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R Data Analysis without Programming: Explanation and Interpretation

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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.

    Author(s): David W. Gerbing
    Edition: 2
    Publisher: Routledge
    Year: 2023

    Language: English
    Pages: 376
    City: New York

    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