Hi,

I think a better option is conducting a principal factor analysis with an oblimin rotation (Oblique). In that case, you will see how the items are related to each other and if they form factors. In the Oblimin analysis you have one table (the last table) that tells you how related your factors are. 

So, if I understand your question correctly I think this could be your path. 

Regards,

Juan Carlos

Hi, my data is scale in spss. My factor is technologies and followed by several items under the technologies. This guide to ordinal data i guess. 

Thanks Aloysius. 

Hi Juan, 

That's mean i need to do the factor analysis. I have done my data analysis using Pearson correlation, and i get the value for my items. My items is under one factor, after that i do the same thing again for another factor. I just want to know how can i get the correlation between one factor (technologies) and factor two (participants). 

Thank you. 

CORRELATION COEFFICIENT FOR MULTIPLE REGRESSION: Assuming that the data for Y and X are both quantitative, the model is given as:

(1)   Y = B0 + B1X1 + B2X2 + e

The Pearson correlation coefficient (r = small r will be relevant below) is given as:

(2)   r = Bi(Sx / Sy)

... where Bi is the slope; Si = sample standard deviation. The range of of value is -1 < r < +1. Now the correlation coefficient for multiple regression in (1) is given as R (big R):

(3)   R = sqrt[((A + B) - C) / D]

(3.1)   A = (ry,x1)2

(3.2)   B = (ry,x2)2

(3.3)   C = 2(ry,x1)(ry,x2)(rx1x2)

(3.4)   D = 1 - (rx1x2)2

Again, the range of R is -1 < R < +1. The correlation coefficient tells the level of relationship between variables, this is not a complete analysis. With known correlation coefficient, you need to verify whether it is statistically significant. Set you confidence interval, i.e. 0.95, 0.99, etc. and proceed to the relevant test statistic then conclude. 

REFERENCES: Attached are some articles on multiple regression. I trust this has been helpful. Cheers.

The closer the correlation coefficient to 1 or –1, the stronger the relationship. A statistically significant correlation is indicated by a probability value of less than 0.05 :)