Using Statistics in the Social and Health Sciences with SPSS and Excel

Martin Lee Abbott, PhD, is Professor of Sociology at Seattle Pacific University, where he has served as Executive Director of the Washington School Research Center, an independent research and data analysis center funded by the Bill & Melinda Gates Foundation. Dr. Abbott has held positions in both academia and industry, focusing his consulting and teaching in the areas of statistical procedures, program evaluation, applied sociology, and research methods. He is the author of Understanding Educational Statistics Using Microsoft Excel and SPSS, The Program Evaluation Prism: Using Statistical Methods to Discover Patterns, and Understanding and Applying Research Design, also from Wiley.

Provides a step-by-step approach to statistical procedures to analyze data and conduct research, with detailed sections in each chapter explaining SPSS(r) and Excel(r) applications
 
This book identifies connections between statistical applications and research design using cases, examples, and discussion of specific topics from the social and health sciences. Researched and class-tested to ensure an accessible presentation, the book combines clear, step-by-step explanations for both the novice and professional alike to understand the fundamental statistical practices for organizing, analyzing, and drawing conclusions from research data in their field.
 
The book begins with an introduction to descriptive and inferential statistics and then acquaints readers with important features of statistical applications (SPSS and Excel) that support statistical analysis and decision making. Subsequent chapters treat the procedures commonly employed when working with data across various fields of social science research. Individual chapters are devoted to specific statistical procedures, each ending with lab application exercises that pose research questions, examine the questions through their application in SPSS and Excel, and conclude with a brief research report that outlines key findings drawn from the results. Real-world examples and data from social and health sciences research are used throughout the book, allowing readers to reinforce their comprehension of the material.
 
Using Statistics in the Social and Health Sciences with SPSS(r) and Excel(r) includes:
 
* Use of straightforward procedures and examples that help students focus on understanding of analysis and interpretation of findings
 
* Inclusion of a data lab section in each chapter that provides relevant, clear examples
 
* Introduction to advanced statistical procedures in chapter sections (e.g., regression diagnostics) and separate chapters (e.g., multiple linear regression) for greater relevance to real-world research needs
 
Emphasizing applied statistical analyses, this book can serve as the primary text in undergraduate and graduate university courses within departments of sociology, psychology, urban studies, health sciences, and public health, as well as other related departments. It will also be useful to statistics practitioners through extended sections using SPSS(r) and Excel(r) for analyzing data.
 
Martin Lee Abbott, PhD, is Professor of Sociology at Seattle Pacific University, where he has served as Executive Director of the Washington School Research Center, an independent research and data analysis center funded by the Bill & Melinda Gates Foundation. Dr. Abbott has held positions in both academia and industry, focusing his consulting and teaching in the areas of statistical procedures, program evaluation, applied sociology, and research methods. He is the author of Understanding Educational Statistics Using Microsoft Excel(r) and SPSS(r), The Program Evaluation Prism: Using Statistical Methods to Discover Patterns, and Understanding and Applying Research Design, also from Wiley.

Preface xv
 
Acknowledgments xix
 
1 INTRODUCTION 1
 
Big Data Analysis, 1
 
Visual Data Analysis, 2
 
Importance of Statistics for the Social and Health Sciences and Medicine, 3
 
Historical Notes: Early Use of Statistics, 4
 
Approach of the Book, 6
 
Cases from Current Research, 7
 
Research Design, 9
 
Focus on Interpretation, 9
 
2 DESCRIPTIVE STATISTICS: CENTRAL TENDENCY 13
 
What is the Whole Truth? Research Applications (Spuriousness), 13
 
Descriptive and Inferential Statistics, 16
 
The Nature of Data: Scales of Measurement, 16
 
Descriptive Statistics: Central Tendency, 23
 
Using SPSS and Excel to Understand Central Tendency, 28
 
Distributions, 35
 
Describing the Normal Distribution: Numerical Methods, 37
 
Descriptive Statistics: Using Graphical Methods, 41
 
Terms and Concepts, 47
 
Data Lab and Examples (with Solutions), 49
 
Data Lab: Solutions, 51
 
3 DESCRIPTIVE STATISTICS: VARIABILITY 55
 
Range, 55
 
Percentile, 56
 
Scores Based on Percentiles, 57
 
Using SPSS and Excel to Identify Percentiles, 57
 
Standard Deviation and Variance, 60
 
Calculating the Variance and Standard Deviation, 61
 
Population SD and Inferential SD, 66
 
Obtaining SD from Excel and SPSS, 67
 
Terms and Concepts, 70
 
Data Lab and Examples (with Solutions), 71
 
Data Lab: Solutions, 73
 
4 THE NORMAL DISTRIBUTION 77
 
The Nature of the Normal Curve, 77
 
The Standard Normal Score: Z Score, 79
 
The Z Score Table of Values, 80
 
Navigating the Z Score Distribution, 81
 
Calculating Percentiles, 83
 
Creating Rules for Locating Z Scores, 84
 
Calculating Z Scores, 87
 
Working with Raw Score Distributions, 90
 
Using SPSS to Create Z Scores and Percentiles, 90
 
Using Excel to Create Z Scores, 94
 
Using Excel and SPSS for Distribution Descriptions, 97
 
Terms and Concepts, 99
 
Data Lab and Examples (with Solutions), 99
 
Data Lab: Solutions, 101
 
5 PROBABILITY AND THE Z DISTRIBUTION 105
 
The Nature of Probability, 106
 
Elements of Probability, 106
 
Combinations and Permutations, 109
 
Conditional Probability: Using Bayes' Theorem, 111
 
Z Score Distribution and Probability, 112
 
Using SPSS and Excel to Transform Scores, 117
 
Using the Attributes of the Normal Curve to Calculate Probability, 119
 
"Exact" Probability, 123
 
From Sample Values to Sample Distributions, 126
 
Terms and Concepts, 127
 
Data Lab and Examples (with Solutions), 128
 
Data Lab: Solutions, 129
 
6 RESEARCH DESIGN AND INFERENTIAL STATISTICS 133
 
Research Design, 133
 
Experiment, 136
 
Non-Experimental or Post Facto Research Designs, 140
 
Inferential Statistics, 143
 
Z Test, 154
 
The Hypothesis Test, 154
 
Statistical Significance, 156
 
Practical Significance: Effect Size, 156
 
Z Test Elements, 156
 
Using SPSS and Excel for the Z Test, 157
 
Terms and Concepts, 158
 
Data Lab and Examples (with Solutions), 161
 
Data Lab: Solutions, 162
 
7 THET TEST FOR SINGLE SAMPLES 165
 
Introduction, 166
 
Z Versus T: Making Accommodations, 166
 
Research Design, 167
 
Parameter Estimation, 169
 
The T Test, 173
 
The T Test: A Research Example, 176