Introduction to Nonparametric Statistics for the Biological Sciences Using R:

Description: This book contains a rich set of tools for nonparametric analyses, and the purpose of this text is to provide guidance to students and professional researchers on how R is used for nonparametric data analysis in the biological sciences: To introduce when nonparametric approaches to data analysis are appropriate To introduce the leading nonparametric tests commonly used in biostatistics and how R is used to generate appropriate statistics for each test To introduce common figures typically associated with nonparametric data analysis and how R is used to generate appropriate figures in support of each data set The book focuses on how R is used to distinguish between data that could be classified as nonparametric as opposed to data that could be classified as parametric, with both approaches to data classification covered extensively. Following an introductory lesson on nonparametric statistics for the biological sciences, the book is organized into eight self-contained lessons on various analyses and tests using R to broadly compare differences between data sets and statistical approach. Thomas W. MacFarland, Ed.D., is Associate Professor (Computer Technology) at Nova Southeastern University in Fort Lauderdale, Florida. He joined the Graduate School of Computer and Information Sciences in 1988 and provides consulting services to the university community on research methods and statistical design as well as individual research on institutional concerns and assessment of student learning. Dr. MacFarland's areas of research include institutional research, assessment of student learning outcomes, federal data resources, and K-12 computer science education. Jan Yates, Ph.D., is Associate Professor of Educational Media and Computer Science Education at Nova Southeastern University's Abraham S. Fischler College of Education in Fort Lauderdale, Florida. Since 2001, she has worked in the areas of curriculum development, program assessment and review, and accreditation. 1 Nonparametric Statistics for the Biological Sciences1.1 Purpose of This Lesson 1.2 Data Types 1.2.1 Nominal Data 1.2.2 Ordinal Data1.2.3 Interval Data and Ratio Data1.3 Graphical Presentation of Populations1.3.1 Samples that Exhibit Normal Distribution1.3.2 Samples that Fail to Exhibit Normal Distribution1.4 R and Nonparametric Analyses1.4.1 Precision of Scales: Ordinal v Interval1.4.2 Deviation from Normal Distribution1.4.3 Sample Size: Number of Subjects for Each Breakout Group1.5 Definition of Nonparametric Analysis1.6 Statistical Tests and Graphics Associated with Distribution Patterns1.7 Prepare to Exit, Save, and Later Retrieve This R Session 2 Sign Test2.1 Background on This Lesson2.1.1 Description of the Data2.1.2 Null Hypothesis (Ho)2.2 Data Import of a .csv Spreadsheet-Type Data File into R2.3 Organize the Data and Display the Code Book2.4 Conduct a Visual Data Check2.5 Descriptive Analysis of the Data2.6 Conduct the Statistical Analysis2.7 Summary 2.8 Prepare to Exit, Save, and Later Retrieve This R Session 3 Chi-Square3.1 Background on This Lesson3.1.1 Description of the Data3.1.2 Null Hypothesis (Ho)3.2 Data Import of a .csv Spreadsheet-Type Data File into R3.3 Organize the Data and Display the Code Book3.4 Conduct a Visual Data Check3.5 Descriptive Analysis of the Data3.6 Conduct the Statistical Analysis3.7 Summary3.8 Addendum: Calculate the Chi-Square Statistic from Contingency Tables3.9 Prepare to Exit, Save, and Later Retrieve This R Session 4 Mann-Whitney U Test4.1 Background on This Lesson4.1.1 Description of the Data4.1.2 Null Hypothesis (Ho)4.2 Data Import of a .csv Spreadsheet-Type Data File into R4.3 Organize the Data and Display the Code Book4.4 Conduct a Visual Data Check4.5 Descriptive Analysis of the Data4.6 Conduct the Statistical Analysis4.7 Summary4.8 Addendum: Stacked Data v Unstacked Data4.9 Prepare to Exit, Save, and Later Retrieve This R Session 5 Wilcoxon Matched-Pairs Signed-Ranks Test5.1 Background on This Lesson5.1.1 Description of the Data5.1.2 Null Hypothesis (Ho)5.2 Data Import of a .csv Spreadsheet-Type Data File into R5.3 Organize the Data and Display the Code Book5.4 Conduct a Visual Data Check5.5 Descriptive Analysis of the Data5.6 Conduct the Statistical Analysis5.7 SummaryTest5.9 Addendum 2: Similar Functions from Different Packages5.10 Addendum 3: Nonparamteric v Parametric Confirmation of Outcomes5.11 Prepare to Exit, Save, and Later Retrieve This R Session 6 Kruskal-Wallis H-Test for Oneway Analysis of Variance (ANOVA) by Ranks6.1 Background on This Lesson6.1.1 Description of the Data6.1.2 Null Hypothesis (Ho)6.2 Data Import of a .csv Spreadsheet-Type Data File into R6.3 Organize the Data and Display the Code Book6.4 Conduct a Visual Data Check6.5 Descriptive Analysis of the Data6.6 Conduct the Statistical Analysis6.7 Summary6.8 Addendum: Comparison of Kruskal-Wallis Test Differences by Breakout Group6.9 Prepare to Exit, Save, and Later Retrieve This R Session 7 Friedman Two Way Analysis of Variance (ANOVA) by Ranks7.1 Background on This Lesson7.1.1 Description of the Data7.1.2 Null Hypothesis (Ho)7.2 Data Import of a .csv Spreadsheet-Type Data File into R7.3 Organize the Data and Display the Code Book7.4 Conduct a Visual Data Check7.5 Descriptive Analysis of the Data7.6 Conduct the Statistical Analysis7.7 Summary7.8 Addendum: Similar Functions from Different Packages7.9 Prepare to Exit, Save, and Later Retrieve This R Session 8 Spearman's Rank-Difference Coefficient of Correlation8.1 Background on This Lesson8.1.1 Description of the Data8.1.2 Null Hypothesis (Ho)8.2 Data Import of a .csv Spreadsheet-Type Data File into R8.3 Organize the Data and Display the Code Book8.4 Conduct a Visual Data Check8.5 Descriptive Analysis of the Data8.6 Conduct the Statistical Analysis8.7 Summary8.8 Addendum: Kendall Rank Correlation8.9 Prepare to Exit, Save, and Later Retrieve This R Session 9 Other Nonparametric Tests for the Biological Sciences9.1 Binominal Test9.2 Walsh Test9.3 Kolmogoroy-Smirnov Two-Sample Test9.4 Binominal Logistic Regression9.5 Future Applications of Nonparametric Statistics9.6 Contact the Authors

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