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Arunachalam, Hariharan, "TOWARDS BUILDING AN INTELLIGENT INTEGRATED MULTI-MODE TIME DIARY SURVEY FRAMEWORK", Master's thesis, University of Nebraska-Lincoln, May, 2016
Enabling true responses is an important characteristic in surveys; where the responses are free from bias and satisficing. In this thesis, we examine the current state of surveys, briefly touching upon questionnaire surveys, and then on time diary surveys (TDS). TDS are open-ended conversational surveys of a free-form nature with both, the interviewer and the respondent, playing a part in its progress and successful completion. With limited research available on how intelligent and assistive components can affect TDS respondents, we explore ways in which intelligent systems such as Computer Adaptive Testing, Intelligent Tutoring Systems, Recommender Systems, and Decision Support Systems can be leveraged for use in TDS. The motivation for this work is from realizing the opportunity that an enhanced web based instrument can offer the survey domain to unite the various facets of web based surveys to create an intelligent integrated multi-mode TDS framework. We envision the framework to provide all the advantages of web based surveys and interviewer assisted surveys. The two primary challenges are in determining what data is to be used by the system and how to interact with the user – specifically integrating the (1) Interviewer-assisted mode, and (2) Self-administered mode. Our proposed solution – the intelligent integrated multi-mode framework – is essentially the solution to a set of modeling problems and we propose two sets of overreaching mechanisms: (1) Knowledge Engineering Mechanisms (KEM), and (2) Interaction Mechanisms (IxM), where KEM serves the purpose of understanding what data can be created, used and stored while IxM deals with interacting with the user. We build and study a prototype instrument in the interviewer-assisted mode based on the framework. We are able to determine that the instrument improves the interview process as intended and increases the data quality of the response data and is able to assist the interviewer. We also observe that the framework’s mechanisms contribute towards reducing interviewers’ cognitive load, data entry times and interview time by predicting the next activity.
Advisor: Leenkiat Soh