Literaturnachweis - Detailanzeige
| Autor/inn/en | Deng, Lifang; Yuan, Ke-Hai |
|---|---|
| Titel | Which Method Is More Powerful in Testing the Relationship of Theoretical Construct? A Meta Comparison of Structural Equation Modeling and Path Analysis with Weighted-Composites |
| Quelle | (2022), (42 Seiten)
PDF als Volltext (1); PDF als Volltext kostenfreie Datei (2) |
| Zusatzinformation | Weitere Informationen |
| Sprache | englisch |
| Dokumenttyp | gedruckt; online; Monographie |
| Schlagwörter | Structural Equation Models; Path Analysis; Weighted Scores; Error of Measurement; Effect Size; Statistical Significance; Least Squares Statistics; Robustness (Statistics) |
| Abstract | Structural equation modeling (SEM) has been deemed as a proper method when variables contain measurement errors. In contrast, path analysis with composite-scores is preferred for prediction and diagnosis of individuals. While path analysis with composite-scores has been criticized for yielding biased parameter estimates, recent literature pointed out that the population values of parameters in a latent-variable model depend on artificially assigned scales. Consequently, bias in parameter estimates is not a well-grounded concept for models involving latent constructs. This article compares path analysis with composite-scores against SEM with respect to effect size and statistical power in testing the significance of the path coefficients, via the z- or t-statistics. The data come from many sources with various models that are substantively determined. Results show that SEM is not as powerful as path analysis even with equally-weighted-composites. But path analysis with Bartlett-factor- scores and the partial-least-squares approach to SEM perform the best with respect to effect size and power. [This paper will be published in "Behavior Research Methods." Discrepancy between the title of the article and authored paper.] (As Provided). |
| Erfasst von | ERIC (Education Resources Information Center), Washington, DC |
| Update | 2024/1/01 |
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