BibTex RIS Cite

Year 2016, Volume: 36 Issue: 2, 0 - 0, 21.12.2016

Şeref Tan

Abstract

References

  • Aktaş, Özlem. (2012). "İlköğretimde Kavram ve Zihin Haritaları ile Desteklenmiş Fen ve Teknoloji Eğitiminin Öğrenme Ürünleri Üzerindeki Etkileri". Yayımlanmamış Yüksek Lisans Tezi, İzmir: Dokuz Eylül Üniversitesi.
  • Arbuckle, J. L. (2013). Amos 22.0 User’s Guide, Amos Development Corporation, USA.
  • Anderson, J. C., & Gerbing, D. W. (1984). The effect of sampling error on convergence, improper solutions, and goodness-of-fit indices for maximum likelihood confirmatory factor analysis. Psychometrika, 49(2), 155-173.
  • Bayram, N. (2009). Sosyal Bilimlerde SPSS ile veri analizi. Bursa: Ezgi Kitapevi.
  • Bayram, N. (2010). Yapısal eşitlik modellemesine giriş AMOS uygulamaları. Bursa:Ezgi Kitapevi.
  • Bentler, P. M., and J. A. Woodward. 1979. Nonexperimental evaluation research: Contributions of causal modeling. In: Improving evaluations, L. Datta and R. Perloff, eds. Beverly Hills: Sage Publications.
  • Bentler, P. M. (1983). Some contributions to efficient statistics in structural models: Specification and estimation of moment structures. Psychometrika, 48, 493–517.
  • Büyüköztürk, Ş. (2006). Sosyal bilimler için veri analizi el kitabı (6. Baskı). Ankara:PegemA Yayıncılık.
  • Cook, T. D., & Campbell, D. T. (1979). Quasi-experimentation: Design and analysis issues for field settings. Chicago: Rand McNally.
  • Cox, D. R. & McCullagh, P. (1982). A Biometrics Invited Paper with Discussion. Some Aspects of Analysis of Covariance. Biometrics. 38-3, 541-561.
  • Glass, G. V., Peckham, P. D. & Sanders, J. R. (1972). Consequences of Failure to Meet Assumptions Underlying the Fixed Effects Analyses of Variance and Covariance. Review of educational research, 42-3, 237-288
  • D’Alonzo, K. T. (2004). The Johnson-Neyman procedure as an alternative to ANCOVA. Western Journal of Nursing Research, 26(7), 804-812.
  • Hays, W. L. (1994). Statistics (5th. ed.). USA:Holt, Rinehart and Winston, Inc.
  • Henson, R. K. (1998). ANCOVA with Intact Groups: Don't Do It!. Paper presented at the Annual Meeting of Educational Research Association.
  • Huitema, E. B. (1980). The analysis of covariance and alternatives. NewYork: John Wiley & Sons.
  • Joreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34, 183–202.
  • Joreskog, K. G. (1970). A general method for analysis of covariance structures. Biometrika, 57, 239–251.
  • Joreskog, K. G. (1971). Statistical analysis of sets of congeneric tests. Psychometrika, 36, 109–133.
  • Joreskog, K. G. (1978). Structural analysis of covariance and correlation matric. Psychometrika, 43, 443–477.
  • Kalaycı, Ş. (Editör) (2006). SPSS uygulamalı çok değişkenli istatistik teknikleri (2. Baskı). Ankara: Asil Yayın Dağıtım.
  • Lawal, B. (2014). Analysis of Covariance. In Applied Statistical Methods in Agriculture, Health and Life Sciences (pp. 503-529). Springer International Publishing.
  • Lesaffre, E., & Senn, S. (2003). A note on non‐parametric ANCOVA for covariate adjustment in randomized clinical trials. Statistics in medicine, 22(23), 3583-3596.
  • Olsson, S. (1973). An experimental study of the effects of training on test scores and factor structure. Uppsala, Sweden: University of Uppsala, Department of Education.
  • Raykov, T. (2010). Analysis of Covariance. Corsini Encyclopedia of Psychology.
  • Stevens, J. P. (2009). Applied multivariate statistics for the social sciences (5th ed.). Taylor & Francis Group.
  • Tan, Ş. (2016). SPSS ve Excel Uygulamalı Temel İstatistik-1. PegemA Yayıncılık

Modified Models of Bentler and Woodward Model of Confirmatory Factor Analysis to Analysis of Covariance

Year 2016, Volume: 36 Issue: 2, 0 - 0, 21.12.2016

Şeref Tan

Abstract

Analysis of covariance is a technique used to fix effects of covariates on dependent variable to test treatment effect in experimental studies. In analysis of covariance it is assumed that covariates are measured perfectly reliable. The assumption of perfectly reliable covariates is almost impossible to meet. In reality, it is almost impossible to measure a covariate without error. In this study, a structural model suggested by Bentler and Woodward (1979) was modified both observed and latent and observed models for a reel data set. These modified models do not require perfectly reliable covariates. In these models the amount of errors in covariates are accounted for testing fit of models and significance of treatment effect.  


* Associate Prof Dr, Gazi Universy Ankara-Türkiye, sereftan4@yahoo.com

References

  • Aktaş, Özlem. (2012). "İlköğretimde Kavram ve Zihin Haritaları ile Desteklenmiş Fen ve Teknoloji Eğitiminin Öğrenme Ürünleri Üzerindeki Etkileri". Yayımlanmamış Yüksek Lisans Tezi, İzmir: Dokuz Eylül Üniversitesi.
  • Arbuckle, J. L. (2013). Amos 22.0 User’s Guide, Amos Development Corporation, USA.
  • Anderson, J. C., & Gerbing, D. W. (1984). The effect of sampling error on convergence, improper solutions, and goodness-of-fit indices for maximum likelihood confirmatory factor analysis. Psychometrika, 49(2), 155-173.
  • Bayram, N. (2009). Sosyal Bilimlerde SPSS ile veri analizi. Bursa: Ezgi Kitapevi.
  • Bayram, N. (2010). Yapısal eşitlik modellemesine giriş AMOS uygulamaları. Bursa:Ezgi Kitapevi.
  • Bentler, P. M., and J. A. Woodward. 1979. Nonexperimental evaluation research: Contributions of causal modeling. In: Improving evaluations, L. Datta and R. Perloff, eds. Beverly Hills: Sage Publications.
  • Bentler, P. M. (1983). Some contributions to efficient statistics in structural models: Specification and estimation of moment structures. Psychometrika, 48, 493–517.
  • Büyüköztürk, Ş. (2006). Sosyal bilimler için veri analizi el kitabı (6. Baskı). Ankara:PegemA Yayıncılık.
  • Cook, T. D., & Campbell, D. T. (1979). Quasi-experimentation: Design and analysis issues for field settings. Chicago: Rand McNally.
  • Cox, D. R. & McCullagh, P. (1982). A Biometrics Invited Paper with Discussion. Some Aspects of Analysis of Covariance. Biometrics. 38-3, 541-561.
  • Glass, G. V., Peckham, P. D. & Sanders, J. R. (1972). Consequences of Failure to Meet Assumptions Underlying the Fixed Effects Analyses of Variance and Covariance. Review of educational research, 42-3, 237-288
  • D’Alonzo, K. T. (2004). The Johnson-Neyman procedure as an alternative to ANCOVA. Western Journal of Nursing Research, 26(7), 804-812.
  • Hays, W. L. (1994). Statistics (5th. ed.). USA:Holt, Rinehart and Winston, Inc.
  • Henson, R. K. (1998). ANCOVA with Intact Groups: Don't Do It!. Paper presented at the Annual Meeting of Educational Research Association.
  • Huitema, E. B. (1980). The analysis of covariance and alternatives. NewYork: John Wiley & Sons.
  • Joreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34, 183–202.
  • Joreskog, K. G. (1970). A general method for analysis of covariance structures. Biometrika, 57, 239–251.
  • Joreskog, K. G. (1971). Statistical analysis of sets of congeneric tests. Psychometrika, 36, 109–133.
  • Joreskog, K. G. (1978). Structural analysis of covariance and correlation matric. Psychometrika, 43, 443–477.
  • Kalaycı, Ş. (Editör) (2006). SPSS uygulamalı çok değişkenli istatistik teknikleri (2. Baskı). Ankara: Asil Yayın Dağıtım.
  • Lawal, B. (2014). Analysis of Covariance. In Applied Statistical Methods in Agriculture, Health and Life Sciences (pp. 503-529). Springer International Publishing.
  • Lesaffre, E., & Senn, S. (2003). A note on non‐parametric ANCOVA for covariate adjustment in randomized clinical trials. Statistics in medicine, 22(23), 3583-3596.
  • Olsson, S. (1973). An experimental study of the effects of training on test scores and factor structure. Uppsala, Sweden: University of Uppsala, Department of Education.
  • Raykov, T. (2010). Analysis of Covariance. Corsini Encyclopedia of Psychology.
  • Stevens, J. P. (2009). Applied multivariate statistics for the social sciences (5th ed.). Taylor & Francis Group.
  • Tan, Ş. (2016). SPSS ve Excel Uygulamalı Temel İstatistik-1. PegemA Yayıncılık
There are 26 citations in total.

Details

Journal Section Articles
Authors

Şeref Tan

Publication Date December 21, 2016
Published in Issue Year 2016 Volume: 36 Issue: 2

Cite

APA Tan, Ş. (2016). Modified Models of Bentler and Woodward Model of Confirmatory Factor Analysis to Analysis of Covariance. Gazi Üniversitesi Gazi Eğitim Fakültesi Dergisi, 36(2).
AMA Tan Ş. Modified Models of Bentler and Woodward Model of Confirmatory Factor Analysis to Analysis of Covariance. GUJGEF. December 2016;36(2).
Chicago Tan, Şeref. “Modified Models of Bentler and Woodward Model of Confirmatory Factor Analysis to Analysis of Covariance”. Gazi Üniversitesi Gazi Eğitim Fakültesi Dergisi 36, no. 2 (December 2016).
EndNote Tan Ş (December 1, 2016) Modified Models of Bentler and Woodward Model of Confirmatory Factor Analysis to Analysis of Covariance. Gazi Üniversitesi Gazi Eğitim Fakültesi Dergisi 36 2
IEEE Ş. Tan, “Modified Models of Bentler and Woodward Model of Confirmatory Factor Analysis to Analysis of Covariance”, GUJGEF, vol. 36, no. 2, 2016.
ISNAD Tan, Şeref. “Modified Models of Bentler and Woodward Model of Confirmatory Factor Analysis to Analysis of Covariance”. Gazi Üniversitesi Gazi Eğitim Fakültesi Dergisi 36/2 (December 2016).
JAMA Tan Ş. Modified Models of Bentler and Woodward Model of Confirmatory Factor Analysis to Analysis of Covariance. GUJGEF. 2016;36.
MLA Tan, Şeref. “Modified Models of Bentler and Woodward Model of Confirmatory Factor Analysis to Analysis of Covariance”. Gazi Üniversitesi Gazi Eğitim Fakültesi Dergisi, vol. 36, no. 2, 2016.
Vancouver Tan Ş. Modified Models of Bentler and Woodward Model of Confirmatory Factor Analysis to Analysis of Covariance. GUJGEF. 2016;36(2).