Compara mètodes

	Anàlisi de Fiabilitat (Alpha de Cronbach)	Modelització Lineal Jeràrquica (HLM / Modelització Multinivell)	Anàlisi de Components Principals
Camp	Estadística	Estadística	Aprenentatge automàtic
Família	Latent structure	Hypothesis test	Machine learning
Any d'origen	1951	1986	2002
Autor original	Lee J. Cronbach	Raudenbush & Bryk (popularized); Goldstein (parallel development)	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)
Tipus	Reliability / internal consistency coefficient	Parametric nested-data regression	Unsupervised dimensionality reduction
Font seminal	Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297–334.	Raudenbush, S.W. & Bryk, A.S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage. ISBN: 978-0761919049	Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer.
Àlies	coefficient alpha, alpha reliability, internal consistency reliability, Güvenilirlik Analizi (Cronbach Alpha)	HLM, MLM, multilevel modeling, multilevel analysis	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform
Relacionats	4	4	3
Resum	Cronbach's alpha is a coefficient of internal consistency that quantifies the degree to which a set of items on a scale measures the same underlying construct. Introduced by Lee J. Cronbach in 1951, it remains the most widely reported reliability index in social-science, health, and educational research.	Hierarchical Linear Modeling (HLM), also known as Multilevel Modeling (MLM), is a parametric statistical method for analyzing nested or clustered data — for example students within classrooms, patients within hospitals, or employees within organizations. Formalized by Raudenbush and Bryk in their 2002 seminal text (building on work from the mid-1980s), HLM simultaneously estimates individual-level and group-level effects while correctly partitioning variance across levels.	Principal Component Analysis (PCA) is an unsupervised dimensionality-reduction method — given its modern textbook treatment by Ian Jolliffe (2002) — that compresses high-dimensional data into fewer dimensions while preserving the maximum possible variance. It re-expresses correlated variables as a small set of uncorrelated principal components ordered by how much of the data's variation each one captures.