AI Summary & Key Takeaways
AI InsightIt was concluded that there was no gender bias in the three scales for which MG-CFA was performed, and the mean scores were comparable according to gender.
📌 3 Key Takeaways
Abstract
This research examines whether the affective characteristics of the TIMSS 2019 Turkey mathematics application provide measurement invariance according to gender. The research sample consists of 4048 8th-grade students participating in the TIMSS in 2019. Research data were downloaded from the international website of TIMSS. The research data collection tools are “Sense of School Belonging”, “Students Confident in Mathematics”, “Students Like Learning Mathematics”, and “Students Value Mathematics” scales. Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) were performed in the context of validity analyses to examine measurement invariance. In terms of reliability, the Cronbach Alfa internal consistency coefficient was calculated. Accordingly, out of the four scales in the study, only “Students Confident in Mathematics” scale could not be confirmed in confirmatory factor analysis. Therefore, while “Students Confident in Mathematics” scale was not examined for measurement invariance, the other three scales were examined within the scope of measurement invariance. For measurement invariance, research data were tested with Multiple Group Confirmatory Factor Analysis (MG-CFA), one of the Structural Equation Modeling (SEM) techniques. As a result of the analyses, while the strict invariance model was provided in “Students Like Learning Mathematics” scale and “Students Value Mathematics” scale, strong invariance/scale invariance model was provided in “Sense of School Belonging” scale. It was concluded that there was no gender bias in the three scales for which MG-CFA was performed, and the mean scores were comparable according to gender. In this context, it can be said that “Sense of School Belonging”, “Students Like Learning Mathematics”, and “Students Value Mathematics” scales are valid in determining the differences according to gender.