"What else are you worried about?" – Integrating textual responses into quantitative social science research

• Published in: PloS one Vol. 12; no. 7; pp. 1 - 34
• Main Authors: Rohrer, Julia M, Brümmer, Martin, Schmukle, Stefan C, Goebel, Jan, Wagner, Gert G
• Format: Journal Article
• Language: English
• Published: San Francisco Public Library of Science 31.07.2017; Public Library of Science (PLoS)
• Subjects: Age Factors; Analysis; Automatic text analysis; Automation; Biology and Life Sciences; Content analysis; Correlation analysis; Data processing; Dictionaries; Economic analysis; Female; Humans; Language; Male; Natural language processing; People and Places; Polls & surveys; Psychology; Qualitative research; Quantitative research; Research and Analysis Methods; Researchers; Sentiment analysis; Social science research; Social Sciences; Sociology - methods; Sociology - standards; Studies; Surveys; Surveys and Questionnaires - standards; Germany
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0182156

Open-ended questions have routinely been included in large-scale survey and panel studies, yet there is some perplexity about how to actually incorporate the answers to such questions into quantitative social science research. Tools developed recently in the domain of natural language processing offer a wide range of options for the automated analysis of such textual data, but their implementation has lagged behind. In this study, we demonstrate straightforward procedures that can be applied to process and analyze textual data for the purposes of quantitative social science research. Using more than 35,000 textual answers to the question “What else are you worried about?” from participants of the German Socio-economic Panel Study (SOEP), we (1) analyzed characteristics of respondents that determined whether they answered the open-ended question, (2) used the textual data to detect relevant topics that were reported by the respondents, and (3) linked the features of the respondents to the worries they reported in their textual data. The potential uses as well as the limitations of the automated analysis of textual data are discussed.