Taxometric Procedures Research Articles

Feigned neurocognitive deficit: Taxon or dimension?

The purpose of this study was to explore the latent structure of feigned neurocognitive deficit. Scores on the Test of Memory Malingering (TOMM), Letter Memory Test (LMT), and Victoria Symptom Validity Test (VSVT) served as indicators in a taxometric investigation of 527 compensation-seeking adults using three taxometric procedures—mean above minus below a cut (MAMBAC), maximum covariance (MAXCOV), and latent-mode factor analysis (L-Mode). All three procedures showed evidence of dimensional latent structure. The fact that feigned neurocognitive symptomatology is ordered along a continuum rather than bifurcating into distinct categories has important implications for theory, research, and clinical practice.

A taxometric investigation of agoraphobia in a clinical and a community sample

The nosological status of agoraphobia is controversial. Agoraphobia may be a distinct diagnostic entity or a marker of avoidance severity. The current study examines the latent structure of agoraphobia through the use of taxometric analysis. The latent structure of agoraphobia was examined in two independent samples, one comprising outpatients presenting for treatment for panic disorder (PD) with or without agoraphobia ( n = 365), and the other comprising community volunteers to a national mental health survey who experienced fear or avoidance of at least one prototypic agoraphobic situation ( n = 640). Two taxometric procedures were carried out – maximum eigenvalue (MAXEIG) and mean above minus below a cut (MAMBAC) – using indicators derived from questionnaire measures of, and structured diagnostic interviews for, agoraphobia. Results show consistent evidence of dimensional latent structure in both samples. It is concluded that scores on measures of agoraphobia best represent an agoraphobic severity dimension.

The latent structure of posttraumatic stress disorder among adolescents

Debate has arisen over whether posttraumatic stress disorder (PTSD) is most accurately conceptualized as representing a discrete clinical syndrome or an extreme reaction to traumatic life events. Recent taxometric research using predominantly adult samples appears to support a dimensional model of PTSD, raising questions about the utility of current psychiatric nosology which depicts PTSD as a distinct entity. The present study sought to use taxometric procedures to examine the latent structure of posttraumatic stress reactions among a national epidemiologic sample of 2,885 adolescents. Results were consistent with previous taxometric studies in supporting a dimensional model of posttraumatic stress reactions. The implications of these findings for public policy, as well as the etiology and assessment of posttraumatic stress reactions, are discussed.

A taxometric study of alcohol abuse and dependence in a general population sample: evidence of dimensional latent structure and implications for DSM-V

To explore, with the aid of taxometric analysis, whether alcohol abuse and alcohol dependence are each conceptualized most effectively as single latent dimensions or distinct latent categories. Data were taken from a nationally representative cross-sectional epidemiological survey of psychiatric and substance use disorders. General population of Australia. A subsample of all respondents who had consumed at least 12 drinks in the year prior to the survey and who had consumed at least three drinks on at least one single day (n = 4920 of a possible 10,641). DSM-IV criteria for alcohol abuse and dependence were assessed with the Composite International Diagnostic Interview, version 2.1. Two independent taxometric procedures, MAXimum EIGenvalue (MAXEIG) and mean above minus below a cut (MAMBAC), together with analysis of simulated dimensional and categorical data sets, were carried out. Findings Consistent evidence was found for a single latent dimension underlying the symptoms of alcohol dependence. Less consistent evidence of dimensionality was found for the symptoms of alcohol abuse. These findings support the growing consensus regarding the need for continuous measures of alcohol use disorders to complement the traditional categorical representations in upcoming versions of the major psychiatric classification systems.

Latent Structure of Depression in a Japanese Population Sample: Taxometric Procedures

Although the debate on whether mental disorders are categorical or dimensional is critical for researchers and practitioners, past findings of taxometric studies of depression have been somewhat contradictory and difficult to reconcile. One reason for the disagreement was that most researchers relied only on selected samples (students, clinical patients, or screened community samples). The present study investigated whether the latent structure of depression is categorical or dimensional using an unselected general community sample. To determine whether the latent structure of depression assessed by the Center for Epidemiologic Studies Depression Scale is categorical or dimensional, taxometric procedures were carried out on a Japanese general population-based sample of 20 987 individuals selected using a cluster sampling procedure. Both the means above and below a sliding cut (MAMBAC) and the maximum eigenvalue (MAXEIG) procedures, using indicators drawn from the Center for Epidemiologic Studies Depression scale yielded results that the latent structure of depression is dimensional. The dimensional structure of depression should be considered when classifying individual differences and selecting suitable research designs.

Using comparison data to differentiate categorical and dimensional data by examining factor score distributions: Resolving the mode problem.

Factor-analytic research is common in the study of constructs and measures in psychological assessment. Latent factors can represent traits as continuous underlying dimensions or as discrete categories. When examining the distributions of estimated scores on latent factors, one would expect unimodal distributions for dimensional data and bimodal or multimodal distributions for categorical data. Unfortunately, identifying modes is subjective, and the operationalization of counting local maxima has not performed very well. Rather than locating and counting modes, the authors propose performing parallel analyses of categorical and dimensional comparison data and calculating an index of the relative fit of these competing structural models. In an extensive Monte Carlo study, the authors replicated prior results for mode counting and found that trimming distributions' tails helped. However, parallel analyses of comparison data achieved much greater accuracy, improved base rate estimation, and afforded consistency checks with other taxometric procedures. Two additional studies apply this approach to empirical data either known to be categorical or presumed to be dimensional. Each study supports this new method for factor-analytic research on the latent structure of constructs and measures in psychological assessment.

To sum or not to sum: Taxometric analysis with ordered categorical assessment items.

Meehl's taxometric method has been shown to differentiate between categorical and dimensional data, but there are many ways to implement taxometric procedures. When analyzing the ordered categorical data typically provided by assessment instruments, summing items to form input indicators has been a popular practice for more than 20 years. A Monte Carlo study compared the accuracy of taxometric analyses implemented in the traditional way (without summing items) and taxometric analyses implemented with the summed-input method. These analyses generated no support for the summed-input method, which substantially reduced discriminating power for 2 of the 3 procedures studied. Accuracy was highest when 5 or more indicators and 4 or more ordered categories were used. Findings from the simulation study were then used to help interpret the results for taxometric analyses of antisocial personality disorder criteria with real research data. In this example, the traditional method yielded clearer results than the summed-input method. Implications for the use and further study of the taxometric method in assessment research are discussed. (PsycINFO Database Record (c) 2009 APA, all rights reserved).

Abstract: A Comparison of the Factor Mixture Model and Taxometric Procedures

Taxometric procedures and the Factor Mixture Model (FMM) have a complimentary set of strengths and weaknesses. Both approaches purport to detect evidence of a latent class structure. Taxometric procedures, popular in psychiatric and psychopathology literature, make no assumptions beyond those needed to compute means and covariances. However, Taxometric procedures assume that observed items are uncorrelated within a class or taxon. This assumption is violated when there are individual differences in the trait underlying the items (i.e., severity differences within class). FMMs can model within-class covariance structures ranging from local independence to multidimensional within-class factor models and permits the specification of more than two classes. FMMs typically rely on normality assumptions for within-class factors and error terms. FMMs are highly parameterized and susceptible to misspecifications of the within-class covariance structure. The current study compared the Taxometric procedures MAXEIG and the Base-Rate Classification Technique to the FMM in their respective abilities to (1) correctly detect the two-class structure in simulated data, and to (2) correctly assign subjects to classes. Two class data were simulated under conditions of balanced and imbalanced relative class size, high and low class separation, and 1-factor and 2-factor within-class covariance structures. For the 2-factor data, simple and cross-loaded factor loading structures, and positive and negative factor correlations were considered. For the FMM, both correct and incorrect within-class factor structures were fit to the data. FMMs generally outperformed Taxometric procedures in terms of both class detection and in assigning subjects to classes. Imbalanced relative class size (e.g., a small minority class and a large majority class) negatively impacted both FMM and Taxometric performance while low class separation was much more problematic for Taxometric procedures than the FMM. Comparisons of alterative FMMs based on information criteria generally resulted in correct model choice but deteriorated when small class separation was combined with imbalanced relative class size.

Exploring the latent structure of two cognitive components of social anxiety: taxometric analyses of fears of negative and positive evaluation

Recent evidence suggests that social anxiety disorder is best characterized as having a dimensional latent structure. In this study, we examined the latent structure of two cognitive components of social anxiety, fear of negative evaluation (FNE) and fear of positive evaluation (FPE), in a large undergraduate sample. Two taxometric procedures (MAMBAC, Mean Above Minus Below a Cut and MAXEIG, MAXimum EIGenvalue) were performed with indicator sets drawn from self-report measures of FNE and FPE. Taxometric analyses, as well as comparison analyses utilizing simulated dimensional and taxonic datasets, yielded converging evidence that both FNE and FPE have a dimensional latent structure.

Latent structure of self-reported depression in undergraduates: Using taxometric procedures and information-theoretic latent variable modeling

Many depression studies dichotomize the participants as depressed or nondepressed on the basis of a self-reported depression scale score. However, there is little statistical rationale for such a procedure. The purpose of this study was to clarify whether depression assessed by the Zung Self-rating Depression Scale (SDS) is categorical or dimensional by using taxometric procedures and information-theoretic latent variable modeling. The present sample comprises 2187 Japanese undergraduates. The latent structure of depression was evaluated by mean above minus below a cut (MAMBAC) and maximum eigenvalue (MAXEIG). In addition, in order to test convergent validity, we performed information-theoretic latent variable modeling. Both taxometric procedures (MAMBAC and MAXEIG) and information-theoretic latent variable modeling, using indicators drawn from the SDS, were consistent with the dimensional latent structure of depression. The latent structure of depression assessed by the SDS is dimensional in undergraduates. For the assessment of depression, researchers should not stratify the participants on the basis of a conventional cutoff score. In addition, it is recommended that researchers use correlational designs, which include individuals with a wider range of severity with regard to depressive symptoms, rather than group comparative research designs.

On the nature of nicotine addiction: A taxometric analysis.

Taxometric procedures were used to determine whether nicotine addiction is best conceptualized as a dimensional or a categorical (i.e., taxonic) phenomenon. Using data from the 2003 National Survey on Drug Use and Health (NSDUH; N = 12,467), results from MAMBAC, MAXEIG, and LMODE taxometric analyses provided strong evidence that nicotine addiction has a taxonic latent structure. Members of the addiction taxon, which constituted approximately 48% of those who reported smoking in the past 30 days, consumed a higher number of cigarettes per day, had stronger craving, higher levels of nicotine tolerance, more inflexible smoking patterns, and shorter latencies to smoking their first cigarette on waking compared with nontaxon members. These findings of a distinct addiction taxonic structure were replicated using a 2002 NSDUH sample (N = 12,224). Finally, the predictive validity of the taxon variable was compared with a continuous indicator sum. The taxon accounted for most of the predictive variance in the indicator sum, but the latter generally showed significant predictive power even after controlling for the former. Thus, these smoking variables may have both a categorical and a dimensional structure.

The distribution of hand preference is discrete: A taxometric examination

The dominant genetic models of human handedness (Annett, 1985; McManus, 1985a) have contrasting views on its nature. Whereas Annett's model proposes that handedness is distributed continuously (following the distribution of performance asymmetries of the hands), McManus's model proposes that handedness is distributed dichotomously. The aim of this study was to investigate the nature of the underlying distribution. The taxonomy of hand preference was analysed by two independent taxometric procedures, 'mean-above-mean-below-a-cut' and 'maximum-eigenvalue', separately in two geographically, demographically, and culturally distinct samples; one of Australian adults (N=787), and the other of Serbian high school students (N=1,224). The analyses revealed a latent categorical structure in both samples, with estimated mean base-rates of a right-handed taxon of 0.80 and 0.94 from the two analyses of the adult sample and of 0.79 and 0.93 from the two analyses of the high school sample. Subsequent analyses showed that the initial right-handed class could be subdivided into consistent and inconsistent right-handed subclasses in both samples. A supplementary analysis, including a measure of hand proficiency in a subset of the sample of Australian adults (N=596), also revealed a latent categorical structure, showing evidence for a categorical latent structure from a continuous measure of hand performance. We conclude that the distribution of hand preferences in humans is discrete and not continuous.

Is Perfectionism Categorical or Dimensional? A Taxometric Analysis

Considerable debate exists within the perfectionism literature regarding whether perfectionism is most accurately conceptualized as a dimensional or categorical construct. Specifically, some researchers have viewed perfectionism as a continuous construct, with extreme scores being associated with negative psychological outcomes (e.g., obsessive–compulsive disorder, eating disorders, etc.). In contrast, others have argued that two distinct forms of perfectionism exist (i.e., adaptive vs. maladaptive perfectionism) that are associated with both positive and negative outcomes. The purpose of this study was to examine the latent structure of perfectionism using taxometric procedures to determine whether perfectionism is most accurately conceptualized as taxonic (categorical) or dimensional (continuous). We applied four taxometric procedures (maximum eigenvalue [Waller & Meehl, 1998], maximum covariance [Meehl & Yonce, 1996], mean above minus below a cut [Meehl & Yonce, 1994], and latent-mode factor analysis [Waller & Meehl, 1998]) to perfectionism data collected from 2 large nonclinical samples. Results provided convergent evidence for a dimensional conceptualization of perfectionism across samples, perfectionism measures, and statistical procedures. We discuss the implications of these findings for the theory, assessment, and investigation of perfectionism are discussed.

Taxometric Analysis of the Levenson Self-Report Psychopathy Scale

Levenson's Self-Report Psychopathy scale (Levenson, Kiehl, & Fitzpatrick, 1995) was administered to 1,972 male and female federal prison inmates, the results of which were subjected to taxometric analysis. We employed 4 taxometric procedures in this study: mean above minus below a cut (Meehl & Yonce, 1994), maximum slope (Grove & Meehl, 1993), maximum eigenvalue (Waller & Meehl, 1998), and latent-mode factor analysis (Waller & Meehl, 1998). The results showed consistent support for a dimensional interpretation of the latent structure of psychopathy, corroborating previous research conducted on the Psychopathy Checklist (e.g., Psychopathy Checklist–Revised; Hare, 2003) and Psychopathic Personality Inventory (Lilienfeld & Andrews, 1996) and denoting that psychopathy is a dimensional construct (degree of psychopathic characteristics) rather than a qualitatively distinct category of behavior (psychopath).

The Latent Structure of Anxiety Sensitivity—Revisited

Anxiety sensitivity has been implicated as a risk factor for the development and maintenance of panic and other anxiety disorders. Although researchers have generally assumed that anxiety sensitivity is a dimensional, rather than categorical, variable, recent taxometric research has raised questions concerning the accuracy of this assumption. The present study examined the latent structure of anxiety sensitivity by applying four taxometric procedures (MAXEIG, MAXCOV, MAMBAC, and L-Mode) to data collected from two large nonclinical samples (n = 1,025 and n = 744) using two distinct measures of anxiety sensitivity (Anxiety Sensitivity Profile and Anxiety Sensitivity Index-Revised). In contrast to previous taxometric analyses of anxiety sensitivity, results of the present research provided convergent evidence for a latent anxiety sensitivity dimension. Several potential explanations for the discrepancy between these findings and those of previous research are discussed, as well as the implications of these findings for the conceptualization and measurement of anxiety sensitivity.

A taxometric analysis of health anxiety

A long-standing issue in the health anxiety literature is the extent to which health anxiety is a dimensional or a categorical construct. This study explores this question directly using taxometric procedures. Seven hundred and eleven working adults completed an index of health anxiety [the Whiteley Index (WI)] and indicated their current health status. Data from those who were currently healthy (n=501) and receiving no medical treatment were examined using three taxometric procedures: mean above minus below a cut (MAMBAC), maximum eigenvalue (MAXEIGEN) and L-mode factor analysis (L-MODE). Graphical representations (comparing actual to simulated data) and fit indices indicate that health anxiety is more accurately represented as a dimensional rather than a categorical construct. Health anxiety is better represented as a dimensional construct. Implications for theory development and clinical practice are examined.

Taxometric investigation of the latent structure of nicotine dependence: An epidemiological sample

A considerable number of daily tobacco users do not fulfill the DSM-IV and ICD-10 diagnostic criteria for nicotine dependence (ND). This suggests that such a diagnostic boundary may be arbitrary. This paper addresses this question empirically by comparing the viability of two models, respectively hypothesizing a dimensional and a categorical latent structure of ND. An epidemiological sample of 6,926 individuals was selected from a cross-sectional probabilistic stratified sampling design. All participants having smoked in the past 30 days were included in the study. Half of this sample was used to select appropriate composite indicators of tobacco consumption. A factor analysis with oblique PROMAX rotation was used as well as the MAXCOV (Maximum Covariance) procedure to identify indicators that maximized between-class distance, and minimize within-class variance. The remaining half of the sample was submitted to a set of three mathematically independent taxometric procedures: Mean Above Minus Below A Cut (MAMBAC), MAXCOV and Maximum Eigenvalues (MAXEIG). In line with the original hypothesis, the results supported a dimensional latent structure for ND. These findings are discussed in terms of their clinical implications for the validation of adequate screening procedures and the etiology and maintenance of ND.

Latent structure of aversion: Taxometric exploration

Recent research has identified specific disorders that appear to be better characterized by the experience of aversion, rather than anxiety, which evolve from disgust-related affect. Three mathematically distinct taxometric procedures – MAXEIG, MAMBAC, and L-Mode – were applied to data from a large undiagnosed sample ( N = 909) to determine whether aversion forms a distinct psychopathological category (i.e., an aversion taxon). Aversion was operationalized with self-report measures of the frequency of disgust experiences (Disgust Propensity), the perceived emotional impact of disgust experiences (Disgust Sensitivity), and contamination ideation and excessive washing (Contamination Fear). The collective results across procedures, consistency tests, and analysis of simulated comparison data produced converging evidence in support of the conclusion that the latent structure of aversion is dimensional. Implications of these findings for the clinical assessment of aversion and its disorders are discussed.

The latent structure of alcohol use disorders: A taxometric analysis of structured interview data obtained from male federal prison inmates

Archival data from structured interviews of 1193 male prison inmates were subjected to taxometric analysis to determine the latent structure of the alcohol use disorder construct. Analyses were performed using three taxometric procedures: mean above minus below a cut (MAMBAC), maximum eigenvalue (MAXEIG), and latent mode factor analysis (L-Mode). The outcomes were based on three indicators: (1) DSM-IV alcohol dependence criteria 1 and 2 (tolerance/withdrawal), (2) DSM-IV alcohol dependence criteria 3, 4, and 5 and DSM-IV alcohol abuse criterion 3 (loss of control), and (3) DSM-IV alcohol dependence criteria 6 and 7 and DSM-IV alcohol abuse criteria 1, 2, and 4 (negative social/psychological consequences). The outcomes revealed consistent support for a taxonic (categorical) interpretation of alcohol use disorders. There may be a taxonic boundary separating those who do and do not qualify for a diagnosis of alcohol dependence or abuse with important implications for diagnosis and treatment.

Converging Evidence for the Latent Structure of Antisocial Personality Disorder

The latent structure of antisocial personality disorder (ASPD) and psychopathy has been examined using both Meehl's taxometric method and other latent variable models, yielding results that have not been entirely consistent. However, in each instance, researchers used independent data sets for their analyses, obscuring whether the inconsistent findings resulted from the analytic method or from the study's methodology and data. A data set that had been used in a previous latent class analysis (LCA) of ASPD was examined using Meehl's taxometric procedures. Consistent with the prior LCA, the present analyses also supported a dimensional latent structure for ASPD. These converging findings from nonredundant data-analytic procedures provide additional evidence that ASPD is dimensional and exists along a continuum.