Detecting Math Anxiety with a Mixture Partial Credit Model

The purpose of this study was to investigate a new methodology for detection of differences in middle grades students' math anxiety. A mixture partial credit model analysis revealed two distinct latent classes based on homogeneities in response patterns within each latent class. Students in Class 1 had less anxiety about apprehension of math lessons and use of mathematics in daily life, and more self-efficacy for mathematics than students in Class 2. Moreover, students in Class 1 were found to be more successful in mathematics, mostly like mathematics and mathematics teachers, and have better educated mothers in comparison to students in Class 2. However, gender, attending private or public schools, and education levels of fathers did not appear to differ between the classes. Capturing such fine-grained information extends recent advances in measuring math anxiety. [For complete proceedings, see ED581294.]