Socioeconomic Status and Academic Achievement: A Meta-Analytic Review of Research

See discussions, stats, and author profiles for this publication at: https://www.researchgate.
net/publication/235356240
Socioeconomic Status and Academic Achievement: A Meta-Analytic Review of

Research
Article in Review of Educational Research · September 2005

DOI: 10.3102/00346543075003417
CITATIONS READS
2,246 16,456
1 author:
Selcuk R Sirin
New York University
61 PUBLICATIONS 4,420 CITATIONS
SEE PROFILE
Some of the authors of this publication are also working on these related projects:
New York City Academic and Social Engagement Study (NYCASES) View project
Racial and Ethical Sensitivity Test View project
All content following this page was uploaded by Selcuk R Sirin on 31 May 2014.
The user has requested enhancement of the downloaded file.

3179-04_Sirin.qxd 9/2/05 2:07 PM Page 417
Review of Educational Research

Fall 2005, Vol. 75, No. 3, pp. 417–453
Socioeconomic Status and Academic Achievement:

A Meta-Analytic Review of Research
Selcuk R. Sirin
New York University
This meta-analysis reviewed the literature on socioeconomic status (SES) and

academic achievement in journal articles published between 1990 and 2000.
The sample included 101,157 students, 6,871 schools, and 128 school dis-
tricts gathered from 74 independent samples. The results showed a medium
to strong SES–achievement relation. This relation, however, is moderated by
the unit, the source, the range of SES variable, and the type of SES–achieve-
ment measure. The relation is also contingent upon school level, minority sta-
tus, and school location. The author conducted a replica of White’s (1982)
meta-analysis to see whether the SES–achievement correlation had changed
since White’s initial review was published. The results showed a slight
decrease in the average correlation. Practical implications for future
research and policy are discussed.
KEYWORDS: achievement, meta-analysis, SES, social class, socioeconomic status.
Socioeconomic status (SES) is probably the most widely used contextual vari-
able in education research. Increasingly, researchers examine educational
processes, including academic achievement, in relation to socioeconomic back-
ground (Bornstein & Bradley, 2003; Brooks-Gunn & Duncan, 1997; Coleman,
1988; McLoyd, 1998). White (1982) carried out the first meta-analytic study that
reviewed the literature on this subject by focusing on studies published before 1980
examining the relation between SES and academic achievement and showed that
the relation varies significantly with a number of factors such as the types of SES
and academic achievement measures. Since the publication of White’s meta-
analysis, a large number of new empirical studies have explored the same relation.
The new results are inconsistent: They range from a strong relation (e.g., Lamdin,
1996; Sutton & Soderstrom, 1999) to no significant correlation at all (e.g., Ripple
& Luthar, 2000; Seyfried, 1998). Apart from a few narrative reviews that are
mostly exclusive to a particular field (e.g., Entwisle & Astone, 1994; Haveman &
Wolfe, 1994; McLoyd, 1998; Wang, Haertal, & Walberg, 1993), there has been no
systematic review of these empirical research findings. The present meta-analysis
is an attempt to provide such a review by examining studies published between
1990 and 2000.
McLoyd (1998), in her review of recent research on SES and child develop-
ment, and Entwisle and Astone (1994), in their review of SES measures, identified
a number of major factors that differentiate the research published during the 1960s
417
3179-04_Sirin.qxd 9/2/05 2:07 PM Page 418
Sirin
and the 1970s from that published in recent years. The first of these is the change
in the way that researchers operationalize SES. Current research is more likely to
use a diverse array of SES indicators, such as family income, the mother’s educa-
tion, and a measure of family structure, rather than looking solely at the father’s
education and/or occupation.
The second factor is societal change in the United States, specifically in parental
education and family structure. During the 1990s, parental education changed
dramatically in a favorable direction: Children in 2000 were living with better-
educated parents than children in 1980 (U.S. Department of Education, 2000).
Likewise, reductions in family size were also dramatic; only about 48% of 15-to-
18-year-old children lived in families with at most one sibling in 1970, as
compared with 73% in 1990 (Grissmer, Kirby, Berends, & Williamson, 1994).
A third factor is researchers’ focus on moderating factors that could influence
the robust relation between SES and academic achievement (McLoyd, 1998). With
increased attention to contextual variables such as race/ethnicity, neighborhood
characteristics, and students’ grade level, current research provides a wide range
of information about the processes by which SES effects occur.
Thus, because of the social, economic and methodological changes that have
occurred since the publication of White’s (1982) review, it is difficult to estimate
the current state of the relation between SES and academic achievement. This
review was designed to examine the relation between students’ socioeconomic sta-
tus and their academic achievement by reviewing studies published between 1990
and 2000. More specifically, the goals of this review are (a) to determine the mag-
nitude of the relation between SES and academic achievement; (b) to assess the
extent to which this relation is influenced by various methodological characteris-
tics (e.g., the type of SES or academic achievement measure), and student charac-
teristics (e.g., grade level, ethnicity, and school location); and (c) to replicate
White’s meta-analysis with data from recently published studies.
Measuring Socioeconomic Status
Although SES has been at the core of a very active field of research, there seems
to be an ongoing dispute about its conceptual meaning and empirical measurement
in studies conducted with children and adolescents (Bornstein & Bradley, 2003).
As White pointed out in 1982, SES is assessed by a variety of different combina-
tions of variables, which has created an ambiguity in interpreting research findings.
The same argument could be made today. Many researchers use SES and social
class interchangeably, without any rationale or clarification, to refer to social and
economic characteristics of students (Ensminger & Fothergill, 2003). In general
terms, however, SES describes an individual’s or a family’s ranking on a hierar-
chy according to access to or control over some combination of valued commodi-
ties such as wealth, power, and social status (Mueller & Parcel, 1981).
While there is disagreement about the conceptual meaning of SES, there seems
to be an agreement on Duncan, Featherman, and Duncan’s (1972) definition of the
tripartite nature of SES that incorporates parental income, parental education, and
parental occupation as the three main indicators of SES (Gottfried, 1985; Hauser,
1994; Mueller & Parcel, 1981). Many empirical studies examining the relations
among these components found moderate correlations, but more important, these
studies showed that the components of SES are unique and that each one measures
418
3179-04_Sirin.qxd 9/2/05 2:07 PM Page 419
Socioeconomic Status and Academic Achievement

a substantially different aspect of SES that should be considered to be separate
from the others (Bollen, Glanville, & Stecklov, 2001; Hauser & Huang, 1997).
Parental income as an indicator of SES reflects the potential for social and eco-
nomic resources that are available to the student. The second traditional SES com-
ponent, parental education, is considered one of the most stable aspects of SES
because it is typically established at an early age and tends to remain the same
over time. Moreover, parental education is an indicator of parent’s income
because income and education are highly correlated in the United States (Hauser
& Warren, 1997). The third traditional SES component, occupation, is ranked on
on the basis of the education and income required to have a particular occupation
(Hauser, 1994). Occupational measures such as Duncan’s Socioeconomic Index
(1961) produce information about the social and economic status of a household
in that they represent information not only about the income and education
required for an occupation but also about the prestige and culture of a given
socioeconomic stratum.
A fourth indicator, home resources, is not used as commonly as the other three
main indicators. In recent years, however, researchers have emphasized the signif-
icance of various home resources as indicators of family SES background (Cole-
man, 1988; Duncan & Brooks-Gunn, 1997; Entwisle & Astone, 1994). These
resources include household possessions such as books, computers, and a study
room, as well as the availability of educational services after school and in the sum-
mer (McLoyd, 1998; Eccles, Lord, & Midgley, 1991; Entwisle & Astone).
Aggregated SES Measures
Education researchers also have to choose whether to use an individual stu-
dent’s SES or an aggregated SES based on the school that the student attends (Cal-
das & Bankston, 1997) or the neighborhood where the student resides
(Brooks-Gunn, Duncan, & Aber, 1997). School SES is usually measured on the
basis of the proportion of students at each school who are eligible for reduced-price
or free lunch programs at school during the school year. Students from families
with incomes at or below 130% of the poverty level are eligible for free meals.
Those with incomes between 130% and 185% of the poverty level are eligible for
reduced-price meals. Neighborhood SES, on the other hand, is usually measured
as the proportion of neighborhood/county residents at least 20 years old who,
according to the census data, have not completed high school (Brooks-Gunn,
Denner, & Klebanov, 1995). School and neighborhood SES indicators vary in how
they assess SES, but they share the underlying definition of SES as a contextual
indicator of social and economic well-being that goes beyond the socioeconomic
resources available to students at home (see Brooks-Gunn, Denner, & Klebanov).
Using aggregated SES measures may introduce the issue of “ecological fallacy”
into the interpretation of results from various studies with differing units of analy-
sis. The ecological fallacy is simply a misinterpretation wherein an individual-level
inference is made on the basis of group aggregated data. In the context of the cur-
rent review it refers to the erroneous assumption that research findings at the school
or neighborhood level also represent within-school or within-neighborhood rela-
tionships, and vice versa. Aggregated SES data on the school or neighborhood lev-
els cannot be interpreted as if they represented family SES variables, nor should
student-level SES data be used to explain differences between schools.
419
3179-04_Sirin.qxd 9/2/05 2:07 PM Page 420
Student Characteristics
Socioeconomic status is not only directly linked to academic achievement but
also indirectly linked to it through multiple interacting systems, including students’
racial and ethnic background, grade level, and school/neighborhood location
(Brooks-Gunn & Duncan, 1997; Bronfenbrenner & Morris, 1998; Eccles, Lord, &
Midgley, 1991; Lerner 1991). For example, family SES, which will largely deter-
mine the location of the child’s neighborhood and school, not only directly
provides home resources but also indirectly provides “social capital,” that is, sup-
portive relationships among structural forces and individuals (i.e., parent–school
collaborations) that promote the sharing of societal norms and values, which are
necessary to success in school (Coleman, 1988; Dika & Singh, 2002). Thus, in
addition to the aforementioned methodological factors that likely influence the
relation between SES and academic achievement, several student characteristics
also are likely to influence that relation.
Grade Level
The effect of social and economic circumstances on academic achievement may
vary by students’ grade level (Duncan, Brooks-Gunn, & Klebenov, 1994; Lerner,
1991). However, the results from prior studies about the effect of grade or age on the
relation between SES and academic achievement are mixed. On the one hand, Cole-
man et al.’s (1966) study and White’s (1982) review showed that as students become
older, the correlation between SES and school achievement diminishes. White pro-
vided two possible explanations for the diminishing SES effect on academic achieve-
ment. First, schools provide equalizing experiences, and thus the longer students stay
in the schooling process, the more the impact of family SES on student achievement
is diminished. Second, more students from lower-SES backgrounds drop out of
school, thus reducing the magnitude of the correlation. On the other hand, results
from longitudinal studies have contradicted White’s results, by demonstrating that
the gap between low- and high-SES students is most likely to remain the same as stu-
dents get older (Duncan et al., 1994; Walker, Greenwood, Hart, & Carta, 1994), if
not widen (Pungello, Kupersmidt, Burchinal, & Patterson, 1996).
Minority Status
Racial and cultural background continues to be a critical factor in academic
achievement in the United States. Recent surveys conducted by the National Cen-
ter for Education Statistics (NCES) indicated that, on average, minority students
lagged behind their White peers in terms of academic achievement (U.S. Depart-
ment of Education, 2000). A number of factors have been suggested to explain the
lower academic achievement of minority students, but the research indicates three
main factors: Minorities are more likely to live in low-income households or in sin-
gle parent families; their parents are likely to have less education; and they often
attend under-funded schools. All of these factors are components of SES and
linked to academic achievement (National Commission on Children, 1991).
School Location
The location of schools is closely related to the social and economic conditions
of students. A narrative review of research on school location (U.S. Department of
420
3179-04_Sirin.qxd 9/2/05 2:07 PM Page 421

Education, 1996) showed that even after accounting for family SES, there appear
to be a number of significant differences between urban, rural, and suburban
schools. Data from the National Assessment of Educational Progress, for example,
indicated that the achievement of children in affluent suburban schools was signif-
icantly and consistently higher than that of children in “disadvantaged” urban
schools (U.S. Department of Education, 2000).
In summary, the relation between SES and academic achievement was the
focus of much empirical investigation in several areas of education research in
the 1990s. Recent research employed more advanced procedures to best exam-
ine the relation between SES and academic achievement. The present meta-
analytic review was designed to assess the magnitude of the relation between
SES and academic achievement in this literature. Further, it was designed to
examine how the SES–achievement relation is moderated by (a) methodological
characteristics, such as the type of SES measure, the source of SES data, and the
unit of analysis; and (b) student characteristics, such as grade level, minority
status, and school location. Finally, it was designed to determine if there has been
any change in the correlation between SES and achievement since White’s 1982
study.
Methods
Criteria for Including Studies
To be included in this review, a study had to do the following:
1. Apply a measure of SES and academic achievement.
2. Report quantitative data in sufficient statistical detail for calculation of
correlations between SES and academic achievement.
3. Include in its sample students from grades kindergarten through 12.
4. Be published in a professional journal between 1990 and 2000.
5. Include in its sample students in the United States.
Identification of Studies
Several computer searches and manual searches were employed to gather the
best possible pool of studies to represent the large number of existing studies on
SES and academic achievement. The computerized search was conducted using
the ERIC (Education Resources Information Center), PsycINFO, and Sociolog-
ical Abstracts reference databases. For SES, the search terms socioeconomic
status, socio-economic status, social class, social status, income, disadvantaged,
and poverty were used. For academic achievement the terms achievement,
success, and performance were used. The search function was created by using
two Boolean operators: “OR” was used within the SES set and the academic
achievement set of search terms, and “AND” was used between the two sets.
Because the majority of studies used SES as a secondary or control variable and,
therefore, the computerized databases did not always index them by using one
of the above search terms as a keyword, the search was performed by using the
“anywhere” function, not the “keyword” function. All databases were searched
for the period 1990 to 2000 (on November 24, 2001). The search yielded 1,338
PsycINFO documents, 953 ERIC documents, and 426 Sociological Abstracts
421
3179-04_Sirin.qxd 9/2/05 2:07 PM Page 422
Sirin
documents. After double entries were eliminated, there remained 2,014 unique
documents.
Next, the Social Science Citation Index (SSCI) was searched for the studies that
cited either Coleman et al.’s (1966) or White’s (1982) review, or both, because
both of those publications have been highly cited in the literature on SES and aca-
demic achievement. Through this process, an additional 170 articles that refer-
enced White’s study and 266 articles that referenced Coleman’s report were
identified. In addition, I received 27 leads from previous narrative reviews and
from studies that had been identified through the initial search. In total, the final
pool contained 2,477 unique documents.
After the initial examination of the abstracts of each study, I applied the inclu-
sion criteria to select 201 articles for further examination. I made the final deci-
sions for inclusion after examining the full articles. Through this process, I selected
58 published journal articles that satisfied the inclusion criteria.
Coding Procedure
A formal coding form was developed for the current meta-analysis on the basis
of Stock et al.’s (1982) categories, which address both substantive and methodolog-
ical characteristics: Report Identification, Setting, Subjects, Methodology, Treat-
ment, Process, and Effect Size. To further refine the coding scheme, a subsample of
the data (k = 10) was coded independently by two doctoral candidates. Rater agree-
ment for the two coders was between .80 and 1.00 with a mean of 87%. The coders
subsequently met to compare their results and discuss any discrepancies between
their ratings until they reached an agreement upon a final score. The coding form
was further refined on the basis of the results from this initial coding procedure. The
final coding form included the following components:
1. The Identification section codes basic study identifiers, such as the year of
publication and the names and disciplines of the authors.
2. The School Setting section describes the schools in terms of location from
which the data were gathered.
3. The Student Characteristics section codes demographic information about
study participants including grade, age, gender, and race/ethnicity.
4. The Methodology section gathers information about the research methodol-
ogy used in the study, including the design, statistical techniques, as well as
sampling procedures.
5. The SES and Academic Achievement section records data about SES and aca-
demic achievement measures.
6. The Effect Size (ES) section codes the statistics that are needed to calculate
an effect size, such as correlation coefficients, means, standard deviations, t
tests, F ratios, chi-squares, and degrees of freedom on outcome measures
used in the study.
Interrater Agreement
All studies were coded by the author. A doctoral student who helped design the
coding schema coded an additional random sample of 10 studies. Interrater agree-
ment levels for the six coding categories ranged from 89% for the methodology
section to 100% for the names of the coding form.
422
3179-04_Sirin.qxd 9/2/05 2:07 PM Page 423
Analytical Procedures
Calculating Average Effect Sizes
The effect size (ES) used in this review was Pearson’s correlation coefficient r.
Because most results were reported as a correlation (k = 45), the raw correlation
coefficient was entered as the ES measure. There were 8 studies that did not orig-
inally report correlations but provided enough information to calculate correlations
using the formulas taken from Hedges and Olkin (1985), Rosenthal (1991), and
Wolf (1986) to convert the study statistic to r. Correlations oversestimate the pop-
ulation effect size because they are bounded at –1 or 1. As the correlation coeffi-
cients approach –1 or 1, the distribution becomes more skewed. To address this
problem, the correlations were converted into Fisher’s Z score and weighted by the
inverse of the variance to give greater weight to larger samples than smaller sam-
ples (Lipsey & Wilson, 2001). The average ESs were then obtained through a
z-to-r transformation with confidence intervals to indicate the range within which
the population mean was likely to fall in the observed data (Hedges & Olkin). The
confidence interval for a mean ES is based on the standard error of the mean and a
critical value from the z distribution (e.g., 1.96 for α = .05).
Statistical Independence
There are two main alternative choices for the unit of analysis in meta-analysis
(Glass, McGaw, & Smith, 1981). The first alternative is to use each study as the unit of
analysis. The second approach is to treat each correlation as the unit of analysis. Both
of these approaches have shortcomings. The former approach obscures legitimate dif-
ferences across multiple correlations (i.e., the correlation for minority students versus
the correlation for White students), while the latter approach gives too much weight to
those studies that have multiple correlations (Lipsey & Wilson, 2001). A third alterna-
tive, which was chosen for this study, is to use “a shifting unit of analysis” (Cooper,
1998). This approach retains most of the information from each study while avoiding
any violations of statistical independence. According to this procedure, the average
effect size was calculated by using the first alternative; that is, one correlation was
selected from each independent sample. The same procedure was followed when the
focus of analysis was a student characteristic (e.g., minority status, grade level, or
school location). For example, if a study provided one correlation for White students
and another for Black students, the two were included as independent correlations in
the same analysis. The only exception to this rule was the moderation tests for the
methodological characteristic (e.g., the types of SES or academic achievement mea-
sure). For example, if a study provided one correlation based on parental education and
another based on parental occupation, they were both entered only when the modera-
tor analysis was for the type of SES measure. In both alternatives, there was only one
correlation from each study for each construct. When studies provided multiple corre-
lations for each subsample, or multiple correlations for each construct, they were aver-
aged so that the sample on which they were based contributed only one correlation to
any given analysis. Thus, in Tables 1 (page 424) and 2 (page 429), the correlation for
each study is the average correlation (r) for all constructs for that specific sample.
Fixed and Random Effects Models

There is an ongoing discussion about whether one should use a fixed or random
effects model in meta-analysis (Cooper & Hedges, 1994; Hedges & Vevea, 1998).
423
TABLE 1
424
Summary of the independent samples
Author(s) Grade/ Ethnicity School SES Achievement N of students
3179-04_Sirin.qxd
(publication year) school level (or % minority) location measure measure (or N of schools) r
Alexander, Entwisle, Primary 60 Baltimore FRLa GPA 453 .391
& Bedinger (1994); schools Education Achievement Test 489
9/2/05
Entwisle, Alexander,
& Olson (1994)
Alspaugh (1991); Primary N/A Urban/rural % FRL Missouri Mastery Urban school Urban =
Alspaugh (1992) Achievement Test N = 39 .719
2:07 PM
Rural school Rural =

N = 106 .072
Balli, Demo, & Middle White Midwestern Income Achievement 74 .430
Wedman (1998) school Test
Brown et al. (1993) Mean age N/A N/A Hollingshead K-ABC 26 .270
Page 424
9.5 years (1975) Achievement

Composite
Caldas & Bankston Grades 44 Louisiana % FRL Achievement School .680
(1999); Caldas K–12 public Test N = 1,301
(1993) schools
Caldas & Bankston Grade 10 Black and Louisiana Incomea Achievement Test W = 21,263 W = .247
(1999); Bankston White public Education B = 13,279 B = .142
& Caldas (1998) schools School
Carlson et al. (1999) Grades 1–3 17 Minneapolis Duncan’s SEIb PIAT 168 .340
Income
Education
Chen, Lee, & High school N/A Minneapolis Educationa Math and general 213 .307
Stevenson (1996) metropolitan Occupation information tests
area Home
Christian, Morrison, Kindergarten 83 Greensboro, NC Education PIAT-R 317 .403
& Bryant (1998)
3179-04_Sirin.qxd
Dixon-Floyd & Grades 6–8 75 El Paso, TX, FRL Texas Assessment 85 .467
Johnson (1997) school districts for Academic
Scores
Dornbusch, Ritter, High school Black and Suburban Educationa Self-reported W = 3,533 WF = .32
9/2/05
& Steinberg White Neighborhood GPA B = 372 WM = .36

(1991) BF = .07
BM = .05
Black-Urban Education Self-reported GPA W = 1,368 WF = .23
2:07 PM
White-Mixed B = 446 WM = .20

BF = .02
BM = .05
Felner at al. (1995) Grades 7–9 49 Rural Southeast Hollingshead’s CAT 398 .132
(1975) GPA
Page 425
four-factor
Gonzales, Cauce, Grades 7–8 Black Urban Education a GPA 120 .130c
Friedman, & Income
Mason (1996) Neighborhood
Greenberg, Langau, Grade 1 47 Nationwide Education a Woodcock-Johnson 337 .249
Coie, & multi-state Occupation Psycho-
Pinderhughes longitudinal Home Educational
(1999) study Neighborhood Battery–
Revised
Griffith (1997) Grades 3–6 38 Suburban % FRL Criterion School .650
school Referenced N = 119
district Test
Grolnick & Grades 6–8 2 N/A Education GPA 302 .095
Slowiaczek
(1994)
(continued)
425
TABLE 1 (Continued )
426
3179-04_Sirin.qxd
Gullo & Burton Kindergarten 21 Urban FRL Metropolitan 1,573 .124

(1993) Readiness Test
(Nurss &
9/2/05
McGauvran,
1974)
Jimerson, Grade 1 36 Urban Duncan’s SEIb Achievement 143 .300
Egeland, Education Test
2:07 PM
Sroufe, & Occupation

Carlson (2000) Income
Johnson & Lindblad Grade 6 33 Eastern city FRL SRA Assessment 1,686 .175
(1991) Survey
Kennedy (1992) Primary Black and Mixed Educationb Achievement WM = 1,328 WM =
Page 426
school White Occupation Test BM = 1,028 .330

BM = .160
Klingele & Grade 4 19 Arkansas % FRL MAT-6 School .54
Warrick (1990) school N = 332
districts
Lamdin (1996) Grades 79 Baltimore % FRL % of students School .73
K–12 schools above median N = 97
CAT scores
McDermott (1995) Mixed 31 Mixed Education Achievement Test 1,200 .315
Miyamoto et al. Grades 76 Hawaiian Education GPA 696 .180
(2000) 9–12
O’Brien, Grade 11 60 Large Income Pre-Scholastic 415 .150
Martinez-Pons, metropolitan Aptitude Test
& Kopala (1999) area
Otto & Atkinson Grade 11 28 North Carolina Educationa CAT 335 .202
(1997) rural counties Occupation
Overstreet, Holmes, Age 8–16 50 N/A Hollingshead WRAT-R 116 .621
3179-04_Sirin.qxd
Dunlap, & Frentz years (1975)

(1997)
Patterson, Grades 2–4 38 Urban Public SRA Achievement M = 417 M = .409
Kupersmidt, & Assistance Test F = 451 F = .391
9/2/05
Vaden (1990);
Pungello,
Kupersmidt,
Burchinal, &
2:07 PM
Patterson (1996)
Rech & Stevens Grade 4 Black Urban FRL CAT 133 .060
(1996)
Ripple & Luthar Grade 9 85 Urban Hollingshead GPA 96 .010c
(2000). two-factor
Page 427
Schultz (1993) Grades 4–6 Black and Urban FRL BASIS 133 .430
Hispanic
Seyfried (1998) Grades 4–6 96 Suburban near Educationb GPA 113 .005
large Income MAT
Midwest
city
Shaver & Walls Grades 7–8 6 Marion FRL CTBS 335 .166
(1998) County, WV
Strassburger, Grades 7–9 19 N/A Occupation GPA 357 .080
Rosen, Miller,
& Chavez (1990)
Sutton & Soderstrom Grades 3 27 Mixed FRL Achievement School .750c
(1999) and 10 Test N = 2,307
Thompson et al. Mixed N/A N/A Hollingshead Achievement 76 .555
(1992) two-factor Test
(1957)
(continued)
427
TABLE 1 (Continued)
428
3179-04_Sirin.qxd
Trusty, Watts, & Grades 4–6 Black Rural Education b Stanford F = 265 F = .150
House (1995) FRL Achievement M = 298 M = .210
Test
9/2/05
Trusty, Watts, & Grades 7–8 Black Rural Educationb Stanford F = 157 F = .200
Lim (1996) FRL Achievement M = 129 M = .260
Test
Trusty, Peck, & Grade 4 55 Mixed Educationb Stanford 392 .440
2:07 PM
Mathews (1994) FRL Achievement

Test
Unnever, Kerckhoff, Grade 11 N/A Virginia’s 128 Neighborhood Achievement School district .540
& Robinson school SES Test N = 128
(2000) districts
Page 428
Walker, Greenwood, Primary 48 Kansas city Educationa Achievement 29 .334c

Hart, & Carta school area. Occupation Tests (WRAT-R,
(1994) Income MAT, CTBS)
Watkins (1997) Grades 2–5 23 Midwestern Education GPA 150 .360
city
White, Reynolds, Mixed N/A Urban FRL Achievement 15,045 .154
Thomas, & Test School School =
Gitzlaff (1993) N = 102 .720
Note. r = effect size; N/A = information not available; K-ABC = Kaufman Assessment Battery for Children; FRL = free or reduced-price lunch;
W = White; B = Black; SEI = Socioeconomic Index; PIAT = Peabody Individual Achievement Test; PIAT-R = Peabody Individual Achievement
Test–Revised; F = female; M = male; CAT = California Achievement Test; WRAT = Wide Range Achievement Test; SRA = Science Research
Associates; BASIS = Basic Achievement Skills Individual Screener; WRAT-R = Wide Range Achievement Test–Revised; MAT = Metropoli-
tan Achievement Test; CTBS = Comprehensive Test of Basic Skills.
aThis study reported independent results per SES component. b This study combined these components in its SES measure. cOnly the first wave of
data were used to calculate ES from this longitudinal study.

TABLE 2
Summary of nationwide studies included in the meta-analysis
Grade/school Ethnicity (or % Achievement
Name of survey Published data source level minority) SES measure measures N of students r
3179-04_Sirin.qxd
National Educational Kennedy (1995) for NELS Grade 8 Asian Educationa GPA AF = 741 AF = .190
Longitudinal base year; Levine & American Occupation AM = 785 AM = .240
Study: 88/90/94 Painter (1999) for Income
multiple SES Black Educationa GPA BF = 1,538 BF = .280
9/2/05
correlations; Rojewski & Occupation BM = 1,467 BM = .230

Yang (1997) for multiple Income
achievement correlations; Hispanic Educationa GPA HF = 1,538 HF = .180
Singh & Ozturk (2000) Occupation HM = 1,630 HM = .200
for NELS: 88 and Income
2:07 PM
follow-up samples White Educationa GPA WF = 8,166 WF = .330

Occupation WM = 8,151 WM = .350
Income
National Ricciuti (1999); Kindergarten N/A Education PIAT W = 280 W = .215
Longitudinal Dubow & Ippolito (Cohort 1) Income B = 235 B = .235
Study of Youth: (1994) H = 256 H = .256
Page 429
Children of Kindergarten N/A Education Achievement W = 440 W = .165

mothers 1986, (Cohort 2) Income Test B = 260 B = .153
Cohorts I & II H = 240 H = .215
Longitudinal Study Reynolds & Grade 7 38% Homea NEAP Math 3,116 .588
of American Walberg (1992a) minority Education Test
Youth: Three- Expectations
waves, panel Gallagher (1994) Grade 7 Education NEAP 1,166 .320
study: fall, 1987; Science
spring 1987; fall Test
b
1988 Reynolds & Grades 38% Educationa NEAP 2,535 Grade 10
Walberg (1992b) 10–11 minority Duncan’s Science = .535
SEI Test
Expectations
National Transition Chan, Ramey, Kindergarten 43% Educationc Achievement 378 .225
Demonstration Ramey, & minority Income Test
Project: Control Schmitt (2000)
sample
Note. ESr = effect size r; A = Asian American; B = Black; H = Hispanic American; W = White; F = Female; M = Male; PIAT = Peabody Individual Achievement
429
Test; NEAP = National Educational Assessments of Student Progress.
a
This study combined multiple SES components in the SES measure. b Only the first wave of data was used to calculate ES from this longitudinal study. c This study
reported separate results for each SES component.
3179-04_Sirin.qxd 9/2/05 2:07 PM Page 430
Sirin
A fixed effects model allows for generalizations to the study sample, while the ran-
dom effects model allows for generalizations to a larger population. For the pres-
ent review, both fixed and random methods results are provided for the main effect
size analysis. For the moderator analyses, fixed methods were chosen to make
inferences only about the studies reviewed in this meta-analysis.
Test of Homogeneity
The variation among correlations was analyzed using Hedges’s Q test of homo-
geneity (Hedges & Olkin, 1985). This test uses the chi-square statistic, with the
degree of freedom of k − 1, where k is the number of correlations in the analysis.
If the test reveals a nonsignificant result, then the correlations are homogenous and
the average correlation can be said to represent the population correlation. If the
test reveals a significant result, that is, if the correlations are heterogeneous, than
further analyses should be carried out to determine the influence of moderator vari-
ables on the relation between SES and academic achievement.
Test for Moderator Effects

To test for the significance of the moderating factors, the homogeneity analysis
outlined by Hedges and Olkin (1985) was followed. For this step of the analysis,
fixed-effects analyses were used to fit homogeneous effect sizes into either analy-
sis of variance (ANOVA) or a modified weighted least squares regression model
to examine whether the variability in effect sizes could be accounted for by mod-
erator variables. The statistical procedure for this analysis involves partitioning the
Q statistics into two proportions, Q-between (Qb), an index of the variability
between the group means, and Q-within (Qw), an index of variability within the
groups. Therefore, a significant Q-between would indicate that the mean effect
sizes across categories differ by more than sampling error. Regression analysis was
performed only for the minority status moderation analysis. The rest of the analy-
ses were performed using the weighted ANOVA procedure. To keep the results
section consistent, when the moderator variables were investigated, I reported the
Q-between statistics alone.
Publication Bias
It is well documented in meta-analysis literature that there is a publication bias
against the null hypothesis (Lipsey & Wilson, 2001; Rosenthal, 1979). We used two
methods to evaluate publication bias in the current review. First, publication bias in
this review would be minimal partly because the SES–achievement relation was not
the primary hypothesis for most studies, as the bias toward significant results is likely
to be contained within the primary hypothesis (Cooper, 1998). To empirically test
this assumption, we determined whether the SES–achievement relation was one of
the main questions in each study by checking the title, abstract, introduction, research
questions and/or hypotheses. Of the 58 articles included in the review, 24 articles had
the SES–achievement relation as one of the main questions (i.e., central variable) of
the study. The remaining 34 articles did not have the SES–achievement relation as a
central variable, but instead used it as a control variable. To examine the possibility
of bias, articles in which the SES achievement relation was a main question were
treated as the central group, and articles in which the relation was a control variable
were treated as a control group. On the basis of the student-level data (N = 64), there
430
3179-04_Sirin.qxd 9/2/05 2:07 PM Page 431

were 21 independent samples using SES–achievement relation as a main hypothesis
and 43 independent samples using the SES–achievement relation as a control
variable. The results showed that the central group effect size (.28) was slightly
higher than the control group effect size (.27). This difference, however, was not
statistically significant, Q(1, 63) = .13, p = .72.
Second, we plotted study sample size against the ES to evaluate the funnel plot.
While studies with small sample sizes are expected to show more variable effects,
studies with larger sample sizes are expected to show less variable effects. With no
publication bias, the plot should thus give the impression of a symmetrical inverted
funnel. An asymmetrical or skewed shape, on the other hand, suggests the presence
of publication bias. Figure 1 shows the plot for this review, which conformed to a fun-
nel shape. The only exception to the symmetry appears to be from two large sample
studies that used home resources as a measure of SES and which showed the strongest
ES in this review. To better understand the link between sample size and ES, using
Begg’s (1994) formula, the correlation between the ranks of standardized effect sizes
and the ranks of their sampling variances were calculated. The results showed that the
Spearman rank correlation coefficient was, rs (64) = .07, p > .59. The Kendall’s rank
correlation coefficient was t(64) = .06, p > .46. Both of these statistics indicate that
there was no statistically significant evidence of publication bias.
26
76
113
129
143
173
213
270
Sample size
292
302
371
398
440
538
696
1028
1467
1686
2535
8166
21263
-.2 0.0 .2 .4 .6 .8
ESZ
FIGURE 1. Funnel plot is used to visually inspect data for publication bias. The sym-
metrical inverted funnel shape suggests that there is no publication bias. The only
exception to the symmetry appears to be from two large sample studies that used
home resources as a measure of SES.
431
3179-04_Sirin.qxd 9/2/05 2:07 PM Page 432
Results
The results are presented in three subsections. First, to address the first ques-
tion, the magnitude of the relation between SES and academic achievement, we
reported general findings of the review. To address the second question, testing for
the effects of methodological and student characteristics, we reported the results
of the moderator tests. Finally, to compare our findings with that of White’s (1982)
review, we reported results from another set of analyses that was conducted using
White’s procedures.
General Characteristics of the Studies
Table 1 contains information about the studies used in this analysis and the vari-
ables for which they were coded. There were 75 independent samples from 58 pub-
lished journal articles. Summary of nationwide studies, including data from the
National Educational Longitudinal Study, the National Longitudinal Study of
Youth, and the Longitudinal Study of American Youth are presented in Table 2.
Of 75 samples, 64 used students as the unit of analysis, while 11 used aggregated
units of analyses (i.e., schools or school districts). The total student-level data
included 101,157 individual students. The sample sizes for this group ranged from
26 to 21,263, with a mean of 1,580.58 (SD = 3,726.32) and a median of 367.5. The
aggregated level data included 6,871 schools and 128 school districts.
Although the publication years of the studies were limited to the period of
1990–2000, the actual year of data collection varied from 1982 to 2000.The data
collection year was reported in most of the articles (k = 36). The year 1990 had the
largest number of studies (k = 7) followed by 1988 and 1992 with 6 studies each.
A weighted regression analysis revealed no statistically significant association
between publication year and the effect sizes, β = −.03, n.s.
The Effect Size (r)

Most studies had multiple indicators of the variables of interest. As a result,
there were 207 correlations that could be coded. Overall, correlations ranged from
.005 to .77, with a mean of .29 (SD = .19) and a median of .24.
For the samples with the student-level data, the average ES for the fixed effects
model was .28 with a 95% confidence interval of .28 to .29, and it was significantly
different from zero (z = 91.75, p <.001). The average ES for the random effects
model was .27 with a 95% confidence interval of .23 to .30, and it was significantly
different from zero (z = 14.26, p < .001).
For the samples with the aggregated level data, however, the correlations ranged
from .11 to .85, with a mean of .60 (SD = .22). The weighted ES ranged from .11 to
1.25. The average ES for the fixed effects model was .67 with a 95% confidence inter-
val of .66 to .67, and it was significantly different from zero (z = 147.56, p < .001).
The average ES for the random effects model was .64 with a 95% confidence inter-
val of .57 to .70, and it was significantly different from zero (z = 13.27, p < .001).
To avoid committing the ecological fallacy, only studies with student-level data
were investigated for the remainder of data analysis. The Q test of homogeneity
was significant, indicating that the correlations were heterogeneous and other fac-
tors beyond sampling error may be involved in the explanation of the differences
across the studies Q(1, 64) = 1,844.95, p < .001. The possible factors leading to
432
3179-04_Sirin.qxd 9/2/05 2:07 PM Page 433

differences across the correlations will be the focus of the rest of the results sec-
tion. The results of the Q statistic along with the mean ES and the variation around
the mean ES value that encompasses the 95% confidence interval for the different
levels of each moderator variable are presented in Tables 3 and 4.
The Methodological Moderators
There were 102 unique correlations that provided information about one or
more components of SES. Table 3 presents the results of the methodological mod-
erator analyses. The average ES for this distribution (k = 102) was .31. This ES is
significantly different from zero (z = 144.12, p < .001). The test for homogeneity
was significant, indicating that the correlations in this set were not estimating the
same underlying population value, and therefore it is appropriate to look for pos-
sible moderators, Q(1, 102) = 2,068.36, p < .001. The number of SES components
in each study, the type of SES components, and the source of SES data were con-
sidered as methodological moderators.
TABLE 3
Methodological characteristics moderators of the relationship between SES
and academic achievement
Q- Mean −95% +95%
Moderator Categories k between ES CI CI
Type of SES 79 587.14* .32 .32 .33
components
Education 30 .30 .30 .31
Occupation 15 .28 .26 .29
Income 14 .29 .27 .31
Free or reduced- 10 .33 .32 .34
price lunch
Neighborhood 6 .25 .22 .28
Home 4 .51 .49 .53
SES range 102 238.65* .32 .32 .33
restriction
No restriction 78 .35 .35 .36
3 to 7 SES groups 15 .28 .28 .29
2 SES groups only 9 .24 .22 .27
SES data source 62 775.55* .29 .28 .30
Parents 31 .38 .37 .39
Students 18 .19 .19 .20
Secondary sources 13 .24 .21 .26
Achievement 167 884.21* .29 .28 .29
measures
General 45 .22 .22 .23
achievement
Verbal 58 .32 .32 .33
Math 57 .35 .34 .36
Science 7 .27 .27 .29
Note. k = number of effect sizes; ES = effect size; CI = confidence interval for the average
value of ES.
*p < .005.
433
3179-04_Sirin.qxd 9/2/05 2:07 PM Page 434
The Type of SES Component

Six SES components were used to assess SES (see Table 3). Parental education
was the most commonly used SES component (k = 30), followed by parental occu-
pation (k = 15), parental income (k = 14), and eligibility for free or reduced lunch
programs (k = 10). The Q statistic of homogeneity indicated that the type of SES
component significantly moderated the relation between SES and academic
achievement, Qb(5, 79) = 587.14, p < .001. A weighted ANOVA revealed that the
average ES was .28 for parental occupation, .29 for parental income, and .30 for
parental education. SES measures based on “home resources” produced the high-
est mean ES (.51), followed by eligibility for free or reduced lunch programs (.33).
There were six neighborhoods with an average effect size for these measures was
.25. The follow-up tests consisted of all pairwise comparisons among the six types
of SES indicators. Pairwise comparisons were conducted using Bonferroni
adjusted alpha levels of .003 per test (.05/15). Each of the pairwise comparisons
between the three most commonly used indicators (education, occupation, and
income) were nonsignificant. Other pairwise comparisons, however, were all sta-
tistically significant at p < .001, with the exception of the pairwise comparison of
occupation and neighborhood, which was nonsignificant.
Restriction on the SES Variable

Of the 102 correlations, there were 9 student-level correlations where the SES
variable was operationalized as a dichotomy (e.g., high versus low SES). An
additional 15 correlations were based on SES measures that were restricted to
3–7 categories (e.g., low, medium, high). The rest of the correlations (k = 78)
were based on continuous SES variables; that is, there were no reported restric-
tion in the operationalization of SES. Pairwise comparisons between three
restriction categories were conducted using Bonferroni adjusted alpha levels of
.016 per test (.05/3). All of the pairwise comparisons between the groups were
significant at p < .005.
The results of the weighted-ANOVA test showed that there were significant dif-
ferences in mean ES across these three groups, Qb(2, 102) = 238.65, p < .001. As
presented in Table 3, the average ES for the two-SES group only category (e.g., high
versus low SES) was .24, while the average ES for the 3–7 SES groups category
was .28. When there were no restrictions on the range of the SES variable, the aver-
age ES was .35. In other words, placing restrictions on the range of the SES variable
significantly decreased the correlation between SES and academic achievement.
The Source of SES Data

Of 64 independent student-level studies, 62 reported information about the
source of SES data. Studies were coded into the following three categories of data
source: Secondary sources (k = 13), students (k = 18), and parents (k = 31). The
source of the SES data proved to be a significant moderator, Qb(2, 64) = 775.55,
p < .001. The results presented in Table 3 show that the average ES was .38 when
the SES data were gathered from parents, .24 when the data were gathered from
secondary sources, and .19 when the data were gathered from students them-
selves. Pairwise comparisons between the three sources were conducted using
Bonferroni adjusted alpha levels of .016 per test (.05/3), and they were all signif-
icant at p < .005.
434
3179-04_Sirin.qxd 9/2/05 2:07 PM Page 435
Type of Academic Achievement Measure Moderator Analysis

To estimate the effect of the choice of academic achievement measure on the
relation between SES and academic achievement, a separate database was con-
structed using studies that reported correlations on single or multiple academic
achievement variables. In total, there were 167 independent correlations with a
mean ES of .29. As presented in Table 3, there were four different measures used
to assess academic achievement: math achievement (k = 57), verbal achievement
(k = 58), science achievement (k = 7), and general achievement (k = 45).
The choice of academic achievement measure was a significant moderator of the
correlation between SES and academic achievement, Qb(4, 167) = 884.21, p <
.001. The mean ES was .22 for general achievement outcomes. When the stud-
ies chose a single achievement indicator, the average ES was .27 for science
achievement outcomes, .32 for verbal achievement outcomes, and .35 for math
achievement outcomes. Pairwise comparisons between four achievement mea-
sures were conducted using Bonferroni adjusted alpha levels of .008 per test
(.05/6). All of the pairwise comparisons between the measures were significant
at p < .001.
Student Characteristics Moderators

The main sample, with 64 student-level correlations, was used to examine var-
ious student characteristics as possible moderators of the relation between SES and
academic achievement. More specifically, student’s grade level, minority status,
and school location were considered as student characteristics moderators. Table
4 presents the results of these moderator analyses.
TABLE 4
Student characteristics moderators of the relationship between SES and academic
performance
Moderator Q- Mean −95% +95%
variable Categories k between ES CI CI
Grade level 71 162.23** .28 .28 .29
Kindergarten 9 .19 .16 .22
Elementary school 21 .27 .25 .30
Middle school 19 .31 .31 .32
High school 22 .26 .26 .27
Minority status 35 164.86** .24 .23 .25
White students 11 .27 .25 .28
Minority students 24 .17 .16 .19
School location 26 10.15* .25 .23 .27
Suburban 9 .28 .25 .30
Urban 13 .24 .22 .27
Rural 4 .17 .12 .23
Note: k = number of effect sizes; ES = effect size; CI = confidence interval for the average
value of ES.
*p < .005. ** p < .001.
435
3179-04_Sirin.qxd 9/2/05 2:07 PM Page 436
Grade Level
The sample used for this analysis had 71 correlations, which included the orig-
inal 64 student-level correlations and 7 additional correlations that came from the
longitudinal studies that provided multiple correlations for the same students over
time. Because some studies presented data from multiple grades without further
specification, the grade data were coded as Kindergarten (1), Elementary School
(2), Middle School (3) and High School (4).
Student’s grade level was found to be a significant moderator of the correlations
between SES and academic achievement, Qb(3, 70) = 162.23, p < .001. As pre-
sented in Table 4, the mean ES was .19 for the kindergarten students, .27 for the
elementary school students, .31 for middle school students, and .26 for high school
students. Thus, with the exception of the high school students, there seems to be a
trend of increasing ES from kindergarten to middle school. Pairwise comparisons
between the four grade levels were conducted using Bonferroni adjusted alpha lev-
els of .008 per test (.05/6). All of the pairwise comparisons between the four groups
were significant at p < .001, with the exception of the pairwise comparison of ele-
mentary school and high school ES.
Minority Status Moderator Analyses

More than half of the studies in the student-level data (k = 35) reported separate
correlations for White (k = 11) and minority students (k = 24). The Q-between sta-
tistics suggested a significant difference between these two groups, Qb(1, 35) =
164.86, p < 001. The mean ES for White student samples (.27) was significantly
larger than the mean ES for minority student samples (.17).
Because there were 21 additional studies that provided information about the
number of minority students in their sample, an additional analysis was conducted
by taking the ratio of minority students in each sample as a predictor of the corre-
lation between SES and academic achievement. To evaluate the association
between the ratio of minority students and the magnitude of the correlation coeffi-
cient between SES and academic achievement, a modified weighted least squares
regression was run. There were 56 independent correlations used in this analysis.
The minority ratio was the predictor and the ES was the criterion variable. The pro-
portion of minority students in the sample was a significant predictor of the corre-
lation between SES and academic achievement, Q(1, 56) = 131.70, p < .001. The
increase in the number of minorities in a study sample was negatively associated
with SES–achievement correlations, β = −.30. In other words, the correlation
between SES and academic achievement was minimized with the increase in the
proportion of minorities in the study sample.
School Location Moderator Analyses

There were only 26 studies (out of a possible 64) with data about the geograph-
ical location of the schools. These studies were categorized in one of the follow-
ing three groups: suburban (k = 9), urban (k = 13), and rural (k = 4). The Q test of
homogeneity provided evidence for a significant geographic location effect, Qb(2,
26) = 11.62, p < .005. As presented in Table 4, the average ES for the suburban
schools was the largest (.28), and the average ES for the rural schools was the
smallest (.17). The average ES for urban schools was also .23. These results sug-
gest that the relation between SES and academic achievement is stronger for stu-
436
3179-04_Sirin.qxd 9/2/05 2:07 PM Page 437

dents in suburban schools than for students in rural or urban schools. Pairwise com-
parisons between three school locations were conducted using Bonferroni adjusted
alpha levels of .016 per test (.05/3). The only significant pairwise comparison was
between suburban school and rural schools, p < .001.
Replicating White’s (1982) Meta-Analysis
One of the main goals of this meta-analysis was to replicate White’s (1982)
meta-analysis using journal articles published from 1990 to 2000. However, it
is difficult to compare the findings reported so far in this review with the find-
ings from White’s review for three reasons. First, White’s calculation of the
mean ES was based on the average of the unweighted correlations. This proce-
dure is likely to overestimate the results because it treats each correlation
equally by not weighting them with appropriate sample size parameters (Lipsey
& Wilson, 2001). Second, White’s review allowed for multiple correlations
from the same sample to be used in the data analysis as independent correla-
tions. The problem with this approach is that it violates the principle that there
would be only one unique correlation from one unique sample According to
White’s procedure, for example, one study would have 5 correlations and
another study would have only 1 correlation, but for meta-analytic purposes, the
first study would be represented with 5 ESs, whereas the second study would
only be represented with 1 ES. This approach, therefore, assigns disproportion-
ate influence to studies that included multiple measures of SES and/or school
achievement. Finally, the inclusion criteria were different between the two
reviews. For example, unlike the current review, White’s review accepted IQ
scores as a measure of achievement and did not limit its samples to U.S. stu-
dents. For these reasons, to allow for a comparison between these two meta-
analyses, a new meta-analysis was conducted using the same procedures
outlined in White’s study for this section of the results.
Replication Sample
Following sampling procedure in White’s (1982) review, the comparison sam-
ple included 207 correlations. This number is comparable to the 219 correlations
in White’s review. The two reviews were also comparable in terms of the number
of journal articles. The current review was based on 58 journal articles published
between 1990 and 2000, and White’s review was based on 59 journal articles pub-
lished between 1918 and 1975.
The Average Correlation

The following are results of the current meta-analysis and of White’s (1982)
meta-analysis based on journal articles from the two year spans:
Present review (1990–2000): M = .299, SD = .169, k = 207
White’s review (1918–1975): M = .343, SD = .204, k = 219
The average correlation in the present review was .299, as compared with White’s
average correlation of .343. Although it is not possible to provide statistically sig-
nificant evidence for the change over time, it is safe to suggest that the magnitude
of the relation between SES and school achievement is not as strong in the present
review as it was in White’s review.
437
3179-04_Sirin.qxd 9/2/05 2:07 PM Page 438
Sirin
Comparisons across the two meta-analyses also showed that for both studies,
parental education was the most frequently used measure of SES, but parental
income and parental occupation also continued to be commonly used as a single
measure of SES. Among these traditional three components of SES, income was
the strongest correlate in both meta-analyses.
Discussion
The general goals of this study were to (a) determine the extent to which a sig-
nificant relation exists between SES and academic achievement based on research
published between 1990 and 2000; (b) assess the influence of several moderating
factors in this relation; and (c) estimate whether this relation has changed in com-
parison with the findings from White’s (1982) study.
What Is the Relation Between SES and Academic Achievement?

Using Cohen’s (1977) guidelines, the overall ES of the present study reflects a
medium level of association between SES and academic achievement at the stu-
dent level and a large degree of association at the school level. This interpretation,
however, is limited because, as Cooper (1998) pointed out, one has to interpret a
particular ES in comparison with other findings in that particular area of research.
The overall finding from this study compares quite favorably with results from
Lipsey and Wilson’s (1993) review of more than 300 meta-analyses (r = .27, trans-
formed to Cohen’s d = .56 for comparison purposes). Of all the factors examined
in the meta-analytic literature, family SES at the student level is one of the
strongest correlates of academic performance. At the school level, the correlations
were even stronger.
This review’s overall finding, therefore, suggests that parents’ location in the
socioeconomic structure has a strong impact on students’ academic achievement.
Family SES sets the stage for students’ academic performance both by directly pro-
viding resources at home and by indirectly providing the social capital that is nec-
essary to succeed in school (Coleman, 1988). Family SES also helps to determine
the kind of school and classroom environment to which the student has access
(Reynolds & Walberg, 1992a). Past research that compared low-SES schools with
higher-SES schools found several important differences in terms of instructional
arrangements, materials, teacher experience, and teacher-student ratio (Wenglin-
sky, 1998). Finally, in addition to the quality of instruction, family SES also influ-
ences the quality of the relationship between school personnel and parents
(Watkins, 1997). The overall finding, therefore, not only reflects the effect of
resources at home but also may reflect the effect of social capital on academic
achievement.
SES and Academic Achievement: Unraveling a Complex Relationship

Beyond the main findings, the results from this review also show that the mag-
nitude of the relationship between SES and academic achievement is contingent
upon several factors. More specifically, methodological characteristics, such as the
type of SES measure, and student characteristics, such as student’s grade, minor-
ity status, and school location, moderated the magnitude of the relationship
between SES and academic achievement.
438
3179-04_Sirin.qxd 9/2/05 2:07 PM Page 439
Methodological Issues
The findings show that the studies used several conceptual frameworks to cap-
ture students’ social and economic background. Overall, this meta-analysis pro-
vides empirical evidence regarding how the type of SES measure affects the
strength of correlations found. This information suggests that researchers should
consider the following four factors when conceptualizing SES: (a) the unit of
analysis for SES data; (b) the type of SES measure; (c) the range of the SES vari-
able; and (d) the source of SES data.
Unit of Analysis for SES Data

As expected, when researchers chose an aggregated unit of analysis for their SES
variable, the average ES doubled in magnitude in comparison with would be
observed if the student were the unit of analysis. When aggregated SES data were
used to examine the SES–achievement relationship at the student level, the findings
were likely to be contaminated because of the ecological fallacy. In other words, it
is problematic to make assumptions at the student level from aggregated data.
Type of SES Measure

The type of SES measure changed the relationship between SES and academic
achievement. The average correlations between SES and academic achievement
ranged from .25, when SES was operationalized by using neighborhood character-
istics as an indicator of family SES, to .47, when SES was operationalized by using
home resources as an indicator of family SES. These two indicators, however, were
based on a limited number of studies. More commonly used SES components such
as education, occupation, income, and eligibility for school lunch programs pro-
duced similar results.
Range of the SES Variable

The findings suggest that the studies that used dichotomous SES variables—
that is, low as opposed to high SES—were less likely to produce stronger correla-
tions than the studies that did not dichotomize the SES variable. This finding is not
surprising considering the statistical principle of correlations with artificially
dichotomized measures. Because both school achievement and SES are believed
to be continuous in nature (i.e., variables that are normally distributed in the pop-
ulation) placing constraints in the measurement process creates artificial cate-
gories. As a statistical rule, when one of the variables in the correlation is
artificially categorized, as was the case for many of the studies in this review,
observed correlations will be lower than would be observed if a continuous mea-
sure had been used (Hunter & Schmidt, 1990). In other words, when SES has an
artificially restricted range, the correlation will be pushed closer to zero and the
degree of attenuation will increase as the skew of the dichotomy increases (Lipsey
& Wilson, 2001). Thus, artificially restricting student’s SES significantly reduces
the magnitude of the interaction between SES and school achievement.
Source of SES Data

When students provided the data about their family’s SES, the magnitude of the
relationship between SES and academic achievement was the smallest. When the
439
3179-04_Sirin.qxd 9/2/05 2:07 PM Page 440
Sirin
SES data were collected from parents, however, the results were likely to be much
higher. As Entwisle and Astone (1994) suggest, information about students’ SES
should be collected from parents, as they are the authoritative source on their own
socioeconomic status. One could, therefore, argue that students are likely to over-
estimate their family background, which would artificially limit the variability of
the SES measure by pushing it upward. It is also possible that they overestimate
their family resources, because they might be reluctant to admit having limited
resources. In a recent study, Ensminger et al. (2000) examined the extent to which
adolescents accurately report their family’s SES. Both mothers and adolescents
completed questionnaires that included measures of SES. Although the results
show relatively high agreement on SES measures between the two sources of infor-
mants, the agreement level varied by age, family structure, and school perfor-
mance. Older students, students from two-parent households, and higher-achieving
students were more likely to report accurately than were younger students, students
from single-parent households, and lower-achieving students.
Achievement Measures
Studies reviewed in this analysis assessed students’ academic achievement
using different types of academic achievement measures. Single subject achieve-
ment measures, such as verbal achievement, math achievement, and science
achievement, yielded significantly larger correlations than general achievement
measures (e.g., GPA or a composite achievement test). It is possible that global
achievement measures conceal differences between subject areas (math and ver-
bal achievement, for example) and therefore obscure meaningful differences
between subject domains. For example, when the studies assessed academic
achievement at the subject level, the correlations were strongest with math achieve-
ment as compared with verbal and science achievement.
Student’s Grade Level
Unlike the results presented by White (1982) and Coleman et al. (1966), the cur-
rent review suggests that the relationship between SES and academic achievement
increases across various levels of schooling, with the exception of the high school
samples. The overall trend was that the magnitude of the SES–academic achieve-
ment relationship increased significantly by each school level, starting from
primary school and continuing to middle school. For the high school samples,
however, the average ES was similar to that of elementary school samples. In
general, this finding is in agreement with the findings from longitudinal studies,
which show that the gap between low- and high-SES students is most likely to
remain the same, if not to widen. In addition, because academic achievement
typically is a cumulative process, in which early school achievement provides a
basis for subsequent educational achievement in later years of schooling, it is pos-
sible that those students who are doing poorly in elementary school because of their
family SES are more likely to drop out of school in later years and therefore are
not included in research samples in the later years of schooling.
This finding should be interpreted with caution because only longitudinal
studies can provide accurate estimates of true intra-individual change over time.
Although the present review included some longitudinal studies (Carlson et al.,
1999; Chen, Lee, & Stevenson, 1996; Gonzales, Cauce, Friedman, & Mason, 1996;
440
3179-04_Sirin.qxd 9/2/05 2:07 PM Page 441

Walker et al., 1994; and data from nationwide longitudinal studies such as NELS
and NLSY), the majority of the studies included were one-time-only assessments
of SES and academic achievement. Thus, because this review examined grade lev-
els cross-sectionally, not longitudinally, the findings reported here do not reflect
the influence of SES over time on an individual basis.
The Role of Minority Status
Socioeconomic status was a stronger predictor of academic achievement for
White students than for minority students. Additional evidence that minority sta-
tus acts as a moderating factor came from the significant association between the
percentage of minority students in a sample and the magnitude of the correlation
between SES and academic achievement. Stated differently, the more minority stu-
dents in a sample, the weaker the association between SES and achievement.
The finding that family background variables such as parental education, income,
and occupation are less predictive for minorities should be of concern not only for
reasons of future research methodology, but also for its for social policy implica-
tions. Although few in number, some studies suggest that neighborhood and school
SES, not family SES, may exert a more powerful effect on academic achievement
in minority communities, particularly in African American communities. For exam-
ple, Gonzales et al. (1996) examined the combined effect of family and neighbor-
hood influences on the school performance of African American high school
students. They found that family SES variables were not as predictive of academic
achievement as were neighborhood SES factors. Neighborhood factors were related
to lower grades and also moderated several other factors such as parenting support
and control. In a large-scale examination of the same issue, Dornbusch, Ritter, and
Steinberg (1991) compared the differential effects of parental SES and neighbor-
hood SES in relation to academic achievement. They reported that the ability of
family SES to influence academic achievement is minimized when students, regard-
less of their ethnic background, live in a census tract with a substantial proportion
of minority residents. In other words, the weaker SES–achievement correlation
among minorities in general and African Americans in particular is not solely
because of their minority status but partly because most of these families, and fewer
Whites, live in neighborhoods with higher educational risk factors.
The Role of School Location
The relationship between family SES and academic achievement was the weak-
est for urban schools as compared with non-urban schools. This result should be
interpreted in the context of economic segregation of people (Wilson, 1987, 1996),
because the location of the school largely determines the financial resources avail-
able for education. In a nationwide study of more than 17,000 school districts, Par-
rish, Matsumoto, and Fowler (1995) found that higher neighborhood SES, as
measured by the value of owner-occupied housing or by residents’ educational
attainment, is significantly related to greater school expenditures per student. In
addition to this nationwide analysis, several statewide surveys showed the same
phenomenon, in which differential resources were available for schools in differ-
ent locations. For example, Unnever et al. (2000) examined Virginia data indicat-
ing that resources are associated with a school district’s SES characteristics and
that resources were related to students’ academic achievement. Thus the role of
441
3179-04_Sirin.qxd 9/2/05 2:07 PM Page 442
Sirin
school location, combined with the finding that higher concentrations of minority
students in a sample decrease the correlation between SES and school perfor-
mance, suggest that the influence of family SES on school performance is contex-
tual. In other words, the impact of family SES varies for individuals depending on
where they live and the cohort with whom they go to school.
Comparisons With White’s (1982) Review
The final question that this review addressed was whether the relationship
between SES and school achievement is any different in the 1990s from that
reported in White’s (1982) study. To provide a comparison across the two studies,
I used the same meta-analytical procedures adopted by White. The findings for this
replication study are, therefore, slightly different from the ones presented so far
because White’s meta-analytical procedures were different from the ones adopted
by the present meta-analysis.
Overall, the magnitude of the SES–school achievement relationship is not as
strong as was reported in White’s (1982) meta-analysis. Studies published before
1980 reported a mean correlation of .343, which is higher than what was found in
this meta-analysis (.299). This is the most comparable finding because both corre-
lations were drawn from published journal articles. The decline is in line with
White’s observation that there was a slight trend toward lower correlations
between SES and school achievement for the more recent studies in his sample.
The weaker correlation between SES and school achievement in the current review
may be attributed to several factors, including changes in research on SES and
school achievement, and changes experienced in the larger social and economic
context. As outlined before, unlike the earlier research, which conceptualized SES
as a static phenomenon, recent research emphasizes a contextual developmental
approach to both SES and school performance. As a result, there is an increasing
emphasis on using more precise measurements of social and economic background
(Entwisle & Astone, 1994). For example, traditional research measured the father
or father figure’s social and economic characteristics, such as education and labor
force status, as the most salient indicators of SES, whereas current research gener-
ally tries to gather information from both mothers and fathers. It is also possible that
the weaker correlation in the current review, as compared with White’s (1982)
review, may reflect social and overall policy changes over time. For example, the
increasing access to learning materials such as books, TV, and computers, as well
as the availability of compensatory education, may have helped to reduce the impact
of SES on academic achievement in recent years. More important, unlike the ear-
lier research that overlooked and understudied students from diverse ethnic back-
grounds, there seems to be more emphasis on diverse students in recent decades
(McLoyd, 1998). Likewise, the economic desegregation between urban and non-
urban schools was not as pronounced as it currently is. Hence the correlation
reported in this study is likely to be reduced partly because of the increasing num-
ber of minority and urban students in published studies, as reported correlations for
both of these groups were significantly lower than for the rest of the student body.
Limitations of This Review
The results of this review should be interpreted with caution for several reasons.
First, its focus is limited to studies published during a certain period: 1990–2000.
442
3179-04_Sirin.qxd 9/2/05 2:07 PM Page 443

This was the most recent data available on the topic when the review was con-
ducted. Although this study failed to find statistically significant evidence of dif-
ferences between data collected in the 1980s and in the 1990s, a more direct test
of this assumption was not possible because of the partial data available in this
review.
Second, although we found no statistically significant evidence for publication
bias in this review, in the absence of unpublished data the review was limited to
published journal articles. White’s (1982) meta-analysis shows that effect sizes
from published studies were 17% larger than effect sizes from unpublished stud-
ies. This overestimate is much smaller in comparison with other meta-analyses
where effect sizes from published studies were 36% larger than effect sizes from
unpublished studies (Lipsey & Wilson, 1993). Nevertheless, this study did not
empirically test the impact of publication types as a possible moderator factor;
therefore, the results are likely to be overestimated and should be interpreted with
caution.
Third, the sample for this study was limited to students in the U.S. schooling
system. Therefore, it is not possible to draw conclusions about how SES and aca-
demic achievement relate in the context of other countries. This would be an
important direction for future research because educational systems are an integral
part of every country’s unique social and economic system.
Fourth, although every effort was made to locate all relevant studies, there was
no way to be certain that the sample of this meta-analysis included all of the stud-
ies that examined the relationship between SES and achievement that were pub-
lished during the 1990s. We primarily used electronic databases (i.e., PsycINFO,
ERIC, and Sociological Abstracs) and manually searched the Social Science Cita-
tion Index for relevant studies. The only manual searches were of studies cited in
narrative reviews. Thus it is likely that some relevant studies were not found.
Finally, each meta-analysis is limited by the quality of the research on which it
relies (Glass et al., 1981). For the purposes of this study, there was no effort to
eliminate studies on the basis of quality. Although study quality was indirectly
tested through several moderator variables that were geared to assess the study
design (e.g., type of SES and academic achievement measures, unit of analysis,
and SES data source), these variables were only proxy indicators of quality. This
limitation, however, is not detrimental, as past research on the association between
the study quality and effect sizes found no significant correlation (Glass et al.,
1981; Harris & Rosenthal, 1985).
Implications for Research
Despite these limitations, the results of this meta-analysis provide some practi-
cal guidelines for education researchers. As the overall finding suggests,
researchers must continue to assess student’s SES as part of their understanding of
family effects on academic performance. The decision about how to measure SES,
however, is a complicated one. On the basis of the results from this meta-analysis,
the following points may help researchers to better capture students’ social and
economic background in education research.
First, the unit of analysis is of critical importance. The availability of school-
and/or neighborhood-level SES data, through many state and nationwide datasets,
increasingly puts researchers at risk of committing the ecological fallacy. Because
443
3179-04_Sirin.qxd 9/2/05 2:07 PM Page 444
Sirin
of the magnitude of the difference between the student level and the school/
neighborhood levels of analysis, future research should consider using aggregate
data appropriately in understanding individual-level processes. For example mul-
tilevel modeling techniques can now be used for combining individual-level data
with school- or aggregate-level data. This method can deal with the issue of the
ecological fallacy because it simultaneously estimates individual and school-level
effects (Bryk & Raudenbush, 1992).
Second, socioeconomic status is a multi-dimensional construct, and differ-
ent components yield different results. Of six major components of SES,
researchers most often choose the three traditional ones—income, education, and
occupation—as the basis for their SES conceptualization. Researchers should
make an effort to use multiple components of SES in their operationalization
because, when only a single component is chosen, the results are more likely to
overestimate the effect of SES.
Third, the use of participation in school lunch programs as a measure of SES,
though common, is conceptually problematic. The process of determining eligibil-
ity is open to mistakes; and, more important, the effect that participation in a school
lunch program itself might have on students’ school performance is difficult to dif-
ferentiate from the effect of SES. Furthermore, research shows that eligibility for
full or partial school lunch programs only weakly correlates with academic
achievement as grade level rises, possibly because adolescents are less likely than
younger children to file applications (McLoyd, 1998). Despite these limitations,
eligibility for lunch programs is still one of the most commonly used SES measure
in the current literature on academic achievement, partly because it is easier to
obtain than school records and does not require having to gather data from students
and parents. As was also pointed out by Hauser (1994), researchers should avoid
using school lunch eligibility as an SES indicator for students.
Fourth, the findings of this review suggest that only a small number of studies
considered neighborhood characteristics as part of their assessment of students’
social and economic background. Research on neighborhood SES has generally
used census tract data to assess neighborhood SES structure. This approach has its
limitations because it may refer to many communities with different features, and
it only provides a distal marker for community SES, which may not best reflect the
community SES itself. Despite these limitations, the census tract may provide
some insight into the relationship between SES and academic achievement that
may not be possible to delineate with family SES variables alone. In addition to
neighborhood census tract data, future research should find new ways to incorpo-
rate neighborhood characteristics into the operationalization of SES. There are
promising alternatives, such as various neighborhood risk measures (for examples,
see Gonzales et al., 1996; Greenberg, Lengua, Coie, Pinderhughes, 1999). These
alternatives may provide more accurate ways to capture the effects of family SES
in relation to the overall socioeconomic well-being of the neighborhood where they
live and send their children to school.
Fifth, SES seems to have different meanings for students from different ethnic
backgrounds. One of the main findings of this review was that, for minorities, SES
did not seem to be as strongly related to academic achievement as it was for
Whites. For White students, SES is an essential variable that should continue to be
examined; but for minorities, it is limited in its capacity to capture students’ social
444
3179-04_Sirin.qxd 9/2/05 2:07 PM Page 445

and economic background. Although this could be partly explained by the variance
differences in two groups (that is, studies with minority samples are likely to have
less variance in SES variables than studies with White samples), when researchers
investigate SES with minority students they should consider using other indicators
of SES, such as home resources and accumulated wealth. As Krieger and Fee
(1994) have demonstrated, although the median family income of White house-
holds is 50% greater than that of Black households, the median wealth, meaning
the distribution of capital assets such as home and estate ownership, is about ten
times greater. Researchers should use home resources and/or accumulated wealth
as part of their operationalization of SES or present their findings separately for
different ethnic groups.
Sixth, it is important to decide from whom the SES data should be collected. As
the results show, studies that collect SES data from students yield much smaller
correlations with achievement than do studies that collect data from parents. If we
assume that parents are the ultimate authority on their SES (Entwisle & Astone,
1994), then it should be of concern to researchers that students’ reports of SES may
not be accurate. Future research should make an attempt to gather SES data from
parents rather than students.
Finally, the location of schools should be an integral part of research on stu-
dents’ SES and academic achievement. The results of this review suggest that for
urban students SES–achievement relationships were not as strong as they were for
suburban students. Thus, without consideration of the geographical location of the
school, the observed correlations between SES and academic achievement are
likely to confound the differences between rural, urban, and suburban schools.
Implications for Educational and Social Policy
The results of this review have important policy implications. Both White’s
(1982) review and this review strongly suggest that the impact of SES on school
achievement was much higher when the focus was on schools, not individual stu-
dents. These results should not be surprising for those who are familiar with the
school funding system. In the United States, family SES is the most important
determinant of school financing, as nearly half of all public school funding is based
on property taxes within a school district (National Research Council, 1999).
Although districts with limited local funds are compensated within a state, in most
cases this outside financial support fails to create financial equity between school
districts (Parrish et al., 1995). For example, in Illinois financial disparity in per-
pupil expenditures ranged from $3,000 to $15,000 in 1995–1996 (National Con-
ference of State Legislatures, 1998). Thus current school financing policies create
a situation where students who come from lower-family-SES backgrounds are
likely to be in school districts that are at best financially inferior to schools in more
wealthy districts, and at worst, in financial crisis.
As the main finding of this review shows, school success is greatly influenced by
students’ family SES. This finding indicates that our society may be failing in one of
the greatest commitments of every modern society, that is, the responsibility to pro-
vide educational opportunities for each student regardless of social and economic
background. Unfortunately, many poor students come to schools without the social
and economic benefits held by many middle- to high-SES students. At present, one
in five children in the United States lives in poverty, which puts many of these stu-
445
3179-04_Sirin.qxd 9/2/05 2:07 PM Page 446
Sirin
dents at risk for poor school performance or failure (Dalaker & Proctor, 2000). Thus,
to significantly reduce the gap in achievement between low-and high-SES students,
policy decisions at the local, state, and federal levels must aim at leveling the play-
ing field for students deemed to be at risk academically as a result of their family SES.
Furthermore, poverty in the 1990s has become more concentrated in inner-city
neighborhoods and among minorities (Wilson, 1996), two groups for whom, as the
present review indicates, the influence of family SES on academic achievement is
significantly lower than it is for other student groups. Thus, even when the current
school financing system achieves its goal of financial equity between poor and
wealthy school districts, it does not necessarily achieve a comparable “ecological
equity”—because students in poor and wealthy school districts do not enjoy compa-
rable living circumstances outside school (Clune, 1994). In addition to differences
at the family-SES level, children who live in poor school districts, as compared with
children who live in wealthy school districts, also have to deal with limited social
services, more violence, homelessness, and illegal drug trafficking (Wilson, 1987,
1996). Likewise, many poor urban and rural schools need more financial incentives
to attract and keep qualified teaching staff and thus need more funding than their
counterparts in suburban areas (Wenglinsky, 1998). To address these social and
educational inequalities, policymakers should focus on adequacy—that is, sufficient
resources for optimal academic achievement—rather than equity as a primary edu-
cation policy goal (Clune, 1994). Poor school districts have more than their equal
share of challenges to deal with, and consequently they need adequate financial
resources that may be more than equal to those needed by wealthier schools.
As a result of current educational and social policies, students who are at risk
because of family SES are more likely to end up in schools with limited financial
resources. Despite these limitations, there have been many interventions that have
successfully improved the educational achievement of those who might otherwise
fail in school because of their family background. For example, small school and
class size (Glass & Smith, 1989), early childhood education, federal programs such
as Title 1 and Head Start, after-school programs and summer school sessions
(Entwisle & Alexander, 1994), and financially qualified school personnel (Wang et
al., 1993), all have been found to be important factors in reducing the achievement
gap between children of the “haves” and the “have-nots.” Future educational and
social programs should provide more support for these and other innovative pro-
grams that can lift the educational achievement of those who are at risk for school
failure because of family SES. Without such support, the current system is likely to
produce an intergenerational cycle of school failure because of family SES.
Conclusions
This meta-analysis is the second review of literature relating to SES and school
achievement, the last having been conducted 20 years ago (White, 1982). Since
White’s review, there have been several changes both in the literature on the
SES–achievement relationship and in meta-analytical procedures. The current
review uses these advances in research methodology, provides an empirically valid
and conceptually rich statistical summary of the literature, and offers a critical
examination of how several moderating factors influence the relationship between
SES and academic achievement. The findings of this review will serve a practical
use for education researchers and policymakers in their efforts to better assess the
446
3179-04_Sirin.qxd 9/2/05 2:07 PM Page 447

implications of family SES on educational processes and to provide equal educa-
tional opportunities for all.
This meta-analysis also provides several areas where future research should fur-
ther test the complex nature of SES–achievement relationships. As the overall find-
ings suggest, researchers must assess student’s family background regardless of
their main research focus. Although the ongoing trend in the study of school per-
formance suggests that the social and economic context is key in understanding
school success, it is still a common practice to mention SES in the introduction and
discussion sections of journal articles without actually incorporating it in the mea-
surement model. Researchers should no longer limit themselves by discussing only
the context but rather should actually measure and evaluate the social and eco-
nomic context in relation to their special area of interest.
In addition to these general points, this meta-analysis also provides several
methodological challenges for future research in education. For example, given the
finding that there is only a weak relationship between SES and academic achieve-
ment among minority students, should we continue to use SES as an important con-
textual indicator of school success for minority students? Or, given the finding that
there is a discrepancy between the data collected from parents and others, should
we continue to ask students or school administrators to provide SES data? The
results of this study can act as a springboard for research that addresses these
important questions.
Note
I would like to thank Lauren Rogers-Sirin, Jacqueline Lerner, Penny Hauser-Cram,
David Blustein, Albert E. Beaten, and four anonymous reviewers for their feedback on
earlier versions of this article.
References
Begg, C. B. (1994). Publication bias. In H. Cooper & L. V. Hedges (Eds.), Handbook
of research synthesis (pp. 399–409). New York: Russell Sage Foundation.
Bollen, K., Glanville, J. A., & Stecklov, G. (2001). Socioeconomic status and class in
studies of fertility and health in developing countries. Annual Review of Sociology,
27, 153–185.
Bornstein, M. C., & Bradley, R. H. (Eds.). (2003). Socioeconmic status, parenting, and
child development. Mahwah, NJ: Lawrence Erlbaum.
Bronfenbrenner, U., & Morris, P. A. (1998). The ecology of developmental processes.
In W. Damon (Series Ed.) & R. M. Lerner (Vol. Ed.), Handbook of child psychol-
ogy: Vol. 1. Theory (5th ed.). New York: Wiley.
Brooks-Gunn, J., Denner, J., & Klebanov, P. K. (1995). Families and neighborhoods
as contexts for education. In E. Flaxman & A. H. Passow (Eds.), Changing popula-
tions changing schools: Ninety-fourth yearbook of the National Society for the Study
of Education, Part II (pp. 233–252). Chicago: National Society for the Study of
Education.
Brooks-Gunn, J., & Duncan, G. J. (1997). The effects of poverty on children. The future
of children, 7(2), 55–71.
Brooks-Gunn, J., Duncan, G. J., & Aber, J. L. (Eds.). (1997). Neighborhood poverty:
Context and consequences for children: Vol. 1. New York: Russell Sage Foundation
Press.
Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical linear models. Newbury Park,
CA: Sage.
447
3179-04_Sirin.qxd 9/2/05 2:07 PM Page 448
Sirin
Caldas, S. J., & Bankston, C. (1997). Effect of school population socioeconomic sta-
tus on individual academic achievement. Journal of Educational Research, 90(5),
269–277.
Clune, W. H. (1994). The shift from equity to adequacy in school finance. Educational
Policy, 8(4), 376–394.
Cohen, J. (1977). Statistical power analysis for the behavioral sciences (rev. ed.). New
York: Academic Press.
Coleman, J. S. (1988). Social capital in the creation of human capital. American Jour-
nal of Sociology, 94, S95–S120.
Coleman, J. S., Campbell, E. Q., Hobson, C. J., McPartland, J., Mood, A. M., Wein-
feld, F. D., & York, R. L. (1966). Equality of educational opportunity. Washington,
DC: Government Printing Office.
Cooper, H. M. (1998). Synthesizing research: A guide for literature reviews (3rd ed.).
Newbury Park, CA: Sage.
Cooper, H., M., & Hedges, L. V. (Eds.). (1994). Handbook of research synthesis. New
York: Russell Sage Foundation.
Dalaker, J., & Proctor, B. D. (2000). U.S. Census Bureau, current population reports,
Series P60-210: Poverty in the United States, 1999. Washington, DC: Government
Printing Office.
Dika, S., & Singh, K. (2002). Applications of social capital in educational literature: A
critical synthesis. Review of Educational Research, 72(1) 31–60.
Duncan, G. J., & Brooks–Gunn, J. (1997). Income effects across the life span: Integra-
tion and interpretation. In G. J. Duncan & J. Brooks–Gunn (Eds.) Consequences of
growing up poor (pp. 596–610). NY: Russell Sage Foundation Press.
Duncan, G., J. Brooks-Gunn, & P. Klebanov. (1994). Economic deprivation and early
childhood development. Child Development 65(2), 296–318.
Duncan, O. D. (1961). A Socioeconomic index for all occupations. In A. J. Reiss, Jr.
(Ed.), Occupations and Social Status (pp. 139–161). NY: Free Press.
Duncan, O. D., Featherman, D. L., & Duncan, B. (1972). Socio-economic background
and achievement. NY: Seminar Press.
Eccles, J. S., Lord, S., & Midgley, C. (1991). What are we doing to early adolescents?
The impact of educational context on early adolescents. American Journal of Edu-
cation, 99, 521–542.
Ensminger, M., Forrest, C. B., Riley, A.W., Kang, M., Green, B. F., Starfield, B., Ryan,
S. (2000). The validity of measures of socioeconomic status of adolescents. Journal
of Adolescent Research,15(3). 392–419.
Ensminger, M. E. & Fothergill, K. E. (2003). A Decade of measuring SES: What it tells
us and where to go from here. In Bornstein & Bradley (Eds.). Socioeconomic status,
parenting, and child development (pp. 13–27), Mahwah, NJ: Lawrence Erlbaum
Associates.
Entwisle, D. R. & Astone, N. M. (1994). Some practical guidelines for measuring
youth’s race/ethnicity and socioeconomic status. Child Development, 65(6),
1521–1540.
Glass, G. V, McGaw, B., & Smith, M. L. (1981). Meta-Analysis in Social Research.
Beverly Hills, CA: Sage Publications.
Glass, G. V. & Smith, M. L. (1989). Meta-analysis of the relationship between class
size and achievement. Educational Evaluation and Policy Analysis, 1, 2–16.
Gottfried, A. (1985). Measures of socioeconomic status in child development research:
Data and recommendations. Merrill-Palmer Quarterly, 31(1). 85–92.
Grissmer, D. W., Kirby, S. N. Berends, M., & Williamson, S. (1994). Student achieve-
ment and the changing American family. Santa Monica, CA: RAND Corporation.
Harris, M. J., & Rosenthal, R. (1985). Mediation of interpersonal expectancy effects:
31 meta-analyses. Psychological Bulletin, 97, 363–386.
448
3179-04_Sirin.qxd 9/2/05 2:07 PM Page 449

Hauser, R. M. (1994). Measuring socioeconomic status in studies of child development.
Child Development, 65(6), 1541–1545.
Hauser, R. M. & Huang, M. H. (1997). Verbal ability and socioeconomic success: A
Trend Analysis. Social Science Research, 26, 331–376.
Hauser, R. M. & Warren, J.R. (1997). Socioeconomic indexes for occupations: A
Review, update, and critique. In A. E. Raftery (Ed.) Sociological Methodolog,
(pp.177–298), Cambridge, MA: Basil Blackwell.
Haveman, R., & Wolfe, B. (1994). Succeeding generations: On the effects of invest-
ment in children. NY: Russell Sage.
Hedges, L. V. & Olkin, I. (1985). Statistical methods for meta-analysis. NY: Academic
Press.
Hedges, L.V. & Vevea, J.L. (1998). Fixed and random-effects models in meta-
analysis. Psychological Methods, 3, 486–504.
Hunter, J. & Schmidt, F. (1990). Methods of meta-analysis: Correcting error and bias
in research findings. Beverly Hills CA: Sage.
Krieger, N. & Fee, E. (1994) Social class: the missing link in the U.S. health data. Inter-
national Journal of Health Services, 24, 25–44.
Lerner, R. (1991). Changing organism-context relations as the basic process of devel-
opment: A developmental contextual perspective. Developmental Psychology, 27,
27–32.
Lipsey, M. W., & Wilson, D. B. (1993). The efficacy of psychological, educational,
and behavioral treatment: Confirmation from meta-analysis. American Psychologist,
48, 1181–1209.
Lipsey, M. W. & Wilson, D. B. (2001). Practical meta-analysis. Thousand Oaks, CA:
Sage Publications.
McLoyd, V. (1998). Socioeconomic disadvantage and child development. American
Psychologist, 53, 185–204.
Mueller, C.W., & Parcel, T.L. (1981). Measures of socioeconomic status: Alternatives
and recommendations. Child Development, 52, 13–30.
National Commission on Children (U. S.) (1991). Beyond rhetoric: A new American
agenda for children and families. Washington, D.C.: National Commission on Children.
National Conference of State Legislatures (1998). The Education Partners Project.
Educational adequacy: Building an adequate school finance system. Denver:
Author.
National Research Council. (1999). Equity & adequacy in education finance: Issues
and perspectives. Washington, D.C.: National Research Council Committee on Edu-
cation Finance.
Parrish, T. B., Matsumoto, C. S., & Fowler, W. J., Jr. (1995). Disparities in public
school district spending 1989–90: A multi-variate, student-weighted analysis,
adjusted for differences in geographic cost of living and student need. Washington,
DC: National Center for Education Statistics, U.S. Department of Education.
Rosenthal, R. (1979). The “file drawer problem” and tolerance for null results. Psycho-
logical Bulletin, 86, 638–641.
Rosenthal, R. (1991). Meta-analytic procedures for social research (Rev. ed). New-
bury Park, CA: Sage.
Stock, W. A, Okun, M., Haring M., Miller W., Kenney, C., & Ceurvorst, R. (1982).
Rigor in data synthesis: A case study of reliability in meta-analysis. Journal of Edu-
cational Research, 11(6), 10–14.
U.S. Department of Education, National Center for Education Statistics. (1996). Loca-
tion, Poverty and Schools. Washington, D.C.: Government Printing Office.
U.S. Department of Education, National Center for Education Statistics. (2000). The
Condition of Education 2000, NCES 2000-602. Washington, DC: Government Print-
ing Office.
449
3179-04_Sirin.qxd 9/2/05 2:07 PM Page 450
Sirin
Wenglinsky, H. (1998). Finance equalization and within-school equity: The relation-
ship between education spending and the social distribution of achievement. Educa-
tional Evaluation and Policy Analysis, 20(4), 269–283.
White, K. (1982). The relation between socioeconomic status and academic achieve-
ment. Psychological Bulletin, 91, 461–481.
Wilson, W. J. (1987). The hidden agenda. In W. J. Wilson (Ed.), The truly disadvan-
taged: The inner city, the underclass and public policy (pp. 140–164). Chicago: Uni-
versity of Chicago Press.
Wilson, W. J. (1996). When work disappears: The world of the new urban poor. New
York: Alfred A. Knopf.
Wolf, F. M. (1986). Meta-analysis: Quantitative methods for research synthesis. Bev-
erly Hills, CA: Sage.
The following sources were included in the meta-analysis:

Alexander, K. L., Entwisle, D. R., & Bedinger, S. D. (1994). When expectations work:
Race and socioeconomic differences in school performance. Social Psychology
Quarterly, 57(4), 283–299.
Alspaugh, J. (1991). Out-of-school environmental factors and elementary-school
achievement in mathematics and reading. Journal of Research and Development in
Education, 24(3), 53–55.
Alspaugh, J. W. (1992). Socioeconomic measures and achievement: Urban vs. rural.
Rural Educator, 13(3), 2–7.
Balli, S. J., Demo, D. H., & Wedman, J. F. (1998). Family involvement with children’s
homework: An intervention in the middle grades. Family Relations: Interdiscipli-
nary Journal of Applied Family Studies, 47(2), 149–157.
Bankston, C. L., III, & Caldas, S. J. (1998). Family structure, schoolmates, and racial
inequalities in school achievement. Journal of Marriage & the Family, 60(3),
715–723.
Brown, R. T., Buchanan, I., Doepke, K., Eckman, J. R., & et al. (1993). Cognitive and
academic functioning in children with sickle-cell disease. Journal of Clinical Child
Psychology. 22(2), 207–218.
Caldas, S. J. (1993). Reexamination of input and process factor effects on public school
achievement. Journal of Educational Research, 86(4), 206–214.
Caldas, S. J., & Bankston, C. (1998). The inequality of separation: Racial composi-
tion of schools and academic achievement. Educational Administration Quarterly,
34(4), 533–557.
Caldas, S. J., & Bankston, C. L., III. (1999). Multilevel examination of student, school,
and district-level effects on academic achievement. Journal of Educational
Research, 93(2), 91–100.
Carlson, E. A., Sroufe, L. A., Collins, W. A., Jimerson, S., Weinfield, N., Hen-
nighausen, K., et al. (1999). Early environmental support and elementary school
adjustment as predictors of school adjustment in middle adolescence. Journal of
Adolescent Research, 14(1), 72–94.
Chan, D., Ramey, S., Ramey, C., & Schmitt, N. (2000). Modeling intraindividual
changes in children’s social skills at home and at school: A multivariate latent
growth approach to understanding between-settings differences in children’s social
skill development. Multivariate Behavioral Research, 35(3), 365–396.
Chen, C., Lee, S. Y., & Stevenson, H. W. (1996). Long-term prediction of academic
achievement of American, Chinese, and Japanese adolescents. Journal of Educa-
tional Psychology, 88(4), 750–759.
Christian, K., Morrison, F. J., & Bryant, F. B. (1998). Predicting kindergarten academic
skills: Interactions among child care, maternal education, and family literacy envi-
ronments. Early Childhood Research Quarterly, 13(3), 501–521.
450
3179-04_Sirin.qxd 9/2/05 2:07 PM Page 451

Dixon-Floyd, I., & Johnson, S. W. (1997). Variables associated with assigning students
to behavioral classrooms. Journal of Educational Research, 91(2), 123–126.
Dornbusch, S. M., Ritter, P. L., & Steinberg, L. (1991). Community influences on the
relation of family statuses to adolescent school performance: Differences between
African Americans and non-Hispanic whites. American Journal of Education [Spe-
cial issue: Development and education across adolescence], 99(4), 543–567.
Dubow, E. F., & Ippolito, M. F. (1994). Effects of poverty and quality of the home-
environment on changes in the academic and behavioral adjustment of elementary
school-age-children. Journal of Clinical Child Psychology, 23(4), 401–412.
Entwisle, D. R., Alexander, K. L., & Olson, L. S. (1994). The gender gap in math: Its pos-
sible origins in neighborhood effects. American Sociological Review, 59, 822–838.
Felner, R. D., Brand, S., DuBois, D. L., Adan, A. M., Mulhall, P. F., & Evans, E. G.
(1995). Socioeconomic disadvantage, proximal environmental experiences, and
socioemotional and academic adjustment in early adolescence: Investigation of a
mediated effects model. Child Development, 66, 774–792.
Gallagher, S. A. (1994). Middle school predictors of science achievement. Journal for
Research in Science Teaching, 31(7), 721–734.
Gonzales, N. A., Cauce, A. M., Friedman, R. J., & Mason, C. A. (1996). Family, peer,
and neighborhood influences on academic achievement among African-American
adolescents: One-year prospective effects. American Journal of Community Psy-
chology, 24(3), 365–387.
Greenberg, M. T., Lengua, L. J., Coie, J. D., & Pinderhughes, E. E. (1999). Predicting
developmental outcomes at school re-entry using a multiple-risk model: Four Amer-
ican communities. Developmental Psychology, 35, 403–417.
Griffith, J. (1997). Linkages of school structural and socioenvironmental characteris-
tics to parental satisfaction with public education and student academic achievement.
Journal of Applied Social Psychology, 27(2), 156–186.
Grolnick, W. S., & Slowiaczek, M. L. (1994). Parents’ involvement in children’s
schooling: A multidimensional conceptualization and motivational model. Child
Development, 65(1), 237–252.
Gullo, D. F., & Burton, C. B. (1993). The effects of social class, class size and
prekindergarten experience on early school adjustment. Early Child Development &
Care, 88, 43–51.
Jimerson, S. R., Egeland, B., Sroufe, L. A., & Carlson, B. (2000). A prospective lon-
gitudinal study of high school dropouts: Examining multiple predictors across devel-
opment. Journal of School Psychology, 38(6), 525–549.
Johnson, R. A., & Lindblad, A. H. (1991). Effect of mobility on academic performance
of sixth grade students. Perceptual & Motor Skills, 72(2), 547–552.
Kennedy, E. (1992). A multilevel study of elementary male black students and white
students. Journal of Educational Research, 86(2), 105–110.
Kennedy, E. (1995). Correlates of perceived popularity among peers: A study of race
and gender differences among middle school students. Journal of Negro Education,
64(2), 186–195.
Klingele, W. E., & Warrick, B. K. (1990). Influence of cost and demographic factors
on reading achievement. Journal of Educational Research, 83(5), 279–282.
Lamdin, D. J. (1996). Evidence of student attendance as an independent variable in
education production functions. Journal of Educational Research, 89(3), 155–162.
Levine, D. I., & Painter, G. (1999). The NELS curve: Replicating the bell curve analy-
ses with the National Education Longitudinal Study. Industrial Relations, 38(3),
364–401.
McDermott, P. A. (1995). Sex, race, class, and other demographics as explanations for
children’s ability and adjustment: A national appraisal. Journal of School Psychol-
ogy, 33(1), 75–91.
451
3179-04_Sirin.qxd 9/2/05 2:07 PM Page 452
Sirin
Miyamoto, R. H., Hishinuma, E. S., Nishimura, S. T., Nahulu, L. B., Andrade, N. N.,
& Goebert, D. A. (2000). Variation in self-esteem among adolescents in an
Asian/Pacific-Islander sample. Personality & Individual Differences, 29(1), 13–25.
O’Brien, V., Martinez-Pons, M., & Kopala, M. (1999). Mathematics self-efficacy, eth-
nic identity, gender, and career interests related to mathematics and science. Jour-
nal of Educational Research, 92(4), 231–235.
Otto, L. B., & Atkinson, M. P. (1997). Parental involvement and adolescent develop-
ment. Journal of Adolescent Research, 12(1), 68–89.
Overstreet, S., Holmes, C. S., Dunlap, W. P., & Frentz, J. (1997). Sociodemographic
risk factors to intellectual and academic functioning in children with diabetes. Intel-
ligence, 24(3), 367–380.
Patterson, C. J., Kupersmidt, J. B., & Vaden, N. A. (1990). Income level, gender eth-
nicity, and household composition as predictors of children’s school based compe-
tence. Child Development, 61, 485–494.
Pungello, E. P., Kupersmidt, J. B., Burchinal, M. R., & Patterson, C. J. (1996). Envi-
ronmental risk factors and children’s achievement from middle childhood to early
adolescence. Developmental Psychology, 32(2), 755–767.
Rech, J. F., & Stevens, D. J. (1996). Variables related to mathematics achievement
among black students. Journal of Educational Research, 89(6), 346–350.
Reynolds, A. J., & Walberg, H. J. (1992a). A process model of mathematics achieve-
ment and attitude. Journal for Research in Mathematics Education, 23(4), 306–328.
Reynolds, A. H. & Walberg, H. J. (1992b). A structural model of science achievement.
Journal of Educational Psychology, 83(1), 97–107.
Ricciuti, H. N. (1999). Single parenthood and school readiness in white, black, and His-
panic 6- and 7-year-olds. Journal of Family Psychology, 13(3), 450–465.
Ripple, C. H., & Luthar, S. S. (2000). Academic risk among inner-city adolescents: The
role of personal attributes. Journal of School Psychology, 38(3), 277–298.
Rojewski, J. W., & Yang, B. (1997). Longitudinal analysis of select influences on ado-
lescents’ occupational aspirations. Journal of Vocational Behavior, 51(3), 375–410.
Schultz, G. F. (1993). Socioeconomic advantage and achievement motivation: Impor-
tant mediators of academic performance in minority children in urban schools.
Urban Review, 25(3), 221–232.
Seyfried, S. F. (1998). Academic achievement of African American preadolescents:
The influence of teacher perceptions. American Journal of Community Psychology,
26(3), 381–402.
Shaver, A. V., & Walls, R. T. (1998). Effect of parent involvement on student reading
and mathematics achievement. Journal of Research & Development in Education,
31(2), 90–97.
Singh, K., & Ozturk, M. (2000). Effect of part-time work on high school mathematics
and science course taking. Journal of Educational Research, 91(2), 67–74.
Strassburger, L. A., Rosen, L. A., Miller, C. D., & Chavez, E. L. (1990). Hispanic-
Anglo differences in academic achievement: The relationship of self-esteem, locus
of control and socioeconomic level with grade-point average in the USA. School
Psychology International, 11(2), 119–124.
Sutton, A., & Soderstrom, I. (1999). Predicting elementary and secondary school
achievement with school-related and demographic factors. Journal of Educational
Research, 92(6), 330–338.
Thompson, R. J., Gustafson, K. E., Meghdadpour, S., Harrell, E. S., Johndrow, D. A.,
& Spock, A. (1992). The role of biomedical and psychosocial processes in the intel-
lectual and academic functioning of children and adolescents with cystic fibrosis.
Journal of Clinical Psychology, 48(1), 3–10.
Trusty, J., Peck, H. I., & Mathews, J. (1994). Achievement, socioeconomic status and
self-concepts of fourth-grade students. Child Study Journal, 24(4), 281–298.
452
3179-04_Sirin.qxd 9/2/05 2:07 PM Page 453

Trusty, J., Watts, R. E., & House, G. (1996). Relationship between self-concept and
achievement for African American preadolescents. Journal of Humanistic Educa-
tion & Development, 35, 29–39.
Trusty, J., Watts, R. E., & Lim, M. G. (1995). Multidimensional self-concepts and
achievement in African-American middle school students. Education, 114, 522–529.
Unnever, J. D., Kerckhoff, A. C., & Robinson, T. J. (2000). District variations in edu-
cational resources and student outcomes. Economics of Education Review, 19(3),
245–259.
Walker, D., Greenwood, C., Hart, B., & Carta, J. (1994). Prediction of school outcomes
based on early language production and socioeconomic factors. Child Development,
65(2), 606–621.
Wang, M. C., Haertel, G. D., & Walberg, H. J. (1993). Toward a knowledge base for
school learning. Review of Educational Research, 63(3), 249–294.
Watkins, T. J. (1997). Teacher communications, child achievement, and parent traits
in parent involvement models. Journal of Educational Research, 91(1), 3–14.
White, S. B., Reynolds, P. D., Thomas, M. M., & Gitzlaff, N. J. (1993). Socioeconomic
status and achievement revisited. Urban Education, 28(3), 328–343.
Author
SELCUK R. SIRIN is an Assistant Professor in the Department of Applied Psychol-
ogy, New York University, East Building, 239 Greene Street #409, New York, NY
10003; e-mail sirins@nyu.edu. His research examines the impact of developmental
challenges on the lives of minority and low-income children and ways to increase
professionals’ ability to address those challenges. His most recent research exam-
ines developmental trajectories of Muslim-American youth.
453
View publication stats

Socioeconomic Status and Academic Achievement: A Meta-Analytic Review of Research

Uploaded by

Copyright:

Available Formats

Socioeconomic Status and Academic Achievement: A Meta-Analytic Review of Research

Uploaded by

Document Information

Original Title

Copyright

Available Formats

Share this document

Share or Embed Document

Sharing Options

Did you find this document useful?

Is this content inappropriate?

Copyright:

Available Formats

Socioeconomic Status and Academic Achievement: A Meta-Analytic Review of Research

Uploaded by

Copyright:

Available Formats

See discussions, stats, and author profiles for this publication at: https://www.researchgate.

Socioeconomic Status and Academic Achievement: A Meta-Analytic Review of

Article in Review of Educational Research · September 2005

Racial and Ethical Sensitivity Test View project

The user has requested enhancement of the downloaded file.

Review of Educational Research

Socioeconomic Status and Academic Achievement:

This meta-analysis reviewed the literature on socioeconomic status (SES) and

KEYWORDS: achievement, meta-analysis, SES, social class, socioeconomic status.

Socioeconomic Status and Academic Achievement

Socioeconomic Status and Academic Achievement

Fixed and Random Effects Models

Rural school Rural =

9.5 years (1975) Achievement

& Steinberg White Neighborhood GPA B = 372 WM = .36

White-Mixed B = 446 WM = .20

Gullo & Burton Kindergarten 21 Urban FRL Metropolitan 1,573 .124

Sroufe, & Occupation

school White Occupation Test BM = 1,028 .330

Dunlap, & Frentz years (1975)

Mathews (1994) FRL Achievement

Walker, Greenwood, Primary 48 Kansas city Educationa Achievement 29 .334c

data were used to calculate ES from this longitudinal study.

correlations; Rojewski & Occupation BM = 1,467 BM = .230

follow-up samples White Educationa GPA WF = 8,166 WF = .330

Children of Kindergarten N/A Education Achievement W = 440 W = .165

Test for Moderator Effects

Socioeconomic Status and Academic Achievement

The Effect Size (r)

Socioeconomic Status and Academic Achievement

The Type of SES Component

Restriction on the SES Variable

The Source of SES Data

Type of Academic Achievement Measure Moderator Analysis

Student Characteristics Moderators

Minority Status Moderator Analyses

School Location Moderator Analyses

Socioeconomic Status and Academic Achievement

The Average Correlation

What Is the Relation Between SES and Academic Achievement?

SES and Academic Achievement: Unraveling a Complex Relationship

Unit of Analysis for SES Data

Type of SES Measure

Range of the SES Variable

Source of SES Data

Socioeconomic Status and Academic Achievement

Socioeconomic Status and Academic Achievement

Socioeconomic Status and Academic Achievement

Socioeconomic Status and Academic Achievement

Socioeconomic Status and Academic Achievement

The following sources were included in the meta-analysis:

Socioeconomic Status and Academic Achievement

Socioeconomic Status and Academic Achievement

View publication stats

You might also like