Series Editors’ Preface. Preface. Part I Course Preparation. 1 Teaching Statistics: A Beginning. So Why Teach Statistics? Historical Pedagogical Controversies. Who should teach statistics? Statistics labs and related technology. Content of statistics courses. Statistics in Relation to the Discipline. Sequence of the Class and Topics. Introducing Research Methods within the Context of Statistics. Student Populations. Mathematical ability. Cognitive ability and learning styles. Self-efficacy and motivation. Gender. Helping Your Students Survive Statistics. Conclusion. 2 Nuts and Bolts of Teaching Statistics. Syllabus Construction. Textbook Selection. Conceptual orientation. Level of difficulty. Chapter topics and organization. Core formulas and vocabulary. Type of data sets/quality of the exercises. Traditional Versus Electronic Textbooks. Supplemental Materials. Study guides. Companion Web sites. Computer tutorials. Electronic Discussion Boards. Multimedia Tools. Presentation technology. Interactive applications:
Java applets, Flash, Shockwave, and HTML. Multimedia simulation programs. Conclusion. Part II Theoretical and Pedagogical Concerns. 3 Educational Reform in Statistics. Educational Reform. Statistically Educated Students. Statistical Literacy. Knowledge elements. Dispositional elements. Statistical Thinking. Statistical Reasoning. Misconceptions Impacting the Development of Literacy, Thinking, and Reasoning. Final Thoughts on Statistical Literacy, Thinking, and Reasoning. Assessment. What is the role of assessment? What is the role of authentic assessment? Assessment and learning outcomes or goals. Conclusion. 4 In the Classroom. Conceptual Learning, Active Learning, and Real Data. Conceptual learning versus rote memorization. Active learning. Real data. Instructional Techniques. Lecture. The use of questions. Practice problems and examples. Journal assignments.
Activities and demonstrations. Writing assignments. Concept maps. Cooperative learning. Projects. Assessment. Principles of effective assessment. Mastery learning. Confronting Fear and Anxiety. Conclusion. Part III Teaching Specific Statistical Concepts. 5 Descriptive Statistics and Bivariate Distributions. Graphing Data. The use of graphs in science. Elements of good design. Human graphical perception. Available graphing methods. Software design. Normal Distribution. Measures of Central Tendency. Measures of Variability. Correlation. Simple Linear Regression. Computer Applications. Conclusion. 6 Teaching Hypothesis Testing. Samples, Sampling Distributions, and the Central Limit Theorem. Confidence Intervals.
Introduction to Null Hypothesis Testing. Additional Introduction to Hypothesis Testing Concepts. Power. Effect sizes. Type I and Type II errors. Analysis of Variance. Introduction to ANOVA. Violating ANOVA assumptions. Factorial ANOVA. General linear model. The Debate Surrounding Null Hypothesis Significance Testing. Nonparametric Statistics. Computer Applications. Conclusion. Part IV Advanced Topics and Approaches. 7 Data Analysis in Statistical Education. Teaching with Statistical Software Tools. Data Analysis Packages. SPSS. Microsoft Excel. Other commercial data analysis programs. Comparing data analysis programs. Data Analysis Software Textbooks. Using Data Sets in the Classroom. Artificial data sets for the classroom. Reality-based data sets. Finding appropriate reality-based data sets. Drawbacks to using real data sets. Conclusion. 8 Endings and Beginnings. Multivariate Statistics. Multiple regression. Logistic regression. Additional multivariate techniques. Special Topics. Ethics. Diversity. Online Statistical Education. Finishing up Any Statistics Course. Final Thoughts. References. Index.
