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Comparing the Performance of Improved Classify-Analyze Approaches For Distal Outcomes in Latent Profile Analysis
John J Dziak1, Bethany C Bray1, Jieting Zhang2,1
1The Methodology Center, The Pennsylvania State University, University Park, PA, USA.
Methodology : European Journal of Research Methods for the Behavioral & Social Sciences
|June 21, 2017
Summary
The Bolck, Croon, and Hagenaars (BCH) correction method shows excellent performance for estimating latent class membership in latent profile analysis (LPA). Other methods like maximum likelihood (ML) and inclusive approaches were less robust under various conditions.
Area of Science:
- Statistics
- Psychometrics
- Social Sciences Research Methods
Background:
- Estimating the relationship between latent class membership and distal outcomes is crucial in latent profile analysis (LPA).
- The common three-step approach in LPA suffers from estimation bias and poor confidence interval coverage.
- Existing improvements, including the Bolck, Croon, and Hagenaars (BCH) correction, maximum likelihood (ML), and inclusive three-step methods, require further study in LPA with continuous indicators.
Purpose of the Study:
- To investigate the performance of different approaches for estimating distal outcome relationships in latent profile analysis (LPA) with continuous indicators.
- To compare the modified BCH, ML, and inclusive three-step approaches under varying conditions relevant to LPA.
Main Methods:
- Simulation study evaluating the performance of three estimation approaches: modified BCH, ML, and inclusive three-step.
- Conditions varied included distal outcome distribution, latent class measurement quality, relative class size, and class-outcome association strength.
- Focus on latent profile analysis (LPA) with normally distributed indicators.
Main Results:
- The modified Bolck, Croon, and Hagenaars (BCH) method, as implemented in Latent GOLD, demonstrated excellent performance.
- Maximum likelihood (ML) and inclusive approaches exhibited a lack of robustness when distributional assumptions were violated.
- Findings align with and extend previous research on latent class analysis (LCA) with categorical indicators.
Conclusions:
- The modified BCH approach is a reliable method for estimating distal outcome relationships in latent profile analysis (LPA) with continuous indicators.
- Researchers should exercise caution when using ML and inclusive methods if distributional assumptions are questionable.
- The study provides valuable insights for selecting appropriate methods in LPA research.
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