ChatGPTest
Opportunities and Cautionary Tales of Utilizing AI for Questionnaire Pretesting
Bibliographic Data
| ID | 2407736 |
|---|---|
| Authors | Francisco Olivos (0000-0001-6395-6593, Lingnan University, corresponding author), Minhui Liu (0009-0008-8911-4405, University of Hong Kong) |
| Year | 2024 |
| Volume | 37 |
| Issue | 4 |
| Pages | 277-290 |
| Publication date | 2024-09-12 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | CAM (JOURNAL) |
| Journal identifiers | ISSN: 1087-8513 • E-ISSN: 1552-3969 |
| Publisher | SAGE Publications Inc (PUBLISHER) |
| DOI | 10.1177/1525822x241280574 |
| OpenAlex | W4402546166 |
| Language | EN |
| Citations received | 3 |
| References cited | 13 |
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
Data science · Applied Psychology · Artificial Intelligence in Healthcare and Education · Computer Science · Privacy-Preserving Technologies in Data · Psychology · Social Psychology · Survey Methodology and Nonresponse
| Unique citing works | 3 |
|---|---|
| Citations per year | 3 |
| Citation span | 2026 - 2026 (1) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 3 |