GÖRSEL SUNUM İLE GÜVEN ARALIKLARI KAVRAMINI ANLAMA

Abstract

Bu çalışmada doğru kullanım ve yorumlama ile güven aralıklarının araştırmalarda sunulmasının önemi üzerinde durulmuştur. American Psychological Association (APA) (2010) yayım kılavuzu, güven aralıkları değerlerine çok önem vermekte ve çalışmalarda rapor edilmesi gerektiğini belirtmektedir. Araştırmacılar tarafından eksik ve yanlış da yorumlanabilen güven aralıkları bu çalışmada ayrıntılı olarak ele alınmış, kitaplardan da örnekler verilerek doğru ve yanlış tanımlar değerlendirilmiş ve görsel sunum ile örneklendirilerek okuyucuların güven aralıkları konusunu daha kolay anlamaları amaçlanmıştır

Keywords

• güven aralıkları, kavram yanılgısı, doğru yorumlama, etki büyüklüğü için güvenaralıkları

Understanding Confidence Intervals With Visual Representations

Abstract

In the present paper, we showed how confidence intervals (CIs) are valuable and useful in research studies when they are used in the correct form with correct interpretations. The sixth edition of the APA (2010) Publication Manual strongly recommended reporting CIs in research studies, and it was described as “the best reporting strategy” (p. 34). Misconceptions and correct interpretations of CIs were presented from several textbooks. In addition, limitations of the null hypothesis statistical significance test (NHSST) were discussed, and using CIs was discussed as an alternative to the NHSST. Finally, the calculation and the visual representation of CIs for mean and effect size were illustrated to help readers comprehend the concept of CIs

Keywords

• confidence intervals, misconception, correct interpretation, confidence intervals for effect sizes

References

1. American Psychological Association. (2001). Publication manual of the American Psychological Association (5th ed.). Washington, DC: Author.
2. American Psychological Association. (2010). Publication manual of the American Psychological Association (6th ed.). Washington, DC: Author.
3. Algina, J., Keselman, H. J. (2003). Approximate confidence intervals for effect sizes. Educational and Psychological Measurement, 63, 537-553.
4. Belia, S., Fidler, F., Williams, J., & Cumming, G. (2005). Researchers misunderstand confidence intervals and standard error bars. Psychological Methods, 10, 389-396. doi: 10.1037/1082-989X.10.4.389
5. Byrd, J. K. (2007). A call for statistical reform in EAQ. Educational Administration Quarterly, 43, 381-391. doi: 10.1177/0013161X06297137
6. Cumming, G. (2011). Understanding the new statistics: Effect sizes, confidence intervals, and meta-analysis. New York: Routledge.
7. Cumming, G., & Fidler, F. (2009). Confidence intervals: Better answers to better questions. Journal of Psychology, 217, 15-26. doi:10.1027/0044- 3409.217.1.15
8. Cumming, G., & Finch, S. (2001). A primer on the understanding, use and calculation of confidence intervals that are based on central and noncentral distributions. Educational doi: 10.1177/0013164401614002 Measurement, 61, 532-575.

9. Cumming, G., & Finch, S. (2005). Inference by eye: Confidence intervals, and how to read doi: 10.1037/0003-066X.60.2.170 American Psychologist, 60, 170-180.
10. Cumming, G., Williams, J. & Fidler, F. (2004). Replication, and researchers' understanding of confidence intervals and standard error bars. Understanding statistics, 3, 299-311. doi: 10.1207/s15328031us0304_5
11. Di Stefano, J. (2004). A confidence interval approach to data analysis. Forest Ecology and Management, 187, 173–183. doi:10.1016/S0378-1127(03)00331-1
12. Fidler, F., & Loftus, G. (2009). Why figures with error bars should replace p values: Some conceptual arguments and empirical demonstrations. Journal of Psychology, 217, 27-37. doi: 10.1027/0044-3409.217.1.27
13. Fidler, F., & Thompson, B. (2001). Computing correct confidence intervals for ANOVA fixed- and random-effects effect sizes. Educational and Psychological Measurement, 61, 575–604. doi: 10.1177/0013164401614003
14. Finch, S., Cumming, G., & Thomason, N. (2001). Reporting of statistical inference in the Journal of Applied Psychology: Little evidence of reform. Educational and doi: 10.1177/00131640121971167 Measurement, 61, 181-210.
15. Good, P. I., & Hardin, J. W. (2003). Common errors in statistics (and how to avoid them). New York: Wiley.
16. Hagen, R. L. (1997). In praise of the null hypothesis statistical test. American Psychologist, 52, 15-24. doi: 10.1037/0003-066X.52.1.15
17. Howell, D. C. (2008). Fundamental statistics for the behavioral sciences (6th ed.). Belmond, California: Thomson.
18. Keller, G., & Warrack, B. (2003). Statistics for management and economics (6th ed.). Pacific Grove, CA: Thomson Learning.
19. Kelley, K. (2007). Confidence intervals for standardizes effect sizes: Theory, application, and implementation. Journal of Statistical Software, 20, 1-24.
20. Kline, R. (2004). Beyond significance testing: Reforming data analysis methods in behavioral research. Washington, DC: American Psychological Association.
21. Knapp, T. R. & Sawilowsky, S. S. (2001). Constructive criticisms of methodological and editorial practices. The Journal of Experimental Education, 70, 65-79.
22. Lomax, R. G. (2001). An introduction to statistical concepts for education and behavioral sciences. Mahwah, NJ: Lawrence Erlbaum.
23. Lynch, S. M. (2007). Introduction to applied Bayesian statistics and estimation for social scientists. New York: Springer.
24. Oakes, M. (1986). Statistical inference: A commentary for the social and behavioral sciences. New York: Wiley.
25. Ott, R. L., & Longnecker, M. (2010). An introduction to statistical methods and data analysis (6th ed.). Belmond, CA: Brooks/Cole.
26. Pagano, R. R. (2009). Understanding statistics in the behavioral sciences. (9th ed.). Belmont, CA: Wadsworth.
27. Schmidt, F. L. (1996). Statistical significance testing and cumulative knowledge in psychology: Implications for the training of researchers. Psychological Methods, 1, 115-129. doi: 2048/10.1037/1082-989X.1.2.115
28. Smithson, M. (2001). Correct confidence intervals for various regression effect sizes and paramaters: The importance of noncentral distributions in computing intervals. Educational and Psychological Measurement, 61, 605-632. doi: 10.1177/00131640121971392
29. Thompson, B. (1996). AERA editorial policies regarding statistical significance testing: Three suggested reforms. Educational Researcher, 25(2), 26-30. doi: 10.3102/0013189X025002026
30. Thompson, B. (1998). In praise of brilliance: Where that praise really belongs. American Psychologist, 53, 799-800. doi: 2048/10.1037/0003-066X.53.7.799
31. Thompson, B. (1999). If statistical significance tests are broken/misused, what practices should supplement or replace them? Theory & Psychology, 9, 167-183. doi: 10.1177/095935439992006
32. Thompson, B. (2001). Significance, effect sizes, stepwise methods, and other issues: Strong arguments move the field. Journal of Experimental Education, 70, 80-93. doi: 10.1080/00220970109599499
33. Thompson, B. (2002). What future quantitative social science research could look like: Confidence intervals for effect sizes. Educational Researcher, 31(3), 24-31. doi: 10.3102/0013189X031003025
34. Thompson, B. (2006a). Foundations of behavioral statistics: An insight-based approach. New York: Guilford.
35. Thompson, B. (2006b). Research synthesis: Effect sizes. In J. Green, G. Camilli, & P.B. Elmore (Eds.), Handbook of complementary methods in education research (pp. 583-603). Washington, DC: American Educational Research Association.
36. Thompson, B. (2007). Effect sizes, confidence intervals, and confidence intervals for effect sizes. Psychology in Schools, 44, 423-432. doi: 10.1002/pits.20234
37. Wilkinson, L., & APA Task Force on Statistical Inference. (1999). Statistical methods in psychology journals: Guidelines and explanations. American Psychologist, 54, 594-604. doi: 10.1037/0003-066X.54.8.594