Doctoral Dissertation Research: Developing and Testing Novel Strategies to Detect Inattentive Responding in Intensive Longitudinal Self-Report Methods

University Of Southern California, Los Angeles CA

Investigators

Abstract

This doctoral dissertation research project will investigate the accuracy of participants' responses to mobile surveys. Surveys are one of the best ways for researchers to learn about the behaviors and attitudes of a population. Due to the popularity of smartphones, researchers have the option of conducting real time repeated surveys on participants' smartphones. However, participants may choose not to respond to the surveys or may respond without fully paying attention. Currently, there is a lack of data and guidance on how to detect and manage the latter problem of inattentive responding in surveys. Inattentive response to surveys may result in poor data quality and affect the accuracy of research conclusions. This research will examine inattentive responding in longitudinal and repeated surveys. The project will establish models of when and why inattentive responding is likely to occur and provide insights into preventive strategies to increase the validity of self-report measures. The project also will generate recommendations for detecting poor data quality and suggest preventative measures. Data and techniques resulting from this project will be made publicly available. Improved research methodology and more accurate research conclusions will benefit policy formation and evidence-based decision making, as well as society through scientific journalism, education, and research. This research project will translate inattentive response detection elements from online survey research to an intensive ecological monetary assessment (EMA) study to develop novel and practical strategies that could be recommended for future intensive longitudinal studies. Due to advancements in mobile technology, there is increased promise of using smartphones to access individuals in their natural environment. EMA is a real-time sampling strategy that researchers can use to collect repeated measures of self-report data such as current experiences, behaviors, and moods, resulting in more ecologically valid data. However, sustaining participant engagement can be challenging given the potential burden from completing repeated surveys, and there is a lack of research and guidance on how to manage decreased data quality due to burden. This project will 1) test the accuracy or indices to detect which surveys may be invalid, 2) use a mixed methods approach to determine what factors increase the probability of engaging in inattentive responding, and 3) model how including these invalid surveys in data analysis creates deviations in results. As a result of these analyses, guidelines will be developed for conducting longitudinal studies. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.