FURTHER GENERALIZATION TECHNOLOGY: ACCOUNTING FOR NATURAL COVARIATION IN GENERALIZATION ASSESSMENT

Abstract

In recent years, the assessment of generalization effects has become a major priority of applied behavior analysis. In this paper we propose a set of procedures to increase the accuracy of generalization assessments by accounting for the degree of natural covariation between treated and untreated behaviors. Scatterplot analyses were used (a) to assess the amount of baseline and postbaseline covariation betweeen behaviors, (b) to determine if the observed generalization effect was due to a preexisting covariation between the behaviors, and (c) to assess if there is a significant change in the strength of the relationship between the behaviors as a function of the intervention. Six hypothetical sets of data are used to demonstrate how these procedures provide more accurate and detailed generalization assessment.