Universidad de Barcelona, Facultad de Psicología, Barcelona, Spain.
Psicothema. 2010 Nov;22(4):1026-32.
The present study evaluates the performance of four methods for estimating regression coefficients used to make statistical decisions about intervention effectiveness in single-case designs. Ordinary least square estimation is compared to two correction techniques dealing with general trend and a procedure that eliminates 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 approach the nominal ones in the 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.
本研究评估了四种用于估计回归系数的方法在单案例设计中进行干预效果统计决策的性能。将普通最小二乘估计与两种处理趋势的修正技术和一种在存在自相关时消除自相关的方法进行了比较。研究了实验条件下的Ⅰ型错误率和统计功效，这些实验条件由治疗效果（水平变化或斜率变化）、趋势和序列相关性的存在或不存在来定义。结果表明，当应用普通最小二乘法和广义最小二乘法时，自相关或趋势存在时，经验Ⅰ型错误率不会接近名义值。处理趋势的技术显示出较低的误报率，但对现有的治疗效果不够敏感。因此，不建议在短数据系列中使用回归系数的统计显著性来检测治疗效果。
