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The reliable change index has been used to evaluate the significance of individual change in health-related quality of life. We estimate reliable change for two measures (physical function and emotional distress) in the Patient-Reported Outcomes Measurement Information System (PROMIS®) 29-item health-related quality of life measure (PROMIS-29 v2.1). Using two waves of data collected 3 months apart in a longitudinal observational study of chronic low back pain and chronic neck pain patients receiving chiropractic care, and simulations, we compare estimates of reliable change from classical test theory fixed standard errors with item response theory standard errors from the graded response model. We find that unless true change in the PROMIS physical function and emotional distress scales is substantial, classical test theory estimates of significant individual change are much more optimistic than estimates of change based on item response theory. Supplementary Information The online version supplementary material available at 10.1007/s11336-021-09774-1.

Large-scale studies spanning diverse project sites, populations, languages, and measurements are increasingly important to relate psychological to biological variables. National and international consortia already are collecting and executing mega-analyses on aggregated data from individuals, with different measures on each person. In this research, we show that Asparouhov and Muthén’s alignment method can be adapted to align data from disparate item sets and response formats. We argue that with these adaptations, the alignment method is well suited for combining data across multiple sites even when they use different measurement instruments. The approach is illustrated using data from the Whole Genome Sequencing in Psychiatric Disorders consortium and a real-data-based simulation is used to verify accurate parameter recovery. Factor alignment appears to increase precision of measurement and validity of scores with respect to external criteria. The resulting parameter estimates may further inform development of more effective and efficient methods to assess the same constructs in prospectively designed studies.

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.

© 2015, Routledge. All rights reserved. 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.

Although the Positive and Negative Syndrome Scale (PANSS) was developed for use in schizophrenia (SZ), antipsychotic drug trials use the PANSS to measure symptom change also for bipolar (BP) and schizoaffective (SA) disorder, extending beyond its original indications. If the dimensions measured by the PANSS are different across diagnoses, then the same score change for the same drug condition may have different meanings depending on which group is being studied. Here, we evaluated whether the factor structure in the PANSS was consistent across schizophrenia (n = 3647), bipolar disorder (n = 858), and schizoaffective disorder (n = 592). Along with congruency coefficients, Hancock's H, and Jaccard indices, we used target rotations and statistical tests of invariance based on confirmatory factor models. We found the five symptom dimensions measured by the 30-item PANSS did not generalize well to schizoaffective and bipolar disorders. A model based on an 18-item version of the PANSS generalized better across SZ and BP groups, but significant problems remained in generalizing some of the factors to the SA sample. Schizophrenia and bipolar disorder showed greater similarity in factor structure than did schizophrenia and schizoaffective disorder. The Anxiety/Depression factor was the most consistent across disorders, while the Positive factor was the least consistent.

OBJECTIVES: To describe the development and initial psychometric properties of the observer-rated Global Therapist Competence Scale for Youth Psychosocial Treatment (G-COMP) in the context of cognitive-behavioral treatment (CBT) for youth anxiety disorders. METHOD: Independent coders rated 744 sessions from a sample of 68 youth (M age = 10.56 years) using the G-COMP and observational instruments of alliance, involvement, CBT adherence, CBT competence. RESULTS: Inter-rater reliability coefficients (ICCs [2,2]) were greater than .60 for the five G-COMP domain scores. G-COMP scores yielded small to medium correlations with instruments of the child-therapist alliance (rs = .17 - .44) and youth involvement in treatment (rs = .08 - .53), and medium to large correlations with instruments of CBT competence and adherence (rs = .26 - .63). Therapists in the research setting were rated higher compared to newly trained therapists in community clinics. CONCLUSIONS: Preliminary reliability and validity of the G-COMP scores are promising, but future research is needed with non-CBT samples.

The aims of this paper are to present findings related to differential item functioning (DIF) in the Patient Reported Outcome Measurement Information System (PROMIS) depression item bank, and to discuss potential threats to the validity of results from studies of DIF. The 32 depression items studied were modified from several widely used instruments. DIF analyses of gender, age and education were performed using a sample of 735 individuals recruited by a survey polling firm. DIF hypotheses were generated by asking content experts to indicate whether or not they expected DIF to be present, and the direction of the DIF with respect to the studied comparison groups. Primary analyses were conducted using the graded item response model (for polytomous, ordered response category data) with likelihood ratio tests of DIF, accompanied by magnitude measures. Sensitivity analyses were performed using other item response models and approaches to DIF detection. Despite some caveats, the items that are recommended for exclusion or for separate calibration were "I felt like crying" and "I had trouble enjoying things that I used to enjoy." The item, "I felt I had no energy," was also flagged as evidencing DIF, and recommended for additional review. On the one hand, false DIF detection (Type 1 error) was controlled to the extent possible by ensuring model fit and purification. On the other hand, power for DIF detection might have been compromised by several factors, including sparse data and small sample sizes. Nonetheless, practical and not just statistical significance should be considered. In this case the overall magnitude and impact of DIF was small for the groups studied, although impact was relatively large for some individuals.

© 2015 American Psychological Association.The measurement of treatment adherence (a component of treatment integrity defined as the extent to which a treatment is delivered as intended) is a critical element in treatment evaluation research. This article presents initial psychometric data for scores on the Cognitive-Behavioral Therapy Adherence Scale for Youth Anxiety (CBAY-A), an observational measure designed to be sensitive to common practice elements found in individual cognitive- behavioral therapy (ICBT) for youth anxiety. Therapy sessions (N = 954) from 1 efficacy and 1 effectiveness study of ICBT for youth anxiety were independently rated by 2 coders. Interrater reliability (as gauged by intraclass correlation coefficients) for the item scores averaged 0.77 (SD = 0.15; range 48 to 80). The CBAY-A item and scale (skills, model, total) scores demonstrated evidence of convergent and discriminant validity with an observational measure of therapeutic interventions and an observational measure of the alliance. The CBAY-A item and scale scores also discriminated between therapists delivering ICBT in research and practice settings and therapists delivering nonmanualized usual clinical care. We discuss the importance of replicating these psychometric findings in different samples and highlight possible application of an adherence measure in testing integrity-outcome relations.

© 2015 APA, all rights reserved). Visuospatial processing is a commonly assessed neurocognitive domain with deficits linked to dysfunction in right posterior regions of the brain. With the growth of large-scale clinical research studies, there is an increased need for efficient and scalable assessments of neurocognition, including visuospatial processing. The purpose of the current study was to use a novel method that combines item response theory (IRT) and computerized adaptive testing (CAT) approaches to create an abbreviated form of the computerized Penn Line Orientation Test (PLOT). The 24-item PLOT was administered to 8,498 youths (aged 8-21 years) as part of the Philadelphia Neurodevelopmental Cohort study and, by Web-based data collection, in an independent sample of 4,593 adults from Great Britain as part of a TV documentary. IRT-based CAT simulations were used to select the best PLOT items for an abbreviated form by performing separate simulations in each group and choosing only items that were selected as useful (i.e., high item discrimination and in the appropriate difficulty range) in at least 1 of the simulations. Fifteen items were chosen for the final, short form of the PLOT, indicating substantial agreement among the models in how they evaluated each item's usefulness. Moreover, this abbreviated version performed comparably to the full version in tests of sensitivity to age and sex effects. This abbreviated version of the PLOT cuts administration time by 50% without detectable loss of information, which points to its feasibility for large-scale clinical and genomic studies. (PsycINFO Database Record

© 2015, Copyright © Taylor & Francis Group, LLC. We describe and evaluate a factor rotation algorithm, iterated target rotation (ITR). Whereas target rotation (Browne, 2001) requires a user to specify a target matrix a priori based on theory or prior research, ITR begins with a standard analytic factor rotation (i.e., an empirically informed target) followed by an iterative search procedure to update the target matrix. In Study 1, Monte Carlo simulations were conducted to evaluate the performance of ITR relative to analytic rotations from the Crawford-Ferguson family with population factor structures varying in complexity. Simulation results: (a) suggested that ITR analyses will be particularly useful when evaluating data with complex structures (i.e., multiple cross-loadings) and (b) showed that the rotation method used to define an initial target matrix did not materially affect the accuracy of the various ITRs. In Study 2, we: (a) demonstrated the application of ITR as a way to determine empirically informed priors in a Bayesian confirmatory factor analysis (BCFA; Muthén & Asparouhov, 2012) of a rater-report alexithymia measure (Haviland, Warren, & Riggs, 2000) and (b) highlighted some of the challenges when specifying empirically based priors and assessing item and overall model fit.

The authors report on the development and calibration of item banks for depression, anxiety, and anger as part of the Patient-Reported Outcomes Measurement Information System (PROMIS®). Comprehensive literature searches yielded an initial bank of 1,404 items from 305 instruments. After qualitative item analysis (including focus groups and cognitive interviewing), 168 items (56 for each construct) were written in a first person, past tense format with a 7-day time frame and five response options reflecting frequency. The calibration sample included nearly 15,000 respondents. Final banks of 28, 29, and 29 items were calibrated for depression, anxiety, and anger, respectively, using item response theory. Test information curves showed that the PROMIS item banks provided more information than conventional measures in a range of severity from approximately −1 to +3 standard deviations (with higher scores indicating greater distress). Short forms consisting of seven to eight items provided information comparable to legacy measures containing more items.

A psychometric analysis of 2 interview-based measures of cognitive deficits was conducted: the 21-item Clinical Global Impression of Cognition in Schizophrenia (CGI-CogS; Ventura et al., 2008), and the 20-item Schizophrenia Cognition Rating Scale (SCoRS; Keefe et al., 2006), which were administered on 2 occasions to a sample of people with schizophrenia. Traditional psychometrics, bifactor analysis, and item response theory methods were used to explore item functioning and dimensionality and to compare instruments. Despite containing similar item content, responses to the CGI-CogS demonstrated superior psychometric properties (e.g., higher item intercorrelations, better spread of ratings across response categories) relative to the SCoRS. The authors argue that these differences arise mainly from the differential use of prompts and how the items are phrased and scored. Bifactor analysis demonstrated that although both measures capture a broad range of cognitive functioning (e.g., working memory, social cognition), the common variance on each is overwhelmingly explained by a single general factor. Item response theory analyses of the combined pool of 41 items showed that measurement precision is peaked in the mild to moderate range of cognitive impairment. Finally, simulated adaptive testing revealed that only about 10 to 12 items are necessary to achieve latent trait level estimates with reasonably small standard errors for most individuals. This suggests that these interview-based measures of cognitive deficits could be shortened without loss of measurement precision.