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I'm performing a psych study on a group of single people . My binary dependent variable is their relationship status one year in the future (still single/in a relationship). My variables include their gender (male/female) and whether they have a crush on someone (yes/no). My chi square tests show no significance for either variable individually, but when I subgroup (women with a crush, men without a crush, ...), I achieve significance.

I applied logistic regression to my data, but it showed pretty much what my chi square tests showed -- no significance for the group as a whole, but significance when I subgroup. I believe that this is because my variables are not independent? Which is a requirement of logistic regression?

My question: is there a test to demonstrate that my variables are not independent? Or maybe results showing no significance for my larger group demonstrates this? If so, should I just stick with chi square and state in my methods section that logistic regression was investigated but the variables were found to lack independence? I could really use some advice for next steps.

EDIT:

Finally occurred to me I had collected their relationship status at 3 and 12 months and was treating these as separate outcomes when I should OR the values to create a dependent variable that relates whether participants were in a relationship at either of these two points in time. Getting much better results.

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"is there a test to demonstrate that my variables are not independent?" - Yes, this is the chi-squared test.

"If so, should I just stick with chi square and state in my methods section that logistic regression was investigated but the variables were found to lack independence?" - No, you can still build a logistic regression using an interaction term.

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  • $\begingroup$ Thanks! Reading up on interaction terms now. If I build a logistic regression using an interaction term, what will it tell me that my current chi square results showing significance for some subgroups won't? $\endgroup$
    – buttonsrtoys
    Nov 28 '16 at 14:34
  • $\begingroup$ Using a logistic regression you don't just detect a relationship, you also predict the outcome. The chi square test is a tool to identify if a relationship exists in the first place. $\endgroup$
    – Arun Jose
    Nov 28 '16 at 15:28
  • $\begingroup$ Got it. Thanks again. In my reading I see that a benefit of logistic regression over chi sq. is that logistic regression allows for continuous and categorical predictors. However, I don't benefit from this as all my predictors are binary (e.g., gender, or have yes/no values). Are there still advantages to logistic regression for my case? $\endgroup$
    – buttonsrtoys
    Nov 28 '16 at 20:41
  • $\begingroup$ As I mentioned, if you want to predict relationship status, then you need logistic. If you only want to state you found a relationship use chi square $\endgroup$
    – Arun Jose
    Nov 29 '16 at 2:03

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