Identifying the Critical Features That Affect the Job Performance of Survey Interviewers

In an attempt to build a good predictor of the performance of survey interviewers, we propose a feature selection method that derives the features' strength (i.e., degree of usefulness) from various feature subsets drawn from a pool of all the features. The method also builds a predictor by using support vector regression (SVR) as the learning machine and the selected features as variables. Applying the method to a collection of 278 instances obtained from 67 interviewers par-ticipating in eight survey projects, we identified three critical features, experience and two attributional style variables, out of fifteen features. Compared with results of four existing methods, the proposed predictor produced the smallest predictive error. Furthermore, the three features utilized by our method were also identified as the most important features by the four compared methods.