Abstract
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.
Highlights
When sampling methodology is complex, initiatives are employed in statistical analysis to extract the most reliable information from data through the model and its parameters
A classic linear model was suggested to obtain a general relationship between the response and predictive variables
Use of a linear model on binary responses is not recommended [21], since predicted values may be outside of the domain of the response variable. From this point forward, a classic model known as classical test theory (CTT) is considered
Summary
When sampling methodology is complex, initiatives are employed in statistical analysis to extract the most reliable information from data through the model and its parameters. The goal of this manuscript is to apply the item response theory (IRT) to analyze survey data, and compare the output with one classical test theory (CTT) called logistic regression models as a point of reference. While the methodology is simple to describe, the challenge is found in the statistical analysis tool used to make prediction, especially in the presence of behavioral variables, such as driver gender, vehicle type, traffic volume, road segment length, weather conditions, driver cellphone use, passenger presence, lane, and passenger seatbelt use.
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