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Item response theory (IRT) is moving to the forefront of methodologies used to develop, evaluate, and score clinical measures. Funding agencies and test developers are routinely supporting IRT work, and the theory has become closely tied to technological advances within the field. As a result, familiarity with IRT has grown increasingly relevant to mental health research and practice. But to what end? This article reviews advances in applications of IRT to clinical measurement in an effort to identify tangible improvements that can be attributed to the methodology. Although IRT shares similarities with classical test theory and factor analysis, the approach has certain practical benefits, but also limitations, when applied to measurement challenges. Major opportunities include the use of computerized adaptive tests to prevent conditional measurement error, multidimensional models to prevent misinterpretation of scores, and analyses of differential item functioning to prevent bias. Whereas these methods and technologies were once only discussed as future possibilities, they are now accessible because of recent support of IRT-focused clinical research. Despite this, much work still remains in widely disseminating methods and technologies from IRT into mental health research and practice. Clinicians have been reluctant to fully embrace the approach, especially in terms or prospective test development and adaptive item administration. Widespread use of IRT technologies will require continued cooperation among psychometricians, clinicians, and other stakeholders. There are also many opportunities to expand the methodology, especially with respect to integrating modern measurement theory with models from personality and cognitive psychology as well as neuroscience. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

The Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS) is a research project aimed at identifying risk and protective factors for suicide and related mental health outcomes among Army Soldiers. The New Soldier Study component of Army STARRS included the assessment of a range of cognitive- and emotion-processing domains linked to brain systems related to suicidal behavior including posttraumatic stress disorder, mood disorders, substance use disorders, and impulsivity. We describe the design and application of the Army STARRS neurocognitive test battery to a sample of 56,824 soldiers. We investigate its structural and concurrent validity through factor analysis and correlation of scores with demographics. We conclude that, in addition to being composed of previously well-validated measures, the Army STARRS neurocognitive battery as a whole demonstrates good psychometric properties. Correlations of scores with age and sex differences mostly replicate previously published findings, highlighting moderate to large effect sizes even within this restricted age range. Factor structures of scores conform to theoretical expectations. This neurocognitive battery provides a brief, valid measurement of neurocognition that may be helpful in predicting mental health and military performance. These measures can be integrated with neuroimaging to offer a powerful tool for assessing neurocognition in Servicemembers.

Objective: Neuropsychology is an applied measurement field with its psychometric work primarily built upon classical test theory (CTT). We describe a series of psychometric models to supplement the use of CTT in neuropsychological research and test development., Method: We introduce increasingly complex psychometric models as measurement algebras, which include model parameters that represent abilities and item properties. Within this framework of parametric model measurement (PMM), neuropsychological assessment involves the estimation of model parameters with ability parameter values assuming the role of test 'scores'. Moreover, the traditional notion of measurement error is replaced by the notion of parameter estimation error, and the definition of reliability becomes linked to notions of item and test information. The more complex PMM approaches incorporate into the assessment of neuropsychological performance formal parametric models of behavior validated in the experimental psychology literature, along with item parameters. These PMM approaches endorse the use of experimental manipulations of model parameters to assess a test's construct representation. Strengths and weaknesses of these models are evaluated by their implications for measurement error conditional upon ability level, sensitivity to sample characteristics, computational challenges to parameter estimation, and construct validity., Conclusion: A family of parametric psychometric models can be used to assess latent processes of interest to neuropsychologists. By modeling latent abilities at the item level, psychometric studies in neuropsychology can investigate construct validity and measurement precision within a single framework and contribute to a unification of statistical methods within the framework of generalized latent variable modeling.

Wisdom has been reported to be associated with better mental health and quality of life among older adults. Over the past decades, there has been considerable growth in empirical research on wisdom, including the development of standardized measures. The 39-item Three-Dimensional Wisdom Scale (3D-WS) is a useful assessment tool, given its rigorous development and good psychometric properties. However, the measure's length can prohibit use. In this article, we used a sample of 1,546 community-dwelling adults aged 21 to 100 years (M = 66 years) from the Successful AGing Evaluation (SAGE) study to develop an abbreviated 12-item version of the 3D-WS: the 3D-WS-12. Balancing concerns for measurement precision, internal structure, and content validity, factor analytic methods and expert judgment were used to identify a subset of 12-items for the 3D-WS-12. Results suggest that the 3D-WS-12 can provide efficient and valid assessments of Wisdom within the context of epidemiological surveys., (© The Author(s) 2015.)

Introduction: We used cognitive and psychometric modeling techniques to evaluate the construct validity and measurement precision of latent cognitive abilities measured by a test of concept identification learning: the Penn Conditional Exclusion Test (PCET)., Method: Item response theory parameters were embedded within classic associative- and hypothesis-based Markov learning models and were fitted to 35,553 Army soldiers' PCET data from the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS)., Results: Data were consistent with a hypothesis-testing model with multiple latent abilities-abstraction and set shifting. Latent abstraction ability was positively correlated with number of concepts learned, and latent set-shifting ability was negatively correlated with number of perseverative errors, supporting the construct validity of the two parameters. Abstraction was most precisely assessed for participants with abilities ranging from 1.5 standard deviations below the mean to the mean itself. Measurement of set shifting was acceptably precise only for participants making a high number of perseverative errors., Conclusions: The PCET precisely measures latent abstraction ability in the Army STARRS sample, especially within the range of mildly impaired to average ability. This precision pattern is ideal for a test developed to measure cognitive impairment as opposed to cognitive strength. The PCET also measures latent set-shifting ability, but reliable assessment is limited to the impaired range of ability, reflecting that perseverative errors are rare among cognitively healthy adults. Integrating cognitive and psychometric models can provide information about construct validity and measurement precision within a single analytical framework.

There is growing evidence that psychiatric disorders maintain hierarchical associations where general and domain-specific factors play prominent roles (see D. Watson, 2005). Standard, unidimensional measurement models can fail to capture the meaningful nuances of such complex latent variable structures. The present study examined the ability of the multidimensional item response theory bifactor model (see R. D. Gibbons & D. R. Hedeker, 1992) to improve construct validity by serving as a bridge between measurement and clinical theories. Archival data consisting of 688 outpatients' psychiatric diagnoses and item-level responses to the Brief Symptom Inventory (BSI; L. R. Derogatis, 1993) were extracted from files at a university mental health clinic. The bifactor model demonstrated superior fit for the internal structure of the BSI and improved overall diagnostic accuracy in the sample (73%) compared with unidimensional (61%) and oblique simple structure (65%) models. Consistent with clinical theory, multiple sources of item variance were drawn from individual test items. Test developers and clinical researchers are encouraged to consider model-based measurement in the assessment of psychiatric distress.

Item response theory (IRT) and related latent variable models represent modern psychometric theory, the successor to classical test theory in psychological assessment. Although IRT has become prevalent in the measurement of ability and achievement, its contributions to clinical domains have been less extensive. Applications of IRT to clinical assessment are reviewed to appraise its current and potential value. Benefits of IRT include comprehensive analyses and reduction of measurement error, creation of computer adaptive tests, meaningful scaling of latent variables, objective calibration and equating, evaluation of test and item bias, greater accuracy in the assessment of change due to therapeutic intervention, and evaluation of model and person fit. The theory may soon reinvent the manner in which tests are selected, developed, and scored. Although challenges remain to the widespread implementation of IRT, its application to clinical assessment holds great promise. Recommendations for research, test development, and clinical practice are provided.

The adaptation of experimental cognitive tasks into measures that can be used to quantify neurocognitive outcomes in translational studies and clinical trials has become a key component of the strategy to address psychiatric and neurological disorders. Unfortunately, while most experimental cognitive tests have strong theoretical bases, they can have poor psychometric properties, leaving them vulnerable to measurement challenges that undermine their use in applied settings. Item response theory-based computerized adaptive testing has been proposed as a solution but has been limited in experimental and translational research due to its large sample requirements. We present a generalized latent variable model that, when combined with strong parametric assumptions based on mathematical cognitive models, permits the use of adaptive testing without large samples or the need to precalibrate item parameters. The approach is demonstrated using data from a common measure of working memory--the N-back task--collected across a diverse sample of participants. After evaluating dimensionality and model fit, we conducted a simulation study to compare adaptive versus nonadaptive testing. Computerized adaptive testing either made the task 36% more efficient or score estimates 23% more precise, when compared to nonadaptive testing. This proof-of-concept study demonstrates that latent variable modeling and adaptive testing can be used in experimental cognitive testing even with relatively small samples. Adaptive testing has the potential to improve the impact and replicability of findings from translational studies and clinical trials that use experimental cognitive tasks as outcome measures.

The MMPI-2 Restructured Form (MMPI-2-RF; Tellegen & Ben-Porath, 2008) was designed to be psychometrically superior to its MMPI-2 counterpart. However, the test has yet to be extensively evaluated in diverse clinical settings. The purpose of this study was to examine the psychometric properties of the MMPI-2-RF Somatic Complaints (RC1) scale in a clinically relevant population. Participants were 399 patients diagnosed with either epilepsy or psychogenic nonepileptic seizures on the basis of video-electroencephalograph monitoring. The internal structure of the MMPI-2-RF was evaluated using taxometric, confirmatory factor analysis, and item response theory procedures. Data from 4 content-specific scales directly related to RC1 (Malaise, Gastrointestinal Complaints, Head Pain Complaints, and Neurological Complaints) indicated that the latent construct of somatization is a dimensional variable with a bifactor structure. However, consistent with the scale's construction, a unidimensional model also provided adequate fit. A 2-parameter logistic item response theory model better accounted for observed item responses than did 1- or 3-parameter models. Results suggest that the RC1 scale is most precise for T score estimates between 55 and 90. Overall, the scale appears to be well suited for the assessment of somatization. (Contains 6 figures and 4 tables.)

ObjectivesWisdom is a personality trait comprising seven components: self-reflection, pro-social behaviors, emotional regulation, acceptance of diverse perspectives, decisiveness, social advising, and spirituality. Wisdom, a potentially modifiable trait, is strongly associated with well-being. We have published a validated 28-item San Diego Wisdom Scale, the SD-WISE-28. Brief scales are necessary for use in large population-based studies and in clinical practice. The present study aimed to create an abbreviated 7-item version of the SD-WISE.MethodParticipants included 2093 people, aged 20-82 years, recruited and surveyed through the online crowdsourcing platform Amazon Mechanical Turk. The participants' mean age was 46 years, with 55% women. Participants completed the SD-WISE-28 as well as validation scales for various positive and negative constructs. Psychometric analyses (factor analysis and item response theory) were used to select one item from each of the seven SD-WISE-28 subscales.ResultsWe selected a combination of items that produced acceptable unidimensional model fit and good reliability (ω = 0.74). Item statistics suggested that all seven items were strong indicators of wisdom, although the association was weakest for spirituality. Analyses indicated that the 28-item and 7-item SD-WISE are both very highly correlated (r = 0.92) and produce a nearly identical pattern of correlations with demographic and validity variables.ConclusionThe SD-WISE-7, and its derived Jeste-Thomas Wisdom Index (JTWI) score, balances reliability and brevity for research applications.

ObjectiveWisdom has gained increasing interest among researchers as a personality trait relevant to well-being and mental health. We previously reported development of a new 24-item San Diego Wisdom Scale (SD-WISE), with good to excellent psychometric properties, comprised of six subscales: pro-social behaviors, emotional regulation, self-reflection (insight), tolerance for divergent values (acceptance of uncertainty), decisiveness, and social advising. There is controversy about whether spirituality is a marker of wisdom. The present cross-sectional study sought to address that question by developing a new SD-WISE subscale of spirituality and examining its associations with various relevant measures.MethodsData were collected from a national-level sample of 1,786 community-dwelling adults age 20-82 years, as part of an Amazon M-Turk cohort. Participants completed the 24-item SD-WISE along with several subscales of a commonly used Brief Multidimensional Measure of Religiousness/Spirituality, along with validated scales for well-being, resilience, happiness, depression, anxiety, loneliness, and social network.ResultsUsing latent variable models, we developed a Spirituality subscale, which demonstrated acceptable psychometric properties including a unidimensional factor structure and good reliability. Spirituality correlated positively with age and was higher in women than in men. The expanded 28-item, 7-subscale SD-WISE total score (called the Jeste-Thomas Wisdom Index or JTWI) demonstrated acceptable psychometric properties. The Spirituality subscale was positively correlated with good mental health and well-being, and negatively correlated with poor mental health. However, compared to other components of wisdom, the Spirituality factor showed weaker (i.e., small-to-medium vs. medium-to-large) association with the SD-WISE higher-order Wisdom factor (JTWI).ConclusionSimilar to other components as well as overall wisdom, spirituality is significantly associated with better mental health and well-being, and may add to the predictive utility of the total wisdom score. Spirituality is, however, a weaker contributor to overall wisdom than components like pro-social behaviors and emotional regulation. Longitudinal studies of larger and more diverse samples are needed to explore mediation effects of these constructs on well-being and health.

IntroductionWisdom is reportedly associated with better health and quality of life. However, our knowledge of the neurobiology of wisdom is still in the early stages of development. We aimed to improve our understanding by correlating a psychometric measure of the trait with patterns of brain activation produced by a cognitive task theorized to be relevant to wisdom: moral decision-making. In particular, we aimed to determine whether individual differences in wisdom interact with moral task complexity in relation to brain activation.MethodsParticipants were 39 community-dwelling men and women aged 27-76 years, who completed moral and nonmoral decision-making tasks while undergoing functional magnetic resonance imaging. Brain activation in select regions of interest was correlated with participants' scores on the San Diego Wisdom Scale (SD-WISE).ResultsIndividual differences in wisdom were found to interact with brain response to moral versus nonmoral and moral personal versus impersonal dilemmas, particularly in regions in or near the default mode network. Persons with higher scores on the SD-WISE had less contrast between moral and nonmoral dilemmas and greater contrast between moral-personal and moral-impersonal dilemmas than individuals with lower SD-WISE scores.ConclusionsResults confirmed our hypothesis that individual differences in level of wisdom would interact with moral condition in relation to brain activation, and may underscore the relevance of considering one's own and others' actions and experiences in the context of wise thinking. Future studies are needed to replicate these findings and to examine specific neurocircuits.

Wisdom is an ancient concept that has gained new interest among clinical researchers as a complex trait relevant to well-being and healthy aging. As the empirical data regarding wisdom have grown, several measures have been used to assess an individual's level of wisdom. However, none of these measures has been based on a construct of wisdom with neurobiological underpinnings. We sought to develop a new scale, the San Diego Wisdom Scale (SD-WISE), which builds upon recent gains in the understanding of psychological and neurobiological models of the trait. Data were collected from 524 community-dwelling adults age 25-104 years as part of a structured multi-cohort study of adult lifespan. Participants were administered the SD-WISE along with two existing measures of wisdom that have been shown to have good psychometric properties. Factor analyses confirmed the hypothesized measurement model. SD-WISE total scores were reliable, demonstrated convergent and discriminant validity, and correlated, as hypothesized, negatively with emotional distress, but positively with well-being. However, the magnitudes of these associations were small, suggesting that the SD-WISE is not just a global measure of mental state. The results support the reliability and validity of SD-WISE scores. Study limitations are discussed. The SD-WISE, with good psychometric properties, a brief administration time, and a measurement model that is consistent with commonly cited content domains of wisdom based on a putative neurobiological model, may be useful in clinical practice as well as in bio-psycho-social research, especially investigations into the neurobiology of wisdom and experimental interventions to enhance wisdom.

When using functional brain imaging to study neuropsychiatric patients an important challenge is determining whether the imaging task assesses individual differences with equal precision in healthy control and impaired patient groups. Classical test theory (CTT) requires separate reliability studies of patients and controls to determine equivalent measurement precision with additional studies to determine measurement precision for different levels of disease severity. Unlike CTT, item response theory (IRT) provides estimates of measurement error for different levels of ability, without the need for separate studies, and can determine if different tests are equivalently difficult when investigating differential deficits between groups. To determine the potential value of IRT in functional brain imaging, IRT was applied to behavioral data obtained during a multi-center functional MRI (fMRI) study of working memory (WM). Average item difficulty was approximately one standard deviation below the ability scale mean, supporting the task's sensitivity to individual differences within the ability range of patients with WM impairment, but not within the range of most controls. The correlation of IRT estimated ability with fMRI activation during the task recognition period supported the linkage of the latent IRT scale to brain activation data. IRT can meaningfully contribute to the design of fMRI tasks.

Objective: Cognitive tasks are used to probe neuronal activity during functional magnetic resonance imaging (fMRI) to detect signs of aberrant cognitive functioning in patients diagnosed with schizophrenia (SZ). However, nonlinear (inverted-U-shaped) associations between neuronal activity and task difficulty can lead to misinterpretation of group differences between patients and healthy comparison subjects (HCs). In this paper, we evaluated a novel method for correcting these misinterpretations based on conditional performance analysis. Method: Participants included 25 HCs and 27 SZs who performed a working memory (WM) task (N-back) with 5 load conditions while undergoing fMRI. Neuronal activity was regressed onto: 1) task load (i.e., parametric task levels), 2) marginal task performance (i.e., performance averaged over all load conditions), or 3) conditional task performance (i.e., performance within each load condition). Results: In most regions of interest, conditional performance analysis uniquely revealed inverted-U-shaped neuronal activity in both SZs and HCs. After accounting for conditional performance differences between groups, we observed few difference in both the pattern and level of neuronal activity between SZs and HCs within regions that are classically associated with WM functioning (e.g., posterior dorsolateral prefrontal and parietal association cortices). However, SZs did show aberrant activity within the anterior dorsolateral prefrontal cortex. Conclusions: Interpretations of differences in neuronal activity between groups, and of associations between neuronal activity and performance, should be considered within the context of task performance. Whether conditional performance-based differences reflect compensation, dedifferentiation, or other processes is not a question that is easily resolved by examining activation and performance data alone. [ABSTRACT FROM AUTHOR]

Social impairments are common across many psychiatric conditions. Standardized dyadic assessments intended to elicit social affiliation between unacquainted partners are used to elucidate mechanisms that disrupt relationship formation and inform possible treatment targets; however, the psychometric properties of such paradigms remain poorly understood. This study evaluated the psychometric properties of a controlled social affiliation paradigm intended to induce connectedness between a target participant and trained confederate. Individuals with an anxiety or depressive disorder diagnosis (clinical group; n = 132) and those without (control group; n = 35) interacted face-to-face with a trained confederate; partners took turns answering a series of increasingly intimate questions about themselves. Social connectedness, affect, and affiliative behavior measures were collected during the interaction. Participant symptom and social functioning measures were collected to examine validity. The paradigm elicited escalating social connectedness throughout the task for both participants and confederates. Parallel forms (i.e., different question sets) elicited similar affiliation outcomes. Self-reported (but not behavioral) affiliation differed across some demographic variables (e.g., participant gender, Hispanic ethnicity). Within-task affiliation measures were associated with one another and with global social connectedness and social anxiety symptom measures, but not with somatic anxiety measures. Clinical participants reported lower social affiliation and positive affect reactivity and higher negative affect reactivity than healthy participants. These findings provide initial psychometric support for a standardized and controlled dyadic affiliation paradigm that could be used to reliably probe social disconnection mechanisms across psychopathology. [ABSTRACT FROM AUTHOR]

Objective: Deficits in cognitive ability are common among patients with schizophrenia. The MATRICS Consensus Cognitive Battery (MCCB) was designed to assess cognitive ability in studies of patients diagnosed with schizophrenia and has demonstrated high test-retest reliability with minimal practice effects, even in multi-site trials. However, given the motivational challenges associated with schizophrenia, it is unknown whether performance on MCCB tasks affects performance at later stages of testing. The goal of this study was to determine whether there are differences between people with and without schizophrenia in how their performance on individual MCCB tasks influences their performance throughout the battery. Participants and Methods: The sample comprised 92 total participants including 49 cognitively healthy comparison participants and 43 outpatients diagnosed with schizophrenia. The mean age of participants was 44.2 years (SD = 12.0, range 21–69) and 61% identified as male. The Trail Making Test, Brief Assessment of Cognition in Schizophrenia, Hopkins Verbal Learning Test – Revised, Letter-Number Span, and Category Fluency from the MCCB were administered in the same order at 2 different sites and studies from 2016–2022. The autocorrelation between t-scores for task scores within each participant was computed and then compared between control and outpatient participants to determine if there are differences between groups. Group mean t-scores for each task were also compared between groups. Results: We found no significant difference in autocorrelations across MCCB tasks between healthy comparison participants and outpatients. However, mean performance in all tasks was lower for the outpatient group than for the healthy comparison group. None of the tasks used stood out as having significantly lower mean scores than other tasks for either group. Conclusions: Our findings suggest that performance on individual MCCB tasks do not affect performance throughout the battery differently between the healthy comparison group and outpatients. This suggests that participants with schizophrenia are not particularly reactive to past performance on MCCB tasks. Additionally, this finding further supports use of the MCCB in this population. Further research is needed to determine whether subgroups of patients and/or different batteries of measures show different patterns of reactivity. [ABSTRACT FROM AUTHOR]

The adaptation of experimental cognitive tasks into measures that can be used to quantify neurocognitive outcomes in translational studies and clinical trials has become a key component of the strategy to address psychiatric and neurological disorders. Unfortunately, while most experimental cognitive tests have strong theoretical bases, they can have poor psychometric properties, leaving them vulnerable to measurement challenges that undermine their use in applied settings. Item response theory–based computerized adaptive testing has been proposed as a solution but has been limited in experimental and translational research due to its large sample requirements. We present a generalized latent variable model that, when combined with strong parametric assumptions based on mathematical cognitive models, permits the use of adaptive testing without large samples or the need to precalibrate item parameters. The approach is demonstrated using data from a common measure of working memory—the N-back task—collected across a diverse sample of participants. After evaluating dimensionality and model fit, we conducted a simulation study to compare adaptive versus nonadaptive testing. Computerized adaptive testing either made the task 36% more efficient or score estimates 23% more precise, when compared to nonadaptive testing. This proof-of-concept study demonstrates that latent variable modeling and adaptive testing can be used in experimental cognitive testing even with relatively small samples. Adaptive testing has the potential to improve the impact and replicability of findings from translational studies and clinical trials that use experimental cognitive tasks as outcome measures. [ABSTRACT FROM AUTHOR]

Background. The study explored the construct validity of a computational model of working memory (WM) by determining whether model parameters manifested double dissociations of lesion laterality with type of material studied. The data set modeled involved psychometrically matched verbal and figural WM tasks on which a double dissociation between test version and lesion laterality failed to emerge when total test scores were used as the laterality marker.Method. This re-analysis of a previously published study involved investigating the WM performance of 15 demographically matched controls with 15 adult patients with left-hemispheric (LH) lesions and 15 adult patients with right-hemispheric (RH) lesions. Each participant was given verbal and figural versions of a continuous paired associates test (CPAT). The two versions had previously been psychometrically matched in a larger sample of healthy individuals. A WM model composed of encoding, displacement, and episodic memory parameters was fit to each individual’s performance profiles for both versions of the CPAT.Results. Replicating the previous results for raw scores, rank transformed values of total score performance failed to reveal a double dissociation. Nonetheless an absolute double dissociation was observed for the model’s displacement parameter: RH patients demonstrated deficits on the figural but not verbal WM displacement parameter, whereas LH patients demonstrated deficits on the verbal but not figural WM displacement parameter. Additionally, both LH and RH patients were impaired on the figural encode parameter, perhaps explaining the absence of a double dissociation in total score performance.Conclusions. By combining different patterns of profile and level data into theoretically motivated model parameters, computational models of neurocognition can enhance the construct validity of interpretations of neuropsychological performance. [ABSTRACT FROM PUBLISHER]

Childhood maltreatment has diverse, lifelong impact on morbidity and mortality. The Childhood Trauma Questionnaire (CTQ) is one of the most commonly used scales to assess and quantify these experiences and their impact. Curiously, despite very widespread use of the CTQ, scores on its Minimization-Denial (MD) subscale—originally designed to assess a positive response bias—are rarely reported. Hence, little is known about this measure. If response biases are either common or consequential, current practices of ignoring the MD scale deserve revision. Therewith, we designed a study to investigate 3 aspects of minimization, as defined by the CTQ’s MD scale: 1) its prevalence; 2) its latent structure; and finally 3) whether minimization moderates the CTQ’s discriminative validity in terms of distinguishing between psychiatric patients and community volunteers. Archival, item-level CTQ data from 24 multinational samples were combined for a total of 19,652 participants. Analyses indicated: 1) minimization is common; 2) minimization functions as a continuous construct; and 3) high MD scores attenuate the ability of the CTQ to distinguish between psychiatric patients and community volunteers. Overall, results suggest that a minimizing response bias—as detected by the MD subscale—has a small but significant moderating effect on the CTQ’s discriminative validity. Results also may suggest that some prior analyses of maltreatment rates or the effects of early maltreatment that have used the CTQ may have underestimated its incidence and impact. We caution researchers and clinicians about the widespread practice of using the CTQ without the MD or collecting MD data but failing to assess and control for its effects on outcomes or dependent variables. [ABSTRACT FROM AUTHOR]

The MMPI-2 Restructured Form (MMPI-2-RF; Tellegen & Ben-Porath, 2008) was designed to be psychometrically superior to its MMPI-2 counterpart. However, the test has yet to be extensively evaluated in diverse clinical settings. The purpose of this study was to examine the psychometric properties of the MMPI-2-RF Somatic Complaints (RCI) scale in a clinically relevant population. Participants were 399 patients diagnosed with either epilepsy or psychogenic nonepileptic seizures on the basis of video-electroencephalograph monitoring. The internal structure of the MMPI-2-RF was evaluated using taxometric, confirmatory factor analysis, and item response theory procedures. Data from 4 content-specific scales directly related to RCI (Malaise, Gastrointestinal Complaints, Head Pain Complaints, and Neurological Complaints) indicated that the latent construct of somatization is a dimensional variable with a bifactor structure. However, consistent with the scale's construction, a unidimensional model also provided adequate fit. A 2-parameter logistic item response theory model better accounted for observed item responses than did 1- or 3-parameter models. Results suggest that the RCI scale is most precise for T score estimates between 55 and 90. Overall, the scale appears to be well suited for the assessment of somatization. [ABSTRACT FROM AUTHOR]

The use of criterion group validation is hindered by the difficulty of classifying individuals on latent constructs. Latent class analysis (LCA) is a method that can be used for determining the validity of scales meant to assess latent constructs without such a priori classifications. The authors used this method to examine the ability of the L scale of the MMPI-2 (J. N. Butcher et al., 2001), the Impression Management scale of the Balanced Inventory of Desirable Responding (D. L. Paulhus, 1991). and the Endorsement of Excessive Virtue scale of the Psychological Screening Inventory (R. I. Lanyon, 1970) to identify favorable response bias (misrepresentation) in a situation where no criterion for individual classifications existed. Results suggest that LCA can be used as a method for assessing the validity of scales that measure unobservable conditions. [ABSTRACT FROM AUTHOR]

Abstract: Background: As the result of extensive translational and cross-disciplinary research, attachment theory is now a construct with significant neuropsychiatric traction. The correlation of attachment with other influential conceptual models (i.e. temperament and personality) is therefore of interest. Consequently, we explored how two attachment dimensions (attachment anxiety and attachment avoidance) correlated with measures of temperament and personality in 357 psychiatric outpatients. Methods: We performed a retrospective review of four questionnaires (the Experiences in Close Relationship scale (ECR-R), Temperament and Character inventory (TCI), Temperament Evaluation of the Memphis, Pisa, Paris and San Diego questionnaire (TEMPS-A), and Personality Self-Portrait Questionnaire (PSQ)). Frequency measures and correlations were examined, as was the predictive value of attachment security for a personality disorder (PD). Results: Significant, robust correlations were found between attachment anxiety and (1) several negative affective temperaments (dysthymic and cyclothymic); (2) several indices of personality pathology (low self-directedness (TCI), DSM-IV paranoid, borderline, histrionic, avoidant and dependent personality traits). Attachment avoidance had fewer large correlations. In an exploratory model, the negative predictive value of attachment security for a PD was 86%. Limitations: Subjects were a relatively homogeneous subset of ambulatory psychiatric outpatients. PD diagnoses were via self-report. Conclusions: Clinically, these findings highlight the significant overlap between attachment, affective temperament, and personality and support the value of attachment as a screen for PDs. More broadly, given our growing understanding of the neurobiology of attachment (i.e. links with the oxytocin system), these results raise interesting questions about underlying biological systems and psychiatric treatment. [Copyright &y& Elsevier]