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| Analisis Klaster× | Analisis Faktor Eksploratori (EFA)× | Pemodelan Persamaan Struktural (SEM)× | |
|---|---|---|---|
| Bidang | Statistika | Statistika | Statistika |
| Keluarga | Latent structure | Latent structure | Latent structure |
| Tahun asal≠ | 1939–1967 | — | 1970 |
| Pencetus≠ | Robert C. Tryon (early development); Ward (1963) for hierarchical; MacQueen (1967) for k-means | — | Karl Jöreskog (LISREL framework, 1970s) |
| Tipe≠ | Unsupervised classification / grouping | Latent variable / dimension reduction | Latent variable / causal modeling |
| Sumber perintis≠ | Everitt, B. S., Landau, S., Leese, M. & Stahl, D. (2011). Cluster Analysis (5th ed.). Wiley. ISBN: 978-0470749913 | 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 ↗ | Hair, J. F., Black, W. C., Babin, B. J. & Anderson, R. E. (2019). Multivariate Data Analysis (8th ed.). Cengage Learning. ISBN: 978-1473756540 |
| Alias≠ | clustering, unsupervised classification, data clustering, numerical taxonomy | common factor analysis, açımlayıcı faktör analizi, factor analysis | Yapısal Eşitlik Modellemesi (SEM), structural equation modelling, covariance structure analysis, latent variable modeling |
| Terkait≠ | 5 | 4 | 5 |
| Ringkasan≠ | Cluster analysis is a family of unsupervised multivariate techniques that partition a set of objects or observations into internally homogeneous, mutually distinct groups — clusters — based on measured characteristics, without any prior knowledge of group membership. It is widely used in market segmentation, bioinformatics, psychology, and social science to reveal natural groupings in data. | 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. | Structural equation modeling is a multivariate statistical framework that simultaneously estimates a measurement model — relating observed indicators to latent constructs — and a structural model specifying directional or reciprocal relationships among those constructs. Rooted in the LISREL tradition developed by Karl Jöreskog in the 1970s, SEM is the standard tool for testing complex theoretical models in the social, behavioural, and management sciences. |
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