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1 frequency analysis and cross tab

1.1 Handling of terrorism

q3_4 n
5 - Highly satisfied 44
4 25
3 17
2 6
1 - Highly unsatisfied 7
Total 99
p_party_new 5 - Highly satisfied 4 3 2 1 - Highly unsatisfied Total
BJP 62 25 9 2 1 99
INC 23 22 24 14 17 100
Others 32 24 22 11 11 100
Non-Identifier 28 27 27 8 10 100
age_cb_new 5 - Highly satisfied 4 3 2 1 - Highly unsatisfied Total
Pre-Millennials 45 25 17 7 7 101
Millennials 45 25 17 6 7 100
Post-Millennials 44 25 17 6 7 99
religion_in_new 5 - Highly satisfied 4 3 2 1 - Highly unsatisfied Total
Hindu 53 26 14 4 3 100
Muslim 21 20 24 14 21 100
Christian 22 25 28 13 12 100
Others 41 25 18 8 9 101
Undisclosed/Irreligious 31 24 24 9 12 100
caste_in_new 5 - Highly satisfied 4 3 2 1 - Highly unsatisfied Total
General 48 26 16 5 5 100
OBC 43 25 17 7 8 100
SC 42 23 20 9 6 100
ST 38 23 19 9 11 100
Undisclosed/Not-Applicable 32 24 24 9 11 100
sec_in_new 5 - Highly satisfied 4 3 2 1 - Highly unsatisfied Total
High SEC 47 25 16 6 6 100
Middle SEC 43 26 18 7 7 101
Low SEC 41 24 19 7 9 100
gender 5 - Highly satisfied 4 3 2 1 - Highly unsatisfied Total
Male 48 25 15 5 7 100
Female 40 25 20 7 7 99
geo_citytier_in 5 - Highly satisfied 4 3 2 1 - Highly unsatisfied Total
Tier 1 44 26 18 6 6 100
Tier 2 47 26 15 6 5 99
Tier 3 43 23 18 7 8 99
geo_region_in_cb 5 - Highly satisfied 4 3 2 1 - Highly unsatisfied Total
North & Central India 48 26 15 5 5 99
South India 37 24 21 9 9 100
East & Northeast India 46 26 17 5 6 100
West India 47 25 16 6 6 100
partisan_index 5 - Highly satisfied 4 3 2 1 - Highly unsatisfied Total
Strong Partisan 58 22 11 4 5 100
Moderate Partisan 39 29 19 8 5 100
Weak Partisan 30 27 24 8 10 99

1.2 handling of china conflict

q3_3 n
5 - Highly satisfied 34
4 26
3 21
2 8
1 - Highly unsatisfied 10
Total 99
p_party_new 5 - Highly satisfied 4 3 2 1 - Highly unsatisfied Total
BJP 49 30 15 4 2 100
INC 15 21 25 15 23 99
Others 24 22 24 13 16 99
Non-Identifier 20 24 31 11 14 100
gender 5 - Highly satisfied 4 3 2 1 - Highly unsatisfied Total
Male 37 26 19 8 11 101
Female 31 26 24 9 9 99
geo_region_in_cb 5 - Highly satisfied 4 3 2 1 - Highly unsatisfied Total
North & Central India 37 27 20 7 9 100
South India 29 24 25 11 12 101
East & Northeast India 35 27 20 8 10 100
West India 34 27 21 8 11 101
geo_citytier_in 5 - Highly satisfied 4 3 2 1 - Highly unsatisfied Total
Tier 1 34 27 21 8 10 100
Tier 2 35 28 21 8 9 101
Tier 3 33 24 22 10 11 100
caste_in_new 5 - Highly satisfied 4 3 2 1 - Highly unsatisfied Total
General 36 27 21 7 9 100
OBC 32 26 21 10 11 100
SC 37 24 19 9 10 99
ST 27 27 24 10 12 100
Undisclosed/Not-Applicable 22 23 29 11 16 101
religion_in_new 5 - Highly satisfied 4 3 2 1 - Highly unsatisfied Total
Hindu 41 29 19 6 6 101
Muslim 13 17 26 16 28 100
Christian 16 23 32 13 16 100
Others 30 26 23 11 11 101
Undisclosed/Irreligious 24 20 27 11 18 100
age_cb_new 5 - Highly satisfied 4 3 2 1 - Highly unsatisfied Total
Pre-Millennials 34 27 20 8 12 101
Millennials 34 26 21 8 11 100
Post-Millennials 33 26 23 9 9 100
sec_in_new 5 - Highly satisfied 4 3 2 1 - Highly unsatisfied Total
High SEC 35 27 20 8 9 99
Middle SEC 33 26 22 9 10 100
Low SEC 32 24 24 9 12 101
partisan_index 5 - Highly satisfied 4 3 2 1 - Highly unsatisfied Total
Strong Partisan 47 26 14 6 8 101
Moderate Partisan 27 29 24 11 9 100
Weak Partisan 20 25 29 11 15 100

1.3 Israel and Hamas

q9 n
Israel 41
Hamas 59
age_cb_new Israel Hamas
Pre-Millennials 32 68
Millennials 40 60
Post-Millennials 45 55
sec_in_new Israel Hamas
High SEC 38 62
Middle SEC 43 57
Low SEC 44 56
gender Israel Hamas
Male 37 63
Female 44 56
religion_in_new Israel Hamas
Hindu 34 66
Muslim 76 24
Christian 32 68
Others 42 58
Undisclosed/Irreligious 46 54
caste_in_new Israel Hamas
General 38 62
OBC 45 55
SC 43 57
ST 32 68
Undisclosed/Not-Applicable 46 54
geo_region_in_cb Israel Hamas
North & Central India 38 62
South India 47 53
East & Northeast India 37 63
West India 39 61
geo_citytier_in Israel Hamas
Tier 1 40 60
Tier 2 38 62
Tier 3 43 57
p_party_new Israel Hamas
BJP 31 69
INC 55 45
Others 51 49
Non-Identifier 46 54
partisan_index Israel Hamas
Strong Partisan 40 60
Moderate Partisan 39 61
Weak Partisan 43 57

1.4 cross border terrorism and restrain

q7_1 n
India must continue to show restraint in response to cross-border terrorism as it is practically the wisest way to deal with it 54
India must not think twice before authorising military action against its neighbours if cross-border terrorism threatens its security 46
age_cb_new India must continue to show restraint in response to cross-border terrorism as it is practically the wisest way to deal with it India must not think twice before authorising military action against its neighbours if cross-border terrorism threatens its security
Pre-Millennials 50 50
Millennials 54 46
Post-Millennials 56 44
sec_in_new India must continue to show restraint in response to cross-border terrorism as it is practically the wisest way to deal with it India must not think twice before authorising military action against its neighbours if cross-border terrorism threatens its security
High SEC 54 46
Middle SEC 53 47
Low SEC 55 45
gender India must continue to show restraint in response to cross-border terrorism as it is practically the wisest way to deal with it India must not think twice before authorising military action against its neighbours if cross-border terrorism threatens its security
Male 52 48
Female 56 44
religion_in_new India must continue to show restraint in response to cross-border terrorism as it is practically the wisest way to deal with it India must not think twice before authorising military action against its neighbours if cross-border terrorism threatens its security
Hindu 54 46
Muslim 55 45
Christian 57 43
Others 51 49
Undisclosed/Irreligious 55 45
caste_in_new India must continue to show restraint in response to cross-border terrorism as it is practically the wisest way to deal with it India must not think twice before authorising military action against its neighbours if cross-border terrorism threatens its security
General 52 48
OBC 56 44
SC 53 47
ST 57 43
Undisclosed/Not-Applicable 57 43
geo_region_in_cb India must continue to show restraint in response to cross-border terrorism as it is practically the wisest way to deal with it India must not think twice before authorising military action against its neighbours if cross-border terrorism threatens its security
North & Central India 53 47
South India 57 43
East & Northeast India 50 50
West India 54 46
geo_citytier_in India must continue to show restraint in response to cross-border terrorism as it is practically the wisest way to deal with it India must not think twice before authorising military action against its neighbours if cross-border terrorism threatens its security
Tier 1 54 46
Tier 2 54 46
Tier 3 54 46
p_party_new India must continue to show restraint in response to cross-border terrorism as it is practically the wisest way to deal with it India must not think twice before authorising military action against its neighbours if cross-border terrorism threatens its security
BJP 54 46
INC 57 43
Others 55 45
Non-Identifier 52 48
partisan_index India must continue to show restraint in response to cross-border terrorism as it is practically the wisest way to deal with it India must not think twice before authorising military action against its neighbours if cross-border terrorism threatens its security
Strong Partisan 56 44
Moderate Partisan 54 46
Weak Partisan 52 48

1.5 India pakistan cricket

##                                                                             q7_5
##  The Pakistan cricket team should be invited to play a bilateral series in India
##                India should not invite Pakistan to play bilateral cricket series
##                                                                            Total
##    n
##   53
##   47
##  100
p_party_new The Pakistan cricket team should be invited to play a bilateral series in India India should not invite Pakistan to play bilateral cricket series Total
BJP 46 54 100
INC 61 39 100
Others 57 43 100
Non-Identifier 59 41 100
age_cb_new The Pakistan cricket team should be invited to play a bilateral series in India India should not invite Pakistan to play bilateral cricket series Total
Pre-Millennials 50 50 100
Millennials 53 47 100
Post-Millennials 54 46 100
gender The Pakistan cricket team should be invited to play a bilateral series in India India should not invite Pakistan to play bilateral cricket series Total
Male 50 50 100
Female 56 44 100
caste_in_new The Pakistan cricket team should be invited to play a bilateral series in India India should not invite Pakistan to play bilateral cricket series Total
General 51 49 100
OBC 53 47 100
SC 52 48 100
ST 58 42 100
Undisclosed/Not-Applicable 57 43 100
religion_in_new The Pakistan cricket team should be invited to play a bilateral series in India India should not invite Pakistan to play bilateral cricket series Total
Hindu 49 51 100
Muslim 64 36 100
Christian 67 33 100
Others 52 48 100
Undisclosed/Irreligious 59 41 100
sec_in_new The Pakistan cricket team should be invited to play a bilateral series in India India should not invite Pakistan to play bilateral cricket series Total
High SEC 51 49 100
Middle SEC 54 46 100
Low SEC 53 47 100
partisan_index The Pakistan cricket team should be invited to play a bilateral series in India India should not invite Pakistan to play bilateral cricket series Total
Strong Partisan 48 52 100
Moderate Partisan 52 48 100
Weak Partisan 59 41 100

1.6 Indo Pak cross border they way Hamas

##    q10   n
##    Yes  37
##     No  63
##  Total 100
p_party_new Yes No Total
BJP 46 54 100
INC 30 70 100
Others 34 66 100
Non-Identifier 24 76 100
age_cb_new Yes No Total
Pre-Millennials 33 67 100
Millennials 38 62 100
Post-Millennials 39 61 100
gender Yes No Total
Male 40 60 100
Female 34 66 100
caste_in_new Yes No Total
General 39 61 100
OBC 35 65 100
SC 45 55 100
ST 35 65 100
Undisclosed/Not-Applicable 27 73 100
religion_in_new Yes No Total
Hindu 42 58 100
Muslim 24 76 100
Christian 24 76 100
Others 43 57 100
Undisclosed/Irreligious 22 78 100
sec_in_new Yes No Total
High SEC 39 61 100
Middle SEC 36 64 100
Low SEC 36 64 100
partisan_index Yes No Total
Strong Partisan 49 51 100
Moderate Partisan 34 66 100
Weak Partisan 25 75 100
geo_region_in_cb Yes No
North & Central India 41 59
South India 27 73
East & Northeast India 41 59
West India 41 59
geo_citytier_in Yes No Total
Tier 1 39 61 100
Tier 2 38 62 100
Tier 3 35 65 100

1.7 Nuclear

q11a n
Yes 65
No 35
p_party_new Yes No Total
BJP 68 32 100
INC 64 36 100
Others 62 38 100
Non-Identifier 55 45 100
gender Yes No Total
Male 68 32 100
Female 61 39 100
religion_in_new Yes No Total
Hindu 66 34 100
Muslim 56 44 100
Christian 63 37 100
Others 68 32 100
Undisclosed/Irreligious 63 37 100
religion_in_new Yes No Total
Hindu 2393 1181 3574
Muslim 193 151 344
Christian 125 71 196
Others 170 76 246
Undisclosed/Irreligious 146 75 221
caste_in_new Yes No Total
General 67 33 100
OBC 60 40 100
SC 70 30 100
ST 67 33 100
Undisclosed/Not-Applicable 58 42 100
age_cb_new Yes No Total
Pre-Millennials 71 29 100
Millennials 67 33 100
Post-Millennials 61 39 100
geo_region_in_cb Yes No Total
North & Central India 67 33 100
South India 61 39 100
East & Northeast India 64 36 100
West India 66 34 100
age_cb_new Yes No Total
Pre-Millennials 71 29 100
Millennials 67 33 100
Post-Millennials 61 39 100
geo_citytier_in Yes No Total
Tier 1 67 33 100
Tier 2 64 36 100
Tier 3 63 37 100
q11b n
India has a superior military and it can win the war without using the bomb 29
My moral conscience does not allow me to support the usage of the nuclear bomb 29
My religious values do not allow me to support the usage of the bomb 10
If India uses the bomb, it might prompt Pakistan to use the bomb in return 9
If India uses the bomb, it sets a bad precedent for conflicts involving other countries 8
If India uses the bomb, it loses its moral authority to speak in global politics 9
If India uses the bomb, it will turn the world public opinion against India 7
p_party_new India has a superior military and it can win the war without using the bomb My moral conscience does not allow me to support the usage of the nuclear bomb My religious values do not allow me to support the usage of the bomb If India uses the bomb, it might prompt Pakistan to use the bomb in return If India uses the bomb, it sets a bad precedent for conflicts involving other countries If India uses the bomb, it loses its moral authority to speak in global politics If India uses the bomb, it will turn the world public opinion against India Total
BJP 37 24 8 8 8 9 6 100
INC 24 30 11 11 9 8 7 100
Others 23 29 12 9 8 10 9 100
Non-Identifier 26 36 10 8 7 7 6 100
gender India has a superior military and it can win the war without using the bomb My moral conscience does not allow me to support the usage of the nuclear bomb My religious values do not allow me to support the usage of the bomb If India uses the bomb, it might prompt Pakistan to use the bomb in return If India uses the bomb, it sets a bad precedent for conflicts involving other countries If India uses the bomb, it loses its moral authority to speak in global politics If India uses the bomb, it will turn the world public opinion against India
Male 32 25 9 9 9 9 7
Female 27 32 11 8 8 8 6
religion_in_new India has a superior military and it can win the war without using the bomb My moral conscience does not allow me to support the usage of the nuclear bomb My religious values do not allow me to support the usage of the bomb If India uses the bomb, it might prompt Pakistan to use the bomb in return If India uses the bomb, it sets a bad precedent for conflicts involving other countries If India uses the bomb, it loses its moral authority to speak in global politics If India uses the bomb, it will turn the world public opinion against India
Hindu 33 27 8 8 8 9 7
Muslim 19 27 20 10 8 9 7
Christian 20 38 11 9 7 8 7
Others 30 24 9 15 8 9 5
Undisclosed/Irreligious 27 38 7 7 7 9 6
caste_in_new India has a superior military and it can win the war without using the bomb My moral conscience does not allow me to support the usage of the nuclear bomb My religious values do not allow me to support the usage of the bomb If India uses the bomb, it might prompt Pakistan to use the bomb in return If India uses the bomb, it sets a bad precedent for conflicts involving other countries If India uses the bomb, it loses its moral authority to speak in global politics If India uses the bomb, it will turn the world public opinion against India
General 31 29 9 8 8 8 7
OBC 27 28 11 9 8 9 7
SC 29 26 11 9 9 10 7
ST 26 29 7 8 11 10 8
Undisclosed/Not-Applicable 26 33 11 8 7 10 6
age_cb_new India has a superior military and it can win the war without using the bomb My moral conscience does not allow me to support the usage of the nuclear bomb My religious values do not allow me to support the usage of the bomb If India uses the bomb, it might prompt Pakistan to use the bomb in return If India uses the bomb, it sets a bad precedent for conflicts involving other countries If India uses the bomb, it loses its moral authority to speak in global politics If India uses the bomb, it will turn the world public opinion against India
Pre-Millennials 34 30 8 7 6 8 6
Millennials 30 29 10 8 7 8 8
Post-Millennials 26 28 11 10 9 9 7
geo_region_in_cb India has a superior military and it can win the war without using the bomb My moral conscience does not allow me to support the usage of the nuclear bomb My religious values do not allow me to support the usage of the bomb If India uses the bomb, it might prompt Pakistan to use the bomb in return If India uses the bomb, it sets a bad precedent for conflicts involving other countries If India uses the bomb, it loses its moral authority to speak in global politics If India uses the bomb, it will turn the world public opinion against India
North & Central India 31 27 9 10 8 9 6
South India 25 33 11 7 8 9 7
East & Northeast India 32 25 10 9 9 8 8
West India 31 27 11 9 7 8 7
age_cb_new India has a superior military and it can win the war without using the bomb My moral conscience does not allow me to support the usage of the nuclear bomb My religious values do not allow me to support the usage of the bomb If India uses the bomb, it might prompt Pakistan to use the bomb in return If India uses the bomb, it sets a bad precedent for conflicts involving other countries If India uses the bomb, it loses its moral authority to speak in global politics If India uses the bomb, it will turn the world public opinion against India
Pre-Millennials 34 30 8 7 6 8 6
Millennials 30 29 10 8 7 8 8
Post-Millennials 26 28 11 10 9 9 7
geo_citytier_in India has a superior military and it can win the war without using the bomb My moral conscience does not allow me to support the usage of the nuclear bomb My religious values do not allow me to support the usage of the bomb If India uses the bomb, it might prompt Pakistan to use the bomb in return If India uses the bomb, it sets a bad precedent for conflicts involving other countries If India uses the bomb, it loses its moral authority to speak in global politics If India uses the bomb, it will turn the world public opinion against India
Tier 1 28 29 10 9 8 9 7
Tier 2 33 27 8 9 8 9 7
Tier 3 28 29 12 8 8 9 7

2 pass and fail

## Warning: There were 3 warnings in `mutate()`.
## The first warning was:
## ℹ In argument: `not_passed_ac1_fail_pass =
##   as.numeric(not_passed_ac1_fail_pass)`.
## Caused by warning:
## ! NAs introduced by coercion
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 2 remaining warnings.
qc_fail_pass_new n
fail 16
pass 84
Total 100
age_cb fail pass Total
Pre-Millennials 19 81 100
Millennials 16 84 100
Post-Millennials 11 89 100
gender fail pass Total
Male 18 82 100
Female 13 87 100
not_passed_ac1_fail_pass n
fail 62
pass 38
Total 100
age_cb fail pass Total
Pre-Millennials 65 35 100
Millennials 64 36 100
Post-Millennials 57 43 100
gender fail pass Total
Male 64 36 100
Female 60 40 100
not_passed_qc_6_fail_pass n
fail 23
pass 77
Total 100
age_cb fail pass Total
Pre-Millennials 29 71 100
Millennials 23 77 100
Post-Millennials 13 87 100
gender fail pass Total
Male 24 76 100
Female 21 79 100
not_passed_qc_3_fail_pass n
fail 37
pass 63
Total 100
age_cb fail pass Total
Pre-Millennials 45 55 100
Millennials 34 66 100
Post-Millennials 29 71 100
gender fail pass Total
Male 41 59 100
Female 33 67 100

3 new regression

3.1 q9

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## 
## Call:
## glm(formula = q9 ~ partisan_index + gender + age_cb_new + geo_citytier_in + 
##     p_party_new + geo_region_in_cb_new + religion_in_new, family = binomial, 
##     data = ., weights = weight)
## 
## Coefficients:
##                    Estimate Std. Error z value Pr(>|z|)    
## intercept         -0.860252   0.097573  -8.817  < 2e-16 ***
## Moderate Partisan -0.133452   0.049003  -2.723  0.00646 ** 
## Weak Partisan     -0.234856   0.051230  -4.584 4.55e-06 ***
## Millenial          0.262691   0.038954   6.744 1.55e-11 ***
## Post Millenial     0.275858   0.057538   4.794 1.63e-06 ***
## Female             0.509285   0.056742   8.975  < 2e-16 ***
## Tier 2            -0.064783   0.049713  -1.303  0.19253    
## Tier 3            -0.019653   0.044934  -0.437  0.66183    
## BJP               -0.560156   0.049205 -11.384  < 2e-16 ***
## INC               -0.004116   0.067385  -0.061  0.95130    
## South India        0.085160   0.044708   1.905  0.05680 .  
## Hindu             -0.081571   0.050687  -1.609  0.10755    
## Muslim             1.465812   0.074481  19.680  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 16955  on 12543  degrees of freedom
## Residual deviance: 15670  on 12531  degrees of freedom
## AIC: 15433
## 
## Number of Fisher Scoring iterations: 4
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Observations 12544
Dependent variable q9
Type Generalized linear model
Family binomial
Link logit
𝛘²(12) 1284.93
Pseudo-R² (Cragg-Uhler) 0.14
Pseudo-R² (McFadden) 0.08
AIC 15433.46
BIC 15530.14
exp(Est.) 2.5% 97.5% z val. p
intercept 0.42 0.35 0.51 -8.82 0.00
Moderate Partisan 0.88 0.79 0.96 -2.72 0.01
Weak Partisan 0.79 0.72 0.87 -4.58 0.00
Millenial 1.30 1.20 1.40 6.74 0.00
Post Millenial 1.32 1.18 1.47 4.79 0.00
Female 1.66 1.49 1.86 8.98 0.00
Tier 2 0.94 0.85 1.03 -1.30 0.19
Tier 3 0.98 0.90 1.07 -0.44 0.66
BJP 0.57 0.52 0.63 -11.38 0.00
INC 1.00 0.87 1.14 -0.06 0.95
South India 1.09 1.00 1.19 1.90 0.06
Hindu 0.92 0.83 1.02 -1.61 0.11
Muslim 4.33 3.74 5.01 19.68 0.00
Standard errors: MLE
## Warning in vif.default(model_IH): No intercept: vifs may not be sensible.
##                           GVIF Df GVIF^(1/(2*Df))
## partisan_index        3.178792  2        1.335259
## gender               10.384228  1        3.222457
## age_cb_new            6.613261  2        1.603629
## geo_citytier_in       2.745666  2        1.287247
## p_party_new           3.688204  2        1.385810
## geo_region_in_cb_new  1.558872  1        1.248548
## religion_in_new       5.762756  2        1.549379

3.2 q10

Q10. If a terrorist outfit based in Pakistan attacks Indian civilians the way Hamas attacked Israel, which leads to a war with Pakistan, would you support using a nuclear bomb against Pakistan? 1.Yes 2.No

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## 
## Call:
## glm(formula = q10 ~ partisan_index + gender + age_cb_new + geo_citytier_in + 
##     p_party_new + geo_region_in_cb_new + religion_in_new, family = binomial, 
##     data = ., weights = weight)
## 
## Coefficients:
##                   Estimate Std. Error z value Pr(>|z|)    
## intercept         -1.13167    0.11643  -9.719  < 2e-16 ***
## Moderate Partisan -0.59265    0.04761 -12.448  < 2e-16 ***
## Weak Partisan     -0.90587    0.05255 -17.239  < 2e-16 ***
## Millenial         -0.12360    0.03907  -3.164 0.001557 ** 
## Post Millenial     0.29382    0.05698   5.156 2.52e-07 ***
## Female             0.39690    0.05651   7.024 2.16e-12 ***
## Tier 2            -0.11965    0.04940  -2.422 0.015444 *  
## Tier 3            -0.12711    0.04522  -2.811 0.004940 ** 
## BJP                0.06091    0.07083   0.860 0.389816    
## INC                0.23316    0.07034   3.315 0.000918 ***
## South India        0.40139    0.04686   8.565  < 2e-16 ***
## Hindu              0.35893    0.07834   4.582 4.61e-06 ***
## Muslim             0.69465    0.06996   9.929  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 16575  on 12543  degrees of freedom
## Residual deviance: 15558  on 12531  degrees of freedom
## AIC: 15395
## 
## Number of Fisher Scoring iterations: 4
##  dbl+lbl [1:12544] 3, 1, 3, 1, 1, 1, 3, 1, 1, 1, 2, 3, 1, 3, 2, 1, 2, 3, 1,...
##  @ labels: Named num [1:3] 1 2 3
##   ..- attr(*, "names")= chr [1:3] "BJP" "INC" "Others"
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Observations 12544
Dependent variable q10
Type Generalized linear model
Family binomial
Link logit
𝛘²(12) 1016.79
Pseudo-R² (Cragg-Uhler) 0.10
Pseudo-R² (McFadden) 0.06
AIC 15395.38
BIC 15492.06
exp(Est.) 2.5% 97.5% z val. p
intercept 0.32 0.26 0.41 -9.72 0.00
Moderate Partisan 0.55 0.50 0.61 -12.45 0.00
Weak Partisan 0.40 0.36 0.45 -17.24 0.00
Millenial 0.88 0.82 0.95 -3.16 0.00
Post Millenial 1.34 1.20 1.50 5.16 0.00
Female 1.49 1.33 1.66 7.02 0.00
Tier 2 0.89 0.81 0.98 -2.42 0.02
Tier 3 0.88 0.81 0.96 -2.81 0.00
BJP 1.06 0.93 1.22 0.86 0.39
INC 1.26 1.10 1.45 3.31 0.00
South India 1.49 1.36 1.64 8.57 0.00
Hindu 1.43 1.23 1.67 4.58 0.00
Muslim 2.00 1.75 2.30 9.93 0.00
Standard errors: MLE
## Warning in vif.default(model_q10new): No intercept: vifs may not be sensible.
##                           GVIF Df GVIF^(1/(2*Df))
## partisan_index        2.704851  2        1.282436
## gender                9.975006  1        3.158323
## age_cb_new            6.413173  2        1.591359
## geo_citytier_in       2.685702  2        1.280161
## p_party_new          15.133249  2        1.972346
## geo_region_in_cb_new  4.493133  1        2.119701
## religion_in_new      12.484970  2        1.879736

3.3 q11a

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## 
## Call:
## glm(formula = q11a ~ partisan_index + gender + age_cb_new + geo_citytier_in + 
##     p_party_new + geo_region_in_cb_new + religion_in_new, family = binomial, 
##     data = ., weights = weight)
## 
## Coefficients:
##                    Estimate Std. Error z value Pr(>|z|)    
## intercept          1.667036   0.135886  12.268  < 2e-16 ***
## Moderate Partisan -0.358490   0.077152  -4.647 3.38e-06 ***
## Weak Partisan     -0.457048   0.088477  -5.166 2.40e-07 ***
## Millenial         -0.307633   0.063085  -4.876 1.08e-06 ***
## Post Millenial    -0.141902   0.098445  -1.441  0.14946    
## Female            -0.418952   0.096448  -4.344 1.40e-05 ***
## Tier 2            -0.129437   0.079654  -1.625  0.10417    
## Tier 3            -0.126155   0.073325  -1.720  0.08534 .  
## BJP                0.007252   0.118621   0.061  0.95125    
## INC               -0.149316   0.080179  -1.862  0.06256 .  
## South India       -0.145684   0.078747  -1.850  0.06431 .  
## Hindu             -0.354557   0.120897  -2.933  0.00336 ** 
## Muslim             0.111275   0.092713   1.200  0.23006    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 6069.1  on 4580  degrees of freedom
## Residual deviance: 5926.0  on 4568  degrees of freedom
##   (7963 observations deleted due to missingness)
## AIC: 5763.4
## 
## Number of Fisher Scoring iterations: 4
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Observations 4581 (7963 missing obs. deleted)
Dependent variable q11a
Type Generalized linear model
Family binomial
Link logit
𝛘²(12) 143.09
Pseudo-R² (Cragg-Uhler) 0.04
Pseudo-R² (McFadden) 0.02
AIC 5763.43
BIC 5847.02
exp(Est.) 2.5% 97.5% z val. p
intercept 5.30 4.06 6.91 12.27 0.00
Moderate Partisan 0.70 0.60 0.81 -4.65 0.00
Weak Partisan 0.63 0.53 0.75 -5.17 0.00
Millenial 0.74 0.65 0.83 -4.88 0.00
Post Millenial 0.87 0.72 1.05 -1.44 0.15
Female 0.66 0.54 0.79 -4.34 0.00
Tier 2 0.88 0.75 1.03 -1.62 0.10
Tier 3 0.88 0.76 1.02 -1.72 0.09
BJP 1.01 0.80 1.27 0.06 0.95
INC 0.86 0.74 1.01 -1.86 0.06
South India 0.86 0.74 1.01 -1.85 0.06
Hindu 0.70 0.55 0.89 -2.93 0.00
Muslim 1.12 0.93 1.34 1.20 0.23
Standard errors: MLE
## Warning in vif.default(model_q11anew): No intercept: vifs may not be sensible.
##                          GVIF Df GVIF^(1/(2*Df))
## partisan_index       2.477075  2        1.254541
## gender               9.922997  1        3.150079
## age_cb_new           7.609198  2        1.660867
## geo_citytier_in      2.689619  2        1.280627
## p_party_new          2.861006  2        1.300558
## geo_region_in_cb_new 1.372812  1        1.171670
## religion_in_new      1.556786  2        1.117010

##Q11b wider morality

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## 
## Call:
## glm(formula = q11b ~ partisan_index + gender + age_cb_new + geo_citytier_in + 
##     p_party_new + geo_region_in_cb_new + religion_in_new, family = binomial, 
##     data = ., weights = weight, control = list(maxit = 1000))
## 
## Coefficients:
##                    Estimate Std. Error z value Pr(>|z|)    
## intercept         -0.391320   0.114755  -3.410 0.000650 ***
## Moderate Partisan  0.019479   0.059272   0.329 0.742429    
## Weak Partisan      0.166234   0.060265   2.758 0.005809 ** 
## Millenial          0.278492   0.046580   5.979 2.25e-09 ***
## Post Millenial    -0.035387   0.065208  -0.543 0.587354    
## Female            -0.007652   0.064795  -0.118 0.905992    
## Tier 2            -0.146843   0.060054  -2.445 0.014478 *  
## Tier 3             0.005745   0.053738   0.107 0.914864    
## BJP               -0.205364   0.059476  -3.453 0.000555 ***
## INC               -0.106922   0.076266  -1.402 0.160928    
## South India        0.164716   0.051753   3.183 0.001459 ** 
## Hindu             -0.224016   0.059743  -3.750 0.000177 ***
## Muslim             0.120007   0.077761   1.543 0.122760    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 10872  on 7962  degrees of freedom
## Residual deviance: 10676  on 7950  degrees of freedom
## AIC: 10711
## 
## Number of Fisher Scoring iterations: 4
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Observations 7963
Dependent variable q11b
Type Generalized linear model
Family binomial
Link logit
𝛘²(12) 196.58
Pseudo-R² (Cragg-Uhler) 0.03
Pseudo-R² (McFadden) 0.02
AIC 10710.92
BIC 10801.70
exp(Est.) 2.5% 97.5% z val. p
intercept 0.68 0.54 0.85 -3.41 0.00
Moderate Partisan 1.02 0.91 1.15 0.33 0.74
Weak Partisan 1.18 1.05 1.33 2.76 0.01
Millenial 1.32 1.21 1.45 5.98 0.00
Post Millenial 0.97 0.85 1.10 -0.54 0.59
Female 0.99 0.87 1.13 -0.12 0.91
Tier 2 0.86 0.77 0.97 -2.45 0.01
Tier 3 1.01 0.91 1.12 0.11 0.91
BJP 0.81 0.72 0.92 -3.45 0.00
INC 0.90 0.77 1.04 -1.40 0.16
South India 1.18 1.07 1.30 3.18 0.00
Hindu 0.80 0.71 0.90 -3.75 0.00
Muslim 1.13 0.97 1.31 1.54 0.12
Standard errors: MLE
## Warning in vif.default(model_q11bnew): No intercept: vifs may not be sensible.
##                           GVIF Df GVIF^(1/(2*Df))
## partisan_index        3.783372  2        1.394665
## gender               10.610002  1        3.257300
## age_cb_new            5.691013  2        1.544534
## geo_citytier_in       2.852790  2        1.299623
## p_party_new           3.461049  2        1.363961
## geo_region_in_cb_new  1.672051  1        1.293078
## religion_in_new       5.608673  2        1.538916

3.4 q11b with narrow morality

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## 
## Call:
## glm(formula = q11b_new ~ partisan_index + gender + age_cb_new + 
##     geo_citytier_in + p_party_new + geo_region_in_cb_new + religion_in_new, 
##     family = binomial, data = ., weights = weight, control = list(maxit = 1000))
## 
## Coefficients:
##                   Estimate Std. Error z value Pr(>|z|)    
## intercept         -0.86806    0.11810  -7.350 1.98e-13 ***
## Moderate Partisan  0.06799    0.06179   1.100 0.271183    
## Weak Partisan      0.32822    0.06185   5.307 1.12e-07 ***
## Millenial          0.30861    0.04798   6.432 1.26e-10 ***
## Post Millenial    -0.04216    0.06703  -0.629 0.529297    
## Female            -0.07739    0.06669  -1.160 0.245852    
## Tier 2            -0.15678    0.06229  -2.517 0.011831 *  
## Tier 3             0.01756    0.05506   0.319 0.749814    
## BJP               -0.17486    0.06150  -2.843 0.004464 ** 
## INC               -0.01150    0.07743  -0.149 0.881895    
## South India        0.12445    0.05280   2.357 0.018427 *  
## Hindu             -0.21425    0.06076  -3.526 0.000421 ***
## Muslim             0.14087    0.07807   1.804 0.071174 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 10476  on 7962  degrees of freedom
## Residual deviance: 10245  on 7950  degrees of freedom
## AIC: 10285
## 
## Number of Fisher Scoring iterations: 4
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Observations 7963
Dependent variable q11b_new
Type Generalized linear model
Family binomial
Link logit
𝛘²(12) 230.72
Pseudo-R² (Cragg-Uhler) 0.04
Pseudo-R² (McFadden) 0.02
AIC 10285.06
BIC 10375.83
exp(Est.) 2.5% 97.5% z val. p
intercept 0.42 0.33 0.53 -7.35 0.00
Moderate Partisan 1.07 0.95 1.21 1.10 0.27
Weak Partisan 1.39 1.23 1.57 5.31 0.00
Millenial 1.36 1.24 1.50 6.43 0.00
Post Millenial 0.96 0.84 1.09 -0.63 0.53
Female 0.93 0.81 1.05 -1.16 0.25
Tier 2 0.85 0.76 0.97 -2.52 0.01
Tier 3 1.02 0.91 1.13 0.32 0.75
BJP 0.84 0.74 0.95 -2.84 0.00
INC 0.99 0.85 1.15 -0.15 0.88
South India 1.13 1.02 1.26 2.36 0.02
Hindu 0.81 0.72 0.91 -3.53 0.00
Muslim 1.15 0.99 1.34 1.80 0.07
Standard errors: MLE
## Warning in vif.default(model_q11bnew1): No intercept: vifs may not be sensible.
##                           GVIF Df GVIF^(1/(2*Df))
## partisan_index        3.937963  2        1.408698
## gender               10.861709  1        3.295711
## age_cb_new            5.693421  2        1.544697
## geo_citytier_in       2.845901  2        1.298838
## p_party_new           3.333175  2        1.351184
## geo_region_in_cb_new  1.705197  1        1.305832
## religion_in_new       5.385638  2        1.523384

4 new set of regression

4.1 data wrangling

##  dbl+lbl [1:13816] 3, 1, 4, 4, 1, 1, 1, 4, 1, 1, 1, 2, 3, 1, 1, 3, 3, 2, 1,...
##  @ labels: Named num [1:4] 1 2 3 4
##   ..- attr(*, "names")= chr [1:4] "BJP" "INC" "Others" "Non-Identifier"

4.2 q9

##  Factor w/ 3 levels "1","2","3": 2 1 3 3 1 1 1 3 1 1 ...
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## 
## Call:
## glm(formula = q9 ~ partisan_index + age_cb_new + geo_citytier_in + 
##     p_party_new + geo_region_in_cb_new + religion_in_new, family = binomial, 
##     data = ., weights = weight)
## 
## Coefficients:
##                Estimate Std. Error z value Pr(>|z|)    
## intercept      -0.48064    0.10309  -4.662 3.13e-06 ***
## Partisan_Index -0.11518    0.02543  -4.529 5.92e-06 ***
## age             0.22916    0.02693   8.511  < 2e-16 ***
## Rest_Tiers     -0.05155    0.04001  -1.289   0.1975    
## BJP            -0.59208    0.04855 -12.195  < 2e-16 ***
## INC             0.01836    0.06707   0.274   0.7842    
## SE             -0.00148    0.03983  -0.037   0.9704    
## Hindu          -0.10103    0.05116  -1.975   0.0483 *  
## Muslim          1.44835    0.07602  19.053  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 16605  on 12433  degrees of freedom
## Residual deviance: 15441  on 12425  degrees of freedom
## AIC: 15280
## 
## Number of Fisher Scoring iterations: 4
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Observations 12434
Dependent variable q9
Type Generalized linear model
Family binomial
Link logit
𝛘²(8) 1164.14
Pseudo-R² (Cragg-Uhler) 0.13
Pseudo-R² (McFadden) 0.07
AIC 15280.34
BIC 15347.20
exp(Est.) 2.5% 97.5% z val. p
intercept 0.62 0.51 0.76 -4.66 0.00
Partisan_Index 0.89 0.85 0.94 -4.53 0.00
age 1.26 1.19 1.33 8.51 0.00
Rest_Tiers 0.95 0.88 1.03 -1.29 0.20
BJP 0.55 0.50 0.61 -12.20 0.00
INC 1.02 0.89 1.16 0.27 0.78
SE 1.00 0.92 1.08 -0.04 0.97
Hindu 0.90 0.82 1.00 -1.97 0.05
Muslim 4.26 3.67 4.94 19.05 0.00
Standard errors: MLE
## Warning in vif.default(model_q9_1): No intercept: vifs may not be sensible.
##                           GVIF Df GVIF^(1/(2*Df))
## partisan_index        7.356719  1        2.712327
## age_cb_new           11.225335  1        3.350423
## geo_citytier_in       2.653795  1        1.629047
## p_party_new           3.551306  2        1.372768
## geo_region_in_cb_new  1.919723  1        1.385541
## religion_in_new       5.800143  2        1.551886
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## 
## Call:
## glm(formula = q9 ~ partisan_index + age_cb_new + geo_citytier_in + 
##     p_party_new + geo_region_in_cb + religion_in_new, family = binomial, 
##     data = ., weights = weight)
## 
## Coefficients:
##                         Estimate Std. Error z value Pr(>|z|)    
## intercept              -0.510087   0.105958  -4.814 1.48e-06 ***
## Partisan_Index         -0.117944   0.025468  -4.631 3.64e-06 ***
## age                     0.229635   0.026983   8.511  < 2e-16 ***
## Rest_Tiers             -0.053113   0.040067  -1.326    0.185    
## BJP                    -0.581292   0.048719 -11.932  < 2e-16 ***
## INC                     0.009682   0.067189   0.144    0.885    
## South India             0.082099   0.050215   1.635    0.102    
## East & Northeast India -0.094430   0.058463  -1.615    0.106    
## West India              0.044267   0.054518   0.812    0.417    
## Hindu                  -0.084089   0.051498  -1.633    0.102    
## Muslim                  1.457529   0.076129  19.145  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 16605  on 12433  degrees of freedom
## Residual deviance: 15432  on 12423  degrees of freedom
## AIC: 15277
## 
## Number of Fisher Scoring iterations: 4
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Observations 12434
Dependent variable q9
Type Generalized linear model
Family binomial
Link logit
𝛘²(10) 1173.06
Pseudo-R² (Cragg-Uhler) 0.13
Pseudo-R² (McFadden) 0.07
AIC 15276.68
BIC 15358.39
exp(Est.) 2.5% 97.5% z val. p
intercept 0.60 0.49 0.74 -4.81 0.00
Partisan_Index 0.89 0.85 0.93 -4.63 0.00
age 1.26 1.19 1.33 8.51 0.00
Rest_Tiers 0.95 0.88 1.03 -1.33 0.18
BJP 0.56 0.51 0.62 -11.93 0.00
INC 1.01 0.89 1.15 0.14 0.89
South India 1.09 0.98 1.20 1.63 0.10
East & Northeast India 0.91 0.81 1.02 -1.62 0.11
West India 1.05 0.94 1.16 0.81 0.42
Hindu 0.92 0.83 1.02 -1.63 0.10
Muslim 4.30 3.70 4.99 19.15 0.00
Standard errors: MLE
## Warning in vif.default(model_q9_2): No intercept: vifs may not be sensible.
##                       GVIF Df GVIF^(1/(2*Df))
## partisan_index    7.371162  1        2.714988
## age_cb_new       11.263001  1        3.356040
## geo_citytier_in   2.658315  1        1.630434
## p_party_new       3.600242  2        1.377472
## geo_region_in_cb  3.135560  3        1.209816
## religion_in_new   5.876445  2        1.556964
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## 
## Call:
## glm(formula = q9 ~ partisan_index + age_cb_new + geo_citytier_in + 
##     p_party_new + hub_dist + religion_in_new, family = binomial, 
##     data = ., weights = weight)
## 
## Coefficients:
##                  Estimate Std. Error z value Pr(>|z|)    
## intercept      -4.555e-01  1.076e-01  -4.233 2.30e-05 ***
## Partisan_Index -1.153e-01  2.543e-02  -4.535 5.76e-06 ***
## age             2.289e-01  2.693e-02   8.501  < 2e-16 ***
## Rest_Tiers     -4.853e-02  4.020e-02  -1.207   0.2274    
## BJP            -5.976e-01  4.855e-02 -12.309  < 2e-16 ***
## INC             1.880e-02  6.707e-02   0.280   0.7792    
## Distance       -2.407e-05  3.572e-05  -0.674   0.5004    
## Hindu          -1.025e-01  5.119e-02  -2.003   0.0452 *  
## Muslim          1.447e+00  7.601e-02  19.035  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 16605  on 12433  degrees of freedom
## Residual deviance: 15440  on 12425  degrees of freedom
## AIC: 15280
## 
## Number of Fisher Scoring iterations: 4
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Observations 12434
Dependent variable q9
Type Generalized linear model
Family binomial
Link logit
𝛘²(8) 1164.59
Pseudo-R² (Cragg-Uhler) 0.13
Pseudo-R² (McFadden) 0.07
AIC 15280.43
BIC 15347.28
exp(Est.) 2.5% 97.5% z val. p
intercept 0.63 0.51 0.78 -4.23 0.00
Partisan_Index 0.89 0.85 0.94 -4.54 0.00
age 1.26 1.19 1.33 8.50 0.00
Rest_Tiers 0.95 0.88 1.03 -1.21 0.23
BJP 0.55 0.50 0.61 -12.31 0.00
INC 1.02 0.89 1.16 0.28 0.78
Distance 1.00 1.00 1.00 -0.67 0.50
Hindu 0.90 0.82 1.00 -2.00 0.05
Muslim 4.25 3.66 4.93 19.04 0.00
Standard errors: MLE
## Warning in vif.default(model_q9_3): No intercept: vifs may not be sensible.
##                      GVIF Df GVIF^(1/(2*Df))
## partisan_index   7.354956  1        2.712002
## age_cb_new      11.226722  1        3.350630
## geo_citytier_in  2.680762  1        1.637303
## p_party_new      3.554333  2        1.373060
## hub_dist         4.254037  1        2.062532
## religion_in_new  5.804994  2        1.552210
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## 
## Call:
## glm(formula = q9 ~ partisan_index + age_cb_new + geo_citytier_in + 
##     p_party_new + bordering + religion_in_new, family = binomial, 
##     data = ., weights = weight)
## 
## Coefficients:
##                Estimate Std. Error z value Pr(>|z|)    
## intercept      -0.27919    0.17712  -1.576   0.1150    
## Partisan_Index -0.11534    0.02543  -4.536 5.73e-06 ***
## age             0.22910    0.02693   8.507  < 2e-16 ***
## Rest_Tiers     -0.06511    0.04113  -1.583   0.1134    
## BJP            -0.59635    0.04788 -12.454  < 2e-16 ***
## INC             0.01671    0.06708   0.249   0.8032    
## bordering      -0.09959    0.07180  -1.387   0.1654    
## Hindu          -0.10140    0.05114  -1.983   0.0474 *  
## Muslim          1.44795    0.07598  19.056  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 16605  on 12433  degrees of freedom
## Residual deviance: 15439  on 12425  degrees of freedom
## AIC: 15279
## 
## Number of Fisher Scoring iterations: 4
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Observations 12434
Dependent variable q9
Type Generalized linear model
Family binomial
Link logit
𝛘²(8) 1166.05
Pseudo-R² (Cragg-Uhler) 0.13
Pseudo-R² (McFadden) 0.07
AIC 15278.90
BIC 15345.75
exp(Est.) 2.5% 97.5% z val. p
intercept 0.76 0.53 1.07 -1.58 0.11
Partisan_Index 0.89 0.85 0.94 -4.54 0.00
age 1.26 1.19 1.33 8.51 0.00
Rest_Tiers 0.94 0.86 1.02 -1.58 0.11
BJP 0.55 0.50 0.61 -12.45 0.00
INC 1.02 0.89 1.16 0.25 0.80
bordering 0.91 0.79 1.04 -1.39 0.17
Hindu 0.90 0.82 1.00 -1.98 0.05
Muslim 4.25 3.67 4.94 19.06 0.00
Standard errors: MLE
## Warning in vif.default(model_q9_4): No intercept: vifs may not be sensible.
##                      GVIF Df GVIF^(1/(2*Df))
## partisan_index   7.354025  1        2.711831
## age_cb_new      11.227881  1        3.350803
## geo_citytier_in  2.804248  1        1.674589
## p_party_new      3.437414  2        1.361626
## bordering       51.923818  1        7.205818
## religion_in_new  5.792726  2        1.551389

4.2.1 combined q9

So this is integrated table to compare the coefficients of various type of regression model.

## 
## Q9
## ==================================================================
##                                 Dependent variable:               
##                   ------------------------------------------------
##                                          q9                       
##                         (1)          (2)        (3)        (4)    
## ------------------------------------------------------------------
## intercept            -0.481***    -0.510***  -0.456***    -0.279  
##                       (0.103)      (0.106)    (0.108)    (0.177)  
##                                                                   
## Partisan_Index       -0.115***    -0.118***  -0.115***  -0.115*** 
##                       (0.025)      (0.025)    (0.025)    (0.025)  
##                                                                   
## age                  0.229***      0.230***   0.229***   0.229*** 
##                       (0.027)      (0.027)    (0.027)    (0.027)  
##                                                                   
## Rest_Tiers            -0.052        -0.053     -0.049     -0.065  
##                       (0.040)      (0.040)    (0.040)    (0.041)  
##                                                                   
## BJP                  -0.592***    -0.581***  -0.598***  -0.596*** 
##                       (0.049)      (0.049)    (0.049)    (0.048)  
##                                                                   
## INC                    0.018        0.010      0.019      0.017   
##                       (0.067)      (0.067)    (0.067)    (0.067)  
##                                                                   
## SE                    -0.001                                      
##                       (0.040)                                     
##                                                                   
## South India                         0.082                         
##                                    (0.050)                        
##                                                                   
## East              Northeast India              -0.094             
##                                    (0.058)                        
##                                                                   
## West India                          0.044                         
##                                    (0.055)                        
##                                                                   
## Distance                                      -0.00002            
##                                              (0.00004)            
##                                                                   
## bordering                                                 -0.100  
##                                                          (0.072)  
##                                                                   
## Hindu                -0.101**       -0.084    -0.103**   -0.101** 
##                       (0.051)      (0.051)    (0.051)    (0.051)  
##                                                                   
## Muslim               1.448***      1.458***   1.447***   1.448*** 
##                       (0.076)      (0.076)    (0.076)    (0.076)  
##                                                                   
## ------------------------------------------------------------------
## Observations          12,434        12,434     12,434     12,434  
## Log Likelihood      -7,631.171    -7,627.342 -7,631.213 -7,630.448
## Akaike Inf. Crit.   15,280.340    15,276.680 15,280.430 15,278.900
## ==================================================================
## Note:                                  *p<0.1; **p<0.05; ***p<0.01

4.3 set 1

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## 
## Call:
## glm(formula = q10 ~ partisan_index + age_cb_new + geo_citytier_in + 
##     p_party_new + geo_region_in_cb_new + religion_in_new, family = binomial, 
##     data = ., weights = weight)
## 
## Coefficients:
##                Estimate Std. Error z value Pr(>|z|)    
## intercept      -0.17354    0.10386  -1.671  0.09474 .  
## Partisan_Index -0.47869    0.02589 -18.488  < 2e-16 ***
## age             0.17814    0.02693   6.615 3.73e-11 ***
## Rest_Tiers     -0.11174    0.03997  -2.795  0.00518 ** 
## BJP             0.19445    0.04835   4.022 5.78e-05 ***
## INC            -0.07058    0.07019  -1.006  0.31460    
## SE             -0.23225    0.04004  -5.800 6.63e-09 ***
## Hindu           0.34646    0.05463   6.342 2.26e-10 ***
## Muslim         -0.39098    0.08003  -4.885 1.03e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 16283  on 12433  degrees of freedom
## Residual deviance: 15355  on 12425  degrees of freedom
## AIC: 15260
## 
## Number of Fisher Scoring iterations: 4
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Observations 12434
Dependent variable q10
Type Generalized linear model
Family binomial
Link logit
𝛘²(8) 928.33
Pseudo-R² (Cragg-Uhler) 0.10
Pseudo-R² (McFadden) 0.06
AIC 15259.63
BIC 15326.48
exp(Est.) 2.5% 97.5% z val. p
intercept 0.84 0.69 1.03 -1.67 0.09
Partisan_Index 0.62 0.59 0.65 -18.49 0.00
age 1.19 1.13 1.26 6.61 0.00
Rest_Tiers 0.89 0.83 0.97 -2.80 0.01
BJP 1.21 1.10 1.34 4.02 0.00
INC 0.93 0.81 1.07 -1.01 0.31
SE 0.79 0.73 0.86 -5.80 0.00
Hindu 1.41 1.27 1.57 6.34 0.00
Muslim 0.68 0.58 0.79 -4.89 0.00
Standard errors: MLE
## Warning in vif.default(model_q10_1): No intercept: vifs may not be sensible.
##                           GVIF Df GVIF^(1/(2*Df))
## partisan_index        6.708071  1        2.589994
## age_cb_new           11.074052  1        3.327770
## geo_citytier_in       2.612881  1        1.616441
## p_party_new           4.079882  2        1.421222
## geo_region_in_cb_new  1.818259  1        1.348428
## religion_in_new       7.179438  2        1.636902
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## 
## Call:
## glm(formula = q10 ~ partisan_index + age_cb_new + geo_citytier_in + 
##     p_party_new + geo_region_in_cb + religion_in_new, family = binomial, 
##     data = ., weights = weight)
## 
## Coefficients:
##                        Estimate Std. Error z value Pr(>|z|)    
## intercept              -0.18049    0.10684  -1.689 0.091132 .  
## Partisan_Index         -0.47779    0.02599 -18.385  < 2e-16 ***
## age                     0.18281    0.02702   6.767 1.32e-11 ***
## Rest_Tiers             -0.10444    0.04008  -2.606 0.009163 ** 
## BJP                     0.16571    0.04861   3.409 0.000652 ***
## INC                    -0.04374    0.07055  -0.620 0.535297    
## South India            -0.38592    0.05192  -7.433 1.06e-13 ***
## East & Northeast India  0.05704    0.05653   1.009 0.312944    
## West India              0.09258    0.05343   1.733 0.083137 .  
## Hindu                   0.31421    0.05504   5.709 1.14e-08 ***
## Muslim                 -0.41731    0.08042  -5.189 2.11e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 16283  on 12433  degrees of freedom
## Residual deviance: 15300  on 12423  degrees of freedom
## AIC: 15222
## 
## Number of Fisher Scoring iterations: 4
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Observations 12434
Dependent variable q10
Type Generalized linear model
Family binomial
Link logit
𝛘²(10) 982.76
Pseudo-R² (Cragg-Uhler) 0.10
Pseudo-R² (McFadden) 0.06
AIC 15222.03
BIC 15303.74
exp(Est.) 2.5% 97.5% z val. p
intercept 0.83 0.68 1.03 -1.69 0.09
Partisan_Index 0.62 0.59 0.65 -18.38 0.00
age 1.20 1.14 1.27 6.77 0.00
Rest_Tiers 0.90 0.83 0.97 -2.61 0.01
BJP 1.18 1.07 1.30 3.41 0.00
INC 0.96 0.83 1.10 -0.62 0.54
South India 0.68 0.61 0.75 -7.43 0.00
East & Northeast India 1.06 0.95 1.18 1.01 0.31
West India 1.10 0.99 1.22 1.73 0.08
Hindu 1.37 1.23 1.53 5.71 0.00
Muslim 0.66 0.56 0.77 -5.19 0.00
Standard errors: MLE
## Warning in vif.default(model_q10_2): No intercept: vifs may not be sensible.
##                       GVIF Df GVIF^(1/(2*Df))
## partisan_index    6.728810  1        2.593995
## age_cb_new       11.090504  1        3.330241
## geo_citytier_in   2.610539  1        1.615716
## p_party_new       4.143417  2        1.426723
## geo_region_in_cb  2.917674  3        1.195380
## religion_in_new   7.271457  2        1.642122
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## 
## Call:
## glm(formula = q10 ~ partisan_index + age_cb_new + geo_citytier_in + 
##     p_party_new + hub_dist + religion_in_new, family = binomial, 
##     data = ., weights = weight)
## 
## Coefficients:
##                  Estimate Std. Error z value Pr(>|z|)    
## intercept      -7.679e-02  1.085e-01  -0.708   0.4790    
## Partisan_Index -4.774e-01  2.588e-02 -18.442  < 2e-16 ***
## age             1.774e-01  2.693e-02   6.587 4.50e-11 ***
## Rest_Tiers     -9.883e-02  4.012e-02  -2.463   0.0138 *  
## BJP             1.936e-01  4.834e-02   4.006 6.19e-05 ***
## INC            -6.838e-02  7.020e-02  -0.974   0.3300    
## Distance       -2.140e-04  3.601e-05  -5.944 2.79e-09 ***
## Hindu           3.412e-01  5.466e-02   6.243 4.29e-10 ***
## Muslim         -3.914e-01  8.004e-02  -4.889 1.01e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 16283  on 12433  degrees of freedom
## Residual deviance: 15353  on 12425  degrees of freedom
## AIC: 15256
## 
## Number of Fisher Scoring iterations: 4
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Observations 12434
Dependent variable q10
Type Generalized linear model
Family binomial
Link logit
𝛘²(8) 930.01
Pseudo-R² (Cragg-Uhler) 0.10
Pseudo-R² (McFadden) 0.06
AIC 15256.28
BIC 15323.13
exp(Est.) 2.5% 97.5% z val. p
intercept 0.93 0.75 1.15 -0.71 0.48
Partisan_Index 0.62 0.59 0.65 -18.44 0.00
age 1.19 1.13 1.26 6.59 0.00
Rest_Tiers 0.91 0.84 0.98 -2.46 0.01
BJP 1.21 1.10 1.33 4.01 0.00
INC 0.93 0.81 1.07 -0.97 0.33
Distance 1.00 1.00 1.00 -5.94 0.00
Hindu 1.41 1.26 1.57 6.24 0.00
Muslim 0.68 0.58 0.79 -4.89 0.00
Standard errors: MLE
## Warning in vif.default(model_q10_3): No intercept: vifs may not be sensible.
##                      GVIF Df GVIF^(1/(2*Df))
## partisan_index   6.703338  1        2.589080
## age_cb_new      11.072770  1        3.327577
## geo_citytier_in  2.631271  1        1.622119
## p_party_new      4.080832  2        1.421305
## hub_dist         4.072195  1        2.017968
## religion_in_new  7.186250  2        1.637290
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## 
## Call:
## glm(formula = q10 ~ partisan_index + age_cb_new + geo_citytier_in + 
##     p_party_new + bordering + religion_in_new, family = binomial, 
##     data = ., weights = weight)
## 
## Coefficients:
##                Estimate Std. Error z value Pr(>|z|)    
## intercept      -0.18551    0.17720  -1.047   0.2951    
## Partisan_Index -0.47535    0.02583 -18.400  < 2e-16 ***
## age             0.17997    0.02691   6.689 2.25e-11 ***
## Rest_Tiers     -0.13131    0.04110  -3.195   0.0014 ** 
## BJP             0.24243    0.04759   5.094 3.51e-07 ***
## INC            -0.07109    0.07004  -1.015   0.3101    
## bordering      -0.06102    0.07152  -0.853   0.3936    
## Hindu           0.35274    0.05456   6.465 1.01e-10 ***
## Muslim         -0.37922    0.07990  -4.746 2.07e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 16283  on 12433  degrees of freedom
## Residual deviance: 15388  on 12425  degrees of freedom
## AIC: 15296
## 
## Number of Fisher Scoring iterations: 4
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Observations 12434
Dependent variable q10
Type Generalized linear model
Family binomial
Link logit
𝛘²(8) 895.35
Pseudo-R² (Cragg-Uhler) 0.09
Pseudo-R² (McFadden) 0.05
AIC 15295.81
BIC 15362.66
exp(Est.) 2.5% 97.5% z val. p
intercept 0.83 0.59 1.18 -1.05 0.30
Partisan_Index 0.62 0.59 0.65 -18.40 0.00
age 1.20 1.14 1.26 6.69 0.00
Rest_Tiers 0.88 0.81 0.95 -3.20 0.00
BJP 1.27 1.16 1.40 5.09 0.00
INC 0.93 0.81 1.07 -1.02 0.31
bordering 0.94 0.82 1.08 -0.85 0.39
Hindu 1.42 1.28 1.58 6.46 0.00
Muslim 0.68 0.59 0.80 -4.75 0.00
Standard errors: MLE
## Warning in vif.default(model_q10_4): No intercept: vifs may not be sensible.
##                      GVIF Df GVIF^(1/(2*Df))
## partisan_index   6.692747  1        2.587034
## age_cb_new      11.087933  1        3.329855
## geo_citytier_in  2.773342  1        1.665335
## p_party_new      3.951039  2        1.409866
## bordering       51.381389  1        7.168081
## religion_in_new  7.182202  2        1.637059

4.3.1 combined q10 (1-4)

## 
## Q10
## =================================
## Statistic N Mean St. Dev. Min Max
## =================================
## 
## Q10
## ==================================================================
##                                 Dependent variable:               
##                   ------------------------------------------------
##                                         q10                       
##                         (1)          (2)        (3)        (4)    
## ------------------------------------------------------------------
## intercept             -0.174*      -0.180*     -0.077     -0.186  
##                       (0.104)      (0.107)    (0.108)    (0.177)  
##                                                                   
## Partisan_Index       -0.479***    -0.478***  -0.477***  -0.475*** 
##                       (0.026)      (0.026)    (0.026)    (0.026)  
##                                                                   
## age                  0.178***      0.183***   0.177***   0.180*** 
##                       (0.027)      (0.027)    (0.027)    (0.027)  
##                                                                   
## Rest_Tiers           -0.112***    -0.104***   -0.099**  -0.131*** 
##                       (0.040)      (0.040)    (0.040)    (0.041)  
##                                                                   
## BJP                  0.194***      0.166***   0.194***   0.242*** 
##                       (0.048)      (0.049)    (0.048)    (0.048)  
##                                                                   
## INC                   -0.071        -0.044     -0.068     -0.071  
##                       (0.070)      (0.071)    (0.070)    (0.070)  
##                                                                   
## SE                   -0.232***                                    
##                       (0.040)                                     
##                                                                   
## South India                       -0.386***                       
##                                    (0.052)                        
##                                                                   
## East              Northeast India              0.057              
##                                    (0.057)                        
##                                                                   
## West India                          0.093*                        
##                                    (0.053)                        
##                                                                   
## Distance                                     -0.0002***           
##                                              (0.00004)            
##                                                                   
## bordering                                                 -0.061  
##                                                          (0.072)  
##                                                                   
## Hindu                0.346***      0.314***   0.341***   0.353*** 
##                       (0.055)      (0.055)    (0.055)    (0.055)  
##                                                                   
## Muslim               -0.391***    -0.417***  -0.391***  -0.379*** 
##                       (0.080)      (0.080)    (0.080)    (0.080)  
##                                                                   
## ------------------------------------------------------------------
## Observations          12,434        12,434     12,434     12,434  
## Log Likelihood      -7,620.813    -7,600.015 -7,619.138 -7,638.903
## Akaike Inf. Crit.   15,259.620    15,222.030 15,256.280 15,295.810
## ==================================================================
## Note:                                  *p<0.1; **p<0.05; ***p<0.01

4.4 set 2

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## 
## Call:
## glm(formula = q10 ~ partisan_index + age_cb_new + geo_citytier_in + 
##     partisan_index_party + p_party_new + geo_region_in_cb_new + 
##     religion_in_new, family = binomial, data = ., weights = weight)
## 
## Coefficients:
##                Estimate Std. Error z value Pr(>|z|)    
## intercept      -0.31068    0.11263  -2.758  0.00581 ** 
## Partisan_Index -0.42232    0.03134 -13.478  < 2e-16 ***
## age             0.18072    0.02696   6.702 2.06e-11 ***
## Rest_Tiers     -0.11066    0.04000  -2.767  0.00566 ** 
## Partisan_bjp   -0.13402    0.04215  -3.179  0.00148 ** 
## BJP             0.50364    0.10854   4.640 3.48e-06 ***
## INC            -0.03512    0.07090  -0.495  0.62032    
## SE             -0.22884    0.04007  -5.711 1.13e-08 ***
## Hindu           0.35116    0.05461   6.430 1.27e-10 ***
## Muslim         -0.38041    0.07996  -4.757 1.96e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 16283  on 12433  degrees of freedom
## Residual deviance: 15344  on 12424  degrees of freedom
## AIC: 15252
## 
## Number of Fisher Scoring iterations: 4
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Observations 12434
Dependent variable q10
Type Generalized linear model
Family binomial
Link logit
𝛘²(9) 938.49
Pseudo-R² (Cragg-Uhler) 0.10
Pseudo-R² (McFadden) 0.06
AIC 15251.71
BIC 15325.99
exp(Est.) 2.5% 97.5% z val. p
intercept 0.73 0.59 0.91 -2.76 0.01
Partisan_Index 0.66 0.62 0.70 -13.48 0.00
age 1.20 1.14 1.26 6.70 0.00
Rest_Tiers 0.90 0.83 0.97 -2.77 0.01
Partisan_bjp 0.87 0.81 0.95 -3.18 0.00
BJP 1.65 1.34 2.05 4.64 0.00
INC 0.97 0.84 1.11 -0.50 0.62
SE 0.80 0.74 0.86 -5.71 0.00
Hindu 1.42 1.28 1.58 6.43 0.00
Muslim 0.68 0.58 0.80 -4.76 0.00
Standard errors: MLE
## Warning in vif.default(model_q10_5): No intercept: vifs may not be sensible.
##                           GVIF Df GVIF^(1/(2*Df))
## partisan_index        9.866159  1        3.141044
## age_cb_new           11.091759  1        3.330429
## geo_citytier_in       2.612131  1        1.616209
## partisan_index_party 11.249422  1        3.354016
## p_party_new          21.729333  2        2.159045
## geo_region_in_cb_new  1.821451  1        1.349611
## religion_in_new       7.154792  2        1.635495
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## 
## Call:
## glm(formula = q10 ~ partisan_index + age_cb_new + geo_citytier_in + 
##     p_party_new + partisan_index_party + geo_region_in_cb + religion_in_new, 
##     family = binomial, data = ., weights = weight)
## 
## Coefficients:
##                         Estimate Std. Error z value Pr(>|z|)    
## intercept              -0.321491   0.115599  -2.781  0.00542 ** 
## Partisan_Index         -0.420408   0.031451 -13.367  < 2e-16 ***
## age                     0.185551   0.027050   6.860 6.90e-12 ***
## Rest_Tiers             -0.103151   0.040104  -2.572  0.01011 *  
## BJP                     0.480348   0.108925   4.410 1.03e-05 ***
## INC                    -0.007299   0.071280  -0.102  0.91845    
## Partisan_bjp           -0.136302   0.042263  -3.225  0.00126 ** 
## South India            -0.381758   0.051942  -7.350 1.99e-13 ***
## East & Northeast India  0.062092   0.056610   1.097  0.27271    
## West India              0.094521   0.053492   1.767  0.07723 .  
## Hindu                   0.318976   0.055024   5.797 6.75e-09 ***
## Muslim                 -0.406381   0.080340  -5.058 4.23e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 16283  on 12433  degrees of freedom
## Residual deviance: 15290  on 12422  degrees of freedom
## AIC: 15214
## 
## Number of Fisher Scoring iterations: 4
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Observations 12434
Dependent variable q10
Type Generalized linear model
Family binomial
Link logit
𝛘²(11) 993.22
Pseudo-R² (Cragg-Uhler) 0.10
Pseudo-R² (McFadden) 0.06
AIC 15213.82
BIC 15302.96
exp(Est.) 2.5% 97.5% z val. p
intercept 0.73 0.58 0.91 -2.78 0.01
Partisan_Index 0.66 0.62 0.70 -13.37 0.00
age 1.20 1.14 1.27 6.86 0.00
Rest_Tiers 0.90 0.83 0.98 -2.57 0.01
BJP 1.62 1.31 2.00 4.41 0.00
INC 0.99 0.86 1.14 -0.10 0.92
Partisan_bjp 0.87 0.80 0.95 -3.23 0.00
South India 0.68 0.62 0.76 -7.35 0.00
East & Northeast India 1.06 0.95 1.19 1.10 0.27
West India 1.10 0.99 1.22 1.77 0.08
Hindu 1.38 1.24 1.53 5.80 0.00
Muslim 0.67 0.57 0.78 -5.06 0.00
Standard errors: MLE
## Warning in vif.default(model_q10_6): No intercept: vifs may not be sensible.
##                           GVIF Df GVIF^(1/(2*Df))
## partisan_index        9.895268  1        3.145675
## age_cb_new           11.108764  1        3.332981
## geo_citytier_in       2.609546  1        1.615409
## p_party_new          21.927792  2        2.163957
## partisan_index_party 11.272607  1        3.357470
## geo_region_in_cb      2.924706  3        1.195860
## religion_in_new       7.246752  2        1.640725
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## 
## Call:
## glm(formula = q10 ~ partisan_index + age_cb_new + geo_citytier_in + 
##     p_party_new + partisan_index_party + bordering + religion_in_new, 
##     family = binomial, data = ., weights = weight)
## 
## Coefficients:
##                Estimate Std. Error z value Pr(>|z|)    
## intercept      -0.33492    0.18313  -1.829 0.067422 .  
## Partisan_Index -0.41659    0.03126 -13.326  < 2e-16 ***
## age             0.18261    0.02694   6.779 1.21e-11 ***
## Rest_Tiers     -0.12957    0.04113  -3.151 0.001629 ** 
## BJP             0.56438    0.10791   5.230 1.69e-07 ***
## INC            -0.03429    0.07073  -0.485 0.627836    
## Partisan_bjp   -0.13983    0.04209  -3.322 0.000893 ***
## bordering      -0.05681    0.07166  -0.793 0.427956    
## Hindu           0.35762    0.05454   6.557 5.51e-11 ***
## Muslim         -0.36827    0.07982  -4.614 3.95e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 16283  on 12433  degrees of freedom
## Residual deviance: 15376  on 12424  degrees of freedom
## AIC: 15287
## 
## Number of Fisher Scoring iterations: 4
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Observations 12434
Dependent variable q10
Type Generalized linear model
Family binomial
Link logit
𝛘²(9) 906.45
Pseudo-R² (Cragg-Uhler) 0.09
Pseudo-R² (McFadden) 0.05
AIC 15286.91
BIC 15361.20
exp(Est.) 2.5% 97.5% z val. p
intercept 0.72 0.50 1.02 -1.83 0.07
Partisan_Index 0.66 0.62 0.70 -13.33 0.00
age 1.20 1.14 1.27 6.78 0.00
Rest_Tiers 0.88 0.81 0.95 -3.15 0.00
BJP 1.76 1.42 2.17 5.23 0.00
INC 0.97 0.84 1.11 -0.48 0.63
Partisan_bjp 0.87 0.80 0.94 -3.32 0.00
bordering 0.94 0.82 1.09 -0.79 0.43
Hindu 1.43 1.28 1.59 6.56 0.00
Muslim 0.69 0.59 0.81 -4.61 0.00
Standard errors: MLE
## Warning in vif.default(model_q10_7): No intercept: vifs may not be sensible.
##                           GVIF Df GVIF^(1/(2*Df))
## partisan_index        9.843300  1        3.137403
## age_cb_new           11.105399  1        3.332476
## geo_citytier_in       2.772418  1        1.665058
## p_party_new          21.531632  2        2.154117
## partisan_index_party 11.247815  1        3.353776
## bordering            51.512449  1        7.177217
## religion_in_new       7.155096  2        1.635512
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## 
## Call:
## glm(formula = q10 ~ partisan_index + age_cb_new + geo_citytier_in + 
##     p_party_new + partisan_index_party + hub_dist + religion_in_new, 
##     family = binomial, data = ., weights = weight)
## 
## Coefficients:
##                  Estimate Std. Error z value Pr(>|z|)    
## intercept      -2.155e-01  1.170e-01  -1.841  0.06558 .  
## Partisan_Index -4.209e-01  3.133e-02 -13.434  < 2e-16 ***
## age             1.800e-01  2.696e-02   6.675 2.47e-11 ***
## Rest_Tiers     -9.786e-02  4.015e-02  -2.437  0.01479 *  
## BJP             5.035e-01  1.085e-01   4.640 3.48e-06 ***
## INC            -3.286e-02  7.091e-02  -0.463  0.64310    
## Partisan_bjp   -1.344e-01  4.215e-02  -3.188  0.00143 ** 
## distance       -2.112e-04  3.603e-05  -5.861 4.60e-09 ***
## Hindu           3.460e-01  5.464e-02   6.332 2.41e-10 ***
## Muslim         -3.808e-01  7.997e-02  -4.761 1.92e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 16283  on 12433  degrees of freedom
## Residual deviance: 15343  on 12424  degrees of freedom
## AIC: 15248
## 
## Number of Fisher Scoring iterations: 4
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Observations 12434
Dependent variable q10
Type Generalized linear model
Family binomial
Link logit
𝛘²(9) 940.22
Pseudo-R² (Cragg-Uhler) 0.10
Pseudo-R² (McFadden) 0.06
AIC 15248.30
BIC 15322.58
exp(Est.) 2.5% 97.5% z val. p
intercept 0.81 0.64 1.01 -1.84 0.07
Partisan_Index 0.66 0.62 0.70 -13.43 0.00
age 1.20 1.14 1.26 6.67 0.00
Rest_Tiers 0.91 0.84 0.98 -2.44 0.01
BJP 1.65 1.34 2.05 4.64 0.00
INC 0.97 0.84 1.11 -0.46 0.64
Partisan_bjp 0.87 0.80 0.95 -3.19 0.00
distance 1.00 1.00 1.00 -5.86 0.00
Hindu 1.41 1.27 1.57 6.33 0.00
Muslim 0.68 0.58 0.80 -4.76 0.00
Standard errors: MLE
## Warning in vif.default(model_q10_8): No intercept: vifs may not be sensible.
##                           GVIF Df GVIF^(1/(2*Df))
## partisan_index        9.860005  1        3.140064
## age_cb_new           11.090594  1        3.330254
## geo_citytier_in       2.630725  1        1.621951
## p_party_new          21.726393  2        2.158971
## partisan_index_party 11.248985  1        3.353951
## hub_dist              4.077974  1        2.019399
## religion_in_new       7.161398  2        1.635873

4.4.1 combined q10 (5-8)

## 
## Q10
## =================================
## Statistic N Mean St. Dev. Min Max
## =================================
## 
## Q10
## ==================================================================
##                                 Dependent variable:               
##                   ------------------------------------------------
##                                         q10                       
##                         (1)          (2)        (3)        (4)    
## ------------------------------------------------------------------
## intercept            -0.311***    -0.321***   -0.335*    -0.215*  
##                       (0.113)      (0.116)    (0.183)    (0.117)  
##                                                                   
## Partisan_Index       -0.422***    -0.420***  -0.417***  -0.421*** 
##                       (0.031)      (0.031)    (0.031)    (0.031)  
##                                                                   
## age                  0.181***      0.186***   0.183***   0.180*** 
##                       (0.027)      (0.027)    (0.027)    (0.027)  
##                                                                   
## Rest_Tiers           -0.111***     -0.103**  -0.130***   -0.098** 
##                       (0.040)      (0.040)    (0.041)    (0.040)  
##                                                                   
## Partisan_bjp         -0.134***    -0.136***  -0.140***  -0.134*** 
##                       (0.042)      (0.042)    (0.042)    (0.042)  
##                                                                   
## South India                       -0.382***                       
##                                    (0.052)                        
##                                                                   
## East              Northeast India              0.062              
##                                    (0.057)                        
##                                                                   
## West India                          0.095*                        
##                                    (0.053)                        
##                                                                   
## bordering                                      -0.057             
##                                               (0.072)             
##                                                                   
## distance                                                -0.0002***
##                                                         (0.00004) 
##                                                                   
## BJP                  0.504***      0.480***   0.564***   0.504*** 
##                       (0.109)      (0.109)    (0.108)    (0.109)  
##                                                                   
## INC                   -0.035        -0.007     -0.034     -0.033  
##                       (0.071)      (0.071)    (0.071)    (0.071)  
##                                                                   
## SE                   -0.229***                                    
##                       (0.040)                                     
##                                                                   
## Hindu                0.351***      0.319***   0.358***   0.346*** 
##                       (0.055)      (0.055)    (0.055)    (0.055)  
##                                                                   
## Muslim               -0.380***    -0.406***  -0.368***  -0.381*** 
##                       (0.080)      (0.080)    (0.080)    (0.080)  
##                                                                   
## ------------------------------------------------------------------
## Observations          12,434        12,434     12,434     12,434  
## Log Likelihood      -7,615.853    -7,594.912 -7,633.457 -7,614.151
## Akaike Inf. Crit.   15,251.710    15,213.820 15,286.910 15,248.300
## ==================================================================
## Note:                                  *p<0.1; **p<0.05; ***p<0.01

4.5 set 3

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## 
## Call:
## glm(formula = q11a_1 ~ partisan_index + age_cb_new + geo_citytier_in + 
##     p_party_new + geo_region_in_cb_new + religion_in_new, family = binomial, 
##     data = ., weights = weight)
## 
## Coefficients:
##                Estimate Std. Error z value Pr(>|z|)    
## intercept       1.73190    0.17557   9.864  < 2e-16 ***
## Partisan_Index -0.26644    0.04261  -6.253 4.04e-10 ***
## age            -0.21433    0.04511  -4.752 2.02e-06 ***
## Rest_Tiers     -0.10652    0.06459  -1.649  0.09912 .  
## BJP             0.11965    0.07847   1.525  0.12733    
## INC             0.10857    0.12017   0.903  0.36626    
## SE             -0.15107    0.06481  -2.331  0.01975 *  
## Hindu          -0.10633    0.09348  -1.138  0.25532    
## Muslim         -0.41584    0.14098  -2.950  0.00318 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 5967.2  on 4563  degrees of freedom
## Residual deviance: 5861.1  on 4555  degrees of freedom
## AIC: 5718.7
## 
## Number of Fisher Scoring iterations: 4
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Observations 4564
Dependent variable q11a_1
Type Generalized linear model
Family binomial
Link logit
𝛘²(8) 106.06
Pseudo-R² (Cragg-Uhler) 0.03
Pseudo-R² (McFadden) 0.02
AIC 5718.70
BIC 5776.54
exp(Est.) 2.5% 97.5% z val. p
intercept 5.65 4.01 7.97 9.86 0.00
Partisan_Index 0.77 0.70 0.83 -6.25 0.00
age 0.81 0.74 0.88 -4.75 0.00
Rest_Tiers 0.90 0.79 1.02 -1.65 0.10
BJP 1.13 0.97 1.31 1.52 0.13
INC 1.11 0.88 1.41 0.90 0.37
SE 0.86 0.76 0.98 -2.33 0.02
Hindu 0.90 0.75 1.08 -1.14 0.26
Muslim 0.66 0.50 0.87 -2.95 0.00
Standard errors: MLE
## Warning in vif.default(model_q11a_1): No intercept: vifs may not be sensible.
##                           GVIF Df GVIF^(1/(2*Df))
## partisan_index        6.381596  1        2.526182
## age_cb_new           12.406934  1        3.522348
## geo_citytier_in       2.612986  1        1.616473
## p_party_new           4.366042  2        1.445513
## geo_region_in_cb_new  1.731430  1        1.315838
## religion_in_new       8.352838  2        1.700038
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## 
## Call:
## glm(formula = q11a_1 ~ partisan_index + age_cb_new + geo_citytier_in + 
##     p_party_new + geo_region_in_cb + religion_in_new, family = binomial, 
##     data = ., weights = weight)
## 
## Coefficients:
##                        Estimate Std. Error z value Pr(>|z|)    
## intercept               1.79390    0.18041   9.944  < 2e-16 ***
## Partisan_Index         -0.26490    0.04272  -6.201 5.61e-10 ***
## age                    -0.21751    0.04524  -4.807 1.53e-06 ***
## Rest_Tiers             -0.11308    0.06470  -1.748  0.08051 .  
## BJP                     0.10834    0.07889   1.373  0.16966    
## INC                     0.11960    0.12048   0.993  0.32083    
## South India            -0.26971    0.08640  -3.122  0.00180 ** 
## East & Northeast India -0.08920    0.08899  -1.002  0.31617    
## West India             -0.08818    0.08487  -1.039  0.29885    
## Hindu                  -0.12391    0.09394  -1.319  0.18718    
## Muslim                 -0.42860    0.14128  -3.034  0.00242 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 5967.2  on 4563  degrees of freedom
## Residual deviance: 5856.8  on 4553  degrees of freedom
## AIC: 5717.4
## 
## Number of Fisher Scoring iterations: 4
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Observations 4564
Dependent variable q11a_1
Type Generalized linear model
Family binomial
Link logit
𝛘²(10) 110.35
Pseudo-R² (Cragg-Uhler) 0.03
Pseudo-R² (McFadden) 0.02
AIC 5717.42
BIC 5788.11
exp(Est.) 2.5% 97.5% z val. p
intercept 6.01 4.22 8.56 9.94 0.00
Partisan_Index 0.77 0.71 0.83 -6.20 0.00
age 0.80 0.74 0.88 -4.81 0.00
Rest_Tiers 0.89 0.79 1.01 -1.75 0.08
BJP 1.11 0.95 1.30 1.37 0.17
INC 1.13 0.89 1.43 0.99 0.32
South India 0.76 0.64 0.90 -3.12 0.00
East & Northeast India 0.91 0.77 1.09 -1.00 0.32
West India 0.92 0.78 1.08 -1.04 0.30
Hindu 0.88 0.73 1.06 -1.32 0.19
Muslim 0.65 0.49 0.86 -3.03 0.00
Standard errors: MLE
## Warning in vif.default(model_q11a_2): No intercept: vifs may not be sensible.
##                       GVIF Df GVIF^(1/(2*Df))
## partisan_index    6.405152  1        2.530840
## age_cb_new       12.467360  1        3.530915
## geo_citytier_in   2.618527  1        1.618186
## p_party_new       4.452907  2        1.452650
## geo_region_in_cb  2.842938  3        1.190222
## religion_in_new   8.438911  2        1.704400
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## 
## Call:
## glm(formula = q11a_1 ~ partisan_index + age_cb_new + geo_citytier_in + 
##     p_party_new + hub_dist + religion_in_new, family = binomial, 
##     data = ., weights = weight)
## 
## Coefficients:
##                  Estimate Std. Error z value Pr(>|z|)    
## intercept       1.750e+00  1.831e-01   9.561  < 2e-16 ***
## Partisan_Index -2.639e-01  4.258e-02  -6.197 5.74e-10 ***
## age            -2.146e-01  4.510e-02  -4.758 1.95e-06 ***
## Rest_Tiers     -1.013e-01  6.465e-02  -1.566  0.11729    
## BJP             1.279e-01  7.842e-02   1.631  0.10286    
## INC             1.088e-01  1.201e-01   0.906  0.36519    
## Distance       -9.791e-05  5.947e-05  -1.646  0.09968 .  
## Hindu          -1.093e-01  9.353e-02  -1.168  0.24277    
## Muslim         -4.151e-01  1.409e-01  -2.945  0.00323 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 5967.2  on 4563  degrees of freedom
## Residual deviance: 5863.8  on 4555  degrees of freedom
## AIC: 5722.2
## 
## Number of Fisher Scoring iterations: 4
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Observations 12434
Dependent variable q10
Type Generalized linear model
Family binomial
Link logit
𝛘²(8) 930.01
Pseudo-R² (Cragg-Uhler) 0.10
Pseudo-R² (McFadden) 0.06
AIC 15256.28
BIC 15323.13
exp(Est.) 2.5% 97.5% z val. p
intercept 0.93 0.75 1.15 -0.71 0.48
Partisan_Index 0.62 0.59 0.65 -18.44 0.00
age 1.19 1.13 1.26 6.59 0.00
Rest_Tiers 0.91 0.84 0.98 -2.46 0.01
BJP 1.21 1.10 1.33 4.01 0.00
INC 0.93 0.81 1.07 -0.97 0.33
Distance 1.00 1.00 1.00 -5.94 0.00
Hindu 1.41 1.26 1.57 6.24 0.00
Muslim 0.68 0.58 0.79 -4.89 0.00
Standard errors: MLE
## Warning in vif.default(model_q11a_3): No intercept: vifs may not be sensible.
##                      GVIF Df GVIF^(1/(2*Df))
## partisan_index   6.375037  1        2.524884
## age_cb_new      12.409201  1        3.522670
## geo_citytier_in  2.619481  1        1.618481
## p_party_new      4.365085  2        1.445434
## hub_dist         4.001093  1        2.000273
## religion_in_new  8.367648  2        1.700791
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## 
## Call:
## glm(formula = q11a_1 ~ partisan_index + age_cb_new + geo_citytier_in + 
##     p_party_new + bordering + religion_in_new, family = binomial, 
##     data = ., weights = weight)
## 
## Coefficients:
##                Estimate Std. Error z value Pr(>|z|)    
## intercept       1.45488    0.28299   5.141 2.73e-07 ***
## Partisan_Index -0.26434    0.04259  -6.206 5.42e-10 ***
## age            -0.21256    0.04507  -4.716 2.41e-06 ***
## Rest_Tiers     -0.09281    0.06664  -1.393   0.1638    
## BJP             0.14848    0.07759   1.914   0.0557 .  
## INC             0.10754    0.12009   0.895   0.3705    
## bordering       0.09493    0.11239   0.845   0.3983    
## Hindu          -0.10239    0.09337  -1.097   0.2728    
## Muslim         -0.41402    0.14091  -2.938   0.0033 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 5967.2  on 4563  degrees of freedom
## Residual deviance: 5865.8  on 4555  degrees of freedom
## AIC: 5726.8
## 
## Number of Fisher Scoring iterations: 4
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Observations 4564
Dependent variable q11a_1
Type Generalized linear model
Family binomial
Link logit
𝛘²(8) 101.35
Pseudo-R² (Cragg-Uhler) 0.03
Pseudo-R² (McFadden) 0.02
AIC 5726.80
BIC 5784.63
exp(Est.) 2.5% 97.5% z val. p
intercept 4.28 2.46 7.46 5.14 0.00
Partisan_Index 0.77 0.71 0.83 -6.21 0.00
age 0.81 0.74 0.88 -4.72 0.00
Rest_Tiers 0.91 0.80 1.04 -1.39 0.16
BJP 1.16 1.00 1.35 1.91 0.06
INC 1.11 0.88 1.41 0.90 0.37
bordering 1.10 0.88 1.37 0.84 0.40
Hindu 0.90 0.75 1.08 -1.10 0.27
Muslim 0.66 0.50 0.87 -2.94 0.00
Standard errors: MLE
## Warning in vif.default(model_q11_4): No intercept: vifs may not be sensible.
##                      GVIF Df GVIF^(1/(2*Df))
## partisan_index   6.381086  1        2.526081
## age_cb_new      12.400774  1        3.521473
## geo_citytier_in  2.785399  1        1.668952
## p_party_new      4.251763  2        1.435960
## bordering       48.185946  1        6.941610
## religion_in_new  8.345633  2        1.699671

4.5.1 combined set-3

## 
## Q11a
## ==================================================================
##                                 Dependent variable:               
##                   ------------------------------------------------
##                                        q11a_1                     
##                         (1)          (2)        (3)        (4)    
## ------------------------------------------------------------------
## intercept            1.732***      1.794***   1.750***   1.455*** 
##                       (0.176)      (0.180)    (0.183)    (0.283)  
##                                                                   
## Partisan_Index       -0.266***    -0.265***  -0.264***  -0.264*** 
##                       (0.043)      (0.043)    (0.043)    (0.043)  
##                                                                   
## age                  -0.214***    -0.218***  -0.215***  -0.213*** 
##                       (0.045)      (0.045)    (0.045)    (0.045)  
##                                                                   
## Rest_Tiers            -0.107*      -0.113*     -0.101     -0.093  
##                       (0.065)      (0.065)    (0.065)    (0.067)  
##                                                                   
## BJP                    0.120        0.108      0.128      0.148*  
##                       (0.078)      (0.079)    (0.078)    (0.078)  
##                                                                   
## INC                    0.109        0.120      0.109      0.108   
##                       (0.120)      (0.120)    (0.120)    (0.120)  
##                                                                   
## SE                   -0.151**                                     
##                       (0.065)                                     
##                                                                   
## South India                       -0.270***                       
##                                    (0.086)                        
##                                                                   
## East              Northeast India              -0.089             
##                                    (0.089)                        
##                                                                   
## West India                          -0.088                        
##                                    (0.085)                        
##                                                                   
## Distance                                      -0.0001*            
##                                               (0.0001)            
##                                                                   
## bordering                                                 0.095   
##                                                          (0.112)  
##                                                                   
## Hindu                 -0.106        -0.124     -0.109     -0.102  
##                       (0.093)      (0.094)    (0.094)    (0.093)  
##                                                                   
## Muslim               -0.416***    -0.429***  -0.415***  -0.414*** 
##                       (0.141)      (0.141)    (0.141)    (0.141)  
##                                                                   
## ------------------------------------------------------------------
## Observations           4,564        4,564      4,564      4,564   
## Log Likelihood      -2,850.351    -2,847.710 -2,852.101 -2,854.401
## Akaike Inf. Crit.    5,718.703    5,717.420  5,722.201  5,726.801 
## ==================================================================
## Note:                                  *p<0.1; **p<0.05; ***p<0.01

4.6 set 4

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## 
## Call:
## glm(formula = q11a_1 ~ partisan_index + age_cb_new + geo_citytier_in + 
##     p_party_new + partisan_index_party + geo_region_in_cb + religion_in_new, 
##     family = binomial, data = ., weights = weight)
## 
## Coefficients:
##                        Estimate Std. Error z value Pr(>|z|)    
## intercept               1.86602    0.19470   9.584  < 2e-16 ***
## Partisan_Index         -0.29514    0.05243  -5.629 1.81e-08 ***
## age                    -0.21905    0.04528  -4.838 1.31e-06 ***
## Rest_Tiers             -0.11431    0.06472  -1.766  0.07738 .  
## BJP                    -0.04170    0.16948  -0.246  0.80566    
## INC                     0.09969    0.12227   0.815  0.41488    
## partisan_bjp            0.06678    0.06675   1.001  0.31705    
## South India            -0.27211    0.08646  -3.147  0.00165 ** 
## East & Northeast India -0.09168    0.08903  -1.030  0.30315    
## West India             -0.08912    0.08488  -1.050  0.29372    
## Hindu                  -0.12547    0.09402  -1.335  0.18202    
## Muslim                 -0.43332    0.14148  -3.063  0.00219 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 5967.2  on 4563  degrees of freedom
## Residual deviance: 5855.8  on 4552  degrees of freedom
## AIC: 5719.3
## 
## Number of Fisher Scoring iterations: 4
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Observations 4564
Dependent variable q11a_1
Type Generalized linear model
Family binomial
Link logit
𝛘²(11) 111.35
Pseudo-R² (Cragg-Uhler) 0.03
Pseudo-R² (McFadden) 0.02
AIC 5719.29
BIC 5796.40
exp(Est.) 2.5% 97.5% z val. p
intercept 6.46 4.41 9.47 9.58 0.00
Partisan_Index 0.74 0.67 0.82 -5.63 0.00
age 0.80 0.74 0.88 -4.84 0.00
Rest_Tiers 0.89 0.79 1.01 -1.77 0.08
BJP 0.96 0.69 1.34 -0.25 0.81
INC 1.10 0.87 1.40 0.82 0.41
partisan_bjp 1.07 0.94 1.22 1.00 0.32
South India 0.76 0.64 0.90 -3.15 0.00
East & Northeast India 0.91 0.77 1.09 -1.03 0.30
West India 0.91 0.77 1.08 -1.05 0.29
Hindu 0.88 0.73 1.06 -1.33 0.18
Muslim 0.65 0.49 0.86 -3.06 0.00
Standard errors: MLE
## Warning in vif.default(model_q11a_5): No intercept: vifs may not be sensible.
##                           GVIF Df GVIF^(1/(2*Df))
## partisan_index        9.635607  1        3.104127
## age_cb_new           12.483126  1        3.533147
## geo_citytier_in       2.619639  1        1.618530
## p_party_new          21.896019  2        2.163173
## partisan_index_party 10.970861  1        3.312229
## geo_region_in_cb      2.845694  3        1.190414
## religion_in_new       8.460173  2        1.705473
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## 
## Call:
## glm(formula = q11a_1 ~ partisan_index + age_cb_new + geo_citytier_in + 
##     partisan_index_party + p_party_new + geo_region_in_cb_new + 
##     religion_in_new, family = binomial, data = ., weights = weight)
## 
## Coefficients:
##                Estimate Std. Error z value Pr(>|z|)    
## intercept       1.80274    0.18996   9.490  < 2e-16 ***
## Partisan_Index -0.29640    0.05234  -5.663 1.49e-08 ***
## age            -0.21578    0.04514  -4.780 1.75e-06 ***
## Rest_Tiers     -0.10767    0.06461  -1.667  0.09560 .  
## partisan_bjp    0.06617    0.06673   0.991  0.32145    
## BJP            -0.02902    0.16928  -0.171  0.86387    
## INC             0.08884    0.12196   0.728  0.46632    
## SE             -0.15311    0.06485  -2.361  0.01823 *  
## Hindu          -0.10785    0.09355  -1.153  0.24898    
## Muslim         -0.42056    0.14119  -2.979  0.00289 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 5967.2  on 4563  degrees of freedom
## Residual deviance: 5860.1  on 4554  degrees of freedom
## AIC: 5720.6
## 
## Number of Fisher Scoring iterations: 4
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Observations 4564
Dependent variable q11a_1
Type Generalized linear model
Family binomial
Link logit
𝛘²(9) 107.04
Pseudo-R² (Cragg-Uhler) 0.03
Pseudo-R² (McFadden) 0.02
AIC 5720.55
BIC 5784.81
exp(Est.) 2.5% 97.5% z val. p
intercept 6.07 4.18 8.80 9.49 0.00
Partisan_Index 0.74 0.67 0.82 -5.66 0.00
age 0.81 0.74 0.88 -4.78 0.00
Rest_Tiers 0.90 0.79 1.02 -1.67 0.10
partisan_bjp 1.07 0.94 1.22 0.99 0.32
BJP 0.97 0.70 1.35 -0.17 0.86
INC 1.09 0.86 1.39 0.73 0.47
SE 0.86 0.76 0.97 -2.36 0.02
Hindu 0.90 0.75 1.08 -1.15 0.25
Muslim 0.66 0.50 0.87 -2.98 0.00
Standard errors: MLE
## Warning in vif.default(model_q11a_6): No intercept: vifs may not be sensible.
##                           GVIF Df GVIF^(1/(2*Df))
## partisan_index        9.613851  1        3.100621
## age_cb_new           12.419863  1        3.524183
## geo_citytier_in       2.613998  1        1.616786
## partisan_index_party 10.974319  1        3.312751
## p_party_new          21.740268  2        2.159316
## geo_region_in_cb_new  1.732761  1        1.316344
## religion_in_new       8.374698  2        1.701149
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## 
## Call:
## glm(formula = q11a_1 ~ partisan_index + age_cb_new + geo_citytier_in + 
##     p_party_new + partisan_index_party + bordering + religion_in_new, 
##     family = binomial, data = ., weights = weight)
## 
## Coefficients:
##                Estimate Std. Error z value Pr(>|z|)    
## intercept       1.52195    0.29283   5.197 2.02e-07 ***
## Partisan_Index -0.29151    0.05224  -5.580 2.41e-08 ***
## age            -0.21386    0.04510  -4.742 2.12e-06 ***
## Rest_Tiers     -0.09406    0.06667  -1.411  0.15826    
## BJP             0.01353    0.16847   0.080  0.93599    
## INC             0.08953    0.12187   0.735  0.46257    
## partisan_bjp    0.06020    0.06669   0.903  0.36671    
## bordering       0.09304    0.11241   0.828  0.40787    
## Hindu          -0.10374    0.09344  -1.110  0.26688    
## Muslim         -0.41821    0.14110  -2.964  0.00304 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 5967.2  on 4563  degrees of freedom
## Residual deviance: 5865.0  on 4554  degrees of freedom
## AIC: 5728.7
## 
## Number of Fisher Scoring iterations: 4
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Observations 4564
Dependent variable q11a_1
Type Generalized linear model
Family binomial
Link logit
𝛘²(9) 102.16
Pseudo-R² (Cragg-Uhler) 0.03
Pseudo-R² (McFadden) 0.02
AIC 5728.75
BIC 5793.01
exp(Est.) 2.5% 97.5% z val. p
intercept 4.58 2.58 8.13 5.20 0.00
Partisan_Index 0.75 0.67 0.83 -5.58 0.00
age 0.81 0.74 0.88 -4.74 0.00
Rest_Tiers 0.91 0.80 1.04 -1.41 0.16
BJP 1.01 0.73 1.41 0.08 0.94
INC 1.09 0.86 1.39 0.73 0.46
partisan_bjp 1.06 0.93 1.21 0.90 0.37
bordering 1.10 0.88 1.37 0.83 0.41
Hindu 0.90 0.75 1.08 -1.11 0.27
Muslim 0.66 0.50 0.87 -2.96 0.00
Standard errors: MLE
## Warning in vif.default(model_q11a_7): No intercept: vifs may not be sensible.
##                           GVIF Df GVIF^(1/(2*Df))
## partisan_index        9.589556  1        3.096701
## age_cb_new           12.413453  1        3.523273
## geo_citytier_in       2.786692  1        1.669339
## p_party_new          21.522747  2        2.153894
## partisan_index_party 10.970968  1        3.312245
## bordering            48.195265  1        6.942281
## religion_in_new       8.365985  2        1.700706
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## 
## Call:
## glm(formula = q11a_1 ~ partisan_index + age_cb_new + geo_citytier_in + 
##     p_party_new + partisan_index_party + hub_dist + religion_in_new, 
##     family = binomial, data = ., weights = weight)
## 
## Coefficients:
##                  Estimate Std. Error z value Pr(>|z|)    
## intercept       1.819e+00  1.970e-01   9.233  < 2e-16 ***
## Partisan_Index -2.928e-01  5.227e-02  -5.601 2.13e-08 ***
## age            -2.160e-01  4.513e-02  -4.786 1.70e-06 ***
## Rest_Tiers     -1.023e-01  6.466e-02  -1.582  0.11370    
## BJP            -1.560e-02  1.691e-01  -0.092  0.92650    
## INC             8.974e-02  1.219e-01   0.736  0.46168    
## partisan_bjp    6.391e-02  6.669e-02   0.958  0.33790    
## distance       -9.931e-05  5.949e-05  -1.669  0.09508 .  
## Hindu          -1.108e-01  9.361e-02  -1.184  0.23658    
## Muslim         -4.197e-01  1.411e-01  -2.973  0.00295 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 5967.2  on 4563  degrees of freedom
## Residual deviance: 5862.9  on 4554  degrees of freedom
## AIC: 5724.1
## 
## Number of Fisher Scoring iterations: 4
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Observations 4564
Dependent variable q11a_1
Type Generalized linear model
Family binomial
Link logit
𝛘²(9) 104.27
Pseudo-R² (Cragg-Uhler) 0.03
Pseudo-R² (McFadden) 0.02
AIC 5724.10
BIC 5788.36
exp(Est.) 2.5% 97.5% z val. p
intercept 6.17 4.19 9.07 9.23 0.00
Partisan_Index 0.75 0.67 0.83 -5.60 0.00
age 0.81 0.74 0.88 -4.79 0.00
Rest_Tiers 0.90 0.80 1.02 -1.58 0.11
BJP 0.98 0.71 1.37 -0.09 0.93
INC 1.09 0.86 1.39 0.74 0.46
partisan_bjp 1.07 0.94 1.21 0.96 0.34
distance 1.00 1.00 1.00 -1.67 0.10
Hindu 0.90 0.75 1.08 -1.18 0.24
Muslim 0.66 0.50 0.87 -2.97 0.00
Standard errors: MLE
## Warning in vif.default(model_q11a_8): No intercept: vifs may not be sensible.
##                           GVIF Df GVIF^(1/(2*Df))
## partisan_index        9.596459  1        3.097815
## age_cb_new           12.422175  1        3.524511
## geo_citytier_in       2.620185  1        1.618699
## p_party_new          21.706314  2        2.158473
## partisan_index_party 10.967963  1        3.311791
## hub_dist              4.002316  1        2.000579
## religion_in_new       8.389398  2        1.701895

4.6.1 combined set 4

## 
## Q11a
## ==================================================================
##                                 Dependent variable:               
##                   ------------------------------------------------
##                                        q11a_1                     
##                         (1)          (2)        (3)        (4)    
## ------------------------------------------------------------------
## intercept            1.866***      1.803***   1.522***   1.819*** 
##                       (0.195)      (0.190)    (0.293)    (0.197)  
##                                                                   
## Partisan_Index       -0.295***    -0.296***  -0.292***  -0.293*** 
##                       (0.052)      (0.052)    (0.052)    (0.052)  
##                                                                   
## age                  -0.219***    -0.216***  -0.214***  -0.216*** 
##                       (0.045)      (0.045)    (0.045)    (0.045)  
##                                                                   
## Rest_Tiers            -0.114*      -0.108*     -0.094     -0.102  
##                       (0.065)      (0.065)    (0.067)    (0.065)  
##                                                                   
## BJP                   -0.042        -0.029     0.014      -0.016  
##                       (0.169)      (0.169)    (0.168)    (0.169)  
##                                                                   
## INC                    0.100        0.089      0.090      0.090   
##                       (0.122)      (0.122)    (0.122)    (0.122)  
##                                                                   
## SE                                 -0.153**                       
##                                    (0.065)                        
##                                                                   
## partisan_bjp           0.067        0.066      0.060      0.064   
##                       (0.067)      (0.067)    (0.067)    (0.067)  
##                                                                   
## South India          -0.272***                                    
##                       (0.086)                                     
##                                                                   
## East              Northeast India   -0.092                        
##                       (0.089)                                     
##                                                                   
## West India            -0.089                                      
##                       (0.085)                                     
##                                                                   
## bordering                                      0.093              
##                                               (0.112)             
##                                                                   
## distance                                                 -0.0001* 
##                                                          (0.0001) 
##                                                                   
## Hindu                 -0.125        -0.108     -0.104     -0.111  
##                       (0.094)      (0.094)    (0.093)    (0.094)  
##                                                                   
## Muslim               -0.433***    -0.421***  -0.418***  -0.420*** 
##                       (0.141)      (0.141)    (0.141)    (0.141)  
##                                                                   
## ------------------------------------------------------------------
## Observations           4,564        4,564      4,564      4,564   
## Log Likelihood      -2,847.643    -2,850.276 -2,854.374 -2,852.048
## Akaike Inf. Crit.    5,719.287    5,720.553  5,728.748  5,724.096 
## ==================================================================
## Note:                                  *p<0.1; **p<0.05; ***p<0.01

4.7 set 5

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## 
## Call:
## glm(formula = q11b_narrow ~ partisan_index + age_cb_new + geo_citytier_in + 
##     p_party_new + geo_region_in_cb + religion_in_new, family = binomial, 
##     data = ., weights = weight)
## 
## Coefficients:
##                        Estimate Std. Error z value Pr(>|z|)    
## intercept              -1.00142    0.13980  -7.163 7.87e-13 ***
## Partisan_Index          0.27758    0.03405   8.153 3.54e-16 ***
## age                    -0.10130    0.03495  -2.899 0.003746 ** 
## Rest_Tiers             -0.08825    0.05366  -1.645 0.100023    
## BJP                    -0.19365    0.06642  -2.916 0.003549 ** 
## INC                     0.08736    0.08367   1.044 0.296467    
## South India             0.11438    0.06474   1.767 0.077284 .  
## East & Northeast India -0.18202    0.08311  -2.190 0.028511 *  
## West India             -0.03456    0.07533  -0.459 0.646394    
## Hindu                  -0.19018    0.06531  -2.912 0.003591 ** 
## Muslim                 -0.29323    0.08675  -3.380 0.000725 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 9135.2  on 7869  degrees of freedom
## Residual deviance: 8943.9  on 7859  degrees of freedom
## AIC: 9072.8
## 
## Number of Fisher Scoring iterations: 4
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Observations 7870
Dependent variable q11b_narrow
Type Generalized linear model
Family binomial
Link logit
𝛘²(10) 191.34
Pseudo-R² (Cragg-Uhler) 0.03
Pseudo-R² (McFadden) 0.02
AIC 9072.76
BIC 9149.44
exp(Est.) 2.5% 97.5% z val. p
intercept 0.37 0.28 0.48 -7.16 0.00
Partisan_Index 1.32 1.23 1.41 8.15 0.00
age 0.90 0.84 0.97 -2.90 0.00
Rest_Tiers 0.92 0.82 1.02 -1.64 0.10
BJP 0.82 0.72 0.94 -2.92 0.00
INC 1.09 0.93 1.29 1.04 0.30
South India 1.12 0.99 1.27 1.77 0.08
East & Northeast India 0.83 0.71 0.98 -2.19 0.03
West India 0.97 0.83 1.12 -0.46 0.65
Hindu 0.83 0.73 0.94 -2.91 0.00
Muslim 0.75 0.63 0.88 -3.38 0.00
Standard errors: MLE
## Warning in vif.default(model_q11b_narrow_2): No intercept: vifs may not be
## sensible.
##                       GVIF Df GVIF^(1/(2*Df))
## partisan_index    9.083606  1        3.013902
## age_cb_new       10.163201  1        3.187978
## geo_citytier_in   2.697222  1        1.642322
## p_party_new       3.144600  2        1.331654
## geo_region_in_cb  3.432651  3        1.228207
## religion_in_new   5.060923  2        1.499883
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## 
## Call:
## glm(formula = q11b_narrow ~ partisan_index + age_cb_new + geo_citytier_in + 
##     p_party_new + partisan_index_party + geo_region_in_cb + religion_in_new, 
##     family = binomial, data = ., weights = weight)
## 
## Coefficients:
##                        Estimate Std. Error z value Pr(>|z|)    
## intercept              -1.02560    0.15185  -6.754 1.44e-11 ***
## Partisan_Index          0.28645    0.04038   7.094 1.30e-12 ***
## age                    -0.10091    0.03496  -2.886 0.003899 ** 
## Rest_Tiers             -0.08816    0.05366  -1.643 0.100376    
## BJP                    -0.13026    0.16811  -0.775 0.438428    
## INC                     0.09313    0.08489   1.097 0.272605    
## Partisan_bjp           -0.02510    0.06117  -0.410 0.681544    
## South India             0.11524    0.06478   1.779 0.075259 .  
## East & Northeast India -0.18115    0.08313  -2.179 0.029332 *  
## West India             -0.03421    0.07533  -0.454 0.649715    
## Hindu                  -0.18959    0.06534  -2.902 0.003710 ** 
## Muslim                 -0.29170    0.08685  -3.359 0.000783 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 9135.2  on 7869  degrees of freedom
## Residual deviance: 8943.7  on 7858  degrees of freedom
## AIC: 9074.7
## 
## Number of Fisher Scoring iterations: 4
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Observations 7870
Dependent variable q11b_narrow
Type Generalized linear model
Family binomial
Link logit
𝛘²(11) 191.50
Pseudo-R² (Cragg-Uhler) 0.03
Pseudo-R² (McFadden) 0.02
AIC 9074.72
BIC 9158.36
exp(Est.) 2.5% 97.5% z val. p
intercept 0.36 0.27 0.48 -6.75 0.00
Partisan_Index 1.33 1.23 1.44 7.09 0.00
age 0.90 0.84 0.97 -2.89 0.00
Rest_Tiers 0.92 0.82 1.02 -1.64 0.10
BJP 0.88 0.63 1.22 -0.77 0.44
INC 1.10 0.93 1.30 1.10 0.27
Partisan_bjp 0.98 0.87 1.10 -0.41 0.68
South India 1.12 0.99 1.27 1.78 0.08
East & Northeast India 0.83 0.71 0.98 -2.18 0.03
West India 0.97 0.83 1.12 -0.45 0.65
Hindu 0.83 0.73 0.94 -2.90 0.00
Muslim 0.75 0.63 0.89 -3.36 0.00
Standard errors: MLE
## Warning in vif.default(model_q11b_2): No intercept: vifs may not be sensible.
##                           GVIF Df GVIF^(1/(2*Df))
## partisan_index       12.773882  1        3.574057
## age_cb_new           10.170736  1        3.189159
## geo_citytier_in       2.697054  1        1.642271
## p_party_new          21.110668  2        2.143510
## partisan_index_party 11.674641  1        3.416817
## geo_region_in_cb      3.436435  3        1.228433
## religion_in_new       5.072183  2        1.500717

4.7.1 combined set 5

## 
## Q11b
## ==============================================
##                       Dependent variable:     
##                   ----------------------------
##                           q11b_narrow         
##                         (1)            (2)    
## ----------------------------------------------
## intercept            -1.001***      -1.026*** 
##                       (0.140)        (0.152)  
##                                               
## Partisan_Index        0.278***      0.286***  
##                       (0.034)        (0.040)  
##                                               
## age                  -0.101***      -0.101*** 
##                       (0.035)        (0.035)  
##                                               
## Rest_Tiers             -0.088        -0.088   
##                       (0.054)        (0.054)  
##                                               
## BJP                  -0.194***       -0.130   
##                       (0.066)        (0.168)  
##                                               
## INC                    0.087          0.093   
##                       (0.084)        (0.085)  
##                                               
## Partisan_bjp                         -0.025   
##                                      (0.061)  
##                                               
## South India            0.114*        0.115*   
##                       (0.065)        (0.065)  
##                                               
## East              Northeast India   -0.182**  
##                       (0.083)        (0.083)  
##                                               
## West India             -0.035        -0.034   
##                       (0.075)        (0.075)  
##                                               
## Hindu                -0.190***      -0.190*** 
##                       (0.065)        (0.065)  
##                                               
## Muslim               -0.293***      -0.292*** 
##                       (0.087)        (0.087)  
##                                               
## ----------------------------------------------
## Observations           7,870          7,870   
## Log Likelihood       -4,525.378    -4,525.358 
## Akaike Inf. Crit.    9,072.757      9,074.715 
## ==============================================
## Note:              *p<0.1; **p<0.05; ***p<0.01

4.8 set 6

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## 
## Call:
## glm(formula = q11b_narrow ~ partisan_index + age_cb_new + geo_citytier_in + 
##     p_party_new + geo_region_in_cb + religion_in_new, family = binomial, 
##     data = ., weights = weight)
## 
## Coefficients:
##                        Estimate Std. Error z value Pr(>|z|)    
## intercept              -1.00142    0.13980  -7.163 7.87e-13 ***
## Partisan_Index          0.27758    0.03405   8.153 3.54e-16 ***
## age                    -0.10130    0.03495  -2.899 0.003746 ** 
## Rest_Tiers             -0.08825    0.05366  -1.645 0.100023    
## BJP                    -0.19365    0.06642  -2.916 0.003549 ** 
## INC                     0.08736    0.08367   1.044 0.296467    
## South India             0.11438    0.06474   1.767 0.077284 .  
## East & Northeast India -0.18202    0.08311  -2.190 0.028511 *  
## West India             -0.03456    0.07533  -0.459 0.646394    
## Hindu                  -0.19018    0.06531  -2.912 0.003591 ** 
## Muslim                 -0.29323    0.08675  -3.380 0.000725 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 9135.2  on 7869  degrees of freedom
## Residual deviance: 8943.9  on 7859  degrees of freedom
## AIC: 9072.8
## 
## Number of Fisher Scoring iterations: 4
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Observations 7870
Dependent variable q11b_narrow
Type Generalized linear model
Family binomial
Link logit
𝛘²(10) 191.34
Pseudo-R² (Cragg-Uhler) 0.03
Pseudo-R² (McFadden) 0.02
AIC 9072.76
BIC 9149.44
exp(Est.) 2.5% 97.5% z val. p
intercept 0.37 0.28 0.48 -7.16 0.00
Partisan_Index 1.32 1.23 1.41 8.15 0.00
age 0.90 0.84 0.97 -2.90 0.00
Rest_Tiers 0.92 0.82 1.02 -1.64 0.10
BJP 0.82 0.72 0.94 -2.92 0.00
INC 1.09 0.93 1.29 1.04 0.30
South India 1.12 0.99 1.27 1.77 0.08
East & Northeast India 0.83 0.71 0.98 -2.19 0.03
West India 0.97 0.83 1.12 -0.46 0.65
Hindu 0.83 0.73 0.94 -2.91 0.00
Muslim 0.75 0.63 0.88 -3.38 0.00
Standard errors: MLE
## Warning in vif.default(model_q11b_narrow_2): No intercept: vifs may not be
## sensible.
##                       GVIF Df GVIF^(1/(2*Df))
## partisan_index    9.083606  1        3.013902
## age_cb_new       10.163201  1        3.187978
## geo_citytier_in   2.697222  1        1.642322
## p_party_new       3.144600  2        1.331654
## geo_region_in_cb  3.432651  3        1.228207
## religion_in_new   5.060923  2        1.499883
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## 
## Call:
## glm(formula = q11b_narrow ~ partisan_index + age_cb_new + geo_citytier_in + 
##     p_party_new + partisan_index_party + geo_region_in_cb + religion_in_new, 
##     family = binomial, data = ., weights = weight)
## 
## Coefficients:
##                        Estimate Std. Error z value Pr(>|z|)    
## intercept              -1.02560    0.15185  -6.754 1.44e-11 ***
## Partisan_Index          0.28645    0.04038   7.094 1.30e-12 ***
## age                    -0.10091    0.03496  -2.886 0.003899 ** 
## Rest_Tiers             -0.08816    0.05366  -1.643 0.100376    
## BJP                    -0.13026    0.16811  -0.775 0.438428    
## INC                     0.09313    0.08489   1.097 0.272605    
## Partisan_bjp           -0.02510    0.06117  -0.410 0.681544    
## South India             0.11524    0.06478   1.779 0.075259 .  
## East & Northeast India -0.18115    0.08313  -2.179 0.029332 *  
## West India             -0.03421    0.07533  -0.454 0.649715    
## Hindu                  -0.18959    0.06534  -2.902 0.003710 ** 
## Muslim                 -0.29170    0.08685  -3.359 0.000783 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 9135.2  on 7869  degrees of freedom
## Residual deviance: 8943.7  on 7858  degrees of freedom
## AIC: 9074.7
## 
## Number of Fisher Scoring iterations: 4
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Observations 7870
Dependent variable q11b_narrow
Type Generalized linear model
Family binomial
Link logit
𝛘²(11) 191.50
Pseudo-R² (Cragg-Uhler) 0.03
Pseudo-R² (McFadden) 0.02
AIC 9074.72
BIC 9158.36
exp(Est.) 2.5% 97.5% z val. p
intercept 0.36 0.27 0.48 -6.75 0.00
Partisan_Index 1.33 1.23 1.44 7.09 0.00
age 0.90 0.84 0.97 -2.89 0.00
Rest_Tiers 0.92 0.82 1.02 -1.64 0.10
BJP 0.88 0.63 1.22 -0.77 0.44
INC 1.10 0.93 1.30 1.10 0.27
Partisan_bjp 0.98 0.87 1.10 -0.41 0.68
South India 1.12 0.99 1.27 1.78 0.08
East & Northeast India 0.83 0.71 0.98 -2.18 0.03
West India 0.97 0.83 1.12 -0.45 0.65
Hindu 0.83 0.73 0.94 -2.90 0.00
Muslim 0.75 0.63 0.89 -3.36 0.00
Standard errors: MLE
## Warning in vif.default(model_q11b_2): No intercept: vifs may not be sensible.
##                           GVIF Df GVIF^(1/(2*Df))
## partisan_index       12.773882  1        3.574057
## age_cb_new           10.170736  1        3.189159
## geo_citytier_in       2.697054  1        1.642271
## p_party_new          21.110668  2        2.143510
## partisan_index_party 11.674641  1        3.416817
## geo_region_in_cb      3.436435  3        1.228433
## religion_in_new       5.072183  2        1.500717
## 
## Q11b
## ==============================================
##                       Dependent variable:     
##                   ----------------------------
##                           q11b_narrow         
##                         (1)            (2)    
## ----------------------------------------------
## intercept            -1.001***      -1.026*** 
##                       (0.140)        (0.152)  
##                                               
## Partisan_Index        0.278***      0.286***  
##                       (0.034)        (0.040)  
##                                               
## age                  -0.101***      -0.101*** 
##                       (0.035)        (0.035)  
##                                               
## Rest_Tiers             -0.088        -0.088   
##                       (0.054)        (0.054)  
##                                               
## BJP                  -0.194***       -0.130   
##                       (0.066)        (0.168)  
##                                               
## INC                    0.087          0.093   
##                       (0.084)        (0.085)  
##                                               
## Partisan_bjp                         -0.025   
##                                      (0.061)  
##                                               
## South India            0.114*        0.115*   
##                       (0.065)        (0.065)  
##                                               
## East              Northeast India   -0.182**  
##                       (0.083)        (0.083)  
##                                               
## West India             -0.035        -0.034   
##                       (0.075)        (0.075)  
##                                               
## Hindu                -0.190***      -0.190*** 
##                       (0.065)        (0.065)  
##                                               
## Muslim               -0.293***      -0.292*** 
##                       (0.087)        (0.087)  
##                                               
## ----------------------------------------------
## Observations           7,870          7,870   
## Log Likelihood       -4,525.378    -4,525.358 
## Akaike Inf. Crit.    9,072.757      9,074.715 
## ==============================================
## Note:              *p<0.1; **p<0.05; ***p<0.01

4.9 set 6- wider

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## 
## Call:
## glm(formula = q11b_wider ~ partisan_index + age_cb_new + geo_citytier_in + 
##     p_party_new + geo_region_in_cb + religion_in_new, family = binomial, 
##     data = ., weights = weight)
## 
## Coefficients:
##                        Estimate Std. Error z value Pr(>|z|)    
## intercept              -0.05906    0.12623  -0.468  0.63989    
## Partisan_Index          0.09211    0.03036   3.033  0.00242 ** 
## age                    -0.01601    0.03162  -0.506  0.61275    
## Rest_Tiers             -0.09466    0.04847  -1.953  0.05082 .  
## BJP                    -0.26833    0.05913  -4.538 5.67e-06 ***
## INC                    -0.13501    0.07678  -1.758  0.07868 .  
## South India             0.19373    0.05899   3.284  0.00102 ** 
## East & Northeast India -0.06325    0.07229  -0.875  0.38158    
## West India              0.01822    0.06729   0.271  0.78655    
## Hindu                  -0.19849    0.06085  -3.262  0.00111 ** 
## Muslim                  0.16137    0.07931   2.035  0.04190 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 10593  on 7869  degrees of freedom
## Residual deviance: 10437  on 7859  degrees of freedom
## AIC: 10522
## 
## Number of Fisher Scoring iterations: 4
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Observations 7870
Dependent variable q11b_wider
Type Generalized linear model
Family binomial
Link logit
𝛘²(10) 156.32
Pseudo-R² (Cragg-Uhler) 0.03
Pseudo-R² (McFadden) 0.01
AIC 10521.73
BIC 10598.41
exp(Est.) 2.5% 97.5% z val. p
intercept 0.94 0.74 1.21 -0.47 0.64
Partisan_Index 1.10 1.03 1.16 3.03 0.00
age 0.98 0.92 1.05 -0.51 0.61
Rest_Tiers 0.91 0.83 1.00 -1.95 0.05
BJP 0.76 0.68 0.86 -4.54 0.00
INC 0.87 0.75 1.02 -1.76 0.08
South India 1.21 1.08 1.36 3.28 0.00
East & Northeast India 0.94 0.81 1.08 -0.87 0.38
West India 1.02 0.89 1.16 0.27 0.79
Hindu 0.82 0.73 0.92 -3.26 0.00
Muslim 1.18 1.01 1.37 2.03 0.04
Standard errors: MLE
## Warning in vif.default(model_q11b_wider_1): No intercept: vifs may not be
## sensible.
##                       GVIF Df GVIF^(1/(2*Df))
## partisan_index    8.228167  1        2.868478
## age_cb_new       10.386986  1        3.222885
## geo_citytier_in   2.744338  1        1.656604
## p_party_new       3.379798  2        1.355884
## geo_region_in_cb  3.363120  3        1.224025
## religion_in_new   5.704229  2        1.545430
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## 
## Call:
## glm(formula = q11b_wider ~ partisan_index + age_cb_new + geo_citytier_in + 
##     p_party_new + partisan_index_party + geo_region_in_cb + religion_in_new, 
##     family = binomial, data = ., weights = weight)
## 
## Coefficients:
##                        Estimate Std. Error z value Pr(>|z|)    
## intercept              -0.12312    0.13652  -0.902 0.367133    
## Partisan_Index          0.11605    0.03604   3.220 0.001282 ** 
## age                    -0.01482    0.03164  -0.468 0.639529    
## Rest_Tiers             -0.09449    0.04848  -1.949 0.051283 .  
## BJP                    -0.10556    0.14433  -0.731 0.464529    
## INC                    -0.11968    0.07781  -1.538 0.124026    
## Partisan_bjp           -0.06613    0.05352  -1.236 0.216591    
## South India             0.19615    0.05903   3.323 0.000891 ***
## East & Northeast India -0.06074    0.07232  -0.840 0.400940    
## West India              0.01937    0.06731   0.288 0.773462    
## Hindu                  -0.19660    0.06088  -3.229 0.001241 ** 
## Muslim                  0.16598    0.07943   2.090 0.036651 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 10593  on 7869  degrees of freedom
## Residual deviance: 10435  on 7858  degrees of freedom
## AIC: 10523
## 
## Number of Fisher Scoring iterations: 4
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Observations 7870
Dependent variable q11b_wider
Type Generalized linear model
Family binomial
Link logit
𝛘²(11) 157.85
Pseudo-R² (Cragg-Uhler) 0.03
Pseudo-R² (McFadden) 0.01
AIC 10522.91
BIC 10606.56
exp(Est.) 2.5% 97.5% z val. p
intercept 0.88 0.68 1.16 -0.90 0.37
Partisan_Index 1.12 1.05 1.21 3.22 0.00
age 0.99 0.93 1.05 -0.47 0.64
Rest_Tiers 0.91 0.83 1.00 -1.95 0.05
BJP 0.90 0.68 1.19 -0.73 0.46
INC 0.89 0.76 1.03 -1.54 0.12
Partisan_bjp 0.94 0.84 1.04 -1.24 0.22
South India 1.22 1.08 1.37 3.32 0.00
East & Northeast India 0.94 0.82 1.08 -0.84 0.40
West India 1.02 0.89 1.16 0.29 0.77
Hindu 0.82 0.73 0.93 -3.23 0.00
Muslim 1.18 1.01 1.38 2.09 0.04
Standard errors: MLE
## Warning in vif.default(model_q11b_wider_2): No intercept: vifs may not be
## sensible.
##                           GVIF Df GVIF^(1/(2*Df))
## partisan_index       11.573288  1        3.401954
## age_cb_new           10.395252  1        3.224167
## geo_citytier_in       2.744730  1        1.656723
## p_party_new          21.132180  2        2.144056
## partisan_index_party 11.437146  1        3.381885
## geo_region_in_cb      3.367140  3        1.224269
## religion_in_new       5.721244  2        1.546581

4.9.1 combined set-6 wider

## 
## Q11b
## ==============================================
##                       Dependent variable:     
##                   ----------------------------
##                            q11b_wider         
##                         (1)            (2)    
## ----------------------------------------------
## intercept              -0.059        -0.123   
##                       (0.126)        (0.137)  
##                                               
## Partisan_Index        0.092***      0.116***  
##                       (0.030)        (0.036)  
##                                               
## age                    -0.016        -0.015   
##                       (0.032)        (0.032)  
##                                               
## Rest_Tiers            -0.095*        -0.094*  
##                       (0.048)        (0.048)  
##                                               
## BJP                  -0.268***       -0.106   
##                       (0.059)        (0.144)  
##                                               
## INC                   -0.135*        -0.120   
##                       (0.077)        (0.078)  
##                                               
## Partisan_bjp                         -0.066   
##                                      (0.054)  
##                                               
## South India           0.194***      0.196***  
##                       (0.059)        (0.059)  
##                                               
## East              Northeast India    -0.063   
##                       (0.072)        (0.072)  
##                                               
## West India             0.018          0.019   
##                       (0.067)        (0.067)  
##                                               
## Hindu                -0.198***      -0.197*** 
##                       (0.061)        (0.061)  
##                                               
## Muslim                0.161**        0.166**  
##                       (0.079)        (0.079)  
##                                               
## ----------------------------------------------
## Observations           7,870          7,870   
## Log Likelihood       -5,249.867    -5,249.456 
## Akaike Inf. Crit.    10,521.730    10,522.910 
## ==============================================
## Note:              *p<0.1; **p<0.05; ***p<0.01

4.10 set 7

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## 
## Call:
## glm(formula = q10 ~ partisan_index + age_cb_new + geo_citytier_in + 
##     p_party_new + geo_region_in_cb + religion_in_new + q3_4, 
##     family = binomial, data = ., weights = weight)
## 
## Coefficients:
##                        Estimate Std. Error z value Pr(>|z|)    
## intercept               0.05046    0.11532   0.438  0.66166    
## Partisan_Index         -0.46237    0.02616 -17.674  < 2e-16 ***
## age                     0.17783    0.02707   6.570 5.03e-11 ***
## Rest_Tiers             -0.10682    0.04013  -2.662  0.00777 ** 
## BJP                     0.10527    0.04991   2.109  0.03494 *  
## INC                    -0.01252    0.07094  -0.176  0.85993    
## South India            -0.38718    0.05199  -7.447 9.54e-14 ***
## East & Northeast India  0.06054    0.05660   1.070  0.28482    
## West India              0.09307    0.05348   1.740  0.08180 .  
## Hindu                   0.28333    0.05539   5.115 3.14e-07 ***
## Muslim                 -0.37653    0.08096  -4.651 3.30e-06 ***
## Terrorism_Dissatisfied -0.10237    0.01909  -5.363 8.20e-08 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 16283  on 12433  degrees of freedom
## Residual deviance: 15271  on 12422  degrees of freedom
## AIC: 15192
## 
## Number of Fisher Scoring iterations: 4
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Observations 12434
Dependent variable q10
Type Generalized linear model
Family binomial
Link logit
𝛘²(11) 1011.82
Pseudo-R² (Cragg-Uhler) 0.10
Pseudo-R² (McFadden) 0.06
AIC 15191.54
BIC 15280.68
exp(Est.) 2.5% 97.5% z val. p
intercept 1.05 0.84 1.32 0.44 0.66
Partisan_Index 0.63 0.60 0.66 -17.67 0.00
age 1.19 1.13 1.26 6.57 0.00
Rest_Tiers 0.90 0.83 0.97 -2.66 0.01
BJP 1.11 1.01 1.23 2.11 0.03
INC 0.99 0.86 1.13 -0.18 0.86
South India 0.68 0.61 0.75 -7.45 0.00
East & Northeast India 1.06 0.95 1.19 1.07 0.28
West India 1.10 0.99 1.22 1.74 0.08
Hindu 1.33 1.19 1.48 5.12 0.00
Muslim 0.69 0.59 0.80 -4.65 0.00
Terrorism_Dissatisfied 0.90 0.87 0.94 -5.36 0.00
Standard errors: MLE
## Warning in vif.default(model_q10t_2): No intercept: vifs may not be sensible.
##                       GVIF Df GVIF^(1/(2*Df))
## partisan_index    6.805665  1        2.608767
## age_cb_new       11.109713  1        3.333124
## geo_citytier_in   2.611327  1        1.615960
## p_party_new       4.484380  2        1.455210
## geo_region_in_cb  2.916772  3        1.195319
## religion_in_new   7.531587  2        1.656615
## q3_4              4.793630  1        2.189436
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## 
## Call:
## glm(formula = q10 ~ partisan_index + age_cb_new + geo_citytier_in + 
##     p_party_new + geo_region_in_cb_new + religion_in_new + q3_4, 
##     family = binomial, data = ., weights = weight)
## 
## Coefficients:
##                        Estimate Std. Error z value Pr(>|z|)    
## intercept               0.05288    0.11252   0.470  0.63840    
## Partisan_Index         -0.46352    0.02606 -17.785  < 2e-16 ***
## age                     0.17325    0.02698   6.421 1.35e-10 ***
## Rest_Tiers             -0.11421    0.04002  -2.854  0.00432 ** 
## BJP                     0.13558    0.04963   2.732  0.00630 ** 
## INC                    -0.04090    0.07055  -0.580  0.56207    
## SE                     -0.23159    0.04009  -5.776 7.63e-09 ***
## Hindu                   0.31630    0.05497   5.754 8.74e-09 ***
## Muslim                 -0.34977    0.08057  -4.341 1.42e-05 ***
## Terrorism_Dissatisfied -0.10044    0.01905  -5.273 1.34e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 16283  on 12433  degrees of freedom
## Residual deviance: 15326  on 12424  degrees of freedom
## AIC: 15230
## 
## Number of Fisher Scoring iterations: 4
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Observations 12434
Dependent variable q10
Type Generalized linear model
Family binomial
Link logit
𝛘²(9) 956.42
Pseudo-R² (Cragg-Uhler) 0.10
Pseudo-R² (McFadden) 0.06
AIC 15230.32
BIC 15304.60
exp(Est.) 2.5% 97.5% z val. p
intercept 1.05 0.85 1.31 0.47 0.64
Partisan_Index 0.63 0.60 0.66 -17.78 0.00
age 1.19 1.13 1.25 6.42 0.00
Rest_Tiers 0.89 0.82 0.96 -2.85 0.00
BJP 1.15 1.04 1.26 2.73 0.01
INC 0.96 0.84 1.10 -0.58 0.56
SE 0.79 0.73 0.86 -5.78 0.00
Hindu 1.37 1.23 1.53 5.75 0.00
Muslim 0.70 0.60 0.83 -4.34 0.00
Terrorism_Dissatisfied 0.90 0.87 0.94 -5.27 0.00
Standard errors: MLE
## Warning in vif.default(model_q10t_1): No intercept: vifs may not be sensible.
##                           GVIF Df GVIF^(1/(2*Df))
## partisan_index        6.784445  1        2.604697
## age_cb_new           11.094830  1        3.330890
## geo_citytier_in       2.613490  1        1.616629
## p_party_new           4.408958  2        1.449052
## geo_region_in_cb_new  1.817544  1        1.348163
## religion_in_new       7.443501  2        1.651750
## q3_4                  4.797057  1        2.190218
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## 
## Call:
## glm(formula = q10 ~ partisan_index + age_cb_new + geo_citytier_in + 
##     p_party_new + bordering + religion_in_new + q3_4, family = binomial, 
##     data = ., weights = weight)
## 
## Coefficients:
##                        Estimate Std. Error z value Pr(>|z|)    
## intercept               0.02567    0.18184   0.141 0.887737    
## Partisan_Index         -0.46004    0.02601 -17.688  < 2e-16 ***
## age                     0.17527    0.02696   6.502 7.91e-11 ***
## Rest_Tiers             -0.13273    0.04115  -3.226 0.001256 ** 
## BJP                     0.18370    0.04887   3.759 0.000171 ***
## INC                    -0.04169    0.07041  -0.592 0.553780    
## bordering              -0.05356    0.07161  -0.748 0.454445    
## Hindu                   0.32261    0.05491   5.876 4.21e-09 ***
## Muslim                 -0.33775    0.08043  -4.199 2.68e-05 ***
## Terrorism_Dissatisfied -0.10052    0.01903  -5.283 1.27e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 16283  on 12433  degrees of freedom
## Residual deviance: 15359  on 12424  degrees of freedom
## AIC: 15267
## 
## Number of Fisher Scoring iterations: 4
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Observations 12434
Dependent variable q10
Type Generalized linear model
Family binomial
Link logit
𝛘²(9) 923.55
Pseudo-R² (Cragg-Uhler) 0.10
Pseudo-R² (McFadden) 0.06
AIC 15266.63
BIC 15340.91
exp(Est.) 2.5% 97.5% z val. p
intercept 1.03 0.72 1.47 0.14 0.89
Partisan_Index 0.63 0.60 0.66 -17.69 0.00
age 1.19 1.13 1.26 6.50 0.00
Rest_Tiers 0.88 0.81 0.95 -3.23 0.00
BJP 1.20 1.09 1.32 3.76 0.00
INC 0.96 0.84 1.10 -0.59 0.55
bordering 0.95 0.82 1.09 -0.75 0.45
Hindu 1.38 1.24 1.54 5.88 0.00
Muslim 0.71 0.61 0.84 -4.20 0.00
Terrorism_Dissatisfied 0.90 0.87 0.94 -5.28 0.00
Standard errors: MLE
## Warning in vif.default(model_q10t_4): No intercept: vifs may not be sensible.
##                      GVIF Df GVIF^(1/(2*Df))
## partisan_index   6.770994  1        2.602113
## age_cb_new      11.108228  1        3.332901
## geo_citytier_in  2.773961  1        1.665521
## p_party_new      4.275230  2        1.437937
## bordering       51.383886  1        7.168255
## religion_in_new  7.446988  2        1.651943
## q3_4             4.800733  1        2.191057
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## 
## Call:
## glm(formula = q10 ~ partisan_index + age_cb_new + geo_citytier_in + 
##     p_party_new + hub_dist + religion_in_new + q3_4, family = binomial, 
##     data = ., weights = weight)
## 
## Coefficients:
##                          Estimate Std. Error z value Pr(>|z|)    
## intercept               1.464e-01  1.167e-01   1.254  0.20984    
## Partisan_Index         -4.623e-01  2.606e-02 -17.741  < 2e-16 ***
## age                     1.726e-01  2.698e-02   6.396 1.59e-10 ***
## Rest_Tiers             -1.015e-01  4.017e-02  -2.526  0.01153 *  
## BJP                     1.356e-01  4.960e-02   2.734  0.00627 ** 
## INC                    -3.873e-02  7.057e-02  -0.549  0.58312    
## Distance               -2.123e-04  3.606e-05  -5.887 3.93e-09 ***
## Hindu                   3.113e-01  5.500e-02   5.659 1.52e-08 ***
## Muslim                 -3.503e-01  8.058e-02  -4.348 1.37e-05 ***
## Terrorism_Dissatisfied -9.975e-02  1.905e-02  -5.236 1.64e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 16283  on 12433  degrees of freedom
## Residual deviance: 15325  on 12424  degrees of freedom
## AIC: 15228
## 
## Number of Fisher Scoring iterations: 4
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Observations 12434
Dependent variable q10
Type Generalized linear model
Family binomial
Link logit
𝛘²(9) 957.70
Pseudo-R² (Cragg-Uhler) 0.10
Pseudo-R² (McFadden) 0.06
AIC 15227.53
BIC 15301.82
exp(Est.) 2.5% 97.5% z val. p
intercept 1.16 0.92 1.46 1.25 0.21
Partisan_Index 0.63 0.60 0.66 -17.74 0.00
age 1.19 1.13 1.25 6.40 0.00
Rest_Tiers 0.90 0.84 0.98 -2.53 0.01
BJP 1.15 1.04 1.26 2.73 0.01
INC 0.96 0.84 1.10 -0.55 0.58
Distance 1.00 1.00 1.00 -5.89 0.00
Hindu 1.37 1.23 1.52 5.66 0.00
Muslim 0.70 0.60 0.82 -4.35 0.00
Terrorism_Dissatisfied 0.91 0.87 0.94 -5.24 0.00
Standard errors: MLE
## Warning in vif.default(model_q10t_3): No intercept: vifs may not be sensible.
##                      GVIF Df GVIF^(1/(2*Df))
## partisan_index   6.780030  1        2.603849
## age_cb_new      11.093632  1        3.330710
## geo_citytier_in  2.631937  1        1.622325
## p_party_new      4.406691  2        1.448866
## hub_dist         4.070893  1        2.017645
## religion_in_new  7.451834  2        1.652212
## q3_4             4.797720  1        2.190370

4.10.1 combined set 7

## 
## Q10t
## =================================
## Statistic N Mean St. Dev. Min Max
## =================================
## 
## Q10t
## =======================================================================
##                                      Dependent variable:               
##                        ------------------------------------------------
##                                              q10                       
##                              (1)          (2)        (3)        (4)    
## -----------------------------------------------------------------------
## intercept                   0.053        0.050      0.146      0.026   
##                            (0.113)      (0.115)    (0.117)    (0.182)  
##                                                                        
## Partisan_Index            -0.464***    -0.462***  -0.462***  -0.460*** 
##                            (0.026)      (0.026)    (0.026)    (0.026)  
##                                                                        
## age                       0.173***      0.178***   0.173***   0.175*** 
##                            (0.027)      (0.027)    (0.027)    (0.027)  
##                                                                        
## Rest_Tiers                -0.114***    -0.107***   -0.101**  -0.133*** 
##                            (0.040)      (0.040)    (0.040)    (0.041)  
##                                                                        
## BJP                       0.136***      0.105**    0.136***   0.184*** 
##                            (0.050)      (0.050)    (0.050)    (0.049)  
##                                                                        
## INC                        -0.041        -0.013     -0.039     -0.042  
##                            (0.071)      (0.071)    (0.071)    (0.070)  
##                                                                        
## SE                        -0.232***                                    
##                            (0.040)                                     
##                                                                        
## South India                            -0.387***                       
##                                         (0.052)                        
##                                                                        
## East                   Northeast India              0.061              
##                                         (0.057)                        
##                                                                        
## West India                               0.093*                        
##                                         (0.053)                        
##                                                                        
## Distance                                          -0.0002***           
##                                                   (0.00004)            
##                                                                        
## bordering                                                      -0.054  
##                                                               (0.072)  
##                                                                        
## Hindu                     0.316***      0.283***   0.311***   0.323*** 
##                            (0.055)      (0.055)    (0.055)    (0.055)  
##                                                                        
## Muslim                    -0.350***    -0.377***  -0.350***  -0.338*** 
##                            (0.081)      (0.081)    (0.081)    (0.080)  
##                                                                        
## Terrorism_Dissatisfied    -0.100***    -0.102***  -0.100***  -0.101*** 
##                            (0.019)      (0.019)    (0.019)    (0.019)  
##                                                                        
## -----------------------------------------------------------------------
## Observations               12,434        12,434     12,434     12,434  
## Log Likelihood           -7,605.158    -7,583.772 -7,603.767 -7,623.316
## Akaike Inf. Crit.        15,230.320    15,191.550 15,227.530 15,266.630
## =======================================================================
## Note:                                       *p<0.1; **p<0.05; ***p<0.01

4.11 set 8

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## 
## Call:
## glm(formula = q11b_wider ~ partisan_index + age_cb_new + geo_citytier_in + 
##     p_party_new + geo_region_in_cb + religion_in_new + q3_4, 
##     family = binomial, data = ., weights = weight)
## 
## Coefficients:
##                        Estimate Std. Error z value Pr(>|z|)    
## intercept              -0.30966    0.13693  -2.261 0.023732 *  
## Partisan_Index          0.08099    0.03050   2.655 0.007922 ** 
## age                    -0.00952    0.03170  -0.300 0.763933    
## Rest_Tiers             -0.09092    0.04855  -1.873 0.061105 .  
## BJP                    -0.20319    0.06074  -3.345 0.000822 ***
## INC                    -0.16971    0.07730  -2.196 0.028127 *  
## South India             0.19291    0.05908   3.265 0.001095 ** 
## East & Northeast India -0.07069    0.07240  -0.976 0.328898    
## West India              0.01861    0.06738   0.276 0.782453    
## Hindu                  -0.16209    0.06144  -2.638 0.008339 ** 
## Muslim                  0.11692    0.08005   1.461 0.144115    
## Terrorism_Dissatisfied  0.10092    0.02119   4.763  1.9e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 10593  on 7869  degrees of freedom
## Residual deviance: 10414  on 7858  degrees of freedom
## AIC: 10496
## 
## Number of Fisher Scoring iterations: 4
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Observations 7870
Dependent variable q11b_wider
Type Generalized linear model
Family binomial
Link logit
𝛘²(11) 179.07
Pseudo-R² (Cragg-Uhler) 0.03
Pseudo-R² (McFadden) 0.02
AIC 10496.44
BIC 10580.09
exp(Est.) 2.5% 97.5% z val. p
intercept 0.73 0.56 0.96 -2.26 0.02
Partisan_Index 1.08 1.02 1.15 2.66 0.01
age 0.99 0.93 1.05 -0.30 0.76
Rest_Tiers 0.91 0.83 1.00 -1.87 0.06
BJP 0.82 0.72 0.92 -3.35 0.00
INC 0.84 0.73 0.98 -2.20 0.03
South India 1.21 1.08 1.36 3.26 0.00
East & Northeast India 0.93 0.81 1.07 -0.98 0.33
West India 1.02 0.89 1.16 0.28 0.78
Hindu 0.85 0.75 0.96 -2.64 0.01
Muslim 1.12 0.96 1.31 1.46 0.14
Terrorism_Dissatisfied 1.11 1.06 1.15 4.76 0.00
Standard errors: MLE
## Warning in vif.default(model_q11b_t_2): No intercept: vifs may not be sensible.
##                       GVIF Df GVIF^(1/(2*Df))
## partisan_index    8.274739  1        2.876585
## age_cb_new       10.407724  1        3.226101
## geo_citytier_in   2.745154  1        1.656850
## p_party_new       3.665164  2        1.383641
## geo_region_in_cb  3.362375  3        1.223980
## religion_in_new   6.037464  2        1.567522
## q3_4              5.219931  1        2.284717
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## 
## Call:
## glm(formula = q11b_wider ~ partisan_index + age_cb_new + geo_citytier_in + 
##     p_party_new + geo_region_in_cb_new + religion_in_new + q3_4, 
##     family = binomial, data = ., weights = weight)
## 
## Coefficients:
##                         Estimate Std. Error z value Pr(>|z|)    
## intercept              -0.293051   0.133434  -2.196 0.028076 *  
## Partisan_Index          0.084935   0.030445   2.790 0.005274 ** 
## age                    -0.007589   0.031621  -0.240 0.810341    
## Rest_Tiers             -0.081944   0.048368  -1.694 0.090234 .  
## BJP                    -0.218031   0.060530  -3.602 0.000316 ***
## INC                    -0.155756   0.077091  -2.020 0.043339 *  
## SE                      0.096687   0.047716   2.026 0.042733 *  
## Hindu                  -0.186666   0.060993  -3.060 0.002210 ** 
## Muslim                  0.105500   0.079888   1.321 0.186637    
## Terrorism_Dissatisfied  0.099479   0.021164   4.700  2.6e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 10593  on 7869  degrees of freedom
## Residual deviance: 10427  on 7860  degrees of freedom
## AIC: 10504
## 
## Number of Fisher Scoring iterations: 4
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Observations 7870
Dependent variable q11b_wider
Type Generalized linear model
Family binomial
Link logit
𝛘²(9) 166.12
Pseudo-R² (Cragg-Uhler) 0.03
Pseudo-R² (McFadden) 0.02
AIC 10503.65
BIC 10573.36
exp(Est.) 2.5% 97.5% z val. p
intercept 0.75 0.57 0.97 -2.20 0.03
Partisan_Index 1.09 1.03 1.16 2.79 0.01
age 0.99 0.93 1.06 -0.24 0.81
Rest_Tiers 0.92 0.84 1.01 -1.69 0.09
BJP 0.80 0.71 0.91 -3.60 0.00
INC 0.86 0.74 1.00 -2.02 0.04
SE 1.10 1.00 1.21 2.03 0.04
Hindu 0.83 0.74 0.94 -3.06 0.00
Muslim 1.11 0.95 1.30 1.32 0.19
Terrorism_Dissatisfied 1.10 1.06 1.15 4.70 0.00
Standard errors: MLE
## Warning in vif.default(model_q11b_t_1): No intercept: vifs may not be sensible.
##                           GVIF Df GVIF^(1/(2*Df))
## partisan_index        8.260061  1        2.874032
## age_cb_new           10.375277  1        3.221068
## geo_citytier_in       2.731065  1        1.652593
## p_party_new           3.621680  2        1.379518
## geo_region_in_cb_new  2.052868  1        1.432783
## religion_in_new       5.956319  2        1.562228
## q3_4                  5.219175  1        2.284551
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## 
## Call:
## glm(formula = q11b_wider ~ partisan_index + age_cb_new + geo_citytier_in + 
##     p_party_new + bordering + religion_in_new + q3_4, family = binomial, 
##     data = ., weights = weight)
## 
## Coefficients:
##                         Estimate Std. Error z value Pr(>|z|)    
## intercept              -0.284624   0.222086  -1.282  0.19998    
## Partisan_Index          0.083659   0.030444   2.748  0.00600 ** 
## age                    -0.007908   0.031616  -0.250  0.80248    
## Rest_Tiers             -0.070101   0.049475  -1.417  0.15652    
## BJP                    -0.238843   0.059747  -3.998  6.4e-05 ***
## INC                    -0.154942   0.077081  -2.010  0.04442 *  
## bordering               0.022757   0.088276   0.258  0.79657    
## Hindu                  -0.189094   0.060964  -3.102  0.00192 ** 
## Muslim                  0.099121   0.079820   1.242  0.21431    
## Terrorism_Dissatisfied  0.099991   0.021163   4.725  2.3e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 10593  on 7869  degrees of freedom
## Residual deviance: 10431  on 7860  degrees of freedom
## AIC: 10510
## 
## Number of Fisher Scoring iterations: 4
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Observations 7870
Dependent variable q11b_wider
Type Generalized linear model
Family binomial
Link logit
𝛘²(9) 162.08
Pseudo-R² (Cragg-Uhler) 0.03
Pseudo-R² (McFadden) 0.02
AIC 10509.93
BIC 10579.64
exp(Est.) 2.5% 97.5% z val. p
intercept 0.75 0.49 1.16 -1.28 0.20
Partisan_Index 1.09 1.02 1.15 2.75 0.01
age 0.99 0.93 1.06 -0.25 0.80
Rest_Tiers 0.93 0.85 1.03 -1.42 0.16
BJP 0.79 0.70 0.89 -4.00 0.00
INC 0.86 0.74 1.00 -2.01 0.04
bordering 1.02 0.86 1.22 0.26 0.80
Hindu 0.83 0.73 0.93 -3.10 0.00
Muslim 1.10 0.94 1.29 1.24 0.21
Terrorism_Dissatisfied 1.11 1.06 1.15 4.72 0.00
Standard errors: MLE
## Warning in vif.default(model_q11b_t_4): No intercept: vifs may not be sensible.
##                      GVIF Df GVIF^(1/(2*Df))
## partisan_index   8.263517  1        2.874633
## age_cb_new      10.379318  1        3.221695
## geo_citytier_in  2.860161  1        1.691201
## p_party_new      3.519686  2        1.369702
## bordering       54.743602  1        7.398892
## religion_in_new  5.948988  2        1.561747
## q3_4             5.221358  1        2.285029
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## 
## Call:
## glm(formula = q11b_wider ~ partisan_index + age_cb_new + geo_citytier_in + 
##     p_party_new + hub_dist + religion_in_new + q3_4, family = binomial, 
##     data = ., weights = weight)
## 
## Coefficients:
##                          Estimate Std. Error z value Pr(>|z|)    
## intercept              -3.219e-01  1.382e-01  -2.330 0.019812 *  
## Partisan_Index          8.473e-02  3.044e-02   2.783 0.005383 ** 
## age                    -7.471e-03  3.162e-02  -0.236 0.813215    
## Rest_Tiers             -8.642e-02  4.870e-02  -1.775 0.075969 .  
## BJP                    -2.200e-01  6.051e-02  -3.636 0.000277 ***
## INC                    -1.563e-01  7.709e-02  -2.028 0.042590 *  
## Distance                7.896e-05  4.237e-05   1.864 0.062382 .  
## Hindu                  -1.855e-01  6.101e-02  -3.040 0.002362 ** 
## Muslim                  1.052e-01  7.989e-02   1.317 0.187858    
## Terrorism_Dissatisfied  9.920e-02  2.117e-02   4.686 2.78e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 10593  on 7869  degrees of freedom
## Residual deviance: 10428  on 7860  degrees of freedom
## AIC: 10505
## 
## Number of Fisher Scoring iterations: 4
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Observations 7870
Dependent variable q11b_wider
Type Generalized linear model
Family binomial
Link logit
𝛘²(9) 165.49
Pseudo-R² (Cragg-Uhler) 0.03
Pseudo-R² (McFadden) 0.02
AIC 10504.94
BIC 10574.65
exp(Est.) 2.5% 97.5% z val. p
intercept 0.72 0.55 0.95 -2.33 0.02
Partisan_Index 1.09 1.03 1.16 2.78 0.01
age 0.99 0.93 1.06 -0.24 0.81
Rest_Tiers 0.92 0.83 1.01 -1.77 0.08
BJP 0.80 0.71 0.90 -3.64 0.00
INC 0.86 0.74 0.99 -2.03 0.04
Distance 1.00 1.00 1.00 1.86 0.06
Hindu 0.83 0.74 0.94 -3.04 0.00
Muslim 1.11 0.95 1.30 1.32 0.19
Terrorism_Dissatisfied 1.10 1.06 1.15 4.69 0.00
Standard errors: MLE
## Warning in vif.default(model_q11b_t_3): No intercept: vifs may not be sensible.
##                      GVIF Df GVIF^(1/(2*Df))
## partisan_index   8.259464  1        2.873928
## age_cb_new      10.376807  1        3.221305
## geo_citytier_in  2.768753  1        1.663957
## p_party_new      3.621235  2        1.379476
## hub_dist         4.459989  1        2.111868
## religion_in_new  5.957984  2        1.562337
## q3_4             5.220956  1        2.284941

4.11.1 combined set 8

## 
## Q11b
## =================================
## Statistic N Mean St. Dev. Min Max
## =================================
## 
## Q11b
## =======================================================================
##                                      Dependent variable:               
##                        ------------------------------------------------
##                                           q11b_wider                   
##                              (1)          (2)        (3)        (4)    
## -----------------------------------------------------------------------
## intercept                 -0.293**      -0.310**   -0.322**    -0.285  
##                            (0.133)      (0.137)    (0.138)    (0.222)  
##                                                                        
## Partisan_Index            0.085***      0.081***   0.085***   0.084*** 
##                            (0.030)      (0.030)    (0.030)    (0.030)  
##                                                                        
## age                        -0.008        -0.010     -0.007     -0.008  
##                            (0.032)      (0.032)    (0.032)    (0.032)  
##                                                                        
## Rest_Tiers                 -0.082*      -0.091*    -0.086*     -0.070  
##                            (0.048)      (0.049)    (0.049)    (0.049)  
##                                                                        
## BJP                       -0.218***    -0.203***  -0.220***  -0.239*** 
##                            (0.061)      (0.061)    (0.061)    (0.060)  
##                                                                        
## INC                       -0.156**      -0.170**   -0.156**   -0.155** 
##                            (0.077)      (0.077)    (0.077)    (0.077)  
##                                                                        
## SE                         0.097**                                     
##                            (0.048)                                     
##                                                                        
## South India                             0.193***                       
##                                         (0.059)                        
##                                                                        
## East                   Northeast India              -0.071             
##                                         (0.072)                        
##                                                                        
## West India                               0.019                         
##                                         (0.067)                        
##                                                                        
## Distance                                           0.0001*             
##                                                   (0.00004)            
##                                                                        
## bordering                                                      0.023   
##                                                               (0.088)  
##                                                                        
## Hindu                     -0.187***    -0.162***  -0.185***  -0.189*** 
##                            (0.061)      (0.061)    (0.061)    (0.061)  
##                                                                        
## Muslim                      0.105        0.117      0.105      0.099   
##                            (0.080)      (0.080)    (0.080)    (0.080)  
##                                                                        
## Terrorism_Dissatisfied    0.099***      0.101***   0.099***   0.100*** 
##                            (0.021)      (0.021)    (0.021)    (0.021)  
##                                                                        
## -----------------------------------------------------------------------
## Observations                7,870        7,870      7,870      7,870   
## Log Likelihood           -5,241.826    -5,236.218 -5,242.469 -5,244.964
## Akaike Inf. Crit.        10,503.650    10,496.440 10,504.940 10,509.930
## =======================================================================
## Note:                                       *p<0.1; **p<0.05; ***p<0.01

4.12 set 9

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## 
## Call:
## glm(formula = q10 ~ partisan_index + age_cb_new + geo_citytier_in + 
##     p_party_new + geo_region_in_cb_new + religion_in_new + q7_5, 
##     family = binomial, data = ., weights = weight)
## 
## Coefficients:
##                Estimate Std. Error z value Pr(>|z|)    
## intercept      -0.78631    0.11980  -6.563 5.26e-11 ***
## Partisan_Index -0.47241    0.02601 -18.160  < 2e-16 ***
## age             0.18422    0.02707   6.805 1.01e-11 ***
## Rest_Tiers     -0.11681    0.04015  -2.909 0.003622 ** 
## BJP             0.16837    0.04863   3.462 0.000536 ***
## INC            -0.06695    0.07060  -0.948 0.342987    
## SE             -0.21167    0.04027  -5.256 1.47e-07 ***
## Hindu           0.32293    0.05491   5.881 4.07e-09 ***
## Muslim         -0.37922    0.08042  -4.716 2.41e-06 ***
## Cricket         0.40925    0.03913  10.458  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 16283  on 12433  degrees of freedom
## Residual deviance: 15245  on 12424  degrees of freedom
## AIC: 15157
## 
## Number of Fisher Scoring iterations: 4
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Observations 12434
Dependent variable q10
Type Generalized linear model
Family binomial
Link logit
𝛘²(9) 1037.90
Pseudo-R² (Cragg-Uhler) 0.11
Pseudo-R² (McFadden) 0.06
AIC 15156.57
BIC 15230.85
exp(Est.) 2.5% 97.5% z val. p
intercept 0.46 0.36 0.58 -6.56 0.00
Partisan_Index 0.62 0.59 0.66 -18.16 0.00
age 1.20 1.14 1.27 6.80 0.00
Rest_Tiers 0.89 0.82 0.96 -2.91 0.00
BJP 1.18 1.08 1.30 3.46 0.00
INC 0.94 0.81 1.07 -0.95 0.34
SE 0.81 0.75 0.88 -5.26 0.00
Hindu 1.38 1.24 1.54 5.88 0.00
Muslim 0.68 0.58 0.80 -4.72 0.00
Cricket 1.51 1.39 1.63 10.46 0.00
Standard errors: MLE
## Warning in vif.default(model_10c_1): No intercept: vifs may not be sensible.
##                           GVIF Df GVIF^(1/(2*Df))
## partisan_index        6.710081  1        2.590382
## age_cb_new           11.083415  1        3.329176
## geo_citytier_in       2.611301  1        1.615952
## p_party_new           4.096095  2        1.422632
## geo_region_in_cb_new  1.820680  1        1.349326
## religion_in_new       7.203118  2        1.638250
## q7_5                 10.214372  1        3.195993
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## 
## Call:
## glm(formula = q10 ~ partisan_index + age_cb_new + geo_citytier_in + 
##     p_party_new + geo_region_in_cb + religion_in_new + q7_5, 
##     family = binomial, data = ., weights = weight)
## 
## Coefficients:
##                        Estimate Std. Error z value Pr(>|z|)    
## intercept              -0.77393    0.12243  -6.321 2.59e-10 ***
## Partisan_Index         -0.47184    0.02610 -18.076  < 2e-16 ***
## age                     0.18835    0.02714   6.939 3.95e-12 ***
## Rest_Tiers             -0.10994    0.04025  -2.731   0.0063 ** 
## BJP                     0.14228    0.04887   2.911   0.0036 ** 
## INC                    -0.04208    0.07093  -0.593   0.5530    
## South India            -0.35619    0.05221  -6.822 8.98e-12 ***
## East & Northeast India  0.05884    0.05680   1.036   0.3003    
## West India              0.08734    0.05365   1.628   0.1035    
## Hindu                   0.29326    0.05531   5.302 1.14e-07 ***
## Muslim                 -0.40441    0.08078  -5.006 5.55e-07 ***
## Cricket                 0.39683    0.03923  10.115  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 16283  on 12433  degrees of freedom
## Residual deviance: 15198  on 12422  degrees of freedom
## AIC: 15125
## 
## Number of Fisher Scoring iterations: 4
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Observations 12434
Dependent variable q10
Type Generalized linear model
Family binomial
Link logit
𝛘²(11) 1085.21
Pseudo-R² (Cragg-Uhler) 0.11
Pseudo-R² (McFadden) 0.07
AIC 15124.98
BIC 15214.12
exp(Est.) 2.5% 97.5% z val. p
intercept 0.46 0.36 0.59 -6.32 0.00
Partisan_Index 0.62 0.59 0.66 -18.08 0.00
age 1.21 1.14 1.27 6.94 0.00
Rest_Tiers 0.90 0.83 0.97 -2.73 0.01
BJP 1.15 1.05 1.27 2.91 0.00
INC 0.96 0.83 1.10 -0.59 0.55
South India 0.70 0.63 0.78 -6.82 0.00
East & Northeast India 1.06 0.95 1.19 1.04 0.30
West India 1.09 0.98 1.21 1.63 0.10
Hindu 1.34 1.20 1.49 5.30 0.00
Muslim 0.67 0.57 0.78 -5.01 0.00
Cricket 1.49 1.38 1.61 10.11 0.00
Standard errors: MLE
## Warning in vif.default(model_10c_2): No intercept: vifs may not be sensible.
##                       GVIF Df GVIF^(1/(2*Df))
## partisan_index    6.730975  1        2.594412
## age_cb_new       11.096739  1        3.331177
## geo_citytier_in   2.608948  1        1.615224
## p_party_new       4.157647  2        1.427946
## geo_region_in_cb  2.928743  3        1.196135
## religion_in_new   7.295276  2        1.643465
## q7_5             10.232139  1        3.198771
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## 
## Call:
## glm(formula = q10 ~ partisan_index + age_cb_new + geo_citytier_in + 
##     p_party_new + hub_dist + religion_in_new + q7_5, family = binomial, 
##     data = ., weights = weight)
## 
## Coefficients:
##                  Estimate Std. Error z value Pr(>|z|)    
## intercept      -6.960e-01  1.240e-01  -5.612 2.00e-08 ***
## Partisan_Index -4.712e-01  2.601e-02 -18.119  < 2e-16 ***
## age             1.835e-01  2.707e-02   6.780 1.20e-11 ***
## Rest_Tiers     -1.046e-01  4.030e-02  -2.597 0.009413 ** 
## BJP             1.665e-01  4.862e-02   3.424 0.000617 ***
## INC            -6.494e-02  7.062e-02  -0.920 0.357797    
## Distance       -1.994e-04  3.623e-05  -5.504 3.72e-08 ***
## Hindu           3.178e-01  5.494e-02   5.784 7.29e-09 ***
## Muslim         -3.797e-01  8.043e-02  -4.720 2.35e-06 ***
## Cricket         4.109e-01  3.912e-02  10.504  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 16283  on 12433  degrees of freedom
## Residual deviance: 15242  on 12424  degrees of freedom
## AIC: 15152
## 
## Number of Fisher Scoring iterations: 4
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Observations 12434
Dependent variable q10
Type Generalized linear model
Family binomial
Link logit
𝛘²(9) 1040.56
Pseudo-R² (Cragg-Uhler) 0.11
Pseudo-R² (McFadden) 0.06
AIC 15152.44
BIC 15226.73
exp(Est.) 2.5% 97.5% z val. p
intercept 0.50 0.39 0.64 -5.61 0.00
Partisan_Index 0.62 0.59 0.66 -18.12 0.00
age 1.20 1.14 1.27 6.78 0.00
Rest_Tiers 0.90 0.83 0.97 -2.60 0.01
BJP 1.18 1.07 1.30 3.42 0.00
INC 0.94 0.82 1.08 -0.92 0.36
Distance 1.00 1.00 1.00 -5.50 0.00
Hindu 1.37 1.23 1.53 5.78 0.00
Muslim 0.68 0.58 0.80 -4.72 0.00
Cricket 1.51 1.40 1.63 10.50 0.00
Standard errors: MLE
## Warning in vif.default(model_q10c_3): No intercept: vifs may not be sensible.
##                      GVIF Df GVIF^(1/(2*Df))
## partisan_index   6.705824  1        2.589561
## age_cb_new      11.082580  1        3.329051
## geo_citytier_in  2.629426  1        1.621551
## p_party_new      4.097809  2        1.422781
## hub_dist         4.078403  1        2.019506
## religion_in_new  7.209944  2        1.638638
## q7_5            10.207034  1        3.194845
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## 
## Call:
## glm(formula = q10 ~ partisan_index + age_cb_new + geo_citytier_in + 
##     p_party_new + bordering + religion_in_new + q7_5, family = binomial, 
##     data = ., weights = weight)
## 
## Coefficients:
##                Estimate Std. Error z value Pr(>|z|)    
## intercept      -0.80198    0.18707  -4.287 1.81e-05 ***
## Partisan_Index -0.46918    0.02596 -18.071  < 2e-16 ***
## age             0.18609    0.02705   6.879 6.02e-12 ***
## Rest_Tiers     -0.13522    0.04130  -3.274  0.00106 ** 
## BJP             0.21116    0.04790   4.408 1.04e-05 ***
## INC            -0.06728    0.07046  -0.955  0.33968    
## bordering      -0.06064    0.07183  -0.844  0.39853    
## Hindu           0.32787    0.05485   5.977 2.27e-09 ***
## Muslim         -0.36883    0.08031  -4.593 4.37e-06 ***
## Cricket         0.41935    0.03905  10.739  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 16283  on 12433  degrees of freedom
## Residual deviance: 15272  on 12424  degrees of freedom
## AIC: 15186
## 
## Number of Fisher Scoring iterations: 4
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Observations 12434
Dependent variable q10
Type Generalized linear model
Family binomial
Link logit
𝛘²(9) 1010.95
Pseudo-R² (Cragg-Uhler) 0.11
Pseudo-R² (McFadden) 0.06
AIC 15186.05
BIC 15260.34
exp(Est.) 2.5% 97.5% z val. p
intercept 0.45 0.31 0.65 -4.29 0.00
Partisan_Index 0.63 0.59 0.66 -18.07 0.00
age 1.20 1.14 1.27 6.88 0.00
Rest_Tiers 0.87 0.81 0.95 -3.27 0.00
BJP 1.24 1.12 1.36 4.41 0.00
INC 0.93 0.81 1.07 -0.95 0.34
bordering 0.94 0.82 1.08 -0.84 0.40
Hindu 1.39 1.25 1.55 5.98 0.00
Muslim 0.69 0.59 0.81 -4.59 0.00
Cricket 1.52 1.41 1.64 10.74 0.00
Standard errors: MLE
## Warning in vif.default(model_10c_4): No intercept: vifs may not be sensible.
##                      GVIF Df GVIF^(1/(2*Df))
## partisan_index   6.696361  1        2.587733
## age_cb_new      11.095856  1        3.331044
## geo_citytier_in  2.771928  1        1.664911
## p_party_new      3.970333  2        1.411584
## bordering       51.321970  1        7.163935
## religion_in_new  7.204149  2        1.638308
## q7_5            10.192069  1        3.192502
## 
## Q10c
## =================================
## Statistic N Mean St. Dev. Min Max
## =================================
## 
## Q10c
## ==================================================================
##                                 Dependent variable:               
##                   ------------------------------------------------
##                                         q10                       
##                         (1)          (2)        (3)        (4)    
## ------------------------------------------------------------------
## intercept            -0.786***    -0.774***  -0.696***  -0.802*** 
##                       (0.120)      (0.122)    (0.124)    (0.187)  
##                                                                   
## Partisan_Index       -0.472***    -0.472***  -0.471***  -0.469*** 
##                       (0.026)      (0.026)    (0.026)    (0.026)  
##                                                                   
## age                  0.184***      0.188***   0.184***   0.186*** 
##                       (0.027)      (0.027)    (0.027)    (0.027)  
##                                                                   
## Rest_Tiers           -0.117***    -0.110***  -0.105***  -0.135*** 
##                       (0.040)      (0.040)    (0.040)    (0.041)  
##                                                                   
## BJP                  0.168***      0.142***   0.166***   0.211*** 
##                       (0.049)      (0.049)    (0.049)    (0.048)  
##                                                                   
## INC                   -0.067        -0.042     -0.065     -0.067  
##                       (0.071)      (0.071)    (0.071)    (0.070)  
##                                                                   
## SE                   -0.212***                                    
##                       (0.040)                                     
##                                                                   
## South India                       -0.356***                       
##                                    (0.052)                        
##                                                                   
## East              Northeast India              0.059              
##                                    (0.057)                        
##                                                                   
## West India                          0.087                         
##                                    (0.054)                        
##                                                                   
## Distance                                     -0.0002***           
##                                              (0.00004)            
##                                                                   
## bordering                                                 -0.061  
##                                                          (0.072)  
##                                                                   
## Hindu                0.323***      0.293***   0.318***   0.328*** 
##                       (0.055)      (0.055)    (0.055)    (0.055)  
##                                                                   
## Muslim               -0.379***    -0.404***  -0.380***  -0.369*** 
##                       (0.080)      (0.081)    (0.080)    (0.080)  
##                                                                   
## Cricket              0.409***      0.397***   0.411***   0.419*** 
##                       (0.039)      (0.039)    (0.039)    (0.039)  
##                                                                   
## ------------------------------------------------------------------
## Observations          12,434        12,434     12,434     12,434  
## Log Likelihood      -7,568.286    -7,550.489 -7,566.222 -7,583.027
## Akaike Inf. Crit.   15,156.570    15,124.980 15,152.440 15,186.050
## ==================================================================
## Note:                                  *p<0.1; **p<0.05; ***p<0.01

4.12.1 combined set 9

## 
## Q10c
## =================================
## Statistic N Mean St. Dev. Min Max
## =================================
## 
## Q10c
## ==================================================================
##                                 Dependent variable:               
##                   ------------------------------------------------
##                                         q10                       
##                         (1)          (2)        (3)        (4)    
## ------------------------------------------------------------------
## intercept            -0.786***    -0.774***  -0.696***  -0.802*** 
##                       (0.120)      (0.122)    (0.124)    (0.187)  
##                                                                   
## Partisan_Index       -0.472***    -0.472***  -0.471***  -0.469*** 
##                       (0.026)      (0.026)    (0.026)    (0.026)  
##                                                                   
## age                  0.184***      0.188***   0.184***   0.186*** 
##                       (0.027)      (0.027)    (0.027)    (0.027)  
##                                                                   
## Rest_Tiers           -0.117***    -0.110***  -0.105***  -0.135*** 
##                       (0.040)      (0.040)    (0.040)    (0.041)  
##                                                                   
## BJP                  0.168***      0.142***   0.166***   0.211*** 
##                       (0.049)      (0.049)    (0.049)    (0.048)  
##                                                                   
## INC                   -0.067        -0.042     -0.065     -0.067  
##                       (0.071)      (0.071)    (0.071)    (0.070)  
##                                                                   
## SE                   -0.212***                                    
##                       (0.040)                                     
##                                                                   
## South India                       -0.356***                       
##                                    (0.052)                        
##                                                                   
## East              Northeast India              0.059              
##                                    (0.057)                        
##                                                                   
## West India                          0.087                         
##                                    (0.054)                        
##                                                                   
## Distance                                     -0.0002***           
##                                              (0.00004)            
##                                                                   
## bordering                                                 -0.061  
##                                                          (0.072)  
##                                                                   
## Hindu                0.323***      0.293***   0.318***   0.328*** 
##                       (0.055)      (0.055)    (0.055)    (0.055)  
##                                                                   
## Muslim               -0.379***    -0.404***  -0.380***  -0.369*** 
##                       (0.080)      (0.081)    (0.080)    (0.080)  
##                                                                   
## Cricket              0.409***      0.397***   0.411***   0.419*** 
##                       (0.039)      (0.039)    (0.039)    (0.039)  
##                                                                   
## ------------------------------------------------------------------
## Observations          12,434        12,434     12,434     12,434  
## Log Likelihood      -7,568.286    -7,550.489 -7,566.222 -7,583.027
## Akaike Inf. Crit.   15,156.570    15,124.980 15,152.440 15,186.050
## ==================================================================
## Note:                                  *p<0.1; **p<0.05; ***p<0.01

4.13 set 10

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## 
## Call:
## glm(formula = q10 ~ partisan_index + age_cb_new + geo_citytier_in + 
##     p_party_new + geo_region_in_cb_new + religion_in_new + q7_1, 
##     family = binomial, data = ., weights = weight)
## 
## Coefficients:
##                Estimate Std. Error z value Pr(>|z|)    
## intercept      -0.42748    0.11876  -3.599 0.000319 ***
## Partisan_Index -0.48302    0.02593 -18.627  < 2e-16 ***
## age             0.18265    0.02698   6.771 1.28e-11 ***
## Rest_Tiers     -0.11369    0.04000  -2.842 0.004483 ** 
## BJP             0.19545    0.04837   4.040 5.34e-05 ***
## INC            -0.06753    0.07025  -0.961 0.336416    
## SE             -0.22973    0.04007  -5.733 9.87e-09 ***
## Hindu           0.34566    0.05468   6.322 2.59e-10 ***
## Muslim         -0.39034    0.08012  -4.872 1.10e-06 ***
## Restrain        0.17202    0.03889   4.423 9.71e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 16283  on 12433  degrees of freedom
## Residual deviance: 15335  on 12424  degrees of freedom
## AIC: 15240
## 
## Number of Fisher Scoring iterations: 4
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Observations 12434
Dependent variable q10
Type Generalized linear model
Family binomial
Link logit
𝛘²(9) 947.91
Pseudo-R² (Cragg-Uhler) 0.10
Pseudo-R² (McFadden) 0.06
AIC 15240.47
BIC 15314.75
exp(Est.) 2.5% 97.5% z val. p
intercept 0.65 0.52 0.82 -3.60 0.00
Partisan_Index 0.62 0.59 0.65 -18.63 0.00
age 1.20 1.14 1.27 6.77 0.00
Rest_Tiers 0.89 0.83 0.97 -2.84 0.00
BJP 1.22 1.11 1.34 4.04 0.00
INC 0.93 0.81 1.07 -0.96 0.34
SE 0.79 0.73 0.86 -5.73 0.00
Hindu 1.41 1.27 1.57 6.32 0.00
Muslim 0.68 0.58 0.79 -4.87 0.00
Restrain 1.19 1.10 1.28 4.42 0.00
Standard errors: MLE
## Warning in vif.default(model_10r_1): No intercept: vifs may not be sensible.
##                           GVIF Df GVIF^(1/(2*Df))
## partisan_index        6.717425  1        2.591800
## age_cb_new           11.089832  1        3.330140
## geo_citytier_in       2.611485  1        1.616009
## p_party_new           4.078217  2        1.421077
## geo_region_in_cb_new  1.818336  1        1.348457
## religion_in_new       7.186341  2        1.637295
## q7_1                  9.659471  1        3.107969
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## 
## Call:
## glm(formula = q10 ~ partisan_index + age_cb_new + geo_citytier_in + 
##     p_party_new + geo_region_in_cb + religion_in_new + q7_1, 
##     family = binomial, data = ., weights = weight)
## 
## Coefficients:
##                        Estimate Std. Error z value Pr(>|z|)    
## intercept              -0.42036    0.12167  -3.455 0.000550 ***
## Partisan_Index         -0.48204    0.02603 -18.522  < 2e-16 ***
## age                     0.18707    0.02706   6.914 4.72e-12 ***
## Rest_Tiers             -0.10635    0.04011  -2.652 0.008008 ** 
## BJP                     0.16738    0.04863   3.442 0.000578 ***
## INC                    -0.04138    0.07061  -0.586 0.557810    
## South India            -0.37808    0.05199  -7.273 3.52e-13 ***
## East & Northeast India  0.05387    0.05657   0.952 0.340909    
## West India              0.09533    0.05348   1.782 0.074674 .  
## Hindu                   0.31431    0.05509   5.706 1.16e-08 ***
## Muslim                 -0.41592    0.08049  -5.168 2.37e-07 ***
## Restraint               0.16129    0.03900   4.135 3.54e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 16283  on 12433  degrees of freedom
## Residual deviance: 15283  on 12422  degrees of freedom
## AIC: 15205
## 
## Number of Fisher Scoring iterations: 4
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Observations 12434
Dependent variable q10
Type Generalized linear model
Family binomial
Link logit
𝛘²(11) 999.87
Pseudo-R² (Cragg-Uhler) 0.10
Pseudo-R² (McFadden) 0.06
AIC 15205.11
BIC 15294.25
exp(Est.) 2.5% 97.5% z val. p
intercept 0.66 0.52 0.83 -3.46 0.00
Partisan_Index 0.62 0.59 0.65 -18.52 0.00
age 1.21 1.14 1.27 6.91 0.00
Rest_Tiers 0.90 0.83 0.97 -2.65 0.01
BJP 1.18 1.07 1.30 3.44 0.00
INC 0.96 0.84 1.10 -0.59 0.56
South India 0.69 0.62 0.76 -7.27 0.00
East & Northeast India 1.06 0.94 1.18 0.95 0.34
West India 1.10 0.99 1.22 1.78 0.07
Hindu 1.37 1.23 1.53 5.71 0.00
Muslim 0.66 0.56 0.77 -5.17 0.00
Restraint 1.18 1.09 1.27 4.14 0.00
Standard errors: MLE
## Warning in vif.default(model_10r_2): No intercept: vifs may not be sensible.
##                       GVIF Df GVIF^(1/(2*Df))
## partisan_index    6.738170  1        2.595798
## age_cb_new       11.106370  1        3.332622
## geo_citytier_in   2.609164  1        1.615291
## p_party_new       4.141460  2        1.426554
## geo_region_in_cb  2.923550  3        1.195781
## religion_in_new   7.278213  2        1.642503
## q7_1              9.678312  1        3.110999
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## 
## Call:
## glm(formula = q10 ~ partisan_index + age_cb_new + geo_citytier_in + 
##     p_party_new + hub_dist + religion_in_new + q7_1, family = binomial, 
##     data = ., weights = weight)
## 
## Coefficients:
##                  Estimate Std. Error z value Pr(>|z|)    
## intercept      -3.306e-01  1.230e-01  -2.688  0.00718 ** 
## Partisan_Index -4.817e-01  2.592e-02 -18.581  < 2e-16 ***
## age             1.818e-01  2.697e-02   6.741 1.57e-11 ***
## Rest_Tiers     -1.010e-01  4.015e-02  -2.515  0.01189 *  
## BJP             1.948e-01  4.836e-02   4.028 5.63e-05 ***
## INC            -6.535e-02  7.027e-02  -0.930  0.35236    
## Distance       -2.110e-04  3.604e-05  -5.854 4.81e-09 ***
## Hindu           3.405e-01  5.471e-02   6.225 4.82e-10 ***
## Muslim         -3.907e-01  8.013e-02  -4.875 1.09e-06 ***
## Restraint       1.708e-01  3.889e-02   4.391 1.13e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 16283  on 12433  degrees of freedom
## Residual deviance: 15334  on 12424  degrees of freedom
## AIC: 15237
## 
## Number of Fisher Scoring iterations: 4
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Observations 12434
Dependent variable q10
Type Generalized linear model
Family binomial
Link logit
𝛘²(9) 949.30
Pseudo-R² (Cragg-Uhler) 0.10
Pseudo-R² (McFadden) 0.06
AIC 15237.42
BIC 15311.70
exp(Est.) 2.5% 97.5% z val. p
intercept 0.72 0.56 0.91 -2.69 0.01
Partisan_Index 0.62 0.59 0.65 -18.58 0.00
age 1.20 1.14 1.26 6.74 0.00
Rest_Tiers 0.90 0.84 0.98 -2.52 0.01
BJP 1.22 1.11 1.34 4.03 0.00
INC 0.94 0.82 1.08 -0.93 0.35
Distance 1.00 1.00 1.00 -5.85 0.00
Hindu 1.41 1.26 1.56 6.22 0.00
Muslim 0.68 0.58 0.79 -4.88 0.00
Restraint 1.19 1.10 1.28 4.39 0.00
Standard errors: MLE
## Warning in vif.default(model_q10r_3): No intercept: vifs may not be sensible.
##                      GVIF Df GVIF^(1/(2*Df))
## partisan_index   6.712870  1        2.590921
## age_cb_new      11.088546  1        3.329947
## geo_citytier_in  2.629877  1        1.621689
## p_party_new      4.079264  2        1.421168
## hub_dist         4.072915  1        2.018146
## religion_in_new  7.193276  2        1.637690
## q7_1             9.661287  1        3.108261
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## 
## Call:
## glm(formula = q10 ~ partisan_index + age_cb_new + geo_citytier_in + 
##     p_party_new + bordering + religion_in_new + q7_1, family = binomial, 
##     data = ., weights = weight)
## 
## Coefficients:
##                Estimate Std. Error z value Pr(>|z|)    
## intercept      -0.44792    0.18670  -2.399  0.01644 *  
## Partisan_Index -0.47981    0.02588 -18.543  < 2e-16 ***
## age             0.18444    0.02695   6.844 7.70e-12 ***
## Rest_Tiers     -0.13266    0.04113  -3.225  0.00126 ** 
## BJP             0.24282    0.04762   5.099 3.42e-07 ***
## INC            -0.06798    0.07011  -0.970  0.33222    
## bordering      -0.05808    0.07161  -0.811  0.41732    
## Hindu           0.35199    0.05461   6.445 1.15e-10 ***
## Muslim         -0.37899    0.07999  -4.738 2.16e-06 ***
## Restraint       0.17483    0.03883   4.502 6.72e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 16283  on 12433  degrees of freedom
## Residual deviance: 15367  on 12424  degrees of freedom
## AIC: 15276
## 
## Number of Fisher Scoring iterations: 4
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Observations 12434
Dependent variable q10
Type Generalized linear model
Family binomial
Link logit
𝛘²(9) 915.64
Pseudo-R² (Cragg-Uhler) 0.10
Pseudo-R² (McFadden) 0.06
AIC 15275.68
BIC 15349.96
exp(Est.) 2.5% 97.5% z val. p
intercept 0.64 0.44 0.92 -2.40 0.02
Partisan_Index 0.62 0.59 0.65 -18.54 0.00
age 1.20 1.14 1.27 6.84 0.00
Rest_Tiers 0.88 0.81 0.95 -3.23 0.00
BJP 1.27 1.16 1.40 5.10 0.00
INC 0.93 0.81 1.07 -0.97 0.33
bordering 0.94 0.82 1.09 -0.81 0.42
Hindu 1.42 1.28 1.58 6.45 0.00
Muslim 0.68 0.59 0.80 -4.74 0.00
Restraint 1.19 1.10 1.29 4.50 0.00
Standard errors: MLE
## Warning in vif.default(model_10r_4): No intercept: vifs may not be sensible.
##                      GVIF Df GVIF^(1/(2*Df))
## partisan_index   6.702512  1        2.588921
## age_cb_new      11.103402  1        3.332177
## geo_citytier_in  2.771898  1        1.664902
## p_party_new      3.950403  2        1.409809
## bordering       51.415419  1        7.170455
## religion_in_new  7.189259  2        1.637461
## q7_1             9.658804  1        3.107862

4.13.1 combined set 10

## 
## Q10c
## =================================
## Statistic N Mean St. Dev. Min Max
## =================================
## 
## Q10c
## ==================================================================
##                                 Dependent variable:               
##                   ------------------------------------------------
##                                         q10                       
##                         (1)          (2)        (3)        (4)    
## ------------------------------------------------------------------
## intercept            -0.427***    -0.420***  -0.331***   -0.448** 
##                       (0.119)      (0.122)    (0.123)    (0.187)  
##                                                                   
## Partisan_Index       -0.483***    -0.482***  -0.482***  -0.480*** 
##                       (0.026)      (0.026)    (0.026)    (0.026)  
##                                                                   
## age                  0.183***      0.187***   0.182***   0.184*** 
##                       (0.027)      (0.027)    (0.027)    (0.027)  
##                                                                   
## Rest_Tiers           -0.114***    -0.106***   -0.101**  -0.133*** 
##                       (0.040)      (0.040)    (0.040)    (0.041)  
##                                                                   
## BJP                  0.195***      0.167***   0.195***   0.243*** 
##                       (0.048)      (0.049)    (0.048)    (0.048)  
##                                                                   
## INC                   -0.068        -0.041     -0.065     -0.068  
##                       (0.070)      (0.071)    (0.070)    (0.070)  
##                                                                   
## SE                   -0.230***                                    
##                       (0.040)                                     
##                                                                   
## South India                       -0.378***                       
##                                    (0.052)                        
##                                                                   
## East              Northeast India              0.054              
##                                    (0.057)                        
##                                                                   
## West India                          0.095*                        
##                                    (0.053)                        
##                                                                   
## Distance                                     -0.0002***           
##                                              (0.00004)            
##                                                                   
## bordering                                                 -0.058  
##                                                          (0.072)  
##                                                                   
## Hindu                0.346***      0.314***   0.341***   0.352*** 
##                       (0.055)      (0.055)    (0.055)    (0.055)  
##                                                                   
## Muslim               -0.390***    -0.416***  -0.391***  -0.379*** 
##                       (0.080)      (0.080)    (0.080)    (0.080)  
##                                                                   
## Restrain             0.172***                                     
##                       (0.039)                                     
##                                                                   
## Restraint                          0.161***   0.171***   0.175*** 
##                                    (0.039)    (0.039)    (0.039)  
##                                                                   
## ------------------------------------------------------------------
## Observations          12,434        12,434     12,434     12,434  
## Log Likelihood      -7,610.235    -7,590.557 -7,608.710 -7,627.841
## Akaike Inf. Crit.   15,240.470    15,205.110 15,237.420 15,275.680
## ==================================================================
## Note:                                  *p<0.1; **p<0.05; ***p<0.01

4.14 set 11

## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## 
## Call:
## glm(formula = q10 ~ partisan_index + age_cb_new + geo_citytier_in + 
##     p_party_new + geo_region_in_cb_new + religion_in_new + caste_in_new, 
##     family = binomial, data = ., weights = weight)
## 
## Coefficients:
##                Estimate Std. Error z value Pr(>|z|)    
## intercept      -0.15524    0.10789  -1.439  0.15018    
## Partisan_Index -0.47829    0.02590 -18.468  < 2e-16 ***
## age             0.18016    0.02712   6.642 3.10e-11 ***
## Rest_Tiers     -0.11040    0.04003  -2.758  0.00581 ** 
## BJP             0.19123    0.04862   3.933 8.38e-05 ***
## INC            -0.07197    0.07022  -1.025  0.30535    
## SE             -0.23026    0.04017  -5.732 9.90e-09 ***
## Hindu           0.34237    0.05502   6.223 4.88e-10 ***
## Muslim         -0.39543    0.08035  -4.921 8.60e-07 ***
## Caste          -0.01126    0.01800  -0.626  0.53159    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 16283  on 12433  degrees of freedom
## Residual deviance: 15354  on 12424  degrees of freedom
## AIC: 15261
## 
## Number of Fisher Scoring iterations: 4
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Observations 12434
Dependent variable q10
Type Generalized linear model
Family binomial
Link logit
𝛘²(9) 928.72
Pseudo-R² (Cragg-Uhler) 0.10
Pseudo-R² (McFadden) 0.06
AIC 15261.29
BIC 15335.57
exp(Est.) 2.5% 97.5% z val. p
intercept 0.86 0.69 1.06 -1.44 0.15
Partisan_Index 0.62 0.59 0.65 -18.47 0.00
age 1.20 1.14 1.26 6.64 0.00
Rest_Tiers 0.90 0.83 0.97 -2.76 0.01
BJP 1.21 1.10 1.33 3.93 0.00
INC 0.93 0.81 1.07 -1.03 0.31
SE 0.79 0.73 0.86 -5.73 0.00
Hindu 1.41 1.26 1.57 6.22 0.00
Muslim 0.67 0.58 0.79 -4.92 0.00
Caste 0.99 0.95 1.02 -0.63 0.53
Standard errors: MLE
## Warning in vif.default(model_10cas_1): No intercept: vifs may not be sensible.
##                           GVIF Df GVIF^(1/(2*Df))
## partisan_index        6.710504  1        2.590464
## age_cb_new           11.232543  1        3.351499
## geo_citytier_in       2.620174  1        1.618695
## p_party_new           4.125800  2        1.425204
## geo_region_in_cb_new  1.829482  1        1.352584
## religion_in_new       7.291667  2        1.643262
## caste_in_new          3.795605  1        1.948231
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## 
## Call:
## glm(formula = q10 ~ partisan_index + age_cb_new + geo_citytier_in + 
##     p_party_new + geo_region_in_cb + religion_in_new + caste_in_new, 
##     family = binomial, data = ., weights = weight)
## 
## Coefficients:
##                         Estimate Std. Error z value Pr(>|z|)    
## intercept              -0.168895   0.110721  -1.525 0.127157    
## Partisan_Index         -0.477559   0.025994 -18.372  < 2e-16 ***
## age                     0.184103   0.027210   6.766 1.32e-11 ***
## Rest_Tiers             -0.103561   0.040139  -2.580 0.009879 ** 
## BJP                     0.163717   0.048865   3.350 0.000807 ***
## INC                    -0.044632   0.070586  -0.632 0.527183    
## South India            -0.384265   0.052089  -7.377 1.62e-13 ***
## East & Northeast India  0.057875   0.056568   1.023 0.306257    
## West India              0.092761   0.053431   1.736 0.082546 .  
## Hindu                   0.311602   0.055427   5.622 1.89e-08 ***
## Muslim                 -0.420229   0.080752  -5.204 1.95e-07 ***
## Caste                  -0.007191   0.018045  -0.399 0.690244    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 16283  on 12433  degrees of freedom
## Residual deviance: 15300  on 12422  degrees of freedom
## AIC: 15224
## 
## Number of Fisher Scoring iterations: 4
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Observations 12434
Dependent variable q10
Type Generalized linear model
Family binomial
Link logit
𝛘²(11) 982.92
Pseudo-R² (Cragg-Uhler) 0.10
Pseudo-R² (McFadden) 0.06
AIC 15223.92
BIC 15313.06
exp(Est.) 2.5% 97.5% z val. p
intercept 0.84 0.68 1.05 -1.53 0.13
Partisan_Index 0.62 0.59 0.65 -18.37 0.00
age 1.20 1.14 1.27 6.77 0.00
Rest_Tiers 0.90 0.83 0.98 -2.58 0.01
BJP 1.18 1.07 1.30 3.35 0.00
INC 0.96 0.83 1.10 -0.63 0.53
South India 0.68 0.61 0.75 -7.38 0.00
East & Northeast India 1.06 0.95 1.18 1.02 0.31
West India 1.10 0.99 1.22 1.74 0.08
Hindu 1.37 1.23 1.52 5.62 0.00
Muslim 0.66 0.56 0.77 -5.20 0.00
Caste 0.99 0.96 1.03 -0.40 0.69
Standard errors: MLE
## Warning in vif.default(model_10cas_2): No intercept: vifs may not be sensible.
##                       GVIF Df GVIF^(1/(2*Df))
## partisan_index    6.731195  1        2.594455
## age_cb_new       11.250455  1        3.354170
## geo_citytier_in   2.618440  1        1.618159
## p_party_new       4.187163  2        1.430474
## geo_region_in_cb  2.938522  3        1.196800
## religion_in_new   7.385025  2        1.648496
## caste_in_new      3.794444  1        1.947933
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## 
## Call:
## glm(formula = q10 ~ partisan_index + age_cb_new + geo_citytier_in + 
##     p_party_new + hub_dist + religion_in_new + caste_in_new, 
##     family = binomial, data = ., weights = weight)
## 
## Coefficients:
##                  Estimate Std. Error z value Pr(>|z|)    
## intercept      -6.013e-02  1.120e-01  -0.537    0.591    
## Partisan_Index -4.770e-01  2.589e-02 -18.424  < 2e-16 ***
## age             1.793e-01  2.712e-02   6.611 3.82e-11 ***
## Rest_Tiers     -9.767e-02  4.017e-02  -2.432    0.015 *  
## BJP             1.906e-01  4.860e-02   3.920 8.84e-05 ***
## INC            -6.973e-02  7.023e-02  -0.993    0.321    
## Distance       -2.123e-04  3.613e-05  -5.875 4.24e-09 ***
## Hindu           3.374e-01  5.504e-02   6.129 8.83e-10 ***
## Muslim         -3.956e-01  8.036e-02  -4.923 8.53e-07 ***
## Caste          -1.075e-02  1.800e-02  -0.597    0.550    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 16283  on 12433  degrees of freedom
## Residual deviance: 15352  on 12424  degrees of freedom
## AIC: 15258
## 
## Number of Fisher Scoring iterations: 4
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Observations 12434
Dependent variable q10
Type Generalized linear model
Family binomial
Link logit
𝛘²(9) 930.37
Pseudo-R² (Cragg-Uhler) 0.10
Pseudo-R² (McFadden) 0.06
AIC 15257.98
BIC 15332.26
exp(Est.) 2.5% 97.5% z val. p
intercept 0.94 0.76 1.17 -0.54 0.59
Partisan_Index 0.62 0.59 0.65 -18.42 0.00
age 1.20 1.13 1.26 6.61 0.00
Rest_Tiers 0.91 0.84 0.98 -2.43 0.02
BJP 1.21 1.10 1.33 3.92 0.00
INC 0.93 0.81 1.07 -0.99 0.32
Distance 1.00 1.00 1.00 -5.87 0.00
Hindu 1.40 1.26 1.56 6.13 0.00
Muslim 0.67 0.58 0.79 -4.92 0.00
Caste 0.99 0.96 1.02 -0.60 0.55
Standard errors: MLE
## Warning in vif.default(model_q10cas_3): No intercept: vifs may not be sensible.
##                      GVIF Df GVIF^(1/(2*Df))
## partisan_index   6.705690  1        2.589535
## age_cb_new      11.232633  1        3.351512
## geo_citytier_in  2.637213  1        1.623950
## p_party_new      4.126523  2        1.425267
## hub_dist         4.099425  1        2.024704
## religion_in_new  7.296794  2        1.643550
## caste_in_new     3.796486  1        1.948457
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## 
## Call:
## glm(formula = q10 ~ partisan_index + age_cb_new + geo_citytier_in + 
##     p_party_new + bordering + religion_in_new + caste_in_new, 
##     family = binomial, data = ., weights = weight)
## 
## Coefficients:
##                Estimate Std. Error z value Pr(>|z|)    
## intercept      -0.15464    0.17951  -0.861  0.38901    
## Partisan_Index -0.47470    0.02584 -18.371  < 2e-16 ***
## age             0.18340    0.02710   6.768 1.30e-11 ***
## Rest_Tiers     -0.12872    0.04117  -3.127  0.00177 ** 
## BJP             0.23627    0.04793   4.929 8.26e-07 ***
## INC            -0.07346    0.07007  -1.048  0.29441    
## bordering      -0.05991    0.07152  -0.838  0.40222    
## Hindu           0.34567    0.05496   6.290 3.18e-10 ***
## Muslim         -0.38687    0.08022  -4.823 1.42e-06 ***
## Caste          -0.01921    0.01796  -1.070  0.28470    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 16283  on 12433  degrees of freedom
## Residual deviance: 15386  on 12424  degrees of freedom
## AIC: 15297
## 
## Number of Fisher Scoring iterations: 4
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Observations 12434
Dependent variable q10
Type Generalized linear model
Family binomial
Link logit
𝛘²(9) 896.50
Pseudo-R² (Cragg-Uhler) 0.09
Pseudo-R² (McFadden) 0.05
AIC 15296.68
BIC 15370.97
exp(Est.) 2.5% 97.5% z val. p
intercept 0.86 0.60 1.22 -0.86 0.39
Partisan_Index 0.62 0.59 0.65 -18.37 0.00
age 1.20 1.14 1.27 6.77 0.00
Rest_Tiers 0.88 0.81 0.95 -3.13 0.00
BJP 1.27 1.15 1.39 4.93 0.00
INC 0.93 0.81 1.07 -1.05 0.29
bordering 0.94 0.82 1.08 -0.84 0.40
Hindu 1.41 1.27 1.57 6.29 0.00
Muslim 0.68 0.58 0.79 -4.82 0.00
Caste 0.98 0.95 1.02 -1.07 0.28
Standard errors: MLE
## Warning in vif.default(model_10cas_4): No intercept: vifs may not be sensible.
##                      GVIF Df GVIF^(1/(2*Df))
## partisan_index   6.693905  1        2.587258
## age_cb_new      11.244785  1        3.353324
## geo_citytier_in  2.782756  1        1.668160
## p_party_new      4.008663  2        1.414979
## bordering       51.374073  1        7.167571
## religion_in_new  7.296877  2        1.643555
## caste_in_new     3.783423  1        1.945102

4.14.1 combined set 11

## 
## Q10cas
## =================================
## Statistic N Mean St. Dev. Min Max
## =================================
## 
## Q10cas
## ==================================================================
##                                 Dependent variable:               
##                   ------------------------------------------------
##                                         q10                       
##                         (1)          (2)        (3)        (4)    
## ------------------------------------------------------------------
## intercept             -0.155        -0.169     -0.060     -0.155  
##                       (0.108)      (0.111)    (0.112)    (0.180)  
##                                                                   
## Partisan_Index       -0.478***    -0.478***  -0.477***  -0.475*** 
##                       (0.026)      (0.026)    (0.026)    (0.026)  
##                                                                   
## age                  0.180***      0.184***   0.179***   0.183*** 
##                       (0.027)      (0.027)    (0.027)    (0.027)  
##                                                                   
## Rest_Tiers           -0.110***    -0.104***   -0.098**  -0.129*** 
##                       (0.040)      (0.040)    (0.040)    (0.041)  
##                                                                   
## BJP                  0.191***      0.164***   0.191***   0.236*** 
##                       (0.049)      (0.049)    (0.049)    (0.048)  
##                                                                   
## INC                   -0.072        -0.045     -0.070     -0.073  
##                       (0.070)      (0.071)    (0.070)    (0.070)  
##                                                                   
## SE                   -0.230***                                    
##                       (0.040)                                     
##                                                                   
## South India                       -0.384***                       
##                                    (0.052)                        
##                                                                   
## East              Northeast India              0.058              
##                                    (0.057)                        
##                                                                   
## West India                          0.093*                        
##                                    (0.053)                        
##                                                                   
## Distance                                     -0.0002***           
##                                              (0.00004)            
##                                                                   
## bordering                                                 -0.060  
##                                                          (0.072)  
##                                                                   
## Hindu                0.342***      0.312***   0.337***   0.346*** 
##                       (0.055)      (0.055)    (0.055)    (0.055)  
##                                                                   
## Muslim               -0.395***    -0.420***  -0.396***  -0.387*** 
##                       (0.080)      (0.081)    (0.080)    (0.080)  
##                                                                   
## Caste                 -0.011        -0.007     -0.011     -0.019  
##                       (0.018)      (0.018)    (0.018)    (0.018)  
##                                                                   
## ------------------------------------------------------------------
## Observations          12,434        12,434     12,434     12,434  
## Log Likelihood      -7,620.645    -7,599.962 -7,618.989 -7,638.342
## Akaike Inf. Crit.   15,261.290    15,223.920 15,257.980 15,296.680
## ==================================================================
## Note:                                  *p<0.1; **p<0.05; ***p<0.01