Making Sense of Statistics : A Conceptual Overview book cover
7th Edition

Making Sense of Statistics
A Conceptual Overview

ISBN 9781138894761
Published June 18, 2018 by Routledge
252 Pages 21 B/W Illustrations

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Book Description

Making Sense of Statistics is the ideal introduction to the concepts of descriptive and inferential statistics for students undertaking their first research project. It presents each statistical concept in a series of short steps, then uses worked examples and exercises to enable students to apply their own learning.

It focuses on presenting the why as well as the how of statistical concepts, rather than computations and formulae, so is suitable for students from all disciplines regardless of mathematical background. Only statistical techniques that are almost universally included in introductory statistics courses, and widely reported in journals, have been included. Once students understand and feel comfortable with the statistics that meet these criteria, they should find it easy to master additional statistical concepts.

New to the Seventh Edition

Retaining the key features and organization that have made this book an indispensable text for teaching and learning the basic concepts of statistical analysis, this new edition features:

  • discussion of the use of observation in quantitative and qualitative research
  • the inclusion of introductions to the book, and each Part.
  • section objectives listed at the beginning of each section to guide the reader.
  • new material on key topics such as z-scores, probability, Central Limit Theorem, Standard Deviation and simple and multiple regression
  • Expanded discussion on t test with separate sections for independent and dependent samples t tests, as well as one-sample t test
  • progressive analysis of bivariate vs multivariate statistics (starts with the basic concepts and moves to more complex analysis as the student progresses)
  • updated and extended pedagogical material such as Chapter Objectives, exercises and worked examples to test and enhance student’s understanding of the material presented in the chapter
  • Bolded key terms, with definitions and Glossary for quick referral
  • expanded Appendices include a brief reference list of some common computational formulas and examples.
  • a Glossary of key terms has been added at the end of the book, with references to sections in parenthesis.
  • New online instructor resources for classroom use consisting of test bank questions and Powerpoint slides, plus material on basic math review

Table of Contents

Introduction to the Seventh Edition

Introduction: What is Research?


Part A. The Research Context

1. The Empirical Approach to Knowledge

2. Types of Empirical Research

3. Scales of Measurement

4. Descriptive, Correlational, and Inferential Statistics

Part B. Sampling

5. Introduction to Sampling

6. Variations on Random Sampling

7. Sample Size

8. Standard Error of the Mean and Central Limits Theorem

Part C. Descriptive Statistics

9.Frequencies, Percentages, and Proportions

10. Shapes of Distributions

11. The mean: An Average

12. Mean, Median, and Mode

13. Range and Interquartile Range

14. Standard Deviation

15. Z Score

Part D Correlational Statistics

16. Correlation

17. Pearson r

18. Scattergram

Part E Inferential Statistics

19. Introduction to Null Hypothesis

20. Decisions About the Null Hypothesis

Part F Means Comparison

21. Introduction to the t Test

22. Independent Sample t Test

23. Paired Sample t Test

24. One Sample t Test

25. Reports of the Results of t Tests

26. One-Way ANOVA

27. Two-Way ANOVA

Part G Predictive Significance

28. Chi-Square

29. Limits of Significance Testing

30. Effect Size

31. Coefficient of Determination

32. Multiple Correlations

33. Simple and Multiple Regressions

Appendix A. Computations

Appendix B. Notes on Interpreting Pearson r and Linear Regression

Appendix C. Table of Random Numbers

Appendix D. More About the Null Hypothesis

Comprehensive Review Questions



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Dr Deborah M. Oh is professor in Research Methods and Statistics at California State University, Los Angeles since 1998. She earned her PhD from Columbia University, New York and teaches statistics to a diverse student population in both undergraduate and graduate levels.