Journal of the Association for Information Science and Technology· 2017Q1
Comparing grounded theory and topic modeling: Extreme divergence or unlikely convergence?
- 228citations
- Q1SCImago
- 2017year
Short summary
Grounded theory and topic modeling, applied to the same social media nonuse survey data, yielded both similar and complementary insights, suggesting potential for combined use.
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Key points
- Grounded theory and topic modeling were applied to the same dataset on social media nonuse.
- Both methods generated similar insights regarding the phenomenon of interest.
- Each method also produced complementary findings, revealing unique advantages.
- The comparison informs decisions about choosing between interpretive and statistical text analysis methods.
AI-generated from the title and abstract; the full text is not read.
Abstract
Researchers in information science and related areas have developed various methods for analyzing textual data, such as survey responses. This article describes the application of analysis methods from two distinct fields, one method from interpretive social science and one method from statistical machine learning, to the same survey data. The results show that the two analyses produce some similar and some complementary insights about the phenomenon of interest, in this case, nonuse of social media. We compare both the processes of conducting these analyses and the results they produce to derive insights about each method's unique advantages and drawbacks, as well as the broader roles that these methods play in the respective fields where they are often used. These insights allow us to make more informed decisions about the tradeoffs in choosing different methods for analyzing textual data. Furthermore, this comparison suggests ways that such methods might be combined in novel and compelling ways.
The authors' abstract, as published at the source. Journal of the Association for Information Science and Technology, 2017 · DOI ↗
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Field: General Social Sciences
General Social SciencesSocial Sciences