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 223 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. 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)
  4. Zhang, Yanqing; Tang, Niansheng: Bayesian empirical likelihood estimation of quantile structural equation models (2017)
  5. Asbeh, Nuaman; Lerner, Boaz: Learning latent variable models by pairwise cluster comparison. II: Algorithm and evaluation (2016)
  6. Bentler, Peter M.: Covariate-free and covariate-dependent reliability (2016)
  7. Chaudhuri, Kausik; Kumbhakar, Subal C.; Sundaram, Lavanya: Estimation of firm performance from a MIMIC model (2016) ioport
  8. Gu, Fei; Wu, Hao: Raw data maximum likelihood estimation for common principal component models: a state space approach (2016)
  9. Jöreskog, Karl G.; Olsson, Ulf H.; Wallentin, Fan Y.: Multivariate analysis with LISREL (2016)
  10. Katsikatsou, Myrsini; Moustaki, Irini: Pairwise likelihood ratio tests and model selection criteria for structural equation models with ordinal variables (2016)
  11. Kreiberg, David; Söderström, Torsten; Yang-Wallentin, Fan: Errors-in-variables system identification using structural equation modeling (2016)
  12. Lin, Shu-Hui; Huang, Yun-Chen: Development and application of a Chinese version of the short attitudes toward mathematics inventory (2016) MathEduc
  13. McNicholas, Paul D.: Model-based clustering (2016)
  14. 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)
  15. 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
  16. Ranalli, Monia; Rocci, Roberto: Mixture models for ordinal data: a pairwise likelihood approach (2016)
  17. Xia, Yemao; Gou, Jianwei: Bayesian semiparametric analysis for latent variable models with mixed continuous and ordinal outcomes (2016)
  18. Xia, Ye-Mao; Tang, Nian-Sheng; Gou, Jian-Wei: Generalized linear latent models for multivariate longitudinal measurements mixed with hidden Markov models (2016)
  19. Abdelmoula, Makki; Chakroun, Wafa; Akrout, Fathi: The effect of sample size and the number of items on reliability coefficients: alpha and rh^o: a meta-analysis (2015)
  20. Backhaus, Klaus; Erichson, Bernd; Weiber, Rolf: Advanced multivariate analysis methods. An application oriented introduction (2015)

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