Hi statalist community,
I am using xthybrid command for the analysis of data. How to handle interaction terms, it has not been specified by Schunck and Perales ( 2017). Since I have two categorical independent variables, whose interaction is important to know.
In xthybrid output that is below, I want to study the interaction of stud_SCST and Teach_SCST (within effect) on the outcome variable. Can anyone suggest, how that can be done? Or steps to take, going further.
regards,
ajay
I am using xthybrid command for the analysis of data. How to handle interaction terms, it has not been specified by Schunck and Perales ( 2017). Since I have two categorical independent variables, whose interaction is important to know.
In xthybrid output that is below, I want to study the interaction of stud_SCST and Teach_SCST (within effect) on the outcome variable. Can anyone suggest, how that can be done? Or steps to take, going further.
Code:
. xthybrid Positive_disc01 stud_SCST stud_OBC Teach_SCST Teach_OBC Teach_nature_1 Teach_nature_2 Teach_gender_
> 1 course1_com course1_eco course1_eng course1_hin course1_his course1_mat course1_pol sem_1 sem_2 sem_3 sem_4 sem_
> 5 attendence_percent , clusterid ( group_teacherID_paper ) se test p link(logit) family(bernoulli)
The variable 'Teach_SCST' does not vary sufficiently within clusters
and will not be used to create additional regressors.
[~0% of the total variance in 'Teach_SCST' is within clusters]
The variable 'Teach_OBC' does not vary sufficiently within clusters
and will not be used to create additional regressors.
[~0% of the total variance in 'Teach_OBC' is within clusters]
The variable 'Teach_nature_1' does not vary sufficiently within clusters
and will not be used to create additional regressors.
[~0% of the total variance in 'Teach_nature_1' is within clusters]
The variable 'Teach_nature_2' does not vary sufficiently within clusters
and will not be used to create additional regressors.
[~0% of the total variance in 'Teach_nature_2' is within clusters]
The variable 'Teach_gender_1' does not vary sufficiently within clusters
and will not be used to create additional regressors.
[~0% of the total variance in 'Teach_gender_1' is within clusters]
The variable 'course1_com' does not vary sufficiently within clusters
and will not be used to create additional regressors.
[~0% of the total variance in 'course1_com' is within clusters]
The variable 'sem_1' does not vary sufficiently within clusters
and will not be used to create additional regressors.
[~0% of the total variance in 'sem_1' is within clusters]
The variable 'sem_2' does not vary sufficiently within clusters
and will not be used to create additional regressors.
[~0% of the total variance in 'sem_2' is within clusters]
The variable 'sem_3' does not vary sufficiently within clusters
and will not be used to create additional regressors.
[~0% of the total variance in 'sem_3' is within clusters]
The variable 'sem_4' does not vary sufficiently within clusters
and will not be used to create additional regressors.
[~0% of the total variance in 'sem_4' is within clusters]
The variable 'sem_5' does not vary sufficiently within clusters
and will not be used to create additional regressors.
[~0% of the total variance in 'sem_5' is within clusters]
Hybrid model. Family: bernoulli. Link: logit.
+-----------------------------------+
| Variable | model |
|----------------------+------------|
| Positive_disc01 | |
| R__Teach_SCST | -0.2539 |
| | 0.3005 |
| | 0.3981 |
| R__Teach_OBC | 0.1782 |
| | 0.3008 |
| | 0.5537 |
| R__Teach_nature_1 | 0.0376 |
| | 0.2425 |
| | 0.8767 |
| R__Teach_nature_2 | (omitted) |
| | |
| | |
| R__Teach_gender_1 | -0.3657 |
| | 0.3221 |
| | 0.2562 |
| R__course1_com | -2.6670 |
| | 0.6928 |
| | 0.0001 |
| R__sem_1 | -0.7267 |
| | 0.4611 |
| | 0.1150 |
| R__sem_2 | -0.6509 |
| | 0.4247 |
| | 0.1254 |
| R__sem_3 | -0.3182 |
| | 0.3366 |
| | 0.3444 |
| R__sem_4 | 0.4698 |
| | 0.2948 |
| | 0.1110 |
| R__sem_5 | (omitted) |
| | |
| | |
| W__stud_SCST | -0.1963 |
| | 0.0659 |
| | 0.0029 |
| W__stud_OBC | -0.1427 |
| | 0.0653 |
| | 0.0290 |
| W__course1_eco | 0.1309 |
| | 0.3041 |
| | 0.6668 |
| W__course1_eng | 0.4095 |
| | 0.3379 |
| | 0.2256 |
| W__course1_hin | 0.4922 |
| | 0.3608 |
| | 0.1725 |
| W__course1_his | -0.1280 |
| | 0.3773 |
| | 0.7345 |
| W__course1_mat | 0.1302 |
| | 0.4678 |
| | 0.7808 |
| W__course1_pol | (omitted) |
| | |
| | |
| W__attendence_perc~t | 0.0100 |
| | 0.0016 |
| | 0.0000 |
| B__stud_SCST | -0.9315 |
| | 1.8668 |
| | 0.6178 |
| B__stud_OBC | -1.7112 |
| | 2.9201 |
| | 0.5579 |
| B__course1_eco | -2.3956 |
| | 0.6492 |
| | 0.0002 |
| B__course1_eng | -3.5290 |
| | 0.6188 |
| | 0.0000 |
| B__course1_hin | -1.5348 |
| | 0.3884 |
| | 0.0001 |
| B__course1_his | -2.9572 |
| | 0.7245 |
| | 0.0000 |
| B__course1_mat | -2.4438 |
| | 0.7351 |
| | 0.0009 |
| B__course1_pol | (omitted) |
| | |
| | |
| B__attendence_perc~t | 0.0371 |
| | 0.0131 |
| | 0.0046 |
| _cons | 1.0844 |
| | 1.2169 |
| | 0.3729 |
|----------------------+------------|
| var(_cons[group~r])| |
| _cons | 1.7310 |
| | 0.2225 |
| | 0.0000 |
|----------------------+------------|
| Statistics | |
| ll | -5185.5979 |
| chi2 | 193.1889 |
| p | 0.0000 |
| aic | 10425.1959 |
| bic | 10619.3819 |
+-----------------------------------+
Legend: b/se/p
Level 1: 9819 units. Level 2: 201 units.
Tests of the random effects assumption:
_b[B__stud_SCST] = _b[W__stud_SCST]; p-value: 0.6939
_b[B__stud_OBC] = _b[W__stud_OBC]; p-value: 0.5913
_b[B__course1_eco] = _b[W__course1_eco]; p-value: 0.0004
_b[B__course1_eng] = _b[W__course1_eng]; p-value: 0.0000
_b[B__course1_hin] = _b[W__course1_hin]; p-value: 0.0001
_b[B__course1_his] = _b[W__course1_his]; p-value: 0.0005
_b[B__course1_mat] = _b[W__course1_mat]; p-value: 0.0031
_b[B__course1_pol] = _b[W__course1_pol]; p-value: .
_b[B__attendence_percent] = _b[W__attendence_percent]; p-value: 0.0400
.
regards,
ajay
