Computerized Adaptive Testing Provides Reliable and Efficient Depression Measurement Using the CES-D Scale.

Background: The Center for Epidemiologic Studies Depression Scale (CES-D) is a measure of depressive symptomatology which is widely used internationally. Though previous attempts were made to shorten the CES-D scale, few have attempted to develop a Computerized Adaptive Test (CAT) version for the CES-D.
Objective: The aim of this study was to provide evidence on the efficiency and accuracy of the CES-D when administered using CAT using an American sample group.
Methods: We obtained a sample of 2060 responses to the CESD-D from US participants using the myPersonality application. The average age of participants was 26 years (range 19-77). We randomly split the sample into two groups to evaluate and validate the psychometric models. We used evaluation group data (n=1018) to assess dimensionality with both confirmatory factor and Mokken analysis. We conducted further psychometric assessments using item response theory (IRT), including assessments of item and scale fit to Samejima's graded response model (GRM), local dependency and differential item functioning. We subsequently conducted two CAT simulations to evaluate the CES-D CAT using the validation group (n=1042).
Results: Initial CFA results indicated a poor fit to the model and Mokken analysis revealed 3 items which did not conform to the same dimension as the rest of the items. We removed the 3 items and fit the remaining 17 items to GRM. We found no evidence of differential item functioning (DIF) between age and gender groups. Estimates of the level of CES-D trait score provided by the simulated CAT algorithm and the original CES-D trait score derived from original scale were correlated highly. The second CAT simulation conducted using real participant data demonstrated higher precision at the higher levels of depression spectrum.
Conclusions: Depression assessments using the CES-D CAT can be more accurate and efficient than those made using the fixed-length assessment.
(©Bao Sheng Loe, David Stillwell, Chris Gibbons. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 20.09.2017.)