I am currently doing my MBA and I am attempting to find out what variables affect new student intake at the university which I am working at. My model is based on the 7Ps of the marketing mix (Product, Place, Price, Promotion, People, Process and Physical Evidence) and non-marketing mix factors (such as influence of people, influence of media, influence of the internet, etc). Essentially I want to find out
- what impact the 7Ps have on new student intake,
- what impact non-marketing mix factors have on new student intake,
- what impact both the 7Ps AND non-marketing mix factors combined have on new student intake.
I will be sending out questionnaires and for each dependent variable (e.g. each 7P and non-marketing mix factors) there will be a variety of 'sub' variables. For example, under price, the 'sub' variables are: tuition fees, development fees, cost of study materials, cost of field trips, cost of university provided food, cost of study related clothing, payment arrangements, method of payment and early bird payment discounts. Each variable has at least 4 'sub' variables.
Can multiple regression analysis find out which 'sub' variables are valid or not? I.e. which are significantly correlated to the main variable? Going back to price, maybe only three or four of the variables are sufficiently correlated to have any impact on price, which then impacts on new student intake.
From what I understand, multiple regression is best suited if the main variables do not have 'sub' variables. Or should I use SEM (AMOS)? Please describe when multiple regression should be used and when SEM (AMOS) should be used. I do not want to use an inappropriate data analysis technique!
Best Answer
Before delving into any data collection I would recommend you to read at least a basic chapter on what factors and/or latent variables are. Then I'd suggest you to read what SEM does do!
Take a look at the latest edition of Hair et al Multivariate Analysis http://www.amazon.com/Multivariate-Data-Analysis-5th-Edition/dp/0138948585