Provides a step-by-step approach to statistical procedures to analyze data and conduct research, with detailed sections in each chapter explaining SPSS(R) and Excel(R) 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(R) and Excel(R) 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(R) and Excel(R) 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(R) and SPSS(R), 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 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 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 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 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 x 2 Chi-square, 468 Effect Size in 2 x 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 2 (Chi-Square) Distribution, 553 REFERENCES 555 Index 557
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