title: Estimating Skin Cancer Risk: Evaluating Mobile Computer-Adaptive Testing.
abstract: BACKGROUND
  Response burden is a major detriment to questionnaire completion rates. Computer adaptive testing may offer advantages over non-adaptive testing, including reduction of numbers of items required for precise measurement.
  
  
  OBJECTIVE
  Our aim was to compare the efficiency of non-adaptive (NAT) and computer adaptive testing (CAT) facilitated by Partial Credit Model (PCM)-derived calibration to estimate skin cancer risk.
  
  
  METHODS
  We used a random sample from a population-based Australian cohort study of skin cancer risk (N=43,794). All 30 items of the skin cancer risk scale were calibrated with the Rasch PCM. A total of 1000 cases generated following a normal distribution (mean [SD] 0 [1]) were simulated using three Rasch models with three fixed-item (dichotomous, rating scale, and partial credit) scenarios, respectively. We calculated the comparative efficiency and precision of CAT and NAT (shortening of questionnaire length and the count difference number ratio less than 5% using independent t tests).
  
  
  RESULTS
  We found that use of CAT led to smaller person standard error of the estimated measure than NAT, with substantially higher efficiency but no loss of precision, reducing response burden by 48%, 66%, and 66% for dichotomous, Rating Scale Model, and PCM models, respectively.
  
  
  CONCLUSIONS
  CAT-based administrations of the skin cancer risk scale could substantially reduce participant burden without compromising measurement precision. A mobile computer adaptive test was developed to help people efficiently assess their skin cancer risk.
authors.affiliation: School of Public Health and Social Work, Institute for Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Australia.
authors.forename: Ngadiman
authors.identifier: http://orcid.org/0000-0003-4584-6299
authors.initials: N
authors.lastname: Djaja
authors.forename: Monika
authors.identifier: http://orcid.org/0000-0002-1728-8085
authors.initials: M
authors.lastname: Janda
authors.forename: Catherine M
authors.identifier: http://orcid.org/0000-0003-4483-1888
authors.initials: CM
authors.lastname: Olsen
authors.forename: David C
authors.identifier: http://orcid.org/0000-0003-2563-9559
authors.initials: DC
authors.lastname: Whiteman
authors.forename: Tsair-Wei
authors.identifier: http://orcid.org/0000-0003-1329-0679
authors.initials: TW
authors.lastname: Chien
mesh_terms: D000368:Aged; D001315:Australia; D002138:Calibration; D015331:Cohort Studies; D003196:Computer Graphics; D003201:Computers; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D063731:Mobile Applications; D008962:Models, Theoretical; D019212:Multimedia; D018570:Risk Assessment / Q000379:methods*; D012878:Skin Neoplasms; D011795:Surveys and Questionnaires
keywords: Rasch analysis; computer adaptive testing; non adaptive test; partial credit model; skin cancer risk scale