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Using regression mixture models with non-normal data: Examining an ordered polytomous approach.

Melissa R W George1, Na Yang, M Lee Van Horn

  • 1Department of Psychology, University of South Carolina, Columbia, South Carolina, USA.

Journal of Statistical Computation and Simulation
|May 21, 2013
PubMed
Summary
This summary is machine-generated.

Transforming outcomes into ordered categorical variables for regression mixture models can preserve differential effects but may introduce bias in parameter estimates, especially with higher error skew. Caution is advised when interpreting effect magnitudes.

Keywords:
Regression mixture modelsdifferential effectsnon-normal errors

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Area of Science:

  • Statistics
  • Quantitative Psychology
  • Econometrics

Background:

  • Regression mixture models are sensitive to error distribution skew.
  • Transforming outcomes to ordered categorical variables is a proposed method to mitigate skew.
  • Potential biases in parameter estimates and model fit require investigation at higher skew levels.

Purpose of the Study:

  • To evaluate the effectiveness of polytomous regression mixture models in handling skewed error distributions.
  • To examine the impact of varying skew levels on parameter estimates and model fit.
  • To assess the preservation of differential population effects under skew.

Main Methods:

  • Monte Carlo simulations were employed.
  • Two subpopulations with differing X-Y effects were simulated.
  • Ten scenarios with varying skew levels in one or both classes were tested, with 500 simulations each.

Main Results:

  • Model comparison criteria accurately identified the two-class model structure.
  • Differential population effects were successfully preserved.
  • Parameter estimates exhibited notable bias, particularly at higher skew levels.

Conclusions:

  • Polytomous regression mixture models can capture the correct number of effects even with skewed data.
  • While preserving differential effects, the magnitude of these effects may be biased.
  • Researchers should exercise caution when interpreting effect sizes from these models with skewed data.