International Association of Educators   |  ISSN: 2834-7919   |  e-ISSN: 1554-5210

Original article | International Journal of Progressive Education 2019, Vol. 15(2) 30-43

Investigation of the Mediator Variable Effect Using BK, Sobel and Bootstrap Methods (Mathematical Literacy Case)

Selda Örs Özdil & Ömer Kutlu

pp. 30 - 43   |  DOI: https://doi.org/10.29329/ijpe.2019.189.3   |  Manu. Number: MANU-1901-30-0001

Published online: April 06, 2019  |   Number of Views: 341  |  Number of Download: 1106


Abstract

This study aimed to compare different mediation analysis methods (BK, Sobel, and bootstrapping) based on single mediation models for groups of different sizes. For this purpose, the PISA 2012 data for Turkey were used. In order to compare the mediation analysis methods, 4,848 students from Turkey that participated in PISA 2012 were divided into sample groups of 100, 200, 500 and 1,000 individuals. Among the mediation analysis methods discussed within the scope of the research, the BK method was implemented assisted by a regression analysis while for the remaining two methods, SPSS macros were utilized. For the analysis, syntax files were created to be run on SPSS. The results of the analysis of single mediation models revealed that the mathematics anxiety variable mediated the relationship between classroom climate and mathematical literacy. According to the analyses based on all three methods, it was observed that the standard error value increased as the sample group became smaller. Although the standard errors of the Sobel test and bootstrap method were close to each other in large study groups, the former produced less erroneous results in large samples whereas the latter yielded more reliable results in smaller samples.

Keywords: Mediator variable, mediation effect, Sobel test, bootstrap, BK method, PISA


How to Cite this Article?

APA 6th edition
Ozdil, S.O. & Kutlu, O. (2019). Investigation of the Mediator Variable Effect Using BK, Sobel and Bootstrap Methods (Mathematical Literacy Case) . International Journal of Progressive Education, 15(2), 30-43. doi: 10.29329/ijpe.2019.189.3

Harvard
Ozdil, S. and Kutlu, O. (2019). Investigation of the Mediator Variable Effect Using BK, Sobel and Bootstrap Methods (Mathematical Literacy Case) . International Journal of Progressive Education, 15(2), pp. 30-43.

Chicago 16th edition
Ozdil, Selda Ors and Omer Kutlu (2019). "Investigation of the Mediator Variable Effect Using BK, Sobel and Bootstrap Methods (Mathematical Literacy Case) ". International Journal of Progressive Education 15 (2):30-43. doi:10.29329/ijpe.2019.189.3.

References

    Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic and statistical considerations. Journal of Personality and Social Psychology, 51, 1173-1182.
    Burmaoğlu, S., Polat, M., & Meydan, C. H. (2013). Örgütsel davranış alanında ilişkisel analiz yöntemleri ve Türkçe yazında aracılık modeli kullanımı üzerine bir inceleme. Anadolu Üniversitesi Sosyal Bilimler Dergisi, 13(1), 13-26.
    Cheung, G. W., & Lau, R. S. (2008). Testing mediation and suppression effects of latent variables: Bootstrapping with structural equation models. Organizational Research Methods, 11(2), 296-325.
    Cohen, J. P., Cohen, S. G., West, L. S., & Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.). Mahwah, NJ: Lawrence Erlbaum Associates.
    Fairchild, A. J., & MacKinnon, D. P. (2009). A general model for testing mediation and moderation effects. Prevention Science, 10, 87-99.
    Frazier, P. A., Tix, A. P., & Barron, K. E. (2004). Testing moderator and mediator effects in counseling psychology research. Journal of Counseling Psychology, 51, 115-134.
    Hayes, A. F. (2009). Beyond Baron and Kenny: Statistical mediation analysis in the new millenium. Communication Monographs, 76, 408-420.
    Hayes, A. F. (2013). An introduction to mediation, moderation, and conditional process analysis. New York: The Guilford Press.
    Hayes, A. F., & Preacher, K. J. (2014). Statistical mediation analysis with a multicategorical independent variable. British Journal of Mathematical and Statistical Psychology, 67, 451-470.
    Karasar, N. (2008). Bilimsel araştırma yöntemi. Ankara: Nobel Yayın Dağıtım.
    Kenny, D. A., Kashy, D., & Bolger, N. (1998). Data analysis in socialpsychology. In D. Gilbert, S. T. Fiske, and G. Lindzey (Eds.), Handbook of socialpsychology (4th ed., pp. 233-265). New York: McGraw-Hill.
    MacKinnon, D. P. (2008). Introduction to statistical mediation analysis. Mahwah, NJ: Erlbaum.
    MacKinnon, D. P., & Dwyer, J. H. (1993). Estimating mediated effects in prevention studies. Evaluation Review, 17(2), 144-158.
    MacKinnon, D. P., Fairchild, A. J., & Fritz, M. S. (2007). Mediation analysis. Annual Review of Psychology, 58, 593-614.
    MacKinnon, D. P., Lockwood, C. M., Hoffman, J. M., West, S. G., & Sheets, V. (2002). A comparison of methods to test mediation and other intervening variable effects. Psychological Methods, 7, 83-104.
    MacKinnon, D. P., Warsi, G., & Dwyer, J. H. (1995). A simulation study of mediated effect measures. Multivariate Behavioral Research, 30, 41-62.
    Mallinckrodt, B., Abraham, W. T., Wei, M., & Russell, D. W. (2006). Advances in testing the statistical significance of mediation effects. Journal of Counseling Psychology, 53, 372-378.
    MEB. (2013). PISA 2012 ulusal ön raporu. Ankara: YEĞİTEK.
    Mertler, C. A., & Vannatta, R. A. (2005). Advanced and multivariate statistical methods: Practical application and interpretation (3th ed.). CA: Pyrczak Publishing.
    Muller, D., Judd, C. M., & Yzerbyt, V. Y. (2005). When moderation is mediated and mediation is moderated. Journal of Personality and Social Psychology, 89, 852-863.
    OECD (2013). PISA 2012 Results: Ready to Learn: Students’ engagement, driveand self-beliefs (Volume III). OECD Publishing. http://dx.doi.org/10.1787/9789264201170-en
    Pardo, A., & Román, M. (2013). Reflections on the Baron and Kenny model of statistical mediation. Anales de Psicología, 29(2), 614-623.
    Preacher, K. J., & Hayes, A. F. (2004). SPSS and SAS procedures for estimating indirect effects in simple mediation models. Behavior Research Methods, Instruments, and Computers, 36, 717-731.
    Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40, 879-891.
    Shrout, P. E., & Bolger, N. (2002). Mediation in experimental and nonexperimental studies: New procedures and recommendations. Psychological Methods, 7, 422-445.
    Sobel, M. E. (1982). Asymptotic confidence intervals for indirect effects in structural equation models. In S. Leinhardt (Ed.), Sociological methodology, 1982 (pp. 290-312). Washington, DC: American Sociological Association.
    Taylor, A. B., MacKinnon, D. P., & Tein, J. Y. (2008). Tests of three-path mediated effect. Organizational Research Methods, 11(2), 241-269.
    Van Buuren, S. (2011). Multiple imputation of multilevel data. In J. J Hoxand J. K. Roberts (Eds.). The handbook of advanced multilevel analysis (pp. 173-196). New York: Routledge.
    Wu, A. D., & Zumbo, B. D. (2007). Understanding and using mediators and moderators. Social Indicators Research, 87, 367-392.
    Zhao, X., Lynch, J. G., & Chen, Q. (2010). Reconsidering Baron and Kenny: Myths and truths about mediation analysis. Journal of Consumer Research, 37, 197-210