Dears,
Hope all is fine ,
I'm trying to rank 10 factors (independent variables) that affecting performance of organizations (dependent variable) , I have measured the availability of the10 factors in multiple organizations by a questionnaire and also I have measured the performance for these organizations by another questionnaire .
Now I'm thinking to rank these 10 factors based on their effect on the performance and I'm wondering which one of the following method is more professional and can be defended scientifically :
The first Method : is to conducted multiple linear regression , as performance is the dependent variable and the 10 factors as its predictor. Based on the regression model I will summarize up the Standardized Coefficient of each predictor and the largest coefficient will be the most critical one that affect performance, and so on....
The Second Method: is to create a new survey to measure the expert opinion about the affect of these 10 factors in performance by asking " on a scale from 1-5, kindly evaluate the effect of each factors on the performance". Based on expert answers I will take the Means for each factors and the highest mean means the highest factor based on expert thoughts.
I'm looking for your discussion for which method is more robust and scientifically correct, Thanks.
My friend you're confused.. see the attached paper from Researchgate. David Booth
@Khalil Both are different methods and used with different purposes. Multiple regression is used to test statistical relationships among predictors and outcome variables, whereas mean rankings for all ten predictor would be of subjective importance as per the opinion of the experts and used to identify important predictors among many with the help of the experts of the field.
David Eugene Booth Thanks sharing this valuable paper, however it seems that this paper discussed the first method I stated above in my question.
The second method I stated is based on subjective judgment by the expert by ranking the predictor from 1 to 5 . Both method are valid but I want to check which one is more robust.
the first method is statistical approach, while the second one is not.
The way you have described it, the first method is about the conditional association between each predictor and the outcome, not whether the predict is (unconditionally) related to the outcome. I am guessing these predictors will be correlated so the meaning of the coefficients for the OLS estimation will be difficult to understand. Further, without knowing the sample size it is not easy to guess the reliability of the approach and therefore compare with the other approach. Similarly, for the second you have not described the sample (the size or why they are experts).
I think the key thing though is that these methods would be getting at different things (one conditional, one not).
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