03105nas a2200445   4500008004100000020002200041245007100063210006900134250001500203300001200218490000700230520183100237653003802068653001902106653002102125653002002146653001402166653001102180653003002191653001102221653000902232653002502241653004602266653001802312653002602330100001302356700001402369700001702383700001302400700001502413700001502428700001702443700001402460700001802474700002302492700001602515700001602531700001502547856009702562       2007                        eng d  a0962-9343 (Print)00aIRT health outcomes data analysis project: an overview and summary0 aIRT health outcomes data analysis project an overview and summar  a2007/03/14  a121-1320 v163 aBACKGROUND: In June 2004, the National Cancer Institute and the Drug Information Association co-sponsored the conference, "Improving the Measurement of Health Outcomes through the Applications of Item Response Theory (IRT) Modeling: Exploration of Item Banks and Computer-Adaptive Assessment." A component of the conference was presentation of a psychometric and content analysis of a secondary dataset. OBJECTIVES: A thorough psychometric and content analysis was conducted of two primary domains within a cancer health-related quality of life (HRQOL) dataset. RESEARCH DESIGN: HRQOL scales were evaluated using factor analysis for categorical data, IRT modeling, and differential item functioning analyses. In addition, computerized adaptive administration of HRQOL item banks was simulated, and various IRT models were applied and compared. SUBJECTS: The original data were collected as part of the NCI-funded Quality of Life Evaluation in Oncology (Q-Score) Project. A total of 1,714 patients with cancer or HIV/AIDS were recruited from 5 clinical sites. MEASURES: Items from 4 HRQOL instruments were evaluated: Cancer Rehabilitation Evaluation System-Short Form, European Organization for Research and Treatment of Cancer Quality of Life Questionnaire, Functional Assessment of Cancer Therapy and Medical Outcomes Study Short-Form Health Survey. RESULTS AND CONCLUSIONS: Four lessons learned from the project are discussed: the importance of good developmental item banks, the ambiguity of model fit results, the limits of our knowledge regarding the practical implications of model misfit, and the importance in the measurement of HRQOL of construct definition. With respect to these lessons, areas for future research are suggested. The feasibility of developing item banks for broad definitions of health is discussed.10a*Data Interpretation, Statistical10a*Health Status10a*Quality of Life10a*Questionnaires10a*Software10aFemale10aHIV Infections/psychology10aHumans10aMale10aNeoplasms/psychology10aOutcome Assessment (Health Care)/*methods10aPsychometrics10aStress, Psychological1 aCook, KF1 aTeal, C R1 aBjorner, J B1 aCella, D1 aChang, C-H1 aCrane, P K1 aGibbons, L E1 aHays, R D1 aMcHorney, C A1 aOcepek-Welikson, K1 aRaczek, A E1 aTeresi, J A1 aReeve, B B  uhttp://mail.iacat.org/content/irt-health-outcomes-data-analysis-project-overview-and-summary02517nas a2200265   4500008004100000020004100041245013200082210006900214250001500283260000800298300001100306490000700317520154000324653003101864653003701895653003301932653002401965653001101989653002402000653002702024653003302051653003002084100001602114856012102130       2006                        eng d  a0025-7079 (Print)0025-7079 (Linking)00aOverview of quantitative measurement methods. Equivalence, invariance, and differential item functioning in health applications0 aOverview of quantitative measurement methods Equivalence invaria  a2006/10/25  cNov  aS39-490 v443 aBACKGROUND: Reviewed in this article are issues relating to the study of invariance and differential item functioning (DIF). The aim of factor analyses and DIF, in the context of invariance testing, is the examination of group differences in item response conditional on an estimate of disability. Discussed are parameters and statistics that are not invariant and cannot be compared validly in crosscultural studies with varying distributions of disability in contrast to those that can be compared (if the model assumptions are met) because they are produced by models such as linear and nonlinear regression. OBJECTIVES: The purpose of this overview is to provide an integrated approach to the quantitative methods used in this special issue to examine measurement equivalence. The methods include classical test theory (CTT), factor analytic, and parametric and nonparametric approaches to DIF detection. Also included in the quantitative section is a discussion of item banking and computerized adaptive testing (CAT). METHODS: Factorial invariance and the articles discussing this topic are introduced. A brief overview of the DIF methods presented in the quantitative section of the special issue is provided together with a discussion of ways in which DIF analyses and examination of invariance using factor models may be complementary. CONCLUSIONS: Although factor analytic and DIF detection methods share features, they provide unique information and can be viewed as complementary in informing about measurement equivalence.10a*Cross-Cultural Comparison10aData Interpretation, Statistical10aFactor Analysis, Statistical10aGuidelines as Topic10aHumans10aModels, Statistical10aPsychometrics/*methods10aStatistics as Topic/*methods10aStatistics, Nonparametric1 aTeresi, J A  uhttp://mail.iacat.org/content/overview-quantitative-measurement-methods-equivalence-invariance-and-differential-item