Purpose The purpose of this study was to calibrate the items for the Communicative Participation Item Bank (CPIB) using Item Response Theory (IRT). One overriding objective was to examine if the IRT item parameters would be consistent across different diagnostic groups, thereby allowing creation of a disorder-generic instrument. The intended outcomes were the final item bank and a short form ready for clinical and research applications. Methods Self-report data were collected from 701 individuals representing four diagnoses: multiple sclerosis, Parkinson’s disease, amyotrophic lateral sclerosis and head and neck cancer. Participants completed the CPIB and additional self-report questionnaires. CPIB data were analyzed using the IRT Graded Response Model (GRM). Results The initial set of 94 candidate CPIB items were reduced to an item bank of 46 items demonstrating unidimensionality, local independence, good item fit, and good measurement precision. Differential item function (DIF) analyses detected no meaningful differences across diagnostic groups. A 10-item, disorder-generic short form was generated. Conclusions The CPIB provides speech-language pathologists with a unidimensional, self-report outcomes measurement instrument dedicated to the construct of communicative participation. This instrument may be useful to clinicians and researchers wanting to implement measures of communicative participation in their work.

Purpose This study evaluated psychometric properties of the Patient Health Questionnaire-9 (PHQ-9), the Center for Epidemiological Studies Depression Scale-10 (CESD-10), and the eight-item PROMIS Depression Short Form (PROMIS-D-8; 8b short form) in a sample of individuals living with multiple sclerosis (MS). Research Method Data were collected by a self-reported mailed survey of a community sample of people living with MS (n=455). Factor structure, inter-item reliability, convergent/discriminant validity and assignment to categories of depression severity were examined. Results A one factor, confirmatory factor analytic model had adequate fit for all instruments. Scores on the depression scales were more highly correlated with one another than with scores on measures of pain, sleep disturbance, and fatigue. The CESD-10 categorized about 37% of participants as having significant depressive symptoms. At least moderate depression was indicated for 24% of participants by PHQ-9. PROMIS-D-8 identified 19% of participants as having at least moderate depressive symptoms and about 7% having at least moderately-severe depression. None of the examined scales had ceiling effects, but the PROMIS-D-8 had a floor effect. Conclusions Overall, scores on all three scales demonstrated essential unidimensionality and had acceptable inter-item reliability and convergent/discriminant validity. Researchers and clinicians can choose any of these scales to measure depressive symptoms in individuals living with MS. The PHQ-9 offers validated cut off scores for diagnosing clinical depression. The PROMIS-D-8 measure minimizes the impact of somatic features on the assessment of depression and allows for flexible administration, including Computerize Adaptive Testing (CAT). The CESD-10 measures two aspects of depression, depressed mood and lack of positive affect, while still providing an interpretable total score.

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This study compared the use of the conventional multilevel model (MM) with that of the multiple membership multilevel model (MMMM) for handling multiple membership data structures. Multiple membership data structures are commonly encountered in longitudinal educational data sets in which, for example, mobile students are members of more than one higher-level unit (e.g., school). While the conventional MM requires the user either to delete mobile students' data or to ignore prior schools attended, MMMM permits inclusion of mobile students' data and models the effect of all schools attended on student outcomes. The simulation study identified underestimation of the school-level predictor coefficient, as well as underestimation of the level-two variance component with corresponding overestimation of the level-one variance when multiple membership data structures were ignored. Results are discussed along with limitations and ideas for future MMMM methodological research as well as implications for applied researchers.