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Abstract: Sample Size Planning for Latent Curve Models.

Keke Lai1

  • 1a University of Notre Dame.

Multivariate Behavioral Research
|January 7, 2016
PubMed
Summary
This summary is machine-generated.

Planning sample size for structural equation modeling (SEM) is crucial. This study proposes using confidence intervals (CIs) for more informative sample size planning in latent curve models (LCMs) instead of traditional power analysis.

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Area of Science:

  • Psychometrics
  • Statistical Modeling
  • Longitudinal Data Analysis

Background:

  • Traditional sample size determination in structural equation modeling (SEM) relies on power analysis, focusing on rejecting false null hypotheses.
  • Hypothesis testing in SEM often leads to excessive power, failing to provide effect size information and potentially rejecting true null hypotheses.
  • Confidence intervals (CIs) offer a more informative approach by providing a range of plausible population effect sizes.

Purpose of the Study:

  • To propose an alternative sample size planning method for latent curve models (LCMs) using a confidence interval (CI) perspective.
  • To shift the focus from achieving sufficient power to obtaining sufficiently narrow CIs for SEM parameters.
  • To provide a framework for sample size determination in LCMs that yields more meaningful effect size estimates.

Main Methods:

  • The proposed method inverts the confidence interval formation process for LCMs.
  • It utilizes maximum likelihood estimation and the expected information matrix.
  • Sample size (N) is determined based on a desired CI width (ω) and proxies for population covariance (Σ) and mean (μ) matrices.

Main Results:

  • The confidence interval approach provides a more direct link between sample size and the precision of population effect size estimates.
  • Inverting the CI formation process allows for direct sample size planning based on desired precision.
  • The method requires specifying population parameters (Σ, μ) and desired precision (ω) for sample size calculation.

Conclusions:

  • Sample size planning for latent curve models (LCMs) should prioritize achieving narrow confidence intervals (CIs) for precise effect size estimation.
  • The confidence interval perspective offers a more informative alternative to traditional power analysis in structural equation modeling (SEM).
  • This approach enhances the interpretability of study findings by focusing on the range of plausible population parameters.