KARESEL ATAMA İLE ÇOKLU REGRESYON VE LOJİSTİK REGRESYON SONUÇLARININ TEORİ ÇEŞİTLEMESİ KAPSAMINDA KARŞILAŞTIRILMASI

Year 2021, Volume: 21 Issue: 4, 1159 - 1171, 15.12.2021

Mehmet Taha Eser , Gökhan Aksu

https://doi.org/10.17240/aibuefd.2021..-608891

Cited By: 1

Abstract

Bu çalışmada karesel atama ile çoklu regresyon analizinin tanıtılması, karesel atama ile çoklu regresyon ve lojistik regresyon sonuçlarının karşılaştırılması amaçlanmıştır. Araştırma bu yönüyle kuramsal bir araştırmadır. Lojistik regresyon nicel, karesel atama ile çoklu regresyon ise nitel analiz bağlamında kullanılmıştır. Araştırmada karesel atama yöntemi ile çoklu regresyon ve lojistik regresyon kullanılmıştır. Araştırma kapsamında, PISA 2015 sınavına katılan Türk ve Singapurlu öğrencilerin tamamına ilişkin fen okuryazarlığı olası başarı puanları bağımlı değişken ve öğrenci anketinde ST011 kodu ile başlayan ilk yedi madde ise bağımsız değişken olarak kullanılmıştır. Araştırma sonuçlarına göre karesel atama ile çoklu regresyon sonuçlarının yorumlanabilir olma anlamında lojistik regresyona göre daha avantajlı bir yapıya sahip olduğu söylenebilir. Az sayıda istatistik ve yorumlanması daha kolay çıktılar elde edilmesine yardımcı olan karesel atama ile çoklu regresyonun bu anlamda lojistik regresyona alternatif olabileceği düşünülmektedir. Aynı zamanda analizin, lojistik regresyon ve özellikle de sağlık bilimleri alanında lojistik regresyona alternatif bir şekilde kullanılan probit regresyon ile elde edilen ölçme sonuçlarına ilişkin bir geçerlik yöntemi olarak kullanılabileceği göz önünde bulundurulmalıdır.

Keywords

Karesel atama ile çoklu regresyon, lojistik regresyon, teori çeşitlemesi, PISA

COMPARISON OF MULTIPLE REGRESSION QUADRATIC ASSIGNMENT PROCEDURE’S AND LOGISTIC REGRESSION ANALYSIS’ RESULTS WITHIN THE SCOPE OF THEORY TRIANGULATION

Abstract

In this study, it was aimed to introduce Multiple Regression Quadratic Assignment Procedure and to compare the results of Multiple Regression Quadratic Assignment Procedure and Logistic Regression. The research falls within the scope of theory diversification from mixed research types. Logistic regression was used for quantitative analysis, whereas Multiple Regression Quadratic Assignment Procedure was employed for qualitative analysis. Within the scope of the research, the estimated science literacy values of all Turkish and Singaporean students who participated in PISA 2015 were used as dependent variable and the first seven items that started with ST011 code were used as independent variables. According to the results of the research, it can be said that the results of Multiple Regression Quadratic Assignment Procedure have a more advantageous structure than logistic regression in terms of being interpretable. It is thought that Multiple Regression Quadratic Assignment Procedure, which helps to obtain outputs with a small number of statistics and easier interpretation, can be an alternative to logistic regression in this sense. At the same time, it should be considered that the analysis can be used as a validation method for the measurement results obtained with logistic regression, and especially probit regression, which is used as an alternative to logistic regression in the field of health sciences.

Keywords

Multiple regression quadratic assignment procedure, logistic regression, theory triangulation, PISA

References

• Baker, F. B., & Hubert, L. J. (1981). The analysis of social interaction data: A nonparametric technique. Sociological Methods and Research, 9(3), 339-361. https://doi.org/10.1177/004912418100900305
• Cohen, L., Manion, L., & Morrison, K. (2011). Research methods in education (7th ed.). Routledge.
• Coleman, J. S. (1968). The concept of equality of educational opportunity. Harvard Educational Review, 38(1), 7-22. https://doi.org/10.17763/haer.38.1.m3770776577415m2
• Çokluk, Ö., Şekercioğlu, G. & Büyüköztürk, Ş. (2021). Sosyal bilimler için çok değişkenli istatistik: SPSS ve LISREL uygulamaları (6. baskı). Pegem Akademi.
• Dekker, D., Krackhardt, D., & Snijders, T. A. B. (2003, June 22-25). Multicollinearity robust QAP for multiple regression [Paper Presantation]. Annual Conference of the North American Association for Computational Social and Organizational Science, Pittsburgh.
• Dekker, D., Krackhardt, D., & Snijders, T. A. B. (2007). Sensitivity of MRQAP tests to collinearity and autocorrelation conditions. Psychometrika, 72(4), 563-581. https://doi.org/10.1007/s11336-007-9016-1
• Denzin, N. K. (1978). The research act: A theoretical introduction to sociological methods (2nd ed.). McGraw-Hill.
• Denzin, N. K. (1989). Interpretive biography. Sage.
• Denzin, N. K., & Lincoln, Y. S. (1994). Handbook of qualitative research. Sage.
• Efron, B., & Tibshirani, R. J. (1983). An introduction to the bootstrap. Chapman and Hall/CRC.
• Erlandson, D. A., Harris, E. L., Skipper, B. L., & Allen, S. D. (1993). Doing naturalistic inquiry: A guide to methods. Sage.
• Evans, G., & Rosenbaum, J. (2008). Self-regulation and the income-achievement gap. Early Childhood Research Quarterly, 23(4), 504-514. https://doi.org/10.1016/j.ecresq.2008.07.002
• Field, A. P. (2013). Discovering statistics using IBM SPSS Statistics: And sex and drugs and rock’n roll (4th ed.). Sage.
• Flick, U. (2002). An introduction to qualitative research (2nd ed.). Sage.
• Golafshani, N. (2003). Understanding reliability and validity in qualitative research. The Qualitative Report, 8(4), 597-606. https://doi.org/10.46743/2160-3715/2003.1870
• Güzeller, C. O., Eser, M. T., Aksu, G. (2016). UCINET ile sosyal ağ analizi. Maya Akademi.
• Honorone, J. (2017). Understanding the role of triangulation in research. Scholarly Research Journal for Interdisciplinary Studies, 4(31), 91-95.
• Hoque, Z., Covaleski, M. A., & Gooneratne, T. N. (2013). Theoretical triangulation and pluralism in research methods in organizational and accounting research. Accounting, Auditing and Accountability Journal, 26(7), 1170-1198. https://doi.org/10.1108/AAAJ-May-2012-01024
• Hubert, L. J. (1987). Assignment methods in combinatorial data analysis. Dekker.
• Jaccard, J. (2001). Interaction effects in logistic regression. Sage.
• Kleinbaum, D. G., & Klein, M. (2002). Logistic regression: A self-learning text (2nd ed.). Springer.
• Knox, H., Savage, M., & Harvey, P. (2006). Social networks and the study of relations: Networks as method, metaphor and form. Economy and Society, 35(1), 113-140. https://doi.org/10.1080/03085140500465899
• Krackhardt, D. (1987). QAP Partialling as a test of spuriousness. Social Networks, 9(2), 171-186. https://doi.org/10.1016/0378-8733(87)90012-8
• Krackhardt, D. (1988). Predicting with networks: Nonparametric multiple regression analyses of dyadic data. Social Networks, 10(4), 359-382. https://doi.org/10.1016/0378-8733(88)90004-4
• Krackhardt, D., & Kilduff, M. (1999). Whether close or far: Perceptions of balance in friendship networks in organizations. Journal of Personality and Social Psychology, 76(5), 770–782. https://doi.org/10.1037/0022-3514.76.5.770
• Mantel, N. (1967). The detection of disease clustering and a generalized regression approach. Cancer Research, 27(2), 209–220.
• Mertens, D. M., & Hesse-Biber, S. (2012). Triangulation and mixed methods research: Provocative positions. Journal of Mixed Methods Research, 6(2), 75-79. https://doi.org/10.1177/1558689812437100
• Nagelkerke, N. J. D. (1991). A note on the general definition of the coefficient of determination. Biometrika, 78(3), 691-692. https://doi.org/10.1093/biomet/78.3.691
• Nelson, R. E. (1989). The strength of strong ties: Social networks and intergroup conflict in organizations. Academy of Management Journal, 32(2), 377–401. https://doi.org/10.5465/256367
• O’Connell, A. A. (2006). Logistic regression models for ordinal response variables. Sage.
• Sokal, R. R., & Oden, N. L. (1991). Spatial Autocorrelation Analysis as an Inferential Tool in Population Genetics. The American Naturalist, 138(2), 518-521.
• Organisation for Economic Co-operationand Development (OECD). (2015). PISA 2015 technical report. http://www.oecd.org/pisa/data/2015-technical-report/
• Patton, M. Q. (2002). Qualitative research and evaluation methods (3rd ed.). Sage.
• Pitre, N. Y., & Kushner, K. E. (2015). Theoretical triangulation as an extension of feminist intersectionality in qualitative family research. Journal of Family Theory & Review, 7(3), 284-298. https://doi.org/10.1111/jftr.12084
• Riles, A. (2001). The network inside out. University of Michigan Press.
• Sarantakos, S. (2000). Social research. MacMillan.
• Sperandei, S. (2014). Understanding logistic regression analysis. Biochemia Medica, 24(1), 12–18. https://doi.org/10.11613/BM.2014.003
• Streiner, D. L. (1994). Figuring our factors: The use and misuse of Factor analysis. The Canadian Journal of Psychiatry, 39(3), 135-140.
• Yin, R. K. (2003). Case study research design and methods (3th ed.). Sage.