indicators. 

The Head Start Quality Research Center (QRC) 

Consortium examines the efficacy of interventions 

designed to enhance the school readiness of preschool 

children in Head Start. These five-year grants fund 

partnerships between eight academic researchers and 

Head Start programs designed to improve child 

outcomes in the areas of literacy, social-emotional 

development, and other domains of school readiness, 

through enhancements to curriculum, teacher training 

and mentoring, parent involvement, and assessment 

practices. Research teams are implementing and 

evaluating their interventions with Head Start program 

partners in an initial site and then replicating the 

successful interventions in additional sites. Cross-site 

data collection uses the FACES measures. 

The Early Head Start Tracking/Pre-Kindergarten 

Follow-up of the Early Head Start Research and 

Evaluation project includes 17 local universities funded 

during the Birth to Three Phase. These sites will 

conduct cross-site and site-specific research, building 

4 See the Appendix for details on data collection instruments. 

4 
Introduction Head Start FACES 2000 

upon earlier research and following the original 

children and families from the time they leave the Early 

Head Start program until they are ready to enter 

kindergarten. 

ACF has also partnered with other federal agencies to 

study children in Head Start and other low-income 

children. One such effort is the ECLS Kindergarten 

Cohort, a longitudinal study of approximately 23,000 

children nationwide who began kindergarten in the fall 

of 1998 and will be assessed through the fifth grade.An 

estimated 3,000 have been verified as former Head 

Start children. In addition to contributing to this study, 

FACES utilizes measures developed in ECLS-K in its 

kindergarten follow-up. 

In addition, ACF is partnering with the National 

Institute of Child Health and Human Development 

(NICHD), and the Assistant Secretary for Planning and 

Evaluation (ASPE) within the Department of Health 

and Human Services (DHHS), and the Office of Special 

Education and Rehabilitation Services (OSERS) of the 

U.S. Department of Education, to solicit grant 

applications to study the Effectiveness of Early 

Childhood Programs, Curricula, and Interventions in 

Promoting School Readiness. The purpose of these 

grants will be to study the effectiveness of integrative 

early childhood interventions and programs across a 

variety of early childhood settings in promoting school 

readiness for children, from birth through age five, 

who are at risk of later school difficulties. 

D. Overview of Report 

This current document is the fourth progress report of 

FACES findings, and the first detailing findings from 

FACES 2000, the second full cohort of FACES. 

Subsequent chapters describe: 

• The cognitive gains made by Head Start children in 

preschool and their achievement in kindergarten; 

• Improvements in social skills and the amelioration of 

problem behavior in Head Start, and children’s 

classroom adjustment and performance in 

kindergarten; 

• The relationship between curricula and program, 

classrooms and child characteristics, including the 

types of curricula used by Head Start programs, 

sources of training, teacher access to and satisfaction 

with curricula, and the relationship between 

curricula and child, family, and program 

characteristics; 

• The quality of Head Start centers and classes as early 

childhood care environments based on classroom 

observations, as well as the link between teacher 

education and beliefs and classroom quality, and 

among program resources and family characteristics 

and program quality; 

• The relationship of program and classroom 

characteristics (including teacher salaries, use of an 

integrated curriculum, and traditional measures of 

classroom quality) to children’s cognitive gains and 

social development in Head Start; 

• The relationship between parent and family 

characteristics (including risk factors such as 

maternal depression, exposure to violence, domestic 

violence, substance use, and involvement in the 

criminal justice system) and children’s social and 

cognitive outcomes, as well as the moderating effects 

of overall Head Start experiences; and 

• The predictive validity of cognitive and behavioral 

measures, and specifically relationships within and 

across cognitive and social developmental domains. 

5 
Head Start FACES 2000 Introduction 

REFERENCES

Administration on Children, Youth and Families. 

(2002). A descriptive study of Head Start families: FACES 

technical report I.Washington, DC: U.S. Department of 

Health and Human Services. 

Administration on Children, Youth and Families. 

(2001). Head Start FACES: Longitudinal findings on 

program performance. Third progress report. Washington, 

DC: U.S. Department of Health and Human Services. 

Administration on Children, Youth and Families. 

(1998). Head Start Program Performance Measures: Second 

progress report. Washington, DC: U.S. Department of 

Health and Human Services. 

Administration on Children, Youth and Families. 

(1997). First progress report on the Head Start Program 

Performance Measures. Washington, DC: U.S. 

Department of Health and Human Services. 

Administration on Children, Youth and Families. 

(1995). Charting our progress: Development of the Head 

Start Program Performance Measures. Washington, DC: 

U.S. Department of Health and Human Services. 

Goal One Technical Planning Group. (1991). The Goal 

One Technical Planning Subgroup report on school 

readiness. In National Education Goals Panel (Ed.), 

Potential strategies for long-term indicator development: 

Reports of the technical planning subgroups (Report No. 

91-0, pp. 1-18). Washington, DC: National Education 

Goals Panel. 

Goal One Technical Planning Group. (1993). 

Reconsidering children’s early development and learning: 

Toward shared beliefs and vocabulary. Draft report to the 

National Education Goals Panel. Washington, DC: 

National Education Goals Panel. 

6 
CHAPTER I

Cognitive Gains Made by Head Start Children and Their Achievement 

in Kindergarten 

The Head Start Family and Child Experiences Survey 

(FACES) provides information about the knowledge 

and skills that children who attend Head Start have 

when they enter the program and the gains they make 

during the Head Start year and the first year of 

elementary school. The information is helpful in 

assessing how well the Head Start program is 

performing, and what changes and reforms may be 

needed to improve program performance. The 

information is gained through direct, one-on-one 

assessment of nationally representative samples of 

Head Start students in the fall and spring of the 

program and at the end of their kindergarten year. 

Although there is no non-Head Start comparison 

group in FACES, the use of assessment measures with 

national norms permits comparisons between the skills 

of children in the sample and children of the same ages 

in the norming samples.5 

This chapter focuses on cognitive measures with national 

norms. See Chapter II for information about social-emotional 

measures and the Appendix for a complete listing of measures 

used in FACES. 

FINDINGS 

Children who entered Head Start in the fall of 2000 

had academic skill and knowledge levels well below 

national averages. They were comparable to the levels 

found in the initial round of FACES, conducted three 

years earlier in fall 1997. As in the earlier study, 

children made significant gains during the Head Start 

year, most notably in the areas of vocabulary 

knowledge and pre-writing skills. In the areas of letter 

5 

RESEARCH QUESTIONS 

Assessment data from FACES 2000 were used to 

address the following research questions: 

1. What skills and knowledge do children 

have when they enter Head Start 

programs? 

2. Do children make significant gains in 

knowledge and skills during the Head Start 

year? During the kindergarten year? 

3. How do these gains vary across skill areas 

and among children who enter the 

program with lower or higher knowledge 

levels? 

4. Are the gains that Head Start children 

make changing? Did they change 

significantly between the 1997-1998 

program year and the 2000-2001 year? 

In making these comparisons, the analysis 

focused on children who were assessed in English 

in both the fall and spring of the Head Start year. 

Information is presented in a later section about 

the skills and knowledge of children who were 

initially assessed in Spanish because they came 

from Spanish-speaking homes and their 

knowledge of English was insufficient for testing 

in English in the fall. 

7
Head Start FACES 2000 CHAPTER I 

recognition and knowledge of book and print 

conventions, children in 2000-2001 made significantly 

greater gains than Head Start children had in 1997­

1998. In the areas of vocabulary and early math, gains 

were similar across the two studies. Children who 

entered Head Start with lower skill levels made greater 

gains than those who entered with higher skill levels, as 

was the case in the first round of FACES. 

A. Emergent Literacy and 

Mathematics Skills of Head Start 

Children Compared With Those of the 

General Population of Preschoolers 

A primary focus of FACES was to measure the 

knowledge and skills children brought with them when 

they entered the Head Start program, and how this 

varied across academic skill areas. Cognitive measures 

with national norms available for comparison with the 

Head Start results included tests in vocabulary, early 

writing, letter identification, and early math. 

Majority Entered Head Start With Academic Skills 

Below National Norms 

As in FACES 1997, the majority of children who 

entered Head Start in fall 2000 came into the program 

with early literacy and numeracy skills that were less 

developed than those of most children of the same age. 

This was to be expected with a group of young 

children who came from families with low parent 

education and income levels. The association between 

family socioeconomic status and children’s 

achievement has often been demonstrated in education 

research (e.g., Phillips, Brooks-Gunn, Duncan, 

Klebanov, & Crane, 1998). FACES found that Head 

Start entrants had a mean standard score of 85.3 on the 

Peabody Picture Vocabulary Test (PPVT-III). They had 

mean standard scores of 85.1 on the Dictation (early 

writing) task of the Woodcock-Johnson Revised (WJR) achievement battery; 87.9 on the Applied Problems 

(early math) task, and 92.4 on the Letter-Word 

Identification (pre-reading) task. Standard scores are 

constructed to have an overall mean of 100 and a 

standard deviation of 15, and are based on a sample of 

children of a given age, across all income levels. Thus, 

the literacy and number skills that the average Head 

Start child brought to the program were from half a 

standard deviation to a full standard deviation below 

national averages. These scores imply that the typical 

Head Start child was at about the 16th percentile in 

vocabulary and early writing skills, at about the 31st 

percentile in letter recognition, and at about the 21st 

percentile in early math, when compared to the full 

spectrum of American children in the same age range. 

Diversity in Skills at Program Entry 

Though most children had below-average literacy 

skills, FACES 2000 found considerable diversity in the 

Head Start population (Figure 1.1). For example, 

mean standard scores for the highest quarter of 

children entering Head Start were at national averages: 

102.8 in vocabulary, 104.0 in letter recognition, 104.7 

in early math, and 101.1 in early writing skills. Thus, 

these students would rank above the 50th percentile 

when compared to all U.S. preschoolers. On the other 

hand, mean standard scores for the lowest quarter of 

Head Start children were two standard deviations or 

more below national averages: e.g., 67.0 in vocabulary 

and 70.8 in early writing skills. These scores would 

rank the bottom quarter of Head Start students in the 

lowest 2 percent of all U.S. preschoolers. Similar 

diversity of skills was found in FACES 1997. 

B. Change in Knowledge and Skills 

Over the Head Start Year 

A primary focus of FACES was to measure the extent 

of change in children’s knowledge and skills from the 

fall to the spring of the Head Start year, especially in 

comparison to national averages for all children of the 

same ages. These changes were meant to serve as key 

8 
CHAPTER I Head Start FACES 2000 

Figure 1.1. Most Children Entering Head Start Have Academic Skills Below National Norms 

110.0 

104.0 104.7 

102.8 

101.1 

100.0 

90.0 

80.0 

70.0 

60.0 

50.0 

85.3 85.1 

92.4 

87.9 

67.0 

70.8 

82.9 

69.0 

Vocabulary Early Writing Letter Identification Early Math 

Skill Area 

Mean Standard Score (Norm = 100)

Mean 

Lowest Quartile 

Highest Quartile 

SOURCE: Head Start Family and Child Experiences Survey (FACES), fall 2000, children assessed in English in both fall and spring SOURCE: Head Start Family and Child Experiences Survey (FACES), fall 2000, children assessed in English in both .

fall and spring. 

indicators of the extent to which programs were 

enhancing children’s school readiness. 

Gains in Vocabulary Knowledge and Writing Skills 

Children in Head Start showed significant expansion of 

their vocabularies between the beginning and end of 

the program year. By the spring of the Head Start year, 

mean standard scores were 89.1 for the PPVT-III and 

87.1 on the WJ-R Dictation writing task. The mean 

standard score on the vocabulary test went up by 3.8 

points (p < .001), or more than one quarter of a 

standard deviation. The mean standard score on the 

writing task increased by 2.0 points (p < .05), or .15 

of a standard deviation (Figure 1.2). The vocabulary 

gain seen in FACES 2000 was very comparable to that 

observed in FACES 1997, while the early writing gain, 

though still significant, was smaller (p = .0028)6 

(Figures 1.3, 1.4). 

While the gains shown by Head Start children from fall 

to spring were relatively modest, they fell within the 

range that has been deemed “educationally meaningful” 

(Rosenthal & Rosnow, 1984). They were in line with 

earlier findings on the immediate effects of Head Start 

on children’s intellectual performance (Haskins, 1989, 

p. 277; McKey et al., 1985). On the other hand, the 

vocabulary gains found in Head Start were about half 

the size of standard-score gains in IQ and achievement 

that have been obtained in some earlier studies of more 

intensive interventions with children from 

disadvantaged families (Barnett, 1998, pp. 13-14). 

6 The fall and spring mean scores and gains are those for children who were assessed in English in both fall and spring. These figures 

are comparable to those reported for FACES 1997. For information about the mean scores and gains of Spanish-speaking children 

from language-minority families, see below. 

9 
Head Start FACES 2000 CHAPTER I 

Figure 1.2. Head Start Students Show Gains in Vocabulary and Early Writing Skills During Program Year 

100.0

95.0

92.9 92.4

Mean Standard Score

90.0 89.1 89.0

87.9

87.1

85.3 85.1

85.0

80.0

75.0

70.0

Vocabulary (PPVT-III) Early Writing Letter Identification (WJR Early Math (WJR Applied 

(WJR Dictation) Letter-Word ID) Problems) 

Skill Area 

Mean at Beginning 

Mean at Year End 

SOURCE: Head Start Family and Child Experiences Survey (FACES), fall 1997-spring 1998, fall 2000-spring 2001, children receiving English language

assessments both times.

SOURCE: Head Start Family and Child Experiences Survey (FACES), fall 2000 and spring 2001, children 

assessed in English in both fall and spring. 

Figure 1.3. Vocabulary Standard Scores of Children in Fall and Spring of Head Start Year: FACES 1997 Versus 

FACES 2000 

100

90

80

70

83.5 85.3 

87.7 89.1 

4.2 3.8 

FACES 1997 FACES 2000 

Mean Standard Score

60 

50 

40 

30 

20 

10 

Fall 

Spring 

Difference 

0 

SOURCE: Head Start Family and Child Experiences Survey (FACES), fall 1997-spring 1998, fall 2000-spring 2001, 

children receiving English language assessments both times. 

10 
CHAPTER I Head Start FACES 2000 

Most children left Head Start with vocabulary 

knowledge and early writing skills that were still below 

national averages. 

Greater Gains in Letter Recognition and Book 

Knowledge 

In recent years, the national Head Start program has 

been stressing the importance of early literacy skills 

like letter recognition. FACES 2000 found that Head 

Start children were making greater gains in letter 

recognition from fall to spring than was the case in 

1997-1998. In fall 2000, the typical child was found to 

enter the program knowing about four letters (3.9) of 

the alphabet, and to leave knowing about nine (8.9). 

The equivalent gain observed in FACES 1997 was from 

about 3 letters (3.2) to seven letters (7.2).7 This meant 

that children in the FACES 2000 cohort held their own 

in terms of standard score gains on the WJ-R LetterWord Identification task, whereas in FACES 1997, they 

actually showed a small decline in standard scores from 

fall to spring (Figure 1.5). The difference between the 

change observed in FACES 2000 and that observed in 

FACES 1997 was statistically significant (p = .0005). 

While this finding indicates that children were making 

more progress in Head Start in the important prereading skill of letter recognition, the result shows that 

Head Start programs still have a way to go in bringing 

children closer to or even up to the national norm. The 

Early Childhood Longitudinal Study of the 

Figure 1.4. Early Writing Standard Scores of Children in Fall and Spring of Head Start Year: FACES 1997 Versus 

FACES 2000 

100 

88.2 90 87.1 85.1

80

70

60

FACES 1997 FACES 2000 

Fall

50

84.3 

Spring 

Difference 

40 

30 

20

10

3.8 2.0

0

Mean Standard Score

SOURCE: Head Start Family and Child Experiences Survey (FACES), fall 1997-spring 1998, fall 2000-spring 2001, 

children receiving English language assessment both times. 

7 The mean number of letters known out of the entire English alphabet are estimates based on the mean scores children received on 

the WJ-R Letter-Word Identification (LWI) task. Estimates were derived using a known relationship between scores on the WJ-R LWI 

and a Letter Naming task in which children are shown all the letters of the alphabet. There are no national norms on the number of 

letters of the alphabet the typical 4-year-old can name. For further details, see the Appendix. 

11 
000 CHAPTER I 

SOURCE: Head Start Family and Child Experiences Survey (FACES), fall 1997-spring 1998, fall 2000-spring 2001, children receiving English language

assessments both times.

Head Start FACES 2

Figure 1.5. Letter Identification Standard Scores of Children in Fall and Spring of Head Start Year: FACES 1997 

Versus FACES 2000 

100 

80 

60 

91.2 92.4 90.0 

92.9 

FACES 1997 -1.2 

0.5 

FACES 2000 

Fall

40 Spring 

Difference 

20 

0 

Mean Standard Score

-20

SOURCE: Head Start Family and Child Experiences Survey (FACES), fall 1997-spring 1998, fall 2000-spring 2001,

children receiving English language assessments both times.

Kindergarten Class of 1998 found that a majority of 

U.S. children knew the letters of the alphabet upon 

entering kindergarten (Zill & West, 2001). 

Preliminary findings from randomized intervention 

studies conducted in Head Start programs in New York 

state as part of the Head Start Quality Research 

Consortium studies suggest that children in Head Start 

can make strikingly larger gains in letter recognition 

and related skills with certain research-based, literacyfocused curricula (Fischel, Storch, Spira, & Stolz, 

2003). 

Head Start children in FACES 2000 demonstrated 

greater gains in knowledge of book and print 

conventions than they had in FACES 1997. In both 

studies, when Head Start children were asked, they 

could show the assessor the front of a storybook and 

open it to where the adult should start reading. But 

children in FACES 1997 showed no advance in this sort 

of book knowledge between the fall and the spring. 

Mean scores on this task were 1.83 in the fall and 1.85 

in the spring. By contrast, in FACES 2000, mean 

scores went from 1.61 in the fall to 2.46 in the spring. 

This was a statistically significant gain (p < .0001), 

with the raw score change equal to two thirds of a 

standard deviation. The difference in fall-spring gains 

from FACES 1997 to FACES 2000 was statistically 

significant (p < .0001). 

Little Gain in Early Math Skills 

Children in FACES 2000 showed very slight gains in 

early math skills with respect to national averages. 

Their mean standard scores on the WJ-R Applied 

Problems task went from 87.9 in the fall to 89.0 in the 

spring. While the gain of 1.2 standard score points was 

statistically significant (p < .05), it amounted to only 

12 
CHAPTER I Head Start FACES 2000 

Figure 1.6. Early Math Standard Scores of Children in Fall and Spring of Head Start Year: FACES 1997 Versus 

FACES 2000 

100 

87.9 89.0 

Mean Standard Score

90 85.8 87.0 

80 

70 

60 

50 

40 

30 

20 

10 

0 

1.2 

FACES 1997 FACES 2000 

Fall 

Spring 

Difference 

1.2 

SOURCE: Head Start Family and Child Experiences Survey (FACES), fall 1997-spring 1998, fall 2000-spring 2001, SOURCE: Head Start Family and Child Experiences Survey (FACES), Fall 1997-Spring 1998, Fall 2000-Spring 2001, children receiving English language

children receiving English language assessments both times. assessments both times.

.08 of a standard deviation. The difference in fallspring gains from FACES 1997 to FACES 2000 was not 

statistically significant (p = .87) (Figure 1.6). 

Greater Gains for Those Who Entered With Lower 

Skills 

Like the 1997 study, FACES 2000 found that children 

who came to Head Start with lower early literacy and 

math skills made greater gains in the program than 

those who came with average skills (Figure 1.7). For 

example, whereas the average gain in vocabulary 

standard scores was 3.8 points, the mean gain for those 

in the lowest quarter of the distribution was 8.4 points. 

This amounted to more than half of a standard 

deviation. By contrast, children who were in the 

highest quarter of the distribution in the fall showed no 

gain (-0.5) in standard scores by spring. (Differences 

between mean gain in lowest quartile and overall mean 

gain, in highest quarter and overall mean gain, and 

between mean gains in lowest and highest quartiles 

were all statistically significant, p < .001.) However, 

this finding may be related in part to the tendency of 

scores to move close to the population mean over 

successive assessments. 

FACES 2000 found a similar picture with respect to 

changes in early writing and math skills (Figure 1.7). 

With respect to letter recognition, there was evidence 

of a slight but significant increase in standard scores 

(1.26, or .08 of a standard deviation) for children in 

the lowest quartile in the fall. In FACES 1997, the 

lowest quartile group had not shown even this 

relatively slight gain in standard scores. 

Despite the greater gains shown by children who 

entered in the lowest quarter of the Head Start 

population, these children ended the year with skills 

that were still well below average. 

13 
Head Start FACES 2000 CHAPTER I 

C. Emergent Literacy Gains of 

Language-Minority Children 

In FACES 2000, children whose English-language skills 

were not sufficient to enable them to be assessed fully 

in English, and whose parents spoke Spanish at home, 

were given a Spanish-language version of the FACES 

battery in the fall. In the spring, these children 

received the full assessment battery in English. 

However, these English-language learners also received 

two additional tests that enabled the research team to 

track their language development and pre-reading 

skills in both English and Spanish. In the fall, they 

received two components of the battery—vocabulary 

and letter identification—in English as well as Spanish. 

In the spring, they received the same two components 

in Spanish as well as English. Thus, the levels of 

proficiency achieved and the gains made in vocabulary 

and letter identification could be determined in both 

languages.8 

The dual assessment procedure made it possible to 

investigate the following research questions: 

1. How do the literacy levels and gains of languageminority children in Head Start compare with 

those of language-majority children? 

2. How do the literacy levels and gains of these 

children vary across the two languages? 

Of course, in comparing children to test norms, a 

different set of norms has to be used for test 

performance in Spanish. The Spanish-language 

Figure 1.7. Children Who Enter Head Start With Lower Skills Show Larger Gains in Comparison to National 

Norms 

10.0 

8.4 

8.0 

6.0 

4.0 3.8 

2.0 

0.5 

1.2 

7.0 

1.3 

5.9 

-0.5 

-2.7 

-0.6 

-3.2 

Vocabulary Early Writing Letter Identification Early Math 

Mean 

Lowest Quartile 

Highest Quartile 2.0 

0.0 

-2.0 

-4.0 

Skill Area 

Difference in Mean Standard Scores from fall to spring of Head

Start year

SOURCE: Head Start Family and Child Experiences Survey (FACES), fall 2000 and spring 2001, children 

receiving assessments in English both times. 

English-language learners whose home language was something other than Spanish were not given any direct assessment in their 

native languages. They received only the full assessment battery in English in the spring. For further details, see the Appendix. 

14 

8 
CHAPTER I Head Start FACES 2000 

knowledge of Head Start children from Spanishspeaking homes was compared to norms based on 

samples of children drawn in Mexico and Puerto Rico 

combined (Dunn, Padilla, Lugo, & Dunn, 1986). 

Knowing the Meaning of English Words 

Spanish-speaking children in Head Start entered with 

English vocabulary skills considerably behind those of 

children who came from households where English 

was the primary language spoken in the home. They 

made greater gains over the course of the Head Start 

year. But their English vocabulary knowledge 

remained behind that of other children at the end of 

the year. The mean standard score on the PPVT-III for 

Spanish-speaking children in fall 2000 was 59.7, two 

standard deviations below the national norm. By the 

spring, their mean score had risen to 66.7, a gain of 7 

points. But this was still more than 20 points (or one 

and a third standard deviations) lower than the mean 

score in vocabulary for language-majority children in 

Head Start (Figure 1.8). 

When the vocabulary scores of language-minority and 

language-majority children were added together, the 

combined Head Start student population began the 

year with a mean standard score of 81.4, and ended the 

year with a mean standard score of 85.7. This 

represented a gain of 4.3 standard score points. 

Identifying Letters in English 

Spanish-speaking children in Head Start entered with 

English letter identification skills that were only 

slightly behind those of language-majority children. 

However, they did not make gains in these skills over 

the course of the year, at least not in comparison to 

national averages.The mean standard score of Spanishspeaking children tested in English on the WJ-R 

Letter-Word Identification test for Spanish-speaking 

children in fall 2000 was 89.5, about two thirds of a 

standard deviation below the national norm. By the 

spring, their mean score was 87.5, which was not 

significantly different from the fall score (Figure 1.9). 

When the letter identification scores of languageminority and language-majority children were added 

together, the combined Head Start student population 

began and ended the Head Start year with a mean 

standard score of 91.9. 

Changes in English Language Skills Versus Changes 

in Spanish Skills 

The Spanish vocabulary knowledge of Spanish-speaking 

Head Start children was quite comparable, in standard 

score terms, to the knowledge levels of Englishspeaking Head Start children in English. While the 

Spanish children made gains in English vocabulary 

knowledge during the year, they did not lose ground in 

Spanish vocabulary knowledge. The mean standard 

score for Spanish vocabulary knowledge was 84.9 in 

the fall and 84.4 in the spring (Figure 1.10). 

The ability of Spanish-speaking Head Start children to 

identify letters of the alphabet in Spanish was 

comparable, in standard score terms, to the ability of 

English-speaking Head Start children to identify letters 

in English. It was also comparable to their own ability 

to identify letters in English. However, the Spanishspeaking children showed no gains against norms in 

their ability to identify letters in either language. The 

mean standard score for Spanish letter identification 

was 89.6 in the fall and 86.2 in the spring (Figure 

1.10). 

D. Growth of Knowledge and Skills in 

Kindergarten 

Assessment data are not yet available from FACES 

2000 on the knowledge and skills of Head Start 

graduates by the time they reach the end of 

kindergarten. But analysis show something about the 

kindergarten experiences of Head Start graduates from 

longitudinal follow-ups to FACES 1997. For example, 

whereas Head Start children in 1997-1998 could 

identify about one third of the letters of the alphabet by 

the end of Head Start, by the end of kindergarten 82 

15 
Head Start FACES 2000 CHAPTER I 

Figure 1.8. English-Language Vocabulary Skills of Head Start Children: Spanish-Language Minority, Language 

Majority, and Combined Population 

100.0

Mean Standard Score on PPVT-III 

90.0

80.0

70.0

60.0

59.7

85.2 

66.7

89.1 

85.7

81.4

7.0

3.8 4.3

Language Minority Language Majority Combined 

Fall 

Spring 

Difference 

50.0

40.0

30.0

20.0

10.0

0.0

Figure 1.9. Letter-Identification in English By Head Start Children: Spanish-Speaking Language Minority, Language 

Majority, and Combined Population 

89.5 

92.4 91.9 

87.5 

92.9 91.9 

-2.0 

0.5 0.0 

0.0 

20.0 

40.0 

60.0 

80.0 

100.0 

Language Majority Combined 

Mean Standard Score on WJ-R LWI Task

Fall 

Spring 

Difference 

Language Minority 

16 
CHAPTER I Head Start FACES 2000 

percent could identify most or all letters. More than 

half—52 percent—could recognize beginning sounds 

of words by the end of kindergarten. 

Analysis from assessments also show in follow-ups to 

FACES 1997 that Head Start children continue to 

make advances against national norms during their first 

year in elementary school. For example, vocabulary 

standard scores went from 88.0 at the end of Head 

Start to 92.7 at the end of kindergarten. This was an 

increase of 4.7 points, or nearly one third of a standard 

deviation. Similarly, early math scores went from a 

mean standard score of 86.5 at the end of Head Start 

to a mean of 92.3 at the end of kindergarten. This was 

a gain of 5.8 points, or nearly 40 percent of a standard 

deviation. Early writing scores showed a gain in 

kindergarten of more than half a standard deviation. 

They went from 86.5 at the end of Head Start to 92.3 

at the end of kindergarten, an increase of 8.3 points 

(Figure 1.11). Similar analysis will be accomplished on 

follow-ups to the FACES 2000 sample, once data 

collection is completed in spring 2003. 

Another important finding from the follow-up studies 

to FACES 1997 was that children’s cognitive scores in 

Head Start were predictive of their achievement by the 

end of kindergarten. Not only were children’s scores at 

the end of Head Start predictive, the size of the gains 

they made in Head Start from the skill levels at which 

they entered the program were predictive of later 

achievement. The evidence for these conclusions is 

presented in Chapter VII of this report. These 

conclusions support the predictive validity of the 

cognitive assessments carried out in FACES. 

Figure 1.10. Vocabulary and Letter-Identification Skills in English and in Spanish of Head Start Children From 

Spanish-Speaking Language-Minority Families 

100.0 

80.0 

60.0 59.7 

84.9 

89.5 

84.4 

89.6 

66.7 

87.5 86.2 

7.0 

-0.6 -2.0 -3.4 

Vocabulary Vocabulary Letter I.D. Letter I.D. 

in English in Spanish in English in Spanish 

Fall 

40.0 Spring 

Difference 

20.0 

0.0 

-20.0 

Mean Standard Score

17 
Head Start FACES 2000 CHAPTER I 

Figure 1.11. Head Start Graduates Show Further Progress Toward National Norms in Kindergarten 

100.0 

96.2 

95.0 

90.0 

85.0 

End of Head Start 

End of Kindergarten 

80.0 

75.0 

70.0 

88.0 87.9 

86.5 

92.7 92.3 

Vocabulary (PPVT-III) Early Writing (WJR Dictation) Early Math (WJR Applied Problems) 

Skill Area 

Mean Standard Score

SOURCE: Head Start Family and Child Experiences Survey (FACES), spring 1998 and 1999 (combined) and spring 1999 

SOURCE:Head Start Family and Child Experiences Survey (FACES), Spring 1998 and 1999 (combined) and Spring 1999 and 2000 (combined), Ns = and 2000 (combined), Ns = 1215-1252. 1215-1252.

CONCLUSIONS 

Cognitive assessment data from FACES 2000 showed 

that most children entered Head Start with early 

literacy and math skills well below national averages. 

The typical Head Start child was found to enter at 

about the 16th percentile in vocabulary and early 

writing skills, at about the 31st percentile in letter 

recognition, and at about the 21st percentile in early 

math, when compared to the full spectrum of 

American children in the same age range. There was 

considerable diversity in skill levels among Head Start 

children, however. The highest quarter of Head Start 

children were at or above the national average (50th 

percentile) in early language and number skills, while 

the lowest quarter of children ranked in the lowest 2 

percent of all U.S. preschoolers in these areas. 

Children made gains toward national averages during 

the Head Start year, especially with respect to 

vocabulary knowledge and early writing skills. 

Children showed greater progress in letter recognition 

skills than they had in 1997-1998, but they still did not 

advance as much as the typical child in this area. Nor 

did they make gains toward national averages with 

respect to early math skills. Children who entered the 

program with lower levels of knowledge and skill 

showed larger gains during the program year, yet still 

lagged considerably behind national averages. Children 

who started with higher assessment scores in the fall 

wound up with higher scores in the spring, but showed 

less dramatic gains. Language-minority children in 

Head Start showed significant gains in English 

vocabulary skills without declines in their Spanish 

vocabulary skills. They did not gain in letter 

recognition skills. 

Based on follow-up of the 1997-1998 cohort, Head 

Start graduates showed further progress toward 

national averages during kindergarten. Gains of 

18 
CHAPTER I Head Start FACES 2000 

between a third to more than half a standard deviation 

were observed in vocabulary, math, and writing skills 

during kindergarten. Most Head Start graduates could 

identify the letters of the alphabet by the end of 

kindergarten and more than half could recognize 

beginning sounds of words. Nevertheless, Head Start 

graduates remained behind their more advantaged 

peers in early achievement. The size of gains that 

children made while in Head Start were predictive of 

their achievement levels by the end of kindergarten. 

This seems to suggest that efforts to bolster 

achievement gains in Head Start should result in higher 

achievement for program graduates in early 

elementary school. 

19 
Head Start FACES 2000 CHAPTER I 

REFERENCES

Barnett, W.S. (1998). Long-term effects on cognitive 

development and school success. Pp. 11-44 in Early 

Care and Education for Children in Poverty, edited by W.S. 

Barnett & S.S. Boocock. Albany, NY: State University 

of New York Press. 

Dunn, L.M., Padilla, E.R., Lugo, D.E., & Dunn, L.M. 

(1986). Test de Vocabulario en Imagenes Peabody. Circle 

Pines, MN: American Guidance Service. 

Fishel, J.E., Storch, S.A., Spira, E.G., & Stolz, B.M. 

(2003). Enhancing emergent literacy skills in Head Start: 

First year curriculum evaluation results. Presented at 

Biennial Meeting of the Society for Research in Child 

Development,Tampa, FL. 

Haskins, R. (1989). Beyond metaphor: The efficacy of 

early childhood education. American Psychologist, 44(2), 

274-282. 

McKey, R.H., Condelli, L., Ganson, H., Barrett, B.J., 

McConkey, C., & Plantz, M.C. (1985). The impact of 

Head Start on children, families, and communities. (DHHS 

Publication No. OHDS 85-31193). Washington, DC: 

U.S. Government Printing Office. 

Phillips, M., Brooks-Gunn, J., Duncan, G.J., Klebanov, 

P., & Crane, J. (1998). Family background, parenting 

practices, and the black-white test score gap. Pp. 103­

145 in The Black-White Test Score Gap, edited by C. 

Jencks & M. Phillips, Washington DC: Brookings 

Institution Press. 

Rosenthal, R. & Rosnow, R.L. (1984). Essentials of 

behavioral analysis: Methods and data analysis. New York: 

McGraw-Hill. 

Zill, N., & West, J. (2001). Entering kindergarten: A 

portrait of American children when they begin school. 

Washington, DC: National Center for Education 

Statistics, U.S. Department of Education. 

20 
CHAPTER II

Social Skills and Problem Behavior of Head Start Children and Their 

Adjustment to Kindergarten 

The Head Start Family and Child Experiences Survey 

(FACES) provides information about desirable and 

undesirable social behavior that children who attend 

Head Start display when they enter the program, at the 

end of the program year, and at the end of the first year 

of elementary school. The information is obtained 

through behavior ratings provided by parents, Head 

Start teachers, and kindergarten teachers. Desirable 

behavior includes cooperation with adults, friendly 

play, and caring for and sharing with other children. 

Undesirable behaviors include disruptive or overly 

aggressive behavior toward other children or defiant 

behavior toward adults, hyperactivity, excessive 

shyness, lack of self-confidence, and social withdrawal. 

Information about children’s social behavior is 

important because children’s academic achievement 

and adjustment once they get to school depend not 

only on their mastery of basic intellectual skills, but 

also on their acquisition of social skills and positive 

approaches to learning. They also depend on the 

amelioration of negative behavior patterns they may 

have acquired that are likely to be disruptive or 

counterproductive in the school setting (Pianta & 

McCoy, 1997; Zill & West, 2001). Thus, data on 

behavioral changes that occur during and after 

program participation are helpful in evaluating how 

well the Head Start program is performing in 

preparing children for school, and what changes and 

reforms may be needed to improve program 

performance.9 

This chapter focuses on social-emotional measures of school 

readiness. See Chapter I for information about cognitive 

measures and the Appendix for a complete listing of measures 

used in FACES. 

RESEARCH QUESTIONS 

Behavior rating data from FACES 2000 were 

used to address the following research questions: 

1. Do children show significant gains in social 

skills and significant declines in problem 

behavior during the Head Start year? 

2. How do gains vary across behavior areas 

and among children who enter the 

program with lower levels of social skills or 

higher levels of problem behavior? 

3. Are the behavioral gains that Head Start 

children make changing? Did they change 

significantly between the 1997-1998 

program year and the 2000-2001 year? 

4. How well do Head Start graduates adjust 

to the demands of elementary school? Is 

their behavior at the end of Head Start 

predictive of adjustment in kindergarten? 

In making these comparisons, data are presented 

on the full sample of children, including both 

those who were assessed in English in both the 

fall and spring of the Head Start year, and those 

who were assessed primarily in Spanish in the fall 

and primarily in English in the spring. 

21

9 
Head Start FACES 2000 CHAPTER II 

FINDINGS 

Children who entered Head Start in the fall of 2000 

showed gains in cooperative classroom behavior over 

the course of the program year. These gains were 

similar to but significantly larger than those shown by 

Head Start children in the 1997-1998 program year. 

Children who entered Head Start with lower levels of 

social skill made greater gains than those who entered 

with higher skill levels did. This was also the case in the 

first round of FACES. According to Head Start 

teachers, children showed small but significant declines 

in hyperactive behavior during the program year. They 

did not show significant declines in overall problem 

behavior, aggressive behavior, or withdrawn behavior. 

However, children who entered the program with high 

levels of problem behavior showed significant 

improvement, and this was true in all areas of problem 

behavior. According to Head Start parents, children 

showed significant improvement in overall problem 

behavior, aggressive behavior, and hyperactive behavior 

during the program year. Those who displayed high 

levels of problem behavior at the start of the year 

showed greater improvement than those who entered 

with average or lower levels of undesirable behavior 

did. 

A. Cooperative Classroom Behavior of 

Head Start Children 

Lead teachers in the FACES 2000 national sample of 

Head Start classes were asked to rate the cooperative 

classroom behavior of children in their classes. These 

ratings were obtained early in the program year 

(October-November) and toward the end of the year 

(April-June). The scale was partly composed of items 

dealing with helpful and compliant behavior like, 

“Follows the teacher’s directions,” “Joins an activity or 

group without being told to do so,” and “Helps in 

putting work materials or center property away.” 

Other items dealt with the child’s maturity and skill in 

interacting with other children. Examples include, 

“Invites others to join in activities,” “Waits her or his 

turn in games or other activities,” and “Accepts 

classmates’ ideas for sharing and playing.” For each 

item, the teacher was asked to judge whether the child 

behaved in the indicated way, “never,” “sometimes,” or 

“very often.”There were 12 rating items in all, and the 

scale score could range from zero (all items marked 

“never”) to 24 (all items marked “very often”). 

Head Start Children Show Gain in Cooperative 

Behavior From Fall to Spring 

The mean score on the cooperative classroom behavior 

scale for children in the fall of 2000 was 14.6. This 

meant that the typical Head Start child engaged in 

most of the specified forms of cooperative behavior at 

least “sometimes,” and a few forms, “very often.” By 

the spring of 2001, the mean score on the scale had 

risen to 16.6. This was a statistically significant gain of 

two scale score points (p < .001), or 41 percent of a 

standard deviation. It meant that the typical child was 

engaging in two additional forms of cooperative 

behavior “very often.” The fall-spring gain was slightly 

larger (by .56 points, p = .0001) than the comparable 

gain (1.41 points, p<.001) found among Head Start 

children in the 1997–1998 program year (Figure 2.1). 

Children Who Show Less Cooperative Behavior 

Initially Make Larger Gains 

There was considerable diversity in the cooperative 

behavior levels with which Head Start children entered 

Head Start. Children in the lowest quartile of the scale 

score distribution, for example, had a mean score in 

the fall of 8.6. This implied that these children “never” 

engaged in several types of cooperative behavior. 

Children in the highest quartile of the scale score 

distribution, on the other hand, had a mean score in the 

fall of 20.5. This implied that, even in the fall, those 

children engaged in most of the specified forms of 

cooperative behavior “very often.” By the spring, the 

children in the lowest quartile received a mean score 

on the cooperative behavior scale of 13.3, whereas the 

22 
CHAPTER II Head Start FACES 2000 

SOURCE: Head Start Family and Child Experiences Survey (FACES), 2000-01 (n = 2,191), 1997-98 (n = 1,982).

Figure 2.1. Head Start Students Show Gains in Cooperative Classroom Behavior During Program Year 

24.0 

20.0 

16.0 

14.6 14.7 

8.6 

9.7 

16.6 16.1 

13.3 13.2 

All Children All Children Lowest Quartile Lowest Quartile 

2000-2001 1997-1998 2000-2001 1997-1998 

Fall 12.0 Spring 

8.0 

4.0 

0.0 

Mean Cooperative Behavior Scale Score -- Teacher Ratings

Group Rated and Program Year 

SOURCE: Head Start Family and Child Experiences Survey (FACES), 2000-2001 (n = 2,191), 1997-1998 (n = 1,982). 

children in the highest quartile had a mean score of 

19.8. The gain shown by the children in the lowest 

quartile in the fall—4.7 points, or nearly a full 

standard deviation—was more than double that shown 

by the average Head Start child. It was significantly 

larger than the gain shown by the average child 

(difference = 2.7, p<.001) or by children in the 

highest quartile of the fall distribution (difference = 

5.4, p<.001). 

Despite the larger gain shown by children in the lowest 

quartile in the fall, their mean score remained below 

the overall spring mean. Likewise, children in the 

highest quartile in the fall had a mean score in the 

spring that was significantly higher than the overall 

mean. This occurred despite a lack of progress (in fact, 

a slight decline) in their scale scores from fall to spring. 

Thus, while the gaps between these groups narrowed 

during the program year, they did not completely 

close. 

The larger gain shown by children who entered Head 

Start with lower levels of cooperative behavior — and 

the decline shown by children who entered with higher 

levels of cooperative behavior — may be at least partly 

attributable to the statistical phenomenon of 

“regression to the mean.” Note however, that the gain 

shown by children in the lowest quartile was 

substantially larger than the decline shown by children 

in the highest quartile, whereas if only regression to 

the mean was operating, one would expect both 

changes to be of roughly equal magnitude. The 

reliability of the teacher rating scale was sufficient (see 

Appendix) to give us confidence that the larger gain 

shown by children who entered with lower social skills 

was not merely a statistical artifact. The larger gain 

23 
Head Start FACES 2000 CHAPTER II 

SOURCE: Head Start Family and Child Experiences Survey (FACES), 2000-2001, (n = 2,073-2,130).

may indicate that Head Start is having a 

“compensatory” influence on children who entered 

with lower social skills. In the absence of a control 

group, however, we cannot say whether the larger gain 

is attributable to participation in the program or 

represents a growth spurt that would have occurred 

among these children whether or not they participated 

in the program. 

B. Problem Behavior of Head Start 

Children 

Lead teachers in the FACES 2000 national sample of 

Head Start classes were asked to rate the undesirable or 

problem behavior of children in their classes. These 

ratings were obtained early in the program year 

(October-November) and toward the end of the year 

(April-June). The Behavior Problems scale was partly 

composed of items dealing with aggressive or defiant 

behavior like, “Hits or fights with others,” “Disrupts 

ongoing activities,” and “Has temper tantrums or hot 

temper.” Other items dealt with inattentive or 

hyperactive behavior. Examples include, “Can’t 

concentrate, can’t pay attention for long,” and “Is very 

restless, fidgets all the time, can’t sit still.” A third set 

of items dealt with shy, withdrawn, or depressed 

behavior. Examples of this set include, “Keeps to 

herself or himself, tends to withdraw,” “Lacks 

confidence in learning new things or trying new 

activities,” and “Often seems unhappy, sad, or 

depressed.” For each item, the teacher was asked to 

judge whether the behavioral description was “not 

true,” “somewhat or sometimes true,” or “very true or 

often true” of the child. There were 14 rating items in 

all and the total scale score could range from zero (all 

items marked “not true”) to 28 (all items marked “very 

true or often true”). 

The teacher ratings were also used to calculate three 

subscale scores. The Aggressive Behavior subscale 

Figure 2.2. Head Start Students Show Significant Reductions in Hyperactive Behavior During Program Year 

16.0 

14.0 

12.0 

10.0 

5.6 

1.8 

1.4 1.7 

2.5 

5.2 

2.3 

1.2 

Total Problem Aggressive Hyperactive Withdrawn 

Behavior Behavior Behavior Behavior 

Fall 

8.0 Spring 

6.0 

4.0 

2.0 

0.0 

Mean Behavior Scale Score -- Teacher Ratings

Type of Problem Behavior 

SOURCE: Head Start Family and Child Experiences Survey (FACES), 2000-2001, (n = 2,073-2,130). 

24 
CHAPTER II Head Start FACES 2000 

16.0 

14.0 

12.0 

10.0 

5.6 5.5 

11.9 11.8 

5.2 5.2 

8.7 

9.2 

All Children All Children Highest Quartile Highest Quartile 

2000-2001 1997-1998 2000-2001 1997-1998 

Fall 8.0 

Spring 

6.0 

4.0 

2.0 

0.0 

Mean Problem Behavior Scale Score -- Teacher Ratings

Group Rated and Program Year 

contained 4 items and could range in value from zero 

to 8.The Hyperactive Behavior subscale was composed 

of three items and could range in value from zero to 6. 

And the Withdrawn Behavior subscale contained seven 

items and could range in value from zero to 14. 

Children Show Reductions in Hyperactive Behavior 

During Program Year 

The mean score on the Total Behavior Problems scale 

for children in the fall of 2000 was 5.6. This meant that 

the typical Head Start child engaged in several of the 

specified forms of aggressive, hyperactive, or 

withdrawn behavior at least “somewhat or sometimes,” 

or one or two forms of undesirable behavior, “often.” 

By the spring of 2001, the mean score on the scale was 

5.2. This was not a statistically significant change 

(-.37, p=.071) (Figure 2.2). The fall and spring levels 

of problem behavior were similar to those found 

among Head Start children in the 1997-1998 program 

year. (The mean in the spring of 1998 was 5.2, a 

difference of .02, p = .91.) The lack of statistically 

significant change in Total Behavior Problems was also 

indistinguishable from the change picture found in the 

earlier cohort (difference in differences = -.08, p=.57) 

(Figure 2.3). 

There was, however, a small but statistically significant 

change in mean scores on the Hyperactive Behavior 

subscale. These went from a mean of 1.36 in the fall to 

a mean of 1.20 in the spring. This was a change of -.16 

scale points (p = .008), or 10 percent of a standard 

deviation. There was a similar decline in Hyperactive 

Behavior noted in the 1997-1998 program year. (The 

difference in differences was -.003, p= .94.) 

Mean scores on the Aggressive Behavior subscale did 

not change significantly from (mean = 1.76) to spring 

Figure 2.3. Head Start Students Showed Similar Patterns of Problem Behavior in 2000-2001 and 1997-1998 

Program Years 

SOURCE: Head Start Family and Child Experiences Survey (FACES), 2000-2001 (n = 2,130), 1997-1998 (n = 1,953). 

25 
Head Start FACES 2000 CHAPTER II 

Highest Quartile in Fall Same Children in Spring 

Mean Problem Behavior Scale Score -- Teacher Ratings 

16.0 

14.0 

12.0 

10.0 

8.0 

6.0 

4.0 

2.0 

0.0 

11.9 

4.4 

3.0 

5.9 

8.7 

3.4 

2.1 

4.0 

Total Problem Aggressive Hyperactive Withdrawn 

Behavior Behavior Behavior Behavior 

Type of Problem Behavior 

(mean = 1.71) of the program year (difference = 

-0.05, p=.492). An apparent decline in mean scores 

on the Withdrawn Behavior subscale from fall (mean = 

2.50) to spring (mean = 2.32) was of only marginal 

statistical significance (difference = -0.18, p=.06). 

The subscale scores and change patterns were similar 

to those found in the 1997-1998 program year. 

Children Who Display More Problem Behavior 

Initially Show Improvement 

There was substantial diversity in the frequency and 

severity of problem behavior that children displayed on 

entry to Head Start. For example, children in the 

highest quartile of the Total Behavior Problems scale 

score distribution, for example, had a mean score in 

the fall of 11.9. This meant that these children 

displayed nearly a dozen of the specific types of 

problem behavior covered in the scale at least 

“somewhat or sometimes.” Or they displayed about half 

a dozen forms of such behavior “often.” By contrast, 

children in the lowest quartile of the scale score 

distribution had a mean score in the fall of 0.80. 

Similar diversity was found for the Aggressive, 

Hyperactive, and Withdrawn Behavior subscales. 

Children who entered the program with relatively high 

levels of problem behavior showed significant declines 

in their behavior scores from fall to spring. For children 

in the highest quartile in the fall, the mean score on the 

Total Behavior Problems scale declined by more than 

three points (-3.23. p<.001), or two thirds of a 

standard deviation. The mean score on the Withdrawn 

Behavior subscale declined by nearly two points 

(-1.88, p<.001), while mean scores on the Aggressive 

(-1.02, p<.001) and Hyperactive (-0.94, p<.001) 

subscales declined by about one point each (Figure 

2.4). On the other hand, children in the lowest quartile 

of the problem behavior distribution in the fall showed 

Figure 2.4. Head Start Students Who Enter With High Levels of Problem Behavior Show Improvement During 

Program Year 

SOURCE: Head Start Family and Child Experiences Survey (FACES), 2000-2001, (n = 2,073-2,130). 

26 
CHAPTER II Head Start FACES 2000 

slight increases in their mean scores from fall to spring. 

For example, children in the lowest quartile on the 

Total Behavior Problems scale showed an increase of 

1.53 points from fall to spring (p<.001). 

Despite the larger declines shown by children in the 

highest quartile in the fall, their mean scores remained 

above overall spring means. Likewise, children in the 

lowest quartile in the fall had mean scores in the spring 

that were significantly lower than the overall means. 

This despite the slight increases they showed in rated 

problem behavior. Thus, as with cooperative behavior, 

gaps between high, average, and low problem behavior 

groups narrowed during the program year, but did not 

completely close. 

The significant declines in problem behavior shown by 

children who enter with higher levels of such behavior 

may indicate that Head Start is having a 

“compensatory” influence on these children, as the 

program was intended to have. In the absence of a 

control group, however, we cannot say whether the 

larger declines are attributable to participation in the 

program or represent maturation processes that would 

have occurred among these children whether or not 

they participated in the program. Also, the changes 

that were found in the high and low behavior problem 

groups may at least partly reflect measurement error 

and “regression to the mean,” as noted in connection 

with the cooperative behavior ratings. Once again, 

however, we note that the changes were much larger in 

the group that showed initial difficulties than in the 

group that was relatively problem-free at program 

entry. 

Parents Report Improvements in Children’s 

Problem Behavior During Head Start 

Parents of children studied in FACES 2000 were asked 

to rate the problem behavior of their children, using 

scales similar to those completed by teachers. The fall 

and spring behavior ratings completed by parents 

showed significant improvements in overall problem 

behavior.They showed improvements in the Aggressive 

as well as the Hyperactive subscale. Significant 

declines were found not just among children who 

entered the program with high levels of problem 

behavior, but in overall mean scores as well (Figure 

2.5). 

Scores on the Total Behavior Problem scale completed 

by parents could range from zero to 24. Mean scores 

went from 6.17 in the fall to 5.64 in the spring. The 

decline of 0.52 points was statistically significant (p < 

.001) and represented a change of 14 percent of a 

standard deviation. Scores on the Aggressive Behavior 

subscale could range from zero to 8. Mean scores went 

from 3.15 in the fall to 2.85 in the spring (difference 

= -0.31, p < .001). Scores on the Hyperactive 

Behavior subscale could range from zero to 6. Mean 

scores went from 1.87 in the fall to 1.66 in the spring 

(difference = -0.22, p < .001). Scores on the 

Withdrawn Behavior subscale could range from zero to 

6. Mean scores were 0.61 in the fall and 0.63 in the 

spring (difference = 0.02, p = .539). 

Parent behavior ratings in the 2000-2001 FACES 

sample showed lower levels of problem behavior in 

their children at the end of the program year than had 

parents in the 1997-1998 FACES study. This was the 

case for Total Behavior Problems (p = .023),Aggressive 

Behavior (p = .025), and Withdrawn Behavior (p < 

.001). The FACES 2000 parents also reported larger 

declines in problem behavior from fall to spring than 

did the FACES 1997 parents. This was the case for 

Total Behavior Problems (p < .002) and Aggressive 

Behavior (p < .001), but not for Hyperactive or 

Withdrawn Behavior. 

Parent Ratings Show Greater Improvement for 

Children With Higher Initial Levels of Problem 

Behavior 

As found with the behavior ratings completed by 

27 
Head Start FACES 2000 CHAPTER II 

Figure 2.5. Parents of Head Start Children Report Reductions in Problem Behavior During Program Year 

16.0 

14.0 

12.0 

10.0 

8.0 Fall 

Spring 

6.0 

4.0 

2.0 

0.0 

6.2 

3.2 

1.9 

0.6 

1.7 

5.6 

2.8 

0.6 

Total Problem Aggressive Hyperactive Withdrawn 

Behavior Behavior Behavior Behavior 

Mean Problem Behavior Scale Score -- Parent Ratings

Type of Problem Behavior 

SOURCE: Head Start Family and Child Experiences Survey (FACES) 2000-2001. 

teachers, parent behavior ratings indicated that 

children who began Head Start with higher levels of 

problem behavior showed significant and significantly 

larger declines in such behavior during the program 

year than children who entered with average or lower 

levels of problem behavior. For children in the highest 

quartile of Total Behavior Problem ratings, mean scores 

declined more than two points from fall to spring 

(difference = -2.27, p < .001). This was significantly 

larger than the mean decline (difference of differences 

= 1.74, p < .001) and than the change shown by the 

lowest quartile children (difference of differences = 

3.03, p < .001). Children in the highest quartile of 

Aggressive Behavior or Hyperactive Behavior showed 

declines of more than one rating point. Children in the 

highest quartile of Withdrawn Behavior showed 

declines of 0.61 of a rating point. All of these changes 

were significantly larger than the corresponding 

changes for all children or for the lowest quartile of 

children (all p < .001) (Figure 2.6). 

The parent ratings of problem behavior produced 

results that were generally consistent with the results 

from teacher ratings of problem behavior. Both sets of 

ratings support the conclusion that children show 

significant improvement in undesirable behavior, and 

that this improvement is more pronounced for 

children who enter Head Start with higher levels of 

problem behavior. Parents were also asked to rate 

social skills and positive approaches to learning in their 

children. Social skills items were similar to those 

presented to teachers (e.g., “Makes friends easily,” 

“Comforts or helps others,” and “Accepts friends’ ideas 

in sharing and playing”). Examples of the approaches 

to learning items included, “Enjoys learning,” “Likes to 

try new things,” and “Shows imagination in work and 

play.” There were seven items in this scale, and scores 

could range from zero (meaning all the items were 

rated “not true” of the child) to 14 (meaning all the 

items were rated “very true or often true” of the child). 

28 
CHAPTER II Head Start FACES 2000 

On this scale, parent ratings did not show overall 

change from fall to spring of the program year. Part of 

the reason for this may be that, even in the fall, parents 

tended to rate their children as exhibiting most or all 

of these positive behaviors “very often.” Average scores 

were so high that there was not much room for further 

improvement. Mean scores were 12.1 in the fall and 

12.1 in the spring (difference = .004; p = .958). There 

was significant improvement, however, for those 

children who received relatively low Social Skills and 

Positive Approaches to Learning ratings from their 

parents. Children in the lowest quartile of the 

distribution in the fall showed a gain of 1.2 scale 

points, going from 9.98 in the fall to 11.18 in the 

spring (p < .001). This change was significantly 

greater than that for the overall mean (p < .001) and 

that for the highest quartile (difference = 2.22, p < 

.001). Children in the highest quartile showed a slight 

decline in their mean scores, going from a perfect 

mean score of 14.0 in the fall to a near perfect mean 

score of 12.98 in the spring (difference = -1.02, p < 

.001). 

C. Behavior and Adjustment in 

Kindergarten 

Teacher ratings data are not yet available from FACES 

2000 on the cooperative behavior and problem 

behavior of Head Start graduates by the time they 

reach the end of kindergarten. But the data reveal 

something about the kindergarten experiences of Head 

Start graduates from longitudinal follow-ups to FACES 

1997. Behavioral ratings by kindergarten teachers 

were obtained for 955 children who participated in 

Head Start during the 1997-1998 and 1998-1999 

program years. (The latter children were those, 

usually aged three at program entry, who participated 

in Head Start for two years.) The kindergarten 

teachers rated the behavior of these children on the 

Figure 2.6. Parents Report Greater Improvement in Head Start Students Who Enter With High Levels of Problem 

Behavior 

16.0 

14.0 

12.0 

10.0 

8.0 

6.0 

4.0 

2.0 

0.0 

Mean Problem Behavior Scale Score -- Parent Ratings 

10.4 

4.9 

3.7 

1.6 

8.1 

3.7 

2.6 

1.0 

Total Problem Aggressive Hyperactive Withdrawn 

Behavior Behavior Behavior Behavior 

Type of Problem Behavior 

Highest Quartile in Fall Same Children in Spring 

SOURCE: Head Start Family and Child Experiences Survey (FACES) 2000-2001, (n=694-883). 

29 
Head Start FACES 2000 CHAPTER II 

same Cooperative Classroom Behavior and Problem 

Behavior scales used by Head Start teachers. 

The picture that these ratings provided was generally 

consistent with that provided by Head Start teachers at 

the end of the child’s last year in Head Start. For 

example, the subset of children for whom 

kindergarten ratings were obtained had a mean score 

on the Cooperative Behavior scale of 17.7 at the end of 

Head Start.10 These same children received a mean 

score from their kindergarten teachers of 18.8. This 

implies that the Head Start graduates displayed a 

majority of the cooperative behaviors “sometimes” or 

“often.” The 1.2 scale-point increase in the mean 

scores from Head Start to kindergarten was statistically 

significant (p < .001). 

The mean score on the Total Behavior Problems scale 

was 4.27 at the end of Head Start, and 4.57 at the end 

of kindergarten. The difference in means (0.30) was 

not statistically significant (p = .208). This implies that 

Head Start graduates in kindergarten displayed 

between four and five kinds of problem behavior at 

least “sometimes,” or one or two types “often.” The 

frequency of problem behavior was similar at the end 

of Head Start and kindergarten. The one type of 

behavior that showed a slight but significant increase 

from the Head Start teacher ratings to the 

kindergarten teacher ratings was Hyperactive 

Behavior. Mean scores on this subscale went from 0.88 

at the end of Head Start to 1.20 at the end of 

kindergarten (difference = .31, p < .001). Mean 

scores on the Aggressive Behavior subscale were not 

significantly different (1.38 in Head Start, 1.30 in 

kindergarten, difference = -.08, p = .318). Nor were 

means on the Withdrawn Behavior subscale (1.99 in 

Head Start, 2.05 in kindergarten, difference = .06, 

p = .208). 

As shown in greater detail in Chapter VII of this report, 

behavior ratings by Head Start teachers and parents 

were predictive of children’s behavior and adjustment 

in kindergarten. When teacher and parent ratings at 

the end of Head Start were combined in a multiple 

regression equation, they related moderately well (R = 

.42) to Cooperative Behavior ratings by kindergarten 

teachers. Teacher ratings of aggressive behavior and 

cooperative behavior were the scales that showed the 

strongest relationship with cooperative behavior in 

kindergarten. A similar result was obtained with 

respect to problem behavior in kindergarten. When 

the Head Start rating scales were put into a regression 

model, they also related moderately well (R = .49) to 

Total Behavior Problems as rated by kindergarten 

teachers. The individual scales or subscales that related 

most strongly to kindergarten behavior problems were 

Head Start teachers’ ratings of aggressive behavior, 

withdrawn behavior, and cooperative classroom 

behavior. 

These findings show the importance of children’s 

behavioral adjustment in preschool as a foreshadowing 

of how they will adjust and perform in elementary 

school. They also suggest that the positive changes in 

behavior that Head Start children display during Head 

Start may well boost the odds of their doing well in 

elementary school. 

REFERENCES 

Pianta, R.C., & McCoy, S.J. (1997). The first day of 

school: The predictive validity of early school 

screening. Journal of Applied Developmental Psychology, 

18, 1-22. 

10 The fact that the mean Cooperative Behavior rating was slightly higher for the children for whom kindergarten teacher ratings were 

obtained than for the overall FACES 1997 sample suggests that the follow-up subsample was somewhat biased toward children with 

better adjustment. 

30 
CHAPTER II Head Start FACES 2000 

Zill, N., & West, J. (2001). Entering kindergarten: A 

portrait of American children when they begin school. 

Washington, DC: National Center for Education 

Statistics, U.S. Department of Education. 

31 
32
CHAPTE III

Relationship Between Curricula and Family, Program, and 

Classroom Characteristics 

CHAPTER III

There is a lack of substantial evidence about the 

relative efficacy of various types of standardized 

curricula available to preschool programs and their 

relationship to children’s school readiness. 

Accordingly, the Head Start Program Performance 

Standards require that programs have a curriculum, 

and delineate the areas that must be covered by it, but 

do not prescribe one. Programs may use curricula 

from a variety of sources, develop one of their own, or 

use a combination of curricula. In 2001-2002, the 

Head Start Program Information Report queried local 

programs about the curricula they use. In descending 

order of frequency, center-based programs were most 

likely to use the Creative Curriculum, High/Scope, a 

locally designed curriculum, and High Reach (beyond 

these a variety of other curricula were used). This 

chapter examines the following: the curricula Head 

Start programs in the FACES sample are using; the 

training and ongoing support teachers receive in the 

use of their curricula; teacher satisfaction with their 

curricula; and the relationships between the type of 

curricula used and child, family, program, and 

classroom characteristics. The relationship between 

curricula and Head Start classroom quality is discussed 

in Chapter IV. Chapter V discusses the relationships 

between curricula and children’s cognitive gains and 

social development in Head Start. 

METHODS 

The sample for this chapter includes 231 center-based 

Head Start teachers from 43 Head Start programs in 

the FACES 2000 study. Field staff conducted personal 

RESEARCH QUESTIONS 

In this chapter, the following research questions 

will be addressed: 

1. What percentage of Head Start programs 

use a curriculum? 

2. What types of curricula are used in Head 

Start programs? 

3. From whom do Head Start teachers 

receive training and ongoing support in the 

use of their curriculum? 

4. What percentage of Head Start teachers 

have access to a copy of their curriculum? 

5. What aspects of the curriculum do Head 

Start teachers like? 

6. What is the relationship between the type 

of curriculum used and the characteristics 

of children and families served? 

7. Are there regional and rural-urban 

differences in the types of curriculum used 

by Head Start programs? 

8. What is the relationship between the type 

of curriculum used and classroom quality? 

33
Head Start FACES 2000 CHAPTER III 

100 

80 

60 

39.1 40.9 

40

20

20

0

Creative Curriculum High/Scope Other 

Percent of Teachers 

interviews with these teachers. The monthly family 

income data (N = 1,859) and child ethnicity data (N = 

1,988) come from parent interviews conducted in fall 

2000. These parent interview numbers are less than 

the total number of parents interviewed in fall 2000 (N 

=2,488) because of non-response or missing data on 

monthly family income or child ethnicity variables. 

Weighted percentages, correlations, independentsample t-tests, and multivariate analysis of variance 

were used to answer the research questions. Data 

presented in this chapter are weighted to represent the 

universe of Head Start programs. 

FINDINGS 

A. Head Start Teachers Report Using 

a Specific Curriculum, Receiving 

Training, and Having Access to Their 

Curriculum 

Types of Curricula 

Head Start teachers were asked if they used a single 

specific curriculum, a combination of curricula, or no 

curriculum. About 70 percent of the teachers used a 

single curriculum, 21 percent used a combination of 

curricula, and 9 percent did not use a curriculum. 

Teachers who reported using a single curriculum or a 

combination of curricula were asked to name their 

principal curriculum. The majority (59.1 percent) said 

that they used either Creative Curriculum or 

High/Scope (see Figure 3.1). Almost 41 percent used 

a curriculum other than Creative Curriculum or 

High/Scope, which will be referred to as Other 

curricula. Other curricula mentioned by teachers 

were High Reach, Scholastic, Los Cantos Los Ninos, 

R.E.A.L, Global Curriculum, Creating Child 

Centered Classrooms - Step by Step, Building Bridges, 

Northern Kentucky Curriculum, Teacher Planning 

Wellbook, Therapeutic Intervention Program, Kid 

College Curriculum, Newport, and Montessori. 

Some said they used the “Head Start” curriculum or 

“theme units” (although there is no “official” Head Start 

curriculum). 

Curricular Training and Support 

Teachers were asked if they received training in their 

curriculum and who provided this training (teachers 

could respond with only one source). Ninety-three 

percent reported receiving training in their curricula. 

Of those that received training, most had received it 

from their own program staff (58.5 percent), followed 

by curricula developers (14 percent), a Head Start 

Figure 3.1. The Majority of Teachers Used Either Creative Curriculum or High/Scope 

34 
CHAPTER III Head Start FACES 2000 

Quality Improvement Center or HSQIC (10.3 

percent), another Head Start program (5 percent), a 

University School of Education (4.4 percent), or 

another source (7.9 percent). 

Almost 92 percent of the teachers received ongoing 

support in the use of their curriculum and this support 

could come from several sources. Of those who 

received support, it most often came from their 

supervisor or the education coordinator (70.3 

percent), other teachers (23.9 percent), the HSQIC 

(20 percent), curriculum developers (19.3 percent), 

or a mentor/master teacher (14 percent). Support 

also came from other Head Start programs (10.8 

percent), Schools of Education (6.6 percent), the 

disability services quality improvement center or 

DSQIC (4.1 percent), and other sources (8.6 percent). 

Teacher Access to Curricula 

Ninety-seven percent of the teachers responded that 

teachers and assistant teachers in their program had 

access to a copy of their curriculum. 

Teacher Satisfaction With Curricula 

The majority of the teachers (92.3 percent) said that 

they liked their curriculum. Figure 3.2 indicates that 

an overwhelming majority of the teachers said they 

liked their curriculum because it addressed multiple 

domains of learning (99.1 percent), was easy to use 

and adapt (98.3 percent), involved parents (96.6 

percent), had room for teacher creativity (96.4 

percent), and had adequate learning 

materials/resources/examples of activities (92.1 

percent). 

B. Relationship Between Curricula 

and Family, Program, and Classroom 

Characteristics 

Relationship Between Curricula and Family 

Characteristics 

In the context of the study’s conceptual framework 

described in Chapter IV, the relationship between the 

use of particular curricula and the characteristics of the 

families served by the programs were examined. The 

mean monthly family income of families from 

Figure 3.2. Teachers Liked Their Respective Curricula for a Variety of Reasons 

99.1 98.3 96.6 96.4 92.1 

100 

80 

60 

40 

20 

0 

Percent of Teachers 

Multiple Easy to Involves Room for Adequate 

domains use/adapt parents teacher learning 

addressed creativity materials 

35 
Head Start FACES 2000 CHAPTER III 

100 

Percent of Teachers 

80 

61.4 

60 54.4 

44.5 42.7 40.6

40

30.5 

21.3 24.5 24.9 21.1 17.3 20 16.7 

0 

Northeast Midwest South West 

Program Region 

Creative Curriculum High/Scope Other 

Figure 3.3. Most Teachers From Northeast, Midwest, and West Used Creative Curriculum or High/Scope While 

Majority of Teachers From the South Used Other Curricula 

classrooms using the High/Scope curriculum was 

$1,641.18, from classrooms using Creative 

Curriculum was $1,559.60, and from classrooms using 

Other curricula was $1,319.43. This indicates that 

classrooms using Other curricula served the poorest 

families compared to classrooms using Creative 

Curriculum or High/Scope. The percentage of nonWhite children served by teachers using Other 

curricula was 75.8 percent, for teachers using 

High/Scope it was 69.6 percent and for teachers using 

Creative Curriculum it was 48.8 percent. Thus, 

teachers using Other curricula served the highest 

percentage of non-White children. 

Relationship Between Curricula and Program 

Characteristics 

Figure 3.3 shows that more teachers from the 

Northeast (78.7 percent), Midwest (59.4 percent), 

and West (69.4 percent) used Creative Curriculum or 

High/Scope than Other curricula while the majority of 

teachers from the South used Other Curricula (54.4 

percent). 

The majority of urban teachers (51.4 percent) used 

either Creative Curriculum or High/Scope while the 

majority of rural teachers (57.5 percent) used Creative 

Curriculum. As shown in Table 3.1, although urban 

and rural teachers were equally likely to use the 

High/Scope curriculum (20 percent), urban teachers 

were more likely than rural teachers to use Other 

curricula (48.6 percent versus 22.4 percent). 

Relationship Between Curricula and Classroom 

Quality 

The relationship between curricula and classroom 

quality was examined using three different scores of 

Table 3.1. The Majority of Urban Teachers Used Either Creative Curriculum or High/Scope While the Majority of 

Rural Teachers Used Creative Curriculum 

Creative Curriculum High/Scope Other 

Urban 31.4 20 48.6 

Rural 57.5 20 22.4 

36 
CHAPTER III Head Start FACES 2000 

6 

5.02 5.03 5.04 5.12 

4.55 4.58 

4 

2 

0 

Creative Curriculum High/Scope Other Curricula 

Average ECERS-R Total & Language Scores 

Average ECERS-R Total Score 

Average ECERS-R Language Score 

classroom quality—ECERS-R Total score, ECERS-R 

Language score, and Quality Composite score. 

The ECERS-R Total score is derived from the Early 

Childhood Environment Rating Scale-Revised 

(ECERS-R), which provides a global rating of 

classroom quality based on structural features of the 

classroom. Scores can range from 1 (inadequate) to 7 

(excellent). As displayed in Figure 3.4, the average 

ECERS-R Total scores for classrooms using Creative 

Curriculum (5.02) and High/Scope (5.04) were 

significantly higher (p < .05) than those for classrooms 

using Other curricula (4.55). However, the average 

ECERS-R Total scores for High/Scope and Creative 

Curriculum were not significantly different from each 

other. 

The ECERS-R Language score is a subscale of the 

ECERS-R and assesses classroom quality as it pertains 

to encouraging language-reasoning experiences 

(Figure 3.4). Scores can range from 1 (inadequate) to 

7 (excellent). Classrooms of teachers who used 

Creative Curriculum (5.03) and High/Scope (5.12) 

had significantly higher average ECERS-R Language 

scores (p < .05) than classrooms of teachers using 

Other curricula (4.58). However, the average ECERSR Language scores of classrooms that used Creative 

Curriculum (5.03) and High/Scope (5.12) were not 

significantly different from one another. 

The Quality Composite score is derived from a 

principal components factor analysis of the ECERS-R 

Language score, Assessment Profile Scheduling raw 

score, and Assessment Profile Learning Environment 

raw score. The standardized factor scores were 

calculated, so that the scores are in standard deviation 

units, with a mean of 0 and a standard deviation of 1. 

As displayed in Figure 3.5, the classrooms of teachers 

Figure 3.4. The Classrooms of Teachers Who Used Creative Curriculum or High/Scope Had Significantly Higher 

Average ECERS-R Total Scores and ECERS-R Language Scores Than Classrooms of Teachers Who Used Other 

Curricula 

37 
Head Start FACES 2000 CHAPTER III 

Figure 3.5. The Classrooms of Teachers Who Used Creative Curriculum and High/Scope Had Significantly Higher 

Quality Composite Scores Than Classrooms Using Other Curricula 

-0.18 

0.26 0.27 

-0.25 

-0.2 

-0.15 

-0.1 

-0.05 

0 

0.05 

0.1 

0.15 

0.2 

0.25 

0.3 

Creative Curriculum High/Scope 

Other 

Average Quality Composite Score 

Table 3.2. Type of Curricula and Classroom Quality Scores (N = 228) 

Type of 

Curricula Used 

Weighted 

Percentage of 

Head Start 

Teachers 

Average Total 

ECERS-R Score 

SD Average ECERS-R 

Language Score 

SD Quality 

Composite Score 

SD 

Creative

Curriculum 

39.1 5.02 .92 5.03 1.32 0.27 .96 

High/Scope 20.0 5.04 .73 5.12 1.01 0.26 .84 

Widely Available 

Curricula 

9.8 4.82 .63 5.15 .96 0.07 .95 

All Other 

Curricula 

31.1 4.47 .86 4.4 1.19 -0.27 .95 

• Widely Available Curricula include: High Reach, Scholastic Curriculum, Newport Curriculum & Montessori. 

• All Other Curricula include: curriculum not specified, Head Start curriculum, Los Cantos Los Ninos, Theme Units, 

R.E.A.L, Global Curriculum, Creating Child Centered Classrooms - Step by Step, Building Bridges, Early Childhood 

Lesson Plan, Curriculum Workbook, Northern Kentucky Curriculum, Teacher Planning Wellbook, Therapeutic 

Intervention Program, and Kid College Curriculum 

38 
CHAPTER III Head Start FACES 2000 

6 

4 

2

Average ECERS-R Total & Language Scores 

0 Creative High/Scope Widely All Other 

Curriculum Available Curricula 

Curricula 

Average ECERS-R Total Score Average ECERS-R Language Score 

4.47 

5.12 5.15 5.02 4.82 

4.4 

5.03 5.04 

Figure 3.6. Classrooms That Used Creative Curriculum and High/Scope Had Significantly Higher Average 

ECERS-R Total Scores and ECERS-R Language Scores Than Classrooms That Used All Other Curricula 

who used Creative Curriculum (0.27) and High/Scope 

(0.26) had significantly higher Quality Composite 

scores (p < .05) than classrooms of teachers using 

Other curricula (-0.18). However, the Quality 

Composite scores of classrooms that used Creative 

Curriculum (0.27) and High/Scope (0.26) were not 

significantly different from one another. 

Other Curricula and Classroom Quality 

In order to determine the relationship of curricula 

within the “Other” category to classroom quality, 

“Other” curricula have been further categorized as 

Widely Available Curricula and All Other Curricula. 

Widely Available Curricula include High Reach, 

Scholastic Curriculum, Newport Curriculum, and 

Montessori. These curricula appear to be established 

(for example High Reach has been commercially 

available for about 17 years, Scholastic for more than 

82 years, and Montessori for 96 years), have 

information about the curricular goals and practices, 

printed materials, and in some cases have research 

available on their efficacy. All Other curricula includes 

curricula not specified by teachers, as well as curricula 

termed “Head Start”, Los Cantos Los Ninos, Theme 

Units, R.E.A.L, Global Curriculum, Creating Child 

Centered Classrooms - Step by Step, Building Bridges, 

Early Childhood Lesson Plan, Curriculum Workbook, 

Northern Kentucky Curriculum, Teacher Planning 

Wellbook,Therapeutic Intervention Program, and Kid 

College Curriculum. This group of curricula appears 

to be designed by programs locally or from sources not 

widely available. 

The classroom quality scores for Widely Available 

curricula, All Other curricula, Creative Curriculum, 

and High/Scope are presented in Table 3.2. The 

average ECERS-R Total scores and average ECERS-R 

Language scores for Creative Curriculum, 

High/Scope, and Widely Available Curricula are 

similar, although the Quality Composite score for 

Widely Available Curricula is lower (see Figures 3.6 

and 3.7). 

Multivariate analyses of variance were used to examine 

if the classroom quality scores were significantly 

different between classrooms using Creative 

39 
Head Start FACES 2000 CHAPTER III 

Figure 3.7. Classrooms That Used Creative Curriculum and High/Scope Had Significantly Higher Quality Factor 

Scores Than Classrooms That Used All Other Curricula Average Quality Composite Score 

0.3 

0.2 

0.1 

0 

-0.1 

-0.2 

-0.3 -0.27 

0.07 

0.26 0.27 

Creative 

Curriculum 

High/Scope Widely Available 

Curricula 

All Other Curricula 

Curriculum versus High/Scope, Creative Curriculum 

versus Widely Available curricula, Creative 

Curriculum versus All Other curricula, High/Scope 

versus Widely Available curricula, High/Scope versus 

All Other curricula, and Widely Available Curricula 

versus All Other curricula. Significant differences (p < 

.05) were found between classrooms using Creative 

Curriculum versus All Other on all three classroom 

quality scores; and between classrooms using 

High/Scope versus All Other on all three classroom 

quality scores. Classroom quality scores between 

classrooms using Widely Available Curricula versus All 

Other curricula were not significantly different. 

• Classrooms using the Creative Curriculum when 

compared to classrooms using All Other curricula 

had significantly higher average ECERS-R Total 

scores (5.02 versus 4.47), average ECERS-R 

Language scores (5.03 versus 4.4), and Quality 

Composite scores (0.27 versus - 0.27). 

• Classrooms using the High/Scope curriculum when 

compared to classrooms using All Other curricula 

had significantly higher average ECERS-R Total 

scores (5.04 versus 4.47), average ECERS-R 

Language scores (5.12 versus 4.4), and Quality 

Composite Scores (0.26 versus - 0.27). 

• Classrooms using Widely Available Curricula when 

compared to classrooms using All Other curricula 

were not significantly different. Classrooms using 

Widely Available Curricula were also not 

significantly different from those using Creative 

Curriculum, or those using High/Scope. Classrooms 

using Creative Curriculum were not significantly 

different from those using High/Scope. 

Overall, these findings suggest that classrooms using 

curricula such as Creative Curriculum and 

High/Scope have relatively higher classroom quality 

than Head Start classrooms using other curricula. 

40 
CHAPTER III Head Start FACES 2000 

IMPLICATIONS 

• The Head Start Performance Standards mandate that 

programs use a curriculum although curricula are 

not prescribed. Findings indicate that the great 

majority of the Head Start programs comply with 

this standard consistent with the goal of providing a 

planned, developmentally appropriate early 

childhood program for children. 

• There is a relationship between program 

characteristics (region, urban-rural, characteristics 

of children/families) and whether or not a program 

uses a curriculum and the type of curriculum used. 

•The relationship between curricula and classroom 

quality may reflect the influences of other factors 

(such as the resources available to programs for 

purchasing and training in specific curricula), or may 

demonstrate the effect of certain curricula on quality 

(see Chapter IV). Programs may want to consider 

whether using curricula related to higher levels of 

classroom quality would enhance their programs. 

41 
42
CHAPTER IV HC APTER IV

Understanding Quality in Head Start Classrooms 

Reports on the first cohort of FACES have 

demonstrated that Head Start is providing children 

with high-quality educational services as measured by 

standard observational instruments. In this chapter, we 

present results of quality analysis from the second 

FACES cohort (FACES 2000), consisting of a new 

national sample of 43 Head Start programs and 

classrooms, collected during the fall of 2000 and the 

spring of 2001. This chapter describes the nature of 

quality in Head Start programs and the factors that 

help explain variations in quality across Head Start 

classrooms. Findings about the relationship between 

classroom quality and child outcomes are described in 

Chapter V. 

A. What Quality Means and How We 

Appraise It 

A variety of indices were used to measure quality in 

FACES because of the different elements of quality 

cited in the research literature (Phillips, Mekos, Scarr, 

McCartney, & Abbott-Shim, 2000). In addition to 

“structural” indices, such as staff-child ratio (Cost, 

Quality and Outcome Studies Team, 1995; Howes, 

Phillips, & Whitebook, 1992) and group size (Ruopp, 

Travers, Glantz, & Coelen, 1979), the backgrounds 

and experiences of early childhood teachers are 

important (Cost, Quality and Outcome Studies Team, 

1995).Additionally, a variety of “process” aspects of the 

classroom environment, including teacher-child 

interactions, child-child interactions, as well as 

curriculum, the schedule of activities, and materials 

have been considered important (Bredekamp, 1986; 

Hayes, Palmer, & Zaslow, 1990; Phillips & Howes, 

1987; Scarr & Eisenberg, 1993). Recent research has 

RESEARCH QUESTIONS 

Information collected through classroom 

observations and interviews with Head Start 

teachers and parents were analyzed to answer the 

following research questions: 

1. What is the quality of Head Start 

classrooms in 2000-2001, and how does it 

compare to quality reported in 1997­

1998? 

2. What are the backgrounds, qualifications, 

and experiences of Head Start teachers in 

2000-2001, and were there any changes 

from the earlier cohort (1997-1998)? 

3. What is the relationship between the 

quality of Head Start classrooms and 

teacher qualifications, experience, and 

attitudes and knowledge; which of these 

factors seems most important in explaining 

variations in quality? 

4. Do factors beyond the classroom, such as 

the types of curricula Head Start programs 

provide, the average teacher salaries, and 

characteristics of families served by the 

Head Start program explain variations in 

the quality of Head Start classrooms? 

43
Head Start FACES 2000 CHAPTER IV 

also shown that factors “beyond the classroom door,” 

which are features of the centers in which classrooms 

are located, may have potentially powerful influences 

on children’s experiences of quality care in early 

childhood classrooms (Blau, 1997; Hofferth & 

Chaplin, 1998 cited in Phillips, Mekos, Scarr, 

McCartney, & Abbott-Shim, 2000). 

In FACES, quality was considered to include not only 

the number of children and adults in each classroom, 

but process factors such as the availability of learning 

materials, the types of classroom activities, the 

scheduling, and the variety of learning opportunities 

provided to all children. We also interviewed lead 

teachers11 in Head Start classrooms to collect teacher 

background information (experience and 

qualifications) as well as more detailed information 

about their curriculum, classroom activities, and 

attitudes and knowledge about early childhood 

education practices. 

The classroom quality measures used in FACES 2000 

provide direct evidence for the extent to which Head 

Start programs employ qualified teachers and provide 

rich learning environments and curricula for the 

children. In FACES 2000, we used the same measures 

as those employed in the earlier cohort, in order to 

make some comparisons in quality, and we added 

several key measures. The classroom quality measures 

are fully described in the Appendix, and will only be 

briefly mentioned here. 

The Early Childhood Environment Rating Scale (ECERS-R). 

This revised version (Harms, Clifford, & Cryer, 1998) 

consists of 37 scales measuring a wide variety of quality 

related processes occurring in the classroom, 

including: routines; teacher-child interaction, 

particularly in the use of language; learning activities; 

classroom tone, creative, dramatic, and gross and fine 

motor activities; equipment and furnishings; and staff 

and parent facilities. A high score on the total ECERSR indicates higher classroom quality, in terms of 

equipment, space, and play materials, as well as the 

range of activities and staff-child interactions. 

The ECERS-R Language Subscale.This measure consists of 

four items from the ECERS-R that assess the quality of 

the language environment in Head Start classrooms. A 

high score indicates a classroom with a rich language 

environment, in terms of the availability and use of 

books and printed materials, receptive and expressive 

language activities, language to engage logical and 

reasoning skills, and the informal use of language 

throughout the classroom day. 

The Assessment Profile for Early Childhood Programs: 

Research Edition Scheduling Scale. This scale from the 

Assessment Profile for Early Childhood Programs 

(Abbott-Shim & Sibley, 1998) assesses the written 

plans for classroom scheduling and how classroom 

activities are implemented. A high score indicates that 

the teacher takes a planful approach to the classroom 

schedule and has been intentional regarding a variety of 

socialization and learning experiences for children 

throughout the day. 

The Assessment Profile for Early Childhood Programs: 

Research Edition Learning Environment Scale. This scale 

from the Assessment Profile for Early Childhood 

Programs (Abbott-Shim & Sibley, 1998) measures the 

variety of learning materials available and accessible in 

the classroom that provide learning experiences in 

different developmental areas. It also assesses the 

degree to which the classroom provides for a 

“language-rich” environment through language 

learning materials as well as the labeling of objects, and 

the amount of printed material in the classroom. A 

high score on this scale is indicative of a greater variety 

The results of teacher data presented in this chapter were based on interviews and ratings of the lead or senior teacher in each 

classroom. 

44 

11 
CHAPTER IV Head Start FACES 2000 

of materials accessible, that stimulate growth in all 

developmental domains. 

The Assessment Profile for Early Childhood Programs: 

Research Edition Individualizing Scale. This is based on a 

scale from the Assessment Profile for Early Childhood 

Programs (Abbott-Shim & Sibley, 1998). For FACES 

2000 it has been revised and shortened to five 

observational items measuring how the teacher plans 

the classroom activities to meet the varying learning 

needs of each child, how the teacher keeps track of the 

children’s work during the year through the use of 

individual child portfolios, and how the teacher is able 

to accommodate children with disabilities.A high score 

indicates that teachers are able to adjust classroom 

activities to meet the learning needs of individual 

children. 

The Arnett Caregiver Interaction Scale (Arnett, 1989). This 

rating scale consists of 26 items that measure the 

teacher’s sensitivity, punitiveness, detachment, 

permissiveness, and encouragement of child 

independence and self-help skills. A high score 

indicates greater teacher sensitivity, responsiveness and 

encouragement of children’s independence and selfhelp skills, and lower levels of punitiveness and 

detachment. 

Teacher Interview. The teacher from each classroom was 

asked specific questions about the nature of the 

curriculum used, attitudes and knowledge about early 

childhood education practice, how they monitor the 

progress of individual children, and what 

accommodations the teacher makes to meet the 

learning needs of each student, including those with 

special needs. The interview also collected extensive 

information about the teachers’ backgrounds (e.g., 

age, ethnicity), experience (e.g., total years teaching, 

years teaching Head Start), and qualifications (e.g., 

whether the teacher has a BA or AA, whether the 

teacher had some graduate school education, whether 

the teacher has a Child Development Associate, the 

course of study, and licensure). Ethnicity was included 

in these analyses because it may be linked to differences 

in teacher qualifications and experience and because 

the types of teachers in the classrooms may be 

influenced by the backgrounds of the families and 

children attending the Head Start program as well as 

the larger community served by the program.12 

Teacher Attitudes and Knowledge. The 24-item Teacher 

Beliefs Scale (Burts, Hart, Charlesworth, & Kirk, 

1990) was included in the teacher interview, and 

consists of statements worded to reflect positive 

attitudes and knowledge of generally accepted 

practices in preschool settings, or to reflect a lack of 

these attitudes and knowledge. In FACES 2000 we 

used one factor comprising 9 items that explained 

most of the variation in scores for the entire scale. A 

high score indicates higher positive attitudes and 

knowledge about early childhood education practices. 

Quality Composite Score. We found that several of the 

key quality indicators were highly correlated with each 

other, suggesting that for analytical purposes we can 

explain a greater amount of variation in quality by 

reducing the three indicators in question to one 

measure. Scores from the ECERS-R Language Scale 

score and the Assessment Profile Scheduling and 

Learning Environment were combined to form this 

single score for quality. A higher score indicates higher 

levels of quality. 

Child:Adult Ratio. Classroom observers counted the 

number of children, the number of adults, and the 

number of paid staff at two time periods during the 

12 While age was also included in the teacher interview, it was so highly correlated with the teacher’s years of experience that it was 

not included in analysis beyond the descriptive level. However, since ethnicity did prove to be related to other factors in classroom 

quality, we continued to include it in our analysis. 

45 
Head Start FACES 2000 CHAPTER IV 

classroom day. The two occasions were separated by at 

least one hour and involved one structured (teacherdirected) and one unstructured activity. A higher 

child-adult ratio is indicative of lower quality. 

During the fall 2000 data collection period, which 

operated from September 2000 to December 2000, 

we collected classroom quality and teacher data from 

225 Head Start centers in 43 programs around the 

country. We observed 278 classrooms out of 286 

possible for a completion rate of 97 percent.13 Percent 

agreement between two independent observers in a 

sample of classrooms averaged 93.5 percent for the 

Assessment Profile Scheduling Scale, 87.9 percent for 

the Assessment Profile Learning Environment Scale, 

and 86.7 percent for the Assessment Profile 

Individualizing Scale. Percent agreement across all 

ECERS scales (which includes direct hits and being off 

by one on a seven-point scale) averaged 79.5 percent, 

agreement on the ECERS-R Language subscale 

averaged 85.7 percent, and agreement on the 

Caregiver Interaction Scale averaged 93.9 percent. 

FINDINGS 

B. The Quality of Most Head Start 

Classrooms Continues To Be Good 

In the first FACES cohort, fall 1997 to spring 1998, we 

reported that the quality of Head Start classrooms was 

good and above that usually found among center-based 

preschools. In FACES 2000, using a new sample of 43 

programs, we continue to find that quality is good and 

above that found among other center-based 

preschools. Figure 4.1 compares the two FACES 

cohorts with other studies using the overall ECERS 

score as the measure of quality. Not only is the average 

score higher, but the variation is less than that found in 

many other preschool settings.14 

The Early Childhood Environment Rating Scale 

(ECERS) 

In the new cohort of 43 programs, the overall average 

ECERS-R score for the 258 classrooms observed in fall 

2000 was 4.84 (with a standard deviation of .87). This 

compares with an overall average ECERS score for the 

518 classrooms in the first FACES cohort (fall 1997) of 

4.93 (with a standard deviation of 0.63). While the 

FACES 2000 average score is comparable to that found 

earlier, the variability in the scores was higher, and 

approached that reported by the test developers for the 

revised measure. Overall, the average scores are 

consistent and indicate that Head Start classrooms 

continue to show good quality. 

Figure 4.2 compares the FACES 1997 and FACES 2000 

cohorts on the ECERS total score, with 1 indicating 

“inadequate” quality and 7 indicating “excellent” 

quality. In FACES 1997, no classrooms were scored in 

the “inadequate” range (1 or 2) and only 4 of 518 

classrooms were scored in the “minimal” range (score 

of 3). In FACES 2000, using the revised version of the 

ECERS which more strictly differentiated the highest 

and lowest ends of the scale, 5 of 258 classrooms (1.9 

percent) were scored in the “inadequate” range and 15 

of 258 classrooms (5.7 percent) scored in the 

“minimal” range. Thus, compared to the earlier 

cohort, in FACES 2000 a small number of classrooms 

were rated lower in quality, but low-scoring 

classrooms still represented only 20 of 258 classrooms 

overall (7.6 percent). On the other hand, the number 

of classrooms rated “excellent” (ECERS scores of 6 or 

higher) increased from FACES 1997 to FACES 2000. 

13 Due to some missing data among one or more of the quality indicators, the analytic sample was comprised of 258 classrooms. 

It should be noted that, with the exception of the New Jersey study, all other studies used the original ECERS, and at this writing 

there are no published reports attesting to the appropriateness of comparing scores on the two versions. 

46 

14 
CHAPTER IV Head Start FACES 2000 

Figure 4.1. Classroom Quality in Head Start Compared With Other Preschool and Child Care Settings 

4.0 

7 

6 

4.0 4.2 4.5 4.5 4.6 4.9 

4.8 4.9 

Mean ECERS Score 

5

4

3 

2 Minimal Quality 

1 

Head Start Head Start 39 246 38 50 42 120 227 

FACES fall FACES fall Head Start Community School- Non-profit Child care Preschool Preschool 

2000 (258 1997 (518 classrooms and School- based Child care Centers Centers Centers 

classrooms) classrooms) (Layzer et based Centers Centers (Layzer et (Scarr et al, (National 

al, 1993) Centers in (Layzer et (Cost, al, 1993) 1992) Child Care 

New Jersey al, 1993) Quality and Staffing 

(Resnick et al, Outcomes Study, 

2001) Study, Whitebook 

Variability defined as +/- 1.5 SD's 1995) et al, 1989) 

100.0 

Percent of Classrooms 

75.0 

53.7 

48.1 50.0 

26.8 

22.8 20.9 25.0 18.1 

5.7 

0 0 0 1.9 0.8 0.6 0.7 

0.0 

1 2 3 4 5 6 7 

"Inadequate" "Minimal" "Good" "Excellent" 

ECERS Scale 

Fall 1997 (N=518) 

Fall 2000 (N=258) 

47 

Figure 4.2. Distribution of Classrooms on ECERS and ECERS-R, Fall 1997 and 2000 

ECERS Scale Categories were coded as follows: 

0-1.49='1', 1.5-2.49='2', 2.50-3.49='3', 3.5-4.49='4' 

4.5-5.49='5', 5.5-6.49='6', 6.5-7='7' 
Head Start FACES 2000 CHAPTER IV 

In FACES 1997, there were 97 out of 518 (18.7%) 

classrooms rated “excellent” while in FACES 2000, 

21.6 percent of classrooms (56 out of 258) were rated 

“excellent” (scores of 6 or higher). 

Assessment Profile Scheduling, Learning 

Environment, and Individualizing Scales 

On the Scheduling scale, the raw scores were virtually 

identical from FACES 1997 to FACES 2000 (average 

raw scores of 11.17 in 1997 and 11.12 in 2000). On 

the Learning Environment scale, there was a slight but 

non-statistically significant increase in raw scores over 

the two cohorts, from 13.46 in 1997 to 14.44 in 2000. 

Finally, on the Individualizing Scale, a new measure in 

FACES 2000, the average raw score was 3.58 (out of a 

maximum raw score of 5), with a standard deviation of 

1.2, indicating that overall Head Start classrooms 

provide an environment that takes into account the 

learning needs of individual students, but there is room 

for improvement. In particular, on the Individualizing 

Scale, 60 percent of classrooms maintained portfolios 

on individual children but only 37 percent of 

classrooms provided opportunities for children to 

evaluate their work, or to decide which products are 

included in their portfolios. However, with regard to 

the inclusion of children with disabilities, and making 

accommodations to allow these children to be included 

in classroom activities, 94 percent of classrooms were 

rated as having full inclusion and 90 percent of 

classrooms had provisions for accommodating children 

with special needs. 

Arnett Caregiver Interaction Scale 

On this scale, measuring the sensitivity and 

responsiveness of teachers in Head Start classrooms, 

the average score in FACES 2000 was 71.5, almost 

identical to the score of 71.3 in FACES 1997. 

The fact that the scores for three measures of 

classroom quality, the ECERS, the Assessment Profile 

measures, and the Arnett Caregiver Interaction Scale, 

were consistent across both cohorts adds support to 

the conclusion that, in general, quality in Head Start 

classrooms was consistent over the two cohorts of 

classrooms, from 1997 to 2000. 

Child:Adult Ratio 

The average child:adult ratio for the FACES Head Start 

classrooms in Fall 2000 was 5.4 children per adult, 

compared with 6.3 children per adult at the fall 1997 

observation and 6.2 children per adult during the 

spring 1998 observation.15 Looking only at paid staff, 

fall 2000 classrooms averaged 6.5 children per paid 

staff. When we include other adults assisting paid staff 

in classroom activities, the child:adult ratio averaged 

5.4, which is somewhat lower than that found in fall 

1997. The difference in these ratios suggests the 

important influence of volunteers on improving 

child:adult ratios in Head Start classrooms (lower 

ratios indicate higher quality). These ratios are far 

better than the NAEYC accreditation standard of eight 

or fewer 3-year-olds or 10 or fewer 4-year-olds for 

each adult and exceed the Head Start Program 

Performance Standards of 7.5 to 8.5 or fewer 3-yearolds or 10 or fewer 4-year-olds per adult. 

Teacher Backgrounds, Qualifications, and 

Experience 

In fall 2000, we continue to find that Head Start 

teachers overall are experienced and qualified (see 

Table 4.1). Teachers in Head Start classrooms have 

been teaching in Head Start for 7.9 years and they have 

been teaching for an average of 11.8 years in all 

educational settings. These data are almost identical to 

those reported for the earlier cohort, fall 1997. 

Teachers spent most of their teaching careers in Head 

In fall 1997 we only measured total number of adults in the classroom so that the discrepancy between 1997 and 2000 could be 

due to the slightly different measurement methods. In the remaining analysis in this chapter, we use the child:adult ratio rather than 

the child:staff ratio, to remain consistent with the earlier cohort. In fact, the two measures are so highly correlated that results of 

statistical analysis using each will be almost identical. 

48 

15 
CHAPTER IV Head Start FACES 2000 

Start classrooms but there was a wide range of teaching 

experience. Approximately 21 percent of the Head 

Start teachers were relatively new, having been 

teaching in Head Start for less than two years, and 28 

percent had taught in Head Start for 10 years or more. 

Compared to the fall 1997 cohort, there were several 

statistically significant differences.16 More new teachers 

(21 percent) taught in fall 2000, compared with fall 

1997 when only 14 percent had taught Head Start for 

less than 2 years. Also, the percentage of teachers who 

taught from five to nine years declined from 34 

percent in fall 1997 to 28 percent in fall 2000. 

Approximately the same number of teachers in both 

cohorts had been teaching in Head Start for ten years 

or more (28 percent in fall 2000 and 29 percent in fall 

1997). 

Most Head Start teachers have good teaching 

qualifications, but lower than those of teachers in 

public elementary schools. In a survey of prekindergarten classrooms in the U. S. public schools in 

2000-2001, 86 percent of pre-kindergarten teachers 

had a bachelor’s or higher degree (Smith, Kleiner, 

Parsad, & Farris, 2002). In the FACES fall 2000 

cohort, 27.8 percent had a bachelor’s degree, 18.6 

percent had an associate’s degree, and another 32.2 

percent had some college but no degree. Overall, 46.4 

percent of teachers had either a bachelor’s or an 

associate’s degree with 38.7 percent having a 

bachelor’s degree or higher and 57 percent having an 

associate’s degree or higher. Seventy-four percent of 

all teachers reported having the Child Development 

Associate (CDA) credential or a state-awarded 

preschool certificate, with 58 percent having the Child 

Development Associate only. 

The proportion of teachers with a bachelor’s degree or 

higher increased significantly from 28.1 percent in fall 

1997 to 38.7 percent in fall 2000, primarily due to an 

increase in the proportion of teachers with graduate 

level degrees, defined as a master’s degree, its 

equivalent, or higher. In fall 2000, 10.9 percent of 

teachers reported having these advanced degrees 

compared with only 3.2 percent in fall 1997, and in fall 

2000 16 percent of teachers reported having received 

some graduate level training.17 This increase was 

significantly correlated with the increase in the 

proportion of new teachers in Head Start, from 14 

percent to 21 percent over the same time period. In 

fall 2000, 32.9 percent of teachers with graduate level 

education or higher were new teachers, compared 

with only 13.3 percent in fall 1997.18 These results 

suggest that there is an increased number of new 

teachers with advanced degrees entering Head Start. 

In addition to an increase in teachers with advanced 

degrees, more teachers are reporting having studied 

Early Childhood Education or Child Development in 

their studies for their highest degree, whether for an 

associate’s or bachelor’s degree or higher. In fall 2000, 

78 percent indicated that their field of study included 

Early Childhood Education or Child Development, 

compared with approximately 62 percent in fall 1997. 

However, the proportion of teachers having a Child 

Development Associate or state preschool certificate 

has stayed approximately the same (74 percent in fall 

2000 compared with 76 percent in fall 1997). 

There was also an increase in the membership of 

teachers in a national professional association for early 

childhood educators (e.g., NAEYC, NHSA, and NEA), 

from 53 percent in fall 1997 to 62 percent in fall 2000. 

16 The chi-square comparison using unweighted data on the independent samples was statistically significant at p<.05. Unweighted 

data were used because no classroom weights were created for the fall 1997 sample. 

17 The chi-square comparison using unweighted data on the independent samples was statistically significant at p<.01. 

18 In fall 2000 the chi-square test using weighted data was statistically significant at p<.001. Further, a comparison of the two 

proportions from fall 1997 to fall 2000 using unweighted data also revealed a statistically significant increase at p<.01 (z = 4.441). 

49 
Head Start FACES 2000 CHAPTER IV 

Table 4.1. Comparison of Lead Teacher Backgrounds, FACES Fall 1997 and Fall 

Fall 1997 

(N=437) 

Fall 2000 

(N=257) 

Years Teaching Head Start 1-2 YRS 14.2% 21.1% 

3-4 YRS 22.7% 23.5% 

5-9 YRS 34.1% 27.6% 

10+ YRS 29.0% 27.8% 

Total 100% 100% 

Highest Level of Education Achieved High School or Equivalent 10.8% 10.4% 

Some College 31.4% 32.2% 

Associates Diploma 29.7% 18.6% 

Bachelor’s Degree or Equivalent 24.9% 27.8% 

Graduate or Professional Degree 3.2% 10.9% 

Total 100% 100% 

Teacher Age Category 18-29 14.7% 14.9% 

30-39 33.3% 33.4% 

40-49 31.8% 28.3% 

50-59 15.9% 16.3% 

60 or Older 4.3% 7.1% 

Total 100% 100% 

Membership in Early Childhood 

Education Association 

No 47.1% 38.0% 

Yes 52.9% 62.0% 

Total 100% 100% 

CDA Certificate/Credential No 23.9% 26.0% 

Yes 76.1% 74.0% 

Total 100% 100% 

Teacher Ethnicity Caucasian, non-Hispanic 41.1% 48.1% 

African-American, non-Hispanic 34.2% 33.7% 

Hispanic* 22.4% 15.4% 

Asian 2.3% 1.2% 

Multiple Race/Other na 1.7% 

Total 100% 52% 

* Puerto Rico was represented in FACES 1997, but not in the FACES 2000 sample, explaining the lower percentage of

Hispanic teachers.

Head Start teachers were, on average, 41.4 years old 

with a range from 23 to 73 years of age (standard 

deviation of 11.1 years). One third of teachers 

belonged to the 30 to 39 year age group and another 

28 percent were between the ages of 40 and 49 years. 

There were no differences in the proportions of 

teachers in each of the ten-year age groupings between 

the earlier cohort (fall 1997) and this cohort (fall 

50 
CHAPTER IV Head Start FACES 2000 

2000), with the exception that this cohort reflects an 

aging of teachers at the upper end of the distribution. 

Whereas only 4 percent of teachers were 60 years of 

age or older in fall 1997, in this newest cohort (fall 

2000), 7 percent of teachers were 60 years of age or 

older. 

In terms of racial and ethnic background, 33.7 percent 

of the teachers were African American, 15 percent 

were Hispanic, 1 percent were Asian, and 48 percent 

were Caucasian. Compared with the fall 1997 data, 

teachers in fall 2000 were slightly less likely to be Black 

(the percentage of African American teachers declined 

to 33.7 percent from 34.2 percent in Fall 1997). 

Although it may appear that there was a strong decline 

in the percentage of Hispanic teachers, this decline is 

due entirely to the fact that Puerto Rico was included 

in the fall 1997 but excluded from the fall 2000 

cohort. In FACES 2000, there was an increase in the 

proportion of teachers who were Caucasian, from 41 

percent in fall 1997 to 48 percent in fall 2000. 

In general, the data reveal that Head Start teachers are 

experienced and qualified to teach early childhood 

education. Compared with the first FACES cohort, 

Head Start teachers in FACES 2000 are more qualified 

than were teachers in 1997. They are more likely to be 

Caucasian, younger, new to teaching Head Start and 

entering with higher educational levels including 

graduate degrees. They are also more likely to be 

trained in early childhood education and to be 

members of a professional organization. 

Teacher Attitudes and Knowledge 

In fall 2000, on average, Head Start teachers showed 

generally positive attitudes and knowledge about 

instructional practice in early childhood education, 

with a mean score for all teachers of 7.9 out of a 

maximum score of 10. For example, they tended to 

agree with statements indicating positive attitudes and 

knowledge about early childhood education practices 

such as: “Head Start classroom activities should be 

responsive to individual differences in development” 

and “Children should be allowed to select many of their 

own activities from a variety of learning areas that the 

teacher has prepared (writing, science center, etc.).” 

Head Start teachers tended to disagree with statements 

indicating negative attitudes and a lack of knowledge 

about early childhood education practices such as: 

“Each curriculum area should be taught as a separate 

subject at separate times” and “Students should work 

silently and alone on seatwork.” 

C. Teacher Qualifications and 

Experience Are Related to Classroom 

Quality 

When correlating teacher backgrounds, qualifications, 

and experience with quality, we found that teachers 

with more experience and higher levels of education 

tended to be in classrooms rated higher in classroom 

quality. Specifically, we found that: 

• teachers with higher levels of education tend to be in 

classrooms rated higher on a number of quality 

indicators, including the ECERS-R Language 

subscale, the Caregiver Interaction Scale, and the 

ECERS-R total score; 

• teachers with a BA or AA degree were in classrooms 

with lower child:adult ratios (indicating higher 

quality) but teachers with a Child Development 

Associate tended to be in classrooms with higher 

ratios, indicative of lower quality; 

• teachers who had more years teaching overall (not 

just in Head Start) were rated higher in their 

sensitivity and responsiveness, as measured by the 

Caregiver Interaction Scale and had higher overall 

ECERS-R scores; 

• teachers with more years teaching preschool or Head 

Start were in classrooms rated higher on the Quality 

Composite score; 

51 
Head Start FACES 2000 CHAPTER IV 

• teachers who were members of an early childhood 

education professional association were rated higher 

in their sensitivity and responsiveness, as measured 

by the Caregiver Interaction Scale, and were in 

classrooms rated higher in ECERS-R Language, the 

Assessment Profile Individualizing Scale, with higher 

Quality Composite scores; 

• teachers with a Child Development Associate 

certificate or a state-sponsored equivalent (versus no 

certificate) were in classrooms rated higher on the 

Assessment Profile Individualizing scale; 

• teachers with at least some graduate school 

education (versus no graduate school education) 

were also in classrooms rated higher in quality as 

measured by the ECERS-R total score; and 

• teachers who had a teaching certificate (versus no 

certificate) were in classrooms rated higher on the 

overall ECERS-R and the ECERS Language scale, 

with lower child:adult ratios (indicating higher 

quality). 

However, years of experience teaching Head Start 

(rather than overall years of teaching at any level) and 

the teachers’ total annual salaries were not significantly 

correlated with any of the quality indicators. 

These results indicate that teacher backgrounds and 

qualifications are related to higher levels of quality in 

Head Start classrooms. However, the above results are 

based only on simple correlations, and do not take into 

account the complex interplay between the various 

teacher-related factors. To understand these links 

between factors, we must also consider how teacher 

backgrounds and experience are related to their 

attitudes and knowledge about early childhood 

education practice. 

D. Teacher Backgrounds and 

Experience Are Related to Their 

Attitudes and Knowledge 

Teachers who had higher levels of knowledge about 

early childhood education practices tended to be more 

experienced and better educated. Specifically, 

teachers with higher scores for attitudes and 

knowledge were more likely to: 

• have higher levels of educational attainment, 

• have some graduate school education or higher, 

• have more total years teaching, 

• belong to an early childhood education association, 

• teach in another language (not including Spanish or 

English),19 and 

• be Caucasian, Hispanic, or Asian. 

Teachers’ attitudes and knowledge were not 

significantly correlated with: teacher salary, years 

teaching Head Start, having a teaching certificate, 

having a Child Development Associate certificate, or 

having a course of study in early childhood education. 

This relationship between teacher education and 

attitudes and knowledge about early childhood 

education practices is supported by findings from other 

studies. Abbott-Shim, Lambert, and McCarty (2000) 

reported that teachers with higher levels of education 

also showed more positive attitudes and knowledge 

about early childhood education practices. 

Our results suggest that teachers who are better 

trained, with higher levels of educational attainment 

especially at the graduate school level, with more years 

of teaching experience overall, and who enroll in a 

19 In FACES 2000 only 1 percent of teachers indicated they taught at least part of the instructional day in a language other than English 

or Spanish. 

52 
CHAPTER IV Head Start FACES 2000 

related professional association are more likely to have 

knowledge and positive attitudes about early childhood 

education practices. These attitudes and knowledge 

should be expected to influence classroom quality, 

particularly since teacher credentials were related to 

classroom quality. In the next step toward building a 

model explaining the connection between teacher 

backgrounds, qualifications, and experience and 

classroom quality, we test the relationship between 

teacher attitudes and knowledge and classroom quality. 

E. Teacher Attitudes and Knowledge 

Are Related to Classroom Quality in 

Head Start 

Teachers with more positive attitudes and knowledge 

about early childhood education practices tend to be in 

classrooms rated higher in quality.This relationship was 

found for a wide variety of quality indicators, including 

the Assessment Profile Learning Environment scale, 

the ECERS-R total score, the ECERS-R Language 

scale, the Assessment Profile Individualizing scale, the 

Quality Composite score, and the Arnett Caregiver 

Interaction Scale (see Figure 4.3). 

Teachers holding more positive attitudes and 

knowledge of early childhood education practices were 

more sensitive and responsive to children, as measured 

by the Arnett Caregiver Interaction Scale. They were 

also more likely to adjust activities to meet the varying 

needs of individual children, as indicated by the 

Assessment Profile Individualizing scale. Teachers 

holding more positive attitudes and knowledge of 

practices in early childhood education were also in 

classrooms rated higher in overall quality on the 

ECERS-R and in classrooms with higher quality 

language activities, as rated by the ECERS-R Language 

scale. 

The above findings show that teacher backgrounds, 

experience, and qualifications, notably their level of 

education, are significantly related to both teachers’ 

Figure 4.3. Teacher Beliefs Are Correlated With Classroom Quality, Fall 2000 

Child:Adult Ratio -0.06 

AP Scheduling

AP Individualizing Composite

AP Learning Environment *

AP Individualizing Observed **

ECERS-R Personal Care **

ECERS-R Furnishings **

ECERS-R Social ***

ECERS-R Motor Skills ***

Quality Factor Score ***

Arnett Lead Teacher Total ***

ECERS-R Creative ***

ECERS-R Language ***

ECERS-R Program Structure ***

ECERS-R Total Score ***

0.05 

0.09 

0.13 

0.15 

0.17 

0.18 

0.24 

0.25 

0.27 

0.28 

0.29 

0.29 

0.31 

0.32 

-0.10 -0.05 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 

Correlation Coefficients 

*** indicates significance at p<.001; ** indicates significance at p<.01; * indicates significant at p<.05 

53 
Head Start FACES 2000 CHAPTER IV 

attitudes and knowledge about early childhood 

education practice, and classroom quality. The teacher 

attitudes and knowledge score alone was also 

significantly correlated with classroom quality. Thus, 

in predicting high or low quality in Head Start 

classrooms, both teacher qualifications as well as their 

attitudes and knowledge about early childhood 

education practices are important. 

These results are limited because we cannot specify 

from the simple correlations above whether the 

relationships with classroom quality are independent, 

suggesting that teacher education, for example, and 

teacher attitudes and knowledge contribute separately 

and uniquely to variations in quality, or whether they 

are linked in some way. Additionally, the research 

literature and earlier analysis of FACES data indicate 

that there are factors existing at the level of the 

program that may influence classroom quality. The 

Second Performance Measures Report described how 

classroom quality varies across classrooms and across 

programs suggesting that factors at the level of the 

Head Start program may help to explain variations in 

quality. Thus, in developing a comprehensive model 

for understanding the many sources of influence on 

classroom quality, it is important to look at factors at 

both the classroom and the program levels. 

F. Teacher and Program-Level Factors 

Explain Significant Variations in 

Classroom Quality 

To determine the joint influence of both program- and 

classroom-level factors in predicting classroom quality, 

we used a multilevel approach with two levels of 

factors: program and classroom.20 This approach tests 

a model for explaining quality in Head Start that builds 

from the previous analyses, which were done only at 

the level of the classroom, and then adds factors at the 

level of the Head Start program. Head Start programs 

consist of the grantee and delegate agencies that 

administer the centers and classrooms within its 

purview. A program comprises the administrative 

entity primarily responsible for determining budgets, 

staffing, and the allocation of resources across centers 

and classrooms, as well as for choosing curricula for its 

classrooms and providing training and resources to 

support the curricula. Programs also define the 

geographic areas they serve and identify the needs of 

low-income families and children living there. Thus, in 

order to understand variations in classroom quality we 

must include program-level factors. The model we are 

testing will explain how variations in the quality of 

Head Start classrooms may be due to those teacher 

factors identified earlier, as well as factors that exist at 

the level of the program, such as the characteristics of 

families participating in the program, the curriculum 

used in the majority of classrooms, and the average 

annual teacher salary (see Figure 4.4). 

The results of these analysis are summarized in Table 

4.2. 

Predicting ECERS-R Total Score 

High quality classrooms are those in which programs 

have a higher percentage of non-minority students, a 

higher percentage of language-minority students (e.g., 

Spanish speakers), and teachers who have greater 

knowledge about early childhood education practices. 

Predicting ECERS-R Language Scale 

Classrooms with higher quality of language activities 

and materials were those whose teachers had higher 

scores for attitudes and knowledge about early 

childhood education practice. Having a teacher with a 

BA or AA, or having a higher percentage of nonminority students or a higher percentage of languageminority students was associated with higher quality at 

the trend level, but did not reach statistical 

significance. 

54 

20 We used the PROC MIXED statistical procedure in SAS, as outlined by Singer (1998). 
CHAPTER IV Head Start FACES 2000 

Head Start Program

• Characteristics of Families Served: Primary Curriculum (Majority of Classrooms)

• Percent Non-Minority • High/Scope Curriculum• Percent Language Minority • Creative Curriculum

• Percent Parents with Some College Experience • Other/No Curriculum• Percent Parents Earning $1500/month or higher

• Average Annual Teacher Salary (Program)

PROGRAM LEVELCLASSROOM LEVEL

Teacher Backgrounds

• Ethnicity (African-American,Hispanic)Teacher Educationand Experience• Years Teaching Overall(Head Start and other) • BA, AA only• Teacher salary (deviation

Teacher Attitudesand Knowledge• 9-Item Attitudes andKnowledge Scale 

Classroom Process Quality• ECERS-R Total Score• Quality Composite• Assessment Profile Individualizing• Assessment Profile Scheduling• Assessment Profile Individualizing

Quality of Language/Literacy Environment• ECERS-R Language ScoreTeacher-Child Interactions• Arnett Caregiver Interaction ScaleClassroom Structural Quality• Child-Adult Ratio

from program mean) 

Figure 4.4. A Model of Program- and Classroom-Level Factors Explaining Quality in Head Start

55 
Table 4.2. Summary of Program- and Classroom-Level Factors Predicting Classroom Quality, Fall 2000

Predicting

ECERS-R Total

(Intercept=4.8)Estimate Signif1 

Predicting AP

Individualizing

(Intercept=3.5) 

Estimate Signif1 

Predicting Caregiver

Interaction Scale

(Intercept = 70.96)

Estimate Signif1 

Predicting

Child-Adult Ratio

(Intercept= 5.4)

Estimate Signif1 

Predicting

ECERS-R Language(Intercept=4.80)

Estimate Signif1 

Predicting Quality

Composite Score(Intercept=.003)

Estimate Signif1 

Program-Level FactorsPercent parents with somecollege or higher

0.55 0.52 10.59 -2.57 0.4645 0.4629 0.06 

Percent parents earning$1500/month or more 

0.44 3.82 0.01 2.83 0.50 0.3601 0.1883 

Percent non-minority students 0.82 0.01 0.14 3.24 - 0.87 0.6781 0.06 0.4254 0.06 

Percent language-minoritystudents

1.04 0.02 0.22 -1.98 -1.31 0.8444 0.10 1.2704 0.01 

High/Scope Curriculum 0.30 -0.40 5.33 0.10 -0.88 0.2408 0.2171 

Creative Curriculum 0.33 -0.38 6.41 0.02 -0.56 0.2676 0.2639 

Average Annual Teacher Salary 0.10 -0.34 0.10 1.62 -0.81 0.01 0.08252 -0.1082 

Classroom-Level FactorsTeacher having BA or AA -0.09 0.10 -1.09 -0.04 -0.2349 0.09 -0.09462 

Teacher Attitudes & Knowledge 0.11 0.00 0.04 1.55 0.00 0.04 0.159 0.00 0.1363 0.00 

Years Teaching Experience 0.01 0.01 0.17 0.05 0.00 0.01154 0.006333 

African American Teacher -0.14 0.06 -1.75 0.14 -0.2208 -0.04489 

Hispanic Teacher -0.18 0.04 -3.91 0.21 -0.3437 -0.2133 

Teacher Salary (deviation fromprogram mean) 

0.03 0.15 1.09 0.04 0.1074 -0.03496 

1. Significance levels in bold indicate statistically significant effects at 

p<.05. 

56 
CHAPTER IV Head Start FACES 2000 

Predicting Assessment Profile Individualizing Score 

Family income was significant and teacher salary was a 

non-significant trend in explaining the variation in 

teacher individualizing practices. Classrooms with a 

focus on individualizing the classroom activities for 

individual students (an indicator of greater quality) are 

located in Head Start programs with greater numbers 

of families with relatively higher incomes. 

Predicting Teacher Arnett Caregiver Interaction 

Scale Score 

Curriculum, teacher experience, and teacher attitudes 

and knowledge were significant predictors, with the 

strongest being the teachers’ attitudes and knowledge 

of early childhood education practice, followed by 

programs that used the Creative Curriculum. 

Programs that used the High/Scope Curriculum had 

classrooms with somewhat higher scores for teacher 

sensitivity, although this did not reach statistically 

significant levels. Classrooms with sensitive and 

responsive teachers are those that use the Creative 

Curriculum, where the teachers have more years of 

teaching experience, and where the teachers hold 

more positive attitudes and knowledge about practices 

in early childhood education. 

The teachers’ education level, indicated by whether 

teachers had an associate’s or bachelor’s degree or not, 

was not significantly related to teacher sensitivity, even 

though it was related in earlier analysis without 

program level factors included. These results further 

suggest that the relationship between teacher 

education and classroom quality is not direct, but 

rather is mediated by their knowledge and attitudes 

towards early childhood education practice as well as 

by the type of curriculum used and the teacher’s level 

of experience. 

Predicting Quality Composite Score 

Classrooms with higher scores for quality on this 

indicator (comprising the ECERS-R Language Scale, 

and the Assessment Profile Scheduling and Learning 

Environment Scales) were those from Head Start 

programs with a higher percentage of languageminority students, and whose teachers had more 

positive attitudes and knowledge of early childhood 

education practice. 

Predicting Child:Adult Ratio 

Even after controlling for other factors within the 

classroom, high quality classrooms in terms of lower 

child:adult ratios are those where teachers are paid 

more. 

Summary of Multilevel Model Results 

Overall the results support the model and enhance our 

understanding of factors that explain quality in Head 

Start. The results suggest that Head Start programs 

that provide for a common integrative curriculum 

across classrooms and that pay their teachers well have 

sufficient resources available to positively influence 

classroom quality, through the quality of teachers 

hired, and their experience and attitudes and 

knowledge. The factors included in the model do a 

good job of explaining the more “process-oriented” 

aspects of quality, such as learning materials, quality of 

language activities, and teacher-child interactions, but 

they do less well at predicting the more structural 

aspects of quality, such as child-adult ratios. 

G. Summary 

Head Start classrooms continue to show good levels of 

quality, based on the indicators of quality measured in 

FACES 2000. These levels of quality are consistent 

from the first cohort, and the consistency is evident 

across a wide variety of the indicators. Head Start 

teachers are qualified and experienced (although as a 

group they do not have the same level of credentials as 

public school teachers), and there appear to be 

substantially more teachers with higher educational 

attainment in this cohort compared with the first 

FACES cohort, in 1997. 

57 
Head Start FACES 2000 CHAPTER IV 

The role of teacher attitudes and knowledge, 

experience, and education has been illuminated by 

these analysis. In general, when all three factors are 

included, the direct relationships occur most 

consistently and strongly for attitudes and knowledge 

about early childhood education practice, and to a 

lesser extent, teacher experience, rather than with 

teacher education. When these other factors were not 

included we found a significant relationship between 

teacher education and classroom quality, suggesting 

that the role of teacher education in influencing 

classroom quality is an indirect one. Teachers with 

higher levels of education have more positive attitudes 

and knowledge about early childhood education 

practice, and they are more likely to be in classrooms 

rated higher in quality. Thus, teacher attitudes and 

knowledge mediate the role of teacher education in 

explaining classroom quality. 

Interestingly, programs using an integrated curriculum 

(such as High/Scope and Creative Curriculum) also 

have teachers with positive attitudes and knowledge 

about early childhood education practice. Both of these 

factors appear to have the strongest effect on teacher 

sensitivity and responsiveness compared with other 

indicators of quality. 

The multilevel results suggest that variations in the 

quality of Head Start classrooms may be explained by 

characteristics of the families and children they serve, 

by the curriculum used in the program, and by teacher 

attitudes and knowledge about early childhood 

education practice.The results suggest that Head Start 

classroom quality may be affected by factors beyond 

the classroom door that are characteristics of the 

program and the families who participate. 

KEY FINDINGS 

1. Head Start quality has been observed to be 

consistently good over time, using a variety of 

indicators; 

2. Head Start teachers have lower teaching 

qualifications compared with pre-k teachers in 

public elementary schools, but as a group they 

have become more qualified in 2000, with more 

of them having a graduate school degree; 

3. Head Start teachers in 2000 are also younger, 

compared with those in 1997-1998, and more of 

them have been teaching in Head Start for two 

years or less. These newer teachers are also the 

ones most likely to have a graduate school 

degree; 

4. Teacher backgrounds, qualifications, and 

experience are related to their attitudes and 

knowledge of early child development practices; 

5. Classrooms with higher levels of quality are those 

whose teachers have higher levels of education, 

experience, and knowledge and attitudes of early 

childhood education practices; 

6. The relationship between teacher education and 

classroom quality is explained by teachers’ 

attitudes and knowledge of early childhood 

education practices, so that teachers who are 

more educated have more positive attitudes and 

knowledge, which translates into higher levels of 

classroom quality; and 

7. Factors at the program level, including 

curriculum use, teacher salaries, and parent 

demographics, are also related to observed 

quality in Head Start classrooms and to the 

characteristics of teachers placed in these 

classrooms. 

58 
CHAPTER IV Head Start FACES 2000 

REFERENCES

Abbott-Shim, M. & Sibley, A. (1998). Assessment Profile 

for Early Childhood Programs: Research version. Atlanta, 

GA: Quality Assist. 

Abbott-Shim, M., Lambert, R., & McCarty, F. (2000). 

Structural model of Head Start classroom quality. Early 

Childhood Research Quarterly, 15(1), 115-134. 

Arnett, J. (1989). Caregivers in day-care centers: 

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Blau, D. (1997).The production of quality in child care 

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the early childhood classroom observation scale for 

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behaviors observed in kindergarten children in 

classrooms with developmentally appropriate versus 

developmentally inappropriate practices. Early 

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report, second edition. Denver: Economics Department, 

University of Colorado at Denver. 

Harms,T., Clifford, R. M., & Cryer, D. (1998). Early 

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York:Teachers College Press. 

Hayes, C., Palmer, J., & Zaslow, M. (1990). Who cares 

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on Child Development Research and Public Policy, 

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Howes, C., Phillips, D., & Whitebook, M. (1992). 

Thresholds of quality: Implications for the social 

development of children in center-based child care. 

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Layzer, J., Goodson., B., & Moss, M. (1993). Final 

report volume 1: Life in preschool. Cambridge, MA: Abt 

Associates, Inc. 

Phillips, D. & Howes, C. (1987). Indicators of quality in 

child care: Review of the research. In D. A. Phillips (Ed.), 

Quality in child care: What does research tell us? (pp. 

1-19). Research Monograph of the National 

Association for the Education of Young Children, Vol. 

1.Washington, DC: NAEYC. 

Phillips, D., Mekos, D., Scarr, S., McCartney, K., & 

Abbott-Shim, M. (2000). Within and beyond the 

classroom door: Assessing quality in child care centers. 

Early Childhood Research Quarterly, 15 (4), 475-496. 

Resnick, G., McKey, R. H., & Klayman, D. (2001). 

The evaluation of early childhood education programming in 

the 30 Abbott School Districts: First-year report on program 

implementation and descriptions of children and families. 

(Contract No. A43078). Trenton, NJ: New Jersey 

Department of Human Services and New Jersey 

Department of Education. 

59 
Head Start FACES 2000 CHAPTER IV 

Ruopp, R., Travers, J., Glantz, F., & Coelen, C. 

(1979). Children at the center: Final results of the National 

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Scarr, S., & Eisenberg, M. (1993). Child care research: 

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Scarr, S., Eisenberg, M., & Deater-Deckard, K. 

(1992). Measurement of quality in child care centers. 

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Singer, J. (1998). Using SAS PROC MIXED to fit 

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60 
CHAPTER V

Relationship of Program and Classroom Characteristics to Children’s 

Cognitive Gains and Social Development in Head Start 

The Family and Child Experiences Survey that began in 

fall 2000 (FACES 2000) found that the quality of Head 

Start centers and classes was generally good as judged 

by observational instruments that are widely employed 

to assay the quality of early childhood learning 

environments. An earlier round of the study that began 

in fall 1997 (FACES 1997) had similar findings. Both 

longitudinal studies also found that children in Head 

Start made significant progress toward national 

averages in some areas of early academic knowledge 

and skill, notably vocabulary knowledge and early 

writing skills. But in other areas, notably letter 

recognition and early math skills, children held their 

own but did not draw closer to national averages 

during the Head Start year. Even in those areas where 

they made significant progress, they still entered 

kindergarten considerably behind their more 

advantaged peers. 

Why was this the case? Variations across local Head 

Start programs—in the achievement gains that 

children make in Head Start and in the levels of skill 

and knowledge with which they leave the 

program—offer potential explanations. Variations in 

cognitive and social-emotional development could be 

related to differences in classroom quality. Or they 

could be associated with differences in the type of 

curriculum or instructional approach that the 

programs or classroom teachers employ. 

If this were indeed the case, then the performance of 

the national Head Start program might be improved by 

encouraging more local programs to improve their 

RESEARCH QUESTIONS 

The analysis reported in this chapter used 

multilevel regression models to address the 

following research questions: 

1. Do Head Start programs and classes differ 

in the average achievement levels that 

children have attained when they leave the 

program? Do they differ in the cognitive 

gains children make during the program 

year? Do they differ in the extent of changes 

children show in their cooperative social 

behavior or conduct problems? 

2. Do children in programs that employ one of 

the two integrated curricula that are widely 

used in Head Start—Creative Curriculum 

or High/Scope—show larger cognitive 

gains or behavioral improvements than 

children in programs that employ other 

curricula? 

3. Do children in programs that have more 

ample resources, as indicated by paying 

higher salaries to their lead teachers, show 

larger cognitive gains or behavioral 

improvements than children in programs 

that have less ample resources? 

61
Head Start FACES 2000 CHAPTER V 

RESEARCH QUESTIONS CONT. 

4. Do children in Head Start classes that are of 

higher quality, as indicated by their receiving 

higher scores on the Language scale of the Early 

Childhood Environment Ratings Scale–Revised 

(ECERS-R) or the Caregiver Interaction Scale 

(CIS), show larger cognitive gains or behavioral 

improvements than children in classes that are of 

lower quality on these measures? 

5. Do children in Head Start classes led by better 

prepared teachers show larger cognitive gains or 

behavioral improvements than children in classes 

led by teachers who are less well prepared? 

Indicators of teacher preparation that were 

examined included whether the teacher had a 

bachelor’s degree or associate’s degree, her years 

of teaching experience, her annual salary as a 

deviation from the program mean salary, and her 

score on a scale that measured positive attitudes 

and knowledge about early childhood 

educational practices. 

6. Do children who participate in Head Start 

classes for a longer period each day—who attend 

“full-day” classes—show larger cognitive gains or 

behavioral improvements than children who 

participate for a shorter period of time—who 

attend “part-day” classes? 

7. Do children in Head Start classes with lower 

child:staff ratios, and that provide more attention 

to the needs and preferences of individual 

children, as indicated by a higher score on the 

Assessment Profile Individualizing scale, show 

larger cognitive gains or behavioral 

improvements than children who are in classes 

with higher child:staff ratios or lower 

Individualizing scores? 

8. Do children whose parents do more educational 

activities at home with their children show larger 

cognitive gains or behavioral improvements than 

children whose parents do fewer educational 

activities? We examined whether children whose 

parents read to them on a daily basis at home 

showed greater gains in Head Start than children 

whose parents read to them less often. 

Frequency of reading was reported by the 

parents themselves in parent interviews in the 

fall of 2001. 

quality or make use of curricula or instructional 

approaches found to be associated with greater gains in 

children’s knowledge and skills or greater 

improvements in their classroom conduct and socialemotional well-being. 

This chapter explores variations in child achievement 

and behavior across local Head Start programs and 

classes. It uses multilevel modeling to test hypotheses 

about early education program and classroom 

characteristics that many child development scholars 

believe to be associated with enhanced cognitive 

growth or emotional maturation in preschool children 

(Phillips, Mekos, Scarr, McCartney, & Abbott-Shim, 

2000; Whitebook, Howes, & Phillips, 1989). The 

characteristics that are thought to make a difference for 

program effectiveness include the following: 

• using an integrated and comprehensive preschool 

curriculum; 

62 
CHAPTER V Head Start FACES 2000 

• having more ample program resources; 

• providing classrooms that are of higher quality as 

early learning environments; 

• employing a better prepared teaching staff; 

• providing preschool services for a longer period each 

day; 

• conducting educational activities in smaller groups 

with more personal adult attention to the needs and 

preferences of individual children; and 

• encouraging parents to engage in more educational 

activities with their children at home. 

This chapter examines relationships between these 

characteristics and several measures of children’s 

cognitive development and classroom behavior, while 

controlling for the influence of other variables. 

Cognitive dependent variables consisted primarily of 

direct assessment measures of children’s letter 

recognition and pre-reading skills, vocabulary 

knowledge, early writing skills, and early math skills. 

Dependent variables in the social-emotional realm 

consisted of teacher and parent ratings of children’s 

approaches to learning tasks and cooperative and 

problem behavior. Control variables included measures 

of the socioeconomic and ethnic composition of the 

families and children participating in each program and 

classroom. Other control variables were characteristics 

of the child like age, sex, and disability status and 

measures of parents’ literacy skills. 

FACES 2000 included a wider range of program and 

classroom characteristics that could be related to 

differences in achievement than did FACES 1997. 

FACES 2000 added interview questions and 

observational procedures that looked more 

systematically at areas like the curriculum used in each 

center and classroom, training and support for that 

curriculum, teacher salary levels, teacher knowledge 

and beliefs, and the use of child portfolios and other 

procedures aimed at individualizing instruction. In 

addition, the sample design was modified to yield a 

larger number of sample children in each sample 

classroom. This produced more stable estimates of 

class means and more variation in child characteristics 

in each classroom subsample. The modified design 

made it possible to carry out multilevel regression 

analysis at three levels: the program, classroom, and 

child level. In FACES 1997, multilevel regression 

analysis could be carried out at only the center and 

child levels. 

The multilevel analysis of FACES 2000 data did indeed 

show that some of the program and classroom level 

characteristics listed above were significantly related to 

variations in the size of the gains children made in Head 

Start. In presenting the analysis findings, we enumerate 

the program and classroom characteristics that seemed 

to make a difference. We describe the nature and size 

of the relationships involved. We also list factors that 

were not significantly related to gains in achievement 

or behavior and discuss possible reasons why 

hypothesized relationships failed to materialize. 

CONCEPTUAL FRAMEWORK 

The conceptual framework that guided our analysis 

was a multilevel, multi-causal model of the influences 

that shape children’s cognitive and social-emotional 

development and the factors that help determine the 

nature of the experience children have in Head Start. 

(See Figure 5.1.) This view posits that children’s 

development in the early years is primarily a function 

of the experiences they have in their families. Children 

from low-income families, whose parents tend to have 

lower educational attainments than other parents, 

often do not experience the same extent or quality of 

intellectual stimulation at home as children from 

middle-class families (Phillips et al., 1998). 

Furthermore, their parents are less able to purchase 

high-quality supplementary or substitute care in the 

63 
Head Start FACES 2000 CHAPTER V 

Figure 5.1. Analytical Model of Multilevel Factors Predicting to Classroom Quality and Children’s Achievement 

and Gains in the Head Start Year 

PROGRAM LEVEL 

Program Characteristics 

• Characteristics of Families 

• Primary Curriculum 

• Teacher Salaries 

Teacher Characteristics 

• Experience and Training

 (education, years teaching) 

• Attitudes and Knowledge about

 ECE Instructional Practices 

• Demographics (age, ethnicity) 

• Salary level (as deviation from

 program mean) 

Child and Family 

Characteristics 

• Child Characteristics 

• Parent Education, Household

 Income, Welfare Status, Family

 Structure 

• Parent Literacy Level 

• Books in Home 

• Educational Activities at home 

CLASSROOM LEVEL

CHILD LEVEL

Classroom Quality 

• ECERS-R Language 

• Teacher Sensitivity 

• Teacher Individualizing 

• Child-Adult Ratio 

Full-Day versus 

Part-Day Class 

Children's Level of 

Achievement and 

Fall-Spring Gains 

• Cognitive Skills 

• Behavioral and Social Skills 

64 
CHAPTER V Head Start FACES 2000 

marketplace. In some cases, children from low-income 

families may also not receive as much emotional 

support from parents as they need for optimal 

development. A center-based early childhood learning 

environment such as Head Start may help provide 

experiences that would be beneficial for the 

development of all children, but especially for those 

from higher-risk family environments (NICHD Early 

Child Care Research Network, 2000). Furthermore, 

parents’ involvement in their children’s educational 

experiences may also be an important factor, and one 

that programs can foster. 

The nature of the learning environment that a given 

child experiences in Head Start depends on the 

training and experience of teachers in the program, 

and the resources available to them in terms of 

facilities, materials, and teaching assistants. Programs 

with more resources are likely to be better able to 

provide adequate facilities and materials and recruit 

and retain talented and well-prepared teachers 

(Whitebook, Howes, & Phillips, 1989). 

But the character of the classroom environment that a 

given Head Start program is able to provide for a child 

is not just a question of program resources. It also 

depends on the educational philosophy to which the 

program adheres, and the kind of curriculum centers 

and teachers are encouraged to follow. Other things 

equal, children would be expected to do better in 

programs that employ well-thought-out curricula that 

are comprehensive and integrated in terms of 

educational activities and assessment methods. This is 

especially the case if the program is able to provide 

teachers with adequate training and support in the 

curriculum. At the same time, children’s progress in a 

given cognitive or social-emotional area depends on 

whether the program’s basic philosophy and 

curriculum of choice are supportive of efforts to 

bolster that area of child development. 

ANALYSIS METHOD 

The analysis method used to examine associations 

between Head Start program and class characteristics 

and children’s cognitive and social-emotional 

development was multilevel linear regression 

modeling, using the SAS PROC MIXED computer 

program (Singer, 1998; Bryk & Raudenbush, 1992). 

Multilevel modeling shows how the average 

achievement scores of a sample of classes, schools, or 

other educational units (in the present case, Head Start 

programs and classes) relate to a set of characteristics 

of those units, such as measures of program 

demographics and classroom quality. Simultaneously, 

this type of modeling can examine how the 

achievement scores of individual children in each 

program and class relate to a set of child-level 

characteristics, such as child demographics and home 

literacy activities. The method provides a numerical 

estimate of how sizable the program-to-program and 

class-to-class variation in average scores is, relative to 

the child-to-child variation in scores within classes. 

The primary dependent variables were the gains each 

child made between the fall and the spring of the Head 

Start year in their cognitive assessment or behavior 

ratings scale scores. Models were also constructed of 

assessment and ratings scores attained by Head Start 

children in the FACES national sample in the fall of 

2000 and the spring of 2001.21 Each analytic model 

had three levels. The first level involved variation in 

average assessment scores or average gain scores across 

the 43 programs in the FACES national sample, 

In the multilevel regression modeling, assessment scores were converted to “W-ability scores,” based on IRT scaling of item 

difficulties carried out by the test developers. These scale scores are purported to have equal-interval properties that are desirable in 

regression modeling, particularly of gain scores. In other analyses, standard score versions of the assessment scores were used. These 

scores show how Head Start children performed compared to national norms. But they do not have as strong equal-interval properties 

as the W-ability scores. 

65 

21 
Head Start FACES 2000 CHAPTER V 

expressed as deviations of the program means from the 

overall mean score for the entire sample. The second 

level involved variations of class means from the overall 

program means. And the third level involved variation 

in individual children’s scores or gain scores around the 

class means. 

There were three levels of independent variables used 

to model or predict the assessment scores or gain 

scores. At the program level, the independent 

variables consisted of measures that represented the 

curriculum employed by the program, average teacher 

salary levels in the program, and average demographic 

and socioeconomic characteristics of the children who 

attended each program and their families. At the 

classroom level, independent variables consisted of 

measures of teacher preparation, teacher background 

characteristics, whether the class was of full-day or 

part-day duration, and indicators of classroom quality 

such as the ECERS-R Language scale and Caregiver 

Interaction Scale. Class-level variables also included 

measures of the demographic composition of the class, 

expressed as deviations from the average demographic 

characteristics of the program. At the child level, the 

independent variables were measures that represented 

demographic characteristics of the child; 

socioeconomic, cultural, and structural characteristics 

of the family; parent literacy levels; disability status of 

the child; and the frequency of parental reading to the 

child. 

Statistical tests were made as to whether a given set of 

independent variables (program-level, class-level, and 

child-level) improved the model’s fit to the data, over 

and above simpler models that did not include that set. 

Tests were also done as to whether the regression 

coefficient for a given independent variable was 

reliably greater than zero. Details about variable 

definitions, means and ranges, reliability of measures, 

and statistical tests used to ascertain the reliability of 

findings are described in the Appendix of the report. 

FINDINGS 

Multilevel regression analysis of the assessment and 

ratings measures showed that there were significant 

relationships between some of the program and class 

characteristics identified above and variations in 

children’s cognitive or behavioral gains. The following 

factors seemed to make a difference for children’s 

progress: 

1. programs using an integrated curriculum, 

particularly the High/Scope Curriculum; 

2. programs having higher teacher salaries; 

3. teachers having bachelor’s or associate’s degrees; 

4. children attending full-day rather than part-day 

classes; and, 

5. parents reporting that they read to their children 

more frequently. 

None of these factors was related to increased gains in 

all cognitive or behavioral indices examined. But most 

were related to gains on two or more outcome 

measures, and the relationships were in the 

hypothesized direction. The following sections 

describe the nature and size of these relationships. 

A. Use of an Integrated Curriculum 

The High/Scope Curriculum is a comprehensive, 

integrated preschool curriculum that has a long history 

of research and development. It grew out of the Perry 

Preschool Project, an intensive, relatively small-scale 

intervention that was found to have long-term effects 

on children’s achievement in a random-assignment 

evaluation study. It is the second most popular 

curriculum employed in Head Start programs, as 

described in Chapter III of this report. In the present 

analysis, children in programs that employed the 

High/Scope Curriculum were found to have made 

greater gains than children in programs that did not 

66 
CHAPTER V Head Start FACES 2000 

employ one of the two integrated curricula that are 

most widely used by local Head Start programs. (In the 

interests of simplicity, henceforth we shall refer to 

these curricula other than Creative Curriculum or 

High/Scope by the term “other curricula.”) The 

greater gains were on measures in both the cognitive 

and social-emotional domains. 

Gains in Pre-Reading and Oral Communication 

Skills 

Children in programs that employed the High/Scope 

Curriculum made small but significantly greater gains 

in letter recognition skills than children in programs 

that employed other curricula. In IRT scale-score 

terms, the average Head Start child made a gain on the 

Wo o d c o c k - J o h n s o n - R e v i s e d - L e t t e r - Wo r d 

Identification task of just under 10 scale points from 

fall to spring of the Head Start year. Children in 

programs employing the High/Scope Curriculum 

showed an average gain of 12.6 scale points on the WJR LWI from fall to spring (p < .001), whereas children 

in programs employing the Creative Curriculum or 

other curricula made gains of about 9 scale points (p < 

.001). The regression coefficient for the High/Scope 

Curriculum in the three-level regression analysis, 

which is an estimate of the difference in average gains 

between it and other curricula adjusted for the 

influence of related variables, was 3.66 (p = .01). 

The other popular integrated curriculum, Creative 

Curriculum, had a positive coefficient in the regression 

analysis of Letter-Word Identification scores in the 

spring, but did not reach significance (2.66, p < .10). 

It also had a positive coefficient in the gain analysis, but 

one that was not significantly different from zero 

(1.92, n.s.). 

When differences were expressed in standard score 

terms, the High/Scope group showed a mean gain of 

1.6 standard score points, going from a mean standard 

score of 92.2 in the fall to 93.8 in the spring. (Figure 

5.2) By comparison, children in programs employing 

the other widely popular integrated curriculum, the 

Creative Curriculum, or other curricula held their 

own against national norms. 

Children in programs using the High/Scope 

Curriculum were found to make greater gains as well 

on a criterion-referenced measure of oral 

communication skills. This difference was only at the 

trend level, however. The “Social Awareness” measure 

assessed children’s ability to tell an adult basic 

information about themselves such as their age, and 

month and year of birth.The regression coefficient for 

the High/Scope Curriculum showed a gain 0.21 points 

greater than that for other curricula (p = .098). 

Improvement in Cooperative Behavior 

The use of an integrated curriculum was found to be 

associated with greater gains for children in the socialemotional realm. Children in programs employing the 

High/Scope Curriculum showed larger gains in 

cooperative classroom behavior from fall to spring of 

the program year than children in programs employing 

other curricula. They also showed more pronounced 

declines in hyperactive behavior from fall to spring. 

Children in programs following the High/Scope 

Curriculum showed a mean increase of 2.3 points on 

the Cooperative Classroom Behavior rating scale 

completed by Head Start teachers (a change equivalent 

to .48 of a standard deviation).They went from a mean 

score of 14.5 in the fall to a mean of 16.8 in the spring 

(p<.001). By comparison, children in programs 

following the Creative Curriculum showed an increase 

of 1.8 points (p<.001), and those in programs 

following other curricula increased by 1.7 points 

(p<.001) (.37 and .36 of a standard deviation, 

respectively). The regression coefficient for the 

High/Scope Curriculum in the three-level regression 

analysis, which is an estimate of the difference in 

average gains between it and other curricula adjusted 

67 
Head Start FACES 2000 CHAPTER V 

100.0 

95.0 

92.2 

91.7 

91.9 

93.8 

90.7 

91.6 

High/Scope 

Creative 

Other 

90.0 

85.0 

Fall 2000 Spring 2001 

Time of Assessment 

Mean Standard Score on WJ-R Letter-Word Identification

Task 

Figure 5.2. Children in Head Start Programs Using High/Scope Curriculum Show Greater Gains in Letter 

Recognition Skills 

for the influence of related variables, was 1.26 (p < 

.05). 

Decline in Hyperactive Behavior 

Children in programs following the High/Scope 

Curriculum exhibited significant improvement in their 

scores on a Total Behavior Problems rating scale 

completed by Head Start teachers (p = .03). In 

particular, they showed greater improvement on the 

Hyperactive subscale of the Problem Behavior rating 

scale. They showed a mean decline of 0.19 points (p < 

.10) on this scale, going from a mean rating of 1.39 in 

the fall to a mean of 1.20 in the spring (a change of .13 

of a standard deviation). By comparison, children in 

programs following the Creative Curriculum showed a 

non-significant mean decline of -.08 points, while 

children in programs following other curricula showed 

a non-significant mean decline of -.13 points. The 

regression coefficient for the High/Scope Curriculum 

in the three-level regression analysis, which is an 

estimate of the difference in average declines between 

it and other curricula, adjusted for the influence of 

related variables, was -0.32 (p < .05). In the 

regression analysis of Total Behavior Problems, the 

coefficient for the High/Scope Curriculum had a value 

of -1.19 (p = .01). 

B. Higher Teacher Salary Levels 

We explored whether children attending Head Start 

programs with higher average teacher salary levels 

would make greater progress in their cognitive and 

social-emotional development. The multivariate 

analysis showed this to be the case with respect to 

children’s pre-reading and oral communication skills, 

and their cooperative and problem behavior in the 

classroom. 

68 
CHAPTER V Head Start FACES 2000 

Gains in Pre-Reading and Oral Communication 

Skills 

Average Annual Salary for Lead Teachers was associated 

with greater gains in letter recognition. The regression 

coefficient for Mean Teacher Salary Level in the threelevel regression analysis of gains, which is an estimate 

of the difference in LWI scale scores associated with 

each $10,000 increment in average teacher salaries, 

adjusted for the influence of related variables, was 1.96 

(p = .009). In standard score terms, the highest 

teacher salary group (top quartile) showed a gain of 

less than one standard score point. Children in 

programs with lower average teacher salary levels 

(bottom three quartiles) showed a slight and nonsignificant decline in their standard scores. 

Children in programs with higher average teacher 

salaries made greater gains as well on the criterionreferenced “Social Awareness” measure.The regression 

coefficient for the Mean Teacher Salary Level showed 

an increased gain of 0.18 points for every $10,000 

increment in mean salary (p = .008). 

Improvement in Cooperative Behavior 

Higher teacher salaries were found to be associated 

with greater gains for children in the social-emotional 

realm. Children in programs with higher average 

teacher salary levels showed larger gains in cooperative 

classroom behavior from fall to spring of the program 

year than children in programs with lower teacher 

salary levels. They also showed more pronounced 

declines in hyperactive behavior from fall to spring. 

Children in programs in the highest quartile of teacher 

salaries showed a mean increase of 3.1 points on the 

Cooperative Classroom Behavior rating scale 

completed by Head Start teachers (a change equivalent 

of .65 of a standard deviation). They went from a mean 

score of 14.6 in the fall to a mean of 17.7 in the spring 

(p<.001). By comparison, children in programs in the 

middle two quartiles of teacher salary showed an 

increase of 1.7 points (p<.001), while those in 

programs in the lowest quartile increased by 1.5 points 

(p<.001) (equivalent to .34 and .30 of a standard 

deviation, respectively). (Figure 5.3.) The regression 

coefficient for Mean Teacher Salary Level in the threelevel regression analysis, which is an estimate of the 

increase in average gains for every $10,000 increment 

in mean salary, adjusted for the influence of related 

variables, was 1.18 (p < .001). 

Decline in Hyperactive Behavior 

Children in programs with higher teacher salary levels 

exhibited significant improvement in their scores on 

the Hyperactive subscale of the Problem Behavior 

rating scale completed by Head Start teachers. 

Children in programs in the highest quartile on teacher 

salary levels showed a mean decline of 0.35 point (p = 

.013) on this scale, going from a mean rating of 1.39 in 

the fall to a mean of 1.04 in the spring (an effect size of 

.23 of a standard deviation). By comparison, children 

in programs in the middle two quartiles on teacher 

salary level showed no change (-0.12 point), while 

children in programs in the lowest quartile also showed 

no change (-.01 point). (Figure 5.4.) The regression 

coefficient for Mean Teacher Salary Level in the threelevel regression analysis, which is an estimate of the 

difference in average declines for every $10,000 

increment in mean teacher salary, adjusted for the 

influence of related variables, was -0.18 point (p < .05). 

C. Teachers With Bachelor’s or 

Associate’s Degrees 

The possession of a four-year college degree or an 

associate’s degree in education or a closely related field 

is among the most widely accepted indicators of 

teacher preparation. One of the current performance 

goals of the national Head Start program is to have all 

local programs staffed by teachers of whom a majority 

have bachelor’s degrees or associate’s degrees. We 

explored whether the lead teacher having a BA or AA 

69 
Head Start FACES 2000 CHAPTER V 

Figure 5.3. Children in Head Start Programs With Higher Teacher Salaries Show Larger Gains in Cooperative 

Classroom Behavior Mean Score on Cooperative Classroom Behavior Rating Scale 

18.0 

17.5 

17.0 

16.5 

16.0 

15.5 

17.7 

16.3 

16.3 

14.6 14.6 

14.8 

Fall 2000 Spring 2001 

Time of Teacher Ratings 

Highest Quartile 

Middle Quartiles 

Lowest Quartile 

15.0 

14.5 

14.0 

Figure 5.4. Children in Head Start Programs With Higher Teacher Salaries Show Larger Declines in Hyperactive 

Behavior 

1.0 

1.4 

1.3 

1.4 

1.3 

1.3 

0.0 

0.2 

0.4 

0.6 

0.8 

1.0 

1.2 

1.4 

1.6 

Fall 2000 Spring 2001 

Time of Teacher Ratings 

Mean Score on Hyperactive Rating Scale

Highest Quartile 

Middle Quartiles 

Lowest Quartile 

70 
CHAPTER V Head Start FACES 2000 

95.0 

90.0 

88.8 

87.1 

86.3 85.5 

BA 

AA 

Less than AA 

85.0 84.5 

83.9 

Mean Standard Score on WJ-R Dictation Task

80.0 

Fall 2000 Spring 2001 

Time of Assessment 

degree made a difference in children’s progress on 

cognitive or social measures. 

On several of the cognitive assessment measures, 

children in classes taught by teachers with BA or AA 

degrees ended the program year with mean scores that 

were higher than those of children in classes taught by 

teachers with less than an AA degree. However, these 

children had also had higher mean scores at the 

beginning of the year. This may reflect a situation in 

which Head Start programs that hire teachers with 

college credentials tend to serve families with higher 

parent education and income levels than are typical for 

Head Start nationwide. What was less certain was 

whether the children taught by teachers with higher 

educational credentials made greater gains from fall to 

spring than children taught by teachers with lesser 

credentials. Early writing skills was one cognitive area 

in which there was evidence of greater gains as well as 

higher achievement levels. However, the evidence was 

not unambiguous. 

Gains in Early Writing Skills 

In standard score terms, and without adjustment for 

the effects of related variables, the picture was 

reasonably clear. Children in Head Start classes taught 

by lead teachers with bachelor’s degrees or associate’s 

degrees had higher mean scores on the Woodcock 

Johnson-Revised Dictation task in the fall—86.3 and 

84.5, respectively—than children in classes taught by 

teachers with less than an associate’s degree, who had 

a mean score of 83.9. But the children in the former 

classes also made significant gains toward national 

averages, whereas children in the latter group did not. 

The gains were 2.48 standard score points (p = .03) 

for children whose teachers had bachelor’s degrees; 

2.55 standard score points (p = .03) for children 

whose teachers had associate’s degrees; and 1.67 

standard score points (p = .21) for children whose 

teachers had less than an associate’s degree. (Figure 

5.5.) The respective gains represented effect sizes of 

.18, .19, and .12 of a standard deviation. 

Figure 5.5. Children in Head Start Classes Taught by Teachers With Bachelor’s or Associate’s Degrees Show 

Gains in Early Writing Skills 

71 
Head Start FACES 2000 CHAPTER V 

In the three-level analysis of spring Dictation scores, 

children in classes taught by teachers with BAs or AAs 

had a significant regression coefficient of 6.14 IRT 

scale points (p = .01). This meant that children in 

these classes had a mean score in the spring that much 

higher than the mean for children in classes taught by 

teachers without those credentials. In the fall analysis, 

children in classes taught by teachers with BAs or AAs 

had had a regression coefficient that was also 

significant, though apparently smaller (5.46 scale 

points, p = .009), which meant that they started with 

higher scores in the fall. The class-level variables as a 

set did not improve model fit in the fall, whereas they 

did in the spring. However, in the multilevel analysis of 

gains on the Dictation task, the regression coefficient 

for full-day classes (0.63) was not significant (p = .77). 

D. Full-Day Versus Part-Day Classes 

As of the 2000-2001 school year, the majority of 

children who attended Head Start participated in partday classes that were conducted in morning or 

afternoon sessions only.We explored whether children 

benefited more from the program in terms of 

academic achievement if they attended full-day classes. 

Children in FACES 2000 who did attend full-day Head 

Start programs made greater gains in several areas than 

children who attended part-day. 

Gains in Pre-Reading and Early Writing Skills 

Children in full-day Head Start classes made larger 

gains in letter recognition skills than children in partday classes. Children in full-day Head Start classes 

showed a mean gain on the Woodcock-Johnson Revised 

Letter-Word Identification task of 12 points in IRT 

scale-score terms (p < .001). Children in part-day 

classes showed a mean gain of 8.7 points (p < .001). 

The regression coefficient for full-day classes in the 

three-level regression analysis, which is an estimate of 

the difference between these classes and part-day 

classes adjusted for the influence of related variables, 

was only significant at the trend level (1.81, p = .067). 

In standard score terms, the full-day group showed an 

average gain on the WJ-R LWI task of 1.2 standard 

score points (p = .06), whereas the part-day group 

merely held their own against national averages, 

showing a non-significant decline of 0.9 points (p = 

.12). (Figure 5.6.) 

Children in full-day Head Start classes made greater 

gains as well in early writing skills, although the 

statistical evidence here was more ambiguous. In 

standard score terms, and without adjustments for the 

effects of related variables, children in full-day classes 

had a mean gain from fall to spring of 3.5 standard 

score points (p = .004) on the Woodcock-Johnson 

Revised Dictation task.They went from a mean of 84.8 

in the fall to a mean of 88.3 in the spring. The gain was 

equivalent to an effect size of .25. By contrast, 

children in part-day classes went from a mean of 85.0 

in the fall to a mean of 86.1 in the spring, a nonsignificant difference of 1.1 standard score points (p = 

.162). (Figure 5.7.) 

In the three-level analysis of spring Dictation scores, 

children in full-day classes had a significant regression 

coefficient of 7.80 IRT scale points (p = .005). This 

meant that children in full-day classes had a mean score 

in the spring that much higher than the mean for 

children in part-day classes, with related factors 

controlled. In the fall analysis, children in full-day 

classes had had a regression coefficient that was only 

marginally higher (4.10 scale points, p = .086). 

Furthermore, the class-level variables as a whole did 

not improve model fit in the fall, whereas they did in 

the spring. However, in the multilevel analysis of gains 

on the Dictation task, the regression coefficient for 

full-day classes (3.85) was not significant (p = .129). 

E. More Frequent Parental Reading to 

Children 

Children are in preschool programs for only a limited 

time, both in terms of hours of each day and months 

72 
CHAPTER V Head Start FACES 2000 

Figure 5.6. Head Start Children in Full-Day Classes Show Larger Gains in Letter Recognition Skills Than Those in 

Part-Day Classes Mean Standard Score on WJ-R Letter-Word Identification Task 100.0 

95.0 

90.0 

85.0 

92.8 

91.7 91.1 

92.0 

Fall 2000 Spring 2001 

Time of Assessment 

Full-Day Classes 

Part-Day Classes 

out of the child’s life. However, preschool programs 

may extend their influence by encouraging parents to 

engage in more frequent and more effective 

educational activities at home with their children. The 

national Head Start program recognized the 

importance of this function by stating, in its 

performance measures framework, that one of the 

major objectives of the program is, to “strengthen 

parents as the primary nurturers of their children.” 

Therefore, we decided to consider frequency of 

parental reading as an additional variable of interest in 

considering children’s outcomes. Analysis showed that 

more frequent parental reading in the fall was 

associated not only with higher initial achievement for 

children as they entered the program, but also with 

larger gains during the program year. Larger gains 

were observed both in vocabulary knowledge and 

letter recognition skills. 

Gains in Vocabulary Knowledge 

Parents were asked whether they read to their 

children, “not at all,” “once or twice,” “three to six 

times,” or “every day” during the previous week. 

Parental responses to the question were entered into 

the three-level regression analysis as a set of 

dichotomous variables, with the most frequent 

response, “three to six times,” as the omitted reference 

category. The reading responses were entered as childlevel independent variables. 

In the regression analysis of fall vocabulary test scores, 

children whose parents reported reading to their 

children “not at all” or “once or twice” had significantly 

lower mean scores than children whose parents 

reported reading “three to six” times. The mean score 

for children whose parents said they read “every day” 

was not significantly different from that of the “three to 

six times” group. In terms of IRT scale scores on the 

73 
Head Start FACES 2000 CHAPTER V 

95.0 

85.0 

90.0 

Mean Standard Score on WJ-R Dictation Test

84.9 

85.0 

88.3 

86.1 

80.0 

Fall 2000 

Time of Assessment 

Spring 2001 

Full-Day Class

Part-Day Class

Figure 5.7. Children in Full-Day Head Start Classes Show Greater Gains in Early Writing Skills 

Peabody Picture Vocabulary Test, Third Edition, the 

mean for the “not at all” group was 1.93 points lower 

(p < .05), and the mean for the “once or twice” group 

was 1.83 points lower (p < .001), than the mean for 

the “three to six times” reference group. 

In the regression analysis of the spring PPVT-III scores, 

the “not at all” and “once or twice” groups again had 

mean scores that were significantly lower (by 1.83 

scale points, p < .05 and 1.35 points, p < .01, 

respectively) than that for the “three to six times” 

reference group. But now the “every day” group had a 

mean score that was significantly higher than the 

reference group mean (by 1.17 scale points, p < .01). 

Thus, there was a 3 scale-point difference between the 

vocabulary means of the highest and lowest reading 

groups. In the regression analysis of vocabulary gains, 

the “every day” group had a larger gain than the 

reference group (by 0.68 scale points), although the 

difference was only significant at the trend level (p = 

.093). 

These differences related to frequency of parental 

reading were obtained even after controlling for parent 

education level, the mother’s score on a measure of 

adult literacy (the K-FAST), and an indicator of the 

presence of books in the home. These measures were 

also entered into the regression analysis as child-level 

variables, and all were significantly related to children’s 

vocabulary test scores in the fall and spring of the 

program year. None was related to the size of fallspring gains in vocabulary scores, however. 

The picture was similar, though not identical, when 

looked at in terms of mean standard scores for the 

parental reading groups without adjustments for the 

effects of related variables. (Figure 5.8.) All four 

groups showed significant gains in their vocabulary 

standard scores from fall to spring of the Head Start 

year. But the gain was smallest for the group whose 

parents said they read to the child “not at all” in the 

previous week (2.1 standard score points, p < .05). 

And the gain was largest for the group whose parents 

74 
CHAPTER V Head Start FACES 2000 

Mean Standard Score on PPVT-III

100.0 

95.0 

92.5 

90.0 89.4 

87.9 

86.1 84.7 85.0 

84.5 

82.6 

80.0 80.8 

75.0 

Fall 2000 Spring 2001 

Time of Assessment 

Every day 

Three to six times 

Once or twice 

Not at all 

said they read to the child “every day” (4.6 standard 

score points, p < .001). However, the gain for the 

group whose parents read only “once or twice” was also 

sizable (3.8 standard score points, p < .001). When 

the gains were seen in terms of effect sizes, they ranged 

from .14 to .32 of a standard deviation, with the “every 

day” reading group having the largest effect size. 

Gains in Pre-Reading Skills 

The parental reading groups showed differences in the 

gains children made on the Woodcock-Johnson Revised 

Letter-Word Identification task. Children whose 

parents reported reading to them only once or twice a 

week or less did not make as large gains in letter 

recognition skills as children whose parents reported 

reading to them three times a week or more. In the 

regression analysis of fall scores on the LWI test, the 

group means lined up in a fashion similar to that seen 

in the vocabulary analysis, but differences were not 

statistically significant. In the regression analysis of 

spring LWI scores, however, both the “not at all” 

reading group and the “once or twice” reading group 

had significantly lower means than the “three to six 

times” reference group. The respective differences, in 

terms of IRT scale scores, were -3.74 points (p < .10) 

and -2.95 points (p < .05). The “every day” reading 

group had a mean score that was not significantly 

different from that of the reference group. 

Similar results were obtained in the regression analysis 

of fall-spring gains on the LWI task. Both the “not at 

all” reading group and the “once or twice” reading 

group had significantly smaller gains than the “three to 

six times’ reference group. The respective differences 

in gains, in terms of IRT scale scores, were -3.23 points 

(p < .10) and -2.30 points (p < .05). The “every day” 

reading group had a mean gain that was not 

significantly different from that of the reference group. 

Again, these results controlled for the effects of parent 

education, parental literacy level, and the presence of 

books in the home. 

Figure 5.8. Head Start Children Whose Parents Read To Them More Often Show Larger Vocabulary Gains 

75 
Head Start FACES 2000 CHAPTER V 

F. Classroom Quality Indicators Not 

Found to Relate to Gains 

The five program and class characteristics described 

above showed significant or marginally significant 

relationships with children’s gains in Head Start. But 

there were two sets of characteristics that did not show 

the relationships with children’s gains that were 

hypothesized. These were the indicators of classroom 

quality, and the indicators of child:staff ratio and 

individualized attention to the needs of each child. 

As described in detail in Chapter IV and the Appendix, 

the FACES 2000 classroom observation battery 

contained a number of widely accepted indicators of 

the general quality of Head Start (or other preschool 

or childcare) environments. Two of these measures 

were chosen for inclusion in the three-level regression 

analysis of children’s gains. One was the Language 

scale, a component of the Early Childhood 

Environment Rating Scale-Revised (ECERS-R). This 

component scale consists of a series of observational 

items and ratings that deal with the frequency and 

quality of class activities related to oral language 

development, vocabulary building, and the nurturing 

of pre-reading and early writing skills. Thus, it seemed 

likely to relate to children’s gains in these areas. 

The second classroom quality indicator included in the 

three-level models was the Caregiver Interaction Scale, 

an observational rating measure of the emotional tone 

of teacher-child interaction and the lead teacher’s 

sensitivity to children’s needs and feelings. It seemed 

likely to relate to children’s gains in the socialemotional domain. Neither of these expectations was 

supported by the FACES findings. 

ECERS-R Language Scale 

Children in classrooms with higher ECERS-R 

Language scores had higher vocabulary test scores in 

the fall than children in classrooms with moderate or 

lower ECERS-R Language scores. In terms of standard 

scores, the mean score on the PPVT-III in the fall for 

children in Head Start classes in the highest quartile on 

the ECERS-R Language scale was 84.4. For children 

in classes in the middle two quartiles on the Language 

scale, the mean score was 80.5, while for children in 

classes in the lowest quartile, the mean score was 79.4. 

But children in higher, moderate, and lower quality 

classes all showed similar gains in vocabulary 

knowledge from fall to spring. Children in the highest 

quartile classes showed standard score gains of 4.26 

points (p < .001, effect size of .25). Children in the 

middle two quartile classes showed standard score 

gains of 4.32 points, (p < .001, effect size of .25). And 

children in the lowest quartile classes showed standard 

score gains of 4.59 points (p < .001, effect size of .27). 

Thus, the same differences in vocabulary knowledge 

between higher and lower quality classes that were 

observed in the fall were still present in the spring. 

(Figure 5.9.) The higher quality language 

environments were not associated with larger gains in 

vocabulary knowledge. 

Indeed, when relationships with other variables were 

controlled in the three-level regression analysis, even 

the differences in fall vocabulary scores associated with 

ECERS-R Language scores proved not statistically 

significant. This implies that the differences were 

better accounted for by the operation of other, related 

variables. 

Higher ECERS-R Language scores were not associated 

with larger gains in letter recognition, early writing 

skills, or early math skills. Nor were they associated 

with improvements in children’s cooperative or 

problem behavior in the classroom. Although the 

ECERS-R Language scale was the classroom quality 

indicator entered in the three-level regression models 

reported here, other correlation and regression 

analysis showed that the conclusions would not have 

been different had another quality indicator, such as the 

ECERS-R Total Score, been used instead. A Quality 

Factor weighted composite score was developed that 

76 
CHAPTER V Head Start FACES 2000 

Figure 5.9. Children in Head Start Classes With Higher, Moderate, and Lower Quality Language Activities Show 

Parallel Gains in Vocabulary Knowledge 

Highest Quartile on ECERS Language Scale Middle Quartiles Lowest Quartile 

Mean Standard Score on PPVT-III 

100 

95 

90 

85 

80 

75 

88.6 

84.4 84.1 

84.8 

80.5 

79.5 

Fall 2000 Spring 2001 

Time of Assessment 

incorporated the Assessment Profile Learning 

Environment and Scheduling scales as well as the 

ECERS-R Language scale. Analysis with that score 

produced non-confirmatory results as well. 

Caregiver Interaction Scale 

We hypothesized that the Caregiver Interaction Scale, 

an observation-based rating scale that reflects the 

sensitivity and emotional tone of teacher-child 

interaction, would be associated with improvements in 

children’s cooperative and problem behavior in the 

classroom. This proved not to be the case. Higher CIS 

scores for the classroom teacher were associated with 

higher cooperative behavior ratings in the spring 2001 

at only the trend level (p < .10). In the three-level 

analysis of gains in cooperative behavior, the coefficient 

for CIS scores had a value of zero. CIS scores were not 

significantly associated with reductions in Hyperactive 

Behavior or Total Behavior Problems either. 

Higher CIS scores were not associated with greater 

gains in vocabulary, letter recognition, or early math 

skills. There was a significant positive association 

between higher CIS scores and higher mean scores on 

the WJ-R Dictation task in the spring of 2001. The 

regression coefficient signified a Dictation score that 

was .29 IRT scale points higher for each unit increase 

in the lead teacher’s CIS score (p = .03). However, 

higher CIS scores were not significantly associated with 

greater fall-spring gains in this measure of early writing 

skills (p = .20). 

Child:Staff Ratio and More Individual Attention 

We hypothesized that lower child:adult ratios in Head 

Start classroom activities, and more attention to the 

needs and preferences of individual children would 

result in greater gains for children. These hypotheses 

were not confirmed by the data. Indeed, on some 

outcome measures, children actually showed greater 

gains in classrooms with more children per adult. 

77 
Head Start FACES 2000 CHAPTER V 

Child:Staff Ratios 

The mean child:staff ratio was a figure derived from 

counting the number of children in the Head Start 

classroom and dividing that number by the number of 

teachers or other adult staff members actively 

interacting with children. These counts were taken at 

two separate occasions on the day that the classroom 

was observed, and the two resulting ratios were 

averaged. When the mean child:staff ratio was entered 

into the three-level regression analysis of children’s 

cognitive gains as a class-level variable, it proved not to 

be significantly associated with gains in vocabulary 

knowledge, early writing, or early math skills. In the 

vocabulary analysis, the results were much like those 

for the ECERS-R Language score. That is, children in 

classes with lower, moderate, or higher child:staff 

ratios all showed roughly equivalent gains in vocabulary 

knowledge from fall to spring of the Head Start year. 

In the analysis of children’s pre-reading skills, results 

were opposite to what was predicted. Children in 

classes with higher child:staff ratios made significantly 

larger gains in letter recognition skills than those in 

classes with lower ratios. In terms of standard scores, 

children in the highest quartile of classes on the 

child:staff ratio showed an average gain of 1.2 standard 

score points on the WJ-R Letter-Word Identification 

task from fall to spring. They went from a mean 

standard score of 92.1 in the fall to a mean score of 

93.3 in the spring. By comparison, children in classes 

in the middle two quartiles and lowest quartile merely 

held their own against national norms, showing nonsignificant declines of -0.6 standard score points (p = 

.28) and -0.9 standard score points (p = .55), 

respectively. (Figure 5.10.) 

Figure 5.10. Children in Head Start Classes With Higher Child:Adult Ratios Show Larger Gains in Letter 

Recognition Mean Standard Score on WJ-R Letter-Word Identification Task 100.0 

95.0 

90.0 

85.0 

Fall 2000 

92.1 

91.7 

92.0 

93.3 

91.1 

91.1 

Highest Quartile of 

Child:Adult Ratios 

Middle Quartiles 

Lowest Quartile 

Spring 2001 

Time of Assessment 

78 
CHAPTER V Head Start FACES 2000 

In the three-level regression analysis of LWI gains, the 

value of the regression coefficient for the mean 

child:staff ratio was 0.57 (p < .05). This may be 

interpreted as the change in gain in LWI IRT scale 

scores that would be expected for every unit increase 

in the child:staff ratio, net of the effects of other 

related variables. If the original hypothesis had been 

confirmed, this coefficient would be negative. Instead, 

it was reliably greater than zero in the positive 

direction. 

Higher child:staff ratios were also associated with 

behavioral gains. In the three-level regression analysis 

of gains in Cooperative Classroom Behavior, the 

coefficient for mean child:staff ratio was 0.15 (p = 

.086). In the analysis of declines in the Total Behavior 

Problems, the coefficient for mean child:staff ratio was 

-0.20 (p = .03). Both of these relationships were in the 

direction opposite to the expected one. 

Assessment Profile Individualizing Scale 

The Individualizing scale of the Assessment Profile 

instrument uses both observational and interview 

methods to assess the degree to which preschool 

teachers track the accomplishments of children in their 

classes and provide activities suited to the capabilities 

and interests of individual pupils. Class-level scores on 

this instrument did not relate to gains in any of the 

cognitive development areas. Nor did they show 

associations with improvements in the measures of 

social-emotional development. 

SUMMARY AND DISCUSSION 

This chapter explored variations in child achievement 

and behavior across local Head Start programs and 

classes. It used multilevel modeling to test hypotheses 

about early education program and class characteristics 

that many child development scholars believe to be 

associated with enhanced cognitive growth or 

emotional maturation in preschool children. The 

conceptual framework posited that the gains a child 

makes in Head Start depend on the nature of the 

learning environment that he or she experiences in the 

local program. The nature of the learning environment 

depends in turn on the training and experience of 

teachers in the program and the resources available to 

them in terms of facilities, materials, and teaching 

assistants. Programs with more resources are likely to 

be better able to provide adequate facilities and 

materials and recruit and retain well-prepared 

teachers. Another hypothesis was that children would 

make larger gains in programs employing curricula 

that are comprehensive and integrated in terms of 

educational activities and assessment methods. Other 

expectations were that children would make more 

sizable gains in higher quality classrooms, in full-day as 

opposed to part-day classes, in classes with better 

child:staff ratios and more individualized attention to 

pupils, and in families where parents engaged in more 

educational activities with their children. 

Analysis of longitudinal data from FACES 2000 showed 

that children’s gains in Head Start were significantly 

related to several of the hypothesized characteristics of 

programs and classes. Specifically: 

• Use of an integrated curriculum was linked to 

greater gains in several cognitive and socialemotional areas. Children in Head Start programs 

using High/Scope showed larger fall-spring gains in 

letter identification and cooperative classroom 

behaviors than children in programs using other 

curricula. Children in programs using High/Scope 

also showed greater improvement in total behavior 

problems and hyperactive problem behavior. 

• Higher teacher salaries were linked to greater gains 

in several cognitive and social-emotional areas, 

including letter identification and cooperative 

classroom behavior. Children in programs with 

higher teacher salaries also showed greater 

improvement in hyperactive problem behavior 

during the Head Start year. 

79 
Head Start FACES 2000 CHAPTER V 

• Teachers’ educational credentials were linked to 

greater gains in early writing skills. Children taught 

by Head Start teachers with bachelor’s degrees or 

associate’s degrees showed gains toward national 

averages in an assessment of early writing skills, 

whereas children taught by teachers with lesser 

credentials merely held their own against national 

norms. 

• Provision of preschool services for a longer period 

each day was linked to greater cognitive gains. 

Children in full-day classes in Head Start showed 

larger fall-spring gains in letter recognition and early 

writing skills than did children in part-day classes. 

• There was indirect evidence that encouraging 

parents to engage in more educational activities with 

their children at home could serve as a pathway to 

greater cognitive gains. Children whose parents 

reported reading to them every day showed larger 

fall-spring gains in vocabulary knowledge and letter 

recognition skills than children whose parents 

reported reading once or twice or less frequently per 

week. 

Other analytic results were not in line with 

expectations. In particular: 

• Within the generally good quality range of Head 

Start classrooms, variation in quality as measured by 

the ECERS-R Language scale or the Caregiver 

Interaction Scale was not associated with differences 

in fall-spring achievement gains across classes. 

• Within the narrow range of child:staff ratios in Head 

Start, variation in child:staff ratios was not associated 

with or was negatively associated with differences in 

fall-spring achievement gains across classes. 

The analysis results were generally supportive of the 

conceptual framework that the amount of resources 

available to a Head Start program and the curricular 

approach it uses can make a difference for children’s 

progress in the program. The results also supported 

the notions that children could make greater gains if 

they had more exposure to comprehensive, integrated 

preschool activities. While we were not able to 

consider the program’s effect on parental reading, it 

appears that benefits accrue from increased frequency 

of educational activities at home. At the same time, 

some provisos about the results should be noted.These 

include the following points: 

1. Differences in cognitive gains, while statistically 

significant, were relatively modest in magnitude. 

By itself, each of the differences was not large 

enough to close the gap between where Head 

Start children typically end up at the end of the 

program year and the average achievement levels 

of American children at the start of elementary 

school. If several of the positive characteristics 

could be implemented simultaneously in a 

program, they might jointly make a more sizable 

difference, however. 

2. Program- and classroom-related gains varied 

across cognitive areas. Significant gains were seen 

primarily with respect to letter recognition and 

early writing skills. The important areas of 

vocabulary and early math skills showed little 

variation in gains that was linked to specific 

program or class characteristics. Rather, 

children in programs and classes with different 

characteristics tended to show parallel gains (in 

vocabulary) or similar lack of gains (in early math 

skills). 

3. Differences in achievement levels at the end of 

the Head Start year between children in Head 

Start programs with differing socioeconomic and 

ethnic composition were substantial. This was 

particularly the case with respect to vocabulary 

knowledge.The program and class characteristics 

studied here did little to narrow these gaps. This 

80 
CHAPTER V Head Start FACES 2000 

was partly because, as just noted, the studied 

characteristics were not linked to differential 

gains in vocabulary and math skills. But it was 

also because local programs with higher average 

parent education and income levels tended to 

have more of the desirable program and class 

characteristics. 

4. Findings of significant links between program 

and class characteristics and improvements in 

children’s behavior have to be tempered by the 

realization that the measures of children’s 

behavior made use of ratings by teachers and 

parents. Thus, it is possible that the observed 

relationships were partly due to differences in 

the rating patterns of different groups of teachers 

rather than (or as well as) to actual behavioral 

differences between groups of children. 

5. The failure to find significant links between 

children’s cognitive gains in Head Start and classlevel scores on the ECERS-R Language scale may 

have to do with the generally good quality of 

Head Start classrooms and the limited range of 

variation in classroom quality that FACES found 

in its national samples of programs and classes 

(Peisner-Feinberg et al, 2001). The same may be 

said of the failure to find significant links between 

children’s improvements in the social-emotional 

domain and class-level scores on the Caregiver 

Interaction Scale. Studies encompassing broader 

ranges of quality of childcare and early education 

facilities have shown greater variations in 

classroom quality measures and significant 

relationships between quality measures and 

children’s gains (Peisner-Feinberg & Burchinal, 

1997; Bryant, Burchinal, Lau, & Sparling, 1994; 

NICHD Early Child Care Research Network, 

2000; Phillips, McCartney, & Scarr, 1987; 

Whitebook, Howes & Phillips, 1989; but for 

another failure to find a relationship, see Kontos 

& Fiene, 1987). 

At the same time, the FACES results should make us 

wary of claims that Head Start could produce 

dramatically larger achievement gains in children from 

low-income families simply by raising ECERS scores or 

other indicators of classroom quality. It may be that 

good classroom quality is a necessary but not sufficient 

condition for practically significant gains in specific 

cognitive or behavioral areas. It may be that further 

progress depends on discovering and applying 

instructional approaches that can bolster gains in 

specific areas. Preliminary findings from randomized 

intervention studies conducted in Head Start programs 

in New York state as part of the Head Start Quality 

Research Consortium studies suggest that children in 

Head Start can make strikingly larger gains in letter 

recognition and related skills with appropriate, 

research-based supplementary curricula (Fischel, 

Storch, Spira & Stoltz, 2003). 

81 
Head Start FACES 2000 CHAPTER V 

REFERENCES

Bryant, D. M., Burchinal, M., Lau, L. B., & Sparling, 

J. J. (1994). Family and classroom correlates of Head 

Start children’s developmental outcomes. Early 

Childhood Research Quarterly, 9, 289-309. 

Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical 

linear models: Applications and data analysis methods. 

Newbury Park, CA: Sage Publications. 

Fischel, J., Storch, S. A., Spira, E. G., & Stoltz, B. M. 

(2003). Enhancing emergent literacy skills in Head Start: 

First year curriculum evaluation results. Poster presented 

at Society for Research in Child Development Biennial 

Meeting,Tampa Bay, Florida, April 26. 

Kontos, S., & Fiene, R. (1987). Child care quality, 

compliance with regulations, and children’s 

development: The Pennyslvania Study. In D. Phillips 

(ed.), Quality in child care: What does the research tell us? 

(pp. 57-79). Washington, DC: National Association 

for the Education of Young Children. 

NICHD Early Child Care Research Network 

(ECCRN). (2000). The relation of child care to 

cognitive and language development. Child 

Development, 71, 823-839. 

Peisner-Feinberg, E. S. & Burchinal, M. R. (1997). 

Relations between preschool children’s child-care 

experiences and concurrent development: The Cost, 

Quality and Outcomes Study. Merrill-Palmer Quarterly. 

43, 3, 451-477. 

Peisner-Feinberg, E. S., Burchinal, M. R., Clifford, R. 

M., Culkin, M. L, Howes, C., Kagan, S. L., & Yazejian, 

N. (2001). The relation of preschool child-care quality 

to children’s cognitive and social developmental 

trajectories through second grade. Child Development, 

72(5), 1534-1553. 

Phillips, D., McCartney, K., & Scarr, S. (1987). Child 

care quality and children’s social development. 

Developmental Psychology, 23, 537-543. 

Phillips, D., Mekos, D., Scarr, S., McCartney, K. & 

Abbott-Shim, M. (2000). Within and beyond the 

classroom door: Assessing quality in child care centers. 

Early Childhood Research Quarterly, 15 (4), 475-496. 

Phillips, M., Brooks-Gunn, J., Duncan, G. J., 

Klebanov, P., & Crane, J. (1998). Family background, 

parenting practices, and the black-white test score gap. 

Pp. 103-145 in The Black-White Test Score Gap, edited by 

C. Jencks & M. Phillips, Washington DC: Brookings 

Institution Press. 

Singer, J. (1998). Using SAS PROC MIXED to fit 

multilevel models, hierarchical models, and individual 

growth models. Journal of Educational and Behavioral 

Statistics, 24(4), 323-355. 

Whitebook, M., Howes, C., & Phillips, D. (1989). 

Who Cares? Child care teachers and the quality of care in 

America (Final report of the National Child Care 

Staffing Study). Oakland, CA: Child Care Employee 

Project. 

82 
CHAPTER V Head Start FACES 2000 

Table 5.1. Three-Level Regression Models of Assessment Scale Scores of Head Start Children in Fall and Spring of 

Program Year, and Fall-Spring Gains, 2000-2001 

Fall 

Letter ID 

Spring 

Letter ID 

Letter ID 

Gains 

Program-Level Predictor Variables Unstandardized Regression Coefficients 

High Scope Curriculum 1.01 4.48 * 3.66 * 

Creative Curriculum 0.88 2.66 † 1.92 

Mean Teacher Salary Level 1.21 † 3.10 ** 1.96 ** 

Proportion Non-Minority Children -0.07 -0.38 -0.22 

Program Mean Parent Education Level 3.55 ** 4.76 * 1.20 

Program Mean Family Income Level -0.67 0.02 0.30 

Proportion Language-Minority Children 5.20 3.07 -2.06 

Class-Level Predictor Variables 

Full-Day Class 0.25 1.99 1.81 † 

Average ECERS Language Score -0.49 -0.92 -0.41 

Average Child-Adult Ratio 0.34 0.91 ** 0.57 * 

AP Individualizing Score 0.68 † 0.37 -0.27 

Average Lead Teacher Arnett Score 0.03 0.02 -0.01 

Teacher BA or AA 1.93 * 0.19 -1.71 † 

Years Teaching Experience -0.02 0.00 0.02 

Teacher DAP Beliefs Score -0.36 -0.76 * -0.40 

Black Teacher -2.50 * -0.16 2.33 † 

Hispanic Teacher -0.13 -0.41 -0.36 

Teacher Salary Deviation Score -0.15 -1.55 -1.39 † 

Class Parent Education Level (deviation) 1.62 * 2.72 * 1.09 

Class Family Income Level (deviation) -0.73 -0.42 0.32 

Class Proportion Non-minority (deviation) 1.86 5.23 3.54 

Proportion Language-Minority (deviation) -1.59 2.34 3.84 

*** p < .001 

** p < .01 

* p < .05 

† p < .10 

N = 957 

83 
Head Start FACES 2000 CHAPTER V 

Total Variance 15% ** 20%*** 12% ** 

Table 5.1. Three-Level Regression Models of Assessment Scale Scores of Head Start Children in Fall and Spring of 

Program Year, and Fall-Spring Gains, 2000-2001, continued 

Fall 

Letter ID 

Spring 

Letter ID 

Letter ID 

Gains 

Child-Level Predictor Variables 

Parent Literacy Standard Score (KFAST) 0.10 ** 0.06 -0.04 

Parent Education Level (deviation) 0.98 ** 1.39 ** 0.39 

Family Income Level (deviation) 0.39 0.66 0.26 

Welfare Status -1.40 -1.51 -0.08 

Books In Home 1.50 0.79 -0.72 

Frequency of Reading to Child 

Not At All -0.55 -3.74 † -3.23 † 

Once or twice -0.68 -2.95 * -2.30 * 

Every day 0.47 0.65 0.22 

Age of Child in Months 0.51*** 0.68*** 0.17 † 

Sex of Child 0.46 1.24 0.79 

Black Child 1.77 1.97 0.29 

Hispanic Child -0.55 -1.92 -1.51 

Language Minority Family 1.07 -1.49 -2.45 

Disability Status -2.12 * -4.07 ** -2.00 † 

Mother-Father Family -1.07 -0.49 0.64 

Neither Birth Parent In Home 2.13 -1.55 -3.69 † 

Intercept 301.22*** 297.66*** -2.90 

Proportion of Variance Accounted For: 

Between-Programs Variance 100%*** 95%*** 100%** 

Between-Classes Variance 100%*** 92%*** 99% ** 

Within-Classes Variance 7%*** 6% *** 1% ** 

*** p < .001 

** p < .01 

* p < .05 

† p < .10 

N = 957 

84 
CHAPTER V Head Start FACES 2000 

Table 5.2. Three-Level Regression Models of Assessment Scale Scores of Head Start Children in Fall and Spring of 

Program Year, and Fall-Spring Gains, 2000-2001 

Fall 

Vocabulary 

Spring 

Vocabulary 

Vocabulary 

Gains 

Program-Level Predictor Variables Unstandardized Regression Coefficients 

High Scope Curriculum 0.38 1.26 0.91 

Creative Curriculum 0.88 0.71 -0.15 

Mean Teacher Salary Level 0.57 0.34 -0.17 

Proportion Non-Minority Children 4.65*** 5.77*** 1.25 

Program Mean Parent Education Level 2.22 * 2.59 ** 0.31 

Program Mean Family Income Level 0.39 -1.14 -1.61 

Proportion Language-Minority Children -1.11 2.16 3.01 † 

Class-Level Predictor Variables 

Full-Day Class -0.41 -0.36 0.10 

Average ECERS Language Score 0.06 -0.13 -0.24 

Average Child-Adult Ratio 0.20 0.20 0.00 

AP Individualizing Score 0.17 0.06 -0.04 

Average Lead Teacher Arnett Score -0.09 0.03 0.01 

Teacher BA or AA 0.07 -0.01 0.00 

Years Teaching Experience -0.04 -0.05 † -0.02 

Teacher DAP Beliefs Score -0.05 -0.02 0.02 

Black Teacher -0.28 -0.07 0.04 

Hispanic Teacher 0.55 0.73 0.36 

Teacher Salary Deviation Score -0.25 -0.24 0.04 

Class Parent Education Level (deviation) 1.64*** 1.61*** 0.02 

Class Family Income Level (deviation) -1.08 -0.51 0.44 

Class Proportion Non-minority (deviation) 3.25 * 3.87 ** 0.69 

Proportion Language-Minority (deviation) -0.59 -2.46 -2.38 

*** p < .001 

** p < .01 

* p < .05 

† p < .10 

N = 1,984 

85 
Head Start FACES 2000 CHAPTER V 

Table 5.2. Three-Level Regression Models of Assessment Scale Scores of Head Start Children in Fall and Spring of 

Program Year, and Fall-Spring Gains, 2000-2001, continued 

Fall 

Vocabulary 

Spring 

Vocabulary 

Vocabulary 

Gains 

Child-Level Predictor Variables 

Parent Literacy Standard Score (KFAST) 0.13*** 0.12*** -0.02 

Parent Education Level (deviation) 0.64*** 0.65*** 0.01 

Family Income Level (deviation) 0.20 0.05 -0.13 

Welfare Status -0.66 -0.92 † -0.21 

Books In Home 1.63 ** 1.26 ** -0.31 

Frequency of Reading to Child 

Not At All -1.93 * -1.83 * 0.12 

Once or twice -1.83*** -1.35 ** 0.44 

Every day 0.57 1.17 ** 0.68 † 

Age of Child in Months 0.81*** 0.73*** -0.08** 

Sex of Child -0.23 0.06 0.32 

Black Child -3.31*** -3.73*** -0.33 

Hispanic Child -1.97*** -2.62*** -0.64 

Language Minority Family -8.51*** -7.19*** 1.32 † 

Disability Status -2.47*** -2.59*** -0.13 

Mother-Father Family -0.65 -0.83 * -0.17 

Neither Birth Parent In Home 2.05 * 0.46 -1.52 † 

Intercept 0.55 13.65 ** 13.23** 

Proportion of Variance Accounted For: 

Between-Programs Variance 99%*** 97%*** 56% * 

Between-Classes Variance 72%*** 80%*** 21% 

Within-Classes Variance 34%*** 33%*** 1% * 

Total Variance 53%*** 53%*** 3% † 

*** p < .001 

** p < .01 

* p < .05 

† p < .10 

N = 1,984 

86 
CHAPTER V Head Start FACES 2000 

Table 5.3. Three-Level Regression Models of Behavior Rating Scores of Head Start Children in Fall and Spring of 

Program Year, and Fall-Spring Gains, 2000-2001 

Fall Cooperative 

Behavior 

Spring Cooperative 

Behavior 

Cooperative Behavior 

Gains 

Program-Level Predictor Variables Unstandardized Regression Coefficients 

High Scope Curriculum -0.01 1.30 * 1.26 * 

Creative Curriculum -0.53 0.06 0.57 

Mean Teacher Salary Level -0.58 † 0.59 * 1.18 *** 

Proportion Non-Minority Children -1.00 -0.47 0.45 

Program Mean Parent Education Level 0.04 -0.20 -0.26 

Program Mean Family Income Level -0.80 -1.79 * -0.92 

Proportion Language-Minority Children -0.08 0.37 0.31 

Class-Level Predictor Variables 

Full-Day Class -0.29 0.13 0.45 

Average ECERS Language Score -0.06 0.04 0.06 

Average Child-Adult Ratio 0.03 0.17 * 0.15 † 

AP Individualizing Score -0.12 -0.35 * -0.22 

Average Lead Teacher Arnett Score 0.03 0.03 † 0.00 

Teacher BA or AA -0.10 -0.53 -0.43 

Years Teaching Experience -0.03 -0.02 0.00 

Teacher DAP Beliefs Score -0.06 0.01 0.07 

Black Teacher -0.73 -0.96 † -0.33 

Hispanic Teacher -0.06 -0.39 -0.28 

Teacher Salary Deviation Score 0.23 -0.02 -0.24 

Class Parent Education Level (deviation) -0.44 -0.40 0.07 

Class Family Income Level (deviation) 0.62 0.10 -0.48 

Class Proportion Non-minority (deviation) 1.03 1.16 -0.02 

Proportion Language-Minority (deviation) 0.34 -0.47 -0.79 

*** p < .001 

** p < .01 

* p < .05 

† p < .10 

N = 2,138 

87 
Head Start FACES 2000 CHAPTER V 

Table 5.3. Three-Level Regression Models of Behavior Rating Scores of Head Start Children in Fall and Spring of 

Program Year, and Fall-Spring Gains, 2000-2001, continued 

Fall Cooperative 

Behavior 

Spring Cooperative 

Behavior 

Cooperative Behavior 

Gains 

Child-Level Predictor Variables 

Parent Literacy Standard Score (KFAST) 0.00 0.01 * 0.01 

Parent Education Level (deviation) 0.14 * 0.13 † -0.01 

Family Income Level (deviation) 0.22 * -0.01 -0.23 * 

Welfare Status -0.27 -0.77*** -0.50 * 

Books In Home 0.40 † 0.07 -0.34 

Frequency of Reading to Child 

Not At All -0.08 0.22 0.32 

Once or twice -0.13 -0.14 -0.01 

Every day 0.44 * 0.26 -0.17 

Age of Child in Months 0.22*** 0.17 *** -0.05 *** 

Sex of Child 1.31 *** 1.48*** 0.18 

Black Child -0.02 -0.26 -0.24 

Hispanic Child 0.23 -0.18 -0.42 

Language Minority Family 0.08 0.50 0.40 

Disability Status -1.46*** -0.99*** 0.47 † 

Mother-Father Family -0.11 0.38 * 0.49 * 

Neither Birth Parent In Home -0.54 0.07 0.60 

Intercept 5.00 6.79 * 1.66 

Proportion of Variance Accounted For: 

Between-Programs Variance 77% 100% 65% * 

Between-Classes Variance 11% 22% * 8% 

Within-Classes Variance 14%*** 12%*** 2% ** 

Total Variance 16%*** 17%*** 7% *** 

*** p < .001 

** p < .01 

* p < .05 

† p < .10 

N = 2,138 

88 
CHAPTER V Head Start FACES 2000 

Table 5.4. Three-Level Models of Behavior Rating Scores of Head Start Children in Fall and Spring of Program Year, 

and Fall-Spring Gains, 2000-2001 

Fall Hyperactive 

Behavior 

Spring Hyperactive 

Behavior 

Hyperactive Behavior 

Declines 

Program-Level Predictor Variables Unstandardized Regression Coefficients 

High Scope Curriculum 0.08 -0.23 -0.32 * 

Creative Curriculum 0.08 -0.07 -0.15 

Mean Teacher Salary Level 0.08 -0.11 -0.18 * 

Proportion Non-Minority Children -0.31 -0.09 0.24 

Program Mean Parent Education Level 0.12 -0.33 * -0.45 ** 

Program Mean Family Income Level 0.00 0.47 † 0.47 * 

Proportion Language-Minority Children 0.25 0.29 0.06 

Class-Level Predictor Variables 

Full-Day Class 0.20 † 0.14 -0.04 

Average ECERS Language Score 0.00 0.06 0.06 

Average Child-Adult Ratio -0.02 -0.06 * -0.04 

AP Individualizing Score 0.09 † 0.06 -0.03 

Average Lead Teacher Arnett Score -0.01 † -0.01 0.00 

Teacher BA or AA -0.12 0.04 0.17 † 

Years Teaching Experience 0.01 † 0.01 0.00 

Teacher DAP Beliefs Score -0.02 0.01 0.03 

Black Teacher -0.24 -0.10 0.14 

Hispanic Teacher -0.21 0.00 0.21 

Teacher Salary Deviation Score 0.04 0.12 0.09 

Class Parent Education Level (deviation) 0.14 0.17 † 0.03 

Class Family Income Level (deviation) -0.03 -0.03 0.00 

Class Proportion Non-minority (deviation) -0.28 -0.16 0.13 

Proportion Language-Minority (deviation) 0.39 0.18 -0.21 

*** p < .001 

** p < .01 

* p < .05 

† p < .10 

N = 2,045 

89 
Head Start FACES 2000 CHAPTER V 

Table 5.4. Three-Level Models of Behavior Rating Scores of Head Start Children in Fall and Spring of Program Year, 

and Fall-Spring Gains, 2000-2001, continued 

Fall Hyperactive 

Behavior 

Spring Hyperactive 

Behavior 

Hyperactive Behavior 

Declines 

Child-Level Predictor Variables 

Parent Literacy Standard Score (KFAST) 0.00 0.00 0.00 

Parent Education Level (deviation) -0.06 * -0.06 * 0.00 

Family Income Level (deviation) -0.06 † -0.01 0.05 

Welfare Status 0.01 0.04 0.03 

Books In Home -0.17 * -0.04 0.14 † 

Frequency of Reading to Child 

Not At All -0.18 -0.01 0.17 

Once or twice -0.11 0.11 0.22 ** 

Every day -0.19 * -0.10 0.10 

Age of Child in Months -0.05*** -0.03*** 0.02 *** 

Sex of Child -0.41*** -0.53*** -0.13 * 

Black Child -0.21 † -0.05 0.17 

Hispanic Child -0.12 -0.07 0.07 

Language Minority Family -0.09 -0.20 -0.11 

Disability Status 0.62*** 0.35 *** -0.26 ** 

Mother-Father Family -0.07 -0.27*** -0.19 ** 

Neither Birth Parent In Home 0.02 0.09 0.07 

Intercept 4.38 *** 3.84 *** -0.61 

Proportion of Variance Accounted For: 

Between-Programs Variance 0% 100% 100% * 

Between-Classes Variance 22% 16% 11% 

Within-Classes Variance 10%*** 9% *** 3% *** 

Total Variance 11%*** 12%*** 6% *** 

*** p < .001 

** p < .01 

* p < .05 

† p < .10 

N = 2,045 

90 
CHAPTER VI

Relationship of Family and Parental Characteristics to Children’s 

Cognitive and Social Development in Head Start 

Head Start maintains a strong interest in supporting 

parents as the primary educators of their children. 

Considering this parental role, the FACES Technical 

Report I (ACYF, 2002)22 highlighted the diversity of 

Head Start families as well as noting many of the 

challenges that they encountered, such as balancing 

work and child care responsibilities, and coping with 

maternal depression, exposure to violence, 

involvement with the criminal justice system, and 

substance use. It was also reported that many of these 

families possessed strengths and resilience in the face of 

such challenges, and that when it came to their 

children, they held many of the same fundamental 

hopes, goals, values, and beliefs that are commonly 

shared among families, particularly with regard to 

education. 

This chapter will explore how parental and family 

characteristics are related to aspects of children’s 

school readiness. FACES Technical Report I (ACYF, 

2002) noted that parents held optimistic expectations 

for their children’s early schooling and valued the longterm benefits of education. While the majority of 

parents’ short-term goals for their children were 

focused on general academic skills, such as completing 

age-appropriate tasks and success in school, most also 

held specific long-term educational attainment goals 

for their children, such as graduating from high school 

and attending college. One essential theme that 

emerged was that parents felt a strong desire to have 

22 Findings cited from the FACES Technical Report I include 

quantitative data from the main study, as well as qualitative 

data from the case study. 

RESEARCH QUESTIONS 

In this chapter, the following research questions 

will be addressed: 

1. How are family and parental characteristics 

related to children’s cognitive and social 

development? 

2. How are family interactions with children 

related to children’s cognitive and social 

development? 

3. How do parents interact with Head Start 

and how is their interaction related to 

children’s cognitive and social 

development? 

Using fall 2000 and spring 2001 FACES parent 

interview data, this chapter explores relationships 

between family and parental characteristics and 

child outcomes (including teacher ratings of 

social skills and behavior problems, and direct 

cognitive assessments). These data provide 

further insights into the development of cognitive 

and social skills, particularly with regard to family 

challenges and strengths. The family risk factors 

investigated in this chapter include maternal 

depression, exposure to violence, domestic 

violence, involvement in the criminal justice 

system, substance use, and cumulative social and 

economic risks. The chapter concludes with a 

presentation of protective family factors and how 

the Head Start experience may help families cope 

with multiple life challenges. 

91
Head Start FACES 2000 CHAPTER VI 

the best for their children and to instill values focused 

on education. 

Despite facing various barriers to participation, Head 

Start families had a strong desire to be involved in their 

children’s Head Start experience and valued their own 

involvement in the program. Although parents 

identified barriers to their participation in Head Start 

(primarily time constraints related to work or school), 

many still participated in Head Start activities. Parents 

felt it was important for them to participate because 

they felt their involvement helped their children, 

because their children seemed to enjoy it, or because it 

was meaningful to their children to have their parents 

participate in activities at their schools. 

With this background information in mind, this 

chapter explores specific characteristics of families and 

parents that may be related to the development of 

school readiness in the current sample of Head Start 

children. 

FINDINGS 

The findings presented in this chapter are primarily 

based on partial correlations, tests of group differences 

(t-test, ANOVA), and multivariate logistic regression 

models used to estimate levels of risk while controlling 

for demographic variables. Unless noted in the text, 

all findings tested were significant (p < .05). 

Additional information on specific parent interview 

scales and child assessment measures is found in the 

Appendix. Means and standard deviations for scales 

from the parent interview and from the teachers’ 

reports of children’s behavior are also included in the 

Appendix. 

A. Prevalence of Family and Parental 

Risk Factors and Their Relationship to 

Children’s Outcomes 

Maternal Depression 

Because depression is a frequent challenge facing lowincome families with young children (Hall,Williams & 

Greenberg, 1985; Liaw & Brooks-Gunn, 1994), 

depression among the Head Start parents was 

measured using the CES-D Depression Scale (Radloff, 

1975). Overall, parents had a mean score of 6.8 in the 

fall of 2000, which was in the mildly depressed range. 

While most parents were classified as not depressed 

(47.7 percent) or only mildly depressed (27.0 

percent), one fourth of the parents (25.3 percent) 

were classified as moderately depressed (13.6 percent) 

or severely depressed (11.7 percent). From fall to 

spring, there was a small decline in the overall mean 

depression scores (spring 2001 mean score of 6.6), but 

the difference was not statistically significant. 

The level of depression did not vary significantly by 

ethnicity. Similar proportions of African American 

parents (27.7 percent), Hispanic parents (27.4 

percent), and White parents (25.0 percent) were 

classified as moderately or severely depressed. 

Group differences, evaluated using t-tests, were found 

between parents who were moderately or severely 

depressed and those who were not at all or only mildly 

depressed. Parents who were moderately or severely 

depressed reported a lower household income, had a 

more external locus of control, had a more 

authoritarian parenting style (i.e., more directive and 

harsh), and spanked their children more frequently. 

When asked about activities with the children, parents 

who were moderately or severely depressed were 

more likely to report participating in fewer activities 

with their children and were also less likely to be 

involved with their children’s Head Start program. A 

higher proportion of mothers living without a father in 

the home were classified as moderately or severely 

92 
CHAPTER VI Head Start FACES 2000 

depressed (30.4 percent) than those who had a father 

present in the home (20.3 percent). 

Results of partial correlations controlling for parental 

income, education, and employment and for child 

gender, age, and ethnicity also revealed that the overall 

parental depression scale score was significantly 

correlated with parent and teacher reports of the 

children’s behavior. Parents who were more depressed 

reported that children had more problem behavior, 

including being more aggressive, hyperactive, and 

withdrawn. Similarly, teachers also reported more 

aggressive, hyperactive, and overall problem behavior 

for children of parents who were more depressed. 

Negative correlations were also found between 

parental depression and children’s positive social 

behavior ratings and emergent literacy, indicating that 

less depressed parents reported having children with 

better social and academic skills. 

Significant correlations were also found between 

parental depression and selected child cognitive 

outcomes. Children of parents who were depressed did 

worse on one-to-one counting and early math tasks, as 

well as on the teacher reports of creativity 

(descriptions of the child outcome measures are found 

in the Appendix). 

Additional risks associated with maternal depression 

were explored more fully through multivariate logistic 

regression models, controlling for mother’s education 

and employment, household income, and for child’s 

race, age, and gender. Odds ratios and 95% confidence 

intervals for each model are presented in Table 6.2 

(located at the end of the chapter).These risk estimates 

indicate that parents who were moderately or severely 

depressed, compared to parents who were not at all or 

only mildly depressed, were 1.5 times more likely to 

be single parents, and almost twice as likely to report 

that they, another household member, or a nonhousehold biological parent had been arrested or 

charged with a crime since the birth of their Head Start 

children. In addition, depressed parents were 1.3 

times more likely than non-depressed parents to drink 

or live with someone who drank alcohol, and 1.6 times 

more likely to have been exposed to violence in their 

neighborhoods or homes. The risk for screening 

positive for domestic violence greatly increased for 

those parents who were moderately or severely 

depressed. These parents were almost 3 times more 

likely to have been victims of domestic violence 

compared to parents who were only mildly depressed 

or not depressed. 

Exposure to Violence 

Neighborhoods have long been recognized in theory 

and research as important contexts for child 

development (Leventhal & Brooks-Gunn, 2000), but 

they can provide unique challenges when families are 

exposed to violence. Parents were asked about the 

violence they knew to occur in their neighborhoods, 

and were asked additional questions about their own 

personal exposure to violence and domestic violence, 

as well as their Head Start children’s exposure to 

violence. 

More than one fifth of all parents (22.5 percent) 

reported seeing nonviolent crime such as selling drugs 

or stealing in their neighborhoods in fall 2000 (15.3 

percent more than once), as well as having been a 

witness to violent crime (22.4 percent; 17.2 percent 

more than once). Approximately 17.0 percent of the 

parents knew someone who was the victim of a violent 

crime in their neighborhood, bringing the reality of 

violence very close to many of the Head Start families. 

Five percent of parents reported being a victim of 

violent crime in their neighborhood, and similarly, 5.0 

percent of the parents reported being victims of 

violence in their homes. 

Exposure to violence varied across ethnic groups. 

Among parents of African American children, 34.5 

percent reported seeing nonviolent crimes in their 

neighborhoods, a figure that was over twice the rate 

93 
Head Start FACES 2000 CHAPTER VI 

reported by parents of White children (15.0 percent) 

and approximately one third more than parents of 

Hispanic children (24.2 percent). This pattern held for 

each type of exposure to violent crime. Over twice as 

many parents of African American children (36.3 

percent) reported witnessing violent crimes in their 

neighborhoods compared to parents of White children 

(15.5 percent) and Hispanic children (17.5 percent). 

For reports of victimization, parents of African 

American children were again highest, with 7.4 

percent indicating they were victims of crime in their 

neighborhoods, and 6.2 percent reporting they were 

victims of violent crime in their homes. These 

victimization rates were closer to reports for parents of 

Hispanic children (5.8 percent in the neighborhoods; 

5.4 percent in their homes) than for parents of White 

children (4.4 percent in the neighborhoods; 4.5 

percent in their homes). 

As for the Head Start children, 3.8 percent were 

reported by parents to have witnessed a violent crime 

and 8.6 percent were reported to have witnessed 

domestic violence during the previous year. Slightly 

over one percent of the children were reported by 

their parents to have been victims of violent crime (1.1 

percent), while almost two percent were victims of 

domestic violence (1.7 percent) during the previous 

year. 

Exposure to violence had direct and indirect 

associations with child outcomes. Partial correlations 

controlling for parental income, education, and 

employment and for child gender, age, and ethnicity 

revealed small but significant positive correlations 

between parents’ reports of exposure to violence and 

parents’ reports of child problem behavior. In these 

analyses, exposure to violence is represented as a 

summary score of how often each of the five types of 

exposure to violence (as noted above) was reported by 

parents. Scores ranged from 5 (no exposure) to 15 

(more than one exposure to every type). The mean 

score for the sample was 6.1. Parents who reported 

greater exposure reported their children engaged in 

fewer positive social behaviors and more overall 

problem behavior; teachers of these children also 

reported they were more aggressive. This contrasts 

with the parents with less violence exposure, who 

reported more positive behaviors for their children. 

Exposure to violence did not have a direct relationship 

with child cognitive outcomes. Parents who reported 

more exposure to violence were significantly more 

depressed, but interestingly, they also were more likely 

to have an authoritative parenting style (e.g., less 

harsh, more use of rationales). 

Controlling for mother’s education and employment, 

household income, and for child’s race, age, and 

gender, multivariate logistic regression models were 

used to further explore the risks associated with 

exposure to violence. Odds ratios and 95% confidence 

intervals for risk factors (alcohol use, single parent, 

domestic violence, criminal justice system 

involvement) included in the models are presented in 

Table 6.2 (located at the end of the chapter). Parents 

who reported exposure to violence in their 

neighborhoods or homes, compared to parents who 

were not exposed to violence, were 1.5 times more 

likely to be single parents as well as to live in 

households where either they or someone else drank 

alcohol. Parents who were exposed to violence in 

their neighborhoods or homes, compared to parents 

not exposed to violence, were also 1.7 times more 

likely to have reported that they, another household 

member, or a non-household biological parent had 

been arrested or charged with a crime since the birth 

of their Head Start children, and over 2.7 times more 

likely to have screened positive for domestic violence 

(see measure below). 

Domestic Violence 

A three-item screening measure for domestic violence 

was administered to the parents (Feldhaus, Loziol94 
CHAPTER VI Head Start FACES 2000 

McLain & Amsury, 1997). They were asked if they had 

ever been hit, kicked, punched, or otherwise hurt by 

anyone within the past year, if they felt safe in their 

current relationship, or if they currently felt unsafe 

from a partner in a previous relationship. Almost 13 

percent of the parents answered ‘yes’ to one of these 

questions, thereby screening positively for 

experiencing domestic violence. Differences on family 

and child outcomes were found between families with 

a parent who screened positively for domestic violence 

versus those who did not. For example, parents 

experiencing domestic violence were significantly 

more depressed, and they reported their children to be 

more aggressive, more hyperactive, more withdrawn, 

and to have more overall problem behavior. Teachers 

also reported these children to be more withdrawn and 

have more overall problem behavior than children 

whose parents were not experiencing domestic 

violence. However, no direct relationship was found 

between parent-reported domestic violence and child 

cognitive outcomes. 

Multivariate logistic regression models, controlling for 

mother’s education and employment, household 

income, and for child’s race, age, and gender, estimated 

that parents who screened positive for domestic 

violence, compared to those who did not, were 2.5 

times more likely to be single parents, 1.5 times more 

likely to drink alcohol or live with a drinker, and 

almost 3.5 times more likely to report that they, 

another household member, or a non-household 

biological parent had been arrested or charged with a 

crime since the birth of their Head Start children. 

Substance Use in the Home 

The occurrence of substance use in homes, whether 

cigarette smoking, alcohol use, or drug use, is another 

challenge that faced a number of Head Start families. 

Almost one half of the Head Start children (45.1 

percent) lived in households with at least one 

individual who smoked cigarettes. Smoking varied by 

ethnicity. Cigarette smoking was reported less 

frequently in households where African American 

children lived (38.8 percent) than in households of 

Hispanic children (52.2 percent) or White children 

(54.3 percent). More than one fourth of the parents 

(28.1 percent) reported drinking alcohol such as beer, 

wine, or liquor in the past 30 days; 20.0 percent drank 

less than once a week, and 7.8 percent reported 

drinking between 1-2 times per week to every day. 

Slightly over 40 percent of all households reported 

having at least one individual who drank alcohol. 

Among families who lived in households where 

someone drank alcohol, 7.9 percent reported alcoholrelated problems with family members, 5.9 percent 

experienced trouble with the police because of 

alcohol, and 4.2 percent missed work or school due to 

alcohol-related illness. Less than one percent of the 

families reported having anyone in the household who 

used drugs. 

The findings suggest that living in a household with 

someone who drinks increased the risk of negative 

family and child outcomes. Parents who drank or who 

lived with a drinker were significantly more likely to be 

depressed and to report their children to have more 

overall problem behavior than parents who did not 

drink or live in a household with a drinker. Children 

who lived with someone who drank alcohol scored 

lower on vocabulary, color naming, and social 

awareness tasks. 

Further exploration of the risks associated with 

drinking or having a drinker in the household was done 

using multivariate logistic regression models, again 

controlling for mother’s education and employment, 

household income, and for child’s race, age, and 

gender. Odds ratios and 95% confidence intervals for 

the associated risk factors included in the models are 

presented in Table 6.2. Parents who drank alcohol or 

lived with a drinker were approximately 1.5 times 

more likely to report involvement in the criminal 

95 
Head Start FACES 2000 CHAPTER VI 

justice system, exposure to violence, or domestic 

violence in their lives compared to parents who did not 

drink or live with a drinker. Parents who drank or lived 

with a drinker were no more likely to be single parents 

than those who lived in alcohol-free households. 

Involvement With the Criminal Justice System 

In order to assess how many families had involvement 

with the criminal justice system, parents were asked if 

they, another household member, or a non-household 

biological parent had been arrested or charged with a 

crime since the birth of their Head Start children. 

Almost one fifth (19.2 percent) of the parents 

reported that someone had been arrested and charged 

with a crime and 16.7 percent reported someone who 

spent time in jail. Involvement with the criminal 

justice system did not vary much by ethnicity. About 

one fifth of parents of White children, parents of 

Hispanic children, and parents of African American 

children reported having someone in their family who 

was arrested (21.8 percent, 20.1 percent, and 18.9 

percent, respectively). 

T-tests were conducted to examine if child behavior 

and cognitive outcomes differed between children who 

were members of families with involvement in the 

criminal justice system and children from families 

without such involvement. Children from families 

who had someone arrested scored lower on assessed 

vocabulary and were reported by their parents to be 

more aggressive, more hyperactive, more withdrawn, 

and to have more overall problem behavior. They were 

also reported by their teachers to be more aggressive 

and have more overall problem behavior than children 

from families who did not have someone arrested. 

Multivariate logistic regression models, controlling for 

mother’s education and employment, household 

income, and for child’s race, age, and gender were used 

to determine estimates of risk among those families 

who had someone close to them who was involved in 

the criminal justice system (odds ratios and 95% 

confidence intervals are reported in Table 6.2). These 

risk estimates indicated that parents who reported that 

they, another household member, or a non-household 

biological parent had been arrested or charged with a 

crime since the birth of their Head Start children were 

twice as likely to be depressed, over 2.5 times more 

likely to be single parents, and over 3.5 times more 

likely to have been a victim of domestic violence than 

parents who did not have someone in their families 

involved in the criminal justice system. 

Children in families from which someone had been 

arrested were at great risk for witnessing or being a 

victim of violent crime and domestic violence 

compared to children in families where no one had 

been arrested or charged with a crime. These Head 

Start children were more than 3 times more likely to 

have been a witness to violent crime and almost 3.5 

times more likely to have witnessed domestic violence 

in the past year. Their risk of victimization also 

increased greatly. These children were 3 times more 

likely to have been both a victim of domestic violence 

and a victim of violent crime in the past year than 

children whose families did not report involvement 

with the criminal justice system. While there is a 

strong literature to suggest that children of 

incarcerated parents are at increased risk for negative 

outcomes (Lange, 2000), these findings suggest that 

risk greatly increases when there is any family 

involvement with the criminal justice system. 

Cumulative Social and Economic Risk 

Previous work has noted that social and economic risk 

factors may be associated with negative child 

outcomes, particularly when multiple risks are present 

(Annie E. Casey Foundation, 1999; ACYF, 2002). To 

explore this further, we adapted a standard list of 

family risk factors (Annie E. Casey Foundation, 1999) 

to investigate relationships with child outcomes. These 

risk factors include: 

96 
CHAPTER VI Head Start FACES 2000 

• The child lived with a single parent; 

• The mother was a high school dropout; 

• The family income was below the poverty line; 

• The child was living with a parent(s) who did not 

have steady, full-time employment; 

• The family was receiving welfare benefits; and 

• The child did not have private health insurance. 

While a number of these risks are already included in 

broader analytic models looking at the child outcomes 

(see earlier chapters), the cumulative effects of these 

risks may be associated with reduced school readiness. 

Across all six risks, at least one risk was evident in 89 

percent of the Head Start families. For all families, the 

most prevalent risks were having a mother without a 

high school education (38.2 percent), being in a singleparent household (48.2 percent), and children not 

having private health insurance (64.6 percent).23 

None of the other risks were reported for more than 

30 percent of the families. 

Of greater concern is that increases in the number of 

risk factors, particularly counts of four or more risks, 

increased the likelihood of negative child outcomes. 

About one fifth of the families (20.3 percent) were 

found to have four or more risk factors, with almost 10 

percent having five or six risks. Less than one fifth of 

the families of White children (18.6 percent) and 

Hispanic children (18.6 percent) had 4 or more of the 

risk factors, while one quarter of the families of African 

American children (24.9 percent) were found to have 

this high level of risk. 

Parents reporting four or more risk factors also had 

higher depression scores, a lower locus of control, and, 

interestingly, scored higher on both the authoritarian 

and authoritative parenting style scales. These same 

parents also reported more problem behavior for their 

children, including aggressive and withdrawn behavior, 

and gave lower ratings on emergent literacy. In 

addition, the Head Start teachers also rated the 

children from families with four or more risks as 

having more withdrawn and hyperactive behavior. 

In terms of the child cognitive assessments, the 

cumulative risk of four or more risk factors was clearly 

associated with lower child outcomes. This high level 

of risk meant significantly poorer performance on 

social awareness, design copying, color naming, oneto-one counting, book knowledge, comprehension, 

print concepts, vocabulary, letter identification, early 

math, and early writing. 

B. Family Involvement and Its 

Relationship to School Readiness 

Family Activities 

While the findings presented above suggest that a 

number of family characteristics are likely to have 

negative associations with children’s school readiness, 

the frequency of families’ interactions with their 

children reflects family strengths that could improve 

school readiness. For example, do families that are 

more active with their children have children with 

better behavior and better cognitive skills? 

Parents were asked how often family members engaged 

in selected activities over the previous week as well as 

the previous month with their Head Start children.The 

weekly activities included telling the child a story; 

teaching letters, words, or numbers; teaching songs or 

music; doing arts and crafts; playing toys or games; 

doing errands; or doing household chores. Parents 

were also asked about how often they read to their 

children over the past week, and findings related to this 

The fall 2000 FACES parent interview identified children without private health insurance, but was not clear on whether children 

received Medicaid or SCHIP. 

97 

23 
Head Start FACES 2000 CHAPTER VI 

variable are addressed in Chapter V. The monthly 

activities included visiting the library, shows, 

museums, and zoos; attending community or sporting 

events; and discussing family history. Total activity 

scores are based on the sum of the weekly and monthly 

activity scores. 

The total activity score for combined weekly and 

monthly activities indicated that families engaged in a 

mean of 7.6 activities with the children, out of a 

possible 14 activities (National Center for Education 

Statistics, 1996). Weekly activities made up most of 

that total, with a reported mean of 5.7 activities of a 

possible seven, while a mean of 1.9 monthly activities 

was reported, also out of a possible seven. 

Ethnic differences were noted in the number of 

activities families engaged in with their children. For 

the activities in the previous week, African American 

children had higher family activity than both White or 

Hispanic children, and White children had higher 

activity scores than Hispanic children. For the 

activities covered for the previous month, there was a 

similar effect for ethnicity, as African American 

children were engaged in more activity than White or 

Hispanic children. 

There were several positive relationships between 

activities with children and child behavioral and 

cognitive outcomes. Partial correlations were run 

controlling for parent income, education, and 

employment; child gender, age, and ethnicity; and how 

often the child was read to in the previous week. The 

frequency of weekly, monthly, and combined family 

activities was positively correlated with parent reports 

on positive child behaviors and emergent literacy 

skills. All three types of activity scores were negatively 

correlated with parent reports of aggressive behavior, 

while weekly and total behavior scores were negatively 

associated with overall problem behavior and the 

hyperactive behavior subscale. Among parents, being 

engaged in activities with their children was correlated 

positively with both locus of control and authoritative 

parenting. Teacher ratings of hyperactive and overall 

problem behavior were negatively correlated with 

monthly activities. 

With regard to child cognitive scores, partial 

correlations identified a number of positive 

relationships, with weekly activities having the 

strongest association with cognitive skills. For 

example, engaging in more activities with the children 

during the previous week was positively correlated 

with higher scores for the children on the color naming 

and vocabulary assessments. Participating in the 

monthly activities also had small positive correlations 

with the social awareness, color naming, one-to-one 

counting, book knowledge, and print concepts 

assessments. 

Parenting Style 

FACES 2000 provided the opportunity to look at the 

correlations of parenting style with selected child 

cognitive and behavioral outcomes. The parents were 

scored on two different parenting styles: authoritarian 

and authoritative. The first of these styles is generally 

considered a stricter, directive style, while the 

authoritative style reflects a less harsh style with 

greater use of rationales. The differences in these styles 

were apparent in their relationships with the parent 

and child outcomes. As expected, the authoritative and 

authoritarian scales were negatively correlated with 

each other. 

Partial correlations, controlling for parent income, 

education, and employment; child gender, age, and 

ethnicity; and how often the child was read to in the 

previous week, were used to assess the relationship 

between parenting style and child behavioral and 

cognitive outcomes. Parents who scored higher on the 

authoritative subscale also had higher internal locus of 

control (i.e., felt more in control of their lives rather 

98 
CHAPTER VI Head Start FACES 2000 

than at the mercy of external factors) and gave their 

children higher ratings of positive social behavior and 

emergent literacy. In contrast, the authoritarian 

subscale scores were positively correlated with the 

overall ratings of children’s problem behavior, as well 

as the subscales of aggression, hyperactivity, and 

withdrawn behavior, but negatively correlated with 

parents’ locus of control. 

There were generally weak relationships between 

parenting style and the cognitive outcomes. The 

authoritative parenting style scores were positively 

correlated with children’s social awareness and the 

authoritarian parenting style was negatively correlated 

with color naming, but neither parenting style was 

correlated with any of the other cognitive measures. 

Hyperactive behavior as rated by teachers was 

positively correlated with an authoritarian parenting 

style. 

Family Support From Head Start 

In the spring of 2001, parents were asked about the 

ways that they were involved in the Head Start 

program throughout the past school year. Parents 

most frequently reported attending parent-teacher 

conferences (79.0 percent), observing in their 

children’s classrooms for at least 30 minutes (74.9 

percent), and meeting with a Head Start staff member 

in their homes (69.4 percent). More than one half of 

the parents volunteered in their children’s classrooms 

(60.0 percent) or prepared food or materials for 

special events (58.1 percent) and slightly less than one 

half helped with field trips (42.5 percent), or attended 

bazaars (42.3 percent) and workshops (42.5 percent). 

Less than one fourth of the parents participated in a 

Policy Council (22.5 percent). 

A summary score measuring total involvement was 

created for each parent who responded to the 

questionnaire. A series of partial correlations, 

controlling for parent income, education, and 

employment; child gender, age, and ethnicity; were 

conducted to examine the relationship between 

involvement at Head Start and other family and child 

factors. Higher levels of involvement at Head Start 

were significantly related to positive outcomes for the 

children. Parents who were more involved at Head 

Start were likely to report more positive social 

behavior for their children and less aggressive and 

overall problem behavior, as well as higher emergent 

literacy skills than parents who were less involved at 

Head Start. There was also a relationship between 

involvement and teachers’ reports of the children’s 

skills and with the children’s scores on cognitive 

assessments. For example, teachers rated the children 

of more involved parents as more creative and more 

socially aware. On the cognitive measures, children 

whose parents were more involved at Head Start 

scored higher on vocabulary, book knowledge, early 

writing, early math, and letter-identification tasks. 

Consistent with previous findings (ACYF, 2002), most 

parents reported high levels of satisfaction with the 

Head Start program. For example, while more than 

one half were very satisfied with Head Start in every 

area, more than 82 percent of the parents reported 

they were very satisfied with how the program helped 

their children grow and develop, respected the family’s 

culture, identified child services, maintained a safe 

program, and prepared the children for kindergarten. 

There were very few unsatisfied parents, with reports 

of dissatisfaction generally under 2.5 percent. The area 

with the greatest degree of reported dissatisfaction was 

how Head Start helped parents become involved in 

their community, but even this concern was cited by 

less than 7 percent of the parents. 

Parents also reported that they and their children had 

positive experiences at Head Start. Almost 85 percent 

of the parents reported that their children ‘often’ or 

‘always’ received individual attention, while more than 

90 percent reported their children ‘often’ or ‘always’ 

99 
Head Start FACES 2000 CHAPTER VI 

felt safe and secure at Head Start, were happy in the 

program, were treated with respect, and felt accepted 

by their teachers. With regard to the teachers, more 

than 90 percent of the parents reported that their 

children’s teachers ‘often’ or ‘always’ were open to 

new learning, were warm towards the children, were 

interested in the children, were supportive to parents, 

were welcoming, did not use harsh discipline, and were 

happy. 

C. Head Start’s Protective Role for 

Families and Children 

Earlier findings from the FACES 1997 cohort of 

families suggest that Head Start may play an important 

role in protecting families and children from the 

challenges that many families face. To validate this 

finding in the FACES 2000 cohort, a series of linear 

regression models, controlling for parent income, 

education, and employment; child gender, age, and 

ethnicity; and parent level of activity with their 

children, tested whether the effect of maternal 

depression, exposure to violence, domestic violence, 

alcohol use, or involvement in the criminal justice 

system on child behavior and child cognitive outcomes 

varied as a function of (or was moderated by) Head 

Start satisfaction, experience, or involvement. 

Summary scores were created for parents’ involvement 

at Head Start, for parents’ reports of having a positive 

experience at Head Start, and parents’ reports of 

satisfaction with the program. Means and standard 

deviations for these three variables are found in the 

Appendix. 

Table 6.1 presents the interaction terms found to be 

significant moderators of risk factors in the regression 

equations. These findings support the earlier results 

and suggest that Head Start may play an important role 

in protecting families from the negative outcomes 

associated with these challenges. For example, there 

is a direct positive relationship between domestic 

violence and parent and teacher reports of children’s 

problem behavior. This relationship remains consistent 

for children of parents who had fewer positive 

experiences with the program; however, when parents 

had positive experiences at Head Start, the effects of 

domestic violence on parent and teacher reports of 

increased aggressive, hyperactive, withdrawn, and 

overall problem behavior for children were no longer 

evident. These findings indicate that the negative 

effects of being exposed to domestic violence on 

children were buffered by parents’ positive 

experiences at Head Start. 

The moderating effects of Head Start on negative 

outcomes for children are consistent across all risk 

factors presented in Table 6.1, and include moderation 

of the relationship between risk factors and lower 

cognitive outcomes, as well as negative child behavior. 

For example, parents’ depression, exposure to 

violence, or involvement in the criminal justice system 

were related, in some cases, to poorer cognitive 

outcomes for children, such as early math, book 

knowledge, color naming, one-to-one counting, and 

vocabulary. Having a parent who reported positive 

Head Start experiences or who was more involved 

with the program moderated this relationship and 

appeared to protect the children from these negative 

outcomes. All significant modifying relationships are 

presented below. 

The difficult challenges or risks that parents 

face—depression, exposure to violence, domestic 

violence, alcohol use, and involvement with the 

criminal justice system—are often associated with 

negative behavioral and cognitive outcomes for their 

children. The findings cited above provide some 

evidence that Head Start may play an important role in 

protecting children and their families from the 

consequences of these risk factors. Parent involvement 

at Head Start, parent reports that they and their 

children had positive experiences at Head Start, and 

100 
CHAPTER VI Head Start FACES 2000 

parent satisfaction with the program, significantly 

moderated relationships between these risk factors and 

many negative child behavior and cognitive outcomes. 

These findings provide support for the theory that 

children’s school readiness is enhanced when programs 

work with families as well as with children. 

SUMMARY 

This chapter examined family and parent 

characteristics that are related to school readiness in 

early childhood, and parents’ important role in 

preparing children for school. The challenges families 

face and the strengths they possess are important 

considerations in understanding how best to prepare 

children for school. The main findings are summarized 

below. 

• One quarter of the parents were classified as 

moderately or severely depressed. Parents who were 

more depressed reported that their children had 

Table 6.1. Significant Head Start Moderators of Negative Outcomes Associated With Risk Factors 

Head Start Satisfaction, Experience, and Involvement as Moderators of the 

Negative Outcomes for Children and Families Related to Maternal Depression 

Interaction Term 

(Independent Variable x Moderator) 

Outcome Variable Sig 

Depression x Head Start Satisfaction Withdrawn Behavior (parent report) p < .01 

Depression x Head Start Involvement Book Knowledge p < .05 

Depression x Head Start Experience Early Math p < .01 

Depression x Head Start Experience Creativity (teacher report) p < .05 

Head Start Satisfaction, Experience, and Involvement as Moderators of the 

Negative Outcomes for Children and Families Related to Exposure to Violence 

Interaction Term 

(Independent Variable x Moderator) 

Outcome Variable Sig 

Violence x Head Start Involvement Hyperactive Behavior (parent report) p < .05 

Violence x Head Start Involvement Emergent Literacy (parent report) p < .01 

Violence x Head Start Involvement Social Awareness p < .01 

Violence x Head Start Involvement Color Naming p < .01 

Violence x Head Start Experience One-to-One Counting p < .05 

Head Start Satisfaction, Experience, and Involvement as Moderators of the 

Negative Outcomes for Children and Families Related to Domestic Violence 

Interaction Term 

(Independent Variable x Moderator) 

Outcome Variable Sig 

Domestic Viol x Head Start Experience Social Skills (parent report) p < .05 

Domestic Viol x Head Start Experience Total Problem Behavior (parent report) p < .001 

Domestic Viol x Head Start Experience Aggressive Behavior (parent report) p < .05 

Domestic Viol x Head Start Experience Withdrawn Behavior (parent report) p < .01 

Domestic Viol x Head Start Satisfaction Hyperactive Behavior (parent report) p < .05 

Domestic Viol x Head Start Experience Total Problem Behavior (teacher report) p < .05 

Domestic Viol x Head Start Experience Social Score (teacher report) p < .05 

101 
Head Start FACES 2000 CHAPTER VI 

Table 6.1: Significant Head Start Moderators of Negative Outcomes Associated With Risk Factors 

(continued) 

Head Start Satisfaction, Experience, and Involvement as Moderators of the Negative 

Outcomes for Children and Families Related to Alcohol Use by a Family Member 

Interaction Term 

(Independent Variable x Moderator) 

Outcome Variable Sig 

Alcohol Use x Head Start Experience Social Skills (parent report) p < .01 

Alcohol Use x Head Start Satisfaction Total Problem Behavior (parent report) p < .05 

Alcohol Use x Head Start Experience Withdrawn Behavior (parent report) p < .01 

Alcohol Use x Head Start Satisfaction Hyperactive Behavior (parent report) p < .01 

Alcohol Use x Head Start Experience Total Problem Behavior (teacher report) p < .01 

Alcohol Use x Head Start Experience Aggressive Behavior (teacher report) p < .01 

Alcohol Use x Head Start Involvement Emergent Literacy (parent report) p < .05 

Alcohol Use x Head Start Experience Social Score (teacher report) p < .05 

Head Start Satisfaction, Experience, and Involvement as Moderators of the Negative Outcomes 

for Children and Families Related to Involvement with the Criminal Justice System 

Interaction Term 

(Independent Variable x Moderator) 

Outcome Variable Sig 

Crime x Head Start Experience Social Skills (parent report) p < .05 

Crime x Head Start Experience Withdrawn Behavior (parent report) p < .01 

Crime x Head Start Involvement Vocabulary p < .05 

more problem behaviors and fewer positive social 

behaviors, a finding supported by the teachers’ 

reports of children’s behavior. Their children also 

had lower scores on one-to-one counting, creativity, 

design copying, early writing, letter identification, 

and early math assessments. 

• More than one fifth of the parents reported they had 

witnessed violent crime. Five percent reported 

being victims of violent crime in the neighborhood, 

while a similar percentage reported being victims of 

violence in their homes. Almost 10 percent of the 

children were reported to have witnessed domestic 

violence during the previous year. Less than two 

percent of the children were reported to have been 

victims of violent crime or victims of domestic 

violence. Positive correlations were found between 

increased exposure to neighborhood violence and 

reports of child problem behavior, while children in 

more violent neighborhoods had lower assessment 

scores on the color naming and book knowledge 

assessments. 

• Almost 13 percent of the parents indicated that they 

have been victims of domestic violence. Teachers and 

parents reported children in these families had more 

overall problem behaviors. However, no direct 

relationship was found between parent reports of 

domestic violence and child cognitive outcomes. 

• Almost one half of the Head Start children lived in 

households with at least one individual who smoked 

cigarettes, and about two fifths of the households 

reported having at least one individual who drank 

alcohol. Living in a household with someone who 

drinks increased the risk of maternal depression, 

while the children in these homes were reported to 

102 
CHAPTER VI Head Start FACES 2000 

have more overall problem behavior and scored 

lower on vocabulary, color naming, and social 

awareness assessments. 

• Almost one fifth of the parents reported that 

someone in their household had been arrested and 

charged with a crime. Children in these families were 

more than three times more likely to have been a 

witness to violent crime or domestic violence in the 

past year. These children were also three times more 

likely to have been a victim of domestic violence or 

violent crime. These children had lower vocabulary 

scores, and were reported by both parents and 

teachers to be more aggressive and have more overall 

problem behaviors. 

• At least one of a set of selected risk factors was 

evident in over 90 percent of the families. Almost 

one quarter of the families had four or more risk 

factors. Children in these families had lower parent 

ratings on emergent literacy and higher teacher and 

parent ratings of problem behavior. In the 

assessments, these children scored lower on design 

copying, color naming, one-to-one counting, book 

knowledge, vocabulary, early math, early writing, 

and letter identification. 

• Families engaged their children in a number of 

weekly and monthly activities. The number of 

activities was positively correlated with positive child 

behaviors and emergent literacy and negatively 

correlated with problem behaviors. In particular, the 

weekly activities had positive correlations with 

scores on the social awareness, color naming, one-toone counting, book knowledge, vocabulary, early 

math, early writing, and letter identification tasks. 

• Higher authoritative parenting style scores were 

significantly positively correlated with children’s 

social awareness, but not with any of the other 

cognitive measures. On the other hand, higher 

authoritarian scores were significantly negatively 

correlated with comprehension, color naming, 

vocabulary, and early math assessments. 

• More than two thirds of parents had attended parentteacher conferences, observed in their children’s 

classrooms for at least 30 minutes, or met with a 

Head Start staff member in their homes. Parent 

involvement in Head Start was positively correlated 

with parental reports of positive social behavior and 

higher emergent literacy skills and negatively 

correlated with aggressive and overall problem 

behavior. Children with more involved parents 

scored higher on vocabulary, book knowledge, early 

writing, early math, and letter identification tasks. 

• Head Start may play an important role in protecting 

families and children from the challenges that lowincome families face. Parent involvement at Head 

Start, parent reports that they and their children had 

positive experiences at Head Start, or parent 

satisfaction with the program, significantly 

moderated relationships between risk factors such as 

maternal depression, exposure to violence and 

domestic violence, substance use, and involvement 

with the criminal justice system, and many negative 

child behavior and lower cognitive outcomes. 

103 
Head Start FACES 2000 CHAPTER VI 

REFERENCES 

Annie E. Casey Foundation. (1999). 1999 Kids count 

data book: State profiles of child well-being. Baltimore, 

MD: Annie E. Casey Foundation. 

Administration on Children, Youth and Families 

(2002). A Descriptive Study of Head Start Families: FACES 

Technical Report I. Washington, D.C.: U.S. Department 

of Health and Human Services. 

Feldhaus, K. M., Koziol-McLain, J., & Amsbury, H. L. 

(1997). Accuracy of 3 brief screening questions for 

detecting partner violence in the emergency 

department. Journal of the American Medical Association, 

227(17), 1357. 

Hall, L., Williams, C., & Greenberg, R. (1985). 

Supports, stressors, and depressive symptoms in lowincome mothers of young children. American Journal of 

Public Health, 75, 518-522. 

Lange, S. M. (2000). The challenges confronting 

children of incarcerated parents. Journal of Family 

Psychotherapy, 11(4), 61-68. 

Leventhal, T., & Brooks-Gunn, J. (2000). The 

neighborhoods they live in: The effects of 

neighborhood residence on child and adolescent 

outcomes. Psychological Bulletin, 126(2), 309-337. 

Liaw, F., & Brooks-Gunn, J. (1994). Cumulative 

familial risks and low birth weight children’s cognitive 

and behavioral development. Journal of Clinical Child 

Psychology, 23, 360-372. 

National Center for Education Statistics. (1996). 

National Household Education Survey Parent and Family 

Involvement Questionnaire. Washington, DC: U.S. 

Department of Education. 

Radloff, L. S. (1977). The CES-D scale: A self report 

depression scale for research in the general population. 

Applied Psychological Measurement, 1, 385-401. 

104 
CHAPTER VI Head Start FACES 2000 

Table 6.2. Significant Adjusted Odds Ratios and 95% Confidence Intervals for Maternal Depression, 

Exposure to Violence, Domestic Violence, Alcohol Use, and Involvement With the Criminal Justice 

System 

Maternal Depression 

Risk Factors Adjusted Odds Ratio 95% Confidence Interval 

Exposure to Violence 1.69 (1.38 - 2.06) 

Domestic Violence 2.99 (2.33 - 3.84) 

Alcohol Use 1.36 (1.12 - 1.65) 

Criminal Justice System Involvement 1.97 (1.57 - 2.47) 

Single Parent 1.45 (1.17 - 1.79) 

Exposure to Violence 

Risk Factors Adjusted Odds Ratio 95% Confidence Interval 

Domestic Violence 2.68 (2.09 - 3.44) 

Criminal Justice System Involvement 1.73 (1.38 - 2.16) 

Alcohol Use 1.46 (1.21 - 1.75) 

Single Parent 1.43 (1.18 - 1.74) 

Domestic Violence 

Risk Factors Adjusted Odds Ratio 95% Confidence Interval 

Criminal Justice System Involvement 3.47 (2.68 - 4.50) 

Single Parent 2.61 (1.97 - 3.46) 

Alcohol Use 1.50 (1.18 - 1.91) 

Alcohol Use 

Risk Factors Adjusted Odds Ratio 95% Confidence Interval 

Criminal Justice System Involvement 1.48 (1.19 - 1.83) 

Exposure to Violence 1.45 (1.21 - 1.74) 

Domestic Violence 1.48 (1.17 - 1.89) 

Criminal Justice System Involvement 

Risk Factors Adjusted Odds Ratio 95% Confidence Interval 

Maternal Depression 1.97 (1.57 - 2.47) 

Single Parent 2.64 (2.06 - 3.39) 

Domestic Violence 3.47 (2.68 - 4.50) 

Child Witness to Violence 3.23 (2.04 - 5.12) 

Child Witness to Domestic Violence 3.60 (2.63 - 2.90) 

Child Victim of Violence 3.06 (1.35 - 6.97) 

Child Victim of Domestic Violence 3.14 (1.57 - 6.30) 

105 
106
CHAPTER V CHAPTER VII

Predictive Validity of Cognitive and Behavioral Measures: Relationships 

Within and Across Cognitive and Social Developmental Domains 

The child assessment battery for the Family and Child 

Experiences Survey (FACES) was designed to provide 

a comprehensive description of Head Start children’s 

development in both the cognitive and socio-emotional 

domains. As an indicator of the validity of the FACES 

instruments, criterion-related validity, that is the 

degree to which the test or questionnaire correlates 

with one or more outcome criteria, was assessed. 

Systematic evidence for criterion validity is often 

described in terms of predictive validity. Predictive 

validity is assessed when the outcome criterion is 

measured at a later time point after the evaluated test 

(e.g., later school year). This chapter evaluates how 

well the FACES measures predict children’s 

achievement and school adjustment at the end of their 

kindergarten year.24 

FINDINGS 

A. Children’s Scores on the FACES 

Instruments at the End of Head Start 

Predict Kindergarten Outcomes 

The FACES cognitive measures were designed to assess 

preliteracy skills, as well as general school readiness. 

In order to determine the predictive validity of these 

measures, relationships between the FACES assessment 

scale scores obtained during the Head Start year and 

the Reading and General Knowledge scale scores 

24 Final kindergarten data from FACES 2000 are not yet 

available, therefore the following analyses are conducted with 

FACES 1997-1998 data. All of the described analyses were 

conducted on children who were assessed in English at all 

timepoints. 

RESEARCH QUESTIONS 

This chapter will address the following research 

questions: 

1. How well does the FACES battery predict 

children’s early reading skills and general 

knowledge at the end of kindergarten? 

2. How well do the FACES parent and teacher 

ratings of children’s social competence 

predict children’s school adjustment at the 

end of kindergarten? 

3. Do the FACES parent and teacher ratings of 

children’s social competence contribute to 

the FACES battery’s prediction of children’s 

reading skills and general knowledge at the 

end of kindergarten? 

4. How well do cognitive gains during Head 

Start predict reading and general 

knowledge at the end of kindergarten? 

5. How well do changes in behavior ratings 

during Head Start predict social 

competence at the end of kindergarten? 

6. Do FACES cognitive and behavior measures 

predict promotion to first grade? 

107
Head Start FACES 2000 CHAPTER VII 

obtained at the end of kindergarten were examined by 

two approaches. In the first approach, Reading and 

General Knowledge scale scores from the end of the 

kindergarten year were correlated with the scores 

from each of the FACES subtests from the end of the 

Head Start year. The second approach assessed the 

ability of the FACES scale scores from the end of the 

Head Start year to predict Reading and General 

Knowledge scale scores at the end of the kindergarten 

year in two multiple regression analysis. 

Children’s scores on each of the component tasks in the 

FACES battery at the end of Head Start correlated 

significantly with their Reading scale scores at the end 

of kindergarten. Bivariate correlations with the 

Reading scale ranged from .55 (for the WoodcockJohnson-R Letter-Word Identification) to .23 (for the 

Social Awareness). These moderate to high 

correlations indicate that the FACES measures have 

predictive power on outcome criteria at later time 

points. When the subtest scores were combined in a 

multiple regression model, the model did quite well at 

predicting children’s early reading skills at the end of 

kindergarten, accounting for 46 percent of the 

variance in Reading scale scores. The best predictor of 

Reading scale scores was the Woodcock-Johnson-R 

Letter-Word Identification task, which also showed the 

highest bivariate correlation with the Reading scale 

(Figure 7.1). 

Similarly, children’s scores on each of the component 

tasks in the FACES battery at the end of Head Start also 

correlated significantly with their scores on the 

General Knowledge scale at the end of kindergarten. 

Bivariate correlations with the General Knowledge 

scale ranged from .77 (for the Peabody Picture 

Vocabulary Test-III or PPVT-III) to .30 (for the Drawa-Design and Social Awareness), again indicating that 

Figure 7.1. Correlations and Standardized Multiple Regression Coefficients Between Reading Scale Scores at End 

of Kindergarten Year and FACES Assessment Scale Scores at End of Head Start Year 

WJR Letter-Word ID r = .55 

beta = .32 

WJR Applied r = .52 Problems 

beta = .16 

r = .48 WJR Dictation Reading Scale 

beta = .14 

r = .40 Multiple R for multiple 

One-to-One Counting regression analyses = .68 beta = .12 

r = .33 

Draw-A-Design beta = .10 

SOURCE: Head Start Family and Child Experiences Survey (FACES), Spring 1998 Head Start and Spring 1999 Kindergarten 

Followup, Ns=718-786 for bivariate correlations, 623 for multiple regression coefficients. 

108 
CHAPTER VII Head Start FACES 2000 

Figure 7.2. Correlations and Standardized Multiple Regression Coefficients Between General Knowledge Scale 

Scores at End of Kindergarten Year and FACES Assessment Scale Scores at End of Head Start Year 

WJ R Applied 

Problems 

PPVT-III Vocabulary 

Book Knowledge 

Draw-A-Design 

r = .77 

beta = .62 

r = .62 

r = .52 

r = .46 

r = .30 

beta = .09 

beta = .06 

beta = .11 

beta = .06 

WJR Dictation 

General Knowledge

Scale

Multiple R for multiple

 regression analyses = .81 

SOURCE: Head Start Family and Child Experiences Survey (FACES), Spring 1998 Head Start and Spring 1999 Kindergarten 

Followup, Ns =720-787 for bivariate correlations, 624 for multiple regression coefficients. 

the FACES measures have predictive power on 

outcome criteria at later time points. When the subtest scores were combined in a multiple regression 

model, the model accounted for 65 percent of the 

variance in General Knowledge scale scores. The best 

predictor in the multiple regression was the PPVT-III 

(beta = .62), which also showed the highest bivariate 

correlation with the General Knowledge scale (Figure 

7.2). 

Neither the PPVT-III nor the Book Knowledge score 

were significant predictors of the Reading scale score 

in its multiple regression model, though scores on both 

of these tasks correlated significantly with the Reading 

score (rs = .42 and .39, respectively). It is noteworthy 

to also mention that the best predictor of the Reading 

score, namely, Woodcock-Johnson-R Letter-Word 

Identification, was not a significant predictor of 

General Knowledge in its multiple regression analysis, 

despite the fact that the Letter-Word Identification task 

did correlate significantly with the General Knowledge 

task (r = .40). Conversely, the best predictor of 

General Knowledge, namely, the PPVT-III, did not 

have a significant role in the multivariate prediction of 

children’s Reading scores. 

It appears that the Reading and General Knowledge 

assessments may be tapping two distinct clusters of 

skills, both of which have been shown to be important 

for children’s future reading proficiency and academic 

achievement. The skills tapped by the Reading scale 

during kindergarten were primarily what Whitehurst 

and Lonigan (1998) have called “inside-out” skills such 

as letter recognition, letter-sound association, and 

word decoding. The skills tapped by the General 

Knowledge assessment were “outside-in” skills, such as 

109 
Head Start FACES 2000 CHAPTER VII 

Figure 7.3. Correlations and Standardized Multiple Regression Coefficients Between Reading Scale Scores at End 

of Kindergarten Year and FACES Assessment Scale Scores at End of Head Start Year—Norm-Referenced Tests Only 

WJR Letter-Word ID r = .55 

WJR Applied 

Problems 

WJR Dictation 

beta = .33 

r = .52 

r = .48 

r = .42 

beta = .24 

beta = .19 

Reading Scale 

Multiple R for multiple 

PPVT-III Vocabulary beta = .05 regression analyses = .66 

SOURCE: Head Start Family and Child Experiences Survey (FACES), Spring 1998 Head Start and Spring 1999 

Kindergarten Followup, Ns=718-737 for bivariate correlations, 640 for multiple regression coefficients. 

general information, word knowledge, and conceptual 

understanding that children need to help them 

comprehend and evaluate what they read and relate it 

to facts and concepts they have previously acquired. 

The FACES battery showed its validity by predicting 

well to children’s later learning in both skill domains. 

In addition, these results suggest that development in 

both skills domains should receive attention in 

preschool curricula and practice, in order to foster 

both types of school-age abilities. 

Although both domains were predicted well, the 

combination of subtests that produced the best 

forecasts differed across the two skill clusters. The 

Letter-Word Identification test was the best predictor 

of inside-out skills, with Applied Problems, Dictation, 

One-to-One Counting, and McCarthy Draw-a-Design 

contributing additional predictive power. The PPVT-III 

was by far the best predictor of outside-in skills, with 

Applied Problems, Book Knowledge, Dictation, and 

the McCarthy Draw-a-Design showing much smaller 

but significant regression coefficients as well. It is 

noteworthy that seven of the eight FACES subtests 

contributed significantly to either the Reading or 

General Knowledge regression model or both. 

Predictive Validity of Abbreviated FACES Battery 

In order to further explore the validity of the FACES 

battery, multivariate regression analyses were carried 

out with the set of norm-referenced tests (i.e., PPVTIII and the Woodcock-Johnson-R subtests) at the end of 

Head Start, predicting the Reading and General 

Knowledge scale at the end of kindergarten. These 

analyses were repeated with the set of criterionreferenced measures (i.e., Social Awareness, McCarthy 

Draw-a-Design, Color Names, One-to-One Counting, 

and Book Knowledge) as the predictors. 

The four norm-referenced tests at the end of Head 

Start did almost as well as the full battery at predicting 

the Reading scores at the end of kindergarten (Figure 

7.3), predicting 43 percent of the variance. The 

Letter-Word Identification was most closely associated 

with the Reading scores. Applied Problems and 

Dictation had significant coefficients as well. But, as 

before, the PPVT-III did not. 

110 
CHAPTER VII Head Start FACES 2000 

Figure 7.4. Correlations and Standardized Multiple Regression Coefficients Between General Knowledge Scale 

Scores at End of Kindergarten Year and FACES Assessment Scale Scores at End of Head Start—Norm-Referenced 

Tests Only 

WJR Applied 

Problems 

PPVT-III Vocabulary 

WJR Dictation 

r = .77 

beta = .63 

r = .62 

r = .46 

r = .40 

beta = .13 

beta = .15 

General Knowledge

Scale

WJR Letter -Word ID beta = .02 

Multiple R for multiple 

regression analyses = .81 

SOURCE: Head Start Family and Child Experiences Survey (FACES), Spring 1998 Head Start and Spring 1999 Kindergarten 

Followup, Ns=720-748 for bivariate correlations, 642 for multiple regression coefficients. 

Figure 7.5. Correlations and Standardized Multiple Regression Coefficients Between Reading Scale Scores at End 

of Kindergarten Year and FACES Assessment Scale Scores at End of Head Start Year—Criterion-Referenced Tests 

Only 

Book Knowledge 

One-to -One 

Counting 

Color Naming 

Draw-A-Design 

Social Awareness 

Reading Scale 

r = .40 

beta = .23 

r = .39 

r = .36 

r = .33 

r = .23 

beta = .19 

beta = .16 

beta = .20 

beta = .08 

Multiple R for multiple 

regression analyses = .55 

SOURCE: Head Start Family and Child Experiences Survey (FACES), Spring 1998 Head Start and Spring 1999 Kindergarten 

Followup, Ns=772-786 for bivariate correlations, 761 for multiple regression coefficients. 

111 
Head Start FACES 2000 CHAPTER VII 

The four norm-referenced tests at the end of Head 

Start also did as well as the full battery at predicting the 

General Knowledge scores at the end of kindergarten, 

predicting 65 percent of the variance. The PPVT-III 

was most closely associated with the General 

Knowledge scores. Dictation and Applied Problems 

had significant regression coefficients, but LetterWord Identification did not (Figure 7.4). 

The five criterion-referenced FACES tasks significantly 

predicted the Reading and General Knowledge 

assessments at the end of kindergarten, though notably 

less well than either the full battery or the abbreviated 

battery composed of norm-referenced tests, predicting 

30 percent and 36 percent of the respective variances. 

All five subtests were significant predictors of the 

Reading score (Figure 7.5). As with the full battery, 

tasks that tapped perceptual-motor skills and 

knowledge of print conventions were more closely 

related to kindergarten Reading scores than were tasks 

that tapped vocabulary or general information. 

All five sub-tests were significant predictors of General 

Knowledge as well, but the pattern of relative 

magnitudes was different than that for Reading. Book 

Knowledge was most closely related with General 

Knowledge, while the other four sub-tests had 

relatively lower, but significant regression coefficients 

ranging from .15 to .13 (Figure 7.6). 

The combination of the four norm-referenced tests at 

the end of Head Start that were more extended (and 

hence more reliable in terms of internal consistency) 

did a better job of predicting to children’s kindergarten 

achievement than the combination of the five FACES 

criterion-referenced tasks. However, further analysis 

revealed that the five criterion-referenced measures 

provide significant unique contributions to the 

Figure 7.6. Correlations and Standardized Multiple Regression Coefficients Between General Knowledge Scale 

Scores at End of Kindergarten Year and FACES Assessment Scale Scores at End of Head Start—Criterion-Referenced 

Tests Only 

Book Knowledge r = .52 

beta = .35 

Color Naming r = .38 

beta = .14 

r = .36 General Knowledge One -to -One Counting 

r = .30 

beta = .13 Scale 

Multiple R for multiple 

Draw-A-Design regression analyses = .60 beta = .15

r = .30

Social Awareness beta = .13 

SOURCE: Head Start Family and Child Experiences Survey (FACES), Spring 1998 Head Start and Spring 1999 

Kindergarten Followup, Ns=773-787 for bivariate correlations, 762 for multiple regression coefficients. 

112 
CHAPTER VII Head Start FACES 2000 

Figure 7.7. Correlation Coefficients Between Parent and Teacher Ratings of Children’s Social Competence at the 

End of Head Start and Teacher Reported Ratings of Cooperative Classroom Behavior at the End of Kindergarten 

0 

0.4 

0.33 

0.3 

-0.23 

-0.14 -0.14 

-0.23 

0.08 

-0.25 -0.24 

Parent Reported Aggression 

Parent Reported Hyperactivity 

0.2 Parent Reported Withdrawal 

Parent Reported Total Behavior 

Problems 

Parent Reported Positive 

Approach to Learning 

Teacher Reported Aggression 

Teacher Reported Hyperactivity 

0.1 

-0.1 Teacher Reported W ithdrawal 

Teacher Reported Total Behavior 

-0.2 Problems 

Teacher Reported Cooperative 

Classroom Behavior -0.3 

-0.36 -0.36 -0.4 

Magnitude of correlation coefficient

Behavior Ratings 

prediction of the Reading scale score, over and above 

the contributions of the norm-referenced tests, 

increasing the predictive power of the assessment 

battery by almost 3 percent. The criterion-referenced 

measures also provided unique contributions to the 

prediction of the General Knowledge scale score 

increasing the predictive power by .6 percent, an 

association at the trend level (p < .10). These results 

indicate that the criterion-referenced measures 

significantly contribute to the assessment battery, by 

picking up variance not accounted for by the normreferenced tests. 

B. FACES Behavioral Ratings at the 

End of Head Start Predict Children’s 

Social Competence in Kindergarten 

Social competence is an important developmental 

domain measured by the FACES battery. Two sources 

of information are tapped for assessing children’s social 

competencies by collecting ratings of the children’s 

behavior from their teachers and parents. These 

analyses examine the ability of the teacher and parent 

ratings of children’s social competencies during Head 

Start to predict children’s school adjustment at the end 

of kindergarten. Analysis of the predictive validity of 

the behavior ratings mirror those for the cognitive 

measures. First, teacher ratings of cooperative 

classroom behavior and total problem behaviors from 

the end of the kindergarten year were correlated with 

the behavior ratings from teachers and parents at the 

end of the Head Start year. Then, the teacher and 

parent ratings were combined in multiple regressions 

predicting teacher ratings of cooperative classroom 

behavior and total problem behaviors at the end of the 

kindergarten year in two multiple regression 

analyses.25 

In all multiple regression analysis involving the behavior ratings, teacher and parent ratings of total behavior were excluded as 

predictor variables from the models. Because these ratings are summative scores of the ratings for aggression, withdrawal, and 

hyperactivity, including them would introduce multicollinearity among these predictor variables to the model. Therefore, they were 

excluded from the regressions. They are included in the bivariate correlation analysis. 

113 

25 
Head Start FACES 2000 CHAPTER VII 

Figure 7.8. Correlations and Significant Standardized Multiple Regression Coefficients Between Teacher Ratings 

of Cooperative Classroom Behavior at End of Kindergarten Year and Parent and Teacher Behavior Ratings at End 

of Head Start Year 

Teacher ratings of

Parent ratings of 

Aggression 

Aggression r =-.36

beta = -.25 

r =-.23 Teacher Ratings of 

Cooperative 

beta = -.13 Classroom Behavior 

r =-.24 Multiple R for multiple Teacher ratings of regression analyses = .42 Withdrawal beta = -.11 

SOURCE: Head Start Family and Child Experiences Survey (FACES), Spring 1998 Head Start and Spring 1999 Kindergarten

Followup, Ns=556-575 for bivariate correlations, 542 for multiple regression coefficients.

Parent and teacher ratings of behavior at the end of 

Head Start were moderately correlated with teacher 

ratings of cooperative classroom behavior at the end of 

kindergarten, indicating that the FACES measures have 

predictive power on kindergarten outcomes (Figure 

7.7). Correlations were significant in the expected 

directions. Problem behaviors as rated by both parents 

and teachers at the end of Head Start had significantly 

negative correlations with teacher ratings at the end of 

kindergarten, ranging from -.36 (for teacher reported 

ratings of total behavior problems and aggressive 

behavior) to -.14 (for parent reported ratings of 

hyperactive behavior and withdrawal behavior). 

Parent ratings of positive approaches to learning and 

teacher ratings of cooperative classroom behavior were 

both positively correlated with teacher ratings of 

cooperative classroom behavior at the end of 

kindergarten (rs = .08 and .33, respectively). In 

general, teacher ratings showed stronger relationships 

than the parent ratings did with the kindergarten 

outcomes. 

Figure 7.9. Correlations and Significant Standardized Multiple Regression Coefficients Between Teacher Ratings 

of Total Problem Behavior at End of Kindergarten Year and Parent and Teacher Behavior Ratings at End of Head 

Start Year 

Teacher ratings of 

Parent ratings of 

Withdrawal 

r = .37 Aggression 

beta = .23 

r = .23 

beta = .12 

Teacher Ratings of 

Total Problem 

Behavior 

r = .28 Multiple R for multiple Parent ratings of regression analyses = .49 Hyperactivity beta = .09 

SOURCE: Head Start Family and Child Experiences Survey (FACES), Spring 1998 Head Start and Spring 1999 Kindergarten 

Followup, Ns=556-575 for bivariate correlations, 542 for multiple regression coefficients. 

114 
CHAPTER VII Head Start FACES 2000 

When the ratings were combined in a multiple 

regression, the model accounted for 18 percent of the 

variance in teacher ratings of cooperative classroom 

behavior. The best predictor in the multiple regression 

was teacher reported ratings of aggressive behavior 

(beta = -.25), which also had the strongest bivariate 

correlation (Figure 7.8). 

Similarly, parent and teacher ratings of behavior at the 

end of Head Start were moderately correlated with 

teacher ratings of total problem behaviors at the end of 

kindergarten, again indicating that the FACES 

measures have predictive power on kindergarten 

outcomes. Correlations were significant in the 

expected directions. Problem behaviors as rated by 

both parents and teachers at the end of Head Start had 

significantly positive correlations with teacher ratings 

of total problem behaviors at the end of kindergarten, 

ranging from .37 (for teacher reported ratings of 

aggressive behavior) to .23 (for parent reported ratings 

of aggressive and withdrawal behavior). Parent ratings 

of positive approaches to learning and teacher ratings 

of cooperative classroom behavior were both 

negatively correlated with teacher ratings of total 

problem behavior at the end of kindergarten (rs = -.15 

and -.36 respectively). In general, teacher ratings 

showed stronger relationships than the parent ratings 

did with the kindergarten outcomes. 

When the ratings were combined in a multiple 

regression, the model accounted for 24 percent of the 

variance in teacher ratings of total problem behavior. 

The best predictor in the multiple regression was 

teacher reported ratings of aggressive behavior (beta = 

.23), which also had the strongest bivariate correlation 

(Figure 7.9). 

Predictive Validity of Abbreviated Sets of Behavior 

Ratings 

In order to further explore the validity of the behavior 

ratings, multivariate regression analyses were carried 

out with the set of teacher reported ratings at the end 

Figure 7.10. Correlation Coefficients Between Parent and Teacher Ratings of Children’s Social Competence at 

the End of Head Start and Reading Scale Scores at End of Kindergarten 

0.3 

0.25 0.24 

0.2 

0.15 

0.1 

0.05 

Magnitude of correlation coefficient

0.05 

0 

-0.05 

-0.1 

-0.15 -0.13 

-0.16 

-0.2 -0.18 -0.19 -0.19 -0.19 

-0.25 

Behavior ratings 

-0.11 -0.1 

Parent Reported Aggression 

Parent Reported Hyperactivity 

Parent Reported Withdrawal 

Parent Reported Total Behavior 

Problems 

Parent Reported Positive

Approach to Learning

Teacher Reported Aggression 

Teacher Reported Hyperactivity 

Teacher Reported W ithdrawal 

Teacher Reported Total Behavior 

Problems 

Teacher Reported Cooperative 

Classroom Behavior 

115 
Head Start FACES 2000 CHAPTER VII 

Teacher ratings of

Parent ratings of 

Hyperactivity 

Cooperative r = .24

Classroom Behavior

beta = .19 

r = -.18 

beta = - .10 

Reading Scale 

r = -.19 Multiple R for multiple Teacher ratings of 

Withdrawal beta = - .10 regression analyses = .28 

Figure 7.11. Correlations and Significant Standardized Multiple Regression Coefficients Between Reading Scale 

Scores at End of Kindergarten Year and Parent and Teacher Behavior Ratings at End of Head Start 

SOURCE: Head Start Family and Child Experiences Survey (FACES), Spring 1998 Head Start and Spring 1999 Kindergarten 

Followup, Ns=741-771 for bivariate correlations, 719 for multiple regression coefficients. 

of Head Start predicting the teacher reported ratings 

of cooperative classroom behavior and total problem 

behavior at the end of kindergarten. 

The teacher reported ratings at the end of Head Start 

did almost as well as the full set of behavior ratings at 

predicting the teacher reported ratings of cooperative 

classroom behavior at the end of kindergarten. The 

model explained 16 percent of the variance in 

kindergarten cooperative classroom behavior ratings, 

and the rating of aggressive behavior was most closely 

associated with the cooperative classroom behavior 

ratings (beta = -.26). The rating of cooperative 

classroom behavior had a significant regression 

coefficient as well (beta = .14). But, as before, the 

ratings of hyperactive and withdrawal behavior did not. 

The four parent reported behavior ratings significantly 

predicted the teacher ratings of cooperative classroom 

behavior at the end of kindergarten, though notably 

less well than either the full set of behavior ratings or 

the set of teacher reported ratings. The model 

explained 6 percent of the variance in teacher reported 

cooperative classroom behavior, and only parent 

reported ratings of aggressive behavior had a significant 

regression coefficient (beta = -.19). 

The combination of the four teacher reported behavior 

ratings at the end of Head Start did a better job of 

predicting children’s kindergarten behavior than the 

combination of the four parent reported behavior 

ratings. However, the four parent ratings provided 

significant unique contributions to the prediction of 

the cooperative classroom behavior ratings, over and 

above the contributions of the teacher ratings, 

increasing the predictive power of the assessment 

battery by almost 2 percent.These results indicate that 

the parent ratings significantly contribute to the 

assessment battery, by picking up variance not 

accounted for by the teacher reported ratings. 

Similarly, the teacher reported ratings at the end of 

Head Start did almost as well as the full set of behavior 

ratings at predicting the teacher reported ratings of 

total problem behavior at the end of kindergarten. The 

model explained 18 percent of the variance in 

kindergarten total problem behavior ratings, and the 

rating of aggressive behavior was most closely 

associated with the total problem behavior ratings 

(beta = .22). The rating of cooperative classroom 

behavior had a significant regression coefficient as well 

(beta = -.17). But the ratings of hyperactive and 

withdrawal behavior did not. 

116 
CHAPTER VII Head Start FACES 2000 

The four parent reported behavior ratings also 

significantly predicted the teacher ratings of total 

problem behavior at the end of kindergarten, though 

notably less well than either the full set of behavior 

ratings or the set of teacher reported ratings. The 

model explained 11 percent of the variance in teacher 

reported total problem behavior, and parent reported 

ratings of hyperactive behavior had the largest 

regression coefficient (beta = .18). 

The combination of the four teacher reported behavior 

ratings at the end of Head Start did a better job of 

predicting children’s kindergarten behavior than the 

combination of the four parent reported behavior 

ratings. However, the four parent ratings provided 

significant unique contributions to the prediction of 

the cooperative classroom behavior ratings, over and 

above the contributions of the teacher ratings, 

increasing the predictive power of the assessment 

battery by almost 6 percent.These results indicate that 

the parent ratings significantly contribute to the 

assessment battery, by picking up variance not 

accounted for by the teacher reported ratings. 

C. Behavior Ratings at the End of 

Head Start Predict Reading Skills and 

General Knowledge at the End of 

Kindergarten 

Given that certain positive behaviors may foster 

learning, while other negative behaviors may impede 

learning, the ability of these behavior ratings obtained 

during the Head Start year to predict Reading and 

General Knowledge scale scores obtained at the end of 

kindergarten was also examined. 

Almost all of the parent and teacher ratings of behavior 

at the end of Head Start correlated significantly with 

Reading scale scores at the end of kindergarten, 

indicating that the FACES measures have predictive 

power on outcome criteria at later time points. Only 

parent reported ratings of positive approaches to 

Figure 7.12. Correlation Coefficients Between Parent and Teacher Ratings of Children’s Social Competencies at 

the End of Head Start and General Knowledge Scale Scores at End of Kindergarten Magnitude of correlation coefficient 

0.3 

0.25 

0.2 

0.15 

0.1 

0.05 

0 

-0.05 

-0.1 

-0.15 

0.23

-0.09 

-0.18 -0.17 

0.03 

-0.06 

-0.11 

-0.13 -0.13 -0.12 

-0.2

Parent Reported Aggression 

Parent Reported Hyperactivity 

Parent Reported Withdrawal 

Parent Reported Total Behavior 

Problems 

Parent Reported Positive 

Approach to Learning 

Teacher Reported Aggression 

Teacher Reported Hyperactivity 

Teacher Reported W ithdrawal 

Teacher Reported Total Behavior 

Problems 

Teacher Reported Cooperative 

Classroom Behavior 

-0.25 

Behavior ratings 

117 
Head Start FACES 2000 CHAPTER VII 

learning were not significantly related with Reading 

scores at the end of kindergarten. Correlations were 

significant in the expected directions. Ratings of 

problem behaviors were negatively correlated with 

kindergarten Reading scale scores, suggesting that 

behaviors that may impede learning are associated with 

lower reading skills in kindergarten (Figure 7.10). 

When the behavior ratings were combined in a 

multiple regression model, the model accounted for 8 

percent of the variance in Reading scale scores. The 

best predictor in the multiple regression was teacher 

reported ratings of cooperative classroom behavior 

(beta = .19), followed by parent reported ratings of 

hyperactive behavior (beta = -.10), followed by 

teacher reported ratings of withdrawal behavior (beta 

= -.10) (Figure 7.11). 

Significant correlations were also found between 

children’s scores on each of the parent and teacher 

reported behavior ratings at the end of Head Start and 

General Knowledge scale scores at the end of 

kindergarten, but these relationships were weaker than 

those found with Reading scale scores reported above. 

Significant bivariate correlations with General 

Knowledge scale scores ranged in absolute value from 

.23 (for teacher ratings of cooperative classroom 

behavior) to .09 (for parent reports of aggression), 

again indicating that the FACES measures have 

predictive power on outcome criteria at later time 

points. Significant correlations were in the expected 

directions. Ratings of problem behaviors were 

negatively correlated with kindergarten General 

Knowledge scale scores, suggesting that behaviors that 

may impede learning are associated with lower skills in 

the natural sciences and social studies in kindergarten 

(Figure 7.12). 

When the behavior ratings were combined in a 

multiple linear regression model, the model accounted 

for 7 percent of the variance in General Knowledge 

scale scores. The strongest predictor in the multiple 

regression was teacher reported ratings of cooperative 

classroom behavior (beta = .26), which also showed 

the strongest bivariate correlation with General 

Knowledge scale scores, followed by parent reported 

Figure 7.13. Correlations and Significant Standardized Multiple Regression Coefficients Between General 

Knowledge Scale Scores at End of Kindergarten Year and Parent and Teacher Behavior Ratings at End of Head 

Start Year 

Teacher ratings of

Parent ratings of 

Hyperactivity 

Cooperative r = .23

Classroom Behavior

beta = .26

r =-.18 

beta = -.12 

General Knowledge

Scale

r =-.06 (ns) Multiple R for multiple Teacher ratings of regression analyses = .27 Aggression beta = .11 

SOURCE: Head Start Family and Child Experiences Survey (FACES), Spring 1998 Head Start and Spring 1999 Kindergarten 

Followup, Ns=742-772 for bivariate correlations, 719 for multiple regression coefficients. 

118 
CHAPTER VII Head Start FACES 2000 

Figure 7.14. Correlations and Standardized Multiple Regression Coefficients Between Reading Scale Scores at 

End of Kindergarten Year and FACES Assessment Scale Gain Scores During Head Start Year 

One-to-One 

Counting 

WJR Letter-Word ID 

WJR Dictation 

r = .39 

beta = .27 

r = .31 

r = .31 

r = .24 

r = .29 

beta = .18 

beta = .15 

beta = .14 

beta = .10 

Multiple R for multiple 

regression analyses = .56 

PPVT-III Vocabulary 

r = .19 

beta = .09 

Draw-A-Design 

WJR Applied

Problems

Reading Scale 

SOURCE: Head Start Family and Child Experiences Survey (FACES), Fall 1997, Spring 1998 Head Start and Spring 1999 

Kindergarten Followup, Ns=580-697 for bivariate correlations, 441 for multiple regression coefficients. 

ratings of hyperactive behavior (beta = -.12). 

Surprisingly, teacher reported ratings of aggressive 

behavior had a significant positive regression 

coefficient (beta = .11). None of the other behavior 

ratings were significant predictors in the model (Figure 

7.13). 

FACES Behavior Ratings Contribute to the 

Prediction of Reading at the End of Kindergarten 

In order to further examine the usefulness of the 

behavior ratings, the unique contribution of the set of 

behavior rating measures to the prediction of 

kindergarten outcomes was assessed. Neither for 

reading nor for general knowledge did behavior ratings 

contribute significantly over and above the cognitive 

assessments (p > .05). However, the eight behavior 

ratings did provide unique contributions at the trend 

level (p < .10) to the prediction of the Reading scale 

score, over and above the contributions of the cognitive 

tests, increasing the predictive power of the assessment 

battery by 1.3 percent. This indicates that the behavior 

ratings provide some unique contributions to the 

prediction of kindergarten outcomes. 

D. Head Start Fall to Spring Gain 

Scores From the FACES Battery 

Predict Kindergarten Outcomes 

As a further assessment of the predictive validity of the 

cognitive measures from the FACES battery, the 

predictive validity of their fall to spring gain scores was 

assessed.To determine the children’s gains during their 

Head Start year, differences between the fall 1997 and 

spring 1998 mean scores were calculated for each of 

119 
Head Start FACES 2000 CHAPTER VII 

the measures by subtracting the fall score from the 

spring score for each child in the study. 

The ability of the fall to spring gains scores from the 

cognitive measures of the FACES battery to predict 

later school readiness was assessed by two approaches. 

In the first approach, Reading and General Knowledge 

scale scores from the end of the kindergarten year 

were correlated with the gain scores from each of the 

FACES sub-tests from the end of the Head Start year. 

In these analyses, partial correlations were calculated, 

controlling for individual difference in fall 1997 

baseline scores.26 The second approach assessed the 

ability of the fall to spring gain scores from the FACES 

instruments to predict Reading and General 

Knowledge scale scores at the end of the kindergarten 

year in a multiple linear regression analysis.27 

Children’s fall to spring gain scores on each of the 

component tasks in the FACES battery correlated 

significantly with their Reading scale scores at the end 

of kindergarten. Bivariate correlations with the 

Reading scale ranged from .39 (for the WoodcockJohnson-R Letter-Word Identification gain score) to 

.11 (for the Social Awareness gain score). These 

significant correlations indicate that the gain scores 

from the FACES measures are predictive of 

kindergarten outcomes. When the gain scores were 

combined in a multiple regression model, the model 

did quite well at predicting children’s early reading 

skills at the end of kindergarten, accounting for 31 

percent of the variance in Reading scale scores. The 

best predictor of Reading scale scores was the gain 

score for Woodcock-Johnson-R Letter-Word 

Identification task (beta = .27), which also showed the 

highest bivariate correlation with the Reading scale 

(Figure 7.14). 

Similarly, children’s fall to spring gain scores on all but 

one of the component tasks (Color Naming; r = .03) 

in the FACES battery correlated significantly with their 

General Knowledge scale scores at the end of 

kindergarten. Significant bivariate correlations with 

the General Knowledge scale ranged from .33 (for the 

Book Knowledge gain score) to .10 (for the Social 

Awareness gain score). These significant correlations 

indicate that the gain scores from the FACES measures 

are predictive of kindergarten outcomes. When the 

gain scores were combined in a multiple regression 

model, the model did quite well at predicting 

children’s general knowledge at the end of 

kindergarten, accounting for 28 percent of the 

variance in General Knowledge scale scores. The best 

predictor in the model was the gain score for the 

PPVT-III (beta = .24), followed by the Book 

Knowledge gain score (beta = .19), which had the 

highest bivariate correlation with the General 

Knowledge scores (Figure 7.15). 

Similar to the analysis with the FACES battery scores at 

the end of Head Start, the Book Knowledge gain score 

was not a significant predictor of the Reading scale 

score in its multiple regression model, though it 

correlated significantly with the Reading score (r = 

.26). Another similarity to the analysis with the scores 

at the end of Head Start is that the best predictor of the 

Reading score, namely the Woodcock-Johnson-R 

Letter-Word Identification gain score, was not a 

26 In analysis of gains scores, baseline scores are controlled for, effectively examining the effect of the gain scores, if all students had 

the same baseline score. 

27 Control of the fall baseline score for each gain score in the multiple linear regression models was accomplished through the use of 

residual scores. A residual score was created for each gain score with the effects of its respective baseline score partialled out. These 

residual scores were then entered as independent variables in the multiple linear regression, predicting the kindergarten outcome 

variables. 

120 
CHAPTER VII Head Start FACES 2000 

Figure 7.15. Correlations and Standardized Multiple Regression Coefficients Between General Knowledge Scale 

Scores at End of Kindergarten Year and FACES Assessment Scale Gain Scores During Head Start Year 

Book Knowledge 

PPVT-III Vocabulary 

Draw-A-Design 

r = .31 

beta = .24 

r = .33 

r = .27 

r = .26 

r = .23 

beta = .19 

beta = .18 

beta = .13 

beta = .12 

Multiple R for multiple 

regression analyses = .53 

WJR Applied 

Problems 

r = .30 

beta = .09 

WJR Dictation 

One -to-One Counting 

General Knowledge

Scale

SOURCE: Head Start Family and Child Experiences Survey (FACES), Fall 1997, Spring 1998 Head Start and Spring 1999 

Kindergarten Followup, Ns=580-697 for bivariate correlations, 441 for multiple regression coefficients. 

significant predictor of General Knowledge in its 

multiple regression analysis, despite the fact that its 

gain score correlated significantly with the General 

Knowledge task (r = .18). Conversely, the best 

predictor of General Knowledge, namely, the PPVTIII, was the weakest significant predictor in the 

multivariate prediction of children’s Reading scores 

(beta = .09). 

The findings of the gain scores analysis support the 

conclusions of the analysis with the scores at the end of 

Head Start that the Reading and General Knowledge 

assessments may be tapping two distinct clusters of 

skills: “inside-out” skills and “outside-in” skills. The 

gain scores from the FACES battery again showed its 

validity by predicting well to children’s later learning 

in both skill domains. 

Although both domains were predicted well, the 

combination of sub-tests that produced the best 

forecasts differed across the two skill clusters. The gain 

score from the Letter-Word Identification test was the 

best predictor of inside-out skills, with gain scores 

from One-to-One Counting, Dictation, McCarthy 

Draw-a-Design, Applied Problems, and PPVT-III 

contributing additional predictive power. The PPVT-III 

gain score was by far the best predictor of outside-in 

skills, with gain scores from Book Knowledge, 

McCarthy Draw-a-Design, Dictation, One-to-One 

Counting, and Applied Problems showing much 

smaller but significant regression coefficients as well. 

It is noteworthy that seven of the eight FACES sub-tests 

contributed significantly to either the Reading or 

General Knowledge regression model or both. 

121 
Head Start FACES 2000 CHAPTER VII 

E. Head Start Fall to Spring Gain 

Scores From the Behavior Ratings 

Predict Social Competence in 

Kindergarten 

The fall to spring gain scores from the parent and 

teacher ratings of behavior were moderately correlated 

with teacher ratings of cooperative classroom behavior 

at the end of kindergarten, indicating that the FACES 

measures have predictive power on kindergarten 

outcomes. Correlations were significant in the 

expected directions. Increases in problem behaviors as 

rated by both parents and teachers at the end of Head 

Start had significantly negative correlations with 

teacher ratings of cooperative classroom behavior at 

the end of kindergarten, ranging from -.24 (for the 

change score for parent reported ratings of total 

problem behaviors) to -.14 (for the change score for 

parent reported ratings of hyperactive behavior). The 

gain score for teacher ratings of cooperative classroom 

behavior was positively correlated with teacher ratings 

of cooperative classroom behavior at the end of 

kindergarten (r = .15), but the gain score for parent 

ratings of positive approaches to learning was not. In 

general, gain score for the teacher ratings showed 

stronger relationships with the kindergarten outcomes 

than those for the parent ratings. 

When the rating gain scores were combined in a 

multiple regression, the model accounted for 11 

percent of the variance in teacher ratings of 

cooperative classroom behavior. The best predictors in 

the multiple regression were the gain scores for 

teacher reported ratings of withdrawal behavior and 

parent reported ratings of aggressive and withdrawal 

behavior (betas = -.13), which had the stronger 

bivariate correlations among the set of behavior ratings 

(Figure 7.16). 

Similarly, the fall to spring gain scores from the parent 

and teacher ratings of behavior were moderately 

correlated with teacher ratings of total problem 

behavior at the end of kindergarten, again indicating 

that the FACES measures have predictive power on 

kindergarten outcomes. Correlations were significant 

in the expected directions. Increases in problem 

behaviors as rated by both parents and teachers at the 

end of Head Start had significantly positive correlations 

with teacher ratings of total problem behavior at the 

end of kindergarten, ranging from .29 (for the change 

score for parent reported ratings of total problem 

behaviors) to .12 (for the change score for teacher 

reported ratings of aggressive behavior). The gain 

score for teacher ratings of cooperative classroom 

behavior was negatively correlated with teacher ratings 

of cooperative classroom behavior at the end of 

kindergarten (r = -.13), but the gain score for parent 

ratings of positive approaches to learning was not 

significantly related. In general, gain score for the 

teacher ratings showed stronger relationships with the 

kindergarten outcomes than those for the parent 

ratings.When the rating gain scores were combined in 

a multiple regression, the model accounted for 15 

percent of the variance in teacher ratings of total 

problem behaviors. The best predictors in the multiple 

regression were the gain scores for parent reported 

ratings of withdrawal behavior, (beta = .21) (Figure 

7.17). 

F. Head Start Fall to Spring Gain 

Scores From the Behavior Ratings 

Predict Reading Skills and General 

Knowledge at the End of Kindergarten 

The fall to spring gain scores for parent and teacher 

ratings of behavior correlated significantly with 

Reading scale scores at the end of kindergarten, 

indicating that the FACES measures have predictive 

power on kindergarten outcomes. Correlations were 

significant in the expected directions. Increases in 

problem behaviors were negatively correlated with 

kindergarten Reading scale scores, adding more 

evidence that behaviors that may impede learning are 

associated with lower reading skills in kindergarten. 

122 
CHAPTER VII Head Start FACES 2000 

Figure 7.16. Correlations and Standardized Multiple Regression Coefficients Between Teacher Ratings of 

Cooperative Classroom Behavior at End of Kindergarten Year and Parent and Teacher Behavior Ratings Gain 

Scores During Head Start Year 

Parent ratings of

Aggression

Parent ratings of 

Withdrawal 

r =-.19 

beta = -.13 

r =-.20 

r =-.14 

beta = -.13 

Teacher ratings of

Withdrawal r =-.16 

beta = -.13 

Teacher ratings of

Cooperative

Classroom Behavior

Multiple R for multiple Parent ratings of regression analyses = .33 Hyperactivity beta = -.10 

SOURCE: Head Start Family and Child Experiences Survey (FACES), Fall 1997, Spring 1998 Head Start and Spring 1999 

Kindergarten Followup, Ns=474-510 for bivariate correlations, 432 for multiple regression coefficients. 

When the behavior ratings gain scores were combined 

in a multiple regression model, the model accounted 

for 4 percent of the variance in Reading scale scores. 

The best predictor in the multiple regression was the 

gain score for teacher reported ratings of withdrawal 

behavior (beta = -.12) followed by the gain score for 

parent reported ratings of withdrawal behavior (beta = 

-.11). 

The correlations of the fall to spring gain scores of 

parent and teacher ratings of behavior and General 

Knowledge scale scores at the end of kindergarten 

were not as strong as those for the Reading scale 

scores. Four of the eight behavior rating gain scores 

(parent ratings of aggressive, hyperactive, and 

withdrawal behavior, and teacher ratings of 

cooperative classroom behavior) were significantly 

Figure 7.17. Correlations and Standardized Multiple Regression Coefficients Between Teacher Ratings of Total 

Problem Behavior at End of Kindergarten Year and Parent and Teacher Behavior Ratings Gain Scores During 

Head Start Year 

Parent ratings of 

Parent ratings of 

Hyperactivity 

Withdrawal r = .27 

beta = .21

r = .25

beta = .18 

Teacher ratings of

Total Problem

Behavior

r = .16 Multiple R for multiple Teacher ratings of regression analyses = .39 Hyperactivity beta = .10 

SOURCE: Head Start Family and Child Experiences Survey (FACES), Fall 1997, Spring 1998 Head Start and Spring 1999 

Kindergarten Followup, Ns=474-510 for bivariate correlations, 432 for multiple regression coefficients. 

123 
Head Start FACES 2000 CHAPTER VII 

related to General Knowledge scores, indicating that 

these FACES measures have some predictive power on 

kindergarten outcomes. Significant correlations were 

in the expected directions. Increases in problem 

behaviors were negatively correlated with 

kindergarten General Knowledge scale scores, adding 

more evidence that behaviors that may impede 

learning are associated with lower skills in natural 

sciences and social studies in kindergarten. When the 

behavior ratings were combined in a multiple 

regression model, the model accounted for 2.5 

percent of the variance in General Knowledge scale 

scores, which is an association at the trend level (p < .10). 

Head Start Fall to Spring Gain Scores From the 

Behavior Ratings Contribute to the Prediction of 

Reading and General Knowledge at the End of 

Kindergarten 

In order to further examine the usefulness of the 

behavior ratings, the unique contribution of the set of 

behavior rating gain scores to the prediction of 

kindergarten outcomes was assessed. The eight 

behavior ratings provided unique contributions at the 

trend level (p < .10) to the prediction of the Reading 

scale score, over and above the contributions of the 

cognitive tests, increasing the predictive power of the 

assessment battery by almost 3 percent. Similarly, the 

eight behavior ratings provided unique contributions at 

the trend level (p < .10) to the prediction of the 

General Knowledge scale score, over and above the 

contributions of the cognitive tests, increasing the 

predictive power of the assessment battery by almost 3 

percent. These results indicate that the addition of the 

behavior rating gain scores provide some unique 

contributions to the prediction of kindergarten 

outcomes. 

G. Children’s Scores on the FACES 

Instruments and Behavior Ratings at 

the End of Head Start Predict 

Promotion to First Grade 

Another measure of the predictive validity of the 

FACES battery is to examine how well scores on the 

FACES instruments and behavior ratings at the end of 

Head Start are related to practical decision-making at 

the end of kindergarten, namely, the teacher’s decision 

of whether the child gets promoted to first grade or 

repeats the kindergarten year. In this set of analysis, 

teachers’ decisions at the end of the kindergarten year 

to have the child repeat another year of kindergarten 

(versus promote the child to first grade) were first 

correlated with the scores from each of the FACES 

instruments from the end of the Head Start year.Then 

the ability of the FACES scale scores from the end of 

the Head Start year to predict teachers’ decisions to 

have the child repeat a year of kindergarten was 

examined in a multiple logistic regression analysis. 

This approach was then repeated with the parent- and 

teacher-reported behavior ratings. 

Children’s Scores on the FACES Instruments at the 

End of Head Start Predict Repeating Kindergarten 

Children’s scores on each of the component tasks in the 

FACES battery at the end of Head Start correlated 

significantly with teacher decisions to have the child 

repeat another year of kindergarten. Bivariate 

correlations were in the expected directions, ranging 

from -.31 (for Book Knowledge) to -.12 (for Draw-ADesign), indicating that lower subtest scores were 

associated with repeating kindergarten. These 

significant correlations indicate that the FACES 

measures have predictive power on outcome criteria at 

later time points. 

When the subtest scores were combined in a multiple 

logistic regression model, the model did quite well at 

predicting whether children repeated kindergarten, 

accounting for 24 percent of the variance in the 

prediction of repeating kindergarten. Information of 

scores from the FACES instruments led to an 82 

percent accuracy rate in predicting or not a child was 

assigned by her teacher to repeat kindergarten. The 

strongest predictor of whether or not children were 

assigned by their teachers to repeat kindergarten was 

124 
CHAPTER VII Head Start FACES 2000 

Figure 7.18. Correlations and Odds-Ratio Estimates Between Teachers’ Decisions at End of Kindergarten Year to 

Assign Child to Repeat Kindergarten and FACES Assessment Scale Scores at End of Head Start Year 

One -to-One 

Counting 

Book Knowledge 

WJR Letter-Word ID 

Color Naming 

Social Awareness 

r = -.31 

odds ratio = .50 

r = -.23 

r = -.23 

r = -.21 

r =-.14 

odds ratio = .77 

odds ratio = .96 

odds ratio = .97 

odds ratio = .98 

Draw-A-Design 

r =-.12 

odds ratio = .98 

R2

Logit for multiple

logistic regression analyses = .24

Percent Concordant = 82%

Teacher’s Decision 

to Assign Child to 

Repeat Kindergarten 

SOURCE: Head Start Family and Child Experiences Survey (FACES), Spring 1998 Head Start and Spring 1999 Kindergarten 

Followup, Ns=551-583 for bivariate correlations, 474 for multiple logistic regression odds ratio estimates. 

the Book Knowledge task, in which for every unit 

increase in Book Knowledge scores, children were 50 

percent less likely to repeat kindergarten (Figure 

7.18). 

Children’s Behavior Ratings at the End of Head 

Start Predict Repeating Kindergarten 

Parent and teacher behavior ratings at the end of Head 

Start correlated significantly with teacher decisions to 

have the child repeat another year of kindergarten. 

Correlations were in the expected directions. 

Problematic behaviors were positively correlated with 

repeating kindergarten, ranging from .26 (for teacher 

ratings of withdrawal behavior) to -.09 (for teacher 

ratings of aggressive behavior). Teacher ratings of 

cooperative classroom behavior at the end of Head 

Start were negatively correlated with teacher decisions 

to repeat kindergarten (r = -.14); parent ratings of 

positive approaches to learning, however, were not 

significantly related. The significant correlations 

indicate that the FACES behavior ratings also have 

predictive power on outcome criteria at later time 

points. 

When the parent and teacher ratings were combined in 

a multiple logistic regression model, the model did 

quite well at predicting whether children repeated 

kindergarten, accounting for 11 percent of the 

variance in the prediction of repeating kindergarten. 

Information from the FACES behavior ratings led to a 

125 
Head Start FACES 2000 CHAPTER VII 

Figure 7.19. Correlations and Odds-Ratio Estimates Between Teachers’ Decisions at End of Kindergarten Year to 

Assign Child to Repeat Kindergarten and Parent and Teacher Behavior Ratings at End of Head Start Year 

Parent ratings of 

Withdrawal 

Teacher ratings of 

Withdrawal 

Teacher ratings of 

Hyperactivity 

Teacher’s Decision 

to Assign Child to 

Repeat Kindergarten 

r = .26 

odds ratio = 1.31 

r = .14 

r = .19 

odds ratio = 1.25 

odds ratio = 1.23 

Parent ratings of

Hyperactivity

r = .11 

odds ratio = 1.11 

R2

Logit for multiple

logistic regression analyses = .11

Percent Concordant = 72% 

SOURCE: Head Start Family and Child Experiences Survey (FACES), Spring 1998 Head Start and Spring 1999 Kindergarten 

Followup, Ns=551-583 for bivariate correlations, 474 for multiple logistic regression odds ratio estimates. 

72 percent accuracy rate in predicting whether or not 

a child was assigned by her teacher to repeat 

kindergarten. The strongest predictor of whether or 

not children were assigned by their teachers to repeat 

kindergarten was teacher ratings of withdrawal 

behavior, in which for every unit increase in these 

teacher ratings, children were 31 percent more likely 

to repeat kindergarten (Figure 7.19). 

The Combination of Children’s Scores on the 

FACES Instruments and Behavior Ratings at the End 

of Head Start Predict Repeating Kindergarten 

When the parent and teacher ratings were combined 

with the subtest scores from the FACES instruments in 

a multiple logistic regression model, the model did 

quite well at predicting whether children repeated 

kindergarten, accounting for 30 percent of the 

variance in the prediction of repeating kindergarten. 

This is an increase of 6 percent of the variance that was 

explained by the subtest scores alone. Information 

from the combination of the FACES behavior ratings 

and the subtest scores led to an 83 percent accuracy 

rate in predicting whether or not a child was assigned 

by her teacher to repeat kindergarten. This suggests 

that the additional information provided by the 

behavior ratings adds to the predictive validity of the 

FACES instruments in predicting kindergarten 

repetition. 

CONCLUSIONS 

The FACES battery has strong predictive validity with 

outcomes at the end of kindergarten. As an indicator 

of preliteracy skills, the cognitive measures show 

strong associations with reading ability at the end of the 

kindergarten year. As an indicator of school 

adjustment and social competence, the behavior ratings 

demonstrate ability to predict kindergarten behaviors 

that promote learning. These analyses show that: 

• The instruments used in FACES predict later 

behavior and performance in kindergarten. 

126 
CHAPTER VII Head Start FACES 2000 

• The instruments used in FACES also predict the later 

practical decision of whether a child gets promoted 

to first grade. 

• The instruments used in FACES tap different types of 

abilities (“inside-out” versus “outside-in”) that are 

important for children’s future reading proficiency 

and academic achievement. 

• The multi-measure and multi-method approach to 

the measurement of children’s abilities provides a 

variety of information sources that significantly 

contribute to the prediction of kindergarten 

outcomes. 

Children who had higher scores at the end of the Head 

Start year, and who made greater gains during the year 

on the Letter-Word Identification test, Applied 

Problems, Dictation, One-to-One Counting, and 

McCarthy Draw-a-Design tasks, tended to have greater 

early reading skills at the end of kindergarten. 

Children’s improved scores on the PPVT-III, Applied 

Problems, Book Knowledge, Dictation, and the 

McCarthy Draw-a-Design tasks at the end of Head 

Start were associated with greater General Knowledge 

scores at the end of kindergarten. These results suggest 

that efforts in improving children’s performance and 

behavior in preschool can result in greater school 

readiness and school adjustment when these children 

are preparing to enter first grade. 

In the assessment of children’s social competencies, the 

use of parent and teacher ratings provides data on 

children’s coping skills in different situations and 

provides a more comprehensive picture of their 

behavior. Equally important, both parent and teacher 

ratings significantly contribute to the prediction of 

social skills at the end of kindergarten. The parent and 

teacher ratings also significantly predict reading skills 

and general knowledge at the end of kindergarten. 

Ratings of problem behaviors were negatively 

correlated with kindergarten Reading and General 

Knowledge scale scores, suggesting that behaviors that 

may impede learning are associated with lower reading 

skills in kindergarten. High ratings of behaviors that 

enhance learning, positive approaches to learning and 

cooperative classroom behavior, were positively 

correlated with kindergarten outcomes. These 

analyses suggest that curricula that strengthen 

children’s social skills will also have beneficial effects 

on their later school readiness. 

The multi-measure and multi-method approach to the 

measurement of children’s development and school 

readiness provides a comprehensive assessment of 

children’s abilities. The addition of the criterionreferenced measures to the norm-referenced measures 

improves the assessment battery in many ways. First, 

they are short tasks that cover more specific topic areas 

that are typically taught in preschool curricula and are 

also fun for the children to do in the assessment. And 

second, they significantly (although moderately) 

increase the battery’s ability to predict kindergarten 

outcomes, improving its predictive validity. The 

addition of the parent and teacher behavior ratings 

adds another source of information that predict 

kindergarten outcomes, namely, an assessment of 

behaviors that can either foster or impede learning. 

They also provide some unique contributions to the 

battery’s ability to predict kindergarten outcomes, 

particularly in the practical decision of whether a child 

is promoted to first grade or repeats kindergarten. 

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132 
HEAD START FACES 2000

A Whole-Child Perspective on Program Performance 

Appendix Methodology 

I. Sample Design Overview 

The third cohort of Head Start children for FACES was 

selected as a two-stage sample.28 The first stage 

sampling units were Head Start programs; the second 

stage units were classes within sampled programs. In 

each sampled classroom, all eligible children in their 

first year of Head Start were taken into the sample. 

A. Programs 

The sampling frame of eligible Head Start programs 

was constructed from the 1998-1999 Program 

Information Report (PIR). Migrant and Seasonal Head 

Start programs, American Indian/Alaska Native Head 

Start programs, Early Head Start programs, programs 

in the territories, and programs that do not serve 

children directly were excluded, resulting in a frame of 

1,675 programs. The programs were stratified by 

Census region (NE, NC, S, W), percent minority 

(above/below 50%), and metro or urban/rural status 

(MSA/non-MSA). These are the same stratification 

variables used in sampling programs for the first 

(Spring 1997) and second (fall 1997 – spring 2000) 

FACES cohorts. 

A sample of 45 programs was selected in spring 2000. 

The sample size in each stratum was proportional to 

the stratum first year Head Start enrollment. The 

programs were selected with probability proportional 

to the program’s first year enrollment using systematic 

sampling. The first year enrollment was calculated 

from the PIR by subtracting the reported second and 

third year enrollment from the total enrollment. A 

Keyfitz procedure was used to minimize the overlap 

with the 40 programs sampled previously by Abt 

Associates for the first and second FACES cohorts. As 

a result, there was no overlap with the previous 

program sample. Of the 45 programs selected for the 

third cohort, two were later discovered to be ineligible 

because they had been defunded. 

B. Classrooms 

In the 43 remaining programs, lists of the anticipated 

classes for fall 2000 were obtained in late summer 

2000. The programs also provided the expected 

number of first year Head Start children in each class. 

These lists formed the basis for the classroom sampling 

frame, after excluding classes with no first year 

children. Classes with fewer than five first year 

children expected were combined with another class in 

the same center to form a “class group.” The class 

groups were treated as a single unit for sampling 

purposes and sample size calculations. The total target 

sample size of first year children was 2,825, or 66 per 

program. In general, the desired sample size of classes 

in each program was determined as 66 / (average class 

size for the program), where the average class size was 

28 The first cohort consisted of children sampled for the spring 1997 Field Test. The second cohort consisted of an additional sample 

of children selected for the FACES study that commenced in fall 1997. The combination of these two cohorts comprised the total 

sample for the FACES 1997 study. The third cohort consists of an entirely new sample of children selected for the current FACES 

study that commenced in fall 2000. 

133
Head Start FACES 2000 Appendix 

in terms of number of first year children. The actual 

initial sample size was increased by 2 classes to allow 

for a reserve sample in each program. In programs 

where the total first year enrollment as obtained from 

the class rosters was more than twice the measure of 

size used to sample the program, the initial class 

sample size was increased to prevent large variation in 

class weights. In small programs where the initial 

sample size exceeded the number of classes available, 

all classes were taken with certainty. 

Classes were sorted by center within program and 

were sampled with equal probabilities. A subsample of 

the initial sample was selected with equal probabilities 

to obtain a main sample of the desired sample size and 

a reserve sample of two classes in each program. A 

total of 367 classes were selected: 279 classes for the 

main sample and 88 for the reserve sample. The 

number of main sample classes in each program varied 

from 3 to 15, with an average of 6. (In terms of 

collapsed classrooms, a total of 252 classroom groups 

were sampled for the main sample and 82 for the 

reserve sample, for an average of 6 per program and a 

range of 3 to 10.) 

In fall 2000 the eligibility status of the main sample 

classes was determined. One or two reserve classes 

were added in some programs to prevent a shortfall in 

the target number of first year children for the study. 

The final sample for weighting purposes included all 

sampled classes where an attempt was made to collect 

data from the classroom, including those discovered to 

be ineligible. The rationale for this is because ineligible 

classes in the sample represent ineligible classes on the 

Head Start program frame. A total of 307 main and 

reserve classes were in sample in fall 2000. Twenty of 

the 307 classes were discovered to be ineligible when 

the program was contacted by field staff in fall 2000 

because they no longer existed, they didn’t receive 

Head Start funding, or they had no first year Head 

Start children. In 286 of the remaining 287 eligible 

classes (one teacher refused to allow the children in her 

class to be sampled), all first year children were taken 

into the sample. 

II. Response Rates 

Fall 2000 

• 2,508 child assessments were completed out of 

2,790 for a completion rate of 90 percent. 

• 2,488 parent interviews were completed out of 

2,790 families selected for the sample (89 percent). 

• Teacher report forms were obtained on 2,532 of the 

sample children (91 percent). 

• Assessment, parent, and teacher data were obtained 

on 2,396 of the 2,790 sample children (86 percent). 

• A total of 278 classrooms were observed out of 286 

in the sample for a completion rate of 97 percent. 

Spring 2001 

• 2,232 child assessments were completed out of 

2,288, representing 98 percent of the children who 

remained in the program, and 80 percent of the 

original sample (2,790). 

• 2,166 parent interviews were completed out of 

2,288, representing 95 percent of the children who 

remained in the program, and 78 percent of the 

original sample. 

• Teacher report forms were obtained on 2,236 of the 

sample children, representing 98 percent of the 

children who remained in the program and 80 

percent of the original sample. 

• Assessment, parent, and teacher data were obtained 

on 2,115 of the 2,288 sample children who remained 

in the program (92 percent). 

• A total of 275 classrooms were observed out of 284 

in the sample for a completion rate of 97 percent. 

134 
Appendix Head Start FACES 2000 

Spring 2002 (Kindergartners Only) 

• 831 child assessments were completed out of 979, 

representing 85 percent of the children who were in 

kindergarten in spring 2002. 

• 901 parent interviews were completed out of 979, 

representing 92 percent of the children who were in 

kindergarten in spring 2002. 

• Teacher report forms were obtained on 681 of the 

children, representing 70 percent of the children 

who were in kindergarten in spring 2002. 

• Assessment, parent, and teacher data were obtained 

on 624 of the 979 children who were in kindergarten 

in spring 2002 (64%). 

III. Program Weights 

The program weight was calculated as the inverse of 

the program’s probability of selection. As mentioned 

earlier, a Keyfitz procedure was used to minimize the 

overlap with the cohort 1 and 2 program sample drawn 

earlier by Abt Associates. This procedure involved 

calculating conditional probabilities of selection, which 

are based on whether the program was sampled 

previously or not, and whether its probability of 

selection increased compared with the previous

sample. Prior to sampling for cohort 3, the

unconditional probability of selection for each

program on the cohort 3 frame was calculated as 

FIRSTYRi 

NEWPSELi = ∑

Nh 

( FIRSTYRi)/NEWSMPSZh 

i=1 

where Nh is the number of programs on the frame in 

stratum h, NEWSMPSZh is the sample size for stratum 

h for the cohort 3 design, and FIRSTYRi is the first 

year enrollment for program i from the PIR. The 

probability of selection for each program under the 

Abt sample design for cohorts 1 and 2 was also 

calculated as 

ENRTOT

ORIGPSELi = i 

∑

Nh 

( ENRTOTi )/SAMPSIZEh 

i=1

where Nh was the number of programs on the Abt 

frame in stratum h, SAMPSIZEh was the sample size for 

stratum h under the Abt design, and ENRTOTi was the 

total enrollment for the i-th program from an earlier 

PIR. 

The conditional probability of selection was calculated 

for each program on the cohort 3 frame according to 

the Keyfitz procedure as: 

Case 1: NEWPSEL 3 1 - ORIGPSEL 

CONDPROB = NEWPSEL − (1 − ORIGPSEL) 

ORIGPSEL 

if the program was sampled for cohorts 1 and 2,

= 1 if the program was not sampled for cohorts 1 and 2.

Case 2: NEWPSEL < 1 - ORIGPSEL

CONDPROB = 0 if the program was sampled for

cohorts 1 and 2,

NEWPSEL 

= 1-ORIGPSEL 

if the program was not sampled for cohorts 1 and 2. 

These conditional probabilities of selection were the 

measures of size used to select the cohort 3 program 

sample. It can be shown that the Keyfitz procedure 

preserves the unconditional cohort 3 program 

probabilities of selection, while at the same time 

minimizing the overlap. Thus the cohort 3 program 

weight is the inverse of NEWPSEL, the unconditional 

probability of selection under the cohort 3 design. 

135 
Head Start FACES 2000 Appendix 

All 43 eligible programs cooperated with the study, so 

that nonresponse adjustments at the program level 

were unnecessary. 

For each program, a set of 43 jackknife replicate 

weights was created for calculating standard errors. 

The replicate weights were created using a standard 

stratified jackknife procedure. One program at a time 

was dropped (i.e. given a zero replicate weight) and 

the weights of the remaining programs in the same 

stratum were adjusted by a factor of nh/(nh-1), where 

nh is the number of sampled programs in stratum h. 

The program weights in the other strata were left 

unchanged. By repeating this 43 times, 43 replicate 

weights were obtained for each program. For 

estimates involving child or classroom data from all 43 

programs, the degrees of freedom for the variance of 

the estimate is #PSUs - #varstrat = 43 - 12 = 31. 

(One of the 13 original sampling strata was collapsed 

with an adjacent stratum for variance estimation 

purposes because it contained only one eligible 

sampled program.) 

A. Classroom Weighting 

Two sets of class weights were produced for classroom 

level estimation: one set for fall 2000 cross-sectional 

estimates and a second set for fall 2000 – spring 2001 

longitudinal classroom analysis. Class base weights 

were first created that reflected the overall probability 

of selection for the class, including the program 

probability of selection. These base weights were 

adjusted for classroom level nonresponse, using the 

following criteria for a complete classroom: 

Fall 2000 cross-sectional estimates: the classroom 

must have complete fall 2000 observation data. 

Classroom observation data include counts of children 

and adults, Assessment Profile (Scheduling, Learning 

Environment, and Individualizing), ECERS-R, Arnett 

Caregiver Interaction Scale, Teacher-Directed 

Activities Checklist and Wrap-Up measures. 

Fall 2000-Spring 2001 longitudinal analysis: the 

classroom must have complete observation data for 

either fall 2000 or spring 2001 and child assessment 

data for both fall 2000 and spring 2001. 

A1. Class Base Weights 

A class base weight was created for each of the 367 

initially sampled classes in Fall 2000. Fifty-four 

reserve classes that were never used were given base 

weights of zero. Six main sample classes were sampled 

out on an ad hoc basis by field staff to reduce burden 

and to have independence between classes. They were 

assigned base weights of zero, since they were not part 

of the final sample. In this situation, a teacher had both 

a morning and afternoon class in the sample. One class 

out of the morning/afternoon pair was subsampled. 

The remaining 307 classes considered to constitute the 

sample were each assigned a class base weight equal to 

the inverse of their overall probability of selection. The 

overall probability of selection is the product of the 

program probability of selection and the probability of 

selecting the class within the program. The inverse of 

the overall probability of selection can also be written 

as the product of the program weight and the withinprogram class weight: 

Class Base Weight = Program Weight * (Total # 

Classes in Program / # sampled classes fielded) 

Collapsed classrooms were counted as one classroom 

in the base weight calculations, since they were treated 

as a single unit in sampling. The ad hoc subsampling 

was reflected by multiplying the base weight of the 

retained class in the am/pm pair by a factor of 2 and 

the dropped class by zero. One class that had merged 

with another was given a zero base weight, and the 

newly merged class had its base weight multiplied by a 

factor of .5 to reflect its increased probability of 

selection. 

136 
Appendix Head Start FACES 2000 

Forty-three jackknife class replicate base weights were 

created from the program replicate weights: 

Class Replicate Base Weight j = Program Replicate 

Weight j * (Total #Classes in Program / #sampled 

classes fielded); j = 1, 2, ...43. 

A2. Cross-sectional Fall 2000 Class Weights 

Of the 307 sampled classes that were fielded in fall 

2000, 279 were eligible and had complete classroom 

data, 8 were eligible but didn’t complete data 

collection, and 20 were discovered to be ineligible. A 

class nonresponse adjustment factor was applied to the 

class base weights of the 279. The nonresponse 

adjustment factor was computed separately by 

program. Both the 8 incomplete and 20 ineligible 

classes were given a zero final class weight. The 

classroom replicate base weights were also adjusted for 

nonresponse by program, so that the sampling 

variability in the nonresponse adjustments will be 

reflected in the standard error estimates. 

The sum of the nonresponse-adjusted fall 2000 

classroom weights is 34,638. The unweighted and 

weighted completion rates are both 97%, excluding 

ineligibles from both numerator and denominator. 

The unweighted and weighted eligibility rates are both 

94%. The class base weight was used in calculating the 

weighted rates. 

A3. Longitudinal Fall 2000 - Spring 2001 Class 

Weights 

Of the 286 eligible classes in fall 2000, 280 completed 

data collection in spring 2001. Note that the 279 fall 

2000 classroom completes are not a subset of the 280 

spring 2001 completes. Five classes that completed fall 

2000 data collection did not complete the spring 2001, 

and six classes that completed spring 2001 data 

collection did not complete the fall 2000. There were 

79 new classes added in spring 2001 because children 

who switched classes after the fall 2000 data collection 

were followed to the new class. However, no classroom 

observations were done at these new classes, so they 

were not considered to be part of the classroom sample 

and were assigned a zero base weight. 

A class nonresponse adjustment factor was applied to 

the class base weights of the 280 eligible completes. 

The nonresponse adjustment factor was computed 

separately by program. The classroom replicate base 

weights were also adjusted for nonresponse by 

program, so that the sampling variability in the 

nonresponse adjustments will be reflected in the 

standard error estimates.The incomplete and ineligible 

classes, along with the 79 new classes, were given a 

zero final class weight. 

The sum of the nonresponse-adjusted fall 2000–spring 

2001 classroom weights is 34,768. The unweighted 

and weighted completion rates are both 98%, 

excluding ineligibles from both numerator and 

denominator. Both unweighted and weighted eligibility 

rates are 94%. The class base weight was used in 

calculating the weighted rates. 

B. Child Weights 

Two sets of child weights were produced: a crosssectional set for fall 2000 estimates, and a fall 2000 ­

spring 2001 set for longitudinal analysis. Child base 

weights were first created that reflected the overall 

probability of selection for the child, including the 

program and classroom stages of sampling. These base 

weights were adjusted for child nonresponse, using the 

following criteria for a complete child case: 

Fall 2000 cross-sectional analysis: a child is 

considered a complete case if the child has a parent 

interview from either fall 2000 or spring 2001, and a 

fall 2000 child assessment or teacher rating. 

Fall 2000-Spring 2001 longitudinal analysis: a 

child is considered a complete case if the child has 

137 
Head Start FACES 2000 Appendix 

either a fall 2000 or spring 2001 parent interview, and 

one of the following data pairs: a child assessment for 

both fall 2000 and spring 2001, or a teacher rating for 

both fall 2000 and spring 2001. 

B1. Child Base Weights 

In 286 eligible fall 2000 classes, all eligible children in 

their first year of Head Start were taken into the 

sample with certainty. A base weight was created for 

each child as the product of their program weight and 

nonresponse-adjusted classroom weight. Note that 

these nonresponse adjusted class weights are not the 

same as those described earlier, which were designed 

for use in classroom level analysis. The creation of 

special classroom weights for the child weights was 

necessary because there were eligible classrooms that 

did not have complete classroom observations, but did 

allow their children to be sampled, and vice versa. To 

create this special classroom weight, the classroom 

base weight was adjusted for classes that had eligible 

children but where “sampling” of children did not take 

place.This nonresponse-adjusted classroom weight was 

then used in calculating the child base weight. Since 

there was no subsampling of children within 

classrooms, the within-classroom child weight is equal 

to one, and the overall child weight can be written as: 

Child Base Weight = Program Weight * Nonresponseadjusted Classroom Weight. 

A set of 43 jackknife (JKn) replicate base weights was 

also created for each child using the program replicate 

weights and the special full-sample nonresponseadjusted classroom weight: 

Child Replicate Base Weight j = Program Replicate 

Weight j * Nonresponse-adjusted Classroom Weight; j 

= 1, 2, ...43. 

B2. Child Fall 2000 Cross-Sectional Weights 

Of the 3,100 children in the fall 2000 sample, 2,535 

were considered completes for the fall 2000 data 

collection, 251 were eligible but incomplete (30 of 

these had assessments but no parent interview), and 

314 were ineligible. Children could be ineligible if they 

came from classrooms that were ineligible, or they 

were discovered to be in their second year of Head 

Start, or were otherwise ineligible when fall 2000 data 

collection began. 

The child base weights of the eligible, complete 

children in each classroom were adjusted for 

nonresponse separately by classroom. The ineligible 

and incomplete children were given a zero final child 

weight and were dropped from the sample for the 

spring 2001 data collection. The replicate child base 

weights were also adjusted for nonresponse by 

classroom, so that the sampling variability in the 

nonresponse adjustments will be reflected in the 

standard error estimates. 

The sum of the nonresponse-adjusted fall 2000 child 

weights is 337,247. The unweighted and weighted 

completion rates are both 91%, excluding ineligibles 

from both the numerator and denominator. The 

unweighted and weighted eligibility rates are 90% and 

91%, respectively. The child base weight was used in 

calculating the weighted rates. 

B3. Child Fall 2000-Spring 2001 Longitudinal 

Weights 

In spring 2001 the eligible first year children were 

again given assessments, a teacher rating, and an 

attempt was made to interview the child’s parent(s). 

Of the 2,535 eligible children who had completed fall 

2000 data collection, 2,359 were eligible, complete 

cases for the fall 2000 – spring 2001 data collection; 

171 were eligible, incompletes; and five became 

ineligible because they moved out of the area. 

Children who had switched to new classes in the spring 

2001 were followed up, but classroom observations 

were not done at the new classes. There were 91 

children from the fall 2000 sample who were followed 

138 
Appendix Head Start FACES 2000 

to 79 new classrooms in spring 2001. In calculating 

their base weights, these children were given the 

classroom probability of selection associated with the 

classroom from which they were originally sampled in 

fall 2000. 

The child base weights of the eligible, complete 

children in each classroom were adjusted for 

nonresponse separately by classroom. The ineligible 

and incomplete children were given a zero final child 

weight. The replicate child base weights were also 

adjusted for nonresponse by classroom, so that the 

sampling variability in the nonresponse adjustments 

will be reflected in the standard error estimates. 

The sum of the nonresponse-adjusted fall 2000–spring 

2001 child weights is 338,047. The unweighted and 

weighted conditional spring 2001 completion rates are 

both 93%. The conditional rate is the percent of fall 

2000 eligible completes who also completed the spring 

2001 data collection. The overall (unconditional) 

completion rate is the product of the completion rates 

for the fall 2000 and spring 2001 data collections: 91% 

* 93% = 85%. This rate is the percent of eligible, 

sampled children in fall 2000 who completed the 

spring 2001 data collection. 

IV. Data Collection Instruments 

A. Direct Child Assessment 

A1. Peabody Picture Vocabulary Test - Third 

Edition - Revised 

The Peabody Picture Vocabulary Test (PPVT-III) (Dunn 

& Dunn, 1997) is designed to assess children’s 

knowledge of the meaning of words by asking them to 

say or indicate by pointing which of four pictures best 

shows the meaning of a word that is said aloud by the 

assessor. A series of words is presented, ranging from 

easy to difficult for children of a given age, each 

accompanied by a picture plate consisting of four line 

drawings. The test requires about 10 minutes to 

administer. It is suitable for a wide range of ages from 

2 1⁄2 through adulthood and has established age norms 

based on a national sample of 2,725 children and adults 

tested at 240 sites across the U.S. 

The PPVT-III has been extensively revised from earlier 

versions of the test. These improvements were 

undertaken to promote easier testing and more 

accurate scoring. Also, new drawings have been added 

and dated illustrations dropped so as to achieve better 

gender and ethnic balance. Individual test items that 

showed statistical bias by race or ethnicity, gender, or 

region were deleted from the item pool for the scale 

prior to standardization. PPVT-III was reported to be 

highly reliable utilizing FACES data with internalconsistency reliability (alpha) coefficients ranging from 

.96 for fall 2000 to .97 for spring 2001. 

A Spanish-language test, the Test de Vocabulario en 

Imagenes Peabody (TVIP), is also available, but has not 

been updated to be directly comparable to the PPVT

III. For FACES, the TVIP was used with children 

whose primary language was Spanish. 

­

A screener was used to determine whether Englishlanguage learners were to be administered the direct 

child assessment battery in English or not. The 

screener involved information provided by teachers 

and assessors which was used to determine the 

language of administration. In fall 2000, Englishlanguage learners who were determined to be 

primarily Spanish-speaking, received the entire direct 

child assessment battery in Spanish, e.g., TVIP, 

Woodcock Munoz Letter-Word Identification,Applied 

Problems, Dictation, etc. They also were administered 

the PPVT and Woodcock Johnson Letter-Word 

Identification in English, as well. In spring 2001, these 

same children received the entire direct child 

assessment battery in English. They were also 

administered the TVIP and Woodcock Munoz Letter 

Word Identification in Spanish for the purpose of 

139 
Head Start FACES 2000 Appendix 

comparison. In fall 2000, English-language learners 

who were determined to primarily speak a language 

other than Spanish did not receive any portion of the 

direct child assessment battery in their native 

languages. In spring 2001, these same children 

received the entire direct child assessment battery in 

English. 

A2. Woodcock-Johnson Psycho-Educational 

Battery - Revised 

The updated edition of the Woodcock-Johnson Battery 

(WJ-R) is a carefully constructed and widely used test 

battery. The set of individually administered tests is 

designed to assess the intellectual and academic 

development of individuals from preschool through 

adulthood (Woodcock & Johnson, 1989; Salvia & 

Ysseldyke, 1991). FACES used three subtests from the 

Achievement Battery that together constitute an “Early 

Development—Skills” cluster, according to the test 

developers. The cluster is comprised of the LetterWord Identification, Applied Problems, and Dictation 

tests. The same three subtests of the Spanish version 

(Woodcock-Muñox Pruebas de Aprovechamiento-Revisada) 

were used in the Spanish version of the FACES 

assessment battery. 

Letter-Word Identification. The first five LetterWord Identification items involve symbolic learning, 

or the ability to match a rebus (pictographic 

representation of a word) with an actual picture of the 

object. The remaining items measure children’s 

reading identification skills in identifying isolated 

letters and words that appear in large type on the pages 

of the test book. As well as being part of the Early 

Development cluster, this subtest is also part of the 

Basic Reading Skills cluster.The internal consistency of 

the Letter-Word Identification subtest with FACES 

children averaged .84 for fall 2000 and .86 for spring 

2001. 

Letter Naming. The Letter Naming task is a test 

developed for use in the Head Start Quality Research 

Centers curricular intervention studies. Children are 

shown all 26 upper-case letters of the alphabet, divided 

into three groups of 8, 9, and 9 letters, arranged in 

approximate order of item difficulty.They are asked to 

identify the letters they know by name. It has the virtue 

of providing specific numeric information about how 

many letters Head Start children learn and which ones 

they are more or less likely to acquire. The Letter 

Naming task provides complementary information to 

the Woodcock Johnson Letter Word Identification task 

regarding children’s knowledge and awareness of 

letters. Children’s knowledge and awareness of letters 

is an essential prerequisite to their learning how to 

read. 

Applied Problems. This subtest measures children’s 

skill in analyzing and solving practical problems in 

mathematics. In order to solve the problems, the child 

must recognize the procedure to be followed and then 

perform relatively simple counting, addition, or 

subtraction operations. Because many of the problems 

include extraneous stimuli or information, the child 

must also decide which data to include in the count or 

calculation. As well as being part of the Early 

Development cluster, the subtest is also part of a Broad 

Mathematics cluster. The internal consistency of the 

Applied Problems subtest with FACES children 

averaged .90 for fall 2000 and .91 for spring 2001. 

Dictation. The first six items in this subtest measure 

prewriting skills, such as drawing lines and copying 

letters. The remaining items measure the child’s skill 

in providing written responses when asked to write 

specific upper- or lower-case letters of the alphabet. 

Later parts of the test ask the child to write specific 

words and phrases, punctuation, and capitalization.The 

internal consistency of the Dictation subtest with 

FACES children averaged .77 for both fall 2000 and 

spring 2001. 

A3. McCarthy Scales of Children’s Abilities 

The McCarthy Scales of Children’s Abilities is a widely 

140 
Appendix Head Start FACES 2000 

used and well-documented test battery. FACES 

employed one subtest from the battery, the Draw-ADesign Task. The Draw-A-Design Task was used to 

assess children’s perceptual-motor skills. This task asks 

the child to draw copies of a series of increasingly 

complex geometric figures. For FACES, this task was 

directly translated as part of the Spanish version of the 

assessment. The Draw-A-Design Task was reported, 

utilizing FACES data, with internal-consistency 

reliability (alpha) coefficients ranging from .58 for fall 

2000 to .68 for spring 2001. 

A4. Story and Print Concepts 

The Story and Print Concepts task was an adaptation of 

earlier prereading assessment procedures developed by 

Marie Clay (1979), William Teale (1988, 1990), and 

Mason and Stewart (1989). In these procedures, a 

child is handed a children’s storybook (FACES Battery 

- Where’s My Teddy? (Alborough, 1992) or ¿Dónde Está Mi 

Osito? (Alborough, Castro, Trans. 1992)) upside down 

and backwards. The assessor asks a series of questions 

designed to test the children’s knowledge of books. 

These include questions regarding the location of the 

front of the book, the point at which one should begin 

reading, and information relating to the title and 

author of the book. The assessor reads the story to the 

child and asks basic questions about both the mechanics 

(print conventions) of reading and the content 

(comprehension) of the story. The print conventions 

questions pertain to children’s knowledge of the leftto-right and up-and-down conventions of reading, 

while the comprehension questions pertain to 

children’s recall of key facts from the story. 

Additionally, for FACES, questions were added tapping 

rhyming awareness (e.g., “I’ll say some words from the 

story and you tell me whether they rhyme, OK - bawl 

and small, etc.”) and phonological awareness (e.g., 

“What word would be left if I took “teh” away from 

Ted?”). These additions were only included in the fall 

2000 direct child assessment battery. FACES 

reliabilities (internal consistencies) for these concepts 

for both the fall and the spring were as follows: 1.) 

Book Knowledge (.57 and .59); 2.) Print Conventions 

(.73 and .74); and 3.) Comprehension (.43 and .41). 

A5. Social Awareness 

This measure was adapted from a subtest of the 

Comprehensive Assessment Program (CAP) Early 

Childhood Diagnostic Instrument used by Snow et al. 

(1995) among others to test children’s general 

knowledge and awareness of the social environment. 

The child is asked to give his/her “full name,” which 

includes both first and last name, his/her age (either 

verbally, which is given full credit, or by holding up the 

correct number of fingers, which is given partial 

credit), and month/day of birth. The FACES 

reliabilities for the Social Awareness measure were .63 

for fall 2000 and .61 for spring 2001. 

A6. Color Names and One-to-One Counting 

This was also a subtest of the CAP Early Childhood 

Diagnostic Instrument used by Snow et al. (1995) and 

developed by Marie Clay (1979),William Teale (1988, 

1990) and Mason and Stewart (1989) as a battery of 

emergent literacy and school readiness measures. For 

the FACES battery, 10 teddy bears of different colors 

are presented randomly arranged on a page and the 

child is asked to point to each in turn and name the 

color. Following the Color Names task, the child is 

asked to count the bears and the assessor marks the 

final number the child arrives at when finished 

counting (correct answer is “10”). After this, the child 

is asked to report the total number of bears. The 

verbatim response is then recorded. Following these 

questions, the assessor must rate the child’s one-to-one 

counting performance using a 5-point scale. At the 

extremes, a score of 5 indicated that the child made no 

mistakes and score of 1 indicated that the child could 

not count or did not try to count. The FACES 

reliabilities for the Color Naming task were .95 for 

Fall, 2000 and .94 for Spring, 2001. 

141 
Head Start FACES 2000 Appendix 

A7. Leiter International Performance Scale -

Revised (Leiter-R) - Attention Sustained 

The Leiter-R by Roid and Miller (1997) assesses 

cognitive function in children and adolescents. The 

battery includes measures of nonverbal intelligence in 

fluid reasoning and visualization, as well as appraisals of 

visuospatial memory and attention. In spring 2001, the 

Leiter-R AS (Attention Sustained) Subtest was added 

to the FACES direct child assessment battery to permit 

assessments of children’s visuospatial memory and 

attention. The subtest is primarily nonverbal and is 

administered in two subsections—the first being for 

those 2-3 years of age and the second being for those 

4-5 years of age. Assessors provide minimal 

instructions throughout the administration of the 

Leiter-R AS. Children are presented with a series of 

pages containing pictures and are instructed to mark 

off all pictures that resemble a reference picture. The 

assessor times the child, with times ranging from 30 

seconds to 120 seconds allotted for completion of the 

tasks. FACES reliabilities for the Leiter Attention 

Sustained subtask by age groupings for spring 2001 

were as follows: 1.) 2 - 3 year old - .71; and 2.) 4 - 5 

year olds - .81. 

A8. Interviewer Ratings 

At the end of the one-on-one testing sessions with the 

children, the assessor completes a set of rating scales 

evaluating the child’s behavior in the test situation, 

including the child’s approaches to learning and 

problem behaviors. There are two sections to these 

ratings. The first consists of eight scales rating the 

child’s response during the assessment on eight 

different domains: task persistence, attention span, 

body movement, attention to directions, 

comprehension of directions, verbalization, ease of 

relationship, and the child’s level of confidence. 

Ratings use 4-point scales with descriptive anchors at 

each point. For example, the “task persistence” scale 

consists of the following anchor points: persists with 

task (4), attempts task briefly (3), attempts task after 

much encouragement (2), refuses (1). The FACES 

reliabilities for the Interviewer Ratings were .82 for 

fall 2000 and .81 for spring 2001. 

The second section asks the assessor to indicate any 

special concerns regarding the child’s ability to 

complete the assessment: responding nonverbally, 

using nonstandard English such as dialect, speaking 

English as a second language, having limited English 

proficiency, experiencing difficulty hearing or seeing 

the assessor/test materials, or reporting the child’s 

speech was difficult to understand. These items use 3­

point ratings to indicate the degree to which the child 

displayed any of these characteristics (i.e., “not at all,” 

“somewhat,” and “very much”). 

A9. Kindergarten Follow-Up ECLS-K Measures 

Two additional measures were included in the followup kindergarten assessment battery (spring 2002): the 

Reading scale and the General Knowledge scale, which 

were adapted from the Early Childhood Longitudinal 

Study - Kindergarten Cohort (ECLS-K). 

In ECLS-K, the Reading scale taps a variety of skills 

that indicate reading ability (including familiarity with 

print), recognition of letters and phonemes, 

vocabulary, and reading comprehension skills (e.g., 

children’s understanding of the text), as well as their 

personal reflection and critical evaluation of the text. 

The General Knowledge scale taps skills in the natural 

sciences (e.g., their conceptual understanding of why 

things occur as they do, and their ability to pose 

questions and investigate answers in the natural 

sciences) and social studies (e.g., their basic knowledge 

of History, Government, and Culture). Both scales 

follow the guidelines of the 1996 National Assessment 

of Educational Progress, have been reviewed by 

curriculum experts, as well as elementary school 

teachers, and have been found to be both reliable and 

valid measures of reading achievement and basic 

knowledge acquisition.29 

142 
Appendix Head Start FACES 2000 

The Reading assessment was administered in two 

stages. First, a routing test was administered to 

estimate the child’s reading ability. Based on his/her 

performance on the routing test (either “high,” 

“medium,” or “low”), an appropriate “second stage” test 

was administered. The Reading assessment had three 

levels of second stage tests: low (red), medium 

(yellow), and high (blue). For the General Knowledge 

assessment, each child was administered only the 

routing test. Estimates of reliability with FACES data, 

as measured by Cronbach’s coefficient alpha, will be 

provided at a later point when the data become 

available. 

B. Classroom Observation Instruments 

In FACES, two distinctive types of observation 

instruments (i.e., classroom observation and child 

observation) were used to measure peer interactions, 

friendships of children, and the extent to which Head 

Start programs employed skilled teachers and provided 

developmentally appropriate environments and 

curricula for their children. 

B1. Counts of Children and Adults 

The Counts of Children and Adults provide 

information needed to calculate child/adult ratios and 

for other calculations to be used in assessing specific 

measures of classroom quality. Classroom observers 

are tasked with counting the number of children (boys 

and girls), the number of paid staff, and the number of 

adult volunteers at two separate time periods during 

the classroom observation. The two counts must be at 

least an hour apart and must involve one structured 

(teacher-directed) activity and one unstructured 

activity. 

B2. Assessment Profile 

The Assessment Profile (Abbott-Shim & Sibley, 1987) 

is a structured observation guide designed to provide a 

quantitative assessment of classrooms and teaching 

practices that facilitate the learning and development 

of children. Three subscales were used in FACES: 

Scheduling, Learning Environment, and 

Individualizing. 

The Scheduling subscale assesses the written plans 

for classroom scheduling and how classroom activities 

are implemented. The appropriateness and 

completeness of the classroom activity plan are also 

noted. The subscale also assesses the balance and 

variety of learning contexts (e.g., individual, small 

group, and large group) and learning opportunities 

(i.e., child- vs. teacher-directed and active vs. quiet 

activities). The 14 observation items are scored in a 

yes/no format. High scores on this measure are 

indicative of a teacher that uses a “planful” approach to 

classroom activities. The reliability of the Scheduling 

subscale was reported as .89 for fall 2000 and .87 for 

spring 2001. 

The Learning Environment subscale focuses on the 

accessibility of a variety of learning materials to 

children in the classroom. Variety is assessed across 

various conceptual areas, such as science, math, 

language, fine motor, etc. and also within each 

conceptual area. The subscale also assesses how 

classroom space is arranged to determine whether the 

classroom encourages independence (e.g., whether the 

learning materials are located on low shelves and 

clearly labeled) and reflects the child as an individual. 

When materials are both available and accessible, and 

in sufficient numbers (typically a minimum of three in 

each group) the item is given a positive score. High 

scores on this 7-item measure indicate a “learning rich” 

environment, filled with toys and learning materials 

that address a variety of developmental domains. The 

For more information on the Reading and General Knowledge measures, please refer to the Early Childhood Longitudinal Study 

website at http://nces.ed.gov/ecls/. 

143 

29 
Head Start FACES 2000 Appendix 

reliability of the Learning Environment subscale was 

reported as .68 for fall 2000 and .77 for spring 2001. 

The Individualizing subscale focuses on the extent 

to which emphasis is placed on children, individually, 

in the classroom setting. This includes whether or not 

there are periodic individual assessments of each 

child’s performance using portfolios of his/her work, 

performance inventories, and teacher notations. Also 

included is whether or not child assessment 

information is used for planning individualized 

learning experiences. The final inclusion involves 

whether or not teachers have the ability to make 

provisions for children with special needs. The 

reliability of the Individualizing subscale was reported 

as .50 for fall 2000 and .54 for spring 2001. 

B3. Early Childhood Environment Rating ScaleRevised (ECERS-R) 

The Early Childhood Environment Rating ScaleRevised (ECERS-R) is a global rating of classroom 

quality based on structural features of the classroom 

(Harms & Clifford, 1980). It has been widely used in 

child development research and has predicted optimal 

child outcomes in a number of studies (e.g., Phillips, 

Voran, Kisker, Howes, & Whitebook, 1994). The 

revised version of the ECERS provides improvements 

to the items and represents an improvement on the 

standardization of the observational methods. In 

addition, the ECERS-R is easier to train and gain interrater reliability. The ECERS-R contains 37 items 

representative of classroom quality. Each item is coded 

on a 7-point scale with a score of 1 representing 

“inadequate,” a score of 3 representing “minimal 

quality,” a score of 5 representing “good quality,” and a 

score of 7 representing “excellent quality.” The internal 

consistency of the ECERS-R mean score for all 

combined items was .92 for both fall 2000 and spring 

2001. 

Seven subscales were derived from the ECERS-R for 

usage in analysis of FACES classroom quality, each 

pertaining to different elements of classroom quality. 

These are as follows: 1.) Personal Care Routines are 

measured using six items: greeting/departing, 

meals/snacks, nap/rest, toileting/diapering, health 

practices, and safety practices; 2.) Furnishings are 

measured using four items: indoor space, furniture for 

routine care, play, and learning, furniture for 

relaxation and comfort, and room arrangement for 

play; 3.) Language Skills are measured using four 

items: books and pictures, encouraging children to 

communicate, using language to develop reasoning 

skills, and informal use of language; 4.) Motor Skills 

are measured using four items: space for gross motor 

play, gross motor equipment, fine motor activities, and 

supervision of gross motor activities; 5.) Creativity is 

measured using six items: child-related display, art, 

music/movement, blocks, sand/water, and dramatic 

play; 6.) Social Skills are measured using four items: 

supervision, other than gross motor activity, discipline, 

staff-child interactions, and interactions among 

children; and 7.) Program Structure is measured using 

four items: space for privacy, schedule, free play, and 

group time. Five items were not incorporated into any 

of the subscales and are as follows: nature/science, 

math/numbers, use of TV, video, and/or computers, 

promoting acceptance of diversity, and provisions for 

children with disabilities. Thus there were only 32 of 

the 37 available items included in the subscales. 

A separate subscale, labeled ECERS-R Language, was 

comprised of four items and was devised to assess the 

quality of the language environment in Head Start 

classrooms. Additional information about this subscale 

can be found in Chapter 4. 

B4. Classroom Observation of Teacher-Directed 

Activities 

The Classroom Observation of Teacher-Directed 

Activities is a checklist completed by classroom 

observers of observed teacher-directed activities in 21 

specific areas, e.g., reading stories, singing songs, etc. 

The classroom observer indicates whether observed 

144 
Appendix Head Start FACES 2000 

activities were directed toward individual children 

(Individual Attention), a small group of children (Small 

Group = 3 to 8 children), or a whole group of children 

(Whole Group = entire classroom). Observers were 

instructed to mark down, only once for any item, any 

teacher-directed activities observed throughout the 

course of the classroom observation and if these 

observed activities were directed toward individuals, a 

small group of children, or the entire classroom. This 

checklist was introduced in spring, 2001. 

B5. Arnett Caregiver Interaction Scale 

The Arnett Caregiver Interaction Scale is a rating scale 

of teacher behavior towards the children in the 

classroom. It consists of 26 items that assess five areas 

of teacher behavior: sensitivity, punitiveness, 

detachment, permissiveness, and prosocial interaction 

(Arnett, 1989). The version of the Arnett Caregiver 

Interaction Scale utilized in the current round of 

FACES consists of 30 items and five subscales with the 

subscale labels being as follows: Sensitivity, Harshness, 

Detachment, Permissiveness, and Independence. At 

the end of the observational period, the observer 

completes the scale for an individual teacher, typically 

the lead teacher in the classroom. For example, in 

evaluating whether the teacher “speaks warmly to the 

children,” the observer will assign ratings indicating the 

extent to which the statement is characteristic of the 

teacher, from 1 “never seen” to 4 “always or almost 

always.” The Cronbach Coefficient Alpha for all of the 

items was .94 for fall 2000 and .69 for spring 2001. 

C. Teacher’s Child Ratings and Teacher 

Background 

Teacher ratings of children were important sources of 

information about children’s learning and behavior 

because teachers see children over extended periods of 

time and in a variety of settings. Using a rating form 

known as the Teacher’s Child Report (TCR), teacher’s 

were first asked to rate each child on a set of behaviors 

that assessed the child’s basic social skills and classroom 

behavior. In these two sections, the teacher is asked to 

indicate the extent to which a given statement (e.g., 

“follows the teacher’s directions”) is characteristic of 

the child, from 1 “never” to 3 “very often.” The items 

making up these ratings form two scales: 

C1. Cooperative classroom behavior: 

There are 12 ratings items for the teacher to indicate 

how often the child engages in cooperative classroom 

behaviors such as following teacher’s directions, 

helping put things away, complimenting classmate, and 

following rules when playing games. The ratings 

include items drawn from the Personal Maturity Scale 

(Alexander & Entwisle, 1988) and the Social Skills 

Rating System (Elliott, Gresham, Freeman, & 

McCloskey, 1988) to assess positive behavior such as 

cooperation, sharing, and expression of feelings. A 

summary score is created from the 3-point scale items 

which ranges from zero to 24, with high scores 

indicating more frequent cooperative behavior. The 

internal consistency for this measure was .88 in both 

fall 2000 and spring 2001. 

C2. Total behavior problems: 

The Behavior Problems scale is based on measures of 

negative child behaviors that are associated with 

learning problems and later grade retention. Items 

come from an abbreviated adaptation of the Personal 

Maturity Scale (Alexander & Entwisle, 1988), the 

Child Behavior Checklist for Preschool-Aged 

Children, Teacher Report (Achenbach, Edelbrock, & 

Howell, 1987) and The Behavior Problems Index (Zill, 

1990). The items ask about the frequency of aggressive 

behavior (e.g., hits/fights with others), hyperactive 

behavior (e.g., is very restless), and anxious or 

depressed and withdrawn behavior (e.g., is unhappy). 

The summary score from the scale’s 14 behavior items 

ranges from zero to 28, with higher scores 

representing more frequent or severe negative 

behavior. The reliabilities (internal consistency) for 

these measures for both Fall and Spring are as follows: 

145 
Head Start FACES 2000 Appendix 

1.) Total Problem Behaviors - .86 for both; 2.) 

Aggression - .83 and .85; 3.) Hyperactivity - .72 for 

both; and 4.) .77 and .76. 

The teacher is then asked to rate the child’s problem 

solving skills and initiative, social relationships, creative 

representations, music/movement skills, and 

language/math skills. The teacher is asked to rate the 

child’s highest level of behavior in each of the above 

domains observed in the past week. Scale points for 

each item are described on paper and there is a glossary 

that provides concrete examples of each anchor point. 

For the purpose of FACES, fourteen items from the 

Child Observation Record (COR; High/Scope 

Educational Research Foundation, 1992) were selected 

with a demonstrated reliability of .94 for both fall 

2000 and spring 2001. These 14-items were further 

divided up into the following scales: social 

relationships, creative representations, music and 

movement, and cognitive. 

C3. Social Relationships (3 items): 

A composite score based on teacher’s ratings of how 

well the child makes friends, works with other 

children, and understands and expresses feelings. Each 

item is rated on a five-point scale with higher scores 

representing greater skill in coping with social 

situations and expressing feelings appropriately. The 

summary score is the average of the three items and 

ranges from one to five. The measure shows good 

reliability with the FACES study, with Alpha 

Coefficients of .83 for both fall 2000 and spring 2001. 

C4. Creative Representations (3 items): 

A composite score based on the teacher’s ratings of 

how well the child uses creative materials for selfexpression in making and building things, drawing and 

painting, and engaging in pretend play. Each item is 

rated on a five-point scale with higher scores 

representing greater proficiency. The summary score 

is the average of the three items and ranges from one 

to five. The measure shows good reliability with the 

FACES study, with Alpha Coefficients of .80 for fall 

2000 and .81 for spring 2001. 

C5. Music and Movement (4 items): 

A composite score based on teacher’s ratings of how 

well the child can imitate movements to a steady beat, 

follow music and movement directions, exhibit body 

coordination, and manipulate small objects and 

perform precise actions. Each item is rated on a fivepoint scale with higher scores representing greater 

proficiency. The summary score is the average of the 

four items and ranges from one to five. The measure 

shows good reliability with the FACES study, with 

Alpha Coefficients of .88 for both fall 2000 and spring 

2001. 

C6. Cognitive (4 items): 

A composite score based on teacher’s ratings of how 

well the child can solve problems, engage in complex 

play, show interest in reading, and exhibit classification 

skills by sorting objects. Each item is rated on a fivepoint scale with higher scores representing greater 

proficiency. The summary score is the average of the 

four items and ranges from one to five. The measure 

shows good reliability with the FACES study, with 

Alpha Coefficients of .82 for fall 2000 and .83 for 

spring 2001. 

The Lead Teacher Background Information 

consists of questions asking the teacher about 

himself/herself, including sociodemographic and 

educational background and professional experience. 

Information about the curriculum being used, his/her 

attitude and knowledge about early childhood 

education practice (see Teacher Beliefs Scale write-up 

referenced in Chapter 4), and accommodations he/she 

has made or that others have made to meet the learning 

needs of children in his/her classroom, particularly 

children with special needs, are included, as well. 

D. Parent Interview 

Data from the FACES Parent Interview, administered 

146 
Appendix Head Start FACES 2000 

in fall 2000 and spring 2001, provide Head Start with 

a comprehensive understanding of the families that 

they serve, including the characteristics of households 

and household members, levels and types of 

participation in the program and in other community 

services, involvement with their children, and 

understanding of their children’s development. 

Parents were also asked to rate each child on a set of 

behaviors that assessed the child’s basic social skills and 

behavior problems. In this section, the parent is asked 

to indicate the extent to which a given statement (e.g., 

“makes friends easily”) is characteristic of the child, 

from 1 “not true” to 3 “very true or often true.” The 

items making up these ratings were drawn from two 

well-known measures of children’s positive behavior 

and behavior problems: the Entwisle scale of Personal 

Maturity (Entwisle, Alexander, Cadigan, & Pallis, 

1987) and the Child Behavior Checklist for PreschoolAged Children (Achenbach, Edelbrock, & Howell, 

1987). Two scales were formed to assess children’s 

social competence: 

D1. Social skills and positive approaches to 

learning: 

Parents were asked to rate their child’s social skills and 

positive approaches to learning by describing their 

children’s skills in making friends and accepting their 

ideas, as well as enjoying learning and trying new 

things. A summary score based on the scale’s seven 

items ranges from zero to 14, with higher scores 

representing more positive behavior.Table A-10 shows 

the reliabilities for the Social Skills measure in both fall 

2000 and spring 2001. 

D2. Total Problem Behaviors: 

Parents were also asked to rate their children on 

negative behaviors that are relatively common among 

preschool children and that are associated with 

adjustment problems in elementary school. Parents 

were asked about three domains of problem behavior: 

hyperactive behavior, aggressive behavior, and 

depressed or withdrawn behavior. The 12 behavior 

items were combined in a summary score ranging from 

zero to 24, with higher scores representing more 

frequent or severe negative behavior. Table A-10 shows 

the reliabilities for all of these behavior problem 

measures in both fall 2001 and spring 2001. 

D3. Other Parent Interview Scales/Measures 

Referenced in the Report: (see chart on next page) 

V. Field Staff Training 

A weeklong training was conducted prior to each data 

collection period to prepare field staff for successful 

completion of data collection. The training included a 

wide variety of activities covering all the procedures, 

techniques, and contents required to carry out 

successful data collection in the Head Start centers: 

• Lecture, incorporating slides, overheads, and 

videotapes; 

• Exercises that simulate various procedures such as 

assessing classroom scheduling; 

• Video demonstration of assessment techniques and 

components of classroom scoring procedures; 

• Exercises to achieve pre-established levels of interrater reliability; 

• Participatory involvement of all trainees in small 

groups so that trainers may evaluate individual 

performance; 

• Multiple occasions of practice in real classroom 

settings that simulate what they are expected to do in 

the field, with the presence of a trainer and a small 

group of trainees to discuss the classroom ratings and 

provide valuable guidance on scoring reliability and 

agreement; and 

• One-on-one practice and role-play in the 

administration of child assessment procedures under 

supervision of training staff. 

147 
Head Start FACES 2000 Appendix 

NAMES AND SOURCES FOR OTHER PARENT INTERVIEW SCALES/MEASURES REFERENCED IN THE REPORT 

Name Source 

Pearlin Mastery Scale (Locus of Control) Pearlin, L. I. and Schooler, C. (1978). The structure of coping. 

Journal of Health and Social Behavior, 22, 337-356. 

CES-D Depression Scale Radloff, L. S. (1977). The CES-D: A self-report depression scale for 

research in the general population. Applied Psychological 

Measurement, 1, 385-401. 

Family Activities with Children National Household Education Survey - FACES Research Team 

Parental Involvement in Head Start Head Start Quality Research Consortium (QRC) 

Exposure to Violence FACES Research Team 

Domestic Violence Screener Feldous, K. M., Koziol-McLain, J., Amsbury, H. L. et. al. (1997). 

Accuracy of three brief screening questions for detecting partner 

violence in the emergency room. JAMA, 227(17), 1357. 

Substance Abuse Screener Administration for Children and Families (1997). National Impact 

Evaluation of the Comprehensive Child Development Program. 

Washington, D.C.: U.S. Department of Health and Human Services. 

Involvement with Criminal Justice System FACES Research Team 

Parenting Style National Longitudinal Study of Youth (NLSY), Early Head Start 

Evaluation (EHS), QRC 

The field procedures manual contained information 

about working with a research team, appropriate 

behaviors within a classroom, and how to orchestrate 

Head Start center visits. Moreover, the manual 

covered an overview of all data collection instruments 

and administrative and travel procedures. Complete 

scoring rules and question-by-question specifications 

for the child assessment and child and classroom 

observation instruments were also discussed in the 

manual. 

During the training, trainees were introduced to the 

purpose and goals of the study and background 

information on Head Start. Trainees were also 

introduced to the data collection materials and general 

issues regarding children and early childhood learning 

environments. Each day of training included a 

morning question and answer period regarding the 

previous day’s training, a daily review of the current 

day’s material, and a brief discussion of the next day’s 

events. 

An additional practice session was given to provide 

trainees with more practice in either observation or 

assessment. Assignment of this practice was based on 

the measures in which the trainees needed more 

practice. For administering child assessments in 

Spanish, a special training for English-Spanish speaking 

trainees was held. The bilingual trainees had an 

opportunity to practice assessments with Spanishspeaking children. 

VI. Data Collection Procedures 

A. Site Visit Arrangements 

The research team obtained feasible dates for the 2­

week site visit from each of the sampled Head Start 

programs. Site visit dates for each program were 

coordinated within the data collection period and 

programs were notified about the visit dates. Three 

weeks before the site visit, a scheduling packet which 

contained the final visit schedule, a master list, 

148 
Appendix Head Start FACES 2000 

organized by classroom, a reminder list, and a request 

for maps and directions to aid the research team was 

sent to the on-site coordinator (OSC). OSCs are 

members of the Head Start program staff specially 

designated to coordinate the data collection efforts by 

scheduling parent interviews, classroom visits with 

program teachers, and obtaining consent forms, 

among other related duties. 

VII. Quality Control Visits 

In FACES, Quality Control (QC) visits were built into 

every step of the data collection to ensure the highest 

quality data possible. The QC visitors consisted of the 

FACES project staff who were involved in designing 

the instruments, preparing the training materials, and 

conducting the training. The QC visitors were trained 

in both observation and assessment data collection and 

also served as technical consultants in the field. During 

the fall 2000 data collection, one 3-day QC visit to 

program sites was made. 

VIII. Data Preparation & Data File 

Creation 

A. Data Entry 

Key entry and verification were performed on the 

study instruments using a sophisticated production 

data entry system. This system provides entry form 

layout, application of edit specification, data 

verification control, and provides data entry quality 

and production reports. 

B. Frequency Review 

The frequencies of responses to all data items (both 

individually and in conjunction with related data items) 

were reviewed to ensure that appropriate skip patterns 

were followed. Members of the data preparation team 

checked each item to make sure the correct number of 

responses was represented for all items. If a 

discrepancy was discovered, the problem case was 

identified and reviewed. 

C. Data Edit 

To code and edit questionnaire data, an integrated 

collection of software was utilized. Through this 

system of software, coding manuals and codebooks 

were developed, data editing was performed, and SAS 

source code was generated. 

D. Data File Creation 

Data files were created and analysis performed to 

provide summaries and assessments of Head Start 

children and their families during this period and to 

assess the reliability and validity of information 

contained within the data collection instruments. 

Numerous derived variables were created to increase 

the magnitude and scope of analytical capabilities. The 

coding for these derived variables may be obtained 

upon request. 

IX. Reliability and Data Summary 

In FACES, various data collection instruments were 

used to assess the accomplishments and behaviors of 

children in Head Start programs, as well as the 

educational and familial support that is provided to 

them. As noted in Section IV: Data Collection 

Instruments, these instruments are widely used and 

report mostly high reliabilities. The reliabilities for 

each data collection instrument and summaries for 

these data collection instruments are provided in the 

following Tables:Table A-2 - Tables A-11. 

149 
Head Start FACES 2000 Appendix 

Table A-1. Summary of Measures Administered from Fall 2000 to Spring 2001 

Fall 2000 

(Head Start) 

Spring 2001 

(Head Start) 

Social Awareness Social Awareness 

PPVT-III / TVIP PPVT-III / TVIP 

McCarthy Draw-A-Design McCarthy Draw-A-Design 

----- Leiter-R AS (Attention Sustained) Subset 

Color Names and Counting Color Names and Counting 

Woodcock Johnson (Munoz): 

Letter-Word Identification 

Woodcock Johnson (Munoz): 

Letter-Word Identification 

Woodcock Johnson (Munoz): Applied Problems Woodcock Johnson (Munoz): Applied Problems 

Woodcock Johnson (Munoz): Dictation Woodcock Johnson (Munoz): Dictation 

Story and Print Concepts Story and Print Concepts 

Interviewer Rating: Assessment Behavior Interviewer Rating: Assessment Behavior 

Table A-2. Reliability of Fall 2000 and Spring 2001 FACES Child Assessment Data - English Assessments 

Only (Spring 2001 Leiter Results are for Children Assessed in Both Eng. & Span.) 

Fall 2000 Spring 2001 

Scales Number 

of Items 

Number 

of Cases 

Cronbach 

Alphas 

Number 

of Items 

Number 

of Cases 

Cronbach 

Alphas 

Social Awareness 5 2,068 .63 5 1,948 .61 

PPVT-III 144 2,116 .96 144 1,980 .97 

McCarthy: Draw-A-Design 9 2,068 .58 9 1,943 .68 

Leiter-R AS - Ages 2 to 3 - - - 4 406 .71 

Leiter-R AS - Ages 4 to 5 - - - 4 1,758 .81 

Color Names 10 2,055 .95 10 1,940 .94 

WJR: Letter-Word Identification 23 1,054 .84 23 1,595 .86 

WJR: Applied Problems 23 1,054 .90 23 1,595 .91 

WJR Dictation 12 1,054 .77 12 1,595 .77 

Story and Print Concepts: 

Print Conventions 

2 2,116 .73 2 1,980 .74 

Story and Print Concepts: 

Book Knowledge 

5 2,116 .57 5 1,980 .59 

Story and Print Concepts: 

Comprehension 

2 2,116 .43 2 1,980 .41 

Interviewer Rating: 

Assessment Behavior 

8 2,021 .82 8 1,901 .81 

150 
Appendix Head Start FACES 2000 

Table A-3. Reliability of Fall 2000 and Spring 2001 FACES Child Assessment Data - Spanish Assessments 

Only (Spring 2001 Leiter Results are Referenced in Table A-2.) 

Fall 2000 Spring 2001 

Scales Number 

of Items 

Number 

of Cases 

Cronbach 

Alphas 

Number 

of Items 

Number 

of Cases 

Cronbach 

Alphas 

Social Awareness 5 385 .36 5 356 .45 

TVIP 144 392 .92 144 364 .92 

McCarthy: Draw-A-Design 9 375 .57 9 355 .74 

Leiter-R AS - Ages 2 to 3 - - - - - -

Leiter-R AS - Ages 4 to 5 - - - - - -

Color Names 10 378 .92 10 358 .93 

WM: Letter-Word Identification 23 219 .75 23 307 .78 

WM: Applied Problems 23 219 .85 231 307 .89 

WM: Dictation 12 219 .77 121 307 .73 

Story and Print Concepts: 

Print Conventions 

2 392 .59 2 364 .77 

Story and Print Concepts: 

Book Knowledge 

5 392 .43 5 364 .48 

Story and Print Concepts: 

Comprehension 

2 392 .39 2 364 .43 

Interviewer Rating: 

Assessment Behavior 

8 372 .77 8 353 .68 

1 Spring 2001 Applied Problems & Dictation are Woodcock Johnson, not Woodcock Munoz 

151 
Table A-4. Summary Statistics for Fall 2000 and Spring 2001 FACES Child Assessment Data - English Assessments Only

(Spring 2001 Leiter Results are for Children Assessed in Both Eng. & Span.)

Fall 2000 Spring 2001 

Scales Number

of Cases 

Mean SD Reported

Response

Range 

Possible

Response

Range 

Number

of Cases 

Mean SD Reported

Response

Range 

Possible

Response

Range 

Social Awareness 2,101 3.36 1.69 0 - 6 0 - 6 1,967 3.98 1.58 0 - 6 0 - 6 

PPVT-III* 2,031 35.06 17.65 0 - 98 0 - 144 1,932 45.30 18.72 1 - 98 0 - 144 

McCarthy: Draw-A-Design 2,112 2.92 1.33 0 - 13 0 - 19 1,980 3.52 1.70 0 - 15 0 - 19 

Leiter-R AS - Ages 2 to 5 - - - - - 2,253 40.72 10.79 1 - 70 0 - 70 

Color Names 2,101 11.32 7.37 0 - 20 0 - 20 1,969 15.59 5.98 0 - 20 0 - 20 

WJR: Letter-Word Identification* 948 5.30 2.61 0 - 21 0 - 23 1,511 6.59 3.19 0 - 22 0 - 23 

WJR: Applied Problems* 963 7.52 4.36 0 - 21 0 - 23 1,542 8.98 4.70 0 - 22 0 - 23 

WJR: Dictation* 916 5.11 1.83 0 - 12 0 - 12 1,491 5.64 2.11 0 -12 0 - 12 

Story and Print Concepts:Print Conventions 

2,089 0.23 0.57 0 - 2 0 - 2 1,968 0.37 0.69 0 - 2 0 - 2 

Story and Print Concepts:Book Knowledge

2,087 1.62 1.27 0 - 5 0 - 5 1,961 2.41 1.30 0 -5 0 - 5 

Story and Print Concepts:Comprehension

2,102 0.54 0.70 0 - 2 0 - 2 1,967 0.71 0.75 0 - 2 0 - 2 

Interviewer Rating:Assessment Behavior 

2,094 17.14 5.02 0 -24 0 - 24 1,950 19.05 4.38 0 - 24 0 - 24 

*Raw scores were used. 

152 
Table A-5. Summary Statistics for Fall 2000 and Spring 2001 FACES Child Assessment Data

Spanish Assessments Only(Spring 2001 Leiter Results are Referenced in Table A-4.)

Fall 2000 Spring 2001 

Scales Number

of Cases 

Mean SD Reported

Response

Range 

Possible

Response

Range 

Number

of Cases 

Mean SD Reported

Response

Range 

Possible

Response

Range 

Social Awareness 390 2.62 1.21 0 - 6 0 - 6 360 2.56 1.30 0 - 6 0 - 6 

TVIP* 369 11.34 8.38 1 - 47 0 - 144 322 16.27 10.04 1 - 48 0 - 144 

McCarthy: Draw-A-Design 392 3.37 1.34 0 - 13 0 - 19 364 4.05 1.90 0 -12 0 - 19 

Leiter-R AS - Ages 2 to 5 - - - - - - - - - -Color Names 386 8.90 6.62 0 - 20 0 - 20 362 13.46 6.49 0 - 20 0 - 20 

WM: Letter-Word Identification* 195 4.37 1.20 0 - 10 0 - 23 295 5.01 1.68 0 - 12 0 - 23 

WM: Applied Problems* 200 5.29 3.40 0 - 14 0 - 231 294 5.81 4.13 0 - 19 0 - 23 

WM: Dictation* 188 4.99 1.28 1 - 11 0 - 121 293 5.72 1.74 0 - 11 0 - 12 Story and Print Concepts:Print Conventions 

391 0.17 0.53 0 - 3 0 - 2 360 0.14 0.47 0 - 2 0 - 2 

Story and Print Concepts:Book Knowledge

376 1.25 1.13 0 - 5 0 - 5 355 1.70 1.13 0 - 5 0 - 5 

Story and Print Concepts:Comprehension

386 0.47 0.67 0 -2 0 - 2 360 0.50 0.68 0 - 2 0 - 2 

Interviewer Rating:Assessment Behavior 

383 17.54 4.41 0 -24 0 - 24 360 17.99 3.50 3 - 24 0 - 24 

*Raw scores were used.

1 Spring 2001 Applied Problems & Dictation are Woodcock Johnson, not Woodcock Munoz

153 
Head Start FACES 2000 Appendix 

Table A-6. Reliability of Fall 2000 and Spring 2001 FACES Classroom Observation Data Selected Measures 

Fall 2000 Spring 2001 

Scales Number 

of Items 

Number 

of Cases 

Cronbach 

Alphas 

Number 

of Items 

Number 

of Cases 

Cronbach 

Alphas 

Assessment Profile: Scheduling 14 227 .89 14 243 .87 

Assessment Profile: 

Learning Environment 

18 228 .68 18 228 .77 

Assessment Profile: 

Individualizing 

5 250 .50 5 250 .54 

ECERS Total Mean 37 270 .92 37 235 .92 

Personal Care 6 146 .73 6 269 .70 

Furnishings 4 263 .52 4 263 .60

Language 4 260 .77 4 272 .76

Motor Skills 4 248 .67 4 274 .64 

Creative 6 253 .60 6 262 .71 

Social 4 264 .86 4 269 .91

Program Structure 4 256 .60 4 261 .69 

Arnett Scale of Caregiver 

Behavior: Lead Teacher (Total) 

30 256 .94 30 258 .94 

Sensitivity 10 262 .94 10 266 .94 

Harshness 9 265 .83 9 271 .82 

Detachment 4 266 .71 4 272 .82 

Permissiveness 3 266 .52 3 274 .53 

Independence 4 263 .58 4 269 .56 

154 
Table A-7. Summary Statistics for Fall 2000 and Spring 2001 FACES Classroom Observation Data

Selected Measures 

Fall 2000 Spring 2001 

Scales Number

of Cases 

Mean SD Reported

Response

Range 

Possible

Response

Range 

Number

of Cases 

Mean SD Reported

Response

Range 

Possible

Response

Range 

Assessment Profile: Scheduling 270 11.12 3.20 0 -14 0 - 14 270 11.00 3.15 3 - 14 0 - 14 

Assessment Profile:Learning Environment

270 14.44 2.57 5 - 18 0 - 18 274 14.22 2.92 4 - 18 0 -18 

Assessment Profile:Individualizing

260 3.58 1.15 0 - 5 0 - 5 271 3.42 1.11 0 - 5 0 - 5 

ECERS Total Mean 258 4.84 0.87 1.8 - 6.7 1.0 -7.0 265 4.91 1.00 1.9 - 6.9 1.0 - 7.0 

Personal Care 261 5.22 1.33 1.0 - 7.0 1.0 -7.0 273 4.84 1.63 1.0 - 7.0 1.0 - 7.0 

Furnishings 268 5.48 1.01 1.3 - 7.0 1.0 -7.0 266 5.52 1.02 2.3 - 7.0 1.0 - 7.0 

Language 270 4.86 1.20 1.0 - 7.0 1.0 -7.0 274 5.01 1.27 1.0 - 7.0 1.0 - 7.0 

Motor Skills 264 4.79 1.27 1.3 - 7.0 1.0 -7.0 277 4.89 1.34 1.3 - 7.0 1.0 - 7.0 

Creative 268 4.31 0.90 2.0 - 6.3 1.0 -7.0 277 4.26 1.02 1.8 - 6.7 1.0 - 7.0 

Social 266 5.39 1.38 1.3 - 7.0 1.0 -7.0 276 5.59 1.56 1.0 - 7.0 1.0 - 7.0 

Program Structure 268 4.87 1.24 1.0 - 7.0 1.0 -7.0 276 5.12 1.28 1.0 - 7.0 1.0 - 7.0 

Arnett Scale of CaregiverBehavior: Lead Teacher (Total)

267 71.48 12.42 26 - 90 0 - 90 274 72.84 12.99 20 - 89 0 - 90 

Sensitivity 262 20.99 6.52 1 - 30 0 - 30 266 21.74 6.42 3 - 30 0 - 30 

Harshness 265 24.28 3.22 4 - 27 0 - 27 271 24.34 3.16 6 - 27 0 - 27 Detachment 266 11.06 1.54 4 - 12 0 -12 272 11.08 1.79 2 - 12 0 - 12 

Permissiveness 266 7.47 1.37 1 - 9 0 - 9 274 7.39 1.45 2 - 9 0 - 9 Independence 263 7.77 2.31 1 - 12 0 - 12 269 8.39 2.32 1 - 12 0 - 12 

155 
Head Start FACES 2000 Appendix 

Table A-8. Reliability of Fall 2000 and Spring 2001 FACES Teacher’s Child Report Data Selected Measures 

Fall 2000 Spring 2001 

Scales Number 

of Items 

Number 

of Cases 

Cronbach 

Alphas 

Number 

of Items 

Number 

of Cases 

Cronbach 

Alphas 

Social Skills 12 2,522 .88 12 2,254 .88 

Behavioral Problems (Total) 14 2,522 .86 14 2,254 .86 

Withdrawn 7 2,522 .77 7 2,254 .76 

Aggressive 4 2,522 .83 4 2,254 .85 

Hyperactive 3 2,522 .72 3 2,254 .72 

Child Observation Record (Total) 14 2,522 .94 14 2,254 .94 

Social Relationships 3 2,522 .83 3 2,254 .83 

Creative Representations 3 2,522 .80 3 2,254 .81 

Music and Movement 4 2,522 .88 4 2,254 .88 

Cognitive 4 2,522 .82 4 2,254 .83

156 
Table A-9. Summary Statistics for Fall 2000 and Spring 2001 FACES Teacher’s Child Report Data

Selected Measures 

Fall 2000 Spring 2001 

Scales Number

of Cases 

Mean SD Reported

Response

Range 

Possible

Response

Range 

Number

of Cases 

Mean SD Reported

Response

Range 

Possible

Response

Range 

Social Skills 2,518 14.52 4.85 0 - 24 0 - 24 2,251 16.58 4.63 0 -24 0 - 24 

Behavioral Problems (Total) 2,484 5.76 5.06 0 - 28 0 - 28 2,215 5.30 4.89 0 - 27 0 - 28 

Withdrawn 2,453 2.59 2.65 0 - 14 0 - 14 2,186 2.34 2.49 0 -14 0 - 14 

Aggressive 2,449 1.80 2.04 0 - 8 0 - 8 2,189 1.74 2.03 0 - 8 0 - 8 

Hyperactive 2,466 1.37 1.53 0 - 6 0 - 6 2,194 1.21 1.47 0 - 6 0 - 6 

Child Observation Record (Total) 2,403 2.82 0.77 1 - 5 1 - 5 2,174 3.50 0.81 1 - 5 1 - 5 

Social Relationships 2,464 2.83 1.01 1 - 5 1 - 5 2,230 3.51 0.99 1 - 5 1 - 5 

Creative Representations 2,459 2.83 0.83 1 - 5 1 - 5 2,230 3.52 0.87 1 - 5 1 - 5 

Music and Movement 2,446 2.95 0.85 1 - 5 1 - 5 2,217 3.65 0.89 1 - 5 1 - 5 

Cognitive 2,403 2.75 0.82 1 - 5 1 - 5 2,184 3.41 0.89 1 - 5 1 - 5 

157 
Head Start FACES 2000 Appendix 

Table A-10. Means and Standard Deviations for Scales From the Parent Interview and the Teachers’ Ratings 

of Children’s Behavior 

Fall 2000 Spring 2001 

Scales Mean1 SD N Mean SD N 

CES-D 6.76 6.67 2473 6.57 6.31 2285 

Pearlin-Mastery Score 14.80 3.27 2471 15.28 3.31 2284 

Exposure to Violence 6.08 1.85 2485 Not Asked 

Weekly Activities with Children 5.91 1.14 2485 6.10 1.05 2290 

Monthly Activities with Children 1.93 1.48 2485 2.30 1.58 2290 

Emergent Literacy 1.97 1.45 2469 2.99 1.50 2290 

Authoritative Parenting Style 4.22 0.61 2483 4.23 0.59 2284 

Authoritarian Parenting Style 2.20 0.71 2482 2.17 0.69 2283 

Positive Social Behavior - Parent Report 12.14 1.74 2479 12.14 1.77 2287 

Problem Behavior- Parent Report 6.16 3.57 2480 5.61 3.56 2287 

Aggressive Behavior- Parent Report 3.15 1.74 2468 2.83 1.73 2275 

Hyperactive Behavior- Parent Report 1.86 1.51 2471 1.65 1.47 2275 

Withdrawn Behavior- Parent Report 0.60 0.94 2459 0.62 0.93 2267 

Problem Behavior- Teacher Report 5.64 5.01 2484 5.23 4.89 2215 

Aggressive Behavior- Teacher Report 1.75 2.02 2449 1.72 2.03 2189 

Hyperactive Behavior- Teacher Report 1.36 1.52 2466 1.20 1.46 2194 

Withdrawn Behavior- Teacher Report 2.51 2.64 2453 2.30 2.48 2186 

Satisfaction with Head Start --- --- --- 30.88 4.15 2185 

Positive Experience with Head Start --- --- --- 49.97 6.71 2185 

Involvement with Head Start --- --- --- 31.30 6.02 2181 

1 Means and Standard Deviations are based on weighted data. 

158 
Appendix Head Start FACES 2000 

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