I was wondering if it would be possible to get some feedback on my interpretation of interaction effects in my logistic regression model? I have done a lot of research but have found it hard to find a clear answer as to how interaction effects should be interpreted.
For context, my model aims to test whether exposure to online political media has an effect on individual-level voter volatility during an election campaign - that is, the likelihood that a voter switches their party preference between the election campaign and the election itself. My hypothesis is tested using panel data with a pre- and post-election wave.
- The dependent variable (totalvolatility) is a binary variable, with 1 indicating the voter switched their party preference from between the pre- and post-election wave, and 0 indicating the voter's preference did not change.
- The main independent variable (onlinemed) is also a binary variable, with 1/0 indicating high/low exposure to online political news.
- The moderating variable (highknow) is coded as 1/0 indicating a respondent with a high/low level of political knowledge.
One of my hypotheses states that the effect of onlinemed on totalvolatility will be moderated by highknow, such that only those with high levels of political knowledge will become less volatile in response to exposure to online political media. I have included the results of the regression models along with marginal effects outputs below.
Code:
logit totalvolatility i.onlinemed##i.highknow socmedqw4 age_i female income_i partyclose_binary leftright_i politicalmood2 networkhet12_i nptvnews [pw = w5_weightp]
Code:
-----------------------------------------------------------------------------------------------
| Robust
totalvolatility | Coef. Std. Err. z P>|z| [95% Conf. Interval]
------------------------------+----------------------------------------------------------------
onlinemed |
high exposure | .0790074 .2651313 0.30 0.766 -.4406405 .5986553
|
highknow |
high knowledge | .3470785 .2892094 1.20 0.230 -.2197616 .9139185
|
onlinemed#highknow |
high exposure#high knowledge | -1.198582 .4915158 -2.44 0.015 -2.161935 -.2352283
|
socmedqw4 | .0293968 .0625581 0.47 0.638 -.0932147 .1520084
age_i | -.0178674 .0075429 -2.37 0.018 -.0326512 -.0030836
female | -.4590877 .2108023 -2.18 0.029 -.8722527 -.0459227
income_i | .0053713 .0166909 0.32 0.748 -.0273422 .0380848
partyclose_binary | -1.478461 .5034873 -2.94 0.003 -2.465278 -.4916444
leftright_i | -.1820593 .0411686 -4.42 0.000 -.2627483 -.1013703
politicalmood2 | .0233802 .0074988 3.12 0.002 .0086827 .0380777
networkhet12_i | .0351173 .0390751 0.90 0.369 -.0414686 .1117032
nptvnews | .0649076 .0397017 1.63 0.102 -.0129063 .1427214
_cons | -1.4158 .6960173 -2.03 0.042 -2.779969 -.0516316
-----------------------------------------------------------------------------------------------Code:
margins, dydx(onlinemed) at(highknow=(0(1)1)) atmeans vsquish
Conditional marginal effects Number of obs = 1,540
Model VCE : Robust
Expression : Pr(totalvolatility), predict()
dy/dx w.r.t. : 1.onlinemed
1._at : 0.onlinemed = .6599114 (mean)
1.onlinemed = .3400886 (mean)
highknow = 0
socmedqw4 = 4.131148 (mean)
age_i = 46.30343 (mean)
female = .4765431 (mean)
income_i = 13.44298 (mean)
partyclose~y = .1003665 (mean)
leftright_i = 5.204464 (mean)
politicalm~2 = 31.69004 (mean)
networkhet~i = 4.545937 (mean)
nptvnews = 6.076544 (mean)
2._at : 0.onlinemed = .6599114 (mean)
1.onlinemed = .3400886 (mean)
highknow = 1
socmedqw4 = 4.131148 (mean)
age_i = 46.30343 (mean)
female = .4765431 (mean)
income_i = 13.44298 (mean)
partyclose~y = .1003665 (mean)
leftright_i = 5.204464 (mean)
politicalm~2 = 31.69004 (mean)
networkhet~i = 4.545937 (mean)
nptvnews = 6.076544 (mean)
------------------------------------------------------------------------------
| Delta-method
| dy/dx Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
0.onlinemed | (base outcome)
-------------+----------------------------------------------------------------
1.onlinemed |
_at |
1 | .00811 .0274501 0.30 0.768 -.0456913 .0619112
2 | -.0964874 .0367689 -2.62 0.009 -.168553 -.0244218
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.Code:
Adjusted predictions Number of obs = 1,540
Model VCE : Robust
Expression : Pr(totalvolatility), predict()
1._at : onlinemed = 0
highknow = 0
socmedqw4 = 4.131148 (mean)
age_i = 46.30343 (mean)
female = .4765431 (mean)
income_i = 13.44298 (mean)
partyclose~y = .1003665 (mean)
leftright_i = 5.204464 (mean)
politicalm~2 = 31.69004 (mean)
networkhet~i = 4.545937 (mean)
nptvnews = 6.076544 (mean)
2._at : onlinemed = 0
highknow = 1
socmedqw4 = 4.131148 (mean)
age_i = 46.30343 (mean)
female = .4765431 (mean)
income_i = 13.44298 (mean)
partyclose~y = .1003665 (mean)
leftright_i = 5.204464 (mean)
politicalm~2 = 31.69004 (mean)
networkhet~i = 4.545937 (mean)
nptvnews = 6.076544 (mean)
3._at : onlinemed = 1
highknow = 0
socmedqw4 = 4.131148 (mean)
age_i = 46.30343 (mean)
female = .4765431 (mean)
income_i = 13.44298 (mean)
partyclose~y = .1003665 (mean)
leftright_i = 5.204464 (mean)
politicalm~2 = 31.69004 (mean)
networkhet~i = 4.545937 (mean)
nptvnews = 6.076544 (mean)
4._at : onlinemed = 1
highknow = 1
socmedqw4 = 4.131148 (mean)
age_i = 46.30343 (mean)
female = .4765431 (mean)
income_i = 13.44298 (mean)
partyclose~y = .1003665 (mean)
leftright_i = 5.204464 (mean)
politicalm~2 = 31.69004 (mean)
networkhet~i = 4.545937 (mean)
nptvnews = 6.076544 (mean)
------------------------------------------------------------------------------
| Delta-method
| Margin Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
_at |
1 | .1121286 .0157127 7.14 0.000 .0813323 .1429249
2 | .1516006 .0323344 4.69 0.000 .0882264 .2149748
3 | .1202385 .0220698 5.45 0.000 .0769824 .1634946
4 | .0551132 .0175233 3.15 0.002 .0207682 .0894581
------------------------------------------------------------------------------Overall, my question is the following: I am correct in saying that "there is a significant interaction between onlinemed and highknow, such that high exposure to online media reduces the likelihood of an individual being volatile compared to an individual with low exposure to online media (by 9.64874% on average), but this effect is only significant for individuals with high levels of political knowledge (p = 0.009). For those with low levels of knowledge, the effect of moving from low to high online media consumption on the probability of volatility is positive (+0.811%), but insignificant (p = 0.768)?"
Thanks in advance.
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