Research Article
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Yeniden Örnekleme Yöntemleri: Kavram ve R Uygulamaları

Year 2019, Volume: 27 Issue: 6, 2747 - 2766, 15.11.2019
https://doi.org/10.24106/kefdergi.3756

Abstract

Parametrik testler evren dağılımına ilişkin bir takım
varsyaımların karşılanmasını gerektirir. Bu varsayımların karşılanmadığı durumlarda
araştırmacılar parametrik olmayan istatistiksel yöntemler kullanılır.
Geleneksel parametrik olmayan yöntemlerde sıra puanları ve sıra ortalamalarına
dayalı işlemler gerçekleştirirken yeniden örnekleme yöntemleri bu sürece farklı
bir bakış açısı getirmiştir. En yaygın kullanılan yeniden örnekleme
yöntemlerinin başında randomizasyon testleri gelir. Randomizasyon testlerinin temel mantığı
orijinal örneklemden hesaplanan test istatistiğinin rastgele olarak oluşturulan
örneklemlerdeki test istatistiği ile karşılaştırılmasına dayalıdır.  Bu anlamda kullanımı dünyada gitgide
yaygınlaşan randomizasyon testlerine ilişkin yeterli sayıda kaynak olmaması,
özellikle Türkiye’de kullanımının çok sınırlı olması bu makalenin yazımına
temel oluşturmuştur. Ayrıca randomizasyon testlerinin son yıllarda gündemde
önemli bir yer tutan R progrlamla dili üzerinden örneklendirilerek açıklanması
çalışmanın diğer bir önemli unsuru olarak düşünülmektedir. Bu çalışmada
randomizasyon testlerinin R proglamla dili ile örneklendirilerek açıklanması [1]amaçlanmıştır.
Bu bağlamda randomizasyon testlerinin temel kavramları açıklanmış akabinde
sosyal bilimler alanında yaygun kullanıldığı düşünülen bağımsız ve tekrarlı
örneklemler için t testi, bağınmsız gruplar için tek yönlü varyans analizi ve
korelasyon analizi özelinde R kodlarından faydalanılarak örneklendirmelere
gidilmiştir. Bu çalışma ile özellikle Türkiye’de randomizasyon testlerinin ve R
progrlamlama dilinin kullanımının yaygınlaştırılması beklenmektedir.
















 







 







 





References

  • Albert, J., & Rizzo, M. (2012). R by example. Springer New York. USA
  • Banjanovic, Erin S. & Osborne, Jason W. (2016). Confidence Intervals for Effect Sizes: Applying Bootstrap Resampling. Practical Assessment, Research & Evaluation, 21(5).
  • Beaujean, A. A. (2013). Factor analysis using R. Practical Assessment: Research & Evaluation. 18(4) http://pareonline.net/getvn.asp?v=18&n=4
  • Bradley, J. V. (1968). Distribution free statistical tests. Englewood Cliffs, NJ: Prentice-Hall.
  • Chernick, M. R., & Labudde, R. A. (2011). An introduction to bootstrap methods with applications to R. A john Wiley & Sons, Inc. New Jersey.
  • Davison, A. C., & Hinkley, D. V. (1997). Bootstrap methods and their application. Cambridge, United Kingdom: Cambridge University Press.
  • Doğan, C.D. (2017). Applying Bootstrap Resampling to Compute Confidence Intervals for Various Statistics with R. Eurasian Journal of Educational Research (68), 1-18.
  • Edgington, E. S. (1986). Randomization tests. In S. Kotz & N. L. Johnson (Eds.), Encyclopedia of statistical scienc-es, Vol. 7 New York, NY: Wiley. 530–538.
  • Field, A. (2018). Discovering Statistics Using SPSS. 5th ed. London: Sage Publication.
  • Gibbons, J. D. (1986). Permutation tests. In S. Kotz & N. L. Johnson (Eds.), Encyclopedia of statistical sciences, Vol. 6 . New York, NY: Wiley. 690
  • Howel, D. C. (2007). Statistical Method for Psychology. Wadsworth, Cengage Learning. USA
  • Onghena, P., & May, R. B. (1995). Pitfalls in computing and interpreting randomization test p values: A commentary on Chen and Dunlap. Behavior Research Methods, Instruments, & Computers, 27, 408–411.
  • Onghena, P. (2018). Randomization tests or permutation tests? A historical and terminological clarification. In V. Berger (Ed.), Randomization, masking, and allocatio concealment (pp. 209‐227). Boca Raton/FL: Chapman & Hall/CRC Press.

Resampling Methods: Concept and R Applications

Year 2019, Volume: 27 Issue: 6, 2747 - 2766, 15.11.2019
https://doi.org/10.24106/kefdergi.3756

Abstract

Parametric tests such as t-test, ANOVA,
etc. requires some assumptions about the distribution of the scores in the
universe. If those assumptions are not met it is a good idea to compute
non-parametric tests instead of parametric tests. Traditional non-parametric
tests such as Wilcoxon Sum of Ranks and Kruskal Wallis tests etc. focus on the
sum of ranks and mean ranks to compare the group scores. On the other hand,
resampling methods present a different point of view on this process. One of the
mostly used resampling methods is the randomization test. The basic principles
of randomization tests are comparing the original test statistic (t values, F
values, r coefficient, etc.) to the test statistics derived from randomly
generated samples. Although usage of randomization tests in the world is
pervading day by day in Turkey it is very rarely used. This may be because of
insufficient written source published in Turkey. Moreover, the R programming
language has become very popular recently. So in this study, it is aimed to
explain the computation process of randomization tests using R codes. In this
study, at first, some basic concepts about randomization tests were presented.
Then randomization tests were exemplified for independent samples t-test,
repeated sample t-test, one-way analysis of variance (one way ANOVA) using R
codes. It is hoped that this study guide and motivate researchers to use
randomizations tests and r programming language in their research.

References

  • Albert, J., & Rizzo, M. (2012). R by example. Springer New York. USA
  • Banjanovic, Erin S. & Osborne, Jason W. (2016). Confidence Intervals for Effect Sizes: Applying Bootstrap Resampling. Practical Assessment, Research & Evaluation, 21(5).
  • Beaujean, A. A. (2013). Factor analysis using R. Practical Assessment: Research & Evaluation. 18(4) http://pareonline.net/getvn.asp?v=18&n=4
  • Bradley, J. V. (1968). Distribution free statistical tests. Englewood Cliffs, NJ: Prentice-Hall.
  • Chernick, M. R., & Labudde, R. A. (2011). An introduction to bootstrap methods with applications to R. A john Wiley & Sons, Inc. New Jersey.
  • Davison, A. C., & Hinkley, D. V. (1997). Bootstrap methods and their application. Cambridge, United Kingdom: Cambridge University Press.
  • Doğan, C.D. (2017). Applying Bootstrap Resampling to Compute Confidence Intervals for Various Statistics with R. Eurasian Journal of Educational Research (68), 1-18.
  • Edgington, E. S. (1986). Randomization tests. In S. Kotz & N. L. Johnson (Eds.), Encyclopedia of statistical scienc-es, Vol. 7 New York, NY: Wiley. 530–538.
  • Field, A. (2018). Discovering Statistics Using SPSS. 5th ed. London: Sage Publication.
  • Gibbons, J. D. (1986). Permutation tests. In S. Kotz & N. L. Johnson (Eds.), Encyclopedia of statistical sciences, Vol. 6 . New York, NY: Wiley. 690
  • Howel, D. C. (2007). Statistical Method for Psychology. Wadsworth, Cengage Learning. USA
  • Onghena, P., & May, R. B. (1995). Pitfalls in computing and interpreting randomization test p values: A commentary on Chen and Dunlap. Behavior Research Methods, Instruments, & Computers, 27, 408–411.
  • Onghena, P. (2018). Randomization tests or permutation tests? A historical and terminological clarification. In V. Berger (Ed.), Randomization, masking, and allocatio concealment (pp. 209‐227). Boca Raton/FL: Chapman & Hall/CRC Press.
There are 13 citations in total.

Details

Primary Language Turkish
Journal Section Research Article
Authors

C. Deha Doğan 0000-0003-0683-1334

Publication Date November 15, 2019
Acceptance Date August 1, 2019
Published in Issue Year 2019 Volume: 27 Issue: 6

Cite

APA Doğan, C. D. (2019). Yeniden Örnekleme Yöntemleri: Kavram ve R Uygulamaları. Kastamonu Education Journal, 27(6), 2747-2766. https://doi.org/10.24106/kefdergi.3756

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