A Systematic Overview of Meta-Analyses on Socioeconomic Status,Cognitive Ability,and Achievement: The Need to Focus on Specific Pathways

Abstract

Meta-analyses on the relation between socioeconomic status (SES) and performance on measures of cognitive ability and achievement arrive at the same general conclusion of a small to medium association. Advancements in methods make possible for meta-analyses to examine specific pathways linking SES to cognitive ability and achievement, as well as the moderators of these pathways. In this study, we conducted a systematic overview of meta-analyses on SES to address three research questions: 1) what is the direction and overall strength of association between SES and performance on measures of cognitive ability and achievement, and how precise are the effect sizes reported? 2) to what extent have meta-analyses examined moderation by components of SES, age, sex, and race/ethnicity? and 3) to what extent have meta-analyses examined mechanisms linking SES to cognitive ability and achievement? We conducted a systematic search using online archives (i.e., PsycINFO, ERIC, PubMed, Sociological Abstracts, and Web of Science), searching issues in Psychological Bulletin and Review of Educational Research, and examining references and citations. We identified 14 meta-analyses published between 1982 and 2019. These meta-analyses consistently reported positive associations of small to medium magnitude, indicating that SES is a meaningful contributor to the development of cognitive ability and achievement. Fewer meta-analyses reported evidence of moderation by age, sex, and race/ethnicity. None of the meta-analyses directly examined mechanisms, but provided evidence of possible mechanisms for future research. We suggest that meta-analyses can increase their contribution to future research, interventions, and policy by narrowing their focus on specific pathways.

Keywords

systematic overview socioeconomic status meta-analysis cognitive ability achievement

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References

References marked with an asterisk (*) were meta-analyses included in the overview.

Adams

Ryan

White

(2005). How accurate are Townsend Deprivation Scores as predictors of self-reported health? A comparison with individual level data. Journal of Public Health, 27(1), 101–106. https://doi.org/10.1093/pubmed/fdh193

Adler

N. E.

Boyce

Chesney

M. A.

Cohen

Folkman

Kahn

R. L.

Syme

S. L.

(1994). Socioeconomic status and health: The challenge of the gradient. American Psychologist, 49(1), 15–24. https://doi.org/10.1037/0003-066x.49.1.15

American Psychological Association, APA Task Force on Race and Ethnicity Guidelines in Psychology. (2019). Race and ethnicity guidelines in psychology: Promoting Responsiveness and equity. Retrieved from http://www.apa.org/about/policy/race-and-ethnicity-in-psychology.pdf

Assari

(2017). Unequal gain of equal resources across racial groups. International Journal of Health Policy and Management, 7(1), 1–9. https://doi.org/10.15171/ijhpm.2017.90

Assari

Boyce

Bazargan

(2020a). Subjective family socioeconomic status and adolescents’ attention: Blacks’ diminished returns. Children, 7(80), 1–14. https://doi.org/10.3390/children7080080

Assari

Boyce

Bazargan

(2020b). Subjective socioeconomic status and children’s amygdala volume: Minorities’ diminish returns. NeuroScience, 1(2), 59–74. https://doi.org/10.3390/neurosci1020006

Assari

Boyce

Bazargan

Caldwell

C. H.

(2020a). Diminished returns of parental education in terms of youth school performance: Ruling out regression toward the mean. Children, 7(74), 1–14. https://doi.org/10.3390/children7070074

Assari

Boyce

Bazargan

Caldwell

C. H.

(2020b). Mathematical performance of American youth: Diminished returns of educational attainment of Asian-American parents. Education Sciences, 10(32), 1–11. https://doi.org/10.3390/educsci10020032

10.

Assari

Caldwell

C. H.

Bazargan

(2019). Association between parental educational attainment and youth outcomes and role of race/ethnicity. JAMA Network Open, 2(11), e1916018. https://doi.org/10.1001/jamanetworkopen.2019.16018

11.

Ayoub

Gosling

S. D.

Potter

Shanahan

Roberts

B. W.

(2018). The relations between parental socioeconomic status, personality, and life outcomes. Social Psychological and Personality Science, 9(3), 338–352. https://doi.org/10.1177/1948550617707018

12.

Baker

B. D.

Farrie

Sciarra

D. G.

(2016). Mind the gap: 20 years of progress and retrenchment in school funding and achievement gaps. ETS Research Report Series, 2016(1), 1–37. https://doi.org/10.1002/ets2.12098

13.

Battle

Lewis

(2002). The increasing significance of class: The relative effects of race and socioeconomic status on academic achievement. Journal of Poverty, 6(2), 21–35. https://doi.org/10.1300/J134v06n02_02

14.

Becker

B. J.

Aloe

A. M.

Cheung

M. W.-L.

(2020). Meta-analysis of correlations, correlation matrices, and their functions. In Schimd

C. H.

Stijnen

White

(Eds.), Handbook of meta-analysis (pp. 347–370). CRC Press.

15.

Becker

S. P.

Leopold

D. R.

Burns

G. L.

Jarrett

M. A.

Langberg

J. M.

Marshall

S. A.

McBurnett

Waschbusch

D. A.

Willcutt

E. G.

(2016). The internal, external, and diagnostic validity of sluggish cognitive tempo: A meta-analysis and critical review. Journal of the American Academy of Child & Adolescent Psychiatry, 55, 163–178. https://doi.org/10.1016/j.jaac.2015.12.006

16.

Beydoun

M. A.

Beydoun

H. A.

Gamaldo

A. A.

Teel

Zonderman

A. B.

Wang

(2014). Epidemiologic studies of modifiable factors associated with cognition and dementia: Systematic review and meta-analysis. BMC Public Health, 14, 1–33. https://doi.org/10.1186/1471-2458-14-643

17.

Bradley

R. H.

(2016a). Economic status and geographic influences on development and learning. In D. Couchenour & J. K. Chrisman (Eds.), The SAGE encyclopedia of contemporary early childhood education (pp. 514–518). SAGE Publications, Inc. https://doi.org/10.4135/9781483340333

18.

Bradley

R. H.

(2016b). Socioeconomic status. In H. S. Friedman (Ed.), Encyclopedia of mental health (pp. 196–210). Elsevier. https://doi.org/10.1016/B978-0-12-397045-9.00223-8

19.

Bradley

R. H.

(2017). Social and contextual risks. In E. Votruba-Drzal & E. Dearing (Eds.), The Wiley handbook of early childhood development programs, practices, and policies (pp. 66–96). Wiley.

20.

Bradley

R. H.

(2018). Socioeconomic status. In M. H. Borenstein (Ed.), SAGE encyclopedia of lifespan human development (pp. 2108–2133). SAGE Publications.

21.

Bradley

R. H.

Corwyn

R. F.

(2002). Socioeconomic status and child development. Annual Review of Psychology, 53, 371–399. https://doi.org/10.1146/annurev.psych.53.100901.135233

22.

Bradley

R. H.

Corwyn

(2005). Caring for children around the world: A view from HOME. International Journal of Behavioral Development, 29(6), 468–478. https://doi.org/10.1080/01650250500146925

23.

Bradley

R. H.

Corwyn

R. F.

McAdoo

H. P.

García Coll

(2001). The home environments of children in the United States part I: Variations by age, ethnicity, and poverty status. Child Development, 72(6), 1844–1867. https://doi.org/10.1111/1467-8624.t01-1-00382

24.

Brooks-Gunn

Klebanov

P. K.

Liaw

(1995). The learning, physical, and emotional environment of the home in the context of poverty: The infant health and development program. Children and Youth Services Review, 17(1–2), 251–276. https://doi.org/10.1016/0190-7409(95)00011-Z

25.

Browman

A. S.

Destin

Carswell

K. L.

Svoboda

R. C.

(2017). Perceptions of socioeconomic mobility influence academic persistence among low socioeconomic status students. Journal of Experimental Social Psychology, 72, 45–52. https://doi.org/10.1016/j.jesp.2017.03.006

26.

Bullock

J. G.

Green

D. P.

S. E.

(2010). Yes, but what’s the mechanism? (Don’t expect an easy answer). Journal of Personality and Social Psychology, 98(4), 550–558. https://doi.org/10.1037/a0018933

27.

Card

N. A.

(2012). Applied meta-analysis for social science research ( Little

T. D.

, Ed.). The Guilford Press.

28.

Caro

D. H.

McDonald

J. T.

Willms

J. D.

(2009). Socio‐economic status and academic achievement trajectories from childhood to adolescence. Canadian Journal of Education, 32, 558–590.

29.

Causadias

J. M.

(2013). A roadmap for the integration of culture into developmental psychopathology. Development and Psychopathology, 25, 1375–1398. https://doi.org/10.1017/S0954579413000679

30.

Causadias

J. M.

Umaña-Taylor

A. J.

(2018). Reframing marginalization and youth development: Introduction to the special issue. American Psychologist, 73(6), 707–712. https://doi.org/10.1037/amp0000336

31.

Cheung

M. W.-L.

(2015). Meta-analysis: A structural equation modeling approach. John Wiley & Sons.

32.

Cheung

M. W.

‐ L. (2019). Some reflections on combining meta‐analysis and structural equation modeling. Research Synthesis Methods, 10, 15–22. https://doi.org/10.1002/jrsm.1321

33.

Chmielewski

A. K.

(2019). The global increase in the socioeconomic achievement gap, 1964 to 2015. American Sociological Review, 84(3), 517–544. https://doi.org/10.1177/0003122419847165

34.

Cohen

(1992). A power primer. Psychological Bulletin, 112(1), 155–159. https://doi.org/10.1037/0033-2909.112.1.155

35.

Cohn

L. D.

Becker

B. J.

(2003). How meta-analysis increases statistical power. Psychological Methods, 8(3), 243–253. https://doi.org/10.1037/1082-989X.8.3.243

36.

Cole

E. R.

(2009). Intersectionality and research in psychology. American Psychologist, 64(3), 170–180. https://doi.org/10.1037/a0014564

37.

Coleman

J. S.

Campbell

E. Q.

Hobson

C. J.

McPartland

Mood

A. M.

Weinfeld

F. D.

York

R. L.

(1966). Equality of educational opportunity (RIE OE-38001; pp. 1–575). National Center for Education Statistics. https://eric.ed.gov/?id=ED012275

38.

Conger

R. D.

Donnellan

M. B.

(2007). An interactionist perspective on the socioeconomic context of human development. Annual Review of Psychology, 58, 175–199. https://doi.org/10.1146/annurev.psych.58.110405.085551

39.

Cooper

Koenka

A. C.

(2012). The overview of reviews: Unique challenges and opportunities when research syntheses are the principal elements of new integrative scholarship. American Psychologist, 67(6), 446–462. https://doi.org/10.1037/a0027119

40.

Cowan

C. D.

Hauser

R. M.

Kominski

R. A.

Levin

H. M.

Lucas

S. R.

Morgan

S. L.

Beale Spencer

Chapman

(2012). Improving the measurement of socioeconomic status for the national assessment of educational progress: A theoretical foundation. National Center for Education Statistics. https://nces.ed.gov/nationsreportcard/pdf/researchcenter/ socioeconomic_factors.pdf

41.

Deer

L. K.

Hastings

P. D.

Hostinar

C. E.

(2020). The role of childhood executive function in explaining income disparities in long‐term academic achievement. Child Development. Advanced online publication. https://doi.org/10.1111/cdev.13383

42.

Devenish

Hooley

Mellor

(2017). The pathways between socioeconomic status and adolescent outcomes: A systematic review. American Journal of Community Psychology, 59, 219–238. https://doi.org/10.1002/ajcp.12115

43.

*Devine

R. T.

Hughes

(2018). Family correlates of false belief understanding in early childhood: A meta-analysis. Child Development, 89(3), 971–987. https://doi.org/10.1111/cdev.12682

44.

Dickersin

(2006). Publication bias: Recognizing the problem, Understanding its origins and scope, and preventing harm. In H. R. Rothstein, A. J. Sutton, & M. Borenstein (Eds.), Publication bias in meta-analysis (pp. 9–33). Wiley. https://doi.org/10.1002/0470870168.ch2

45.

Diemer

M. A.

Mistry

R. S.

Wadsworth

M. E.

López

Reimers

(2013). Best practices in conceptualizing and measuring social class in psychological research: Social class measurement. Analyses of Social Issues and Public Policy, 13, 77–113. https://doi.org/10.1111/asap.12001

46.

Diez-Roux

A. V.

(1998). Bringing context back into epidemiology: Variables and fallacies in multilevel analysis. American Journal of Public Health, 88(2), 216–222. https://doi.org/10.2105/AJPH.88.2.216

47.

Dohrenwend

B. P.

(2000). The role of adversity and stress in psychopathology: Some evidence and Its implications for theory and research. Journal of Health and Social Behavior, 41(1), 1–19. https://doi.org/10.2307/2676357

48.

Duncan

G. J.

Magnuson

Votruba-Drzal

(2017). Moving beyond correlations in assessing the consequences of poverty. Annual Review of Psychology, 68, 413–434. https://doi.org/10.1146/annurev-psych-010416-044224

49.

Duncan

G. J.

Yeung

W. J.

Brooks-Gunn

Smith

J. R.

(1998). How much does childhood poverty affect the life chances of children? American Sociological Review, 63(3), 406. https://doi.org/10.2307/2657556

50.

Ebbert

A. M.

Kumar

N. L.

Luthar

S. S.

(2019). Complexities in adjustment patterns amon gthe “best and the brightest”: Risk and resilience in the context of high achieving schools. Research in Human Development, 16, 21–34. https://doi.org/10.1080/15427609.2018.1541376

51.

Ekholm

Chow

(2018). Addressing publication bias in educational psychology. Translational Issues in Psychological Science, 4(4), 425–439. https://doi.org/10.1037/tps0000181

52.

Elgar

F. J.

McKinnon

Torsheim

Schnohr

C. W.

Mazur

Cavallo

Currie

(2016). Patterns of socioeconomic inequality in adolescent health differ according to the measure of socioeconomic position. Social Indicators Research, 127, 1169–1180. https://doi.org/10.1007/s11205-015-0994-6

53.

Evans

G. W.

Kim

(2012). Childhood poverty and young adults’ allostatic load: The mediating role of childhood cumulative risk exposure. Psychological Science, 23(9), 979–983. https://doi.org/10.1177/0956797612441218

54.

Evans

G. W.

Kim

(2013). Childhood poverty, chronic stress, self-regulation, and coping. Child Development Perspectives, 7(1), 43–48. https://doi.org/10.1111/cdep.12013

55.

Farah

M. J.

Shera

D. M.

Savage

J. H.

Betancourt

Giannetta

J. M.

Brodsky

N. L.

Malmud

E. K.

Hurt

(2006). Childhood poverty: Specific associations with neurocognitive development. Brain Research, 1110(1), 166–174. https://doi.org/10.1016/j.brainres.2006.06.072

56.

*Fisher

E. L.

(2017). A systematic review and meta-analysis of predictors of expressive-language outcomes among late talkers. Journal of Speech, Language, and Hearing Research, 60, 2935–2948. https://doi.org/10.1044/2017_JSLHR-L-16-0310

57.

*Fleming

M. L.

Malone

M. R.

(1983). The relationship of student characteristics and student performance in science as viewed by meta-analysis research. Journal of Research in Science Teaching, 20(5), 481–495. https://doi.org/10.1002/tea.3660200510

58.

Funder

D. C.

Ozer

D. J.

(2019). Evaluating effect size in psychological research: Sense and nonsense. Advances in Methods and Practices in Psychological Science, 2, 156–168. https://doi.org/10.1177/2515245919847202

59.

García Coll

Lamberty

Jenkins

McAdoo

H. P.

Crnic

Wasik

B. H.

García

H. V.

(1996). An integrative model for the study of developmental competencies in minority children. Child Development, 67(5), 1891–1914. https://doi.org/10.1111/j.1467-8624.1996.tb01834.x

60.

Geyer

Hemström

Ö.

Peter

Vågerö

(2006). Education, income, and occupational class cannot be used interchangeably in social epidemiology. Empirical evidence against a common practice. Journal of Epidemiology & Community Health, 60(9), 804–810. https://doi.org/10.1136/jech.2005.041319

61.

Glass

G. V.

(1977). Integrating findings: The meta-analysis of research. Review of Research in Education, 5, 351–379. https://doi.org/10.2307/1167179

62.

Göllner

Damian

R. I.

Nagengast

Roberts

B. W.

Trautwein

(2018). It’s not only who you are but who you are with: High school composition and individuals’ attainment over the life course. Psychological Science, 29(11), 1785–1796. https://doi.org/10.1177/0956797618794454

63.

Guo

Harris

K. M.

(2000). The mechanisms mediating the effects of poverty on children’s intellectual development. Demography, 37(4), 431–447. https://doi.org/10.1353/dem.2000.0005

64.

Gurevitch

Koricheva

Nakagawa

Stewart

(2018). Meta-analysis and the science of research synthesis. Nature, 555, 175–182. https://doi.org/10.1038/nature25753

65.

Hackman

D. A.

Farah

M. J.

(2009). Socioeconomic status and the developing brain. Trends in Cognitive Sciences, 13(2), 65–73. https://doi.org/10.1016/j.tics.2008.11.003

66.

Hackman

D. A.

Farah

M. J.

Meaney

M. J.

(2010). Socioeconomic status and the brain: Mechanistic insights from human and animal research. Nature Reviews Neuroscience, 11(9), 651–659. https://doi.org/10.1038/nrn2897

67.

Hackman

D. A.

Gallop

Evans

G. W.

Farah

M. J.

(2015). Socioeconomic status and executive function: Developmental trajectories and mediation. Developmental Science, 18(5), 686–702. https://doi.org/10.1111/desc.12246

68.

*Hall

J. A.

Schmid Mast

Latu

I.-M.

(2015). The vertical dimension of social relations and accurate interpersonal perception: A meta-analysis. Journal of Nonverbal Behavior, 39, 131–163. https://doi.org/10.1007/s10919-014-0205-1

69.

Hanushek

Peterson

Talpey

Woessmann

(2020). Long-run trends in the U.S. SES-achievement gap (IZA Discussion Papers No. 12971; p. w26764). Institute of Labor Economics (IZA). https://doi.org/10.3386/w26764

70.

Harwell

(2018). Don’t expect too much: The limited usefulness of common SES measures and a prescription for change. National Education Policy Center. http://nepc.colorado.edu/publication/SES

71.

*Harwell

Maeda

Bishop

Xie

(2017). The surprisingly modest relationship between SES and educational achievement. The Journal of Experimental Education, 85(2), 197–214. https://doi.org/10.1080/00220973.2015.1123668

72.

Haveman

Wolfe

(1995). The determinants of children’s attainments: A review of methods and findings. Journal of Economic Literature, 33(4), 1829–1878.

73.

Hennessy

E. A.

Johnson

B. T.

Keenan

(2019). Best practice guidelines and essential methodological steps to conduct rigorous and systematic meta‐reviews. Applied Psychology: Health and Well-Being, 11(3), 353-381. https://doi.org/10.1111/aphw.12169

74.

Henry

D. A.

Votruba-Drzal

Miller

(2019). Child development at the intersection of race and SES: An overview. In D. A. Henry, E. Votruba-Drzal, & P. Miller (Eds.), Advances in child development and behavior (Vol. 57, pp. 1–25). Elsevier. https://doi.org/10.1016/bs.acdb.2019.05.002

75.

Ioannidis

J. P. A.

(2018). Meta-research: Why research on research matters. PLOS Biology, 16, 1–6. https://doi.org/10.1371/journal.pbio.2005468

76.

Ioannidis

J. P. A.

Munafò

M. R.

Fusar-Poli

Nosek

B. A.

David

S. P.

(2014). Publication and other reporting biases in cognitive sciences: Detection, prevalence, and prevention. Trends in Cognitive Sciences, 18(5), 235–241. https://doi.org/10.1016/j.tics.2014.02.010

77.

Jackson

Riley

White

I. R.

(2011). Multivariate meta-analysis: Potential and promise. Statistics in Medicine, 30, 2481–2498. https://doi.org/10.1002/sim.4172

78.

Jak

Cheung

M. W.-L.

(2019). Meta-analytic structural equation modeling with moderating effects on SEM parameters. Psychological Methods, 25(4), 430–455. https://doi.org/10.1037/met0000245

79.

Johnson

B. T.

Eagly

A. H.

(2014). Meta-analysis of social-personality psychological research. In H. T. Reis & C. M. Judd (Eds.), Handbook of research methods in social and personality psychology (2nd ed., pp. 675–707). Cambridge University Press.

80.

Juster

R.-P.

McEwen

B. S.

Lupien

S. J.

(2010). Allostatic load biomarkers of chronic stress and impact on health and cognition. Neuroscience & Biobehavioral Reviews, 35(1), 2–16. https://doi.org/10.1016/j.neubiorev.2009.10.002

81.

*Kim

S. W.

Cho

Song

(2018). Revisiting the explanations for Asian American scholastic success: A meta-analytic and critical review. Educational Review, 71(6), 1–21. https://doi.org/10.1080/00131911.2018.1471664

82.

Korous

K. M.

Causadias

J. M.

Bradley

R. H.

Luthar

S. S.

(2018). Unpacking the link between socioeconomic status and behavior problems: A second-order meta-analysis. Development and Psychopathology, 30(5), 1889–1906. https://doi.org/10.1017/S0954579418001141

83.

Kraus

M. W.

Park

J. W.

Tan

J. J. X.

(2017). Signs of social class: The experience of economic inequality in everyday life. Perspectives on Psychological Science, 12(3), 422–435. https://doi.org/10.31234/osf.io/qye9a

84.

Krieger

Williams

D. R.

Moss

N. E.

(1997). Measuring social class in US public health research: Concepts, methodologies, and guidelines. Annual Review of Public Health, 18, 341–378. https://doi.org/10.1146/annurev.publhealth.18.1.341

85.

Last

B. S.

Lawson

G. M.

Breiner

Steinberg

Farah

M. J.

(2018). Childhood socioeconomic status and executive function in childhood and beyond. PLOS ONE, 13(8), e0202964. https://doi.org/10.1371/journal.pone.0202964

86.

*Lawson

G. M.

Hook

C. J.

Farah

M. J.

(2018). A meta-analysis of the relationship between socioeconomic status and executive function performance among children. Developmental Science, 21, 1–22. https://doi.org/10.1111/desc.12529

87.

Lerner

R. M.

(2002). What are SES effects effects of?: A developmental systems perspective. In Borenstein

M. H.

Bradley

R. H.

(Eds.), Socioeconomic status, parenting, and child development (pp. 231–255). Lawrence Erlbaum Associates.

88.

*Letourneau

N. L.

Duffett-Leger

Levac

Watson

Young-Morris

(2011). Socioeconomic status and child development: A meta-analysis. Journal of Emotional and Behavioral Disorders, 21(3), 211–224. https://doi.org/10.1177/1063426611421007

89.

Little

T. D.

Widaman

K. F.

Levy

Rodgers

J. L.

Hancock

G. R.

(2017). Error, error in my model, who’s the fairest error of them all? Research in Human Development, 14(4), 271–286. https://doi.org/10.1080/15427609.2017.1370965

90.

Loignon

A. C.

Woehr

D. J.

(2018). Social class in the organizational sciences: A conceptual integration and meta-analytic review. Journal of Management, 44(1), 61–88. https://doi.org/10.1177/0149206317728106

91.

López-López

J. A.

Page

M. J.

Lipsey

M. W.

Higgins

J. P. T.

(2018). Dealing with effect size multiplicity in systematic reviews and meta-analyses. Research Synthesis Methods, 9(3), 336–351. https://doi.org/10.1002/jrsm.1310

92.

Lövdén

Fratiglioni

Glymour

M. M.

Lindenberger

Tucker-Drob

E. M.

(2020). Education and cognitive functioning across the life span. Psychological Science in the Public Interest, 21(1), 6–41. https://doi.org/10.1177/1529100620920576

93.

Luthar

S. S.

Ebbert

A. M.

Kumar

N. L.

(in press). Risk and resilience among Asian American youth: Ramifications of discrimination and low authenticity in self-presentations. American Psychologist.

94.

Luthar

S. S.

Kumar

N. L.

Zillmer

(2020). High-achieving schools connote risks for adolescents: Problems documented, processes implicated, and directions for interventions. American Psychologist, 75(7), 983–995. https://doi.org/10.1037/amp0000556

95.

Luthar

S. S.

Suh

B. C.

Ebbert

A. M.

Kumar

N. L.

(2020). Students in high-achieving schools: Perils of pressures to be “standouts.” Adversity and Resilience Science, 1(2), 135–147. https://doi.org/10.1007/s42844-020-00009-3

96.

McLoyd

V. C.

(1998). Socioeconomic disadvantage and child development. American Psychologist, 53(2), 185–204. https://doi.org/10.1037/0003-066x.53.2.185

97.

Merz

E. C.

Wiltshire

C. A.

Noble

K. G.

(2018). Socioeconomic inequality and the developing brain: Spotlight on language and executive function. Child Development Perspectives, 13(1), 15–20. https://doi.org/10.1111/cdep.12305

98.

Miech

R. A.

Caspi

Moffitt

T. E.

Wright

B. R. E.

Silva

P. A.

(1999). Low socioeconomic status and mental disorders: A longitudinal study of selection and causation during young adulthood. American Journal of Sociology, 104(4), 1096–1131. https://doi.org/10.1086/210137

99.

Morrison

Polisena

Husereau

Moulton

Clark

Fiander

Mierzwinski-Urban

Clifford

Hutton

Rabb

(2012). The effect of English-language restriction on systematic review-based meta-analyses: A systematic review of empirical studies. International Journal of Technology Assessment in Health Care, 28(2), 138–144. https://doi.org/10.1017/S0266462312000086

100.

Mueller

C. W.

Parcel

T. L.

(1981). Measures of socioeconomic status: Alternatives and recommendations. Child Development, 52, 13–30. https://doi.org/10.2307/1129211

101.

Murry

V. M.

Butler-Barnes

S. T.

Mayo-Gamble

T. L.

Inniss-Thompson

M. N.

(2018). Excavating new constructs for family stress theories in the context of everyday life experiences of Black American families. Journal of Family Theory & Review, 10, 384–405. https://doi.org/10.1111/jftr.12256

102.

National Academies of Sciences, Engineering, and Medicine. (2019a). A roadmap to reducing child poverty. National Academies Press. https://doi.org/10.17226/25246

103.

National Academies of Science, Engineering, and Medicine. (2019b). Vibrant and healthy kids: Aligning science, practice, and policy to advance health equity. National Academies Press. https://doi.org/10.17226/25466.

104.

Noble

K. G.

Houston

S. M.

Brito

N. H.

Bartsch

Kan

Kuperman

J. M.

Akshoomoff

Amaral

D. G.

Bloss

C. S.

Libiger

Schork

N. J.

Murray

S. S.

Casey

B. J.

Chang

Ernst

T. M.

Frazier

J. A.

Gruen

J. R.

Kennedy

D. N.

Van Zijl

, . . . Sowell

E. R.

(2015). Family income, parental education and brain structure in children and adolescents. Nature Neuroscience, 18(5), 773–778. https://doi.org/10.1038/nn.3983

105.

Oakes

J. M.

Rossi

P. H.

(2003). The measurement of SES in health research: Current practice and steps toward a new approach. Social Science & Medicine, 56, 769–784. https://doi.org/10.1016/S0277-9536(02)00073-4

106.

Ouzzani

Hammady

Fedorowicz

Elmagarmid

(2016). Rayyan—A web and mobile app for systematic reviews. Systematic Reviews, 5, 1–10. https://doi.org/10.1186/s13643-016-0384-4

107.

Oyserman

(2013). Not just any path: Implications of identity-based motivation for disparities in school outcomes. Economics of Education Review, 33, 179–190. https://doi.org/10.1016/j.econedurev.2012.09.002

108.

Peng

Kievit

R. A.

(2020). The development of academic achievement and cognitive abilities: A bidirectional perspective. Child Development Perspectives, 14(1), 15–20. https://doi.org/10.1111/cdep.12352

109.

Pickett

K. E.

Pearl

(2001). Multilevel analyses of neighbourhood socioeconomic context and health outcomes: A critical review. Journal of Epidemiology & Community Health, 55, 111–122. https://doi.org/10.1136/jech.55.2.111

110.

Piff

P. K.

Kraus

M. W.

Keltner

(2018). Unpacking the inequality paradox: The psychological roots of inequality and social class. In Advances in experimental social psychology (Vol. 57, pp. 53–124). Elsevier. https://doi.org/10.1016/bs.aesp.2017.10.002

111.

Pigott

T. D.

Polanin

J. R.

(2020). Methodological guidance papers: High-quality meta-analysis in a systematic review. Review of Educational Research, 90(1), 24–46. https://doi.org/10.3102/0034654319877153

112.

Polanin

J. R.

Hennessy

E. A.

Tanner-Smith

E. E.

(2017). A review of meta-analysis packages in R. Journal of Educational and Behavioral Statistics, 42(2), 206–242. https://doi.org/10.3102/1076998616674315

113.

Polanin

J. R.

Maynard

B. R.

Dell

N. A.

(2017). Overviews in education research: A systematic review and analysis. Review of Educational Research, 87(1), 172–203. https://doi.org/10.3102/0034654316631117

114.

Priem

R. L.

Lyon

D. W.

Dess

G. G.

(1999). Inherent limitations of demographic proxies in top management team heterogeneity research. Journal of Management, 25(6), 935–953. https://doi.org/10.1177/014920639902500607

115.

Riley

R. D.

Lambert

P. C.

Abo-Zaid

(2010). Meta-analysis of individual participant data: Rationale, conduct, and reporting. BMJ: British Medical Journal, 340, 521–525.

116.

*Robbins

S. B.

Lauver

Davis

Langley

Carlstrom

(2004). Do psychosocial and study skill factors predict college outcomes? A meta-analysis. Psychological Bulletin, 130, 261–288. https://doi.org/10.1037/0033-2909.130.2.261

117.

Rosen

M. L.

Hagen

M. P.

Lurie

L. A.

Miles

Z. E.

Sheridan

M. A.

Meltzoff

A. N.

McLaughlin

K. A.

(2020). Cognitive stimulation as a mechanism linking socioeconomic status with executive function: A longitudinal investigation. Child Development, 91(4), e762–e779. https://doi.org/10.1111/cdev.13315

118.

*Sackett

P. R.

Kuncel

N. R.

Arneson

J. J.

Cooper

S. R.

Waters

S. D.

(2009). Does socioeconomic status explain the relationship between admissions tests and post-secondary academic performance? Psychological Bulletin, 135(1), 1–22. https://doi.org/10.1037/a0013978

119.

Sastry

Pebley

A. R.

(2010). Family and neighborhood sources of socioeconomic inequality in children’s achievement. Demography, 47(3), 777–800. https://doi.org/10.1353/dem.0.0114

120.

*Scherer

Siddiq

(2019). The relation between students’ socioeconomic status and ICT literacy: Findings from a meta-analysis. Computers & Education, 138, 13–32. https://doi.org/10.1016/j.compedu.2019.04.011

121.

Schmidt

F. L.

Hunter

J. E.

(2015). Methods of meta-analysis: Correcting error and bias in research findings. Sage Publications.

122.

Shamseer

Moher

Clarke

Ghersi

Liberati

Petticrew

Shekelle

Stewart

L. A.

, & PRISMA-P Group. (2015). Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015: Elaboration and explanation. British Medical Journal, 349, 1–25. https://doi.org/10.1136/bmj.g7647

123.

Shea

B. J.

Grimshaw

J. M.

Wells

G. A.

Boers

Andersson

Hamel

Porter

A. C.

Tugwell

Moher

Bouter

L. M.

(2007). Development of AMSTAR: A measurement tool to assess the methodological quality of systematic reviews. BMC Medical Research Methodology, 7, 1–7. https://doi.org/10.1186/1471-2288-7-10

124.

Shea

B. J.

Hamel

Wells

G. A.

Bouter

L. M.

Kristjansson

Grimshaw

Henry

D. A.

Boers

(2009). AMSTAR is a reliable and valid measurement tool to assess the methodological quality of systematic reviews. Journal of Clinical Epidemiology, 62, 1013–1020. https://doi.org/10.1016/j.jclinepi.2008.10.009

125.

*Sirin

S. R.

(2005). Socioeconomic status and academic achievement: A meta-analytic review of research. Review of Educational Research, 75(3), 417–453. https://doi.org/10.3102/00346543075003417

126.

Sommer

Griebler

Mahlknecht

Thaler

Bouskill

Gartlehner

Mendis

(2015). Socioeconomic inequalities in non-communicable diseases and their risk factors: An overview of systematic reviews. BMC Public Health, 15(914), 1-12. https://doi.org/10.1186/s12889-015-2227-y

127.

Song

Eastwood

A. J.

Gilbody

Duley

Sutton

A. J.

(2000). Publication and related biases. Health Technology Assessment, 4(10), 1–111. https://doi.org/10.3310/hta4100

128.

Spears Brown

Mistry

R. S.

Yip

(2019). Moving from the margins to the mainstream: Equity and justice as key considerations for developmental science. Child Development Perspectives, 13(4), 235–240. https://doi.org/10.1111/cdep.12340

129.

Spencer

N. J.

Blackburn

C. M.

Read

J. M.

(2015). Disabling chronic conditions in childhood and socioeconomic disadvantage: A systematic review and meta-analyses of observational studies. BMJ Open, 5, e007062. https://doi.org/10.1136/bmjopen-2014-007062

130.

Stephens

N. M.

Townsend

S. S. M.

Dittmann

A. G.

(2018). Social-class disparities in higher education and professional workplaces: The role of cultural mismatch. Current Directions in Psychological Science, 28(1), 67–73. https://doi.org/10.1177/0963721418806506

131.

*Strenze

(2007). Intelligence and socioeconomic success: A meta-analytic review of longitudinal research. Intelligence, 35, 401–426. https://doi.org/10.1016/j.intell.2006.09.004

132.

Tipton

Pustejovsky

J. E.

Ahmadi

(2019). Current practices in meta-regression in psychology, education, and medicine. Research Synthesis Methods, 10, 180–194. https://doi.org/10.1002/jrsm.1339

133.

Valentine

J. C.

Pigott

T. D.

Rothstein

H. R.

(2010). How many studies do you need?: A primer on statistical power for meta-analysis. Journal of Educational and Behavioral Statistics, 35(2), 215–247. https://doi.org/10.3102/1076998609346961

134.

Van den Noortgate

López-López

J. A.

Marín-Martínez

Sánchez-Meca

(2013). Three-level meta-analysis of dependent effect sizes. Behavior Research Methods, 45(2), 576–594. https://doi.org/10.3758/s13428-012-0261-6

135.

von Stumm

(2017). Socioeconomic status amplifies the achievement gap throughout compulsory education independent of intelligence. Intelligence, 60, 57–62. https://doi.org/10.1016/j.intell.2016.11.006

136.

von Stumm

Smith‐Woolley

Ayorech

McMillan

Rimfeld

Dale

P. S.

Plomin

(2020). Predicting educational achievement from genomic measures and socioeconomic status. Developmental Science, 23(3), 1–8. https://doi.org/10.1111/desc.12925

137.

*White

K. R.

(1982). The relation between socioeconomic status and academic achievement. Psychological Bulletin, 91(3), 461–481. https://doi.org/10.1037/0033-2909.91.3.461

138.

White

R. M. B.

Pasco

M. C.

Korous

K. M.

Causadias

J. M.

(2020). A systematic review and meta-analysis of the association of neighborhood ethnic-racial concentrations and adolescent behaviour problems in the U.S. Journal of Adolescence, 78, 73–84. https://doi.org/10.1016/j.adolescence.2019.12.005