Hello everyone,
I'm currently analyzing cross-sectional survey data from 28 country-waves, totaling 30,988 observations. My study focuses on the relationship between "satisfaction with democracy" (measured across four categories) and "Asian Values," with a consideration for the moderating effect of "corruption levels," which I assess using either V-dem or CPI.
I utilized multilevel ordered logistic estimate(meologit), I include an interaction between individual-level and country-level continuous variables (Asian values * Corruption level).
I'm seeking guidance on calculating the marginal effects and predicted probabilities of Asian values on satisfaction with democracy across different levels of corruption.
Despite my efforts, I'm encountering challenges in determining the appropriate methods for calculating a continuous-by-continuous interaction in a multilevel model.
Specifically, I aim to predict the probability of receiving scores 3 and 4 on the democratic satisfaction scale.
Below, I've provided my code and results.
I'd greatly appreciate any insights or suggestions on how to proceed with my analysis.
Thank you in advance for your assistance!
And I calculate the marginal effects by utilizing margins function.
Is it appropriate way to calculate the marginal effects?
Also, I am not sure why there are non-linear results in this marginal effects when I chose to predict 3 score of democratic satisfaction.
Is it accurate to expect nonlinear results when calculating the marginal effects of an ordered logit model?
I'm currently analyzing cross-sectional survey data from 28 country-waves, totaling 30,988 observations. My study focuses on the relationship between "satisfaction with democracy" (measured across four categories) and "Asian Values," with a consideration for the moderating effect of "corruption levels," which I assess using either V-dem or CPI.
I utilized multilevel ordered logistic estimate(meologit), I include an interaction between individual-level and country-level continuous variables (Asian values * Corruption level).
I'm seeking guidance on calculating the marginal effects and predicted probabilities of Asian values on satisfaction with democracy across different levels of corruption.
Despite my efforts, I'm encountering challenges in determining the appropriate methods for calculating a continuous-by-continuous interaction in a multilevel model.
Specifically, I aim to predict the probability of receiving scores 3 and 4 on the democratic satisfaction scale.
Below, I've provided my code and results.
I'd greatly appreciate any insights or suggestions on how to proceed with my analysis.
Thank you in advance for your assistance!
Code:
sum demo_satisfy AVS_4D_goertzian_cses_z_1 r_cpi r_v2x_corr Variable | Obs Mean Std. dev. Min Max -------------+--------------------------------------------------------- demo_satisfy | 37,335 2.588376 .7045862 1 4 AVS_4D_goe~1 | 35,595 -4.35e-11 .1879719 -.4720078 .6834241 r_cpi | 37,559 .5182177 .1728828 .2 .76 r_v2x_corr | 38,703 .4324576 .2602557 .088 .789
Code:
meologit demo_satisfy c.AVS_4D_goertzian_cses_z_1 c.r_v2x_corr ///
> c.AVS_4D_goertzian_cses_z_1#c.r_v2x_corr ///
> $control ///
> || n_country: , diff
Fitting fixed-effects model:
Iteration 0: log likelihood = -33828.686
Iteration 1: log likelihood = -32584.359
Iteration 2: log likelihood = -32572.189
Iteration 3: log likelihood = -32572.177
Iteration 4: log likelihood = -32572.177
Refining starting values:
Grid node 0: log likelihood = -32277.857
Fitting full model:
Iteration 0: log likelihood = -32277.857 (not concave)
Iteration 1: log likelihood = -32268.079 (not concave)
Iteration 2: log likelihood = -32261.949 (not concave)
Iteration 3: log likelihood = -32256.217 (not concave)
Iteration 4: log likelihood = -32254.663
Iteration 5: log likelihood = -32252.719
Iteration 6: log likelihood = -32252.403
Iteration 7: log likelihood = -32252.351
Iteration 8: log likelihood = -32252.351
Mixed-effects ologit regression Number of obs = 32,046
Group variable: n_country Number of groups = 29
Obs per group:
min = 632
avg = 1,105.0
max = 1,401
Integration method: mvaghermite Integration pts. = 7
Wald chi2(27) = 1782.44
Log likelihood = -32252.351 Prob > chi2 = 0.0000
-------------------------------------------------------------------------------
demo_satisfy | Coefficient Std. err. z P>|z| [95% conf. interval]
--------------+----------------------------------------------------------------
AVS_4D_goer~1 | 1.042765 .1332343 7.83 0.000 .7816307 1.3039
r_v2x_corr | 1.211922 2.228575 0.54 0.587 -3.156005 5.579849
|
c. |
AVS_4D_goer~1#|
c.r_v2x_corr | -.7401299 .241652 -3.06 0.002 -1.213759 -.2665007
|
national_ec~1 | .4902225 .01353 36.23 0.000 .4637042 .5167408
inter_polit~1 | .125871 .0138005 9.12 0.000 .0988226 .1529195
female_1 | -.04697 .0222005 -2.12 0.034 -.0904821 -.0034579
education_l~1 | -.0276095 .0059244 -4.66 0.000 -.0392212 -.0159978
income_leve~1 | .0175865 .0095815 1.84 0.066 -.0011929 .036366
age_group_1 | .0074725 .0095896 0.78 0.436 -.0113227 .0262678
urban_1 | -.1941105 .0266462 -7.28 0.000 -.246336 -.141885
log_gdp_per | .0650359 .5343701 0.12 0.903 -.9823104 1.112382
real_gdp_gr~h | .0059382 .0622354 0.10 0.924 -.116041 .1279174
v2x_polyarchy | 1.725386 2.140482 0.81 0.420 -2.469881 5.920652
year_democr~y | -.0062621 .0072102 -0.87 0.385 -.0203938 .0078696
log_district1 | -.3696868 .3125295 -1.18 0.237 -.9822335 .2428598
|
year |
2002 | -.0658129 .5084933 -0.13 0.897 -1.062441 .9308157
2003 | .5028068 .5990552 0.84 0.401 -.6713197 1.676933
2005 | -.8269738 .5592569 -1.48 0.139 -1.923097 .2691495
2006 | .4219778 .583083 0.72 0.469 -.7208438 1.564799
2007 | .4208095 .6899593 0.61 0.542 -.931486 1.773105
2010 | .1867433 .6929426 0.27 0.788 -1.171399 1.544886
2011 | .6369974 .5696362 1.12 0.263 -.479469 1.753464
2014 | .3855318 .5933459 0.65 0.516 -.7774048 1.548468
2015 | .7495475 .599657 1.25 0.211 -.4257586 1.924854
2016 | 1.102011 .6494188 1.70 0.090 -.1708261 2.374849
2018 | .5106638 .5976895 0.85 0.393 -.6607861 1.682114
2019 | 1.181593 .5769045 2.05 0.041 .0508812 2.312305
--------------+----------------------------------------------------------------
/cut1 | -.6529692 3.987586 -8.468494 7.162556
/cut2 | 1.773676 3.987562 -6.041802 9.589153
/cut3 | 5.049279 3.987684 -2.766437 12.865
--------------+----------------------------------------------------------------
n_country |
var(_cons)| .0857079 .0234122 .0501769 .1463988
-------------------------------------------------------------------------------
LR test vs. ologit model: chibar2(01) = 639.65 Prob >= chibar2 = 0.0000
Code:
margins , dydx(AVS) at(r_v2x_corr=(0.08(0.1)0.79)) expression(predict (outcome(4) mu fixed) + predict(outcome(3) mu fixed))
> vsquish atmean
Conditional marginal effects Number of obs = 32,046
Model VCE: OIM
Expression: predict (outcome(4) mu fixed) + predict(outcome(3) mu fixed)
dy/dx wrt: AVS
------------------------------------------------------------------------------
| Delta-method
| dy/dx std. err. z P>|z| [95% conf. interval]
-------------+----------------------------------------------------------------
AVS_4D_goe~1 |
_at |
1 | .2457272 .0295098 8.33 0.000 .1878892 .3035653
2 | .227108 .0245175 9.26 0.000 .1790545 .2751614
3 | .2069876 .0209084 9.90 0.000 .1660079 .2479673
4 | .1858139 .0166791 11.14 0.000 .1531235 .2185044
5 | .1640518 .014552 11.27 0.000 .1355305 .1925731
6 | .1421609 .0185196 7.68 0.000 .1058632 .1784586
7 | .1205754 .0262524 4.59 0.000 .0691217 .1720291
8 | .0996866 .0340355 2.93 0.003 .0329783 .1663948
------------------------------------------------------------------------------
Also, I am not sure why there are non-linear results in this marginal effects when I chose to predict 3 score of democratic satisfaction.
Code:
margins , dydx(AVS) at(r_cpi=(0.2(0.2)0.8)) expression(pred
> ict(outcome(3) mu fixed)) vsquish atmean
------------------------------------------------------------------------------
| Delta-method
| dy/dx std. err. z P>|z| [95% conf. interval]
-------------+----------------------------------------------------------------
AVS_4D_goe~1 |
_at |
1 | -.0057405 .1134455 -0.05 0.960 -.2280896 .2166087
2 | .1106163 .0333242 3.32 0.001 .0453022 .1759305
3 | .1409644 .0138573 10.17 0.000 .1138047 .1681242
4 | .0994199 .0248382 4.00 0.000 .050738 .1481018
------------------------------------------------------------------------------
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