Principal Component Analysis and Exploratory Factor Analysis
You’d like to create an index from a group of related variables. At least you think they’re related — they ought to be.
But what’s the best way to combine them? Add them together? Take the mean? Which ones go together anyway?
Principal component analysis and exploratory factor analysis are both data reduction techniques — techniques to combine a group of correlated variables into fewer variables. You can then use those combination variables — indices or subscales — in other analyses.
But they have important theoretical differences and are used in different research contexts. There are a number of steps to go through — each with many decisions — to come up with a reasonable way to combine those variables.
There is a lot of new vocabulary involved, but once you learn the concepts and the logic, both PCA and EFA are very straightforward.
This workshop will give you the strong foundation you need to conduct Principal Component and Exploratory Factor Analysis. You will learn what each technique does, the considerations to be made, and the steps to conduct it within SPSS, Stata, and SAS.
- Begins: January 31
- 6 hours of live instruction, plus 3 Q&As
- 1-year access to workshop website
- Instructor: Karen Grace-Martin
- Stat Software: Demonstrations in R, Stata, SAS, and SPSS
- Level: Intermediate
- Investment: $297 / $197 (Student)
About the Instructor
Karen Grace-Martin is the workshop instructor and founder of The Analysis Factor. Karen helps statistics practitioners gain an intuitive understanding of how statistics is applied to real data in research studies.
She has guided and trained researchers through their statistical analysis for over 15 years as a statistical consultant at Cornell University and through The Analysis Factor.
She has Master’s degrees in both applied statistics and social psychology and has been using SPSS and SAS since the early 90s (yikes!).
About the Software Specialists
As a software specialist for this workshop, Jeff will be writing the Stata code to use in the examples and exercises and making Stata demonstrations. He’ll also be at the live Q&A sessions and on the workshop website to answer any Stata questions you have.
He has an MBA from the Thunderbird School of Global Management at Arizona State University and an MPA with a focus on policy from the Wagner School of Public Service at New York University.
Audrey Schnell is a statistical consultant at The Analysis Factor. She has particular expertise in biostatistics, including Inter Rater Reliability, Case control studies, and linear models.
As your software specialist for this workshop, Audrey will be creating the SAS demonstrations and code for examples and exercises. She’ll be at the live Q&A sessions to answer your SAS questions.
Audrey has a Master’s Degree in Clinical Psychology and a PhD in Epidemiology and Biostatistics.
Kim Love is the owner of and lead consultant at K. R. Love Quantitative Consulting and Collaboration.
As a software specialist for this workshop, Kim will be creating the R demonstrations and code to use in the examples and exercises. She will be at the live Q&A sessions and on the workshop website to answer your R questions.
She has worked as a statistical consultant and collaborator in multiple professional roles, most recently as the associate director of the University of Georgia Statistical Consulting Center. She has a B.A. in mathematics from the University of Virginia, and an M.S. and Ph.D. in statistics from Virginia Tech.
Comments from Past Participants in Karen’s Workshops:
3 Live, Interactive Webinar Sessions with Karen Grace-Martin
Sessions start at 12 pm (US EST) and last 1.5 – 2 hours, depending on the number of questions.
3 Live Q&A Sessions with Karen Grace-Martin, Jeff Meyer, Audrey Schnell and Kim Love
Q&As are from 12 – 1 pm (US EST).
Exclusive Access to a Participants-Only Website, Your Home Base for the Workshop
You’ll find everything you need here:
Each topic we cover will include demonstrations in R, Stata, SAS, and SPSS.
✅ Principal Component Analysis
- The difference between Principal Component and Exploratory Factor Analysis and when to use each
- Preparing data
- The steps in R, SPSS, Stata, and SAS
- Initial Extraction of Components, Eigenvalues, Communalities, and Variance explained
- Scree Plots, Loadings, Parallel Analysis for choosing the number of components to retain
- Rotation of components and the differences among rotation techniques
- How to interpret all that output
✅ Exploratory Factor Analysis
- How EFA differs from PCA theoretically and practically and why it’s important
- Factor Scores and Factor-Based Scores
- More methods of initial extraction
- Sample Size and Reverse Coding
- EFA on ordinal and categorical variables
- Tricky data situations: non-normal data, missing data, likert scale data
✅ Assessing Scale Reliability
- Scale Reliability
- Calculating Cronbach’s alpha
Is This Workshop Right for Me?
This workshop is for you if:
- You have a good foundation in doing basic data analysis. You should have a good understanding of correlations and working with data.
- You need to do any scale development or work with large, many-variable data sets.
- You have time each week to do the exercises and practice what you’ve learned.
- You are comfortable with using R, SPSS, SAS, or Stata — or are able to apply concepts to another package.
Sorry, this workshop is full, and registration has closed.To get first notice when it opens again, please join our Advance Discount List. Check out some of our other online workshops too!
Your registration fee is fully refundable up to 72 hours in advance. Because enrollment is limited, no refunds will be granted after the program begins.
As with all our programs, your satisfaction is guaranteed. If you participate fully in this workshop — watch the videos, read the materials, complete the homework, and try out everything included — and find you are not satisfied for any reason, we will give you a full refund, no questions asked. Just notify us within 90 days of purchasing the workshop.