Using derived relational responding to model statistics learning across participants with varying degrees of statistics anxiety. Issue 1 (2nd January 2017)

• Record Type: Journal Article
• Authors: Sandoz, Emily K.; Hebert, Emmie R.
• Is Part Of: European journal of behavior analysis. Volume 18:Issue 1(2017)
• Page Start: 113
• Page End: 131
• Publication Date: 2017-01-02
• Subjects: Relational frame theory -- statistics -- anxiety -- academic -- college students; Behavioral assessment -- Periodicals; 150.287
• DOI: 10.1080/15021149.2016.1146552
• Languages: English
• ISSNs: 1502-1149

Abstract

ABSTRACT: Statistics courses offer a challenge for students in behavioral science programs. Many students experience statistics-related anxiety resulting in deficits in comprehension and performance with potential long-term consequences. This may be attributable to the avoidance that often accompanies statistics anxiety. However, it may also be attributable to disruptions of fundamental learning processes that are necessary for statistics performance. Relational frame theory may provide an analysis of how individuals learn to respond to statistical concepts in terms of derived relational responding (DRR). Students who experience statistics anxiety may perform poorly because the DRR involved in learning statistics is disrupted. This study aimed to model statistics learning using a DRR task and to explore the relationship of statistics anxiety and DRR with statistics stimuli. Twenty-seven undergraduate students completed a measure of statistics anxiety and a conditional discrimination task in which they learned to relate statistics stimuli. DRR training with statistics stimuli resulted in quick and accurate relational responding with both familiar and novel stimuli in this sample. High rates of correct responding on DRR testing were associated with posttraining statistics quiz accuracy. Additionally, statistics anxiety was related to poor DRR accuracy and greater difficulty meeting pass criterion.