Using Statistics in the Social and Health Sciences with SPSS and ExcelISBN: 9781119121046
600 pages
August 2016

Description
Provides a stepbystep 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 classtested to ensure an accessible presentation, the book combines clear, stepbystep 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. Realworld 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 realworld 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.
Table of Contents
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
NonExperimental 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
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 TwoTailed 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 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 OneWay ANOVA, 282
The Need for Diagnostics, 289
NonParametric ANOVA Tests: The Kruskal–Wallis Test, 289
Terms and Concepts, 292
Data Lab and Examples (with Solutions), 293
Data Lab: Solutions, 294
10 FACTORIAL ANOVA 297
Extensions of ANOVA, 297
ANCOVA, 298
MANOVA, 299
MANCOVA, 299
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 η 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
11 CORRELATION 329
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
12 BIVARIATE REGRESSION 371
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
13 INTRODUCTION TO MULTIPLE LINEAR REGRESSION 417
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
14 CHISQUARE AND CONTINGENCY TABLE ANALYSIS 455
Contingency Tables, 455
The Chisquare Procedure and Research Design, 456
Chisquare Design One: Goodness of Fit, 457
A Hypothetical Example: Goodness of Fit, 458
Effect Size: Goodness of Fit, 462
Chisquare Design Two: The Test of Independence, 463
A Hypothetical Example: Test of Independence, 464
Special 2 × 2 Chisquare, 468
Effect Size in 2 × 2 Tables: PHI, 470
Cramer's V: Effect Size for the Chisquare Test of Independence, 471
Repeated Measures Chisquare: Mcnemar Test, 472
Using SPSS and Excel with Chisquare, 474
Using SPSS for the Chisquare Test of Independence, 475
Using Excel for Chisquare Analyses, 481
Terms and Concepts, 483
Data Lab and Examples (with Solutions), 483
Data Lab: Solutions, 484
15 REPEATED MEASURES PROCEDURES: Tdep AND ANOVAWS 489
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
WithinSubject ANOVA (ANOVAWS), 498
Experimental Designs, 499
Post Facto Designs, 500
WithinSubject Example, 501
Using SPSS for WithinSubject Data, 501
The SPSS Procedure, 502
The SPSS Output, 504
Nonparametric Statistics, 508
Terms and Concepts, 508
APPENDICES
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
Appendix C STATISTICAL TABLES 545
Table C.1: ZScore Table (Values Shown are Percentages – %), 545
Table C.2: Exclusion Values for the TDistribution, 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 χ2 (ChiSquare) Distribution, 553
REFERENCES 555
Index 557