## Check Reducibility Strength

The CheckReducibilityStrength Class in Quorum implements suitability checks before factor analysis. It determines what amount of variance in our data or variables may be caused by underlying factors.

The test listed below is used to check sampling adequacy. For example, is it random? What is the size of the sample? Is it likely to change? How accurate do we want to be?

Formal Test | Action in CheckReducibilityStrength Class |
---|---|

Kaiser-Meyer-Olkin Measure of Sampling Adequacy | CheckReducibilityStrength |

### Check if Data is Good Enough

#### Technical Test Name: Kaiser-Meyer-Olkin Measure of Sampling Adequacy

A Kaiser-Meyer-Olkin Measure of Sampling Adequacy is a way to see if the data that we have collected is good enough to use in certain statistical analysis.

Let's say we have a bunch of puzzle pieces that we want to put together to see a big picture. This test is basically making sure that we have all the pieces of the puzzle before we start actually putting things together.

It looks at how each puzzle piece is related to other pieces. If they are alike, it means we might now have data that is good enough to get a good analysis. But if they are different from each other, then we have a lot of different things to work with.

Data needs to be suitable for factor analysis to work. Basically, this test measures how suited your data is for factor analysis and what quantity of variance is in the data that might be caused by underlying factors. The higher the difference, the more suited your data is for factor analysis

Example of a Kaiser-Meyer-Olkin Measure of Sampling Adequacy

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## Next Tutorial

In the next tutorial, we will discuss Principal Component Analysis, which describes how to reduce big datasets.