ABSTRACT

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 .

摘要

开放式调查问题对研究人员理解受访者的经历至关重要。然而，使用人类编码器分析开放式响应是劳动密集型的。结构主题建模（STM）是一种从文本数据中发现主题的文本挖掘方法。我们演示了使用 STM 来分析开放式调查答复，以了解父母在新加坡 COVID-19 封锁期间的应对方式。在 COVID-19 封锁期间，我们对新加坡的 199 名家长进行了在线调查。为了展示 STM 分析，我们展示了一个工作流程，其中包括数据预处理、模型估计、模型选择和模型解释等步骤。一个包含 18 个主题的模型最适合基于模型诊断和研究人员专业知识的数据。受访者描述的普遍应对方法包括“配偶支持，” “例程/时间表”和“管理期望”。某些话题的话题流行度因受访者的育儿压力水平以及父母是父亲还是母亲而异。STM 提供了一种高效、有效且可复制的方法来分析文本数据，例如可以补充研究人员知识和技能的开放式调查回复和案例说明。STM 可用作多阶段研究过程的一部分或支持其他分析，例如澄清定量结果和从定性数据中识别初步主题。和可复制的方法来分析文本数据，例如可以补充研究人员的知识和技能的开放式调查答复和案例说明。STM 可用作多阶段研究过程的一部分或支持其他分析，例如澄清定量结果和从定性数据中识别初步主题。和可复制的方法来分析文本数据，例如可以补充研究人员知识和技能的开放式调查答复和案例说明。STM 可用作多阶段研究过程的一部分或支持其他分析，例如澄清定量结果和从定性数据中识别初步主题。

本文的补充数据可在 https://doi.org/10.1080/15228835.2022.2036301 在线获取。