I hope I got the right subforum! I would really appreciate if someone takes the time to help me and guide me into th right direction.

Generally, I am trying to calculate correlation coefficients (best case even Pearson r) of several independent variables on one single dependent variable. I am looking at several studies, where some use Likert scales for the measurement of the independent variables, others use agreement statements.

Several motivational factors are the independent variables; [the likelihood of] participation is the dependent. The problem is, that for some studies, the data was collected on a basis of people who were participating anyway. i.e. the binary dependent variable is 1(=did participate) for all subjects. Those are all individual observations (i.e. a cross section of individuals) by the way.

I want to know which factor influences the likelyhood of participation the most. I am looking at many studies though and I want to transform the data into a comparable and aggregatable format. (I am just dealing with a constant dependent variable for some of those studies).

Going the normal way, the correlation equals zero, as no matter what they answered to the questions (e.g. "I participate because I want to earn money"), the subjects all participated anyway.

I was thinking that maybe I can assume, that for the certain study, all 4 independent variables are responsible for x% of the total variance of the dependent variable. From there, I could maybe allocate fractions of x to the respective independent variables by considering the agreement level of the regarded independent variable as a fraction of the total agreement.

A short example might clarify what I am looking at:

651 participants were asked why they participated in a crowd-sourcing application. They could either agree with a statement("I participated because I...") or not agree. The results (% of participants agreeing) are the following:

71,9% Fun

79,1% Improving skills

89,8% Payment

49,8% Recognition

If I would assume that those 4 variables explain x% of the variance for the dependent variable, (estimation based on other studies concerned with the same correlations, that explicitly state that R^2) is there any way I could calculate correlation coefficients?

I would really appreciate if somebody would take the time to help me or point me towards the right direction! ]]>

I am sampling air and using a gas chromatograph (GC) quantify my results. I want to calculate a total combined error for my final number, but I am stuck on the standard error of the mass that I get back from my GC's calibration's linear regression.

Here's the whole story: I have an instrument that I have calibrated with known concentrations, x, to get instrument responses, y. I perform a linear regression to get an equation, y = mx + b. I take a sample for a measured time at a measured flow rate to get my total volume of air collected. This number has simple errors associated with them that I have not problem combining to get my volume and associated error. I then run the sample on my calibrated GC, get an instrument response, y, then use the equation for the line I got from my linear regression to calculate the mass in the sample, x = (y-b)/m.

So how do I calculate the error associated with that calculated x! The closest I have come is the equation at the bottom of the table in this link:

http://chemlab.truman.edu/DataAnalys...ionofError.htm

When I use that, I get reasonable numbers, I was just hoping for a better reference to use. Any ideas?

Thanks in advance! ]]>

y=a+b1x1+b2x2+b3x1x2+b4x3+other terms $

Let's say, I am only interested in coefficient b3. The magnitude of coefficient b3 is 0.54 and its t value is 4.50 and the magnitude of the coefficient b2 is 2.00 and it's t value is 3.00. Now after including the interaction term x2x3

y=a+b1x1+b2x2+b3x1x2+b4x3+b5x2x3+other terms $

, the magnitude of the coefficient b3 is 0.53 and t value is 4.30 and the magnitude of the coefficient b2 is 300 and t value is 0.001. Note that the coefficient b1 doesn't change much.

I would like to know whether I should consider adding the interaction term x2x3 . I want to reiterate that I am only interested in coefficient b3. ]]>