SMART-LCA Checklist

This Vignette describes the SMART-LCA Checklist: Standards for More Accuracy in Reporting of different Types of Latent Class Analysis, introduced in Van Lissa, C. J., Garnier-Villarreal, M., & Anadria, D. (2023). Recommended Practices in Latent Class Analysis using the Open-Source R-Package tidySEM. Structural Equation Modeling. This version of the checklist corresponds to the tidySEM R-package’s version , 0.2.7.

The paper discusses the best practices on which the checklist is based and describes specific points to check when writing, reviewing, or reading a paper in greater detail. However, this vignette may be updated to keep pace with tidySEM package development, whereas the print publication will remain static.

Note that, although the steps below are numbered for reference purposes, we acknowledge the process of conducting and reporting research is not always linear.

  1. Pre-Analysis
  2. Examining Observed Data
  3. Data Preprocessing
  4. Missing data
  5. Model specification
  6. Number of classes
  7. Justify the criteria used for class enumeration, which can include:
  8. Justify the criteria used to eliminate models from consideration, including:
  9. Transparency and Reproducibility
  10. Estimation and Convergence
  11. Reporting Results
  12. Inference
  13. Visualization
  14. Follow-up analyses.