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 212 articles , 1 standard article )

Showing results 1 to 20 of 212.
Sorted by year (citations)

1 2 3 ... 9 10 11 next

  1. Asbeh, Nuaman; Lerner, Boaz: Learning latent variable models by pairwise cluster comparison. II: Algorithm and evaluation (2016)
  2. Chaudhuri, Kausik; Kumbhakar, Subal C.; Sundaram, Lavanya: Estimation of firm performance from a MIMIC model (2016)
  3. Gu, Fei; Wu, Hao: Raw data maximum likelihood estimation for common principal component models: a state space approach (2016)
  4. Jöreskog, Karl G.; Olsson, Ulf H.; Wallentin, Fan Y.: Multivariate analysis with LISREL (2016)
  5. Kreiberg, David; Söderström, Torsten; Yang-Wallentin, Fan: Errors-in-variables system identification using structural equation modeling (2016)
  6. Lin, Shu-Hui; Huang, Yun-Chen: Development and application of a Chinese version of the short attitudes toward mathematics inventory (2016)
  7. 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)
  8. 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)
  9. Ranalli, Monia; Rocci, Roberto: Mixture models for ordinal data: a pairwise likelihood approach (2016)
  10. Xia, Yemao; Gou, Jianwei: Bayesian semiparametric analysis for latent variable models with mixed continuous and ordinal outcomes (2016)
  11. Xia, Ye-Mao; Tang, Nian-Sheng; Gou, Jian-Wei: Generalized linear latent models for multivariate longitudinal measurements mixed with hidden Markov models (2016)
  12. 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)
  13. Backhaus, Klaus; Erichson, Bernd; Weiber, Rolf: Advanced multivariate analysis methods. An application oriented introduction (2015)
  14. Foldnes, Njål; Grønneberg , Steffen: How general is the Vale-Maurelli simulation approach? (2015)
  15. Hwang, Heungsun; Takane, Yoshio: Generalized structured component analysis. A component-based approach to structural equation modeling (2015)
  16. Jones, Jeff A.; Waller, Niels G.: The normal-theory and asymptotic distribution-free (ADF) covariance matrix of standardized regression coefficients: theoretical extensions and finite sample behavior (2015)
  17. Lombardi, Luigi; Pastore, Massimiliano; Nucci, Massimo; Bobbio, Andrea: SGR modeling of correlational effects in fake good self-report measures (2015)
  18. Molenaar, Dylan: Heteroscedastic latent trait models for dichotomous data (2015)
  19. Passolunghi, Maria Chiara; Lanfranchi, Silvia; Altoè, Gianmarco; Sollazzo, Nadia: Early numerical abilities and cognitive skills in kindergarten children (2015)
  20. Pek, Jolynn; Wu, Hao: Profile likelihood-based confidence intervals and regions for structural equation models (2015)

1 2 3 ... 9 10 11 next