The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Comparing groups in a before–after design: When t test and ANCOVA produce different results
Abstract
Background. Researchers often test people before and after some treatment and compare these scores with a control group. Sometimes it is not possible to allocate people into conditions randomly, which means the initial scores for the two groups may differ. There are two main approaches: t test on the gain scores and ANCOVA partialling out the initial scores. showed that these can lead to different conclusions. This is an often‐discussed paradox in psychology and education. Aims. The reasons why these approaches can lead to different conclusions, the assumptions that each approach makes and how the approaches relate to group allocation, are discussed Methods. Three sets of simulations are reported that investigate the relationships among effect size, group allocation, measurement error and Lord's paradox. Conclusions. Recommendations are given that stress careful examination of the research questions, sampling and allocation of participants and graphing the data. ANCOVA is appropriate when allocation is based on the initial scores, t test can be appropriate if allocation is associated non‐causally with the initial scores, but often neither approach provides adequate results.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
An ANCOVA controlling for pre-intervention differences would indicate a benefit of the intervention for the treatment group despite no improvement from the intervention (Lord's paradox).
“An ANCOVA controlling for pre-intervention differences would indicate a benefit of the intervention for the treatment group despite no improvement from the intervention (i.e., Lord's paradox, Lord, 1967; see also Wright, 2006).”
From Do growth mindset interventions impact students’ academic achievement? A systematic review and meta-analysis with recommendations for best practices. (2023) · cites this paper as Wright, 2006
Citations
Cites
Statistics and Causal Inference
Holland, P. W. (1986) · Journal of the American Statistical Association · doi:10.1080/01621459.1986.10478354 · Cited by 5,293
Flagged first, then by citations
Research works printed but not linked: 16
- 10.1016/j.apergo.2004.01.002 · DOI
- Cook T. D., 1979, Quasi‐experimentation: Design & analysis issues for field settings
- Galton F., 1886, Regression towards mediocrity in hereditary stature, Journal of the Anthropological Institute, 15, 246
- 10.2307/2983526 · DOI
- 10.1037/h0025105 · DOI · Paper page
- 10.1037/1082-989X.3.3.309 · DOI
- Maxwell S. E., 2004, Designing experiments and analysing data: A model comparison perspective
- 10.1136/bmj.326.7398.1083 · DOI
- 10.1177/0193841X0102500101 · DOI
- Rutherford A., 2000, Introducing ANOVA and ANCOVA: A GLM approach
- 10.1037/0033-2909.109.1.147 · DOI
- 10.1198/000313004268 · DOI
- Wright D. B., 1997, Football standings and measurement levels, Journal of the Royal Statistical Society: D, 46, 105
- 10.1348/000709903762869950 · DOI
- Wright D. B., 2006, Handbook of educational psychology
- 10.1037/1076-8971.12.2.190 · DOI
Cited by
Sorted by citations
Macnamara, B. N., & Burgoyne, A. P. (2023) · Psychological Bulletin · doi:10.1037/bul0000352 · Cited by 415
Huisman, B., Saab, N., van den Broek, P., & van Driel, J. (2019) · Assessment & Evaluation in Higher Education · doi:10.1080/02602938.2018.1545896 · Cited by 306
Valente, M. J., & MacKinnon, D. P. (2017) · Structural Equation Modeling: A Multidisciplinary Journal · doi:10.1080/10705511.2016.1274657 · Cited by 173
Fu, R., & Holmer, H. K. (2016) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2016.01.034 · Cited by 71
Language-Capable Robots may Inadvertently Weaken Human Moral Norms
Jackson, R. B., & Williams, T. (2019) · 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1109/hri.2019.8673123 · Cited by 64
Metadata
- Authors
- Wright, D. B.
- Affiliations
- University of Sussex (GB)
- Corresponding author
- Wright, Daniel B.
- ORCID
- Wright, Daniel B.
- Venue
- British Journal of Educational Psychology
- Publisher
- Wiley
- Published
- 2006-09
- Volume
- 76
- Issue
- 3
- Pages
- 663-675
- ISSN
- 0007-0998 (ISSN-L)
- 2044-8279
- OpenAlex
- W2011895155
- Cited by
- 145
- References
- 1
- Citations by year
- 2026: 7 · 2025: 8 · 2024: 7 · 2023: 14 · 2022: 10 · 2021: 7 · 2020: 14 · 2019: 11 · 2018: 5 · 2017: 5 · 2016: 10 · 2015: 9 · 2014: 5 · 2013: 7 · 2012: 6
- FWCI
- 3.27
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- British Journal of Educational Psychology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
- Physical Sciences › Mathematics › Statistics and Probability › Statistics Education and Methodologies
- Keywords
- before-after study, covariance analysis, t-test, gain score, baseline differences, group allocation, measurement error, effect size, causal inference
- MeSH terms
- Analysis of Variance, Data Interpretation, Statistical, Humans, Models, Psychological, Psychology, Time Factors
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 ›
Are you an author, or do you see a problem with this page? Request a review