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Bayesian estimates of autocorrelations in single-case designs
William R Shadish1, David M Rindskopf, Larry V Hedges
1School of Social Sciences, Humanities and Arts, University of California, Merced, 5200 North Lake Rd, Merced, CA 95343, USA. wshadish@ucmerced.edu
Behavior Research Methods
|December 15, 2012
Summary
Bayesian estimates reduce sampling error in single-case designs. This method offers more accurate autocorrelation estimates than traditional methods, improving data analysis and debate in research.
Area of Science:
- Statistics
- Behavioral Research Methods
Background:
- Single-case designs often involve small sample sizes, leading to significant sampling error in autocorrelation estimates.
- Traditional methods rely on observed autocorrelation estimates, which can be unreliable due to this sampling error.
Purpose of the Study:
- To present Bayesian estimation methods for autocorrelations in single-case designs.
- To demonstrate how Bayesian approaches reduce sampling error compared to traditional estimators.
Main Methods:
- Introduction of empirical Bayes estimates to illustrate shrinkage and sampling error concepts.
- Presentation of fully Bayesian estimates, explaining the differences from empirical Bayes methods.
- Provision of analysis scripts as supplemental materials.
Main Results:
- Bayesian estimates significantly reduce the impact of sampling error on autocorrelation estimation.
- The proposed methods provide more accurate population parameter estimates compared to observed estimates.
Conclusions:
- Bayesian estimation is a valuable tool for analyzing autocorrelations in single-case designs.
- This approach enhances statistical methods requiring independent autocorrelation estimates for data analysis.
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