The comparison data forest: A new comparison data approach to determine the number of factors in exploratory factor analysis.

Developing psychological assessment instruments often involves exploratory factor analyses, during which one must determine the number of factors to retain. Several factor-retention criteria have emerged that can infer this number from empirical data. Most recently, simulation-based procedures like the comparison data approach have shown the most accurate estimation of dimensionality.

Asking People to Explain Complex Policies Does Not Increase Political Moderation: Three Preregistered Failures to Closely Replicate Fernbach, Rogers, Fox, and Sloman's (2013) Findings.

Fernbach et al. (2013) found that political extremism and partisan in-group favoritism can be reduced by asking people to provide mechanistic explanations for complex policies, thus making their lack of procedural-policy knowledge salient. Given the practical importance of these findings, we conducted two preregistered close replications of Fernbach et al.

Weighing People Rather Than Food: A Framework for Examining External Validity.

Research training in psychological science emphasizes common threats to internal validity, with no comparably systematic or rigorous treatment of external validity. Trade-offs between internal and external validity are well known in some areas (e.g.

A taxometric investigation of psychopathy in women.

The taxonomic status of psychopathy is the topic of considerable research interest. The latent structure of psychopathy will latent structure will guide the determination of the best assessment approaches, maximize the reliability and validity, will help to establish optimal cutting scores that minimize decision errors and will also facilitate the selection of the best research designs to advance the study of the construct. In the present study, taxometric analyses were used for assessing taxonicity, and they were applied to Psychopathy Checklist-Revised (PCL-R) ratings of 1218 female offenders.

Using the comparison curve fix index (CCFI) in taxometric analyses: Averaging curves, standard errors, and CCFI profiles.

Determining whether a construct is more appropriately conceptualized and assessed in a categorical or a dimensional manner has received considerable research attention in recent years. There are a variety of statistical techniques to address this empirically, and Meehl's (1995) taxometric method has been among the most widely used methods applied to constructs in the areas of personality and psychopathology. In taxometric analysis, the comparison curve fit index (CCFI; Ruscio, Ruscio, & Meron, 2007) is an objective measure of whether parallel analysis of categorical or dimensional comparison data better reproduce empirical data results.

Taking Advantage of Citation Measures of Scholarly Impact: Hip Hip h Index!

Professional decisions about hiring, tenure, promotion, funding, and honors are informed by assessments of scholarly impact. As a measure of influence, citations are produced by experts but accessible to nonexperts. The h index is the largest number h such that an individual has published at least h works cited at least h times apiece.

Do recognizable lifetime eating disorder phenotypes naturally occur in a culturally asian population? A combined latent profile and taxometric approach.

Background: We examined whether empirically derived eating disorder (ED) categories in Hong Kong Chinese patients (N = 454) would be consistent with recognizable lifetime ED phenotypes derived from latent structure models of European and American samples.

Method: We performed latent profile analysis (LPA) using indicator variables from data collected during routine assessment, and then applied taxometric analysis to determine whether latent classes were qualitatively versus quantitatively distinct.

Results: Latent profile analysis identified four classes: (i) binge/purge (47%); (ii) non-fat-phobic low-weight (34%); (iii) fat-phobic low-weight (12%); and (iv) overweight disordered eating (6%).

Categories or dimensions: lessons learned from a taxometric analysis of Adult Attachment Interview data.

Booth-LaForce and Roisman's monograph on the Adult Attachment Interview (AAI) featured a taxometric analysis to determine whether variation along two components, dismissing and preoccupied states of mind, was categorical or dimensional. Empirically evaluating the latent structure of these constructs helps to avoid spurious categories or dimensions. This benefits researchers working with measures of adult attachment to maintain as much predictive validity and statistical power as possible, and it benefits researchers who build or test theories of adult attachment by steering the search for causal factors in fruitful directions.

Generalizations and Extensions of the Probability of Superiority Effect Size Estimator.

Researchers are strongly encouraged to accompany the results of statistical tests with appropriate estimates of effect size. For 2-group comparisons, a probability-based effect size estimator (A) has many appealing properties (e.g.

Trajectories of youthful antisocial behavior: categories or continua?

The purpose of this study was to determine whether qualitatively distinct trajectories of antisocial behavior could be identified in 1,708 children (843 boys, 865 girls) from the 1979 National Longitudinal Survey of Youth-Child Data (NLSY-C). Repeated ratings were made on the Behavior Problems Index (BPI: Peterson and Zill Journal of Marriage and the Family, 48, 295-307, 1986) antisocial scale by the mothers of these children when the children were 6, 8, 10, 12, and 14 years of age. Scores on three indicators constructed from the six BPI Antisocial items (callousness, aggression, noncompliance) were then analyzed longitudinally (by summing across the rating periods) and cross-sectionally (by testing each individual rating period) in the full sample as well as in subsamples of boys and girls.

Scientific communication in clinical psychology: examining patterns of citations and references.

Unlabelled: Previous studies of scientific communication used citation mapping, establishing psychology as a 'hub science' from which many other fields draw information. Within psychology, the clinical and counselling discipline is a major 'knowledge broker'. This study analyzed scientific communication among three major subdisciplines of clinical psychology-the cognitive-behavioural, psychodynamic and humanistic schools of thought-by examining patterns of references within and citations to 305 target articles published in leading journals of these subdisciplines.

Confidence Intervals for the Probability of Superiority Effect Size Measure and the Area Under a Receiver Operating Characteristic Curve.

It is good scientific practice to the report an appropriate estimate of effect size and a confidence interval (CI) to indicate the precision with which a population effect was estimated. For comparisons of 2 independent groups, a probability-based effect size estimator (A) that is equal to the area under a receiver operating characteristic curve and closely related to the popular Wilcoxon-Mann-Whitney nonparametric statistical tests has many appealing properties (e.g.

Determining the number of factors to retain in an exploratory factor analysis using comparison data of known factorial structure.

Exploratory factor analysis (EFA) is used routinely in the development and validation of assessment instruments. One of the most significant challenges when one is performing EFA is determining how many factors to retain. Parallel analysis (PA) is an effective stopping rule that compares the eigenvalues of randomly generated data with those for the actual data.

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.

Performing Taxometric Analysis to Distinguish Categorical and Dimensional Variables.

A fundamental question facing clinical scientists is whether the constructs they are studying are categorical or dimensional in nature. The taxometric method was developed expressly to answer this question and is being used by a growing number of investigators to inform theory, research, and practice in psychopathology. The current paper provides a practical introduction to the method, updating earlier tutorials based on the findings of recent methodological studies.

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

A number of recent studies have used Meehl's (1995) taxometric method to determine empirically whether one should model assessment-related constructs as categories or dimensions. The taxometric method includes multiple data-analytic procedures designed to check the consistency of results. The goal is to differentiate between strong evidence of categorical structure, strong evidence of dimensional structure, and ambiguous evidence that suggests withholding judgment.

Where do we draw the line? Assigning cases to subsamples for MAMBAC, MAXCOV, and MAXEIG taxometric analyses.

There are several important decisions that must be made when implementing taxometric procedures such as mean above minus below a cut (MAMBAC), maximum covariance (MAXCOV), and maximum eigenvalue (MAXEIG). A Monte Carlo study was performed with 10,000 (5,000 categorical, 5,000 dimensional) samples to examine 5 ways to locate the first and last MAMBAC cuts and 24 ways to perform MAXCOV and MAXEIG. For MAMBAC, there was little difference across conditions, with slightly more accurate results obtained when a small, fixed number of cases (n = 10 or 25) was located beyond the most extreme cuts.

Taxometric evidence for the dimensional structure of cluster-C, paranoid, and borderline personality disorders.

Despite a lively debate about the dimensional vs. categorical nature of Personality Disorders (PDs), direct empirical tests of the underlying structure are missing for most PDs. Taxometrics can be used to investigate whether latent structures are categorical or dimensional.

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.

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.

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.

A taxometric investigation of DSM-IV major depression in a large outpatient sample: interpretable structural results depend on the mode of assessment.

Most taxometric studies of depressive constructs have drawn indicators from self-report instruments that do not bear directly on the Diagnostic and Statistical Manual of Mental Disorders, 4th edition (DSM-IV) diagnostic construct of major depressive disorder (MDD). The present study examined the latent structure of MDD using indicator sets constructed from a semistructured clinical interview, self-report questionnaires, and a combination of the two. Taxometric analyses were performed in a large sample of outpatients with primary mood or anxiety disorders.

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.