Our little group at uni is investigating if there is a relationship between 3 measures of social well-being (social anxiety, social connectedness and self esteem) and usage time (on-screen time in hours) on several social media platforms. Would you recommend that we use correlation or regression in this case?
Thanks in advance :)
You need to provide us with more information on your variables, are they categorical or numerical? If usage time is numerical then you can build a linear regression model and the get the coefficients and associated p-values out of it.
If all variables are numerical you could calculate the correlation between them, but the linear model gives more information / is superior.
usage time is you DV, the other 3 are your IV's, this is multiple regression. In R lm(usage~v1+v2+v3)
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Commented
Sep 14, 2018 at 9:47
If you want to predict things that can't be precisely measured (such as social anxiety, social connectedness, and self esteem) I recommend using your numerical screen time data to perform principal component analysis.
In short, principal component analysis "uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components."
The principal components CAN be interpreted as measurements of something you don't have a measurement for, in this case self esteem, etc... This is a pretty big stretch so if you are doing this in a rigorous academic environment then you are going to need something more to back up your claims.
Read paragraph 2.5 in this paper to see what I mean: http://www.math.montana.edu/jobo/writing/documents/barbour.pdf
