09 SES 05 A, Assessing Instructional Quality in International Large-scale Assessments
Authors: Heike Wendt (presenting author), Trude Nilsen, Daniel Kasper, Jan Van Damme, Ole Kristian Bergem In order to use instructional quality as a predictor in international large scale assessments, the same construct has to be assessed in different countries. Thus, in TIMSS 2015 several countries included student’s ratings of instructional quality at Grade 4 level on four dimensions of teaching quality of mathematics instruction (cognitive activation, clarity of instruction, classroom management, and supportive climate). The focus of this paper is to investigate measurement invariance of these scales across selected countries. We use the national datasets for TIMSS 2015 from five different countries (Belgium, Germany, Norway, Croatia). To investigate the dimensionality structure of the construct within and across countries, the data sets were analyzed with hierarchical structural equation models (Goldstein & McDonald, 1988; Muthén, 1989; Rabe-Hesketh, Skrondal & Pickles, 2004; Mehta & Neale, 2005). Building on previous research (e.g. Fauth et al., 2014; Prenzel & Lankes, 2013) and analyses of the TIMSS Field Test data we expect high psychometric quality of the scales within the countries. However, due to the nature of the scales (newly developed items) we expected that at least some items show no measurement invariance across the countries (e.g. some item parameters should be varied across countries). Nevertheless, the four dimensional latent structure of instructional quality as measured by the scales should be given in every country (i.e. factorial invariance should be hold across countries). The usefulness of the developed scales to measure instructional quality in international large-scale assessment studies is reflected. In the course of this we will also provide a critical reflection of the strong connection between measurement invariance and predictive invariance, favoring a more flexible approach of measurement invariance (e.g. bootstrapping standard errors or linking items).
Fauth, B., Decristan, J., Rieser, S., Klieme, E., & Büttner, G. (2014): Students ratings of teaching quality in primary school: Dimensions and prediction of student outcomes. Learning and Instruction 29, 1-9. Goldstein, H., & McDonald, R. P. (1988): A general model for the analysis of multilevel data. Psychometrika, 53 (4), 455-467. Muthén, B. O. (1989). Latent variable modeling in heterogeneous populations. Psychometrika, 54 (4), 557-585. Rabe-Hesketh, S., Skrondal, A., & Pickles, A. (2004). Generalized multilevel structural equation modeling. Psychometrika, 69 (2), 167-190. Mehta, P. D., & Neale, M. C. (2005). People are variables too: multilevel structural equation modeling. Pschological Methods, 10 (3), 259-284. Prenzel, M.& Lankes, E.-M. (2013): Was können Schülerinnen und Schüler über ihren Unterricht sagen? Ein Blick in die Schülerfragebogen von internationalen Vergleichsstudien. In: McElvany, N./Holtappels, H. G. (Eds.): Empirische Bildungsforschung. Theorien, Methoden, Befunde und Perspektiven. Festschrift für Wilfried Bos. Münster: Waxmann, 93-107.
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