Psychology undergraduates find identifying appropriate analyses for common research designs difficult. Resources have been developed to aid this process, including decision trees commonly included in statistics textbooks. The use of such trees is supported by research demonstrating their efficacy and popularity. In recent years, decision trees to aid statistic selection have been adapted for digital media. One such adaptation is StatHand, a free iOS and web app that that aids statistic selection by prompting users to focus systematically on each structural feature of their research design. Previous research has suggested that simply providing students with an app like StatHand is not enough to promote accurate statistic selection. Rather, students need to be trained in its use. In this chapter we describe a brief statistic selection training activity built around the use of StatHand. The development of the activity was informed by two sets of literature. The first suggests that accurate statistic selection is a consequence of ‘structural awareness’. The second pertains to the success of ‘wise’ psychological interventions across a range of contexts, including education. The students we have trained using our methods (N = 50) demonstrated substantially greater statistic selection proficiency than untrained students in previous research. Our training methods can be adapted for a range of contexts. The chapter appendices include our training materials and over 40 research scenarios spanning the range of analyses covered in StatHand. These can be freely adapted by instructors for both formative and summative learning activities.
|Title of host publication||For the love of teaching undergraduate statistics|
|Editors||Alisa Bayer, Janet Peters|
|Place of Publication||Washington, DC|
|Publisher||Society for the Teaching of Psychology|
|Number of pages||26|
|Publication status||Published - 21 Feb 2020|