The Pennsylvania State University

The Graduate School

College of Education

ENGLISH LANGUAGE PROFICIENCY AND TEACHER JUDGMENTS OF THE

ACADEMIC AND INTERPERSONAL COMPETENCE OF

ENGLISH LANGUAGE LEARNERS

A Dissertation in

School Psychology

by

Miranda E. Freberg

© 2014 Miranda E. Freberg

Submitted in Partial Fulfillment of the Requirements

for the Degree of

Doctor of Philosophy

May 2014

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The dissertation of Miranda E. Freberg was reviewed and approved* by the following:

Beverly J. Vandiver Associate Professor Emeritus of Education Dissertation Adviser Co-Chair of Committee

James D. DiPerna Associate Professor of Education Co-Chair of Committee Professor in Charge of the School Psychology Program

Barbara A. Schaefer Associate Professor of Education Keith B. Wilson Professor of Education Shirley A. Woika Associate Professor of Education *Signatures are on file in the Graduate School.

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ABSTRACT

The purpose of the study was to investigate how English language proficiency is related to

teacher judgments of students’ academic and interpersonal competence. It was hypothesized that

English Language Learner (ELL) students would generally be perceived as having weaker

academic and interpersonal skills than their non-ELL counterparts regardless of race/ethnicity.

Additionally, it was proposed that teachers’ ratings would be more predictive of the performance

of non-ELL versus ELL students. Data were obtained from the Early Childhood Longitudinal

Study–Kindergarten Class of 1998-1999 (ECLS-K). Participants were 260 third-grade students

whose academic and interpersonal skills were rated by their teachers on the Academic Rating

Scale (ARS; Atkins-Burnett, Meisels, & Correnti, 2000) and Social Rating Scale (SRS; Atkins-

Burnett, Meisels, & Correnti, 2000), respectively. Teachers’ academic ratings were compared to

students’ actual performance on the reading and math sections of the ECLS-K direct cognitive

assessment and teachers’ interpersonal ratings were compared to students’ self-ratings on the

Self-Description Questionnaire (SDQ; Marsh, 1990). Multiple regression analyses were used to

assess the effects of language status and race/ethnicity on teacher ratings. Additional regression

analyses were conducted to investigate whether teacher ratings were predictive of students’

academic performance and students’ self-ratings of interpersonal skills. Results showed that, in

contrast to what was hypothesized, teacher ratings were not significantly related to language

status, but race/ethnicity was found to be a significant predictor of both academic and social

ratings. Specifically, teachers rated African American students as having weaker reading and

interpersonal skills than their Hispanic counterparts. As hypothesized, teacher ratings were

found to be more predictive for non-ELL students on math and reading skills than ELL students.

These findings suggest that race/ethnicity may be more of an influential factor than language

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status when teachers make academic and interpersonal judgments and support previous research

(e.g., Hodson, Dovidio, & Gaertner, 2002; Jussim & Eccles, 1995) that teachers may have pre-

existing biases towards students of different races or ethnicities. Additionally, given the lower

predictive accuracy of teacher ratings of ELL than non-ELL students, teachers may need more

training to work with and to ensure a fair assessment of ELL students’ academic capabilities.

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TABLE OF CONTENTS

LIST OF TABLES……………………………………………………………………………...viii

INTRODUCTION………………………………………………………………………………...1

LITERATURE REVIEW……………………………………………………………....................3

The Self-Fulfilling Prophecy………………………...........................................................3

Teacher Expectations and Student Outcomes……………………………………………..5

Accuracy of Teacher Judgments…………………………………………………………..8

Potential Influences on Teacher Judgments and Expectations…………………………..11

English Language Learners in Mainstream Classrooms………………………………....19

Perceptions of Language and Academic Competence…………………………………...27

Conclusions………………………………………………………………………………29

Current Study: Research Questions and Hypotheses………………………………….....31

METHOD………………………………………………………………………………………..33

Overview…………………………………………………………………………………33

Participants……………………………………………………………………………….33

Measures…………………………………………………………………………………39

Oral Language Development Scale (OLDS)…………………………………….39

Direct Cognitive Assessments…………………………………………………. .41

Self-Description Questionnaire (SDQ)…………………………………………..45

Academic Rating Scale (ARS)…………………………………………………...46

Social Rating Scale (SRS)……………………………………………………….48

Procedure………………………………………………………………………………...50

ECLS-K Data Collection………………………………………………………...50

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Data Acquisition…………………………………………………………………51

RESULTS………………………………………………………………………………………..52

Descriptive Statistics……………………………………………………………………..52

Preliminary Analyses………………………………………………………………….....55

Student Variables………………………………………………………………...56

Parent Variables………………………………………………………………….57

Teacher Variables………………………………………………………………..59

School Variables………………………………………………………………....60

Language Status and Teacher Perceptions…………………………………………….…61

Reading Skills……………………………………………………………………62

Math Skills……………………………………………………………………….65

Interpersonal Skills………………………………………………………………65

Language Status and Teacher Perceptions across Racial/Ethnic Groups………………..65

Unweighted Analyses……………………………………………………………66

Weighted Analyses………………………………………………………………68

Additional Analyses…………………………………………………………………...…69

Socioeconomic Status…………………………………………………………....71

National Origin of Mother……………………………………………………….72

School Location………………………………………………………………….73

Teacher Ratings as Predictors of Student Performance………………………………….73

Unweighted Analyses……………………………………………………………74

Reading Skills……………………………………………………………74

Math Skills……………………………………………………………….80

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Interpersonal Skills………………………………………………………81

Weighted Analyses……………………………………………………………....81

Reading Skills……………………………………………………………83

Math Skills……………………………………………………………….85

Interpersonal Skills………………………………………………………87

Post-Hoc Analyses……………………………………………………………………….88

DISCUSSION……………………………………………………………………………………96

Language Status and Teacher Perceptions……………………………………………….96

Language Status and Teacher Perceptions across Racial/Ethnic Groups………………..98

Additional Analyses…………………………………………………………………….100

Teacher Ratings as Predictors of Student Performance………………………………...101

Limitations……………………………………………………………………………...103

Implications for Practice and Future Research…………………………………………105

Conclusion……………………………………………………………………………...106

REFERENCES…………………………………………………………………………………108

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LIST OF TABLES

Table 1 Demographic Characteristics of Unweighted Sample of Students………………35

Table 2 Demographic Characteristics of Weighted Sample of Students…………………37 Table 3 Demographic Characteristics of the Unweighted and Weighted Sample of Teachers………………………………………………………………39 Table 4 Descriptive Statistics for Teacher Ratings, Self-Ratings, and

Reading and Math Assessment Scores for All Students…………………………53 Table 5 Descriptive Statistics for Teacher Ratings, Self-Ratings, and

Reading and Math Assessment Scores for Students of Hispanic Ethnicity based on Language Status……………………………………………..54

Table 6 Descriptive Statistics for Teacher Ratings, Self-Ratings, and

Reading and Math Assessment Scores for Non-ELL Students of Caucasian and African American Race……………………………………….55

Table 7 Unweighted Frequency (Percentage) of ELL and Non-ELL

Students by SES Level…………………………………………………………...57 Table 8 Unweighted Frequency (Percentage) of ELL and Non-ELL

Students by Parent’s National Origin....................................................................58 Table 9 Unweighted Frequency (Percentage) of ELL and Non-ELL

Students by Teacher’s Level of Education………………………………………59 Table 10 Unweighted Frequency (Percentage) of ELL and Non-ELL

Students by School Location……….....................................................................60 Table 11 Summary of Unweighted Hierarchical Regression Analyses on Teacher

Ratings of Reading, Math, and Interpersonal Skills based on Students’ Language Status and Teacher’s Education and ESL Training…………………...63

Table 12 Summary of Weighted Hierarchical Regression Analyses on Teacher

Ratings of Reading, Math, and Interpersonal Skills based on Students’ Language Status and Teachers’ Education and ESL Training…………………..64

Table 13 Summary of Unweighted Regression Analyses for the Prediction of

Teacher Ratings of Reading, Math, and Interpersonal Skills for Students Grouped by Language Status and Race/Ethnicity……………………………….67

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Table 14 Summary of Weighted Regression Analyses for the Prediction of Teacher Ratings of Reading, Math, and Interpersonal Skills for Students Grouped by Language Status and Race/Ethnicity……………………………….70

Table 15 Summary of Unweighted ANOVA for Teacher Ratings of Reading,

Math, and Interpersonal Skills based on SES, National Origin, and School Location………………………………………………………………….71

Table 16 Summary of Weighted ANOVA for Teacher Ratings of Reading,

Math, and Interpersonal Skills based on SES, National Origin, and School Location …………………………………………………………………72

Table 17 Summary of Unweighted Regression Analyses for the Prediction of

Students’ Reading Scores by Language Status, Race/Ethnicity, and Teacher Ratings………………………………………………………………….75

Table 18 Summary of Unweighted Regression Analyses for the Prediction of Students’ Math Scores by Language Status, Race/Ethnicity, and Teacher Ratings………………………………………………………………….77

Table 19 Summary of Unweighted Regression Analyses for the Prediction of Students’ Interpersonal Self-Ratings by Language Status, Race/Ethnicity, and Teacher Ratings……………………………………………………………...79

Table 20 Summary of Weighted Regression Analyses for the Prediction of Students’ Reading Scores by Language Status, Race/Ethnicity, and Teacher Ratings………………………………………………………………….82

Table 21 Summary of Weighted Regression Analyses for the Prediction of Students’ Math Scores by Language Status, Race/Ethnicity, and Teacher Ratings………………………………………………………………….84

Table 22 Summary of Weighted Regression Analyses for the Prediction of Students’ Interpersonal Self-Rating by Language Status, Race/Ethnicity, and Teacher Ratings……………………………………………………………...86

Table 23 Summary of Unweighted Regression Analyses Investigating the

Influence of Time in ESL Classroom on the Accuracy of Teacher Ratings of ELL Students’ Reading Skills……………………………………….89

Table 24 Summary of Unweighted Regression Analyses Investigating the

Influence of Time in ESL Classroom on the Accuracy of Teacher Ratings of ELL Students’ Math Skills…………………………………………..90

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Table 25 Summary of Unweighted Regression Analyses Investigating the Influence of Time in ESL Classroom on the Accuracy of Teacher Ratings of ELL Students’ Interpersonal Skills…………………………………..91

Table 26 Summary of Weighted Regression Analyses Investigating the

Influence of Time in ESL Classroom on the Accuracy of Teacher Ratings of ELL Students’ Reading Skills……………………………………….92

Table 27 Summary of Weighted Regression Analyses Investigating the

Influence of Time in ESL Classroom on the Accuracy of Teacher Ratings of ELL Students’ Math Skills…………………………………………..93

Table 28 Summary of Weighted Regression Analyses Investigating the

Influence of Time in ESL Classroom on the Accuracy of Teacher Ratings of ELL Students’ Interpersonal Skills………………………………….94

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INTRODUCTION

Recent estimates suggest that English Language Learners (ELLs) currently constitute

approximately 10% of the total student population in the Unites States – an increase of over 80%

in the past decade (Gottlieb, 2006; National Center for Education Statistics, 2007). As the ELL

population continues to grow in U.S. schools, it becomes increasingly important to conduct

research that addresses the overall well-being and functioning of these students in schools. In

alignment with the No Child Left Behind Act of 2001, which mandates increased focus on equal

opportunities and the promotion of academic success for all students, research is needed about

ways to promote successful outcomes for the growing ELL population.

Existing research indicates that teachers’ perceptions of, and expectations for, students

can influence their academic outcomes (Hoge & Coladarci, 1989) and, in some cases, can even

become self-fulfilling prophecies (Lumsden, 1997). Additionally, the findings of some studies

(e.g., Gill & Reynolds, 1999; Jussim, Eccles, & Madon, 1996 Jussim & Harber, 2005) suggest

that teacher expectancy effects, particularly negative ones, may be most prominent for students

from stigmatized groups, such as students of diverse ethnic minority backgrounds, of low

socioeconomic status, or of limited English proficiency. Given these findings, teachers need to

become more aware of their beliefs, perhaps subconsciously, towards different groups of

students. Armed with this awareness, teachers can take conscious action to positively influence

students’ achievement on a consistent basis by maintaining positive perceptions and

communicating high expectations to all of their students, regardless of differences.

Much of the existing research on teachers’ perceptions and expectations has focused on

English-speaking students in regular education classrooms. However, some research exists that

specifically examines mainstream teachers’ perceptions of ELL students. Early ethnographic

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studies (e.g., Clair, 1993; Penfield, 1987) exploring teachers’ perceptions of ELL students

suggest a general lack of awareness and knowledge for working with these students. Later

surveys (Mantero & McVicker, 2006; Reeves, 2006; Vollmer, 2000; Young & Youngs, 2001)

indicate somewhat increased awareness and knowledge of ELL students, but the general

consensus continues to be that more teacher training in this area is needed.

One recurring theme in the extant literature on mainstream teachers’ perceptions of ELL

students is the potential impact of limited English proficiency on ELL students’ performance in

the regular education classroom. While some researchers have explored teachers’ attitudes

toward language diversity and development (e.g., Byrnes & Kiger, 1994; Byrnes, Kiger, &

Manning, 1997; Williams, Whitehead, & Miller, 1972), the relationship between level of English

language proficiency and teacher perceptions of academic and interpersonal competence has not

been extensively investigated. As such, the purpose of the current study is to specifically

investigate how teachers’ perceptions of English language proficiency are related to their

judgments of the academic and interpersonal competence of ELL students.

Several areas relevant to the current study are examined in the following literature

review. First, the concept of the self-fulfilling prophecy will be introduced, followed by a

general review of studies about the influence of the self-fulfilling prophecy in the classroom and

the potential impact of teachers’ expectations on student outcomes. Next, the predictive

accuracy of teachers’ judgments will be examined in conjunction with potential intervening

factors, such as student behavior, gender, race/ethnicity, socioeconomic status, and physical

attractiveness. Also, literature that specifically focuses on ELL students and teachers’

perceptions of them and their use of language will be reviewed. Finally, the purpose of the

current study, including research questions and hypotheses, will be presented.

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LITERATURE REVIEW

The Self-Fulfilling Prophecy

It has been proposed that teachers’ beliefs, perceptions, and expectations affect students’

overall educational experiences (Alva, 1991). Cummins (2001) states that teachers’ perceptions

and expectations can have a major impact on teacher-student relationships, which are central to

student learning. Lumsden (1997), specifically, suggests that teachers’ general beliefs about

students and academic expectations have an impact on students’ attitudes and performance in the

classroom, as students may internalize beliefs about their abilities that teachers have. Lumsden

additionally indicates that teachers’ expectations for students can become a self-fulfilling

prophecy as students adapt to these expectations—whether high or low.

The self-fulfilling prophecy, a prediction that becomes true because people act as if it is

true, has been studied repeatedly since the introduction of the concept by 20th century

sociologist, Robert Merton (1957). Rosenthal (1963, 1966) conducted a series of studies

investigating the self-fulfilling prophecy and found that experimenters’ expectations indeed had

an effect on experimental outcomes. A few years later, Rosenthal and Jacobson (1968) tested the

related phenomenon known as the Pygmalion effect (higher expectations lead to better

performance) in the classroom setting. Specifically, teachers were told that certain children (who

were in fact chosen randomly) could be expected to be “growth-spurters” based on their

supposed results on a nonexistent test. In this ground-breaking study, Rosenthal and Jacobson

found support for the self-fulfilling prophecy and Pygmalion effect, as the students expected to

show greater intellectual growth actually demonstrated larger gains on an IQ test.

Rosenthal and Jacobson’s (1968) findings sparked a lot of debate about whether teachers’

expectations could actually have an effect on students’ performance. Since this time, hundreds of

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related studies have been conducted with mixed results; self-fulfilling prophecy effects have

been found to occur in some cases, but not in others. In essence, all perceptions and expectations

are not automatically self-fulfilling.

Blease (1983) proposed that in order for expectation effects to occur, expectations must

first be successfully communicated. The successful transmission of expectations depends on the

existence of certain conditions. First, the school must provide an environment in which

expectations will be formed and articulated. Such an environment would include prolonged

student-teacher interactions, activities that facilitate verbal communication, and the opportunity

for teachers to make regular subjective judgments about their students. Second, the teacher’s

behavior is important. Blease notes that based on their expectations, teachers must consistently

provide qualitatively different classroom experiences for each student. Third, when a group of

individuals share similar perceptions of particular children, there is likely to be a cumulative

effect, increasing the likelihood that those expectations will be transmitted. This “expectation

network” could include other school staff, parents, siblings, and peers. As information

accumulates, expectations within this network are more likely to become firmly established and

more resistant to change. A final important factor is the receptivity of the students to whom the

expectations are being transmitted. Blease indicates that students must believe that their teachers

are legitimate and competent judges of their behavior and performance. That is, the student must

accept as true the situation, which has been defined by the teacher (Blease, 1983).

While recognizing that self-fulfilling prophecy effects do not automatically occur

between every teacher and student, and not every teacher expectation is self-fulfilled, Babad

(2009) states, “Today there is no doubt that the phenomenon of teachers’ SFP [self-fulfilling

prophecy] does indeed exist and can be measured empirically” (p. 79). He further suggests, “In

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the reality of the classroom, teachers form differential expectations about all students, and they

interact with students according to their expectations and interpretations” (Babad, p. 87).

Teacher expectations and student outcomes. Studies to date vary in the reported

magnitude of teachers’ expectancy effects. In an early meta-analysis of 47 studies, Smith (1980)

investigated the effect of teacher expectations on students’ IQ test performance and academic

achievement. While Smith found a small average effect size (Cohen’s d = .16) for teacher

expectations on students’ IQ test performance, teacher expectations had a larger effect on

students’ academic achievement (Cohen’s d = .38). Raudenbush (1984) conducted a meta-

analysis of 18 studies also examining the effects of teacher expectations on student IQ test

performance. Like Smith (1980), Raudenbush found a small average effect size (Cohen’s d =

.11). However, expectancy effects varied (-.04 to .32), depending on how long the teacher had

known the student. Specifically, the longer the teacher had known a student, the smaller the

expectancy effect.

In another study on teachers’ expectations and self-fulfilling prophecy, Brophy (1983)

found that teachers’ expectation effects occur in only a minority of cases and that such effects are

minimal because teachers’ expectations are generally accurate. However, Brophy noted that it is

difficult to fully predict the direct effects of teachers’ expectations due to the possible

interactions with various factors, such as teachers’ beliefs about learning and instruction, or

students’ perceptions, interpretations, and responses to teacher expectations.

In 1989, Jussim began a series of studies on teachers’ expectations. Jussim (1989)

examined whether students’ academic performance confirmed teachers’ expectations due to the

creation of self-fulfilling prophecies or due to the accuracy of teachers’ expectations.

Longitudinal data collected over the course of a year were obtained from 27 teachers and 429

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students in sixth grade math classes in a public school district in southeastern Michigan. More

than 90% of the students sampled were White, with a majority coming from middle- or upper

middle-class backgrounds. Teachers were given questionnaires, which assessed perceptions of

each student’s talent, effort, and performance in math; students were given questionnaires, which

were designed to measure self-concept of ability in math, effort in math, time spent on math

homework, and value placed on math. Standardized test scores and math grades were used as

measures of students’ achievement.

Path analytic techniques were used to assess the relationship between teachers’

expectations, students’ motivation, and students’ achievement. Consistent with the self-fulfilling

prophecy hypothesis, Jussim (1989) found a modest self-fulfilling prophecy effect on students’

achievement and motivation, but also found that teachers’ expectations predicted student

achievement due to their accuracy rather than actually “causing” students to perform in a certain

way. Results of a subsequent study by Jussim and Eccles (1992) using the same measures and an

expanded sample (98 teachers & 1,731 students from 11 school districts in southeastern

Michigan) also found that teachers’ expectations were significant predictors of changes in

students’ achievement.

A more recent review of 35 years of empirical research led Jussim and Harber (2005) to

conclude that self-fulfilling prophecies do occur in the classroom, but the effects are generally

small (averaging r = .10 to .20), with self-fulfilling prophecies affecting approximately 5 to 10%

of students. However, expectancy effects may be stronger for stigmatized social groups or

children for whom teachers hold lower expectations. In an early longitudinal study of a group of

urban African American children in the lower elementary grades, Rist (1970) observed the self-

fulfilling prophecy first-hand when children were placed into reading groups reflective of social

7

class and treated differently by the teacher, with subsequent effects on the children’s academic

achievement.

In a later study, Jussim, Eccles, and Madon (1996) studied self-fulfilling prophecies

among students from stigmatized demographic groups based on sex, race/ethnicity, and social

class. Although no significant effects were found based on sex, students from lower social class

backgrounds were more susceptible to self-fulfilling prophecies. Teachers’ expectations for low

achieving students from lower social class backgrounds produced a self-fulfilling prophecy

effect size of .60. Additionally, effect sizes based on teachers’ expectations for African

American students ranged from .40 to .60.

Using a subsample of 712 students (out of a total sample of 1,539 students) from the

Chicago Longitudinal Study, Gill and Reynolds (1999) also examined teachers’ expectations on

the achievement of low-income African American sixth graders. Parents and teachers were

asked to rate their expectations for students’ educational attainment, and students rated their

perceptions of both parents’ and teachers’ academic expectations. Prior achievement was

determined based on students’ third grade math and reading scores on the Iowa Tests of Basic

Skills (ITBS: Hoover, Hieronymus, Frisbie, & Dubar, 1993). Current reading and math

achievement was also assessed by the ITBS. The results of path analyses revealed that teachers’

expectations had the largest direct effect on both reading and math achievement (R2 = .32 and

.35, respectively; p < .01).

While results across studies are somewhat varied, the findings indicate that expectation

effects are likely to exist to some extent, most notably among marginalized or lower achieving

groups of students. However, the reviewed literature also suggests that teachers may just be

accurate predictors rather than potential “causers” of students’ academic achievement levels. An

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extensive amount of research has been conducted on the predictive accuracy of teachers’

judgments.

Accuracy of teacher judgments. In a survey of literature on teacher-based judgments of

academic achievement, Hoge and Coladarci (1989) reviewed 16 published studies to determine

the overall match between teacher-based assessments of students’ achievement and objective

measures of students’ learning. In most of the reviewed studies, researchers employed

correlational analyses to assess the accuracy of teacher judgments while only a few researchers

examined the exact agreement between student performance and teacher judgments. Overall, the

results revealed moderate to strong correspondence between teacher judgments and student

achievement with correlations ranging from .28 to .92 (Mdn = .66).

Hoge and Coladarci (1989) suggested that the underlying variability in the results of the

reviewed studies may be due to notable differences between the studies. For example, nine of

the reviewed studies used indirect ratings or rankings of student achievement whereas the seven

remaining studies contained direct estimates of how students would perform on a specific

achievement test. Additionally, some studies used norm-referenced judgments whereas others

employed peer-independent judgments. Across studies, five different types of judgment

measures were used, each differing in level of judgment specificity (presented here in order from

lowest to highest specificity level): (a) rating of each student’s academic ability, (b) ranking

students according to academic ability, (c) estimating grade equivalents likely to be obtained on

a concurrently administered achievement test, (d) estimating the number of items a student

would get correct on an achievement test, and (e) estimating actual item responses or whether or

not a student would get a particular item correct on an achievement test.

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In a follow-up to Hoge and Coladarci’s (1989) review, Demaray and Elliott (1998)

investigated the relationship between teachers’ judgments of students’ academic achievement

and actual performance on an academic achievement test, using both direct and indirect methods.

Participants were 12 teacher volunteers and their 47 randomly selected first through fourth grade

students (30 female and 17 male) from Wisconsin public schools. No information was provided

on the race/ethnicity of the participating students and teachers. Teachers completed the

Academic Competence scale from the Social Skills Rating System–Teacher Version (SSRS;

Gresham & Elliott, 1990) as well as a questionnaire specifically developed to measure teachers’

direct predictions of student performance on the Kaufman Test of Education Achievement –

Brief Form (K-TEA; Kaufman & Kaufman, 1985). Subsequently, students were administered

the K-TEA.

Pearson correlations and percent of agreements were used to investigate proposed

relationships. The results showed moderately strong correspondence between teacher

predictions (both direct and indirect) and actual student achievement, which were similar to

Hoge and Coladarci’s (1989) review of prior findings. Demaray and Elliott (1998) also found

moderately high (r = .70) correlations between indirect teacher ratings on the SSRS and actual

student performance on the K-TEA. Additionally, there was a mean 79% agreement between

teachers’ direct item predictions and students’ actual item performance.

Subsequent studies have also shown support for the predictive accuracy of teachers’

judgments. Alvidrez and Weinstein (1999) found that preschool teachers’ judgments of student

ability had a predictive relationship with students’ later high school performance. Additionally,

Hecht and Greenfield (2002) determined that teachers were able to accurately predict the future

reading ability of a sample of first grade students. Much of the previously summarized research

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has compared teachers’ judgments/ratings with students’ performance on norm-referenced

measures of academic achievement. In contrast, Eckert, Dunn, Coding, Begeny, and Kleinmann

(2006) compared teacher ratings with students’ performance on Curriculum-Based Measurement

(CBM) probes, a more direct and curriculum-relevant estimate of students’ skill levels in math

and reading. Participants were 33 students (51.5% male) from two second-grade classrooms in

an elementary school in a Northeastern suburban school district. The mean age of the students

was 7.3 years and a majority (78.8%) of the students were Caucasian with the remaining students

classified as African American (18.2%) or Latino/Hispanic (3%). Almost 6% of the participants

received special education services and approximately 32% participated in Title I programming.

Teacher ratings were assessed through interviews and the creation of teacher reading and

mathematics assessment charts. On the charts, teachers were asked to rate students on five

reading grade levels (i.e., Grades 1-5) and four hierarchically arranged basic mathematics skills

involving addition and subtraction. During interviews, teachers were asked to estimate targeted

students’ reading and math abilities, including general skill, instructional level, and class-wide

comparisons of skills. Student participants were given specifically developed CBM reading and

math probes to assess their oral reading and math computational fluency.

Overall, the results of this study suggested that teachers were not consistently accurate in

assessing their students’ reading and math fluency. In general, correlations between judgments

of students’ instructional levels in reading and their actual reading performance ranged from

moderate (r = .59) to high (r = .83), whereas correlations between teachers’ judgments and math

CBM performance were low (ranging from .09 to .32). Specific analyses of patterns of

correspondence indicated that teachers often overestimated student performance in math as well

as performance on reading material that was at or below grade level.

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While there is some variability in research findings on the accuracy of teachers’ academic

judgments and predictions, especially as related to using different types of measures to assess

students’ skills (i.e., norm-referenced versus CBM), existing research indicates that with some

exceptions, teachers are generally fairly accurate judges of their students’ academic skills.

However, the accuracy of such judgments may vary across academic domain and may also

decrease when certain variables are introduced into the prediction equation. In some of the

previous studies and others, researchers have explored intervening factors, which may have an

impact on teachers’ judgments and subsequently their expectations as well.

Potential influences on teacher judgments and expectations. In addition to examining

the accuracy of teachers’ judgments, researchers have also explored specific variables that have

been proposed to influence teachers’ perceptions or judgments of students’ academic potential or

achievement. Based on meta-analysis, Hoge and Coladarci (1989) suggested that differences

among teachers, student gender, subject matter, and student ability were potential moderating

variables. In another meta-analysis of 77 studies, Dusek and Joseph (1983) used Stouffer’s

(1949) method of adding z-scores to provide a summary of statistically significant influences on

teachers’ academic and social expectations and used Cohen’s (1977) d for effect size. Based on

these calculations, Dusek and Joseph concluded that student attractiveness, behavior/conduct,

race, and social class were also potentially related to teachers’ expectations.

Bennett, Gottesman, Rock, and Cerullo (1993) explored the possible influence of gender

and perceived student behavior on teachers’ judgments of academic skills. Participants in this

study were 794 regular education students, the entire student population in kindergarten through

second grade at three parochial schools in Cleveland, Ohio, and one public school in the Bronx,

New York. Approximately half of the participants were male, with 45% of the sample classified

12

as White, 33% African American, 21% Hispanic, and fewer than 1% classified as belonging to

other racial/ethnic minority groups. Participants were administered the Einstein Assessment of

School-Related Skills (Gottesman, Doino-Ingersoll, & Cerullo, 1990), a brief academic screener

consisting of five to seven subtests (depending on grade level): Arithmetic, Language-Cognition,

Auditory Memory, Visual-Motor Integration, Letter Recognition, Word Recognition, Oral

Reading, and Reading Comprehension. Behavioral perceptions were based on grades assigned to

behavior at the end of the term; academic judgments were report card grades and structured

ratings in relation to word recognition, reading comprehension, mathematics, handwriting, and

language. A path model was created to examine the relationship between tested academic skill,

gender, behavior grades, and teachers’ academic judgments.

Results indicated that across grades and schools, teachers’ perceptions of students’

behavior accounted for a significant amount of variance (i.e., R2 = .36 to .49) in their academic

judgments. Students who were perceived as exhibiting bad behaviors were also regarded as

weaker academically, regardless of their gender or actual measured academic skill. While

gender appeared to influence behavior perceptions, with girls consistently receiving higher

behavior grades than boys, no direct effect was found between gender and teachers’ academic

judgments.

This study contained some notable limitations, including considerable missing data for

behavior and academic grades in one of the school districts (approximately 40 to 45% of students

were missing data in these areas), and the use of measures limited in generalizability and scope.

Different criteria were used across districts for behavior and report card grades making

generalizability of teachers’ judgments difficult. Additionally, the range and degree of assessed

academic skills were limited, and there was a mismatch between measured academic skills and

13

teachers’ academic judgments. While the findings of this study suggest that variables do exist

that affect teachers’ judgments and expectations, these findings should be interpreted with

caution due to the methodological flaws.

Helwig, Anderson, and Tendal (2001) focused primarily on the potential influence of

gender, but also considered classroom behavior and effort. The purpose of the study was to

examine whether the accuracy of elementary school teachers’ predictions of math achievement

was influenced by gender (after controlling for effort and compliance with classroom rules). The

sample consisted of 15 third-grade and 14 fifth-grade teachers and their 512 students in six

public school districts in a western state. Teachers were given three 5-point Likert scale items to

rate students on math skill, amount of effort in math, and overall classroom behavior and

compliance with rules. Students were given computer-based multiple choice math and reading

tests designed by the Northwest Evaluation Association.

Contrary to what was hypothesized, results of correlation and regression analyses

revealed that gender was not a statistically significant contributor to teacher predictions of math

achievement. Instead, for both third and fifth graders, actual math and reading achievement test

scores, together with student effort, were statistically significant predictors of teachers’ ratings of

math achievement. When participants were divided according to educational setting (i.e., regular

education versus special education), the results of analyses were similar to those previously

stated. Overall, these results indicated that teachers did not make academic judgments primarily

based on gender, but instead focused on other more relevant factors (e.g., past performance).

Hecht and Greenfield (2002) further explored potential factors that influence teacher

judgments of their students’ academic ability. In this longitudinal study teachers predicted the

future (third grade) reading proficiency of students currently in first grade. The sample consisted

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of 170 children from low socioeconomic status backgrounds who were part of a larger multi-site

study, the National Head Start/Public School Early Childhood Transition Demonstration Project

(Kagan & Neuman, 1998). Using specific scales from the Social Skills Rating System (SSRS;

Gresham & Elliott, 1990), teachers rated each child on academic competence and classroom

behavior in comparison to other children in the classroom. Additionally, various tests were used

to assess students’ print knowledge, phonemic awareness, word identification, receptive

vocabulary, and reading comprehension.

Using hierarchical regression analyses, Hecht and Greenfield (2002) examined the extent

to which first grade child characteristics (i.e., emergent literacy skills, classroom behavior, and

gender) were related to third grade reading skills; the extent to which first grade child

characteristics were related to teachers’ ratings of students’ reading skills; and finally, the extent

to which child characteristics accounted for associations between initial teacher ratings and later

reading outcomes. Results of the first set of hierarchical regression analyses indicated that

classroom behavior explained approximately 37% and 38% of the variance in later word reading

and reading comprehension skills, respectively. Independently, classroom behavior was not

found to substantially influence later reading acquisition. In the second set of analyses,

approximately 59% and 49% of the variability in teacher ratings was accounted for by emergent

literacy skills in first and third grade, respectively. Finally, while the predictive accuracy of

teachers’ ratings was almost entirely accounted for by students’ emergent literacy skills, gender

and classroom behavior appeared to act as extraneous child characteristics that may reduce the

accuracy of teacher judgments.

Other student characteristics have also been investigated in relation to teachers’

judgments and expectations, including physical attractiveness, name, and ethnicity. Tompkins

15

and Boor (1980) investigated whether students who were deemed more physically attractive or

had more popular first names were rated higher academically or socially. Forty-four male and

ninety-seven female student teachers were given a packet of information about a seventh grade

boy. All of the presented information remained the same except that the cases presented to each

participant contained pictures of students varying in attractiveness (attractive, unattractive, or no

picture) and first name popularity (popular first name, unpopular first name, or no name

indicated). After reading the presented information, participants were asked to rate the described

student on six academic attributes (intelligence, class standing, creativity, probability of learning

disabilities, level of future educational attainment, and severity of behavioral problems in class)

and five social attributes (popularity with peers, general personality, family socioeconomic

status, extent of participation in extracurricular club activities, and extent of participation in

sports). Tompkins and Boor (1980) reported that teachers rated students who were physically

attractive higher across all five social attributes whereas this same characteristic did not appear to

influence their ratings of academic attributes. Additionally, first name popularity was found to

have no effect on ratings of either social or academic attributes.

More recent research on the relationship between first names and teacher expectations

has indicated the opposite to be true. Anderson-Clark, Green, and Henley (2008) asked 130

elementary school teachers in a Dallas school district to rate academically-related behaviors

based on a presented vignette of a “typical” fifth grade student. Exactly half of the teachers in

the sample were African American while the other half was Caucasian. Teachers ranged in age

from 20 to 75 years (M = 40.3 years) and had a range of teaching experience from 6 months to

40 years (M = 12.5 years).

16

Each participant was presented with one of four versions of a brief description of a fifth

grade boy. The only notable difference between the four versions was the name (Xavier or

Ethan) and race (African American or Caucasian) of the identified student. (Names were chosen

based on popularity ratings of the local Social Security Administration). After reviewing the

presented description, participants were asked to complete the School Achievement Motivation

Rating Scale (SAMRS; Chiu, 1997), which consists of 15 five-point Likert scale items. Results

revealed a statistically significant main effect for teachers’ expectations based on student name,

while the effect for student ethnicity was not statistically significant. Teachers held more

negative expectations for the student with the African American sounding name, but did not hold

correspondingly negative expectations for the student actually designated as African American.

Additional analyses based on rater’s ethnicity, age, gender, and years of teaching experience

showed no statistically significant differences.

Unlike the previous study, Rubie-Davies, Hattie, and Hamilton (2006) found differences

in teachers’ expectations based on student ethnicity. In this study, 21 primary teachers from 12

schools in Auckland, New Zealand were surveyed in regards to 540 students, who were

classified as New Zealand European (n = 261), Pacific Islander (n = 97), Asian (n = 94), or

Maori (n = 88). Teachers were given two Likert-scale surveys, one at the beginning of the

school year and one at the end. In the first survey they were asked to indicate students’ expected

reading achievement at the end of the year while in the subsequent survey they were asked to

judge their students’ actual reading achievement. In addition to the teacher surveys, running

records of students’ oral reading were also reviewed.

A group (ethnicity) x time (beginning or end of school year) mixed model repeated

measures ANOVA was conducted. Results indicated statistically significant differences between

17

Maori students and all other ethnic groups. Even though Maori students’ achievement was not

below that of any other ethnic group at the beginning of the year, teachers had lower

expectations for Maori students’ achievement over time. In alignment with these lower

expectations, Maori students made the fewest academic gains as compared to the other ethnic

groups. As such, Rubie-Davies et al. (2006) suggest that ethnic stereotypes may lead to

sustaining teacher expectation effects, which results in altered teaching practices and student

opportunity to learn. These altered teaching practices in turn are likely to have an impact on the

amount of academic progress made by students for whom teachers have lowered expectations,

such as reported about the Maori.

While providing important information about what may influence teachers’ academic

judgments and expectations for students, one limitation of the summarized research is that the

primary source of measurement has been teacher self-report. There may be a difference between

what teachers report and how they actually behave. Hodson, Dovidio, and Gaertner (2002)

propose that a difference exists between what people know and believe is “right” versus how

they actually behave towards people who do not fit into their “in-group” (i.e., people of the same

ethnic and linguistic background who maintain similar beliefs and worldview).

In 1970, Kovel coined the term “aversive racism,” which refers to a subtle type of racial

bias rationalized by appeal to rules or stereotypes. The premise of the aversive racism theory

(Gaertner & Dovidio, 1986) is that negative evaluations of racial/ethnic minorities are realized

by a persistent avoidance of interaction with other racial and ethnic groups. Hodson, Dovidio,

and Gaertner (2002) further describe aversive racism as “socialization practices and normal

cognitive biases which form the basis of negative feelings that exist under the surface of

consciousness, conflicting with more deliberative, consciously-held beliefs regarding the positive

18

values of equality and justice among racial groups” (p. 2). This subtle form of bias may apply to

teachers and their expectations for students from typically stigmatized groups (e.g., ethnic and

language minorities or low SES students). That is, while teachers may be aware of the influence

their expectations have and believe that they should maintain and communicate high

expectations for all students, they may still unintentionally treat certain groups of children

differently based on subconsciously lower expectations.

While much of the more recent research on aversive racism has been conducted in the

laboratory, workplace, or a higher education institution (e.g., Dovidio, Gaertner, Kawakami, &

Hodson, 2002; Wolfe & Spencer, 1996), an early observational study conducted by Rist (1970)

provides what may be an illustration of this effect in an elementary school setting. Rist’s goal

was to “describe the manner in which inequalities imposed on children become manifest within

an urban ghetto school and the resultant differential educational experience for children from

dissimilar social class backgrounds” (p. 270). Data were collected through twice-weekly

observations of a single group of African American children in an urban school. Formal

observations were conducted throughout the children’s kindergarten year and then again during

the first half of their second grade year. Additionally, the children were observed four times

during their first grade year. Interviews were also conducted with the children’s kindergarten

and second grade teachers. Based on his observations, Rist argued that the children were placed

in reading groups that reflected social class and persisted through second grade. Additionally,

Rist noted that teachers consistently treated the groups differently, which ultimately influenced

children’s achievement.

In summary, general research on teachers’ academic judgments indicates moderate to

strong correlation (i.e., r = .50-.80) with actual academic outcomes. Research further suggests,

19

however, that the accuracy of teachers’ judgments is sometimes affected by extraneous variables,

such as gender (e.g., Hoge & Coladarci, 1989), student behavior (e.g., Bennett, Gottesman,

Rock, & Cerullo, 1993), socioeconomic status (e.g., Dusek & Joseph, 1983), physical

attractiveness (e.g., Tompkins & Boor, 1980), ethnicity (e.g., Rubie-Davies, Hattie, & Hamilton,

2006), and name (e.g., Anderson-Clark, Green, & Henley, 2008). It is important to further

investigate these and other variables and their effect on teachers’ judgments and expectations.

These investigations are especially important in light of the body of research on aversive racism,

which suggests the existence of contemporary subtle bias towards racial/ethnic minority groups.

Much of the research on teachers’ judgments and expectations has been conducted with

regular education English-speaking students. However, there is a growing body of literature that

focuses on the educational needs and outcomes of ELL students. Survey research exists which

investigates mainstream teachers’ perceptions of ELL students and the specific variables which

moderate these perceptions.

English Language Learners in Mainstream Classrooms

Research has been conducted on teachers’ perceptions of and expectations for

students who have limited English proficiency. Cummins (2001) suggests that teachers’

attitudes toward and perceptions of ELL students are especially important due to the potential

impact on student-teacher relationships and ultimately students’ achievement, which may be

undermined by struggles to fit in and learn a new language. Within the past 20 years, research

has gradually evolved that examines teachers’ perceptions and attitudes towards ELL students in

the mainstream classroom (e.g., Clair, 1993, 1995; Mantero & McVicker, 2006; Penfield, 1987;

Reeves, 2002, 2006; Vollmer, 2000; Youngs & Youngs, 2001). The focus of this line of

research has been not only on identifying teachers’ perceptions and attitudes towards ELL

20

students, but also investigating variables that might predict or influence these overall perceptions

and attitudes.

In 1987, Penfield administered an open-ended questionnaire to 162 New Jersey teachers,

who had ELL students in their classrooms. Eighty five percent of respondents taught grades K-8

and the remainder taught Grades 9-12. While the ELL students were reportedly from many

different countries, the majority were said to have originated from Taiwan, India, or Puerto Rico.

A majority of respondents (61%) suggested that the regular classroom was a better instructional

setting for ELL students than segregated classrooms. However, this integration was also noted as

problematic; for example, some statements reflected concern about the possible impact on non-

ELL students, such as slowing of their academic progress. Responses also indicated a general

belief that ELL instruction should primarily be the role of the ELL teacher, not the regular

education teacher. The most commonly noted frustration was the inability to communicate

effectively with ELL students and their parents. Additionally, more than half (54%) of the

respondents indicated an overall lack of knowledge on how to work with ELL students as well as

a need for more training and access to appropriate, adapted curricular materials. Other

comments reflected teachers’ perceptions of the stigmatization of ELL students and the tendency

of ELL students to stick together and to isolate themselves from their English-speaking peers.

Despite some limitations (e.g., lack of standardization and thus limited generalizability),

Penfield’s (1987) research reveals a sample of teacher perspectives, both positive and negative,

regarding ELL students in the mainstream classroom. Based on the results, Penfield makes some

broad recommendations, such as increased training for working with ELL students (including

coursework and inservice training) and increased cooperation and collaboration between ELL

and mainstream teachers.

21

Limited research was published immediately following Penfield’s (1987) study, but later

studies (Clair, 1993; Vollmer, 2000) have investigated mainstream teachers’ perceptions of ELL

students. Clair conducted a year-long qualitative study exploring the beliefs, self-reported

practices, and professional development needs of three mainstream classroom teachers (Grades

4, 5, and 10) with ELL students. Case histories were compiled based on transcripts of in-depth

interviews, notes from classroom observations, and entries from teachers’ and researcher

journals. In general, the three teachers reported not knowing much about their ELL students

beyond speculation of national origin and native language. To some extent all three teachers

erroneously believed that academic or social difficulties stemmed solely from internal factors

within the ELL student and thus these teachers tended to have unconsciously lower expectations

for these students. Additionally, all three teachers expressed beliefs about their ELL students’

cultural background based heavily on stereotypes and assumptions. Finally, while the three

teachers indicated their belief that ELL students were generally accepted by non-ELL peers,

there were some contradictory responses describing community prejudice and the stigma

attached to participating in an English as a Second Language (ESL) program.

Clair (1993) concluded that many of the beliefs held by the teachers interviewed were

based on hearsay and misinformation, and that their beliefs appeared to stem from individual

experience. All three teachers seemed to have minimal understanding of their ELL students and

tended to express the belief that solely internal factors cause academic and social difficulties,

while neglecting to consider important external factors, such as societal attitudes, political

structures, and acculturation patterns. In light of the role beliefs may play in shaping actual

behavior, Clair suggests that more education and staff development is needed to change teachers’

beliefs and their resulting instructional practices for working with ELL students.

22

In 2000, Vollmer examined teachers’ constructions of the “typical ESL student,” based

on data collected as part of a year-long ethnographic study in an urban, public high school. For

the purposes of the current study, Vollmer chose to examine a relatively new group of ELL

students, 17 Russian-speaking students from republics of the former Soviet Union. Responses

from seven semi-structured teacher interviews were analyzed as well as informal interactions and

unsolicited comments gathered from the same teachers throughout the year. Interview questions

focused on teachers’ perceptions of the “typical” ELL student, the fit of this image to the Russian

students, their perceptions of individual students, and teacher experiences with the students in the

classroom.

While Vollmer (2000) focused on a group of Russian students, the teachers who were

interviewed frequently compared this group of Russian students to ELL students of different

ethnic/racial backgrounds in the same high school. For example Vollmer’s critical discourse

analysis revealed that Russian students were frequently singled out for praise whereas other ELL

students in the school, including Chinese and Latino/a (primarily Mexican and Central

American) students, were often stigmatized. Teachers consistently described the Russian

students as bright, confident, assertive, and passionate. Additional comments highlighted the

Russian students’ unique individuality, level of communication, and interpersonal skills. In

general, Vollmer observed that the Russian students were frequently described as a distinct group

of second language learners with atypical characteristics as compared to other ELL groups.

Vollmer’s (2000) findings highlight some important implications. Most notably, this “atypical”

positive conceptualization of Russian students may be related to the general

perception/acceptance of Russians as White/European, whereas the “typical” ELL student (e.g.,

Asian or Latino/a) is viewed more conclusively as non-White. Overall, these findings support

23

the idea that teacher perceptions and expectations may differ between ethnic groups and perhaps

within groups as well.

While Clair’s (1993) and Vollmer’s (2000) ethnographic studies reveal valuable in-depth

information, one limitation of these studies is the small sample size and thus potential lack of

generalizability. Additionally, due to the qualitative nature of the studies, these results may be

interpreted somewhat differently. The next set of studies expands on these limitations by

including a larger sample size and using different measurement techniques (e.g., survey).

Youngs and Youngs (2001) investigated what influences teachers’ general perceptions of

ELL students by constructing a general model containing five categories of possible predictors:

(a) general educational experiences (i.e., the quantity, quality, and content of general coursework

completed); (b) specific ELL training, (c) direct personal contact with diverse cultures, (d) prior

contact with ELL students (including frequency, diversity, and intensity), and (e) demographic

characteristics (e.g., gender, ethnicity, and age). A survey was distributed to all teachers in three

middle/junior high schools in a U.S. Great Plains community; out of 224 distributed surveys, 143

usable surveys were returned. The sample of respondents was relatively balanced with respect to

gender, age, and grade level/subject taught.

Overall, the results supported a multi-predictor model of teacher attitudes with most

respondents reporting a neutral to slightly positive attitude toward teaching ELL students.

Youngs and Youngs (2001) suggested that most of the identified predictors (i.e., training to work

with ESL students, participation in a foreign language or multicultural class, living or teaching

outside of the United States, teaching humanities or social sciences versus more applied

disciplines, and interaction with a diverse population of ELL students) collectively represented a

teacher’s exposure to cultural diversity. Together, these predictors accounted for 26% of the

24

variance in teachers’ attitudes. The researchers conclude that it is collective exposure rather than

any one variable that leads to positive teacher attitudes towards ELL students in their classrooms.

Reeves (2002, 2006) created and piloted a survey instrument designed to gauge teachers’

agreement or disagreement with 16 Likert-scale items related to attitudes toward inclusion of

ELL students in mainstream classrooms. Other sections of the survey tapped the frequency of

teaching behaviors, open-ended responses about the benefits and challenges of ELL inclusion,

and demographic information about the participating teachers. Participants were 279 high school

teachers, primarily women (60.9%) and native-English speakers (98.2%), from a Southeastern

school district.

Results revealed that 72% of the surveyed teachers had a welcoming attitude toward

inclusion of ELL students in their classrooms. However, almost half of the respondents

indicated that not all students benefited from the inclusion of ELLs in mainstream classrooms

and that ELL students should not be mainstreamed until they had attained a minimal level of

English proficiency. While supportive of students using their native language at school, a

majority of the teachers indicated that English should be made the official language of the United

States and that ELL students should be able to learn English within two years of enrollment in a

U.S. school. More than half of respondents suggested that certain modifications (e.g., extended

time) were justifiable for ELL students; however, nearly 70% of surveyed teachers reported that

they did not have enough time to deal with the additional needs of ELL students. A majority of

the surveyed teachers indicated that they had not received sufficient training to work with ELL

students; however, only half of the teachers indicated interest in obtaining specialized ELL

training.

25

As Reeves (2006) pointed out, these findings illuminate a discrepancy between teachers’

general viewpoints toward ELL inclusion and their attitudes towards specific aspects of ELL

inclusion. In particular, some teachers appeared to have conceptions regarding second language

learning that have not been supported by research. Specifically, ELL students should be able to

acquire English within two years and that ELL students should avoid using their native language

because it interferes with the acquisition of English. While the amount of time needed to acquire

a second language has not been agreed on, some experts (e.g., Cummins, 1979; Thomas &

Collier, 1997) suggest that it can take more than seven years. Additionally, according to research

(e.g., Cummins, 1981; Krashen, 2003), continued first-language use can facilitate and improve

the development of second-language literacy.

Mantero and McVicker (2006) also investigated differences in the beliefs of mainstream

and ELL teachers in regard to middle school ELL students and second language learning.

Participants were 148 mainstream teachers and 12 ELL teachers from a sixth grade academy and

a middle school located in Atlanta, Georgia. Researchers used a modified survey designed by

Reeves (2002, 2006). Demographic information was also collected about general subject area

taught, years of teaching, gender, number of undergraduate and graduate course taken with an

ELL focus, and hours spent in professional development on ELL students.

Results of t-tests and ANOVAs showed a statistically significant difference between

mainstream and ELL teachers’ perceptions of ELL students in the regular education classroom;

ELL teachers indicated more positive perceptions than mainstream teachers. While ELL

teachers reported more positive perceptions, mainstream teachers were generally neutral rather

than negative. The number of years of teaching was found to be positively related to teacher

perceptions of ELL students, with the most positive perceptions expressed by teachers who had

26

between 6 and 10 years of teaching experience. Additionally, both ELL and mainstream teachers

who had taken more undergraduate coursework about working with ELL students had more

positive perceptions of this population. In regard to graduate studies, this finding was also true

for mainstream teachers, but not for ELL teachers. As such, it appears that the completion of

more coursework on the graduate level does not modify ELL teachers’ perceptions, which was

generally positive, regardless of number of courses taken. Finally, the more hours both types of

teachers spent in professional development, the more positive their perception of ELL students;

professional development experiences had a slightly greater impact on ELL teachers than

mainstream teachers. Mantero and McVicker (2006) concluded that it is imperative for teacher

education programs to incorporate courses that specifically address the learning needs of ELL

students. Not only does this increase teachers’ overall knowledge and awareness, but these types

of courses also appear to have a positive impact on how teachers perceive ELL students.

There appears to be some consistent trends across the studies reviewed. Over time

multicultural awareness among teachers has gradually increased with resultant changes in

viewpoints (in a positive direction), perhaps due to expanded educational opportunities and

increased experiences interacting and working with ELL students. However, researchers (e.g.,

Reeves, 2006) continue to note mainstream teachers’ overall lack of specific training for working

with this ever-growing population and suggest more training in this area. Recent studies

(Mantero & McVicker, 2006; Young & Youngs, 2001) suggest that increased training leads to

better awareness and knowledge, which ultimately facilitates more positive perceptions of ELL

students in general.

A major limitation of the extant literature on teachers’ perceptions of ELL students is the

broad grouping of all ELL students together. Vollmer (2000) notes that not all ELL students are

27

perceived equally and that teachers’ perceptions and expectations may differ significantly

depending on a student’s ethnic and/or cultural background. Future research needs to move

beyond this typical monolithic view of ELL students to more specific examinations of distinct

ELL groups and to focus on the nature of teacher perceptions about specific racial/ethnic

minorities, such as Mexicans or Puerto Ricans.

Perceptions of Language and Academic Competence

Some researchers have investigated teachers’ attitudes towards language and linguistic

diversity. In some cases there have been unfavorable attitudes toward bilingualism or the use of

languages other than English (e.g., Rueda & Garcia, 1996). It is possible that negative attitudes

toward other languages or lack of knowledge about second language development may influence

teachers’ judgments and expectations for ELL students.

Byrnes and Kiger (1994) developed the Language Attitudes of Teachers Scale (LATS) to

investigate 191 regular education teachers’ attitudes about linguistic diversity in a standardized

fashion. The scale is used to assess language politics (e.g., English as official language),

tolerance of ELL students in the regular classroom, and language support through teacher

training and more resources to provide better programs for ELL students. ANOVAs were

conducted to investigate the relationship of five specific teacher characteristics to teacher

attitudes toward language diversity: (a) previous experience with linguistically diverse students,

(b) formal training in second-language learning, (c) graduate education, (d) geographical region,

and (e) grade level taught. Results revealed that language attitudes differed with experience and

across region, with the most positive attitudes maintained by teachers from Arizona in

comparison to those in Utah and Virginia. Additionally, teachers who held a graduate degree or

had more formal training also had more positive language attitudes.

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Edl, Jones, and Estell (2008) examined the teachers’ attitude about language proficiency

and ethnicity in relation to their ratings of students’ academic and interpersonal competence.

Participants were fourth grade students in seven schools from two suburban school districts in a

major Midwestern city; the final sample consisted of 703 European Americans (53.9% female)

and 172 Latino/a students (50.0% female) in regular classrooms and 99 Latino/a students (44.4%

female) in bilingual classrooms. Teachers rated students on the Interpersonal Competence Scale

–Teacher (ICS-T; Cairns, Leung, Gest, & Cairns, 1995), which contains six distinct subscales:

Aggression, Popularity, Academics, Affiliative, Olympian-like traits, and Internalizing.

Using discriminant function analyses at four different time points (fall and spring of

fourth and fifth grade), Edl et al. (2008) found that Latino/a students in bilingual classrooms

were consistently rated lower by teachers on several of the competence variables. In the fall of

fourth grade, three factors most strongly differentiated the groups: Popularity, Academic

Competence, and Olympian-like traits. Multivariate contrasts indicated that teachers rated

European American students in both regular and bilingual classrooms highest on Academic

Competence, followed by Latino/a students in regular classrooms; Latino/a students in bilingual

classrooms were rated as least academically competent. In contrast, for Popularity and

Olympian-like traits, all regular education students (both European American and Latino/a) were

rated similarly while Latino/as in bilingual classrooms were rated lower than their regular

classroom counterparts. Edl et al. (2008) suggest that language proficiency may influence

teacher ratings of competence more so than ethnicity.

Also, some of the variables that were determined to differentiate between the groups in

the fall of both years no longer distinguished the groups in the spring, suggesting that teacher

perceptions likely changed over time, perhaps based in part on more experience with and

29

knowledge of their students. While teacher ratings of Academic Competence continued to

distinguish between European American and Latino students in the spring of fourth grade,

ratings of Popularity no longer differentiated the groups.

The results of this study have important implications for educational practice. Edl et al.

(2008) highlight the need for school psychologists to be aware of the academic and social

challenges that ELL students face, indicating “a key component of this awareness is knowing

how teachers perceive them in relation to their European counterparts” (p. 43). Awareness of

specific challenges and the impact of culture, ethnicity, and language proficiency on ELL

students’ educational performance and social involvement will assist school professionals in

determining ways to better meet all students’ needs in accordance with recent educational

regulations.

Conclusions

In summary, findings vary somewhat, but research generally suggests that the self-

fulfilling prophecy can occur in the classroom, especially given certain conditions (Blease,

1983). The reviewed studies show that teachers’ expectations can influence student outcomes

(e.g., Hoge & Coladarci, 1989; Jussim, 1989; Jussim & Eccles, 1992) and furthermore that

stronger expectancy effects may exist for stigmatized groups (e.g., Gill & Reynolds, 1999;

Jussim, Eccles, & Madon, 1996). While research generally supports the predictive accuracy of

teachers’ judgments, several researchers (e.g., Bennett, Gottesman, Rock, & Cerullo, 1993;

Dusek & Joseph, 1983; Hoge & Coladarci, 1989) have found evidence for intervening factors,

which may influence teachers’ beliefs and expectations, such as student behavior (e.g., Bennett,

Gottesman, Rock, & Cerullo, 1993), gender (Hoge & Coladarci, 1989), race/ethnicity (e.g.,

Rubie-Davies, Hattie, & Hamilton, 2006), socioeconomic status (e.g., Dusek & Joseph, 1983),

30

physical attractiveness (e.g., Tompkins & Boor, 1980), and even names (e.g., Anderson-Clark,

Green, & Henley, 2008). Teachers’ expectations and behavior may even be influenced by a

subtle form of bias termed aversive racism (Kovel, 1970; Gaertner & Dovido, 1986), in which

individuals, such as teachers, have a tendency to unknowingly act in a discriminatory manner

towards those who are perceived as different.

Early in-depth ethnographic studies (e.g., Clair, 1993; Penfield, 1987) on teachers’

perceptions of ELL students showed a general lack of knowledge as well as the existence of

beliefs and practices based heavily on assumptions and stereotypes. More recent surveys (e.g.,

Mantero & McVicker, 2006; Youngs & Youngs 2001) of teachers’ perceptions of ELL students

show an overall increase in awareness and knowledge for working with these students, but the

general consensus continues to be that more education and training to work with ELL students is

needed (Reeves, 2006). In particular, efforts are needed to increase awareness of the possible

subtle biases which teachers may exhibit towards ELL students. To develop adequate, effective

training that increases teachers’ awareness and knowledge, more research is needed to further

investigate the proposed link between teachers’ perceptions, expectations, and outcomes for ELL

students.

Edl et al. (2008), in particular, provide fertile ground for future research. As noted by the

authors, there are several opportunities to expand based on their study’s limitations. First, while

their findings implicate that it is language proficiency more than ethnicity that influences

teachers’ perceptions, this contention has not been specifically investigated. They also noted that

the research was conducted in an area in which less than 20% of the students were of Latino/a

heritage. Communities with larger Latino/a or multicultural populations may present different

results. Additionally, Edl et al. grouped all Latino/a ELL students together. However, previous

31

research findings (e.g., Clair, 1993; Vollmer, 2000) suggest that not all ELL students are viewed

the same; further research is needed that focuses on teachers’ perceptions of specific groups of

ELL students.

Current Study

The purpose of the current study was to further examine the impact of English language

proficiency on teachers’ perceptions of ELL students’ competence by expanding on the

limitations noted in Edl et al.’s (2008) study. Edl et al. suggest that English language proficiency

may have a stronger influence than ethnicity on teachers’ ratings of ELL students. To investigate

this premise, both ethnicity and English language proficiency were included as variables in the

current study. Additionally, in the current study, the data examined were based on a larger

multicultural population across a wider geographical region. Finally, instead of considering ELL

students as a whole, the current study considered the national origin of students and their parents.

Research questions. Three research questions guided the study:

1. Do teachers’ perceptions of academic and interpersonal abilities differ for Spanish-

speaking ELL and English-speaking non-ELL students of the same (Hispanic non-

White) general ethnic background?

2. Do teachers’ perceptions of academic and interpersonal abilities differ for Spanish-

speaking ELL and English-speaking non-ELL students of different racial/ethnic

backgrounds (i.e., Hispanic, African American, and Caucasian)?

3. How accurate are teachers’ perceptions of ELL and non-ELL students’ academic and

interpersonal competence?

Hypotheses. Three hypotheses guided the study as well:

1. It is hypothesized that Spanish-speaking ELL students will be judged as having

32

weaker academic and interpersonal skills than their English-speaking non-ELL

counterparts of the same (Hispanic non-White) general ethnic background.

2. It is hypothesized that Spanish-speaking ELL students will be judged as having

weaker academic and interpersonal skills than their English-speaking non-ELL

counterparts of different racial/ethnic backgrounds.

3. It is hypothesized that teachers’ ratings would be more predictive of the academic

performance and interpersonal self-ratings of non-ELL versus ELL students.

33

METHOD

Overview

An archival data set was used for this study and was obtained from the Early Childhood

Longitudinal Study – Kindergarten Class of 1998-1999 (ECLS-K), a longitudinal data set

collected by the U.S. Department of Education. The ECLS-K contains data on 21,399 students, a

sample considered nationally representative of U.S. kindergarteners in 1998-1999. The children

in the ECLS-K came from both public and private schools and are considered representative of

diverse socioeconomic and racial/ethnic backgrounds. Data contained in the ECLS-K are

information about family, school, community, and individual factors associated with school

performance, as the focus of the ECLS-K study has been on children’s early school experiences

beginning with kindergarten and following these same children through middle school.

Participants

Participants were a sample of 260 third-grade students (133 boys, 127 girls) taken from

the ECLS-K database representative of the cohort of children who were in kindergarten in 1998–

99 or in first grade in 1999–2000, but were not necessarily representative of all third graders in

2001-02. This sample contained native English-speaking students as well as non-native English

speaking students, normally noted as English language learners (ELL). For the purposes of the

current study, ELL students were defined as students who spoke a language other than English in

the home per parent report and who also did not pass the English-proficiency screening test used

by the ECLS-K (the Oral Language Development Scale [OLDS]). The sample of ELL students

obtained scores on the OLDS ranging from 0 to 36 (M = 22.31, SD =10.99). Anything above a

36 was considered a passing score.

34

Four subsamples of 65 participants each were obtained from the ECLS-K: (a) ELL

students of Hispanic ethnicity (33 boys, 32 girls), (b) non-ELL students of Hispanic ethnicity (30

boys, 35 girls), (c) non-ELL Caucasian students (36 boys, 29 girls), and (d) non-ELL African

American students (34 boys, 31 females). Participants ranged in age from 8.75 to 9.75 years. In

accordance with the recommendations of ECLS-K staff (due to the oversampling of certain

groups of participants (including ELL students) and to correct for differential nonresponse as

well as students moving away over time), the ECLS-K data were weighted accordingly. While

the weighted data allowed for generalization of the findings beyond the sample, the weighting

significantly reduced the sample size (from 260 to 97) in the current study. In comparison to the

four equal subsamples of 65 students each in the unweighted sample, the weighted sample

contained the following: (a) 24 ELL students of Hispanic ethnicity, (b) 28 non-ELL students of

Hispanic ethnicity, (c) 17 non-ELL Caucasian students, and (d) 28 non-ELL African American

students. A summary of the demographic characteristics of the unweighted and weighted

samples of students are presented in Tables 1 and 2, respectively.

Participants also included 238 teachers from the ECLS-K study who provided ratings of

the students. (This number was reduced to 90 teachers when the sample was weighted).

Nineteen of these teachers provided ratings for 2 or 3 students. The teachers ranged in age from

24 to 62 years (M= 42.61; SD = 11.55) and years of teaching experience ranged from 1 to 35

years (M= 14.15; SD = 10.43). Information on the gender and race/ethnicity of teachers was not

available in the public-use dataset of the ECLS-K database. Further teacher characteristics, for

both the unweighted and weighted sample, are presented in Table 3. These characteristics

include teachers’ highest level of education, number of ESL training classes taken in college, and

whether or not teachers had obtained ESL certification.

35

Table 1

Demographic Characteristics of Unweighted Sample of Students (N = 260)

ELL Students Non-ELL Students Hispanic Hispanic African American Caucasian

Gender n % n % n % n % Total Male 33 50.8 30 46.2 34 52.3 36 55.4 133 Female 32 49.2 35 53.8 31 47.7 29 44.6 127 Age < 8.75 years 6 9.2 9 13.8 2 3.1 3 4.6 20 8.75 to < 9 years 18 27.7 16 24.6 14 21.5 14 21.5 62 9 to < 9.25 years 17 26.2 13 20.0 6 9.2 15 23.1 51 9.25 to < 9.5 years 13 20.0 15 23.1 23 35.4 13 20.0 64 9.5 to < 9.75 years 8 12.3 10 15.4 16 24.6 10 15.4 44 > 9.75 years 3 4.6 2 3.1 4 6.2 10 15.4 19 SES First Quintile 49 75.4 9 13.8 16 24.6 1 1.5 75 Second Quintile 9 13.8 16 24.6 17 26.2 10 15.4 52 Third Quintile 5 7.7 16 24.6 16 24.6 14 21.5 51 Fourth Quintile 1 1.5 15 23.1 11 16.9 14 21.5 41 Fifth Quintile 1 1.5 9 13.8 5 7.7 26 40.0 41 School Location Rural - - 14 21.5 13 20.0 16 24.6 43 Suburban 18 72.3 21 32.3 28 43.1 31 47.7 98 Urban 47 27.7 30 46.2 24 36.9 18 27.7 119 Mother’s Origin Not Applicable - - 3 4.6 4 6.2 1 1.5 8 United States 5 7.7 50 76.9 56 86.2 61 94.8 172 Bermuda - - - - 1 1.5 - - 1 Cuba - - 2 3.1 - - - - 2 Dominica - - 1 1.5 - - - - 1 Dominican Republic 1 1.5 - - - - - - 1 El Salvador 3 4.6 - - - - 1 1.5 4 France - - - - - - 1 1.5 1 Haiti - - - - 1 1.5 - - 1 Honduras 1 1.5 - - - - - - 1 Liberia - - - - 1 1.5 - - 1 Mexico 53 81.5 7 10.8 - - - - 60 Netherlands 1 1.5 - - - - - - 1 Nigeria - - - - 1 1.5 - - 1 Puerto Rico 1 1.5 1 1.5 - - - - 2 United Kingdom - - - - 1 1.5 - - 1 Venezuela - - - - - - 1 1.5 1 Vietnam - - 1 1.5 - - - - 1

36

Table 1 continued ELL Students Non-ELL Students

Hispanic Hispanic African American Caucasian Father’s Origin n % n % n % n % Total Not Applicable 14 21.5 11 16.9 23 35.4 5 7.7 53 United States 4 6.2 42 64.6 36 55.4 58 89.2 140 Bermuda - - - - 1 1.5 - - 1 Dominica - - 1 1.5 - - - - 1 El Salvador 1 1.5 - - - - - - 1 The Gambia - - - - 1 1.5 - - 1 Guatemala 1 1.5 - - - - - - 1 Haiti - - - - 1 1.5 - - 1 Honduras 1 1.5 - - - - - - 1 Hungary - - 1 1.5 - - - - 1 Italy - - - - - - 1 1.5 1 Jamaica - - - - 1 1.5 - - 1 Liberia - - - - 1 1.5 - - 1 Mayotte - - 1 1.5 - - - - 1 Mexico 43 66.2 6 9.2 - - - - 49 Nigeria - - - - 1 1.5 - - 1 Philippines - - 1 1.5 - - - - 1 Puerto Rico 1 1.5 2 3.1 - - - - 3 Switzerland - - - - - - 1 1.5 1 Mother’s Education Not Applicable - - 3 4.6 4 6.2 1 1.5 8 8th grade or below 20 30.8 - - 1 1.5 - - 21 9th to 12th grade 10 15.4 6 9.2 7 10.8 1 1.5 24 HS diploma 19 29.2 18 27.7 15 23.1 16 24.6 68 Vocational/Tech. 6 9.2 4 6.2 3 4.6 3 4.6 16 Some college 8 12.3 21 32.3 24 36.9 23 35.4 76 Bachelor’s degree 1 1.5 8 12.3 8 12.3 9 13.8 26 Some grad. School - - 2 3.1 2 3.1 2 3.1 6 Master’s degree - - 3 4.6 1 1.5 8 12.3 12 Doctorate 1 1.5 - - - - 2 3.1 3 Father’s Education Not Applicable 14 21.5 11 16.9 23 35.4 5 7.7 53 8th grade or below 20 30.8 - - - - - - 20 9th to 12th grade 7 10.8 4 6.2 7 10.8 2 3.1 20 HS diploma 11 16.9 24 36.9 15 23.1 15 23.1 65 Vocational/Tech. 3 4.6 1 1.5 4 6.2 4 6.2 12 Some college 8 12.3 15 23.1 10 15.4 10 15.4 43 Bachelor’s degree 1 1.5 6 9.2 2 3.1 14 21.5 23 Some grad. School - - - - 1 1.5 1 1.5 2 Master’s degree 1 1.5 4 6.2 3 4.6 8 12.3 16 Doctorate - - - - - - 6 9.2 6

37

Table 2 Demographic Characteristics of Weighted Sample of Students (N = 97)

ELL Students Non-ELL Students

Hispanic Hispanic African American Caucasian Gender n % n % n % n % Total Male 13 53.5 15 54.5 17 60.9 9 52.7 54 Female 11 46.5 13 45.5 11 39.1 8 47.3 43 Age < 8.75 years 3 11.6 3 11.8 0 1.5 1 3.0 7 8.75 to < 9 years 8 31.3 6 23.5 8 29.6 5 28.3 27 9 to < 9.25 years 5 20.1 7 26.4 1 4.8 3 18.8 16 9.25 to < 9.5 years 5 19.8 4 15.5 10 38.1 3 17.6 22 9.5 to < 9.75 years 3 13.5 5 19.7 6 20.4 3 18.8 17 > 9.75 years 1 3.7 1 3.1 2 5.7 2 17.6 6 SES First Quintile 17 71.4 8 29.8 6 21.6 0 1.4 31 Second Quintile 4 16.8 5 19.5 7 23.8 3 16.4 19 Third Quintile 2 10.1 5 18.9 8 28.5 4 24.1 19 Fourth Quintile 0 1.3 5 17.2 5 17.1 3 20.2 13 Fifth Quintile 0 0.3 4 14.5 2 8.9 6 37.9 12 School Location Rural - - 8 29.4 3 10.8 3 17.6 14 Suburban 6 26.8 8 30.7 11 38.8 6 37.5 31 Urban 18 73.2 11 39.8 14 50.4 7 42.8 50 Mother’s Origin Not Applicable - - 1 4.9 3 12.1 0 2.2 4 United States 1 4.5 22 78.8 21 77.1 16 91.8 60 Bermuda - - - - 0 0.7 - - 0 Cuba - - 1 4.6 - - - - 1 Dominica - - 0 .4 - - - - 0 Dominican Republic 0 1.8 - - - - - - 0 El Salvador 1 3.9 - - - - 1 4.1 2 France - - - - - - 0 0.9 0 Haiti - - - - 0 .3 - - 0 Honduras 0 1.2 - - - - - - 0 Mexico 20 84.6 2 6.0 - - - - 22 Liberia - - - - 0 .6 - - 0 Netherlands 1 2.9 - - - - - - 1 Nigeria - - - - 2 5.9 - - 2 Puerto Rico 0 1.0 1 5.0 - - - - 1 United Kingdom - - - - 1 3.3 - - 1 Venezuela - - - - - - 0 0.9 0 Vietnam - - 0 0.3 - - - - 0

38

Table 2 continued ELL Students Non-ELL Students

Hispanic Hispanic African American Caucasian Father’s Origin n % n % n % n % Total Not Applicable 6 25.2 7 25.9 10 35.2 1 7.2 24 United States 2 8.3 17 62.8 15 54.7 16 90.5 50 Bermuda - - - - 0 0.7 - - 0 Dominica - - 0 0.4 - - - - 0 El Salvador 0 1.2 - - - - - - 0 The Gambia - - - - 1 2.5 - - 1 Guatemala 0 1.2 - - - - - - 0 Haiti - - - - 0 0.3 - - 0 Honduras 0 1.2 - - - - - - 0 Hungary - - 1 1.9 - - - - 1 Italy - - - - - - 0 1.3 0 Jamaica - - - - 0 0.0 - - 0 Liberia - - - - 0 0.6 - - 0 Mayotte - - 0 .3 - - - - 0 Mexico 15 62.0 2 6.9 - - - - 17 Nigeria - - - - 2 5.9 - - 2 Philippines - - 0 0.4 - - - - 0 Puerto Rico 0 1.0 0 1.3 - - - - 0 Switzerland - - - - - - 0 0.9 0 Mother’s Education Not Applicable - - 1 4.9 3 12.1 0 1.4 4 8th grade or below 8 33.1 - - 0 1.2 - - 8 9th to 12th grade 3 12.2 7 26.4 3 10.1 0 1.4 6 HS diploma 7 30.6 6 22.0 8 27.5 4 26.1 25 Vocational/Tech. 3 12.7 0 1.7 3 10.6 1 8.6 7 Some college 2 9.8 7 26.2 7 25.4 5 28.3 21 Bachelor’s degree 0 0.3 4 14.1 3 11.3 3 15.1 10 Some grad. school - - 0 1.3 0 1.5 2 10.8 2 Master’s degree - - 1 3.4 0 .3 1 6.2 2 Doctorate 0 1.3 - - - - 0 2.0 0 Father’s Education Not Applicable 6 25.2 7 25.9 10 35.2 1 6.4 24 8th grade or below 7 29.2 - - - - - - 7 9th to 12th grade 2 8.5 4 13.3 4 14.6 1 3.4 11 HS diploma 4 16.3 8 29.3 5 17.7 3 20.0 20 Vocational/Tech. 2 6.7 0 0.7 2 7.6 1 5.8 5 Some college 3 13.4 5 16.8 4 15.7 3 16.3 15 Bachelor’s degree 0 0.4 2 6.6 0 1.6 6 34.2 8 Some grad. school - - - - 0 0.4 0 0.8 0 Master’s degree 0 0.3 2 7.3 2 7.1 1 8.3 5 Doctorate - - - - - - 1 4.8 1

39

Table 3

Demographic Characteristics of the Unweighted and Weighted Sample of Teachers

Unweighted (N = 238) Weighted (N = 90) n % n % Highest Educational Level Not Ascertained 2 0.8 1 1.5 Bachelor’s Degree 80 33.6 32 36.0 Bachelor’s Degree plus 1 year 69 29.0 24 26.3 Master’s Degree 72 30.3 26 29.2 Ed. Specialist/Prof. Diploma/ Doctorate

15

6.3

6

7.1

Teachers ESL Training Courses 0 166 69.8 63 70.5

1 19 8.0 6 7.0 2 8 3.4 5 5.6 3 16 6.7 6 6.5 4 7 2.9 2 2.4 5 4 1.7 2 1.7 6+ 18 7.6 6 6.3

ESL Certification Yes 41 17.2 15 16.8 No 197 82.7 75 83.2

Measures

Oral Language Development Scale (OLDS). The OLDS (Montgomery, 1997) was

developed specifically for use in the ECLS-K study to determine the English language

proficiency of students who speak a language other than English in the home. The OLDS

consists of a subset of tests taken from the Pre-Language Assessment Scales (Pre-LAS) 2000

(Duncan & DeAvila, 1998), which was designed to assess the English and Spanish language

proficiency and pre-literacy skills of early childhood learners (ages 4-6) and is one of the most

commonly used instruments to assess oral English language proficiency (Genesee, Lindholm-

Leary, Saunders, & Christian, 2006). Staff from the American Institutes for Research

recommended using the Pre-LAS 2000 on the basis of a literature search, advice from experts in

40

language minority assessment issues, and information from the departments of education in the

four states with the largest percentages of ELL individuals. The Pre-LAS 2000 was ultimately

chosen as the basis for the ECLS-K English language screener due to its widespread use and

acceptance for the targeted age group (K-1), content matching the ECLS-K requirements, and

similarity to the ECLS-K cognitive battery in format and administration procedures.

Edward De Avila, co-author of the Pre-LAS 2000, consulted with ECLS-K project staff

in selecting three of the six scales from the Pre-LAS 2000 to form the OLDS. The first subtest

chosen is “Simon Says,” which measures listening comprehension of directives presented in

English. This subtest consists of ten items, which are presented orally and direct the child to

follow a one-step command (e.g., touch your ear, pick up a piece of paper, or knock on the

table). Each item is scored 0 or 1 point depending on whether the child is able to follow the

given directive. The next subtest selected, “Art Show,” uses images to assess children’s naming

and descriptive vocabulary. This subtest also consists of ten items, which are scored on a 1-point

basis. Each item presents a picture to the child which he or she is to name, with one point given

for each correct response. The third and final subtest, “Let’s Tell Stories,” is used to obtain a

sample of a child’s natural speech by asking the child to retell a story read by the examiner. The

child reads two different stories (selected at random from three possibilities) and is asked to

retell it in his or her own words using pictures as prompts. The child’s version of the story is

evaluated based on the use of appropriate receptive and expressive language, sequencing, syntax,

complexity of sentence structure, and vocabulary. This subtest is scored 0 to 5 points for each

story and weighted at four times the items from the first 2 subtests resulting in a total of 60

points possible for the three OLDS subtests. Based on De Avila’s recommendation, 37 out of 60

41

points was determined as the minimal passing score. This score is based on the results of a

national norming sample for the Pre-LAS 2000, extrapolated to the three selected subtests.

Split-half reliability coefficients were found to be 96 or higher for the scores from each

administration of the English OLDS test. While limited research has been completed on the

psychometric properties and factor structure of the OLDS, research on the Pre-LAS 2000, the

basis for the OLDS, has provided some support for the factor structure and psychometric

strength of the measure. The concurrent and predictive validity of the Pre-LAS was investigated

with a sample kindergarten ELL students (Schrank, Fletcher, & Alvarado, 1996), in which a

significant and positive correlation (r = 0.91) was found among the oral subtest scores of the

original Pre-LAS and the Woodcock Language Proficiency Battery–Revised (WLPB-R;

Woodcock, 1991). A lesser yet still statistically significant correlation was found between scores

on the Pre-LAS and teacher ratings of English language proficiency (r = .74). Schrank et al. also

reported that correlations among the Pre-LAS subtests ranged from .55 to .93 (Mdn = .75).

Correlations between the subtests on the Pre-LAS and the WLPB-R subtests ranged from .36 to

.90, with the higher correlations suggesting similarity between what aspects of oral language

proficiency the subtests are measuring.

Direct cognitive assessments. The National Center for Education Statistics and

contractor staff assembled school curriculum specialists, teachers, and academicians to consult

on the design and development of the assessment instruments. The direct cognitive assessments

were created specifically for the ECLS-K study by this panel of experts and were based on

existing and commonly used commercial assessments, including the Peabody Individual

Achievement Test–Revised (Markwardt, 1989), the Peabody Picture Vocabulary Test-Revised

(Dunn & Dunn, 1981), the Primary Test of Cognitive Skills (Huttenlocher & Levine, 1990), the

42

Test of Early Reading Ability–Second Edition (Reid, Hresko, & Hammill, 1989), the Test of

Early Mathematics Ability–Second Edition (Ginsburg & Baroody, 1990), and the Woodcock-

Johnson Tests of Achievement–Revised (Woodcock & Bonner, 1989). The direct assessments

were derived from national and state standards, and designed to measure children’s knowledge in

various subject areas (reading, math, science, and general knowledge) at designated time points

as well as track children’s academic growth over time. These assessments were administered by

trained evaluators1 and were typically computer-assisted as the evaluators read questions from

and entered children’s responses into a computer program.

Prior to administering the direct child assessments, evaluators were trained in Los

Angeles, California in March of 2002 across 5 days. Two hundred sixty six assessors completed

the training, which included an overview of study activities, interactive lectures on the direct

child assessments and parent interviews, role-play scripts to practice parent interviews and direct

child assessments, precertification exercises on each form of the direct child domain

assessments, techniques for parent refusal avoidance, and strategies for building rapport with

children. The culmination of the child assessments training was a practice session administering

the cognitive assessment battery to children. Staff already certified on the child assessments

observed trainees during the practice administrations and gave feedback on performance using a

specially designed assessment certification form.

Pools of over 200 test items in each of the content domains (reading, mathematics, and

science) were developed by a team of educational specialists. Then curriculum specialists

reviewed test items for appropriateness of content and difficulty as well as sensitivity to

1 Information about specific evaluator demography (e.g., ethnicity/race, age, gender, educational level) was not available.

43

minority2 concerns. Items that passed screenings of content, construct, and sensitivity were pilot

tested. Evidence for the validity of the direct cognitive assessments was derived from several

sources, including a review of national and state performance standards, comparison with state

and commercial assessments, and the judgments of curriculum experts and teachers. The content

validity of the ECLS-K items was established by comparing the results of the ECLS-K with

scores on the Woodcock-McGrew-Werder Mini-Battery of Achievement (Woodcock, McGrew,

& Werder, 1994), which was also administered during the field test. Support for the construct

validity of the direct cognitive assessments was evidenced in the correlational patterns between

test scores both within and across rounds. The correlation between the third-grade reading and

mathematics scores was .73, which is relatively consistent with the correlations found in both the

kindergarten (.77) and first grade (.74) rounds of data collection (U.S. Department of Education,

2005).

The direct cognitive assessments were individually administered, two-stage adaptive

tests; that is, all students first took a routing test to determine what difficulty level should be

administered of the second-stage test form. Adaptive assessments were utilized to maximize the

accuracy of measurement and to minimize floor and ceiling effects. Some items overlapped

between the three different levels of forms to ensure a sufficient number of test items. A variety

of scores were obtained: (a) number of items answered correctly, (b) item response theory (IRT)

scale scores, (c) standardized scores (T scores), (d) item cluster scores, and (e) proficiency level

scores. For the purposes of this study, only standardized scores from the third-grade reading and

math assessments were analyzed. Reliability estimates and validity information for the scores

2 No elaboration was provided regarding the use of the term minority. It was not clear whether the term was used to define only racial/ethnic minorities or to reflect various diverse groups, who are not a numerical majority.

44

from the direct cognitive assessment as well as the results of factor analyses are summarized

below for each respective area.

Reading. The reading assessment was designed to measure basic skills, such as print

familiarity, letter identification, phonemic awareness, decoding skills, sight word recognition,

vocabulary knowledge, and passage comprehension. Passages used represent a variety of literary

genres, such as fiction, nonfiction, poetry, and letters. While the focus in the earlier grades was

on basic reading skills, greater emphasis was placed on reading comprehension by the third

grade. Five proficiency levels are contained within the third-grade reading assessment: (a) sight

word recognition, (b) passage comprehension, (c) literal inferences, (d) extrapolation or

identifying clues to make inferences, and (e) evaluation and application of narrative to real life

situations.

Internal consistency coefficients ranged from .75 to .84 for the scores obtained from the

reading assessment (U.S. Department of Education, 2005). Evidence for the construct validity of

the ECLS-K reading item pool was supported by its correlation in the mid-to upper .80 range

with the Kaufman Test of Educational Achievement (K-TEA; Kaufman & Kaufman 1985).

Additionally, there was a strong correlation between the reading scores on the Mini-Battery of

Achievement (Woodcock, McGrew, & Werder, 1994) and the IRT ability estimate (theta) from

the reading field test items (r = .83). Factor analyses suggested a single underlying factor for

reading as the percentage of variance accounted for by the first factor was more than four times

that accounted for by the potential second factor. Attempts to identify additional distinct factors

resulted in factors related to item difficulty, but not content (U. S. Department of Education,

2005), but no elaborations or examples were provided to clarify this conclusion.

45

Math. The math assessment was designed to measure both conceptual and procedural

knowledge of math as well as applied problem solving. Depending on grade-level, math content

included (a) number sense, properties, and operations; (b) measurement; (c) geometry and spatial

sense; (d) data analysis, statistics, and probability; and/or (e) patterns, algebra, and functions.

Proficiency levels within the third-grade math assessment involved (a) solving simple addition

and subtraction problems, (b) solving multiplication and division problems, (c) determining

place value, and (d) calculating rate and measurement. Internal consistency coefficients ranged

from .72 to .86 for scores obtained from the math assessment. The correlation between the math

scores on the Mini-Battery of Achievement and theta scores from the math field test items on the

ECLS-K was .84. Similar to the reading section, factor analyses also suggested a single factor

underlying math performance as the percentage of variance accounted for by the first factor was

more than six times the percentage of variance accounted for by the potential second factor (U.S.

Department of Education, 2005).

Self-Description Questionnaire (SDQ). The SDQ (Marsh, 1990) is a measure of

students’ perception about themselves in two major areas: (a) academic interest/competence and

(b) social competence. The SDQ also contains subscales, which are designed to assess targeted

problem behaviors (both externalizing and internalizing) that may interfere with academic and

social competence. The 42-item SDQ is divided into six subscales: Perceived

Interest/Competence in Reading (8 items), Perceived Interest/Competence in Math (8 items),

Perceived Interest/Competence in All Subjects (6 items), Perceived Interest/Competence in Peer

Relations (6 items), Externalizing Problems (6 items), and Internalizing Problems (8 items).

For the purposes of this study only the mean score for the Perceived Interest/Competence

in Peer Relations subscale was used. This subscale contains six items, which assess how easily

46

children make friends and get along with others as well as the perception of their popularity.

Specifically, students were asked to provide ratings on the following items: “I have lots of

friends,” “I make friends easily,” “I get along with kids easily,” “I am easy to like,” “Other kids

want me to be their friend,” and “I have more friends than most other kids.” On each of these

items children were asked to rate themselves using a 1 to 4 response scale: 1 (not at all true), 2

(a little bit true), 3 (mostly true), or 4 (very true). To avoid effects of reading ability on

responses, the items were read aloud to each child.

The internal consistency coefficient for the scores of third-graders’ ratings on the peer

relations subscale was .79 (U.S. Department of Education, 2005). Correlations of children’s self-

ratings on the SDQ with the other direct and indirect measures were low, ranging from .03 to .21

(U.S. Department of Education, 2005). This finding suggests that children use different criteria

than teachers when rating their academic competence and skills. Children’s ratings of social

competence (i.e., Peer Relations) were not included in these cross-correlational studies. No

rationale was provided for the exclusion of the children’s ratings.

Academic Rating Scale (ARS). The ARS (Atkins-Burnett, Meisels, & Correnti, 2000)

was developed for the ECLS-K to measure teachers’ evaluations of students’ academic

achievement in four domains: (a) literacy (reading and writing), (b) science, (c) social studies,

and (d) math. Furthermore, the ARS was designed to measure children’s skills within the same

broad curricular domains as the direct cognitive assessment and was intended to overlap and

supplement this information. Content experts familiar with the early grades and teachers from

both public and private schools and from different regions of the country reviewed proposed

items and made recommendations. Items were then piloted and tested in the field to gather

statistical evidence of the appropriateness of the items. For the purposes of the current study,

47

only overall teacher ratings from the literacy and math sections will be used. Ratings on all

items were made on a 5-point Likert scale: 1 = child has not yet demonstrated skill, 2 = child is

just beginning to demonstrate skill, but does so very inconsistently, 3 = child demonstrates skills

with some regularity, but varies in level of competence, 4 = child demonstrates skill with

increasing regularity and average competence, but is not completely proficient, and 5 = child

demonstrates skills consistently and competently. Teachers were also given the option of

selecting “not applicable” to indicate that the skill had not been introduced in the classroom.

The one-parameter item response theory (IRT) model (Rasch, 1960) was used to estimate

the scores on ARS. The resulting reliabilities for the scores of the scales were .95 for literacy

and .94 for math (U.S. Department of Education, 2005). Evidence for the discriminant and

convergent validity of the ARS items was based on correlations among thirteen scores from the

third grade measures, including four teacher ratings of children’s academic achievement (taken

from the ARS), three selected teacher ratings of children’s attitudes and behaviors (taken from

the SRS), three children’s self-ratings of achievement (taken from the SDQ), and direct cognitive

scores in the three subject areas assessed (reading, math, and science). No other validity

information was readily available and no factor structure information for either the literacy or

mathematics sections of the ARS was reported. Teachers’ ratings of students’ literacy and math

skills on the ARS correlated at .82.

Literacy. The literacy section of the ARS consists of eight items designed to assess a

student’s reading and writing abilities. Using the abovementioned 5-point Likert scale, teachers

are asked to rate each child’s proficiency in reading grade-level text, using strategies to gain

information, and expressing ideas through writing. The person reliability (analogous to

Cronbach’s alpha) was .95 for the scores from the literacy section. The correlation between

48

teachers’ ratings on the literacy section of the ARS and reading theta scores from the direct

cognitive measure was .65.

Math. The math section consists of nine items on which teachers rate each child’s skills

in the following areas: number concepts (e.g., place value, fractions, and estimation), data

analysis, measurement, basic operations, geometry, application of mathematical strategies, and

pattern analysis. The person reliability for the math scores was .94. The correlation between

teachers’ ratings on the math section of the ARS and math theta scores from the direct cognitive

measure was .59.

Social Rating Scale (SRS). The SRS (Atkins-Burnett, Meisels, & Correnti, 2000) is an

adaptation of the Social Skills Rating System (SSRS; Gresham & Elliot, 1990). The SRS

contains five subscales adapted from the original nine (Cooperation, Empathy, Assertion, Self-

Control, Responsibility, Externalizing Problems, Internalizing Problems, Hyperactivity, and

Academic Competence) of the SSRS. The first subscale of the SRS, Approaches to Learning,

measures behaviors that affect the ease with which children can benefit from the learning

environment, such as attentiveness, task persistence, eagerness to learn, learning independence,

flexibility, and organization. Self-Control assesses children’s ability to control their behavior by

respecting the property of others, managing anger, accepting peer ideas for group activities, and

responding appropriately to pressure from peers. Interpersonal Skills assesses children’s skills in

establishing and maintaining friendships, getting along with people who are different, responding

empathetically to or helping other children, appropriately expressing feelings and opinions, and

showing sensitivity to others’ feelings. Externalizing Problem Behaviors assesses “acting out”

behaviors such as arguing, fighting, disturbing others, and acting impulsively. Lastly,

Internalizing Problem Behaviors asks about the apparent presence of anxiety, loneliness, low

49

self-esteem, and sadness. On each SRS scale, third-grade teachers were asked to indicate how

often students exhibited the identified social skills and behaviors using a 4-point rating scale (1 =

never to 4 = very often).

Factor analyses (both exploratory analyses and confirmatory factor analyses using

LISREL) were used to confirm the SRS scales. Intercorrelations between the five SRS factors

ranged from .59 to.81 for third graders (U.S. Department of Education, 2002). No further

psychometric information or details about the strength of the evidence for the factor structure

was provided. However, results of studies examining the psychometric properties of the SSRS

on which the SRS is based have provided support for the proposed factor structure and internal

consistency of the SSRS scores. For example, Van der Oord et al. (2004) found a similar factor

structure to that found by Gresham and Elliot (1990). The phi coefficients for all subscales were

.78 or higher and coefficients of congruence for factors that were not proposed to be related were

all far below .80, ranging from .00 to .24. All internal consistency coefficients were above .76

with a mean alpha of .83 (range = .76 to .88).

Due to a pattern of SRS ratings in the third-grade round of data collection, suggesting a

strong positive correlation between interpersonal skills and self-control (r = .81), an additional

scale was created that combined all five items from the Interpersonal Skills scale and four items

from the Self-Control scale that were specifically related to control of emotions and behavior in

social interactions. The resulting Peer Relations scale was designed to comprehensively

represent a child’s overall skill in establishing and maintaining peer relationships. This newly

created Peer Relations scale was the only SRS scale used in the current study. The split-half

reliability coefficient for the scores on the Peer Relations scale was .92 (U.S. Department of

Education, 2005). Intercorrelations between teachers’ ratings on the Peer Relations subscale and

50

children’s’ ratings on the SDQ ranged from .03 to .12. However, only children’s ratings of

academic (not social) competence were compared.

Procedure

ECLS-K data collection. As indicated earlier, data were obtained from the third-grade

round of the ECLS-K public-use database. The ECLS-K third-grade data collection was

conducted in the fall and spring of the 2001–2002 school year. Several in-person training

sessions were conducted to prepare staff for the third-grade data collection. Data collection

contained the direct child assessments, parent interviews, teacher and school questionnaires,

student record abstract, and facilities checklist.

Self-administered questionnaires were used to gather information from teachers, school

administrators, and student records. Packets of hard-copy teacher and school administrator

questionnaires were assembled and mailed to schools in February 2002. Teachers and school

administrators were asked to either complete the questionnaires for pickup on assessment day, or

to return the questionnaires by mail in the enclosed postage-paid, self-addressed envelopes.

Teachers were specifically asked to complete a questionnaire consisting of three distinct parts.

The first section, Part A, asked about the teacher’s classroom and the characteristics of the

students, instructional activities and curricular focus, instructional practices in different subject

areas, and student evaluation methods. Part B included questions on school and staff activities

and the teacher’s views on teaching, the school environment, and overall school climate.

Background questions about the teachers were also included in this section. Lastly, teachers

were requested to complete one copy of Part C for each of the sampled children in their

classrooms. This part contained 39 questions about the child’s academic performance as well as

questions from the Social Rating Scale.

51

Direct child assessments were conducted from March through June 2002. These

assessments were conducted in 90-minute one-on-one sessions using both hard-copy instruments

and computer-assisted interviewing (e.g., interviewers read questions from and entered responses

into a computer program).

Data acquisition. The ECLS-K public-use data set is available for no fee on CD-ROM or

DVD from the Department of Education (Ed Pubs at http://edpubs.ed.gov). The CD-ROMs and

DVDs contain data files, electronic codebooks, user’s manuals, methodology reports, survey

instruments, and file record layouts. The ECLS-K public-use data are also available through an

online version of the electronic codebook called the EDAT system, which can be accessed at

http://nces.ed.gov/edat/. As the public-use data are made readily available to the public, no

additional authorization to use the data was required from the National Center for Education

Statistics - Institute of Education Sciences (NCES-IES). However, the researcher did attend a

three-day long training seminar to learn how to access and use the ECLS-K public-use data and

obtained a DVD with the data on it at that time. Additionally, approval was obtained from The

Pennsylvania State University’s Institutional Review Board (IRB) and Office of Research

Protections (ORP) to conduct the current study. This study was classified as exempt from further

review by the IRB and ORP as it was determined that it did not meet the definition of “human

participant research”.

52

RESULTS

Descriptive Statistics

Descriptive statistics, including means, standard deviations, ranges, reliability estimates

of the scores (on scales for which item-level data were available) and correlations, for the major

variables (teacher ratings, student ratings, and reading and math T scores) are presented in Table

4. All descriptive statistics were calculated based on the unweighted data. Statistically

significant correlations (p < .01) were found between students’ reading and math scores as well

as between teacher ratings of students’ literacy and math skills on the Academic Rating Scale

(ARS). Coefficients ranged from .38 to .48 (Mdn = .46). Teacher ratings of interpersonal skills

on the Social Rating Scale (SRS) were also statistically significantly correlated with teacher

ratings of literacy (r = .32) and math (r = .25), although the effect size for the latter correlation

was less than 10%. Additionally, teacher ratings of interpersonal skills on the SRS were

statistically significantly correlated with student T scores on the reading assessment (r = .19); the

effect size was less than 4%. For math, none of the correlations between the relevant variables

were statistically significant. Finally, no relationship was found between students’ self-

perceptions of their social competence and any other variable.

Descriptive statistics for the same variables are presented in Table 5 for Hispanic students

based on language status (ELL vs. non-ELL), and in Table 6 based on the race of monolingual

English speakers: Caucasian and African American students. Similar to the overall sample,

teacher ratings of students’ reading and math skills were statistically significantly correlated for

all of the subsamples, with coefficients ranging from .78 to .84 (Mdn = .82). Teacher ratings of

interpersonal skills were not statistically significantly correlated with teacher ratings of academic

skills for any of the non-ELL groups. However, there was a statistically significant correlation

53

Table 4

Descriptive Statistics for Teacher Ratings, Self-Ratings, and Reading and Math Assessment Scores for All Students (unweighted N = 260) Variable

1

2

3

4

5

6

M

SD

Range

α

Academic Rating Scale

.91

1. Literacy -- .81* .32* -.01 .45* .48* 3.23

.82

4.00 .85

2. Math

-- .25* .01 .38* .46* 3.00

.68 3.66 .86

Social Rating Scale

3. Interpersonal Skills

-- .03 .19* .14

3.00

.63 2.60 --

Self-Description Questionnaire

.83

4. Peer Relations

-- -.06 -.09 3.11 .64 3.00 .77

Direct Cognitive Assessment

5. Reading

-- .73* 48.34

9.94 52.84 --

6. Math

-- 48.49 9.73 52.11 --

Note. Reliability estimates of the scores could not be calculated for the Social Rating Scale and reading and math assessments due to the lack of access to item-level data. *p < .01. between teacher ratings of ELL students’ interpersonal skills and (a) reading (r = .35) and (b)

math (r = .29) skills; the effect size of the latter correlation was less than 10%. The pattern was

opposite between teacher ratings of reading and math skills, and students’ actual performance on

the reading and math assessments. For the racial/ethnic subsamples (Hispanic, Caucasian, and

African American) of non-ELL students, there were statistically significant correlations between

teachers’ academic ratings and student performance on the cognitive assessments, with

54

Table 5

Unweighted Descriptive Statistics for Teacher Ratings, Self-Ratings, and Reading and Math Assessment Scores for Students of Hispanic Ethnicity based on Language Status Variable

1

2

3

4

5

6

M

SD

Range

α

1. Literacy

--

.84*

.35*

.12

.16

.19

3.06

.88

3.68

.93

2. Math

.78* -- .29 .19 .13 .29 2.94 .75 3.66 .93

3. Interpersonal Skillsa

.30 .32 -- -.14 .25 .03 2.94 .59 2.20 --

4. Peer Relations

-.07 -.14 .06 -- .06 .07 3.07 .70 2.67 .76

5. Reading Assessmenta

.46 .40* .20 -.19 -- .40* 39.06 7.48 37.73 --

6. Math Assessmenta

.50 .62* .12 -.17 .64* -- 42.40 7.41 35.70 --

M

3.35 3.02 3.13 3.08 51.35 51.07

SD

.78 .56 .58 .58 7.63 8.49

Range

3.09 2.89 2.40 2.50 41.59 38.09

α .81 .78 -- .75 -- -- Note. Intercorrelations for ELL participants of Hispanic ethnicity (n = 65) are presented above the diagonal, and intercorrelations for non-ELL participants of Hispanic ethnicity (n = 65) are presented below the diagonal. Means, standard deviations, ranges, and Cronbach’s internal reliability coefficients (α) for ELL Hispanic students are presented in the vertical columns, and means, standard deviations, ranges, and reliability coefficients for non-ELL Hispanic students are presented in the horizontal rows. aReliability coefficients of the scores could not be calculated for the SRS, and reading and math assessments due to the lack of access to item-level data. * p < .01. coefficients ranging from .39 to .62 (Mdn = .50). In contrast, teachers’ academic ratings of ELL

students were not statistically significantly correlated with ELL students’ actual reading and

math scores. Additionally, a statistically significant relationship was not observed between

student and teacher ratings of interpersonal skills for any of the subsamples. Furthermore,

55

Table 6

Unweighted Descriptive Statistics for Teacher Ratings, Self-Ratings, and Reading and Math Assessment Scores for Non-ELL Students of Caucasian and African American Race Variable

1

2

3

4

5

6

M

SD

Range

α

1. Literacy

--

.80*

.24

-.07

.56*

.57*

3.50

.78

3.32

.88

2. Math

.83* -- .21 -.03 .48* .50* 3.24 .71 3.04 .88

3. Interpersonal Skillsa

.24 .12 -- .08 .10 .10 3.15 .60 2.00 --

4. Peer Relations

-.01 -.03 .19 -- -.07 -.02 3.09 .60 2.17 .85

5. Reading Assessmenta

.53* .49* -.06 -.16 -- .64 56.23 8.40 48.08 --

6. Math Assessmenta

.46* .39* -.08 -.24 .75 -- 55.54 9.09 49.46 --

M

3.02 2.93 2.77 3.20 46.72 44.94

SD

.76 .65 .69 .68 7.24 8.13

Range

4.00 3.45 2.60 3.00 34.03 39.08

α .81 .85 -- .76 -- -- Note. Intercorrelations for Caucasian participants (n = 65) are presented above the diagonal, and intercorrelations for African American participants (n = 65) are presented below the diagonal. Means, standard deviations, ranges, and Cronbach’s internal reliability coefficients (α) for Caucasian students are presented in the vertical columns, and means, standard deviations, ranges, and reliability coefficients for African American students are presented in the horizontal rows. aReliability coefficients of scores could not be calculated for the SRS, and reading and math assessments due to the lack of access to item-level data. * p < .01. student ratings of interpersonal competence were not statistically significantly correlated with

student scores on the reading and math assessments for any of the subgroups.

Preliminary Analyses

Preliminary analyses were also conducted using the unweighted data. One set of analyses

56

focused on whether the two sets of participants (ELL vs. non-ELL) differed based on various

demographic features, namely personal ones (age and gender), parent characteristics

(socioeconomic status and national origin), teacher characteristics (age, years of teaching

experience, educational level, and ESL training), and school features (school type and

geographical location). If any of these findings was statistically significant (p < .01), then

consideration was given to using the specific variable as a covariate in the major analyses.

Parametric assumptions for the major statistical analyses are reported as well.

Student variables. Preliminary analyses were conducted to assess whether the ELL and

non-ELL groups differed on the demographic variables of age and gender. All students were in

third grade and varied only slightly in age. The only available data for age of students were

categorized into six age groups based on months: (a) < 105 months, (b) 105 to < 108 months, (c)

108 to < 111 months, (d) 111 to < 114 months, (e) 114 to < 117 months, and (f) > 117 months.

A 2 (language status) x 6 (age group) contingency table analysis was conducted to determine if

the frequency of ELL and non-ELL students varied as a result of the age range. The finding was

not statistically significant, χ2 (5, N = 260) = 5.39, p = .37, Cramer’s V = .14. A 2 (language

status) x 2 (gender) chi-square analysis was also conducted and found not to be statistically

significant, χ2 (1, N = 260) = .01, p = .94, Cramer’s V .004. Thus, the number of ELL and non-

ELL students did not differ across the various age ranges or gender.

Other demographic variables. Based on prior literature (e.g., Montero & McVicker,

2006; Reeves, 2006; Youngs & Youngs, 2001), other potentially relevant variables were also

investigated to determine whether there were statistically significant differences between the two

language groups. These variables were socioeconomic status (SES); parents’ national origin;

teacher demographics, such as age, years of teaching experience, highest educational level, and

57

English as a Second Language (ESL) training and certification; and school characteristics, such

as school status (public or private) and geographical location (rural, suburban, or urban).

Parent variables. In the available dataset, SES had been categorized into five quintiles,

with the first quintile representing the lowest SES and the fifth quintile representing the highest

SES. A 2 (language status) x 5 (SES) contingency table analysis was conducted to determine

whether the frequency of the ELL and non-ELL students varied as a result of SES. The finding

was statistically significant, χ2 (4, N = 260) = 95.25, p < .01. The effect size was .61 (Cramer’s

V), which is considered to be a moderate effect (Cohen, 1992). More than five times the number

of ELL students (75.4%) fell into the first (lowest) quintile for SES in comparison to the non-

ELL students (13.3%). The opposite held true for the other end of the spectrum, with 41% of

non-ELL students falling into the upper (highest) two quintiles compared to only 3% of ELL

students. The percentage of students in each SES category by language status is presented in

Table 7.

Table 7

Frequency (Percentage) of ELL and Non-ELL Students by SES Level

First Quintile

(n = 75)

Second Quintile

(n = 52)

Third Quintile

(n = 51)

Fourth Quintile

(n = 41)

Fifth Quintile

(n = 41) ELL

75.4%

13.8%

7.7%

1.5%

1.5%

Non-ELL

13.3%

22.1%

23.6%

20.5%

20.5%

Note. Unweighted N = 260. The first quintile represents the lowest SES and the fifth quintile represents the highest SES.

Two 2-way contingency table analyses were conducted to determine whether the

frequency of language status differed due to parents’ national origin (one for mothers; the other

58

for fathers). However, the analysis could not be conducted because of the numerous (25)

countries represented by a small number of each parent (less than 5). Thus, national origin was

grouped into three categories based on the geographical location frequently reported: United

States, Mexico, and Other (e.g., Puerto Rico, El Salvador, Cuba).3 Subsequently, two 2

(language status) x 3 (national origin) contingency table analyses, one for each parent, were

conducted and statistically significant differences were found: mother’s country of origin, χ2 (2,

N = 260) = 176.04, p < .01, Cramer’s V = .82; father’s country of origin, χ2 (2, N = 260) =

127.66, p < .01, Cramer’s V = .70.

The frequency and percentages of ELL and non-ELL students by parents’ national origin

are presented in Table 8. A higher percent of both parents of non-ELL students (67.2%) were

born in the United States compared to the percent of parents of ELL students (6.9% of fathers

and 2% of mothers) born in the United States. In contrast, approximately 22% of the sampled

ELL fathers and 23% of ELL mothers were born in a country other than the United States

compared to only 3.8% of the sampled non-ELL fathers and 7.7% of non-ELL mothers.

Table 8

Unweighted Frequency (Percentage) of ELL and Non-ELL Students by Parent’s National Origin Mother’s National Origin Father’s National Origin

U.S. (n = 180)

Mexico (n = 60)

Other

(n = 20)

U.S.

(n = 193)

Mexico (n = 49)

Other

(n = 18) ELL

1.9%

20.5%

2.7%

6.9%

16.6%

5.4%

Non-ELL 67.2% 2.7% 5.0% 67.2% 2.3% 1.5%

3See Tables 1 and 2 for a complete listing of all represented countries.

59

Teacher variables. In regard to the teacher demographics, two independent t-tests were

conducted on age and teaching experience (years) based on students’ language status. Neither

finding was statistically significant, age (t = .51, p = .61); years of teaching experience (t = 1.40,

p = .16). Additionally, a two-way contingency analysis was conducted between students’

language status and teachers’ educational level. In the available dataset, teachers’ educational

level had been categorized into four groups: (a) Bachelor’s Degree, (b) At least one year beyond

Bachelor’s Degree, (c) Master’s Degree, or (d) Educational Specialist/Professional

Diploma/Doctorate. A 2 (language status) x 4 (educational level) contingency table analysis was

conducted and was found to be statistically significant, χ2 (3, N = 260) = 17.03, p = .002,

Cramer’s V = .26. A higher percent of teachers of non-ELL students had obtained a master’s

degree (32.3%) or higher (7.2%) compared to teachers of ELL students (18.5% and 1.5%,

respectively). The percent of ELL and non-ELL students taught by teachers’ educational level is

depicted in Table 9.

Table 9

Unweighted Frequency (Percentage) of ELL and Non-ELL Students by Teacher’s Level of

Education

Bachelor’s

Degree

At least one year beyond Bachelor’s

Degree

Master’s Degree

Ed. Specialist/ Prof. Diploma/

Doctorate (n =89) (n = 79) (n = 75) (n = 15)

ELL

30.8%

49.2%

18.5%

1.5%

Non-ELL

36.4%

24.1%

32.3%

7.2%

Note. N = 258 (Teacher’s level of education was not ascertained for two of the participants).

Two 2 x 2 chi-square analyses were conducted between language status and (a) whether

teachers had taken classes on ESL and (b) whether teachers had received ESL certification. Both

60

analyses were statistically significant, ESL Training, χ2 (1, N = 260) = 65.57, p = .000, Cramer’s

V = .55; ESL certification, χ2 (1, N = 260) = 66.91, p = .000, Cramer’s V = .51. Approximately

84% of teachers of ELL students had taken one or more ESL training classes compared to 22.8%

of teachers of non-ELL students. Additionally, 55% of teachers of ELL students had ESL

certification versus 7.9% of teachers of non-ELL students.

School variables. A final set of preliminary analyses were conducted to investigate

potential differences between the language status of students and two school characteristics: (a)

type of school (public versus private), and (b) geographical location of school (urban, suburban,

or rural). The two-way contingency table analysis was not statistically significant for type of

school, χ2 (1, N = 260) = 6.85, p = .033, Cramer’s V = .17, but was for the geographical location

of the school, χ2 (2, N = 260) = 28.42, p = .000, Cramer’s V = .34. As depicted in Table 10, the

number of non-ELL students was more evenly spread across the different types of school

locations compared to the ELL students. Approximately 72% percent of the ELL students

attended an urban school compared to 38% of the non-ELL students. In contrast, none (0%) of

the ELL students attended a rural school whereas 23% of the non-ELL students did. The

difference was less for suburban schools (28% of ELL students and 39% of non-ELL students).

Table 10

Unweighted Frequency (Percentage) of ELL and Non-ELL Students by School Location Urban

(n = 119) Suburban (n= 91)

Rural (n = 43)

ELL

72.3%

27.7%

0%

Non-ELL 38.3% 38.8% 22.9% Note. N = 253 (The school’s geographical location was not available for 7 of the participants).

61

Selection of covariates. Based on the findings of the preliminary analyses, two of the

previously investigated variables were selected to be used as covariates in the primary analyses

for the first two hypotheses. Two of the teacher characteristics, level of education and training to

work with ELL students, were used as covariates because of their potential relationship with

teacher judgments of ELL and non-ELL students’ academic and social skills. Additionally, in

the preliminary analyses these two variables were found to be statistically significantly different

across the ELL and non-ELL students, with more teachers of non-ELL students holding a more

advanced degree (e.g., Master’s or higher) and more teachers of ELL students having received at

least one ESL training class. Thus, teacher level of education and ESL training were included as

possible contributors to teachers’ academic and interpersonal judgments.

Statistical assumptions. The basic assumptions of multiple regression analysis

(normality, linearity, and homoscedasticity of residuals) were tested and met. Visual inspection

of histograms suggested normally distributed standardized residual values around the mean of

zero and normal P-P plots indicated little deviation of the observed values from the expected

values. Independence of errors was assumed because the Durbin-Watson statistic for each

analysis was within the recommended range of one to three (Field, 2000). Multicollinearity,

examined through the Variance Inflation Factor (VIF), was not present as the VIF values were

within the recommended range (Bowerman & O’Connell, 1990). For comparison purposes, all

subsequent analyses were conducted with both the unweighted and weighted data, and the

findings for both are presented and interpreted in the text and the tables.

Language Status and Teacher Perceptions

For Hypothesis I, teachers of Spanish-speaking ELL students of Hispanic ethnicity were

expected to judge them as having weaker academic and interpersonal skills than their English-

62

speaking non-ELL peers, also of Hispanic ethnicity. Three hierarchical regression analyses were

conducted to test this hypothesis with each analysis using a different outcome variable: (a)

teacher ratings of reading skills, (b) teacher ratings of math skills, and (c) teacher ratings of

interpersonal skills. In all analyses, two steps were used. In the first step, covariates were

entered and in the second step, the predictor variable, language status, was entered. Based on

preliminary analyses reported above, teacher’s level of education and ESL training were used as

covariates. Teacher’s level of education was dummy coded, with 0 representing a Bachelor’s

degree and 1 representing more than a Bachelor’s degree. The other covariate, teacher’s ESL

training, was dummy coded with 0 representing no ESL training and 1 representing some ESL

training. The predictor variable, language status, was dummy coded (0 for the non-ELL group; 1

for the ELL group). A summary of the regression analyses (unstandardized and standardized

regression coefficients, standard errors, t values, and the proportion of variance accounted for) is

reported for all three outcome variables in Table 11 for the unweighted data and Table 12 for the

weighted data.

Reading skills. Whether the data were weighted or not, the omnibus test was not

statistically significant for teacher judgments of students’ reading skills at any step: Unweighted

step 1: F (2, 129) = 1.02, p = .36, R2 = .016; step 2: F (3, 129) = 1.53, p = .21, R2 ∆ = .019;

Weighted step 1: F (2, 51) = .28, p =.75, R2 = .012; step 2: F (3, 51) = .19, p = .91, R2 ∆ = .000.

Less than 2% of the variance in teachers’ perceptions of students’ reading skills was accounted

for by the covariates of teacher’s level of education and ESL training. Above and beyond the

covariates, language status was not found to be a predictor of teachers’ judgments of student

reading skills, accounting for 0% (weighted) to less than 2% (unweighted) of the variance.

63

Table 11 Summary of Hierarchical Regression Analyses on Teacher Ratings of Reading, Math, and Interpersonal Skills based on Student’s Language Status and Teacher’s Education and ESL Training (Unweighted n =130)

Reading Skills Math Skills Interpersonal Skills Variable

B

SE B

β

t

B

SE B

β

t

B

SE B

β

t

Step 1

F (2, 129) = 1.02, p = .36

R2 = .016

F (2, 129) = .43, p = .65

R2 = .007

F (2, 129) = 1.82, p =.17

R2 = .028

Teacher Level of Education

-.01 .17 -.01 -.06 -.01 .13 -.01 -.04 -.11 .12 -.08 -.94

Teacher ESL Training -.21 .15 -.13 -1.43 -.11 .12 -.08 -.92 -.17 .10 -.14 -1.64 Step 2

F (3, 129) = 1.53, p = .21

R2 = .019

F (3, 129) = .32, p =.81

R2 = .001

F (3, 129) = 2.1, p = .10

R2 = .020

Teacher Level of Education

-.06

.17

-.03

-.33

-.01

.13

-.01

-.09

-.14

.12

-.11

-1.21

Teacher ESL Training

-.13 .16 -.07 -.79 -.09 .13 -.07 -.75 -.11 .11 -.09 -.99

Language Status

.25 .16 .15 1.59 .04 .13 .03 .32 .18 .11 .15 1.61

Note. R2Δ = R2 change. ELL = 65; Non-ELL = 65. Teacher level of education was dummy coded with 0 representing teachers who hold a Bachelor’s degree or less and 1 representing teacher who hold a Master’s degree or higher. Teacher ESL training was dummy coded with 0 representing teachers who hadn’t taken any ESL training classes and 1 representing teachers who have taken one or more ESL training class. ELL status was dummy coded with 0 representing the non-ELL group and 1 representing the ELL group. None of the findings were statistically significant at p < .01.

64

Table 12 Summary of Hierarchical Regression Analyses on Teachers’ Ratings of Reading, Math, and Interpersonal Skills based on Students’ Language Status and Teachers’ Education and ESL Training (Weighted n =52)

Reading Skills Math Skills Interpersonal Skills Variable

B

SE B

β

t

B

SE B

β

t

B

SE B

β

t

Step 1

F (2, 51) = .28, p =.75

R2 = .012

F (2, 51) = .32, p = .73

R2 = .013

F (2, 51) = .07, p = .94

R2 = .003

Teacher Level of Education

.09

.28

.05

.31

.02

.22

.01

.09

-.01

.18

-.01

-.06

Teacher ESL Training -.18 .25 -.11 -.73 -.16 .20 -.12 -.79 -.06 .16 -.05 -.34 Step 2

F (3, 51) = .19, p = .91

R2 = .000

F (3, 51) = .28, p = .84

R2 = .004

F (3, 51) = .27, p = .85

R2 = .014

Teacher Level of Education

.09

.29

.05

.31

.04

.23

.03

.19

-.05

.19

-.04

-.24

Teacher ESL Training

-.19 .28 -.11 -.67 -.20 .22 -.14 -.91 .01 .18 .01 .02

Language Status

-.01 .28 -.01 -.04 -.10 .22 -.07 -.46 .15 .18 .13 .83

Note. R2Δ = R2 change. The sample size was reduced from 130 to 52 when the data were weighted. Teacher level of education was dummy coded with 0 representing teachers who hold a Bachelor’s degree or less and 1 representing teacher who hold a Master’s degree or higher. Teacher ESL training was dummy coded with 0 representing teachers who hadn’t taken any ESL training classes and 1 representing teachers who have taken one or more ESL training class. ELL status was dummy coded with 0 representing the non-ELL group and 1 representing the ELL group. None of the findings were statistically significant at p < .01.

65

Math skills. The omnibus test for teacher judgments of student math skills for both the

weighted and unweighted data was not statistically significant at either step, Unweighted step 1:

F (2, 129) = .43, p = .65, R2 = .013; step 2: F (3, 129) = .32, p = .81, R2 ∆ = .001; Weighted step

1: F (2, 51) = .32, p = .73, R2 = .013; step 2: F (3, 51) = .28, p = .84, R2 ∆ = .004.

Approximately 1% (weighted) or less (unweighted) of the variance in teachers’ perceptions of

students’ math skills was accounted for by the teacher covariates, level of education and ESL

training. Language status accounted for less than 1% of the variance in the teachers’ ratings of

math skills.

Interpersonal skills. The omnibus test for teacher judgments of student interpersonal

skills was also not statistically significant for either the unweighted or weighted data,

Unweighted step 1: F (2, 129) = 1.82, p = .17, R2 = .028; step 2: F (3, 129) = 2.1, p = .10, R2 ∆ =

.020; Weighted step 1: F (2, 51) = .07, p = .94, R2 = .003; step 2: F (3, 51) = .27, p = .85, R2 ∆ =

.014. The covariates of teacher level of education and teacher ESL training were not statistically

significant contributors to teacher predictions of interpersonal skills, with the effect sizes ranging

between less than 1% (weighted) to less than 3% (unweighted). Furthermore, after controlling

for the covariates, language status was not a statistically significant predictor of teacher

judgments of interpersonal skills, contributing only 1% (weighted) to 2% (unweighted) of the

variance.

Language Status and Teacher Perceptions across Racial/Ethnic Groups

For Hypothesis II, it was proposed that teachers would rate ELL students as having

weaker academic and interpersonal skills in comparison to non-ELL students, regardless of the

ethnic/racial background of the non-ELL students (Hispanic, Caucasian, or African American).

Three standard multiple regression analyses were conducted to test this hypothesis, with teacher

66

ratings as the outcome variables: (a) reading, (b) math, and (c) interpersonal skills. Participants

were divided into four groups based on language status and race/ethnicity: (a) ELL Hispanic, (b)

non-ELL Hispanic, (c) non-ELL Caucasian, and (d) non-ELL African American. This combined

language/race variable was coded (with three resulting contrasts) and entered as the predictor

variable. The first contrast examined potential differences between teacher ratings of the non-

ELL African American students and the non-ELL Hispanic students. The second contrast

compared non-ELL Caucasian students to both non-ELL Hispanic and non-ELL African

American students. The final contrast considered the comparison between the ELL Hispanic

students and all three non-ELL groups (Hispanic, African American, and Caucasian). Teacher

level of education (dummy coded with 0 representing Bachelor’s degree and 1 representing

Master’s degree or higher) and teacher ESL training (dummy coded with 0 representing no ESL

training and 1 representing some ESL training) were included as covariates in these analyses.

These two covariates were entered in the first step of the regression analyses and the three

contrast variables for the predictor were entered into the second step of the regression analyses.

Unweighted analyses. The results for the unweighted data, including unstandardized and

standardized regression coefficients, standard errors, t values, and the proportion of variance

accounted for (R2 and R2 change) by the two covariates and the combined language/race variable

for all three outcome variables are reported in Table 13. For the analyses investigating teacher

ratings of math skills, the omnibus test for the unweighted teacher covariates was statistically

nonsignificant for the first step, F (2, 257) = 1.67, p = .19, R2 = .013). Additionally, the omnibus

test was statistically nonsignificant at the second step, F (5, 257) = 1.87, p = .10, R2 ∆ = .036),

suggesting that above and beyond the teacher covariates, race/ethnicity of the students was not a

predictor of teacher ratings of students’ math skills.

67

Table 13 Summary of Regression Analyses for the Prediction of Teacher Ratings of Reading, Math, and Interpersonal Skills for Students Grouped by Language Status and Race/Ethnicity (Unweighted N = 260)

Reading Skills Math Skills Interpersonal Skills Variable

B

SE B

β

t

B

SE B

β

t

B

SE B

β

t

Step 1 R2 = .021 R2 = .013 R2 = .000 Teacher Level of Education

.21

.11

.12

1.92

.10

.09

.07

1.12

-.01

.08

-.01

-.11

Teacher ESL Training -.15 .11 -.09 -1.39 -.13 .09 -.09 -1.46 -.01 .08 -.01 -.03 Step 2 R2 = .047 R2 = .023 R2 = .059 Teacher Level of Education

.17

.11

.10

1.53

.07

.09

.05

.78

-.03

.08

-.03

-.40

Teacher ESL Training

-.13 .13 -.07 -.97 -.10 .11 -.07 -.94 -.01 .10 -.01 -.04

Language Status and Race/Ethnicity (C1) Non-ELL Minority (C2) Caucasian (C3) ELL

-.19 .09

-.03

.07 .04 .03

-.16 .13

-.07

-2.54*

2.15

-.91

-.06 .08

-.01

.06 .04 .03

-.06 .14

-.03

-.91

2.21

-.41

-.18 .06

-.02

.06

.03

.03

-.20

.12

-.06

-3.21*

1.99

-.77 Note. R2Δ = R2 change. The predictor variable was divided into four equal groups (n =65 for each group): ELL Hispanic, non-ELL Hispanic, non-ELL African American, and non-ELL Caucasian. Three contrasts were created: contrast 1 (C1) = non-ELL African American vs. non-ELL Hispanic; contrast 2 (C2) = non-ELL Caucasian vs. non-ELL Hispanic and African American; and contrast 3 (C3) = ELL Hispanic vs. non-ELL Hispanic, African American, and Caucasian. Teacher level of education was dummy coded with 0 representing a Bachelor’s degree or less and 1 representing a Master’s degree or higher. Teacher ESL training was dummy coded with 0 representing teachers who hadn’t taken any ESL training classes and 1 representing teachers who have taken one or more ESL training class. *p < .01

68

The omnibus test for the unweighted teacher covariates was also nonsignificant at the

first step for teacher ratings of reading skills, F (2, 257) = 2.78, p = .06, R2 = .021). However, the

omnibus test was statistically significant at the second step, F (5, 257) = 3.69, p < .01, R2 ∆ =

.047), suggesting that, above and beyond teacher covariates (level of education and ESL

training), the race/ethnicity of the student was a predictor of teachers’ ratings of students’

reading skills. An examination of the contrast variables revealed that teachers rated non-ELL

African American students more negatively than their non-ELL Hispanic counterparts on reading

skills (t = -2.54, p < .01, semipartial r = -.16). As noted in Table 13, the second contrast between

non-ELL Caucasian and the other two non-ELL groups (Hispanic and African American), and

the third contrast between all three non-ELL groups (Hispanic, African American, and

Caucasian) and the ELL Hispanic group, were both statistically nonsignificant.

The omnibus test for teacher ratings of interpersonal skills was also statistically

nonsignificant for teacher covariates at the first step, F (2, 257) = .01, p = .99, R2 = .000), but

statistically significant at the second step F (5, 257) = 3.18, p < .01, R2 ∆ = .059). This finding

indicated that teachers’ level of education and teachers’ ESL training were not predictors of

teacher judgments, but that race/ethnicity of students was a predictor of teachers’ interpersonal

ratings of students. An examination of the contrast variables revealed higher negative ratings of

the interpersonal skills of non-ELL African American students than their non-ELL Hispanic

counterparts (t = -3.21, p < .01, semipartial r = -.20). The second and third racial/ethnic contrasts

were statistically nonsignificant for these analyses.

Weighted analyses. The results of analyses conducted with the weighted data were all

statistically nonsignificant, including the covariates. The only weighted analysis that came close

to statistical significance was the contrast between non-ELL Hispanic students and non-ELL

69

African American students in predicting teacher ratings of interpersonal skills (t = -2.27, p = .03,

semipartial r = -.23). The complete results of the regression analyses for the weighted data are

presented in Table 14.

Additional Analyses: SES, Mother’s National Origin, and School Location

Based on prior literature (e.g., Montero & McVicker, 2006; Reeves, 2006; Youngs &

Youngs, 2001), and the findings of preliminary analyses reported above (see pp. 54-59),

additional analyses were conducted to investigate how the various characteristics of the student,

parent, and school location were related to teachers’ academic and interpersonal judgments of

ELL and non-ELL students. The variables examined were SES, mother’s national origin, and

the school’s geographical location. These variables were chosen based on the preliminary

findings that SES, national origin, and school location were statistically significantly different

across the ELL and non-ELL groups. Because the aim of these analyses was solely to

investigate differences based on language status and not race/ethnicity, the subsample of just

ELL and non-ELL Hispanic students (n = 130) was used for these analyses. Nine 2-way

ANOVAs were conducted to investigate the nature of the relationship between each

demographic variable (SES, mother’s national origin, and school location) and language status

(ELL versus non-ELL) on each of the teacher ratings of students’ reading, math, and

interpersonal skills.

Assumptions. Visual examination of P-P plots indicated minimal deviation of the

observed values from the expected values, suggesting that the data met the assumption of

univariate normality. Homogeneity of variance was investigated through Levene’s Test of

Equality. For each analysis, the Levene’s test was not statistically significant (p = .05). Results

of the additional analyses are presented in Table 15 (unweighted) and Table 16 (weighted).

70

Table 14 Summary of Regression Analyses for the Prediction of Teacher Ratings of Reading, Math, and Interpersonal Skills for Students Grouped by Language Status and Race/Ethnicity (Weighted n = 97)

Reading Skills Math Skills Interpersonal Skills Variable

B

SE B

β

t

B

SE B

Β

t

B

SE B

β

t

Step 1 R2 = .049 R2 = .036 R2 = .006 Teacher Level of Education

.35

.19

.19

1.84

.20

.16

.13

1.26

-.05

.14

-.04

-.34

Teacher ESL Training -.24 .19 -.13 -1.25 -.23 .16 -.15 -1.43 .10 .14 .07 .69 Step 2 R2 = .010 R2 = .020 R2 = .082 Teacher Level of Education

.32

.20

.17

1.58

.17

.17

.11

1.01

-.08

.14

-.06

-.53

Teacher ESL Training

-.23 .25 -.12 -.93 -.22 .20 -.14 -1.08 .06 .17 .04 .34

Language Status and Race/Ethnicity (C1) Non-ELL Minority (C2) Caucasian (C3) ELL

-.01 .08

-.01

.13 .08 .07

-.01 .10

-.01

-.08 .97

-.07

.02 .09 .01

.11 .07 .05

.02 .14 .01

.18

1.33 .06

-.20 .09

-.02

.09

.06

.05

-.24

.16

-.04

-2.27

1.52

-.33 Note. R2Δ = R2 change. The sample size was reduced from 260 to 97 when the data were weighted. The predictor variable was divided into four groups: ELL Hispanic (n = 24), non-ELL Hispanic (n = 28), non-ELL African American (n = 28), and non-ELL Caucasian (n = 17). Three contrasts were created: contrast 1 (C1) = non-ELL African American vs. non-ELL Hispanic; contrast 2 (C2) = non-ELL Caucasian vs. non-ELL Hispanic and African American; and contrast 3 (C3) = ELL Hispanic vs. non-ELL Hispanic, African American, and Caucasian. Teacher level of education was dummy coded with 0 representing a Bachelor’s degree or less and 1 representing a Master’s degree or higher. Teacher ESL training was dummy coded with 0 representing teachers who hadn’t taken any ESL training classes and 1 representing teachers who have taken one or more ESL training class. *p < .01

71

Table 15

Summary of ANOVA for Teacher Ratings of Reading, Math, and Interpersonal Skills based on SES, National Origin, and School Location (Unweighted n =130) Reading Skills Math Skills Interpersonal Skills df F p ηρ2 df F p ηρ2 df F p ηρ2

SES

A. Language Status

1 .52 .47 .004 1 .39 .54 .003 1 .03 .87 .000

B. SES

4 2.08 .09 .065 4 .91 .46 .029 4 1.40 .24 .044

Interaction (A x B)

4 2.24 .07 .070 4 .79 .53 .026 4 .42 .80 .014

Error (Within Groups)

120 120 120

Mother’s National Origin

A. Language Status

1 .83 .36 .007 1 .08 .78 .001 1 .54 .21 .012

B. National Origin

1 .07 .79 .001 1 .01 .94 .000 1 .01 .88 .000

Interaction (A x B)

1 .92 .34 .007 1 .17 .68 .001 1 .03 .76 .001

Error (Within Groups)

126 126 126

School Location A. Language Status

1 6.82 .01 .052 1 1.73 .19 .014 1 2.79 .10 .022

B. School Location

2 1.54 .21 .036 2 1.08 .36 .026 2 .48 .70 .012

Interaction (A x B)

2 .12 .73 .001 2 .73 .40 .006 2 .25 .62 .002

Error (Within Groups)

124 124 124

Note. ηρ2 = partial eta squared.

Socioeconomic status. Three 2 (language status) by 5 (SES) ANOVAs were conducted

to examine the effect of language status and SES (categorized into five levels) on teacher

judgments of students’ academic and interpersonal skills. None of the analyses for both the

unweighted and weighted data were statistically significant for any of the outcome variables.

72

Table 16 Summary of ANOVA for Teacher Ratings of Reading, Math, and Interpersonal Skills based on SES, National Origin, and School Location (Weighted n = 26) Reading Skills Math Skills Interpersonal Skills df F p ηρ2 df F p ηρ2 df F p ηρ2

SES

A. Language Status

1 .75 .40 .040 1 1.47 .24 .075 1 .32 .58 .018

B. SES

4 1.01 .43 .184 4 1.80 .17 .285 4 .98 .44 .179

Interaction (A x B)

2 .14 .87 .016 2 1.50 .25 .143 2 .11 .90 .012

Error (Within Groups)

18 18 18

Mother’s National Origin

A. Language Status

1 .15 .70 .006 1 .16 .69 .007 1 1.36 .26 .056

B. National Origin

1 .45 .51 .019 1 .49 .49 .021 1 3.02 .10 .116

Interaction (A x B)

0 -- -- .000 0 -- -- .000 0 -- -- .000

Error (Within Groups)

23 23

School Location A. Language Status

1 .76 .39 .037 1 .09 .76 .005 1 .06 .81 .003

B. School Location

2 1.60 .22 .193 2 .46 .71 .064 2 .27 .84 .039

Interaction (A x B)

1 1.09 .31 .051 1 .44 .52 .021 1 1.39 .25 .065

Error (Within Groups)

20 20 20

Note. ηρ2 = partial eta squared.

National origin of mother. For the additional analyses, mother’s national origin

(country) was recoded further into two groups, United States (n =58) and Other (n = 72). Similar

to the results for SES, the results of three 2 (language status) by 2 (mother’s national origin)

ANOVAs revealed no statistically significant effects between mother’s national origin and

language status for any of the outcome variables.

73

School location. Three 2 (language status) by 3 (school location) ANOVAs were

conducted to investigate the potential relationship of school location to teacher judgments of

ELL and non-ELL students. Schools were classified as rural (n = 14), suburban (n = 39), or

urban (n = 77). Again none of the effects were statistically significant for either the unweighted

or weighted data.

Teacher Ratings as Predictors of Student Performance

Hypothesis III was that teachers’ ratings of academic and interpersonal competence were

expected to be more predictive of non-ELL students’ actual performance and self-ratings in

comparison to teacher ratings of ELL students. Three hierarchical regression analyses were

conducted with the entire sample (unweighted N = 260) to investigate this hypothesis. Each

analysis was conducted on a different student outcome variable: (a) actual reading scores, (b)

actual math scores, and (c) self-ratings of interpersonal skills. Based on the findings of

preliminary analyses, five covariates were entered into the first step: SES, teachers’ highest level

of education, teachers’ training to work with ELL students, mother’s national origin, and the

geographical location of the school. These variables were coded in the same manner as for

previous analyses. Participants were divided into four groups based on language status and

race/ethnicity: (a) ELL Hispanic, (b) non-ELL Hispanic, (c) non-ELL Caucasian, and (d) non-

ELL African American. This combined language/race variable was coded in the same manner as

previous analyses (with three resulting contrasts) and entered as the first predictor variable. The

first contrast compared non-ELL African American and non-ELL Hispanic students, the second

contrast compared non-ELL Caucasian students to both non-ELL Hispanic and non-ELL African

American students, and the third contrast compared the ELL Hispanic students and all three

groups of non-ELL students (Hispanic, African American, and Caucasian). Teacher ratings of

74

students’ skills (reading, math, or interpersonal) was entered as the second predictor in the

second step of each analysis. In accordance with recommendations by Cohen et al. (2003),

teachers’ ratings were centered to make interpretation more meaningful. The third step

contained three interaction variables, including the interaction of each contrast with teacher

ratings. Due to the number of tests included in each analysis, the criteria for interpretation of

statistical significance were based on a fixed alpha level of .001 to minimize Type I error.

Unweighted analyses. A summary of the results for the unweighted data is reported in

Table 17 (Reading), Table 18 (Math), and Table 19 (Interpersonal). Unstandardized and

standardized regression coefficients, standard errors, t values, and the proportion of variance

accounted for (R2 and R2 change) are presented in the tables for the five covariates, the combined

language/race variable, teacher ratings, and the interactions between the combined language/race

variable and teacher ratings for all three outcome variables.

Reading skills. For the unweighted analyses, the omnibus test for teachers’ ratings of

students’ reading skills was statistically significant for the covariates at the first step, F (5, 256) =

21.07, p < .001, R2 = .296). SES was the only covariate that was a statistically significant

contributor to the prediction of students’ reading scores (t = 7.76, p < .001, semipartial r = .41).

Specifically, students from higher SES categories had higher scores on the reading assessment.

The omnibus test was also statistically significant at the second step (language status,

race/ethnicity, and teacher ratings), F (9, 256) = 30.59, p < .001, R2 ∆ = .231). Above and

beyond the covariates, the combined language/race variable as well as teacher ratings of

students’ reading skills were statistically significant predictors of students’ actual reading scores.

The significance was associated with the second and third contrasts of race/ethnicity, and teacher

75

Table 17 Summary of Regression Analyses for the Prediction of Students’ Reading Scores by Language Status, Race/Ethnicity and Teacher Ratings (Unweighted N = 260) Variable

B

SE B

β

t

Step 1

R2 =.296

SES

3.07 .40 .44 7.76*

Teacher Level of Education

1.10 1.13 .05 .97

Teacher ESL Training

-4.02 1.18 -.19 -3.40

Mother’s National Origin

-.07 .19 -.02 -.36

School Location

.34 .28 .07 1.23

Step 2

R2Δ =.231

SES

1.06 .39 .15 2.73*

Teacher Level of Education

-.43 .96 -.02 -.45

Teacher ESL Training

-.99 1.13 -.05 -.88

Mother’s National Origin

-.09 .16 -.03 -.59

School Location

.20 .23 .04 .86

(C1) Non-ELL Minority

-1.55 .65 -.11 -2.39

(C2) Caucasian

1.59 .39 .20 4.07*

(C3) ELL

-2.30 .34 -.40 -6.80*

Teacher Ratings

3.83 .55 .32 6.96*

Step 3

R2Δ =.023

SES

1.06 .38 .15 2.77*

Teacher Level of Education

-.66 .95 -.03 -.70

76

Table 17 continued B SE B β t

Teacher ESL Training

-1.36

1.12

-.06

-1.21

Mother’s National Origin

-.06 .16 -.02 -.40

School Location

.24 .23 .05 1.06

(C1) Non-ELL Minority

-1.47 .65 -.11 -2.28

(C2) Caucasian

1.36 .40 .17 3.44*

(C3) ELL

-2.34 .34 -.41 -6.96*

Teacher Ratings

4.08 .55 .34 7.47*

Non-ELL Minority x Teacher Ratings

.19 .80 .01 .24

Caucasian x Teacher Ratings

.50 .46 .05 1.10

ELL x Teacher Ratings -.98 .29 -.15 -3.35* Note. R2Δ = R2 change. The covariates were dummy coded as follows: teacher level of education (0 = Bachelor’s degree or less, 1 = Master’s degree or higher), teacher ESL training (0 = no ESL training classes, 1 = one or more ESL training class), mother’s national origin (0 = United States, 1 = other), and school location (0 = urban, 1 = rural/suburban). The first predictor variable was divided into four equal groups (n =65 for each group): ELL Hispanic, non-ELL Hispanic, non-ELL African American, and non-ELL Caucasian. Three contrasts were created: contrast 1 (C1) = non-ELL African American vs. non-ELL Hispanic; contrast 2 (C2) = non-ELL Caucasian vs. non-ELL Hispanic and African American; and contrast 3 (C3) = ELL Hispanic vs. non-ELL Hispanic, African American, and Caucasian. *p < .001 ratings of students’ reading skills. Further examination of the second and third race/ethnicity

contrast variables revealed that non-ELL Caucasian students had higher reading scores than

those of their non-ELL Hispanic and African American counterparts (t = 4.07, p < .001,

semipartial r = .18), and conversely, that ELL students had lower reading scores than the non-

ELL students (t = -6.80, p < .001, semipartial r = -.30). Teacher ratings of students’ reading

skills were also a statistically significant predictor of students’ actual reading performance (t =

6.96, p < .001, semipartial r = .31). In general, teacher ratings predicted student scores on the

direct reading assessment.

77

Table 18

Summary of Regression Analyses for the Prediction of Students’ Math Scores by Language Status, Race/Ethnicity and Teacher Ratings (Unweighted N = 260) Math Skills Variable

B

SE B

β

t

Step 1

R2 = .189

SES

2.55 .41 .38 6.22*

Teacher Level of Education

.95 1.18 .05 .80

Teacher ESL Training

-2.31 1.23 -.11 -1.88

Mother’s National Origin

.12 .20 .04 .62

School Location

-.11 .29 -.02 -.37

Step 2

R2Δ =.246

SES

1.03 .41 .15 2.51*

Teacher Level of Education

.11 1.01 .01 .11

Teacher ESL Training

-.94 1.20 -.05 -.78

Mother’s National Origin

.11 .17 .03 .66

School Location

-.27 .25 -.05 -1.11

(C1) Non-ELL Minority

-2.74 .68 -.20 -4.03*

(C2) Caucasian

1.59 .41 .20 3.85*

(C3) ELL

-1.32 .36 -.24 -3.68*

Teacher Ratings

5.26 .69 .37 7.57*

Step 3

R2Δ =.023

SES

.95 .41 .14 2.34*

Teacher Level of Education .19 1.00 .01 .19

78

Table 18 continued B SE B β t

Teacher ESL Training

-1.44

1.19

-.07

-1.21

Mother’s National Origin

.11 .17 .03 .64

School Location

-.25 .24 -.05 -1.03

(C1) Non-ELL Minority

-2.88 .68 -.21 -4.27*

(C2) Caucasian

1.53 .42 .19 3.69*

(C3) ELL

-1.33 .35 -.24 -3.75

Teacher Ratings

5.67 .70 .40 8.10*

Non-ELL Minority x Teacher Ratings

-2.05 1.09 -.09 -1.89

Caucasian x Teacher Ratings

-.29 .57 -.03 -.51

ELL x Teacher Ratings -1.01 .37 -.13 -2.72* Note. R2Δ = R2 change. The covariates were dummy coded as follows: teacher level of education (0 = Bachelor’s degree or less, 1 = Master’s degree or higher), teacher ESL training (0 = no ESL training classes, 1 = one or more ESL training class), mother’s national origin (0 = United States, 1 = other), and school location (0 = urban, 1 = rural/suburban). The first predictor variable was divided into four equal groups (n =65 for each group): ELL Hispanic, non-ELL Hispanic, non-ELL African American, and non-ELL Caucasian. Three contrasts were created: contrast 1 (C1) = non-ELL African American vs. non-ELL Hispanic; contrast 2 (C2) = non-ELL Caucasian vs. non-ELL Hispanic and African American; and contrast 3 (C3) = ELL Hispanic vs. non-ELL Hispanic, African American, and Caucasian. *p < .001

Finally, the omnibus test was statistically significant at the third step (interaction effects

of language status, race/ethnicity & teachers’ ratings), F (12, 256) = 24.86, p < .001, R2 ∆ =

.023). Thus, the prediction of students’ reading performance varied as the result of the combined

effects of language status, race/ethnicity, and teacher ratings. The interaction between the third

contrast variable (ELL vs. non-ELL) and teacher ratings of reading skills was statistically

significant (t = -3.35, p < .001, semipartial r = -.14). The pattern indicated that teacher ratings of

reading were more predictive for non-ELL students’ actual reading scores than for ELL students.

79

Table 19 Summary of Regression Analyses for the Prediction of Students’ Interpersonal Self-Ratings by Language Status, Race/Ethnicity and Teacher Ratings (Unweighted N = 260) Interpersonal Skills Variable

B

SE B

β

t

Step 1

R2 = .008

SES

-.01 .03 -.01 -.13

Teacher Level of Education

.04 .09 .03 .50

Teacher ESL Training

-.05 .09 -.04 -.61

Mother’s National Origin

.02 .01 .08 1.18

School Location

-.01 .02 -.02 -.38

Step 2

R2Δ =.014

SES

.01 .04 .01 .16

Teacher Level of Education

.04 .09 .03 .50

Teacher ESL Training

-.02 .10 -.02 -.21

Mother’s National Origin

.02 .01 .08 1.29

School Location

-.01 .02 -.02 -.33

(C1) Non-ELL Minority

.08 .06 .09 1.35

(C2) Caucasian

-.04 .04 -.08 -1.16

(C3) ELL

-.01 .03 -.02 -.18

Teacher Ratings

.06 .07 .06 .93

Step 3

R2Δ =.016

SES

.01 .04 .01 .16

Teacher Level of Education .03 .09 .02 .33

80

Table 19 continued B SE B β t

Teacher ESL Training

-.06

.11

-.04

-.52

Mother’s National Origin

.02 .01 .08 1.21

School Location

-.01 .02 -.02 -.31

(C1) Non-ELL Minority

.09 .06 .10 1.52

(C2) Caucasian

-.05 .04 -.10 -1.35

(C3) ELL

-.01 .03 -.02 -.23

Teacher Ratings

.05 .07 .05 .73

Non-ELL Minority x Teacher Ratings

.06 .09 .05 .71

Caucasian x Teacher Ratings

-.01 .06 -.01 -.17

ELL x Teacher Ratings -.07 .04 -.12 -1.82 Note. R2Δ = R2 change. The covariates were dummy coded as follows: teacher level of education (0 = Bachelor’s degree or less, 1 = Master’s degree or higher), teacher ESL training (0 = no ESL training classes, 1 = one or more ESL training class), mother’s national origin (0 = United States, 1 = other), and school location (0 = urban, 1 = rural/suburban). The first predictor variable was divided into four equal groups (n =65 for each group): ELL Hispanic, non-ELL Hispanic, non-ELL African American, and non-ELL Caucasian. Three contrasts were created: contrast 1 (C1) = non-ELL African American vs. non-ELL Hispanic; contrast 2 (C2) = non-ELL Caucasian vs. non-ELL Hispanic and African American; and contrast 3 (C3) = ELL Hispanic vs. non-ELL Hispanic, African American, and Caucasian. *p < .001

Math skills. A similar pattern of statistical significance was found for the unweighted

regression analyses on the prediction of students’ math scores. The omnibus test was again

significant at all three steps: step 1 F (5, 256) = 11.72, p < .001, R2 = .189), step 2 F (9, 256) =

21.13, p < .001, R2 ∆ = .246), and step 3 F (12, 256) = 17.18, p < .001, R2 ∆ = .023). In the first

step, SES was again the only statistically significant covariate (t = 6.22, p < .001, semipartial r =

.35). Similar to the findings for reading, students from higher SES categories obtained higher

scores on the reading assessment.

81

Unlike the previous analyses for reading, all three contrasts were statistically significant

predictors of students’ math scores in the second step. For the first contrast, non-ELL African

American students had lower math scores than the non-ELL Hispanic students (t = -4.03, p <

.001, semipartial r = -.19). The second contrast revealed that non-ELL Caucasian students had

higher math scores than the groups of non-ELL Hispanic and non-ELL African American

students (t = 3.85, p < .001, semipartial r = .18). Finally, the third contrast showed that ELL

students had lower math scores than the non-ELL students (t = -6.80, p < .001, semipartial r =

-.30). Teacher ratings were also a statistically significant predictor of students’ actual math

scores (t = 7.57, p < .001, semipartial r = .36), suggesting that teacher ratings were closely

aligned with students’ performance on the math assessment. And at the third step, the interaction

between teacher ratings and the third contrast (ELL vs. non-ELL) was statistically significant (t =

-2.72, p < .001, semipartial r = .36). Teachers’ ratings of students’ math skills varied as a result

of language status. Teachers’ math ratings were more predictive of non-ELL students’ actual

math scores than their ratings of ELL students’ actual math scores.

Interpersonal skills. Unlike the previous sets of analyses on the prediction of students’

actual reading and math scores, none of the omnibus tests were significant for the prediction of

students’ self-ratings of interpersonal skills, including the covariates. The complete results for

the unweighted analyses investigating the prediction of interpersonal skills are presented in Table

19.

Weighted analyses. A summary of the results for the weighted data is reported in Table

20 (Reading), Table 21 (Math), and Table 22 (Interpersonal). Unstandardized and standardized

regression coefficients, standard errors, t values, and the proportion of variance accounted for

(R2 and R2 change) are presented in the tables for the five covariates, the combined language/race

82

Table 20 Summary of Regression Analyses for the Prediction of Students’ Reading Scores by Language Status, Race/Ethnicity and Teacher Ratings (Weighted n = 95) Variable

B

SE B

β

t

Step 1

R2 = .253

SES

2.40 .74 .34 3.25*

Teacher Level of Education

4.53 2.02 .21 2.24

Teacher ESL Training

-3.20 2.17 -.15 -1.47

Mother’s National Origin

-.19 .31 -.06 -.62

School Location

-.11 .39 -.03 -.27

Step 2

R2Δ = .244

SES

.66 .70 .09 .95

Teacher Level of Education

1.61 1.77 .08 .91

Teacher ESL Training

1.73 2.14 .08 .81

Mother’s National Origin

-.21 .26 -.07 -.81

School Location

-.19 .33 -.04 -.56

(C1) Non-ELL Minority

.05 1.10 .01 .05

(C2) Caucasian

1.41 .77 .15 1.83

(C3) ELL

-2.83 .60 -.49 -4.68*

Teacher Ratings

4.15 .92 .36 4.47*

Step 3

R2Δ = .045

SES

.51 .69 .07 .74

Teacher Level of Education

1.30 1.75 .06 .75

83

Table 20 continued B SE B β t Teacher ESL Training

.64 2.13 .03 .30

Mother’s National Origin

-.14 .26 -.04 -.55

School Location

-.14 .32 -.03 -.43

(C1) Non-ELL Minority

-.13 1.08 -.01 -.12

(C2) Caucasian

1.2 .77 .13 1.57

(C3) ELL

-2.92 .60 -.50 -4.91*

Teacher Ratings

4.51 .91 .39 4.94*

Non-ELL Minority x Teacher Ratings

-.06 1.32 -.01 -.05

Caucasian x Teacher Ratings

.13 .76 .01 .17

ELL x Teacher Ratings -1.38 .49 -.22 -2.82* Note. R2Δ = R2 change. The covariates were dummy coded as follows: teacher level of education (0 = Bachelor’s degree or less, 1 = Master’s degree or higher), teacher ESL training (0 = no ESL training classes, 1 = one or more ESL training class), mother’s national origin (0 = United States, 1 = other), and school location (0 = urban, 1 = rural/suburban). The first predictor variable was divided into four equal groups (n =65 for each group): ELL Hispanic, non-ELL Hispanic, non-ELL African American, and non-ELL Caucasian. Three contrasts were created: contrast 1 (C1) = non-ELL African American vs. non-ELL Hispanic; contrast 2 (C2) = non-ELL Caucasian vs. non-ELL Hispanic and African American; and contrast 3 (C3) = ELL Hispanic vs. non-ELL Hispanic, African American, and Caucasian. *p < .001 variable, teacher ratings, and the interactions between the combined language/race variable and

teacher ratings for all three outcome variables.

Reading skills. Similar to the unweighted data, the omnibus test for the weighted

analyses of reading skills were statistically significant at all three steps: step 1 F (5, 94) = 6.01, p

< .001, R2 = .253), step 2 F (9, 94) = 9.30, p < .001, R2 ∆ = .244), and step 3 F (12, 94) = 8.05, p

< .001, R2 ∆ = .045). In the first step, SES was the only statistically significant covariate (t =

3.25, p < .001, semipartial r = .30), with students from higher SES categories obtaining higher

reading scores.

At step 2, above and beyond the covariates, the ELL contrast (t = -4.68, p < .001,

semipartial r = -.36) and teacher ratings of students’ reading skills (t = 4.46, p < .001, semipartial

84

Table 21 Summary of Regression Analyses for the Prediction of Students’ Math Scores by Language Status, Race/Ethnicity and Teacher Ratings (Weighted n = 95) Variable

B

SE B

β

t

Step 1

R2 = .181

SES

1.89 .77 .27 2.46

Teacher Level of Education

3.61 2.11 .17 2.24

Teacher ESL Training

-3.90 2.26 -.18 -1.47

Mother’s National Origin

-.03 .32 -.01 -.62

School Location

-.11 .41 -.03 -.27

Step 2

R2Δ = .252

SES

.44 .74 .06 .60

Teacher Level of Education

1.93 1.86 .09 1.04

Teacher ESL Training

-1.42 2.27 -.07 -.62

Mother’s National Origin

-.04 .28 -.01 -.15

School Location

-.34 .35 -.08 -.97

(C1) Non-ELL Minority

-2.21 1.16 -.17 -1.91

(C2) Caucasian

.44 .74 .06 .60

(C3) ELL

-2.83 1.28 .82 .14

Teacher Ratings

4.15 -1.64 .64 -.28

Step 3

R2Δ = .069

SES

.50 .72 .07 .70

Teacher Level of Education

1.81 1.78 .09 1.01

85

Table 21 continued B SE B β t

Teacher ESL Training

-2.49 2.24 -.11 -1.12

Mother’s National Origin

.02 .27 .01 .08

School Location

-.32 .34 -.08 -.96

(C1) Non-ELL Minority

-2.39 1.13 -.18 -2.13

(C2) Caucasian

.76 .81 .08 .94

(C3) ELL

-1.47 .62 -.25 -2.39

Teacher Ratings

6.49 1.19 .47 5.46*

Non-ELL Minority x Teacher Ratings

.47 1.87 .02 .25

Caucasian x Teacher Ratings

1.18 .98 .10 1.21

ELL x Teacher Ratings -1.75 .60 -.25 -2.90* Note. R2Δ = R2 change. The covariates were dummy coded as follows: teacher level of education (0 = Bachelor’s degree or less, 1 = Master’s degree or higher), teacher ESL training (0 = no ESL training classes, 1 = one or more ESL training class), mother’s national origin (0 = United States, 1 = other), and school location (0 = urban, 1 = rural/suburban). The first predictor variable was divided into four equal groups (n =65 for each group): ELL Hispanic, non-ELL Hispanic, non-ELL African American, and non-ELL Caucasian. Three contrasts were created: contrast 1 (C1) = non-ELL African American vs. non-ELL Hispanic; contrast 2 (C2) = non-ELL Caucasian vs. non-ELL Hispanic and African American; and contrast 3 (C3) = ELL Hispanic vs. non-ELL Hispanic, African American, and Caucasian. *p < .001 r = .34) were statistically significant predictors of students’ actual reading performance. ELL

students had lower reading scores than their non-ELL counterparts. At the third step, the

interaction between the ELL contrast and teachers’ ratings was statistically significant (t = -2.82,

p < .001, semipartial r = -.21). Teachers’ ratings of reading skills were more predictive of non-

ELLs’ actual reading scores in comparison to those of ELL students.

Math skills. The three omnibus tests for the weighted analyses were also statistically

significant: step 1 F (5, 94) = 3.93, p < .001, R2 = .181), step 2 F (9, 94) = 7.21, p < .001, R2 ∆ =

.252), and step 3 F (12, 94) = 6.89, p < .001, R2 ∆ = .069). SES was statistically significant in the

first step (t = 2.46, p < .001, semipartial r = .24). After controlling for the covariates at the

86

Table 22 Summary of Regression Analyses for the Prediction of Students’ Interpersonal Self-Ratings by Language Status, Race/Ethnicity and Teacher Ratings (Weighted n = 95) Variable

B

SE B

β

t

Step 1

R2 = .027

SES

-.01 .06 -.01 -.02

Teacher Level of Education

.09 .15 .07 .62

Teacher ESL Training

-.04 .16 -.03 -.22

Mother’s National Origin

.03 .02 .15 1.40

School Location

-.01 .03 -.02 -.20

Step 2

R2Δ = .028

SES

-.01 .06 -.01 -.10

Teacher Level of Education

.11 .16 .08 .69

Teacher ESL Training

-.02 .19 -.01 -.08

Mother’s National Origin

.04 .02 .17 1.51

School Location

-.01 .03 -.02 -.21

(C1) Non-ELL Minority

-.02 .10 -.02 -.16

(C2) Caucasian

-.07 .07 -.12 -1.04

(C3) ELL

-.03 .05 -.07

-.50

Teacher Ratings

.10 .12 .10 .88

Step 3

R2Δ = .010

SES

-.01 .06 -.01 -.10

Teacher Level of Education

.11 .16 .08 .67

87

Table 22 continued B SE B β t Teacher ESL Training

-.05 .20 -.03 -.24

Mother’s National Origin

.04 .02 .17 1.48

School Location

-.01 .03 -.02 -.20

(C1) Non-ELL Minority

-.01 .10 -.01 -.07

(C2) Caucasian

-.08 .07 -.13 -1.10

(C3) ELL

-.03 .06 -.07 -.46

Teacher Ratings

.10 .12 .10 .82

Non-ELL Minority x Teacher Ratings

.02 .16 .01 .11

Caucasian x Teacher Ratings

.01 .11 .01 .02

ELL x Teacher Ratings -.06 .07 -.10 -.89 Note. R2Δ = R2 change. The covariates were dummy coded as follows: teacher level of education (0 = Bachelor’s degree or less, 1 = Master’s degree or higher), teacher ESL training (0 = no ESL training classes, 1 = one or more ESL training class), mother’s national origin (0 = United States, 1 = other), and school location (0 = urban, 1 = rural/suburban). The first predictor variable was divided into four equal groups (n =65 for each group): ELL Hispanic, non-ELL Hispanic, non-ELL African American, and non-ELL Caucasian. Three contrasts were created: contrast 1 (C1) = non-ELL African American vs. non-ELL Hispanic; contrast 2 (C2) = non-ELL Caucasian vs. non-ELL Hispanic and African American; and contrast 3 (C3) = ELL Hispanic vs. non-ELL Hispanic, African American, and Caucasian. *p < .001 second step, the ELL contrast was a statistically significant predictor of students’ math scores.

ELL students had lower math scores than their non-ELL counterparts (t = -2.56, p < .001,

semipartial r = -.21). Teacher ratings of students’ math skills were also a significant predictor

ofstudents’ actual math performance (t = 5.10, p < .001, semipartial r = .42). In the final step,

the interaction between the third contrast and teacher ratings was statistically significant (t = -

2.90, p < .001, semipartial r = .86). Teacher ratings of math were more predictive of actual math

scores for non-ELL than ELL students.

Interpersonal skills. Similar to the unweighted analyses on the prediction of students’

self-ratings of interpersonal skills, none of the omnibus tests were significant for the prediction

88

of students’ self-ratings of interpersonal skills, including the covariates. The complete results for

the weighted analyses investigating the prediction of interpersonal skills are presented in Table

22.

Post-hoc analyses. Post-hoc regression analyses were conducted with both the

unweighted and weighted data to examine whether time spent in the ESL classroom had an

impact on teachers’ academic and interpersonal ratings of ELL students. Only the sample of

ELL students (n = 65) was used in these analyses. All five previously investigated covariates

(SES, teachers’ highest level of education, teachers’ ESL training, mother’s national origin, and

geographical location of school) plus time spent in the ESL classroom were entered into the first

step of the analyses. In the available data, time spent in the ESL classroom were collected as a

categorical variable based on four ranges of time in minutes: (a) 1-30 minutes, (b) 31-60

minutes, (c) 61-90 minutes, and (d) more than 90 minutes. This variable was dummy coded with

0 representing students who spent 60 minutes or less per day in the ESL classroom and 1

representing students who spent more than 60 minutes in the ESL classroom. Teacher ratings of

students’ reading, math, and interpersonal skills were entered into the second step of the

analyses. The three student outcome variables were again actual reading and math scores, and

students’ self-ratings of interpersonal skills.

None of these analyses were statistically significant. Time in the ESL classroom was not

a statistically significant contributor to the prediction of students’ academic scores and

interpersonal ratings. Teacher ratings of ELL students were also statistically nonsignificant. A

summary of the unweighted results is reported in Table 23 (Reading), Table 24 (Math), and

Table 25 (Intepersonal). The weighted results are presented in Table 26 (Reading), Table 27

(Math), and Table 28 (Interpersonal). Unstandardized and standardized regression coefficients,

89

Table 23

Summary of Regression Analyses Investigating the Influence of Time in ESL Classroom on the Accuracy of Teacher Ratings of ELL Students’ Reading Skills (Unweighted n = 65) Variable

B

SE B

β

t

Step 1

R2 =.195

Time in ESL Classroom

-.06 .29 -.03 -.22

SES

-.95 1.16 -.11 -.82

Teacher Level of Education

2.00 2.29 .11 .88

Teacher ESL Training

-1.47 2.04 -.09 -.72

Mother’s National Origin

-.52 .28 -.22 -1.84

School Location

6.42 2.04 .39 3.15

Step 2

R2Δ =.032

Time in ESL Classroom

-.01 .29 -.01 -.02

SES

-1.0 1.15 -.11 -.87

Teacher Level of Education

1.97 2.26 .10 .87

Teacher ESL Training

-1.01 2.04 -.06 -.49

Mother’s National Origin

-.52 .28 -.22 -1.87

School Location

6.44 2.02 .39 3.19

Teacher Ratings

1.55 1.02 .18 1.51

Note. R2Δ = R2 change. The covariates were dummy coded as follows: time in ESL classroom (0 = 60 minutes or less, 1 = more than 60 minutes), teacher level of education (0 = Bachelor’s degree or less, 1 = Master’s degree or higher), teacher ESL training (0 = no ESL training classes, 1 = one or more ESL training class), mother’s national origin (0 = United States, 1 = other), and school location (0 = urban, 1 = rural/suburban). None of the findings were statistically significant at p < .001.

90

Table 24 Summary of Regression Analyses Investigating the Influence of Time in ESL Classroom on the Accuracy of Teacher Ratings of ELL Students’ Math Skills (Unweighted n = 65) Variable

B

SE B

β

t

Step 1

R2 = .042

Time in ESL Classroom

.03 .31 .01 .09

SES

-.48 1.25 -.05 -.38

Teacher Level of Education

.29 2.46 .02 .12

Teacher ESL Training

-1.51 2.20 -.09 -.68

Mother’s National Origin

.37 .30 .16 1.23

School Location

1.35 2.20 .08 .61

Step 2

R2Δ =.133

Time in ESL Classroom

.03 .30 .01 .09

SES

-.48 1.20 -.05 -.40

Teacher Level of Education

.07 2.37 .01 .03

Teacher ESL Training

-.48 2.16 -.03 -.22

Mother’s National Origin

.38 .29 .16 1.30

School Location

1.43 2.11 .09 .77

Teacher Ratings

3.05 1.26 .31 2.43

Note. R2Δ = R2 change. The covariates were dummy coded as follows: time in ESL classroom (0 = 60 minutes or less, 1 = more than 60 minutes), teacher level of education (0 = Bachelor’s degree or less, 1 = Master’s degree or higher), teacher ESL training (0 = no ESL training classes, 1 = one or more ESL training class), mother’s national origin (0 = United States, 1 = other), and school location (0 = urban, 1 = rural/suburban). None of the findings were statistically significant at p < .001.

91

Table 25 Summary of Regression Analyses Investigating the Influence of Time in ESL Classroom on the Accuracy of Teacher Ratings of ELL Students’ Interpersonal Skills (Unweighted n = 65) Variable

B

SE B

β

t

Step 1

R2 = .077

Time in ESL Classroom

.03 .03 .14 1.02

SES

.24 .11 .28 2.08

Teacher Level of Education

.11 .23 .06 .48

Teacher ESL Training

.09 .20 .06 .46

Mother’s National Origin

-.01 .03 -.03 -.23

School Location

.01 .20 .01 .04

Step 2

R2Δ =.047

Time in ESL Classroom

.04 .03 .18 1.30

SES

.26 .11 .31 2.30

Teacher Level of Education

.03 .23 .02 .14

Teacher ESL Training

-.01 .21 -.01 -.05

Mother’s National Origin

-.02 .03 -.07 -.54

School Location

.06 .20 .04 .31

Teacher Ratings

-.28 .16 -.24 -1.73

Note. R2Δ = R2 change. The covariates were dummy coded as follows: time in ESL classroom (0 = 60 minutes or less, 1 = more than 60 minutes), teacher level of education (0 = Bachelor’s degree or less, 1 = Master’s degree or higher), teacher ESL training (0 = no ESL training classes, 1 = one or more ESL training class), mother’s national origin (0 = United States, 1 = other), and school location (0 = urban, 1 = rural/suburban). None of the findings were statistically significant at p < .001.

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Table 26 Summary of Regression Analyses Investigating the Influence of Time in ESL Classroom on the Accuracy of Teacher Ratings of ELL Students’ Reading Skills (Weighted n = 25) Variable

B

SE B

β

t

Step 1

R2 = .205

Time in ESL Classroom

-.22 .49 -.10 -.44

SES

-1.24 2.26 -.13 -.55

Teacher Level of Education

2.40 4.11 .13 .58

Teacher ESL Training

.02 3.79 .01 .01

Mother’s National Origin

-.63 .50 -.28 -1.26

School Location

5.86 3.78 .36 1.55

Step 2

R2Δ =.010

Time in ESL Classroom

-.24 .50 -.11 -.47

SES

-1.21 2.31 -.13 -.52

Teacher Level of Education

2.34 4.21 .13 .56

Teacher ESL Training

.56 4.06 .03 .14

Mother’s National Origin

-.64 .52 -.28 -1.25

School Location

5.97 3.88 .37 1.54

Teacher Ratings

.81 1.81 .11 .45

Note. R2Δ = R2 change. The covariates were dummy coded as follows: time in ESL classroom (0 = 60 minutes or less, 1 = more than 60 minutes), teacher level of education (0 = Bachelor’s degree or less, 1 = Master’s degree or higher), teacher ESL training (0 = no ESL training classes, 1 = one or more ESL training class), mother’s national origin (0 = United States, 1 = other), and school location (0 = urban, 1 = rural/suburban). None of the findings were statistically significant at p < .001.

93

Table 27 Summary of Regression Analyses Investigating the Influence of Time in ESL Classroom on the Accuracy of Teacher Ratings of ELL Students’ Math Skills (Weighted n = 25) Variable

B

SE B

β

t

Step 1

R2 = .085

Time in ESL Classroom

-.18 .52 -.08 -.34

SES

.47 2.41 .05 .19

Teacher Level of Education

1.16 4.39 .06 .26

Teacher ESL Training

-1.67 4.05 -.10 -.41

Mother’s National Origin

.38 .54 .17 .71

School Location

2.60 4.04 .16 .64

Step 2

R2Δ =.044

Time in ESL Classroom

-.25 .53 -.12 -.48

SES

.35 2.43 .04 .14

Teacher Level of Education

1.08 4.42 .06 .24

Teacher ESL Training

-.43 4.31 -.03 -.10

Mother’s National Origin

.38 .54 .17 .69

School Location

2.76 4.07 .17 .68

Teacher Ratings

1.94 2.19 .23 .89

Note. R2Δ = R2 change. The covariates were dummy coded as follows: time in ESL classroom (0 = 60 minutes or less, 1 = more than 60 minutes), teacher level of education (0 = Bachelor’s degree or less, 1 = Master’s degree or higher), teacher ESL training (0 = no ESL training classes, 1 = one or more ESL training class), mother’s national origin (0 = United States, 1 = other), and school location (0 = urban, 1 = rural/suburban). None of the findings were statistically significant at p < .001.

94

Table 28 Summary of Regression Analyses Investigating the Influence of Time in ESL Classroom on the Accuracy of Teacher Ratings of ELL Students’ Interpersonal Skills (Weighted n = 25) Variable

B

SE B

β

t

Step 1

R2 = .149

Time in ESL Classroom

.06 .05 .30 1.29

SES

.25 .23 .27 1.11

Teacher Level of Education

.08 .41 .05 .20

Teacher ESL Training

-.01 .38 -.01 -.02

Mother’s National Origin

.01 .05 .02 .10

School Location

-.11 .38 -.07 -.28

Step 2

R2Δ =.047

Time in ESL Classroom

.07 .05 .35 1.46

SES

.27 .23 .29 1.20

Teacher Level of Education

.02 .42 .01 .06

Teacher ESL Training

-.11 .40 -.07 -.28

Mother’s National Origin

-.01 .05 -.01 -.05

School Location

-.07 .38 -.04 -.18

Teacher Ratings

-.29 .30 -.24 -.95

Note. R2Δ = R2 change. The covariates were dummy coded as follows: time in ESL classroom (0 = 60 minutes or less, 1 = more than 60 minutes), teacher level of education (0 = Bachelor’s degree or less, 1 = Master’s degree or higher), teacher ESL training (0 = no ESL training classes, 1 = one or more ESL training class), mother’s national origin (0 = United States, 1 = other), and school location (0 = urban, 1 = rural/suburban). None of the findings were statistically significant at p < .001.

95

standard errors, t values, and the proportion of variance accounted for (R2 and R2 change) are

presented in the tables for the five covariates, the combined language/race variable, teacher

ratings, and the interactions between the combined language/race variable and teacher ratings for

all three outcome variables.

96

DISCUSSION

The purpose of the current study was to investigate whether teacher judgments differed

for ELL and non-ELL students on academic abilities, specifically reading and mathematics, and

interpersonal competence. It was hypothesized that teachers would judge ELL students as

having weaker academic and interpersonal skills than their non-ELL counterparts, regardless of

the latter’s racial/ethnic background. Additionally, it was hypothesized that teacher perceptions

would be more predictive of the academic performance and interpersonal self-ratings for non-

ELL versus ELL students. These hypotheses were partially supported. In the sections that

follow, the meaning of each set of findings will be examined by hypothesis, with a consideration

of both the unweighted and weighted findings. The discussion will also focus on additional

analyses regarding the effect that specific demographic variables (SES, mother’s national origin,

and location of school) might have had on teacher ratings. Limitations of the study will be noted

as well as the implications for practice and future research.

Language Status and Teacher Perceptions

For the first hypothesis teachers were expected to judge Spanish-speaking ELL Hispanic

students as having weaker academic and interpersonal skills than their English-speaking non-

ELL Hispanic counterparts. Teachers’ level of education and training in ESL were included in

the analyses as potential covariates and language status (ELL versus non-ELL) was entered as

the predictor variable. The findings did not support the hypothesis. Regardless of teachers’ level

of education and training in ESL, the language status of Hispanic students was not found to be a

predictor of teacher ratings of reading, math, or interpersonal skills.

In contrast to what was hypothesized, these results suggest that teachers do not rate

students’ academic and social skills differently based on their English language ability. As noted

97

in previous studies (e.g., Demaray & Elliott, 1998), differences in teacher judgments may be

more strongly related to actual student ability and performance, rather than preconceived notions

about students based on language status. Or it may not be the actual language spoken that

influences teacher judgments, but rather how it is spoken (e.g., grammar) or whether a student is

speaking in a vernacular versus using more formal language (McClendon, 2010). Research has

also been conducted on code-switching (i.e., alternating between two languages in the context of

one conversation), suggesting that this practice is generally perceived negatively, particularly by

monolingual speakers and majority cultural groups, in terms of understandability, attractiveness,

and correctness (Hidalgo, 1988). Historically, code-switching has been viewed negatively in

schools as well; it has been seen as a sign of limited language proficiency (Cheng & Butler,

1989) and considered detrimental to students’ development of English and academic skills in

English (Aitchison, 1991; Hughes, Shaunessy, Brice, Ratliff, & McHatton, 2006). Thus,

teachers’ academic and interpersonal ratings of students may be more affected by students who

speak informally, using vernacular English or code-switching between languages, as opposed to

students who simply speak limited English.

Additionally, while some teacher characteristics were investigated as covariates in the

current study (level of education and ESL training), there may be other differences between

teachers (e.g., experience working with ELL students, teacher race/ethnicity, languages spoken)

which influence their ratings of students, particularly those of different language and

racial/ethnic backgrounds. Research conducted on the effect of the race/ethnicity of teachers’

perceptions of students has found mixed results. For example, some studies (e.g., Bates & Glick,

2013; Saft & Pianta, 2001) have shown that racial/ethnic congruence between teachers and

students were consistently related to teachers’ perceptions and positive relationships with

98

students. Other studies (Beady & Hansell, 1981; Mashburn, Hamre, Downer, & Pianta, 2006;

Pigott & Cowen, 2000) have revealed that teachers of non-Caucasian backgrounds tended to rate

children more positively in terms of competencies, expectations, and behavior, regardless of the

students’ race/ethnicity or racial/ethnic match with the teacher. Dominguez de Ramirez and

Shapiro (2005) have suggested that teacher perceptions of students, particularly in regards to

deviance from behavioral norms, may be partially mediated by teachers’ cultural values more

than race/ethnicity.

It may also be that more apparent attributes of students have more of an effect on teacher

judgments. For example, previous researchers (e.g., Dusek & Joseph, 1983, Hoge & Coladarci,

1989; Tenenbaum & Ruck, 2007) have posited that teachers may rate students differently based

on race/ethnicity, skin color, physical attractiveness, or student behavior. One of these variables,

race/ethnicity, was investigated more closely in the second hypothesis.

Language Status and Teacher Perceptions across Racial/Ethnic Groups

The second hypothesis was that teachers were expected to judge ELL students to have

weaker academic and interpersonal skills than their non-ELL counterparts of Hispanic, African

American, and Caucasian backgrounds. Teachers’ level of education and ESL training were

again considered as potential covariates and a combined language/race variable, divided into four

groups (ELL Hispanic, non-ELL Hispanic, non-ELL African American, and non-ELL

Caucasian), was entered as the predictor with three resulting contrasts ([a] ELL Hispanic versus

all three non-ELL groups, [b] non-ELL Caucasian versus the other two non-ELL groups [African

American and Hispanic], and [c] non-ELL Hispanic versus non-ELL African American). This

hypothesis was also not supported; teacher ratings of ELL students were not found to be different

from their ratings of non-ELL students regardless of the ethnic/racial background of the non-

99

ELL students. While teacher ratings were not different across the ELL and non-ELL students,

results from the other contrasts revealed differences between the non-ELL groups based on

race/ethnicity. Specifically, teachers rated non-ELL African American students as having

weaker reading and social skills than non-ELL Hispanic students.

While the second hypothesis was not supported, concurrent findings suggest that teacher

ratings may be influenced by race/ethnicity more so than language status. The potential role of a

student’s race/ethnicity on teacher expectations aligns with existing research. A review of

relevant literature suggests that teachers may have more negative perceptions of and lower

expectations for ethnic “minority” students (e.g., Jussim & Eccles, 1995; Rist, 1970; Rubie-

Davies, Hattie, & Hamilton, 2006) or even students with more “ethnic-sounding” names (e.g.,

Anderson-Clark, Green, & Henley, 2008). However, much of the existing literature suggests that

African American and Hispanic students are rated similarly by teachers (in a more negative

manner) whereas European American and Asian American students are judged more favorably

(e.g., Dusek & Joseph, 1983; Tenenbaum & Ruck, 2007). In contrast, teachers in the current

study rated Hispanic students more favorably than African American students, but no differences

were found between Caucasian students and the other racial/ethnic groups. That is, there were

differences in the way that teachers rated Hispanic and African American students, but together

as a group, the African American and Hispanic students were not rated significantly differently

than Caucasian students.

There are several possible explanations for this finding. It may be that teachers are

becoming more aware of their biases and preconceived notions based on race/ethnicity, and that

gradually, different racial/ethnic groups have become less stigmatized over time. Or, there may

be differences based on perceptions of race (e.g., African American) versus ethnicity (e.g.,

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Hispanic). Future research could further tease out this distinction between race and ethnicity,

and how individuals primarily identify themselves. The Hispanic students in the current study

may have been rated more similarly to the Caucasian students based on fewer differences in

characteristics, such as skin color/tone or other facets of appearance as compared to the African

American students. Hispanic students may have even been misperceived as Caucasian in some

cases. It may be that it is not race/ethnicity in itself that is influencing teacher judgments, but

other corresponding characteristics such as skin color (Elmore, 2010; Smith, 1977), physical

appearance (Dare, 1992; Song, 1998), or cultural differences (Dominguez de Ramirez & Shapiro,

2005; Narvaez, 2013).

A growing body of research has been conducted that has investigated phenotype or skin

color, with some studies (e.g., Elmore, 2010; Fergus, 2009) providing evidence for

discrimination based on phenotype or skin color. These findings indicated that those with lighter

skin are treated more favorably than those with darker skin, regardless of race/ethnicity (Elmore,

2010; Fergus, 2009). While the data used in the current study did not contain details about

students’ skin color or phenotype, it may be these physical or outward characteristics influenced

teacher perceptions more so than language status.

Additional Analyses

Based on preliminary findings that the ELL and non-ELL groups were systematically

different in regards to socioeconomic status (SES), parents’ national origin, and school location,

additional analyses were conducted to investigate the potential relationship of these demographic

variables to teacher judgments of ELL and non-ELL students. Results revealed that SES,

national origin, and school location were not related to teachers’ academic and interpersonal

judgments, either alone or in conjunction with language status.

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While previous research (e.g., Bennett, Gottesman, Rock, & Cerullo, 1993; Dusek &

Joseph, 1983; Hoge & Coladarci, 1989) suggests that there may be moderating variables, which

influence teacher expectations/judgments, these findings are inconsistent. For example, Dusek

and Joseph found through meta-analysis that SES was only sometimes related to teacher

expectations. As a result, the authors indicated that the mixed findings may be indicative of the

location of the analyses, within or across classrooms/schools or the influence of other factors

(students’ age or grade level) on teacher expectations. The results of the current study add to

these inconsistent findings as none of the variables investigated in the additional analyses (SES,

parents’ national origin, and school location) directly influenced teacher judgments of students’

academic and interpersonal skills. Teachers may be less aware of these particular variables or

there may be other factors that hold more significance, such as students’ actual

ability/achievement (e.g., Hecht & Greenfield, 2002; Hoge and Coladarci, 1989) or student

behavior (e.g., Bennett, Gottesman, Rock, & Cerullo, 1993; Dusek & Joseph, 1983). Factors like

SES or school location may not be significant predictors of teacher judgments. However, SES or

school location may be related to other factors that are directly or indirectly related to teacher

ratings, such as students’ actual achievement, parent involvement, and exposure to/opportunities

for learning outside of school.

Teacher Ratings as Predictors of Student Performance

The third hypothesis was that teachers’ ratings would be more predictive of the academic

performance and interpersonal self-ratings of non-ELL versus ELL students. The findings

partially supported this hypothesis. Five covariates (SES, teachers’ highest level of education,

teachers’ ESL training, mother’s national origin, and school location) were entered in the first

step; the combined race/language variable (with three contrasts) used to test the second

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hypothesis and teacher ratings were entered into the second step. The interaction between

teacher ratings and each race/language contrast was entered into the third step. Students’ actual

reading and math scores and their self-ratings of interpersonal skills were the outcome variables.

As predicted, teachers’ ratings were more predictive of the academic (reading and mathematics)

skills of non-ELL students; however, there was no relationship found between teachers’ ratings

of students’ interpersonal skills and students’ own ratings of their interpersonal skills for either

the ELL or non-ELL sample.

In all of the analyses (unweighted and weighted), SES was the only covariate that was

found to be related to students’ performance in reading and math. That is, students from a higher

SES category were found to have higher math and reading scores. Above and beyond SES,

race/ethnicity and language status were also found to be significant predictors of student

performance for the unweighted data only. Specifically, non-ELL Caucasian students had higher

reading and math scores than their non-ELL Hispanic and African American counterparts. For

math only, non-ELL Hispanic students had higher scores than non-ELL African American

students. In regard to language status, non-ELL students demonstrated stronger reading and

math performance than ELL students.

For both the unweighted and weighted analyses teacher ratings were found to predict

students’ reading and math performance on the direct assessments. Analyses revealed an

interaction effect; teachers consistently rated non-ELL students more accurately than their ELL

counterparts on both reading and math skills. No relationships were found for interpersonal

skills.

Most of the existing literature indicates moderate to strong correspondence between

teachers’ academic judgments and students’ actual academic performance (e.g., Demaray &

103

Elliott, 1998; Hoge & Coladarci, 1989), but some research (e.g., Dusek & Joseph, 1983) has

suggested that the accuracy of teachers’ academic ratings may be influenced by various factors,

including race/ethnicity. The current findings suggest that language status may be an additional

factor that may affect the accuracy of teacher judgments of students’ academic skills as teachers

generally rated non-ELL students more accurately than ELL students. This difference may be

because mainstream teachers are better equipped to make academic judgments about non-ELL

students. Previous research (e.g., Williams, Whitehead, & Miller, 1972) indicates that

mainstream teachers may confuse ELL students’ language differences with academic or

cognitive deficits. Or it may be that ELL students’ true abilities are masked by their English

language proficiency; they may have limited means to express what they know and can do.

Limitations

Several limitations should be noted in the current study. First, the study was conducted

using archival data, which were collected over ten years ago and may not be representative of

current trends of teachers’ perceptions or students’ performance. Additionally, for the purposes

of the ECLS-K study from which the data for the current study were taken, ELL students were

oversampled, purposefully selected in numbers that were disproportionate to their actual

representation in the population. As such, the data needed to be weighted to obtain results that

were applicable beyond just the present sample. While the overall sample in this study actually

consisted of 260 students (including 65 ELL students), these numbers were reduced to 95 (24

ELL students) when the data were weighted to be more representative of the population.

Furthermore, the use of weighted or unweighted data resulted in some inconsistency in findings

and thus made it difficult to draw reliable conclusions. While weighting the data allows for the

findings to be generalized beyond the sample to the population (National Center for Education

104

Statistics, 2004) this statistical technique reduced the current sample size and thus decreased the

power of the statistical analyses (Cohen, 1992).

There are also some limitations in regard to the ECLS-K measures selected for use in the

study. Limited psychometric information was available for the direct assessments of reading and

math, Oral Language Development Scale (OLDS), Academic Rating Scale (ARS), Social Rating

Scale (SRS), and Self-Description Questionnaire (SDQ). These measures were collections of

items derived for the purposes of the ECLS-K, and thus, had not been established as

psychometrically sound scales outside of the ECLS-K study. This lack of psychometric research

limits both the internal and external validity of the obtained scores.

Another limitation of the current study is that the sample was restricted in range on

parents’ national origin, which made in-depth analyses in this area not viable. Specifically, a

majority of the sampled participants were from the United States, with the second largest group

coming from Mexico. Some participants’ parents did originate from other countries and regions

besides U.S. and Mexico, but the sizes of these subsamples were too small to conduct any

meaningful comparisons.

There was also a lack of access to certain information from ECLS-K or the unavailability

of information. Some information had been collected but was restricted from public access, and

other information had not been collected for the ECLS-K study. For example, data on parents’

national origin were available for public use, but the same data on the children’s national origin

had restricted access. Information on teacher race/ethnicity was collected, but also had restricted

access and thus was not investigated in the current study. Additional information that may have

been particularly relevant to the current study, such as status of citizenship, length of time in the

105

United States, and length of time speaking English, were not collected as part of the ECLS-K

study.

Implications for Practice and Future Research

The results of the current study reinforce the importance of teacher training to work with

students from diverse backgrounds. While teachers have been found to be generally accurate in

their academic ratings of students, the current study supports previous findings (e.g., Dusek &

Joseph, 1983), which suggest that accuracy of judgment may decrease when other factors, such

as race/ethnicity and language, are present. As such, it is important for teachers to develop

awareness and knowledge of the diversity of the students they teach. Additionally, given the

finding that teachers’ academic and interpersonal judgments may be influenced by students’

race/ethnicity, it is critical that teachers become aware of their potential biases and find ways to

minimize these unfair preconceptions when working with students.

The current study has important implications for school psychologists and other related

service providers in the schools as well. School psychologists should take teachers’ potential

biases into account when collecting teacher ratings of students as part of conducting

comprehensive psychoeducational evaluations. Particular caution should be used when

interpreting and making conclusions about teachers’ academic and behavioral ratings of students

from diverse backgrounds, including, but not limited to, race/ethnicity and language status. The

findings of the current study also highlight the potential role of the school psychologist in

providing in-service trainings to teachers for working with and fairly assessing ELL and

racially/ethnically diverse students, including recognizing and addressing potential biases.

The findings of the current study also provide direction for expansion and future research

on investigating the potential influences on teacher judgments and expectations of students.

106

Future studies could consider more specific differences between national origin, skin

color/appearance, U.S. citizenship, length of time in the United States, and length of time

speaking English. Additional research could also be conducted that further examines students’

degree or level of English language proficiency and the operationalization and measurement of

proficiency. A larger sample size and data from more established and psychometrically sound

rating scales/measures would help to eliminate some of the current study’s limitations of external

and internal validity. Other potentially influential variables that could be addressed in future

studies are other teacher characteristics (e.g., race/ethnicity, languages spoken) and student

characteristics (e.g., student behavior, skin color, physical attractiveness).

Conclusion

The purpose of the current study was to investigate teacher judgments of the academic

and interpersonal competence of students based on language status, specifically Spanish-

speaking students versus non-ELL (i.e., non-Spanish-speaking) students. The findings did not

support the hypothesis that teachers would rate non-ELL students (regardless of race/ethnicity)

as having stronger academic and interpersonal skills than their Spanish-speaking counterparts.

Instead, the results suggested that race/ethnicity may be more of an influential factor when

teachers make academic and interpersonal judgments. Non-ELL African American students in

particular were rated as having weaker reading and interpersonal skills than their non-ELL

Hispanic counterparts. This finding supports previous research (e.g., Hodson et al., 2002;

Jussim, 1989; Jussim & Eccles, 1995; Kovel, 1970; Rist, 1970) that teachers may have pre-

existing biases towards students of different races or ethnicities. The third hypothesis that

teachers’ academic and interpersonal judgments would be more accurate for non-ELL versus

ELL students was supported by the current results. Teachers may generally be more accurate

107

about non-ELL students’ academic and social functioning than ELL students. Thus, teachers and

other school service providers may need more training to work with, fairly assess, and make

better judgments about ELL students’ academic capabilities.

108

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Curriculum Vita

Miranda E. Freberg EDUCATION Ph.D. School Psychology, The Pennsylvania State University, University Park, PA, 2014

Specialization in Culture and Language Education M.Ed. School Psychology, The Pennsylvania State University, University Park, PA 2007 B.S. Psychology, Magna Cum Laude, Colby College, Waterville, ME 2000 Boston University London Internship Program, 1999 PROFESSIONAL CREDENTIAL Certified School Psychologist – Pennsylvania Department of Education PROFESSIONAL EXPERIENCE School Psychologist, ASPIRA, Inc., Olney Charter High School, Philadelphia, PA (2013 – present) School Psychologist, ASPIRA, Inc., Antonia Pantoja Bilingual Community Charter School, Philadelphia, PA (2011- 2013) School Psychologist, CORA Services, Philadelphia, PA (2009-2011) School Psychology Intern, CORA Services, Philadelphia, PA (2008-2009) Specialization in Culture and Language Education Practicum Student, State College School District, State College, PA (2007-2008) Student Supervisor, The Pennsylvania State University CEDAR Clinic, University Park (2007-2008) School Psychology Student Clinician, The Pennsylvania State University CEDAR Clinic, University Park (2005-2008) Graduate Teaching Assistant, Department of Human Development and Family Studies, The Pennsylvania State University, University Park, PA (2005-2006) Youth Minister, St. Thomas Episcopal Church, Lancaster, PA (2002-2004) Therapeutic Support Staff, Philhaven Behavioral Healthcare Services, Mt. Gretna, PA (2001-2004) Paraeducator, Life Skills Support, Lancaster-Lebanon Intermediate Unit 13, Lancaster, PA (2000) AFFILIATIONS American Psychological Association – Division 16 National Association of School Psychologists Association of School Psychologists of Pennsylvania School Psychology Student Group, The Pennsylvania State University, Founding Member Charter Committee (Chair), Outreach Committee Psi Chi Honor Society PUBLICATIONS AND PRESENTATIONS Reid, E. E., Goffreda, C. T., Culler, E. D., McGinnis, A. M., Miller, A. R., Reid, M. A., Freberg., M. E., Meyer, E.

L., & Hahn, K. R. (2009, February). Construct validity of the WJ-III Cognitive among adjudicated adolescents. Poster presentation at the annual convention of the National Association of School Psychologists, Boston, MA.

Freberg, M. E., Vandiver, B. J., Watkins, M. W., & Canivez, G. L. (2008). Factor score variability and the validity of the WISC-III FSIQ in predicting later academic achievement. Applied Neuropsychology, 15, 131-139.

Freberg, M. E., Vandiver, B. J., Watkins, M. W. & Canivez, G. L. (2008, February). Significant Factor Score Variability and the Validity of the WISC-III FSIQ. Poster presentation at the annual convention of the National Association of School Psychologists, New Orleans, LA.