Using Testlet Response Theory to analyze data from a survey of attitude change among breast cancer survivors.
In this paper we examine alternative measurement models for fitting data from health surveys. We show why a testlet-based latent trait model that includes covariate information, embedded within a fully Bayesian framework, can allow multiple simultaneous inferences and aid interpretation. We illustrate our approach with a survey of breast cancer survivors that reveals how the attitudes of those patients change after diagnosis toward a focus on appreciating the here-and-now, and away from consideration of longer-term goals. Using the covariate information, we also show the extent to which individual-level variables such as race, age and Tamoxifen treatment are related to a patient's change in attitude.The major contribution of this research is to demonstrate the use of a hierarchical Bayesian IRT model with covariates in this application area; hence a novel case study, and one that is certainly closely aligned with but distinct from the educational testing applications that have made IRT the dominant test scoring model.
Wang
Xiaohui
X
Department of Statistics, University of Virginia, Charlottesville, VA, USA. xw5a@virginia.edu
Baldwin
Su
S
Wainer
Howard
H
Bradlow
Eric T
ET
Reeve
Bryce B
BB
Smith
Ashley W
AW
Bellizzi
Keith M
KM
Baumgartner
Kathy B
KB
D001294:Attitude to Health; D001499:Bayes Theorem; D001943:Breast Neoplasms / Q000523:psychology*; D003627:Data Interpretation, Statistical; D005260:Female; D006306:Health Surveys; D006801:Humans; D009364:Neoplasm Recurrence, Local / Q000517:prevention & control; D011594:Psychometrics; D017741:Survivors / Q000523:psychology*; D013629:Tamoxifen / Q000627:therapeutic use
D013629:Tamoxifen