An Introduction to Statistical Analysis in Research

With Applications in the Biological and Life Sciences
 
 
Standards Information Network (Verlag)
  • erschienen am 4. August 2017
  • |
  • 608 Seiten
 
E-Book | PDF mit Adobe-DRM | Systemvoraussetzungen
978-1-119-29969-1 (ISBN)
 
Provides well-organized coverage of statistical analysis and applications in biology, kinesiology, and physical anthropology with comprehensive insights into the techniques and interpretations of R, SPSS(r), Excel(r), and Numbers(r) output
An Introduction to Statistical Analysis in Research: With Applications in the Biological and Life Sciences develops a conceptual foundation 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 real-world research questions and how to perform them using easily available software packages including R, SPSS(r), Excel(r), and Numbers(r). 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.
In addition, this book:
* Aims to develop readers' skills including how to report research outcomes, determine the appropriate experimental methodology and statistical analysis, and identify the needed research questions and testable hypotheses
* Includes pedagogical elements throughout that enhance the overall learning experience including case studies and tutorials, all in an effort to gain full comprehension of designing an experiment, considering biases and uncontrolled variables, analyzing data, and applying the appropriate statistical application with valid justification
* Fills the gap between theoretically driven, mathematically heavy texts and introductory, step-by-step type books while preparing readers with the programming skills needed to carry out basic statistical tests, build support figures, and interpret the results
* Provides a companion website that features related R, SPSS, Excel, and Numbers data sets, sample PowerPoint(r) lecture slides, end of the chapter review questions, software video tutorials that highlight basic statistical concepts, and a student workbook and instructor manual
An Introduction to Statistical Analysis in Research: With Applications in the Biological and Life Sciences is an ideal textbook for upper-undergraduate and graduate-level courses in research methods, biostatistics, statistics, biology, kinesiology, sports science and medicine, health and physical education, medicine, and nutrition. The book is also appropriate as a reference for researchers and professionals in the fields of anthropology, sports research, sports science, and physical education.
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.
1. Auflage
  • Englisch
  • USA
John Wiley & Sons Inc
  • Für Beruf und Forschung
  • 196,32 MB
978-1-119-29969-1 (9781119299691)
weitere Ausgaben werden ermittelt
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

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