PubMed · 14253182
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E H RUNYON. 1965. TYPICAL MYOBACTERIA: THEIR CLASSIFICATION.. https://doi.org/10.1164/arrd.1965.91.2.288
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The taxometric analytic procedures developed by P. E. Meehl and colleagues represent powerful statistical tools for use in the evaluation of observed quantitative data for the possible existence of a qualitative latent class, natural subgroup, or, more commonly, a taxon. Taxometric methods, like any other statistical procedures, require thoughtful application, and the responsibility for proper interpretation of the results from a taxometric study lies with the investigator. Any investigator using taxometric methods must be mindful of those substantive and methodological issues that will facilitate the most fruitful utilization of the general taxometric approach, as well as those issues that will handicap or, perhaps, fatally flaw a taxometric investigation. A number of these issues are highlighted and discussed in this article, and a reader's, writer's, and reviewer's guide for the evaluation of taxometric research reports is provided.
Joining the debate on the structure of depression, S. R. H. Bearh and N. Amir (2003) analyzed college students' responses to 6 Beck Depression Inventory (BDI) items with predominantly somatic content and concluded that they identified a small latent taxon corresponding to involuntary defeat syndrome. An exact replication of these analyses yielded virtually identical taxometric results, but parallel analyses of simulated taxonic and dimensional comparison data matching the intercorrelations and skewed distributions of the BDI items showed the results to be more consistent with dimensional than with taxonic latent structure. Analyses in a clinical sample with nonskewed indicators further supported a dimensional interpretation. The authors discuss methodological strategies for conducting and interpreting taxometric analyses under the adverse conditions commonly encountered in psychopathology research, including skewed indicators and small putative taxa.
Despite decades of debate, important questions about the boundaries that separate psychological disorder from normality and that distinguish 1 disorder from another remain largely unanswered. These issues pose empirical questions that may be addressed by assessing the latent structure of psychopathological constructs. Because these constructs are likely to be structurally complex, and no single statistical tool addresses all structural questions, it is proposed in this article that boundary issues be examined through programmatic research grounded in the taxometric method and elaborated by complementary analyses. The authors describe how such a program could delimit the structure of disorders and test competing explanations of diagnostic co-occurrence, emphasizing the potential to enhance the reliability and validity of assessment, maximize the power of research designs, and improve diagnostic classification.