Factor Analysis and Principal Component Analysis: A Simple Explanation
Got lots of variables? Need to condense the data into something your audience is going to be able to actually understand? Factor analysis/PCA is a great place to start. Find out how you can find patterns in your data faster and easier today. What Is Factor Analysis & Principal Component Analysis (PCA)? Factor analysis and […]
Principal Component Analysis of Text Data
Principal Component Analysis (PCA) is all about reducing the number of dimensions in large datasets into smaller ‘principal’ components, that still retain most of the original information. As you can imagine, it’s a useful technique when it comes to analyzing text data. It takes a single text variable as an input, and returns numeric variables […]
Working with Principal Components Analysis Results
Principal Components Analysis (PCA) is a technique for taking many variables and creating a new, smaller set of variables. These aim to capture as much of the variation in the data as possible. In this post, we show you how to save, access, and export the PCA results and output. For information on how to […]
Learn More about Dimension Reduction in Displayr
Correspondence Analysis Webinar: DIY Market Mapping Using Correspondence Analysis Ebook: DIY Correspondence Analysis How Correspondence Analysis Works (A Simple Explanation) Understanding the Math of Correspondence Analysis How to Interpret Correspondence Analysis Plots Correspondence Analysis Versus Multiple Correspondence Analysis Principal Component Analysis Principal Component Analysis (Wiki example) How to Do Principal Components Analysis in Displayr The […]
How to Do Principal Components Analysis in Displayr
Data setup Principal Components Analysis always views data numerically. This means that you need to be careful with the question Structure assigned to your variables to ensure the analysis views their numeric values. The variables in a PCA should be part of a Numeric, Numeric – Multi, or Binary – Multi question. In most cases, you should […]
