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How to Interpret Unexpected Negative Beta in HMR

• 1.  How to Interpret Unexpected Negative Beta in HMR

Posted Thu August 13, 2020 01:15 PM
Hello!
I'm hoping someone can help me interpret an unexpected negative value for a beta that I obtained in a hierarchical multiple linear regression. I do not believe it is a suppressor variable as a) the sum of the squared semi-partials are not greater than the r-squared, b) the r-squared is less than .5, and c) there doesn't seem to be anything too wonky in the correlations (technical term!)
A little background about my study... I'm trying to see if one of three executive function assessments better predict academic achievement. The 3 IVs are a performance-based measure of inhibition, a teacher's rating of inhibition, and a teacher's rating of attention. The DV is a score on an academic test (i.e., reading, math, or science-- each one run separately). The teacher's rating of inhibition has negative beta values in about half of the models (I'm looking at 3 different subjects across 2 different grades).
I'm not sure how to interpret it or what to do about it, if it is uninterpretable.
I would greatly appreciate any help.
Thank you!!
--Emily

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Emily Ciesielski
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• 2.  RE: How to Interpret Unexpected Negative Beta in HMR

Posted Thu August 13, 2020 01:39 PM
Are you running a regression with all three variables or is this three one-variable regressions?  If the former, remember that the beta for each variable is interpreted as holding the others constant, what is the effect.  It is easy to imagine an unexpected sign in that case due to the correlations among the regressors.

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Jon Peck
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• 3.  RE: How to Interpret Unexpected Negative Beta in HMR

Posted Thu August 13, 2020 01:45 PM
This is 3 one-variable regressions... theory-driven step-wise regression.

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Emily Ciesielski
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• 4.  RE: How to Interpret Unexpected Negative Beta in HMR

Posted Thu August 13, 2020 03:01 PM
I would not call that a stepwise procedure.  But, anyway, I suggest looking at a scatterplot to see if there is something anomalous in the data.

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Jon Peck
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