1

Survival Literacy Training for 

Non-Native-English-Speaking Workers 

Authors 

Lee Bruno 

Ying Jin 

Dwayne Norris 

American Institutes for Research

October 30, 2010 

Contents 

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

Characteristics of NonNative-English-Speaking 

Workers ................................ 2

Demographic and 

Socioeconomic Comparisons.... 2

Prose and Quantitative 

Literacy Comparisons ............... 2

Literacy and Participation in 

Formal Instruction..................... 4

Literacy and Labor Force 

Behavior .................................... 4

Survival Literacy for NonNative-English-Speaking 

Workers ................................ 7

A Review of the Models for 

Teaching ESL and Literacy. 8

Vocational English as a 

Second Language ...................... 8

Immersion Methods .................. 9

Integrated Work-Based 

Methods..................................... 9

Addressing Survival Literacy 

for Non-Native-EnglishSpeaking Workers With a 

New Model............................ 9

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

References........................... 10

Appendix A: Methodology 

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

The 2003 NAAL 

Assessment............................A-1

Descriptions of Background 

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

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

Linking O*NET Occupation 

Descriptors to NAAL 

Literacy Scales ......................A-3

Introduction 

Prior to the recession of 2008, immigrant workers, 50 percent of whom were foreign-born 

Hispanics, had a higher employment rate than native workers and represented 16 percent of 

the total U.S. workforce. A disproportionate share of immigrant workers is concentrated in 

the agriculture, construction, production, and service industries (Cytron, 2009). The physical 

nature of the work in these industries increases the potential for accidents and the need for 

safety training. The U.S. Department of Labor’s (USDOL) Occupational Safety and Health 

Administration (OSHA) recently issued an enforcement memorandum that instructs its 

compliance officers “to check and verify that workers are receiving OSHA-required training 

in a language they understand” (Occupational Health & Safety, 2010). In another issuance, 

OSHA sets forth the requirement that safety training materials match workers’ literacy 

proficiency (Michaels, 2010). Although this measure is meant to protect non-native-Englishspeaking workers, it also could be an incentive for employers to restrict hiring to English 

speakers rather than conduct safety training in English and then again in as many languages 

as necessary to accommodate all non-native-English speakers. The OSHA directive places 

immigrants with limited English-speaking ability at risk for being excluded from industries 

where they are most dependent for employment because employers may view them either as 

greater training challenges or at greater risk for failing to follow workplace safety rules. 

USDOL’s Employment and Training Administration (ETA) is the locus for the Federal 

Government’s resources for the nation’s workforce development, with particular 

responsibility for training segments of the population who face disadvantages in the labor 

market. Under contract to ETA, the American Institutes for Research (AIR) produced a 

series of papers on literacy findings from the 2003 National Assessment of Adult Literacy 

(NAAL).1

 One paper in the series examined the characteristics of non-native-Englishspeaking adults with emphasis on their literacy levels, in comparison with the literacy 

levels of native-English-language speakers. In that paper, Overcoming the Language 

Barrier: The Literacy of Non-Native-English-Speaking Adults, AIR analyzed data from 

NAAL to study 2,807 adults older than 16 years of age who did not speak English before 

starting school. The findings of that analysis provided useful information on the 

characteristics of non-native-English speakers that is relevant to their challenges in the 

labor market. This paper, using the same guidelines for designating non-native-English 

speakers,2

 is based on NAAL data for 2,728 adult workers older than 18 years of age. 

The purpose of this paper is to explore options for addressing the specific challenges to 

remediating the literacy and limited English skills of non-native-English-speaking workers, 

options that will address both the OSHA safety training requirements and ETA’s future 

occupational training of non-native-English speakers. 

Study questions that guide this analysis are 

 What characteristics of non-native-English-speaking workers will influence 

literacy and English as a Second Language (ESL) training? 

 What levels of English and literacy skills are needed to master the requirements of 

industries where non-native-English speakers are concentrated? 

 What training methods address both literacy and ESL training at these levels? 

 What are the ETA’s workforce investment system’s3

 strategic options for 

implementing a systemic response to literacy and ESL training? 

 1

 The 2003 NAAL study (http://nces.ed.gov/naal/) provided information on the literacy proficiency of nearly 18,000 adults, 16 years of age or older. See the 

appendix of this report for a description of the study. For an interpretation of the literacy scales and performance levels on the NAAL assessment, see Kutner et al. 

(2007). 2

 “Non-native-English-speaking adults” were defined as adults who reported they did not learn to speak English before starting school. 3

 ETA funds job training, income maintenance, labor market information, and job placement services through a system of State and local One-Stop Service 

Centers, http://www.doleta.gov/etainfo/wrksys/WIMission 
2 

Characteristics of Non-Native-EnglishSpeaking Workers 

Demographic and Socioeconomic Comparisons 

In addition to assessing the literacy skills of respondents, 

NAAL gathered extensive background information on their 

demographic and socioeconomic characteristics (e.g., age, 

gender, nativity status, schooling, labor force status, 

household income), along with their literacy practices. The 

size of the sample of non-native-English speakers is 

sufficient for NAAL findings to be representative of all nonnative-English speakers in the United States. 

Table 1 compares race/ethnicity, native language, age 

learned to speak English, country of birth, and highest 

educational attainment of the native-English speakers with 

non-native-English speakers.4

Generally, the adjustment of the population studied from all 

adults older than 16 years of age to workers older than 

18 years of age did not reveal any new findings about the 

characteristics of non-native-English speakers and their 

comparison with native-English speakers. However, some 

characteristics of non-native-English speakers are 

particularly pertinent to workforce literacy training. 

 4

 Many of the background variables examined in this report are based on 

self-reported data. Due to the limited scope of this report and because the 

variables 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 NAAL data show that 31 percent of non-native-Englishspeaking workers learned English at an early age—during 

the first 10 years. Another group (15 percent) learned 

English between the ages of 11 and 15. A safe assumption, 

then, is that perhaps 46 percent of the workers have a good 

command of working English, having gone through years of 

education before reaching the working age of 18. The older 

students are when they learn English, that is, the closer they 

are to the age at which they are first employed, usually 

between the ages of 16 and 20, the less likely it is that they 

enter the labor market with a good command of the English 

language. This group represents 32 percent of non-nativeEnglish-speaking workers. Another 22 percent of the nonnative- English-speaking workers have yet to learn English. 

Therefore, the workers who are most in need of English 

proficiency represent about half (54 percent) of the nonnative-English-speaking workers. 

The NAAL data also show that 62 percent of the non-nativeEnglish-speaking workers speak Spanish. There was no 

other language with concentrations of non-native- Englishspeaking respondents. These findings support earlier cited 

research showing that Spanish-speaking workers make up 

the largest segment of our non-native-English-speaking 

workforce. 

Prose and Quantitative Literacy Comparisons 

NAAL measured respondents’ proficiencies on three literacy 

scales: prose, document, and quantitative. Because there 

was such a high correlation between the prose and document 

scores, the analyses in this paper are based on the prose and 

quantitative literacy scales only. Prose and quantitative 

proficiency was measured on a scale that ranged from 0 to 

Table 1: Percentage distribution of native- and non-native-English-speaking workers, by selected characteristics 

Characteristic Native-English-speaking adults Non-native-English-speaking adults 

Race/ethnicity 

White 79 19* 

Black 13 3* 

Hispanic 5 62* 

Other 4 16* 

Language (if not English) spoken before starting school 

Spanish and other language N/A 62 

Non-Spanish language N/A 38 

Age learned to speak English 

1–10 years N/A 31 

11–15 years N/A 15 

16–20 years N/A 11 

21 years or older N/A 21 

Does not speak English N/A 22 

Country of birth 

United States 96 16* 

Other 4 84* 

Highest educational attainment 

Still in high school 1 2 

Less than/some high school 12 36* 

High School grad/GED/equivalency 33 23* 

Postsecondary but less than 4-year college 31 21* 

College grad/graduate studies/degree 24 19* 

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

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

500. Scores on each of the literacy scales were characterized 

in terms of four literacy proficiency levels: Below Basic, 

Basic, Intermediate, and Proficient. A brief description of 

the four levels of literacy is presented below (Hauser, Edley, 

Koenig, & Elliot, 2005; White & Dillow, 2005). 

Prose 

 Below Basic—locating easily identifiable 

information in short, commonplace prose texts, 

with literacy assessment scores ranging from 

0 to 209. 

 Basic—reading and understanding information in 

short commonplace texts, with literacy assessment 

scores ranging from 210 to 264. 

 Intermediate—reading and understanding 

moderately dense, less commonplace prose texts, as 

well as summarizing, making simple inferences, 

determining cause and effect, and recognizing the 

author’s purpose, with literacy assessment scores 

ranging from 265 to 339. 

 Proficient—reading lengthy, complex, abstract 

prose tests, as well as synthesizing information and 

making complex inferences, with literacy 

assessment scores ranging from 340 to 500. 

Quantitative 

 Below Basic—locating numbers and using them to 

perform simple quantitative operations (primarily 

addition) when the mathematics information is very 

concrete and familiar, with literacy assessment 

scores ranging from 0 to 234. 

 Basic—locating easily identifiable quantitative 

information and using it to solve simple, one-step 

problems when the arithmetic operation is specified 

or easily inferred, with literacy assessment scores 

ranging from 235 to 289. 

 Intermediate—locating less familiar quantitative 

information and using it to solve problems when 

the arithmetic operation is not specified or easily 

inferred, with literacy assessment scores ranging 

from 290 to 349. 

 Proficient—locating more abstract quantitative 

information and using it to solve multistep 

problems when the arithmetic operations are not 

easily inferred and the problems are more complex, 

with literacy assessment scores ranging from 

350 to 500. 

There is a dramatic difference between the average literacy 

levels of native-English-speaking workers and non-nativeEnglish-speaking workers in both prose and quantitative 

literacy. Table 2, which compares literacy levels of nonnative-English speakers with native-English speakers, shows 

that the discrepancy between the two groups is particularly 

striking when comparing the lowest and highest levels of 

literacy. Non-native-English-speaking workers are more 

than five times as likely to fall within the category of Below 

Basic as are native-English-speaking workers. Below Basic 

means that they had no more than the simplest and most 

concrete prose5

 and quantitative6 skills. 

By contrast, native-English-speaking workers were more 

than twice as likely to fall within the Intermediate literacy 

range and nearly four times as likely to fall within the 

Proficient literacy range for prose literacy. 

 5

 Below Basic prose literacy is being able to locate easily identifiable 

information in short, commonplace prose texts. 6

 Below Basic quantitative literacy is being able to locate numbers and 

use them to perform simple quantitative operations, such as comparing two 

prices by subtracting. 

Table 2: Means and distribution of non-native-English-speaking workers in each prose and quantitative literacy level 

Overall % Prose Mean Quant Mean 

Native-English speakers 87 283 289 

Non-native-English speakers 13 211 235 

Below Basic Basic Intermediate Proficient 

Prose 

Native-English speakers 9 27 49 15 

Non-native-English speakers 49* 28 20* 4* 

Quantitative 

Native-English speakers 18 33 35 15 

Non-native-English speakers 48* 28* 18* 6* 

* Significantly different from native English speakers workers at the significance level of < 0.05. 
4 

These findings are consistent with the findings from the 

analysis of non-native-English-speaking adults versus native 

English-speaking adults cited in the earlier paper, 

Overcoming the Language Barrier: The Literacy of NonNative-English-Speaking Adults, which showed that 

“Spanish-speaking [non-native-English-speaking] adults had 

lower prose and quantitative literacy levels than nonSpanish-speaking [native-English-speaking] adults. Sixtytwo percent of the Spanish speakers had Below Basic prose 

literacy, compared with 26 percent of the non-Spanish 

speakers with Below Basic prose literacy.” 

In addition, findings from the multivariate analyses 

conducted for this paper revealed the following: 

 More non-native-English-speaking adults with low 

literacy (Below Basic and Basic) failed to complete 

high school or receive a General Education 

Development (GED) certificate than nativeEnglish-speaking adults with low literacy. 

However, there was little difference in education 

attainment between the two groups when they 

scored either Intermediate or Proficient. 

 A higher percentage of non-native-Englishspeaking adults with low literacy (10 percent) 

earned a college degree or completed graduate 

studies or a degree program than native-Englishspeaking adults with low literacy (7 percent ). 

 Non-native-English speakers (39 percent) with low 

literacy were less likely to have some computer 

literacy than their native-English-speaking 

counterparts (51 percent). 

Non-native-English speakers seem to have greater difficulty 

completing high school when they also have low literacy 

levels than English speakers with low literacy levels. 

However, once they enter postsecondary education 

programs, the effects of low literacy are less apparent. In 

addition, non-native-English speakers with low literacy 

levels are not as likely as native-English speakers to benefit 

from computer-based education programs. 

Literacy and Participation in Formal Instruction 

If low literacy infringes on non-native-English speakers’ 

ability to finish high school, will it also affect participation 

in ESL and Basic Skills (literacy) courses? Table 3 

summarizes the percentage of non-native-English-speaking 

workers who have participated in formal instruction in ESL 

and Basic Skills by literacy level. The NAAL data show 

that non-native-English-speaking workers with lower prose 

literacy levels were more likely to participate in ESL classes 

than their counterparts with Proficient prose literacy levels. 

Participation in the ESL services among the low-literacy 

non-native-English-speaking workers indicates their felt 

need for English skills that enable them to function in an 

English-speaking environment. However, those with lower 

prose literacy levels were less likely to participate in Basic 

Skills training, indicating either a language barrier or the 

lack of understanding of the connection between prose and 

quantitative literacy and employment or job training. 

Interestingly, participation in Basic Skills classes was the 

same, regardless of literacy level. 

Literacy and Labor Force Behavior 

Tables 4–7 summarize data that show how prose and 

quantitative literacy correlate to various aspects of labor 

force behavior—probability of full-time employment, length 

of employment, and occupations held. All are aspects of 

workplace survival. 

Full-time employment 

It is not surprising to see that non-native-English-speaking 

workers with low levels of prose and quantitative literacy are 

less likely to be employed full time than their more literate 

counterparts. The same pattern was found among the nativeEnglish-speaking workers, as well. This finding is similar to 

studies of other populations and the effects of literacy on 

employment. Literacy is clearly a factor in gaining full-time 

employment. 

Table 3: Proportion of non-native-English-speaking workers participating in ESL and Basic Skills instruction 

ESL Basic Skills 

Yes (%) No (%) Yes (%) No (%) 

Overall % 38 62 11 89 

Below Basic 39 61 10 91 

Basic 43 57 14 86 

Intermediate 31 69 12 88 Prose mean 

Proficient 18 82 11 90 

Below Basic 38 63 11 89 

Basic 42 58 12 88 

Intermediate 35 65 12 88 Quantitative mean 

Proficient 28 72 9 91 
5 

Duration of employment 

Literacy is also related to length of employment, as shown in 

Table 5. Regardless of whether the workers were nativeEnglish speakers or non-native-English speakers, the more 

literacy proficient they were, the more they worked. 

Table 4: Literacy and labor force behavior 

Prose 

Employed full 

time (%) 

Employed part 

time (%) 

Employed, not 

at work (%) 

Unemployed 

(%) 

Out of labor 

force (%) 

Below Basic 49 11 2 8 33 

Basic 53 12 2 8 26 

Intermediate 61 11 2 6 21 Non-native 

Proficient 68 10 1 4 19 

Below Basic 27 8 2 8 55 

Basic 42 11 3 8 36 

Intermediate 54 13 4 6 23 Native 

Proficient 62 13 5 4 16 

Quantitative 

Employed full 

time (%) 

Employed part 

time (%) 

Employed, not 

at work (%) 

Unemployed 

(%) 

Out of labor 

force (%) 

Below Basic 47 11 2 5 34 

Basic 56 12 2 6 24 

Intermediate 62 11 1 5 21 Non-native 

Proficient 63 10 1 7 19 

Below Basic 30 10 3 10 47 

Basic 46 12 4 7 31 

Intermediate 56 13 4 5 23 Native 

Proficient 64 13 4 4 15 

Table 5: Literacy and length of employment 

Prose 

26 weeks or less (%) 27–51 weeks (%) 52 weeks (%) 

Below Basic 42 17 41 

Basic 37 14 49 

Intermediate 31 17 52 Non-native 

Proficient 27 21 52 

Below Basic 65 12 23 

Basic 46 16 38 

Intermediate 31 19 50 Native 

Proficient 22 20 58 

Quantitative 

26 weeks or less (%) 27–51 weeks (%) 52 weeks (%) 

Below Basic 45 14 41 

Basic 33 17 50 

Intermediate 31 18 51 Non-native 

Proficient 29 23 48 

Below Basic 60 14 26 

Basic 40 18 42 

Intermediate 29 19 52 Native 

Proficient 0.20 0.19 0.61 
6 

Prose literacy levels and types of occupations held by 

non-native-English speakers 

In terms of the effects of literacy on the types of occupations 

held by non-native-English speakers, both prose and 

quantitative literacy levels were very similar and had similar 

effects. For that reason, Table 6 uses data from prose 

literacy scores to demonstrate the effects of both. 

Non-native-English speakers had lower prose and 

quantitative literacy than their native-English-speaking 

counterparts across all occupational groups (i.e., workers 

holding the same types of occupations.) The most common 

types of occupations held by non-native-English speakers

were these: 

1. Services, where the workers’ average prose literacy 

score was 188 

2. Professional and related, where workers’ average prose 

literacy score was 288 

3. Production, where the workers’ average prose literacy 

score was 178 

4. Office/Administrative support, where the workers’ 

average prose literacy score was 237 

Among the non-native-English speakers, 63 percent held 

occupations in these four categories, where the workers’ 

average prose literacy scores fell in the range of 178 to 288. 

This range corresponds to below basic and basic literacy levels. 

Table 6 also displays the most common types of occupations 

held by native-English-speaking workers. They are 

1. Professional and related fields, where the workers’ 

average prose literacy score was 320 

2. Service, where the workers’ average prose literacy score 

was 272 

3. Office/administrative support, where the workers’ 

average prose literacy score was 292 

4. Management/business/financial, where the workers’ 

average prose literacy score was 310 

Among native-English-speaking workers, 66 percent held 

occupations in these four categories, where the average prose 

literacy score ranged from 272 to 320, a range that corresponds 

to intermediate literacy levels. 

Across all occupational groups, more non-native-English 

speakers had Below Basic or Basic prose and quantitative 

literacy than their native-English-speaking counterparts, and the 

gap was fairly large. For example, among non-native-Englishspeaking workers in the service occupations, 61 percent had 

Below Basic prose literacy, in contrast with only 10 percent 

among the native-English speakers. Among non-nativeEnglish-speaking service workers, 66 percent had Below Basic 

quantitative literacy, compared with 25 percent of nativeEnglish speakers. 

For both native- and non-native-English-speaking workers, 

increased prose literacy proficiency levels were associated with 

strong and steady gains in weekly earnings, as shown in 

Table 7. Similar relationships were found between quantitative 

literacy and earnings, as well. 

Table 6: Percentage of non-native-English-speaking workers versus native-English-speaking workers in each prose literacy level,

by occupation 

Prose 

Non-native Native 

Occupational groups 

Below Basic 

(%) 

Basic 

(%) 

Intermediate 

(%) 

Proficient 

(%) 

Below Basic 

(%) 

Basic 

(%) 

Intermediate 

(%) 

Proficient 

(%) 

Management/business/ 

 financial 

20 39 36 5 2 14 59 25 

Professional and related 7 26 53 15 2 12 51 35 

Service 61 25 12 2 10 34 48 8 

Sales and related 33 39 26 3 5 28 56 12 

Office/administrative 

 support 

32 34 31 3 3 23 59 14 

Farming/fishing/forestry 83 14 2 0 20 30 40 11 

Construction/extraction 68 21 10 1 14 34 44 8 

Installation/maintenance/ 

 repair 

44 34 21 2 4 27 62 8 

Production 67 24 8 0 10 36 48 6 

Transportation/material 

 moving 

63 25 10 2 13 41 42 4 
7 

Among non-native-English-speaking workers with Below Basic 

prose literacy, 67 percent earned less than $500 per week and 

9 percent earned $850 or more. In comparison, 61 percent of 

those with Proficient literacy earned $850 or more and 

7 percent earned less than $500, as shown in Table 8. Non- 

 native-English speakers had lower prose literacy than their 

native-English-speaking counterparts earning the same weekly 

wage. The gap was largest among those earning less than $500 

weekly. 

All the above findings show a consistent relationship between 

literacy and survival in the workplace, in terms of duration and 

type of job, hours worked, and wages earned. 

Survival Literacy for Non-Native-EnglishSpeaking Workers 

Survival literacy is the prose and quantitative literacy 

proficiency needed to function safely in an English-speaking 

environment and compete successfully for self-sustaining 

employment. Determining what these essential literacy 

skills are for the workplace requires an examination of 

occupational requirements that represent a sample from 

industries where significant numbers of non-native-Englishspeaking workers are employed—production, services, and 

construction.7

 For demonstration purposes, occupations 

were chosen that did not call for education beyond high 

school and required the lowest literacy levels, which 

provides the minimum base for determining survival literacy 

skills for self-sustaining employment. 

On the basis of the multiple regression models developed by 

AIR that integrate Occupational Information Network 

(O*NET)8

 data and the NAAL literacy measures, each 

O*NET occupation’s literacy requirements were derived so 

that they could be compared with the average literacy levels 

of the NAAL respondents who were non-native-Englishspeaking workers.9

 Table 9 shows the resulting range of 

literacy levels for the sample of occupations from the target 

industries. 

 7

 Farm work is an occupation for which there are no minimal knowledge 

requirements according to O*NET and so was not included in the analysis. 8

 O*NET is a database of information describing all U.S. occupations. 

See http://online.onetcenter.org/ 9

 The method used to link the O*NET data and the NAAL literacy 

measures is described in Appendix A. 

Table 7: Prose literacy and labor market outcomes, by income 

Non-native Native 

Weekly salary Overall (%) Score (mean) Overall (%) Score (mean) 

Less than $300 13 175 8 275 

$300–$499 34 180 21 274 

$500–$649 15 210 17 285 

$650–$849 13 243 17 296 

$850–$1149 12 263 16 306 

≥$1150 13 273 22 320 

Table 8: Literacy and labor market outcomes, by prose literacy level 

Less than 

$300 (%) 

$300–$499 

(%) 

$500–$649 

(%) 

$650–$849 

(%) 

$850–$1149 

(%) 

More than

$1150 (%) 

Below Basic 20 47 16 9 5 4 

Basic 11 30 17 14 13 15 

Intermediate 6 15 13 17 22 27 Non-native 

Proficient 4 3 9 25 23 38 

Below Basic 14 34 22 15 9 6 

Basic 12 31 19 17 12 11 

Intermediate 7 19 17 18 16 22 Native 

Proficient 4 9 11 17 21 40 
8 

Collectively, these occupations represent a prose literacy range 

of 226 to 275 and a quantitative literacy range of 240 to 278. 

(See page 3 for corresponding literacy levels.) The prose mean 

for non-native-English-speaking workers is 211, and the 

quantitative mean is 235. Therefore, on the basis of this sample 

of occupations, non-native-English-speaking workers are 

employed in industries in which they cannot function safely 

because they fail to meet the literacy levels common to these 

occupations. It is reasonable to assume that each employer 

selects OSHA safety training materials that best match its 

workers’ literacy levels because this is an OSHA requirement 

(Michaels, 2010). According to the concerns basic to Michaels’ 

position on this matter, minority, non-native-English-speaking 

workers with average literacy proficiency below that of their 

native-English-speaking counterparts are subject to a 

disadvantage in the workplace that is unquestionably linked to 

their safety. This point was made by the USDOL Assistant 

Secretary for OSHA, and is confirmed by the NAAL data. 

OSHA’s proposed solution is for employers to conduct safety 

training in the native language of workers and at the literacy 

levels within their range of understanding. However, that 

would require employers to replicate safety training in many 

different languages and at many different levels of literacy 

proficiency. ETA has the option of examining its approach to 

occupational training to find alternative approaches that would 

be either more attractive to employers or more efficient to 

implement in its training institutions. 

A Review of the Models for Teaching ESL 

and Literacy 

Workforce investment system professionals are familiar with 

traditional methods for teaching ESL and literacy that require 

attendance in a classroom, usually located within a public 

school or college, during evening hours that accommodate 

working students. Teachers use a didactic approach for 

covering vocabulary, grammar, and numeracy, and provide 

practice through repetitive drills. Students rely on rote memory 

to recall and present information. ESL and Basic Skills 

(literacy) are usually taught as separate subjects, where ESL 

precedes Basic Skills teaching. Because non-native-English 

speakers can usually accommodate only one class at a time, 

some level of English is mastered before the Basic Skills 

program is attempted. As the NAAL data showed, non-nativeEnglish speakers were far more likely to attend ESL classes 

than Basic Skills training. In fact, non-native-English speakers 

rarely enroll in Basic Skills programs. NAAL data also show 

that non-English speakers are less likely to own or use personal 

computers or the Internet, which precludes online training. 

Altogether, the NAAL data raise questions about the benefits of 

traditional teaching methods for non-native-English-speaking 

workers. 

New models for teaching ESL and Basic Skills, currently in 

evidence, could offer better solutions to teaching survival 

literacy to non-native-English-speakers. These models 

incorporate one or more of the following methods: 

 Teaching ESL in the context of a vocational 

training program 

 Teaching ESL through the immersion method 

 Teaching workplace literacy using a work-based 

approach 

Vocational English as a Second Language 

One of the oldest of the new models is Vocational English as a 

Second Language (VESL), which was first used to address the 

language barriers of waves of new immigrants arriving from 

Third World countries during the late 1970s and early 1980s. 

The lack of employability skills created a need to teach English 

and vocational skills together to expedite job readiness. English 

is taught on an as-needed basis by a team of ESL and vocational 

instructors. This instructor team starts with the vocational 

learning objectives and creates a lesson plan for each unit that 

includes vocational subjects, language skills associated with 

each subject, and relevant cultural information (Harrison, 1986). 

Several examples of modern applications of VESL involve 

partnerships among community colleges, nonprofit advocacy 

organizations, and sometimes corporations. One is the Instituto 

del Progreso Latino, a community-based organization in 

Chicago that is partnering with Humbolt Park Vocational 

Education Center to prepare first-generation Hispanic 

immigrants for jobs in advanced manufacturing and licensed 

practical nursing. Seattle’s Shoreline Community College uses 

Washington State’s Integrated Basic Education and Skills 

Training program to deliver training that integrates automotive 

skills, ESL, and employability skills (Cytron, 2009). These 

examples demonstrate a shift from sequential learning to 

integrated learning with a focus on priorities relevant to the 

vocational objective. This concentration on relevant language 

and numeracy skills is far more appealing to the students and 

more efficient than traditional methods; however, instruction 

still depends on bilingual teachers teaching students in their 

native language. 

Table 9: Literacy requirements for occupations common to non-native-English-speaking workers 

Occupations Industry Prose literacy Quantitative literacy 

Rough carpenter Construction 275 278 

Operating engineer* Construction 245 255 

Team assembler Production 250 257 

Extruding & forming machine operator Production 257 270 

Sewing machine operator Production 226 240 

*High growth occupation 
9 

Immersion Methods 

Immersion is based on the way children learn language—

by listening and imitating and not worrying about getting 

everything correct. Immersion language training is now a 

common way to teach foreign languages in public elementary 

and secondary schools. Typical academic courses—for 

example, science—are taught in a foreign language so that the 

foreign language is the language of instruction. Homework 

assignments are in the foreign language, and students are 

encouraged to engage in activities with non-English speakers 

to practice. Students as young as sixth graders spend a week 

living in the home of a host family in a country corresponding 

to their language studies. Fairfax County, Virginia, public 

schools offer immersion language classes in Chinese, Japanese, 

French, Spanish, and German in a number of elementary and 

middle school classrooms.10

Immersion has proved to be a far faster way to acquire speaking 

and listening skills in a second language, although the grammar 

lags behind. Using immersion methods would enable nonnative-English speakers to learn English at the same time that 

they are learning occupational skills or safety requirements. It 

also offers the advantage of not necessitating bilingual 

instructors. 

Integrated Work-Based Methods 

Work-based literacy programs teach literacy tailored to the 

needs of a specific employer or community of employers with 

common needs. Unlike traditional teaching methods and VESL 

programs, work-based programs occur at the workplace. 

Learning objectives are organized around needs identified by 

the employer or union representatives. Employers embrace the 

training as a means of ameliorating a particular problem, while 

the trainees see the immediacy and relevance of literacy 

instruction to their job success. 

A report by Isserlis (1991) based on work done by AIR sees 

work-based literacy as especially well suited to the needs of 

non-native-English-speaking workers. AIR set out four major 

components of successful workplace literacy programs: 

 Systematic analysis of literacy requirements 

 Involvement of workers in defining the types of 

tasks they perform 

 Involvement of stakeholders—employers, unions, 

and teachers 

 Development of instructional materials that 

incorporate the first three elements 

Work-based literacy programs address some of the logistical 

and motivational issues of literacy training. Workers learn 

where they work and in some cases are given paid time off to 

attend classes. Because the literacy training is directly relevant 

to their jobs and is sanctioned by their bosses, trainees are 

highly motivated learners. 

 10 See http://www.fcps.edu/dis/OHSICS/forlang/partial.htm. 

Addressing Survival Literacy for Non-NativeEnglish-Speaking Workers With a New Model 

There is a very specific challenge before ETA’s workforce 

investment system that concerns non-native-English-speaking 

workers: how to make the workplace safer and in compliance 

with OSHA requirements. This challenge affects the ETA 

occupational training programs that include OSHA safety 

training, as well as employer-managed safety training. ESL 

training alone will not address the literacy issues that also are at 

play. The answer is an integrated ESL/literacy program that is 

taught at the workplace in English and is tailored to the specific 

needs of each employer.11 This program includes all workers 

with literacy deficiencies, regardless of English proficiency. 

Thus, the model addresses all OSHA concerns about the 

adequacy of safety training. Rather than “dumb down” the 

training, the model improves the literacy of all workers to 

achieve a common standard that matches all training, including 

the safety training materials. 

Evidence-based best practices show the utility of integrating 

ESL and job-related literacy: It motivates learners and is more 

efficient. There are also numerous examples of the efficacy of 

teaching literacy to non-native-English speakers at the 

workplace. Malden Mills retrained its entire workforce of lowlevel fabric mill workers, many of whom were non-nativeEnglish speakers, to operate advanced manufacturing processes 

that produce Polartec fabric.12 Malden Mills engaged a 

contractor that constructed a curriculum around the 

requirements of the new equipment, set up classes, and 

retrained all hourly workers during the shifts when they would 

normally work. All workers were paid their normal salaries, 

and learning became part of their job. 

Using immersion methods will enhance the English speaking 

and listening skills of non-native-English-speaking workers on 

the job, while allowing all workers to be trained together on 

specific safety issues. Malden Mills did not use bilingual 

instructors, and only English was spoken. The non-nativeEnglish speakers came from a wide range of ethnic groups and 

had learned some English as a result of their previous 

employment at Malden. 

Further research through demonstration grants would establish 

the feasibility and effectiveness of this combination of program 

elements. The optimal balance of methods could not only 

provide a solution to OSHA safety training but enable nonnative-English-speaking workers to advance their progress to 

safer and more secure employment in the industries in which 

they are concentrated. 

Summary 

According to the 2003 NAAL data, a high percentage 

(62 percent) of non-native-English-speaking workers are 

Hispanic and more than a third do not have a high school 

 11 Small employers can combine forces to bring about the economy of 

scale needed. 12 Although there are numerous examples of employer-based adult basic 

education classes, Malden Mills is the most famous and well documented 

(www.compassionatwork.com/art_malden_mills.html). 
10 

diploma. About 20 percent do not speak English at all, while 

another 25 percent learned English as adults and are likely to 

have limited English skills. Their literacy levels are markedly 

lower than those of native-English speakers, even though they 

demonstrate higher employment rates than native-Englishspeaking workers. Comparisons of the literacy proficiency of 

non-native-English-speaking workers show that literacy is 

closely associated with length of employment, wages earned, 

hours worked, and occupations held. 

Non-native-English speakers’ limited English skills have raised 

concerns about the efficacy of OSHA safety training in the 

industries in which these workers are concentrated—

agriculture, production, construction, and service industries. 

Employers are now directed to conduct training that meets the 

literacy and language skills limitations of their workers. This 

requirement poses a burden on employers, who may choose not 

to hire workers who lack the requisite literacy skills to master 

safety instruction. This same mandate must be addressed by 

ETA’s workforce investment system, as well. Where OSHA 

safety training is an integral part of occupational skills training, 

ETA must ensure that the safety training is conducted within 

the language and literacy skills of the trainees. 

A proposed solution to this dilemma incorporates evidencebased best practices into a new training model. USDOL has an 

opportunity to test and evaluate the model for use among 

employers who employ large numbers of non-native-Englishspeaking workers. 

References 

Cohen, J. (1988). Statistical power analysis for the 

behavioral sciences (2nd ed.). Hillsdale, NJ: 

Erlbaum. 

Cytron, N. (2009). Workforce development needs for immigrant 

job-seekers. Community Investment, 19–20, 39. 

Available at http://www.frbsf.org/publications/ 

community/investments/0905/Cytron_Naomi.pdf 

Harrison, C. (1986). Bilingual vocational education for 

immigrants (ERIC Digest No. 49). Retrieved 

October 1, 2010, from ERIC database (ED268302), 

http://www.eric.ed.gov/ 

Hauser, R. M., Edley, C. F. , Jr., Koenig, J. A., & Elliot, S. W. 

(Eds.). (2005). Measuring literacy: Performance 

levels for adults, Interim report. Washington, DC: 

National Academies Press. 

Isserlis, J. (1991). Workplace literacy programs for nonnative 

English speakers. Retrieved October 1, 2010, from 

ERIC database (ED334874), http://www.eric.ed.gov/ 

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

Dunleavy, E. (2007). Literacy in everyday life: 

Results from the 2003 National Assessment of Adult 

Literacy (NCES 2007-480). Washington, DC: 

U.S. Department of Education, National Center for 

Education Statistics. 

LaPolice, C. C., Carter, G. W., & Johnson, J. W. (2008). 

Linking O*NET descriptors to occupational literacy 

requirement using job component validation. 

Personnel Psychology, 61, 405–441. 

Michaels, D. (2010). OSHA Training Standards Policy 

Statement (Memorandum: April 28, 2010). 

Available at http://www.osha.gov/dep/standardspolicy-statement-memo-04-28-10.html 

Occupational Health & Safety. (2010, April). OSHA 

enforcement memo focused on non-English speakers. 

Available at http://ohsonline.com/articles/2010/ 

04/29osha-enforcement-memo-focused-on-latinoworker-safety.aspx 

U.S. Census Bureau. (2000). 2000 census industry and 

occupational codes. Washington, DC: Author. 

White, S., & Dillow, S. (2005). Key concepts and feature of 

the 2003 National Assessment of Adult Literacy

(NCES 2006-471). Washington, DC: U.S. 

Department of Education, National Center for 

Education Statistics. 
A–1 

Appendix A: Methodology and 

Technical Notes 

This appendix provides more information about the 

methodology and research that are referenced in this paper, 

starting with an overview of the 2003 National Assessment 

of Adult Literacy (NAAL). This section also describes the 

background variables and statistical procedures used in this 

particular paper. A final section discusses the methods used 

to link the Occupational Information Network (O*NET} 

occupation descriptors to the NAAL literacy scales, so that 

literacy gaps between selected high-growth occupations and 

non-Native-English-speaking workers could be determined. 

The 2003 NAAL Assessment 

The 2003 NAAL assessed the English literacy of adults 

(16 years of age and older) in the United States for the first 

time since the 1992 National Adult Literacy Survey. NAAL 

provided information on the literacy proficiencies of a 

nationally representative sample of approximately 18,000 

adults living in households and 1,200 prison inmates. In 

addition to assessing the literacy skills of respondents, 

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 how they obtain information. 

The NAAL measured respondents’ proficiencies on three 

literacy scales: 

 Prose literacy—the knowledge and skills needed to 

search, comprehend, and use information from 

continuous texts. Prose examples include editorials, 

news stories, brochures, and instructional materials. 

 Document literacy—the knowledge and skills 

needed to search, comprehend, and use information 

from noncontinuous texts. Document examples 

include job applications, payroll forms, 

transportation schedules, maps, tables, and drug and 

food labels. 

 Quantitative literacy—the knowledge and skills 

needed to identify and perform computations using 

numbers that are embedded in printed materials. 

Examples include balancing a checkbook, figuring 

out a tip, completing an order form, and 

determining the amount of interest on a loan from 

an advertisement. 

For each of the literacy scales, proficiency was measured on 

a scale that ranged from 0 to 500. Scores on each of the 

literacy scales were characterized in terms of four literacy 

proficiency levels: Below Basic, Basic, Intermediate, and 

Proficient. For more information on the methodology and 

findings from the NAAL assessment, see Literacy in 

Everyday Life: Results from the 2003 National Assessment 

of Adult Literacy (Kutner et al., 2007). 

Descriptions of Background Variables 

Race and ethnicity 

In 2003, all respondents were asked two questions about 

their race and ethnicity. The first asked them to indicate 

whether they were Hispanic or Latino. Then all the 

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. 

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 when 

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

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

currently taking an ESL class. 

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. 
A–2 

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 

 General Education Development (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., 

J.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. 

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 U.S. Census Bureau (2000) 

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 

timeframe 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. 

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: 

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: 

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

subgroup. 

Minimum sample sizes for reporting subgroup results 

In the NAAL study, 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. 
A–3 

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). 

Linking O*NET Occupation Descriptors to NAAL 

Literacy Scales 

Several studies support the use of the O*NET data to 

determine job requirement levels of employee aptitudes 

(LaPolice, Carter, & Johnson, 2008). To identify the literacy 

requirements of the selected occupations, including highgrowth occupations, that potentially offer economic 

independence for the non-native-English-speaking workers, 

occupation data related to job analysis ratings of knowledge, 

skills, and abilities were collected through the O*NET 

database. However, the ratings of the O*NET occupation 

descriptors are not on the same scale as the NAAL literacy 

measures. Therefore, linkage needs to be established 

between the two measures so that the average literacy scores 

of the non-native-English speakers can be compared with the 

literacy requirements of those selected occupations. This 

section describes the method used to link the O*NET 

occupation descriptors to the NAAL literacy scales. 

Following the approach taken by LaPolice, Carter, and 

Johnson. (2008) in their study “Linking O*NET Descriptors 

to Occupational Literacy Requirements Using Job 

Component Validation,” AIR used multiple regression 

models to estimate the literacy requirement for the 

50 selected occupations on the NAAL 2003 literacy scale. 

The LaPolice study used a job component validity approach 

to relate O*NET knowledge, skill, ability, and generalized 

work activity descriptor data to literacy test scores on 

NALS, conducted in 1992. The study estimated mean 

NALS 1992 literacy scores for 902 O*NET–Standard 

Occupational Classification (SOC) (http://www.bls.gov/soc/) 

occupations using multiple regression models. The study 

also showed that the NALS literacy scores were highly 

predictable from the O*NET descriptors. 

Specifically, for the purpose of this report, the prose, 

document, and quantitative literacy scores for 

100 occupations were first estimated based on the NAAL 

database. More than 400 occupations were available 

through the NAAL database, but these 100 occupations had 

sufficient sample sizes to allow reliable estimates. These 

literacy scores were used as the dependent variable in the 

multiple regression models for each of the prose, document, 

and quantitative scales, respectively. 

The NAAL occupations were classified according to the 

2000 Census Industry and Occupational Codes (U.S. Census 

Bureau, 2000). The O*NET descriptor data were at the level 

of O*NET–SOC code. The Census occupational codes were 

matched to the O*NET-SOC codes before the multiple 

regression analyses were conducted. 

To select the potential predictor variables for the multiple 

regression models, a team of AIR experts first identified the 

O*NET descriptors that were conceptually relevant to prose, 

document, and quantitative literacy. Correlations of the 

selected predictors and literacy scores were checked, and 

those predictors with negative or zero correlations were 

deleted. Then several models were compared in terms of 

how the descriptors could be further combined. The final set 

of predictors was determined on the basis of the published 

O*NET factor model, and is presented in Table A.1. 

The results of the multiple regression analyses are presented in 

Table A.2. Two coefficients in the models are negative, which 

is contrary to what would be expected. This does not mean that 

those two variables are negatively related to the criterion; rather, 

this is very likely due to the effect of multicollinearity13

(LaPolice, Carter, & Johnson, 2008). The regression 

coefficients were then applied to the models described above to 

estimate the literacy scores of the 50 selected occupations. 

 13 Multicollinearity is a problem in multiple regression that occurs when 

variables are so highly correlated with each other that it is difficult to 

separate the effects of two (or more) variables on an outcome variable and 

produce reliable estimates of their individual regression coefficients. 

Table A.1: O*NET descriptors identified to predict NAAL literacy scores 

Prose Document Quantitative 

Basic Skills Basic Skills Complex Problem Solving 

English Language English Language English Language 

Getting Information Getting Information Computers and Electronics 

Interacting With Others Performing Administrative Activities Cognitive Abilities 

Cognitive Abilities Cognitive Abilities 
A–4 

Table A.2: Regression coefficients for each regression model 

Regression coefficients 

Standardized 

Regression coefficients 

Literacy scale Predictors B Std. Error Beta t p 

Adjusted 

R square 

(Constant) 170 6.2 27.5 0 

Basic Skills P 12.5 2.7 0.4 4.7 0 

English Language 6.7 2.0 0.2 3.4 0 

Getting Information –4.9 2.2 –0.1 –2.2 0 

Interacting With Others 7.6 2.2 0.2 3.5 0 

Prose 

Cognitive Abilities P 9.5 3.3 0.2 2.9 0 

0.8 

(Constant) 184.9 5.7 32.6 0 

Basic Skills D 7.5 2.2 0.3 3.4 0 

English Language 8.8 1.7 0.4 5.3 0 

Getting Information –4.3 2.0 –0.2 –2.2 0 

Performing Administrative Activities 5.0 1.4 0.2 3.5 0 

Document 

Cognitive Abilities D 10.0 3.0 0.2 3.4 0 

0.7 

(Constant) 185.1 5.3 34.9 0 

Complex Problem Solving 5.9 1.4 0.2 4.2 0 

English Language 9.1 1.5 0.3 6.1 0 Quantitative 

Computers and Electronics 3.6 1.3 0.1 2.8 0 

0.8