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Cited by 48Psychometric Methodologies and TestingAdvanced Causal Inference TechniquesStatistical Methods and Bayesian Inference

Enhancing validity in psychological research.

Kenny, David A.·2019·American Psychologist
DOI 10.1037/amp0000531
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Abstract

Methods to increase Campbell's (1957) internal and external validity as well as Cook and Campbell's (1979) construct and conclusion validity are reviewed. For internal validity or valid causal inference, designs and methods to draw causal conclusions from nonrandomized studies are considered. Greater collaboration between the causal inference and structural equation modeling traditions would benefit both. For external validity, generalizing results, treating partners and studies as well as participants as random is strongly encouraged. For construct validity, particularly the psychological meaning of measures, multivariate models that treat measures from both overtime and dyadic data as being a combination of multiple constructs are discussed. For conclusion validity or valid statistical inference, the problem of low power when generalizability is high and the assumption of independence are discussed. Finding the truth in psychological research is a challenge, and seemingly insurmountable difficulties are often encountered. Nonetheless, persistent and diligent efforts using strategies developed by generations of methodologists should lead to scientific advancement. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

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  • Providing participants with instructions on a set of prompts to provide GPT-4 and requesting that the creative output derivative of this process is inputted back into the experimental interface poses a clear threat to internal validity because the experimenter has little to no control over participant adherence to instructions.

    “An illustrative example of a typical approach here is to provide participants with instructions on a set of prompts to provide GPT-4 and request that the creative output derivative of this process is inputted back into the experimental interface (e.g., Qualtrics). Such an approach, however, poses a clear threat to internal validity because the experimenter has little to no control over participant”

    From Establishing the importance of co-creation and self-efficacy in creative collaboration with artificial intelligence (2024) · cites this paper as [19]

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Authors
Kenny, D. A.
Affiliations
University of Connecticut (US)
Corresponding author
Kenny, David A.
ORCID
Kenny, David A.
Venue
American Psychologist
Publisher
American Psychological Association (APA)
Published
2019-12
Volume
74
Issue
9
Pages
1018-1028
ISSN
  • 0003-066X (ISSN-L)
  • 1935-990X
DOI
10.1037/amp0000531
OpenAlex
W2995977041
Cited by
48
References
0
Citations by year
2026: 2 · 2025: 14 · 2024: 8 · 2023: 6 · 2022: 6 · 2021: 5 · 2020: 7
FWCI
3.22
Type
journal article
Language
en
Open access
closed
Locations
  • American Psychologist (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
  • Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
Keywords
research validity, internal validity, generalizability, construct validity, causal inference, non-randomized studies, structural equation modeling, dyadic data, multivariate models, statistical power, independence assumption
MeSH terms
Humans, Psychology, Research, Reproducibility of Results

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