LISREL

During the last thirty eight years, the LISREL model, methods and software have become synonymous with structural equation modeling (SEM). SEM allows researchers in the social sciences, management sciences, behavioral sciences, biological sciences, educational sciences and other fields to empirically assess their theories. These theories are usually formulated as theoretical models for observed and latent (unobservable) variables. If data are collected for the observed variables of the theoretical model, the LISREL program can be used to fit the model to the data.


References in zbMATH (referenced in 303 articles , 1 standard article )

Showing results 1 to 20 of 303.
Sorted by year (citations)

1 2 3 ... 14 15 16 next

  1. Ouyang, Ming; Song, Xinyuan: Bayesian local influence of generalized failure time models with latent variables and multivariate censored data (2020)
  2. Panagiotis Papastamoulis, Ioannis Ntzoufras: On the identifiability of Bayesian factor analytic models (2020) arXiv
  3. Pan, Junhao; Ip, Edward Haksing; Dubé, Laurette: Multilevel heterogeneous factor analysis and application to ecological momentary assessment (2020)
  4. Po-Hsien Huang: lslx: Semi-Confirmatory Structural Equation Modeling via Penalized Likelihood (2020) not zbMATH
  5. Cui, Ruifei; Bucur, Ioan Gabriel; Groot, Perry; Heskes, Tom: A novel Bayesian approach for latent variable modeling from mixed data with missing values (2019)
  6. Luo, S.; Song, R.; Styner, M.; Gilmore, J. H.; Zhu, H.: FSEM: functional structural equation models for twin functional data (2019)
  7. Meshcheryakov Georgy, Igolkina Anna: semopy: A Python package for Structural Equation Modeling (2019) arXiv
  8. Noh, Maengseok; Lee, Youngjo; Oud, Johan H. L.; Toharudin, Toni: Hierarchical likelihood approach to non-Gaussian factor analysis (2019)
  9. Papageorgiou, Ioulia; Moustaki, Irini: Sampling of pairs in pairwise likelihood estimation for latent variable models with categorical observed variables (2019)
  10. Sergio Venturini, Mehmet Mehmetoglu: plssem: A Stata Package for Structural Equation Modeling with Partial Least Squares (2019) not zbMATH
  11. Xia, Ye-Mao; Tang, Nian-Sheng: Bayesian analysis for mixture of latent variable hidden Markov models with multivariate longitudinal data (2019)
  12. Bakk, Zsuzsa; Kuha, Jouni: Two-step estimation of models between latent classes and external variables (2018)
  13. Chun, So Yeon; Browne, Michael W.; Shapiro, Alexander: Modified distribution-free goodness-of-fit test statistic (2018)
  14. Edgar Merkle; Yves Rosseel: blavaan: Bayesian Structural Equation Models via Parameter Expansion (2018) not zbMATH
  15. Fattore, Marco; Pelagatti, Matteo; Vittadini, Giorgio: A least squares approach to latent variables extraction in formative-reflective models (2018)
  16. Jin, Shaobo; Moustaki, Irini; Yang-Wallentin, Fan: Approximated penalized maximum likelihood for exploratory factor analysis: an orthogonal case (2018)
  17. Schlittgen, Rainer: Estimation of generalized structured component analysis models with alternating least squares (2018)
  18. Worku, Hailemichael M.; de Rooij, Mark: A multivariate logistic distance model for the analysis of multiple binary responses (2018)
  19. An, Ji; Stapleton, Laura M.: Book review of: W. H. Finch et al., Multilevel modeling using R (2017)
  20. Epskamp, Sacha; Rhemtulla, Mijke; Borsboom, Denny: Generalized network psychometrics: combining network and latent variable models (2017)

1 2 3 ... 14 15 16 next