The DOI matches its Crossref record; 73 of the 73 references it cites were found in a registry or an index.
The substantiveness of socioeconomic school compositional effects in Australia: measurement error and the relationship with academic composition
Published version of a preprint
Abstract
This study examines the effect of school socioeconomic composition on student achievement growth in Australian schooling, and its relationship with academic composition utilising the National Assessment Program—Literacy and Numeracy (NAPLAN) dataset. Previous research has found that school composition predicts a range of schooling outcomes. A critique of school compositional research has been that measurement error may have biased findings of compositional effects. Prior studies have found that socioeconomic compositional effect sizes are small when models include academic composition. The relationship between socioeconomic and academic compositions has yet to be fully determined. Multi-level regressions and structural equation models were compared to estimate the degree of bias in socioeconomic compositional effects due to measurement error. Multi-level path models were used to test if academic composition mediated the relationship between socioeconomic composition and achievement growth. The results showed that measurement error did not bias compositional effects in the dataset, and academic composition mediates the relationship between socioeconomic composition and achievement growth. We argue that school value-add research should include academic composition to account for contextual effects. The socioeconomic compositional effect is of practical significance to policy makers and educational researchers due to its relative size compared to average student achievement growth. Potential reforms include ensuring public subsidies to private schools in Australia do not increase school segregation and the amelioration of the effects of residential segregation through school funding reforms.
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
Hierarchical Linear Models: Applications and Data Analysis Methods
Davidian, M. (2003) · Journal of the American Statistical Association · doi:10.1198/jasa.2003.s288 · Cited by 13,723
(2010) · Cited by 7,034
Socioeconomic Status and Academic Achievement: A Meta-Analytic Review of Research
Sirin, S. R. (2005) · Review of Educational Research · doi:10.3102/00346543075003417 · Cited by 4,687
A general multilevel SEM framework for assessing multilevel mediation
Preacher, K. J., Zyphur, M. J., & Zhang, Z. (2010) · Psychological Methods · doi:10.1037/a0020141 · Cited by 3,796
Dunteman, G. H. (1989) · Sage eBooks · doi:10.4135/9781412985475 · Cited by 2,713
PISA 2015 Results (Volume I): Excellence and Equity in Education
OECD (2016) · PISA · doi:10.1787/9789264266490-en · Cited by 1,316
Effects of Parental Involvement on Eighth-Grade Achievement
Sui-Chu, E. H., & Willms, J. D. (1996) · Sociology of Education · doi:10.2307/2112802 · Cited by 1,128
Alternative methods for assessing mediation in multilevel data: The advantages of multilevel SEM
Preacher, K. J., Zhang, Z., & Zyphur, M. J. (2011) · Structural Equation Modeling: A Multidisciplinary Journal · doi:10.1080/10705511.2011.557329 · Cited by 889
Lüdtke, O., Marsh, H. W., Robitzsch, A., Trautwein, U., Asparouhov, T., & Muthén, B. (2008) · Psychological Methods · doi:10.1037/a0012869 · Cited by 886
PISA 2018 Results (Volume II): Where All Students Can Succeed
OECD (2019) · PISA · doi:10.1787/b5fd1b8f-en · Cited by 807
Research works printed but not linked: 3
- Greenwell, T., & Bonnor, C. (2022). Waiting for Gonski: How Australia failed its schools. University of New South Wales Press.
- Muthén, L. K., & Muthén, B. O. (2017). Mplus user’s guide (8th ed.). Muthén & Muthén.
- Organisation for Economic Co-operation and Development. (2017). PISA 2015 technical report. OECD.
Cited by
School Finance Reform and the Distribution of Student Achievement
Lafortune, J., Rothstein, J., & Schanzenbach, D. W. (2018) · American Economic Journal: Applied Economics · doi:10.1257/app.20160567 · Cited by 415
Metadata
- Authors
- Sciffer, M. G., Perry, L. B., & McConney, A.
- Venue
- Large-scale Assessments in Education
- Published
- 2022-11-23
- Volume
- 10
- Issue
- 1
- ISSN
- 2196-0739
- Cited by
- 16
- References
- 73
- Language
- en
- Licence
- creativecommons.org/licenses/by/4.0 (tdm)
- creativecommons.org/licenses/by/4.0 (vor)
- Funders
- Australian Government Research Training Program
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
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.