विधियों की तुलना करें

	अव्यक्त वर्ग विश्लेषण (LCA)	समूह विश्लेषण	अन्वेषणात्मक कारक विश्लेषण (EFA)
क्षेत्र	सांख्यिकी	सांख्यिकी	सांख्यिकी
परिवार	Latent structure	Latent structure	Latent structure
उद्भव वर्ष	1950	1939–1967	—
प्रवर्तक	Paul F. Lazarsfeld	Robert C. Tryon (early development); Ward (1963) for hierarchical; MacQueen (1967) for k-means	—
प्रकार	Latent variable / probabilistic clustering	Unsupervised classification / grouping	Latent variable / dimension reduction
मौलिक स्रोत	Hagenaars, J. A. & McCutcheon, A. L. (Eds.) (2002). Applied Latent Class Analysis. Cambridge University Press. ISBN: 978-0521594516	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.
उपनाम	Gizil Sınıf Analizi (LCA), latent class model, latent structure analysis	clustering, unsupervised classification, data clustering, numerical taxonomy	common factor analysis, açımlayıcı faktör analizi, factor analysis
संबंधित	3	5	4
सारांश	Latent class analysis is a probabilistic model-based clustering technique that identifies unobserved subgroups — latent classes — within a population on the basis of patterns of categorical, binary, or ordinal indicator responses. Originating in sociological measurement theory with Lazarsfeld's latent structure work around 1950 and formalised computationally by Goodman in the 1970s, it is widely used in the social, health, and behavioural sciences to reveal hidden population heterogeneity.	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.