Schizotypy, taxometrics, and disconfirming theories in soft science: Comment on

Among the most consistently replicated findings in literature addressing the latent structure of psychopathology is evidence for a discrete latent class of individuals who are vulnerable to schizophrenia (schizotypes). Rawlings, Williams, Haslam, and Claridge (2008) challenge these findings by subjecting schizotypy scale scores to taxometric analysis, using a data simulation technique to accommodate variable skew. The authors conclude that schizotypy reflects a latent dimension, and that evidence for discrete latent structure from previous studies is likely an artifact of skewed variables. In this comment, we discuss (a) the philosophical implications of disconfirming well replicated findings in soft science with a single study, (b) important considerations when defining the schizotypy construct, (c) intricacies in executing and interpreting a taxometric analysis, and (d) problems with drawing strong conclusions from null results. Considerable evidence suggests that schizotypy is a discrete latent class, a conclusion that is unlikely the result of skewed variables.     

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.     

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.     

Does attention-deficit/hyperactivity disorder have a dimensional latent structure? A taxometric analysis

An understanding of the latent structure of attention-deficit/hyperactivity disorder (ADHD) is essential for developing causal models of this disorder. Although some researchers have presumed that ADHD is dimensional and others have assumed that it is taxonic, there has been relatively little research directly examining the latent structure of ADHD. The authors conducted a set of taxometric analyses using data from the NICHD Study of Early Child Care and Youth Development (ns between 667 and 1,078). The results revealed a dimensional latent structure across a variety of different analyses and sets of indicators for inattention, hyperactivity/impulsivity, and ADHD. Furthermore, analyses of correlations with associated features indicated that dimensional models demonstrated stronger validity coefficients with these criterion measures than dichotomous models. These findings jibe with recent research on the genetic basis of ADHD and with contemporary models of ADHD.     

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.     

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.     

Psychopathic, not psychopath: taxometric evidence for the dimensional structure of psychopathy

Although psychopathy is frequently regarded as qualitatively distinct from other conditions, relatively little research has examined whether psychopaths represent a distinct class of individuals. Using a sample of 876 prison inmates and court-ordered substance abuse patients who were administered the Psychopathy Checklist-Revised (R. D. Hare, 2003), the authors examined the latent structure of psychopathy using several taxometric procedures developed by Meehl and colleagues (P. E. Meehl & L. J. Yonce, 1994; N. G. Waller & P. E. Meehl, 1998). The results across these procedures offer no compelling support for the contention that psychopathy is a taxonic construct and contradict previous reports that psychopathy is underpinned by a latent taxon. The authors discuss the theoretical, public policy, and practice-level implications of these findings.     

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.     

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.     

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.     

Consideration of the challenges, complications, and pitfalls of taxometric analysis

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.     

Dimensional Latent Structure of Callous-Unemotional Traits in German Adolescents: Results from Taxometric Analyses

Research on Child and Adolescent Psychopathology - A large amount of research has addressed the issue of the latent status of psychiatric disorders and related phenomena. We used a new taxometric...     

Are infant attachment patterns continuously or categorically distributed? A taxometric analysis of strange situation behavior

Contemporary attachment research is based on the assumption that at least three types of infant attachment patterns exist: secure, avoidant, and resistant. It is not known, however, whether individual differences in attachment organization are more consistent with a continuous or a categorical model. The authors addressed this issue by applying P. E. Meehl's (1973, 1992) taxometric techniques for distinguishing latent types (i.e., classes, natural kinds) from latent continua (i.e., dimensions) to Strange Situation data on 1,139 fifteen-month-old children from the NICHD Study of Early Child Care. The results indicate that variation in attachment patterns is largely continuous, not categorical. The discussion focuses on the implications of dimensional models of individual differences for attachment theory and research.     

Inferential errors in taxometric analyses of ordered three-class constructs

Taxometric analysis (Waller & Meehl, 1998) has become a popular tool for investigating whether personality and psychopathology constructs are better modeled as dimensional or categorical. Monte Carlo evidence for the validity of taxometric methods, however, has been restricted to data that are derived either from latent dimensional or dichotomous models. There are various circumstances in which the correct model for a psychosocial construct could involve three or more ordered classes. It was demonstrated both mathematically and empirically that under these circumstances, the results of taxometric analyses can lead to incorrect conclusions about the population structure. Recommendations are provided for future studies intended to distinguish between categorical and dimensional structures.     

Differences in conceptualization and differences in methodology: Reply to Gavett and Stern (2012)

Gavett and Stern (2012, Commentary: Dementia has a categorial, not dimensional, latent structure, Psychology and Aging, 27, 791-797) and I have come to opposite conclusions about the latent structure of dementia using the same taxometric procedures and data source. Whereas I discerned the presence of dimensional latent structure (Walters, 2010, Dementia: Continuum or distinct entity? Psychology and Aging, 25, 534-544), they have uncovered evidence of a dementia category. Gavett and Stern (this issue) attribute these different outcomes to their decision to add indicators of cognitive change and functional independence to their analyses. In this article, I argue that methodological differences between the two studies may be the primary reason for the discrepant results. (PsycINFO Database Record (c) 2012 APA, all rights reserved).     

Assigning cases to groups using taxometric results: an empirical comparison of classification techniques

Determining whether individuals belong to different latent classes (taxa) or vary along one or more latent factors (dimensions) has implications for assessment. For example, no instrument can simultaneously maximize the efficiency of categorical and continuous measurement. Methods such as taxometric analysis can test the relative fit of taxonic and dimensional models, but it is not clear how best to assign individuals to groups using taxometric results. The present study compares the performance of two classification techniques-Bayes' theorem and a base-rate technique--across a wide range of data conditions. The base-rate technique achieves greater classification accuracy and a more even balance between sensitivity and specificity. In addition, the base-rate classification technique is easier to implement than Bayes' theorem and is more versatile in that it can be used when the context of assessment requires that cases be classified despite the absence of latent classes.     

Dimensional Latent Structure of Early Disruptive Behavior Disorders: A Taxometric Analysis in Preschoolers

Although disruptive behavior disorders (DBDs) are used as a distinct categorical diagnosis in clinical practice, they have repeatedly been described as having a dimensional structure in taxometric analyses. In the current study the authors analyzed the latent status of disruptive behaviors (DB) in a large sample (N?=?2,808) of German preschool children (2–6 years old, mean age 53.7 months, SD?=?13.5, 48.4% girls). The Eyberg Child Behavior Inventory (ECBI) as well as the Strengths and Difficulties Questionnaire (SDQ) were used to compile indicators of the DB core dimensions (Temper Loss, Aggression, Noncompliance, and Low Concern for others). Three widely used taxometric methods (a) MAXEIG, (b) MAMBAC, and (c) L-Mode were applied. Simulation data were created to evaluate the Comparison Curve Fit Index values (CCFIs), which were below 0.45, supporting a dimensional solution. Hence, in the current study the latent structure of DB in preschoolers encompassed differences in degree rather than kind. Researchers and practitioners should be mindful of the dimensional latent status of DB in theory building, assessment, classification, and labeling.     

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.     

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.     

Continuity and discontinuity models of bulimia nervosa: a taxometric investigation

Past research evaluating the continuity and discontinuity models of bulimia has produced inconclusive results. In the current study, we performed a taxometric analysis of bulimia nervosa using means above minus below a sliding cut and maximum covariance analysis with a sample of women diagnosed with bulimia nervosa (n = 201) or women college students (n = 412). Indicators were derived from the Bulimia Test--Revised and the Eating Attitudes Test--26, and both a mixed sample and the nonclinical sample were analyzed. With both taxometric methods and both mixed and nonclinical samples, results were consistently suggestive of a latent taxon for bulimia. These results challenge a dimensional model of bulimia nervosa.