@article {328,
	title = {Psychometric evaluation and calibration of health-related quality of life item banks: plans for the Patient-Reported Outcomes Measurement Information System (PROMIS)},
	journal = {Medical Care},
	volume = {45},
	number = {5 Suppl 1},
	year = {2007},
	note = {Reeve, Bryce BHays, Ron DBjorner, Jakob BCook, Karon FCrane, Paul KTeresi, Jeanne AThissen, DavidRevicki, Dennis AWeiss, David JHambleton, Ronald KLiu, HonghuGershon, RichardReise, Steven PLai, Jin-sheiCella, DavidPROMIS Cooperative GroupAG015815/AG/United States NIAResearch Support, N.I.H., ExtramuralUnited StatesMedical careMed Care. 2007 May;45(5 Suppl 1):S22-31.},
	month = {May},
	pages = {S22-31},
	edition = {2007/04/20},
	abstract = {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.},
	keywords = {*Health Status, *Information Systems, *Quality of Life, *Self Disclosure, Adolescent, Adult, Aged, Calibration, Databases as Topic, Evaluation Studies as Topic, Female, Humans, Male, Middle Aged, Outcome Assessment (Health Care)/*methods, Psychometrics, Questionnaires/standards, United States},
	isbn = {0025-7079 (Print)},
	author = {Reeve, B. B. and Hays, R. D. and Bjorner, J. B. and Cook, K. F. and Crane, P. K. and Teresi, J. A. and Thissen, D. and Revicki, D. A. and Weiss,  D. J. and Hambleton, R. K. and Liu, H. and Gershon, R. C. and Reise, S. P. and Lai, J. S. and Cella, D.}
}
@article {384,
	title = {Overview of quantitative measurement methods. Equivalence, invariance, and differential item functioning in health applications},
	journal = {Medical Care},
	volume = {44},
	number = {11 Suppl 3},
	year = {2006},
	note = {Teresi, Jeanne AAG15294/AG/NIA NIH HHS/United StatesResearch Support, N.I.H., ExtramuralResearch Support, Non-U.S. Gov{\textquoteright}tReviewUnited StatesMedical careMed Care. 2006 Nov;44(11 Suppl 3):S39-49.},
	month = {Nov},
	pages = {S39-49},
	edition = {2006/10/25},
	abstract = {BACKGROUND: 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.},
	keywords = {*Cross-Cultural Comparison, Data Interpretation, Statistical, Factor Analysis, Statistical, Guidelines as Topic, Humans, Models, Statistical, Psychometrics/*methods, Statistics as Topic/*methods, Statistics, Nonparametric},
	isbn = {0025-7079 (Print)0025-7079 (Linking)},
	author = {Teresi, J. A.}
}
