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 336 articles , 1 standard article )

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  1. Kang, Kai; Song, Xinyuan: Consistent estimation of a joint model for multivariate longitudinal and survival data with latent variables (2022)
  2. Papastamoulis, Panagiotis; Ntzoufras, Ioannis: On the identifiability of Bayesian factor analytic models (2022)
  3. Burghgraeve, Elissa; De Neve, Jan; Rosseel, Yves: Estimating structural equation models using James-Stein type shrinkage estimators (2021)
  4. Cho, Eunseong: Neither Cronbach’s alpha nor McDonald’s omega: a commentary on Sijtsma and Pfadt (2021)
  5. Fernando Palluzzi, Mario Grassi: SEMgraph: An R Package for Causal Network Analysis of High-Throughput Data with Structural Equation Models (2021) arXiv
  6. Geminiani, Elena; Marra, Giampiero; Moustaki, Irini: Single- and multiple-group penalized factor analysis: a trust-region algorithm approach with integrated automatic multiple tuning parameter selection (2021)
  7. Merkle, E. C., Fitzsimmons, E., Uanhoro, J., Goodrich, B. : Efficient Bayesian Structural Equation Modeling in Stan (2021) not zbMATH
  8. Rockwood, Nicholas J.: Efficient likelihood estimation of generalized structural equation models with a mix of normal and nonnormal responses (2021)
  9. Ye, Ai; Gates, Kathleen M.; Henry, Teague Rhine; Luo, Lan: Path and directionality discovery in individual dynamic models: a regularized unified structural equation modeling approach for hybrid vector autoregression (2021)
  10. Fisher, Zachary F.; Bollen, Kenneth A.: An instrumental variable estimator for mixed indicators: analytic derivatives and alternative parameterizations (2020)
  11. Grønneberg, Steffen; Moss, Jonas; Foldnes, Njål: Partial identification of latent correlations with binary data (2020)
  12. Ouyang, Ming; Song, Xinyuan: Bayesian local influence of generalized failure time models with latent variables and multivariate censored data (2020)
  13. Panagiotis Papastamoulis, Ioannis Ntzoufras: On the identifiability of Bayesian factor analytic models (2020) arXiv
  14. Pan, Junhao; Ip, Edward Haksing; Dubé, Laurette: Multilevel heterogeneous factor analysis and application to ecological momentary assessment (2020)
  15. Po-Hsien Huang: lslx: Semi-Confirmatory Structural Equation Modeling via Penalized Likelihood (2020) not zbMATH
  16. Rockwood, Nicholas J.: Maximum likelihood estimation of multilevel structural equation models with random slopes for latent covariates (2020)
  17. Wang, Jichuan; Wang, Xiaoqian: Structural equation modeling. Applications using Mplus (2020)
  18. Cui, Ruifei; Bucur, Ioan Gabriel; Groot, Perry; Heskes, Tom: A novel Bayesian approach for latent variable modeling from mixed data with missing values (2019)
  19. Luo, S.; Song, R.; Styner, M.; Gilmore, J. H.; Zhu, H.: FSEM: functional structural equation models for twin functional data (2019)
  20. Meshcheryakov Georgy, Igolkina Anna: semopy: A Python package for Structural Equation Modeling (2019) arXiv

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