Synopses & Reviews
Utilizing the latest software, this book presents the essential statistical procedures for drawing valuable results from data in the social sciencesMobilizing interesting real-world examples from the field of education, Understanding Educational Statistics Using Microsoft Excel and SPSS supplies a seamless presentation that identifies valuable connections between statistical applications and research design. Class-tested to ensure an accessible presentation, the book combines clear, step-by-step explanations and the use of software packages that are accessible to both the novice and professional alike to present the fundamental statistical practices for organizing, understanding, and drawing conclusions from educational research data.
The book begins with an introduction to descriptive and inferential statistics and then proceeds to acquaint readers with the various functions for working with quantitative data in the Microsoft Excel environment, such as spreadsheet navigation; sorting and filtering; and creating pivot tables. Subsequent chapters treat the procedures that are commonly employed when working with data across various fields of social science research, including:
Individual chapters are devoted to specific procedures, each ending with a lab exercise that highlights the importance of that procedure by posing a research question, examining the question through its application in Excel and SPSS, and concluding with a brief research report that outlines key findings drawn from the results. Real-world examples and data from modern educational research are used throughout the book, and a related web site features additional data sets, examples, and labs, allowing readers to reinforce their comprehension of the material.
Bridging traditional statistical topics with the latest software and applications in the field of education, Understanding Educational Statistics Using Microsoft Excel and SPSS is an excellent book for courses on educational research methods and introductory statistics in the social sciences at the upper-undergraduate and graduate levels. It also serves as a valuable resource for researchers and practitioners in the fields of education, psychology, and the social sciences who require a statistical background to work with data in their everyday work.
Synopsis
This class-tested book equips readers with the fundamental statistical practices that are essential for organizing, understanding, and drawing conclusions from educational research data. It is the first to utilize both SPSS and Excel to work all problems, examples, and data. Each data procedure is given its own chapter, which concludes with a lab exercise that poses a research question, examines the question by performing that chapter's statistical topic of focus in Excel and SPSS, and outlines key findings. This text is meant for courses on introductory statistics in the social sciences at the upper-undergraduate level and as a resource for statistical professionals.
About the Author
MARTIN LEE ABBOTT, PhD, is Professor of Sociology at Seattle Pacific University, where he also serves as Executive Director of the Washington School Research Center, an independent research and data analysis center funded by the Bill and Melinda Gates Foundation. He has held positions in both academia and in industry, focusing his consulting and teaching in the areas of program evaluation, applied sociology, statistics, and research methods. Dr. Abbott is the author of The Program Evaluation Prism: Using Statistical Methods to Discover Patterns (Wiley).
Table of Contents
Preface.
Acknowledgments.
1 Introduction 1
2 Getting Acquainted with Microsoft Excel.
3 Using Statistics in Excel.
4 SPSSBasics.
5 Descriptive Statistics—Central Tendency.
6 Descriptive Statistics—Variablity.
7 The Normal Distribution.
8 The Z Distribution and Probability.
9 The Nature of Research Design and Inferential Statistics.
10 The T Test for Single Samples.
11 Independent-Samples T Test.
12 Analysis of Variance.
13 Factorial Anova.
14 Correlation.
15 Bivariate Regression.
16 Introduction to Multiple Linear Regression.
17 Chi Square and Contingency Table Analysis.
18 Repeated Measures Procedures: Tdep and ANOVAws.
References.
Appendix: Statistical Tables.
Index.