A Guide to Teaching Statistics: Innovations and Best Practices addresses the critical aspects of teaching statistics to undergraduate students, acting as an invaluable tool for both novice and seasoned teachers of statistics.
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Michael R. Hulsizer is Associate Professor of Experimental Psychology at Webster University in St. Louis, Missouri, where he was honored with the prestigious William T. Kemper Award for Excellence in Teaching (2002). He has attended numerous National Institute on the Teaching of Psychology conferences and has won awards for posters presented at the conference. Michael has coauthored several teaching resources available at the Office of Teaching Resources in Psychology – Online. In addition, he recently contributed a chapter with Linda on incorporating diversity into research methods for Best Practices for Teaching Statistics and Research Methods in the Behavioral Sciences. Michael has also authored articles on mass violence, hate groups, and interpersonal aggression.
Linda M. Woolf is Professor of Experimental and Peace Psychology at Webster University. Linda is the recipient of several teaching awards including the 1988 Early Career Award from the Society for the Teaching of Psychology (Division 2, APA), Emerson Electric Excellence in Teaching Award (1990, 2000), and William T. Kemper Award for Excellence in Teaching (2000). She has authored numerous curriculum resources, book chapters, and journal articles concerning international psychology, peace psychology, mass violence, human rights, and research methods. Linda is Past-President of the Society for the Study of Peace, Conflict, and Violence (Division 48, APA) and former Secretary and Newsletter Editor for the Society for the Teaching of Psychology.
A Guide to Teaching Statistics: Innovations and Best Practices, by Michael R. Hulsizer and Linda M. Woolf, is an invaluable guide for both novice and seasoned teachers of statistics. Based on an extensive review of the research in fields such as education, health sciences, mathematics, statistics, psychology, and the social sciences, the book covers a range of statistics education and assessment topics. The book also includes novel classroom exercises, pedagogical tools, and computer applications designed to enhance active learning. Topics include descriptive, inferential, and multivariate statistics as well as the importance of using real data in the classroom, the role of ethics and diversity in statistics, and the effectiveness of online statistical education. The authors also provide extensive coverage of the research concerning statistical literacy, thinking, and reasoning.
A Guide to Teaching Statistics: Innovations and Best Practices, by Michael R. Hulsizer and Linda M. Woolf, is an invaluable guide for both novice and seasoned teachers of statistics. Based on an extensive review of the research in fields such as education, health sciences, mathematics, statistics, psychology, and the social sciences, the book covers a range of statistics education and assessment topics. The book also includes novel classroom exercises, pedagogical tools, and computer applications designed to enhance active learning. Topics include descriptive, inferential, and multivariate statistics as well as the importance of using real data in the classroom, the role of ethics and diversity in statistics, and the effectiveness of online statistical education. The authors also provide extensive coverage of the research concerning statistical literacy, thinking, and reasoning.
For some students, it is the course about which their peers have warned them. They have heard the horror stories and believed them. - Schutz, Drogosz, White, & DiStefano, 1998, p. 292
Statistics as both a course to take and one to teach has a dreaded reputation. If they are able, students invariably put off the course to the very last moment and appear visibly anxious on the first day of class. They seem to believe the scuttlebutt that any statistics course really deserves the title, "Stadistics." Of course, faculty are not much better. Our departmental chairperson joked that he does not like the three of us who teach statistics traveling together to a conference. "What if something happened! Who would teach statistics?" If truth be told, most of our colleagues, with a bit of time to prepare, could teach introductory statistics. However, they also seem to believe the mythology that the course is a drudge and more importantly, the notion that the course is ripe for less than stellar course evaluations.
However, nothing could be farther from the truth. Statistics can be one of the most fun and gratifying courses to teach. When we talk to fellow statistics teachers at various conferences, it is not unusual for one of us to comment on how much we enjoy teaching statistics. Oddly, what we have noticed is that individuals will often lower their voices a tad and look around before expressing similar thoughts. It is as if some teachers do not want others to know about one of the best-kept secrets in academia. Teaching statistics can be eminently rewarding and, more importantly, meets a fundamental need in helping students develop a solid knowledge foundation in psychology.
Nonetheless, as Mulhern and Wylie (2004) commented, "Teaching statistics and research methods to psychology undergraduates is a major pedagogic challenge" (p. 355). The challenge, however, lies not with the complexity of the material, which ranges in difficulty from easy to conceptually complex, but rather with the type of information communicated. Evans (1976) provided an interesting perspective on the differences between teaching most content-oriented courses in psychology and quantitative methods courses. In most content courses, we teach students to "know that," whereas in statistics we teach students to "know how." Evans draws the following apropos analogy: Teaching statistics via lecture and handouts, with a clear explication of concepts, is as useful as providing someone with a lecture and handout on how to ride a bicycle. The pedagogical challenge for statistics teachers is to move beyond the lectern, put away the static PowerPoint (the current equivalent of yellowing notes), and to try out some alternate teaching strategies.
Students also face new challenges when taking statistics or research methods courses for the first time. Unfortunately, students may perceive these challenges principally as threats versus opportunities. This point is particularly true for those students who may not utilize sophisticated learning techniques. If students have succeeded primarily by studying in spurts, memorizing materials, or relying heavily on recall for exams, they may find statistics to be difficult terrain to navigate. Hence, the familiar lament from struggling students that they feel "lost" in the course. If students cling to their traditional study methods and learning strategies, they may experience a drop in their usual performance level and hence, a subsequent drop in their self-efficacy in relation to the course, which can then spiral into a well of deepening frustration and potential failure. Therefore, statistics teachers might consider structuring their courses in ways that facilitate new and more adaptive learning strategies.
The aim of this book is to provide statistics teachers with the best information available to assist in the development or restructuring of their statistics course. We designed this book to meet the needs of both novice and seasoned teachers of statistics. In addition, we have created a companion Web site (www.teachstats.org) that contains additional instructional techniques, activities, topics, and resources. Throughout the book, we provide information concerning a range of topics from pedagogical methods and activities designed for teaching specific concepts to broader issues related to the unique learning needs of statistics students. We draw heavily on the small but growing empirical and scholarly literature related to the teaching of statistics in each chapter (Becker, 1996). As a result, this book extends beyond the content you might typically find in an instructor's manual. Our goal is to introduce you to the best practices in teaching statistics so that you can turn a potential course prison-the incoming perception of many students-into a pedagogical haven for learning.
So Why Teach Statistics?
Although statistics may be tangential to your primary area of research, it is beneficial to examine why the course is an important one to teach. After all, if you do not find meaning in the material, neither will your students. On the most transparent level, it simply is a good idea for everyone to have a basic understanding of statistics. In other words, knowledge of elementary statistics is an end goal in itself. In today's world, statistical literacy is fundamental given the tendency for the media, politicians, and corporate America to deluge us daily with quantitative information (Ben-Zvi & Garfield, 2004; Gal, 2004; Rumsey, 2002; Utts, 2003). Individuals need to be able to make sense of numerical information to avoid falling prey to the influence of data that looks incontrovertible simply because it is quantitative in nature. Over a half century ago, Wishart (1939), an early statistician, commented that the teaching of statistics is important because it protects individuals from the misleading practices of "the propagandists" (p. 549). It is just as important an issue today.
Two similes often describe the teaching of statistics. Hotelling (1940), perhaps best known for the multivariate technique called Hotelling's T, remarked that teaching students statistics is like teaching them to use a tool. More commonly, instructors comment that teaching of statistics is like teaching a foreign language (Hastings, 1982; Lalonde & Gardner, 1993; Walker, 1936). Both comparisons are insufficient, as they emphasize discrete skills that, once learned, students may fail to apply to other domains of knowledge or to the broader research process. Hence, one can learn to use a power sander and circular saw but not necessarily see any connection from those skills to building a doghouse. Students need to be able to apply their underlying knowledge to other contexts. We also do not want students to perceive statistics as a foreign language requirement only to be left unvisited once completed. It is imperative that students come to see statistics as a set of critical thinking skills and knowledge structures designed to enhance their ability to explore, understand, reason, and evaluate psychological science. In teaching the course, instructors need to make connections to material from other courses to emphasize the role that research methods and statistics plays in creating a foundation for the study of psychology as well as other disciplines.
We all cringe when we see a paper handed in that has as its most scholarly reference, Rolling Stone or Newsweek. Students need to be able to read and evaluate the empirical literature. This ability is particularly important given the dangers associated with blindly trusting the translations presented in the popular press. Consequently, we often ask our students how many of them actually read the results section of an empirical paper and how many simply skip over that section hoping that the author will eventually put it into English for them. Sheepishly, a large percentage of our students confess to such practices. As demonstrated by Rossi (1987), the statistical computations themselves in journal articles may even be incorrect. Therefore, our students need basic statistical literacy, thinking, and reasoning skills with which to begin their evaluation of empirical results. Buche and Glover (1988) demonstrated that students who are provided with training in the fundamental skills necessary to review and study research articles, particularly in relation to methods and an understanding of statistical techniques, are better able to read, evaluate, and appreciate research in their field. Thus, such training is not only essential in their other coursework, but also beneficial for their future careers regardless of whether they choose a path as a researcher, clinician, lawyer, manager, or medical practitioner.
Hotelling (1940) commented that "a good deal of [statistics] has been conducted by persons engaged in research, not of a kind contributing to statistical theory, but consisting of the application of statistical methods and theory to something else" (p. 465). The vast majority of our students will not develop careers specializing in quantitative methods or theory. However, we may hope, and in some instances require, that our students engage in research as part of a class project or independent study. Unfortunately, not all students immediately see the connection between research methods and statistics. They may hold the false belief that one can simply design a study, collect data, and then hire a statistician to analyze those data. Of course, the concepts of research methods and statistics are inextricably interwoven and students must recognize the interrelationships to conduct research effectively. Indeed, students must begin their statistical planning while designing their study.
Finally, and perhaps it should go without saying, psychology is a science. Thus, research methods and statistics are foundation courses necessary for understanding and critically evaluating all of the research presented, studied, and evaluated in the remainder of our students' coursework. Psychology instructors can enhance students' appreciation of statistics by drawing connections to other content-focused domains of psychology. Although taking statistics alone does not decrease students' beliefs in pseudoscientific claims (Mill, Gray, & Mandel, 1994), statistical literacy combined with other content-focused coursework stressing research evaluation, may better prepare our students to be critical consumers of information both within and outside of psychology.
Historical Pedagogical Controversies
Occasionally, one may hear statistics teachers state that they love teaching the course because the material never changes. This point is simply not true. Although there is much that has remained the same, the field of statistics and its application to psychological research is constantly developing. Three main pedagogical controversies have been associated with the teaching of statistics since the field was in its infancy: (a) who should teach statistics; (b) the use of statistics labs and technology; and (c) the content of statistics courses.
Who should teach statistics?
One source of discussion among statisticians, decades ago, was the question of who should teach statistics. Should statisticians and mathematicians be the only individuals allowed to teach statistics or is it more appropriately taught within the departments, such as psychology, conducting research? Wishart (1939) argued that non-statisticians should not teach statistics. He believed that such practices were fraught with danger, as non-statisticians were unprepared to handle the difficulties of teaching and supervising statistical research. However, Fisher (1937) felt that the goal of teaching statistics should be toward the application of these concepts to research in one's field and he argued for offering statistics coursework in research departments such as psychology or biology. Hotelling (1940) commented that professors usually do not want to teach a class outside their main area of interest. He noted further that anyone attempting to digest mathematical statistics outside of one's discipline faces a largely unreadable task. Therefore, he made a case for individuals within particular disciplines keeping current with the quantitative methods literature in their field and teaching the statistics course within respective academic departments. Although some individuals may feel unprepared to teach statistics due to a lack of extensive training in quantitative methods, Hotelling argued that being an excellent mathematician is, in and of itself, a poor predictor for becoming a good statistics instructor. Rather, Hotelling stated that in addition to knowledge of the fundamentals, statistics instructors need to have "a really intimate acquaintance with the problems of one or more empirical subjects in which statistical methods are taught" (p. 463). Accordingly, psychologists today are in a good position to make the world of statistics contextually meaningful for students by relating statistical concepts to applied problems in psychology.
By 1950, it was evident that psychology had adopted Hotelling's (1940) approach to teaching statistics and the majority of psychology departments included coursework in statistics, research methods, experimental, and tests and measurements (Sanford & Fleishman, 1950). More recently, approximately 77% of universities and colleges required statistics courses within departments of psychology (Bartz, 1981). According to Garfield (2000), today's students receive the vast majority of statistical training from instructors outside the field of mathematics (e.g., education, psychology). Many individuals who teach statistics within psychology departments do not have quantitative methods as their primary focus of scholarship (Hayden, 2000). The departmental location of a statistics class may reflect philosophical differences and pragmatic concerns due to limited numbers of faculty within any one department (Fraser, 1962; Friedrich, Buday, & Kerr, 2000; Perlman & McCann, 1999).
Statistics labs and related technology
Many early statisticians cared deeply about the pedagogy of statistics and endeavored to sort out best practices in relation to their craft. For example, there was uniform agreement that teaching statistics primarily through lecture was a death knoll for learning. Indeed, Cohen and Firestone (1939) commented that "a lecture is a process whereby the notes of the professor become the notes of the student without passing through the minds of either" (p. 714). Although there was agreement on some issues related to teaching methods, there were still significant areas of disagreement among statistics instructors. For example, Walker (1936) and Olds (1954) argued for the importance of laboratory work. On the other hand, Cohen and Firestone stated that a lecture-laboratory combination was not enough to facilitate learning and only assisted the best students. They suggested that students take smaller, informal statistical workshops designed to provide them with the opportunity to learn a range of concepts and apply these techniques to real-world problems.
Few teachers today would argue that lecture alone is ideal for any course. However, Perlman and McCann (1999) found that only 12% of statistics courses included an identified laboratory component. Although one can argue that Perlman and McCann's methods may have undercounted the number of available statistics courses including a laboratory experience, the reported limited availability of laboratory experiences for students studying statistics is still a potential concern.
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