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Attribution theory plays a crucial role in social psychology, helping to explain how individuals interpret the causes of behavior. One prominent model within this field is Harold Kelley's covariation theory, which provides a systematic approach to determining whether internal traits or external circumstances drive a person's actions. The model posits that individuals rely on three key types of information—consensus, consistency, and distinctiveness—to make these judgments.Consensus:...
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Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
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Relating latent class membership to external variables: An overview.

Zsuzsa Bakk1, Jouni Kuha2

  • 1Department of Methodology and Statistics, Leiden University, The Netherlands.

The British Journal of Mathematical and Statistical Psychology
|November 17, 2020
PubMed
Summary
This summary is machine-generated.

This study reviews methods for linking latent class membership to external variables. It extends prior work by detailing modeling options for covariates and multiple latent variables, using real-world survey data for illustration.

Keywords:
covariatesdistal outcomelatent class analysisthree-step estimationtwo-step estimation

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Area of Science:

  • Statistics
  • Social Sciences
  • Quantitative Psychology

Background:

  • Latent class analysis (LCA) is a statistical method for identifying unobserved subgroups within a population.
  • Relating latent class membership to external variables is crucial for understanding subgroup differences.
  • Existing literature primarily focuses on models with distal outcomes.

Purpose of the Study:

  • To provide a comprehensive overview of approaches for relating latent class membership to external variables.
  • To extend existing work by detailing recommended modeling options for covariates and multiple latent variables in LCA.
  • To illustrate these modeling approaches with real-world data.

Main Methods:

  • Review and synthesis of existing statistical modeling approaches for latent class analysis.
  • Extension of previous work by Nylund-Gibson et al. (2019).
  • Application of recommended modeling strategies to a distal outcome model and a multiple latent variable model using General Social Survey data.

Main Results:

  • The article presents recommended modeling options for latent class analysis with covariates.
  • It demonstrates approaches for models involving multiple latent variables.
  • Practical examples using Latent GOLD software syntax are provided for both model types.

Conclusions:

  • The study offers a practical guide to advanced latent class analysis techniques.
  • It highlights the utility of these methods for analyzing complex relationships between latent classes and external factors.
  • The findings support the application of these models in social science research.