Applying the Bootstrap to Taxometric Analysis: Generating Empirical Sampling Distributions to Help Interpret Results

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Abstract
Meehl's taxometric method was developed to distinguish categorical and continuous constructs. However, taxometric output can be difficult to interpret because expected results for realistic data conditions and differing procedural implementations have not been derived analytically or studied through rigorous simulations. By applying bootstrap methodology, one can generate empirical sampling distributions of taxometric results using data–based estimates of relevant population parameters. We present iterative algorithms for creating bootstrap samples of taxonic and dimensional comparison data that reproduce important features of the research data with good precision and negligible bias. In a series of studies, we demonstrate the utility of these comparison data as an interpretive aid in taxometric research. Strengths and limitations of the approach are discussed along with directions for future research.

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