I am performing a "dry run" statistical analysis on my dissertation data pretest, and I'm looking for a little guidance regarding the specific analyses I need to perform.
I have three primary variables:
The other two questions are where I'm getting a little stuck. For #2, you can't do a one-way ANOVA using a continuous independent variable, so would it make sense to break subjects up into quartiles and just run the ANOVA as if the continuous variable was four groups? Or is there a better way?
As for #3, I'm not sure what test at all that would be. Any recommendations?
I have three primary variables:
- Environment. Categorical, 1 of 3 conditions. Independent, assigned by experimenter randomly.
- Self-Monitoring Score. Continuous variable, 0-18. Independent, calculated using responses to Snyder's self-monitoring scale.
- Uses & grats. These are the dependent variables. 59 5-point Likert-type items that have been put through factor analysis resulting in 12 factors with eigenvalues > 1.
- Are there statistically significant differences in uses & grats based on environment?
- Are there statistically significant differences in uses & grats based on self-monitoring score?
- Are there any discernible interactions between environment, self-monitoring score, and uses & grats?
The other two questions are where I'm getting a little stuck. For #2, you can't do a one-way ANOVA using a continuous independent variable, so would it make sense to break subjects up into quartiles and just run the ANOVA as if the continuous variable was four groups? Or is there a better way?
As for #3, I'm not sure what test at all that would be. Any recommendations?