Comparer des méthodes

	Analyse des conditions nécessaires	Modélisation par équations structurelles par moindres carrés partiels
Domaine	Psychométrie	Psychométrie
Famille	Latent structure	Latent structure
Année d'origine	2016	1985
Auteur d'origine	Jan Dul	Herman Wold
Type	Set-theoretic configurational analysis	Component-based structural equation model
Source fondatrice	Dul, J. (2016). Necessary Condition Analysis (NCA): Logic and methodology of "necessary but not sufficient" causality. Organizational Research Methods, 19(1), 10-52.	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
Alias	NCA	PLS-SEM, PLS path modeling
Apparentées	5	5
Résumé	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.	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.