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 327 articles , 1 standard article )

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  1. Fernando Palluzzi, Mario Grassi: SEMgraph: An R Package for Causal Network Analysis of High-Throughput Data with Structural Equation Models (2021) arXiv
  2. Fisher, Zachary F.; Bollen, Kenneth A.: An instrumental variable estimator for mixed indicators: analytic derivatives and alternative parameterizations (2020)
  3. Ouyang, Ming; Song, Xinyuan: Bayesian local influence of generalized failure time models with latent variables and multivariate censored data (2020)
  4. Panagiotis Papastamoulis, Ioannis Ntzoufras: On the identifiability of Bayesian factor analytic models (2020) arXiv
  5. Pan, Junhao; Ip, Edward Haksing; Dubé, Laurette: Multilevel heterogeneous factor analysis and application to ecological momentary assessment (2020)
  6. Po-Hsien Huang: lslx: Semi-Confirmatory Structural Equation Modeling via Penalized Likelihood (2020) not zbMATH
  7. Rockwood, Nicholas J.: Maximum likelihood estimation of multilevel structural equation models with random slopes for latent covariates (2020)
  8. Wang, Jichuan; Wang, Xiaoqian: Structural equation modeling. Applications using Mplus (2020)
  9. Cui, Ruifei; Bucur, Ioan Gabriel; Groot, Perry; Heskes, Tom: A novel Bayesian approach for latent variable modeling from mixed data with missing values (2019)
  10. Luo, S.; Song, R.; Styner, M.; Gilmore, J. H.; Zhu, H.: FSEM: functional structural equation models for twin functional data (2019)
  11. Meshcheryakov Georgy, Igolkina Anna: semopy: A Python package for Structural Equation Modeling (2019) arXiv
  12. Noh, Maengseok; Lee, Youngjo; Oud, Johan H. L.; Toharudin, Toni: Hierarchical likelihood approach to non-Gaussian factor analysis (2019)
  13. Papageorgiou, Ioulia; Moustaki, Irini: Sampling of pairs in pairwise likelihood estimation for latent variable models with categorical observed variables (2019)
  14. Sergio Venturini, Mehmet Mehmetoglu: plssem: A Stata Package for Structural Equation Modeling with Partial Least Squares (2019) not zbMATH
  15. Xia, Ye-Mao; Tang, Nian-Sheng: Bayesian analysis for mixture of latent variable hidden Markov models with multivariate longitudinal data (2019)
  16. Bakk, Zsuzsa; Kuha, Jouni: Two-step estimation of models between latent classes and external variables (2018)
  17. Chun, So Yeon; Browne, Michael W.; Shapiro, Alexander: Modified distribution-free goodness-of-fit test statistic (2018)
  18. Edgar Merkle; Yves Rosseel: blavaan: Bayesian Structural Equation Models via Parameter Expansion (2018) not zbMATH
  19. Fattore, Marco; Pelagatti, Matteo; Vittadini, Giorgio: A least squares approach to latent variables extraction in formative-reflective models (2018)
  20. Jin, Shaobo; Moustaki, Irini; Yang-Wallentin, Fan: Approximated penalized maximum likelihood for exploratory factor analysis: an orthogonal case (2018)

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