Estimating skin cancer risk: Evaluating mobile computer adaptive testing
Djaja, Ngadiman, Janda, Monika, Olsen, Catherine M., Whiteman, David C., & Chien, Tsair-Wei (2016) Estimating skin cancer risk: Evaluating mobile computer adaptive testing. Journal of Medical Internet Research, 18(1), e22.
- 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.
- 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.
- 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 ) 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).
- 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.
- 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.
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|Item Type:||Journal Article|
|Keywords:||Rasch analysis, computer adaptive testing, non adaptive test, partial credit model, skin cancer risk scale|
|Subjects:||Australian and New Zealand Standard Research Classification > MEDICAL AND HEALTH SCIENCES (110000)
Australian and New Zealand Standard Research Classification > MEDICAL AND HEALTH SCIENCES (110000) > PUBLIC HEALTH AND HEALTH SERVICES (111700)
Australian and New Zealand Standard Research Classification
|Divisions:||Current > QUT Faculties and Divisions > Faculty of Health
Current > Institutes > Institute of Health and Biomedical Innovation
Current > Schools > School of Public Health & Social Work
|Deposited On:||30 Aug 2016 04:14|
|Last Modified:||02 Sep 2016 05:31|
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