Moving from IBM® SPSS® to R and RStudio®

A Statistics Companion

Häftad, Engelska, 2021

679 kr

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Are you a researcher or instructor who has been wanting to learn R and RStudio®, but you don't know where to begin? Do you want to be able to perform all the same functions you use in IBM® SPSS® in R? Is your license to IBM® SPSS® expiring, or are you looking to provide your students guidance to a freely-available statistical software program?

Moving from IBM® SPSS® to R and RStudio®: A Statistics Companion is a concise and easy-to-read guide for users who want to know learn how to perform statistical calculations in R. Brief chapters start with a step-by-step introduction to R and RStudio, offering basic installation information and a summary of the differences. Subsequent chapters walk through differences between SPSS and R, in terms of data files, concepts, and structure. Detailed examples provide walk-throughs for different types of data conversions and transformations and their equivalent in R. Helpful and comprehensive appendices provide tables of each statistical transformation in R with its equivalent in SPSS and show what, if any, differences in assumptions factor to into each function. Statistical tests from t-tests to ANOVA through three-factor ANOVA and multiple regression and chi-square are covered in detail, showing each step in the process for both programs. By focusing just on R and eschewing detailed conversations about statistics, this brief guide gives adept SPSS® users just the information they need to transition their data analyses from SPSS to R.

Produktinformation

Utgivningsdatum2021-06-15
Mått152 x 228 x 19 mm
Vikt450 g
FormatHäftad
SpråkEngelska
Antal sidor312
Upplaga1
FörlagSAGE Publications
ISBN9781071817001

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Howard 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 Ph.D. 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 author of Moving from IBM SPSS to R and RStudio: A Statistics Companion, and co-author (with G. Keppel) of Introduction to Design and Analysis: A Student’s Handbook. In his spare time, he enjoys the outdoors, museums, live theater, and being physically, socially, and politically active as possible.

About the AuthorAcknowledgmentsIntroductionChapter 1. Introduction to R1.1 What Is R?1.2 Why are Some Features of R?1.3 Installing R and Getting Help Learning R1.4 Conducting Statistical Analyses in Spss Versus R: A First Example1.5 Comparing Spss and RChapter 2. Preparing to Use R and Rstudio2.1 Tasks to Perform Before Your First R Session2.2 Tasks to Perform Before Any R Session2.3 Tasks To Perform During Any R SessionChapter 3. R Terms, Concepts, and Command Structure3.1 Data-Related Terms3.2 Command-Related Terms3.3 Object-Related Terms3.4 File-Related TermsChapter 4. Introduction to Rstudio4.1 What Is RStudio?4.2 Installing RStudio4.3 Components of RStudio4.4 Writing and Executing R Commands in RStudioChapter 5. Conducting Rstudio Sessions: A Detailed Example5.1 1. Start RStudio5.2 2. Create a New Script File (Optional)5.3 3. Define the Working Directory5.4 4. Import CSV File to Create a Data Frame5.5 5. Change Any Missing Data in Data Frame to NA5.6 6. Save Data Frame With NAs As CSV File in the Working Directory5.7 7. Read the Modified CSV File to Create a Data Frame5.8 8. Download and Install Packages (If Not Already Done)5.9 9. Load Installed Packages (As Needed)5.10 10. Conduct Desired Statistical Analyses5.11 11. Open a New Markdown File5.12 12. Copy Commands and Comments into the Markdown File5.13 13. Knit the Markdown File to Create a Markdown Document5.14 Exiting Rstudio (Save the Workspace Image?)5.15 Getting Help With RChapter 6. Conducting Rstudio Sessions: A Brief Example6.1 1. Start RStudio6.2 2. Create a New Script File (Optional)6.3 3. Define the Working Directory6.4 4. Import CSV File to Create a Data Frame6.5 5. Change Any Missing Data in Data Frame to NA6.6 6. Save Data Frame with NAs as CSV File in the Working Directory6.7 7. Read the Modified CSV File to Create a Data Frame6.8 8. Download and Install Packages (If Not Already Done)6.9 9. Load Installed Packages (As Needed)6.10 10. Conduct Desired Statistical Analyses6.11 11. Open a New Markdown File6.12 12. Copy Commands and Comments into the Markdown File6.13 13. Knit the Markdown File to Create a Markdown Document6.14 Exiting RStudioChapter 7. Conducting Statistical Analyses Using This Book: A Detailed Example7.1 1. Start RStudio7.2 2. Copy and Paste an Example Script into a Script File7.3 3. Modify the Example Script as Needed for the Desired Statistical Analysis7.4 4. Execute the Script to Confirm It Works Properly7.5 5. Copy and Paste the Script into a Markdown File7.6 6. Knit the Markdown File to Create a Markdown DocumentChapter 8. Conducting Statistical Analyses Using This Book: A Brief Example8.1 1. Start RStudio8.2 2. Copy and Paste an Example Script into a Script File8.3 3. Modify the Example Script as Needed for the Desired Statistical Analysis8.4 4. Execute the Script to Confirm it Works Properly8.5 5. Copy and Paste the Script into a Markdown File8.6 6. Knit the Markdown File to Create a Markdown DocumentChapter 9. Working With Data Frames and Variables in R9.1 Working with Data Frames9.2 Working With VariablesChapter 10. Conducting Statistical Analyses Using SPSS Syntax10.1 Conducting Analyses in SPSS Using Menu Choices10.2 Conducting Analyses in Spss Using Syntax Commands10.3 Editing SPSS Output FilesAppendix A: Data TransformationsReverse 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 ProceduresDescriptive Statistics (All Variables)Descriptive Statistics (Selected Variables)Descriptive Statistics (Selected Variables) by GroupFrequency Distribution TableHistogramt-Test for One MeanConfidence Interval for the MeanT-Test for Independent MeansT-Test for Dependent Means (Repeated-Measures T-Test)One-Way Anova and Tukey Post-Hoc ComparisonsOne-Way Anova and Trend AnalysisSingle-Factor Within-Subjects (Repeated Measures) AnovaTwo-Factor Between-Subjects AnovaTwo-Factor Between-Subjects Anova (Simple Effects)Two-Factor Between-Subjects Anova (Simple Comparisons)Two-Factor Between-Subjects Anova (Main Comparisons)Two-Factor Mixed Factorial AnovaTwo-Factor Within-Subjects AnovaThree-Factor Between-Subjects AnovaPearson Correlation (One Correlation)Pearson Correlation (Correlation Matrix)ScatterplotInternal Consistency (Cronbach’s Alpha)Principal Components Analysis (Varimax Rotation)Principal Components Analysis (Oblique Rotation)Factor Analysis (Principal Axis Factoring)Linear RegressionMultiple 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 AnalysisDiscriminant Analysis (Two Groups)Discriminant Analysis (Three Groups)Cross-Tabulation and the Chi-Square Test of IndependenceFurther Resources