Dear Mosayeb Dashtpeyma ,

Factor extraction in EFA is a bit like a dance. In order to extract stable factors you are likely to lose a lot (maybe half) of your items along the way for reasons such as:

• High correlations (> 0.8; remove one from the pair using a qualitative argument)
• Low communality scores (< 0.2)
• Low scores on all the factors (< 0.3)
• Cross loading on several factors (greater than 3:4)

The eigenvalue > 1 rule tends to be too generous but it can help you as you remove items incrementally and work towards a stable solution. My advice would be to reduce the number of factors manually one by one from an initial solut derived using the eigenvalue rule.

Please see my study guide for more information.

I provided several criteria on that. Please refer to this article:

Article Korean Patient-Perceived Satisfaction Scale of Community-Bas...

Hi Mosayeb

You have a few options-

If you use maximum likelihood extraction you can get a chi-square test; keep extracting factors until the p value is > .05.

You can use Horn's parallel test using the guidance at http://global.oup.com/us/companion.websites/9780199734177/supplementary/factors/factors_a/

..or you could use other software, such as Mplus, that has a very comprehensive set of tools to do EFA in a much better way than SPSS.

Mark

Section 6.4 of http://www.personality-project.org/r/book/Chapter6.pdf discusses some methods. This is written assuming the person is doing analyses in R, but the conceptual and mathematical issues are relevant. Following on from Mark Shevlin 's suggestion, fa.parallel in the R package psych using the parallel test is described there.

Thank you so much Claire Su-Yeon Park

Thank you so much Mark Shevlin

Thank you so much Daniel Wright

Thank you so much Jimmy Y. Zhong

Dear Mosayeb Dashtpeyma ,

Factor extraction in EFA is a bit like a dance. In order to extract stable factors you are likely to lose a lot (maybe half) of your items along the way for reasons such as:

• High correlations (> 0.8; remove one from the pair using a qualitative argument)
• Low communality scores (< 0.2)
• Low scores on all the factors (< 0.3)
• Cross loading on several factors (greater than 3:4)

The eigenvalue > 1 rule tends to be too generous but it can help you as you remove items incrementally and work towards a stable solution. My advice would be to reduce the number of factors manually one by one from an initial solut derived using the eigenvalue rule.

Please see my study guide for more information.

Dear\ Mosayeb Dashtpeyma

pls read this article :-

• https://www.unimuenster.de/imperia/md/content/ew/personen/obermeier/methodisches_vorgehen
• http://hosted.jalt.org/test/PDF/Brown31.pdf
• https://www.youtube.com/watch?v=x3u1G5JAuiY

Good luck

Thank you so much Peter Samuels

Thank you so much Mohamed Abdallah Turky

Hello Mosayeb,

In my experience, and in agreement with Peter Samuels above, using the eigenvalue > 1 "rule", also known as the Kaiser criterion, almost always results in too many factors when conducting EFA. There are other ways of getting an idea about how many factors might lie within a data set. Some are described in the following article:

Fives, H., & Buehl, M. M. (2010). Examining the factor structure of the Teachers’ Sense of Efficacy Scale. Journal of Experimental Education, 78, 118–134.

I have found that the scree test is often the most helpful method, although some "cleaning up" of items, as suggested by Peter S. above, might also be desirable in some situations.

Following