title: A computer adaptive testing simulation applied to the FIM instrument motor component.
abstract: OBJECTIVE
  To determine whether computer adaptive testing (CAT) can be used to decrease the number of FIM trade mark instrument motor component items administered in assessing persons with spinal cord injury (SCI).
  
  
  METHODS
  For a CAT simulation, a 3-step algorithm was used to select 6 FIM items for each individual; items were selected according to the subject's motor ability as estimated by 2 initial items. Separate estimates of motor ability for admission, discharge, and follow-up data (plus combined time points) derived from 6 items were compared statistically with estimates derived from 14 items (walking and wheelchair mobility were split).
  
  
  METHODS
  Records from the Spinal Cord Injury Model Systems (SCIMS).
  
  
  METHODS
  Patients served by the SCIMS, for whom complete motor FIM information was available for rehabilitation admission (N=5,969), discharge (N=5,964), or follow-up at a first or later anniversary (N=5,176).
  
  
  METHODS
  Not applicable.
  
  
  METHODS
  Similarity of mean, standard deviation, skewness, kurtosis, and Rasch reliability and separation of persons and items based on 6 and 13 items; intraclass correlation coefficient (ICC) for parallel estimates.
  
  
  RESULTS
  Calibrations for FIM items and FIM steps differed for the 3 time points, but showed sufficient agreement (ICC, >.90) that combined calibration was feasible. Means and other distribution characteristics differed minimally between the 6- and 13-item estimates. The person and item separations and reliabilities were somewhat lower and the mean measurement errors somewhat higher for the 6-item estimates, but only marginally so. ICCs between 6- and 13-item estimates were .95 or higher.
  
  
  CONCLUSIONS
  CAT can be used to reduce data collection time; the level of precision of estimates is minimally less than that provided by traditional assessment approaches.
authors.affiliation: Department of Rehabilitation Medicine, Mount Sinai School of Medicine, New York, NY 10029-6574, USA. marcel.dijkers@mountsinai.org
authors.forename: Marcel P
authors.initials: MP
authors.lastname: Dijkers
mesh_terms: D000465:Algorithms; D002138:Calibration; D003198:Computer Simulation / Q000706:statistics & numerical data*; D016208:Databases, Factual / Q000706:statistics & numerical data; D003936:Diagnosis, Computer-Assisted / Q000379:methods* / Q000592:standards*; D004185:Disability Evaluation; D006801:Humans; D015233:Models, Statistical; D017063:Outcome Assessment, Health Care / Q000379:methods*; D015203:Reproducibility of Results; D013119:Spinal Cord Injuries / Q000175:diagnosis* / Q000503:physiopathology; D011795:Surveys and Questionnaires; D013997:Time Factors