Hostname: page-component-5cf477f64f-r2nwp Total loading time: 0 Render date: 2025-04-04T02:37:28.737Z Has data issue: false hasContentIssue false
  • English
  • Français

D. Kaplan (2014). Bayesian Statistics for the Social Sciences. The Guilford Press

Review products

D. Kaplan (2014). Bayesian Statistics for the Social Sciences. The Guilford Press

Published online by Cambridge University Press:  01 January 2025

Chun Wang Open the ORCID record for Chun Wang [Opens in a new window]  and
Jack Kostal
Chun Wang
Affiliation:
University of Minnesota
Jack Kostal
Affiliation:
University of Minnesota
Get access Rights & Permissions [Opens in a new window]

Abstract

An abstract is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.
Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'
Type
Book Review
Information
Psychometrika , Volume 82 , Issue 1 , March 2017 , pp. 267 - 272
Copyright
Copyright © 2016 The Psychometric Society

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Andrews, M., & Baguley, T. (2013). Prior approval: The growth of Bayesian methods in psychology. The British Journal of Mathematical and Statistical Psychology, 66, 1–7.CrossRefGoogle ScholarPubMed
Cowles, M. K., & Carlin, B. P. (1996). Markov chain Monte Carlo convergence diagnostics: A comparative review. Journal of the American Statistical Association, 91, 883–904.CrossRefGoogle Scholar
Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (2014). Bayesian data analysis, Boca Raton: Chapman & Hall/CRC.Google Scholar
Gigerenzer, G., & Marewski, J. N. (2015). Surrogate science: The idol of a universal method for scientific inference. Journal of Management, 41, (2), 421–440.CrossRefGoogle Scholar
Kruschke, J. K., Aguinis, H., & Joo, H. (2012). The time has come Bayesian methods for data analysis in the organizational sciences. Organizational Research Methods, 15, (4), 722–752.CrossRefGoogle Scholar
McKee, R. A., & Miller, C. C. (2015). Institutionalizing Bayesianism within the organizational sciences: A practical guide featuring comments from eminent scholars. Journal of Management, 41, (2), 471–490.CrossRefGoogle Scholar
Muthén, B., & Asparouhov, T. (2011). Bayesian SEM: A more flexible representation of substantive theory. Psychological Methods, 17, 313–335.CrossRefGoogle Scholar
Schmidt, F. L., & Hunter, J. E. (1977). Development of a general solution to the problem of validity generalization. Journal of Applied Psychology, 62, (5), 529CrossRefGoogle Scholar