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Abstract

We examined the role of six psychosocial factors (PSFs) in explaining gender gaps in English Composition (n = 8,633) and College Algebra (n = 2,261) using data of first-year female (55%) and male students from 42 colleges. Using a multilevel model and controlling for prior achievement, we found that PSFs mediated between 3% and 41% of the gender gap in English Composition, but only one PSF (Academic Discipline), helped explain the gender gap in College Algebra. Gender gaps significantly narrowed when students were more motivated to turn in homework assignments in a timely and consistent manner, meet deadlines, and obtain satisfactory academic achievement. These findings may help stakeholders prepare students for success in these foundational college courses.

Keywords

psychosocial factors mediation academic discipline commitment to college communication skills general determination goal striving study skills

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References

Achieving the Dream. (n.d.). Achieving the Dream interventions showcase. Retrieved from http://achievingthedream.org/resources/achieving-the-dream-interventions-showcase

ACT. (1997). ACT assessment technical manual. Iowa City, IA: Author.

ACT. (2012). ACT Engage® college—User’s guide. Iowa City, IA: Author. Retrieved from http://media.act.org/documents/engage_college_users_guide.pdf

ACT. (2013a). ACT technical manual. Iowa City, IA: Author. Retrieved from http://www.act.org/content/dam/act/unsecured/documents/ACT_Technical_Manual.pdf

ACT. (2013b). ACT Engage® college domains and scales overview. Iowa City, IA: Author. Retrieved from https://www.act.org/content/dam/act/unsecured/documents/EngageCollegeHandout.pdf

ACT. (2013c). Student- and teacher-reported behavioral measures: Do they agree? Iowa City, IA: Author. Retrieved from http://www.act.org/research/policymakers/pdf/BehavioralMeasures.pdf

Adams

E. J.

Esliger

D. W.

Taylor

I. M.

Sherar

L. B.

(2017). Individual, employment and psychosocial factors influencing walking to work: Implications for intervention design. PLoS ONE, 12(2), 1-14. doi:10.1371/journal.pone.0171374

Adelman

(2006). The toolbox revisited: Paths to degree completion from high school through college. Washington, DC: U.S. Department of Education.

Allen

(2013). Updating the ACT college readiness benchmarks (ACT Research Report Series, 2013-6). Iowa City, IA: ACT.

10.

Allen

Robbins

S. B.

(2008). Prediction of college major persistence based on vocational interests, academic preparation, and first-year academic performance. Research in Higher Education, 49, 162-179. doi:10.1007/s11162-007-9064-5

11.

Allen

Robbins

S. B.

Sawyer

(2010). Can measuring psychosocial factors promote college success? Applied Measurement in Education, 23, 11-22. doi:10.1080/08957340903423503

12.

Allen

J. D.

(2005). Grades as valid measures of academic achievement of classroom learning. The Clearing House: A Journal of Educational Strategies, Issues and Ideas, 78, 218-223. doi:10.3200/TCHS.78.5.218-223

13.

Bandura

(1989). Human agency in social cognitive theory. American Psychologist, 44, 1175-1184. doi:10.1037/0003-066X.44.9.1175

14.

Baron

R. M.

Kenny

D. A.

(1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51, 61173-61182. doi:10.1037/0022-3514.51.6.1173

15.

Barrow

Reilly

Woodfield

(2009). The determinants of undergraduate degree performance: How important is gender? British Educational Research Journal, 35, 575-597. doi:10.1080/01411920802642322

16.

Bembenutty

(2007). Self-regulation of learning and academic delay of gratification: Gender and ethnic differences among college students. Journal of Advanced Academics, 18, 4586-4616. doi:10.4219/jaa-2007-553

17.

Bielinski

Davison

M. L.

(2001). A sex difference by item difficulty interaction in multiple-choice mathematics items administered to national probability samples. Journal of Educational Measurement, 38, 51-77. doi:10.1111/j.1745-3984.2001.tb01116.x

18.

Bridgeman

Wendler

(1991). Gender differences in predictors of college mathematics performance and in college mathematics course grades. Journal of Educational Psychology, 83, 2275-2284. doi:10.1037/0022-0663.83.2.275

19.

Briggs

(2006). Estimation of the standard error and confidence interval of the indirect effect in multiple mediator models. Dissertation Abstracts International, 37, 475B. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1158693880

20.

Brouse

H. C.

Basch

E. C.

LeBlanc

McKnight

R. K.

Lei

(2010). College students’ academic motivation: Differences by gender, class, and source of payment. College Quarterly, 13(1). Retrieved from https://eric.ed.gov/?id=EJ912093

21.

Cheung

M. L.

(2009). Comparison of methods for constructing confidence intervals of standardized indirect effects. Behavior Research Methods, 41, 2425-2438. doi:10.3758/BRM.41.2.425

22.

Combs

J. P.

Slate

J. R.

Moore

G. W.

Bustamante

R. M.

Onwuegbuzie

A. J.

Edmonson

S. L.

(2010). Gender differences in college preparedness: A statewide study. Urban Review, 42, 5441-5457. doi:10.1007/s11256-009-0138-x

23.

Conger

Long

C. M.

(2010). Why are men falling behind? Gender gaps in college performance and persistence. The ANNALS of the American Academy of Political and Social Science, 627, 184-214. doi:10.1177/0002716209348751

24.

Crawford

MacLeod

(1990). Gender in the college classroom: An assessment of the “chilly climate” for women. Sex Roles, 23101-23122. doi:10.1007/BF00289859

25.

Crocker

Quinn

D. M.

Karpinski

Chase

S. K.

(2003). When grades determine self-worth: Consequences of contingent self-worth for male and female engineering and psychology majors. Journal of Personality and Social Psychology, 85, 3507-3516. doi:10.1037/0022-3514.85.3.507

26.

Dee

T. S.

(2006). Teachers and the gender gaps in student achievement. The Journal of Human Resources, 42, 528-554. doi:10.3386/w11660

27.

DeMars

C. E.

(2000). Test stakes and item format interactions. Applied Measurement in Education, 13, 55-77. doi:10.1207/s15324818ame1301_3

28.

Ding

C. S.

Song

Richardson

(2007). Do mathematical gender differences continue? A longitudinal study of gender difference and excellence in mathematics performance in the U.S. Educational Studies, 40, 3279-3295. doi:10.1080/00131940701301952

29.

D’Lima

G. M.

Winsler

Kitsantas

(2014). Ethnic and gender differences in first-year college students’ goal orientation, self-efficacy, and extrinsic and intrinsic motivation. Journal of Educational Research, 107, 341-356. doi:10.1080/00220671.2013.823366

30.

Dorans

J. N.

(1999). Correspondences between ACT™ and SAT® I scores (College Board Research Report No. 99-1). New York, NY: The College Board. doi:10.1002/j.2333-8504.1999.tb01800.x

31.

Duckworth

L. A.

Seligman

E. P.

(2006). Self-discipline gives girls the edge: Gender in self-discipline, grades, and achievement test scores. Journal of Educational Psychology, 98, 198-208. doi:10.1037/0022-0663.98.1.198

32.

Farley

B. L.

(1995). “A” is for average: The grading crisis in today’s colleges. Princeton, NJ: Princeton University Press. Retrieved from https://eric.ed.gov/?id=ED384384

33.

Fong

C. J.

Davis

C. W.

Kim

Y. W.

Marriott

Kim

(2017). Psychosocial factors and community college student success. Review of Educational Research, 87, 388-424. doi:10.3102/0034654316653479

34.

Fowler

P. P.

Boylan

H. R.

(2010). Increasing student success and retention: A multidimensional approach. Journal of Developmental Education, 34(2), 2-10. Retrieved from https://eric.ed.gov/?id=EJ986268

35.

Fraser

B. J.

Treagust

D. F.

Dennis

N. C.

(1986). Development of an instrument for assessing classroom psychosocial environment at universities and colleges. Studies in Higher Education, 11, 143-154. doi:10.1080/03075078612331378451

36.

Freeman

C. E.

(2004). Trends in educational equity of girls & women: 2004 (NCES 2005-016). Washington, DC: U.S. Department of Education, National Center for Education Statistics, U.S. Government Printing Office. Retrieved from https://nces.ed.gov/pubs2005/2005016.pdf

37.

Ganley

C. M.

Mingle

L. A.

Ryan

A. M.

Ryan

Vasilyeva

Perry

(2013). An examination of stereotype threat effects on girls’ mathematics performance. Developmental Psychology, 49, 1886-1897. doi:10.1037/a0031412

38.

Ganley

C. M.

Vasilyeva

(2014). The role of anxiety and working memory in gender differences in mathematics. Journal of Educational Psychology, 106, 105-120. doi:10.1037/a0034099

39.

Goetz

Bieg

Lüdtke

Pekrun

Hall

C. N.

(2013). Do girls really experience more anxiety in mathematics? Psychological Science, 24, 102079-102087. doi:10.1177/0956797613486989

40.

Goldstein

(2003). Multilevel statistical models (3rd ed.). London, England: Arnold.

41.

González-Muñiz

Klingler

Moosai

Raviv

(2012). Taking a College Algebra course: An approach that increased students’ success rate. PRIMUS: Problems, Resources, and Issues in Mathematics Undergraduate Studies, 22, 201-213. doi:10.1080/10511970.2010.507621

42.

Goonatilake

Chappa

(2010). Early intervention in college mathematics courses: A component of the STEM RRG program funded by the US Department of Education. The Mathematics Enthusiast, 7, 63-74. Retrieved from http://scholarworks.umt.edu/tme/vol7/iss1/4

43.

Hayes

A. F.

(2009). Beyond Baron and Kenny: Statistical mediation analysis in the new millennium. Communication Monographs, 76, 408-420. doi:10.1080/03637750903310360

44.

Hopkins

C. D.

Raymond

M. A.

Carlson

(2011). Educating students to give them a sustainable competitive advantage. Journal of Marketing Education, 33, 337-347. doi:10.1177/0273475311420241

45.

Hox

J. J.

(2002). Multilevel analysis: Techniques and applications (2nd ed.). Mahwah, NJ: Lawrence Erlbaum.

46.

Johnson

J. P.

Krase

E. E.

(2012). Coming to learn: From first-year composition to writing in the disciplines. Across the Disciplines, 9(2), 1. Retrieved from https://wac.colostate.edu/atd/articles/johnson_krase2012/index.cfm

47.

Judge

T. A.

Erez

Bono

J. E.

Thoresen

C. J.

(2002). Are measures of self-esteem, neuroticism, locus of control, and generalized self-efficacy indicators of a common core construct? Journal of Personality and Social Psychology, 83, 693-710. doi:10.1037//0022-3514.83.3.693

48.

Kenny

D. A.

Kashy

D. A.

Bolger

(1998). Data analysis in social psychology. In Gilbert

Fiske

Lindzey

(4th ed.). The handbook of social psychology (Vol. 1, pp. 233-265). Boston, MA: McGraw-Hill.

49.

Kinzie

Gonyea

Kuh

G. D.

Umbach

Blaich

Korkmaz

(2007, November). The relationship between gender and student engagement in college. Paper presented at the Association for the Study of Higher Education, Louisville, KY. Available at https://pdfs.semanticscholar.org/8352/7135f260de6eda03bdc30c7bb9130713e813.pdf

50.

Kling

C. K.

Noftle

E. E.

Robins

W. R.

(2012). Why do standardized tests underpredict women’s academic performance? The role of conscientiousness. Social Psychological and Personality Science, 4, 5600-5606. doi:10.1177/1948550612469038

51.

Krull

J. L.

MacKinnon

D. P.

(2001). Multilevel modeling of individual and group level mediated effects. Multivariate Behavioral Research, 36, 2249-2277. doi:10.1207/S15327906MBR3602_06

52.

Krumrei-Mancuso

E. J.

Newton

F. B.

Kim

Wilcox

(2013). Psychosocial factors predicting first-year college student success. Journal of College Student Development, 54, 247-266. doi:10.1353/csd.2013.0034

53.

Casillas

Robbins

Langley

(2005). Motivational and skills, social, and self-management predictors of college outcomes: Constructing the student readiness inventory. Educational and Psychological Measurement, 65, 482-508. doi:10.1177/0013164404272493

54.

Ligon

N. Y.

(2006). Assessing the achievement motivation across grades and gender (Dissertation Abstracts International Section A). Humanities and Social Sciences, 67(6-A), 2052.

55.

Lindberg

M. S.

Hyde

S. J.

Petersen

L. J.

Linn

C. M.

(2010). New trends in gender and mathematics performance: A meta-analysis. Psychology Bulletin, 136, 61123-61135. doi:10.1037/a0021276

56.

Liu

L. O.

Wilson

(2009). Gender differences in large-scale math assessments: PISA trend 2000 and 2003. Applied Measurement in Education, 22, 164-184. doi:10.1080/08957340902754635

57.

Locke

(1968). Toward a theory of task motivation and incentives. Organizational Behavior and Human Performance, 3, 157-189. doi:10.1016/0030-5073(68)90004-4

58.

Loeys

Moerkerke

De Smet

Buysse

Steen

Vansteellandt

(2013). Flexible mediation analysis in the presence of nonlinear relations: Beyond the mediation formula. Multivariate Behavioral Research, 48, 871-894. doi:10.1080/00273171.2013.832132

59.

Lorah

J. E.

Ndum

(2013). Trends in achievement gaps in first-year college courses for racial/ethnic, income, and gender subgroups: A 12-year study (ACT Research Report Series 2013-8). Iowa City, IA: ACT. Retrieved from https://eric.ed.gov/?id=ED546852

60.

MacKinnon

D. P.

Fritz

M. S.

Williams

Lockwood

C. M.

(2007). Distribution of the product confidence limits for the indirect effect: Program PRODCLIN. Behavior Research Methods, 39, 384-389.

61.

MacKinnon

D. P.

Lockwood

C. M.

Hoffman

J. M.

West

S. G.

Sheets

(2002). A comparison of methods to test mediated and other intervening variable effects. Psychological Methods, 7, 83-104. doi:10.1037/1082-989X.7.1.83

62.

MacKinnon

D. P.

Lockwood

C. M.

Williams

(2004). Confidence limits for the indirect effect: Distribution of the product and resampling methods. Multivariate Behavioral Research, 30, 41-62. doi:10.1207/s15327906mbr3901_4

63.

Maisel

Cokins

(2014). Why analytics will be the next competitive edge. Journal of Corporate Accounting & Finance, 25(5), 63-72. doi:10.1002/jcaf.21974

64.

Mattern

K. D.

Patterson

B. F.

(2014). Synthesis of Recent SAT Validity Findings: Trend Data over Time and Cohort. (Research Report No. 2014-1). New York: The College Board. Available at http://research.collegeboard.org/sites/default/files/info2go/2014/6/Synthesis-of-Recent-SAT-Validity-Findings.pdf

65.

Mattern

K. D.

Shaw

J. E.

Marini

(2013). Does college readiness translate to college completion? (Research Note 2013-9). College Board. Available at http://files.eric.ed.gov/fulltext/ED562613.pdf

66.

Mattern

K. D.

Sanchez

Ndum

(2016, April). Underprediction of female performance: An omitted variable problem? Paper session presented at the Annual Meeting of the American Educational Research Association, Washington, DC. doi:10.1111/emip.12138

67.

McNeish

(2014). Modeling sparsely clustered data: Design-based, model-based, and single-level methods. Psychological Methods, 19, 552-563.

68.

Meece

J. L.

Glienke

B. B.

Burg

(2006). Gender and motivation. Journal of School Psychology, 44, 351-373. doi:10.1016/j.jsp.2006.04.004

69.

Mikyake

Kost-Smith

L. E.

Finkelstein

N. D.

Pollock

S. J.

Cohen

G. L.

Ito

T. A.

(2010). Reducing the gender achievement gap in college science: A classroom study of values affirmation. Science, 330, 1234-1237. doi:10.1126/science.1195996

70.

Mori

Gobel

(2006). Motivation and gender in the Japanese EFL classroom. System. An International Journal of Educational Technology and Applied Linguistics, 34, 194-210. doi:10.1016/j.system.2005.11.002

71.

Mouw

J. T.

Khanna

R. K.

(1993). Prediction of academic success: A review of the literature and some recommendations. College Student Journal, 27, 328-336.

72.

National Center for Education Statistics. (2014). Digest of Education Statistics: 2014—Table 303.10. Retrieved from https://nces.ed.gov/programs/digest/d14/tables/dt14_303.10.asp?referrer=report

73.

Newman

L. M.

Groom

J. C.

Handelman

D. L.

Pennebaker

W. J.

(2008). Gender differences in language use: An analysis of 14,000 text samples. Discourse Processes, 45, 211-236. doi:10.1080/01638530802073712

74.

Oswald

Schmitt

Kim

Ramsay

Gillespie

(2004). Developing a biodata measure and situational judgment inventory as predictors of college student performance. Journal of Applied Psychology, 89, 187-207. doi:10.1037/0021-9010.89.2.187

75.

Patterson

H. D.

Robin

(1971). Recovery of inter-block information when block sizes are unequal. Biometrika, 58, 545-554. doi:10.2307/2334389

76.

Pintrich

P. R.

Zusho

(2007). Student motivation and self-regulated learning in the college classroom. In Perry

R. P.

Smart

J. C.

(Eds.), The scholarship of teaching and learning in higher education: An evidence-based perspective (pp. 731-810). New York, NY: Springer.

77.

Powell

J. C.

(2014). Leveraging information for a competitive advantage. Army Sustainment, 46(3), 14-18. Retrieved from https://www.army.mil/article/125007

78.

Preacher

K. J.

Hayes

A. F.

(2004). SPSS and SAS procedures for estimating indirect effects in simple mediation models. Behavior Research Methods, Instruments, & Computers, 36, 717-731. doi:10.3758/BF03206553

79.

Radunzel

Noble

(2012). Tracking 2003 ACT®-tested high school graduates: College readiness, enrollment, and long-term success (ACT Research Report Series, 2012 (2)). Iowa City, IA: ACT. Retrieved from https://eric.ed.gov/?id=ED542012

80.

Rau

Durand

(2000). The academic ethic and college grades: Does hard work help students to “make the grade”? Sociology of Education, 73, 19-38. doi:10.2307/2673197

81.

Raudenbush

S. W.

Bryk

A. S.

(2002). Hierarchical linear models: Applications and data analysis methods. Newbury Park, CA: Sage.

82.

Robbins

S. B.

Allen

Casillas

Peterson

(2006). Unraveling the differential effects of motivational and skills, social, and self-management measures from traditional predictors of college outcomes. Journal of Educational Psychology, 98, 3598-3616. doi:10.1037/0022-0663.98.3.598

83.

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. DOI: 10.1037/0033-2909.130.2.261

84.

Robbins

S. B.

Button

(2009). Intervention effects on college performance and retention as mediated by motivational, emotional, and social control factors: Integrated meta-analytic path analyses. Journal of Applied Psychology, 94, 51163-51184. doi:10.1037/a0015738

85.

Robinson

J. P.

Lubienski

S. T.

(2011). The development of gender achievement gaps in mathematics and reading during elementary and middle school: Examining direction cognitive assessments and teacher ratings. American Educational Research Journal, 48, 2268-2302. doi:10.3102/0002831210372249

86.

Rodriguez

Elo

(2003). Intra-class correlation in random-effects models for binary data. The Stata Journal, 3, 32-46. Retrieved from http://ageconsearch.tind.io/record/116030/files/sjart_st0031.pdf

87.

Rousson

(2014). Measuring an effect size from dichotomous data: Contrasted results whether using a correlation or an odds ratio. Journal of Educational and Behavioral Statistics, 39, 2144-2163. doi:10.3102/1076998614524597

88.

Rucker

D. D.

Preacher

K. J.

Tormala

Z. L.

Petty

R. E.

(2011). Mediation analysis in social psychology: Current practices and new recommendations. Social and Personality Psychology Compass, 5, 6359-6371. doi:10.1111/j.1751-9004.2011.00355.x

89.

Ryu

West

S. G.

Sousa

K. H.

(2009). Mediation and moderation: Testing relationships between symptom status, functional health, and quality of life in HIV patients. Multivariate Behavioral Research, 44, 2213-2232.

90.

Sanchez

E. I.

(2013). Differential effects of using ACT college readiness assessment scores and high school GPA to predict first-year college GPA among racial/ethnic, gender, and income groups (ACT Research Report Series 2013-4). Iowa City, IA: ACT. Retrieved from http://files.eric.ed.gov/fulltext/ED555597.pdf

91.

Sander

(2012). Colleges confront a gender gap in student engagement. Chronicle of Higher Education, 59, 10B14-10B17. Retrieved from http://www.chronicle.com/article/Men-Like-to-Chill-Women-Are/135290

92.

Sawyer

(1986). Using demographic subgroup and dummy variable equations to predict college freshman grade average. Journal of Educational Measurement, 23, 131-145. doi:10.1111/j.1745-3984.1986.tb00239.x

93.

Sax

L. J.

(1994). Mathematical self-concept: How college reinforces the gender gap. Research in Higher Education, 35, 2141-2166. doi:10.1007/BF02496699

94.

Shekhar

Devi

(2012). Achievement motivation across gender and different academic majors. Journal of Educational and Developmental Psychology, 2(2). doi:10.5539/jedp.v2n2p105

95.

Shrout

E. P.

Bolger

(2002). Mediation in experimental and non-experimental studies: New procedures and recommendations. Psychological Methods, 7, 422-444. doi:10.1037//1082-989X.7.4.422

96.

Sibulkin

A. E.

Butler

J. S.

(2008). Should College Algebra be a prerequisite for taking psychology statistics? Teaching of Psychology, 35, 214-217. doi:10.1080/00986280802286399

97.

Smithwick-Rodriguez

(2011, January 1). A study of college readiness programs among high school students in South Texas. ProQuest. Retrieved from https://search.proquest.com/docview/914225380

98.

Sobel

M. E.

(1982). Asymptotic confidence intervals for indirect effects in structural equation models. Sociological Methodology, 13, 290-312. doi:10.2307/270723

99.

Speed

T. P.

(2006). Restricted maximum likelihood (REML). Encyclopedia of Statistical Sciences, 11. doi:10.1002/0471667196.ess0337.pub2

100.

Spielhagen

F. R.

(2006). Closing the achievement gap in math: The long-term effects of eighth-grade Algebra. Journal of Advanced Academics, 18, 34-59. doi:10.4219/jaa-2006-344

101.

Stricker

Rock

Burton

(1991). Sex differences in SAT prediction of college grades (College Board Research Report No. 91-2). New York, NY: The College Board. doi:10.1002/j.2333-8504.1991.tb01405.x

102.

Tannous

Moore

(2013). Improving upper-class engagement and retention: Academic and co-curricular strategies. Educational Advisory Board: Academic Affairs Forum. Retrieved from https://pdfs.semanticscholar.org/1e99/2172f9f539a9b9a99533b357566371ca2466.pdf

103.

Theil

Peterman

Brown

(2008). Addressing the crisis in college mathematics: Designing courses for student success. Change: The Magazine of Higher Learning, 40, 44-49. doi:10.3200/CHNG.40.4.44-49

104.

U.S. Department of Education, & Office for Civil Rights. (2000). The use of tests as part of high-stakes decision-making for students: A resource guide for educators and policy-makers. Washington, DC. Retrieved from http://files.eric.ed.gov/fulltext/ED456173.pdf

105.

Volpe

D. A.

(2011, January 1). English Composition I: An effective predictor of persistence and retention at a community college. Theses and Dissertations (All), 711. Retrieved from http://knowledge.library.iup.edu/etd/711

106.

Voyer

S. D.

(2014). Gender differences in scholastic achievement: A meta-analysis. Psychological Bulletin. Advance online publication, 140(4):1174-204. Doi:10.1037/a0036620

107.

Vrugt

Oort

F. J.

Waardenburg

(2009). Motivation of men and women in mathematics and language. International Journal of Psychology, 44, 5351-5359. doi:10.1080/00207590802324831

108.

Weinberger

J. C.

(2005, September). Is the science and engineering workforce drawn from the far upper tail of the math ability distribution? Paper presented at the NBER Conference on Diversity in the Science and Engineering, University of Massachusetts, Amherst.

109.

Westrick

A. P.

Allen

(2014). Validity evidence for ACT Compass® placement tests (ACT Research Report Series, 2014-2). Iowa City, IA: ACT. Retrieved from http://files.eric.ed.gov/fulltext/ED546849.pdf

110.

Wigfield

Eccles

J. S.

(2000). Expectancy–value theory of achievement motivation. Contemporary Educational Psychology, 25, 68-81. doi:10.1006/ceps.1999.1015

111.

Williams

MacKinnon

D. P.

(2008). Resampling and distribution of the product methods for testing indirect effects in complex models. Structural Equation Modeling, 15, 23-51. doi:10.1080/10705510701758166

112.

Willis

D. E.

Fitzpatrick

K. M.

(2016). Psychosocial factors as mediators of food insecurity and weight status among middle school students. Appetite, 103, 236-243. doi:10.1016/j.appet.2016.04.022

113.

A. D.

Zumbo

B. D.

(2008). Understanding and using mediators and moderators. Social Indicators Research, 87, 3367-3392. doi: 10.1007/s11205-007-9143-1

114.

Crespi

C. M.

Wong

W. K.

(2012). Comparison of methods for estimating the intraclass correlation coefficient for binary responses in cancer prevention cluster randomized trials. Contemporary Clinical Trials, 33, 869-880. doi:10.1016/j.cct.2012.05.004

115.

Jia

(2013). A new procedure to test mediation with missing data through nonparametric bootstrapping and multiple imputation. Multivariate Behavioral Research, 48, 5663-5691. doi:10.1080/00273171.2013.816235

116.

Young

J. W.

(2001). Differential validity, differential prediction, and college admission testing: A comprehensive review and analysis (Research Report 2001-6). New York, NY: College Entrance Examination Board.

117.

Zhang

Zyphur

M. J.

Preacher

K. J.

(2009). Testing multilevel mediation using hierarchical linear models: Problems and solutions. Organizational Research Methods, 12, 695-719. doi:10.1177/1094428108327450

118.

Zhao

Lynch

J. G.

Jr. Chen

(2010). Reconsidering Baron and Kenny: Myths and truths about mediation analysis. Journal of Consumer Research, 37, 2197-2206. doi:10.1086/651257

Explaining Gender Gaps in English Composition and College Algebra in College: The Mediating Role of Psychosocial Factors

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