Students' EA-PGI interacts with schools such that school differences in achievement are wider among students with lower EA-PGI (compensation, not Scarr-Rowe suppression)¶

Claim · All claims
Evidence · 1 study (2 entries) · associational r2–r3 · q3
Currency · newest study 2022 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q3 i? In Norwegian multilevel models, school effects on achievement vary by student EA-PGI: schools explain ~4% of achievement variance for students 2 SD below the mean EA-PGI versus ~2% for those 2 SD above. → Rosa Cheesman 2022 q3 i? The slope-intercept correlation was negative, meaning EA-PGI differences are less influential for achievement in schools with overall higher student achievement. → Rosa Cheesman 2022 (2)

Evidence¶

Rosa Cheesman 2022¶

Rosa Cheesman, Nicolai T. Borgen, Torkild H. Lyngstad, Espen M. Eilertsen, Ziada Ayorech, Fartein A. Torvik, Ole A. Andreassen, Henrik D. Zachrisson and Eivind Ystrom. (2022). A population-wide gene-environment interaction study on how genes, schools, and residential areas shape achievement. npj Science of Learning 7:29. https://doi.org/10.1038/s41539-022-00145-8

q3 · i? · associational · r2

Multilevel model results from the population-wide MoBa sample of 23,471 students in 2578 schools, with schools explaining achievement variance at different EA-PGI values. Figure 3 caption states "schools explain ~4% of the phenotypic variance" for students 2 SD below versus ~2% for students 2 SD above the mean.

"School effects on achievement vary across values of the within-family EA-PGI. For students with within-family EA-PGI that are 2 SD below the mean, schools explain ~4% of the phenotypic variance, whereas for students with PGI 2 SD above the mean, schools explain ~2%."

Rosa Cheesman 2022 (2)¶

Rosa Cheesman, Nicolai T. Borgen, Torkild H. Lyngstad, Espen M. Eilertsen, Ziada Ayorech, Fartein A. Torvik, Ole A. Andreassen, Henrik D. Zachrisson and Eivind Ystrom. (2022). A population-wide gene-environment interaction study on how genes, schools, and residential areas shape achievement. npj Science of Learning 7:29. https://doi.org/10.1038/s41539-022-00145-8

q3 · i? · associational · r3

Result from the best-fitting multilevel model (Model 3a) in the Results section, shown in Fig. 2: the slope-intercept correlation was negative, meaning EA-PGI variation is less influential in higher-performing schools, interpreted by the authors as compensation.

"the slope-intercept correlation was negative, meaning that variation in students' EA-PGI is less influential for achievement in the schools with overall higher student achievement."

Discussion¶

Related Claims¶

• Neighbourhood, district, and municipality variation contributes little to achievement (under 2% of variance collectively) and does not interact with student EA-PGI — related
• Five measured school sociodemographic characteristics do not explain the EA-PGI-by-school interaction — related
• Between-school variation in EA-PGI effects is substantial: the average EA-PGI effect is 0.22 with slope SD 0.034, and variance explained by EA-PGI ranges from ~2% in the weakest-effect schools to ~8% in the strongest — related
• The EA-PGI-by-school interaction is stronger for maths and reading than for English — related