Evaluating measurement properties of attitudinal items related to learning science in taiwan from timss 2007

Abstract

In the recent empirical studies Pey-Yan Liou utilizing existing items and derived variables of international large-scale assessment (ILSA) data, the three major methodological deficiencies, including the use of a single item to define a construct, the statistical properties of ordinal data, and the fitness of the measurement structure for different scenarios, are examined. To overcome these issues, this study proposes an integrated approach to evaluating items and constructing derived variables in a given situation. Exploratory factor analysis, confirmatory factor analysis, and the item response model are utilized to evaluate student attitudinal items and derived variables from the Trends in International Mathematics and Science Study (TIMSS) 2007 Taiwanese data. The results suggest that the three-factor model composed of 12 items is optimal for the data, not the default factor structure in the database. The implications of evaluating items and creating derived variables from ILSA data for the education research community are also discussed.