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Measuring mathematics self-efficacy: Multitrait-multimethod comparison.

Wenhua Yu1, Shuodi Zhou1, Yu Zhou2

  • 1School of Mathematics and Statistics, Shandong Normal University, Jinan, China.

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|March 24, 2023
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Summary
This summary is machine-generated.

This study investigated measuring mathematics self-efficacy using a multitrait-multimethod design. "Synthesis and Practice" and the Motivated Strategies for Learning Questionnaire (MSLQ) best reflected self-efficacy, offering insights for improvement.

Keywords:
academic performance in mathematicsconfirmatory factor analysismathematics self-efficacymultitrait-multimethod designself-efficacy

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Area of Science:

  • Educational Psychology
  • Psychometrics

Background:

  • Mathematics self-efficacy is linked to math performance.
  • Accurate measurement of self-efficacy is crucial for students, parents, and educators.
  • Existing research necessitates a deeper understanding of how different facets and methods of measurement reflect mathematics self-efficacy.

Purpose of the Study:

  • To measure mathematics self-efficacy using a multitrait-multimethod (MTMM) design.
  • To identify which of the three traits (Number and Algebra, Graphics and Geometry, Synthesis and Practice) and three methods (General-Math-Task-referenced, Unconventional-Math-Problem-referenced, MSLQ self-efficacy) best reflect individual self-efficacy.
  • To validate the use of Confirmatory Factor Analysis (CFA) in modeling MTMM data.

Main Methods:

  • Employed a multitrait-multimethod (MTMM) design with 100 students.
  • Utilized Confirmatory Factor Analysis (CFA) to construct and validate eight first-order and second-order models.
  • Assessed self-efficacy across three traits and three measurement methods, collecting data via questionnaires and math scores.

Main Results:

  • The "Synthesis and Practice" trait significantly reflected self-efficacy.
  • The Motivated Strategies for Learning Questionnaire (MSLQ) self-efficacy measure was the most significant among the methods.
  • Model D, a second-order CFA model, was selected as the optimal model.
  • Higher correlation found between Unconventional-Math-Problem-referenced self-efficacy and MSLQ compared to General-Math-Task-referenced self-efficacy in the second-order model.

Conclusions:

  • Mathematics self-efficacy can be effectively improved by focusing on the "Synthesis and Practice" domain.
  • The MSLQ provides a robust measure for assessing mathematics self-efficacy.
  • CFA is a suitable statistical tool for analyzing MTMM data in educational research.
  • Findings contribute to the theoretical understanding and practical enhancement of students' mathematics self-efficacy.