PRELIS

Prelis (Lisrel pre-processor). PRELIS is a 32-bit application which can be used for: Data manipulation; Data transformation; Data generatiion; Computing moment matrices; Computing asymptotic covariance matrices of sample moments; Imputation by matching; Multiple imputation; Multiple linear regression; Logistic regression; Univariate and multivariate censored regression; ML and MINRES exploratory factor analysis.

Keywords for this software

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References in zbMATH (referenced in 26 articles )

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  1. An, Ji; Stapleton, Laura M.: Book review of: W. H. Finch et al., Multilevel modeling using R (2017)
  2. Lombardi, Luigi; Pastore, Massimiliano; Nucci, Massimo; Bobbio, Andrea: SGR modeling of correlational effects in fake good self-report measures (2015)
  3. Thompson, Ross; Wylie, Judith; Mulhern, Gerry; Hanna, Donncha: Predictors of numeracy performance in undergraduate psychology, nursing and medical students (2015) MathEduc
  4. Byrne, Barbara M.: Structural equation modeling with Lisrel, Prelis, and Simplis. Basic concepts, applications, and programming (2014)
  5. Giofrè, David; Mammarella, Irene Cristina; Cornoldi, Cesare: The relationship among geometry, working memory, and intelligence in children (2014) MathEduc
  6. Tamayo-Torres, Javier; Gutierrez-Gutierrez, Leopoldo; Ruiz-Moreno, Antonia: The relationship between exploration and exploitation strategies, manufacturing flexibility and organizational learning: an empirical comparison between non-ISO and ISO certified firms (2014) ioport
  7. Morlini, Isabella: A latent variables approach for clustering mixed binary and continuous variables within a Gaussian mixture model (2012)
  8. Jain, Sachin; Dowson, Martin: Mathematics anxiety as a function of multidimensional self-regulation and self-efficacy (2009) MathEduc
  9. Muis, Krista R.: Epistemic profiles and self-regulated learning: examining relations in the context of mathematics problem solving (2008) MathEduc
  10. Alwin, Duane F.: Margins of error. A study of reliability in survey measurement. (2007)
  11. Koo, Chulmo; Song, Jaeki; Kim, Yong Jin; Nam, Kichan: Do e-business strategies matter? the antecedents and relationship with firm performance (2007) ioport
  12. Cziráky, Dario; Sambt, Jože; Rovan, Jože; Puljiz, Jakša: Regional development assessment: A structural equation approach (2006)
  13. Goetz, Thomas; Hall, Nathan C.; Frenzel, Anne C.; Pekrun, Reinhard: A hierarchical conceptualization of enjoyment in students (2006) MathEduc
  14. Pituch, Keenan A.; Lee, Yao-kuei: The influence of system characteristics on e-learning use (2006) MathEduc
  15. Chellappa, Ramnath K.; Sin, Raymond G.: Personalization versus privacy: an empirical examination of the online consumer’s dilemma (2005) ioport
  16. Steyer, Rolf; Riedl, Katrin: It is possible to feel good and bad at the same time? New evidence on the bipolarity of mood-state dimensions (2004)
  17. Olinsky, Alan; Chen, Shaw; Harlow, Lisa: The comparative efficacy of imputation methods for missing data in structural equation modeling. (2003)
  18. Alles, Michael; Amershi, Amin; Datar, Srikant; Sarkar, Ratna: Information and incentive effects of inventory in JIT production (2000)
  19. Byrne, Barbara M.: Structural equation modeling with Lisrel, Prelis, and Simplis. Basic concepts, applications, and programming (1998)
  20. Mueller, Ralph O.: Basic principles of structural equation modeling. An introduction to LISREL and EQS (1996)

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