Randomization Procedures for Changing Criterion Designs.
To strengthen the scientific credibility arguments for single-case intervention studies, randomization design-and-analysis methods have been developed for the multiple-baseline, ABAB, and alternating treatment designs, including options for preplanned designs, wherein the series and phase lengths are established prior to gathering data, as well as options for response-guided designs, wherein ongoing visual analyses guide decisions about when to intervene. Our purpose here is to develop randomization methods for another class of single-case design, the changing criterion design. We first illustrate randomization design-and-analysis methods for preplanned changing criterion designs and then develop and illustrate methods for response-guided changing criterion designs. We discuss the limitations associated with the randomization methods and the validity of the corresponding intervention-effect inferences.
A Priori Justification for Effect Measures in Single-Case Experimental Designs
Due to the complex nature of single-case experimental design data, numerous effect measures are available to quantify and evaluate the effectiveness of an intervention. An inappropriate choice of the effect measure can result in a misrepresentation of the intervention effectiveness and this can have far-reaching implications for theory, practice, and policymaking. As guidelines for reporting appropriate justification for selecting an effect measure are missing, the first aim is to identify the relevant dimensions for effect measure selection and justification prior to data gathering. The second aim is to use these dimensions to construct a user-friendly flowchart or decision tree guiding applied researchers in this process. The use of the flowchart is illustrated in the context of a preregistered protocol. This is the first study that attempts to propose reporting guidelines to justify the effect measure choice, before collecting the data, to avoid selective reporting of the largest quantifications of an effect. A proper justification, less prone to confirmation bias, and transparent and explicit reporting can enhance the credibility of the single-case design study findings.
Combining Probabilities from Discrete Probability Distributions
The combining of probabilities from separate studies has been discussed frequently in regard to probabilities based on continuous probability distributions. Little has been written, however, regarding the combining of probabilities from discrete distributions, such as probability distributions for nonparametric tests. This paper explains how to combine probabilities when some or all of them are from discrete probability distributions.