Categories and Dimensions

ABSTRACT— The distinction between categories and dimensions has important consequences for basic and applied science in many areas of psychological research. Decisions as to whether individuals should be assigned to groups or located along one or more continua often are based on personal preferences or discipline-specific measurement traditions, which can lead to the creation, use, or reification of spurious categories or dimensions. Methods for evaluating the latent structure of psychological constructs, using powerful and informative tests between competing models, are available. Rather than choosing on a priori grounds, investigators can perform structural research to evaluate the strength and consistency with which results tease apart categorical and dimensional models. Here, we review why researchers should make this distinction empirically, briefly discuss methods available for doing so, and describe the breadth of areas ripe for exploiting the largely untapped potential of structural research.     

Inferential Errors in Taxometric Analyses of Ordered Three-Class Constructs

Hermann Rorschach's theory of perception is first presented, and then a method is described which was aimed at testing the theory's implications. Group Rorschach contents were selected, and two types of scoring procedures were worked out. In one case, a simple percentage of total responses was used, and in a second case a weighting was made according to whether or not the S was reporting an interpretation or a perception of the inkblot. Three all-male samples were studied, attending two different high schools. Both social class and racial differences were represented in the samples. Where one or the other of the two Rorschach scoring methods resulted in a significant correlation with independent measures of personality within the samples, it was the measure taking into consideration Rorschach's theory which reflected the over-all advantage.     

Creativity and Psychopathology: Categories,Dimensions, and Dynamics

The scientific method may be of limited value when investigating topics such as religion, spirituality, or aesthetic matters. A novel, hybrid approach is described that uses a supported philosophical-deductive methodology. This involves seeking scientific evidence to corroborate or refute the "alternative" premise in each of a series of deductive arguments. To test this, the notion that musical appreciation is an innate gift was pitted first against the alternative that it is a learned phenomenon and, second, that music has survival value and could therefore be explained purely in terms of evolution theory. So far, research to date failed to fully confirm either of the alternative propositions, thus leaving open the possibility that the ability to appreciate music is an innate gift for our aesthetic pleasure. The proposed approach could encourage investigations that otherwise might falter at an early stage, due to the constraints of conventional scientific methodology.     

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...     

Dimensions and Categories Underlying Thinking About College Student Types1

Previous work on implicit personality theory and the college student culture suggests that thinking about college student types should include academic involvement and social involvement dimensions. Further, positive and negative social, positive academic, and oppositional clusters of types were predicted based on studies of high school crowds. Using 85 college student type labels as stimuli, a free‐sorting method coupled with multidimensional scaling and hierarchical clustering analyses provided support for these hypotheses. Also, the results suggest convergence in the cognitive structure underlying perception of college student types across major demographic categories.     

亲环境行为研究中情感的类别、维度及其作用机理

从近20年来境外学者对亲环境行为的研究文献看, 情感的作用越来越受到关注。一个大致结论是, 情感对亲环境行为的影响往往大于认知, 有更重要的驱动作用。首次对境外学者的情感与亲环境行为研究进行了梳理和回顾。在亲环境行为研究中, 情感的类别和维度有4种划分标准, 即呈现形态、体验结构、时间维度和责任归属; 情感对亲环境行为的作用机理可以通过情感因素在亲环境行为模型中的三种角色加以分析; 情感在规范激活模型和计划行为理论模型的拓展中扮演了重要角色, 有效提升了拓展模型的解释力。未来研究需要更重视情感在亲环境行为中的测量范式、研究深度和动态影响机制。     

不同维度特征的共存对归类不确定性特征推理的影响

探讨在归类不确定的情境下,目标特征和预测特征的共存对特征推理的影响。共包括了三个实验,其中实验1、2考察了非靶类别中目标特征和预测特征的共存性对特征推理的影响,实验3考察了靶类别中目标特征和预测特征的共存性对特征推理的影响。三个实验五个分实验的结果一致支持了修正后的Bayesian规则,排除了“单类说”、“综合条件概率模型”的假设,并进一步修正了Bayesian规则为：     

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.     

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).     

Taxometric Analysis of the Levenson Self-Report Psychopathy Scale

Fifteen HIT cards were administered to 12 Ss before and after paired placebo and alcohol intravenous infusions in a Latin-square cross-over design. Separate sets of cards were used for placebo and alcohol infusions with cards from the A form administered before and the counterpart cards from the B form after each infusion. A significant increase in score in color was noted with the low level of intoxication as compared with placebo infusion. There were no significant differences between placebo and alcohol infusion on other HIT variables. Collateral data from previous alcohol intoxication experiments and other HIT studies suggest that the increase in color may be related to change in affect which accompanies the intoxication.     

Taxometric analyses of sexual orientation and gender identity

Taxa are nonarbitrary classes whose existence is an empirical question and not a matter of mere semantic convenience. Taxometric procedures detect whether numerical relations between purported indicators of conjectured taxa bear the hallmarks of true taxa. On the basis of theoretical considerations, the current study tested whether taxa underlie sexual orientation and related measures of gender identity. Two taxometric procedures, maximum covariance, making hits maximum (MAXCOV) and mean above minus below a cut (MAMBAC), were applied to Kinsey Scales and measures of childhood gender nonconformity and adult gender identity in a sample of nearly 5,000 members of the Australian Twin Registry. Results suggest that latent taxa underlie these measures. About 12-15% of men and 5-10% of women belong to latent taxa associated with homosexual preference. These percentages are greater than those of individuals who report homosexual preference, however, and hence it appears that an appreciable proportion of individuals in these taxa have heterosexual preference. An understanding of the origins of these latent taxa may be important to understanding the development of sexual orientation and gender identity.     

Taxometric analysis of fuzzy categories: a Monte Carlo study

A small Monte Carlo study examined the performance of a form of taxometric analysis (the MAXCOV procedure) with fuzzy data sets. These combine taxonic (categorical) and nontaxonic (continuous) features, containing a subset of casts with intermediate degrees of category membership. Fuzzy data sets tended to yield taxonic findings on plot inspection and two popular consistency tests, even when the degree of fuzziness, i.e., the proportion of intermediate cases, was large. These results suggest that fuzzy categories represent a source of pseudotaxonic inferences, if on is understood in the usual binary, "either-or" fashion. This in turn implies that dichotomous causes cannot be confidently inferred when taxometric analyses yield apparently taxonic findings.     

Categories and causality: the neglected direction

The standard approach guiding research on the relationship between categories and causality views categories as reflecting causal relations in the world. We provide evidence that the opposite direction also holds: categories that have been acquired in previous learning contexts may influence subsequent causal learning. In three experiments we show that identical causal learning input yields different attributions of causal capacity depending on the pre-existing categories to which the learning exemplars are assigned. There is a strong tendency to continue to use old conceptual schemes rather than switch to new ones even when the old categories are not optimal for predicting the new effect, and when they were motivated by goals that differed from the present context of causal discovery. However, we also found that the use of prior categories is dependent on the match between categories and causal effect. Whenever the category labels suggest natural kinds which can be plausibly related to the causal effects, transfer was observed. When the categories were arbitrary, or could not be plausibly related to the causal effect learners abandoned the categories, and used different categories to predict the causal effect.     

The Latent Structure of Psychopathy in Youth: A Taxometric Investigation

Using taxometric procedures, the latent structure of psychopathy was investigated in two studies of children and adolescents. Prior studies have identified a taxon (i.e., a natural category) associated with antisocial behavior in adults as well as children and adolescents. However, features of this taxon suggest that it is not psychopathy but rather a broader class consistent with the construct of life course persistent antisocial behavior. Because the only prior study of youth used a non-clinical sample, the base rate of psychopathy may have been too low to reveal a psychopathy taxon, especially against the background of a broader and more prevalent antisocial behavior taxon. Therefore, this investigation sought to increase the likelihood of finding a psychopathy taxon (should one exist) by increasing its expected base rate through inclusion of clinical cases in the samples studied. Results produced evidence for both a broad antisocial behavior problem taxon consistent with past research and a much lower base rate taxon consistent with prevalence expectations for psychopathy. These findings support the existence of latent taxa for both psychopathy and a broader class of antisocial behavior problems. Further taxometric research appears to be warranted, which should use a broader array of indicators, with greater specificity to psychopathy.     

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.     

Differentiating anxiety and depression: the State-Trait Anxiety-Depression Inventory

ABSTRACT

The differentiation of trait anxiety and depression in nonclinical and clinical populations is addressed. Following the tripartite model, it is assumed that anxiety and depression share a large portion of negative affectivity (NA), but differ with respect to bodily hyperarousal (specific to anxiety) and anhedonia (lack of positive affect; specific to depression). In contrast to the tripartite model, NA is subdivided into worry (characteristic for anxiety) and dysthymia (characteristic for depression), which leads to a four-variable model of anxiety and depression encompassing emotionality, worry, dysthymia, and anhedonia. Item-level confirmatory factor analyses and latent class cluster analysis based on a large nation-wide representative German sample (N?=?3150) substantiate the construct validity of the model. Further evidence concerning convergent and discriminant validity with respect to related constructs is obtained in two smaller nonclinical and clinical samples. Factors influencing the association between components of anxiety and depression are discussed.     

Taxometric and factor analytic models of anxiety sensitivity: integrating approaches to latent structural research

This study represents an effort to better understand the latent structure of anxiety sensitivity (AS), as indexed by the 16-item Anxiety Sensitivity Index (ASI; S. Reiss, R. A. Peterson, M. Gursky, & R. J. McNally, 1986), by using taxometric and factor-analytic approaches in an integrative manner. Taxometric analyses indicated that AS has a taxonic latent class structure (i.e., a dichotomous latent class structure) in a large sample of North American adults (N=2,515). As predicted, confirmatory factor analyses indicated that a multidimensional 3-factor model of AS provided a good fit for the AS complement class (normative or low-risk form) but not the AS taxon class (high-risk form). Exploratory factor analytic results suggested that the AS taxon may demonstrate a unique, unidimensional factor solution, though there are alternative indications that it may be characterized by a 2-factor solution. Findings suggest that the latent structural nature of AS can be conceptualized as a taxonic latent class structure composed of 2 types or forms of AS, each of these forms characterized by its own unique latent continuity and dimensional structure.     

The Underlying Structure of Grief: A Taxometric Investigation of Prolonged and Normal Reactions to Loss

Recent studies have supported the distinctiveness of complicated and prolonged forms of grief as a cluster of symptoms that is separate from other psychiatric disorders. The distinction between prolonged and normal reactions to loss remains unclear, however, with some believing that prolonged grief represents a qualitatively distinct clinical entity and others conceptualizing it as the extreme end of a continuum. Thus, in this study a taxometric methodology was used to examine the underlying structure of grief. Participants included 1,069 bereaved individuals who had lost a first-degree relative. Each participant completed the Dutch version of the Inventory of Complicated Grief–Revised, which was used to create indicators of prolonged grief. The mean above and mean below a cut (MAMBAC) and maximum eigenvalue (MAXEIG) tests supported a dimensional conceptualization, indicating that pathological reactions might be best defined by the severity of grief symptoms rather than the presence or absence of specific symptoms.

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

Meehl's 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.