An Introduction to Statistical Analyses in Research: With Applications in the Biological and Life Sciences focuses on developing a conceptual foundation of knowledge in statistical analysis while providing readers with opportunities to practice these skills via research-based data sets in biology, kinesiology, and physical anthropology. Readers are provided with a detailed introduction and orientation to statistical analysis as well as practical examples to ensure a thorough understanding of the concepts and methodology. In addition, the book addresses not just the statistical concepts researchers should be familiar with, but also demonstrates their relevance to actual research questions and how to perform them using easily available software packages including R, SPSS, Microsoft Excel Office, and Numbers. The authors successfully fill the gap between theoretically driven, mathematically heavy texts and introductory, step-by-step type of books while preparing readers with the programming skills needed to carry out basic statistical tests and interpret the results. Specific emphasis is on the practical application of statistics in the biological and life sciences, while enhancing reader skills in identifying the research questions and testable hypotheses, determining the appropriate experimental methodology and statistical analyses, processing data, and reporting the research outcomes. Topical coverage includes: experimental design; data presentation; central tendency and distribution; parametric vs. non-parametric tests; student's t-test; analysis of variance (ANOVA); Mann Whitney U-test and Wilcoxon signed rank test; Kruskal-Wallis test; Chi-square test; Pearson correlation and Spearman rank order test; simple linear regression; basics in Excel; basics in SPSS; basics in Numbers; and basics in R. An optimized, interactive ebook is also available. This version contains identical text to that of the print book, but is enhanced with multiple choice questions, quizzes, animated figures, and video tutorials for each of the four software platforms.

KATHLEEN F. WEAVER, PhD, is Associate Dean of Learning, Innovation, and Teaching and Professor in the Department of Biology at the University of La Verne. The author of numerous journal articles, she received her PhD in Ecology and Evolutionary Biology from the University of Colorado. VANESSA C. MORALES, BS, is Assistant Director of the Academic Success Center at the University of La Verne. SARAH L. DUNN, PhD, is Associate Professor in the Department of Kinesiology at the University of La Verne and is Director of Research and Sponsored Programs. She has authored numerous journal articles and received her PhD in Health and Exercise Science from the University of New South Wales. KANYA GODDE, PhD, is Assistant Professor in the Department of Anthropology and is Director/Chair of Institutional Review Board at the University of La Verne. The author of numerous journal articles and a member of the American Statistical Association, she received her PhD in Anthropology from the University of Tennessee. PABLO F. WEAVER, PhD, is Instructor in the Department of Biology at the University of La Verne. The author of numerous journal articles, he received his PhD in Ecology and Evolutionary Biology from the University of Colorado.

Contents Preface ix Acknowledgments xi About the Companion Website xiii 1 Experimental Design 1 1.1 Experimental Design Background 1 1.2 Sampling Design 2 1.3 Sample Analysis 7 1.4 Hypotheses 9 1.5 Variables 10 2 Central Tendency and Distribution 13 2.1 Central Tendency and Other Descriptive Statistics 13 2.2 Distribution 18 2.3 Descriptive Statistics in Excel 34 2.4 Descriptive Statistics in SPSS 48 2.5 Descriptive Statistics in Numbers 52 2.6 Descriptive Statistics in R 57 3 Showing Your Data 61 3.1 Background on Tables and Graphs 61 3.2 Tables 62 3.3 Bar Graphs, Histograms, and Box Plots 63 3.4 Line Graphs and Scatter Plots 136 3.5 Pie Charts 165 4 Parametric versus Nonparametric Tests 191 4.1 Overview 192 4.2 Two-Sample and Three-Sample Tests 194 5 t-Test 195 5.1 Student's t-Test Background 195 5.2 Examples t-Tests 196 5.3 Case Study 201 5.4 Excel Tutorial 205 5.5 Paired t-Test SPSS Tutorial 209 5.6 Independent t-Test SPSS Tutorial 213 5.7 Numbers Tutorial 218 5.8 R Independent/Paired-Samples t-Test Tutorial 223 6 ANOVA 227 6.1 ANOVA Background 227 6.2 Case Study 236 6.3 One-Way ANOVA Excel Tutorial 241 6.4 One-Way ANOVA SPSS Tutorial 247 6.5 One-Way RepeatedMeasures ANOVA SPSS Tutorial 252 6.6 Two-Way Repeated Measures ANOVA SPSS Tutorial 261 6.7 One-Way ANOVA Numbers Tutorial 272 6.8 One-Way R Tutorial 288 6.9 Two-Way ANOVA R Tutorial 291 7 Mann-Whitney U andWilcoxon Signed-Rank 297 7.1 Mann-Whitney U andWilcoxon Signed-Rank Background 297 7.2 Assumptions 298 7.3 Case Study - Mann--Whitney U Test 299 7.4 Case Study -Wilcoxon Signed-Rank 302 7.5 Mann-Whitney U Excel Tutorial 305 7.6 Wilcoxon Signed-Rank Excel Tutorial 313 7.7 Mann-Whitney U SPSS Tutorial 319 7.8 Wilcoxon Signed-Rank SPSS Tutorial 324 7.9 Mann-Whitney U Numbers Tutorial 328 7.10 Wilcoxon Signed-Rank Numbers Tutorial 337 7.11 Mann-Whitney U/Wilcoxon Signed-Rank R Tutorial 350 8 Kruskal-Wallis 353 8.1 Kruskal-Wallis Background 353 8.2 Case Study 1 354 8.3 Case Study 2 358 8.4 Kruskal-Wallis Excel Tutorial 362 8.5 Kruskal-Wallis SPSS Tutorial 368 8.6 Kruskal-Wallis Numbers Tutorial 375 8.7 Kruskal-Wallis R Tutorial 386 9 Chi-Square Test 393 9.1 Chi-Square Background 393 9.2 Case Study 1 394 9.3 Case Study 2 401 9.4 Chi-Square Excel Tutorial 405 9.5 Chi-Square SPSS Tutorial 418 9.6 Chi-Square Numbers Tutorial 426 9.7 Chi-Square R Tutorial 429 10 Pearson's and Spearman's Correlation 435 10.1 Correlation Background 435 10.2 Example 435 10.3 Case Study - Pearson's Correlation 442 10.4 Case Study - Spearman's Correlation 445 10.5 Pearson's Correlation Excel and Numbers Tutorial 448 10.6 Spearman's Correlation Excel Tutorial 455 10.7 Pearson/Spearman's Correlation SPSS Tutorial 462 10.8 Pearson/Spearman's Correlation R Tutorial 467 11 Linear Regression 473 11.1 Linear Regression Background 473 11.2 Case Study 480 11.3 Linear Regression Excel Tutorial 484 11.4 Linear Regression SPSS Tutorial 497 11.5 Linear Regression Numbers Tutorial 508 11.6 Linear Regression R Tutorial 517 12 Basics in Excel 523 12.1 Opening Excel 524 12.2 Installing the Data Analysis ToolPak 525 12.3 Cells and Referencing 529 12.4 Common Commands and Formulas 532 12.5 Applying Commands to Entire Columns 534 12.6 Inserting a Function 536 12.7 Formatting Cells 537 13 Basics in SPSS 539 13.1 Opening SPSS 539 13.2 Labeling Variables 541 13.3 Setting Decimal Placement 543 13.4 Determining the Measure of a Variable 544 13.5 Saving SPSS Data Files 545 13.6 Saving SPSS Output 547 14 Basics in Numbers 551 14.1 Opening Numbers 551 14.2 Common Commands 553 14.3 Applying Commands 555 14.4 Adding Functions 557 15 Basics in R 561 15.1 Opening R 561 15.2 Getting Acquainted with the Console 562 15.3 Loading Data 566 15.4 Installing and Loading Packages 570 15.5 Troubleshooting 576 Appendix 579 Flow Chart 579 Literature Cited 581 Glossary 585 Index 591