On the Application of Bootstrapping and Monte Carlo Simulations to Clinical Studies: Psychometric Intelligence Research and Juvenile Delinquency
- 1 Faculty of Human and Social Science, Osaka Ohtani University, Tondabayashi, Japan
Abstract
The common problems in the methodology of clinical psychology research are sampling issues, both in the case of biased clinical groups and inappropriate control groups. This study aimed to mitigate this problem by using the following procedures: 1) using a bootstrapping approach for the biased clinical sample; 2) generating a random number dataset as a control population; 3) resampling both the bootstrapped targeted datasets and the normed control population; and 4) conducting a repeated analysis to create averaged statistics using the Monte Carlo simulation. The dataset used in the present study included 273 children with a history of delinquency and was assessed using the WISC-IV. Compared with conventional analyses, the proposed approach in the present study was found to generate the characteristics of the targeted clinical group on the basis of averaged statistics. Given that the norm had been identified in past research on psychometric intelligence, the use of bootstrapping and Monte Carlo simulations led to more robust findings compared with the use of conventional clinical studies.
- Alfaro, M. E., Zoller, S., & Lutzoni, F. (2003). Bayes or Bootstrap? A Simulation Study Comparing the Performance of Bayesian Markov Chain Monte Carlo Sampling and Bootstrapping in Assessing Phylogenetic Confidence. Molecular Biology and Evolution, 20, 255-266. https://doi.org/10.1093/molbev/msg028
- Andrew, J. M. (1977). Delinquency: Intellectual Imbalance? Correctional Psychologist, 4, 99-104. https://doi.org/10.1177%2F009385487700400108
- Bray, J. H., & Maxwell, S. E. (1982). Analyzing and Interpreting Significant MANOVAs. Review of Educational Research, 52, 340-367. https://doi.org/10.3102/00346543052003340
- Carpenter, J., & Bithell, J. (2000). Bootstrap Confidence Intervals: When, Which, What? A Practical Guide for Medical Statisticians. Statistics in Medicine, 19, 1141-1164. https://doi.org/10.1002/(SICI)1097-0258(20000515)19:9%3C1141::AID-SIM479%3E3.0.CO;2-F
- Davidson, R., & MacKinnon, J. G. (2000). Bootstrap Tests: How Many Bootstraps? Econometric Reviews, 19, 55-68. https://doi.org/10.1080/07474930008800459
- Del Moral, P., Doucet, A., & Jasra, A. (2012). On Adaptive Resampling Strategies for Sequential Monte Carlo Methods. Bernoulli, 18, 252-278. https://doi.org/10.3150/10-BEJ335
- Deng, L. Y., & Lin, D. K. (2000). Random Number Generation for the New Century. The American Statistician, 54, 145-150. https://doi.org/10.1080/00031305.2000.10474528
- Doubilet, P., Begg, C. B., Weinstein, M. C., Braun, P., & McNeil, B. J. (1985). Probabilistic Sensitivity Analysis Using Monte Carlo Simulation: A Practical Approach. Medical Decision Making, 5, 157-177. https://doi.org/10.1177/0272989X8500500205
- Drabick, D. A., & Goldfried, M. R. (2000). Training the Scientist-Practitioner for the 21st Century: Putting the Bloom Back on the Rose. Journal of Clinical Psychology, 56, 327-340. https://doi.org/10.1002/(SICI)1097-4679(200003)56:3%3C327::AID-JCLP9%3E3.0.CO;2-Y
- Efron, B., & Tibshirani, R. (1986). Bootstrap Methods for Standard Errors, Confidence Intervals, and Other Measures of Statistical Accuracy. Statistical Science, 1, 77. https://doi.org/10.1214/ss/1177013817
- Enders, C. K. (2003). Performing Multivariate Group Comparisons Following a Statistically Significant MANOVA. (Methods, Plainly Speaking). Measurement and Evaluation in Counseling and Development, 36, 40-56. https://doi.org/10.1080/07481756.2003.12069079
- Hall, P., & Martin, M. A. (1988). On Bootstrap Resampling and Iteration. Biometrika, 75, 661-671. https://doi.org/10.1093/biomet/75.4.661