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Part 1: Document Description
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Citation |
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Title: |
Replication materials for: Categorizing topics versus inferring attitudes: a theory and method for analyzing open-ended survey responses |
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Identification Number: |
doi:10.7910/DVN/FSK6NZ |
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Distributor: |
Harvard Dataverse |
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Date of Distribution: |
2024-10-30 |
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Version: |
1 |
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Bibliographic Citation: |
Hobbs, William; Green, Jon, 2024, "Replication materials for: Categorizing topics versus inferring attitudes: a theory and method for analyzing open-ended survey responses", https://doi.org/10.7910/DVN/FSK6NZ, Harvard Dataverse, V1 |
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Citation |
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Title: |
Replication materials for: Categorizing topics versus inferring attitudes: a theory and method for analyzing open-ended survey responses |
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Identification Number: |
doi:10.7910/DVN/FSK6NZ |
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Authoring Entity: |
Hobbs, William (Cornell University) |
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Green, Jon (Duke University) |
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Distributor: |
Harvard Dataverse |
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Access Authority: |
Hobbs, William |
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Depositor: |
Code Ocean |
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Holdings Information: |
https://doi.org/10.7910/DVN/FSK6NZ |
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Study Scope |
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Keywords: |
Social Sciences |
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Abstract: |
Article abstract: Past work on closed-ended survey responses demonstrates that inferring stable political attitudes requires separating signal from noise in “top of the head” answers to researchers’ questions. We outline a corresponding theory of the open-ended response, in which respondents make narrow, stand-in statements to convey more abstract, general attitudes. We then present a method designed to infer those attitudes. Our approach leverages co-variation with words used relatively frequently across respondents to infer what else they could have said without substantively changing what they meant – linking narrow themes to each other through associations with contextually prevalent words. This reflects the intuition that a respondent may use different specific statements at different points in time to convey similar meaning. We validate this approach using panel data in which respondents answer the same open-ended questions (concerning healthcare policy, most important problems, and evaluations of political parties) at multiple points in time, showing that our method’s output consistently exhibits higher within-subject correlations than hand-coding of narrow response categories, topic modeling, and large language model output. Finally, we show how large language models can be used to complement – but not, at present, substitute – our “implied word” method. |
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Methodology and Processing |
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Sources Statement |
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Data Access |
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Other Study Description Materials |
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Label: |
capsule-5767693.zip |
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Notes: |
application/zip |
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Label: |
result-997ac6a3-8ccf-464b-a150-9648e5dc3614.zip |
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Notes: |
application/zip |