@article {168,
	title = {Activity outcome measurement for postacute care},
	journal = {Medical Care},
	volume = {42},
	number = {1 Suppl},
	year = {2004},
	note = {0025-7079Journal ArticleMulticenter Study},
	pages = {I49-161},
	abstract = {BACKGROUND: Efforts to evaluate the effectiveness of a broad range of postacute care services have been hindered by the lack of conceptually sound and comprehensive measures of outcomes. It is critical to determine a common underlying structure before employing current methods of item equating across outcome instruments for future item banking and computer-adaptive testing applications. OBJECTIVE: To investigate the factor structure, reliability, and scale properties of items underlying the Activity domains of the International Classification of Functioning, Disability and Health (ICF) for use in postacute care outcome measurement. METHODS: We developed a 41-item Activity Measure for Postacute Care (AM-PAC) that assessed an individual{\textquoteright}s execution of discrete daily tasks in his or her own environment across major content domains as defined by the ICF. We evaluated the reliability and discriminant validity of the prototype AM-PAC in 477 individuals in active rehabilitation programs across 4 rehabilitation settings using factor analyses, tests of item scaling, internal consistency reliability analyses, Rasch item response theory modeling, residual component analysis, and modified parallel analysis. RESULTS: Results from an initial exploratory factor analysis produced 3 distinct, interpretable factors that accounted for 72\% of the variance: Applied Cognition (44\%), Personal Care \& Instrumental Activities (19\%), and Physical \& Movement Activities (9\%); these 3 activity factors were verified by a confirmatory factor analysis. Scaling assumptions were met for each factor in the total sample and across diagnostic groups. Internal consistency reliability was high for the total sample (Cronbach alpha = 0.92 to 0.94), and for specific diagnostic groups (Cronbach alpha = 0.90 to 0.95). Rasch scaling, residual factor, differential item functioning, and modified parallel analyses supported the unidimensionality and goodness of fit of each unique activity domain. CONCLUSIONS: This 3-factor model of the AM-PAC can form the conceptual basis for common-item equating and computer-adaptive applications, leading to a comprehensive system of outcome instruments for postacute care settings.},
	keywords = {*Self Efficacy, *Sickness Impact Profile, Activities of Daily Living/*classification/psychology, Adult, Aftercare/*standards/statistics \& numerical data, Aged, Boston, Cognition/physiology, Disability Evaluation, Factor Analysis, Statistical, Female, Human, Male, Middle Aged, Movement/physiology, Outcome Assessment (Health Care)/*methods/statistics \& numerical data, Psychometrics, Questionnaires/standards, Rehabilitation/*standards/statistics \& numerical data, Reproducibility of Results, Sensitivity and Specificity, Support, U.S. Gov{\textquoteright}t, Non-P.H.S., Support, U.S. Gov{\textquoteright}t, P.H.S.},
	author = {Haley, S. M. and Coster, W. J. and Andres, P. L. and Ludlow, L. H. and Ni, P. and Bond, T. L. and Sinclair, S. J. and Jette, A. M.}
}
@article {191,
	title = {Item response theory and health outcomes measurement in the 21st century},
	journal = {Medical Care},
	volume = {38},
	number = {9 Suppl II},
	year = {2000},
	note = {204349670025-7079Journal Article},
	pages = {II28-II42},
	abstract = {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.},
	keywords = {*Models, Statistical, Activities of Daily Living, Data Interpretation, Statistical, Health Services Research/*methods, Health Surveys, Human, Mathematical Computing, Outcome Assessment (Health Care)/*methods, Research Design, Support, Non-U.S. Gov{\textquoteright}t, Support, U.S. Gov{\textquoteright}t, P.H.S., United States},
	author = {Hays, R. D. and Morales, L. S. and Reise, S. P.}
}
