Latent GOLD

Latent GOLD® 4.0 User’s Guide. Latent classes are unobservable (latent) subgroups or segments. Cases within the same latent class are homogeneous on certain criteria, while cases in different latent classes are dissimilar from each other in certain important ways. Formally, latent classes are represented by K distinct categories of a nominal latent variable X. Since the latent variable is categorical, LC modeling differs from more traditional latent variable approaches such as factor analysis, structural equation models, and random-effects regression models that are based on continuous latent variables. Latent class (LC) analysis was originally introduced by Lazarsfeld (1950) as a way of explaining respondent heterogeneity in survey response patterns involving dichotomous items. During the 1970s, LC methodology was formalized and extended to nominal variables by Goodman (1974a, 1974b) who also developed the maximum likelihood algorithm that serves as the basis for the Latent GOLD program. Over the same period, the related field of finite mixture (FM) models for multivariate normal distributions began to emerge, through the work of Day (1969), Wolfe (1965, 1967, 1970) and others. FM models seek to separate out or ’un-mix’ data that is assumed to arise as a mixture from a finite number of distinctly different populations.

References in zbMATH (referenced in 93 articles )

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  1. Giordani, Paolo; Ferraro, Maria Brigida; Martella, Francesca: An introduction to clustering with R (2020)
  2. Wenchao Ma, Jimmy de la Torre: GDINA: An R Package for Cognitive Diagnosis Modeling (2020) not zbMATH
  3. Boeschoten, L.; Croon, M. A.; Oberski, D. L.: A note on applying the BCH method under linear equality and inequality constraints (2019)
  4. Durante, Daniele; Canale, Antonio; Rigon, Tommaso: A nested expectation-maximization algorithm for latent class models with covariates (2019)
  5. Piccolo, Domenico; Simone, Rosaria: Rejoinder to the discussion of “The class of CUB models: statistical foundations, inferential issues and empirical evidence” (2019)
  6. Piccolo, Domenico; Simone, Rosaria: The class of \textsccubmodels: statistical foundations, inferential issues and empirical evidence (2019)
  7. Ranalli, Monia; Rocci, Roberto: An overview on the URV model-based approach to cluster mixed-type data (2019)
  8. Bakk, Zsuzsa; Kuha, Jouni: Two-step estimation of models between latent classes and external variables (2018)
  9. Jacques, Julien; Biernacki, Christophe: Model-based co-clustering for ordinal data (2018)
  10. Michel Meulders; Philippe De Bruecker: Latent Class Probabilistic Latent Feature Analysis of Three-Way Three-Mode Binary Data (2018) not zbMATH
  11. Wadsworth, Ian; Van Horn, M. Lee; Jaki, Thomas: A diagnostic tool for checking assumptions of regression mixture models (2018)
  12. Erosheva, Elena A.; Curtis, S. McKay: Dealing with reflection invariance in Bayesian factor analysis (2017)
  13. Francesco Bartolucci; Silvia Pandolfi; Fulvia Pennoni: LMest: An R Package for Latent Markov Models for Longitudinal Categorical Data (2017) not zbMATH
  14. Ken Beath: randomLCA: An R Package for Latent Class with Random Effects Analysis (2017) not zbMATH
  15. Ranalli, Monia; Rocci, Roberto: Mixture models for mixed-type data through a composite likelihood approach (2017)
  16. Bassi, Francesca: Dynamic segmentation with growth mixture models (2016)
  17. Bennink, Margot; Croon, Marcel A.; Kroon, Brigitte; Vermunt, Jeroen K.: Micro-macro multilevel latent class models with multiple discrete individual-level variables (2016)
  18. Biernacki, Christophe; Jacques, Julien: Model-based clustering of multivariate ordinal data relying on a stochastic binary search algorithm (2016)
  19. Chew, Cindy S.; Forte, Jason D.; Reeve, Robert A.: Cognitive factors affecting children’s nonsymbolic and symbolic magnitude judgment abilities: A latent profile analysis (2016) MathEduc
  20. Chiu, Chia-Yi; Köhn, Hans-Friedrich: The reduced RUM as a logit model: parameterization and constraints (2016)

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