R Data Analysis without Programming: Explanation and Interpretation, 2nd Edition

Length: 358 pages
Edition: 2
Language: English
Publisher: Routledge
Publication Date: 2023-01-30
ISBN-10: 1032244038
ISBN-13: 9781032244037
Sales Rank: #0 (See Top 100 Books)

The new edition of this innovative book, R Data Analysis without Programming, prepares the readers to quickly analyze data and interpret statistical results using R. Professor Gerbing has developed lessR, a ground-breaking method in alleviating the challenges of R programming. The lessR extends R, removing the need for programming. This edition expands upon the first edition’s introduction to R through lessR, which enables the readers to learn how to organize data for analysis, read the data into R, and generate output without performing numerous functions and programming exercises first. With lessR, readers can select the necessary procedure and change the relevant variables with simple function calls. The text reviews and explains basic statistical procedures with the lessR enhancements added to the standard R environment. Using lessR, data analysis with R becomes immediately accessible to the novice user and easier to use for the experienced user.

Highlights along with content new to this edition include:

Explanation and Interpretation of all data analysis techniques; much more than a computer manual, this book shows the reader how to explain and interpret the results.
Introduces the concepts and commands reviewed in each chapter.
Clear, relaxed writing style more effectively communicates the underlying concepts than more stilted academic writing.
Extensive margin notes highlight, define, illustrate, and cross-reference the key concepts. When readers encounter a term previously discussed, the margin notes identify the page number for the initial introduction.
Scenarios that highlight the use of a specific analysis followed by the corresponding R/lessR input, output, and an interpretation of the results.

Numerous examples of output from psychology, business, education, and other social sciences, that demonstrate the analysis and how to interpret results.

Two data sets are analyzed multiple times in the book, provide continuity throughout.
Comprehensive: A wide range of data analysis techniques are presented throughout the book.
Integration with machine learning as regression analysis is presented from both the traditional perspective and from the modern machine learning perspective.
End of chapter problems help readers test their understanding of the concepts.
A website at www.lessRstats.com that features the data sets referenced in both standard text and SPSS formats so readers can practice using R/lessR by working through the text examples and worked problems, R/lessR videos to help readers better understand the program, and more.

This book is ideal for graduate and undergraduate courses in statistics beyond the introductory course, research methods, and/or any data analysis course, taught in departments of psychology, business, education, and other social and health sciences; this book is also appreciated by researchers doing data analysis. Prerequisites include basic statistical knowledge, though the concepts are explained from the beginning in the book. Previous knowledge of R is not assumed.

Cover
Half Title
Title Page
Copyright Page
Dedication
Contents
List of Figures
List of Tables
Preface
1. R for Data Analysis
	1.1. Introduction
		1.1.1. Data Analysis
		1.1.2. R with lessR
	1.2. Prepare R for Analysis
		1.2.1. Download R
		1.2.2. Download RStudio
		1.2.3. R in the Cloud
		1.2.4. Start R
		1.2.5. Extend R
		1.2.6. Access lessR
		1.2.7. Get Help
		1.2.8. R Functions for Analysis
		1.2.9. Vectors
	1.3. Data
		1.3.1. Data Example I: Employee Data
		1.3.2. Data Example II: Machiavellianism
		1.3.3. Create a Data File
	1.4. Analysis Problems
2. Read and Write Data
	2.1. Quick Start
	2.2. Types of Variables
		2.2.1. Variables as a Concept
		2.2.2. Variables in the Computer
	2.3. Read Data
		2.3.1. Access the Data
		2.3.2. Output
		2.3.3. Missing Values
		2.3.4. Row Names
	2.4. More Data Formats
		2.4.1. lessR Data
		2.4.2. SPSS, SAS, and Stata Data
		2.4.3. Fixed-Width Data
		2.4.4. More Options
	2.5. Variable Labels
		2.5.1. Definition
		2.5.2. Variable Labels File
		2.5.3. Variable Labels with R Functions
	2.6. Write Data
		2.6.1. Choose an Output Format
		2.6.2. Write a Data Frame to a File
	2.7. Analysis Problems
3. Manage Data
	3.1. Quick Start
	3.2. Categorical Variables as Factors
		3.2.1. Order Levels
		3.2.2. Value Labels
		3.2.3. Add Levels
	3.3. Transform Data
		3.3.1. Arithmetic Operators
		3.3.2. Mathematical Functions
	3.4. Recode Data
		3.4.1. Reverse Score Items
		3.4.2. Missing Data
	3.5. Sort Data
		3.5.1. Sort by Variables
		3.5.2. Sort by Other Criteria
	3.6. Subset Data
		3.6.1. Select Rows and/or Columns
		3.6.2. Randomly Select Rows
	3.7. Revise Data
		3.7.1. Change an Individual Data Value
		3.7.2. Change a Variable Name
	3.8. Merge Data
		3.8.1. Inner Join
		3.8.2. Outer and Full Joins
		3.8.3. Add Rows to a Data Frame
	3.9. Analysis Problems
4. Categorical Variables
	4.1. Quick Start
	4.2. One Categorical Variable
		4.2.1. Bar Chart
		4.2.2. Pie Chart
		4.2.3. Customization
		4.2.4. Bar Chart from the Summary Table
		4.2.5. Bar Chart of Deviation Scores
		4.2.6. Stack the Bars across Multiple Variables
		4.2.7. Generalize Beyond the One Sample
	4.3. Two Categorical Variables
		4.3.1. Bar Chart from Joint Frequencies
		4.3.2. 100% Stacked Bar Chart
		4.3.3. Description with Summary Tables
		4.3.4. Inferential Analysis
	4.4. Analysis Problems
5. Continuous Variables
	5.1. Quick Start
	5.2. Histogram
		5.2.1. Bins
		5.2.2. Default Histogram
		5.2.3. Customize the Bins
		5.2.4. Smooth the Bins
		5.2.5. Bandwidth
		5.2.6. Cumulative Histogram
		5.2.7. Histograms for All
	5.3. Histogram Alternatives
		5.3.1. Box Plot and Outliers
		5.3.2. Violin-Box-Scatter Plot
	5.4. Visualize Data over Time
		5.4.1. Run Chart
		5.4.2. Time series
	5.5. Analysis Problems
6. Statistics
	6.1. Quick Start
	6.2. Types of Summary Statistics
		6.2.1. Parametric Statistics
		6.2.2. Order Statistics
		6.2.3. Obtain the Statistics
		6.2.4. Data Aggregation
	6.3. Evaluate a Single Mean
		6.3.1. Description
		6.3.2. Basis of Inference
		6.3.3. Application
		6.3.4. One-Tailed vs. Two-Tailed Tests
	6.4. Evaluate a Proportion
	6.5. Analysis Problems
7. Compare Two Samples
	7.1. Quick Start
	7.2. Independent-Samples
		7.2.1. Research Design for Independent-Samples
		7.2.2. Example 1: Two Existing Groups
		7.2.3. Description
		7.2.4. Inference
		7.2.5. Nonparametric Alternative
		7.2.6. Example 2: Two Experimental Groups
	7.3. Dependent Samples
		7.3.1. Dependent-Samples t-test
		7.3.2. Nonparametric Comparison
	7.4. Multiple Proportions
	7.5. Analysis Problems
8. Compare Multiple Samples
	8.1. Quick Start
	8.2. Experimental Design
	8.3. One-Way Design
		8.3.1. Variability
		8.3.2. Example
		8.3.3. Data and Input
		8.3.4. Description
		8.3.5. Inference
		8.3.6. Search for Outliers
		8.3.7. Nonparametric Alternative
	8.4. Randomized Block Design
		8.4.1. Example
		8.4.2. Data
		8.4.3. Input
		8.4.4. Description
		8.4.5. Inference
		8.4.6. Other Output
		8.4.7. Nonparametric Alternative
		8.4.8. Advantage of Blocking
	8.5. Analysis Problems
9. Factorial Designs
	9.1. Quick Start
	9.2. Two-Way Factorial Design
		9.2.1. Example
		9.2.2. Data
		9.2.3. Input
		9.2.4. Description
		9.2.5. Inference
	9.3. More Advanced Designs
		9.3.1. Randomized Block Factorial Design
		9.3.2. Split-Plot Factorial Design
		9.3.3. Unbalanced Designs
	9.4. Analysis Problems
10. Correlation
	10.1. Quick Start
	10.2. Relation of Two Numeric Variables
		10.2.1. Scatterplot
		10.2.2. Correlation Coefficient
		10.2.3. Two Unrelated Variables
		10.2.4. Two Variables Positively Related
		10.2.5. Scatterplot Classification Variable
		10.2.6. Bubble Plot
	10.3. Correlation Matrix
		10.3.1. All Numeric Variables
		10.3.2. List of Variables
		10.3.3. Missing Data
		10.3.4. Visualizations
		10.3.5. Save the Correlations
		10.3.6. Cluster Analysis
	10.4. Nonparametric Correlation Coefficients
	10.5. Analysis Problems
11. Regression Analysis
	11.1. Quick Start
	11.2. Regression Models
		11.2.1. Supervised Machine Learning
		11.2.2. Functions
	11.3. Model Estimation
		11.3.1. Analysis
		11.3.2. Standardization
		11.3.3. Inference for the Slope
	11.4. Model Fit
		11.4.1. Residuals
		11.4.2. Fit Indices
	11.5. Prediction Intervals
		11.5.1. Prediction Error
		11.5.2. Predict from Existing Data
		11.5.3. Predict from New Data
	11.6. Outliers and Diagnostics
		11.6.1. Bivariate Outliers
		11.6.2. Case-Deletion Statistics
		11.6.3. Predictive Residuals
	11.7. Model Assumptions
		11.7.1. Properties of the Residuals
		11.7.2. Curvilinear Relationships
	11.8. Analysis Problems
12. Multiple Regression
	12.1. Quick Start
	12.2. Multiple Regression Model
		12.2.1. Multiple Predictor Variables
		12.2.2. Partial Slope Coefficients
	12.3. Model Estimation
		12.3.1. Total Effects
		12.3.2. Net Effects
	12.4. Model Fit
		12.4.1. Fit Indices
		12.4.2. Outliers and Assumptions
	12.5. Prediction
		12.5.1. Predictive Precision
		12.5.2. Training vs. Testing Data
		12.5.3. Data Splitting
	12.6. Model Selection
		12.6.1. Collinearity
		12.6.2. Best Subsets
		12.6.3. Nested Models
	12.7. Analysis of Covariance
		12.7.1. Covariates
		12.7.2. Homogeneity of Regression
		12.7.3. Group Differences
		12.7.4. Conclusion
		12.7.5. More Advanced Designs
	12.8. Analysis Problems
13. Categorical Regression Variables
	13.1. Quick Start
	13.2. Indicator Variables
		13.2.1. Dummy Variables
		13.2.2. Dummy Variable Regression
		13.2.3. General Linear Model
	13.3. Custom Indicator Variables
		13.3.1. Contrast Matrix
		13.3.2. Effects Coding Regression
	13.4. Binary Logistic Regression
		13.4.1. Motivation
		13.4.2. Logic
		13.4.3. Estimation
		13.4.4. Odds Ratio
		13.4.5. Fit Indices
		13.4.6. Classification
		13.4.7. Outliers
		13.4.8. Multiple Predictors
	13.5. Analysis Problems
14. Causality
	14.1. Quick Start
	14.2. Correlation is not Causation
		14.2.1. Example
		14.2.2. Real Life Consequences
	14.3. Moderation
		14.3.1. The Concept
		14.3.2. Example
		14.3.3. Manual Analysis
	14.4. Mediation
		14.4.1. The Concept
		14.4.2. Example
		14.4.3. The Indirect Effect
	14.5. Path Analysis
	14.6. Analysis Problems
15. Item and Factor Analysis
	15.1. Quick Start
	15.2. Overview of Factor Analysis
		15.2.1. Latent Variables
		15.2.2. Measurement Models
	15.3. Exploratory Factor Analysis
		15.3.1. Extraction then Rotation
		15.3.2. Exploratory Analysis of Mach IV Items
	15.4. Confirmatory Factor Analysis
		15.4.1. Covariance Structure
		15.4.2. Analysis of a Population Model
		15.4.3. Proportionality
	15.5. Confirmatory Analysis of Mach IV Items
		15.5.1. Analysis of Model from Exploratory Analysis
		15.5.2. Revised Model
		15.5.3. Scale Reliability
		15.5.4. Total Score Correlations
		15.5.5. Beyond the Basics
	15.6. Analysis Problems
References
Index