John Ruscio

A taxometric analysis of the latent structure of psychopathy: evidence for dimensionality.

The taxonomic status of psychopathy is controversial. Whereas some studies have found evidence that psychopathy, at least its antisocial component, is distributed as a taxon, others have found that both major components of psychopathy-callousness/unemotionality and impulsivity/antisocial behavior-appear to distribute as dimensions and show little evidence of taxonicity. In the present study, recent advances in taxometric analysis were added to P. Meehls (1995) multiple consistency tests strategy for assessing taxonicity, and they were applied to Psychopathy Checklist-Revised (R. D. Hare, 2003) ratings of 4,865 offenders sampled from multiple forensic settings. The results indicated that both the individual components of psychopathy and their interface are distributed dimensionally. Both the implications of these results for research in psychopathy and the integration of these findings with previous taxometric studies of psychopathy are discussed.

Informing the continuity controversy: a taxometric analysis of depression.

Researchers and practitioners have long debated the structural nature of mental disorders. Until recently, arguments favoring categorical or dimensional conceptualizations have been based primarily on theoretical speculation and indirect empirical evidence. Within the depression literature, methodological limitations of past studies have hindered their capacity to inform this important controversy. Two studies were conducted using MAXCOV and MAMBAC, taxometric procedures expressly designed to assess the underlying structure of a psychological construct. Analyses were performed in large clinical samples with high base rates of major depression and a broad range of depressive symptom severity. Results of both studies, drawing on 3 widely used measures of depression, corroborated the dimensionality of depression. Implications for the conceptualization, investigation, and assessment of depression are discussed.

A Taxometric Investigation of the Latent Structure of Worry

Researchers have described 2 types of worriers, normal and pathological, who differ in the frequency, intensity, and controllability of their worry experiences. Although normal and pathological worry are generally treated as separate though related phenomena, no study has tested for separateness against the alternative hypothesis that all worry exists along a single dimension. In the present study, worry ratings of 1,588 college students were submitted to taxometric procedures designed to evaluate latent structure. Results provided evidence for the dimensionality of worry. These findings suggest that generalized anxiety disorder (GAD), whose central feature is worry, may also be quantitatively rather than qualitatively different from normal functioning. The authors argue that a focus on normal and pathological extremes has constrained the study of worry phenomena and that dimensional conceptualization of worry may significantly enhance understanding of both worry and GAD.

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

Meehls 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.

Determining the Number of Factors to Retain in an Exploratory Factor Analysis Using Comparison Data of Known Factorial Structure.

Exploratory factor analysis (EFA) is used routinely in the development and validation of assessment instruments. One of the most significant challenges when one is performing EFA is determining how many factors to retain. Parallel analysis (PA) is an effective stopping rule that compares the eigenvalues of randomly generated data with those for the actual data. PA takes into account sampling error, and at present it is widely considered the best available method. We introduce a variant of PA that goes even further by reproducing the observed correlation matrix rather than generating random data. Comparison data (CD) with known factorial structure are first generated using 1 factor, and then the number of factors is increased until the reproduction of the observed eigenvalues fails to improve significantly. We evaluated the performance of PA, CD with known factorial structure, and 7 other techniques in a simulation study spanning a wide range of challenging data conditions. In terms of accuracy and robustness across data conditions, the CD technique outperformed all other methods, including a nontrivial superiority to PA. We provide program code to implement the CD technique, which requires no more specialized knowledge or skills than performing PA.

The Latent Structure of Analogue Depression: Should the Beck Depression Inventory Be Used to Classify Groups?

Research on depression is often conducted with analogue samples that have been divided into depressed and nondepressed groups using a cutoff score on the Beck Depression Inventory (BDI). Although the relative merits of different cut scores are frequently debated, no study has yet determined whether the use of any cut score is valid, that is, whether the latent structure of BDI depression is categorical or dimensional in analogue samples. The BDI responses of 2,260 college students were submitted to 3 taxometric procedures whose results were compared with those of simulated data sets with equivalent parameters. Analyses provided converging evidence for the dimensionality of analogue depression, arguing against the use of the BDI to classify analogue participants into groups. Analyses also illustrated the notable impact of pronounced skew on taxometric results and the value of using simulated comparison data as an interpretive aid.

Clarifying Boundary Issues in Psychopathology: The Role of Taxometrics in a Comprehensive Program of Structural Research.

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.

Comparing the relative fit of categorical and dimensional latent variable models using consistency tests.

A number of recent studies have used Meehls (1995) taxometric method to determine empirically whether one should model assessment-related constructs as categories or dimensions. The taxometric method includes multiple data-analytic procedures designed to check the consistency of results. The goal is to differentiate between strong evidence of categorical structure, strong evidence of dimensional structure, and ambiguous evidence that suggests withholding judgment. Many taxometric consistency tests have been proposed, but their use has not been operationalized and studied rigorously. What tests should be performed, how should results be combined, and what thresholds should be applied? We present an approach to consistency testing that builds on prior work demonstrating that parallel analyses of categorical and dimensional comparison data provide an accurate index of the relative fit of competing structural models. Using a large simulation study spanning a wide range of data conditions, we examine many critical elements of this approach. The results provide empirical support for what marks the first rigorous operationalization of consistency testing. We discuss and empirically illustrate guidelines for implementing this approach and suggest avenues for future research to extend the practice of consistency testing to other techniques for modeling latent variables in the realm of psychological assessment.

Taxometric Analysis An Empirically Grounded Approach to Implementing the Method

Whether individual differences are treated as categorical or continuous has consequences for theory, assessment, classification, and research in criminal justice. Paul Meehls (1995) taxometric method allows investigators to test between these two competing structural models. This article provides an overview of the methods inferential framework and data-analytic procedures. Because guidelines for implementing taxometric analyses and interpreting their results have received little research attention, investigators are encouraged to adopt an empirically grounded approach to taxometric analysis rather than following conventions or relying on personal opinion. The guidance afforded by Monte Carlo studies, including the two reported here, can be supplemented by simulating comparison data. This empirically grounded approach, described and illustrated below, helps to implement the taxometric method effectively and to draw valid conclusions.

A Probability-Based Measure of Effect Size : Robustness to Base Rates and Other Factors

Calculating and reporting appropriate measures of effect size are becoming standard practice in psychological research. One of the most common scenarios encountered involves the comparison of 2 groups, which includes research designs that are experimental (e.g., random assignment to treatment vs. placebo conditions) and nonexperimental (e.g., testing for gender differences). Familiar measures such as the standardized mean difference (d) or the point-biserial correlation (rpb) characterize the magnitude of the difference between groups, but these effect size measures are sensitive to a number of additional influences. For example, R. E. McGrath and G. J. Meyer (2006) showed that rpb is sensitive to sample base rates, and extending their analysis to situations of unequal variances reveals that d is, too. The probability-based measure A, the nonparametric generalization of what K. O. McGraw and S. P. Wong (1992) called the common language effect size statistic, is insensitive to base rates and more robust to several other factors (e.g., extreme scores, nonlinear transformations). In addition to its excellent generalizability across contexts, A is easy to understand and can be obtained from standard computer output or through simple hand calculations.