Detection Probability of Poor Responses in Questionnaires with Quality Control Questions

Abstract

In the realm of web-based surveys, ensuring the quality of responses is a crucial yet challenging task. This study addresses the issue of detecting poor responses, particularly focusing on the phenomenon of ‘satisficing’ - a situation where respondents provide minimal effort responses. Traditional methods such as the Instructional Manipulation Check (IMC) and Directed Question Scale (DQS) have been commonly employed to tackle this issue. However, their effectiveness is often limited due to various constraints. This paper introduces a theoretical framework and a generalized model for the design of questionnaires. This framework aims to improve the detection of poor responses, thus enhancing the reliability and validity of survey data. Through numerical experiments, the paper demonstrates the applicability and effectiveness of the proposed model. The study’s approach is based on a thorough analysis of response patterns and the integration of quality control questions within the survey structure. The findings of this research have significant implications for the field of survey methodology, providing a more robust and systematic way of ensuring data integrity in web-based surveys.