Is it okay using apllo_weighting for propensity score weighting?
Posted: 11 May 2021, 08:09
Dear Stephane,
I want to use propensity score weighting to control selection biases in experimental groups.
The datasets are Stated Choice survey, and looks like as follows;
ipw row is for propensity scores,
I used ordered logit model since the choice part is in Likert scale, and I have two questions as follows;
1. Can I use apollo_weighting in propensity score weighting?
I tried weights = "ipw" in apollo_control and P = apollo_weighting(P, apollo_inputs, functionality) before prepareprob, which follows the manual.
I just add these two in my original basic code for Ordered logit.
2. If it is okay, however, the result is different from other, MASS package, simple ordered logit also.
I will attach the different results.
(1) Result from apollo
(2) Result for MASS package in r
Sorry for the differences in the variable name, As you can see, the coefficients are different especially t-values are really different.
For your understanding, there are 3 groups, video, experiment, and none. Therefore, I put none as a baseline.
Thank you for your kind supports for questions, I really want to use this package in my analysis since it is really straightforward to expand what I want to do.
Thank you so much.
With kindest regards,
Hyewon
I want to use propensity score weighting to control selection biases in experimental groups.
The datasets are Stated Choice survey, and looks like as follows;
Code: Select all
ID choice attirbute1 attribute2 ... ipw
1 1 1 0 0.3478
1 3 0 1 0.3478
1 5 1 1 0.3478
2 3 0 1 3.7865
2 4 1 1 3.7865
2 3 0 1 3.7865
I used ordered logit model since the choice part is in Likert scale, and I have two questions as follows;
1. Can I use apollo_weighting in propensity score weighting?
I tried weights = "ipw" in apollo_control and P = apollo_weighting(P, apollo_inputs, functionality) before prepareprob, which follows the manual.
I just add these two in my original basic code for Ordered logit.
2. If it is okay, however, the result is different from other, MASS package, simple ordered logit also.
I will attach the different results.
(1) Result from apollo
Code: Select all
Estimates:
Estimate s.e. t.rat.(0) Rob.s.e. Rob.t.rat.(0)
b_auto_tram -0.05929 0.03786 -1.5658 0.05149 -1.1515
b_auto_vehi 0.04170 0.04618 0.9030 0.16129 0.2585
b_bus -0.18977 0.04646 -4.0851 0.14342 -1.3232
b_truck_person 0.24773 0.07118 3.4805 0.28958 0.8555
b_truck_guard 0.25147 0.04102 6.1298 0.06824 3.6850
b_video 0.10208 0.03814 2.6766 0.33802 0.3020
b_experi 0.68932 0.19335 3.5651 0.40888 1.6859
b_none 0.00000 NA NA NA NA
tau_choice_1 -1.67527 0.04992 -33.5567 0.31206 -5.3685
tau_choice_2 -0.62904 0.04634 -13.5750 0.27961 -2.2497
tau_choice_3 0.77439 0.04662 16.6089 0.34088 2.2717
tau_choice_4 1.87547 0.05072 36.9775 0.54187 3.4611(2) Result for MASS package in r
Code: Select all
Call:
polr(formula = ans ~ auto_tram + auto_vehi + bus + truck_person *
s21_1 + truck_guard + video1 + exp_group, data = rdata3,
weights = ipw)
Coefficients:
Value Std. Error t value
auto_tram -0.06321 0.03787 -1.669
auto_vehi 0.04968 0.04620 1.075
bus -0.18969 0.04646 -4.083
truck_person -0.28461 0.05187 -5.487
s21_1 0.29061 0.05363 5.419
truck_guard 0.09386 0.04635 2.025
video 0.07793 0.03833 2.033
exp_group 0.55516 0.19438 2.856
truck_person:s21_1 0.16706 0.09305 1.795Sorry for the differences in the variable name, As you can see, the coefficients are different especially t-values are really different.
For your understanding, there are 3 groups, video, experiment, and none. Therefore, I put none as a baseline.
Thank you for your kind supports for questions, I really want to use this package in my analysis since it is really straightforward to expand what I want to do.
Thank you so much.
With kindest regards,
Hyewon