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…mehr
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.
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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
