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.

Quick Facts

  • 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)




Our Workshop Approach

Practical, Not Theoretical

You’re not interested in statistics as a field of inquiry: You need to develop skills and get your data analyzed.

We explain concepts in practical, intuitive terms and show you step-by-step how to run the data analysis, how to make choices along the way, and how to interpret results.

Our instructors are applied statisticians and consultants with excellent teaching backgrounds.

They are personally acquainted with the challenges of data analysis and show you how to deal with them.

Examples Are Vital

Data analysis is all about the context of the design, the research question, and the variables.

It’s really hard to learn statistical skills without seeing how the same concept applies in different data situations.

So we always show you multiple examples, pulled from multiple fields.

Real Data Sets Show Real Challenges

The hard part of data analysis is figuring out how to handle imperfect data. You can’t learn how to do real data analysis on textbook-perfect data sets.

We always use real data sets.

Demonstrations in the Software You Use

It doesn’t matter how well you understand an analysis if you can’t get your software to do it, or if you don’t understand what their options mean or their defaults are.

We always give you software demonstrations in multiple packages so you learn how the statistics apply to your software.

Having Syntax Saves You Time Later

Writing syntax from scratch is like reinventing the wheel — and neither is a good use of your time.

We always share the code we use in our examples and exercises so not only can you try these examples yourself, but you can save it for your own analysis.

✅ Many Opportunities to Ask Questions

You learn when you ask–creating a safe space to ask any question (no matter how silly the question feels) is our core value.

You are encouraged to ask questions during webinars, in Q&As, and on the forum.

Access to Instructors During and After the Workshop

We know that even if everything makes sense as you’re learning it, when you encounter a new situation 6 months later in your own data, you can get stuck.

We’re not going to leave you out in the cold.

So in addition to all the opportunities to ask questions during the workshop, our instructors hang around to answer questions on the workshop website forum for a whole year.

And if you still need help after that, you can extend that access each year for just $25.

That includes any live webinars or Q&As if we run the workshop again during your yearlong access.

You Learn When You Practice…

Because statistical analysis is a skill, you need experience.

We give you everything you need — data, code, exercises — to practice.

✅ And When You Ask Questions about Applying What You Learn to Your Own Data.

Practicing on your own data is crucial.

So we encourage you not only to try out what you’re learning on your own data, but to ask questions when you get stuck. That’s when the real insights occur.

Having Multiple Resources Is Important

There are great resources out there on many statistical topics.

We share as many as we find. Sometimes seeing the same issue explained in different ways is just what you need.


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

jeff-meyer-150Jeff Meyer is a statistical consultant, instructor, and writer for The Analysis Factor, specializing in Stata.

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-croppedAudrey 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:

Karen is a fantastically knowledgeable and patient person. There are no wrong or stupid questions when working with her, and everyone is encouraged to participate. As well, really helpful for me was the option to go through all the materials at the time that worked best for me and to access and go through the same materials over and over again if needed… Fantastic workshop!
– Danijela Gasevic, Simon Fraser University

The most helpful feature of the workshop is simply Karen’s command of the topic and her skill in communicating and teaching the material. The illustration and practice of concepts through SPSS is also very helpful. The materials, technology, organization, and topic selection are all excellent; a model of online instruction.

I specifically appreciated the opportunity for the live Q&A, although wasn’t always able to take advantage of it. For that reason, the recordings were also very helpful.

Finally, though this may sound trivial, I appreciated the explicit course outline that was advertised. It helped me to better understand what content the course specifically covered, the time commitment, and address other concerns (e.g., pre-requisite knowledge, organizational format).
– Jason Hurwitz, PhD, Assistant Research Scientist, University of Arizona

Karen Grace Martin… has a gift for explaining and teaching applied statistics. I really appreciate her patient, pleasant, down-to-earth manner. My previous statistics professors were knowledgeable, but un-engaging, and not easy to understand. I wish I would have had a teacher like Karen in my grad program… The Q&As were also helpful because you could hear real-life examples others were working on, from different angles. Helps to reinforce the concepts.
– J. Basta, PhD, Program Evaluation & Research Consultant

While my day job and PhD is in learning theory and teacher education, I also teach doctoral research methods and statistics each term. For teachers, a singular quality of excellence is being able to make powerful disciplinary ideas available to the sense making of students (in this case, me). [Karen’s] teaching is so exceedingly well put together that I leave every one of her sessions feeling a lot smarter than I entered the course. If you have a chance to participate in Karen’s teaching, do treat yourself!
– Daniel R. Lofald, PhD, Associate Professor, University of St. Augustine for Health Sciences

This workshop provided a user-friendly introduction/review that has significantly honed my statistical skills. The most helpful aspect was interactivity — the opportunity to ask specific questions relevant to my own research. I also greatly appreciated the scheduled Q&A sessions.
– Kathleen Miller


The Format

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:


✅ Video recordings of each webinar session

✅ Video recordings of each Q&A session

✅ Data files from real research projects

✅ Video software demos

✅ Syntax for R, SPSS, SAS, and Stata to run the examples

✅ PDF handouts of the presentation slides

✅ Exercises to practice what you learn

✅ A forum to submit questions between webinar sessions

✅ A list of helpful resources and suggestions for further reading

✅ Bonus videos on relevant topics



The Curriculum

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 Mailing List. Check out some of our other online workshops too!

Refund Policy

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.