I have about 5,000 responses to a survey in which users were asked how strongly they agreed with a statement on a 5-point Likert scale. This response is my dependent variable - I want to find out what influences this answer.
In the same questionnaire, the users gave a lot of other data, including age, employment, gender, location, and answers to other Likert questions.
From all of these, I want to find the factors that are most strongly correlated with their answers to the dependent variable - so that I can say "The most important predictive factor for agreeing with the statement was X. Other important predictive factors were Y and Z."
What approach should I use? I've been reading around and it looks like the two best approaches might be:
- individual chi-squared tests for each column, then compare the probability of the chi-squared scores and see which is most significant
- a multiple linear regression model
I'm not completely clear on the second approach: specifically, whether I can use to pull out the importance of individual factors, and whether it's useful for analytical work rather than predictive work. I don't want to dive in without having a good understanding of the best approach.
Suggestions much appreciated.