R for SPSS Users - Online Course
A 3-Day Livestream Seminar Taught by
Christopher L. Aberson10:00am-12:30pm (convert to your local time)
1:30pm-3:30pm
In this seminar, you will learn how to convert SPSS workflow into R code from the perspective of an instructor who is a long-time user of both programs.
SPSS users in the social sciences and other fields are migrating to R. However, R approaches sometimes do not address common tasks relevant to SPSS users or recognize processes and workflow common to SPSS users.
This course will address the following issues:
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- How does SPSS terminology correspond to R terminology (i.e., translations between the two approaches)?
- How to import data from major software packages.
- Basic differences between the R environment and SPSS.
- How to perform common SPSS data visualizations, analyses, and modeling in R.
- Manipulating and cleaning data.
This course addresses these issues and numerous practical ones that allow attendees to move seamlessly from SPSS to R.
Starting March 25, this seminar will be presented as a 3-day synchronous, livestream workshop via Zoom. Each day will feature two lecture sessions with hands-on exercises, separated by a 1-hour break. Live attendance is recommended for the best experience. But if you can’t join in real time, recordings will be available within 24 hours and can be accessed for four weeks after the seminar.
Closed captioning is available for all live and recorded sessions. Captions can be translated to a variety of languages including Spanish, Korean, and Italian. For more information, click here.
ECTS Equivalent Points: 1
More details about the course content
This course takes the perspective of the SPSS user. SPSS users know how to carry out their preferred analyses in SPSS. However, in moving to R, analysis approaches, workflows, and output often differ from R defaults. This course teaches you how to derive all statistics common to SPSS (and commonly reported) in R.
This course is hands-on. There are regular exercises to ensure understanding and online help for those who get stuck. You are encouraged to bring your own data and post questions about translating your SPSS-focused analyses to R. Throughout the course, we will live-code examples (the code is also provided on the slides) and you are encouraged to code along.
This seminar will familiarize you with using R through the RStudio interface and demonstrate how to smoothly transition to working in the R environment. You will leave with course materials including detailed examples of the most common social science analyses and approaches.
This course takes the perspective of the SPSS user. SPSS users know how to carry out their preferred analyses in SPSS. However, in moving to R, analysis approaches, workflows, and output often differ from R defaults. This course teaches you how to derive all statistics common to SPSS (and commonly reported) in R.
This course is hands-on. There are regular exercises to ensure understanding and online help for those who get stuck. You are encouraged to bring your own data and post questions about translating your SPSS-focused analyses to R. Throughout the course, we will live-code examples (the code is also provided on the slides) and you are encouraged to code along.
This seminar will familiarize you with using R through the RStudio interface and demonstrate how to smoothly transition to working in the R environment. You will leave with course materials including detailed examples of the most common social science analyses and approaches.
Computing
Who should register?
This course is for SPSS users who want to move to the R environment. It is designed for attendees who want to transition to R seamlessly with a minimal learning curve.
This course is for SPSS users who want to move to the R environment. It is designed for attendees who want to transition to R seamlessly with a minimal learning curve.
Seminar outline
Introduction to the R computing environment
- Basics of code structure
- Tour of RStudio set up and features
Importing data
- Viewing data in RStudio
- Hands-on work focused on importing data
Introduction to packages
- Installing packages
- Useful packages
- Errors that occur when installing packages and how to fix them
Importing SPSS (and other) files
- Hands-on work importing an SPSS file and installing and loading packages
- Overview of strategies for importing other data formats
- How to point functions to data
Basic data visualizations
- Histograms
- Bar charts
- Scatterplots
- Boxplots
- Hands-on work with graphs
Data types
- Review of SPSS data types (scale, ordinal, nominal)
- Drawing connections between SPSS data types and R data types
- Common problems and solutions – e.g., factors imported as numbers, factor levels in wrong order
Introduction to data manipulation
- Review of common SPSS tasks such as select cases, transform-compute, recode, create summary scores
- Carrying out common SPSS tasks in R
- Hands-on work with data
Descriptive statistics
- CT and dispersion
- Skew, kurtosis
- Percentiles
- Frequencies
- Hands-on work running various descriptive measures
Correlations/simple linear regression
- Pearson’s correlation approaches
- Work with scatterplots
- Linear regression
- Adding regression lines to scatterplots
- Hands-on work
Chi-square tests and variations
- Goodness of fit and test of independence
- Frequency/proportion tables
- Effect sizes
- Hands-on work with chi-square including installing lsr package
ANOVA basics
- One factor and factorial
- Descriptives for ANOVA
- Graphs for ANOVA (introduction to GGPlot2)*
- Setting up R for factorial ANOVA to match SPSS settings*
- Getting information out of the ANOVA via summary
- Hands-on work with ANOVA
Multiple regression
- Basic output
- Getting additional statistics (e.g., standardized coefficients)*
- Assumption plots
- Hands-on work with multiple regression
Mediation
- Basics of mediation in R*
- Using the process macro for R*
- Using lavaan to conduct mediation*
Psychometrics
- Reliability estimates*
- Exploratory factor analysis*
- Confirmatory factor analysis*
*Time permitting. However, materials and annotated code for these topics will be provided.
Introduction to the R computing environment
- Basics of code structure
- Tour of RStudio set up and features
Importing data
- Viewing data in RStudio
- Hands-on work focused on importing data
Introduction to packages
- Installing packages
- Useful packages
- Errors that occur when installing packages and how to fix them
Importing SPSS (and other) files
- Hands-on work importing an SPSS file and installing and loading packages
- Overview of strategies for importing other data formats
- How to point functions to data
Basic data visualizations
- Histograms
- Bar charts
- Scatterplots
- Boxplots
- Hands-on work with graphs
Data types
- Review of SPSS data types (scale, ordinal, nominal)
- Drawing connections between SPSS data types and R data types
- Common problems and solutions – e.g., factors imported as numbers, factor levels in wrong order
Introduction to data manipulation
- Review of common SPSS tasks such as select cases, transform-compute, recode, create summary scores
- Carrying out common SPSS tasks in R
- Hands-on work with data
Descriptive statistics
- CT and dispersion
- Skew, kurtosis
- Percentiles
- Frequencies
- Hands-on work running various descriptive measures
Correlations/simple linear regression
- Pearson’s correlation approaches
- Work with scatterplots
- Linear regression
- Adding regression lines to scatterplots
- Hands-on work
Chi-square tests and variations
- Goodness of fit and test of independence
- Frequency/proportion tables
- Effect sizes
- Hands-on work with chi-square including installing lsr package
ANOVA basics
- One factor and factorial
- Descriptives for ANOVA
- Graphs for ANOVA (introduction to GGPlot2)*
- Setting up R for factorial ANOVA to match SPSS settings*
- Getting information out of the ANOVA via summary
- Hands-on work with ANOVA
Multiple regression
- Basic output
- Getting additional statistics (e.g., standardized coefficients)*
- Assumption plots
- Hands-on work with multiple regression
Mediation
- Basics of mediation in R*
- Using the process macro for R*
- Using lavaan to conduct mediation*
Psychometrics
- Reliability estimates*
- Exploratory factor analysis*
- Confirmatory factor analysis*
*Time permitting. However, materials and annotated code for these topics will be provided.
Payment information
The fee of $995 USD includes all course materials.
PayPal and all major credit cards are accepted.
Our Tax ID number is 26-4576270.
The fee of $995 USD includes all course materials.
PayPal and all major credit cards are accepted.
Our Tax ID number is 26-4576270.