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.
Improved Randomization Tests for a Class of Single-Case Intervention Designs
Forty years ago, Eugene Edgington developed a single-case AB intervention design-and-analysis procedure based on a random determination of the point at which the B phase would start. In the present simulation studies encompassing a variety of AB-type contexts, it is demonstrated that by also randomizing the order in which the A and B phases are administered, a researcher can markedly increase the procedure's statistical power.
An intelligent control system for ventilators.
This study reports on a ventilator system that consists of several intelligent modules for controlling ventilator operation. These modules are software programs in two controllers. One controller is a personal computer used for diagnoses, determining settings and checking the effects of settings. The other controller is a single-chip microprocessor in a ventilator that controls the ventilator's settings in accordance with the computer settings. After setting up the system, an artificial lung model simulating a patient's lung is used to test the system. The result of test run indicated that it always responds to a patient's lung condition in a stable manner. Thus, the proposed system with its intelligent modules may assist clinicians in caring for patients and managing ventilator operation.