%0 Journal Article %J Rehabilitation Psychology %D 2009 %T Development of an item bank for the assessment of depression in persons with mental illnesses and physical diseases using Rasch analysis %A Forkmann, T. %A Boecker, M. %A Norra, C. %A Eberle, N. %A Kircher, T. %A Schauerte, P. %A Mischke, K. %A Westhofen, M. %A Gauggel, S. %A Wirtz, M. %K Adaptation, Psychological %K Adult %K Aged %K Depressive Disorder/*diagnosis/psychology %K Diagnosis, Computer-Assisted %K Female %K Heart Diseases/*psychology %K Humans %K Male %K Mental Disorders/*psychology %K Middle Aged %K Models, Statistical %K Otorhinolaryngologic Diseases/*psychology %K Personality Assessment/statistics & numerical data %K Personality Inventory/*statistics & numerical data %K Psychometrics/statistics & numerical data %K Questionnaires %K Reproducibility of Results %K Sick Role %X OBJECTIVE: The calibration of item banks provides the basis for computerized adaptive testing that ensures high diagnostic precision and minimizes participants' test burden. The present study aimed at developing a new item bank that allows for assessing depression in persons with mental and persons with somatic diseases. METHOD: The sample consisted of 161 participants treated for a depressive syndrome, and 206 participants with somatic illnesses (103 cardiologic, 103 otorhinolaryngologic; overall mean age = 44.1 years, SD =14.0; 44.7% women) to allow for validation of the item bank in both groups. Persons answered a pool of 182 depression items on a 5-point Likert scale. RESULTS: Evaluation of Rasch model fit (infit < 1.3), differential item functioning, dimensionality, local independence, item spread, item and person separation (>2.0), and reliability (>.80) resulted in a bank of 79 items with good psychometric properties. CONCLUSIONS: The bank provides items with a wide range of content coverage and may serve as a sound basis for computerized adaptive testing applications. It might also be useful for researchers who wish to develop new fixed-length scales for the assessment of depression in specific rehabilitation settings. %B Rehabilitation Psychology %7 2009/05/28 %V 54 %P 186-97 %8 May %@ 0090-5550 (Print)0090-5550 (Linking) %G eng %M 19469609 %0 Journal Article %J BMC Psychiatry %D 2004 %T Computerized adaptive measurement of depression: A simulation study %A Gardner, W. %A Shear, K. %A Kelleher, K. J. %A Pajer, K. A. %A Mammen, O. %A Buysse, D. %A Frank, E. %K *Computer Simulation %K Adult %K Algorithms %K Area Under Curve %K Comparative Study %K Depressive Disorder/*diagnosis/epidemiology/psychology %K Diagnosis, Computer-Assisted/*methods/statistics & numerical data %K Factor Analysis, Statistical %K Female %K Humans %K Internet %K Male %K Mass Screening/methods %K Patient Selection %K Personality Inventory/*statistics & numerical data %K Pilot Projects %K Prevalence %K Psychiatric Status Rating Scales/*statistics & numerical data %K Psychometrics %K Research Support, Non-U.S. Gov't %K Research Support, U.S. Gov't, P.H.S. %K Severity of Illness Index %K Software %X Background: Efficient, accurate instruments for measuring depression are increasingly importantin clinical practice. We developed a computerized adaptive version of the Beck DepressionInventory (BDI). We examined its efficiency and its usefulness in identifying Major DepressiveEpisodes (MDE) and in measuring depression severity.Methods: Subjects were 744 participants in research studies in which each subject completed boththe BDI and the SCID. In addition, 285 patients completed the Hamilton Depression Rating Scale.Results: The adaptive BDI had an AUC as an indicator of a SCID diagnosis of MDE of 88%,equivalent to the full BDI. The adaptive BDI asked fewer questions than the full BDI (5.6 versus 21items). The adaptive latent depression score correlated r = .92 with the BDI total score and thelatent depression score correlated more highly with the Hamilton (r = .74) than the BDI total scoredid (r = .70).Conclusions: Adaptive testing for depression may provide greatly increased efficiency withoutloss of accuracy in identifying MDE or in measuring depression severity. %B BMC Psychiatry %V 4 %P 13-23 %G eng %M 15132755