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

1. Auflage 2016
ISBN: 978-1-119-12104-6
Verlag: Wiley

Buch, Englisch, 592 Seiten, Format (B × H): 161 mm x 240 mm, Gewicht: 1035 g

Provides a step-by-step approach to statistical procedures to analyze data and conduct research, with detailed sections in each chapter explaining SPSS® and Excel® 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® and Excel® 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® and Excel® for analyzing data.

Autoren/Hrsg.

Abbott, Martin Lee

Fachgebiete

• Sozialwissenschaften Soziologie | Soziale Arbeit Soziologie Allgemein Empirische Sozialforschung, Statistik
• Sozialwissenschaften Psychologie Psychologie / Allgemeines & Theorie Psychologische Forschungsmethoden
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Epidemiologie, Medizinische Statistik

Inhaltsverzeichnis

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 The T 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

Interpreting the Results of the T Test for a Single Mean 180

The T Distribution 181

The Hypothesis Test for the Single Sample T Test 182

Type I and Type II Errors 183

Effect Size 187

Effect Size for the Single Sample T Test 187

Power Effect Size and Beta 188

One- and Two-Tailed Tests 189

Point and Interval Estimates 192

Using SPSS® and Excel with the Single Sample T Test 196

Terms and Concepts 201

Data Lab and Examples (with Solutions) 201

Data Lab: Solutions 203

8 Independent Sample T Test 207

A Lot of “Ts” 207

Research Design 208

Experimental Designs and the Independent T Test 208

Dependent Sample Designs 209

Between and Within Research Designs 210

Using Different T Tests 211

Independent T Test: The Procedure 213

Creating the Sampling Distribution of Differences 215

The Nature of the Sampling Distribution of Differences 216

Calculating the Estimated Standard Error of Difference with Equal Sample Size 218

Using Unequal Sample Sizes 219

The Independent T Ratio 221

Independent T Test Example 222

Hypothesis Test Elements for the Example 222

Before–After Convention with the Independent T Test 226

Confidence Intervals for the Independent T Test 227

Effect Size 228

The Assumptions for the Independent T Test 230

SPSS® Explore for Checking the Normal Distribution Assumption 231

Excel Procedures for Checking the Equal Variance Assumption 233

SPSS® Procedure for Checking the Equal Variance Assumption 237

Using SPSS® and Excel with the Independent T Test 239

SPSS® Procedures for the Independent T Test 239

Excel Procedures for the Independent T Test 243

Effect Size for the Independent T Test Example 245

Parting Comments 245

Nonparametric Statistics: The Mann–Whitney U Test 246

Terms and Concepts 249

Data Lab and Examples (with Solutions) 249

Data Lab: Solutions 251

Graphics in the Data Summary 254

9 Analysis of Variance 255

A Hypothetical Example of ANOVA 255

The Nature of ANOVA 257

The Components of Varia

Über Autor(innen)

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.