Comparison Curve Fit Index Research Articles

Further Evidence for a Dimensional Latent Structure of Health Anxiety: Taxometric Analyses of the Whiteley Index Based on Two German Representative Samples.

Health anxiety is an intricate part of illness anxiety and somatic symptom disorder. Based on convenience samples, two out of three available studies indicate that it is a dimensional rather than a categorical construct. Using two representative datasets, this study investigates whether previous results can be clarified. Conventional taxometric analyses as well as comparison curve fit indices (CCFI) profile analyses (MAMBAC and MAXSLOPE procedures) were calculated with two datasets of the German adult population assessing the Whiteley Index (WI-14, N = 2,072; WI-7, N = 2,498). Mean CCFIs indicated a dimensional structure for both the WI-7 (mean CCFI = 0.42, mean CCFI profile = 0.40) and the WI-14 (mean CCFI = 0.44, mean CCFI profile = 0.32). The results support and extend previous findings by strongly suggesting a dimensional distribution of health anxiety in the general population. Implications for research and practice comprise the adoption of a dimensional description of psychopathology as well as transdiagnostic treatment approaches.

Moderately Eccentric: Evidence for Dimensionality in the Latent Structure of Schizotypal Personality Features

The authors investigated the latent structure of schizotypal personality features using taxometric analysis in a large sample of individuals from clinical and nonclinical populations. A total of 7,072 community adults and 270 psychiatric patients participated in the study by responding to the eccentricity, distrust, and isolation scales from the Dimensional Clinical Personality Inventory (IDCP). Data were analyzed using three different taxometric techniques, MAMBAC, MAXEIG, and L-Mode. The indicators proved valid for the taxometric analysis (d ≥ 1.25). The comparison curve fit index (CCFI) coefficients from the three methods used were close to zero, and the average CCFI was lower than .40, clearly indicating a dimensional structure for schizotypal personality features. These findings suggest that schizotypal personality traits occur in levels, with no evidence of any natural boundary between clinical and nonclinical manifestations. Results from other studies that were consistent with a categorical structure may be due to methodological weaknesses.

The Somatoform Spectrum Within the Hierarchical Taxonomy of Psychopathology System: A Taxometric Test of the Latent Structure.

The general understanding of disorders related to chronic somatic symptoms (e.g., somatic symptom disorder, functional somatic syndromes) is limited because of current categorical conceptualizations in traditional taxonomies. To improve clinical utility and validity, the Hierarchical Taxonomy of Psychopathology offers an empirically grounded dimensional approach. Therefore, the distribution of persistent somatic symptom distress observed in nature is highly relevant for informing decisions related to classification and treatment. This study analyzes the underlying structure of symptoms associated with the somatoform spectrum. Taxometric analyses were used to examine the latent status of the somatoform spectrum, which was measured via a dimensional questionnaire devised as part of the Hierarchical Taxonomy of Psychopathology scale development effort. We generated Comparison Curve Fit Index (CCFI) profile analyses across a clinical sample of psychotherapy outpatients ( n = 487), a community sample of German adults ( n = 451), and a student sample from New Zealand ( n = 549). In the clinical sample (CCFI mean = 0.38) and in the student sample (CCFI mean = 0.36), a dimensional solution was clearly favored. Results in the community sample (CCFI mean = 0.51) were ambiguous. Across the three independent samples, qualitatively distinct subgroups within the distribution of chronic somatic symptoms could not be identified. Therefore, continuous representations seemed to best represent the structure of somatic symptoms. Implications of these findings for etiology and treatment are discussed.

The latent structure of olfactory reference disorder symptoms: A taxometric analysis

Olfactory reference disorder (ORD), a newly included disorder in the ICD-11, is characterized by ‘pathological’ concerns about emitting body odor. While research is emerging on the construct, no study has directly examined the boundary between ORD and normal body odor concerns. That is, should ORD be considered as categorical in nature versus a more dimensional construct? As such, the current study explored the extent to which ORD symptoms correspond to a distinct category or dimension in a mixed university student and community sample (n = 757). Three indicators, derived from the Yale-Brown Obsessive Compulsive Scale Modified for Olfactory Reference Syndrome, were submitted to three independent taxometric procedures: MAMBAC, MAXEIG, and L-Mode. Two of three procedures showed that the latent structure of ORD is dimensional rather than categorical. The comparison curve fit index profile method yielded dimensional structure. Results suggested that researchers and clinical practitioners would be well-advised to conceptualize, assess, and treat ORD symptoms in a dimensional way.

A taxometric analysis of psychopathy in a Chinese prison sample.

As psychopathy is one of the major personality disorders-relating to severe pathological syndromes of personality-clarity concerning the nature of the latent structure of psychopathy is necessary for accurate assessment. In the present study, psychopathy was assessed in 339 Chinese male prisoners aged 18 to 35 years with three subscales of the Youth Psychopathy Trait Inventory - Short Version (YPI-SV). Three different taxometric analyses were used to assess the taxonicity of psychopathy, and a three-factor solution to the YPI-SV was used as the basis of the analysis. Consistency tests of the three different procedures showed strongly that the latent structure was dimensional rather than categorical. Moreover, the mean comparison curve fit index was close to .40, which also clearly indicates a dimensional result. These results confirm the fact that the factor solution applies in most studies of psychopathy.

The classification of eating disorders in China: A categorical model or a dimensional model.

ObjectiveAccording to the ICD‐10 and DSM‐5, eating disorders (EDs) are classified using a categorical model that assumes the subtypes are qualitatively different from one another. However, it is still intensely debated that a dimensional model is more suitable. The aim of this study is to examine whether EDs have a categorical or dimensional latent structure using a sample of Chinese ED patients.MethodThe sample included 322 patients, diagnosed with an ED from 2010 to 2017 in the Shanghai Mental Health Center, and comparison participants (N = 850), recruited from undergraduate students in one university in Shanghai. Participants were evaluated with the Eating Disorder Inventory‐2 (EDI‐2) questionnaire and another questionnaire developed by the researchers. Three taxometric procedures (MAXimum EIGenvalue [MAXEIG], latent‐mode factor analysis [L‐Mode], and Mean Above Minus Below A Cut [MAMBAC]) were applied, respectively, to analyze the patients' clinical symptoms data.ResultsPatients were divided into three groups according to their clinical diagnosis. The plots of the three taxometric analysis procedures supported the categorical construct in anorexia nervosa, binge‐eating/purging group, and bulimia nervosa group. The Comparison Curve Fit Indices of the MAXEIG, L‐Mode, and MAMBAC procedures were 0.694, 0.709, 0.704 in the AN‐BP group and 0.727, 0.67, 0.62 in the BN group, respectively, which also support the categorical construct.DiscussionThe results support two distinct classes of ED subtypes among Chinese sample. Further work on applying hybrid model in analysis has been discussed.

Schizotypy from the Perspective of the DSM-5 Alternative Model of Personality Traits: a Study on a Sample of 1056 Italian Adult University Students

To assess the relationships between schizotypy measures and DSM-5 Alternative Model of Personality Disorders (AMPD) traits, 1056 (69.4% female; mean age = 23.30 years) University students, were administered the Italian translation of the Schizotypal Personality Questionnaire (SPQ), Schizotypal Personality Scale (STA), Schizotypy Scale (SS), and Personality Inventory for DSM-5 (PID-5). Exploratory structural equation modeling (ESEM) suggested that the SPQ, STA, and SS Schizofrenism scale total scores may represent primary measures of schizotypy/Schizotypal personality disorder (PD), whereas the SS anhedonia (AH) total score represent an index of the general anhedonia level. MAMBAC, MAXCOV, and LMode taxometric analyses showed that both schizotypy and anhedonia constructs had a dimensional distribution (all comparison curve fit index values<.40). Bayesian confirmatory factor analysis (CFA) supported a two-factor model of SPQ, STA, SS SZ and SS AH scale total scores. Hierarchical regression analyses showed that DSM-5 AMPD traits that were hypothesized to define the Schizotypal PD profile (i.e., Cognitive and Perceptual Dysregulation, Unusual Beliefs and Experiences, Eccentricity, Restricted Affectivity, Withdrawal, and Suspiciousness), as well as the additional specifiers (i.e., Anxiousness, and Depressivity) explained 66.0% of the systematic variance in the schizotypy factor scale scores. Our findings suggested that schizotypy could be represented as a continuously-distributed latent variable which may be effectively described in terms of a coherent system of dysfunctional personality traits.

Further evidence that suicide risk is categorical: A taxometric analysis of data from an inpatient sample.

Recent research by Witte and colleagues (2017) revealed a taxonic structure for classifying suicide risk using a sample of predominantly military outpatients. The authors sought to replicate the Witte et al. (2017) findings using data from a sample of 2,385 psychiatric inpatients who completed measures of suicidal ideation and behavior upon admission to the hospital. The comparison curve fit index values for means above minus below a cut (.80), maximum eigenvalue (.71), and latent mode (.52) showed a similar taxonic structure (i.e., dichotomous rather than continuous). Consistent with Witte et al. (2017), differences between the taxon and complement groups were larger for variables conceptually directly related to suicide risk than to broader constructs such as hopelessness or depression. Support for this categorical distinction among a sample of long-term psychiatric inpatients, who are uniformly high in symptom severity, emphasizes the need for additional research on this high-risk group and development of further assessment methods. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

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. The development and use of the CCFI helps to reduce the subjectivity involved in performing taxometric analyses and interpreting the results. In a series of simulation studies, we examine the use of the CCFI to flesh out some empirically supported guidelines. We find that a panel of curves should be averaged to calculate a single CCFI value (rather than calculating the CCFI for each curve and averaging these values), that an ambiguous range of CCFI values should be defined using a fixed-width interval (rather than a multiple of the estimated standard error), and that constructing a CCFI profile can help to differentiate categorical and dimensional data and provide a less biased and more precise estimate of the taxon base rate than conventional methods. Implications of these findings for taxometric research relevant to psychological assessment are discussed along with ways to perform analyses consistent with these recommendations. (PsycINFO Database Record

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 (2-6years old, mean age 53.7months, 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.

Investigating the latent structure of distress intolerance

Distress intolerance (DI) is defined as a perceived or actual inability to withstand distressing emotional or somatic states, which motivates the use of avoidance strategies. Despite widespread interest in DI, key questions about its underlying structure remain unanswered. The current study evaluated the latent structure of DI in two large samples using four-indicators and three taxometric procedures (MAMBAC, MAXEIG, and L-Mode). Data interpretation relied primarily on the Comparison Curve Fit Indices (CCFI). Overall, results from the three non-redundant procedures suggested that DI was more accurately characterized by a dimensional rather than a categorical conceptualization. Implications for assessment and conceptual models of DI are discussed.

Individuals at high risk for suicide are categorically distinct from those at low risk.

Although suicide risk is often thought of as existing on a graded continuum, its latent structure (i.e., whether it is categorical or dimensional) has not been empirically determined. Knowledge about the latent structure of suicide risk holds implications for suicide risk assessments, targeted suicide interventions, and suicide research. Our objectives were to determine whether suicide risk can best be understood as a categorical (i.e., taxonic) or dimensional entity, and to validate the nature of any obtained taxon. We conducted taxometric analyses of cross-sectional, baseline data from 16 independent studies funded by the Military Suicide Research Consortium. Participants (N = 1,773) primarily consisted of military personnel, and most had a history of suicidal behavior. The Comparison Curve Fit Index values for MAMBAC (.85), MAXEIG (.77), and L-Mode (.62) all strongly supported categorical (i.e., taxonic) structure for suicide risk. Follow-up analyses comparing the taxon and complement groups revealed substantially larger effect sizes for the variables most conceptually similar to suicide risk compared with variables indicating general distress. Pending replication and establishment of the predictive validity of the taxon, our results suggest the need for a fundamental shift in suicide risk assessment, treatment, and research. Specifically, suicide risk assessments could be shortened without sacrificing validity, the most potent suicide interventions could be allocated to individuals in the high-risk group, and research should generally be conducted on individuals in the high-risk group. (PsycINFO Database Record

Substance Use Disorders in Adolescence Exist along Continua: Taxometric Evidence in an Epidemiological Sample.

A longstanding nosological question in the clinical literature is whether substance use disorders (SUDs) are best conceptualized as categorical or dimensional phenomena. Taxometric analysis is a statistical approach uniquely developed to address this issue. To date, no taxometric studies have been conducted with SUDs in adolescents. The current taxometric study investigated the latent structure of SUDs in adolescents for four different substances: marijuana, analgesics, hallucinogens, and inhalants. Interview-derived data for DSM-IV SUD symptoms were drawn from the National Survey on Drug Use and Health, pooled across the years 2004 to 2013 (unweighted n=181,573; 48.92% female). Two mathematically non-redundant taxometric methods (MAMBAC [mean above minus below a cut] and MAXEIG [maximum eigenvalue]) were conducted with the data for respondents who had used the substance under study at least once in the past 12months, or on 5 separate days in the case of marijuana (unweighted ns=4900 to 17,517). Consistent evidence emerged in support of a dimensional solution across the analyses for all four substances (mean comparison curve fit index = 0.129 to 0.301). The current findings are consistent with the view of SUDs in adolescents as continuous syndromes rather than discrete entities. These findings are also consistent with theoretical conceptualizations of SUDs as having multi-causal etiologies, and have implications for current diagnostic conceptualizations of SUDs.

Taxometric evidence of a dimensional latent structure for depression in an epidemiological sample of children and adolescents.

A basic phenomenological question of much theoretical and empirical interest is whether the latent structure of depression is dimensional or categorical in nature. Prior taxometric studies of youth depression have yielded mixed findings. In a step towards resolving these contradictory findings, the current taxometric investigation is the first to utilize a recently developed objective index, the comparison curve fit index, to evaluate the latent structure of major depression in an epidemiological sample of children and adolescents. Data were derived from Mental Health of Children and Young People in Great Britain surveys. Participants were administered a structured diagnostic interview to assess for current depression. Parents (n = 683) were interviewed for children aged 5-16 years, and child interviews (n = 605) were conducted for those aged 11-16 years. MAMBAC (mean above minus below a cut), MAXEIG (maximum eigenvalue) and L-Mode (latent mode) analyses provided convergent support for a dimensional latent structure. The current findings suggest that depression in youth is more accurately conceptualized as a continuous syndrome rather than a discrete diagnostic entity.

The dimensional structure of cycling mood disorders

This study examines whether mood disorders differ fundamentally in terms of phase duration. Most clinically significant mood disorders are recurrent and cyclical. The phase duration of these cycles is part of the diagnostic criteria. Specifically, we determined whether a dimensional or taxonic latent structure better captures cycling mood disorders. 319 patients recruited from 5 psychiatrists and a psychoeducational program completed three questionnaires assessing aspects of mood cycling. These were the Affective Lability Scale-Short Form (ALS-SF), Mood Disorders Questionnaire (MDQ), and the Eysenck Neuroticism scale. Patient scores on these instruments were submitted to three taxometric procedures (MAMBAC, MAXEIG, and L-Mode). Comparison curve fit indices (CCFIs) were calculated to distinguish taxonic versus dimensional latent structure. In addition, graphs were produced for each procedure and compared with those of categorical or dimensional prototypes. The CCFIs of the three procedures ranged from 0.25 to 0.27, consistent with dimensional structure. The graphs closely resembled dimensional prototypes. Mood instability and other types of cycling moods probably conform to a dimensional latent structure. Patients with disorders featuring mood cycling might benefit from common treatments.

Latent structure of depression in the elderly: a taxometric analysis

This study aimed to investigate the latent structure of depression with a non-clinical sample of elderly, using taxometric analysis. The latter consists of a family of statistical procedures conceived for testing whether a given psychological construct is best represented by categories or dimensions in which individuals vary. The sample consisted of 570 elderly with a mean age of 71.9 years (SD = 7.45), who answered the Brazilian version of the Geriatric Depression Scale, a cognitive test, and demographic questions. Three taxometric procedures were used: Mean Above Minus Below A Cut (MAMBAC), Maximum Eigenvalue (MAXEIG), and Latent Mode (L-mode). Sets of simulated categorical and dimensional data, along with the comparison curve fit indices (CCFI), oriented the study data's interpretation. The results with the three techniques pointed to a better fit with the dimensional format as opposed to the taxonic one, that is, depression represented better as a syndrome in which subjects are distributed along a continuum rather than as a discrete diagnostic entity.

A contemporary taxometric analysis of the latent structure of self-monitoring.

One of the most provocative findings in the personality psychology literature is evidence that the latent structure of self-monitoring is categorical. That is, individuals can be classified as either high or low self-monitors (Gangestad & Snyder, 1985). Surprisingly, in the three decades since its original publication, this study has never been replicated. Using the sample from the original study (N = 1,918) and a replication sample (N = 2,951), the latent structure of self-monitoring was retested using contemporary taxometric procedures. Preliminary analyses indicated that the eight-item indicator set used in the original study lacked sufficient indicator validities for unambiguously detecting latent categorical structure. In addition, the Other-Directedness subscale, one of the three factor analytically derived subscale indicators used in the original investigation, was likewise found to be unsuitable, because of a combination of low validity and relative orthogonality vis-à-vis its fellow subscales. The 2 remaining subscales, Acting and Extraversion, had excellent properties as indicators, and were subsequently subjected to multiple taxometric procedures and consistency tests. Results failed to support the original taxonic claim; to the contrary, multiple comparison curves and a grand mean comparison curve fit index (CCFI) of .214 provided strong, convergent evidence that the latent structure of self-monitoring is dimensional rather than categorical. Dimensional findings indicate that the conventional model of self-monitoring may merit reexamination, and that theoretical models, measurement practices, and data analytic procedures that assume taxonicity should be replaced by dimensional conceptualizations and corresponding statistical procedures. Findings underscore the importance of replication in psychological science.

DSM-IV schizotypal personality disorder: a taxometric analysis among individuals with and without substance use disorders in the general population.

This study examined the underlying structure of DSM-IV schizotypal personality disorder (SPD) among individuals with and without a substance use disorder. Using a nationally representative sample of U.S. adults, taxometric analyses were conducted on SPD in the total sample and among individuals with and without a substance use disorder. The structure of SPD in the total sample and among individuals without substance use disorders was dimensional (comparison curve fit indices (CCFI): 0.440, 0.365) whereas a taxonic structure was demonstrated among individuals with a substance use disorder (CCFI: 0.679). Taxonicity underlying schizotypy and SPD in prior taxometric research may have been the result of sampling high risk subsamples of the population. Taxometric research on SPD and other personality psychopathology among high risk subgroups of the population can help elucidate the complex etiology of SPD and the role played by comorbid substance use disorders in the expressivity of these disorders.

On the latent structure of problem gambling: a taxometric analysis.

To test whether problem gambling is a categorical or dimensional disorder on the basis of two problem gambling assessments. This distinction discriminates between two different conceptualizations of problem gambling: one that problem gambling is defined by its addictive properties, the other that it is a continuum of harm. Using The British Gambling Prevalence Survey 2010, a nationally representative sample of the United Kingdom conducted by the National Centre for Social Research, five different taxometric analyses were carried out on cases from two problem gambling screens: the Problem Gambling Severity Index (PGSI) and a measure derived from the DSM-IV Pathological Gambling criteria. Two further analyses were conducted on the total scores for these measures. There was strong evidence that both scales were measuring a categorical construct. Fit indices consistently supported a categorical interpretation [comparison curve fit index (CCFI) > 0.6]. The PGSI analysis indicated the presence of a taxon (CCFIs = 0.633, 0.756). The analysis conducted on the adapted DSM-IV criteria indicated stronger quantitative support for a taxon (CCFIs = 0.717, 0.811 and 0.756) but items probing a loss of control were inconsistent. The taxometric analyses of both scales support a categorical interpretation (CCFIs = 0.628, 0.567), but extreme caution should be used due to high nuisance covariance. Two problem gambling screens (the Problem Gambling Severity Index and a measure derived from the DSM-IV Pathological Gambling criteria) appear to measure a categorical construct that taps into a categorical, loss of control model of problem gambling. There is some evidence that the two screens measure different aspects of an addiction construct.

Somatic symptom reporting has a dimensional latent structure: Results from taxometric analyses.

Medically unexplained symptoms (MUS) are one of the key features of somatoform disorders. Although MUS are currently treated as both categorical (in terms of the diagnosis of somatoform disorders) and dimensional (in terms of quantitative measures of somatization/somatic symptom reporting), little is known about the empirical latent structure of MUS. Using taxometric analyses, the latent structure of somatic symptom reporting was analyzed with the Patient Health Questionnaire (PHQ)-15 in two student samples (N=782 and N=2,577) and a primary care sample (N=519). We applied three popular taxometric methods: Maximum Eigenvalue (MAXEIG), Mean Above Minus Below a Cut (MAMBAC) and Latent-Mode (L-Mode). Simulation data were created to evaluate the appropriateness of the data for our analyses and to create the comparison curve fit index (CCFI) as an objective outcome measure. The results of all taxometric methods in any of the three data sets were in favor of a dimensional solution (CCFI<.50). Simulated taxonic and dimensional datasets differed substantially and the samples were appropriate for taxometric analysis. Accordingly, the latent structure of somatization/somatic symptom reporting as assessed by the PHQ-15 is dimensional in both primary care and student samples. Implications regarding the practical application as well as models of etiology and pathogenesis of somatic symptom reporting are discussed.