Compara mètodes

	Anàlisi Factorial	Regressió Logística	Modelatge Multillivell
Camp	Estadística per a la recerca	Estadística per a la recerca	Estadística per a la recerca
Família	Process / pipeline	Process / pipeline	Process / pipeline
Any d'origen	1931	1958	1992
Autor original	Louis Leon Thurstone	David Roxbee Cox	Anthony Bryk and Stephen Raudenbush
Tipus	Method	Method	Method
Font seminal	Thurstone, L. L. (1947). Multiple Factor Analysis. University of Chicago Press.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.	Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications.
Àlies	EFA, CFA, latent variable modeling	logit model, binomial logistic regression, LR	HLM, mixed-effects models, random effects models, MLM
Relacionats	3	3	3
Resum	Factor analysis is a statistical technique for identifying latent (unobserved) dimensions underlying observed variables, developed by Louis Leon Thurstone in the 1930s and formalized by Jöreskog (1969). Exploratory factor analysis (EFA) discovers unknown factor structure from data; confirmatory factor analysis (CFA) tests hypothesized relationships between observed and latent variables. Essential in psychometrics (test development), organizational research (measuring constructs like leadership style), and biomedicine (identifying disease subtypes), factor analysis reduces dimensionality while revealing conceptual organization in multivariate data.	Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science.	Multilevel modeling (also called hierarchical linear modeling, mixed-effects modeling) is a statistical framework for analyzing data organized in nested or clustered structures—students within schools, patients within hospitals, repeated measures within individuals. Developed by Bryk and Raudenbush (1992), it accounts for dependency among observations and partitions variance into levels (within-cluster and between-cluster), enabling valid inference and revealing context effects. Essential in education, medicine, organizational research, and any field where data have natural hierarchies.