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3. Scale-Invariance, Equivariance and Dependency of Structural Equation Models.

4. Replies to comments on "Which method delivers greater signal‐to‐noise ratio: Structural equation modelling or regression analysis with weighted composites?" by Yuan and Fang (2023).

5. Which method delivers greater signal‐to‐noise ratio: Structural equation modelling or regression analysis with weighted composites?

7. Comments on the article "Marketing or methodology? Exposing the fallacies of PLS with simple demonstrations" and PLS-SEM in general.

8. Which method is more powerful in testing the relationship of theoretical constructs? A meta comparison of structural equation modeling and path analysis with weighted composites.

9. An Open-source WYSIWYG Web Application for Drawing Path Diagrams of Structural Equation Models.

10. Sensitivity Analysis of the Weights of the Composites Under Partial Least-Squares Approach to Structural Equation Modeling.

11. A Reply to "Structural Parameters under Partial Least Squares and Covariance-based Structural Equation Modeling: A Comment on Yuan and Deng (2021)" by Schuberth, Rosseel, Rönkkö, Trichera, Kline, and Henseler (2023).

12. Equivalence of Partial-Least-Squares SEM and the Methods of Factor-Score Regression.

13. Which Method is More Reliable in Performing Model Modification: Lasso Regularization or Lagrange Multiplier Test?

14. Using Equivalence Testing to Evaluate Goodness of Fit in Multilevel Structural Equation Models.

15. Mean and Variance Corrected Test Statistics for Structural Equation Modeling with Many Variables.

16. What Causes the Mean Bias of the Likelihood Ratio Statistic with Many Variables?

17. New Effect Size Measures for Structural Equation Modeling.

18. Optimizing Ridge Generalized Least Squares for Structural Equation Modeling.

19. The Performance of Ten Modified Rescaled Statistics as the Number of Variables Increases.

20. Empirically Corrected Rescaled Statistics for SEM with Small N and Large p.

21. Four New Corrected Statistics for SEM With Small Samples and Nonnormally Distributed Data.

22. Improving the convergence rate and speed of Fisher-scoring algorithm: ridge and anti-ridge methods in structural equation modeling.

23. Reliable and More Powerful Methods for Power Analysis in Structural Equation Modeling.

24. New Ways to Evaluate Goodness of Fit: A Note on Using Equivalence Testing to Assess Structural Equation Models.

25. Meta analytical structural equation modeling: comments on issues with current methods and viable alternatives.

26. Assessing Structural Equation Models by Equivalence Testing With Adjusted Fit Indexes.

27. Structural Equation Modeling With Unknown Population Distributions: Ridge Generalized Least Squares.

28. Multiple-Group Analysis for Structural Equation Modeling With Dependent Samples.

29. Empirical Correction to the Likelihood Ratio Statistic for Structural Equation Modeling with Many Variables.

30. Bias and Efficiency for SEM With Missing Data and Auxiliary Variables: Two-Stage Robust Method Versus Two-Stage ML.

31. Evaluation of Test Statistics for Robust Structural Equation Modeling With Nonnormal Missing Data.

32. Data-driven sensitivity analysis to detect missing data mechanism with applications to structural equation modelling.

33. Structural Equation Modeling Diagnostics Using R Package semdiag and EQS.

34. Robust Structural Equation Modeling with Missing Data and Auxiliary Variables.

35. Ridge structural equation modelling with correlation matrices for ordinal and continuous data.

36. FINITE NORMAL MIXTURE SEM ANALYSIS BY FITTING MULTIPLE CONVENTIONAL SEM MODELS.

37. OUTLIERS, LEVERAGE OBSERVATIONS, AND INFLUENTIAL CASES IN FACTOR ANALYSIS: USING ROBUST PROCEDURES TO MINIMIZE THEIR EFFECT.

38. Robust mean and covariance structure analysis through iteratively reweighted least squares.

39. Structural equation modeling with near singular covariance matrices

40. Abstract: Evaluation of Test Statistics for Robust Structural Equation Modeling With Nonnormal Missing Data.

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