Using Statistics to Make Educational Decisions

David Tanner is a Professor in the Department of Educational Research and Administration at California State University, Fresno, where he was the Department Chair from 1990-1995. He received his Ph.D. in Curriculum and Instruction/Measurement from Texas A&M University, and his areas of interest include educational psychology; statistics and measurement; educational research; quantitative and qualitative evaluation; assessing student achievement; evaluating classroom assessment instruments; and evaluating the performance of teachers and teacher candidates. He has authored several journal articles and the textbook Assessing Academic Achievement ( (c)2001).

Part A: Developing a Context for Statistical Analysis Chapter 1: A Context for Solving Quantitative Problems Chapter 2: Describing Data Part B: Presenting Data Chapter 3: Data Distributions: Picturing Data Chapter 4: Working with the Normal Curve: z Scores Part C. Examining Differences Chapter 5: Probability and the Normal Distribution Chapter 6: t for One, or Two Chapter 7: Analysis of Variance Chapter 8: ANOVA with more than One Independent Variable: Factorial ANOVA Chapter 9: Dependent Groups Tests for Interval Data Part D. Association and Prediction Chapter 10: Correlation Chapter 11: Regression with One Predictor Chapter 12: Regression with More than One Predictor Part E. Tests for Nominal and Ordinal Data Chapter 13: Some of the Chi-square Tests Chapter 14: Working with Ordinal, More, or Less-than Data Part F. Tests, Measurement Issues and Selected Advanced Topics Chapter 15: Testing Issues Chapter 16: A Brief Introduction to Selected Advanced Topics Glossary Appendices: Tables of Critical Values Appendices: A Primer in SPSS (R) Appendices: Solutions to Selected Problems

"This text seems to cover the basics of educational decision making. It would make an excellent text for a course preceding the one I am teaching." -- Mack C. Shelley The content is very specific to my design for the course. The book addresses a wide range of prior content affiliation by the student. It reads very well but still provides necessary mathematical rigor for understading the concepts presented...extremely appropriate for most EdD programs and could also function as an excellent transistion from a mathematics, statistical background into the world of research in the social sciences. -- Benjamin Brown