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Two-Step Estimation of Models Between Latent Classes and External Variables.

Zsuzsa Bakk1, Jouni Kuha2

  • 1Leiden University, Leiden, The Netherlands. z.bakk@fsw.leidenuniv.nl.

Psychometrika
|November 19, 2017
PubMed
Summary
This summary is machine-generated.

A new two-step method effectively estimates complex latent class models by first analyzing measurement models, then structural models. This approach offers an attractive alternative for analyzing relationships between latent classes and observed variables.

Keywords:
latent variablesmixture modelspseudo-maximum likelihood estimationstructural equation models

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Area of Science:

  • Statistics
  • Psychometrics
  • Econometrics

Background:

  • Latent class analysis (LCA) models categorical latent variables.
  • Structural regression models analyze relationships between variables.
  • Combining these models is crucial for complex data analysis.

Purpose of the Study:

  • To propose and evaluate a novel two-step estimation method for combined latent class measurement and structural regression models.
  • To provide an alternative to existing one-step and three-step estimation procedures.
  • To develop methods for calculating accurate standard errors for the proposed approach.

Main Methods:

  • A two-step estimation procedure is introduced.
  • Step 1: Estimate the latent class measurement model independently.
  • Step 2: Estimate the structural regression model with measurement model parameters fixed.

Main Results:

  • Simulation studies and applied examples demonstrate the viability of the two-step method.
  • The proposed method is shown to be a competitive alternative to existing estimation techniques.
  • Derived standard errors account for uncertainty from both estimation steps.

Conclusions:

  • The two-step method provides an attractive and practical approach for estimating combined latent class measurement and structural models.
  • The method is implementable in existing latent variable modeling software.
  • This offers researchers a valuable tool for analyzing complex relationships involving latent constructs.