Displayr for Factor Analysis (PCA)

Get factor analysis/ PCA done right

Factor Analysis / Principal Component Analysis should be quick and easy to do. Displayr makes it so.

Book a demo

Displayr for Factor Analysis

See Factor Analysis / PCA in action

See how Displayr will cut your Factor Analysis / Principal Component Analysis time in half

The world has changed, it's time your tools do too.

If you’re feeling the pressure to deliver insights at record-breaking speeds for less but your team is already working at capacity, it may be time to address the elephant in the room. The business world has changed and today’s voracious appetite for insights demands a tool fit for the purpose. Sticking to your conventional and dated tools may mean risking missed opportunities, slower turnaround times and a dwindling customer base.

If you need your business to not just remain competitive, but thrive in this new landscape, it’s time to upgrade your toolbox to one built for the modern market research industry. Displayr’s the all-in-one analysis and reporting solution that will help you prevent time and cost blowouts, simplify your workflow, and deliver better insights. Fuel your business growth and gain a competitive edge with Displayr, the only tool designed for today's market researcher.

How Displayr makes Factor Analysis faster and easier

Fast to use

Drag and drop variables to create a factor analysis / Principal Component Analysis (PCA).

Best practice defaults

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.

Expert systems that guide you

In-built expert systems alert you to problems with your data and analysis, providing suggestions for how to fix them.

Easy-to-interpret outputs

We hand-crafted visualizations designed to make it easy to understand the key insights in the factor analysis.

Automatic automation (no code required)

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.

Complete (not just factor analysis)

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).

Flexible

One tool where you can easily do everything: any analysis goal, any reporting goal, collaboratively, for all skill levels.

Live

All your analysis and reports automatically update when your data changes. The key to saving you so much time.

Complete

One app to replace all your analysis and reporting tools.

TRUSTED BY THOUSANDS OF CUSTOMERS

Factor Analysis
Factor Analysis
Factor Analysis
Factor Analysis
Factor Analysis
Factor Analysis
Factor Analysis

Your next steps

Step 1

Demo

Step 2

Purchase

Step 3

Implement

  • Customized to your needs
  • Annual subscription, see pricing
  • Review & optimize your data file
  • See how to cut your analysis and reporting time in half
  • Customer support
  • Custom 3 hour new user training
  • Confirm that Displayr does everything you want it to
  • Quantity discounts
  • Regular check-ins
Step1

Demo

  • Customized to your needs
  • See how to cut your analysis and reporting time in half
  • Confirm that Displayr does everything you want it to
Step2

Purchase

  • Annual subscription,   see pricing
  • Customer support
  • Quantity discounts
Step3

Implement

  • Review & optimize your data file
  • Custom 3 hour new user training
  • Regular check-ins

Learn more about Displayr for Factor Analysis/PCA

DIY Factor Analysis
Webinar

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Working with Principal Components Analysis Results
Blog

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Factor Analysis and Principal Component Analysis: A Simple Explanation
Blog

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Principal Component Analysis of Text Data
Blog

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