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MESLEKİ KARAR ENVANTERİ FAKTÖR YAPISINA AİT FARKLI ÖLÇME MODELLERİNİN DOĞRULAYICI FAKTÖR ANALİZİ İLE İNCELENMESİ

Year 2019, Volume: 19 Issue: 3, 1068 - 1078, 11.10.2019
https://doi.org/10.17240/aibuefd.2019.19.49440-585745

Abstract

Bu araştırmanın amacı, lise öğrencilerinin
mesleki kararsızlık düzeylerini ölçmek için Çakır (2004) tarafında geliştirilen
Mesleki Karar Envanterinin faktör yapısını farklı modeller çerçevesinde
doğrulayıcı faktör analiziyle incelemektir. Mesleki Karar Envanterinin ön
çalışmasında Çakır (2004) tarafından açımlayıcı faktör analizi ile envanterin
faktör çözümlenmesi yapılmıştır. Envanterin geliştirilme aşamasında 5 faktör
toplam 30 madde içeren formu ortaya konulmuştur. Bu araştırmada ise ön
çalışmada ortaya konulan yapı tek boyut, ilişkili birinci düzey, ilişkisiz
birinci düzey, ikinci düzey ve iki-faktör model olmak üzere 5 farklı model
çerçevesinde doğrulayıcı faktör analizi ile test edilmiştir. Yapılan
doğrulayıcı faktör analizlerinde, Mesleki Karar Envanterinin ikinci düzey
yapısının bir model olarak doğrulandığı sonucuna ulaşılmıştır. Bu doğrultuda
İçsel Çatışma, Kendini Yeterince Tanımama, Meslek ve Alan Bilgisi Eksikliği,
Meslek Seçiminde Akılcı Olmayan İnançlar ve Dışsal Çatışmalar değişkenlerinin
Mesleki Karar yapısını oluşturduğu söylenilebilir. Ayrıca genel yapı olan
Mesleki Karar üzerinde en çok etkiye sahip değişkenin Meslek ve Alan Bilgisi
Eksikliği olduğu belirlenmiştir. Envanter için hesaplanan ortalama açıklanan
varyans ve yapı güvenirlik katsayılarının da yeterli düzeydedir. 

References

  • Alpar, R. (2017). Uygulamalı çok değişkenli istatistiksel yöntemler. Ankara: Detay Yayıncılık.
  • Beaujean, A.A., Parkin, J., & Parker, S. (2014). Comparing Cattell-Horn-Carroll factor models: Differences between bifactor and higher order factor models in predicting language achievement. Psychological. Assessment, 26(3), 789–805.
  • Benson, N., Hulac, D.M., & Bernstein, J.D. (2013). An independent confirmatory factor analysis of the Wechsler Intelligence Scale for Children—Fourth Edition (WISC-IV) Integrated: What do the process approach subtests measure? Psychological Assessment, 25(3), 692–705.
  • Bentler, P. M. & Chou, C. P. (1987). Practical issues in structural equation modeling. Sociological Methods and Research, 16(1), 78–117.
  • Boomsma, A., & Hoogland, J. J. (2001). The robustness of LISREL modeling revisited. In R. Cudeck, S. du Toit, & D. Sörbom (Eds.), Structural equation models: Present and future. A Festschrift in honor of Karl Jöreskog (pp. 139–168), Chicago: Scientific Software.
  • Brown, T. A. (2015). Confirmatory factor analysis for applied research. The Guilford Press: New York London. Byrne, B. M. (1998). Structural equation modelling with LISREL, PRELIS, and SIMPLIS: basic concepts, applications, and programming. Mahwah, NJ: L. Erlbaum.
  • Canivez, G. L. (2016). Bifactor modeling in construct validation of multifactored tests: Implications for multidimensionality and test interpretation. In K. Schweizer & C.
  • DiStefano (Eds.), Principles and methods of test construction: Standards and recent advancements (pp. 247–271). Gottingen, Germany: Hogrefe.
  • Cucina, J. & Byle, K. (2017). The bifactor model fits better than the higher-order model in more than 90% of comparisons for mental abilities test batteries. Journal of Intelligence, 5(3), 27.
  • Çakir, M. A. (2004). Mesleki karar envanterinin geliştirilmesi [The development of career decision inventory]. Ankara Üniversitesi Eğitim Bilimleri Fakültesi Dergisi, 37(2), 1-14.
  • Flores-Kanter, P. E., Dominguez-Lara, S., Trógolo, M. A., & Medrano, L. A. (2018). Best practices in the use of bifactor models: Conceptual grounds, fit indices and complementary indicators. Revista Evaluar, 18(3), 44-48. Retrieved March 15, 2019 from https://revistas.unc.edu.ar/index.php/revaluar
  • Gignac, G. E. (2008). Higher-order models versus direct hierarchical models: g as superordinate or breadth factor? Psychology Science, 50(1), 21–43.
  • Gignac, G.E. (2016). The higher-order model imposes a proportionality constraint: That is why the bifactor model tends to fit better. Intelligence, 55, 57–68.
  • Gignac, G.E. & Watkins, M.W. (2013). Bifactor modeling and the estimation of model-based reliability in the WAIS-IV. Multivariate Behavioral Research, 48(5), 639–662.
  • Hair, J.F., Black, W. C., Barry, J.B., & Anderson, R.E. (2014). Multivariate data analysis. (Seventh edition, Pearson new international edition). Harlow: Pearson Education Limited.
  • Hooper, D., Coughlan, J., & Mullen, M. (2008). Structural equation modeling: Guidelines for determining model fit. Electronic Journal of Business Research Methods, 6(1), 53-60.
  • Hu, L.T. & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1-55. http:l/doi.org/10.1080/10705519909540118
  • Jackson, D. L. (2001). Sample size and number of parameter estimates in maximum likelihood confirmatory factor analysis: A Monte Carlo investigation. Structural Equation Modeling, 8(2), 205–223.
  • Jöreskog, K. & Sörbom, D. (1993). LISREL 8: Structural equation modeling with the SIMPLIS command language. Chicago, IL: Scientific Software International Inc.Kline, B.R. (2005, 2015). Principles and practice of structural modeling. New York-London: The Guilford Press.
  • Meydan, C. H. & Şeşen, H. (2015). Yapısal eşitlik modellemesi AMOS uygulamaları. Ankara: Seçkin Yayıncılık
  • Moshagen, M. & Musch, J. (2014). Sample size requirements of the robust weighted least squares estimator. Methodology, 10(2), 60–70.
  • Murray, A. L. & Johnson, W. (2013). The limitations of model fit in comparing the bi-factor versus higher-order models of human cognitive ability structure. Intelligence, 41(5), 407–422.
  • Worthington, R. L., & Whittaker, T. A. (2006). Scale development research: A content analysis and recommendations for best practices. The Counseling Psychologist, 34(6), 806-838.
  • Reise, S. P. (2012). The rediscovery of bifactor measurement models. Multivariate Behavioral Research, 47(5), 667–696. http://dx.doi.org/10 .1080/00273171.2012.715555
  • Reise, S. P., Kim, K. H., Mansolf, M., & Widaman, K. F. (2016). Is the bifactor model a better model or is it just better at modeling implausible responses? Application of iteratively reweighted least squares to the Rosenberg Self- Esteem Scale. Multivariate Behavioral Research, 51(6):818-838.
  • Rodriguez, A., Reise, S. P., & Haviland, M. G. (2016). Applying bifactor statistical indices in the eval¬uation of psychological measures. Journal of Personality Assessment, 98(3), 223-237. doi: 10.1080/00223891.2015.1089249
  • Schermelleh-Engel, K., Moosbrugger, H., & Müller, H., (2003). Evaluating the fit of structural equation models: Test of significance and descriptive goodness-of-fit measures. Methods of Psychological Research - Online, 8(2), 23-74.
  • Sümer, N. (2000). Yapısal eşitlik modelleri: Temel kavramlar ve örnek uygulamalar. Türk Psikoloji Yazıları, 3(6), 49-74.
  • Şimşek, Ö.F. (2007). Yapısal eşitlik modellemesine giriş: Temel ilkeler ve LISREL uygulamaları. Ankara: Ekinoks Yayınları.
  • Tabachnick, B.G. & Fidell, L.S. (2001). Using multivariate statistics. New York: Allyn &Bacon Inc.
  • Watkins, M. W. (2010). Structure of the Wechsler Intelligence Scale for Children-fourth edition among a national sample of referred students. Psychological Assessment, 22(4), 782–787.
Year 2019, Volume: 19 Issue: 3, 1068 - 1078, 11.10.2019
https://doi.org/10.17240/aibuefd.2019.19.49440-585745

Abstract

References

  • Alpar, R. (2017). Uygulamalı çok değişkenli istatistiksel yöntemler. Ankara: Detay Yayıncılık.
  • Beaujean, A.A., Parkin, J., & Parker, S. (2014). Comparing Cattell-Horn-Carroll factor models: Differences between bifactor and higher order factor models in predicting language achievement. Psychological. Assessment, 26(3), 789–805.
  • Benson, N., Hulac, D.M., & Bernstein, J.D. (2013). An independent confirmatory factor analysis of the Wechsler Intelligence Scale for Children—Fourth Edition (WISC-IV) Integrated: What do the process approach subtests measure? Psychological Assessment, 25(3), 692–705.
  • Bentler, P. M. & Chou, C. P. (1987). Practical issues in structural equation modeling. Sociological Methods and Research, 16(1), 78–117.
  • Boomsma, A., & Hoogland, J. J. (2001). The robustness of LISREL modeling revisited. In R. Cudeck, S. du Toit, & D. Sörbom (Eds.), Structural equation models: Present and future. A Festschrift in honor of Karl Jöreskog (pp. 139–168), Chicago: Scientific Software.
  • Brown, T. A. (2015). Confirmatory factor analysis for applied research. The Guilford Press: New York London. Byrne, B. M. (1998). Structural equation modelling with LISREL, PRELIS, and SIMPLIS: basic concepts, applications, and programming. Mahwah, NJ: L. Erlbaum.
  • Canivez, G. L. (2016). Bifactor modeling in construct validation of multifactored tests: Implications for multidimensionality and test interpretation. In K. Schweizer & C.
  • DiStefano (Eds.), Principles and methods of test construction: Standards and recent advancements (pp. 247–271). Gottingen, Germany: Hogrefe.
  • Cucina, J. & Byle, K. (2017). The bifactor model fits better than the higher-order model in more than 90% of comparisons for mental abilities test batteries. Journal of Intelligence, 5(3), 27.
  • Çakir, M. A. (2004). Mesleki karar envanterinin geliştirilmesi [The development of career decision inventory]. Ankara Üniversitesi Eğitim Bilimleri Fakültesi Dergisi, 37(2), 1-14.
  • Flores-Kanter, P. E., Dominguez-Lara, S., Trógolo, M. A., & Medrano, L. A. (2018). Best practices in the use of bifactor models: Conceptual grounds, fit indices and complementary indicators. Revista Evaluar, 18(3), 44-48. Retrieved March 15, 2019 from https://revistas.unc.edu.ar/index.php/revaluar
  • Gignac, G. E. (2008). Higher-order models versus direct hierarchical models: g as superordinate or breadth factor? Psychology Science, 50(1), 21–43.
  • Gignac, G.E. (2016). The higher-order model imposes a proportionality constraint: That is why the bifactor model tends to fit better. Intelligence, 55, 57–68.
  • Gignac, G.E. & Watkins, M.W. (2013). Bifactor modeling and the estimation of model-based reliability in the WAIS-IV. Multivariate Behavioral Research, 48(5), 639–662.
  • Hair, J.F., Black, W. C., Barry, J.B., & Anderson, R.E. (2014). Multivariate data analysis. (Seventh edition, Pearson new international edition). Harlow: Pearson Education Limited.
  • Hooper, D., Coughlan, J., & Mullen, M. (2008). Structural equation modeling: Guidelines for determining model fit. Electronic Journal of Business Research Methods, 6(1), 53-60.
  • Hu, L.T. & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1-55. http:l/doi.org/10.1080/10705519909540118
  • Jackson, D. L. (2001). Sample size and number of parameter estimates in maximum likelihood confirmatory factor analysis: A Monte Carlo investigation. Structural Equation Modeling, 8(2), 205–223.
  • Jöreskog, K. & Sörbom, D. (1993). LISREL 8: Structural equation modeling with the SIMPLIS command language. Chicago, IL: Scientific Software International Inc.Kline, B.R. (2005, 2015). Principles and practice of structural modeling. New York-London: The Guilford Press.
  • Meydan, C. H. & Şeşen, H. (2015). Yapısal eşitlik modellemesi AMOS uygulamaları. Ankara: Seçkin Yayıncılık
  • Moshagen, M. & Musch, J. (2014). Sample size requirements of the robust weighted least squares estimator. Methodology, 10(2), 60–70.
  • Murray, A. L. & Johnson, W. (2013). The limitations of model fit in comparing the bi-factor versus higher-order models of human cognitive ability structure. Intelligence, 41(5), 407–422.
  • Worthington, R. L., & Whittaker, T. A. (2006). Scale development research: A content analysis and recommendations for best practices. The Counseling Psychologist, 34(6), 806-838.
  • Reise, S. P. (2012). The rediscovery of bifactor measurement models. Multivariate Behavioral Research, 47(5), 667–696. http://dx.doi.org/10 .1080/00273171.2012.715555
  • Reise, S. P., Kim, K. H., Mansolf, M., & Widaman, K. F. (2016). Is the bifactor model a better model or is it just better at modeling implausible responses? Application of iteratively reweighted least squares to the Rosenberg Self- Esteem Scale. Multivariate Behavioral Research, 51(6):818-838.
  • Rodriguez, A., Reise, S. P., & Haviland, M. G. (2016). Applying bifactor statistical indices in the eval¬uation of psychological measures. Journal of Personality Assessment, 98(3), 223-237. doi: 10.1080/00223891.2015.1089249
  • Schermelleh-Engel, K., Moosbrugger, H., & Müller, H., (2003). Evaluating the fit of structural equation models: Test of significance and descriptive goodness-of-fit measures. Methods of Psychological Research - Online, 8(2), 23-74.
  • Sümer, N. (2000). Yapısal eşitlik modelleri: Temel kavramlar ve örnek uygulamalar. Türk Psikoloji Yazıları, 3(6), 49-74.
  • Şimşek, Ö.F. (2007). Yapısal eşitlik modellemesine giriş: Temel ilkeler ve LISREL uygulamaları. Ankara: Ekinoks Yayınları.
  • Tabachnick, B.G. & Fidell, L.S. (2001). Using multivariate statistics. New York: Allyn &Bacon Inc.
  • Watkins, M. W. (2010). Structure of the Wechsler Intelligence Scale for Children-fourth edition among a national sample of referred students. Psychological Assessment, 22(4), 782–787.
There are 31 citations in total.

Details

Primary Language Turkish
Journal Section Articles
Authors

Funda Nalbantoğlu Yılmaz 0000-0002-3228-4605

Publication Date October 11, 2019
Submission Date July 2, 2019
Published in Issue Year 2019 Volume: 19 Issue: 3

Cite

APA Nalbantoğlu Yılmaz, F. (2019). MESLEKİ KARAR ENVANTERİ FAKTÖR YAPISINA AİT FARKLI ÖLÇME MODELLERİNİN DOĞRULAYICI FAKTÖR ANALİZİ İLE İNCELENMESİ. Abant İzzet Baysal Üniversitesi Eğitim Fakültesi Dergisi, 19(3), 1068-1078. https://doi.org/10.17240/aibuefd.2019.19.49440-585745