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Title: Structural equation modelling trees for invariance assessment

Authors: W. Holmes Finch

Addresses: Department of Educational Psychology, Ball State University, Muncie, IN, USA

Abstract: Large-scale assessment data have become increasingly popular in educational research. Factor model invariance testing is also a key feature of educational research, as scholars seek to identify situations where scales work comparably for different subgroups in the population. There exist a variety of methods for assessing invariance; however, standard approaches for this purpose can be cumbersome with a large number of groups, and do not typically accommodate invariance assessment across multiple variables simultaneously. The purpose of this study is to demonstrate the use of Structural Equation Modelling Trees for invariance assessment with complex large scale assessment data. The results of the study, involving Programme for International Student Assessment reading interest inventory data, found a lack of invariance based on nation of residence, language spoken in the home and family socioeconomic status. Advantages, and disadvantages, of SEMtree when compared to other methods for invariance assessment are discussed in light of these findings.

Keywords: factor analysis; invariance testing; recursive partitioning; structural equation modelling.

DOI: 10.1504/IJQRE.2017.086508

International Journal of Quantitative Research in Education, 2017 Vol.4 No.1/2, pp.72 - 93

Received: 11 Oct 2016
Accepted: 21 Apr 2017

Published online: 10 Sep 2017 *

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