Generalized Linear Mixed Effects Modeling (GLMM) of Functional Analysis Graphical Construction Elements on Visual Analysis.

Multielement designs are the quintessential design tactic to evaluate outcomes of a functional analysis in applied behavior analysis. Protecting the credibility of the data collection, graphing, and visual analysis processes from a functional analysis increases the likelihood that optimal intervention decisions are made for individuals. Time-series graphs and visual analysis are the most prevalent method used to interpret functional analysis data. The current project included two principal aims. First, we tested whether the graphical construction manipulation of the x-to-y axes ratio (i.e., data points per x- axis to y-axis ratio [DPPXYR]) influenced visual analyst's detection of a function on 32 multielement design graphs displaying functional analyses. Second, we investigated the alignment between board certified behavior analysts (BCBAs; N = 59) visual analysis with the modified visual inspection criteria (Roane et al., Journal of Applied Behavior Analysis, 46, 130-146, 2013). We found that the crossed GLMM that included random slopes, random intercepts, and did not include an interaction effect (AIC = 1406.1, BIC = 1478.2) performed optimally. Second, alignment between BCBAs decisions and the MVI appeared to be low across data sets. We also leveraged current best practices in Open Science for raw data and analysis transparency.

Consistent Visual Analysis of Multielement Data: A Preliminary Evaluation.

Experimenters provided 33 graphical displays of hypothetical data depicted in a multielement experimental design to editorial board members of prominent, applied, behavior-analytic journals via an online survey. For each display, participants indicated (a) the presence or absence of experimental control and (b) the degree of experimental control (rated on a 1-100 scale). Each depiction varied systematically in (a) the number of data paths, (b) the number of data paths elevated above the control, (c) the mean difference between affected data paths and control conditions, and (d) the degree of variability within conditions. Correspondence among experts' ratings of experimental control was high across all presented graphical displays, supporting the reliability of visual analysis as an evaluative tool for these designs.

Effects of Three Music Therapy Interventions on the Verbal Expressions of Children With Autism Spectrum Disorder: A Combined Single-Subject Design.

The specific aims of this research study were to (a) examine the differential effect of three different music interventions, namely the interactive music playing therapy ("music and singing"), interaction music singing therapy ("singing"), and receptive music therapy ("listening") studying the varying latency periods in the response time it took 3-year-old children diagnosed with autism spectrum disorder (ASD) to elicit the target word vocally; and (b) assess the index of happiness of children with ASD after the implementation of the three music interventions, which can, in turn, be used to influence their overall quality of life through this specific intervention. This study used a combined single-subject research design consisting of delayed multiple baseline across the participants and a multielement design to compare the effects of each music intervention technique targeting the child's verbal response during playback of a practiced song. Findings demonstrated "singing" to be associated with the lowest latency compared to the other two interventions ("listening" and "singing and music") across participants. Additionally, happiness levels varied from neutral to happy, signifying an overall positive experience during participation in the music applied behavior analysis (ABA) intervention.

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.

Using a Visual Structured Criterion for the Analysis of Alternating-Treatment Designs.

Although visual inspection remains common in the analysis of single-case designs, the lack of agreement between raters is an issue that may seriously compromise its validity. Thus, the purpose of our study was to develop and examine the properties of a simple structured criterion to supplement the visual analysis of alternating-treatment designs. To this end, we generated simulated data sets with varying number of points, number of conditions, effect sizes, and autocorrelations, and then measured Type I error rates and power produced by the visual structured criterion (VSC) and permutation analyses. We also validated the results for Type I error rates using nonsimulated data. Overall, our results indicate that using the VSC as a supplement for the analysis of systematically alternating-treatment designs with at least five points per condition generally provides adequate control over Type I error rates and sufficient power to detect most behavior changes.

Effects of fixed versus random condition sequencing during multielement functional analyses.

It has been suggested that a fixed condition sequence might facilitate differential responding during multielement functional analyses (FAs) by capitalizing on or limiting sequence effects (Iwata, Pace, et al., 1994); however, the effects of condition sequence have not been examined empirically. We conducted fixed- and random-sequence FAs for 7 individuals with developmental disabilities to determine the relative effects that sequence may have on assessment outcomes. Experimental conditions during the fixed sequence were conducted in the following order: ignore, attention, play, and demand; condition order during the random sequence was determined randomly. Results showed that sequence had no influence on the FA outcomes for 3 subjects, whereas differential responding emerged either faster (1 subject) or only (3 subjects) under the fixed sequence for the remaining subjects. These results suggest that the fixed sequence, a simple modification, should be used when conducting multielement FAs to accommodate the influence of establishing operations across assessment conditions.