Association for Behavior Analysis International

The Association for Behavior Analysis International® (ABAI) is a nonprofit membership organization with the mission to contribute to the well-being of society by developing, enhancing, and supporting the growth and vitality of the science of behavior analysis through research, education, and practice.

Search

45th Annual Convention; Chicago, IL; 2019

Event Details

Previous Page

 

Symposium #229
CE Offered: BACB
Recent Research on Teaching Graph Construction and Visual Analysis
Sunday, May 26, 2019
10:00 AM–10:50 AM
Fairmont, Lobby Level, Rouge
Area: TBA/EDC; Domain: Applied Research
Chair: Katie Wolfe (University of South Carolina)
CE Instructor: Katie Wolfe, Ph.D.
Abstract: The graphic depiction and visual analysis of data is integral to the science of applied behavior analysis, and it is important to investigate how to most effectively teach these critical skills to new researchers and practitioners. This symposium consists of the three data-based papers that describe recent research on teaching graph construction and visual analysis. The first paper will describe a component analysis of instruction using task analysis to create reversal design graphs in Excel. The second paper will describe the results of a single-case study evaluating the effects of a clinical decision-making model on the accuracy of a) visual analysis and b) data-based decisions made by Registered Behavior Technicians (RBTs). The third paper will report results of a survey of Behavior Analyst Certification Board Verified Course Sequence (VCS) Coordinators on the content and instructional methods used to teach visual and statistical analysis of single-case research data in VCS courses. The results of each study will be described with implications for training future researchers and practitioners in the graphic representation and analysis of data.
Instruction Level: Intermediate
Keyword(s): graphic representation, single-case research, single-subject research, visual analysis
Target Audience: Current BCBAs, researchers, faculty members
Learning Objectives: 1. At the conclusion of the presentation, participants will be able to describe the effective components of task analysis instruction for creating reversal design graphs. 2. At the conclusion of the presentation, participants will be able to describe the evidence for using a clinical decision making model to improve data-based decision making. 3. At the conclusion of the presentation, participants will be able to describe how instructors in VCS sequences report that they are teaching the visual and statistical analysis of data.
 

Optimizing Computer-Based Instructions for Visualizing Data in Microsoft Excel Through Component Analysis

BRYAN TYNER (CUNY Graduate Center), Steven Floumanhaft (Queens College, CUNY), Daniel Mark Fienup (Columbia University)
Abstract:

Task-analysis instruction is widely used for teaching and learning how to visualize data using computer software. Numerous studies demonstrate the efficacy of specific task analyses for teaching students and behavior analysts how to create graphs; however, little is known about the properties of task-analysis instruction that promote skill acquisition. Findings are reported from two component analyses of the instructional content presented in a computer-based tutorial for creating a reversal-design graph in Microsoft Excel. The first study demonstrated the relative effects on learner performance of describing and presenting images of: (a) stimuli to which a learner must respond, (b) the target responses required to create the graph, and (c) stimulus changes in the graph and the software's graphical user interface that are produced by correct responses. The second study further analyzed the relative effects of presenting text and images on graphing performance. The findings informed the development of a checklist of best practices for designing and evaluating graphing instruction, which will be shared.

 

Training Front-Line Employees to Conduct Visual Analysis Using a Clinical Decision-Making Model

KAILIE JAE KIPFMILLER (Michigan State University), Matthew T. Brodhead (Michigan State University), Katie Wolfe (University of South Carolina), Kate La Londe (Michigan State University ), Emma Seliina Sipila (Michigan State University ), M. Y. Savana Bak (Michigan State University), Marisa H Fisher (Michigan State University)
Abstract:

Behavior analysts visually analyze graphs to interpret data in order to make data-based decisions. Front-line employees, such as the Registered Behavior Technician, are the forefront of behavioral intervention and responsible for its direct implementation. Though front-line employees implement behavioral interventions on a daily basis, they are not often trained to interpret these data. A clinical decision-making model may aid front- line employees in learning how to interpret graphs. Such training will allow front-line employees to evaluate whether the data suggest a learner is struggling with a prescribed intervention, if variations in treatment implementation may be affecting obtained data, and to identify these potential outcomes to provide optimal and individualized therapy. A multiple-baseline-across-participants design was used to evaluate the effectiveness of a clinical decision-making model on the percentage of correct clinical decisions interpreted from line graphs. All of the participants increased their percentage of correct responses after the introduction of the clinical decision- making model. Two of the 8 participants required additional feedback. The implications of these findings are discussed.

 
The Analysis of Single-Case Research Data: Current Instructional Practices
MEKA MCCAMMON (University of South Carolina), Katie Wolfe (University of South Carolina)
Abstract: Visual analysis is the predominant method of analysis in single-case research (SCR). However, most research suggests that agreement between visual analysts is often suboptimal. Poor agreement may be due to a lack of clear guidelines and criteria for visual analysis, as well as variability in how individuals are trained. To date, no research has investigated how instructors are teaching this foundational skill to future researchers and practitioners. Therefore, we developed a 36-item survey containing questions about the content and methods used to teach visual and statistical analysis of SCR data in Verified Course Sequences (VCS). Four independent Board Cerified Behavior Analysts reviewed the survey for clarity and comprehensiveness, and then we distributed it via the VCS Coordinator Listserv to approximately 200 VCS coordinators. Thirty-seven (19%) instructors completed the survey. Results suggest that there is variability across instructors in some fundamental aspects of data analysis (i.e., number of replications required for experimental control), but a great deal of consistency in others (i.e., emphasizing visual over statistical analysis). Additional results will be discussed, along with their implications both for teaching students to analyze SCR data and for conducting additional research on content addressed in behavior-analytic training programs.
 

BACK TO THE TOP

 

Back to Top
Modifed by Eddie Soh
SABA DONATE