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 51 articles )

Showing results 1 to 20 of 51.
Sorted by year (citations)

1 2 3 next

  1. Biernacki, Christophe; Jacques, Julien: Model-based clustering of multivariate ordinal data relying on a stochastic binary search algorithm (2016)
  2. Chiu, Chia-Yi; Köhn, Hans-Friedrich: The reduced RUM as a logit model: parameterization and constraints (2016)
  3. Chiu, Chia-Yi; Köhn, Hans-Friedrich: Consistency of cluster analysis for cognitive diagnosis: the reduced reparameterized unified model and the general diagnostic model (2016)
  4. Gray, Sarah A.; Reeve, Robert A.: Number-specific and general cognitive markers of preschoolers’ math ability profiles (2016)
  5. Gudicha, Dereje W.; Tekle, Fetene B.; Vermunt, Jeroen K.: Power and sample size computation for Wald tests in latent class models (2016)
  6. Iannario, Maria; Piccolo, Domenico: A comprehensive framework of regression models for ordinal data (2016)
  7. Paul, Jacob M.; Reeve, Robert A.: Relationship between single digit addition strategies and working memory reflects general reasoning sophistication (2016)
  8. van der Palm, Daniël W.; van der Ark, L.Andries; Vermunt, Jeroen K.: Divisive latent class modeling as a density estimation method for categorical data (2016)
  9. Francis, Brian; Liu, Jiayi: Modelling escalation in crime seriousness: a latent variable approach (2015)
  10. Lu, Yi; Bolt, Daniel M.: Examining the attitude-achievement paradox in PISA using a multilevel multidimensional IRT model for extreme response style (2015)
  11. Paas, Leonard J.; Bijmolt, Tammo H.A.; Vermunt, Jeroen K.: Long-term developments of respondent financial product portfolios in the EU: a multilevel latent class analysis (2015)
  12. Anderlucci, Laura; Hennig, Christian: The clustering of categorical data: a comparison of a model-based and a distance-based approach (2014)
  13. Codd, Casey L.; Cudeck, Robert: Nonlinear random-effects mixture models for repeated measures (2014)
  14. Forero, Carlos G.; Almansa, Josué; Adroher, Núria D.; Vermunt, Jeroen K.; Vilagut, Gemma; De Graaf, Ron; Haro, Josep-Maria; Caballero, Jordi Alonso: Partial likelihood estimation of IRT models with censored lifetime data: an application to mental disorders in the ESEMeD surveys (2014)
  15. Gollini, Isabella; Murphy, Thomas Brendan: Mixture of latent trait analyzers for model-based clustering of categorical data (2014)
  16. Paas, Leonard J.: Comments on: “Latent Markov models: a review of a general framework for the analysis of longitudinal data with covariates” (2014)
  17. Trezise, Kelly; Reeve, Robert A.: Working memory, worry, and algebraic ability (2014)
  18. Cho, Sun-Joo; Cohen, Allan S.; Bottge, Brian: Detecting intervention effects using a multilevel latent transition analysis with a mixture IRT model (2013)
  19. Cho, Sun-Joo; Cohen, Allan S.; Kim, Seock-Ho: Markov chain Monte Carlo estimation of a mixture item response theory model (2013)
  20. Chung, Yeojin; Rabe-Hesketh, Sophia; Dorie, Vincent; Gelman, Andrew; Liu, Jingchen: A nondegenerate penalized likelihood estimator for variance parameters in multilevel models (2013)

1 2 3 next