1

Overcoming the Language Barrier: The Literacy of 

Non-Native-English-Speaking Adults 

Authors 

Ying Jin 

Joanna Kling 

American Institutes for Research

June 2009 

Contents 

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

Profile of Non-NativeEnglish-Speaking Adults ..... 3

Background Characteristics .. 3

Prose and Quantitative 

Literacy .................................... 4

Characteristics of NonNative-English-Speaking 

Adults with Low and High 

Prose Literacy....................... 5

Background Characteristics .. 5

Characteristics Most 

Associated with the Low 

Prose Literacy of the NonNative-English-Speaking 

Adults .................................... 6

Summary............................... 9

References............................. 9

Appendix A: Methodology 

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

Descriptions of Background 

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

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

Estimation of Literacy 

Proficiency ...........................A-4

Introduction 

Over the past few decades, the United States has experienced a substantial influx of foreign 

immigrants, who have accounted for much of the continued population growth in the nation

(Surn, Kirsch, and Yamamoto 2004). The majority of the new foreign immigrants do not 

speak English as their native language. Findings from the 2003 National Assessment of 

Adult Literacy (NAAL) revealed that the average literacy level of U.S. non-native-Englishspeaking adults1

 was considerably below that of their native-English-speaking counterparts, 

even when English is learned shortly after starting school. As the non-native-Englishspeaking share of the population rises, knowing their literacy proficiency becomes 

important for identifying the potential need for public policies to address any English 

literacy and educational deficits that might impede their ability to be fully productive 

workers, parents, and citizens. 

Using data collected from the 2003 NAAL assessment, this report examines the 

characteristics of the non-native-English-speaking adult population and compares their 

prose and quantitative literacy with the literacy of their native-English-speaking peers. 

This report also identifies characteristics that are most associated with low literacy nonnative-English-language speakers, which can help policymakers and practitioners 

understand for whom services should be designed. Many of the background variables 

examined in this report are based on self-reported data, and because they are also 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 proficiency of approximately 18,000 adults living in 

households and 1,200 prison inmates. In the household sample, 2,807 adults did not speak 

English before starting school and formed the non-native-English-speaking 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.

2

 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 Appendix A: 

Methodology and Technical Notes. 

 

1

 Non-native-English-speaking adults were defined in this report as adults who reported they did not learn to speak English before starting school. 2

 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 native- and non-native-English-speaking adults by selected characteristics: 2003

Characteristic 

NativeEnglishspeaking 

adults 

NonnativeEnglishspeaking 

adults Characteristic 

NativeEnglishspeaking 

adults 

NonnativeEnglishspeaking 

adults 

Race/ethnicity Computers with Internet access 

White 78 19* None 30 45* 

Black 13 3* At least 1 70 55* 

Hispanic 5 62* Employment status 

Asian/Pacific Islander 2 15* Employed full-time 48 52* 

American Indian/Alaskan native 1 — Employed part-time 13 12 

Multiracial 2 # Employed not at work 4 2* 

Language (if not English) spoken Unemployed 6 6 

before starting school Out of labor force 30 29 

Spanish and other language N/A 62 Reason out of labor force 

Non-Spanish language N/A 38 In school 10 13 

Age learned to speak English Keeping house 22 42* 

1-10 N/A 32 Retired 35 23* 

11-15 N/A 16 Doing volunteer work 2 — 

16-20 N/A 11 Other 9 9 

21 or older N/A 20 More than one of the above 22 14* 

Does not speak English N/A 22 Occupation 

Country of birth 22 Service 18 24* 

U.S. 96 17* Professional and related 20 14* 

Other 4 83* Office/Administrative support 15 12* 

Highest educational attainment Production 7 12* 

Still in H.S. 3 4 Sales and related 11 10 

Less than/some high school 12 35* Construction/Extraction 6 10* 

H.S. grad/GED/equivalency 32 22* Management/Business/Financial 13 7* 

Postsecondary but less than 4-year college 29 21* Transportation/Material moving 6 6 

College grad/graduate studies/degree 23 18* Installation/Maintenance/Repair 4 3* 

Age obtained high school diploma/GED Farming/Fishing/Forestry 1 2* 

19 or younger 93 87* Weekly wage 

20-24 4 9* Less than $300 8 14* 

25 or older 3 4 $300 - $499 21 33* 

Participation in English-as-a-second- $500 - $649 17 15 

language (ESL) class $650 - $849 17 13* 

Yes N/A 38 $850-$1149 16 12* 

No N/A 62 $1150 or more 22 13* 

Computer literacy Participation in job training 

At least some computer literacy 73 51* Yes 42 26* 

No computer literacy 27 49* No 58 74* 

*Significantly different from native-English speakers at the significance level of .05. 

— Sample size is insufficient to permit a reliable estimate. 

# Rounds to zero. 

NOTE: Percentages may not sum to 100 because of rounding. 
3

Profile of Non-Native-English-Speaking 

Adults 

Background Characteristics 

Demographic Characteristics 

As presented in Table 1, a large percentage (62%) of nonnative-English speakers was Hispanic and spoke Spanish 

before starting school. Nearly a third of the non-nativeEnglish-speaking adults reported they first learned to speak 

English at age 10 or younger, but 20 percent of these 

adults reported learning to speak English after age 21 and 

another 20 percent did not speak English at all. 

Highest Educational Attainment and Computer 

Literacy 

As shown in Table 1, more than a third of the non-nativeEnglish speakers lacked a high school diploma or a 

General Educational Development (GED) certificate. The 

high-school completion rate was lower among the nonnative-English-speaking adults than among the nativeEnglish speakers. In comparison with the native-English 

speakers, a lower percentage (87%) of the non-nativeEnglish-speaking adults who completed at least high 

school or high school equivalency received their high 

school diploma or GED certificate at age 19 or younger. 

There were also fewer non-native speakers than native 

speakers who had received a bachelor’s or graduate degree 

or had taken graduate classes. These data suggest that 

additional educational sources are needed to support nonnative-English-speaking adults. Such services likely need 

to focus on academic content and ESL instruction. 

Thirty-eight percent of the non-native-English-speaking 

adults had participated in an English as a Second Language 

(ESL) class in the United States (Table 1). One source of 

the needed additional services is likely to be through adult 

ESL classes. Despite relatively limited funding for such 

services across the nation as a whole, the fact that nearly 

40 percent of non-native-English-speaking adults report 

participating in these services demonstrates the interest and 

need they have in such classes, even though many are parttime evening classes. 

Table 1 also shows that nearly half of the non-nativeEnglish-speaking adults lacked computer literacy (see 

Appendix A for information on how the computer literacy 

scale was constructed), in comparison with 27 percent of 

native-English speakers. As computer literacy continues 

to become an increasingly important skill for adults to 

possess, the fact that almost half of non-native-Englishspeaking adults characterize themselves as having no 

computer literacy indicates that ESL services for these 

adults should introduce and/or include this topic. 

Additionally, and perhaps in partnership with the One-Stop 

Career Centers administered through the U.S. Department 

of Labor, computer training can include vocational-based 

ESL. Examples of this provision of service with other 

skills and job training classes has demonstrated success. 

There were fewer non-native speakers than native speakers 

who reported having at least one computer in their 

household with Internet access (55% vs. 70%). 

As shown in Table 2, among both native- and non-nativeEnglish speakers, those with higher levels of educational 

attainment were more likely to be computer literate. For 

example, 82 percent of non-native-English speakers who 

did not complete high school lacked computer literacy, 

whereas only 8 percent of non-native-English speakers 

with college degrees lacked computer literacy. The 

percentage of non-native-English speakers having no 

computer literacy decreased to 51 percent among those 

who graduated high school or had received a GED or a 

high school equivalency credential. However, among 

adults who received less than a college degree, more nonnative-English speakers lacked computer literacy than their 

native-speaking counterparts with the same level of 

education. 

Table 2. Percentage distribution of native- and non-native-English-speaking adults, by education and computer literacy: 2003 

 Native-English-speaking 

adults 

Non-native-English- speaking 

adults 

Highest educational attainment 

Some computer 

literacy 

No computer 

literacy 

Some computer 

literacy 

No computer 

literacy 

Less than/some high school 38 62 18 82* 

High school grad/GED/equivalency 61 39 49 51* 

Postsecondary but less than 4-year college 84 16 72 28* 

College grad/graduate studies/degree 92 8 92 8 

*Significantly different from native-English speakers with no computer literacy at the significance level of .05. 

NOTE: Percentages may not sum to 100 because of rounding. 
4

Employment, Earnings, and Job Training 

As shown in Table 1, two-thirds of the non-native-Englishspeaking adults were employed compared with about 6 

percent who were unemployed and 29 percent who were out 

of the labor force. The out-of-the-labor-force group 

included individuals who were classified as neither 

employed nor unemployed: students not looking for work, 

retirees, persons keeping house, and those who did not wish 

to work for other reasons. The rate of unemployment and 

out-of-the-labor-force were similar among the nativeEnglish speakers. However, compared with their nativeEnglish-speaking counterparts, more non-native speakers 

who were out of the labor force reported keeping house 

(42% vs. 22%) and fewer non-native speakers reported being 

retired (23% vs. 35%). 

The occupation distribution in Table 1 shows that more nonnative-English-speaking adults held jobs in Service (24%) 

than in any other occupational groups. Compared with the 

native speakers, fewer non-native speakers held jobs in 

Professional and Related; Management, Business, and 

Financial; and Office and Administrative Support

occupations. In contrast, more non-native speakers held jobs 

in Service, Production, and Construction and Extraction 

than their native-speaking counterparts. 

Table 1 also shows that the non-native speakers earned less 

weekly than their native-speaking counterparts. For 

example, 55 percent of the native-English speakers earned 

$650 or more weekly in comparison to 38 percent of the 

non-native speakers. In contrast, 47 percent of the nonnative speakers earned $499 or less weekly in comparison to 

just 29 percent of the native speakers. 

The NAAL respondents were asked whether, during the 

previous year, they had participated in any training or 

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

training or apprenticeships, to help improve their job 

performance, get a promotion, or get a job. Fewer nonnative-English speakers (26%) than native-English speakers 

(42%) reported participating in some type of job training 

(Table 1). 

Prose and Quantitative Literacy 

Estimates of the mean prose and quantitative literacy scores 

of non-native-English-speaking adults are displayed in 

Figure 1, along with estimates of the mean scores for nativeEnglish speakers. The average scores of non-nativeEnglish-speaking adults were quite low on both the prose 

and the quantitative literacy scales. There were substantial 

differences in mean literacy scores between the native- and 

the non-native-English-speaking populations. 

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. On both 

scales, a majority of the non-native-English-speaking adults 

had Below Basic or Basic literacy, indicating that their 

literacy proficiency was quite limited. More than threequarters (76%) of the non-native-English speakers had 

Below Basic or Basic prose literacy compared with 37 

percent of the native-English-speaking adults. 

As was found in Literacy of Everyday Life (Kutner et al. 

2005), among the non-native-English speakers, Spanishspeaking adults had lower prose and quantitative literacy 

than non-Spanish-speaking adults. Sixty-two percent of the 

Spanish speakers had Below Basic prose literacy, compared 

with 26 percent of the non-Spanish speakers with Below 

Basic prose literacy. 
5

Characteristics of Non-Native-EnglishSpeaking Adults with Low and High Prose 

Literacy 

This section examines the characteristics of the non-nativeEnglish-speaking adults with the two lowest prose 

proficiency levels (i.e., Below Basic and Basic), as well as 

the characteristics of non-native-English-speaking adults 

with the two highest prose proficiency levels (i.e.,

Intermediate and Proficient). This report refers to adults 

with Below Basic and Basic proficiency levels as the lower 

literacy or less literate group, in contrast to those with 

Intermediate and Proficient prose literacy, who are referred 

to as the higher literacy or more literate group. Table 3 

provides comparisons of native- and non-native-Englishspeaking adults with lower and higher prose literacy 

separately. 

Additionally, this section discusses findings from 

multivariate analyses about characteristics significantly 

associated with lower prose literacy among non-nativeEnglish-speaking adults. The focus was on these adults 

because they were considered to be in greatest need of 

literacy interventions to improve their skills and 

employability. 

Background Characteristics 

Demographic Characteristics 

As indicated in Table 3, there were more Hispanic adults 

among the non-native-English speakers with low literacy 

than native-English speakers with low literacy. Similarly, 

among adults with high literacy, there were more Hispanic 

and ‘Other’ non-native-English speakers than native-English 

speakers. 

Educational Attainment and Computer Literacy 

Table 3 also presents the highest educational attainment 

between native- and non-native-English-speaking adults by 

literacy level. 

Relative to native-English-speaking adults with low literacy, 

a higher percentage of non-native-English-speaking adults 

with low literacy lacked a high school diploma or GED 

certificate. However, results for non-native-English 

speakers and native-English speakers with high literacy 

show little difference in attaining at least a high school 

diploma or GED certificate. Interestingly, a higher 

percentage of non-native-English-speaking adults with low 

literacy (10%) earned a college degree or completed 

graduate studies/degree than native-English-speakers with 

low literacy (7%). 

A higher percentage of native-English-speaking adults with 

low literacy (51%) reported having at least some computer 

literacy compared to their non-native-English-speaking 

counterparts (39%). The majority of adults with high 

literacy reported having at least some computer literacy. 

For adults with lower literacy, fewer non-native-Englishspeakers (45%) had computers with Internet access than 

native-English speakers (52%). However, among adults 

with higher literacy, a higher percentage of non-nativeEnglish-speakers reporting having at least one computer at 

home with Internet access than their native-English-speaking 

counterparts (Table 3). 

Employment, Earnings, and Job Training 

Table 3 shows that for both low and high literacy groups, 

non-native-English-speaking adults were more often 

employed full-time than their native-English-speaking 

counterparts. Conversely, for adults with low literacy, more 

native-English-speakers (42%) were out of the labor force 

than non-native-English-speakers (31%). The out-of-laborforce adults included homemakers, retirees, students, 

persons who were disabled, and those who did not wish to 

work at the time of the assessment for other reasons. 

Among the non-native-English-speaking adults with lower 

literacy, the most frequently held jobs were in Service 

(28%), compared with the most frequently held jobs in 

Construction and Extraction (25%) among their nativeEnglish-speaking counterparts. Among adults with higher 

literacy, 36 percent of non-native-English speaking adults 

held jobs in Professional and Related occupations. For 

those with high literacy, more non-native-English speakers 
6

with high literacy (12%) than native-English speakers (4%) 

held jobs in Management, Business, and Financial

occupations. 

Among lower-literacy adults, more than half of the nonnative-English-speaking adults earned $499 or less per 

week. Higher percentages (12% and 41%, respectively) of 

non-native-English-speaking adults with low literacy earned 

less than $300 or $300-$499 per week than their nativeEnglish-speaking counterparts. In contrast to less literate 

non-native English speakers, higher percentages of nativeEnglish-speaking adults with low literacy earned $650-$849 

or $850-$1149 per week. No differences were found in 

weekly wages between non-native- and native-Englishspeaking adults with high literacy. 

More native-English-speakers with low literacy had 

participated in some type of job training (28%) compared to 

non-native-English-speakers with low literacy (19%). By 

comparison, nearly half of both native- and non-nativeEnglish-speaking adults with higher literacy had participated 

in some type of job training activities. 

School Involvement and Public Assistance Participation 

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 adults with low literacy, there was no difference in 

school involvement between native- and non-native-English 

speaking adults. Among adults with higher literacy levels, a 

lower percentage of non-native-English-speaking adults did 

not participate in any school activities than did nativeEnglish speakers. However, for those adults who did 

participate in school activities, more native-Englishspeaking adults participated in four activities over their nonnative counterparts. 

The majority of the native- and non-native-English-speaking 

adults, regardless of their literacy level, had never 

participated in public assistance programs such as 

Temporary Assistance to Needy Families (TANF). 

However, a higher percentage of native-English-speaking 

adults with low literacy had participated in public assistance 

then their non-native-English-speaking counterparts. This 

finding suggests that additional outreach efforts may be 

needed to draw this population to public services for which 

they qualify. 

Characteristics Most Associated with the Low 

Prose Literacy of the Non-Native-EnglishSpeaking Adults 

To identify characteristics most associated with the low 

prose literacy of the non-native-English-speaking adults, we 

conducted multivariate analyses (see Appendix A for details 

on the methodology). Such analyses allowed us to 

disentangle differences in the characteristics among the nonnative-English-speaking adults with Below Basic, Basic, and 

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

literacy. 

In general, Hispanic or Black non-native-English-speaking 

adults, adults who lacked a high school diploma or a GED 

certificate, adults who lacked computer literacy, and adults 

who never participated in job training or ESL classes were 

more likely to have low prose literacy. 

For example, among non-native-English speakers, Black 

adults were five times more likely than White adults to have 

Below Basic prose literacy relative to Above Basic prose 

literacy. Similarly, Hispanic adults were nearly twice as 

likely as White adults to have Basic prose literacy over 

Above Basic prose literacy. 

The results also showed that, as expected, the non-nativeEnglish-speaking adults lacking a high school diploma or a 

GED certificate were 1.5 times more likely to have Below 

Basic literacy relative to Basic literacy and nearly three 

times more likely to have Below Basic relative to Above

Basic literacy, compared with those who had received 

postsecondary education. 

Adults having at least some computer literacy were half as 

likely as those who were computer illiterate to have Below 

Basic prose literacy over Above Basic prose literacy. Those 

adults who had participated in job training were 0.7 times as 

likely as those who never participated to have Below Basic

prose literacy over Above Basic prose literacy. 
7

Table 3. Percentage distribution of native- and non-native-English-speaking adults with low prose literacy, by selected characteristics,

and percentage distribution of native- and non-native-English-speaking adults with high prose literacy, by selected 

characteristics: 2003 

Characteristic 

Non-nativeEnglish- speakers 

with low prose 

literacy

Native English 

speakers with 

low prose 

literacy 

Non-nativeEnglish-speakers 

with high prose 

literacy

Native English 

speakers with 

high prose 

literacy 

Race 

White 15 67* 32 85** 

Black 4 22* 2 7** 

Hispanic 70 7* 35 4** 

Other 12 5* 31 4** 

Language (if not English) spoken before starting school 

Spanish and other language 71 N/A 36 N/A 

Non-Spanish language 29 N/A 64 N/A 

Age learned to speak English 

1-10 23 N/A 60 N/A 

11-15 14 N/A 22 N/A 

16-20 11 N/A 8 N/A 

21 or older 23 N/A 10 N/A 

Does not speak English 29 N/A # N/A 

Country of birth 

U.S. 14 95* 27 97** 

Other 86 5* 73 3** 

Highest educational attainment 

Still in high school 4 4 4 3 

Less than/some high school 45 26* 4 4 

High school grad/GED/equivalency 24 42* 14 26** 

Postsecondary but less than 4-year college 17 22* 33 34 

College grad/graduate studies/degree 10 7* 45 33** 

Age obtained high school diploma 

19 or younger 83 87 94 96 

20-24 12 7* 5 3** 

25 or older 5 6 1 2 

Participation in English-as-a-second-language (ESL) class 

Yes 41 N/A 31 N/A 

No 59 N/A 69 N/A 

Computer literacy 

At least some computer literacy 39 51* 89 87 

No computer literacy 61 50* 11 13 

Computers with Internet access 

None 55 48* 12 19** 

At least 1 45 52* 88 81** 

Employment status 

Employed full-time 49 37* 60 54** 

Employed part-time 12 11 11 13 

Employed not at work 2 3 1 4** 

Unemployed 6 8* 5 6 

Out of labor force 31 42* 23 23 

See notes at end of table. 
8

Table 3. Percentage distribution of native- and non-native-English-speaking adults with low prose literacy, by selected 

characteristics, and percentage distribution of native- and non-native-English-speaking adults with high prose 

literacy, by selected characteristics: 2003—Continued 

Characteristic 

Non-nativeEnglish- speakers 

with low prose 

literacy

Native English 

speakers with

low prose 

literacy

Non-nativeEnglish-speakers 

with high prose 

literacy

Native English 

speakers with 

high prose 

literacy 

Occupation 

Service 28 7 13 16** 

Production 15 9* 4 25** 

Construction/Extraction 12 25 4 15 

Office/Administrative support 10 12 15 11 

Sales and related 9 13 11 16 

Transportation/Material moving 8 1* 3 # 

Professional and related 6 10 36 5 

Management/Business/Financial 6 4 12 4** 

Farming/Fishing/Forestry 3 11* # 6 

Installation/Maintenance/Repair 3 10 2 4 

Weekly wage 

Less than $300 17 12* 6 7 

$300 - $499 41 32* 14 16 

$500 - $649 16 19 12 16 

$650 - $849 11 16* 18 18 

$850-$1149 8 11* 22 17 

$1150 or more 8 10 28 26 

Participation in job training 

Yes 19 28* 50 51 

No 81 72* 50 49 

School involvement 

No activities 12 12 5 9** 

1 activity 12 16 13 10 

2 activities 22 21 24 19 

3 activities 30 26 37 29 

4 activities 24 25 21 33** 

Public assistance participation 

Never participated 94 90* 96 95 

Had participated 6 10* 4 5 

*Significantly different from non-native-English speakers with low literacy at the significance level of .05. 

**Significantly different from non-native-English speakers with high literacy at the significance level of .05. 

# Rounds to zero. 

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. 
9

Summary 

This report, using the 2003 NAAL assessment data, 

examines the characteristics of non-native-English-speaking 

adults, as well as the relationship between various 

characteristics and literacy proficiency of this population 

group. The key findings in this report are as follows: 

 A high percentage (62%) of the non-native-English 

speakers were Hispanic and spoke Spanish before 

starting school. Relative to their native-Englishspeaking peers with low literacy, non-native-English 

speakers with low literacy were more likely to be 

foreign born and be of Black or Hispanic 

race/ethnicity. 

 More than a third of the non-native-Englishspeaking adults lacked a high school diploma or a 

GED certificate. Their high school completion rate 

was lower than that of the general adult population. 

 Nearly half of non-native-English-speaking adults 

were not computer literate. Over half reported 

having at least one computer in their household with 

Internet access. 

 Nearly half (49%) of non-native-English-speaking 

adults with low literacy were employed full-time, a 

higher percentage than their native-English-speaking 

peers with low literacy (37%). 

 Nearly 30 percent of the non-native-Englishspeaking adults were out of the labor force. Among 

adults with lower literacy, fewer non-native-Englishspeaking adults were out of the labor force than 

native-English-speaking adults. 

 More non-native-English-speaking adults held jobs 

in Service (24%) than in any other occupational 

groups. Among the non-native-English-speaking 

adults with lower literacy, the most frequently held 

jobs were in Service, compared with the most 

frequently held jobs in Construction and Extraction 

among their low literacy native-English-speaking 

counterparts. 

 The average prose and quantitative literacy scores of 

the non-native-English-speaking adults were 

substantially lower than those of their nativeEnglish-speaking counterparts. More than threequarters of the non-native-English speakers had 

Below Basic or Basic prose literacy. 

 Hispanic or Black non-native-English-speaking 

adults, adults who lacked a high school diploma or a 

GED certificate, and adults who lacked computer 

literacy were more likely to have low prose literacy. 

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. 

Surn, A., Kirsch, I., and Yamamoto, K. (2004). A Human 

Capital Concern: The Literacy Proficiency of U.S. 

Immigrants. Princeton, NJ: Educational Testing 

Services. 

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) for the 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 

Age 

All respondents were asked to report their birthdates, and 

this information was used to calculate their age. Age groups 

reported are 16 to 18, 19 to 24, 25 to 39, 40 to 49, 50 to 64, 

and 65 and older. Age groups were selected to correspond 

to key life stages of many adults: 

 16–18: Completion of secondary education 

 19–24: College or job training 

 25–39: Early career 

 40–49: Mid career 

 50–64: Late career 

 65 and older: Retirement

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 five groups: English 

only, English and Spanish, English and other language, 

Spanish only or with other language, or other language(s). 

The English and Spanish category includes adults who spoke 

languages in addition to both English and Spanish. 

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 to 20, 

21 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. 

Age Obtained High School Diploma/GED

Respondents were asked to provide the year they graduated 

high school or obtained their GED. Their age was calculated 

on the basis of their birthday and the assumption that they 

obtained their degree in June. Respondents were grouped 

into the following categories: 19 or younger, 20 to 24, 25 or 

older, did not graduate.

Participation in English as a Second Language 

Instruction 

Respondents who spoke a language other than English 

before starting school were asked whether they were 

currently enrolled in or had ever taken part in an ESL class 

in the United States. Respondents were then asked how long 

ago they last took a class to improve their English: within 

the last two years, 2 to 5 years ago, more than 5 years ago, 

currently taking an ESL class. 

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 information on a CD-ROM 

 Find information on the Internet 
A-2

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 

out-of-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. 

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 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.

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 twotailed test. Differences in the means and proportions 

between subgroups were calculated using the following t 

statistic: 
A-3

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

se2 are their corresponding standard errors. When a 

subgroup was compared with 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: 

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 designbased 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).3

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 non-native-English-speaking 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). 

 

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

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

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.4

 

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 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 age, 

sex, race/ethnicity, country of birth, age when first learned to 

speak English, educational attainment, participation in ESL 

class, 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.5

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. 

 

4

 For more information on multinomial logistic regression, see Hosmer, D., 

and Lemeshow, S. (2004). Applied Logistic Regression. New York: John 

Wiley & Sons. 

5 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

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 therefore were not appropriate 

to use for estimates of population statistics. 

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.6

 Estimates for 

average literacy scores and percentages in each literacy 

proficiency level in this report were all obtained using the 

 

6

 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. 

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). 

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

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

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 -- -- -- 3.00 1.46 6.23 4.94 2.16 11.36 

Race/ethnicity: Hispanic vs. White -- -- -- 1.82 1.14 2.92 -- -- -- 

Race/ethnicity: American 

Indian/Alaskan native vs. White -- -- -- 1.94 1.02 3.75 -- -- -- 

Education: Less than high school vs. 

postsecondary 1.54 1.10 2.15 1.77 1.21 2.58 2.71 1.79 4.11 

Education: High school/GED vs. 

postsecondary -- -- -- 1.96 1.34 2.87 2.34 1.50 3.64 

ESL class participation: Yes vs. No -- -- -- -- -- -- 1.49 1.00 2.22 

Computer literacy: Some vs. None -- -- -- -- -- -- 0.52 0.34 0.81 

Job training participation: Yes vs. No -- -- -- 0.63 0.44 0.89 0.66 0.45 0.98 

Oral passage reading score -- -- -- 0.99 0.99 1.00 0.98 0.97 0.98 

-- Estimates not significant and not shown. 

Note: Results are shown only for predictors with significant odds ratio estimates. 
A-5

 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.