Statistical Power to Detect the Correct Number of Classes in Latent Profile Analysis.

@article{Tein2013StatisticalPT,
  title={Statistical Power to Detect the Correct Number of Classes in Latent Profile Analysis.},
  author={Jenn-Yun Tein and Stefany Coxe and Heining Cham},
  journal={Structural equation modeling : a multidisciplinary journal},
  year={2013},
  volume={20 4},
  pages={
          640-657
        }
}
Little research has examined factors influencing statistical power to detect the correct number of latent classes using latent profile analysis (LPA). This simulation study examined power related to inter-class distance between latent classes given true number of classes, sample size, and number of indicators. Seven model selection methods were evaluated. None had adequate power to select the correct number of classes with a small (Cohen's d = .2) or medium (d = .5) degree of separation. With a… CONTINUE READING
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