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


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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.

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.

Preface xv

Acknowledgments xix


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


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


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


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


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


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


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


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


A Hypothetical Example of ANOVA, 255

The Nature of ANOVA, 257

The Components of Variance, 258

The Process of ANOVA, 259

Calculating ANOVA, 260

Effect Size, 268

Post Hoc Analyses, 269

Assumptions of ANOVA, 274

Additional Considerations with ANOVA, 275

The Hypothesis Test: Interpreting ANOVA Results, 276

Are the Assumptions Met?, 276

Using SPSS® and Excel with One-Way ANOVA, 282

The Need for Diagnostics, 289

Non-Parametric ANOVA Tests: The Kruskal–Wallis Test, 289

Terms and Concepts, 292

Data Lab and Examples (with Solutions), 293

Data Lab: Solutions, 294


Extensions of ANOVA, 297




Factorial ANOVA, 299

Interaction Effects, 299

Simple Effects, 301

2XANOVA: An Example, 302

Calculating Factorial ANOVA, 303

The Hypotheses Test: Interpreting Factorial ANOVA Results, 306

Effect Size for 2XANOVA: Partial �� 2, 308

Discussing the Results, 309

Using SPSS® to Analyze 2XANOVA, 311

Summary Chart for 2XANOVA Procedures, 319

Terms and Concepts, 319

Data Lab and Examples (with Solutions), 320

Data Lab: Solutions, 320


The Nature of Correlation, 330

The Correlation Design, 331

Pearson’s Correlation Coefficient, 332

Plotting the Correlation: The Scattergram, 334

Using SPSS® to Create Scattergrams, 337

Using Excel to Create Scattergrams, 339

Calculating Pearson’s r, 341

The Z Score Method, 342

The Computation Method, 344

The Hypothesis Test for Pearson’s r, 345

Effect Size: the Coefficient of Determination, 347

Diagnostics: Correlation Problems, 349

Correlation Using SPSS® and Excel, 352

Nonparametric Statistics: Spearman’s Rank Order Correlation (rs), 358

Terms and Concepts, 363

Data Lab and Examples (with Solutions), 364

Data Lab: Solutions, 365


The Nature of Regression, 372

The Regression Line, 374

Calculating Regression, 376

Effect Size of Regression, 379

The Z Score Formula for Regression, 380

Testing the Regression Hypotheses, 382

The Standard Error of Estimate, 383

Confidence Interval, 385

Explaining Variance Through Regression, 386

A Numerical Example of Partitioning the Variation, 389

Using Excel and SPSS® with Bivariate Regression, 390

The SPSS® Regression Output, 390

The Excel Regression Output, 396

Complete Example of Bivariate Linear Regression, 398

Assumptions of Bivariate Regression, 398

The Omnibus Test Results, 404

Effect Size, 404

The Model Summary, 405

The Regression Equation and Individual Predictor Test of Significance, 405

Advanced Regression Procedures, 406

Detecting Problems in Bivariate Linear Regression, 408

Terms and Concepts, 409

Data Lab and Examples (with Solutions), 410

Data Lab: Solutions, 411


The Elements of Multiple Linear Regression, 417

Same Process as Bivariate Regression, 418

Some Differences between Bivariate Linear Regression and Multiple Linear Regression, 419

Stuff not Covered, 420

Assumptions of Multiple Linear Regression, 421

Analyzing Residuals to Check MLR Assumptions, 422

Diagnostics for MLR: Cleaning and Checking Data, 423

Extreme Scores, 424

Distance Statistics, 428

Influence Statistics, 429

MLR Extended Example Data, 430

Assumptions Met?, 431

Analyzing Residuals: Are Assumptions Met?, 433

Interpreting the SPSS® Findings for MLR, 436

Entering Predictors Together as a Block, 437

Entering Predictors Separately, 442

Additional Entry Methods for MLR Analyses, 447

Example Study Conclusion, 448

Terms and Concepts, 448

Data Lab and Example (with Solution), 450

Data Lab: Solution, 450


Contingency Tables, 455

The Chi-square Procedure and Research Design, 456

Chi-square Design One: Goodness of Fit, 457

A Hypothetical Example: Goodness of Fit, 458

Effect Size: Goodness of Fit, 462

Chi-square Design Two: The Test of Independence, 463

A Hypothetical Example: Test of Independence, 464

Special 2 × 2 Chi-square, 468

Effect Size in 2 × 2 Tables: PHI, 470

Cramer’s V: Effect Size for the Chi-square Test of Independence, 471

Repeated Measures Chi-square: Mcnemar Test, 472

Using SPSS® and Excel with Chi-square, 474

Using SPSS® for the Chi-square Test of Independence, 475

Using Excel for Chi-square Analyses, 481

Terms and Concepts, 483

Data Lab and Examples (with Solutions), 483

Data Lab: Solutions, 484


Independent and Dependent Samples in Research Designs, 490

Using Different T Tests, 491

The Dependent T Test Calculation: The “Long” Formula, 491

Example: The Long Formula, 492

The Dependent T Test Calculation: The “Difference” Formula, 494

Tdep and Power, 496

Conducting The Tdep Analysis Using SPSS®, 496

Conducting The Tdep Analysis Using Excel, 498

Within-Subject ANOVA (ANOVAWS), 498

Experimental Designs, 499

Post Facto Designs, 500

Within-Subject Example, 501

Using SPSS® for Within-Subject Data, 501

The SPSS® Procedure, 502

The SPSS® Output, 504

Nonparametric Statistics, 508

Terms and Concepts, 508


Appendix A SPSS® BASICS 509

Using SPSS®, 509

General Features, 510

Management Functions, 513

Additional Management Functions, 517

Appendix B EXCEL BASICS 531

Data Management, 531

The Excel Menus, 533

Using Statistical Functions, 541

Data Analysis Procedures, 543

Missing Values and “0” Values in Excel Analyses, 544

Using Excel with “Real Data”, 544


Table C.1: Z-Score Table (Values Shown are Percentages – %), 545

Table C.2: Exclusion Values for the T-Distribution, 547

Table C.3: Critical (Exclusion) Values for the Distribution of F, 548

Table C.4: Tukey’s Range Test (Upper 5% Points), 551

Table C.5: Critical (Exclusion) Values for Pearson’s Correlation Coefficient, r, 552

Table C.6: Critical Values of the �� 2 (Chi-Square) Distribution, 553


Index 557

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