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| Explorativ faktoriell analys (EFA)× | Hierarkisk linjär modellering (HLM / Multilevelmodellering)× | Analys av huvudkomponenter× | |
|---|---|---|---|
| Ämnesområde≠ | Statistik | Statistik | Maskininlärning |
| Familj≠ | Latent structure | Hypothesis test | Machine learning |
| Ursprungsår≠ | — | 1986 | 2002 |
| Upphovsperson≠ | — | Raudenbush & Bryk (popularized); Goldstein (parallel development) | Jolliffe, I.T. (textbook); Pearson & Hotelling (origins) |
| Typ≠ | Latent variable / dimension reduction | Parametric nested-data regression | Unsupervised dimensionality reduction |
| Ursprungskälla≠ | Fabrigar, L. R., Wegener, D. T., MacCallum, R. C. & Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4(3), 272–299. DOI ↗ | 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. DOI ↗ |
| Alias≠ | common factor analysis, açımlayıcı faktör analizi, factor analysis | HLM, MLM, multilevel modeling, multilevel analysis | Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform |
| Närliggande≠ | 4 | 4 | 3 |
| Sammanfattning≠ | Exploratory factor analysis reduces a large set of observed variables into a smaller number of latent common factors. It is widely used in scale development and psychometrics to uncover the dimensional structure that underlies a set of correlated items, without specifying that structure in advance. | 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. |
| ScholarGateDatamängd ↗ |
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