Methoden vergleichen

	Bayesianische statistische Inferenz	Faktorenanalyse
Fachgebiet	Forschungsstatistik	Forschungsstatistik
Familie	Process / pipeline	Process / pipeline
Entstehungsjahr	1763	1931
Urheber	Thomas Bayes	Louis Leon Thurstone
Typ	Method	Method
Wegweisende Quelle	Bayes, T. (1763). An essay towards solving a problem in the doctrine of chances. Philosophical Transactions of the Royal Society, 53, 370–418.	Thurstone, L. L. (1947). Multiple Factor Analysis. University of Chicago Press.
Aliasnamen	Bayes theorem, Bayesian inference, posterior probability	EFA, CFA, latent variable modeling
Verwandt	3	3
Zusammenfassung	Bayesian inference is a statistical framework using Bayes' theorem to update beliefs about parameters or hypotheses as data accumulate. Published posthumously in 1763, Thomas Bayes' work lay dormant until the 20th century, when computational advances (Gibbs sampling, Markov Chain Monte Carlo) made Bayesian methods practical. Unlike frequentist inference (which treats parameters as fixed unknowns), Bayesian analysis treats parameters as random variables with probability distributions, enabling direct probability statements about parameters, incorporation of prior knowledge, and sequential updating. Essential in precision medicine, adaptive trials, complex hierarchical models, and any context where prior information enriches inference.	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.