The DOI matches its Crossref record; 56 of the 56 references it cites were found in a registry or an index.
Combining effect size estimates in meta-analysis with repeated measures and independent-groups designs.
Abstract
When a meta-analysis on results from experimental studies is conducted, differences in the study design must be taken into consideration. A method for combining results across independent-groups and repeated measures designs is described, and the conditions under which such an analysis is appropriate are discussed. Combining results across designs requires that (a) all effect sizes be transformed into a common metric, (b) effect sizes from each design estimate the same treatment effect, and (c) meta-analysis procedures use design-specific estimates of sampling variance to reflect the precision of the effect size estimates.
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In the citing paper
“Hedges g is viewed as more conservative than ES calculations that have previously been recommended for within-participant data.12,28,39”
In the citing paper
“The advantages of using Hedges g for correlated data (cf.39) include the fact that it controls for small sample size bias9 and does not risk inflation of the overall treatment effect when the data are correlated across time in excess of .50.28”
In the citing paper
“We selected Hedges’ g as our ES based on recent guidelines that indicate it is more conservative than previously recommended ES for use in correlated data, particularly for bias associated with small sample size.12,28,39”
In the citing paper
“Effect sizes from pre-post designs are generally less reliable and less informative about the effects of the intervention because they are likely to be influenced by confounding variables (Morris and DeShon, 2002).”
From The Power of Feedback Revisited: A Meta-Analysis of Educational Feedback Research (2020) · cites this paper as Morris and DeShon, 2002
In the citing paper
“Generally, and conform to expectations (Light and Pillemer, 1986; Morris and DeShon, 2002), the average effect of feedback as reported in pre-post design studies is higher than in controlled studies and higher reported in published journal articles than in dissertations.”
From The Power of Feedback Revisited: A Meta-Analysis of Educational Feedback Research (2020) · cites this paper as Morris and DeShon, 2002
In the citing paper
“Effect sizes computed for repeated measures designs and independent groups designs are not directly comparable because different degrees of freedom are used to calculate each [62,63].”
From Menstrual cycle effects on cognitive performance: A meta-analysis (2025) · cites this paper as [63]
In the citing paper
“When computing effect sizes, we did not correct for the correlation between the pre- and post-treatment means despite the within-subjects design (Morris and DeShon 2002); given that we used the LOCF method to deal with missing data, this would have artificially inflated the pre- to post-treatment mean correlations leading to less conservative estimates of effect size.”
From The Effectiveness of Mindfulness-Based Cognitive Therapy (MBCT) in Real-World Healthcare Services (2020) · cites this paper as Morris and DeShon 2002
In the citing paper
“The estimation of treatment effects on the dependent variables, for each study, was based on the calculation of the standardized mean change using raw score standardization (SMCR; see Appendix A), taking the standard deviations of the pretest [33,34].”
From Mindfulness-Based Interventions for People with Schizophrenia: A Systematic Review and Meta-Analysis (2020) · cites this paper as [34]
In the citing paper
“Letting φ() denote the cumulative density function of a standard normal distribution, the interpretation of the standardized mean change can also be facilitated by noting that φ(d) estimates the proportion of individuals for which the difference or change scores reflect a preferable outcome [31].”
From How to boost positive interpretations? A Meta-Analysis of the effectiveness of cognitive bias modification for interpretation (2014) · cites this paper as [31]
In the citing paper
“For an effect size of 0.2 (a “small” effect), the positive interpretation bias is expected to increase (and negative mood is expected to decrease) for φ(0.2) = .58 (i.e., 58%) of individuals.”
From How to boost positive interpretations? A Meta-Analysis of the effectiveness of cognitive bias modification for interpretation (2014) · cites this paper as [31]
In the citing paper
“Effect sizes of 0.5 (a “medium” effect) and 0.8 (a “large” effect) correspond to an increase in positive interpretation bias (and a decrease in negative mood) in 69% and 79% of individuals, respectively [31].”
From How to boost positive interpretations? A Meta-Analysis of the effectiveness of cognitive bias modification for interpretation (2014) · cites this paper as [31]
In the citing paper
“as there was no indication for publication bias [31], we cautiously suggest that even had there been other unpublished data, these would not have changed the results.”
From How to boost positive interpretations? A Meta-Analysis of the effectiveness of cognitive bias modification for interpretation (2014) · cites this paper as [31]
In the citing paper
“Based on the premise for comparing ES previously utilized [58-60], for each of the measures of interest based on the therapeutic intervention (Figure 1).”
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Kalmbach, D. A., Cheng, P., Arnedt, J. T., Anderson, J. R., Roth, T. G., Fellman-Couture, C., Williams, R. A., & Drake, C. L. (2018) · Sleep Medicine · doi:10.1016/j.sleep.2018.11.019 · Cited by 140
Mangelsdorf, J., Eid, M., & Luhmann, M. (2019) · Psychological Bulletin · doi:10.1037/bul0000173 · Cited by 120
“In contrast to the standardized mean gain it does not confound individual differences in change with mean-level change and tends to be more conservative (Morris & DeShon, 2002).”
BackgroundAdults are intuitive mind-body dualists.
Forstmann, M., & Burgmer, P. (2014) · Journal of Experimental Psychology General · doi:10.1037/xge0000045 · Cited by 134
Koehler, F., Martin, Z.-S., Hertrampf, R.-S., Gäbel, C., Keßler, J., Ditzen, B., & Warth, M. (2020) · Frontiers in Psychology · doi:10.3389/fpsyg.2020.00651 · Cited by 129
Kebede, M. M., Zeeb, H., Peters, M., Heise, T. L., & Pischke, C. R. (2018) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2018.0216 · Cited by 135
Linetzky, M., Pergamin‐Hight, L., Pine, D. S., & Bar‐Haim, Y. (2017) · Depression and Anxiety · doi:10.1002/da.22690 · Cited by 130
Negt, P., Brakemeier, E., Michalak, J., Winter, L., Bleich, S., & Kahl, K. G. (2016) · Brain and Behavior · doi:10.1002/brb3.486 · Cited by 126
Sheridan, S. M., Ryoo, J. H., Garbacz, S. A., Kunz, G. M., & Chumney, F. L. (2013) · Journal of School Psychology · doi:10.1016/j.jsp.2013.09.003 · Cited by 126
Metadata
- Authors
- Morris, S. B., & DeShon, R. P.
- Affiliations
- Illinois Institute of Technology (US)
- Michigan State University (US)
- Venue
- Psychological Methods
- Published
- 2002
- Volume
- 7
- Issue
- 1
- Pages
- 105-125
- ISSN
- 1082-989X (ISSN-L)
- 1939-1463
- OpenAlex
- W2076029586
- Semantic Scholar
- d88e7804b7df89d229fd5db7a1c567b94ff18f33
- Cited by
- 2,502
- References
- 54
- Citations by year
- 2026: 182 · 2025: 116 · 2024: 90 · 2023: 138 · 2022: 137 · 2021: 148 · 2020: 180 · 2019: 211 · 2018: 219 · 2017: 190 · 2016: 135 · 2015: 147 · 2014: 148 · 2013: 124 · 2012: 81
- FWCI
- 13.35
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Psychological Methods (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Psychology Research and Bibliometrics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Keywords
- meta-analysis, repeated measures designs, treatment effect estimation, experimental design
- MeSH terms
- Humans, Models, Psychological, Meta-Analysis as Topic
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