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. 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. INDICE: 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 × 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 2 (Chi–Square) Distribution, 553 .REFERENCES 555 .Index 557

• ISBN: 978-1-119-12104-6
• Editorial: Wiley–Blackwell
• Encuadernacion: Cartoné
• Páginas: 600
• Fecha Publicación: 24/10/2016
• Nº Volúmenes: 1
• Idioma: Inglés