I see the first uses a chi test and the second uses an F test. The only real difference in results is the p-value for the comparison of level 2 vs 1 in the first is .0733, while it is .08 in the second.
Which command should I use? In this case, there is little difference, but could there be a case where the p-value difference between the two is substantial for interpretation?
Code:
. margins ar.interven123 Contrasts of predictive margins Model VCE : Robust Expression : Linear prediction, predict() df chi2 P>chi2 interven123 (1 vs 0) 1 0.02 0.8963 (2 vs 1) 1 3.21 0.0733 (3 vs 2) 1 12.00 0.0005 Joint 3 17.54 0.0005 Delta-method Contrast Std. Err. [95% Conf. Interval] interven123 (1 vs 0) -1735.885 13315.37 -27833.52 24361.75 (2 vs 1) 37908 21165.87 -3576.35 79392.35 (3 vs 2) -21796.77 6293.4 -34131.6 -9461.928 . contrast ar.interven123, effects Contrasts of marginal linear predictions Margins : asbalanced df F P>F interven123 (1 vs 0) 1 0.02 0.8969 (2 vs 1) 1 3.21 0.0802 (3 vs 2) 1 12.00 0.0012 Joint 3 5.85 0.0019 Denominator 44 Contrast Std. Err. t P>t [95% Conf. Interval] interven123 (1 vs 0) -1735.885 13315.37 -0.13 0.897 -28571.24 25099.47 (2 vs 1) 37908 21165.87 1.79 0.080 -4749.015 80565.01 (3 vs 2) -21796.77 6293.4 -3.46 0.001 -34480.28 -9113.251 . end of do-file
0 Response to margins ar.variable VS contrast ar.variable, effects
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