In order to test whether there is a statistically significant effect of a particular macro level variable on a particular micro level outcome, you will probably wish to use some from of regression analysis. For example, suppose you are interested in whether PR increases trust in politicians and you want to control for the effect of generalized trust in people, you could then run the following regression.

regress trstplt ppltrst pr Source | SS df MS Number of obs = 6148 -------------+------------------------------ F( 2, 6145) = 402.38 Model | 3801.87099 2 1900.9355 Prob > F = 0.0000 Residual | 29030.2259 6145 4.72420276 R-squared = 0.1158 -------------+------------------------------ Adj R-squared = 0.1155 Total | 32832.0969 6147 5.34115779 Root MSE = 2.1735 ------------------------------------------------------------------------------ trstplt | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- ppltrst | .3159584 .0112979 27.97 0.000 .2938106 .3381062 pr | .196908 .0793005 2.48 0.013 .0414513 .3523648 _cons | 2.082625 .0891842 23.35 0.000 1.907793 2.257457 ------------------------------------------------------------------------------

The results suggest that there is a significant positive effect of PR on trust in politicians even after controlling for trust in people generally. Note that macro and micro variables are introduced into the regression in entirely the same way.

This problem can be dealt with by using a multilevel model as described in Unit 6 in this series.