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Jak, Suzanne; Oort, Frans J.; Dolan, Conor V. – Structural Equation Modeling: A Multidisciplinary Journal, 2013
We present a test for cluster bias, which can be used to detect violations of measurement invariance across clusters in 2-level data. We show how measurement invariance assumptions across clusters imply measurement invariance across levels in a 2-level factor model. Cluster bias is investigated by testing whether the within-level factor loadings…
Descriptors: Statistical Bias, Measurement, Structural Equation Models, Hierarchical Linear Modeling
Morin, Alexandre J. S.; Marsh, Herbert W.; Nagengast, Benjamin; Scalas, L. Francesca – Journal of Experimental Education, 2014
Many classroom climate studies suffer from 2 critical problems: They (a) treat climate as a student-level (L1) variable in single-level analyses instead of a classroom-level (L2) construct in multilevel analyses; and (b) rely on manifest-variable models rather than on latent-variable models that control measurement error at L1 and L2, and sampling…
Descriptors: Classroom Environment, Hierarchical Linear Modeling, Structural Equation Models, Grade 5
Wüstenberg, Sascha; Greiff, Samuel; Vainikainen, Mari-Pauliina; Murphy, Kevin – Journal of Educational Psychology, 2016
Changes in the demands posed by increasingly complex workplaces in the 21st century have raised the importance of nonroutine skills such as complex problem solving (CPS). However, little is known about the antecedents and outcomes of CPS, especially with regard to malleable external factors such as classroom climate. To investigate the relations…
Descriptors: Individual Differences, Problem Solving, Difficulty Level, Foreign Countries

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