Data-Division–Specific Robustness and Power of Randomization Tests for ABAB Designs

This study deals with the statistical properties of a randomization test applied to an ABAB design in cases where the desirable random assignment of the points of change in phase is not possible. To obtain information about each possible data division, the authors carried out a conditional Monte Carlo simulation with 100,000 samples for each systematically chosen triplet. The authors studied robustness and power under several experimental conditions—different autocorrelation levels and different effect sizes as well as different phase lengths determined by the points of change. Type I error rates were distorted by the presence of autocorrelation for the majority of data divisions. The authors obtained satisfactory Type II error rates only for large treatment effects. The relation between the lengths of the four phases appeared to be an important factor for the robustness and power of the randomization test.     

Changing Criterion Designs: Integrating Methodological and Data Analysis Recommendations

Abstract

Changing criterion designs (CCD) are single-case experimental designs that entail a step-by-step approximation of the final level desired for a target behavior. Following a recent review on the desirable methodological features of CCDs, the current text focuses on an analytical challenge: the definition of an objective rule for assessing the correspondence between criterion levels and behavior. We review extensively applied research and methodological recommendations for establishing experimental control, such as the range-bound CCD, and integrate them into a proposal for a formal expression of an objective rule. We illustrate how this proposal can lead to establishing the acceptable level of behavior prior to gathering the intervention phase data and how it leaves space for professional judgment. A discussion of software implementing the suggested proposal is included.