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	Partial Least Squares Structural Equation Modeling	Analýza nevyhnutných podmienok
Odbor	Psychometria	Psychometria
Rodina	Latent structure	Latent structure
Rok vzniku	1985	2016
Tvorca	Herman Wold	Jan Dul
Typ	Component-based structural equation model	Set-theoretic configurational analysis
Pôvodný zdroj	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.
Ďalšie názvy	PLS-SEM, PLS path modeling	NCA
Príbuzné	5	5
Zhrnutie	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.