Hello!
I have a dataset with all managers, but some of them manage one another within the dataset. I have a column that shows who manages who (Manager ID; there are about 200 managers and the rest are their followers).
All of the respondents answered questions a self report question, and then all of them answered questions about their own job satisfaction. I want to find out whether leaders who answered the question have team members who are more satisfied.
For some reason, I don’t think that I’m doing this correctly. Any suggestions? Thank you very much for your consideration and advice!
Hello!
It sounds like you're using "vision" as an level-1 (i.e. individual level)-covariate. Therefore, in the model you described the vision of every individual is used as covariate. But as I understand, you want "vision" as a level-2 variable, i.e. restricted to the manager's vision predicting individual job satisfaction? In that case, you need some kind of a "manager_vision" variable (analogue to the managerID) that contains only the managers visions - and then use this variable as covariate.
Could that help?
Hello Amanda,
Perhaps this video will help you with your SPSS calculations:
https://www.youtube.com/watch?v=blfflA-34pQ
Good luck,
Daryn
Maybe you can try the following two ways.
The first way is to use average mean satisfaction score of a manager's followers as dependent variable , and use manager's score as independent variable, and then use ANOVA of SPSS to analyze the effect of the independent.
The second way is to use multiple variable modeling. In the model, the manager's ID, manager's score, follower's ID, follower's score are four variables. Use follower score as dependent variable, manager's score as independent, and then use regression analysis or ANOVA of SPSS.
You may find useful the following books
Ho, R. (2006). Handbook of univariate and multivariate data analysis and interpretation with SPSS. CRC Press.
Leech, N. L., Barrett, K. C., & Morgan, G. A. (2014). IBM SPSS for intermediate statistics: Use and interpretation. Routledge.
I would suggest consulting the following textbook:
Joseph F. Hair Jr., William C. Black, Barry J. Babin, Rolph E. Anderson (2010) Multivariate Data Analysis, Seventh Edition, Pearson, 816 pages.
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