Experimental analysis of stereotypy with applications of nonparametric statistical tests for alternating treatments designs

Single-case intervention research design standards: Additional proposed upgrades and future directions

Single-case intervention research design standards have evolved considerably over the past decade. These standards serve the dual role of assisting in single-case design (SCD) intervention research methodology and as guidelines for literature syntheses within a particular research domain. In a recent article (Kratochwill et al., 2021), we argued for a need to clarify key features of these standards. In this article we offer additional recommendations for SCD research and synthesis standards that have been either underdeveloped or missing in the conduct of research and in literature syntheses. Our recommendations are organized into three categories: expanding design standards, expanding evidence standards, and expanding the applications and consistency of SCDs. The recommendations we advance are for consideration for future standards, research design training, and they are especially important to guide the reporting of SCD intervention investigations as they enter the literature-synthesis phase of evidence-based practice initiatives.

Descriptive Longitudinal Analysis of Stereotypy and Corresponding Changes in Psychotropic Medication

INTRODUCTION

Psychotropic medication is often prescribed to individuals with intellectual and developmental disabilities who engage in challenging and other behavior (e.g., aggression and stereotypy, respectively), but there is limited understanding of the effects of these medications on behavior.

OBJECTIVE

Within the context of a larger study that evaluated the effects of psychotropic medication regimen changes on the presentation of challenging behavior, this study describes the presentation of stereotypic behavior of three individuals diagnosed with autism spectrum disorder.

METHODS

Stereotypy was measured during weekly, one-hour, direct observations and during the control and ignore conditions of functional analyses of challenging behavior (which were conducted following changes in psychotropic medication regimens).

RESULTS

Patterns of stereotypy varied over time, but not significantly, and at times seemed to coincide with medication changes.

DISCUSSION

Our results suggest stereotypy persists throughout adulthood; however, additional research is needed.

Assessing Consistency in Single-Case Data Features Using Modified Brinley Plots

The current text deals with the assessment of consistency of data features from experimentally similar phases and consistency of effects in single-case experimental designs. Although consistency is frequently mentioned as a critical feature, few quantifications have been proposed so far: namely, under the acronyms CONDAP (consistency of data patterns in similar phases) and CONEFF (consistency of effects). Whereas CONDAP allows assessing the consistency of data patterns, the proposals made here focus on the consistency of data features such as level, trend, and variability, as represented by summary measures (mean, ordinary least squares slope, and standard deviation, respectively). The assessment of consistency of effect is also made in terms of these three data features, while also including the study of the consistency of an immediate effect (if expected). The summary measures are represented as points on a modified Brinley plot and their similarity is assessed via quantifications of distance. Both absolute and relative measures of consistency are proposed: the former expressed in the same measurement units as the outcome variable and the latter as a percentage. Illustrations with real data sets (multiple baseline, ABAB, and alternating treatments designs) show the wide applicability of the proposals. We developed a user-friendly website to offer both the graphical representations and the quantifications.

Randomization Tests for Single Case Designs with Rapidly Alternating Conditions: An Analysis of p-Values from Published Experiments

Two common barriers to applying statistical tests to single-case experiments are that single-case data often violate the assumptions of parametric tests and that random assignment is inconsistent with the logic of single-case design. However, in the case of randomization tests applied to single-case experiments with rapidly alternating conditions, neither the statistical assumptions nor the logic of the designs are violated. To examine the utility of randomization tests for single-case data, we collected a sample of published articles including alternating treatments or multielement designs with random or semi-random condition sequences. We extracted data from graphs and used randomization tests to estimate the probability of obtaining results at least as extreme as the results in the experiment by chance alone (i.e., p-value). We compared the distribution of p-values from experimental comparisons that did and did not indicate a functional relation based on visual analysis and evaluated agreement between visual and statistical analysis at several levels of α. Results showed different means, shapes, and spreads for the p-value distributions and substantial agreement between visual and statistical analysis when α = .05, with lower agreement when α was adjusted to preserve family-wise error at .05. Questions remain, however, on the appropriate application and interpretation of randomization tests for single-case designs.