The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
The Other Half of the Story: Effect Size Analysis in Quantitative Research
Open accessAbstract
Statistical significance testing is the cornerstone of quantitative research, but studies that fail to report measures of effect size are potentially missing a robust part of the analysis. We provide a rationale for why effect size measures should be included in quantitative discipline-based education research. Examples from both biological and educational research demonstrate the utility of effect size for evaluating practical significance. We also provide details about some effect size indices that are paired with common statistical significance tests used in educational research and offer general suggestions for interpreting effect size measures. Finally, we discuss some inherent limitations of effect size measures and provide further recommendations about reporting confidence intervals.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Distribution Theory for Glass's Estimator of Effect Size and Related Estimators
Hedges, L. V. (1981) · Journal of Educational Statistics · doi:10.2307/1164588 · Cited by 5,695
Cohen, J. (1994) · American Psychologist · doi:10.1037/0003-066x.49.12.997 · Cited by 4,003
An effect size primer: A guide for clinicians and researchers.
Ferguson, C. J. (2009) · Professional Psychology: Research and Practice · doi:10.1037/a0015808 · Cited by 3,467
Statistical methods in psychology journals: Guidelines and explanations.
Wilkinson, L., & Task Force on Statistical Inference (1999) · American Psychologist · doi:10.1037/0003-066x.54.8.594 · Cited by 2,791
Ellis, P. D. (2010) · doi:10.1017/cbo9780511761676 · Cited by 2,461
Practical significance: a concept whose time has come
Kirk, R. E. (1996) · Educational and Psychological Measurement · doi:10.1177/0013164496056005002 · Cited by 1,778 · cited 5×
“The p value represents the probability of the observed data (or more extreme data) given that the null hypothesis is true: Pr(observed data|H0), assuming that the sampling was random and done without error (Kirk, 1996; Johnson, 1999).”
BackgroundInfluentialEta-squared and partial eta-squared in fixed factor anova designs
Cohen, J. (1973) · Educational and Psychological Measurement · doi:10.1177/001316447303300111 · Cited by 1,333
A common language effect size statistic
McGraw, K. O., & Wong, S. P. (1992) · Psychological Bulletin · doi:10.1037/0033-2909.111.2.361 · Cited by 934 · cited 1×
“Additionally, McGraw and Wong (1992) developed a measure to report what they call “the common language effect size indicator,” which describes the probability that a random value sampled from one group will be greater than a random value sampled from a comparison group (McGraw and Wong, 1992).”
BackgroundGottesman, A. J., & Hoskins, S. G. (2013) · CBE—Life Sciences Education · doi:10.1187/cbe.12-11-0201 · Cited by 123 · cited 1×
“Gottesman and Hoskins (2013) applied Cohen’s d to compare pre/post means of data collected using an instrument measuring students’ critical thinking, experimental design ability, attitudes, and beliefs.”
Method
Cited by
Tsai, Y.-L., & Tsai, C.-C. (2018) · Computers & Education · doi:10.1016/j.compedu.2018.06.020 · Cited by 164
Stevens, L. M., & Hoskins, S. G. (2014) · CBE—Life Sciences Education · doi:10.1187/cbe.13-12-0239 · Cited by 62
Eakman, A. M., Schelly, C., & Henry, K. L. (2016) · American Journal of Occupational Therapy · doi:10.5014/ajot.2016.016519 · Cited by 35
Kenyon, K. L., Onorato, M. E., Gottesman, A. J., Hoque, J., & Hoskins, S. G. (2016) · CBE—Life Sciences Education · doi:10.1187/cbe.15-07-0146 · Cited by 32
Barry, A. E., Szucs, L. E., Reyes, J. V., Ji, Q., Wilson, K. L., & Thompson, B. (2016) · Health Education & Behavior · doi:10.1177/1090198116669521 · Cited by 27
Metadata
- Authors
- Maher, J. M., Markey, J. C., & Ebert-May, D.
- Venue
- CBE—Life Sciences Education
- Published
- 2013-09
- Volume
- 12
- Issue
- 3
- Pages
- 345-351
- ISSN
- 1931-7913 (ISSN-L)
- OpenAlex
- W2128850990
- Cited by
- 625
- References
- 9
- FWCI
- 63.85
- Type
- journal article
- Language
- en
- Open access
- hybrid
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: The Other Half of the Story: Effect Size Analysis in Quantitative Research. https://app.cochat.ai/papers/10.1187/cbe.13-04-0082
- The paper
- Maher, J. M., Markey, J. C., & Ebert-May, D. (2013). The Other Half of the Story: Effect Size Analysis in Quantitative Research. CBE—Life Sciences Education. https://doi.org/10.1187/cbe.13-04-0082
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.