Narrative Criticism of Positivist Researches of Humanities from the Point of View of Generalizability and Reliability of Measures with the Suggestion of Replacing McDonald's Omega (Case Study: Marketing Researches)

Document Type : علمی - پژوهشی

Authors

1 Instructor of methodological courses, quantitative and qualitative data analysis soft ware at Analysis Academy and MPT Academy

2 Associate Professor,Islamic Azad University, Central Tehran Branch, Department of marketing

3 Doctor of Philosophy,Islamic Azad University, Tehran Science and Research Branch, Department of Agricultural Development

Abstract

Scientific research includes philosophical, procedural, and ethical steps, and studies with a positivist philosophical premise, which show the recent meta-method reviews, still include a share of more than 70% of studies in humanities and social sciences. From a comparative point of view, it dictates a quantitative approach to researchers. In this approach, entering the field of measurement and then discussing the identification of metrics and linking them with variables or at a more abstract level with structures is, if not the most important, one of the most prominent cases. The vagueness and conceptual immanence of these structures in these sciences makes it more and more difficult for researchers to fully realize validity and reliability. With a scientific review and in a narrative format, focusing on the reliability aspect and the generalizability aspect of the results, the researcher enumerates the weak but unfortunately very common indicators in Iran research literature. Then, with an evaluative and pathological point of view, it proposes a suitable solution to overcome this procedural structural weakness by analyzing and introducing a suitable and operational alternative for it, i.e. McDonald's Omega Coefficient. It was estimated that the results of the present study will create a stable and satisfying perspective in changing the viewpoint of researchers in related humanities fields.

Keywords

References

1. Aiken, L. S., West, S. G. & Millsap, R. E. (2008), Doctoral training in statistics, measurement, and methodology in psychology: Replication and extension of Aiken, West, Sechrest, and Reno’s (1990) survey of PhD programs in North America. American Psychologist, 63(1), 32.
2. Aikin, S. F. & Talisse, R. B. (2016), Pragmatism and pluralism revisited. Political Studies Review, 14(1), 17–26. https://doi.org/10.1177/1478929915607886.
3. Avkiran, N. K. & Ringle, C. M. (2018), Partial least squares structural equation modeling (PLS-SEM), Springer International Publishing AG. Retrieved from http://www.emeraldinsight.com/doi/abs/10.1108/EBR-10-2013-0128%5Cnhttp://www.emeraldinsight.com/10.1108/EBR-10-2013-0128.
4. Bhattacherjee, A. (2012), Scholar Commons Social Science Research: Principles, Methods, and Practices. Textbooks Collection. Retrieved from http://scholarcommons.usf.edu/oa_textbookshttp://scholarcommons.usf.edu/oa_textbooks/3.
5. Bielik, L. (2019), methodology of science an introduction (1st ed.). Bratislava: comenius university in bratislava.
6. Bollen, K. A. & Diamantopoulos, A. (2017), Notes on measurement theory for causal-formative indicators: A reply to Hardin, Psychological Methods, 22(3), 605–608. https://doi.org/10.1037/met0000149.
7. Cronbach, L. J. (2004), My current thoughts on coefficient alpha and successor procedures, EDUCATIONAL AND PSYCHOLOGICAL MEASUREMENT, 64(3), 391–418. https://doi.org/10.1177/0013164404266386.
8. DiCiccio, T. J., & Efron, B. (1996). Bootstrap confidence intervals. Statistical Science, 11(3). https://doi.org/10.1214/ss/1032280214.
9. Dunn, T. J., Baguley, T. & Brunsden, V. (2014), From alpha to omega: A practical solution to the pervasive problem of internal consistency estimation. British Journal of Psychology, 105(3), 399–412. https://doi.org/10.1111/bjop.12046.
10. Gignac, G. E. (2014), On the Inappropriateness of Using Items to Calculate Total Scale Score Reliability via Coefficient Alpha for Multidimensional Scales. European Journal of Psychological Assessment, 30(2), 130–139. https://doi.org/10.1027/1015-5759/a000181.
11. Goodboy, A. K. & Martin, M. M. (2020), Omega over alpha for reliability estimation of unidimensional communication measures, Annals of the International Communication Association, 44(4), 422–439. https://doi.org/10.1080/23808985.2020.1846135.
12. Graham, J. M. (2006), Congeneric and (essentially) tau-equivalent estimates of score reliability: What they are and how to use them, Educational and Psychological Measurement, 66(6), 930–944.
13. Green, S. B. & Hershberger, S. L. (2000), Correlated errors in true score models and their effect on coefficient alpha, Structural Equation Modeling, 7(2), 251–270.
14. Green, S. B., Lissitz, R. W. & Mulaik, S. A. (1977), Limitations of coefficient alpha as an index of test unidimensionality1, Educational and Psychological Measurement, 37(4), 827–838.
15. Green, S. B. & Yang, Y. (2009), Commentary on coefficient alpha: A cautionary tale, Psychometrika, 74(1), 121–135.
16. Hayes, A. F. & Coutts, J. J. (2020a), Use Omega Rather than Cronbach’s Alpha for Estimating Reliability, But… Communication Methods and Measures, 14(1), 1–24. https://doi.org/10.1080/19312458.2020.1718629.
17. Hayes, A. F. & Coutts, J. J. (2020b), Use Omega Rather than Cronbach’s Alpha for Estimating Reliability, But…. Communication Methods and Measures, 00(00), 1–24. https://doi.org/10.1080/19312458.2020.1718629.
18. Hoekstra, R., Vugteveen, J., Warrens, M. J. & Kruyen, P. M. (2019), An empirical analysis of alleged misunderstandings of coefficient alpha. International Journal of Social Research Methodology, 22(4), 351–364. https://doi.org/10.1080/13645579.2018.1547523.
19. Huysamen, G. K. (2006), Coefficient alpha: unnecessarily ambiguous; unduly ubiquitous, SA Journal of Industrial Psychology, 32(4), 34–40.
20. Johnson Burke., R. & Christensen, L. (2014), Educational Research: Quantitative, Qualitative, and Mixed Approaches (5th ed., Vol.4).
21. Kaushik, V. & Walsh, C. A. (2019), Pragmatism as a research paradigm and its implications for Social Work research, Social Sciences, 8(9). https://doi.org/10.3390/socsci8090255.
22. Kline, R. B. (2016), Principles and practices of structural equation modelling, Methodology in the social sciences.
23. McCain, K. (2016), The Nature of Scientific Knowledge, The Nature of Scientific Knowledge, Springer International Publishing. https://doi.org/10.1007/978-3-319-33405-9.
24. McDonald, R. P. (1999), Test theory: A unified treatment. Test theory: A unified treatment. Mahwah, NJ, US: Lawrence Erlbaum Associates Publishers.
25. Miller, M. B. (1995), Coefficient alpha: A basic introduction from the perspectives of classical test theory and structural equation modeling.
26. Moradi, M. & Miralmasi, A. (2020), Pragmatic research method (F. Seydi, Ed.) (1st ed.), Tehran: School of quantitative and qualitative research, Retrieved from https://analysisacademy.com/
27. Naik, C. N. K., Gantasala, S. B. & Prabhakar, G. V. (2010), Service quality (SERVQUAL) and its effect on customer satisfaction in retailing, European Journal of Social Sciences, 16(2), 231–243.
28. Neuman, W. L. (2014), Social Research Methods: Qualitative and Quantitative Approaches. Pearson Education Limited (7th ed.), Printed in the United States of America. https://doi.org/10.2307/3211488.
29. Revelle, W. & Zinbarg, R. E. (2008), Coefficients Alpha, Beta, Omega and the glb: Comments on Sijtsma, Psychometrika, 74(1), 145. https://doi.org/10.1007/s11336-008-9102-z.
30. Salzberger, T., Sarstedt, M. & Diamantopoulos, A. (2016), Measurement in the social sciences: where C-OAR-SE delivers and where it does not, European Journal of Marketing, 50(11), 1942–1952. https://doi.org/10.1108/EJM-10-2016-0547.
31. Sarstedt, M, Diamantopoulos, A. & Salzberger, T. (2016), Should we use single items? Better notF_. Journal of Business Research, 69, 3199–3203.
32. Sarstedt, Marko, Bengart, P., Shaltoni, A. M. & Lehmann, S. (2018), The use of sampling methods in advertising research: a gap between theory and practice, International Journal of Advertising, 37(4), 650–663. https://doi.org/10.1080/02650487.2017.1348329.
33. Sijtsma, K. (2009), On the use, the misuse and the very limited usefulness of cronbach’s alpha, Psychometrika, 74(1), 107–120. https://doi.org/10.1007/s11336-008-9101-0.
34. Singh, D. (2019), Understanding philosophical underpinnings of research with respect to various paradigms: Perspective of a research scholar, (April).
35. Smith, S. M. & Albaum, G. S. (2013), Basic Marketing Research Building Your Survey, Published by Qualtrics Labs, Inc.
36. Starkweather, J. (2012), Step out of the past: Stop using coefficient alpha; there are better ways to calculate reliability, Research and Statistical Support.
37. Teo, T. & Fan, X. (2013), Coefficient alpha and beyond: Issues and alternatives for educational research, The Asia-Pacific Education Researcher, 22(2), 209–213.
38. Trizano-Hermosilla, I. & Alvarado, J. M. (2016), Best Alternatives to Cronbach’s Alpha Reliability in Realistic Conditions: Congeneric and Asymmetrical Measurements. Frontiers in Psychology, 7, 769. https://doi.org/10.3389/fpsyg.2016.00769.
39. Wertz, F. J., Charmaz, K., McMullen, L. M., Josselson, R., Anderson, R. & McSpadden, E. (2011), Five Ways of Doing Qualtative Analysis, The Guilford Press, New York (1st ed.).
40. Wilkinson, L. (1999), Statistical methods in psychology journals: Guidelines and explanations, American Psychologist, 54(8), 594–604. https://doi.org/10.1037/0003-066X.54.8.594.
41. Wilkinson, L. & Raykov, T. (1998), Coefficient alpha and composite reliability with interrelated nonhomogeneous items, Applied Psychological Measurement, 22(4), 375–385. https://doi.org/10.1037/0003-066X.54.8.594.
42. Yang, Y. & Green, S. B. (2011), Coefficient alpha: A reliability coefficient for the 21st century? Journal of Psychoeducational Assessment, 29(4), 377–392.
43. Zimmerman, D. W., Zumbo, B. D. & Lalonde, C. (1993), Coefficient alpha as an estimate of test reliability under violation of two assumptions, Educational and Psychological Measurement, 53(1), 33–49.
44. Zinbarg, R. E., Revelle, W., Yovel, I. & Li, W. (2005), Cronbach’s, α Revelle’s β and McDonald’s ω H: Their relations with each other and two alternative conceptualizations of reliability, Psychometrika, 70(1), 123–133. https://doi.org/10.1007/s11336-003-0974-7.

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