Abstract
Designs of small samples: Analysis by generalized least squares. The time series analysis (TSA) constitutes an appropriate procedure of analysis for interrupted time series designs (ITSD). The main disadvantage of this analysis technique is that it requires a high number of observations with object of identifying the corresponding ARIMA model (autoregressive Integrated Moving Averages). However, in applied behavioral investigation most of designs have small samples. As alternative to the TSA, it is possible to appeal to the aproaches of generalized least squares (GLS). The main problem for the aplication of GLS approach is the estimate of the residual variancie-covariance matrix. For this reason, in the present paper a new procedure of GLS is studied, it is proposed as alternative solution to the analysis of data of short time series with a single case and two phases (Arnau, en prensa). It is to apply the approach of ordinary least squares (OLS), transforming the original data and the design matrix by the square root or Cholesky factor of the inverse of the covariance matrix, under the assumption of first order autoregressive stationary model (Fox, 1997). In this study is presented, by a Monte Carlo simulation using the MATLAB program (version 5.2), the goodness of the proposed procedure.
