The DOI matches its Crossref record; 93 of the 93 references it cites were found in a registry or an index.
Accomplishment in science, technology, engineering, and mathematics (STEM) and its relation to STEM educational dose: A 25-year longitudinal study.
Abstract
Two studies examined the relationship between precollegiate advanced/enriched educational experiences and adult accomplishments in science, technology, engineering, and mathematics (STEM). In Study 1, 1,467 13-year-olds were identified as mathematically talented on the basis of scores 500 (top 0.5%) on the math section of the Scholastic Assessment Test; subsequently, their developmental trajectories were studied over 25 years. Particular attention was paid to high-level STEM accomplishments with low base rates in the general population (STEM PhDs, STEM publications, STEM tenure, STEM patents, and STEM occupations). Study 2 retrospectively profiled the adolescent advanced/enriched educational experiences of 714 top STEM graduate students (mean age 25), and related these experiences to their STEM accomplishments up to age 35. In both longitudinal studies, those with notable STEM accomplishments manifested past histories involving a richer density of advanced precollegiate educational opportunities in STEM (a higher “STEM dose”) than less highly achieving members of their respective cohorts. While both studies are quasi-experimental, they suggest that for mathematically talented and academically motivated young adolescents, STEM accomplishments are facilitated by a rich mix of precollegiate STEM educational opportunities that are designed to be intellectually challenging, even for students at precocious developmental levels. These opportunities appear to be uniformly important for both sexes.
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
Statistical power analysis for the behavioral sciences
(1990) · Computers, Environment and Urban Systems · doi:10.1016/0198-9715(90)90050-4 · Cited by 65,269
Convergent and discriminant validation by the multitrait-multimethod matrix.
Campbell, D. T., & Fiske, D. W. (1959) · Psychological Bulletin · doi:10.1037/h0046016 · Cited by 17,116
(2002) · Cited by 13,211
(1979) · Medical Entomology and Zoology · Cited by 11,252
Hapeman, D. E. (1991) · Technometrics · doi:10.1080/00401706.1991.10484817 · Cited by 9,373
An Introduction to Categorical Data Analysis
Agresti, A. (2007) · Wiley Series in Probability and Statistics · doi:10.1002/0470114754 · Cited by 6,689
"General Intelligence," Objectively Determined and Measured
Spearman, C. (1904) · The American Journal of Psychology · doi:10.2307/1412107 · Cited by 6,207
(1968) · PubMed · Cited by 4,803
The World Is Flat: A Brief History of the Twenty-First Century
Ikenberry, G. J., & Friedman, T. L. (2005) · Foreign Affairs · doi:10.2307/20031715 · Cited by 4,210
The two disciplines of scientific psychology.
Cronbach, L. J. (1957) · American Psychologist · doi:10.1037/h0043943 · Cited by 2,523
Cited by
The benefits of playing video games.
Granic, I., Lobel, A., & Engels, R. C. M. E. (2014) · American Psychologist · doi:10.1037/a0034857 · Cited by 2,456
The malleability of spatial skills: A meta-analysis of training studies.
Uttal, D. H., Meadow, N. G., Tipton, E., Hand, L. L., Alden, A. R., Warren, C., & Newcombe, N. S. (2013) · Psychological Bulletin · doi:10.1037/a0028446 · Cited by 1,899
Subotnik, R. F., Olszewski-Kubilius, P., & Worrell, F. C. (2011) · Psychological Science in the Public Interest · doi:10.1177/1529100611418056 · Cited by 1,269
Stoet, G., & Geary, D. C. (2013) · PLoS ONE · doi:10.1371/journal.pone.0057988 · Cited by 398
Su, R., & Rounds, J. (2015) · Frontiers in Psychology · doi:10.3389/fpsyg.2015.00189 · Cited by 388
Metadata
- Authors
- Wai, J., Lubinski, D., Benbow, C. P., & Steiger, J. H.
- Venue
- Journal of Educational Psychology
- Published
- 2010-11
- Volume
- 102
- Issue
- 4
- Pages
- 860-871
- ISSN
- 0022-0663 (ISSN-L)
- 1939-2176
- OpenAlex
- W2107811114
- Cited by
- 395
- References
- 93
- FWCI
- 13.58
- 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: Accomplishment in science, technology, engineering, and mathematics (STEM) and its relation to STEM educational dose: A 25-year longitudinal study. https://app.cochat.ai/papers/10.1037/a0019454
- The paper
- Wai, J., Lubinski, D., Benbow, C. P., & Steiger, J. H. (2010). Accomplishment in science, technology, engineering, and mathematics (STEM) and its relation to STEM educational dose: A 25-year longitudinal study. Journal of Educational Psychology. https://doi.org/10.1037/a0019454
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.