A Mixture Partial Credit Analysis of Math Anxiety - İbrahim Burak Ölmez; Allan S. Cohen (International Journal of Assessment Tools in Education)
Thepurpose of this study was to investigate a new methodology for detection ofdifferences in middle grades students’ math anxiety. A mixture partial creditmodel analysis was used to detect distinct latent classes based onhomogeneities in response patterns. The analysis detected two latent classes.Students in Class 1 had less anxiety about apprehensionof math lessons and use ofmathematics in daily life, and more self-efficacyfor mathematics than students in Class 2. Students in both classes weresimilar in terms of test and evaluationanxiety. Moreover, students in Class 1 were found to be more successful inmathematics, mostly like mathematics and mathematics teachers, and have bettereducated mothers than students in Class 2. Manifest variables of gender,attending private or public schools, and education levels of fathers did notdiffer among the latent classes. Characterizing differences between members ofeach latent class extends recent advances in measuring math anxiety.