# What statistical analysis method to use? Multiple regression?

My research is looking at ethnic differences in teenage mental health during the pandemic. Specifically I have 2 independent variables: ethnicity (categorical - 2 groups: white and non-white) and time point (categorical - 2 time points: before the pandemic and after the 1st UK lockdown). My dependent variable is a measure of mental health which is a continuous variable. This measure was repeated at both time points.

I am a bit confused on the analysis and whether I have done it correctly. My aim was to do multiple regression to see if the time point and ethnicity predicted changes in mental health scores. However I got a bit stuck as one of my predictors is independent (ethnicity) and the other is repeated measures (time point). I dummy coded the ethnicity variable but as time point was repeated measures I converted my dataset into long format and then dummy coded, but this led to double the amount of cases for the ethnicity variable. Would this be an issue? I'm not sure if you can even do a multiple regression with one independent measures predictor and one repeated measures predictor?

So I ran the regression and both of the predictors came out as highly significant (β =.157 and -.119, p = 0.000) but together they only account for 3.7% of the variance which I don't understand.

Any help would be much appreciated!

• Why would you use white and non-white over a more granular model? Jan 5 at 16:44
• Wouldn't the interaction between race and pandemic be the actual (only?) term of scientific interest here? I'm not sure why you are surprised that those two predictors explain very little variation. Surely there is more to teenage mental health than whether a teenager is white or not. You don't say anything about the dataset but you would be able to build a more meaningful model if you can include other variables that are (expected) to be predictive of the mental well-being of teenagers. (I'd guess a lot depends on the environment they are growing up in.) Jan 5 at 17:05