Using Statistics in the Social and Health Sciences with SPSS and Excel
John Wiley & Sons Inc (Verlag)
978-1-119-12104-6 (ISBN)
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
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 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
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 𝜂2 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 Chi-Square and Contingency Table Analysis 455
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
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
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
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: 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 𝜒2 (Chi-Square) Distribution 553
References 555
Index 557
| Erscheinungsdatum | 11.11.2016 |
|---|---|
| Verlagsort | New York |
| Sprache | englisch |
| Maße | 163 x 239 mm |
| Gewicht | 953 g |
| Themenwelt | Geisteswissenschaften ► Psychologie |
| Mathematik / Informatik ► Mathematik ► Statistik | |
| Mathematik / Informatik ► Mathematik ► Wahrscheinlichkeit / Kombinatorik | |
| Studium ► Querschnittsbereiche ► Epidemiologie / Med. Biometrie | |
| Studium ► Querschnittsbereiche ► Prävention / Gesundheitsförderung | |
| Sozialwissenschaften ► Soziologie ► Empirische Sozialforschung | |
| ISBN-10 | 1-119-12104-3 / 1119121043 |
| ISBN-13 | 978-1-119-12104-6 / 9781119121046 |
| Zustand | Neuware |
| Haben Sie eine Frage zum Produkt? |
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