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 272 articles , 1 standard article )

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  1. Chun, So Yeon; Browne, Michael W.; Shapiro, Alexander: Modified distribution-free goodness-of-fit test statistic (2018)
  2. Edgar Merkle; Yves Rosseel: blavaan: Bayesian Structural Equation Models via Parameter Expansion (2018)
  3. Fattore, Marco; Pelagatti, Matteo; Vittadini, Giorgio: A least squares approach to latent variables extraction in formative-reflective models (2018)
  4. Schlittgen, Rainer: Estimation of generalized structured component analysis models with alternating least squares (2018)
  5. Worku, Hailemichael M.; de Rooij, Mark: A multivariate logistic distance model for the analysis of multiple binary responses (2018)
  6. An, Ji; Stapleton, Laura M.: Book review of: W. H. Finch et al., Multilevel modeling using R (2017)
  7. Epskamp, Sacha; Rhemtulla, Mijke; Borsboom, Denny: Generalized network psychometrics: combining network and latent variable models (2017)
  8. Grønneberg, Steffen; Foldnes, Njål: Covariance model simulation using regular vines (2017)
  9. Jin, Shaobo; Yang-Wallentin, Fan: Asymptotic robustness study of the polychoric correlation estimation (2017)
  10. Maydeu-Olivares, Alberto: Assessing the size of model misfit in structural equation models (2017)
  11. Nora Umbach and Katharina Naumann and Holger Brandt and Augustin Kelava: Fitting Nonlinear Structural Equation Models in R with Package nlsem (2017)
  12. Ranalli, Monia; Rocci, Roberto: Mixture models for mixed-type data through a composite likelihood approach (2017)
  13. Ranalli, Monia; Rocci, Roberto: A model-based approach to simultaneous clustering and dimensional reduction of ordinal data (2017)
  14. Yemao, Xia; Maolin, Pan: Bayesian analysis for confirmatory factor model with finite-dimensional Dirichlet prior mixing (2017)
  15. Yuan, Ke-Hai; Bentler, Peter M.: Improving the convergence rate and speed of Fisher-scoring algorithm: ridge and anti-ridge methods in structural equation modeling (2017)
  16. Zhang, Yanqing; Tang, Niansheng: Bayesian empirical likelihood estimation of quantile structural equation models (2017)
  17. Asbeh, Nuaman; Lerner, Boaz: Learning latent variable models by pairwise cluster comparison. II: Algorithm and evaluation (2016)
  18. Bentler, Peter M.: Covariate-free and covariate-dependent reliability (2016)
  19. Cantaluppi, Gabriele; Boari, Giuseppe: A partial least squares algorithm handling ordinal variables (2016)
  20. Chaudhuri, Kausik; Kumbhakar, Subal C.; Sundaram, Lavanya: Estimation of firm performance from a MIMIC model (2016) ioport

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