Is Human Behavior Autocorrelated? An Empirical Analysis

Abstract

The current review and analysis investigated the presence of serial dependency (or autocorrelation) in single-subject applied behavior-analytic research. While well researched, few studies have controlled for the number of data points that appeared in the time-series and, thus, the negative bias of the r coefficient, and the power to detect true serial dependency effects. Therefore, all baseline graphs that appeared in the Journal of Applied Behavior Analysis (JABA) between 1968 and 1993 that provided more than 30 data points were examined for the presence of serial dependency (N = 103). Results indicated that 12% of the baseline graphs provided a significant lag-1 autocorrelation, and over 83% of them had coefficient values less than or equal to (±.25). The distribution of the lag-1 autocorrelation coefficients had a mean of .10. Subsequent distributions of partial autocorrelations at lags two through seven had smaller means indicating that as the distance between observations increases (i.e., the lag), serial dependency decreased. Although serial dependency did not appear to be a common property of the single-subject behavioral experiments, it is recommended that, whenever statistical analyses are contemplated, its presence should always be examined. Alternatives for coping with the presence of significant levels of serial dependency were discussed in terms of: (a) using alternative statistical procedures (e.g., ARIMA models, randomization tests, the Shewhart quality-control charts); (b) correcting statistics of traditional parametric procedures (e.g., t, F); or (c) using the autocorrelation coefficient as an indicator and estimate of reliable intervention effects.