Confirmatory Bifactor Modeling Research Articles

Development of the scrupulosity inventory: A factor analysis and construct validity study

Background and objectivesScrupulosity, despite its considerable prevalence and morbidity, remains under-investigated. The present study develops and examines the psychometric properties of a comprehensive assessment tool, the Scrupulosity Inventory (SI). MethodsThe SI, along with other measures of obsessive-compulsive disorder (OCD) and perfectionism, were administered to a sample (N = 150) of college undergraduates similar in size to other scale development studies of related measures. We conducted exploratory and confirmatory factor analyses of the SI, examined its convergent and divergent validity, and assessed its ability to predict categorical diagnoses of scrupulosity using a receiver operator characteristic analysis. ResultsWe found a well-fitting confirmatory bifactor model (RMSEA = 0.049) with a strong general Scrupulosity factor (ωHS=0.907) and specific factors for Personal Violations (ωHS=0.212), Ritualized Behavior (ωHS=0.505), Interference with Life (ωHS=0.254), and Problem Pervasiveness (ωHS=0.430). As predicted, we also found the strongest convergence (r = 0.63) between the SI and the Penn Inventory of Scrupulosity (PIOS), intermediate convergence (r = 0.54) between the SI and Perfectionism Inventory (PI), and weaker convergence (r = 0.47) between the SI and YBOCS. Finally, we found that a categorical diagnosis of scrupulosity was highly predicted by the SI (AUC = 0.84), less well-predicted by the PIOS (AUC = 0.75) and less well predicted by the YBOCS (AUC = 0.69). LimitationsThis study was conducted among a sample of undergraduates at a religiously affiliated university. ConclusionsThese results suggest utility in using the SI to measure the severity of scrupulosity symptoms and that scrupulosity and OCD may present significantly different clinical features.

Misbegotten methodologies and forgotten lessons from Tom Swift's electric factor analysis machine: A demonstration with competing structural models of psychopathology.

Confirmatory factor analysis (CFA) and its bifactor models are popular in empirical investigations of the factor structure of psychological constructs. CFA offers straightforward hypothesis testing but has notable pitfalls, such as the imposition of strict assumptions (i.e., simple structure) that obscure unmodeled complexity. Due to the limitations of bifactor CFAs, they have yielded anomalous results across samples and studies that suggest model misspecification (e.g., evaporating specific factors and unexpected loadings). We propose the use of exploratory factor analysis (EFA) to evaluate the structural validity of CFA solutions-either before or after the estimation of more restrictive CFA models-to (a) identify model misspecifications that may drive anomalous estimates and (b) confirm CFA models by examining whether hypothesized structures emerge with limited researcher input. We evaluated the degree to which predominant factor structures were invariant across contexts along the exploratory-confirmatory continuum and demonstrate how poor methodological choices can distort results and impede theory development. All CFA models fit well, but there were numerous differences in replicability and substantive interpretability. Several similarities emerged between bifactor CFA and EFA models, including evidence of overextraction, the collapse of specific factors onto the general factor, and subsequent shifts in how the general factor was defined. We situate these methodological shortcomings within the broader literature on structural models of psychopathology, articulate implications for theories (such as the p-factor) that are borne out of factor analysis, outline several remedies for problems encountered when performing exploratory bifactor analysis, and propose alternative specifications for confirmatory bifactor models. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Evaluating the dimensionality of L2 grit: A confirmatory bifactor model

This study applied bifactor models to evaluate the dimensionality of two L2 grit models: the 2-factor model (Teimouri et al., 2022) and the 3-factor model we proposed based on Datu et al. (2017). We tested the bifactor models in conjunction with several feasible models including the unidimensional models, the correlated-factors models, and the second-order factor models. Our objective was to investigate whether the grit models effectively measure a unidimensional general construct while also assessing the viability of the domain-specific subconstructs. We utilized a range of bifactor statistical indices to evaluate the unidimensionality of the grit instrument, including omega coefficients (ω), omega subscale coefficients (ωs), omega hierarchical coefficients (ωH), omega hierarchical subscale coefficients (ωHs), explained common variance (ECV), Percent of Uncontaminated Correlations (PUC), item ECV (I-ECV), and construct replicability H-index. The major findings include: (1) the general L2 grit construct comprising 3 domain-specific subconstructs can be treated as essentially unidimensional; (2) the sum scores of the 3-factor grit scale can reliably measure a general L2 grit construct; (3) an alternative version consists of 8 items fits a unidimensional model well. These findings offer valuable implications regarding how to effectively use the sum scores and which items best represent a unidimensional construct.

Model Specification Searches in Structural Equation Modeling Using Bee Swarm Optimization.

Metaheuristics are optimization algorithms that efficiently solve a variety of complex combinatorial problems. In psychological research, metaheuristics have been applied in short-scale construction and model specification search. In the present study, we propose a bee swarm optimization (BSO) algorithm to explore the structure underlying a psychological measurement instrument. The algorithm assigns items to an unknown number of nested factors in a confirmatory bifactor model, while simultaneously selecting items for the final scale. To achieve this, the algorithm follows the biological template of bees' foraging behavior: Scout bees explore new food sources, whereas onlooker bees search in the vicinity of previously explored, promising food sources. Analogously, scout bees in BSO introduce major changes to a model specification (e.g., adding or removing a specific factor), whereas onlooker bees only make minor changes (e.g., adding an item to a factor or swapping items between specific factors). Through this division of labor in an artificial bee colony, the algorithm aims to strike a balance between two opposing strategies diversification (or exploration) versus intensification (or exploitation). We demonstrate the usefulness of the algorithm to find the underlying structure in two empirical data sets (Holzinger-Swineford and short dark triad questionnaire, SDQ3). Furthermore, we illustrate the influence of relevant hyperparameters such as the number of bees in the hive, the percentage of scouts to onlookers, and the number of top solutions to be followed. Finally, useful applications of the new algorithm are discussed, as well as limitations and possible future research opportunities.

Factor structure of post-operative quality of recovery questionnaire (QoR-15): An Italian adaptation and validation.

The Quality of Recovery questionnaire (QoR-15) is an English instrument for measuring quality of recovery in surgical patients, not yet translated and validated in Italian when the Enhanced Recovery After Surgery (ERAS) Piemonte studies were planned. To produce the Italian version of the QoR-15 questionnaire, to evaluate its factorial structure and to assess the invariance between two types of surgery. The Italian version (QoR-15I) was obtained translating and adapting the original version to the Italian context. The validation was performed suppling the QoR-15I to 3,784 patients enrolled in two parallel stepped wedge cluster randomised trials (ERAS Colon-rectum Piemonte; ERAS Gyneco Piemonte). The factor structure and its invariance between types of surgery was tested using confirmatory bifactor model and multi-group analysis. Comparative fit index (CFI), root mean square error of approximation (RMSEA), and standardized root mean square residual (SRMR) fit indices and their changes between nested models were used to assess the factor structure and the invariance. The bifactor model showed good fit (RMSEA = 0.049, CFI =0.957, SRMR = 0.036) and provided a general recovery factor and two specific factors for physical and mental recovery. Eighty-four percent of the common variance is attributable to the general factor, and thus the QoR-15I is sufficiently 'one-dimensional' with an adequate reliability (ωh = 0.70). The ωs values for the physical and mental recovery factors were 0.01 and 0.13, respectively. Multigroup analysis supported configural (RMSEA = 0.053, CFI = 0.950, SRMR = 0.035) and metric invariance (ΔRMSEA = -0.004; ΔCFI = -0.002; ΔSRMR = 0.014), whereas the intercept constraint was removed from item 15 to obtain partial scalar invariance (ΔRMSEA = 0.002; ΔCFI = 0.007; ΔSRMR = 0.004). Construct validity was supported by a negative association of QoR-15I scores with all variables related to worse patient condition and more complex surgery. Our results support the use of the QoR-15I as a valid, reliable, and clinically feasible tool for measuring the quality of recovery after surgery. The results of the confirmatory factor analyses suggest that a unique recovery score can be calculated and support measurement invariance of the QOR-15I across the two type of surgery, suggesting that the questionnaire has the same meaning and the same measurement parameters in colorectal and gynaecologic patients.

A bifactor approach to subscore assessment.

Bifactor confirmatory factor analysis models and statistical indices computed from them have previously been used to provide evidence for the appropriateness of utilizing a unidimensional interpretation of multidimensional data. However, the ability of bifactor indices to aid in the assessment of subscore strength has not been investigated. A simulation study was conducted to relate bifactor indices to the strength of subscores corresponding to specific factors. The bifactor indices OmegaHS and ECVSS were found to be strongly predictive of subscore strength conditional upon OmegaS. The number of factors was also found to play a minor role in this relationship. Cutoffs for assessing the appropriateness of interpreting subscores were constructed based on OmegaHS or ECVSS conditional upon OmegaS and the number of factors. For low subscore reliability (OmegaS = .60), OmegaHS = .25 or ECVSS = .45 is sufficient that the subscore has a good chance of having added value (VAR > 1.1) above and beyond the total score. For moderate reliability (OmegaS = .80), OmegaHS = .20 or ECVSS = .30 is sufficient, and the role of OmegaHS or ECVSS diminishes as OmegaS increases further. A subscore having added value does not necessitate its interpretation. Instead, when subscores are desired to be interpreted, high OmegaHS or ECVSS can be considered as evidence that such an interpretation is statistically appropriate. Finally, we illustrate the use of these cutoffs with an empirical data set. When combined with prior bifactor research, this work extends a framework of using confirmatory bifactor models for dimensionality assessment. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

How Multidimensional is Computational Thinking Competency? A Bi-Factor Model of the Computational Thinking Challenge

Computational thinking (CT) is an emerging and multifaceted competence important to the computing era. However, despite the growing consensus that CT is a competence domain, its theoretical and empirical account remain scarce in the current literature. To address this issue, rigorous psychometric evaluation procedures were adopted to investigate the structure of CT competency, as measured by Computational Thinking Challenge (Lai, 2021a), in a large sample of 1,130 British secondary school students ( M age = 14.14 years, SD age = 1.45). Based on model comparison from an exploratory multidimensional item response theory approach, the results supported the multidimensional operationalization of CT competency. A confirmatory bi-factor item response theory model further suggested CT competency is comprised of a general CT competency factor and two specific factors for programming and non-programming problem-solving. Despite the multidimensionality, the common variance is largely explained by a primary general factor of CT competency, thus the use of a single scale score is recommended. Psychometric evaluation from the bi-factor model indicated good psychometric properties of the assessment tool. Overall, the bi-factor model provides a useful approach to investigating CT competency and serves as a robust test validation tool.

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.

The mediating role of attachment and mentalising in the relationship between childhood maltreatment, self-harm and suicidality

BackgroundAlthough the relationship between childhood maltreatment, self-harm and suicidality is well-established, less is known about the mediating mechanisms explaining it. Based on a developmental mentalisation-based theoretical framework, childhood adversity compromises mentalising ability and attachment security, which in turn increase vulnerability to later stressors in adulthood. ObjectiveThis study aimed to investigate the role of attachment and mentalising as potential mechanisms in the relationship between childhood maltreatment, self-harm and suicidality. Participants and settingWe recruited 907 adults from clinical and community settings in Greater London. MethodsThe study design was cross-sectional. Participants completed self-report questionnaires on retrospectively rated childhood trauma, and current attachment to the romantic partner, mentalising, self-harm, suicidal ideation and attempt. We used structural equation modelling to examine the data and conceptualized childhood maltreatment as a general factor in a confirmatory bifactor model. ResultsThe results showed that childhood maltreatment was both directly associated with self-harm and suicidality and indirectly via the pathways of attachment and mentalising. ConclusionsThese findings indicate that insecure attachment and impaired mentalising partially explain the association between childhood maltreatment, self-harm and suicidality. Clinically, they provide support for the potential of mentalisation-based therapy or other psychosocial interventions that aim to mitigate the risk of self-harm and suicidality among individuals who have experienced childhood maltreatment via increasing understanding of self and other mental states.

Satisfaction with in-patient child and adolescent psychiatric treatment: development and psychometric properties of the BEST questionnaires for adolescents and for parents

BackgroundInterest in the assessment of patient satisfaction with in-patient psychiatric treatment has steadily increased and several measurement tools are available for the quantification of patients’ experience. However, they are often uni-dimensional or focus mainly on therapeutic relationship and environment, and neglect other important issues such as information about treatment and participation. The BEST questionnaires were developed as comprehensive instruments that include items on all of the mentioned topics. The present study evaluates the psychometric properties of the BEST in a version for adolescents and for parents. Furthermore, the dimensionality of the satisfaction ratings is analyzed.MethodDescriptive statistics were applied to data of 1582 adolescents (mean age = 15.0 years, SD = 1.65; 62.4% female) and 1998 parents/guardians assessed in seven in-patient units across Germany. The factorial structure of the BEST questionnaires was determined by exploratory and confirmatory factor analyses, including a bifactor model.ResultsThe psychometric quality of the scales was strong. Correlations with another assessment instrument of patient satisfaction were good to high, indicating good convergent validity. Exploratory factor analyses revealed three factors in adolescents that were labelled as: Therapeutic relationship, environment, and general satisfaction and treatment success. For parents, the same three factors could be distinguished. Confirmatory bifactor models suggested that the vast majority of variance was accounted for by the general factor; the three specific factors provided some additional information. Agreement between the subscales of adolescents and their parents was only moderate. Parents were usually more satisfied.ConclusionsThe BEST questionnaires can be considered as reliable and valid instruments to not only assess the “classical” aspects of patient satisfaction, but to also assess newer fundamental topics such as children’s rights and treatment participation. For scientific usage, the total score seems superior because of the high explained variance by the general factor, but the subscale scores provide further information. The use of single items seems advantageous for quality management purposes.

Rasch testlet model and bifactor analysis: how do they assess the dimensionality of large-scale Iranian EFL reading comprehension tests?

Rasch testlet and bifactor models are two measurement models that could deal with local item dependency (LID) in assessing the dimensionality of reading comprehension testlets. This study aimed to apply the measurement models to real item response data of the Iranian EFL reading comprehension tests and compare the validity of the bifactor models and corresponding item parameters with unidimensional and multidimensional Rasch models. The data collected from the EFL reading comprehension section of the Iranian national university entrance examinations from 2016 to 2018. Various advanced packages of the R system were employed to fit the Rasch unidimensional, multidimensional, and testlet models and the exploratory and confirmatory bifactor models. Then, item parameters estimated and testlet effects identified; moreover, goodness of fit indices and the item parameter correlations for the different models were calculated. Results showed that the testlet effects were all small but non-negligible for all of the EFL reading testlets. Moreover, bifactor models were superior in terms of goodness of fit, whereas exploratory bifactor model better explained the factor structure of the EFL reading comprehension tests. However, item difficulty parameters in the Rasch models were more consistent than the bifactor models. This study had substantial implications for methods of dealing with LID and dimensionality in assessing reading comprehension with reference to the EFL testing.

Predicting depression outcomes throughout inpatient treatment using the general and specific personality disorder factors.

Clinical intuition suggests that personality disorders hinder the treatment of depression, but research findings are mixed. One reason for this might be the way in which current assessment measures conflate general aspects of personality disorders, such as overall severity, with specific aspects, such as stylistic tendencies. The goal of this study was to clarify the unique contributions of the general and specific aspects of personality disorders to depression outcomes. Patients admitted to the Menninger Clinic, Houston, between 2012 and 2015 (N = 2352) were followed over a 6-8-week course of multimodal inpatient treatment. Personality disorder symptoms were assessed with the Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders, 4th edition Axis II Personality Screening Questionnaire at admission, and depression severity was assessed using the Patient Health Questionnaire-9 every fortnight. General and specific personality disorder factors estimated with a confirmatory bifactor model were used to predict latent growth curves of depression scores in a structural equation model. The general factor predicted higher initial depression scores but not different rates of change. By contrast, the specific borderline factor predicted slower rates of decline in depression scores, while the specific antisocial factor predicted a U shaped pattern of change. Personality disorder symptoms are best represented by a general factor that reflects overall personality disorder severity, and specific factors that reflect unique personality styles. The general factor predicts overall depression severity while specific factors predict poorer prognosis which may be masked in prior studies that do not separate the two.

Risk factors associated with general and specific dimensions of psychosis in a nationally representative sample of adults from the United States

ABSTRACT General and specific dimensions of psychosis have been identified in both clinical and general population samples. Demographic and clinical correlates, however, have only been explored within models derived from clinical data. Data were drawn from Wave 3 of the National Epidemiological Survey on Alcohol and Related Conditions (N = 36,309). Confirmatory factor analysis and confirmatory bifactor modelling were used to determine the optimal measurement model of psychosis symptoms among this sample. Structural equation modelling was used to identify variables correlated with the different psychosis dimensions, as per the best fitting measurement model. Measurement modelling results indicated that the latent structure of psychosis was best represented by a bifactor model including a general dimension orthogonal to four specific dimensions representing “Positive”, “Negative”, “Mania”, and “Disorganized” symptoms. Childhood interpersonal trauma, lack of social support, lower socio-economic status and suicidal attempts were associated with higher scores on the general dimension. These results provide further support for the validity of a bifactor model of psychosis in the general population and identify unique risk correlates of specific and general factors of psychosis.

Military Suicide Research Consortium common data elements: Bifactor analysis and longitudinal predictive ability of suicidal ideation and suicide attempts within a clinical sample.

To enhance and standardize the assessment of suicidal self-directed violence (SDV) in military populations, the Military Suicide Research Consortium developed the Common Data Elements (CDEs). Previous research supported the CDEs as assessing a higher-order factor of suicidal SDV in military populations. The present study had two aims: 1) confirm the bifactor structure of the CDEs in a high-risk sample, and 2) assess the ability of the factorially derived suicidal SDV factor to predict suicide attempts and return to care for suicidal ideation over 3-month follow-up. Utilizing a sample of service members referred for a psychiatric evaluation (N = 1,044), the CDE structure was assessed with confirmatory bifactor modeling. Logistic regressions and receiver operating characteristic (ROC) analyses were used to assess the suicidal SDV risk factor's prediction of suicide attempts and return to care for suicidal ideation during follow-up (n = 758). Bifactor modeling suggested adequate fit for the overarching suicidal SDV risk factor. Logistic regressions supported the overarching suicidal SDV risk factor as a predictor of suicide attempts (OR = 4.07, p < .001) and return to care for suicidal ideation (OR = 2.81, p < .001) over follow-up. However, ROC analyses suggested that the model including the suicidal SDV risk factor was only significantly better at classifying suicide attempts over follow-up (not return to care for suicidal ideation) than the model that did not include it (AUC difference = 0.15, p < .001). Findings suggest that the shared variance assessed across CDEs better predicts future suicide attempts beyond any individual suicide-related constructs. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Health-related quality-of-life assessment in dementia: Evidence of cross-cultural validity in Latin America.

All health-related quality-of-life (HRQL) measures for dementia have been developed in high-income countries and none were validated for cross-cultural use. Yet, the global majority of people living with dementia reside in low- and middle-income countries. We therefore investigated the measurement invariance of a set of self- and informant-report HRQL measures developed in the United Kingdom when used in Latin America. Self-reported HRQL was obtained using (DEMQOL) at a memory assessment service in the United Kingdom (n = 868) and a population cohort study in Latin America (n = 417). Informant reports were collected using DEMQOL-Proxy at both sites (n = 909 and n = 495). Multiple-group confirmatory bifactor models for ordered categorical item responses were estimated to evaluate measurement invariance. Results support configural, metric, and scalar invariance for the concept of general HRQL in DEMQOL and DEMQOL-Proxy. The dominant impact of general HRQL on item responses was evident across U.K. English and Ibero American Spanish versions of DEMQOL (ωh = 0.87–0.90) and DEMQOL-Proxy (ωh = 0.88–0.89). Ratings of “positive emotion” did not show a major impact on general HRQL appraisal, particularly for Latin American respondents. Item information curves show that self- and informant-reports were highly informative about the presence rather than the absence of HRQL impairment. We found no major difference in conceptual meaning, sensitivity, and relevance of DEMQOL and DEMQOL-Proxy for older adults in the United Kingdom and Latin America. Further replication is needed for consensus over which HRQL measures are appropriate for making cross-national comparisons in global dementia research.

The CSS-12: Development and Validation of a Short-Form Version of the Cyberchondria Severity Scale.

Cyberchondria is defined as an increase in anxiety about one's health status as a result of excessive online searches. McElroy and Shevlin (2014) developed the first multidimensional, self-report measure of this construct-the Cyberchondria Severity Scale (CSS). The CSS consists of 33 items which can be summed to form a total score, and/or 5 subscale scores. The aim of the present study was to develop a short-form version of the CSS, removing the "Mistrust" subscale. Participants were undergraduate students from two UK universities (N = 661, 73% female, Mage = 22.19 years, SD = 5.88). Students completed the CSS, Short Health Anxiety Inventory (SHAI) and Generalized Anxiety Disorder Assessment (GAD-7). Twelve items were chosen for retention in the short form based on an exploratory factor analysis. These items corresponded to the four factors previously identified in the 33-item scale (minus the "Mistrust" subscale). Confirmatory factor analysis was used to validate the structure of the CSS-12. Confirmatory bifactor modeling indicated that the majority of item covariance was accounted for by a general cyberchondria factor. Construct validity was assessed by examining associations with the SHAI and GAD-7, with stronger correlations observed between the CSS-12 and the SHAI (compared with the GAD-7). The CSS-12 is a brief, reliable, and valid measure of worry/anxiety attributable to excessive online health research.

Evidence for the continuum-severity model of psychosis through scrutiny of the architecture of symptoms associated with schizophrenia

ABSTRACTThe psychosis continuum provides a framework to develop a compelling insight into the architecture of psychotic experiences in non-clinical samples. Using schizophrenia-specific measures within non-clinical samples offers further opportunity to investigate psychotic experiences and compare to those reported in clinical samples.A cross sectional survey method was used to collect data from a non-clinical sample (n = 510) using the Schizophrenia Quality of Life Scale-Revision 4 (SQLS-R4) and the Hospital Anxiety and Depression Scale (HADS).Conducting confirmatory factor analysis and bi-factor modelling found that a bi-factor model offered a better model fit to the data than the established two factor model. A general factor explained most item variance whilst seven domain specific factors explained a further small amount of item variance. Participants with higher anxiety reported comparatively poorer Quality of Life to those with lower anxiety. Comparison with data taken from a clinical sample found similarities in both the internal consistency and correlation coefficients between SQLS-R4 totals and sub-scales and HADS total scores and sub-scales.These results show the presence of a robust general psychosis Quality of Life factor within a non-clinical sample. The use of schizophrenia-specific measures and bi-factor modelling can provide suitable methods for investigating the nature of the psychosis continuum.

The preliminary measurement properties of the person-centred community care inventory (PERCCI)

PurposeResearchers investigating person-centredness in older people’s long-term community care are hindered by the lack of appropriate measures. Studies have tended to rely on proxy indicators or generic instruments, risking invalid results. This new research aimed to develop and psychometrically test a person-centredness scale for use in older people’s community services.MethodsQuestionnaire items were sourced from groups of older people and mapped to a conceptual framework of person-centredness. A postal questionnaire in 2015–2016 tested these items with older people supported by mental health and social care services in five areas of England. Dimensionality was assessed through exploratory factor analysis and a confirmatory bifactor model, with classical item analysis removing weak items. Test–retest analysis was undertaken through a repeated postal questionnaire 3 weeks after the first.ResultsThree factors were identified, representing (i) interpersonal and (ii) organisational aspects of person-centred care; and (iii) negatively phrased items. Removing weaker items resulted in an 18-item scale. The bifactor analysis concluded the summary scale was ‘essentially unidimensional’. The Person-centred community care inventory (PERCCI) had excellent reliability, with Intra-Class Correlation Coefficient of 0.886 [95% CI 0.818–0.929]. A priori hypotheses about associations with satisfaction metrics and support variables were broadly confirmed.ConclusionsThe PERCCI has promising measurement properties and can be recommended for use in research with older adults using community mental health and social care services. Future developments must identify how sensitive the instrument is in detecting changing service quality.

“P” and “DP:” Examining Symptom-Level Bifactor Models of Psychopathology and Dysregulation in Clinically Referred Children and Adolescents

This study examined cross-informant evidence for a general factor of psychopathology ("P") and a narrower, clinically oriented dysregulation general factor based on the Dysregulation Profile ("DP") in a large clinical sample of children and adolescents. We also compared the magnitude of P and DP general factor associations with self-harm and suicidal ideation as an indicator of criterion validity. Itemwise data from the Child Behavior Checklist (N= 2,934; 4-18 years of age) were analyzed using confirmatory bifactor modeling and replicated in a supplementary analysis using Youth Self Report data (N= 2,395). General P and DP bifactor models fit the data better than single-factor and correlated factor models. Cross-informant criterion analyses on a subset of youth (n= 1,552) suggested that whether modeled as latent P or DP, associations with a brief composite index of self-harm and suicidal ideation are essentially of the same magnitude. Our findings provide novel, large-sample support for the existence of general factors of psychopathology and dysregulation in clinically referred children and adolescents using a standardized rating system of psychopathology symptoms. Moreover, our results provide preliminary evidence that general psychopathology and dysregulation factors are clinically meaningful constructs. In addition, our findings raise the possibility that the DP general factor may serve as an efficient proxy for the general psychopathology factor in future clinical applications. Further efforts are necessary to understand the core empirical meaning of the P factor and to determine how it can be applied to clinical assessment and intervention.

Construction of a Potential Telephone Assessment of Dementia Prevalence and Severity

The "δ" (for "dementia") is a latent dementia phenotype that can be constructed by a unique confirmatory bifactor model in a structural equation model framework. Because it is derived from Spearman's general intelligence factor, "g," δ can be constructed from any cognitive battery. This may allow for accurate dementia case-finding by telephone and in the absence of expert clinical evaluation or review. The authors constructed a new δ homolog in a large ethnically diverse convenience sample: the Texas Alzheimer's Research and Care Consortium, comprising 2,016 participants (Alzheimer's disease [AD], N=920; mild cognitive impairment, N=277; normal controls, N=819). A δ composite ("dTEL") was extracted from informant-rated Instrumental Activities of Daily Living and a brief battery of verbal cognitive measures. The entire battery was engineered to be administered over the telephone. dTEL's model had excellent fit. dTEL correlated strongly with dementia severity, as measured by the Clinical Dementia Rating "sum of boxes" scale (r=0.78, p<0.001). The dTEL composite's area under the receiver operating characteristic curve for the discrimination between control subjects and AD patients was 0.97 (95% CI=0.964-0.975). This was superior to all dTEL indicators. Therefore, the authors have demonstrated that a δ homolog composite constructed entirely from verbal measures is strongly associated with dementia severity, can accurately diagnose dementia, and outperforms all observed measures from which it is constructed. Future studies are required to assess dTEL's performance relative to evaluation by expert clinicians when obtained by lay psychometricians over the telephone.