The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Cross-Validation: A Method Every Psychologist Should Know
Open accessAbstract
Cross-validation is a statistical procedure that every psychologist should know. Most are possibly familiar with the procedure in a global way but have not used it for the analysis of their own data. We introduce cross-validation for the purpose of model selection in a general sense, as well as an R package we have developed for this kind of analysis, and we present examples illustrating the use of this package for types of research problems that are often encountered in the social sciences. Cross-validation can be an easy-to-use alternative to null-hypothesis testing, and it has the benefit that it does not make as many assumptions.
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Citations
Cites
A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection
Kohavi, R. (1995) · Zenodo · doi:10.5281/zenodo.19712698 · Cited by 10,909
Kuhn, M., & Johnson, K. (2013) · doi:10.1007/978-1-4614-6849-3 · Cited by 6,788
Cohen, J. (1994) · American Psychologist · doi:10.1037/0003-066x.49.12.997 · Cited by 4,003
Cross‐validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure
Roberts, D. R., Bahn, V., Ciuti, S., Boyce, M. S., Elith, J., Guillera‐Arroita, G., Hauenstein, S., Lahoz‐Monfort, J. J., Schröder, B., Thuiller, W., et al. (2017) · Ecography · doi:10.1111/ecog.02881 · Cited by 3,689
A survey of cross-validation procedures for model selection
Arlot, S., & Celisse, A. (2010) · Statistics Surveys · doi:10.1214/09-ss054 · Cited by 3,522
The New Statistics: Why and How
Cumming, G. (2014) · Psychological Science · doi:10.1177/0956797613504966 · Cited by 3,200
The Predictive Sample Reuse Method with Applications
Geisser, S. (1975) · Journal of the American Statistical Association · doi:10.1080/01621459.1975.10479865 · Cited by 2,748
A practical solution to the pervasive problems ofp values
Wagenmakers, E.-J. (2007) · Psychonomic Bulletin & Review · doi:10.3758/bf03194105 · Cited by 2,490
Bias in error estimation when using cross-validation for model selection
Varma, S., & Simon, R. (2006) · BMC Bioinformatics · doi:10.1186/1471-2105-7-91 · Cited by 2,485
Cited by
Azevedo, F., & Jost, J. T. (2021) · Group Processes & Intergroup Relations · doi:10.1177/1368430221990104 · Cited by 113
Temporomandibular joint segmentation in MRI images using deep learning
Li, M., Punithakumar, K., Major, P. W., Le, L. H., Nguyen, K.-C. T., Pacheco-Pereira, C., Kaipatur, N. R., Nebbe, B., Jaremko, J. L., & Almeida, F. T. (2022) · Journal of Dentistry · doi:10.1016/j.jdent.2022.104345 · Cited by 55
Metadata
- Authors
- de Rooij, M., & Weeda, W.
- Venue
- Advances in Methods and Practices in Psychological Science
- Publisher
- SAGE Publications
- Published
- 2020-05-27
- Volume
- 3
- Issue
- 2
- Pages
- 248-263
- ISSN
- 2515-2459 (ISSN-L)
- 2515-2467
- OpenAlex
- W3031690984
- Cited by
- 182
- References
- 9
- FWCI
- 16.76
- Type
- journal article
- Language
- en
- Open access
- hybrid
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