Students as Experimental Participants: A Defense of the “Narrow Data Base”
Abstract
An experiment entails randomly assigning participants to various conditions or manipulations. Given common consent requirements, this means experimenters need to recruit participants who, in essence, agree to be manipulated. The ensuing practical and ethical challenges of subject recruitment have led many researchers to rely on convenience samples of college students. For political scientists who put particular emphasis on generalizability, the use of student participants often constitutes a critical, and according to some reviewers, fatal problem for experimental studies. In this chapter, we investigate the extent to which using students as experimental participants creates problems for causal inference. First, we discuss the impact of student subjects on a study's internal and external validity. In contrast to common claims, we argue that student subjects do not intrinsically pose a problem for a study's external validity. Second, we use simulations to identify situations when student subjects are likely to constrain experimental inferences. We show that such situations are relatively limited; any convenience sample poses a problem only when the size of an experimental treatment effect depends on a characteristic on which the convenience sample has virtually no variance. Third, we briefly survey empirical evidence that provides guidance on when researchers should be particularly attuned to taking steps to ensure appropriate generalizability from student subjects. We conclude with a discussion of the practical implications of our findings.
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There is no difference between student and non-student samples in terms of a variety of factors, such that selecting any one issue may represent mundane realism as opposed to any real effect.
“However, Druckman and Kam [84] have suggested that there is no difference between student and non-student samples in terms of a variety of factors, such that selecting any one issue may represent mundane realism as opposed to any real effect.”
From Consumer Responses to Generative AI Chatbots Versus Search Engines for Product Evaluation (2025) · cites this paper as [84]
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Berinsky, A. J., Huber, G. A., & Lenz, G. S. (2012) · Political Analysis · doi:10.1093/pan/mpr057 · Cited by 4,008 · see citing works
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Representativeness and Response Validity Across Nine Opt-In Online Samples
Stagnaro, M. N., Druckman, J., Berinsky, A. J., Arechar, A. A., Willer, R., & Rand, D. G. (2024) · doi:10.31234/osf.io/h9j2d · Cited by 68 · see citing works
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Kim, S., & Priluck, R. (2025) · Journal of Theoretical and Applied Electronic Commerce Research · doi:10.3390/jtaer20020093 · Cited by 20 · see citing works
“However, Druckman and Kam [84] have suggested that there is no difference between student and non-student samples in terms of a variety of factors, such that selecting any one issue may represent mundane realism as opposed to any real effect.”
- Authors
- Druckman, J. N., & Kam, C. D.
- Affiliations
- Northwestern University (US)
- Vanderbilt University (US)
- Venue
- Cambridge Handbook of Experimental Political Science
- Publisher
- Cambridge University Press
- Published
- 2011-06-06
- Pages
- 41-57
- OpenAlex
- W1562888238
- Cited by
- 454
- References
- 3
- Citations by year
- 2026: 9 · 2025: 29 · 2024: 31 · 2023: 20 · 2022: 30 · 2021: 40 · 2020: 50 · 2019: 57 · 2018: 26 · 2017: 32 · 2016: 31 · 2015: 31 · 2014: 33 · 2013: 15 · 2012: 12
- FWCI
- 208.50
- Type
- book chapter
- Language
- en
- Open access
- closed
- Locations
- Cambridge University Press eBooks (publishedVersion)
- Topics
- Social Sciences › Political Science and International Relations › Political Science Research and Education
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Social Sciences › Political Science and International Relations › Electoral Systems and Political Participation
- Keywords
- student participants, convenience sampling, generalizability, internal validity, causal inference, treatment effect heterogeneity, random assignment, participant recruitment
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