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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

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• TITLE: On the Application of Bootstrapping and Monte Carlo Simulations to Clinical Studies: Psychometric Intelligence Research and Juvenile Delinquency

  AUTHORS: Kohske Ogata

  KEYWORDS: Bootstrapping, Monte Carlo Simulation, Juvenile Delinquency, Intelligence Testing

  JOURNAL NAME: Psychology, Vol.12 No.8, August 3, 2021

  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.