%0 Generic %D 2011 %T Item banks for measuring emotional distress from the Patient-Reported Outcomes Measurement Information System (PROMIS®): depression, anxiety, and anger %A Pilkonis, P. A. %A Choi, S. W. %A Reise, S. P. %A Stover, A. M. %A Riley, W. T. %A Cella, D. %B Assessment %@ 1073-1911 %G eng %& June 21, 2011 %0 Journal Article %J Quality of Life Research %D 2010 %T Efficiency of static and computer adaptive short forms compared to full-length measures of depressive symptoms %A Choi, S. %A Reise, S. P. %A Pilkonis, P. A. %A Hays, R. D. %A Cella, D. %B Quality of Life Research %V 19(1) %P 125–136 %G eng %0 Journal Article %J Annual Review of Clinical Psychology %D 2009 %T Item response theory and clinical measurement %A Reise, S. P. %A Waller, N. G. %K *Psychological Theory %K Humans %K Mental Disorders/diagnosis/psychology %K Psychological Tests %K Psychometrics %K Quality of Life %K Questionnaires %X In this review, we examine studies that use item response theory (IRT) to explore the psychometric properties of clinical measures. Next, we consider how IRT has been used in clinical research for: scale linking, computerized adaptive testing, and differential item functioning analysis. Finally, we consider the scale properties of IRT trait scores. We conclude that there are notable differences between cognitive and clinical measures that have relevance for IRT modeling. Future research should be directed toward a better understanding of the metric of the latent trait and the psychological processes that lead to individual differences in item response behaviors. %B Annual Review of Clinical Psychology %7 2008/11/04 %V 5 %P 27-48 %@ 1548-5951 (Electronic) %G eng %M 18976138 %0 Journal Article %J Medical Care %D 2007 %T Psychometric evaluation and calibration of health-related quality of life item banks: plans for the Patient-Reported Outcomes Measurement Information System (PROMIS) %A Reeve, B. B. %A Hays, R. D. %A Bjorner, J. B. %A Cook, K. F. %A Crane, P. K. %A Teresi, J. A. %A Thissen, D. %A Revicki, D. A. %A Weiss, D. J. %A Hambleton, R. K. %A Liu, H. %A Gershon, R. C. %A Reise, S. P. %A Lai, J. S. %A Cella, D. %K *Health Status %K *Information Systems %K *Quality of Life %K *Self Disclosure %K Adolescent %K Adult %K Aged %K Calibration %K Databases as Topic %K Evaluation Studies as Topic %K Female %K Humans %K Male %K Middle Aged %K Outcome Assessment (Health Care)/*methods %K Psychometrics %K Questionnaires/standards %K United States %X BACKGROUND: The construction and evaluation of item banks to measure unidimensional constructs of health-related quality of life (HRQOL) is a fundamental objective of the Patient-Reported Outcomes Measurement Information System (PROMIS) project. OBJECTIVES: Item banks will be used as the foundation for developing short-form instruments and enabling computerized adaptive testing. The PROMIS Steering Committee selected 5 HRQOL domains for initial focus: physical functioning, fatigue, pain, emotional distress, and social role participation. This report provides an overview of the methods used in the PROMIS item analyses and proposed calibration of item banks. ANALYSES: Analyses include evaluation of data quality (eg, logic and range checking, spread of response distribution within an item), descriptive statistics (eg, frequencies, means), item response theory model assumptions (unidimensionality, local independence, monotonicity), model fit, differential item functioning, and item calibration for banking. RECOMMENDATIONS: Summarized are key analytic issues; recommendations are provided for future evaluations of item banks in HRQOL assessment. %B Medical Care %7 2007/04/20 %V 45 %P S22-31 %8 May %@ 0025-7079 (Print) %G eng %M 17443115 %0 Journal Article %J Assessment %D 2000 %T Computerization and adaptive administration of the NEO PI-R %A Reise, S. P. %A Henson, J. M. %K *Personality Inventory %K Algorithms %K California %K Diagnosis, Computer-Assisted/*methods %K Humans %K Models, Psychological %K Psychometrics/methods %K Reproducibility of Results %X This study asks, how well does an item response theory (IRT) based computerized adaptive NEO PI-R work? To explore this question, real-data simulations (N = 1,059) were used to evaluate a maximum information item selection computerized adaptive test (CAT) algorithm. Findings indicated satisfactory recovery of full-scale facet scores with the administration of around four items per facet scale. Thus, the NEO PI-R could be reduced in half with little loss in precision by CAT administration. However, results also indicated that the CAT algorithm was not necessary. We found that for many scales, administering the "best" four items per facet scale would have produced similar results. In the conclusion, we discuss the future of computerized personality assessment and describe the role IRT methods might play in such assessments. %B Assessment %V 7 %P 347-64 %G eng %M 11151961 %0 Journal Article %J Medical Care %D 2000 %T Item response theory and health outcomes measurement in the 21st century %A Hays, R. D. %A Morales, L. S. %A Reise, S. P. %K *Models, Statistical %K Activities of Daily Living %K Data Interpretation, Statistical %K Health Services Research/*methods %K Health Surveys %K Human %K Mathematical Computing %K Outcome Assessment (Health Care)/*methods %K Research Design %K Support, Non-U.S. Gov't %K Support, U.S. Gov't, P.H.S. %K United States %X Item response theory (IRT) has a number of potential advantages over classical test theory in assessing self-reported health outcomes. IRT models yield invariant item and latent trait estimates (within a linear transformation), standard errors conditional on trait level, and trait estimates anchored to item content. IRT also facilitates evaluation of differential item functioning, inclusion of items with different response formats in the same scale, and assessment of person fit and is ideally suited for implementing computer adaptive testing. Finally, IRT methods can be helpful in developing better health outcome measures and in assessing change over time. These issues are reviewed, along with a discussion of some of the methodological and practical challenges in applying IRT methods. %B Medical Care %V 38 %P II28-II42 %G eng %M 10982088 %0 Conference Paper %B Unpublished manuscript. %D 1988 %T Fitting the two-parameter model to personality data: The parameterization of the Multidimensional Personality Questionnaire %A Reise, S. P. %A Waller, N. G. %B Unpublished manuscript. %G eng