Motivation and Engagement in Science around the Globe: Testing Measurement Invariance with Multigroup Structural Equation Models across 57 Countries Using PISA 2006

Authored by: Benjamin Nagengast , Herbert W. Marsh

Handbook of International Large-Scale Assessment

Print publication date:  November  2013
Online publication date:  November  2013

Print ISBN: 9781439895122
eBook ISBN: 9781439895146
Adobe ISBN:

10.1201/b16061-18

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Abstract

International large-scale assessment studies offer unprecedented opportunities for studying achievement and achievement-related processes from a cross-cultural perspective. In this chapter, we focus on the cross-cultural study of motivational processes using large-scale assessment data (see Brunner et al. 2009;Chiu and Klassen 2010; Marsh and Hau 2004;Nagengast and Marsh 2012; Nagengast et al. 2011; Seaton et al. 2009, for recent applied examples). While the data quality of large-scale assessment studies such as the Program for International Student Assessment (PISA) or the Trends in International Maths and Science Study (TIMSS) is typically very high due to the rigorous testing protocols and quality assurance measures, we focus on the issue of comparability of scales across countries that cannot be taken as a given (van de Vijver and Leung 2000). Cross-cultural comparisons require that the meaning of constructs remains invariant over the studied countries, so that comparisons are meaningful (Marsh and Grayson 1994). Multigroup confirmatory factor analyses (MG-CFA) and structural equation modeling (MG-SEM) have emerged as standard tools for testing measurement invariance of scales in cross-cultural research (e.g., Lee et al. 2011; Marsh et al. 2006) and are increasingly used to analyze data from international large-scale surveys (e.g., Marsh et al. 2006;OECD 2010). Depending on the parameters that are invariant across countries, different cross-country comparisons are meaningful. In this chapter, we introduce a 13-model taxonomy of measurement invariance models and the possible inferences based on these models (Marsh et al. 2009;Widaman and Reise 1997). We present an exemplary analysis of motivation and engagement measures in the student background questionnaire of PISA 2006. The subject focus is on motivation and engagement in science, an area with important policy implications.

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