Automating Survey Coding for Occupation
Malte Schierholz · RePEc: Research Papers in Economics · 2014
Currently, most surveys ask for occupation with open-ended questions. The verbatim responses are coded afterwards into a classification with hundreds of categories and thou-sands of jobs, which is an error-prone, time-consuming, and costly task. Research related to the coding of occupations is summarized with an international literature review. Special attention is paid to our main topic, the automation of coding. A prominent approach for automated coding is to consult a dictionary on the correct code. In contrast, we focus on data-based methods where codes for new answers are pre-dicted from those answers that are already coded. Four different coding methods are tested on two data sets: (1) Rule-based Coding that consults a dictionary, (2) data-based Naive Bayes that allows coding for text answers with multiple words, (3) data-based Bayesian Categorical is used to improve performance when relatively few answers were coded before, and (4) Combined Methods (Boosting) combining predictions from the first three methods. The proposed Bayesian Categorical model is able to code 38 % of all answers at 3 % error rate without human interaction. In all remaining cases or for higher quality human intellect