Dimensional Latent Structure 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.

Dimensional latent structure of ICD-11 posttraumatic stress disorder, complex PTSD, and adjustment disorder: Evidence from Ghana, Kenya, and Nigeria.

Dimensional latent structure of ICD-11 posttraumatic stress disorder, complex PTSD, and adjustment disorder: Evidence from Ghana, Kenya, and Nigeria.

Taxometric evidence for a dimensional latent structure of hypnotic suggestibility

Hypnotic suggestibility denotes a capacity to respond to direct verbal suggestions in an involuntary manner. Most research on responsiveness to hypnotic suggestions has focused on highly suggestible individuals but it remains unclear whether these individuals constitute a discrete subgroup (taxon) characterized by a distinct mode of responding from the remainder of the population or whether hypnotic suggestibility is better modelled as a dimensional ability. In this study, we applied taxometric analysis, a method for distinguishing between dimensional and categorical models of a psychological ability, to behavioural and involuntariness subscale scores of the Harvard Group Scale of Hypnotic Susceptibility Scale: Form A (HGSHS:A) in a non-clinical sample (N = 584). Analyses of HGSHS:A subscale scores with different a priori taxon base rates yielded consistent evidence for a dimensional structure. These results suggest that hypnotic suggestibility is dimensional and have implications for current understanding of individual differences in responsiveness to direct verbal suggestions.

Are body dysmorphic symptoms dimensional or categorical in nature? A taxometric investigation in adolescents

Body dysmorphic disorder (BDD) is a debilitating mental health condition which usually emerges during adolescence and is characterised by distressing and impairing appearance concerns. It is currently unclear whether body dysmorphic concerns represent an extreme manifestation of normal appearance concerns (a dimensional conceptualisation), or whether they are qualitatively distinct (a categorical conceptualisation). This study aimed to determine whether body dysmorphic symptoms are dimensional or categorical in nature by investigating the latent structure using taxometric procedures. Body dysmorphic symptoms were assessed using validated measures among 11-16-year-old school pupils (N=707). Items of the Body Image Questionnaire Child and Adolescent version were used to construct four indicators that broadly corresponded to the DSM-5 diagnostic criteria for BDD (appearance concerns, repetitive behaviours, impairment, and insight). Indicators were submitted to three non-redundant taxometric procedures (MAMBAC, MAXEIG and L-MODE). Overall, results of all three taxometric procedures indicated a dimensional latent structure of body dysmorphic symptoms. The current study provides preliminary evidence that body dysmorphic symptoms are continuously distributed among adolescents, with no evidence of qualitative differences between mild and severe symptoms. Implications for clinical practice and research are discussed.

Dimensional latent factor structure of psychopathology in adults with chronic illness.

The goal of the present study was to determine if the internalizing and externalizing model of psychopathology is applicable in a sample of adults with chronic illness. Confirmatory factor analyses were used to examine the factor structure of internalizing and externalizing symptoms in a sample of adults (N = 172) with a unique chronic physical health condition (postural orthostatic tachycardia syndrome; POTS) and in a sample of adults without any chronic illness diagnoses (N = 199). Measurement invariance was used to compare levels of internalizing and externalizing symptoms across samples. Confirmatory factor analyses suggested that psychological distress in individuals with chronic illness can be effectively characterized by an internalizing dimension composed of distress and fear subcomponents as well as an externalizing dimension. Measurement invariance testing reached adequate levels of fit, allowing for examination of latent means; individuals with chronic illness had higher scores on the internalizing dimension and lower scores on the externalizing dimension than healthy controls. Regression analyses suggested that among those with a chronic illness, internalizing symptoms were significantly, negatively related to acceptance of illness and higher health-related quality of life. Findings suggest that assessing internalizing symptoms broadly may allow for better identification of chronically ill individuals experiencing psychological distress than a focus on categorical diagnoses. However, professionals also need to be aware of the overlap between physical and psychological symptoms in adults with chronic illnesses in order to avoid inaccurate diagnoses. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

The latent structure of cannabis misuse: A taxometric analysis.

Taxometric research on substance use problems has yielded inconsistent results, with some studies yielding evidence of a taxonic (categorical) latent structure and other studies yielding dimensional findings. The 2 previous studies that specifically examined cannabis misuse supported a dimensional latent structure. However, given the inconsistent findings for other substance use disorders and the changing legal status of, attitudes about, and patterns of cannabis use, the present study examined the latent structure of cannabis misuse in an attempt to replicate these previous findings. Participants were 804 cannabis using college students who completed measures of cannabis use anonymously online to reduce response biases. Consistent with previous research, the taxometric analyses yielded clear evidence that cannabis misuse has a dimensional latent structure. Furthermore, compared to a dichotomous measure of cannabis misuse, continuous measures of cannabis misuse were significantly more strongly associated with external correlates of cannabis misuse (e.g., stress). These findings have implications for the assessment, treatment, and study of cannabis use disorder. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Nearest comoment estimation with unobserved factors

We propose a minimum distance estimator for the higher-order comoments of a multivariate distribution exhibiting a lower dimensional latent factor structure. We derive the influence function of the proposed estimator and prove its consistency and asymptotic normality. The simulation study confirms the large gains in accuracy compared to the traditional sample comoments. The empirical usefulness of the novel framework is shown in applications to portfolio allocation under non-Gaussian objective functions and to the extraction of factor loadings in a dataset with mental ability scores.

Supplemental Material for Dimensional Latent Factor Structure of Psychopathology in Adults With Chronic Illness

Supplemental Material for Dimensional Latent Factor Structure of Psychopathology in Adults With Chronic Illness

DSCD: A Novel Deep Subspace Clustering Denoise Network for Single-Cell Clustering

Single-cell RNA sequencing(scRNA-seq) technology has boomed in the past decade which makes it possible to study biological problems at the resolution of cellular-level. Currently, the research mainly focuses on exploring the cellular heterogeneity, involving studies about identifying cell type identification, cell lineage tracing, spatial model reconstruction of complex organizations, etc. Clustering analysis is always the most effective way in grouping single cells in previous studies. However, existing scRNA-seq clustering methods separate pre-processing and clustering tasks that complicated the problem. In addition, the emergence of big data further limits the traditional clustering algorithms' application on scRNA-seq data. Therefore, developing novel clustering methods and improving clustering accuracy for growing scRNA-seq data is a continuous task. In this paper, we propose a highly integrated Deep Subspace Clustering Denoise Network named DSCD, which integrates denoise, dimension reduction and clustering in a unified framework. Based on the neural network architecture of autoencoder, DSCD discovers the low dimensional latent structure within scRNA-seq data from the compressed representation. Furthermore, we add a novel self-expressive denoise layer to learning the global relationships between single cells, which is the main innovation of DSCD. Experimental results on the synthetic data demonstrate the effectiveness of the novel denoise layer. From the clustering results on 5 real scRNA-seq datasets, we find that DSCD outperforms the related subspace clustering algorithms and state of the art methods. In conclusion, DSCD responds well to the rapidly increasing scRNA-seq data scale, greatly reduces human interference in dimension reduction and handles the noisy scRNA-seq data in proper way thus obtain a higher clustering accuracy.

Optimists or optimistic: Replication of a taxometric study on optimism

Hope and optimism are variants of individual differences regarding positive expectancies for the future. Previous literature describes the relationship of hope and optimism to various outcomes such as psychological adjustment, achievement, problem solving, and health-related concerns; however, few studies investigate its latent structure. Eichner, Kwon, and Marcus (2014) explore optimism’s latent structure through taxometric procedures and provide support for a dimensional construct. In light of recent debate over the replicability of scientific findings, the present study aimed to replicate these findings in an independent sample. Taxometric analyses are consistent with the original study, providing further support for the dimensional latent structure of optimism.

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.

Taxometric Analysis of Secure Base Script Knowledge in Middle Childhood Reveals Categorical Latent Structure.

Taxometric investigation of scripted attachment representations in lateadolescence and adulthood suggests that variations in secure base script knowledge consist of differences in degree (dimensional latent structure) rather than differences in kind (categorical latent structure). However, the latent structure of secure base script knowledge in younger cohorts has gone unexplored. This study presents a downward extension of prior taxometric work using the middle childhood version of the Attachment Script Assessment in a cross-sectional sample of 639 normative-risk children (age 8 to 13years; M=10.77, SD=1.06). Results suggest that secure base script knowledge in middle childhood is categorically distributed. Taxometric curves revealed three distinct taxa, highlighting discontinuity in the latent structure of scripted attachment representations across development.

Probabilistic Encoding Models for Multivariate Neural Data.

A key problem in systems neuroscience is to characterize how populations of neurons encode information in their patterns of activity. An understanding of the encoding process is essential both for gaining insight into the origins of perception and for the development of brain-computer interfaces. However, this characterization is complicated by the highly variable nature of neural responses, and thus usually requires probabilistic methods for analysis. Drawing on techniques from statistical modeling and machine learning, we review recent methods for extracting important variables that quantitatively describe how sensory information is encoded in neural activity. In particular, we discuss methods for estimating receptive fields, modeling neural population dynamics, and inferring low dimensional latent structure from a population of neurons, in the context of both electrophysiology and calcium imaging data.

Latent Structure of Early Adolescent Bullying Perpetration: A Taxometric Analysis of Raw and Ranked Scores.

The purpose of this study was to investigate the latent structure type (categorical vs. dimensional) of bullying perpetration in a large sample of middle school students. A nine-item bullying scale was administered to 1,222 (625 boys, 597 girls) early adolescents enrolled in middle schools in a Midwestern state. Based on the results of a principal axis factoring analysis, the nine items were organized into four homogeneous scales. These scales were then used as indicators in a taxometric analysis of the bullying perpetration construct. According to the results of three nonredundant taxometric procedures (mean above minus below a cut, maximum covariance, and latent mode factor analysis), the total sample showed evidence of dimensional or continuous latent structure, but the male and female subsamples produced outcomes that were more ambiguous. Because the indicators were highly skewed and leptokurtic, raw scores were converted to rank normalized scores and the taxometric analyses recomputed. These recomputed analyses provided unequivocal support for dimensional latent structure in the total sample as well as in both subsamples. From these results, it was surmised that bullying perpetration is continuously organized and that rank normalized scores may improve the interpretability of taxometric findings derived from highly nonnormal indicators.

Understanding the Latent Structure of Hypersexuality: A Taxometric Investigation.

We examined the latent structure and taxonicity of hypersexuality in large university and community samples of male and female respondents. Participants completed the Hypersexual Behavior Inventory (HBI) and Sexual Compulsivity Scale (SCS), each as part of larger anonymous online surveys of sexual behavior. Exploratory factor analyses (EFA) were performed in part to prepare the data for taxometric analysis and also to identify the putative dimensions underpinning each measure. Three latent dimensions were identified from each of the Sexual Compulsivity Scale (dyscontrol, consequences, and preoccupation) and Hypersexual Behavior Inventory (coping, dyscontrol, and consequences). Taxometric analyses of the generated factors using mean above minus below a cut (MAMBAC), maximum covariance (MAXCOV), and latent mode factor analysis (L-Mode) broadly supported a dimensional latent structure for hypersexuality, particularly in female participants. Implications pertaining to the assessment of hypersexuality are discussed.

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.

Nearest Comoment Estimation with Unobserved Factors

We propose a minimum distance estimator for the higher-order comoments of a multivariate distribution exhibiting a lower dimensional latent factor structure. We derive the influence function of the proposed estimator and prove its consistency and asymptotic normality. The simulation study confirms the large gains in accuracy compared to the traditional sample comoments. The empirical usefulness of the novel framework is shown in applications to portfolio allocation under non-Gaussian objective functions and to the extraction of factor loadings in a dataset with mental ability scores. Supplementary appendix with code examples: https://ssrn.com/abstract=3269127.

The latent nature of prolonged grief - A taxometric analysis: Results from a representative population sample

Individuals suffering from prolonged grief disorder (PGD) show severe grief reactions after the death of a significant other, even beyond a period of grieving that is within a person's cultural and religious context. In addition to this core element, PGD can manifest in various ways. Symptoms may include persistent preoccupation, intense emotional pain, or impairment in important life domains. The symptoms, furthermore, have to be of culturally or religiously inappropriate extent or severity, taking into account different norms of grieving. PGD is discussed as a distinct diagnostic category in the revision of the International Classification of Diseases (ICD-11). Nosology of PGD has been highly debated surrounding the suggested inclusion in the DSM-5, which had been declined due to insufficient evidence. This paper addresses the latent nature of PGD. Using a short form of the Inventory of Complicated Grief-Revised (ICG-R), we applied three popular taxometric methods: MAXEIG, MAMBAC and L-Mode. Data stemmed from a subsample of N = 1445 bereaved individuals that participated in a large representative German population survey (N = 2520). The analysis strongly indicated a dimensional latent structure of PGD. Implications of the conceptualization of PGD on a continuum are discussed, regarding measurement, diagnosis, etiology and future research.

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.

Do paranoid delusions exist on a continuum with subclinical paranoia? A multi-method taxometric study

BackgroundThere is widespread interest in whether psychosis exists on a continuum with healthy functioning. Previous research has implied that paranoia, a common symptom of psychosis, exists on a continuum but this has not been investigated using samples including both patients and non-patients and up-to-date taxometric methods. AimTo assess the latent structure of paranoia in a diverse sample using taxometric methods. MethodWe obtained data from 2836 participants, including the general population as well as at-risk mental state and psychotic patients using the P-scale of the Paranoia and Deservedness Scale. Data were analysed using three taxometric procedures, MAMBAC, MAXEIG and L-MODE (Ruscio, 2016), and two sets of paranoia indicators (subscales and selected items from the P scale), including and excluding the patient groups. ResultsEleven of the twelve analyses supported a dimensional model. Using the full sample and subscales as indicators, the MAMBAC analysis was ambiguous. Overall, the findings converged on a dimensional latent structure. ConclusionsA dimensional latent structure of paranoia implies that the processes involved in sub-clinical paranoia may be similar to those in clinical paranoia.