Taxometric analysis supports a dimensional latent structure for schizotypy

The existence of a discrete class of people vulnerable to schizophrenia spectrum disorders is the most replicated finding of taxometric research. Evidence for such a “taxon” has been obtained with diverse measures of schizotypy in clinical, high-risk, and normal samples. However, recent demonstrations that skewed indicators of a latent dimension can yield a spuriously taxonic pattern of results may call some of these findings into question. Normal adults (N = 1073) completed measures of positive (perceptual aberration, magical ideation) and negative (physical and social anhedonia) components of schizotypy. Taxometric curves resembled those obtained previously, but when a simulation procedure took skew into account, dimensional models of schizotypy received stronger support than taxonic models for most schizotypy components, with findings for magical thinking inconclusive. A re-evaluation of previous taxonic conclusions regarding the latent structure of schizotypy is indicated.     

Resolving the latent structure of schizophrenia endophenotypes using expectation-maximization-based finite mixture modeling

Prior research has focused on the latent structure of endophenotypic markers of schizophrenia liability, or schizotypy. The work supports the existence of 2 relatively distinct latent classes and derives largely from the taxometric analysis of psychometric values. The present study used finite mixture modeling as a technique for discerning latent structure and the laboratory-measured endophenotypes of sustained attention deficits and eye-tracking dysfunction as endophenotype indexes. In a large adult community sample (N=311), finite mixture analysis of the sustained attention index d' and 2 eye-tracking indexes (gain and catch-up saccade rate) revealed evidence for 2 latent components. A putative schizotypy class accounted for 27% of the sample. A supplementary maximum covariance taxometric analysis yielded highly consistent results. Subjects in the schizotypy component displayed higher rates of schizotypal personality features and an increased rate of treated schizophrenia in their 1st-degree biological relatives compared with subjects in the other component. Implications of these results are examined in light of major theories of schizophrenia liability, and methodological advantages of finite mixture modeling for psychopathology research, with particular emphasis on genomic issues, are discussed.     

Confirming the latent structure and base rate of schizotypy: a taxometric analysis.

Meehl's (1962, 1990) model of schizotypy and the development of schizophrenia implies that the structure of liability for schizophrenia is dichotomous and that a "schizogene" determines membership in a latent class, or taxon (Meehl & Golden, 1982). The authors sought to determine the latent structure and base rate of schizotypy. They applied Meehl's (1973; Meehl & Golden, 1982) MAXCOV-HITMAX taxometric analytic procedures to a subset of items from the Perceptual Aberration Scale (PAS; Chapman, Chapman, & Raulin, 1978), a prominent psychometric index of schizotypy, derived from a randomly ascertained nonclinical university sample (N = 1,093). The results, in accordance with Meehl's conjectures, strongly suggest that schizotypy, as assessed by the PAS, is taxonic at the latent level with a general population taxon base rate of approximately .10.     

The psychometric detection of schizotypy: do putative schizotypy indicators identify the same latent class?

The authors examined whether three indicators of Schizotypy, the Revised Social Anhedonia Scale (RSAS), Magical Ideation Scale (MIS), and Perceptual Aberration Scale (PAS), identify a common latent class or taxon as conceptualized by P. E. Meehl (1962, 1990). The separate and joint latent structures of these scales were evaluated using taxometric methods in two large, independent samples. Replicating prior findings, the RSAS and PAS each identified latent taxa with base rates approximating.10. Results for the taxonicity of the MIS were less consistent. Analyses of joint latent structure indicated that the RSAS-identified taxon was essentially independent of constructs measured by the PAS or MIS. Measures commonly thought to tap the same latent class, conjectured to be schizotypy, do not share this property.     

The latent structure of social anxiety disorder and the performance only specifier: a taxometric analysis

Social anxiety disorder (SAD) is often treated as a discrete diagnostic entity that represents a naturally occurring class, though empirical evidence largely supports a dimensional conceptualization of social fears. Further, the inclusion of a “performance only” specifier in the DSM-5 implies that individuals who experience intense social anxiety exclusively in performance situations are distinct from those with broader social fears. The purpose of the present research was to examine the latent structure of SAD and the DSM-5 “performance only” specifier in a large nonclinical sample (n = 2019). Three taxometric procedures (MAXCOV, MAMBAC, and L-Mode) were applied to indicators derived from two commonly used measures of social anxiety. Results yielded convergent evidence indicating that social anxiety exhibits a dimensional latent structure. Further, social performance anxiety demonstrates continuous relationships with milder social fears, suggesting that the “performance only” specifier may not represent a discrete entity. The implications of these findings for the assessment, diagnosis, classification, and treatment of social anxiety are discussed.     

Taxometric analysis as a general strategy for distinguishing categorical from dimensional latent structure

Statistical analyses investigating latent structure can be divided into those that estimate structural model parameters and those that detect the structural model type. The most basic distinction among structure types is between categorical (discrete) and dimensional (continuous) models. It is a common, and potentially misleading, practice to apply some method for estimating a latent structural model such as factor analysis without first verifying that the latent structure type assumed by that method applies to the data. The taxometric method was developed specifically to distinguish between dimensional and 2-class models. This study evaluated the taxometric method as a means of identifying categorical structures in general. We assessed the ability of the taxometric method to distinguish between dimensional (1-class) and categorical (2-5 classes) latent structures and to estimate the number of classes in categorical datasets. Based on 50,000 Monte Carlo datasets (10,000 per structure type), and using the comparison curve fit index averaged across 3 taxometric procedures (Mean Above Minus Below A Cut, Maximum Covariance, and Latent Mode Factor Analysis) as the criterion for latent structure, the taxometric method was found superior to finite mixture modeling for distinguishing between dimensional and categorical models. A multistep iterative process of applying taxometric procedures to the data often failed to identify the number of classes in the categorical datasets accurately, however. It is concluded that the taxometric method may be an effective approach to distinguishing between dimensional and categorical structure but that other latent modeling procedures may be more effective for specifying the model.     

Hedonic capacity and schizotypy revisited: a taxometric analysis of social anhedonia

P. E. Meehl (1962) originally conjectured that hedonic capacity was an indicator of the latent class or taxon of schizotypy. However, P. E. Meehl (1989, 1990) subsequently diminished the role of hedonic capacity in his theory, indicating that hypohedonia is one of a dozen normal-range (nontaxonic) individual-differences factors that may potentiate the expression of schizophrenia. This dimensional-only view of hedonic capacity was tested by applying taxometric procedures to the Revised Social Anhedonia Scale (RSAS; M. L. Eckblad, L. J. Chapman, J. P. Chapman, & M. Mishlove, 1982) in a sample of college students (N = 1,526). Analyses indicated that the construct measured by the RSAS is taxonic in nature with a base rate approximating .10. These data are interpreted in the context of other findings suggesting that social anhedonia is an indicator of schizotypy.     

Is schizotypy taxonic? Response to

Beauchaine, Lenzenweger, and Waller (2008) criticize our taxometric study of schizotypy (Rawlings, Williams, Haslam, & Claridge, in press) on a variety of methodological and philosophical grounds. We argue that their critique selectively applies more stringent standards to our work than to studies supporting their view that schizotypy is taxonic (i.e., categorical). Many of their criticisms apply at least equally to existing studies that offer support for a schizotypy taxon, and these studies are vulnerable to biases favouring taxonic conclusions that were controlled for in our study. Contrary to their criticisms, we did not claim to have disconfirmed previous taxonic findings about schizotypy, and our findings positively support dimensional models of schizotypy rather than merely being null results. Similarly, our findings are not artefacts of the sampling or measurement decisions that they question. Even well-replicated taxometric findings are not immune to empirical challenge, and evaluation of such challenges must be even-handed.     

Using taxometric analysis to distinguish a small latent taxon from a latent dimension with positively skewed indicators: the case of involuntary defeat syndrome

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.     

Taxometric Investigation of PTSD: data from two nationally representative samples

Current psychiatric nosology depicts posttraumatic stress disorder (PTSD) as a discrete diagnostic category. However, only one study has examined the latent structure of PTSD, and this study suggested that PTSD may be more accurately conceptualized as an extreme reaction to traumatic life events rather than a discrete clinical syndrome. To build on the existing literature base, the present research examined the latent structure of posttraumatic stress reactions by applying three taxometric procedures (MAXEIG, MAMBAC, and L-Mode) to data collected from large nationally representative samples of women (ns = 2684 and 3033) and adolescents (n = 3775). Results consistently provided evidence for a dimensional PTSD solution across samples and statistical procedures. These findings have important implications for the theory, assessment, and investigation of posttraumatic stress reactions.     

Categorical or Dimensional: A Reanalysis of the Anxiety Sensitivity Construct

Anxiety sensitivity, or the fear of anxiety sensations, has been implicated in the etiology of anxiety disorders, particularly panic disorder. Recently, inconsistent findings have been reported regarding the latent structure of anxiety sensitivity. Whereas some taxometric studies of anxiety sensitivity have reported evidence of categorical latent structure, others have found evidence of a latent dimension. The purpose of the present research was to further examine the latent structure of anxiety sensitivity using taxometric procedures and commonly utilized measures of anxiety sensitivity. To this end, three mathematically independent taxometric procedures (MAXEIG, MAMBAC, and L-Mode) were applied to data collected from two large nonclinical samples (n's = 1,171 and 2,173) that completed the Anxiety Sensitivity Index and the Anxiety Sensitivity Index-Revised. Results from both studies converged in support of a dimensional conceptualization of anxiety sensitivity. A third study was conducted using indicators derived from the newly revised Anxiety Sensitivity Index-3 in a separate sample of 1,462 nonclinical participants. Results of these analyses provided further support for a dimensional anxiety sensitivity solution. The implications of these results for anxiety sensitivity research are discussed, and several potential directions for future research are considered.     

Exploring the latent structure of the Perceptual Aberration, Magical Ideation, and Physical Anhedonia Scales in a German sample.

The schizotypy model proposed by Meehl (1990) assumes a discontinuous distribution of schizophrenia liability. The "schizogene" is thought to determine if one is at risk for psychosis (i.e., whether one is a member of the taxon or its complement, which are considered to be the two latent classes). Using a German non-student sample (n = 809) we wanted to (1) replicate the results of prior research pertaining to the latent structure and base rate of schizotypy assessed by the Perceptual Aberration Scale (PER; Chapman, Chapman, & Raulin, 1978), and (2) investigate whether the same holds true for two other prominent psychometric indices, the Magical Ideation Scale (MI; Eckblad & Chapman, 1983) and the Physical Anhedonia Scale (PhA; Chapman et al., 1976), if one uses the same kind of analysis--the MAXCOV-HITMAX analysis based on subsets of items (Meehl, 1973). Pertaining to PER and PhA, our results are in accordance with prior research showing a latent class structure and a base rate of about 12% for schizotypy. However, for MI, there was no evidence of a taxonic structure. Possible reasons for MI's negative results are discussed as well as the role of the concept "anhedonia."     

The Behavioral Approach System (BAS) Model of Vulnerability to Bipolar Disorder: Evidence of a Continuum in BAS Sensitivity across Adolescence

Behavioral Approach System (BAS) sensitivity has been implicated in the development of a variety of different psychiatric disorders. Prominent among these in the empirical literature are bipolar spectrum disorders (BSDs). Given that adolescence represents a critical developmental stage of risk for the onset of BSDs, it is important to clarify the latent structure of BAS sensitivity in this period of development. A statistical approach especially well-suited for delineating the latent structure of BAS sensitivity is taxometric analysis, which is designed to evaluate whether the latent structure of a construct is taxonic (i.e., categorical) or dimensional (i.e., continuous) in nature. The current study applied three mathematically non-redundant taxometric procedures (i.e., MAMBAC, MAXEIG, and L-Mode) to a large community sample of adolescents (n = 12,494) who completed two separate measures of BAS sensitivity: the BIS/BAS Scales Carver and White (Journal of Personality and Social Psychology, 67, 319–333. 1994) and the Sensitivity to Reward and Sensitivity to Punishment Questionnaire (Torrubia et al. Personality and Individual Differences, 31, 837–862. 2001). Given the significant developmental changes in reward sensitivity that occur across adolescence, the current investigation aimed to provide a fine-grained evaluation of the data by performing taxometric analyses at an age-by-age level (14–19 years; n for each age ≥ 883). Results derived from taxometric procedures, across all ages tested, were highly consistent, providing strong evidence that BAS sensitivity is best conceptualized as dimensional in nature. Thus, the findings suggest that BAS-related vulnerability to BSDs exists along a continuum of severity, with no natural cut-point qualitatively differentiating high- and low-risk adolescents. Clinical and research implications for the assessment of BSD-related vulnerability are discussed.     

Construct Validity of the Rotter Incomplete Sentences Blank With Clinic-Referred and Nonreferred Adolescents

Inferred latent entities, whether those of psychoanalysis, factor analysis, or cluster analysis, have declined in value for many clinical psychologists, both as tools of practice and as objects of theoretical interest. Behavior modification, rational-emotive therapy, crisis intervention, psycho-pharmacology, and actuarial prediction all tend to minimize reliance on latent entities in favor of purely dispositional concepts. Behavior genetics is, however, a powerful movement to the contrary. As regards categorical entities (types, taxa, syndromes, diseases), history reveals no impressive examples of their discovery by cluster algorithms; whereas organic medicine and psychopathology have both discovered many taxonic entities without reliance on formal (statistical) cluster methods. I offer eight reasons for this strange condition, with associated suggestions for ameliorating it. Adopting a realist instead of a fictionist approach to taxonomy, I give high priority to theory-based mathematical derivation of quantitative consistency tests for all taxometric results. I urge a large scale cooperative survey of taxometric methods based on Monte Carlo runs, biological pseudoproblems where the true taxon is independently known, and live problems in genetics, organic medicine, and psychopathology. An empirical example of taxometric bootstrapping and consistency testing was presented from my own current research on schizotypy.     

Is risky sexual behavior continuous or categorical? A taxometric analysis of the Sexual Risk Survey

Risky sexual behaviors are behaviors that involve the possibility of an adverse outcome, such as contracting a sexually transmitted infection or unwanted pregnancy. The question of whether risky sexual behavior exists as a discrete class (i.e., taxon) or as a dimensional construct has not previously been explored. The authors performed a set of taxometric analyses on 4 factor scales derived from the Sexual Risk Survey (Turchik & Garske, 2009) with data from 1,103 college students. The results provided consistent support for a dimensional latent structure in which variations in reported risky sexual behavior reflect differences in degree and not differences in kind. The implications of these findings for the assessment of risky sexual behavior are discussed.     

Differentiating categorical and dimensional data with taxometric analysis: are two variables better than none?

Taxometric analyses have proven helpful for distinguishing categorical and dimensional data. Many taxometric procedures require at least 3 variables for analysis. What if a construct is defined by only 2 conceptually nonredundant characteristics or a data set contains only 2 empirically nonredundant variables? In Study 1, we performed extensive simulations to determine whether informative results can be obtained when only 2 variables are available for taxometric analysis. The mean above minus below a cut (MAMBAC) and maximum slope (MAXSLOPE) procedures, used with parallel analyses of comparison data, successfully differentiated categorical and dimensional structure. With just 2 variables, it seems especially important that indicators vary across as many distinct values as possible and that investigators obtain as large a sample as possible. Additional findings address questions about the most effective way to implement taxometric analyses. In Study 2, the potential utility of 2-variable taxometric analysis is illustrated using data on proactive and reactive childhood aggression, where the results provided strong support for dimensional structure. As long as high-quality data are available, it appears that one can have confidence in the results of taxometric analyses performed with only 2 variables.     

Deeper into the schizotypy taxon: on the robust nature of maximum covariance analysis

Prior research has suggested that the latent structure of the schizotypy construct (P. E. Meehl, 1990) may be qualitative in nature and have a low base rate (L. Korfine & M. F. Lenzenweger, 1995; M. F. Lenzenweger & L. Korfine, 1992). These studies relied on the application of maximum covariance analysis (MAXCOV) to 8 true-false format items from a schizotypy measure. The current study sought to examine the robustness of those prior findings through MAXCOV analysis of fully quantitative measures of schizotypy. Measures of perceptual aberration, magical ideation, and referential thinking were analyzed using MAXCOV in a sample of 429 persons. The results of these analyses strongly support a latent taxonic structure for schizotypy and a low base rate for the schizotypy taxon. Furthermore, the members of the putative taxon reveal an increased level of deviance on a psychometric measure known to be associated with schizophrenia liability. The possibility that the dichotomous item format of those items analyzed previously with MAXCOV lead to spurious pseudotaxonicity is greatly diminished in light of these results.     

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.     

Differentiating Categories and Dimensions: Evaluating the Robustness of Taxometric Analyses

Interest in modeling the structure of latent variables is gaining momentum, and many simulation studies suggest that taxometric analysis can validly assess the relative fit of categorical and dimensional models. The generation and parallel analysis of categorical and dimensional comparison data sets reduces the subjectivity required to interpret results by providing an objective Comparison Curve Fit Index (CCFI). This study takes advantage of developments in the generation of comparison data to examine the robustness of taxometric analyses to unfavorable data conditions. Very large comparison data sets are treated as populations from which many samples are drawn randomly, placing the method on a firmer statistical foundation and increasing its run-time efficiency. The impressive accuracy of the CCFI was consistent with prior findings and robust across novel manipulations of asymmetry, tail weight, and heterogeneous variances. Analyses, an empirical illustration using Minnesota Multiphasic Personality Inventory (MMPI) hypochondriasis data, and discussion focus on the practical implications for differentiating categories and dimensions.     

Malingering as a categorical or dimensional construct: the latent structure of feigned psychopathology as measured by the SIRS and MMPI-2

The 6 nonoverlapping primary scales of the Structured Interview of Reported Symptoms (SIRS) were subjected to taxometric analysis in a group of 1,211 criminal and civil examinees in order to investigate the latent structure of feigned psychopathology. Both taxometric procedures used in this study, mean above minus below a cut (MAMBAC) and maximum covariance (MAXCOV), produced dimensional results. A subgroup of participants (n = 711) with valid Minnesota Multiphasic Personality Inventory-2 (MMPI-2) protocols were included in a second round of analyses in which the 6 nonoverlapping primary scales of the SIRS and the Infrequency (F), Infrequency-Psychopathology (Fp), and Dissimulation (Ds) scales of the MMPI-2 served as indicators. Again, the results were more consistent with dimensional latent structure than with taxonic latent structure. On the basis of these findings, it is concluded that feigned psychopathology forms a dimension (levels of fabrication or exaggeration) rather than a taxon (malingering-honest dichotomy) and that malingering is a quantitative distinction rather than a qualitative one. The theoretical and clinical practice implications of these findings are discussed.