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 249 articles , 1 standard article )

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  1. An, Ji; Stapleton, Laura M.: Book review of: W. H. Finch et al., Multilevel modeling using R (2017)
  2. Jin, Shaobo; Yang-Wallentin, Fan: Asymptotic robustness study of the polychoric correlation estimation (2017)
  3. Maydeu-Olivares, Alberto: Assessing the size of model misfit in structural equation models (2017)
  4. Yemao, Xia; Maolin, Pan: Bayesian analysis for confirmatory factor model with finite-dimensional Dirichlet prior mixing (2017)
  5. 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)
  6. Zhang, Yanqing; Tang, Niansheng: Bayesian empirical likelihood estimation of quantile structural equation models (2017)
  7. Asbeh, Nuaman; Lerner, Boaz: Learning latent variable models by pairwise cluster comparison. II: Algorithm and evaluation (2016)
  8. Bentler, Peter M.: Covariate-free and covariate-dependent reliability (2016)
  9. Cantaluppi, Gabriele; Boari, Giuseppe: A partial least squares algorithm handling ordinal variables (2016)
  10. Chaudhuri, Kausik; Kumbhakar, Subal C.; Sundaram, Lavanya: Estimation of firm performance from a MIMIC model (2016) ioport
  11. Gu, Fei; Wu, Hao: Raw data maximum likelihood estimation for common principal component models: a state space approach (2016)
  12. Jöreskog, Karl G.; Olsson, Ulf H.; Wallentin, Fan Y.: Multivariate analysis with LISREL (2016)
  13. Katsikatsou, Myrsini; Moustaki, Irini: Pairwise likelihood ratio tests and model selection criteria for structural equation models with ordinal variables (2016)
  14. Kreiberg, David; Söderström, Torsten; Yang-Wallentin, Fan: Errors-in-variables system identification using structural equation modeling (2016)
  15. Lee, Jihyun: Attitude toward school does not predict academic achievement (2016) MathEduc
  16. Lin, Shu-Hui; Huang, Yun-Chen: Development and application of a Chinese version of the short attitudes toward mathematics inventory (2016) MathEduc
  17. McNicholas, Paul D.: Model-based clustering (2016)
  18. Neale, Michael C.; Hunter, Michael D.; Pritikin, Joshua N.; Zahery, Mahsa; Brick, Timothy R.; Kirkpatrick, Robert M.; Estabrook, Ryne; Bates, Timothy C.; Maes, Hermine H.; Boker, Steven M.: OpenMX 2.0: extended structural equation and statistical modeling (2016)
  19. Niculescu, Alexandra C.; Tempelaar, Dirk T.; Dailey-Hebert, Amber; Segers, Mien; Gijselaers, Wim H.: Extending the change-change model of achievement emotions: the inclusion of negative learning emotions (2016) MathEduc
  20. Ranalli, Monia; Rocci, Roberto: Mixture models for ordinal data: a pairwise likelihood approach (2016)

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