Configural Invariance of a Model of Student Attrition

Abstract

The model presented used available data to predict whether or not a student will drop out at some time during his or her college career. The model successfully identified students who would or would not drop out approximately 80% of the time. Logistic regression analysis was employed to predict chances of attrition for matriculating freshmen soon after freshman orientation. The dependent variable was the probability a student would, at some time in their college experience, drop out of college. The independent or predictor variables included demographics, high school experiences and factor scores based on a survey of attitudes, opinions and values. A two-stage procedure consisting of factor analysis in the first stage and logistic regression in the second stage provided model parameter estimates for each cohort of matriculating freshman in years 2000 to 2003. Empirical tests revealed the model was fully configural invariant in the first stage and partially configural invariant in the second stage.

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References

Astin

A. W.

(1975). Preventing students from dropping out. San Francisco: Jossey-Bass.

Astin

A. W.

(1987). Student involvement: A developmental theory for higher education. Journal of College Student Personnel, 25, 297–308.

Bean

J. P.

(1982). Student attrition, intentions, and confidence: Interactions effects in a path model. Research in Higher Education, 17, 291–319.

Byrne

B. M.

Shavelson

R. J.

, & Muthen

(1989). Testing for equivalence of factor covariance and mean structures: The issue of partial measurement invariance. Psychological Bulletin, 105, 456–466.

Cabrera

A. F.

Castaneda

M. B.

Nora

, & Hengstler

(1992). The convergence between two theories of college persistence. Journal of Higher Education, 63(2), 143–164.

Cabrera

A. F.

Nora

, & Castaneda

M. B.

(1993). College persistence: Structural equations modeling test of an integrated model of student retention. Journal of Higher Education, 64(2), 123–138.

Delicath

T. A.

(1999). The influence of dual credit programs on students' integration and goal attainment. Journal of College Student Retention, 1(4), 377–398.

Dey

E. L.

, & Astin

A. W.

(1993). Statistical alternatives for studying college student retention: A comparative analysis of probit, logit and linear regression. Research in Higher Education, 34(5), 569–581.

Glynn

J. G.

Sauer

P. L.

, & Miller

T. E.

(2003). Signaling student retention with rematriculation data. NASPA Journal, 41(1), 41–67.

10.

Meade

A. W.

, & Lautenschlager

G. J.

(2004). A Monte-Carlo study of confirmatory factor analytic tests of measurement equivalence/invariance. Structural Equation Modeling, 11(1), 60–72.

11.

Milem

J. F.

, & Berger

J. B.

(1997). A modified model of college student persistence: Exploring the relationship between Astin's theory of involvement and Tinto's theory of student departure. Journal of College Student Development, 38(4), 387–400.

12.

Miller

T. E.

Glynn

J. G.

, & Neuner

J. L.

(1988). Reducing attrition: A college at work in research and practice. NASPA Journal, 25(4), 236–243.

13.

Nora

(1987). Determinants of retention among Chicano college students. Research in Higher Education, 26(1), 31–59.

14.

Steenkamp

J. E. M.

, & Baumgartner

(1998). Assessing measurement invariance in cross-national consumer research. Journal of Marketing Research, 25(1), 78–90.

15.

Tinto

(1975). Dropout from higher education: A theoretical synthesis of recent research. Review of Educational Research, 45, 89–125.

16.

Tinto

(1993). Leaving college: Rethinking the causes and cures of student attrition. Chicago: The University of Chicago Press.

17.

Vandenberg

R. J.

, & Lance

C. E.

(2000). A review and synthesis of the measurement invariance literature: Suggestions, practices and recommendations for organizational research. Organizational Research Methods, 3(1), 4–69.