Three Alternatives for Graphing Behavioral Data: A Comparison of Usability and Acceptability

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.

How to assess and take into account trend in single-case experimental design data

One of the data features that are expected to be assessed when analyzing single-case experimental designs (SCED) data is trend. The current text deals with four different questions that applied researchers can ask themselves when assessing trend and especially when dealing with improving baseline trend: (a) What options exist for assessing the presence of trend?; (b) Once assessed, what criterion can be followed for deciding whether it is necessary to control for baseline trend?; (c) What strategy can be followed for controlling for baseline trend?; and (d) How to proceed in case there is baseline trend only in some A-B comparisons? Several options are reviewed for each of these questions in the context of real data, and tentative recommendations are provided. A new user-friendly website is developed to implement the options for fitting a trend line and a criterion for selecting a specific technique for that purpose. Trend-related and more general data analytical recommendations are provided for applied researchers.Trial registration: ClinicalTrials.gov identifier: NCT04560777.

Slope Identification and Decision Making: A Comparison of Linear and Ratio Graphs

Applied behavior analysts have traditionally relied on visual analysis of graphic data displays to determine the extent of functional relations between variables and guide treatment implementation. The present study assessed the influence of graph type on behavior analysts' (n = 51) ratings of trend magnitude, treatment decisions based on changes in trend, and their confidence in decision making. Participants examined simulated data presented on linear graphs featuring equal-interval scales as well as graphs with ratio scales (i.e., multiply/divide or logarithmic vertical axis) and numeric indicators of celeration. Standard rules for interpreting trends using each display accompanied the assessment items. Results suggested participants maintained significantly higher levels of agreement on evaluations of trend magnitude and treatment decisions and reported higher levels of confidence in making decisions when using ratio graphs. Furthermore, decision making occurred most efficiently with ratio charts and a celeration value. The findings have implications for research and practice.