Hi all,

I am working on my MSc. and I have data for 201 participants who each made 457 choices (total choices ~ 91,000 choices). I am using a binary logit model of choice 1 and choice 2 groupings. I am using STATA 15. I have no problems running my logit regression (see example below) but I am having trouble with the interaction terms showing up when I do the marginal effects. The marginal effects for the interaction term disappears so I can't estimate the effect size for the interaction term. I have been trying to find the answer to this for a few days and I have read a lot about how to operationalize the commands (Thank you, Richard Williams & Clyde Schechter for all your answers especially). Obviously, I am either trying to do something I shouldn't be doing or going about it the wrong way. Any help would be appreciated greatly. I am clustering the standard errors by ID (of each person)-- also unsure if I should be doing that but it isn't the focus of this question.

Background: anything with i. is a factor variable (0,1) and otherwise, it is continuous.

. logit buy i.treatment##i.income_low i.female i.young i.fulltime i.children i.recentimmigrin
> t i.highschool_or_below price nvs_score_total, cluster(ID)

Iteration 0: log pseudolikelihood = -4419.9312
Iteration 1: log pseudolikelihood = -4386.08
Iteration 2: log pseudolikelihood = -4384.7678
Iteration 3: log pseudolikelihood = -4384.765
Iteration 4: log pseudolikelihood = -4384.765

Logistic regression Number of obs = 91,857
Wald chi2(11) = 59.14
Prob > chi2 = 0.0000
Log pseudolikelihood = -4384.765 Pseudo R2 = 0.0080

(Std. Err. adjusted for 201 clusters in ID)
---------------------------------------------------------------------------------------
| Robust
buy | Coef. Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
1.treatment | -.2627016 .0917476 -2.86 0.004 -.4425235 -.0828797
1.income_low | -.2187479 .1241379 -1.76 0.078 -.4620536 .0245579
|
treatment#income_low |
1 1 | .4124181 .217789 1.89 0.058 -.0144404 .8392766
|
1.female | .0782018 .084832 0.92 0.357 -.0880658 .2444695
1.young | -.2152019 .099856 -2.16 0.031 -.4109162 -.0194877
1.fulltime | .0954429 .0888683 1.07 0.283 -.0787357 .2696215
1.children | .0427962 .1040897 0.41 0.681 -.1612158 .2468082
1.recentimmigrint | -.2436619 .1236873 -1.97 0.049 -.4860846 -.0012392
1.highschool_or_below | .2040199 .1194476 1.71 0.088 -.030093 .4381329
price | -.1089045 .0244309 -4.46 0.000 -.1567883 -.0610208
n_score_total | .0225023 .0299007 0.75 0.452 -.0361019 .0811065
_cons | -4.385361 .1775846 -24.69 0.000 -4.73342 -4.037301
---------------------------------------------------------------------------------------

margins, dydx(*) atmeans

---------------------------------------------------------------------------------------
| Delta-method
| dy/dx Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
1.treatment | -.0014183 .0006661 -2.13 0.033 -.0027239 -.0001127
1.income_low | -.0000747 .0009576 -0.08 0.938 -.0019515 .001802
1.female | .0006153 .000663 0.93 0.353 -.0006841 .0019147
1.young | -.0016644 .0007549 -2.20 0.027 -.0031441 -.0001847
1.fulltime | .000764 .000719 1.06 0.288 -.0006453 .0021733
1.children | .0003424 .0008402 0.41 0.684 -.0013044 .0019891
1.recentimmigrint | -.0018043 .0008555 -2.11 0.035 -.0034811 -.0001276
1.highschool_or_below | .0017283 .0010859 1.59 0.111 -.0004 .0038566
price | -.0008627 .0001912 -4.51 0.000 -.0012374 -.0004881
nvs_score_total | .0001783 .0002363 0.75 0.451 -.0002848 .0006413
---------------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.