Sammenlign metoder
Gennemgå dine valgte metoder side om side; rækker, der afviger, er fremhævet.
| Partial Least Squares Structural Equation Modeling× | Necessary Condition Analysis× | |
|---|---|---|
| Fagområde | Psykometri | Psykometri |
| Familie | Latent structure | Latent structure |
| Oprindelsesår≠ | 1985 | 2016 |
| Ophavsperson≠ | Herman Wold | Jan Dul |
| Type≠ | Component-based structural equation model | Set-theoretic configurational analysis |
| Oprindelig kilde≠ | Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) (2nd ed.). Sage Publications. ISBN: 9781483377445 | Dul, J. (2016). Necessary Condition Analysis (NCA): Logic and methodology of "necessary but not sufficient" causality. Organizational Research Methods, 19(1), 10-52. DOI ↗ |
| Aliasser≠ | PLS-SEM, PLS path modeling | NCA |
| Relaterede | 5 | 5 |
| Resumé≠ | PLS-SEM is a variance-based approach to structural equation modeling developed by Herman Wold (1985) that estimates latent variable models by maximizing the variance explained in dependent variables. Unlike covariance-based SEM, PLS-SEM is particularly useful for exploratory research, small to medium samples, complex models with many constructs, and non-normal data. | Necessary Condition Analysis (NCA) is a set-theoretic method developed by Dul (2016) that identifies conditions necessary (but not necessarily sufficient) for an outcome to occur. Unlike regression, which estimates average effects, NCA identifies absolute thresholds: conditions that must be present at a certain level for the outcome to be possible, regardless of other factors. |
| ScholarGateDatasæt ↗ |
|
|