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Using Computational Text Analysis to Explore Open-Ended Survey Question Responses

Using Computational Text Analysis to Explore Open-Ended Survey Question Responses

Copyright: © 2019 |Pages: 33
ISBN13: 9781522585633|ISBN10: 152258563X|EISBN13: 9781522585657
DOI: 10.4018/978-1-5225-8563-3.ch007
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MLA

. "Using Computational Text Analysis to Explore Open-Ended Survey Question Responses." Online Survey Design and Data Analytics: Emerging Research and Opportunities, edited by Shalin Hai-Jew, IGI Global, 2019, pp. 148-180. https://doi.org/10.4018/978-1-5225-8563-3.ch007

APA

. (2019). Using Computational Text Analysis to Explore Open-Ended Survey Question Responses. In S. Hai-Jew (Ed.), Online Survey Design and Data Analytics: Emerging Research and Opportunities (pp. 148-180). IGI Global. https://doi.org/10.4018/978-1-5225-8563-3.ch007

Chicago

. "Using Computational Text Analysis to Explore Open-Ended Survey Question Responses." In Online Survey Design and Data Analytics: Emerging Research and Opportunities, edited by Shalin Hai-Jew, 148-180. Hershey, PA: IGI Global, 2019. https://doi.org/10.4018/978-1-5225-8563-3.ch007

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Abstract

To capture a broader range of data than close-ended questions (often defined and delimited by the survey instrument designer), open-ended questions, such as text-based elicitations (and file-upload options for still imagery, audio, video, and other contents) are becoming more common because of the wide availability of computational text analysis, both within online survey tools and in external software applications. These computational text analysis tools—some online, some offline—make it easier to capture reproducible insights with qualitative data. This chapter explores some analytical capabilities, in matrix queries, theme extraction (topic modeling), sentiment analysis, cluster analysis (concept mapping), network text structures, qualitative cross-tabulation analysis, manual coding to automated coding, linguistic analysis, psychometrics, stylometry, network analysis, and others, as applied to open-ended questions from online surveys (and combined with human close reading).

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