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Evidence That Growth Mixture Model Results Are Highly Sensitive to Scoring Decisions.
James Soland1, Veronica Cole2, Stephen Tavares1
1University of Virginia, Charlottesville, VA, USA.
Multivariate Behavioral Research
|January 15, 2025
Summary
Growth mixture models (GMMs) are highly sensitive to how item responses are scored, potentially biasing results. Measurement decisions, not developmental trends, may drive many GMM findings.
Area of Science:
- Psychology
- Statistics
- Developmental Science
Background:
- Growth mixture models (GMMs) are widely used to identify developmental trajectories.
- Previous research indicates GMMs are sensitive to modeling assumptions.
- The impact of item scoring decisions on GMMs remains understudied.
Purpose of the Study:
- To investigate the sensitivity of GMM results to item scoring decisions.
- To examine how measurement artifacts may influence latent growth profile analysis.
Main Methods:
- Empirical studies and Monte Carlo simulations were employed.
- The impact of scoring decisions on GMM convergence, class enumeration, and trajectory estimation was assessed.
Main Results:
- GMM outcomes, including convergence and class identification, demonstrated extreme sensitivity to measurement decisions.
- Measurement models used for GMM score generation are inherently misspecified.
- Misspecification of measurement models leads to biased GMM results.
Conclusions:
- Current findings raise concerns about the validity of existing GMM literature.
- Measurement artifacts, rather than true developmental differences, may explain many GMM results.
- Re-evaluation of scoring practices in GMM research is warranted.
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