A Mixture Partial Credit Analysis of Math Anxiety
Abstract
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 was used to detect distinct latent classes based on
homogeneities in response patterns. The analysis detected two latent classes.
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. Students in both classes were
similar in terms of test and evaluation
anxiety. Moreover, students in Class 1 were found to be more successful in
mathematics, mostly like mathematics and mathematics teachers, and have better
educated mothers than students in Class 2. Manifest variables of gender,
attending private or public schools, and education levels of fathers did not
differ among the latent classes. Characterizing differences between members of
each latent class extends recent advances in measuring math anxiety.
Keywords
References
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Details
Primary Language
English
Subjects
Studies on Education
Journal Section
Research Article
Authors
İbrahim Burak Ölmez
*
0000-0002-4931-2174
United States
Allan S. Cohen
This is me
0000-0002-8776-9378
United States
Publication Date
December 16, 2018
Submission Date
June 25, 2018
Acceptance Date
August 25, 2018
Published in Issue
Year 2018 Volume: 5 Number: 4
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