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Area of Science:

  • Quantitative psychology
  • Educational research methodology
  • STEM education

Background:

  • Mixture modeling offers a "person-centered" quantitative approach to analyze unobserved subpopulations.
  • Latent class analysis (LCA) is a key cross-sectional mixture modeling technique.
  • LCA can reveal underlying structures within diverse populations.

Purpose of the Study:

  • To introduce Latent Class Analysis (LCA) as a valuable statistical method for discipline-based education research.
  • To explore the application and benefits of LCA in Science, Technology, Engineering, and Math (STEM) education.
  • To examine how LCA can support equity-focused research agendas in STEM education.

Main Methods:

  • The paper describes Latent Class Analysis (LCA), a type of mixture modeling.
  • It provides examples of LCA application in STEM education research.
  • The discussion includes the affordances and limitations of LCA.

Main Results:

  • Latent Class Analysis (LCA) offers a powerful quantitative tool for identifying unobserved subpopulations.
  • LCA can be effectively applied to analyze complex data in STEM education.
  • The method has significant potential for advancing equity-focused research.

Conclusions:

  • Latent Class Analysis (LCA) is a versatile method for quantitative research in STEM education.
  • Researchers are encouraged to consider LCA for its ability to support equity, inclusion, access, and justice agendas.
  • LCA helps leverage quantitative data to align with research goals focused on fairness and equity.