2018 Volume 7 Issue 1 Pages 21-42
When using an open-ended response collected by social surveys as basic data in statistical processing, after-coding must be conducted to classify it into one of the pre-defined codes (classes). After-coding is a heavy burden for a coder and can possibly be misclassified when the open-ended response is ambiguous or insufficient. Hence, we need to collect a response containing sufficient information for classification to avoid such a situation. However, requesting this task to a respondent and a survey taker is not easy. In this paper, we propose a new system in which a survey taker brings a computer with knowledge that can classify an open-ended response into a valid code of all codes. The computer then asks additional question to the respondent if it perceives that a response does not contain sufficient information for classification, and subsequently extracts the remaining information effectively. This decision is determined when a confidence level of the computer to a result, which is estimated by the scores accompanied by the results, is lower. After collecting information, the computer reclassifies a new open-ended response into a valid code. The proposed system has the additional advantage of being able to supply basic data immediately. We are constructing a new system for occupational coding which is a representative after-coding. The system has not been completed yet, but shows efficacy by a small experiment. In future work, we will completely implement the system and evaluate it by respondents, survey takers, and coders. Moreover, we will expand the system for generalization.
