Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 269

The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.

BootES: An R package for bootstrap confidence intervals on effect sizes

Kirby, Kris N., Gerlanc, Daniel·2013·Behavior Research Methods
DOI 10.3758/s13428-013-0330-5
Open this paper in CoChat — free account

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.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.3758/s13428-013-0330-5

Cites

10 of 10 references verified0 citation errors · 0 not checkable

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.

A few free checks a day · papers already verified open instantly · results are public

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

Show all 10 references

Research works printed but not linked: 34

  1. Algina, J., Keselman, H. J., & Penfield, R. D. (2005). An alternative to Cohen’s standardized mean difference effect size: A robust parameter and confidence interval in the two independent groups case. Psychological Methods, 10, 317–328. · DOI · Paper page
  2. Algina, J., Keselman, H. J., & Penfield, R. D. (2006). Confidence interval coverage for Cohen’s effect size statistic. Educational and Psychological Measurement, 66, 945–960. · DOI
  3. APA. (2009). Publication Manual of the American Psychological Association (6th ed.). Washington, DC: American Psychological Association.
  4. Canty, A. J. (2002). Resampling methods in R: The boot package. R News: The Newsletter of the R Project, 2, 2–7.
  5. Carpenter, J., & Bithell, J. (2000). Bootstrap confidence intervals: When, which, what? A practical guide for medical statisticians. Statistics in Medicine, 19, 1141–1164. · doi:10.1002/(sici)1097-0258(20000515)19:9<1141::aid-sim479>3.0.co;2-f
  6. Cumming, G., & Fidler, F. (2009). Confidence intervals: Better answers to better questions. Zeitschrift für Psychologie/Journal of Psychology, 217, 15–26. · DOI · Paper page
  7. Cumming, G., & Finch, S. (2001). A primer on the understanding, use, and calculation of confidence intervals that are based on central and noncentral distributions. Educational and Psychological Measurement, 61, 532–574. · DOI · Paper page
  8. Efron, B. (1979). Bootstrap methods: Another look at the jackknife. Annals of Statistics, 7, 1–26. · DOI · Paper page
  9. Efron, B. (1987). Better bootstrap confidence-intervals. Journal of the American Statistical Association, 82, 171–185. · DOI · Paper page
  10. Efron, B., & Tibshirani, R. J. (1993). An introduction to the bootstrap. New York: Chapman & Hall/CRC. · DOI · Paper page
  11. Finch, S., Cumming, G., Williams, J., Palmer, L., Griffith, E., Alders, C., et al. (2004). Reform of statistical inference in psychology: The case of Memory & Cognition. Behavior Research Methods, Instruments and Computers, 36, 312–324. · DOI
  12. Hall, P. (1986). On the number of bootstrap simulations required to construct a confidence interval. Annals of Statistics, 14, 1453–1462. · DOI
  13. Harlow, L. L., Mulaik, S. A., & Steiger, J. H. (Eds.). (1997). What if there were no significance tests? Mahwah, NJ: Lawrence Erlbaum Associates.
  14. Hedges, L. V., & Olkin, I. (1985). Statistical methods for meta-analysis. Orlando, FL: Academic.
  15. Hess, M. R., Hogarty, K. Y., Ferron, J. M., & Kromrey, J. D. (2007). Interval estimates of multivariate effect sizes: Coverage and interval width estimates under variance heterogeneity and nonnormality. Educational and Psychological Measurement, 67, 21–40. · DOI
  16. Howell, D. C., & McConaughy, S. H. (1982). Nonorthogonal analysis of variance: Putting the question before the answer. Educational and Psychological Measurement, 42, 9–24. · DOI
  17. Kelley, K. (2005). The effects of nonnormal distributions on confidence intervals around the standardized mean difference: Bootstrap and parametric confidence intervals. Educational and Psychological Measurement, 65, 51–69. · DOI · Paper page
  18. Kelley, K. (2007a). Confidence intervals for standardized effect sizes: Theory, application, and implementation. Journal of Statistical Software, 20, 1–24. · DOI
  19. Kelley, K. (2007b). Methods for the Behavioral, Educational, and Social Sciences: An R package. Behavior Research Methods, 39, 979–984. · DOI · Paper page
  20. Killeen, P. R. (2005). An alternative to null-hypothesis significance tests. Psychological Science, 16, 345–353. · DOI · Paper page
  21. Kline, R. B. (2004). Beyond significance testing: Reforming data analysis methods in behavioral research. Washington, DC: American Psychological Association. · DOI · Paper page
  22. Lei, S., & Smith, M. R. (2003). Evaluation of several nonparametric bootstrap methods to estimate confidence intervals for software metrics. IEEE Transactions on Software Engineering, 29, 996–1004. · DOI
  23. Li, J. C., Cui, Y., & Chan, W. (2013). Bootstrap confidence intervals for the mean correlation corrected for case IV range restriction: A more adequate procedure for meta-analysis. Journal of Applied Psychology, 98, 183–193. · doi:10.1037/a0029946
  24. Maxwell, S. E., & Delaney, H. D. (2004). Designing experiments and analyzing data: A model comparison perspective (2nd ed.). Mahwah, NJ: Lawrence Erlbaum Associates.
  25. McGaw, B., & Glass, G. V. (1980). Choice of the metric for effect size in meta-analysis. American Educational Research Journal, 17, 325–337. · DOI
  26. Padilla, M. A., & Veprinsky, A. (2012). Correlation attenuation due to measurement error: A new approach using the bootstrap procedure. Educational and Psychological Measurement, 72, 827–846. · DOI
  27. Pearson, K. (1896). Mathematical contributions to the theory of evolution. III. Regression, heredity and panmixia. Philosophical Transactions of the Royal Society of London, 187, 253–318. · DOI · Paper page
  28. Robertson, C. (1991). Computationally intensive statistics. In P. Lovie & A. D. Lovie (Eds.), New developments in statistics for psychology and the social sciences (Vol. 2, pp. 49–80). Oxford: British Psychological Society.
  29. Rosenthal, R., & Rosnow, R. (1991). Essentials of behavioral research: Methods and data analysis (2nd ed.). New York: McGraw-Hill.
  30. Steiger, J. H. (2004). Beyond the F test: Effect size confidence intervals and tests of close fit in the analysis of variance and contrast analysis. Psychological Methods, 9, 164–182. · DOI · Paper page
  31. Steiger, J. H., & Fouladi, R. T. (1992). R2: A computer program for interval estimation, power calculation, and hypothesis testing for the squared multiple correlation. Behavior Research Methods, Instruments, and Computers, 4, 581–582. · DOI
  32. Steiger, J. H., & Fouladi, R. T. (1997). Noncentrality interval estimation and the evaluation of statistical methods. In L. L. Harlow, S. A. Mulaik, & J. H. Steiger (Eds.), What if there were no significance tests? (pp. 221–257). Mahwah, NJ: Lawrence Erlbaum Associates.
  33. Thompson, B. (2002). What future quantitative social science research could look like: Confidence intervals for effect sizes. Educational Researcher, 31, 25–32. · DOI · Paper page
  34. Wilcox, R. R., & Keselman, H. J. (2003). Modern robust data analysis methods: Measures of central tendency. Psychological Methods, 8, 254–274. · DOI · Paper page

Cited by

Metadata

Authors
Kirby, K. N., & Gerlanc, D.
Venue
Behavior Research Methods
Publisher
Springer Science and Business Media LLC
Published
2013-03-22
Volume
45
Issue
4
Pages
905-927
ISSN
  • 1554-351X (ISSN-L)
  • 1554-3528
DOI
10.3758/s13428-013-0330-5
OpenAlex
W2008130329
Cited by
269
References
10
FWCI
16.13
Type
journal article
Language
en
Update notices
correction (2014-08-30)
Open access
closed
Licence
www.springer.com/tdm (tdm)

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.