ChatGPTest: Opportunities and Cautionary Tales of Utilizing AI for Questionnaire Pretesting

The rapid advancements in generative artificial intelligence have opened new avenues for enhancing various aspects of research, including the design and evaluation of survey questionnaires. However, the recent pioneering applications have not considered questionnaire pretesting. This article explores the use of GPT models as a useful tool for pretesting survey questionnaires, particularly in the early stages of survey design. Illustrated with two applications, the article suggests incorporating GPT feedback as an additional stage before human pretesting, potentially reducing successive iterations. The article also emphasizes the indispensable role of researchers’ judgment in interpreting and implementing AI-generated feedback.

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Publication Details

Journal
Field Methods
Published
2024-09-12
DOI
https://doi.org/10.1177/1525822x241280574
Citations
8
Primary Topic
Survey Methodology and Nonresponse
Type
article
Field-Weighted Citation Impact
9.06
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article

ChatGPTest: Opportunities and Cautionary Tales of Utilizing AI for Questionnaire Pretesting

Minhui Liu, Francisco Olivos
8 citations
Field Methods
Survey Methodology and Nonresponse
9.06
article

ChatGPTest: Opportunities and Cautionary Tales of Utilizing AI for Questionnaire Pretesting

Minhui Liu, Francisco Olivos
article en
8 citations

Abstract

The rapid advancements in generative artificial intelligence have opened new avenues for enhancing various aspects of research, including the design and evaluation of survey questionnaires. However, the recent pioneering applications have not considered questionnaire pretesting. This article explores the use of GPT models as a useful tool for pretesting survey questionnaires, particularly in the early stages of survey design. Illustrated with two applications, the article suggests incorporating GPT feedback as an additional stage before human pretesting, potentially reducing successive iterations. The article also emphasizes the indispensable role of researchers’ judgment in interpreting and implementing AI-generated feedback.

Field MethodsVol. 37(4)
Lingnan University (HK), University of Hong Kong (HK)
Openalex Percentile: Top 2%
Survey Methodology and Nonresponse
9.06
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