Author(s)

Mark K. Ledbetter¹, Norou Diawara^1*, Bryan E. Porter²

¹Mathematics and Statistics Department, Old Dominion University, Norfolk, VA, USA.
²Department of Psychology, Old Dominion University, Norfolk, VA, USA.

Problem: Several approaches to analyze survey data have been proposed in the literature. One method that is not popular in survey research methodology is the use of item response theory (IRT). Since accurate methods to make prediction behaviors are based upon observed data, the design model must overcome computation challenges, but also consideration towards calibration and proficiency estimation. The IRT model deems to be offered those latter options. We review that model and apply it to an observational survey data. We then compare the findings with the more popular weighted logistic regression. Method: Apply IRT model to the observed data from 136 sites within the Commonwealth of Virginia over five years collected in a two stage systematic stratified proportional to size sampling plan. Results: A relationship within data is found and is confirmed using the weighted logistic regression model selection. Practical Application: The IRT method may allow simplicity and better fit in the prediction within complex methodology: the model provides tools for survey analysis.

Item Response Theory, Logistic Regression, Sampling Weight

Ledbetter, M. , Diawara, N. and Porter, B. (2018) The Use of Item Response Theory in Survey Methodology: Application in Seat Belt Data. American Journal of Operations Research, 8, 17-32. doi: 10.4236/ajor.2018.81002.