1

Preparing Youth for the Future: The Literacy of 

America’s Young Adults 

Authors 

Ying Jin 

Joanna Kling 

American Institutes for Research

June 2009 

Contents 

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

Profile of Young Adults 

Ages 16-18 and 19-24 ......... 1

Background Characteristics ....1

Prose and Quantitative 

Literacy ...................................3

Characteristics of Young 

Adults Who Dropped Out of 

High School......................... 4

Background Characteristics ....4

Prose and Quantitative 

Literacy ...................................5

Characteristics Most 

Associated With Low Prose 

Literacy of Young Adults .. 6

Summary............................. 6

References........................... 7

Appendix A: Methodology 

and Technical Notes....... A-1

Descriptions of Background 

Variables .............................A-1

Statistical Procedures..........A-2

Estimation of Literacy 

Proficiency ..........................A-3

 

Introduction 

Young adults are among the most recent entrants into the labor force. Many out-of-school 

young adults, especially those with no postsecondary schooling, have encountered severe 

difficulties in obtaining access to career jobs (Sum, Fogg, and Mangum 2000). Improving the 

literacy of young adults is among the proven strategies for enhancing their labor market 

prospects. Knowing the literacy proficiencies of America’s young adults has become 

increasingly important for providing guidance to public policymakers about effective literacy 

instruction among this population group. 

Using data collected from the 2003 National Assessment of Adult Literacy (NAAL), this report 

examines the characteristics and literacy proficiency of America’s young adults ages 16-18 and 

19-24, with particular attention to high school drop-outs within these age cohorts. This report 

also compares the literacy of young adults with that of older age cohorts and identifies 

characteristics that are most associated with the low literacy of America’s young adults. Many 

of the background variables examined in this report are based on self-reported data, and because 

many of the variables are related to one another, complex interactions and relationships among 

them cannot be explored. Therefore, readers are cautioned not to draw causal inferences based 

solely on the results presented here. 

The 2003 NAAL assessed the English literacy of adults (ages 16 and older) in the United States 

for the first time since the 1992 National Adult Literacy Survey. The NAAL provided 

information on the literacy proficiencies of approximately 18,000 adults living in households 

and 1,200 prison inmates. In the household sample, 2,960 adults were between ages 16 and 24 

and formed the young adult analysis sample in this report. In addition to assessing the literacy 

skills of respondents, the NAAL gathered extensive background information on their 

demographic and socioeconomic characteristics (e.g., their age, gender, nativity status, 

schooling, labor force status, household income), as well as on their literacy practices. 

The NAAL measured respondents’ proficiencies on three literacy scales: prose, document, and 

quantitative. For each, proficiency was measured on a scale that ranged from 0 to 500. Scores 

on each of the three literacy scales were characterized in terms of four literacy proficiency 

levels: Below Basic, Basic, Intermediate, and Proficient.

1

 Given the scope of this report, the 

analyses focused on the prose and quantitative literacy scales; in some sections, the analyses 

focused on the prose literacy scale only. A detailed description of background variables and 

methodology used in this report is provided in the Appendix A: Methodology and Technical 

Notes. 

Profile of Young Adults Ages 16-18 and 19-24 

Background Characteristics 

Demographic Characteristics, Educational Attainment, and Computer Literacy 

Table 1 presents the percentage distribution of young adults ages 16-18 and 19-24 by selected 

characteristics. Based on self-reported data on highest level of educational attainment, although 

nearly half of young adults ages 16-18 were still in high school and 23 percent had completed 

high school or high school equivalency, nearly one quarter still lacked a high school diploma or 

a General Educational Development (GED) certificate. Among young adults ages 19-24, some 

16 percent lacked a high school diploma or a GED certificate. 

 

1 For an interpretation of the literacy scales and performance levels on the NAAL assessment, see Kutner, M., Greenberg, E., Jin, Y., Boyle, B., 

Hsu, Y., and Dunleavy, E. (2007). Literacy in Everyday Life: Results From the 2003 National Assessment of Adult Literacy (NCES 2007-48). 

U.S. Department of Education. Washington, DC: National Center for Education Statistics.
2

Table 1. Percentage distribution of young adults ages 

16-18 and 19-24, by selected characteristics: 

2003 

Characteristic 

Young 

adults 

16-18 

Young 

adults 

19-24 

Gender 

Male 52 52 

Female 48 48 

Race/ethnicity 

White 62 58 

Black 12 15 

Hispanic 17 20 

Other 9 8 

Highest educational attainment 

Still in H.S. 48 — 

Less than/some high school 24 16 

H.S. grad/GED/equivalency 23 33 

Postsecondary but less than 4-year 

college 5 40 

College grad/graduate studies/degree — 10 

Language spoken before starting 

school 

English only 81 77 

English and other 8 8 

Other only 11 16 

Age learned English if not spoken 

before starting school 

1-10 63 39 

11-15 — 22 

16 or older — 16 

Does not speak English — 24 

Computer literacy 

At least some computer literacy 92 81 

No computer literacy 8 20 

Employment status 

Employed full time 11 47 

Employed part time 28 22 

Employed not at work — 3 

Unemployed 13 12 

Out of labor force 44 16 

See notes at end of table.

Table 1. Percentage distribution of young adults ages 

16-18 and 19-24, by selected characteristics: 

2003—Continued 

Characteristic 

Young 

adults 

16-18 

Young 

adults 

19-24 

Occupation 

Management/Business/Financial — 4 

Professional and related — 13 

Service 47 28 

Sales and related 20 16 

Office/Administrative support 9 15 

Farming/Fishing/Forestry — — 

Construction/Extraction — 9 

Installation/Maintenance/Repair — 3 

Production — 6 

Transportation/Material moving 7 6 

Weekly wage 

Less than $300 49 21 

$300 - $499 — 41 

$500 - $649 — 16 

$650 - $849 — 13 

$850 or more — 10 

Participation in job training 

Yes 31 48 

No 70 52 

Number of children 

No children 96 77 

1 child 4 15 

2 or more children — 9 

School involvement 

2 activities or fewer — 37 

3 activities — 41 

4 activities — 22 

Public assistance participation 

Never participated 96 94 

Had participated — 6 

Voting 

Did not vote — 74 

Voted — 26 

— Sample size is insufficient to permit a reliable estimate. 

NOTES: Percentages may not sum to 100 because of rounding. The 

“Other” race/ethnicity category includes Asians, Pacific Islanders, 

Native-Hawaiians, American Indians, Alaska Natives, and multiracial adults.
3

Although the majority of young adults in these age cohorts 

reported speaking English before starting school, 11 percent of 

the 16-18 year olds and 16 percent of the 19-24 year olds 

reported speaking a non-English language before starting 

school. Among the latter, 16 percent first learned to speak 

English after the age of 16, and nearly one quarter did not 

speak English at all. 

The vast majority (92%) of young adults ages 16-18 had at 

least some computer literacy (see Appendix A for information 

on how the computer literacy scale was constructed). A 

higher percentage of young adults ages 19-24 (20%) lacked 

computer literacy compared with the younger age group. 

Employment, Earnings, and Job Training 

As presented in Table 1, 72 percent of young adults ages 19-

24 were employed full or part time at the time of the 

assessment compared with 12 percent who were unemployed 

and 16 percent who were out of the labor force. Forty-four 

percent of young adults ages 16-18 were out of the labor force. 

The out-of-the-labor-force group included individuals who 

were classified as neither employed nor unemployed. They 

included students, persons keeping house, persons who were 

disabled, and those who did not wish to work at the time of the 

assessment for other reasons. 

Of all employed young adults ages 16-18, nearly half held jobs 

in Service occupations and another 20 percent in Sales and 

related occupations. Similarly, the occupations of the 

employed 19-24 year olds were concentrated in Service, Sales 

and related, and Office/Administrative support.

Data on weekly earnings at the time of the assessment were 

also available for young adults who were employed full time. 

Of young adults ages 16-18, nearly half earned less than $300 

weekly. Of the 19-24 age group, 21 percent earned less than 

$300 weekly, and 41 percent earned between $300 and $499. 

In general, young adults ages 19-24 earned more weekly than 

the younger age group. 

The NAAL respondents were asked whether, during the 

previous year, they had participated in any training or 

education to help improve their job performance, get a 

promotion, or get a job. Thirty-one percent of the young 

adults ages 16-18 and nearly half of those ages 19-24 had 

participated in some type of job training. 

School Involvement, Public Assistance Participation, and 

Voting 

Among adults ages 19-24, less than a quarter had one or more 

children. Parents of school-age children were asked whether 

they had been involved in their children’s schools during the 

previous year in any of the following ways: 

 Volunteered to help out at the school, including in the 

classroom, on a field trip, or at a school event such as a 

party or school fair 

 Gone to a parent-teacher or other type of meeting at the 

school 

 Spoken individually with a teacher to see how their 

children were doing in school 

 Sent food or other items to share in the classroom 

Among the 19-24 year olds who had school-age children, 63 

percent reported doing three or four activities during the past 

year. 

The majority of young adults in both age groups had never 

participated in public assistance programs such as Temporary 

Assistance to Needy Families (TANF). Among America’s 

young adult citizens of voting age, 74 percent reported they 

voted in the 2000 presidential election. 

Prose and Quantitative Literacy 

Estimates of the mean prose and quantitative literacy scores of 

adults by age cohorts are displayed in Figure 1. On both the 

prose and the quantitative literacy scales, adults ages 16-18 

had lower average scores than adults in the two older age 

cohorts (i.e., 19-24 and 25-64). For adults ages 19-24 and 

adults ages 25-64, although their average prose literacy scores 

were similar, their average quantitative literacy scores were 

different, with the older age group having higher scores than 

the younger age group. 
4

The literacy scores can also be used to assign individuals to 

one of the four levels of literacy performance. The percentage 

distributions across proficiency levels on prose and 

quantitative scales are displayed in Figure 2 for adults in 

different age cohorts. On the prose literacy scale, nearly half 

of young adults ages 16-18 had Below Basic or Basic literacy, 

indicating that their literacy proficiency was quite limited. 

The percentages of adults having Below Basic or Basic prose 

literacy were lower in the two older age cohorts. On the 

quantitative scale, about two-thirds of young adults ages 16-18 

and 57 percent of those ages 19-24 had Below Basic or Basic

literacy. Both percentages were higher than those of adults 

ages 25-64. 

Characteristics of Young Adults Who Dropped 

Out of High School 

Nineteen percent of all young adults ages 16-24 had not 

completed high school or high school equivalency. This 

section examines the characteristics and literacy proficiency of 

America’s youths who dropped out of high school. 

Background Characteristics 

Table 2: Percentage distribution of young adults ages 16-

18 and 19-24 who dropped out of high school, by 

selected characteristics: 2003 

Characteristic 

Dropped-out 

young adults 

16-18 

Dropped-out 

young adults 

19-24 

Gender 

Male 58 56 

Female 43 44 

Race/ethnicity 

White 48 38 

Black 17 15 

Hispanic 23 43 

Other — — 

Number of years of education 

0-8 years — 25 

9-12 years 90 75 

Computer literacy 

At least some computer 

literacy 85 50 

No computer literacy 16 50 

Number of children 

No children 92 61 

1 child — 23 

2 or more children — 15 

Public Assistance 

Never 95 89 

Had participated — 11 

Voting 

Did not vote — 93 

Voted — — 

— Sample size is insufficient to permit a reliable estimate. 

NOTES: Percentages may not sum to 100 because of rounding. 

The "Other" category includes Asians, Pacific Islanders, Native 

Hawaiians, American Indians, Alaska Natives, and Multiracial 

adults. 

Demographic Characteristics, Years of Schooling, and 

Computer Literacy 

As indicated in Table 2, in both age groups of 16-18 and 19-

24, more male than female young adults dropped out of high 

school. One-quarter of the out-of-school youths ages 19-24 

had less than 9 years of schooling. 

Compared with 12 percent of Hispanics in the NAAL 

household population, higher percentages of the high school 

drop-outs were Hispanics in both age groups (23% and 43%, 

respectively), indicating an over-representation of this ethnic 
5

group. Blacks were also over-represented among high school 

drop-outs ages 16-18 (17%) compared with the general NAAL 

household population (11%). 

Employment and Earnings 

As shown in Table 3, high school drop-outs more frequently 

held jobs in the construction industry compared to graduates. 

With the exception of this difference, there was no other 

significant difference among high school completers and dropouts in types of jobs held. Both groups held jobs most 

frequently in Service and Sales and related industries. 

Table 3: Percentage distribution of young adults ages 16-24 

by occupation, weekly wage and completion of 

high school: 2003  

Characteristic 

Droppedout young 

adults 

High school 

graduated 

young adults 

Occupation 

Management/Business/ 

Financial — — 

Professional and related — — 

Service 39 38 

Sales and related 15 17 

Office/Administrative 

support — 13 

Farming/Fishing/Forestry — — 

Construction/Extraction 16 10 * 

Installation/Maintenance/ 

Repair — — 

Production — 6 

Transportation/Material 

moving 8 6 

Weekly wage 

Less than $300 30 27 

$300 - $499 49 44 

$500 or more — 29 

*Significantly different from dropped-out young adults at the 

significance level of .05. 

— Sample size is insufficient to permit a reliable estimate. 

NOTE: Percentages may not sum to 100 because of rounding.

Thirty percent of the young drop-outs ages 16-24 who were 

employed full time earned less than $300 weekly at the time of 

the assessment. No significant differences in weekly wage 

were found between high school drop-outs and high school 

completers. 

Public Assistance Participation and Voting 

Compared with the general young adult population ages 19-

24, a higher percentage of drop-outs of the same age had 

children (38% vs. 24%) and had received public assistance 

(11% vs. 6%). 

While 74 percent of young adult citizens of voting age 

reported they did not vote in the 2000 presidential election, an 

even higher percentage of those who had dropped out of high 

school (93%) reported not voting. 

Prose and Quantitative Literacy 

Figure 3 shows how the literacy performance distributions 

varied between young adults who lacked a high school 

diploma or a GED certificate and those who completed high 

school or high school equivalency. 

Of young adults who dropped out of high school, more than 

30 percent had Below Basic prose literacy compared with 9 

percent of their peers who completed high school or high 

school equivalency. In total, more than two-thirds of high 

school drop-outs had Below Basic or Basic prose literacy, 

whereas less than half of those with a high school diploma or a 

GED certificate fell into these two lowest literacy levels. 

On the quantitative literacy scale, half of the young adults who 

dropped out of high school had Below Basic literacy, which 

was also higher than the percentage of those having a high 

school diploma or a GED certificate. 

Similar to the findings in the previous section, among 

dropped-out young adults having Below Basic and Basic prose 

literacy, Hispanics and Blacks were over-represented 
6

compared with the general adult population (Table 4). For 

example, over half of drop-outs ages 19-24 who had Below 

Basic and Basic prose literacy were Hispanics, compared with 

12 percent of Hispanics in the total adult population. 

Table 4: Percentage distribution of young adults ages 16-

18 and 19-24 who dropped out of high school and 

had low prose literacy, by selected 

characteristics: 2003 

Characteristic 

Dropped-out 

young adults 

16-18 with low 

prose literacy 

Dropped-out 

young adults 

19-24 with 

low prose 

literacy 

Gender 

Male 56 57 

Female 44 44 

Race/ethnicity 

White 40 28 

Black 22 18 

Hispanic 29 52 

Other — — 

Number of years of education 

0-8 years — 32 

9-12 years 88 68 

Computer literacy 

At least some computer 

literacy 80 39 

No computer literacy 21 61 

— Sample size is insufficient to permit a reliable estimate. 

NOTES: Percentages may not sum to 100 because of rounding. 

The "Other" category includes Asians, Pacific Islanders, Native 

Hawaiians, American Indians, Alaska Natives, and Multiracial 

adults. 

Nearly one-third of the drop-outs ages 19-24 who had Below 

Basic and Basic prose literacy had less than 9 years of 

schooling, and 61 percent demonstrated a lack of computer 

literacy. 

Characteristics Most Associated With Low 

Prose Literacy of Young Adults 

To identify characteristics most associated with the low prose 

literacy of America’s young adults, we conducted multivariate 

analyses (see Appendix A for details on the methodology). 

Such analyses allowed us to disentangle differences in the 

characteristics among young adults with Below Basic, Basic, 

and Above Basic (i.e., Intermediate and Proficient) prose 

literacy. 

In general, among the factors investigated, lacking a high 

school diploma or a GED certificate, lacking computer 

literacy, and not speaking English before starting school were 

found to increase the likelihood that young adults had low 

prose literacy. 

For example, young adults lacking a high school diploma or a 

GED certificate were four times more likely than those having 

received postsecondary education to have Below Basic relative 

to Above Basic prose literacy. 

Young adults having at least some computer literacy were 

only half as likely as those who were not computer literate to 

have Below Basic literacy relative to Above Basic literacy. 

Young adults who spoke a non-Spanish foreign language 

before starting school were over three times more likely than 

their native English-speaking counterparts to have Below 

Basic literacy relative to Above Basic literacy. 

Results also showed that Black young adults were nearly twice 

as likely as White young adults to have Below Basic literacy 

relative to Above Basic literacy. 

Summary 

This report, based on the 2003 NAAL assessment data, 

examines the characteristics and literacy proficiency of 

America’s young adults ages 16-18 and 19-24, with particular 

attention to high school drop-outs within these age cohorts. 

This report also compares the literacy proficiency of young 

adults with that of older age cohorts and discusses 

characteristics that are most associated with the low literacy of 

America’s young adults. 

The key findings in this report are as follows: 

 More male than female young adults dropped out of 

high school. 

 Hispanic and Black young adults were over-represented 

in the high-school drop-out population, especially 

among the drop-outs having low prose literacy. 

 The most frequently held jobs among young adults 

were in Service and Sales and related industries. 

 The vast majority (92%) of young adults ages 16-18 

had at least some computer literacy. The percentage 

among adults ages 19-24 was lower (80%). 

 Compared with the general young adult population ages 

19-24, a higher percentage of the 19-24-year-old dropouts had children and had received public assistance, 

but a lower percentage reported voting in the 2000 

presidential election. 

 Young adults who did not have a high school diploma 

or a GED certificate, lacked computer literacy, and did 

not speak English before starting school were more 

likely to have low prose literacy. 

 Black young adults were nearly twice as likely as White 

young adults to have Below Basic literacy over Above

Basic literacy.
7

References 

Allen, N.L., Carlson, J.E., and Zelenak, C.A. (1999). The 

NAEP 1996 Technical Report (NCES 1999-452). U.S. 

Department of Education. Washington, DC: National 

Center for Education Statistics. 

Kutner, M., Greenberg, E., Jin, Y., Boyle, B., Hsu, Y., and 

Dunleavy, E. (2007). Literacy in Everyday Life: Results 

From the 2003 National Assessment of Adult Literacy 

(NCES 2007-480). U.S. Department of Education. 

Washington, DC: National Center for Education 

Statistics. 

Mislevy, R.J. (1984). Estimating Latent Distributions. 

Psychometrika, 49(3): 359–381. 

Mislevy, R.J. (1985). Estimation of Latent Group Effects. 

Journal of the American Statistical Association, 

80(392): 993–997. 

Mislevy, R.J. (1991). Randomization-Based Inference About 

Latent Variables from Complex Samples. 

Psychometrika, 56(2): 177–196. 

Sum, A., Fogg, N., and Mangum, G. (2000). Confronting the 

Youth Demographic Challenge: The Labor Market 

Prospects of Out-of-School Young Adults. Baltimore: 

Johns Hopkins University, Sar Levitan Center for Social 

Policy Studies. 

Thomas, N. (1993). Asymptotic Corrections for Multivariate 

Posterior Moments with Factored Likelihood Functions. 

Journal of Computational and Graphical Statistics, 

2(3): 309–322. 
A-1

Appendix A: Methodology and Technical Notes 

This section describes the background variables and statistical 

procedures used in this report. It also provides a brief 

explanation of the direct estimation method and the plausible 

values method used to estimate the NAAL proficiency scores. 

For information on survey methodology (e.g., sampling, data 

collection, weighting and variance estimation, scaling) 

followed for NAAL, see Literacy in Everyday Life: Results 

from the 2003 National Assessment of Adult Literacy (Kutner, 

Greenberg, Jin, Boyle, Hsu, and Dunleavy 2007). 

Descriptions of Background Variables 

Race and Ethnicity 

In 2003, all respondents were asked two questions about their 

race and ethnicity. The first question asked them to indicate 

whether they were Hispanic or Latino. Then, all respondents, 

including those who indicated they were Hispanic or Latino, 

were asked to choose one or more of the following groups to 

describe themselves: 

 White 

 Black or African American 

 Asian 

 American Indian or Alaska Native 

 Native Hawaiian or other Pacific Islander 

Individuals who responded “yes” to the first question were 

coded as Hispanic, regardless of their answer to the second 

question. Individuals who identified more than one group on 

the second question were coded as Multiracial. Respondents 

of Native Hawaiian or Pacific Islander origin were grouped 

with those of Asian origin. 

Language Spoken Before Starting School 

All respondents were asked what language or languages they 

learned to speak before starting school. Their responses were 

then used to divide respondents into three groups: English 

only, English and other language, or Other language(s). 

Age Learned English 

Respondents who spoke a language other than English before 

starting school were asked their age when they learned to 

speak English. They were classified into one of the following 

categories: 10 or younger, 11 to 15, 16 or older. 

Highest Educational Attainment 

All respondents were asked to indicate the highest level of 

education they had completed. The following options were 

provided: 

 Still in high school 

 Less than high school 

 Some high school 

 GED or high school equivalency 

 High school graduate 

 Vocational, trade, or business school after high school 

 College: less than 2 years 

 College: Associate’s degree (A.A.) 

 College: 2 or more years, no degree 

 College graduate (B.A. or B.S.) 

 Postgraduate, no degree 

 Postgraduate degree (M.S., M.A., Ph.D., M.D., etc.) 

Respondents who reported less than high school or some high 

school were asked how many years of education they had 

completed. For certain analyses, some of these groups were 

collapsed. 

Participation in Basic Skills Class 

The NAAL respondents were asked whether they had 

participated in a program other than in regular school to 

improve their basic skills, that is, basic reading, writing, and 

arithmetic skills. 

Information Technology (IT) Certification 

All respondents were asked whether they had received any 

type of information technology skill certification sponsored by 

a hardware or software manufacturer or an industry or 

professional association and whether they had passed a test to 

get the certification. Those who answered yes to both 

questions were counted as receiving IT certification. 

Computer Literacy 

The NAAL background questionnaire collected data from 

respondents on using a computer to perform various activities. 

Specifically, respondents were asked How often (every day, a 

few times a week, once a week, less than once a week, never) 

do you: 

 Send or receive an email message 

 Write using a word processing program 

 Use a spreadsheet program or use a financial program 

 Look up info on a CD-ROM 

 Find info on the Internet 

On the basis of these questionnaire items, a computer literacy 

scale was created such that respondents who had never 

performed any of these five computer activities were 

considered to have no computer literacy, whereas those who 

had at least some experience with at least one of the five items 

were considered to have at least some computer literacy. 

Labor Force Participation 

The NAAL background questionnaire also collected 

information on respondents’ labor force and employment 

activities at the time of the assessment and during the previous 

12 months. Responses to the questions on current 

employment status at the time of the assessment were used to 

assign each respondent to one of the following labor force 

statuses: employed full time, employed part time, employed 

not at work, unemployed, and out of the labor force. The outof-the-labor-force group included individuals who were 

classified as neither employed nor unemployed: students not 

looking for work, retirees, persons keeping house, persons 

who were disabled, and those who did not wish to work at the 

present time for other reasons. 
A-2

Occupation 

Respondents who had held a job within the past 3 years were 

asked to provide the title of their occupation and its most 

important activities and duties. This information was used to 

assign each occupation a 2000 Census Bureau code. The 

occupations were then collapsed into eight major occupational 

groups: 

 Management, business, and financial 

 Professional and related 

 Service 

 Sales and related 

 Office and administrative support 

 Construction and extraction 

 Installation, maintenance, and repair 

 Production 

Weekly wage 

Respondents who were employed were asked to report their 

gross weekly wage or salary (before deductions) during the 

previous week. Some respondents were unable to report their 

weekly wage or salary before deductions. In these cases, the 

interviewers asked them to report their take-home pay and 

noted that fact. Some respondents reported their pay per hour, 

day, 2-week period, month, or year, rather than per week as 

requested. This was also noted by the interviewers, who asked 

a follow-up question to clarify the time frame the respondents 

were using. 

All reported pay was adjusted to approximate gross weekly 

wages or salaries. For respondents who reported their 

earnings in units other than weekly (e.g., per hour or per day), 

information on the number of hours worked per week 

(collected in a separate question) was used to compute weekly 

earnings. For respondents who reported take-home pay rather 

than gross pay, adjustments were made to the wage or salary 

they reported by adding a FICA adjustment at a flat rate of 

7.65 percent and an additional adjustment based on IRS 

withholding tables for single taxpayers in 2003. An additional 

10 percent was added as a proxy for state taxes and 

miscellaneous deductions. 

Participation in Job Training 

Respondents were asked in separate questions whether during 

the past year they had participated in any training or 

education, including courses, workshops, formal on-the-job 

training, or apprenticeships, intended to help improve job 

performance, earn a promotion, or obtain a job. 

School Involvement 

Respondents were asked four questions to indicate the number 

of different types of activities they were involved in at their 

child’s or grandchild’s school. They were asked whether 

during the past year they had done the following: 

 Volunteered to help out at their child’s (one of their 

children’s/grandchild/grandchildren) school(s), 

including in the classroom, on a field trip, or at school 

event such as a party or school fair? 

 Gone to a PTA or other type of parent meeting at their 

child’s (one of their children’s/ 

grandchild/grandchildren) school(s)? 

 Spoken individually with their child’s (one of their 

children’s/grandchild/grandchildren) teacher(s) to see 

how he or she was doing in school? 

 Sent food, or other items to share in their child’s (one of 

their children’s/grandchild/ grandchildren) 

classroom(s)? 

Respondents were grouped according to the number of 

questions that they answered “yes” as none, one, two, three, or 

four.

Participation in Public Assistance 

Respondents were asked whether they or anyone in their 

household had received Temporary Assistance for Needy 

Families (TANF), public assistance, or public welfare 

payments from the state or local welfare office during the 

previous 12 months or whether they had ever received public 

assistance in the past. Respondents were identified as never, 

past, or current participants in welfare.

Voting 

All respondents who either were born in the United States or 

indicated in their response to a separate question (that was 

asked only of people not born in the United States) that they 

were citizens of the United States were asked whether they 

remembered whether or not they voted in the 2000 presidential 

election. If they said they remembered whether or not they 

voted in the election, they were asked whether they voted. 

Respondents who did not remember whether they voted were 

treated as missing data for this question. 

Statistical Procedures 

Tests of Statistical Significance 

All comparisons discussed in this report have been tested for 

statistical significance using the t statistic. Statistical 

significance was determined by calculating a t value for the 

difference between a pair of means, or proportions, and 

comparing this value with published tables of values at a 

certain level of significance, called the alpha level. The alpha 

level is an a priori statement of the probability of inferring that 

a difference exists when, in fact, it does not. The alpha level 

used in this report is .05, based on a two-tailed test. 

Differences in the means and proportions between subgroups 

were calculated using the following t statistic: 

where p1 and p2 are the estimates to be compared and se1 and 

se2 are their corresponding standard errors. When a subgroup 

was compared to a total group, a modification of the standard 

error of difference was made to adjust for group dependence. 

The formula for the adjusted standard error of difference was 

as follows: 
A-3

where p is the proportion of the total group contained in the 

subgroup. 

Minimum Sample Sizes for Reporting Subgroup Results 

In the NAAL reports, the sample sizes were not always large 

enough to permit accurate estimates of proficiency and/or 

background results for one or more categories of variables. 

For results to be reported for any subgroup, a minimum 

sample size of 45 was required. This number was arrived at 

by determining the sample size needed to detect an effect size 

of 0.5 with a probability of 0.8 or greater, using a design effect 

of 1.5. This design effect implies a sample design-based 

variance 1.5 times that of a simple random sample. The effect 

size of 0.5 pertains to the true difference in a given mean 

estimate (e.g., mean proficiency) between the subgroup in 

question and the total population, divided by the standard 

deviation of that estimate in the total population. An effect 

size of 0.5 was chosen following Cohen (1988), who classifies 

effect size of this magnitude as “medium” as well as to be 

consistent with what was done in the 1992 National Adult 

Literacy Survey (NALS).2

Multinomial Logistic Regression Analyses 

Proficiency on the NAAL literacy scales (i.e., prose, 

document, and quantitative) is measured on a scale that ranges 

from 0 to 500. The performance of adults on the assessment 

can be reported as either mean scores on the scale or on the 

basis of the distribution of adults across the NAAL 

performance levels (Below Basic, Basic, Intermediate, and 

Proficient). Each performance level describes the abilities 

associated with score ranges on the NAAL scale. 

In investigating the effects of multiple factors on low literacy 

for young adults, we conducted multinomial logistic 

regression analyses. Such analyses allowed us to disentangle 

differences between the characteristics of adults with Below 

Basic literacy and those of adults with Basic literacy, while 

holding constant a series of other explanatory variables. 

Similarly, it also allowed us to examine differences between 

the characteristics of adults with Basic literacy and those of 

adults with literacy levels in the next highest categories 

(Intermediate and Proficient). 

Multinomial logistic regression is a form of regression used 

when the dependent variable is categorical with more than two 

classes and the independent variables are of any type. 3

 It 

allows the simultaneous comparison of more than one contrast 

(e.g., the probability of Below Basic vs. Basic literacy, Basic

vs. Above Basic literacy, Below Basic vs. Above Basic 

 

2 Cohen, J. (1988). Statistical Power Analysis for the Behavioral 

Sciences (second edition). Hillsdale, NJ: Lawrence Erlbaum 

Associates.

3

 For more information on multinomial logistic regression, see 

Hosmer, D., and Lemeshow, S. (2004). Applied Logistic Regression. 

New York: John Wiley & Sons. 

literacy) and usually expresses the impact of predictor 

variables on dependent variables in terms of odds ratios. 

The odds ratio for a given independent variable represents the 

factor by which the odds change in the dependent variable for 

a one-unit change in the independent variable. For example, if 

the odds ratio for success in a given performance test for 

females versus male is 3.5, and if this odds ratio is statistically 

significant, we would say that the odds of success for females 

are 3.5 times as large as for males. 

The statistical significance of the odds ratio estimates are 

indicated by the confidence interval for the odds ratio. If the 

confidence interval around the odds ratio contains the value of 

1.0, then the change in the value of the independent variable is 

not associated with change in the odds of the dependent 

variable. Thus, that independent variable is not considered a 

useful predictor in the logistic model. 

In our multinomial logistic regression analyses, the outcome 

measure was the NAAL literacy performance level: Below 

Basic, Basic, and Above Basic (i.e., Intermediate and 

Proficient combined). Using the literacy levels rather than the 

NAAL scale scores as the dependent variables in the model 

made the analyses more easily interpretable. If the continuous 

NAAL scale scores had been used, the results would need to 

be discussed in terms of unit changes on the NAAL scale per 

unit change in an independent variable. The impact of specific 

variables would be more difficult to grasp in this approach, 

given the abstract nature of the NAAL scale. The predictor 

variables in the model were sex, race/ethnicity, country of 

birth, language spoken before starting school, educational 

attainment, IT certification, computer literacy, employment 

status, participation in job training, participation in basic skills 

training, and oral passage reading scores as measured in the 

Fluency Addition to NAAL.4

Table A-1 reports the odds ratio estimates from the 

multinomial regression of the prose literacy performance level 

on the set of predictor variables described above. 

Estimation of Literacy Proficiency 

The NAAL used a complex assessment design that allowed 

maximum coverage of the broad domain of literacy while 

minimizing the time burden on any one respondent. Under 

this design, the NAAL administered only a fraction of the 

assessment items on each literacy scale to each respondent. 

Although individual respondents were required to take only a 

small portion of the entire pool of assessment questions, the 

aggregate results across the entire assessment allowed broad 

reporting of literacy for the targeted population. However, 

because respondents did not receive enough literacy tasks to 

provide reliable information about individual performance, 

traditional test scores for individual respondents would have 

resulted in biased estimates of population characteristics and 

 

4

As part of the NAAL assessment, adults were asked to read a series of short 

passages aloud. Their responses were recorded and later scored for accuracy 

and speed. 
A-4

therefore were not appropriate to use for estimates of 

population statistics. 

Table A-1. Odds ratio estimates from multinomial regression analyses for young adults 

Below Basic vs. Basic Basic vs. Above Basic 

Below Basic vs. Above 

Basic 

Effect 

Point 

Estimate 

95% Wald 

Confidence 

Limits 

Point 

Estimate 

95% Wald 

Confidence 

Limits 

Point 

Estimate 

95% Wald 

Confidence 

Limits 

Race/ethnicity: Black vs. White -- -- -- 1.90 1.43 2.54 1.92 1.16 3.20 

Language spoken before starting school: English 

with other language vs. English only -- -- -- 2.42 1.01 5.92 -- -- -- 

Language spoken before starting school: Other 

vs. English only -- -- -- 2.39 1.15 5.01 3.30 1.38 7.92 

Education: Still in high school vs. Postsecondary -- -- -- 1.66 1.17 2.36 2.35 1.27 4.36 

Education: Less than high school vs. 

Postsecondary 1.99 1.15 3.44 2.08 1.37 3.15 4.10 2.43 6.95 

Education: High school/GED vs. Postsecondary -- -- -- 1.64 1.15 2.34 2.12 1.27 3.57 

Computer literacy: Some vs. None 0.69 0.48 1.00 0.68 0.46 0.99 0.46 0.30 0.72 

Oral passage reading score 0.99 0.98 0.99 0.99 0.98 0.99 0.97 0.97 0.98 

-- Estimates not significant and not shown. 

Note: Results were only shown for predictors with significant odds ratio estimates. 

To obtain unbiased estimates of population statistics (e.g., 

subgroup means or percentages in each proficiency level), 

the NAAL used methods derived from Marginal Maximum 

Likelihood (MML) estimation. Such MML estimation 

procedures were available with AM software.5

 Estimates 

for average literacy scores and percentages in each literacy 

proficiency level in this report were all obtained using the 

direct estimation method with AM. The multinomial 

logistic regression analyses could not be conducted using 

MML direct estimation because the procedure is not 

available in AM. Instead, an alternative estimation 

procedure called plausible values methodology was used 

for the multinomial logistic regression analyses. Plausible 

values were initially developed for the National 

Assessment of Educational Progress (NAEP; Mislevy 

1984, 1985, 1991; Thomas 1993) to allow secondary users 

to estimate statistics derived from individual data. 

Plausible values are multiple imputations randomly drawn 

from a distribution derived from the MML parameter 

estimates for an extensive conditioning model (Allen, 

Carlson, and Zelenak 1999). 

 

5

 For more information on direct estimation methodology 

followed for NAAL and the use of AM, see Baldi, S. (Ed.) et al. 

(Forthcoming). Technical Report and Data File User’s Manual 

for the 2003 National Assessment of Adult Literacy. U.S. 

Department of Education. Washington, DC: National Center for 

Education Statistics. 

It is important to recognize that plausible values are not 

test scores for individual, and they should not be treated as 

such. Plausible values are randomly drawn from the 

distribution of scores that could be reasonably assigned to 

each individual. As such, the plausible values contain 

random error variance components and are not optimal as 

scores for individuals. 

In our multivariate analyses, five plausible values for each 

adult were obtained as estimates of scores on the prose 

literacy scale. These plausible values were then used to 

assign each individual to one of the NAAL performance 

levels. Five sets of multinomial regression analysis were 

conducted, using each of the five plausible values. The 

reported odds ratio estimates are the average of the five 

odds ratio estimates using each of the five plausible values. 

It should be noted, however, that the standard errors used 

in the significance tests for the reported odds ratio 

estimates were not adjusted for variation among the five 

sets of results given the complexity of the computations 

and the unavailability of an estimation procedure in the 

statistical software. Therefore, the confidence limits 

around the odds ratio estimates might be narrower than 

they would be, had the standard errors been corrected.