The DOI matches its Crossref record; 56 of the 56 references it cites were found in a registry or an index.
OECD's Brief Self-Report Measure of Educational Psychology's Most Useful Affective Constructs: Cross-Cultural, Psychometric Comparisons Across 25 Countries
Abstract
Through a rigorous process of selecting educational psychology's most useful affective constructs, the Organisation for Economic Co-operation and Development (OECD) constructed the Students' Approaches to Learning (SAL) instrument, which requires only 10 min to measure 14 factors that assess self-regulated learning strategies, self-beliefs, motivation, and learning preferences. This study evaluated SAL responses from nationally representative samples of approximately 4,000 15-year-olds from each of 25 countries (N = 107,899)—OECD's Program For International Student Assessment database. In one of the largest and most powerful cross-cultural comparisons of diverse educational psychology constructs, this study used multiple group confirmatory factor analyses to show that SAL's a priori 14-factor solution is well defined and reasonably invariant across the 25 countries, as are relations between SAL factors and 4 criterion variables (gender, socioeconomic status, math achievement, and verbal achievement). The results support posited relations among constructs derived from different theoretical perspectives and their cross-cultural generalizability. The SAL provides a standard set of educational psychological measures that have been translated into many languages with nationally representative norms that have been validated across the world. These should be a useful focus or supplement in diverse educational psychology research settings, and should provide the longitude and latitude against which to map new and existing educational psychology constructs.
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
Hu, L., & Bentler, P. M. (1999) · Structural Equation Modeling A Multidisciplinary Journal · doi:10.1080/10705519909540118 · Cited by 108,788
Multiple regression: testing and interpreting interactions
(1992) · Choice Reviews Online · doi:10.5860/choice.29-3352 · Cited by 36,654
Intrinsic Motivation and Self-Determination in Human Behavior
Deci, E. L., & Ryan, R. M. (1985) · doi:10.1007/978-1-4899-2271-7 · Cited by 26,772
Comparative fit indexes in structural models.
Bentler, P. M. (1990) · Psychological Bulletin · doi:10.1037/0033-2909.107.2.238 · Cited by 25,266
(1985) · Medical Entomology and Zoology · Cited by 19,180
Hierarchical Linear Models: Applications and Data Analysis Methods.
Longford, N. T., Bryk, A. S., & Raudenbush, S. W. (1993) · Contemporary Sociology · doi:10.2307/2075823 · Cited by 18,724
Structural Equations with Latent Variables.
Clogg, C. C., & Bollen, K. A. (1991) · Contemporary Sociology · doi:10.2307/2072165 · Cited by 17,442
Structural Equations with Latent Variables
Bollen, K. A. (1989) · Wiley series in probability and statistics · doi:10.1002/9781118619179 · Cited by 17,277
Statistical Analysis With Missing Data
Lahiff, M., Little, R. J. A., & Rubin, D. B. (1989) · Journal of the American Statistical Association · doi:10.2307/2289883 · Cited by 17,265
Evaluating Goodness-of-Fit Indexes for Testing Measurement Invariance
Cheung, G. W., & Rensvold, R. B. (2002) · Structural Equation Modeling: A Multidisciplinary Journal · doi:10.1207/s15328007sem0902_5 · Cited by 16,280
Cited by
PISA 2018 Assessment and Analytical Framework
OECD (2019) · PISA · doi:10.1787/b25efab8-en · Cited by 1,614
Academic self‐concept and academic achievement: Relations and causal ordering
Marsh, H. W., & Martin, A. J. (2011) · British Journal of Educational Psychology · doi:10.1348/000709910x503501 · Cited by 975
Self-citationLee, J. (2009) · Learning and Individual Differences · doi:10.1016/j.lindif.2008.10.009 · Cited by 493
Classification of learning outcomes: evidence from the computer games literature
O'Neil, H. F., Wainess, R., & Baker, E. L. (2005) · The Curriculum Journal · doi:10.1080/09585170500384529 · Cited by 447
Gogol, K., Brunner, M., Goetz, T., Martin, R., Ugen, S., Keller, U., Fischbach, A., & Preckel, F. (2014) · Contemporary Educational Psychology · doi:10.1016/j.cedpsych.2014.04.002 · Cited by 440
Metadata
- Authors
- Marsh, H. W., Hau, K.-T., Artelt, C., Baumert, J., & Peschar, J. L.
- Venue
- International Journal of Testing
- Publisher
- Informa UK Limited
- Published
- 2006-12
- Volume
- 6
- Issue
- 4
- Pages
- 311-360
- ISSN
- 1530-5058 (ISSN-L)
- 1532-7574
- OpenAlex
- W1993672325
- Cited by
- 593
- References
- 56
- FWCI
- 16.79
- Type
- journal article
- Language
- en
- Open access
- closed
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: OECD's Brief Self-Report Measure of Educational Psychology's Most Useful Affective Constructs: Cross-Cultural, Psychometric Comparisons Across 25 Countries. https://app.cochat.ai/papers/10.1207/s15327574ijt0604_1
- The paper
- Marsh, H. W., Hau, K.-T., Artelt, C., Baumert, J., & Peschar, J. L. (2006). OECD's Brief Self-Report Measure of Educational Psychology's Most Useful Affective Constructs: Cross-Cultural, Psychometric Comparisons Across 25 Countries. International Journal of Testing. https://doi.org/10.1207/s15327574ijt0604_1
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.