How Methodological Features Affect Effect Sizes in Education

By Alan C. K. Cheung, R. Slavin. 2016

424 citations recorded by the source corpus.

Subfields: Education, Information Systems and Management

View Paper: https://www.semanticscholar.org/paper/ba9eab5d4d917c7035d5159c43a21b1b935d186a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Education

1. K. Harris
2. Steve Graham
3. D. Yeager
4. Cynthia Lake
5. Carol S. Dweck
6. M. Kraft
7. A. Aitken
8. Brendan Bartlett

In Information Systems and Management

1. M. Kraft
2. Carol S. Dweck
3. D. Yeager
4. Tim Kaiser
5. Lukas Menkhoff
6. Robert Rueda
7. Steve Graham
8. R. Slavin

Citations in the corpus

• Can Reading Self-Efficacy Be Modified? A Meta-Analysis of the Impact of Interventions on Reading Self-Efficacy (2017, background): “Cheung and Slavin (2016), in their study of how methodological features in educational studies affect effect sizes, found that elementary school children showed slightly greater gains in effect sizes (ES = +0.”
• Interpreting Effect Sizes of Education Interventions (2020, methodology): “First, I update prior reviews (Bloom et al., 2008; Coe, 2002; Lipsey et al., 2012) with insights from a number of new articles (e.g., Baird & Pane, 2019; Cheung & Slavin, 2016; Funder & Ozer, 2019; Lortie-Forgues & Inglis, 2019; Schäfer & Schwarz, 2019; Simpson, 2017; Soland & Thum, 2019).”
• Effective Programs in Elementary Mathematics: A Meta-Analysis (2021, background): “One interesting finding from the present review relates to technology in mathematics education, which has been reviewed previously by Cheung and Slavin (2016); Higgins et al.”
• Mathematics Interventions for Upper Elementary and Secondary Students: A Meta-Analysis of Research (2018, neutral): “The implication is that researcher-developed measures may inflate the intervention effects (Cheung & Slavin, 2016).”
• Reading for Writing: A Meta-Analysis of the Impact of Reading Interventions on Writing (2017, methodology): “In terms of variability in effects, Cheung and Slavin (2016) stress the importance of examining if methodological features of experimental studies are correlated with study outcomes.”
• What Can Be Learned from Growth Mindset Controversies? (2020, background): “This is important because lower-quality research designs (non-experimental, non-randomized, or non-representative) do not provide realistic benchmarks against which interventions should be evaluated (Cheung & Slavin, 2016).”
• The Effects of Tier 2 Mathematics Interventions for Students With Mathematics Difficulties: A Meta-Analysis (2020, background): “Standardized measures had lower and negative effect sizes as they are less likely aligned with the intervention than researcher-designed measures (Cheung & Slavin, 2016).”
• Mindsets: A View From Two Eras (2019, background): “The largest effects in the literature for large-sample, randomized controlled experiments with adolescents are roughly .20 SD (see, e.g., Cheung & Slavin, 2016; Nye, Hedges, & Konstantopoulos, 2000; see also Dynarski, 2017).”
• Financial education in schools: A meta-analysis of experimental studies (2020, background): “This finding confirms the results on adult financial education programs studied in Kaiser and Menkhoff (2017) as well as findings from other educational domains where non-randomized impact evaluations also appear to report inflated effect size estimates (Cheung and Slavin, 2016).”