TL:DR is Pearson correlation acceptable for a binary variable?
For my study I have measured and determined a "change in anxiety" in a group of students (the change has been found significant with large effect size using paired t-test [same group of students: test/re-test]). I have 4 variables that I want to to see if there is any correlation between them and the change in anxiety. I have been at this for 6+ hours reading Dornyei's book on Research Methods and Green and Salkind's SPSS Handbook and posts on ResearchGate, etc, etc, etc. No clear answers.
My variables are
- (Binary) Gender
- Field of study (11 categories)
- (Binary) whether the instructor is a native English speaker or a non-native English speaker
For gender, in SPSS, I used 1 for male and 2 for female.
For #4 I did similar. 1 for native, 2 for non-native speaker of English.
I ran bivariate correlation analysis (in SPSS). I got no significant correlation for #1, #2, or #3.
For number #4 my Pearson output data indicated a significant (but weak) correlation and effect size: r(397) = .109, p .05 (sig = .030).
Is this data okay? If I try to publish a paper saying I used Pearson's correlation are they going to say "okay" or "what the hell are you thinking" because I see no clear answer out there on the forums or in my reference books. Some say binary data is perfectly acceptable for Pearson correlation.
Just for kicks
Kendall tau b: r(397) = .086, p .05 (sig = .037).
Spearman's rho: r(397) = .105, p .05 (sig = .037).
Maybe I am just tired and the answer is right in front of me, but how can I see the relationship between native and non-native English speaking teachers and change in anxiety. I don't just mean "there is/is not a correlation". The above data is showing there is a SMALL but significant correlation, but how do I show (with data or graphs) the effect of native English speaking teachers on the change in anxiety and the effect of non-native English speaking teachers on the change in anxiety.