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.
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