Moving from IBM (R) SPSS (R) to R and RStudio (R)

Howard T. Tokunaga is Professor of Psychology at San Jose State University, where he serves as Coordinator of the MS Program in Industrial/Organizational (I/O) Psychology and teaches undergraduate and graduate courses in statistics, research methods, and I/O psychology. He received his bachelor's degree in psychology at UC Santa Cruz and his PhD in psychology at UC Berkeley. In addition to his teaching, he has consulted with a number of public-sector and private-sector organizations on a wide variety of management and human resource issues. He is coauthor (with G. Keppel) of Introduction to Design and Analysis: A Student's Handbook.

About the Author Acknowledgments Introduction Chapter 1. Introduction to R 1.1 What Is R? 1.2 Why are Some Features of R? 1.3 Installing R and Getting Help Learning R 1.4 Conducting Statistical Analyses in Spss Versus R: A First Example 1.5 Comparing Spss and R Chapter 2. Preparing to Use R and Rstudio 2.1 Tasks to Perform Before Your First R Session 2.2 Tasks to Perform Before Any R Session 2.3 Tasks To Perform During Any R Session Chapter 3. R Terms, Concepts, and Command Structure 3.1 Data-Related Terms 3.2 Command-Related Terms 3.3 Object-Related Terms 3.4 File-Related Terms Chapter 4. Introduction to Rstudio 4.1 What Is RStudio? 4.2 Installing RStudio 4.3 Components of RStudio 4.4 Writing and Executing R Commands in RStudio Chapter 5. Conducting Rstudio Sessions: A Detailed Example 5.1 1. Start RStudio 5.2 2. Create a New Script File (Optional) 5.3 3. Define the Working Directory 5.4 4. Import CSV File to Create a Data Frame 5.5 5. Change Any Missing Data in Data Frame to NA 5.6 6. Save Data Frame With NAs As CSV File in the Working Directory 5.7 7. Read the Modified CSV File to Create a Data Frame 5.8 8. Download and Install Packages (If Not Already Done) 5.9 9. Load Installed Packages (As Needed) 5.10 10. Conduct Desired Statistical Analyses 5.11 11. Open a New Markdown File 5.12 12. Copy Commands and Comments into the Markdown File 5.13 13. Knit the Markdown File to Create a Markdown Document 5.14 Exiting Rstudio (Save the Workspace Image?) 5.15 Getting Help With R Chapter 6. Conducting Rstudio Sessions: A Brief Example 6.1 1. Start RStudio 6.2 2. Create a New Script File (Optional) 6.3 3. Define the Working Directory 6.4 4. Import CSV File to Create a Data Frame 6.5 5. Change Any Missing Data in Data Frame to NA 6.6 6. Save Data Frame with NAs as CSV File in the Working Directory 6.7 7. Read the Modified CSV File to Create a Data Frame 6.8 8. Download and Install Packages (If Not Already Done) 6.9 9. Load Installed Packages (As Needed) 6.10 10. Conduct Desired Statistical Analyses 6.11 11. Open a New Markdown File 6.12 12. Copy Commands and Comments into the Markdown File 6.13 13. Knit the Markdown File to Create a Markdown Document 6.14 Exiting RStudio Chapter 7. Conducting Statistical Analyses Using This Book: A Detailed Example 7.1 1. Start RStudio 7.2 2. Copy and Paste an Example Script into a Script File 7.3 3. Modify the Example Script as Needed for the Desired Statistical Analysis 7.4 4. Execute the Script to Confirm It Works Properly 7.5 5. Copy and Paste the Script into a Markdown File 7.6 6. Knit the Markdown File to Create a Markdown Document Chapter 8. Conducting Statistical Analyses Using This Book: A Brief Example 8.1 1. Start RStudio 8.2 2. Copy and Paste an Example Script into a Script File 8.3 3. Modify the Example Script as Needed for the Desired Statistical Analysis 8.4 4. Execute the Script to Confirm it Works Properly 8.5 5. Copy and Paste the Script into a Markdown File 8.6 6. Knit the Markdown File to Create a Markdown Document Chapter 9. Working With Data Frames and Variables in R 9.1 Working with Data Frames 9.2 Working With Variables Chapter 10. Conducting Statistical Analyses Using SPSS Syntax 10.1 Conducting Analyses in SPSS Using Menu Choices 10.2 Conducting Analyses in Spss Using Syntax Commands 10.3 Editing SPSS Output Files Appendix A: Data Transformations Reverse Score a Variable (Recode) Reduce the Number of Groups in a Categorical Variable (Recode) Create a Categorical Variable from a Continuous Variable (Recode) Create a Variable from Other Variables (Minimum Number of Valid Values) (Compute) Create a Variable from Occurrences of Values of Other Variables (Count) Perform Data Transformations When Conditions are Met (IF) Perform Data Transformations Under Specified Conditions (DO IF/END IF) Perform Data Transformations Under Different Specified Conditions (DO IF/ELSE IF/END IF) Use Numeric Functions in Data Transformations (ABS, RND, TRUNC, SQRT) Appendix B: Statistical Procedures Descriptive Statistics (All Variables) Descriptive Statistics (Selected Variables) Descriptive Statistics (Selected Variables) by Group Frequency Distribution Table Histogram t-Test for One Mean Confidence Interval for the Mean T-Test for Independent Means T-Test for Dependent Means (Repeated-Measures T-Test) One-Way Anova and Tukey Post-Hoc Comparisons One-Way Anova and Trend Analysis Single-Factor Within-Subjects (Repeated Measures) Anova Two-Factor Between-Subjects Anova Two-Factor Between-Subjects Anova (Simple Effects) Two-Factor Between-Subjects Anova (Simple Comparisons) Two-Factor Between-Subjects Anova (Main Comparisons) Two-Factor Mixed Factorial Anova Two-Factor Within-Subjects Anova Three-Factor Between-Subjects Anova Pearson Correlation (One Correlation) Pearson Correlation (Correlation Matrix) Scatterplot Internal Consistency (Cronbach's Alpha) Principal Components Analysis (Varimax Rotation) Principal Components Analysis (Oblique Rotation) Factor Analysis (Principal Axis Factoring) Linear Regression Multiple Regression (Standard) Multiple Regression (Hierarchical With Two Steps) Multiple Regression (Hierarchical With Three Steps) Multiple Regression (Testing Moderator Variables Using Hierarchical Regression) Multiple Regression (Portraying A Significant Moderating Effect) Multiple Regression (Stepwise) Multiple Regression (Backward) Multiple Regression (Forward) Canonical Correlation Analysis Discriminant Analysis (Two Groups) Discriminant Analysis (Three Groups) Cross-Tabulation and the Chi-Square Test of Independence Further Resources