DOI QR코드

DOI QR Code

An Overview of Bootstrapping Method Applicable to Survey Researches in Rehabilitation Science

  • Choi, Bong-sam (Dept. of Physical Therapy, College of Health and Welfare, Woosong University)
  • Received : 2016.04.11
  • Accepted : 2016.05.11
  • Published : 2016.05.21

Abstract

Background: Parametric statistical procedures are typically conducted under the condition in which a sample distribution is statistically identical with its population. In reality, investigators use inferential statistics to estimate parameters based on the sample drawn because population distributions are unknown. The uncertainty of limited data from the sample such as lack of sample size may be a challenge in most rehabilitation studies. Objects: The purpose of this study is to review the bootstrapping method to overcome shortcomings of limited sample size in rehabilitation studies. Methods: Articles were reviewed. Results: Bootstrapping method is a statistical procedure that permits the iterative re-sampling with replacement from a sample when the population distribution is unknown. This statistical procedure is to enhance the representativeness of the population being studied and to determine estimates of the parameters when sample size are too limited to generalize the study outcome to target population. The bootstrapping method would overcome limitations such as type II error resulting from small sample sizes. An application on a typical data of a study represented how to deal with challenges of estimating a parameter from small sample size and enhance the uncertainty with optimal confidence intervals and levels. Conclusion: Bootstrapping method may be an effective statistical procedure reducing the standard error of population parameters under the condition requiring both acceptable confidence intervals and confidence level (i.e., p=.05).

Keywords

References

  1. Anderson AJ, Vingrys AJ. Small samples: Does size matter? Invest Ophthalmol Vis Sci. 2001;42(7): 1411-1413.
  2. Banerjee A, Chaudhury S. Statistics without tears: Populations and samples. Ind Psychiatry J. 2010; 19(1):60-65. https://doi.org/10.4103/0972-6748.77642
  3. Banerjee A, Chitnis UB, Jadhav SL, et al. Hypothesis testing, type I and type II errors. Ind Psychiatry J. 2009;18(2):127-131. http://dx.doi.org/10.4103/0972-6748.62274
  4. Button KS, Ioannidis JP, Mokrysz C, et al. Power failure: Why small sample size undermines the reliability of neuroscience. Nat Rev Neurosci. 2013; 14(5):365-376. http://dx.doi.org/10.1038/nrn3475
  5. Choi BS, Park SY. Responsiveness comparisons of self-report versus therapist-scored functional capacity for workers with low back pain. Phys Ther Korea. 2012;19(3):91-97. http://dx.doi.org/10.12674/ptk.2012.19.3.091
  6. Claudy JG. A comparison of five variable weighting procedures. Educ Psychol Meas. 1972;32:311-322. https://doi.org/10.1177/001316447203200208
  7. Cohen J. Statistical Power Analysis for the Behavioral Sciences. 2nd ed. Hillsdale, N.J, Lawrence Erlbaum Associates Inc., 1988:112-122.
  8. Efron B. Bootstrap methods: Another look at the jackknife. Ann Stat. 1979;7(1):1-26. https://doi.org/10.1214/aos/1176344552
  9. Efron B. Bayesian inference and the parametric bootstrap. Ann Appl Stat. 2012;6(4):1971-1997. https://doi.org/10.1214/12-AOAS571
  10. Efron B, Tibshirani R. An Introduction to the Bootstrap. 1st ed. London, Chapman & Hall/CRC, 1993:23-29.
  11. Green SB. How many subjects it take to do a regression analysis? Multivariate Behav Res. 1991;26(3): 499-510. http://dx.doi.org/10.1207/s15327906mbr2603_7
  12. Hall P. Theoretical comparison of bootstrap confidence intervals. Ann Stat. 1988;16(3):927-953. https://doi.org/10.1214/aos/1176350933
  13. Hall P. On bootstrap confidence intervals in nonparametric regression. Ann Stat. 1992;20(2):695-711. https://doi.org/10.1214/aos/1176348652
  14. Ioannidis JP, Tarone R, McLaughlin JK. The false-positive to false-negative ratio in epidemiologic studies. Epidemiology. 2011;22(4):450-456. http://dx.doi.org/10.1097/EDE.0b013e31821b506e
  15. Ioannidis JP. Why most discovered true associations are inflated. Epidemiology. 2008:19(5):640-648. http://dx.doi.org/10.1097/EDE.0b013e31818131e7
  16. Kulesa A, Krzywinski M, Blainey P, et al. Points of significance: Sampling distributions and the bootstrap. Nat Methods. 2015;12(6):477-478. https://doi.org/10.1038/nmeth.3414
  17. Masicampo EJ, Lalande DR. A peculiar prevalence of p values just below .05. Q J Exp Psychol (Hove). 2012;65(11):2271-2279. http://dx.doi.org/10.1080/17470218.2012.711335
  18. Page P. Beyond statistical significance: Clinical interpretation of rehabilitation research literature. Int J Sports Phys Ther. 2014;9(5):726-736.
  19. Pedhazur EJ, Schmelkin LP. Measurement, Design and Analysis: An integrated approach. 1st ed. Hillsdale, NJ, Lawrence Erlbaum Associates Inc., 1991:89-97.
  20. Pernet CR, Latinus M, Nicholas TE, et al. Cluster-based computational methods for mass univariate analyses of event-related brain potentials/fields: A simulation study. J Neurosci Methods. 2015;250:85-93. http://dx.doi.org/10.1016/j.jneumeth.2014.08.003
  21. Singh K. On the asymptotic accuracy of Efron's bootstrap. Ann Stat. 1981;9(6):1187-1195. https://doi.org/10.1214/aos/1176345636
  22. Singh K, Xie M. Bootlier-plot: Bootstrap based outlier detection plot. Sankhya Ser A. 2003;65(3): 532-559.
  23. Tabachnick BG, Fidell LS. Using Multivariate Statistics. 3rd ed. New York, Haper Collins Publishers, 1996:120-122.
  24. Velozo CA, Choi B, Zylstra SE, et al. Measurement qualities of a self-report and therapist-scored functional capacity instrument based on the Dictionary of Occupational Titles. J Occup Rehabil. 2006;16(1):109-122.
  25. Wolf EJ, Harrington KM, Clark SL, et al. Sample size requirements for structural equation models: An evaluation of power, bias, and solution propriety. Educ Psychol Meas. 2013;76(6):913-934.
  26. Wyrwich KW. Minimal important difference thresholds and the standard error of measurement: Is there a connection? J Biopharm Stat. 2004;14(1): 97-110. https://doi.org/10.1081/BIP-120028508

Cited by

  1. Professionalism as a Predictor of Fieldwork Performance in Undergraduate Occupational Therapy Students: An Exploratory Study vol.34, pp.2, 2020, https://doi.org/10.1080/07380577.2020.1737896
  2. The convergent validity of the Children’s Leisure Assessment Scale (CLASS) and Children’s Assessment of Participation and Enjoyment and Preferences for Activities of Children (CAPE/PAC) vol.27, pp.5, 2016, https://doi.org/10.1080/11038128.2019.1672784
  3. Convergent Validity between Three Self-Report Measures of Children’s Play and Activity Interests vol.13, pp.4, 2016, https://doi.org/10.1080/19411243.2020.1769001