1 Forward teaching: Recommendations for improving statistical literacy in psychological 2 and behavioral sciences 3 Kai Ruggeri12 4 1 Department of Psychology, University of Cambridge, Cambridge, United Kingdom 5 2 Department of Health Policy and Management, Mailman School of Public Health, Columbia 6 University in the City of New York, USA 7 Corresponding Author: 8 Kai Ruggeri 9 10 Email address: [email protected] 1 PeerJ Preprints | https://doi.org/10.7287/peerj.preprints.3265v1 | CC BY 4.0 Open Access | rec: 19 Sep 2017, publ: 19 Sep 2017 11 Abstract 12 To improve outcomes in statistics education - namely the acquisition of statistical literacy - we 13 need a new approach to how the subject is delivered in psychology and behavioral sciences. To do 14 this, key indicators and impacts of barriers to learning statistics can be utilized from existing 15 evidence from recent findings on statistical literacy. Using these, this paper proposes nine elements 16 seen as critical for improving the delivery and associated outcomes for statistics teaching in higher 17 education. Each of the nine elements may be systematically assessed and translated for wider use 18 if effective. 2 PeerJ Preprints | https://doi.org/10.7287/peerj.preprints.3265v1 | CC BY 4.0 Open Access | rec: 19 Sep 2017, publ: 19 Sep 2017 19 Introduction 20 Students do not like the way statistics are taught. This may not come as a surprise to many, but the 21 consequences of this negative experience are significant for students (Chiesi & Primi, 2010) as 22 well as for professors (Uttl & Smibert, 2017) and populations (Calzada Prado & Marzal, 2013). 23 These implications have been highlighted in a recent paper in this journal, which indicates that 24 teaching in quantitative subjects is a strong predictor of lower student evaluations when compared 25 to other topics Uttl & Smibert, 2017). These findings were tied to negative outcomes for academic 26 career progression, which likely has unstudied consequences on the experience level of those 27 responsible for teaching the course. 28 Those who attempt statistical teaching interventions should be commended for recognizing that 29 traditional approaches to teaching statistics may not be as effective or as desirable. The perpetuity 30 of similar findings implies that not everyone acknowledges an issue even exists, though this may 31 also be linked to high turnover in who teaches. While some academics attempt various efforts and 32 even build interventions through a solid theoretical base, this approach has been generally limited. 33 While some techniques may appear obvious, even the cursory scanning of teaching guidance for 34 statistics will reveal much advice has little evidence base and resorts to anecdotes (e.g. ‘In my 35 experience…’), blogs, or untested pieces whose review is limited to the idea rather than empirical 36 evidence – an odd result considering the sources. 37 There is a growing body of evidence for understanding what hinders acquisition of statistical 38 thinking. As those become better identified, academics should be encouraged to attempt 39 scientifically validated improvements to how we engage our classrooms (Kottemann & Salimian, 40 2008), especially when it comes to deep understanding of statistics anxiety (Chew & Dillon, 2014). 41 That educators are now working in earnest to contribute to this presents a positive outlook for a 42 potential future of long-awaited statistically literate populations (Wallman, 1993). Anecdotes, 43 however, have yet to produce a sustained improvement. We need more than this. 44 Take the example of classroom activities used early in the teaching of statistics as a way to improve 45 outcomes (Chiou et al., 2014). This method employs a previously validated one-minute exercise 46 (Stead, 2005) meant to engage students in such a way that statistics anxiety should be reduced and 47 lead to improved test scores. The result: grades increase marginally, a contribution is added to the 48 ‘bag of tricks’ (Gelman & Nolan, 2002), and another anecdote goes into circulation. Without 49 seeking to discredit interventions that show some benefit, the following piece is an argument – 50 even a plea – that if we are truly devoted to gainful change from teaching statistics, it will require 51 more than a minute, clever lecture anecdotes, or new approaches to assessment. 52 We require a new way of thinking about how to deliver the skills, techniques, and mindset 53 necessary for populations that can think statistically. Such abilities enable more people to make 54 better decisions based on information provided, whether in scientific study, government policy, or 55 everyday living (Chew & Dillon, 2014). Unfortunately, the same studies that generate these 56 insights indicate that statistics anxiety is a major barrier to realizing them, largely due to statistics 57 anxiety (Chew & Dillon, 2014). The necessary, substantial change to overcome this begins with 58 the person at the front of the lecture hall. However, this requires implementing evidence-backed 59 approaches to teaching, not simply more assessment of interventions and the students enrolled in 60 them. 3 PeerJ Preprints | https://doi.org/10.7287/peerj.preprints.3265v1 | CC BY 4.0 Open Access | rec: 19 Sep 2017, publ: 19 Sep 2017 61 To respond to this, we propose a way of moving forward with our teaching (or ‘teaching forward’). 62 To do so, this piece offers a brief review of relevant barriers to acquiring statistical literacy, namely 63 statistics anxiety, followed by a set of evidence-based proposals for how to reduce those challenges 64 through better teaching. Though some elements will seem common sense, evidence indicates there 65 are many instances where they are not in place, and it is hoped that this will at least spur meaningful 66 debate toward that end. 67 What we already know 68 Statistics anxiety, its precursors, and its impacts, are well understood. This has largely been 69 through work using the Statistics Anxiety Rating Scale (STARS; Cruise, Cash, & Bolton, 1980). 70 STARS has been the prevailing measure for study, and recent validation work confirms most of 71 its properties continue to hold (DeVaney, 2016). Statistics anxiety is not limited to the social 72 sciences or to English-speaking regions, which has been confirmed by recent work in medical 73 students (Stanisavljevic et al., 2014; Gaudet, et al. 2014) as well as on several continents (Papousek 74 et al. 2012; Liu et al., 2011). To briefly summarize: 75 1. Students in the behavioral, social, and biological sciences are pretty anxious about statistics 76 as a class, assignment, and examination (Field, 2014; Baloglu & Zelhart, 2004). 77 2. Some anxiety is not a bad thing; a lot of anxiety is (Macher et al., 2015). 78 3. Performance in statistics is a subjective measure – we can do better as educators at 79 assessing this (Macher et al., 2013) but should be careful about teaching to the test. 80 4. Teaching is the ultimate moderator: do it right and you are much more likely to get a 81 positive result with a larger number of students; do it wrong and you lose people (Ruggeri 82 et al., 2008). The latter is much easier and more common. 83 5. The people who teach statistics are not always in love with the subject, either (Martins et 84 al., 2012). 85 6. Introducing innovative activities, materials, and teaching styles in the classroom may lead 86 to improved outcomes, but many proposed approaches have not been rigorously evaluated 87 or followed up (Chiou et al., 2014; Gould, 2010; Field, 2009). 88 7. Statistics anxiety is not merely a classroom and performance issue, but one that may impact 89 student well-being, particularly of more vulnerable groups (Jordan, et al., 2014). 90 What can we take from the evidence so far? 91 No first-day activity, no single joke, no vivid anecdote is likely to address all issues faced in 92 ensuring statistical literacy is gained by the greatest number of students. This holds true even if 93 lecturers are able to deliver them. Why is this a problem? If the people who are trained specifically 94 to be analytical (i.e. scientists) struggle to grasp statistical inference, then it is unlikely the general 95 population can easily understand quantitative information presented from any number of sources 96 (Ruggeri et al., 2011). Such arguments are backed up by any number of examples from mainstream 97 media and scientific papers about misunderstanding topics such as risk (e.g. Spiegelhalter, 2013). 98 Instead of a simple response, we need to rethink our approach to how the subject is taught from 99 the outset. One-off activities may offer some impact, but even so, the potential gain from going 100 further than this cannot be missed based on small increases in exam scores. The same may be said 101 of using humor (Field, 2009), guest speakers from professions of interest to students (Ruggeri, 4 PeerJ Preprints | https://doi.org/10.7287/peerj.preprints.3265v1 | CC BY 4.0 Open Access | rec: 19 Sep 2017, publ: 19 Sep 2017 102 20091) and other ‘tricks’ (Gelman & Nolan, 2002). It is unsurprising that these may offer little 103 impact when not utilized within a wider strategy but instead simply called upon for the occasional 104 change of pace. 105 The evidence ultimately indicates that we need systematically assessed approaches to teaching that 106 demonstrably and reliably reduce the legitimate impacts created by mathematical and statistical 107 anxiety. These barriers currently result in low retention of students and knowledge acquisition as 108 well as the avoidance of quantitative information in some more severe cases (Alexander & 109 Onwuegbuzie, 2007). Any new approach should extend beyond simply new forms of presenting 110 the same information for improved test performance, but instead to aim for advances in statistical 111 thinking relevant to modern influences and demands (Cumming, 2014; Gould, 2010).