Disparity between General Symptom Relief and Remission Criteria in the Positive and Negative Syndrome Scale (PANSS): A Post-treatment Bifactor Item Response Theory Model.

Objective: Total scale scores derived by summing ratings from the 30-item PANSS are commonly used in clinical trial research to measure overall symptom severity, and percentage reductions in the total scores are sometimes used to document the efficacy of treatment. Acknowledging that some patients may have substantial changes in PANSS total scores but still be sufficiently symptomatic to warrant diagnosis, ratings on a subset of 8 items, referred to here as the "Remission set," are sometimes used to determine if patients' symptoms no longer satisfy diagnostic criteria. An unanswered question remains: is the goal of treatment better conceptualized as reduction in overall symptom severity, or reduction in symptoms below the threshold for diagnosis? We evaluated the psychometric properties of PANSS total scores, to assess whether having low symptom severity post-treatment is equivalent to attaining Remission. Design: We applied a bifactor item response theory (IRT) model to post-treatment PANSS ratings of 3,647 subjects diagnosed with schizophrenia assessed at the termination of 11 clinical trials. The bifactor model specified one general dimension to reflect overall symptom severity, and five domain-specific dimensions. We assessed how PANSS item discrimination and information parameters varied across the range of overall symptom severity (θ), with a special focus on low levels of symptoms (i.e., θ<-1), which we refer to as "Relief" from symptoms. A score of θ=-1 corresponds to an expected PANSS item score of 1.83, a rating between "Absent" and "Minimal" for a PANSS symptom. Results: The application of the bifactor IRT model revealed: (1) 88% of total score variation was attributable to variation in general symptom severity, and only 8% reflected secondary domain factors. This implies that a general factor may provide a good indicator of symptom severity, and that interpretation is not overly complicated by multidimensionality; (2) Post-treatment, 534 individuals (about 15% of the whole sample) scored in the "Relief" range of general symptom severity, but more than twice that number (n = 1351) satisfied Remission criteria (37%). 2 in 3 Remitted patients had scores that were not in a low symptom range (corresponding to Absent or Minimal item scores); (3) PANSS items vary greatly in their ability to measure the general symptom severity dimension; while many items are highly discriminating and relatively "pure" indicators of general symptom severity (delusions, conceptual disorganization), others are better indicators of specific dimensions (blunted affect, depression). The utility of a given PANSS item for assessing a patient depended on the illness level of the patient. Conclusion: Satisfying conventional Remission criteria was not strongly associated with low levels of symptoms. The items providing the most information for patients in the symptom Relief range were Delusions, Preoccupation, Suspiciousness Persecution, Unusual Thought Content, Conceptual Disorganization, Stereotyped Thinking, Active Social Avoidance, and Lack of Judgment and Insight. Lower scores on these items (item scores ≤2) were strongly associated with having a low latent trait θ or experiencing overall symptom relief. The inter-rater agreement between Remission and Relief subjects suggested that these criteria identified different subsets of patients. Alternative subsets of items may offer better indicators of general symptom severity and provide better discrimination (and lower standard errors) for scaling individuals and judging symptom relief, where the "best" subset of items ultimately depends on the illness range and treatment phase being evaluated

When Are Multidimensional Data Unidimensional Enough for Structural Equation Modeling?:An Evaluation of the DETECT Multidimensionality Index

In structural equation modeling (SEM), researchers need to evaluate whether item response data, which are often multidimensional, can be modeled with a unidimensional measurement model without seriously biasing the parameter estimates. This issue is commonly addressed through testing the fit of a unidimensional model specification, a strategy previously determined to be problematic. As an alternative to the use of fit indexes, we considered the utility of a statistical tool that was expressly designed to assess the degree of departure from unidimensionality in a data set. Specifically, we evaluated the ability of the DETECT “essential unidimensionality” index to predict the bias in parameter estimates that results from misspecifying a unidimensional model when the data are multidimensional. We generated multidimensional data from bifactor structures that varied in general factor strength, number of group factors, and items per group factor; a unidimensional measurement model was then fit and parameter bias recorded. Although DETECT index values were generally predictive of parameter bias, in many cases, the degree of bias was small even though DETECT indicated significant multidimensionality. Thus we do not recommend the stand-alone use of DETECT benchmark values to either accept or reject a unidimensional measurement model. However, when DETECT was used in combination with additional indexes of general factor strength and group factor structure, parameter bias was highly predictable. Recommendations for judging the severity of potential model misspecifications in practice are provided.<br/

Structure and correlates of self-reported empathy in schizophrenia

Research on empathy in schizophrenia has relied on dated self-report scales that do not conform to contemporary social neuroscience models of empathy. The current study evaluated the structure and correlates of the recently-developed Questionnaire of Cognitive and Affective Empathy (QCAE) in schizophrenia. This measure, whose structure and validity was established in healthy individuals, includes separate scales to assess the two main components of empathy: Cognitive Empathy (assessed by two subscales) and Affective Empathy (assessed by three subscales). Stable outpatients with schizophrenia (n=145) and healthy individuals (n= 45) completed the QCAE, alternative measures of empathy, and assessments of clinical symptoms, neurocognition, and functional outcome. Exploratory and confirmatory factor analyses provided consistent support for a two-factor solution in the schizophrenia group, justifying the use of separate cognitive and affective empathy scales in this population. However, one of the three Affective Empathy subscales was not psychometrically sound and was excluded from further analyses. Patients reported significantly lower Cognitive Empathy but higher Affective Empathy than controls. Among patients, the QCAE scales showed significant correlations with an alternative self-report empathy scale, but not with performance on an empathic accuracy task. The QCAE Cognitive Empathy subscales also showed significant, though modest, correlations with negative symptoms and functional outcome. These findings indicate that structure of self-reported empathy is similar in people with schizophrenia and healthy subjects, and can be meaningfully compared between groups. They also contribute to emerging evidence that some aspects of empathy may be intact or hyper-responsive in schizophrenia

The Revised Child Anxiety and Depression Scale-Short Version: Scale reduction via exploratory bifactor modeling of the broad anxiety factor.

Using a school-based (N = 1,060) and clinic-referred (N = 303) youth sample, the authors developed a 25-item shortened version of the Revised Child Anxiety and Depression Scale (RCADS) using Schmid-Leiman exploratory bifactor analysis to reduce client burden and administration time and thus improve the transportability characteristics of this youth anxiety and depression measure. Results revealed that all anxiety items primarily reflected a single “broad anxiety” dimension, which informed the development of a reduced 15-item Anxiety Total scale. Although specific DSM-oriented anxiety subscales were not included in this version, the items comprising the Anxiety Total scale were evenly pulled from the 5 anxiety-related content domains from the original RCADS. The resultant 15-item Anxiety Total scale evidenced significant correspondence with anxiety diagnostic groups based on structured clinical interviews. The scores from the 10-item Depression Total scale (retained from the original version) were also associated with acceptable reliability in the clinic-referred and school-based samples (α = .80 and .79, respectively); this is in contrast to the alternate 5-item shortened RCADS Depression Total scale previously developed by Muris, Meesters, and Schouten (2002), which evidenced depression scores of unacceptable reliability (α = .63). The shortened RCADS developed in the present study thus balances efficiency, breadth, and scale score reliability in a way that is potentially useful for repeated measurement in clinical settings as well as wide-scale screenings that assess anxiety and depressive problems. These future applications are discussed, as are recommendations for continued use of exploratory bifactor modeling in scale development.Psycholog

Advancing PROMIS’s methodology: results of the Third Patient-Reported Outcomes Measurement Information System (PROMIS ® ) Psychometric Summit

In 2002, the NIH launched the ‘Roadmap for Medical Research’. The Patient-Reported Outcomes Measurement Information System (PROMIS®) is one of the Roadmap’s key aspects. To create the next generation of patient-reported outcome measures, PROMIS utilizes item response theory (IRT) and computerized adaptive testing. In 2009, the NIH funded the second wave of PROMIS studies (PROMIS II). PROMIS II studies continue PROMIS’s agenda, but also include new features, including longitudinal analyses and more sociodemographically diverse samples. PROMIS II also includes increased emphasis on pediatric populations and evaluation of PROMIS item banks for clinical research and population science. These aspects bring new psychometric challenges. To address this, investigators associated with PROMIS gathered at the Third Psychometric Summit in September 2010 to identify, describe and discuss pressing psychometric issues and new developments in the field, as well as make analytic recommendations for PROMIS. The summit addressed five general themes: linking, differential item functioning, dimensionality, IRT models for longitudinal applications and new IRT software. In this article, we review the discussions and presentations that occurred at the Third PROMIS Psychometric Summit

A conceptual guide to statistics using SPSS

Bridging an understanding of Statistics and SPSS. This unique text helps students develop a conceptual understanding of a variety of statistical tests by linking the ideas learned in a statistics class from a traditional statistics textbook with the computational steps and output from SPSS. Each chapter begins with a student-friendly explanation of the concept behind each statistical test and how the test relates to that concept. The authors then walk through the steps to compute the test in SPSS and the output, clearly linking how the SPSS procedure and output connect back to the conceptual