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.


References in zbMATH (referenced in 37 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. Kuvvetli, Ümit; Firuzan, Ali Rıza; Alpaykut, Süleyman; Gerger, Atakan: Determining six sigma success factors in Turkey by using structural equation modeling (2016)
  3. Lee, Jihyun: Attitude toward school does not predict academic achievement (2016) MathEduc
  4. Lombardi, Luigi; Pastore, Massimiliano; Nucci, Massimo; Bobbio, Andrea: SGR modeling of correlational effects in fake good self-report measures (2015)
  5. Thompson, Ross; Wylie, Judith; Mulhern, Gerry; Hanna, Donncha: Predictors of numeracy performance in undergraduate psychology, nursing and medical students (2015) MathEduc
  6. Byrne, Barbara M.: Structural equation modeling with Lisrel, Prelis, and Simplis. Basic concepts, applications, and programming (2014)
  7. Giofrè, David; Mammarella, Irene Cristina; Cornoldi, Cesare: The relationship among geometry, working memory, and intelligence in children (2014) MathEduc
  8. Ribes-Giner, Gabriela; Fuentes-Blasco, Maria: Influence of candidate qualities and previous president performance in voting intentions (2014)
  9. 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
  10. Morlini, Isabella: A latent variables approach for clustering mixed binary and continuous variables within a Gaussian mixture model (2012)
  11. Lu, Tong-Yu; Poon, Wai-Yin; Tsang, Yim-Fan: Latent growth curve modeling for longitudinal ordinal responses with applications (2011)
  12. Mateos-Aparicio, Gregoria: Partial least squares (PLS) methods: origins, evolution, and application to social sciences (2011)
  13. Ren, Steven Ji-Fan; Ngai, E. W. T.; Cho, Vincent: Examining the determinants of outsourcing partnership quality in Chinese small- and medium-sized enterprises (2010)
  14. Schermelleh-Engel, Karin; Werner, Christina S.; Klein, Andreas G.; Moosbrugger, Helfried: Nonlinear structural equation modeling: is partial least squares an alternative? (2010)
  15. Wei, Pao-Lien; Huang, Jen-Hung; Tzeng, Gwo-Hshiung; Wu, Shwu-Ing: Causal modeling of web-advertising effects by improving SEM based on DEMATEL technique (2010)
  16. Jain, Sachin; Dowson, Martin: Mathematics anxiety as a function of multidimensional self-regulation and self-efficacy (2009) MathEduc
  17. Pan, Jason Chao-Hsien; Tai, Damon He: Implementing virtual metrology for in-line quality control in semiconductor manufacturing (2009)
  18. Muis, Krista R.: Epistemic profiles and self-regulated learning: examining relations in the context of mathematics problem solving (2008) MathEduc
  19. Alwin, Duane F.: Margins of error. A study of reliability in survey measurement. (2007)
  20. Koo, Chulmo; Song, Jaeki; Kim, Yong Jin; Nam, Kichan: Do e-business strategies matter? the antecedents and relationship with firm performance (2007) ioport

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