Guide to Teaching Statistics

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.

• Series Editors’ Preface xiiiPreface xviiPart I Course Preparation 11 Teaching Statistics: A Beginning 3So Why Teach Statistics? 5Historical Pedagogical Controversies 7Who should teach statistics? 7Statistics labs and related technology 8Content of statistics courses 10Statistics in Relation to the Discipline 11Sequence of the Class and Topics 12Introducing Research Methods within the Context of Statistics 16Student Populations 17Mathematical ability 17Cognitive ability and learning styles 19Self-efficacy and motivation 20Gender 22Helping Your Students Survive Statistics 23Conclusion 252 Nuts and Bolts of Teaching Statistics 27Syllabus Construction 28Textbook Selection 30Conceptual orientation 31Level of difficulty 33Chapter topics and organization 34Core formulas and vocabulary 35Type of data sets/quality of the exercises 36Traditional Versus Electronic Textbooks 37Supplemental Materials 38Study guides 39Companion Web sites 39Computer tutorials 40Electronic Discussion Boards 42Multimedia Tools 44Presentation technology 45Interactive applications: Java applets, Flash, Shockwave, and HTML 46Multimedia simulation programs 48Conclusion 49Part II Theoretical and Pedagogical Concerns 513 Educational Reform in Statistics 53Educational Reform 54Statistically Educated Students 56Statistical Literacy 59Knowledge elements 60Dispositional elements 62Statistical Thinking 63Statistical Reasoning 66Misconceptions Impacting the Development of Literacy, Thinking, and Reasoning 70Final Thoughts on Statistical Literacy, Thinking, and Reasoning 72Assessment 73What is the role of assessment? 73What is the role of authentic assessment? 74Assessment and learning outcomes or goals 75Conclusion 774 In the Classroom 79Conceptual Learning, Active Learning, and Real Data 80Conceptual learning versus rote memorization 80Active learning 82Real data 83Instructional Techniques 84Lecture 85The use of questions 86Practice problems and examples 87Journal assignments 88Activities and demonstrations 89Writing assignments 90Concept maps 93Cooperative learning 94Projects 95Assessment 97Principles of effective assessment 97Mastery learning 98Confronting Fear and Anxiety 99Conclusion 101Part III Teaching Specific Statistical Concepts 1035 Descriptive Statistics and Bivariate Distributions 105Graphing Data 106The use of graphs in science 107Elements of good design 108Human graphical perception 109Available graphing methods 110Software design 111Normal Distribution 112Measures of Central Tendency 114Measures of Variability 117Correlation 119Simple Linear Regression 122Computer Applications 125Conclusion 1276 Teaching Hypothesis Testing 129Samples, Sampling Distributions, and the Central Limit Theorem 131Confidence Intervals 133Introduction to Null Hypothesis Testing 135Additional Introduction to Hypothesis Testing Concepts 138Power 138Effect sizes 140Type I and Type II errors 141Analysis of Variance 142Introduction to ANOVA 142Violating ANOVA assumptions 143Factorial ANOVA 144General linear model 145The Debate Surrounding Null Hypothesis Significance Testing 146Nonparametric Statistics 146Computer Applications 149Conclusion 151Part IV Advanced Topics and Approaches 1537 Data Analysis in Statistical Education 155Teaching with Statistical Software Tools 156Data Analysis Packages 158SPSS 158Microsoft Excel 160Other commercial data analysis programs 162Comparing data analysis programs 163Data Analysis Software Textbooks 165Using Data Sets in the Classroom 166Artificial data sets for the classroom 167Reality-based data sets 168Finding appropriate reality-based data sets 169Drawbacks to using real data sets 174Conclusion 1768 Endings and Beginnings 179Multivariate Statistics 180Multiple regression 182Logistic regression 184Additional multivariate techniques 185Special Topics 186Ethics 187Diversity 190Online Statistical Education 193Finishing up Any Statistics Course 195Final Thoughts 198References 201Index 248

"In the book the reader will not find a collection of statistical tables or formulae but a rich collection of references on teaching of statistics in several fields, e.g. psychology, social sciences, health sciences, education." (Zentralblatt MATH, 2010)