Is the Bifactor Model a Better Model or Is It Just Better at Modeling Implausible Responses? Application of Iteratively Reweighted Least Squares to the Rosenberg Self-Esteem Scale

Construction and Validation of a Scale to Assess Social Judgments Toward Sex Work from the Stereotype Content Model

Sex workers (SW) are subject to social judgment and the associated attitudes, ranging from admiration to contempt. The presence of stereotypical attitudes toward SW is common and can be analyzed using the Stereotype Content Model (SCM), where the concepts of warmth and competence play a central role. The interweaving of both dimensions allows the identification of four emotions and corresponding political positions toward SW: admiration (non-interventionism), pity (abolitionism), contempt (prohibitionism), and fear (regulationism). From the SCM framework, this study offers the construction, validation and performance of a 25-item scale with a snowball sample of 1,543 participants residing in Spanish-speaking countries. The four-factor hypothesized model yielded adequate values. Internal consistency was sufficient on all factors, as was model-based reliability and convergent validity. The scale also showed measurement invariance between gender and age groups, suggesting that the measure is interpreted in a conceptually similar manner by respondents representing different genders or ages. Further analysis revealed that male participants scored significantly higher on admiration. Baby boomers showed less pity and contempt while Millennials showed more fear and less admiration. SW and those who know or work with SW showed less fear and pity and more admiration. The SCM and the process of developing social judgments offer us a way to understand the differences that underlie irreconcilable policy positions. Overcoming these differences requires mutual understanding from scientific frameworks instead of from ideological perspectives.

Detecting non-content-based response styles in survey data: An application of mixture factor analysis

It is common for some participants in self-report surveys to be careless, inattentive, or lacking in effort. Data quality can be severely compromised by responses that are not based on item content (non-content-based [nCB] responses), leading to strong biases in the results of data analysis and misinterpretation of individual scores. In this study, we propose a specification of factor mixture analysis (FMA) to detect nCB responses. We investigated the usefulness and effectiveness of the FMA model in detecting nCB responses using both simulated data (Study 1) and real data (Study 2). In the first study, FMA showed reasonably robust sensitivity (.60 to .86) and excellent specificity (.96 to .99) on mixed-worded scales, suggesting that FMA had superior properties as a screening tool under different sample conditions. However, FMA performance was poor on scales composed of only positive items because of the difficulty in distinguishing acquiescent patterns from valid responses representing high levels of the trait. In Study 2 (real data), FMA detected a minority of cases (6.5%) with highly anomalous response patterns. Removing these cases resulted in a large increase in the fit of the unidimensional model and a substantial reduction in spurious multidimensionality.

The Impact of Measurement Model Misspecification on Coefficient Omega Estimates of Composite Reliability

Coefficient omega indices are model-based composite reliability estimates that have become increasingly popular. A coefficient omega index estimates how reliably an observed composite score measures a target construct as represented by a factor in a factor-analysis model; as such, the accuracy of omega estimates is likely to depend on correct model specification. The current paper presents a simulation study to investigate the performance of omega-unidimensional (based on the parameters of a one-factor model) and omega-hierarchical (based on a bifactor model) under correct and incorrect model misspecification for high and low reliability composites and different scale lengths. Our results show that coefficient omega estimates are unbiased when calculated from the parameter estimates of a properly specified model. However, omega-unidimensional produced positively biased estimates when the population model was characterized by unmodeled error correlations or multidimensionality, whereas omega-hierarchical was only slightly biased when the population model was either a one-factor model with correlated errors or a higher-order model. These biases were higher when population reliability was lower and increased with scale length. Researchers should carefully evaluate the feasibility of a one-factor model before estimating and reporting omega-unidimensional.

The general factor of psychopathology (p): Choosing among competing models and interpreting p

Over the past 10 years, the general factor of psychopathology, p, has attracted interest and scrutiny. We review the history of the idea that all mental disorders share something in common, p; how we arrived at this idea; and how it became conflated with a statistical representation, the Bi-Factor Model. We then leverage the Environmental Risk (E-Risk) longitudinal twin study to examine the properties and nomological network of different statistical representations of p. We find that p performed similarly regardless of how it was modelled, suggesting that if the sample and content are the same the resulting p factor will be similar. We suggest that the meaning of p is not to be found by dueling over statistical models but by conducting well-specified criterion-validation studies and developing new measurement approaches. We outline new directions to refresh research efforts to uncover what all mental disorders have in common.

Validation of the Weiss Functional Impairment Rating Scale (WFIRS-P) as a Functional Impairment Measure in a General Population of Schoolchildren

Functional impairment rating scales are few in numbers and have mainly been examined in clinical populations. The Weiss Functional Impairment Rating Scale-Parent report (WFIRS-P) is a case in point. We tested the psychometric properties of the WFIRS-P in the largest general population study to date and for the first time examined the factor structure of the scale in a general population setting. Participants were 2,027 schoolchildren aged 6 to 11. Parents/caregivers completed the WFIRS-P and criterion measures of quality of life, attention-deficit/hyperactivity disorder, and behavioral/emotional symptoms. Confirmatory factor analysis and convergent/divergent validity analyses supported a six-factor structure: family, life skills, self-concept, social activities, and the separation of the school domain into two smaller domains covering school learning and school behavior. Children with and without a history of referral differed significantly on all six domains supporting the external validity. In conclusion, the WFIRS-P was found to generate valid scores in a general population sample.

The underlying structure of the Personality Inventory for DSM-5 (PID-5): a general factor of personality psychopathology

BACKGROUND

The psychopathology of personality is currently undergoing a paradigm shift from a categorical to a dimensional approach. This work aimed to study the underlying structure of pathological personality traits of the DSM-5 Alternative Model for Personality Disorders (AMPD). For this purpose, the internal structure of a version of the Personality Inventory for the DSM-5 (PID-5) was examined by a confirmatory factor analysis. This version assesses the five higher-order pathological personality domains (negative affectivity, detachment, antagonism, disinhibition, and psychoticism) and the 25 lower-order pathological personality facets through a reduced number of items. Four alternative models were compared: five-factor oblique; second-order (five first-order factors and one second-order factor); bifactor (five specific factors and a general factor), and one-factor.

PARTICIPANTS AND PROCEDURE

We worked with an Argentinean sample of N = 525 subjects from the general population who answered the Argentine version of the PID-5.

RESULTS

The five-factor model was slightly superior to the second order model, and the bifactor model presented the best fit.

CONCLUSIONS

These findings, while preliminary, suggest that the PID-5 facets could reflect five specific pathological personality traits (which correspond to AMPD domains) but also a general factor (which would reflect a general propensity for psychopathology).

Developments of possible clinical diagnostic methods for parkinson's disease: event-related potentials

In this study, Event-Related Potential (ERP) analyzes were performed to detect cognitive impairments in PD with Deep Brain Stimulation (DBS). A total of 85 volunteers underwent ERP analysis and neuropsychological testing (NPT) to determine cognitive level. In ERP analyses, prolonged latencies were observed in PD groups. However, patients implanted with DBS showed a decrease in latencies, a decrease in symptoms and statistical improvements in both cognitive and attention skills. Considering all these data, ERP results are promising as a noninvasive method that can be used in both disease status and diagnosis of PD.

Investigating the Multidimensionality and Psychometric Properties of the Children's Anger Rumination Scale (CARS): A Bifactor Exploratory Structural Equation Modeling Framework

Anger Rumination (AR) represents a maladaptive cognitive process that contributes negatively to psychosocial functioning. The purpose of the present study was to investigate the psychometric properties (e.g., factorial structure, measurement invariance, and reliability) of the Children's Anger Rumination Scale (CARS). Factorial structure was tested by contrasting alternative model representations of the instrument (one- and four-factor independent cluster models-confirmatory factor analysis [ICM-CFA], exploratory structural equation modelling [ESEM], bifactor-CFA and bifactor-ESEM) in a convenience sample of 552 Greek students (M_age = 11.50 years; 53.6% girls). The hypothesized bifactor-ESEM solution, composed by a general anger rumination factor and four specific factors (Angry Afterthoughts, Thoughts of Revenge, Angry Memories, and Understanding of Causes) provided the best fit to the data and revealed the unitary dimensionality of the CARS. Measurement invariance across gender and age in level of the latent means indicated no significant differences in relation to AR tendency. The CARS showed internal consistency, one-month test-retest reliability as well as desirable patterns of convergent and discriminant validity. The predictive power of the instrument was also supported as participants' AR propensity was found to explain both depressive symptoms and bullying behaviors. Overall, our findings indicate that the CARS is a developmentally appropriate and psychometrically sound instrument that conceptualizes AR as an unidimensional construct among children and preadolescents.

Assessing Essential Unidimensionality of Scales and Structural Coefficient Bias

Percentage of uncontaminated correlations (PUC), explained common variance (ECV), and omega hierarchical (ωH) have been used to assess the degree to which a scale is essentially unidimensional and to predict structural coefficient bias when a unidimensional measurement model is fit to multidimensional data. The usefulness of these indices has been investigated in the context of bifactor models with balanced structures. This study extends the examination by focusing on bifactor models with unbalanced structures. The maximum and minimum PUC values given the total number of items and factors were derived. The usefulness of PUC, ECV, and ωH in predicting structural coefficient bias was examined under a variety of structural regression models with bifactor measurement components. Results indicated that the performance of these indices in predicting structural coefficient bias depended on whether the bifactor measurement model had a balanced or unbalanced structure. PUC failed to predict structural coefficient bias when the bifactor model had an unbalanced structure. ECV performed reasonably well, but worse than ωH.

Resolving Dimensionality in a Child Assessment Tool: An Application of the Multilevel Bifactor Model

Multidimensionality and hierarchical data structure are common in assessment data. These design features, if not accounted for, can threaten the validity of the results and inferences generated from factor analysis, a method frequently employed to assess test dimensionality. In this article, we describe and demonstrate the application of the multilevel bifactor model to address these features in examining test dimensionality. The tool for this exposition is the Child Observation Record Advantage 1.5 (COR-Adv1.5), a child assessment instrument widely used in Head Start programs. Previous studies on this assessment tool reported highly correlated factors and did not account for the nesting of children in classrooms. Results from this study show how the flexibility of the multilevel bifactor model, together with useful model-based statistics, can be harnessed to judge the dimensionality of a test instrument and inform the interpretability of the associated factor scores.

Heterogeneity in Reading Achievement and Mindset of Readers with Reading Difficulties

Recent research has focused on evaluating the relation between mindset and reading achievement. We used exploratory factor mixture models (E-FMMs) to examine the heterogeneity in reading achievement and mindset of 650 fourth graders with reading difficulties. To build E-FMMs, we conducted confirmatory factor analyses to examine the factor structure of scores of (a) mindset, (b) reading, and (c) mindset/reading combined. Our results indicated (a) a 2-factor model for mindset (General Mindset vs. Reading Mindset), (b) a 2-factor model for reading (Word Reading vs. Comprehension; four covariances), and (c) a combined model with significant correlations across mindset and reading factors. We ran E-FMMs on the combined model. Overall, we found three classes of students. We situate these results within the existing literature and discuss implications for practice and research.

Factor analysis in personality disorders research: Modern issues and illustrations of practical recommendations

The development of factor analysis is uniquely situated within psychology, and the development of many psychological theories and measures are likewise tethered to the common use of factor analysis. In this article, we review modern methodological controversies and developments of factor analytic techniques through concrete demonstrations that span the exploratory-confirmatory continuum. Also, we provide recommendations for working through common challenges in personality disorders research. To help researchers conduct riskier tests of their theory-implied models, we review what factor analysis is and is not, as well as some dos and don'ts for engaging in the process of model evaluation and selection. Throughout, we also emphasize the need for closer alignment between factor models and our theories, as well as clearer statements about which criteria would support or refute the theories being tested. Consideration of these themes appears promising in terms of advances in theory, research, and treatment surrounding the nature and impact of personality disorders. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

An Empirical Identification Issue of the Bifactor Item Response Theory Model

Using the bifactor item response theory model to analyze data arising from educational and psychological studies has gained popularity over the years. Unfortunately, using this model in practice comes with challenges. One such challenge is an empirical identification issue that is seldom discussed in the literature, and its impact on the estimates of the bifactor model's parameters has not been demonstrated. This issue occurs when an item's discriminations on the general and specific dimensions are approximately equal (i.e., the within-item discriminations are similar in strength), leading to difficulties in obtaining unique estimates for those discriminations. We conducted three simulation studies to demonstrate that within-item discriminations being similar in strength creates problems in estimation stability. The results suggest that a large sample could alleviate but not resolve the problems, at least when considering sample sizes up to 4,000. When the discriminations within items were made clearly different, the estimates of these discriminations were more consistent across the data replicates than that observed when the discriminations within the items were similar. The results also show that the similarity of an item's discriminatory magnitudes on different dimensions has direct implications on the sample size needed in order to consistently obtain accurate parameter estimates. Although our goal was to provide evidence of the empirical identification issue, the study further reveals that the extent of similarity of within-item discriminations, the magnitude of discriminations, and how well the items are targeted to the respondents also play factors in the estimation of the bifactor model's parameters.

Measuring Loneliness in Different Age Groups: The Measurement Invariance of the UCLA Loneliness Scale

Age differences in the prevalence of loneliness have been a key focus among researchers, practitioners, and policy makers. However, the degree to which those reflect genuine differences in the experience of loneliness or the way individuals understand and respond to loneliness measures is yet to be examined. The current study explored the age measurement invariance of the 20-item Revised University of California Los Angeles, Loneliness Scale (UCLA-LSR) and its shorter forms in a U.K. sample of adults aged 18 to 99 years (M = 50.6, SD = 19.7). The fit of different structures/versions was explored through multigroup confirmatory factor analysis (CFA; N = 4,375) and local structural equation modeling (N = 19,521). Results indicated a poor and/or inconsistent structure for the 20-item UCLA-LSR and many of its shorter forms. Of the structures considered, 12 showed acceptable model fit and received age measurement invariance testing through multigroup CFA and alignment; 10 of these achieved full, partial, or approximate measurement invariance. Our findings suggest that the age measurement invariance of loneliness measures should not be assumed, and crucially, this must be explored before accurate and meaningful age comparisons can be made. Implications for measurement research, and clinical and community practice, are discussed.

What are the consequences of ignoring cross-loadings in bifactor models? A simulation study assessing parameter recovery and sensitivity of goodness-of-fit indices

Bifactor latent models have gained popularity and are widely used to model construct multidimensionality. When adopting a confirmatory approach, a common practice is to assume that all cross-loadings take zero values. This article presents the results of a simulation study exploring the impact of ignoring non-zero cross-loadings on the performance of confirmatory bifactor analysis. The present work contributes to previous research by including study conditions that had not been examined before. For instance, a wider range of values of the factor loadings both for the group factors and the cross-loadings is considered. Parameter recovery is analyzed, but the focus of the study is on assessing the sensitivity of goodness-of-fit indices to detect the model misspecification that involves ignoring non-zero cross-loadings. Several commonly used SEM fit indices are examined: both biased estimators of the fit index (CFI, GFI, and SRMR) and unbiased estimators (RMSEA and SRMR). Results indicated that parameter recovery worsens when ignoring moderate and large cross-loading values and using small sample sizes, and that commonly used SEM fit indices are not useful to detect such model misspecifications. We recommend the use of the unbiased SRMR index with a cutoff value adjusted by the communality level (R²), as it is the only fit index sensitive to the model misspecification due to ignoring non-zero cross-loadings in the bifactor model. The results of the present study provide insights into modeling cross-loadings in confirmatory bifactor models but also practical recommendations to researchers.

A Network Analysis of the Fear of COVID-19 Scale (FCV-19S): A Large-Scale Cross-Cultural Study in Iran, Bangladesh, and Norway

The rapid spread of the coronavirus disease 2019 (COVID-19) has led to high levels of fear worldwide. Given that fear is an important factor in causing psychological distress and facilitating preventive behaviors, assessing the fear of COVID-19 is important. The seven-item Fear of COVID-19 Scale (FCV-19S) is a widely used psychometric instrument to assess this fear. However, the factor structure of the FCV-19S remains unclear according to the current evidence. Therefore, the present study used a network analysis to provide further empirical evidence for the factor structure of FCV-19S. A total of 24,429 participants from Iran (n = 10,843), Bangladesh (n = 9906), and Norway (n = 3680) completed the FCV-19S in their local language. A network analysis (via regularized partial correlation networks) was applied to investigate the seven FCV-19S items. Moreover, relationships between the FCV-19S items were compared across gender (males vs. females), age groups (18−30 years, 31−50 years, and >50 years), and countries (Iran, Bangladesh, and Norway). A two-factor structure pattern was observed (three items concerning physical factors, including clammy hands, insomnia, and heart palpitations; four items concerning psychosocial factors, including being afraid, uncomfortable, afraid of dying, and anxious about COVID-19 news). Moreover, this pattern was found to be the same among men and women, across age groups and countries. The network analysis used in the present study verified the two-factor structure for the FCV-19S. Future studies may consider using the two-factor structure of FCV-19S to assess the fear of COVID-19 during the COVID-19 era.

Psychometric Properties of the Chinese Translated Athlete Burnout Questionnaire: Evidence From Chinese Collegiate Athletes and Elite Athletes

The purpose of the study was to translate the athlete burnout questionnaire (ABQ) into Simplified Chinese and examine its psychometric properties in Chinese collegiate athletes and elite athletes. Firstly, the factor structure, internal consistency reliability and nomological validity of the Chinese translated ABQ was examined in a sample of Chinese collegiate athletes (n = 214, 58.9% females). Secondly, abovementioned psychometric properties were examined in a sample of Chinese elite athletes (n = 505, 52.7% females). Finally, measurement invariance of the Chinese translated ABQ was examined across the two samples. It was found that the 12-item three-correlated-factors model outperformed the one factor model and bi-factor model in collegiate athlete sample whereas the 12-item bi-factor model best represented the factor structure of the Chinese translated ABQ in elite athlete sample. Satisfactory internal consistency reliabilities of the Chinese translated ABQ were evidenced in the two samples. Nomological validity was also supported by the results of the two samples that the three subscales of the ABQ were significantly associated with its theoretically related variables. Results of multiple-group confirmatory factor analysis revealed that weak measurement invariance of the Chinese translated ABQ (three-correlated-factors model) was evidenced across the two samples. Collectively, results of this study indicated that the 12-item Chinese translated ABQ could be used for measuring burnout of Chinese collegiate and elite athletes. Significance and implication of the current study as well as recommendations for future study were discussed.

How Specific Are Learning Disabilities?

Despite historical emphasis on "specific" learning disabilities (SLDs), academic skills are strongly correlated across the curriculum. Thus, one can ask how specific SLDs truly are. To answer this question, we used bifactor models to identify variance shared across academic domains (academic g), as well as variance unique to reading, mathematics, and writing. Participants were 686 children ages 8 to 16. Although the sample was overselected for learning disabilities, we intentionally included children across the full range of individual differences in this study in response to growing recognition that a dimensional, quantitative view of SLD is more accurate than a categorical view. Confirmatory factor analysis identified five academic domains (basic reading, reading comprehension, basic math, math problem-solving, and written expression); spelling clustered with basic reading and not writing. In the bifactor model, all measures loaded significantly on academic g. Basic reading and mathematics maintained variance distinct from academic g, consistent with the notion of SLDs in these domains. Writing did not maintain specific variance apart from academic g, and evidence for reading comprehension-specific variance was mixed. Academic g was strongly correlated with cognitive g (r = .72) but not identical to it. Implications for SLD diagnosis are discussed.

Using Constrained Factor Mixture Analysis to Validate Mixed-Worded Psychological Scales: The Case of the Rosenberg Self-Esteem Scale in the Dominican Republic

A common method to collect information in the behavioral and health sciences is the self-report. However, the validity of self-reports is frequently threatened by response biases, particularly those associated with inconsistent responses to positively and negatively worded items of the same dimension, known as wording effects. Modeling strategies based on confirmatory factor analysis have traditionally been used to account for this response bias, but they have recently become under scrutiny due to their incorrect assumption of population homogeneity, inability to recover uncontaminated person scores or preserve structural validities, and their inherent ambiguity. Recently, two constrained factor mixture analysis (FMA) models have been proposed by Arias et al. (2020) and Steinmann et al. (2021) that can be used to identify and screen inconsistent response profiles. While these methods have shown promise, tests of their performance have been limited and they have not been directly compared. Thus the objective of the current study was to assess and compare their performance with data from the Dominican Republic of the Rosenberg Self-Esteem Scale (N = 632). Additionally, as this scale had not yet been studied for this population, another objective was to show how using constrained FMAs could help in the validation of mixed-worded scales. The results indicated that removing the inconsistent respondents identified by both FMAs (≈8%) reduced the amount of wording effects in the database. However, whereas the Steinmann et al. method only cleaned the data partially, the Arias et al. (2020) method was able to remove the great majority of the wording effects variance. Based on the screened data with the Arias et al. method, we evaluated the psychometric properties of the RSES for the Dominican population, and the results indicated that the scores had good validity and reliability properties. Given these findings, we recommend that researchers incorporate constrained FMAs into their toolbox and consider using them to screen out inconsistent respondents to mixed-worded scales.

Matching IRT Models to Patient-Reported Outcomes Constructs: The Graded Response and Log-Logistic Models for Scaling Depression

Item response theory (IRT) model applications extend well beyond cognitive ability testing, and various patient-reported outcomes (PRO) measures are among the more prominent examples. PRO (and like) constructs differ from cognitive ability constructs in many ways, and these differences have model fitting implications. With a few notable exceptions, however, most IRT applications to PRO constructs rely on traditional IRT models, such as the graded response model. We review some notable differences between cognitive and PRO constructs and how these differences can present challenges for traditional IRT model applications. We then apply two models (the traditional graded response model and an alternative log-logistic model) to depression measure data drawn from the Patient-Reported Outcomes Measurement Information System project. We do not claim that one model is "a better fit" or more "valid" than the other; rather, we show that the log-logistic model may be more consistent with the construct of depression as a unipolar phenomenon. Clearly, the graded response and log-logistic models can lead to different conclusions about the psychometrics of an instrument and the scaling of individual differences. We underscore, too, that, in general, explorations of which model may be more appropriate cannot be decided only by fit index comparisons; these decisions may require the integration of psychometrics with theory and research findings on the construct of interest.

Unpacking the impact of the COVID-19 pandemic: identifying structural domains

Background: The novel coronavirus-19 (COVID-19) pandemic is a collective crisis that imposed an abrupt and unprecedented impact on college students, as universities were closed with little warning. Paired with the challenges associated with physical distancing (e.g. economic stress, job loss, food insecurity, housing challenges) and the simultaneous need to balance continued and new academic demands, impact will be wide-ranging. It is critical to determine the structure of the impact of this heterogeneous stressor (e.g. health concerns, pandemic worry, financial concerns) for prevention and intervention planning. Objective: Through an existing recruitment pipeline we were in a unique position to study the wide-ranging reach of this pandemic in a cohort of students for whom their university experiences were like no other cohort in history. Method: Data were collected from students who were in their third year of college during the onset of the pandemic; of the N = 1,899 in the cohort who were invited to participate in this COVID-related survey, 897 (47.2%) completed measures of impact between May and July of 2020. Results: A series of confirmatory and exploratory models were fit to examine the structure of the pandemic-related domains. Following estimation of a single-factor model, a correlated five factors model, as well as two second-order factor structures, the five correlated factors (exposure, worry, housing/food instability, social media, substance use) model was found to represent the data most appropriately, while producing an interpretable solution. Conclusions: These measurement model analyses set the stage for future research to examine how these correlated factors impact psychiatric, substance, and academic outcomes in this vulnerable population.

Experimental evidence that apologies promote forgiveness by communicating relationship value

Robust evidence supports the importance of apologies for promoting forgiveness. Yet less is known about how apologies exert their effects. Here, we focus on their potential to promote forgiveness by way of increasing perceptions of relationship value. We used a method for directly testing these causal claims by manipulating both the independent variable and the proposed mediator. Namely, we use a 2 (Apology: yes vs. no) × 2 (Value: high vs. low) concurrent double-randomization design to test whether apologies cause forgiveness by affecting the same causal pathway as relationship value. In addition to supporting this causal claim, we also find that apologies had weaker effects on forgiveness when received from high-value transgressors, suggesting that the forgiveness-relevant information provided by apologies is redundant with relationship value. Taken together, these findings from a rigorous methodological paradigm help us parse out how apologies promote relationship repair.

Bifactor analysis of the rosenberg self-esteem scale in morbid obesity

Self-esteem is a set of feelings and thoughts a person has about his/her own worth and competence, which is reflected in a positive or negative attitude toward himself/herself. This study examined the factor structure of the Rosenberg Self-Esteem Scale with scores of 288 obese brazilian women waiting for bariatric surgery. Psychometric studies have found controversial results regarding the factor structure of this measure with samples from different profiles. In this investigation, the unifactor, two-oblique-factor and bifactor models were tested by Confirmatory Factor Analysis. The results clearly support the existence of a single dimension for the scale, but also for the method effects relating to the positively and negatively worded items. Further investigations with the inclusion of obese males are suggested.

Item Reduction, Psychometric and Biometric Properties of the Italian Version of the Body Perception Questionnaire-Short Form (BPQ-SF): The BPQ-22

Body awareness disorders and reactivity are mentioned across a range of clinical problems. Constitutional differences in the control of the bodily state are thought to generate a vulnerability to psychological symptoms. Autonomic nervous system dysfunctions have been associated with anxiety, depression, and post-traumatic stress. Though interoception may be a transdiagnostic mechanism promoting the improvement of clinical symptomatology, few psychometrically sound, symptom-independent, self-report measures, informed by brain-body circuits, are available for research and clinical use. We validated the Italian version of the body perception questionnaire (BPQ)-short form and found that response categories could be collapsed from five to three and that the questionnaire retained a three-factor structure with items reduced from 46 to 22 (BPQ-22). The first factor was loaded by body awareness items; the second factor comprised some items from the body awareness scale and some from the subdiaphragmatic reactivity scale (but all related to bloating and digestive issues), and the third factor by supradiaphragmatic reactivity items. The BPQ-22 had sound psychometric properties, good convergent and discriminant validity and test-retest reliability and could be used in clinical and research settings in which the body perception assessment is of interest. Psychometric findings in light of the polyvagal theory are discussed.

Examining the Factor Structure and Incremental Validity of the Barkley Deficits in Executive Functioning Scale - Short Form in a Community Sample

The Barkley Deficits in Executive Functioning Scale - Short Form (BDEFS-SF; Barkley, 2011) was developed to assess deficits in five facets of executive functioning. Theoretical assumptions surrounding the BDEFS-SF presume that executive dysfunction is an overarching construct that consists of five domain-specific factors (i.e., a hierarchical model; Barkley, 2011). Prior research has supported a correlated five-factor model, but the tenability of hierarchical or bifactor models of the BDEFS-SF have not yet been tested. In the present study (N = 1,120 community adults), confirmatory factor analysis was used to compare four theoretically relevant models of the BDEFS-SF (i.e., one-factor, correlated five-factor, hierarchical, and bifactor models). The bifactor model provided the best fit to the data. However, the general factor accounted for the overwhelming majority of variance in BDEFS-SF scores and none of the domain-specific factors exhibited adequate construct replicability or factor determinancy. Further, the general factor accounted for the overhelming majority of variance in criterion variables (i.e., executive attention and health anxiety); the Organization and Emotion factors accounted for a small amount of unique variance in executive attention and the Emotion factor accounted for a small amount of unique variance in health anxiety. Taken together, study findings suggest that the BDEFS-SF has a strong general factor and independent use of the domain-specific factors is contraindicated.

The "Big Everything": Integrating and investigating dimensional models of psychopathology, personality, personality pathology, and cognitive functioning

Common factors are increasingly used to model the structure of psychopathology ("p"), personality (General Factor of Personality [GFP]), pathological personality (General Factor of Pathological Personality [GFPP]), and intelligence ("g"). Using 4 waves spanning ages 18-29 in a cohort of college students (baseline n = 489), this study used indicators of psychopathology, personality, pathological personality, and cognitive functioning to compare models that included Cognitive Functioning, p, GFP, GFPP, and a "Big Everything" factor (which included cross-domain measures as indicators). GFP, GFPP, and p exhibited substantial overlap, and the Big Everything factor accounted for considerable variance in psychopathology, personality, and pathological personality indicators. Only a self-report measure of cognitive functioning loaded significantly onto the Big Everything. This study highlights concerns in the pursuit of identifying and reifying common factors based on the modeling of residual variances and limitations of using factor modeling to determine the structure of psychologically relevant phenomena. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Examining the Short Dark Tetrad (SD4) Across Models, Correlates, and Gender

To date, no studies have examined a range of structural models of the interpersonally aversive traits tapped by the Short Dark Tetrad (SD4; narcissism, Machiavellianism, psychopathy, sadism), in conjunction with their measurement invariance (males vs. females) and how the models each predict external correlates. Using a large sample of young adults (N = 3,975), four latent variable models were compared in terms of fit, measurement invariance, and prediction of intrapersonal and interpersonal functioning. The models tested were as follows: (Model A) confirmatory factor analytic, (Model B) bifactor, (Model C) exploratory structural equation model, and (Model D) a reduced-item confirmatory factor analytic that maximized item information. All models accounted for item covariance with good precision, although differed in incremental fit. Strong invariance held for all models, and each accounted similarly for the external correlates, highlighting differential predictive effects of the SD4 factors. The results provide support for four theoretically distinct but overlapping dark personality domains.

Negative Problems Orientation Questionnaire for Chinese Adolescents: Bifactor Model and Measurement Invariance

The Negative Problem Orientation Questionnaire (NPOQ) is a widely used tool for assessing negative problem orientation (NPO). However, its construct and measurement invariance has not been adequately tested in adolescents. The present study explored the possible construct of the NPOQ and its measurement invariance in a sample of 754 Chinese adolescents (51.6% girls, all 12–18 years old). The results supported a bifactor model of the NPOQ that consists of a general factor NPO and three domain-specific factors including perceived threat, self-inefficacy, and negative outcome expectancy. A multiple-group CFA indicated that the bifactor model showed strict invariance across gender and age. The general and domain factors showed unique variance in indexes of worry, depression, anxiety, and stress, which supported well incremental validity of them. This study confirms for a bifactor conceptualization of the NPOQ and its measurement invariance across gender and age in Chinese adolescents. Additionally, it is recommended that the total score should be used to assess NPO in Chinese adolescents.

The Structure of Psychopathology in a Sample of Clinically Referred, Emotionally Dysregulated Early Adolescents

This investigation answers and amplifies calls to model the transdiagnostic structure of psychopathology in clinical samples of early adolescents and using stringent psychometric criteria. In 162 clinically referred, clinically evaluated 11-13-year-olds, we compared a correlated two-factor model, containing latent internalizing and externalizing factors, to a bifactor model, which added a transdiagnostic general factor. We also evaluated the bifactor model psychometrically, including criterion validity with broad indicators of psychosocial functioning. In doing so, we compared alternative approaches to defining and interpreting criterion validity: a recently proposed incremental definition based on amounts of variance in criterion factors explained, and the more typical definition based on the presence of conceptually meaningful relationships. While traditional fit statistics favored the bifactor model as expected, psychometric analyses added important nuance. Despite moderate reliability, the general factor was not fully transdiagnostic (i.e., was not informed by several externalizing scores), and was partially redundant with internalizing scores. Approaches to criterion validity yielded opposing results. Compared to the correlated two-factor model, the bifactor model redistributed, without incrementally increasing, the total variance explained in criterion indicators of psychosocial functioning. Yet, the bifactor model did improve the precision of clinically important relationships to psychosocial functioning, raising questions about meaningful tests of bifactor psychopathology models.

A transdiagnostic approach to conceptualizing depression across the perinatal period in a high-risk sample

Clinical guidelines recommend assessing depression during pregnancy and postpartum but often overlook potential changes in symptoms across this developmental period. Such changes contribute to difficulties in conceptualizing maternal depression. This study aimed to situate depressive symptoms and related concerns (anxiety, stress, sleep) across the perinatal period within a transdiagnostic framework and to use this framework to better understand how depressive symptoms change across the perinatal period. First, items from seven symptom scales were a priori categorized into six transdiagnostic factors: four based on Research Domain Criteria (loss, potential threat, frustrative nonreward, and sleep-wakefulness) and two based on the depression literature (somatic and coping symptoms). Second, using prospective data from women with a history of an affective disorder (n = 657) in an observational study of neuropsychiatric illness, factor analyses were performed in seven periods (three trimesters of pregnancy and four quarters of first year postpartum). For each period, a bifactor model with six transdiagnostic factors and a general factor fit data better than models that combined or dropped a factor (p < .003). Except around delivery, item loadings and intercepts could be fixed between consecutive periods and still adequately fit data from both periods. Means of sleep-wakefulness and somatic factors increased significantly from second to third trimester (p < .01), with trends reversing early postpartum. In conclusion, depressive symptoms and related concerns exhibit factor structures that are only partly congruent across the perinatal period. This conclusion suggests that greater attention to specific life phases is warranted in the conceptualization of depression during this time in women's lives. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Heritability of overlapping impulsivity and compulsivity dimensional phenotypes

Impulsivity and compulsivity are traits relevant to a range of mental health problems and have traditionally been conceptualised as distinct constructs. Here, we reconceptualised impulsivity and compulsivity as partially overlapping phenotypes using a bifactor modelling approach and estimated heritability for their shared and unique phenotypic variance within a classical twin design. Adult twin pairs (N = 173) completed self-report questionnaires measuring psychological processes related to impulsivity and compulsivity. We fitted variance components models to three uncorrelated phenotypic dimensions: a general impulsive-compulsive dimension; and two narrower phenotypes related to impulsivity and obsessiveness.There was evidence of moderate heritability for impulsivity (A² = 0.33), modest additive genetic or common environmental effects for obsessiveness (A² = 0.25; C² = 0.23), and moderate effects of common environment (C² = 0.36) for the general dimension, This general impulsive-compulsive phenotype may reflect a quantitative liability to related mental health disorders that indexes exposure to potentially modifiable environmental risk factors.

The association between general childhood psychopathology and adolescent suicide attempt and self-harm: A prospective, population-based twin study

Few quantitative behavior genetic studies have examined why psychopathology is associated with suicide attempt (SA) and self-harm (SH) in adolescence. The present study analyzed data from the Child and Adolescent Twin Study in Sweden to examine the extent to which genetic and environmental factors explain SA/SH and its association with psychopathology in childhood, an often-cited risk factor of subsequent SA/SH. When children were 9 or 12 years old (n = 30,444), parents completed the Autism-Tics, AD/HD and other Comorbidities Inventory (Larson et al., 2010) regarding their children's psychiatric problems as part of an ongoing, longitudinal study. At age 18 years (n = 10,269), adolescents completed self-report questionnaires, including SA/SH assessments. In a bifactor model of childhood psychopathology, a general factor of psychopathology was a statistically significant predictor of adolescent SA/SH at a higher magnitude (β, 0.25, 95% confidence interval [CI; 0.15, 0.34] for suicide attempt), as compared with specific factors of inattention, impulsivity, oppositional behavior, and anxiety/emotion symptoms. Quantitative genetic modeling indicated that the additive genetic influences on the general factor accounted for the association with each outcome (β, 0.24, 95% CI [0.13, 0.34] for suicide attempt). The results remained virtually identical when we fit a higher order factors model. Two additional outcomes demonstrated comparable results. The results extend current literature by revealing the shared genetic overlap between general psychopathology during childhood and adolescent SA/SH. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Multilevel Heterogeneous Factor Analysis and Application to Ecological Momentary Assessment

Ansari et al. (Psychometrika 67:49-77, 2002) applied a multilevel heterogeneous model for confirmatory factor analysis to repeated measurements on individuals. While the mean and factor loadings in this model vary across individuals, its factor structure is invariant. Allowing the individual-level residuals to be correlated is an important means to alleviate the restriction imposed by configural invariance. We relax the diagonality assumption of residual covariance matrix and estimate it using a formal Bayesian Lasso method. The approach improves goodness of fit and avoids ad hoc one-at-a-time manipulation of entries in the covariance matrix via modification indexes. We illustrate the approach using simulation studies and real data from an ecological momentary assessment.

The Multiple Deficit Model: Progress, Problems, and Prospects

The multiple deficit model (MDM) was proposed because the prevailing single-deficit model provided an inadequate account of atypical neuropsychological development. Across methods and levels of analysis, there has been support for the two fundamental tenets of the MDM, that multiple predictors contribute probabilistically to neurodevelopmental disorders and shared risk factors contribute to comorbidity. Diagnostically, the multiplicity of factors means that no single cognitive deficit or combination of deficits can be used to rule in or out most neurodevelopmental disorders. Challenges for the MDM are that the theory is difficult to falsify and that current cross-sectional studies cannot establish causality. Prospects for further development of the MDM include incorporating an explicit focus on promotive and protective factors and pursuing mechanistic connections between multiple factors across levels of analysis.

Monte Carlo Modeling of Contemporary Intelligence Test (IQ) Factor Structure: Implications for IQ Assessment, Interpretation, and Theory

Researchers continue to debate the constructs measured by commercial ability tests. Factor analytic investigations of these measures have been used to develop and refine widely adopted psychometric theories of intelligence particularly the Cattell-Horn-Carroll (CHC) model. Even so, this linkage may be problematic as many of these investigations examine a particular instrument in isolation and CHC model specification across tests and research teams has not been consistent. To address these concerns, the present study used Monte Carlo resampling to investigate the latent structure of four of the most widely used intelligence tests for children and adolescents. The results located the approximate existence of the publisher posited CHC theoretical group factors in the Differential Abilities Scales-Second edition and the Kaufman Assessment Battery for Children-Second edition but not in the Wechsler Intelligence Scale for Children-Fifth edition or the Woodcock-Johnson IV Tests of Cognitive Abilities. Instead, the results supported alternative conceptualizations from independent factor analytic research. Additionally, whereas a bifactor model produced superior fit indices in two instruments (Wechsler Intelligence Scale for Children-Fifth edition and Woodcock-Johnson IV Tests of Cognitive Abilities), a higher order structure was found to be superior in the Kaufman Assessment Battery for Children-Second edition and the Differential Abilities Scales-Second edition. Regardless of the model employed, the general factor captured a significant portion of each instrument's variance. Implications for IQ test assessment, interpretation, and theory are discussed.

Psychometric Properties of the SCARED in a Nationally Representative U.S. Sample of 5-12-Year-Olds

OBJECTIVE

To examine the factor structure of the Screen for Child Anxiety Related Emotional Disorders - Parent Report (SCARED-P) in young children and elucidate normative levels of parent-reported anxiety using a nationally representative sample of parents of children ages 5-12 years living in the United States.

METHOD

The 41-item SCARED-P was administered to parents of 1,570 youth who were selected to match the U.S. population on key demographic variables. SCARED-P model fit and mean score differences by age, race/ethnicity, and sex were assessed.

RESULTS

SCARED-P model fit and subscale reliability appeared almost identical in younger children (ages 5-8) and older children (ages 9-12), although model fit for a five-factor model was poor in both groups. Symptoms of generalized anxiety increased from age 5 to 12, while symptoms of separation anxiety disorder decreased. Parents reported significantly more symptoms of social anxiety in females than males. No significant differences by race/ethnicity were found for mean levels of anxiety or model fit.

CONCLUSIONS

The SCARED-P shows some utility as an anxiety screening instrument in a representative sample of U.S. youth as young as 5-years-old, but caution should be used when interpreting subscale scores.

Kidney Disease Quality of Life 36-Item Short Form Survey (KDQOL-36) Normative Values for the United States Dialysis Population and New Single Summary Score

BACKGROUND

The Kidney Disease Quality of Life 36-item short form survey (KDQOL-36) is a widely used, patient-reported outcome measure for patients on dialysis. Efforts to aid interpretation are needed.

METHODS

We used a sample of 58,851 dialysis patients participating in the Medical Education Institute (MEI) KDQOL Complete program, and 443,947 patients from the US Renal Data System (USRDS) to develop the KDQOL-36 Summary Score (KSS) for the kidney-targeted KDQOL-36 scales (Burdens of Kidney Disease [BKD], Symptoms and Problems of Kidney Disease [SPKD], and Effects of Kidney Disease [EKD]). We also used the MEI and USRDS data to calculate normative values for the Short Form-12 Health Survey's Physical Component Summary (PCS) and Mental Component Summary (MCS), and the KDQOL-36's BKD, SPKD, and EKD scales for the United States dialysis population. We used confirmatory factor analysis (CFA) models for KDQOL-36 kidney-targeted items, evaluated model fit with the comparative fit index (CFI; >0.95 indicates good fit) and root-mean-squared error of approximation (RMSEA; <0.06 indicates good fit), and estimated norms by matching the joint distribution of patient characteristics in the MEI sample to those of the USRDS sample.

RESULTS

A bifactor CFA model fit the data well (RMSEA=0.046, CFI=0.990), supporting the KSS (α=0.91). Mean dialysis normative scores were PCS=37.8 and MCS=50.9 (scored on a T-score metric); and KSS=73.0, BKD=52.8, SPKD=79.0, and EKD=74.1 (0-100 possible scores).

CONCLUSIONS

The KSS is a reliable summary of the KDQOL-36. The United States KDQOL-36 normative facilitate interpretation and incorporation of patient-related outcome measures into kidney disease care.

Individual differences in object recognition

There is substantial evidence for individual differences in personality and cognitive abilities, but we lack clear intuitions about individual differences in visual abilities. Previous work on this topic has typically compared performance with only 2 categories, each measured with only 1 task. This approach is insufficient for demonstration of domain-general effects. Most previous work has used familiar object categories, for which experience may vary between participants and categories, thereby reducing correlations that would stem from a common factor. In Study 1, we adopted a latent variable approach to test for the first time whether there is a domain-general object recognition ability, o. We assessed whether shared variance between latent factors representing performance for each of 5 novel object categories could be accounted for by a single higher-order factor. On average, 89% of the variance of lower-order factors denoting performance on novel object categories could be accounted for by a higher-order factor, providing strong evidence for o. Moreover, o also accounted for a moderate proportion of variance in tests of familiar object recognition. In Study 2, we assessed whether the strong association across categories in object recognition is due to third-variable influences. We find that o has weak to moderate associations with a host of cognitive, perceptual, and personality constructs and that a clear majority of the variance in and covariance between performance on different categories is independent of fluid intelligence. This work provides the first demonstration of a reliable, specific, and domain-general object recognition ability, and suggest a rich framework for future work in this area. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

The dimensionality and latent structure of mental health difficulties and wellbeing in early adolescence

Research with adults and older adolescents suggests a general factor may underlie both mental health difficulties and wellbeing. However, the classical bifactor model commonly used to demonstrate this general trait has recently been criticised when a unidimensional structure is not supported. Furthermore, research is lacking in this area with children and early adolescents. We present confirmatory factor analysis models to explore the structure of psychopathology and wellbeing in early adolescents, using secondary data from a large U.K. sample (N = 1982). A simple correlated factors structure fitted the data well and revealed that wellbeing was just as related to internalising as this was to externalising symptoms. The classical bifactor solution also fitted the data well but was rejected as the general factor explained only 55% of the total common variance. S-1 models were therefore used to explore general covariance in a more robust way, and revealed that a general internalising distress factor could play an important role in all item responses. Gender and income differences in mental health were also explored through invariance testing and correlations. Our findings demonstrate the importance of considering mental health difficulties and wellbeing items together, and suggestions are made for how their correspondence could be controlled for.

Seeking certainty about Intolerance of Uncertainty: Addressing old and new issues through the Intolerance of Uncertainty Scale-Revised

Intolerance of Uncertainty is a trans-diagnostic process that spans a range of emotional disorders and it is usually measured through the Intolerance of Uncertainty Scale-12. The current study aims at investigating some issues in the assessment of Intolerance of Uncertainty (IU) through the Italian Intolerance of Uncertainty Scale-Revised, a measure adapted from the Intolerance of Uncertainty Scale-12 to assess IU across the lifespan. In particular we address the factor structure among a large community sample, measurement invariance across gender, age, and over time, together with reliability and validity of the overall scale and its subscales. The questionnaire was administered to community (N = 761; mean age = 35.86 ± 14.01 years) and undergraduate (N = 163; mean age = 21.16 ± 2.64 years) participants, together with other self-report measures assessing constructs theoretically related to IU. The application of a bifactor model shows that the Italian Intolerance of Uncertainty Scale-Revised possesses a robust general factor, thus supporting the use of the unit-weighted total score of the questionnaire as a measure of the construct. Furthermore, measurement invariance across gender, age, and over time is supported. Finally, the Italian Intolerance of Uncertainty Scale-Revised appears to possess adequate reliability and validity. These findings support the unidimensionality of the measure, a conceptually reasonable result in line with the trans-diagnostic nature of Intolerance of Uncertainty. In addition, this study and comparison with published factor structures of the Intolerance of Uncertainty Scale-12 and of the Intolerance of Uncertainty Scale-Revised identify some issues for the internal structure of the measure. In particular, concern is expressed for the Prospective IU subscale. In light of the promising psychometric properties, the use of the Italian Intolerance of Uncertainty Scale-Revised as a univocal measure is encouraged in both research and clinical practice.

Neuropsychological tests of the future: How do we get there from here?

OBJECTIVE

This article reviews current approaches to neuropsychological assessment, identifies opportunities for development of new methods using modern psychometric theory and advances in technology, and suggests a transition path that promotes application of novel methods without sacrificing validity.

METHODS

Theoretical/state-of-the-art review.

CONCLUSIONS

Clinical neuropsychological assessment today does not reflect advances in neuroscience, modern psychometrics, or technology. Major opportunities for improving practice include both psychometric and technological strategies. Modern psychometric approaches including item response theory (IRT) enable linking procedures that can place different measures on common scales; adaptive testing algorithms that can dramatically increase efficiency of assessment; examination of differential item functioning (DIF) to detect measures that behave differently in different groups; and person fit statistics to detect aberrant patterns of responding of high value for performance validity testing. Opportunities to introduce novel technologies include computerized adaptive testing, Web-based assessment, healthcare- and bio-informatics strategies, mobile platforms, wearables, and the 'internet-of-things'. To overcome inertia in current practices, new methods must satisfy requirements for back-compatibility with legacy instrumentation, enabling us to leverage the wealth of validity data already accrued for classic procedures. A path to achieve these goals involves creation of a global network to aggregate item-level data into a shared repository that will enable modern psychometric analyses to refine existing methods, and serve as a platform to evolve novel assessment strategies, which over time can revolutionize neuropsychological assessment practices world-wide.

Shared and unique variances of interpersonal callousness and low prosocial behavior

Although low prosocial behavior (LPB) items have been incorporated into youth measures of callousness, it remains unclear from current factor analytic findings whether callous traits and LPB are best operationalized as a common construct, or distinct dimensions. Using data from a population-representative birth cohort (N = 5,463), this study compared 4 latent factor structures for interpersonal callousness (IC; 6 items) and LPB (5 items) at age 13: (a) unidimensional; (b) two-factor; (c) higher-order (with 2 subfactors); and (d) bifactor (1 general and 2 specific residual factors). Alternative models distinguishing positively and negatively worded items were tested for comparative purposes. To assess the external validity of the factors that emerged from the best-fitting model, associations with early parenting styles and psychiatric comorbidities were examined. A bifactor model, achieving invariance for males and females, offered the best fit for these data. However, additional bifactor-specific indices suggested that the specific IC factor did not offer a unique contribution to the total variance over and above the general factor (IC/LPB). Of the remaining factors, IC/LPB was associated with higher levels of harsh parenting, externalizing and internalizing disorder, and social-cognitive difficulties, and lower levels of warm parenting. The LPB factor, meanwhile, was associated with greater social-cognitive difficulties and externalizing disorder, and lower maternal warmth, evoking a phenotype that may be more indicative of the autism spectrum than IC. These findings suggest that the shared variance underlying IC and LPB taps a severe psychiatric phenotype, while the residual variance for LPB may represent a distinct profile of social-cognitive dysfunction. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Item distribution, internal consistency, and structural validity of the German version of the DEMQOL and DEMQOL-proxy

Background

Accurate assessment of health-related quality of life as an endpoint in intervention studies is a major challenge in dementia research. The DEMQOL (29 items) and the proxy version (32 items), which is partly based on the DEMQOL, are internationally used instruments. To date, there is no information on the structural validity, item distribution, or internal consistency for the German language version of these questionnaires.

Methods

This psychometric study is based on a secondary data analysis of a sample of 201 outpatients with a mild form of Alzheimer’s disease (AD) and their informal caregivers. The informal caregivers who were interviewed were involved in the care of the person with AD several times per week. The analysis for the evaluation of the structural validity was performed using Mokken scale analysis. The internal consistency was calculated using the ρ of the Molenaar Sijtsma statistic and Cronbach’s α.

Results

For both versions, four subscales were identified: [A] “positive emotions”, [B] “negative emotions”, [C] “physical and cognitive functioning”, and [D] “daily activities and social relationships”. For both instruments, the internal consistency of all subscales was considered “good” (ρ = 0.71–0.88, α = 0.72–0.87).

Conclusions

The results are a first indication of good construct validity of the instruments used for the German setting. We recommend further investigations of the test-retest reliability and the inter-rater reliability of the proxy instrument.

Electronic supplementary material

The online version of this article (10.1186/s12877-018-0930-0) contains supplementary material, which is available to authorized users.

Giving G a Meaning: An Application of the Bifactor-(S-1) Approach to Realize a More Symptom-Oriented Modeling of the Beck Depression Inventory–II

The Beck Depression Inventory–II is one of the most frequently used scales to assess depressive burden. Despite many psychometric evaluations, its factor structure is still a topic of debate. An increasing number of articles using fully symmetrical bifactor models have been published recently. However, they all produce anomalous results, which lead to psychometric and interpretational difficulties. To avoid anomalous results, the bifactor-(S-1) approach has recently been proposed as alternative for fitting bifactor structures. The current article compares the applicability of fully symmetrical bifactor models and symptom-oriented bifactor-(S-1) and first-order confirmatory factor analysis models in a large clinical sample ( N = 3,279) of adults. The results suggest that bifactor-(S-1) models are preferable when bifactor structures are of interest, since they reduce problematic results observed in fully symmetrical bifactor models and give the G factor an unambiguous meaning. Otherwise, symptom-oriented first-order confirmatory factor analysis models present a reasonable alternative.

Factor Structure and Incremental Utility of the Distress Tolerance Scale: A Bifactor Analysis

The Distress Tolerance Scale (DTS) is a self-report measure of perceived capacity to withstand aversive emotions. Initial factor analysis of this measure suggested a structure comprising one higher-order factor and four lower-order domain-specific factors. However, there is limited evidence in support of the DTS's purported multidimensionality, and despite use of the DTS subscales, research has yet to assess their incremental utility. The current investigation sought to rectify the paucity of evidence in support of the DTS's factor structure and independent use of DTS subscales via bifactor analysis. In the present study (N = 826 community adults), a bifactor model of the DTS provided the best fit to the data. However, an examination of statistical indices associated with bifactor modeling, as well as results from an examination of incremental utility, suggest that the domain-specific factors are largely redundant with the general factor and do not provide incremental utility in predicting relevant clinical constructs beyond the general factor. Measurement invariance between sexes was confirmed. Taken together, results support use of a DTS total score, but not subscale scores.

Cognitive Abilities Explain Wording Effects in the Rosenberg Self-Esteem Scale

There is consensus that the 10 items of the Rosenberg Self-Esteem Scale (RSES) reflect wording effects resulting from positively and negatively keyed items. The present study examined the effects of cognitive abilities on the factor structure of the RSES with a novel, nonparametric latent variable technique called local structural equation models. In a nationally representative German large-scale assessment including 12,437 students competing measurement models for the RSES were compared: a bifactor model with a common factor and a specific factor for all negatively worded items had an optimal fit. Local structural equation models showed that the unidimensionality of the scale increased with higher levels of reading competence and reasoning, while the proportion of variance attributed to the negatively keyed items declined. Wording effects on the factor structure of the RSES seem to represent a response style artifact associated with cognitive abilities.

Evaluating the Hierarchical Structure of ADHD Symptoms and Invariance Across Age and Gender

The bifactor model of attention-deficit/hyperactivity disorder (ADHD) has been extensively explored, yet the tendency of the bifactor model to overfit data necessitates investigation of alternative, more parsimonious models, such as a modified bifactor structure. The present study used item response theory to compare unidimensional, correlated factors, bifactor, and modified bifactor models of ADHD symptoms in a clinical sample of youth ( N = 1,612) and examined differential item functioning (DIF) by age (<11 and ≥11 years) and gender. Results suggested that two restricted bifactor models showed superior fit compared with alternative models, and support strong general and inattention dimensions, with unreliable hyperactivity and impulsivity dimensions. No DIF was found across gender or age. The present study confirms that the general dimension (i.e., inhibition) and one specific dimension (i.e., sustained attention) represent distinct variability in ADHD symptoms that may improve prediction of symptom persistence, treatment response, or functional outcomes.