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.


36th Annual Convention; San Antonio, TX; 2010

Event Details

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Symposium #139
CE Offered: BACB
Evaluating Data Collection Methodologies and Systems
Sunday, May 30, 2010
9:00 AM–10:20 AM
206AB (CC)
Area: AUT; Domain: Applied Behavior Analysis
Chair: Erin B. Richard (Alpine Learning Group)
CE Instructor: Melissa Nosik, M.S.
Abstract: Data-based behavior analytic techniques have a demonstrated history of teaching a range of skills to people with developmental disabilities. Questions remain, however, as to how the data collection methodologies used by practitioners impact the quality of intervention. In addition, other important considerations include parents’ and staff members’ skill acquisition in using data collection systems that utilize technological innovations, as well the potential time saving impact these systems offer agencies. In the first study, discontinuous and continuous data collection procedures were compared to determine their impact on session duration. The second study compared the effects of levels of detail in data collection on the effectiveness of intervention. The third study evaluated the efficacy of a video recording system to capture episodes of problem behavior in the home setting. The final study compared traditional paper and pencil data collection and graphing with a hand held data collection and web-based graphing program.
An Examination of the Effectiveness and Efficiency of Data Collection and Graphing Procedures in Early Intervention
JASON C. VLADESCU (Central Michigan University), Tiffany Kodak (Munroe-Meyer Institute, University of Nebraska Medical Center), Wayne Fisher (Munroe-Meyer Institute, University of Nebraska Medical Center), Andrea Clements (Munroe-Meyer Institute), Rebecca Arvans-Feeney (Developmental Behavioral Health, Inc.), Kelly J. Bouxsein (Munroe-Meyer Institute, University of Nebraska Medical Center)
Abstract: Previous research has examined the use of discontinuous (i.e., first trial only) and continuous (i.e., all trials) data collection procedures (Cummings & Carr, 2008; Nadjowski et al., in press) in Early Intervention. Although the previous studies reported different findings, both studies described that discontinuous data collection may take less time. However, neither study included session time as a dependent variable. Thus, it remains unclear whether certain data collection procedures are associated with time savings. Furthermore, other therapist responsibilities may be substantially more time consuming then data collection. For example, therapists graph data on each child’s academic programs daily. Each client may have up to 15 programs that require data entry and adjustments to the program’s graph. In the present evaluation, we replicated and extended previous studies by examining discontinuous and continuous data collection while recording session time for each type of data collection. We also extended previous research by examining discontinuous (i.e., twice a week) and continuous (i.e., daily) graphing to determine if the frequency of graphing influenced data-based decisions. Results indicated that the efficacy of each data collection procedure varied across participants. We did not find differences in session duration across data collection procedures. The graphing procedures resulted in substantial differences in identifying mastery of targets, and more sessions were required to reach the mastery criterion based on discontinuous graphing. Although discontinuous graphing showed some time savings, the increase in sessions to mastery as a result of discontinuous graphing outweighed any benefit in time savings.
A Comparison of Different Methods for Collecting Data on Students’ Performance During Discrete Trial Teaching
LAURA HARPER-DITTLINGER (Texana Behavior Treatment & Training Center), Dorothea C. Lerman (University of Houston-Clear Lake), Taira Lanagan (Center for Autism and Related Disorders, Inc.), Susie Balasanyan (Center for Autism and Related Disorders, Inc.), Lynn Williams (Center for Autism and Related Disorders, Inc.)
Abstract: Data collection and progress monitoring are an integral part of effective teaching. Educators use many different forms of data collection. Methods that provide greater precision (e.g., recording the prompt level needed on each instructional trial) are less practical than methods with less precision (e.g., recording the presence or absence of a correct response on the first trial only). However, few studies have examined which method will best suit client needs. In this study, precise data collected by therapists while working on skills with nine children were re-analyzed several different ways to determine if less labor intensive methods would be adequate to make programmatic decisions. Results suggested that, for most of the children and targeted skills, less precise methods of collecting data would have led to similar conclusions about the effectiveness of the intervention.
Validation of Parent Collected Observational Data in the Natural Environment
DANA M. SWARTZWELDER (Marcus Autism Center), Nathan A. Call (Marcus Autism Center), Rosa Arriaga (Georgia Institute of Technology), Addie Jane Findley (Marcus Autism Center), Nazneen Anwer (Georgia Institute of Technology)
Abstract: Data collection in the natural environment for the purpose of assessment and treatment of problem behavior can be problematic for a variety of reasons. The use of video recording has been attempted as a solution to these problems. However, continuous video recording can produce copious amounts of footage that must be scored. Alternatively, video recording may begin at the onset of problem behavior, but this method may fail to capture antecedent events. Innovations in video data collection methods have parents remotely signal an automated video recording system when problem behavior occurs. Because the device maintains a video buffer it is able to store footage of all of the relevant information, including antecedent. The current study evaluated the utility of this technology by recording parent signals but also scoring problem behavior from the corresponding 24 hours of continuously collected video. Specifically at issue was whether the 12 parents who participated would accurately signal the device to record the occurrence of problem behavior. Results suggested that, without parent training that includes corrective feedback, a high number of false positive and false negative parent signals may compromise the effectiveness of this potential solution to capturing video data in the natural environment.
A Comparison of Two Data Collection and Graphing Systems: Paper and Pencil and TeachMe
ERIN B. RICHARD (Alpine Learning Group), Bridget A. Taylor (Alpine Learning Group), Jaime A. DeQuinzio (Alpine Learning Group), Barry Katz (Operant Systems, Inc.)
Abstract: Data collection and graphing are an essential, yet time consuming, component of programs using Applied Behavior Analysis. It would be beneficial to investigate options to decrease time spent completing paperwork in order to increase time available to staff for other tasks such as training and problem solving. This study used an alternating treatment design to compare the duration of time spent graphing data, analyzing those data, and planning for the next teaching session using traditional paper and pencil methods and the TeachMe. The TeachMe uses handheld devices, such as cellular phones or personal digital assistants, to collect data, which are then uploaded directly into a web-based graphing program. In addition, a multiple baseline design was used to assess the training procedures outlined by TeachMe. Finally, social validity measures were taken to compare staff members’ satisfaction with each method. Results indicated that staff members spend substantially less time graphing data with TeachMe and found it easy to use.



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