# name
Regression-based techniques for statistical decision making in single-case designs

# metadata.dc.description.abstract[].value
The present study evaluates the performance of four methods for estimating regression coefficients used to make statistical decisions regarding intervention effectiveness in single-case designs. Ordinary least squares estimation is compared to two correction techniques dealing with general trend and one eliminating autocorrelation whenever it is present. Type I error rates and statistical power are studied for experimental conditions defined by the presence or absence of treatment effect (change in level or in slope), general trend, and serial dependence. The results show that empirical Type I error rates do not approximate the nominal ones in presence of autocorrelation or general trend when ordinary and generalized least squares are applied. The techniques controlling trend show lower false alarm rates, but prove to be insufficiently sensitive to existing treatment effects. Consequently, the use of the statistical significance of the regression coefficients for detecting treatment effects is not recommended for short data series.

# metadata.dc.publisher[].value
Facultad de Psicología de la Universidad de Oviedo y el Colegio Oficial de Psicólogos del Principado de Asturias

# metadata.dc.source[].value
Articles publicats en revistes (Psicologia Social i Psicologia Quantitativa)

# metadata.dc.title[].value
Regression-based techniques for statistical decision making in single-case designs