Text-Mining Open-Ended Survey Responses Using Structural Topic Modeling: A Practical Demonstration to Understand Parents' Coping Methods During the COVID-19 Pandemic in Singapore

Open-ended survey questions crucially contribute to researchers' understandings of respondents' experiences. However, analyzing open-ended responses using human coders is labor-intensive. Structural topic modeling (STM) is a text mining method that discover topics from textual data. We demonstrate the use of STM to analyze open-ended survey responses to understand how parents coped during the COVID-19 lock-down in Singapore. We administered online surveys to 199 parents in Singapore during the COVID-19 lock-down. To show a STM analysis, we demonstrated a workflow that includes steps in data preprocessing, model estimation, model selection, and model interpretation. An 18-topic model best fit the data based on model diagnostics and researchers' expertise. Prevalent coping methods described by respondents include "Spousal Support," "Routines/Schedules," and "Managing Expectations." Topic prevalence for some topics varied with respondents' levels of parenting stress and whether parents were fathers or mothers. STM offers an efficient, valid, and replicable way to analyze textual data such as open-ended survey responses and case notes that can complement researchers' knowledge and skills. STM can be used as part of a multistage research process or to support other analyses such as clarifying quantitative findings and identifying preliminary themes from qualitative data. Supplemental data for this article is available online at https://doi.org/10.1080/15228835.2022.2036301 .