The DOI matches its Crossref record; 93 of the 93 references it cites were found in a registry or an index.
Best Practice Recommendations for Designing and Implementing Experimental Vignette Methodology Studies
Abstract
We describe experimental vignette methodology (EVM) as a way to address the dilemma of conducting experimental research that results in high levels of confidence regarding internal validity but is challenged by threats to external validity versus conducting nonexperimental research that usually maximizes external validity but whose conclusions are ambiguous regarding causal relationships. EVM studies consist of presenting participants with carefully constructed and realistic scenarios to assess dependent variables including intentions, attitudes, and behaviors, thereby enhancing experimental realism and also allowing researchers to manipulate and control independent variables. We describe two major types of EVM aimed at assessing explicit (i.e., paper people studies) and implicit (i.e., policy capturing and conjoint analysis) processes and outcomes. We offer best practice recommendations regarding the design and implementation of EVM studies based on a multidisciplinary literature review, discuss substantive domains and topics that can benefit from implementing EVM, address knowledge gaps regarding EVM such as the need to increase realism and the number and diversity of participants, and address ways to overcome some of the negative perceptions about EVM by pointing to exemplary articles that have used EVM successfully.
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Metadata
- Authors
- Aguinis, H., & Bradley, K. J.
- Venue
- Organizational Research Methods
- Publisher
- SAGE Publications
- Published
- 2014-08-25
- Volume
- 17
- Issue
- 4
- Pages
- 351-371
- ISSN
- 1094-4281 (ISSN-L)
- 1552-7425
- OpenAlex
- W1977793234
- Cited by
- 2,021
- References
- 93
- FWCI
- 118.03
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.