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E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
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Practitioner's Guide to Latent Class Analysis: Methodological Considerations and Common Pitfalls.

Pratik Sinha1,2, Carolyn S Calfee1,2, Kevin L Delucchi3

  • 1Department of Medicine, Division of Pulmonary, Critical Care, Allergy and Sleep Medicine, University of California, San Francisco, San Francisco, CA.

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Latent class analysis (LCA) is a statistical method for data clustering and inference. This review guides researchers through LCA principles and practical application steps, including potential challenges.

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Area of Science:

  • Statistics
  • Biostatistics
  • Data Science

Background:

  • Latent class analysis (LCA) is a probabilistic modeling technique.
  • LCA facilitates data clustering and statistical inference.
  • Recent applications of LCA have surged in critical care and respiratory medicine.

Purpose of the Study:

  • To provide a comprehensive overview of latent class analysis principles.
  • To outline the stepwise process of performing LCA.
  • To highlight common challenges and pitfalls in LCA implementation.

Main Methods:

  • Review of latent class analysis methodology.
  • Stepwise procedural outline for LCA application.
  • Discussion of practical considerations and potential issues.

Main Results:

  • A clear explanation of LCA's foundational concepts.
  • A structured guide for researchers applying LCA.
  • Identification of key challenges encountered during LCA.

Conclusions:

  • Latent class analysis is a valuable tool for data analysis and inference.
  • This review serves as a practical resource for investigators using LCA.
  • Understanding LCA steps and challenges enhances its effective application.