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

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. Research 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, public health, and other related departments. It will also be useful to statistics practitioners through extended sections using SPSS® and Excel® 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 and Melinda Gates Foundation.  Dr. Abbott has held positions in both academia and in 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.

 

Preface

Acknowledgments

CHAPTER ONE: INTRODUCTION

Big Data Analysis

Visual Data Analysis

Importance of Statistics for the Social and Health Sciences and Medicine

Historical Notes: Early Use of Statistics

Approach of the Book

Cases from Current Research

Research Design

Focus on Interpretation

CHAPTER TWO: DESCRIPTIVE STATISTICS CENTRAL TENDENCY

Spuriousness

Descriptive and Inferential Statistics

The Nature of Data Scales of Measurement

Descriptive Statistics––Central Tendency Measures

Using SPSS® and Excel to Understand Central Tendency

Distributions

Describing the Normal Distribution: Numerical Methods

Describing the Normal Distribution: Graphical Methods

Terms and Concepts

Central Tendency: Data Lab and Examples (with Solutions)

CHAPTER THREE: DESCRIPTIVE STATISTICS VARIABILITY

Range

Percentile

Scores Based on Percentiles

Using SPSS® and Excel to Identify Percentiles

Standard Deviation and Variance

Calculating the Variance and Standard Deviation

Population SD and Inferential SD

Obtaining SD from Excel and SPSS®

Terms and Concepts

Data Lab and Examples (with Solutions)

CHAPTER FOUR: THE NORMAL DISTRIBUTION

The Nature of the Normal Curve

The Standard Normal Score: z score

The z Score Table of Values

Navigating the z score Distribution

Calculating Percentiles

Creating Rules for Locating Z Scores

Calculating Z Scores

Using SPSS® to Create Z Scores and Percentiles

Using Excel to Create Z Scores

Using Excel and SPSS® for Distribution Descriptions

Terms and Concepts

Data Lab and Examples (with Solutions)

CHAPTER FIVE: THE Z DISTRIBUTION AND PROBABILITY

The Nature of Probability

Elements of Probability

Combinations and Permutations

Conditional Probability Using Bayes Theorem

Z Score Distribution and Probability

Using SPSS® and Excel to Transform Scores

Using the Attributes of the Normal Curve to Calculate Probability

Exact Probability

From Sample Values to Sample Distributions

Terms and Concepts

Data Lab and Examples (with Solutions)

CHAPTER SIX: RESEARCH DESIGN AND INFERENTIAL STATISTICS

Research Design

Experiment

Non–Experimental or Post Facto Research Designs

Inferential Statistics

Z Test

The Hypothesis Test

Statistical Significance

Practical Significance: Effect Size

Z Test Elements

Using SPSS® and Excel for the Z Test

Terms and Concepts

Data Lab and Examples (with Solutions)

CHAPTER SEVEN: THE T TEST FOR SINGLE SAMPLES

Introduction

Z vs. T: Making Accommodations

Research Design

Parameter Estimation

The T Test

The T Test: A Research Example

Interpreting the Results of the T test for a Single Mean

The T Distribution

The Hypothesis Test for the Single Sample T Test

Type I and Type II Errors

Effect Size

Effect Size for the Single Sample T Test

Power, Effect Size, and Beta

One and Two Tailed Tests

Point and Interval Estimates

Using SPSS® and Excel with the Single Sample T test

Terms and Concepts

Data Lab and Examples (with Solutions)

CHAPTER EIGHT: INDEPENDENT SAMPLES T TEST

A Lot of T s

Research Design

Experimental Designs and the Independent T Test

Dependent Samples Designs

Between and Within Research Designs

Using Different T Tests

Independent T Test: The Procedure

Creating the Sampling Distribution of Differences

The Nature of the Sampling Distribution of Differences

Calculating the Estimated Standard Error of Differences with Equal Sample Size

Using Unequal Sample Sizes

The Independent T Ratio

Independent T Test Example

Hypothesis Test Elements for the Example

Before After Convention with the Independent T Test

Confidence Intervals for the Independent T test

Effect Size

Assumptions for the Independent T Test

SPSS® Explore for Checking the Normal Distribution

Excel Procedures for Checking the Equal Variance Assumption

SPSS® Procedure for Checking the Equal Variance Assumption

Using SPSS® and Excel with the Independent T Test

SPSS® Procedure for the Independent T Test

Excel Procedures for the Independent T Test

Effect Size for the Independent T Test Example

Parting Comments

Non Parametric Statistics: the Mann–Whitney U Test

Terms and Concepts

Data Lab and Examples (with Solutions)

CHAPTER NINE: ANALYSIS OF VARIANCE

A Hypothetical Example of ANOVA

The Nature of ANOVA

The Components of Variance

The Process of ANOVA

Calculating ANOVA

Effect Size

Post Hoc Analyses

Assumptions of ANOVA

Additional Considerations with ANOVA

The Hypothesis Test: Interpreting ANOVA Results

Are the Assumptions Met?

Using SPSS® and Excel with One Way ANOVA

The Need for Diagnostics

Non–Parametric ANOVA Tests: The Kruskal–Wallis Test

Terms and Concepts

Data Lab and Examples (with Solutions)

CHAPTER TEN: FACTORIAL ANOVA

Extensions of ANOVA

Factorial ANOVA

Interaction Effects

Simple Effects   

2XANOVA: An Example

Calculating Factorial ANOVA

The Hypotheses Test: Interpreting Factorial ANOVA Results

Effect Size for 2XANOVA: Partial 2

Discussing the Results

Using SPSS® to Analyze 2XANOVA

Summary Chart for 2XANOVA Procedures

Terms and Concepts

Data Lab and Examples (with Solutions)

CHAPTER ELEVEN: CORRELATION

The Nature of Correlation

The Correlation Design

Pearson s Correlation Coefficient

Plotting the Correlation: The Scattergram

Using SPSS® To Create Scattergrams

Using Excel to Create Scattergrams

Calculating Pearson s r

The Z Score Method

The Computation Method

The Hypothesis Test for Pearson s r

Effect Size: The Coefficient of Determination

Diagnostics: Correlation Problems

Correlation Using SPSS® and Excel

Non Parametric Statistics: Spearman s Rank Order Correlation ( )

Terms and Concepts

Data Lab and Examples (with Solutions)

CHAPTER TWELVE: BIVARIATE REGRESSION

The Nature of Regression

The Regression Line

Calculating Regression

Effect Size of Regression

The Z Score Formula for Regression

Testing the Regression Hypotheses

The Standard Error of Estimate

Confidence Interval

Explaining Variance through Regression

A Numerical Example of Partitioning the Variance

Using Excel and SPSS® with Bivariate Regression

The SPSS® Regression Output

The Excel Regression Output

Complete Example of Bivariate Linear Regression

Assumptions of Bivariate Linear Regression

The Omnibus Test Results

Effect Size

The Model Summary

The Regression Equation and Individual Predictor Test of Significance

Advanced Regression Procedures

Detecting Problems in Bivariate Linear Regression

Terms and Concepts

Data Lab and Examples (with Solutions)

CHAPTER THIRTEEN: INTRODUCTION TO MULTIPLE LINEAR REGRESSION

The Elements of Multiple Linear Regression

Some Differences between Bivariate Regression and Multiple Linear Regression

Stuff Not Covered

Assumptions of Multiple Linear Regression

Analyzing Residuals to Check MLR Assumptions

Diagnostics for MLR – Cleaning and Checking Data

Extreme Scores

Distance Statistics

Influence Statistics

MLR Extended Example Data

Assumptions Met?

Analyzing Residuals: Are Assumptions Met?

Interpreting the SPSS® Findings for MLR

Entering Predictors Together as a Block

Entering Predictors Separately

Additional Entry Methods for MLR Analyses

Example Study Conclusion

Terms and Concepts

Data Lab Example (with Solutions)

CHAPTER FOURTEEN: CHI SQUARE AND CONTINGENCY TABLE ANALYSIS

Contingency Tables

The Chi Square Procedure and Research Design

Chi Square Design One: Goodness of Fit

A Hypothetical Example Goodness of Fit

Effect Size Goodness of Fit

Chi Square Design Two: The Test of Independence

A Hypothetical Example Test of Independence

Special 2 x 2 Chi Square

Effect Size in 2X2 Tables: Phi

Cramer s V: Effect Size for the Chi Square Test of Independence

Repeated Measures Chi–Square: McNemar Test

Using SPSS® and Excel with Chi Square

Using SPSS® for the Chi Square Test of Independence

Using Excel for Chi Square

Terms and Concepts

Data Lab Example (with Solutions)

CHAPTER FIFTEEN: REPEATED MEASURES PROCEDURES: TDEP AND ANOVAWS

Independent and Dependent Samples in Research Designs

Using Different T Tests

The Dependent T Test Calculation: The Long Formula

Example: The Long Formula

The Dependent T Test Calculation: The Difference Formula

Tdep and Power

Conducting the Tdep Analysis Using SPSS®

Conducting the Tdep Analysis Using Excel

Within Subjects ANOVA (ANOVAws)

Experimental Designs

Post Facto Designs

Within Subjects Example

Using SPSS® for Within Subjects Data

The SPSS® Procedure

The SPSS® Output

Non Parametric Statistics

Terms and Concepts

REFERENCES

APPENDICES

APPENDIX A: SPSS® BASICS

Using SPSS®

General Features

Management Functions

Additional Management Functions

Analysis Functions

APPENDIX B: EXCEL BASICS

Data Management

The Excel Menus

Using Statistical Functions

Data Analysis Procedures

Missing Values and 0 Values in Excel Analyses

Using Excel with Real Data

APPENDIX C: STATISTICAL TABLES

Table A: Z–Score Table

Table B: Exclusion Values for the T Distribution

Table C: Critical (Exclusion) Values for the Distribution of F

Table D: Tukey s Range Test

Table E: Critical (Exclusion) Values for Pearson s Correlation Coefficient, r

Table F: Critical Values of the Chi Square Distribution

INDEX