ISSN 2043-8087
Journal of Experimental Psychopathology
 Volume 2, Issue 2, 252-270, 2011
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Using Bootstrap Estimation and the Plug-in Principle for Clinical Psychology Data

Authors
Daniel B. Wright (a), Kamala London (b), Andy P. Field (c)
(a) Psychology Department, Florida International University
(b) Department of Psychology, University of Toledo
(c) School of Psychology, University of Sussex

Volume 2, Issue 2, 2011, Pages 252-270
DOI: http://dx.doi.org/10.5127/jep.013611

Abstract
Psychologists estimate the precision of their statistics both to conduct hypothesis tests and to construct confidence intervals. The methods traditionally used for this are available only for a small set of statistics (e.g., the mean and transformations of it) and often make unrealistic assumptions about the variables' distributions. These assumptions are often particularly unrealistic in data derived from clinical samples, or when looking at groups responding at the extreme end of clinical constructs. Bootstrap estimation is a computer intensive procedure that offers a flexible and automatic alternative. The computer takes thousands of bootstrap samples from the observed data and from these bootstrap samples estimates the precision of the statistic. High-speed personal computers make the bootstrap a viable and appealing technique throughout the sciences. This article offers a tutorial on the theory and practice of applying bootstrap estimation to data from clinical samples and measures relevant to experimental psychopathology.

Table of Contents
Using Bootstrap Estimation and the Plug-in Principle for Clinical Psychology Data
The Plug-in Principle
Bootstrap Sampling and Bootstrap Estimation: Examples
  Bootstrap Estimates for the Median
  Bootstrapping Categorical Data (Kappa, association for a 2x2 table)
  Bootstrapping Correlations
  Bootstrapping Regression Coefficients
  Other Statistics
Doing bootstrapping
  Using R to Bootstrap Estimates for the Median and Mean
  Using R to Bootstrap Estimates for the Correlation Coefficient
  Using R to Bootstrap Regression Parameters
The benefits of bootstrapping
Conclusions
Acknowledgements
References

Correspondence to
Daniel B. Wright, Psychology Department, Florida International University, 11200 S.W. 8th Street, Miami, FL, 33199.

Keywords
Bootstrap, Robust methods

Dates
Received 26 Nov 2010; Revised 11 Jan 2011; Accepted 21 Jan 2011; In Press 5 May 2011







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