A generalized least squares regression approach for computing effect sizes in single-case research: Application examples

A new method for deriving effect sizes from single-case designs is proposed. The strategy is applicable to small-sample time-series data with autoregressive errors. The method uses Generalized Least Squares (GLS) to model the autocorrelation of the data and estimate regression parameters to produce...

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Bibliographic Details
Published in:Journal of school psychology 2011-06, Vol.49 (3), p.301-321
Main Authors: Maggin, Daniel M., Swaminathan, Hariharan, Rogers, Helen J., O'Keeffe, Breda V., Sugai, George, Horner, Robert H.
Format: Article
Language:English
Subjects:
Autocorrelation
Bullying
Comparative Analysis
Deviation
Effect Size
Humans
Least Squares Statistics
Least-Squares Analysis
Magnitude
Methodology
Parameters
Regression (Statistics)
Research Design
Single-case research
Statistical Analysis
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Summary:A new method for deriving effect sizes from single-case designs is proposed. The strategy is applicable to small-sample time-series data with autoregressive errors. The method uses Generalized Least Squares (GLS) to model the autocorrelation of the data and estimate regression parameters to produce an effect size that represents the magnitude of treatment effect from baseline to treatment phases in standard deviation units. In this paper, the method is applied to two published examples using common single case designs (i.e., withdrawal and multiple-baseline). The results from these studies are described, and the method is compared to ten desirable criteria for single-case effect sizes. Based on the results of this application, we conclude with observations about the use of GLS as a support to visual analysis, provide recommendations for future research, and describe implications for practice.
ISSN:0022-4405
1873-3506
DOI:10.1016/j.jsp.2011.03.004