**Real Statistics Data Analysis Tool**: The Real Statistics Resource Pack contains the **Factor Analysis** data analysis tool, which automates most of the Factor Analysis capabilities described elsewhere in this website.

To access this data analysis tool, first press **Ctrl-m **and then choose the **Multivariate**** Analyses** option from the resulting menu. From the dialog box that appears select the **Factor Analysis **option and click on the** OK **button**.** The dialog box in 1 will then appear.

If you click on the **Help** button the following dialog box will appear.

As seen in Figure 1, you are presented with a choice between using **Principal Component **extraction or **Principal Axis** extraction. You can choose to use **Varimax** rotation or not. You can also choose to specify the number of factors to use in the model (**# of Factors**); if this field is left blank then the Kaiser criterion is used, namely that all factors whose eigenvalue is 1 or greater are retained.

**Principal Component Extraction**

If you choose the Principal Component extraction option then the following output will appear (all the data refers to Example 1 of Factor Extraction):

In order to display the rotated factor matrix shown in range B114:E122, the VARIMAX supplemental array function is used. This function is provided in the Real Statistics Resource Pack.

**VARIMAX**(R1, *iter, prec*) = the result of rotating the square matrix defined by range R1 using the Varimax algorithm, where *iter* is the maximum number of iterations (default 100) and *prec* is the value that is considered to be sufficiently close to zero (default 0.00001).

In Figure 7, range B114:E122 contains the formula =VARIMAX(M100:P108).

**Principal Axis Extraction**

If you choose the Principal Axis extraction method then the output is similar to that described above. In fact, the output starts out identically as described in Figure 3 and 4 (except that the title is Factor Analysis – Principal Axis Extraction).

As described in Principal Axis Extraction, the Real Statistics software next calculates the initial communalities and revised communalities (using the ExtractCommunalities supplemental function) as described in Figure 11.

From this point on the data analysis tool calculates its results exactly as in Principal Component extraction except that the revised correlation matrix (range M96:104 in Figure 11) is used as the correlation matrix.

Hi,

It’s great tool thank you.

What are the limitations of the it?

How many rows it can calculate ?

Thank you

Sarah,

I can’t recall the exact limitation in the number of rows, but for many of the data analysis tools it isabout 65,000.

Charles

Thank you

Hello,

Thank you for providing this resource pack. I have very little experience in statistical analysis. I would like to perform a factor analysis on my data set. I am unsure what the input range is. Could you tell me how I determine what the input range is for my data? Thank you.

Tiffany

Tiffany,

The input should consist of the values of each of the k variables for all n subjects.

For the Real Statistics data analysis tool the input should take the format shown in Figure 1 of the following webpage

http://www.real-statistics.com/multivariate-statistics/factor-analysis/principal-component-analysis/

Charles

Thank you 🙂

Another question is:

I have demographic questions, how can I know what is the demographic distribution of each group.

Thanks again

Tur

Hi Charles,

It’s a great tool, Thank you.

I did not understand how to find the size/weight/importance of each group.

i.e. in the teachers example what is the percentage of each one of the 4 groups.

Thanks!

Tur

Tur,

This is explained on the other webpages describing Factor Analysis. Please go to the following webpage:

Factor Analysis

Charles

Hi Charles,

The factor analysis function is wonderful – but I’m having a problem. For the very last chart, the varimax rotation chart (the main one I need) is showing #VALUE! in every cell of the chart. In the initial selection box for the function, I had changed the max # of eigenvalues from the default of 100 to 1. This did give me the scree plot, which I wasn’t given when I left the max eigenvalues at 100.

I’ve probably messed things up by changing it to 1 – but any thoughts on how to fix the #VALUE! problem in the varimax chart while maintaining my scree plot?

Stats aren’t my strong suit so a lot of this is way over my head.

Thanks!

BWhite

Hi,

If you send me the spreadsheet with your data and the results you obtained, I will take a look at it and try to figure out where the problem is.

Charles