Factor Analysis / Principal Component Analysis should be quick and easy to do. Displayr makes it so.
Drag and drop variables to create a factor analysis / Principal Component Analysis (PCA).
Displayr is set up with the correct defaults for most problems: varimax rotation, lots of iterations, and the Kaiser Rule for selecting the number of components. If you’re an expert you can tweak things, but out-of-the-box it gets the job done the right way.
In-built expert systems alert you to problems with your data and analysis, providing suggestions for how to fix them.
We hand-crafted visualizations designed to make it easy to understand the key insights in the factor analysis.
Once you have created your analysis you can have it automatically refresh with new data (e.g., a new clean data file, a new wave of a tracker). Alternatively, you can turn automatic updating off so you can compare to see how results have changed.
Displayr is a general purpose app that does everything from crosstabs to text coding to advanced analysis to dashboards, driver analysis, and segmentation.
Once you have created your factor analysis, you can use the factors as inputs to all your other work (e.g., crosstabs, regression).
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Factor analysis is a statistical technique used to identify underlying patterns in large datasets by grouping related variables into factors. It helps reduce data complexity and uncover hidden relationships. Displayr automates factor analysis, making it easy to extract meaningful insights from survey and business data.
Factor analysis is best used when you want to find patterns in the correlations between variables. It works by converting many variables into a few summary variables. These summary variables are referred to as factors, components, dimensions, and scores.
Factor analysis works by:
Factor analysis is widely used in research for:
Yes, PCA works on text data. In fact, because factor analysis is so powerful when it comes to reducing the number of dimensions in a large dataset, it can be a very effective tool when dealing with a text dataset.
Some common factor analysis examples include:
