Methodological measurement fruitfulness of Exploratory Structural Equation Modeling (ESEM) : new approaches to key substantive issues in motivation and engagement

The most popular measures of multidimensional constructs typically fail to meet standards of good measurement: goodness of fit, measurement invariance, lack of differential item functioning, and well-differentiated factors that are not so highly correlated as to detract from their discriminant valid...

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Published in:Journal of psychoeducational assessment Vol. 29; no. 4; pp. 322 - 346
Main Authors: Marsh, Herbert W., Liem, Gregory Arief D., Martin, Andrew J., Morin, Alexandre J. S., Nagengast, Benjamin
Format: Journal Article
Language:English
Published: Los Angeles, CA SAGE Publications 01.08.2011
Subjects:
Australia
Correlation
Exploratory factor analysis
Factor Analysis
Factor Structure
Foreign Countries
Goodness of Fit
Measurement models
Measurement techniques
Measures (Individuals)
Psychometrics
Structural Equation Models
Student engagement
Student motivation
Student Motivation and Engagement Scale
Students
Australia
Factor analysis
measurement invariance
Motivation and Engagement
differential item functioning
ISSN:0734-2829, 1557-5144
Online Access:Get full text
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Summary:The most popular measures of multidimensional constructs typically fail to meet standards of good measurement: goodness of fit, measurement invariance, lack of differential item functioning, and well-differentiated factors that are not so highly correlated as to detract from their discriminant validity. Part of the problem, the authors argue, is undue reliance on overly restrictive independent cluster models of confirmatory factor analysis (ICM-CFA) in which each item loads on one, and only one, factor. Here the authors demonstrate exploratory structural equation modeling (ESEM), an integration of the best aspects of CFA and traditional exploratory factor analyses (EFA). On the basis of responses to the 11-factor Motivation and Engagement Scale (n = 7,420, M age = 14.22), the authors demonstrate that ESEM fits the data much better and results in substantially more differentiated (less correlated) factors than corresponding CFA models. Guided by a 13-model taxonomy of ESEM full-measurement (mean structure) invariance, the authors then demonstrate invariance of factor loadings, item intercepts, item uniquenesses, and factor variances-covariances, across gender and over time. ESEM has broad applicability to other areas of research that cannot be appropriately addressed with either traditional EFA or CFA and should become a standard tool for use in psychometric tests of psychological assessment instruments. [Author abstract]
Bibliography:Refereed article. Includes bibliographical references.
Journal of Psychoeducational Assessment; v.29 n.4 p.322-346; August 2011
ISSN:0734-2829
1557-5144
DOI:10.1177/0734282911406657