DONE Abedi Standardized Achievement Tests

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Standardized Achievement Testsand English Language Learners:

Psychometrics Issues

Jamal AbediGraduate School of Education and Information Studies

CRESST/University of California, Los Angeles

Using existing data from several locations across the U.S., this study examined the

impact of students language background on the outcome of achievement tests. The

results of the analyses indicated that students assessment results might be con-

founded by their language background variables. English language learners (ELLs)

generally perform lower than non-ELL students on reading, science, and mathastrong indication of the impact of English language proficiency on assessment.

Moreover, the levelof impact of language proficiency on assessment of ELLstudents

is greater in the content areas with higher language demand. For example, analyses

showed that ELL and non-ELL students had the greatest performance differences in

the language-related subscales of tests in areas such as reading. The gap between the

performance of ELL and non-ELL students was smaller in science and virtually non-

existent in the math computation subscale, where language presumably has the least

impact on item comprehension.

The results of our analyses also indicated that test item responses by ELL stu-

dents, particularly ELL students at the lower end of the English proficiency spec-

trum, suffered from low reliability. That is, the language background of students may

add another dimension to the assessment outcome that may be a source of measure-

ment error in the assessment for English language learners.

Further, the correlation between standardized achievement test scores and exter-

nal criterion measures was significantly larger for the non-ELL students than for the

ELL students. Analyses of the structural relationships between individual items and

between items and the total test scores showed a major difference between ELL and

non-ELL students. Structural models forELLstudents demonstrated lowerstatistical

EDUCATIONAL ASSESSMENT,8(3), 231257Copyright 2002, Lawrence Erlbaum Associates, Inc.

Requests for reprints should be sent to Jamal Abedi, UCLACSE/CRESST, 300 Charles E.


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fit. The factor loadings were generally lower for ELL students, and the correlations

between the latent content-based variables were also weaker for them.

We speculate that language factors may be a source of construct-irrelevant vari-ance in standardized achievement tests (Messick, 1994) and may affect their con-

struct validity.

Due to the rapidly changing demographics of the U.S. population, fairness and va-

lidity issues in assessment are becoming top priorities in the national agenda. Be-

tween 1990 and 1997, the number of U.S. residents not born in the United States

increased by 30%, from 19.8 million to 25.8 million (Hakuta & Beatty, 2000). Ac-

cording to theSurvey of the States Limited English Proficient Students and Avail-

able Educational Programs and Services 19992000 Summary Report,over 4.4million limited English proficient1 students were enrolled in public schools (Na-

tional Clearinghouse for English Language Acquisition and Language Instruction

Educational Programs, 2002). To provide fair assessment and uphold standards on

instruction for every child in this country, both federal (e.g., No Child Left Behind

Act of 2001) and state legislation now require the inclusion of all students, includ-

ing ELLs, into large-scale assessments (Abedi, Lord, Hofstetter, & Baker, 2000;

Mazzeo, Carlson, Voelkl, & Lutkus, 2000). Such inclusion requirements have

prompted new interest in modifying assessments to improve the level of English

language learners participation and to enhance validity and equitability of infer-ences drawn from the assessments themselves.

Standardized, high-stakes achievement tests are frequently used for assessment

and classification of ELL students, as well as for accountability purposes. They

shape instruction and student learning (Linn, 1995). About 40% of districts and

schools use achievement tests for assigning ELL students to specific instructional

services within a school, and over 70% of districts and schools use achievement

tests to reclassify students from ELL status (Zehler, Hopstock, Fleischman, &

Greniuk, 1994).

However, as most standardized, content-based tests (such as science and mathtests) are administered in English and normed on native English-speaking test pop-

ulations, they may inadvertently function as English language proficiency tests.

English language learners may be unfamiliar with the linguistically complex struc-

ture of test questions, may not recognize vocabulary terms, or may mistakenly in-

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1The termEnglish language learner(ELL) refers to students who are not native speakers of English

and are not as proficient in English as native speakers. A subgroup of these students with a lower levelof English proficiency is referred to as limitedEnglish proficient(LEP). The term LEP is used primarily

by government-funded programs to classify students as well as by the National Assessment of Educa-


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terpret an item literally (Duran, 1989; Garcia, 1991). They may also perform less

well on tests because they read more slowly (Mestre, 1988).

Thus, language factors are likely to reduce the validity and reliability of infer-ences drawn about students content-based knowledge, as stated in theStandards

for Educational and Psychological Testing(American Educational Research As-

sociation, American Psychological Association, & National Council on Measure-

ment in Education [AERA, APA, & NCME], 1999):

For all test takers, any test that employs language is, in part, a measure of their lan-

guage skills. This is of particular concern for test takers whose first language is not

the language of the test. Test use with individuals who have not sufficiently acquired

the language of the test may introduce construct-irrelevant components to the testingprocess. In such instances, test results may not reflect accurately the qualities and

competencies intended to be measured. Therefore it is important to consider lan-

guage background in developing, selecting, and administering tests and in interpret-

ing test performance. (p. 91)

Asindicatedearlier,amajorcriticismofstandardizedachievementtestsistheex-

clusionofELLstudents fromthe norminggroup for these tests. Linn(1995) refers to

theissuesassociatedwithinclusionofallstudentsasoneofthethreemostnotableof

the new featuresof this reform effort. The inclusion ofall students in its assessmentshas also been among the major issues for NAEP (see, e.g., Mazzeo et al., 2000).

Navarrette and Gustke (1996) expressed several concerns about the exclusion of

ELL students from the norming groups of standardized achievement tests:

Not including students from linguistically diverse backgrounds in the norming

group, not considering the match or mismatch between a students cultural and

school experiences, and not ensuring for English proficiency have led to justified ac-

cusations of bias and unfairness in testing. (p. 2)

Findings from a series of studies conducted by the National Center for Research

on Evaluation,Standards,andStudentTesting(CRESST) on theimpactof students

languagebackgroundontheirperformanceindicatedthat(a)studentlanguageback-

ground affects studentsperformance in content-based areas such as math and sci-

ence,and(b)thelinguisticcomplexityoftestitemsmaythreatenthevalidityandreli-

abilityofachievement tests,particularly forELLstudents (see Abedi& Leon,1999;

Abedi, Leon, & Mirocha, 2001; Abedi & Lord, 2001; Abedi et al., 2000).

Thus, the literature on the assessment of ELLs clearly suggests that language

factors confound the test results of English language learners. However, the litera-ture is not clear on the level of impact that language factors may have on different

STANDARDIZED ACHIEVEMENT TESTS FOR ELLS 233


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guage factors on the validity and reliability of content-based assessments for

ELLs. Available data from four large school sites in the nation enabled us to ex-

plore these issues in greater detail.

METHODOLOGY

Research Questions

1. Could the performance difference between ELL and non-ELL students be

partly explained by language factors in the assessment?

2. Could the linguistic complexity of test items as a possible source of mea-surement error influence the reliability of the assessment?

3. Could the linguistic complexity of test items as a possible source of con-

struct-irrelevant variance influence the validity of the assessment?

Data Sources

The data for this study were obtained from four locations across the U.S. To assure

anonymity, these data sites are referred to as Sites 1 to 4. Item-level standardized

achievement test data and background information were obtained for participatingstudents. The background variables included gender, ethnicity, free/reduced price

lunch participation, parent education, student ELL status, and students with dis-

abilities (SD) status.

Table 1 summarizes some of the main characteristics of the four data sites. As

data in Table 1 show, there were similarities and differences among the four data

sites. All sites used standardized tests for measuring studentsachievement in Eng-

lish and other content-based areas, but they differed in the type of test adminis-

tered. Although all sites had an index of students English language proficiency

status (ELL or bilingual status), and they all provided some student background in-formation, they differed in the type of language proficiency index used and the

type of background variables provided. These differences limited our ability to

perform identical analyses at the different sites for cross-validation purposes.

However, there were enough similarities in the data structures at the four different

sites to allow for meaningful comparisons.

The following is a brief description of each of the four data sites.

Site 1. Site 1 is a large urban school district. Data on the Iowa Tests of Basic

Skills (ITBS) were obtained for Grades 3 through 8 in 1999. No information wasavailable on students ELL status; however, students were categorized as to

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ing bilingual services. In Grade 6 there were 28,313 students in the population,

with 3,341 (11.8%) receiving bilingual services. In Grade 8 there were 25,406 stu-

dents in the population, and 2,306fewer than one in ten (9.1%)were receivingbilingual services.

Site 2. Site 2 is a state with a very large number of ELL students. There were

a total of 414,169 students in the Grade 2 population of the state, and 125,109

(30.2%) of these students were ELLs. In Grade 7 there were 349,581 students, of

whom 73,993 (21.2%) were ELL students. In Grade9 there were 309,930 students,

and 57,991 (18.7%) were ELL students. Stanford Achievement Test, 9th edition

(Stanford 9) test data were obtained for all students in Grades 2 to 11 who were en-

rolled in the statewide public schools for the 19971998 academic year.

Site 3. Site 3 is an urban school district. Stanford 9 test data were available

for all students in Grades 10 and 11 for the 19971998 academic year. Accommo-

dation data were obtained from the district and included both the type and number

of accommodations received. There were 12,919 students in the Grade 10 popula-

tion, and 431 (3.3%) of these students were ELLs. In Grade 11 there were 9,803

students in the population, of whom 339 (3.5%) were ELL students.

Site 4. Site 4 is a state with a large number of ELL students. Access was pro-

vided to Stanford 9 summary test data for all students in Grades 3, 6, 8, and 10 whowere enrolled in the states public schools for the 19971998 academic year. There


TABLE 1

Summary of Characteristics of the Four Data Sites

Data Site Site 1 Site 2 Site 3 Site 4

Location type Large urban district Entire state Large urban district Entire state

Total number of students,

K12

430,914 5,844,111 approx. 200,000 187,969

Percent of ELL, K12 15.6 24.1 N/A 6.9

Language designation Bilingual/

nonbilingual

ELL/non-ELL ELL/non-ELL ELL/non-ELL

Grades data available 18 211 10, 11 3, 6, 8, 10

Achievement tests used ITBS SAT9 SAT9 SAT9

Language proficiency

tests used

N/A LAS N/A LAS

Accommodation data N/A N/A N/A N/A

Years data available 1999 1998 1998 1998

Note. ELL= English language learner; ITBS = Iowa Tests of Basic Skills; SAT9 = Stanford

Achievement Test, 9th edition; LAS = Language Assessment Scales; N/A = not available.


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population, of whom 813 (6.3%) were ELL students. In Grade 8 there were 12,400

students, and 807 (6.5%) were ELL students.

Design and Statistical Approach

To provide responses to the research questions outlined previously, data from the

four sites were analyzed. There were some differences in the type and format of the

data across the four sites; however, similar analyses were performed on the four

data sets, and the four sites were used as cross-validation samples.

The main hypothesis of this study focused on the possible impact of students

language background on their performance. Therefore, the focus of the analyses

was on the comparison between the level of performance of ELL and non-ELL stu-

dents. However, to develop an understanding about the role of other contributing

factors in the assessment of ELL students, comparisons were also made between

students with respect to other background variables, such as family income and

parent education. Studentsmean normal-curve equivalent (NCE) scores on differ-

ent subscales of standardized achievement tests were compared across subgroups

using analysis of variance andttests in a multiple-comparison framework.

To examine the impact of language on the reliability of tests and on the level of

measurement error, internal consistency coefficients were computed for different

tests across categories by students ELL status and other background variables,

such as family income and parent education. This approach was based on the as-

sumption that test items within each strand or subscale were measuring the same

construct; that is, they were unidimensional (see Cortina, 1993). To study the im-

pact of language factors on the validity of tests, the structural equation approach

was used (Bollen, 1989). Through the application of multiple-group factor analy-

ses, the internal structural relationship of test items and the relationships of test

scores with external criteria were examined.

It must be noted at this point that in some of our data sites, we had access to the

data for the entire student population. Therefore, application of inferential statisti-

cal techniques was not necessary. However, to be consistent with the analyses for

the other sites that provided data for subgroups of the population, as well as the en-

tire population, we report statistical analyses for all four data sites. Findings from

these analyses are presented next.

RESULTS

Three main research questions guided the analyses and reporting of the results.

These questions were based on (a) issues concerning content-based performancedifferences between ELLs and non-ELLs due to language factors, (b) the impact of

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The results of analyses are reported in three sections: (a) performance differ-

ences between ELL and non-ELL students, (b) impact of language factors on reli-

ability, and (c) validity.

Performance Differences Between ELL and Non-ELL

Students Due to Possible Impact of Language Factors

The results of analyses of data from the four sites consistently suggested that ELL

students performed substantially lower than non-ELL students. However, the per-

formance gap between ELL and non-ELL students was not the same across the

content areas. In content areas with a higher level of language demand (e.g., read-

ing and writing), the performance gap between ELL and non-ELL students was thehighest, whereas in content areas with less language demand (e.g., math and sci-

ence), the performance gap was much smaller and in some cases was almost non-

existent (e.g., math computation).

To present a picture of the performance gap trend between ELL and non-ELL

students, we report the descriptive statistics on the site with the largest ELL popu-

lation for two grades, an early elementary grade and a secondary school grade. To

conserve space, we have summarized the results of the descriptive analyses for the

other three sites.

Table 2 presents the number and percentage of students in Grades 2 and 9 in Site2 who took the Stanford 9 tests in reading, math, and science, by student ELL and

disability status.


TABLE 2

Site 2 Grades 2 and 9 Stanford 9 Frequencies for Students

Students With a Normal Curve Equivalent Score

All Students Reading Math Science

n % n % n % n %

Grade 2

SD only 17,506 4.2 15,051 4.1 16,720 4.2 NA NA

ELL only 120,480 29.1 97,862 26.5 114,519 28.4 NA NA

ELL and SD 4,629 1.1 3,537 1.0 4,221 1.0 NA NA

Non-ELL/Non-SD 271,554 65.6 252,696 68.5 267,397 66.4 NA NA

All students 414,169 100.0 369,146 100.0 402,857 100.0 NA NA

Grade 9

SD only 18,750 6.0 16,732 5.7 17,350 5.8 17,313 5.8

ELL only 53,457 17.2 48,801 16.6 50,666 17.0 50,179 16.9

ELL and SD 4,534 1.5 3,919 1.3 4,149 1.4 4,108 1.4Non-ELL/Non-SD 233,189 75.2 224,215 76.4 226,393 75.8 225,457 75.9

All students 309 930 100 0 293 667 100 0 298 558 100 0 297 057 100 0


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As data in Table 2 show, over 29% of all Grade 2 students at Site 2 who partici-

pated in Stanford 9 testing were ELL students. This percentage point (29.1%) may

not represent the actual percentage of ELL students at Site 2 because some ELLstudents did not participate in the assessment due to language barriers. The per-

centage of ELL students who participated in the Stanford 9 testing was 17.2% for

Grade 9, which was substantially lower than for Grade 2 (29.1%). There were

slight differences between percentages of ELL students across the different con-

tent areas in this site.

The large number of ELL students in this site provided a unique opportunity

to perform analyses at the subgroup level to examine the impact of students

background variables on academic achievement. Table 3 presents means, stan-

dard deviations, and numbers of students in reading, math, and science for Stan-ford 9 test scores by subgroups of students. In addition to data by students ELL

status, we included subgroup data by school lunch program (a proxy for family

income) and parent education, which were highly confounded with students

ELL status.

In general, the results of analyses reported in Table 3 indicate that:

ELL students performed substantially lower than non-ELL students, particu-larly in content areas with more language demand such as reading. For example,

the mean reading score for ELL students in Grade 2 was 31.6 (SD= 15.9,N=

97,862) compared with a mean of 49.3 (SD= 19.7,N= 252,696) for non-ELL stu-

dents. This difference was significant beyond the .01 nominal level (t= 250.6,df=

350,556,p< .001).2

The performance gap between ELL and non-ELL students was smaller in thelower grades. For example, there was a 17.7-point difference between ELL and

non-ELL students in Grade 2 reading mean scores as compared with a 22-point

difference for students in Grade 9.

The performance gap between ELL and non-ELL students decreased whenthe level of language demand of test items decreased. For example, for Grade 9 stu-

dents, the performance gap between ELL and non-ELL students in reading was 22

points, as compared to 15.4 points in math.

The results of analyses also show that other background variables affect test

performance. Background variables such as family income (as measured by partic-

ipation in free/reduced price lunch program) and parent education may not be di-

rectly related to students ELL status, but are confounded with it.

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2Sinceweareworkingwith thepopulation of students in this site,nostatisticalcomparison isneeded.

Even a minor difference wouldbe real. However, following tradition, we conducted some statistical sig-


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TABLE 3

Site 2 Grade 2 Stanford 9 Subsection Scores

Grade 2 Grade 9

Subgroup/Grade Reading Math Science Reading Math Science

ELL status

ELL

M 31.6 37.7 NA 24.0 38.1 34.9

SD 15.9 19.7 NA 12.5 15.2 12.8

N 97,862 114,519 NA 48,801 50,666 50,179

Non-ELL

M 49.3 50.4 NA 46.0 53.5 49.2

SD 19.7 21.9 NA 18.0 19.4 16.1N 252,696 267,397 NA 224,215 226,393 225,457

School lunch

Free/reduced price

M 35.4 38.8 NA 32.0 42.5 39.4

SD 17.5 20.1 NA 16.2 16.4 14.3

N 106,999 121,461 NA 56,499 57,961 57,553

No free/reduced price

M 47.0 48.5 NA 42.6 50.7 47.0

SD 20.6 22.4 NA 19.7 20.1 17.0

N 304,092 327,409 NA 338,285 343,480 341,663

Parent education

Not high school grad

M 30.1 34.7 NA 29.2 39.6 37.3

SD 15.3 19.1 NA 15.0 15.1 13.5

N 54,855 63,960 NA 69,934 71,697 71,183

High school graduate

M 40.5 42.6 NA 35.6 44.1 41.7

SD 18.1 20.3 NA 17.0 17.1 14.9

N 93,031 101,276 NA 71,986 73,187 72,810

Some college

M 48.8 50.3 NA 44.6 51.6 48.2

SD 18.6 20.6 NA 17.2 18.1 15.4

N 66,530 70,381 NA 70,364 70,971 70,687

College graduate

M 56.5 58.4 NA 48.1 56.3 51.5

SD 18.5 20.6 NA 18.5 19.6 16.4

N 54,391 56,451 NA 87,654 88,241 87,956

Post graduate studies

M 62.1 64.1 NA 57.6 65.8 58.8

SD 18.7 20.4 NA 19.6 20.7 17.1

N 25,571 26,367 NA 34,987 35,087 35,022

Note. ELL = English languge learner.


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Students who did not participate in the free/reduced price lunch program had

higher mean scores in all subject areas than those who did participate in the pro-

gram. For example, the average NCE score for reading for Grade 2 students whoparticipated in the free/reduced price lunch program was 35.4 (SD= 17.5,N=

106,999), as compared with an average score of 47.0 (SD= 20.6,N= 304,092) for

those who did not participate in the program. The difference was statistically sig-

nificant (t= 177.8, df= 411,089,p < .001). For Grade 9 students participating in the

free/reducedprice lunch program, the average NCE score for reading was 32.0 (SD

= 16.2,N= 56,499), as compared with an average of 42.6 (SD = 19.7,N= 338,285)

for those who did not participate in the program. The difference between the per-

formances of the two groups was statistically significant (t= 139.2, df= 394,755,p

< .001).The results also indicate that parent education has a substantial impact on the

Stanford 9 test scores. For example, the average NCE score for reading for Grade 2

students of parents with low education (not high school graduate) was 30.1 (SD=

15.3,N= 54,855), as compared with an average of 62.1 (SD = 18.7,N= 25,571) for

students of parents with high education (post graduate education). This difference

was statistically significant (t= 238.8, df= 80,424,p < .001). For Grade 9 students,

the average NCE score for reading for the low parent education category was 29.2

(SD= 15.0,N= 69,934). For students with parents in the high education category,

the average was 57.6 (SD = 19.6,N= 34,987). This difference was statistically sig-nificant (t= 238.4, df= 104,919,p < .001). The results of our analyses also suggest

that family income and parent education are confounded with students ELL sta-

tus. Table 4 presents frequencies and percentages of family income (free/reduced

price lunch program) and parent education by ELL status.

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TABLE 4

Site 2 Free/Reduced Price Lunch Status and Parent Education

by ELL Status

No Free/Reduced Price Lunch

Parent Education

Free/Reduced Price Lunch

Parent Education

Not

HS Grad Post Grad Total

Not

HS Grad Post Grad Total

Grand

Total

Non-ELL 20,738 22,410 43,148 9,763 980 10,743 53,891

26.2% 28.2% 54.4% 12.3% 1.3% 13.6% 68.0%

ELL 15,384 976 16,360 8,648 358 9,006 25,366

19.4% 1.2% 20.6% 10.9% 0.5% 11.4% 32.0%

Total 36,122 23,386 59,508 18,411 1,338 19,749 79,25745.6% 29.5% 75.1% 23.2% 1.7% 24.9% 100.0%


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A chi-square of 12,096.72, which shows confounding of these variables, was

significant beyond the .01 nominal level (2 = 12,096.72,p< .001). A square con-

tingency coefficientof .132 presents a rough estimate of the proportion of commonvariance (or confounding) among the three variables. These results suggest that a

greater percentage of ELL students are from families with lower income and lower

education. For example, 95% of ELL students had parents with low education,

whereas only 57% of non-ELL students had parents with low education. Thirty-six

percent of all ELL students participated in the free/reduced price lunch program as

compared with only 20% of non-ELL students.

However, the results of analyses in this study suggest that among these back-

ground variables, language factors show a greater impact on assessment, much

greater than family income or parent education.To make a more clear comparison between the performance of subgroups of

students (e.g., by ELL status, family income, and parent education) in different

content areas, a Disparity Index (DI) was computed. For example, to compute DI

by students ELL status, the mean score for ELL students was subtracted from the

mean for non-ELL students. The difference was then divided by the mean for ELL

students, and the result was multiplied by 100. Table 5 shows the DI by student

ELL status, as well as by school lunch program and parent education, for Grades 2

and 7, for Site 2, in four content areas.3 Similar results were obtained for other

grades (see Abedi & Leon, 1999).As the data in Table 5 show, the average DI for ELL status over reading, math,

language, and spelling for Grade 2 was 48.1 (i.e., over all four subject areas,

non-ELL students outperformed ELL students by 48.1%). For Grade 7, the DI was

74.8. We also computed DI by school lunch program and parent education. The DI

for school lunch program for Grade 2 students was 29.6. That is, students who did

not participate in the school lunch program outperformed students who partici-

pated in the program by 29.6%. For Grade 7, the DI was 35.2. We also compared

the performance of students with the lowest level of parent education with students

and the highest level of parent education. The DI for parent education for Grade 2was 99.3; that is, children of parents with the highest level of education (post grad-

uate education) outperformed children of parents with lower levels of education

(no education or elementary level education) by 99.3%. The DI for Grade 7 by

parent education was 83.5.

By comparing the math DI with the DIs of the language-related subscales (read-

ing, language, and spelling), we can see the impact of languageon studentsperfor-

mance. The DIs for all categories (ELL status, school lunch, and parent education)

were smaller for math and larger for reading. For example, for Grade 2 students,

the DI (non-ELL vs. ELL) was 55.8 in reading (non-ELL students outperformed



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ELL students by 55.8%), 60.2 in language, and 42.8 in spelling, as compared with

a DI of 33.5 in math. For Grade 7 students, the DIs (non-ELL vs. ELL) were 96.9

for reading, 70.7 for language, and 81.1 for spelling, compared to 50.4 for math.

The DIs for school lunch program (nonparticipant vs. participant in free/reduced

price lunch) for Grade 2 students were 32.7 for reading, 35.2 for language, and

25.3 for spelling, as compared with 25.1 for math.

However, the difference between DIs for math and language-related subscaleswas largest across the ELL categories. In Table 5, we included these DI differences

undertheDifferencecolumn.TheDIdifferencebyELLstatus4 was19.4forGrade

2and32.5forGrade7,ascomparedwiththeschoollunchprogramDIdifferencesof

6.0and7.7,respectively,andtheparent-educationDIdifferencesof15.8and9.8,re-

spectively. Once again, these data suggest that language factors may have a more

profound impact on the assessment outcome than other backgroundvariables, such

as family income and parent education, particularly for ELL students.

To shed light on the impact of language factors on assessment, analyses by math

subscales were conducted and will be presented. Standardized achievement testssuch as the Stanford 9 and ITBS include in their tests different math subscales that

have varying degrees of language demand. These subscales range from testing

math analytical skills, concepts and estimation, and problem solving with a rela-

tively higher level of language demand to testing math computation with a mini-

mal level of language demand. If the hypothesis concerning the impact of language

on content-based performance is tenable, then the performance difference between

ELL and non-ELL students should be at the minimum level in content-based tests

with a minimal level of language demand, such as math computation. This was ex-

actly what the results of our analyses showed.

242 ABEDI

TABLE 5

Site 2 Grades 2 and 7 Disparity Indexes (DI) by ELL Status,

Free/Reduced Price Lunch, and Parent Education

DI Reading Math Language Spelling Average Difference

Grade 2

ELL/Non-ELL 55.8 33.5 60.2 42.8 48.1 19.4

Free/reduced lunch 32.7 25.1 35.2 25.3 29.6 6.0

Parent education 106.3 84.9 118.5 87.5 99.3 15.8

Grade 7

ELL/Non-ELL 96.9 50.4 70.7 81.1 74.8 32.5

Free/reduced lunch 47.2 29.5 32.9 31.1 35.2 7.7

Parent education 98.4 76.2 79.0 80.5 83.5 9.8

Note. ELL = English language learner.


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Data obtained from some of the sites in this study included different subscale

scores including math computation. Table 6 presents the DIs for bilingual students

compared with nonbilingual5 students by level and grade for math concepts and es-

timation, math problem solving, math computation, and reading in Site 1.

The results of the DIanalyses showninTable 6 present several interestingpatterns:

1. The DIs indicated that the nonbilingual students generally outperformed the

bilingual students. However, the magnitude of the DIs depends, to a greater extent,

on the level of language demand of the test items. The DI for test items with less

language demand was smaller than for other items. For example, in Grade 3, bilin-

gual students performed better on math computation, which has the lowest level of

language demand.

2. Major differences between bilingual and nonbilingual students were found

for students in Grades 3 and above. There seemed to be a positive relationship be-

tween the mean score differences and grade level, in that the difference increasedas the grade level increased, up to Grade 5. Starting with Grade 6, the DI was still

positive, but the rate of increase was not as systematic as before. For example, in

Grade 3, nonbilingual over bilingual students had DIs of 5.3 in math concepts and

estimation, 11.1 in math problem solving and data interpretation, 3.1 in math

computation, and 23.4 in reading. In Grade 4, these indexes increased to 26.9 for

math concepts and estimation, 19.3 for math problem solving and data interpreta-

tion, 6.9 for math computation, and 30.1 for reading. The indexes further increased

in Grade 5 to 36.5 for math concepts and estimation, 32.7 for math problem solv-

ing and data interpretation, 12.6 for math computation, and 41.1 for reading.


TABLE 6

Site 1 Disparity Indexes of Nonbilingual Over Bilingual Students

on Math and Reading

Test

Level

Primary

Grade

Math Concepts

and Estimation

Math Problem Solving

and Data Interpretation

Math

Computation Reading

9 3 5.3 11.1 3.1 23.4

10 4 26.9 19.3 6.9 30.1

11 5 36.5 32.7 12.6 41.1

12 6 27.5 30.9 11.8 43.7

13 7 39.4 32.7 12.9 39.6

14 8 30.5 31.7 12.9 42.7

Average of all levels/grades 27.7 26.4 9.0 36.8


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3. The largest gap between bilingual and nonbilingual students was in reading.

The next largest gaps were in the content areas that appear to have more language

demand. For example, the math concepts and estimation and the math problemsolving and data interpretation subsections seem to have more language demand

than the math computation subsection. Correspondingly, the DIs were higher for

those subsections. The average DI for Grades 3 through 8 was 27.7 for math con-

cepts and estimation. That is, the mean of the nonbilingual group in math concepts

and estimation was 27.7% higher than the bilingual group mean. A similar trend

was observed in math problem solving and data interpretation; the average DI for

this subsection was 26.4. The average DI for math computation, however, was 9.0,

which was substantially lower than the corresponding DIs for the other two math

subsections. These results were consistent across the different data sites.

Table 7 reports the DIs, non-ELL versus ELL students, for reading, math total,

and the math calculation and math analytical subscales for Grades 3, 6, and 8 at

Site 4. Once again, the results of analyses clearly suggest the impact of language

factors on students performance, particularly in areas with more language de-

mand. For example, in reading, ELL students had the largest performance gap with

non-ELL students. The average DI for reading across the three grades was 86.7, as

compared with the average performance gap of 33.4 for math total. Among themath subscale scores, those with less language demand showed a smaller perfor-

mance gap. The average DI was 41.0 for math analytical and 20.1 for math calcula-

tion. The math calculation DI was substantially less than the DI for reading (86.7)

and for math analytical (41.0). However, it must be indicated at this point that lan-

guage demand and cognitive complexity of test items may also be confounded.

That is, items in the math calculation subscale may not only have less language de-

mand, but they may also be less cognitively demanding than other math subscales,

such as math problem solving. This is a caveat in our discussion on the impact of

language on content-based assessments.

244 ABEDI

TABLE 7

Site 4 Disparity Indexes of Non-ELL Versus ELL Students in Reading

and Subscales of Math

Disparity Index

Grade Reading Math Total Math Calculation Math Analytical

3 53.4 25.8 12.9 32.8

6 81.6 37.6 22.2 46.18 125.2 36.9 25.2 44.0

Average over the three grades 86 7 33 4 20 1 41 0


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Possible Impact of Language Factors

on Reliability of Assessments

In classical test theory, reliability is defined as the ratio of the true-score variance

(2T) to observed-score variance (2X) (Allen & Yen, 1979). This observed score

variance (2X) is the sum of two components, the true-score variance (2T) and the

error variance (2E). In a perfectly reliable test, the error variance (2E) would be

zero; therefore, the true-score variance (2T) would be equal to the observed-score

variance.

However, in measurement with human participants there is always an error

component, whether large or small, which is referred to in classical test theory as

the measurement error (see Allen & Yen, 1979; Linn & Gronlund, 1995; Salvia &Ysseldyke, 1998). Appropriate evaluation of the measurementerror is important in

any type of assessment, whether in a traditional, multiple-choice approach or in

performance-based assessments (Linn, 1995; see also AERA, APA, & NCME,

1999). Many different sources (e.g., occasion, task, test administration conditions)

may contribute to measurement error in traditional, closed-ended assessment in-

struments. In addition to these sources, the reliability of performance assessment

measures suffers from yet another source of measurement error, variation in scor-

ing of open-ended items. More important, in the assessment of ELL students, lan-

guage factors may be another serious source of measurementerror, due to unneces-sary linguistic complexity in content-based areas. In the classical approach to

estimating reliability of assessment tools, the level of contribution of different

sources to measurement error may be indeterminable. Through the generaliz-

ability approach, one would be able to determine the extent of the variance each

individual source contributes (such as occasion, tasks, items, scorer, and language

factors) to the overall measurement error (see Cronbach, Gleser, Nanda, &

Rajaratnam, 1972; Shavelson & Webb, 1991).

To estimate reliability of the standardized achievement tests used in this study

and to investigate their measurement error, we considered different approaches.Since parallel forms or testretest data were not available, we decided to use an

internal consistency approach. The main limitation with the internal consistency

approach, however, is the assumption of unidimensionality. For example, the lit-

erature has indicated that the alpha coefficient, which is a measure of internal

consistency, is extremely sensitive to multidimensionality of test items (see, e.g.,

Abedi, 1996; Cortina, 1993). However, because the test items within each con-

tent area are assumed to measure the same construct, we believe this approach

may be appropriate for estimating reliability of the achievement tests used in this

study.Because different data sites used different tests, and because within the individ-



16/28

ducted the internal consistency analyses separately for ELL and non-ELL stu-

dents. The results obtained from analyses at different sites were consistent. Due to

space limitations, only the results from Site 2, the site with the largest number ofstudents, are presented. A complete report of the results of analyses can be found in

Abedi et al. (2001).

Language (and perhaps other variables, such as socioeconomic status and op-

portunity to learn) may cause a restriction of range in the score distribution that

may result in lower internal consistency.

Table 8 presents reliability (internal consistency) coefficients for the Stanford 9

data for Grade 2 students in Site 2. As the data in Table 8 show, non-ELL students

had higher coefficients than the ELL students. There was also a slight difference

between the alpha coefficients across the free/reduced price lunch categories.Nonparticipants in the free/reduced price lunch program had slightly higher alphas

than the participating students. For example, the average reliability for the reading

subscale for the nonparticipant group was .913, as compared with an average reli-

ability of .893 for the participant group (a difference of .021), and for ELL students

the average reliability was .856, as compared with an average reliability of .914 for

non-ELL students, a difference of .058 (non-ELLs refers to English only). The re-

sults of our analyses, which are consistent across the different sites, indicate that

the difference in internal consistency coefficients between ELL and non-ELL stu-

dents is significantly larger than the difference between these coefficients acrossthe free/reduced price lunch and parent education categories.

Table 9 presents the reliability (internal consistency) coefficients for Grade 9

students. Comparing the internal consistency coefficients for Grade 9 students

with those for Grade 2 students (reported in Table 8) once again revealed that re-

liability coefficients for ELL students were lower than the coefficients for

non-ELL students. This was particularly true for students in higher grades,

where language has more impact on performance. In both Grade 3 and Grade 9,

reliabilities were lower for ELL students. However, in Grade 9, the difference

between reliability coefficients for ELL and non-ELL students was larger. Forexample, for Grade 2, the difference between reliability coefficients for ELL and

non-ELL students was .058 in reading, .013 in math, and .062 in language, as

compared with the ELL/non-ELL reliability difference of .109 for reading, .096

for math, and .120 for language in Grade 9. The difference between the overall

reliability coefficient of ELL students and English-only students for Grade 9 was

.167, which was substantially higher than the respective difference of .043 in

Grade 2. Thus, the reliability gap between ELL and non-ELL students increases

with increase in the grade level. This may be due to the use of more complex

language structures in higher grades.The results of these analyses strongly suggest that students language back-

246 ABEDI


17/28

247

TABLE 8

Site 2 Grade 2 Stanford 9 Subscale Reliabilities

Non-ELL Students: Free Lunch

Participation

Subscale (No. of Items) Yes No English Only

Reading N= 209,262 N= 58,485 N= 34,505 N

Word study (48) .917 .895 .916 Vocabulary (30) .913 .897 .915

Reading comp. (30) .908 .888 .910

Average reliability .913 .893 .914

Math N= 220,971 N= 63,146 N= 249,000 N

Problem solving (45) .893 .881 .896

Procedures (28) .892 .892 .891

Average reliability .893 .887 .894

Language N= 218,003 N= 62,028 N= 245,384 N

Total (44) .890 .866 .891

Note. ELL = English language learner; FEP = fluent English proficient; RFEP = redesignated fluen


18/28


19/28

Validity

Research has indicated that complex language in content-based assessments for

nonnative speakers of English may reduce the validity and reliability of infer-

ences drawn about students content-based knowledge. For example, results

from earlier CRESST language background studies (Abedi & Lord, 2001;

Abedi, Lord, & Hofstetter, 1998; Abedi et al., 2000; Abedi, Lord, & Plummer,

1997) provided support for a strong link between language factors and con-

tent-based performance. The linguistic factors in content-based assessments

(such as math and science) may be considered a source of construct-irrelevant

variance because they are not conceptually related to the content being assessed

(Messick, 1994):

With respect to distortion of task performance, some aspects of the task may require

skills or other attributes having nothing to do with the focal constructs in question, so

that deficiencies in the construct-irrelevant skills might prevent some students from

demonstrating the focal competencies. (p. 14)

To examine the impact of studentslanguage background on thevalidity of stan-

dardized achievement tests, analyses were performed to compare criterion validity

coefficients for ELL and non-ELL students and to examine differences betweenthe structural relationship of ELL and non-ELL groups.

Linguistic complexity of test items, as a possible source of construct-irrelevant

variance, may be a threat to the validity of achievement tests, because it could be a

source of measurement error in estimating the reliability of the tests. Inter-

correlation between individual test items, the correlation between items and total

test score (the internal validity coefficient), and the correlation between item score

and total test score with the external criteria (the studentsother achievement data)

were computed. A significant difference across the ELL categories in the relation-

ships between test items, between individual items and total test scores (internalvalidity), and between overall test scores and external criteria may be indicative of

the impact of language on the validity of tests. Since language factors should not

influence the performance of non-ELL students, these relationships may be stron-

ger for non-ELL students.

To examine the hypothesis regarding differences between ELL and non-ELL

students on the structural relationship of the test items, a series of structural equa-

tion models were created for Site 2 and Site 3 data. Fit indexes were compared

across ELL and non-ELL groups. The results generally indicated that the relation-

ships between individual items, items with the total test score, and items with theexternal criteria were higher for non-ELL students than for ELL students.



20/28

cels and latent variables for reading, math, and science for Site 2. As Figure 1

shows, the 54 reading items were grouped into four parcels. Each parcel was

constructed to systematically contain items with three degrees of item difficulty:

easy, difficult, and moderately difficult items (for a description of the item par-

cels and ways to create them, see Catell & Burdsal, 1975). A reading latent vari-

able was constructed based on these four parcels.

Similarly, item parcels and latent variables for math and science were created

from the 48 math items and 40 science items by the same process. The correlations

between the reading, math and science latent variables were estimated. Modelswere tested on randomly selected subsamples to demonstrate the cross-validation

of the results.

Table 10 shows the results of the structural models for Grade 9 at Site 2. Corre-

lations of item parcels with the latent factors were consistently lower for ELL stu-

dents than they were for non-ELL students. This finding was true for all parcels re-

gardless of which grade or which sample of the population was tested. For

example, for Grade 9 ELL students, the correlations for the four reading parcels

ranged from a low of .719 to a high of .779 across the two samples (see Table 10).

In comparison, for non-ELL students, the correlations for the four reading parcelsranged from a low of .832 to a high of .858 across the two samples. The item parcel

250 ABEDI

FIGURE 1 Latent variable model for reading, science, and math.


21/28

The correlations between the latent factors were also larger for non-ELL stu-

dents than they were for ELL students. This gap in latent factor correlations be-

tween non-ELL and ELL students was especially large when there was more lan-

guage demand. For example, in Sample 1 for Grade 9, the correlation between

latent factors for math and reading for non-ELL students was .782 compared to just

.645 for ELL students. When comparing the latent factor correlations between

reading and science from the same population, the correlation was still larger fornon-ELL students (.837) than for ELL students (.806), but the gap between the cor-


TABLE 10

Site 2 Grade 9 Stanford 9 Reading, Math,

and Science Structural Modeling Results (df= 51)

Non-ELL (N = 22,782) ELL (N = 4,872)

Sample 1 Sample 2 Sample 1 Sample 2

Factor loadings

Reading comprehension

Parcel 1 .852 .853 .723 .719

Parcel 2 .841 .844 .734 .739

Parcel 3 .835 .832 .766 .779

Parcel 4 .858 .858 .763 .760

Math factorParcel 1 .818 .821 .704 .699

Parcel 2 .862 .860 .770 .789

Parcel 3 .843 .843 .713 .733

Parcel 4 .797 .796 .657 .674

Science factor

Parcel 1 .678 .681 .468 .477

Parcel 2 .679 .676 .534 .531

Parcel 3 .739 .733 .544 .532

Parcel 4 .734 .736 .617 .614

Factor correlation

Reading vs. Math .782 .779 .645 .674

Reading vs. Science .837 .839 .806 .802

Science vs. Math .870 .864 .796 .789

Goodness of fit

Chi-square 488 446 152 158

NFI .997 .998 .992 .992

NNFI .997 .997 .993 .993

CFI .998 .998 .995 .995

Note. There was significant invariance for all constraints tested with the multiple group model

(Non-ELL/ELL). ELL = English language learner; NFI = Normed Fit Index; NNFI = Nonnormed Fit

Index; CFI = Comparative Fit Index.


22/28

non-ELL and ELL students mentioned previously were significant. There were

significant differences for all constraints tested at thep< .05 level.

The results of simple structure confirmatory factor analyses also showed differ-ences on factor loadings and factor correlations between the ELL and non-ELL

groups for the Site 3 data. The hypotheses of invariance of factor loadings and fac-

tor correlations between the ELL and non-ELL groups were tested. Specifically,

we tested the following null hypotheses:

Correlations between parcel scores and a reading latent variable are the samefor the ELL and non-ELL groups.

Correlations between parcel scores and a science latent variable are the same

for the ELL and non-ELL groups. Correlations between parcel scores and a math latent variable are the same

for the ELL and non-ELL groups.

Correlations between content-based latent variables are the same for the ELLand non-ELL groups.

Table 11 summarizes the results of structural models for reading and math tests

for Site 3 students in Grade 10. Table 11 includes fit indexes for the ELL and

non-ELL groups, correlations between parcel scores and content-based latent vari-

ables (factor loadings), and correlations between latent variables. Hypotheses re-garding the invariance of factor loadings and factor correlations between ELL and

non-ELL groups were tested. Significant differences between the ELL and

non-ELL groups at or below .05 nominal levels were identified. These differences

are indicated by an asterisk next to each of the constraints. There were several sig-

nificant differences between the ELL and non-ELL groups on the correlations be-

tween parcel scores and latent variables. For example, on the math subscale, differ-

ences in factor loadings between the ELL and non-ELL groups on Parcels 2 and 3

were significant. Table 11 also shows a significant difference between the ELL and

non-ELL groups on the correlation between reading and math latent variables.These results indicate that:

1. Findings from the two cross-validation samples are very similar and pro-

vide evidence on the consistency of the results.

2. Structural models show a better fit for non-ELL than for ELL students.

3. Correlations between parcel scores and the content-based latent variables

are generally lower for ELL students.

4. Correlations between the content-based latent variables are lower for ELL

students.

252 ABEDI


23/28

DISCUSSION

The purpose of this study was to examine the impact of students language back-

ground on the outcome of their assessments. Three major research questions

guided the analyses and reporting and will be the basis for discussion of the results

of this study:

1. Could the performance difference between ELL and non-ELL students be

partly explained by language factors in the assessment?

2. Could the linguistic complexity of test items as a possible source of mea-surement error influence the reliability of the assessment?


TABLE 11

Site 3 Grade 10 Stanford 9 Reading and Math Structural Modeling Results

(Parcels Ordered by Item Number)

Goodness of Fit Model 1 (df = 75) Model 2 (df = 74)

Chi-square 2938 2019

NFI .916 .943

NNFI .902 .933

CFI .918 .945

Factor Loadings

Non-ELL

(N = 8,947)

ELL

(N = 303)

Non-ELL

(N = 8,947)

ELL

(N = 303)

ReadingParcel 1 .677 .683 .679 .685

Parcel 2 .683 .612 .684 .613

Parcel 3 .738 .695 .739 .696

Parcel 4 .826 .816 .824 .812

Parcel 5 .693 .723 .690 .720

Math

Parcel 1 .735 .763 .752 .788

Parcel 2 .659 .702* .667 .716*

Parcel 3 .623 .730* .592 .685*

Parcel 4 .724 .774 .722 .774

Parcel 5 .389 .471 .330 .391

Factor correlation

Reading vs. Math .719 .624* .723 .622*

Note. NFI = Normed Fit Index; NNFI = Nonnormed Fit Index; CFI = Comparative Fit Index: ELL

= English language learner.*Significant at or above .05.


24/28

In response to Question 1, results from the analyses of data from several loca-

tions nationwide indicated that students assessment results might be confounded

with language background variables. Descriptive statistics comparing ELL andnon-ELL student performance by subgroup and across different content areas re-

vealed major differences between the performance of the two groups. Included in

the descriptive statistics section was a DI (the disparity of performance of

non-ELL students over that of ELL students). This index showed major differ-

ences in performance between students with different language backgrounds. The

higher the level of English language complexity in the assessment tool, the greater

the DI (the performance gap between ELL and non-ELL students).

Accessto student-leveland item-leveldatafromthesitesprovidedanopportunity

to conduct analyses on student subgroups that were formed based on their back-groundvariables, includinglanguage background.Theexceptionallylargenumbers

ofstudentsinsomesubgroupsenabledustoconductcross-validationstudiestodem-

onstrate consistency of results over different sitesand grade levels. The high degree

of consistency assured us of the validity and interpretability of the results.

Descriptive analyses revealed that ELL students generally perform lower than

non-ELL students on reading, science, and math subtests. The level of impact of

language proficiency on the assessment of ELL students is greater in content areas

with a higher level of language demanda strong indication of the impact of Eng-

lish language proficiency on assessment. For example, analyses show that ELLand non-ELL students had the greatest performance differences in reading, and the

least performance differences in math, where language has less of an impact on the

assessment.

In response to Question 2, the results of our analyses indicated that test items for

ELL students, particularly ELL students at the lower end of the English profi-

ciency spectrum, suffer from lower internal consistency. That is, the language

background of students may add another dimension to the assessment in con-

tent-based areas. Thus, we speculate that language might act as a source of mea-

surement error in such areas. It is therefore imperative that test publishers examinethe impact of language factors on test reliability and publish reliability indexes

separately for the ELL subpopulation.

To shed light on the issues concerning the impact of language factors on validity

(Question3), concurrent validity of standardized achievement tests (Stanford9 and

ITBS) was examined using a latent-variable modeling approach. Standardized

achievement latent variables were correlated with the external-criterion latent vari-

ables. The results suggest that (a) there is a strong correlation between the standard-

ized achievement and external-criterion latent variables, (b) this relationship is

stronger when latent variables rather than measured variables are used, and (c) thecorrelation between standardized achievement and external-criterion latent vari-

254 ABEDI


25/28

stems from language factors. That is, language factors act as construct-irrelevant

sources (Messick, 1994).

Analyses of the structural relationships between individual items and betweenitems with the total test scores revealed a major difference between ELL and

non-ELL students. Structural models for ELL students demonstrated lower sta-

tistical fit. Further, the factor loadings were generally lower for ELL students,

and the correlations between the latent content-based variables were weaker for

ELL students.

The results of this study suggest that ELL test performance may be explained

partly by language factors. That is, linguistic complexity of test items unrelated to

the content being assessed may at least be partly responsible for the performance

gap between ELL and non-ELL students. Based on the findings of this study, werecommend that (a) the issues concerning the impact of language factors on the as-

sessment of ELL students should be examined further; (b) psychometric character-

istics of assessment tools should be carefully reviewed for use with ELL students;

and (c) in assessing ELL students, student language background variables should

always be included, and efforts should be made to reduce confounding effects of

language background on the assessment outcome.

ACKNOWLEDGMENTS

This research was supported in part by the Office of Bilingual Education and Mi-

nority Languages Affairs under Contract R305B960002 as administered by the

U.S. Department of Education. The findings and opinions expressed in this report

do not reflect the position or policies of the Office of Bilingual Education and Mi-

nority Languages Affairs or the U.S. Department of Education.

I acknowledge the valuable contribution of colleagues in preparation of this ar-

ticle. Seth Leon and Jim Mirocha provided assistance with the data analyses.

Kathryn Morrison provided technical assistance in preparation of this article. Joan

Herman and Mary Courtney contributed to this article with their helpful comments

and suggestions. I am grateful to Eva Baker and Joan Herman for their support of

this work.

REFERENCES

Abedi, J. (1996). The interrater/test reliability system (ITRS).Multivariate Behavioral Research, 31,409417.

Abedi, J., & Leon, S. (1999).Impact of studentslanguage background on content-based performance:



26/28

Abedi, J., Leon, S., & Mirocha, J. (2001).Examining ELL and non-ELL student performance differ-

ences and their relationship to background factors: Continued analyses of extant data. Los Angeles:

University of California, National Center for Research on Evaluation, Standards, and Student

Testing.

Abedi, J., & Lord, C. (2001). The language factor in mathematics tests.Applied Measurement in Edu-

cation, 14,219234.

Abedi, J., Lord, C., & Hofstetter, C. (1998). Impact of selected background variables on students

NAEP math performance.Los Angeles: University of California, National Center for Research on

Evaluation, Standards, and Student Testing.

Abedi, J., Lord, C., Hofstetter, C., & Baker, E. (2000). Impact of accommodation strategies on Eng-

lish language learners test performance. Educational Measurement: Issues and Practice, 19(3),

1626.

Abedi, J., Lord, C., & Plummer, J. R. (1997).Final report of language background as a variable in

NAEP mathematics performance(CSE Tech. Rep. No. 429). Los Angeles: University of Califor-nia, National Center for Research on Evaluation, Standards, and Student Testing.

Allen, M. J., & Yen, W. M. (1979).Introduction to measurement theory. Monterey, CA: Brooks/Cole.

American Educational Research Association, American Psychological Association, & National Coun-

cil on Measurement in Education. (1999). Standards for educational and psychological testing.

Washington, DC: American Educational Research Association.

Benjamini, Y., & Hochberg, Y. (1994). Controlling the false discovery rate: A practical and powerful

approach to multiple testing.Journal of the Royal Statistical Society, Series B, 57,289300.

Bollen, K. A. (1989).Structural equations with latent variables. New York: Wiley.

Cattell, B. R., & Burdsal, A. C. (1975). The radial parcel double factoring design: A solution to the

item-vs.-parcel controversy.Multivariate Behavioral Research, 10,165179.

Cortina, J. M. (1993). What is coefficient alpha? An examination of theory and applications.Journal ofApplied Psychology, 78,98104.

Cronbach, L. J., Gleser, G. C., Nanda, H., & Rajaratnam, N. (1972).The dependability of behavioral

measurements: Theory of generalizability of scores and profiles. New York: Wiley.

Duran, R. P. (1989). Assessment and instruction of at-risk Hispanic students.Exceptional Children, 56,

154158.

Garcia, G. E. (1991). Factors influencing the English reading test performance of Spanish-speaking

Hispanic children.Reading Research Quarterly, 26,371391.

Hakuta, K., & Beatty, A. (Eds.). (2000).Testing English language learners in U.S. schools. Washing-

ton, DC: National Academy Press.

Linn, R. L. (1995).Assessment-based reform: Challenges to educational measurement.Princeton, NJ:

Educational Testing Service.

Linn, R. L., & Gronlund, N. E. (1995).Measurement and assessment in teaching(7th ed.). Englewood

Cliffs, NJ: Prentice-Hall.

Mazzeo, J., Carlson J. E., Voelkl, K. E., & Lutkus, A. D. (2000).Increasing the participation of special

needs students in NAEP:A report on 1996 NAEP research activities. Washington, DC:National Cen-

ter for Education Statistics.

Messick, S. (1994). The interplay of evidence and consequences in the validation of performance as-

sessments.Educational Researcher, 23(2), 1323.

Mestre, J. P. (1988). The role of language comprehension in mathematics and problem solving. In R. R.

Cocking & J. P. Mestre (Eds.),Linguistic and cultural influences on learning mathematics (pp.

201220). Hillsdale, NJ: Lawrence Erlbaum, Associates, Inc.

National Clearinghouse for English Language Acquisition and Language Instruction Educational Pro-

grams. (2002).Survey of the states limited English proficient students and available educational

256 ABEDI


27/28

Navarrette, C., & Gustke, C. (1996).A guide to performance assessment for linguistically diverse stu-

dents.Albuquerque: New Mexico Highlands University.

No Child Left Behind Act of 2001, Pub. L. No. 107110, 115 Stat. 1425 (2002).

Salvia, J., & Ysseldyke, J. E. (1998).Assessment(7th ed.). Boston: Houghton Mifflin.

Shavelson, R., & Webb, N. (1991).Generalizability theory: A primer. Newbury Park, CA: Sage.

Zehler, A. M., Hopstock, P. J., Fleischman, H. L., & Greniuk, C. (1994).An examination of assessment

of limited English proficient students(Special Issues Analysis Center Task Order D070 Report).

Arlington, VA: Development Associates.



28/28

Documents

DONE Abedi Standardized Achievement Tests