A Priori Justification for Effect Measures in Single-Case Experimental Designs
Open accessAbstract
Due to the complex nature of single-case experimental design data, numerous effect measures are available to quantify and evaluate the effectiveness of an intervention. An inappropriate choice of the effect measure can result in a misrepresentation of the intervention effectiveness and this can have far-reaching implications for theory, practice, and policymaking. As guidelines for reporting appropriate justification for selecting an effect measure are missing, the first aim is to identify the relevant dimensions for effect measure selection and justification prior to data gathering. The second aim is to use these dimensions to construct a user-friendly flowchart or decision tree guiding applied researchers in this process. The use of the flowchart is illustrated in the context of a preregistered protocol. This is the first study that attempts to propose reporting guidelines to justify the effect measure choice, before collecting the data, to avoid selective reporting of the largest quantifications of an effect. A proper justification, less prone to confirmation bias, and transparent and explicit reporting can enhance the credibility of the single-case design study findings.
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“Therefore, NAP is a more appropriate measure than baseline-corrected TAU (Manolov et al., 2021).”
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Self-citation
- Authors
- Manolov, R., Moeyaert, M., & Fingerhut, J. E.
- Affiliations
- Universitat de Barcelona (ES)
- State University of New York (US)
- Corresponding author
- Manolov, Rumen
- Venue
- Perspectives on Behavior Science
- Published
- 2021-03-25
- Volume
- 45
- Issue
- 1
- Pages
- 153-186
- ISSN
- 2520-8969 (ISSN-L)
- 2520-8977
- PMC
- PMC8894525
- OpenAlex
- W3136855749
- Cited by
- 53
- References
- 175
- Citations by year
- 2026: 6 · 2025: 7 · 2024: 15 · 2023: 10 · 2022: 13 · 2021: 2
- FWCI
- 5.80
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- Perspectives on Behavior Science (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Dipòsit Digital de la Universitat de Barcelona (Universitat de Barcelona) (submittedVersion, public-domain)
- PsyArXiv (OSF Preprints) (submittedVersion)
- PsyArXiv (OSF Preprints) (submittedVersion, cc-by)
- Topics
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Social Sciences › Psychology › Developmental and Educational Psychology › Behavioral and Psychological Studies
- Keywords
- single-case experimental design, effect measures, a priori justification, reporting guidelines, decision tree, flowcharts, preregistration, selective reporting, confirmation bias, intervention effectiveness, research transparency
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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