library(survey)## Loading required package: grid## Loading required package: Matrix## Loading required package: survival##
## Attaching package: 'survey'## The following object is masked from 'package:graphics':
##
## dotchartsource('analysis/chendaniely/model_utils.R')
load('data/model_dataframes.RData')
svy_never_every <- svydesign(ids = ~1, weights = ~weight, data = never_every[!is.na(never_every$weight), ])
svy_never_some <- svydesign(ids = ~1, weights = ~weight, data = never_some[!is.na(never_some$weight), ])
svy_never_someevery <- svydesign(ids = ~1, weights = ~weight, data = never_someevery[!is.na(never_someevery$weight), ])
svy_some_every <- svydesign(ids = ~1, weights = ~weight, data = some_every[!is.na(some_every$weight), ])Demographics + Q13
ne_demo <- svyglm(formula = Q13 ~ ppagecat + PPEDUCAT + income + PPREG4 + work,
design = svy_never_every,
family = quasibinomial(link = "logit"))
print_svy_mod(ne_demo)##
## Call:
## svyglm(formula = Q13 ~ ppagecat + PPEDUCAT + income + PPREG4 +
## work, design = svy_never_every, family = quasibinomial(link = "logit"))
##
## Survey design:
## svydesign(ids = ~1, weights = ~weight, data = never_every[!is.na(never_every$weight),
## ])
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.15875 0.38131 -3.039 0.002410
## ppagecat25-34 -0.12446 0.25785 -0.483 0.629387
## ppagecat35-44 0.11411 0.24482 0.466 0.641197
## ppagecat45-54 0.36594 0.23766 1.540 0.123797
## ppagecat55-64 0.79890 0.22843 3.497 0.000482
## ppagecat65-74 1.23304 0.24325 5.069 4.43e-07
## ppagecat75+ 1.78680 0.31427 5.686 1.53e-08
## PPEDUCATHigh school 0.12528 0.21964 0.570 0.568488
## PPEDUCATSome college 0.04576 0.22502 0.203 0.838890
## PPEDUCATBachelor_s degree or higher 0.56223 0.23241 2.419 0.015662
## income$10k to $25k -0.03187 0.32358 -0.098 0.921563
## income$25k to $50k 0.32064 0.31107 1.031 0.302792
## income$50k to $75k 0.54019 0.31574 1.711 0.087289
## income$75k to $100k 0.59079 0.32660 1.809 0.070644
## income$100k to $150k 0.84520 0.32077 2.635 0.008493
## incomeover $150k 1.11446 0.36584 3.046 0.002352
## PPREG4Northeast 0.08301 0.16879 0.492 0.622954
## PPREG4South 0.07621 0.14528 0.525 0.599952
## PPREG4West 0.18289 0.17063 1.072 0.283932
## workemployed -0.32218 0.13615 -2.366 0.018070
##
## (Intercept) **
## ppagecat25-34
## ppagecat35-44
## ppagecat45-54
## ppagecat55-64 ***
## ppagecat65-74 ***
## ppagecat75+ ***
## PPEDUCATHigh school
## PPEDUCATSome college
## PPEDUCATBachelor_s degree or higher *
## income$10k to $25k
## income$25k to $50k
## income$50k to $75k .
## income$75k to $100k .
## income$100k to $150k **
## incomeover $150k **
## PPREG4Northeast
## PPREG4South
## PPREG4West
## workemployed *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 1.004179)
##
## Number of Fisher Scoring iterations: 4
##
## term or sig or_std_err or_lower
## 1 (Intercept) 0.3139 ** 1.464 -2.5559
## 2 ppagecat25-34 0.8830 1.294 -1.6535
## 3 ppagecat35-44 1.1209 1.277 -1.3828
## 4 ppagecat45-54 1.4419 1.268 -1.0439
## 5 ppagecat55-64 2.2231 *** 1.257 -0.2399
## 6 ppagecat65-74 3.4316 *** 1.275 0.9319
## 7 ppagecat75+ 5.9703 *** 1.369 3.2866
## 8 PPEDUCATHigh school 1.1335 1.246 -1.3080
## 9 PPEDUCATSome college 1.0468 1.252 -1.4078
## 10 PPEDUCATBachelor_s degree or higher 1.7546 * 1.262 -0.7182
## 11 income$10k to $25k 0.9686 1.382 -1.7402
## 12 income$25k to $50k 1.3780 1.365 -1.2972
## 13 income$50k to $75k 1.7163 . 1.371 -0.9714
## 14 income$75k to $100k 1.8054 . 1.386 -0.9116
## 15 income$100k to $150k 2.3284 ** 1.378 -0.3728
## 16 incomeover $150k 3.0479 ** 1.442 0.2222
## 17 PPREG4Northeast 1.0865 1.184 -1.2338
## 18 PPREG4South 1.0792 1.156 -1.1873
## 19 PPREG4West 1.2007 1.186 -1.1240
## 20 workemployed 0.7246 * 1.146 -1.5213
## or_upper estimate std.error statistic p.value
## 1 3.184 -1.15875 0.3813 -3.03891 2.410e-03
## 2 3.419 -0.12446 0.2578 -0.48268 6.294e-01
## 3 3.625 0.11411 0.2448 0.46611 6.412e-01
## 4 3.928 0.36594 0.2377 1.53979 1.238e-01
## 5 4.686 0.79890 0.2284 3.49733 4.820e-04
## 6 5.931 1.23304 0.2433 5.06894 4.435e-07
## 7 8.654 1.78680 0.3143 5.68561 1.530e-08
## 8 3.575 0.12528 0.2196 0.57039 5.685e-01
## 9 3.501 0.04576 0.2250 0.20334 8.389e-01
## 10 4.227 0.56223 0.2324 2.41912 1.566e-02
## 11 3.677 -0.03187 0.3236 -0.09848 9.216e-01
## 12 4.053 0.32064 0.3111 1.03078 3.028e-01
## 13 4.404 0.54019 0.3157 1.71086 8.729e-02
## 14 4.522 0.59079 0.3266 1.80889 7.064e-02
## 15 5.030 0.84520 0.3208 2.63487 8.493e-03
## 16 5.874 1.11446 0.3658 3.04635 2.352e-03
## 17 3.407 0.08301 0.1688 0.49176 6.230e-01
## 18 3.346 0.07621 0.1453 0.52457 6.000e-01
## 19 3.525 0.18289 0.1706 1.07187 2.839e-01
## 20 2.970 -0.32218 0.1361 -2.36646 1.807e-02ns_demo <- svyglm(formula = Q13 ~ ppagecat + PPEDUCAT + income + PPREG4 + work,
design = svy_never_some,
family = quasibinomial(link = "logit"))
print_svy_mod(ns_demo)##
## Call:
## svyglm(formula = Q13 ~ ppagecat + PPEDUCAT + income + PPREG4 +
## work, design = svy_never_some, family = quasibinomial(link = "logit"))
##
## Survey design:
## svydesign(ids = ~1, weights = ~weight, data = never_some[!is.na(never_some$weight),
## ])
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.86890 0.43842 -1.982 0.0477 *
## ppagecat25-34 -0.16824 0.25760 -0.653 0.5138
## ppagecat35-44 -0.33740 0.25325 -1.332 0.1830
## ppagecat45-54 -0.23398 0.24717 -0.947 0.3440
## ppagecat55-64 -0.62973 0.24657 -2.554 0.0108 *
## ppagecat65-74 -0.27059 0.28752 -0.941 0.3468
## ppagecat75+ -0.87755 0.48353 -1.815 0.0698 .
## PPEDUCATHigh school -0.32996 0.27255 -1.211 0.2263
## PPEDUCATSome college 0.42652 0.26473 1.611 0.1074
## PPEDUCATBachelor_s degree or higher 0.55928 0.26815 2.086 0.0372 *
## income$10k to $25k -0.08734 0.39014 -0.224 0.8229
## income$25k to $50k 0.29968 0.35424 0.846 0.3977
## income$50k to $75k 0.39828 0.36387 1.095 0.2739
## income$75k to $100k 0.54574 0.37211 1.467 0.1427
## income$100k to $150k 0.59808 0.36959 1.618 0.1059
## incomeover $150k 0.53540 0.40798 1.312 0.1897
## PPREG4Northeast -0.05572 0.20960 -0.266 0.7904
## PPREG4South -0.22860 0.18411 -1.242 0.2146
## PPREG4West 0.43145 0.19312 2.234 0.0257 *
## workemployed -0.15314 0.16011 -0.956 0.3390
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 0.9956768)
##
## Number of Fisher Scoring iterations: 4
##
## term or sig or_std_err or_lower
## 1 (Intercept) 0.4194 * 1.550 -2.6191
## 2 ppagecat25-34 0.8451 1.294 -1.6907
## 3 ppagecat35-44 0.7136 1.288 -1.8113
## 4 ppagecat45-54 0.7914 1.280 -1.7182
## 5 ppagecat55-64 0.5327 * 1.280 -1.9753
## 6 ppagecat65-74 0.7629 1.333 -1.8500
## 7 ppagecat75+ 0.4158 . 1.622 -2.7629
## 8 PPEDUCATHigh school 0.7190 1.313 -1.8551
## 9 PPEDUCATSome college 1.5319 1.303 -1.0221
## 10 PPEDUCATBachelor_s degree or higher 1.7494 * 1.308 -0.8134
## 11 income$10k to $25k 0.9164 1.477 -1.9789
## 12 income$25k to $50k 1.3494 1.425 -1.4437
## 13 income$50k to $75k 1.4893 1.439 -1.3310
## 14 income$75k to $100k 1.7259 1.451 -1.1177
## 15 income$100k to $150k 1.8186 1.447 -1.0178
## 16 incomeover $150k 1.7081 1.504 -1.2393
## 17 PPREG4Northeast 0.9458 1.233 -1.4712
## 18 PPREG4South 0.7956 1.202 -1.5606
## 19 PPREG4West 1.5395 * 1.213 -0.8380
## 20 workemployed 0.8580 1.174 -1.4423
## or_upper estimate std.error statistic p.value
## 1 3.458 -0.86890 0.4384 -1.9819 0.04772
## 2 3.381 -0.16824 0.2576 -0.6531 0.51380
## 3 3.239 -0.33740 0.2533 -1.3322 0.18303
## 4 3.301 -0.23398 0.2472 -0.9467 0.34400
## 5 3.041 -0.62973 0.2466 -2.5540 0.01077
## 6 3.376 -0.27059 0.2875 -0.9411 0.34684
## 7 3.595 -0.87755 0.4835 -1.8149 0.06979
## 8 3.293 -0.32996 0.2726 -1.2106 0.22628
## 9 4.086 0.42652 0.2647 1.6112 0.10740
## 10 4.312 0.55928 0.2681 2.0857 0.03721
## 11 3.812 -0.08734 0.3901 -0.2239 0.82290
## 12 4.143 0.29968 0.3542 0.8460 0.39772
## 13 4.309 0.39828 0.3639 1.0945 0.27393
## 14 4.569 0.54574 0.3721 1.4666 0.14274
## 15 4.655 0.59808 0.3696 1.6182 0.10587
## 16 4.656 0.53540 0.4080 1.3123 0.18965
## 17 3.363 -0.05572 0.2096 -0.2658 0.79043
## 18 3.152 -0.22860 0.1841 -1.2416 0.21461
## 19 3.917 0.43145 0.1931 2.2341 0.02565
## 20 3.158 -0.15314 0.1601 -0.9565 0.33901nse_demo <- svyglm(formula = Q13 ~ ppagecat + PPEDUCAT + income + PPREG4 + work,
design = svy_never_someevery,
family = quasibinomial(link = "logit"))
print_svy_mod(nse_demo)##
## Call:
## svyglm(formula = Q13 ~ ppagecat + PPEDUCAT + income + PPREG4 +
## work, design = svy_never_someevery, family = quasibinomial(link = "logit"))
##
## Survey design:
## svydesign(ids = ~1, weights = ~weight, data = never_someevery[!is.na(never_someevery$weight),
## ])
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.35017 0.32215 -1.087 0.277165
## ppagecat25-34 -0.12243 0.21134 -0.579 0.562456
## ppagecat35-44 -0.10054 0.20422 -0.492 0.622546
## ppagecat45-54 0.09667 0.19929 0.485 0.627687
## ppagecat55-64 0.28297 0.19367 1.461 0.144144
## ppagecat65-74 0.69794 0.21432 3.257 0.001146
## ppagecat75+ 1.12506 0.29206 3.852 0.000121
## PPEDUCATHigh school -0.01512 0.19489 -0.078 0.938153
## PPEDUCATSome college 0.21655 0.19897 1.088 0.276565
## PPEDUCATBachelor_s degree or higher 0.55083 0.20533 2.683 0.007361
## income$10k to $25k -0.01750 0.27925 -0.063 0.950044
## income$25k to $50k 0.31860 0.26522 1.201 0.229789
## income$50k to $75k 0.48772 0.26983 1.807 0.070830
## income$75k to $100k 0.57717 0.27929 2.067 0.038895
## income$100k to $150k 0.75141 0.27516 2.731 0.006371
## incomeover $150k 0.93784 0.31411 2.986 0.002861
## PPREG4Northeast 0.02439 0.15181 0.161 0.872404
## PPREG4South -0.03050 0.13238 -0.230 0.817824
## PPREG4West 0.30264 0.15085 2.006 0.044951
## workemployed -0.26498 0.12214 -2.169 0.030159
##
## (Intercept)
## ppagecat25-34
## ppagecat35-44
## ppagecat45-54
## ppagecat55-64
## ppagecat65-74 **
## ppagecat75+ ***
## PPEDUCATHigh school
## PPEDUCATSome college
## PPEDUCATBachelor_s degree or higher **
## income$10k to $25k
## income$25k to $50k
## income$50k to $75k .
## income$75k to $100k *
## income$100k to $150k **
## incomeover $150k **
## PPREG4Northeast
## PPREG4South
## PPREG4West *
## workemployed *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 1.00668)
##
## Number of Fisher Scoring iterations: 4
##
## term or sig or_std_err or_lower
## 1 (Intercept) 0.7046 1.380 -2.0004
## 2 ppagecat25-34 0.8848 1.235 -1.5365
## 3 ppagecat35-44 0.9044 1.227 -1.4997
## 4 ppagecat45-54 1.1015 1.221 -1.2908
## 5 ppagecat55-64 1.3271 1.214 -1.0518
## 6 ppagecat65-74 2.0096 ** 1.239 -0.4189
## 7 ppagecat75+ 3.0804 *** 1.339 0.4556
## 8 PPEDUCATHigh school 0.9850 1.215 -1.3968
## 9 PPEDUCATSome college 1.2418 1.220 -1.1497
## 10 PPEDUCATBachelor_s degree or higher 1.7347 ** 1.228 -0.6721
## 11 income$10k to $25k 0.9827 1.322 -1.6087
## 12 income$25k to $50k 1.3752 1.304 -1.1801
## 13 income$50k to $75k 1.6286 . 1.310 -0.9385
## 14 income$75k to $100k 1.7810 * 1.322 -0.8105
## 15 income$100k to $150k 2.1200 ** 1.317 -0.4608
## 16 incomeover $150k 2.5545 ** 1.369 -0.1288
## 17 PPREG4Northeast 1.0247 1.164 -1.2566
## 18 PPREG4South 0.9700 1.142 -1.2675
## 19 PPREG4West 1.3534 * 1.163 -0.9257
## 20 workemployed 0.7672 * 1.130 -1.4474
## or_upper estimate std.error statistic p.value
## 1 3.410 -0.35017 0.3221 -1.08699 0.2771645
## 2 3.306 -0.12243 0.2113 -0.57929 0.5624560
## 3 3.308 -0.10054 0.2042 -0.49232 0.6225456
## 4 3.494 0.09667 0.1993 0.48506 0.6276867
## 5 3.706 0.28297 0.1937 1.46107 0.1441437
## 6 4.438 0.69794 0.2143 3.25650 0.0011456
## 7 5.705 1.12506 0.2921 3.85214 0.0001205
## 8 3.367 -0.01512 0.1949 -0.07760 0.9381533
## 9 3.633 0.21655 0.1990 1.08835 0.2765655
## 10 4.141 0.55083 0.2053 2.68262 0.0073612
## 11 3.574 -0.01750 0.2792 -0.06266 0.9500436
## 12 3.930 0.31860 0.2652 1.20125 0.2297891
## 13 4.196 0.48772 0.2698 1.80747 0.0708304
## 14 4.372 0.57717 0.2793 2.06657 0.0388953
## 15 4.701 0.75141 0.2752 2.73076 0.0063709
## 16 5.238 0.93784 0.3141 2.98576 0.0028610
## 17 3.306 0.02439 0.1518 0.16062 0.8724041
## 18 3.207 -0.03050 0.1324 -0.23037 0.8178240
## 19 3.633 0.30264 0.1508 2.00630 0.0449507
## 20 2.982 -0.26498 0.1221 -2.16945 0.0301590Demographics + Q13 + Q20
ne_demo_belief <- svyglm(formula = Q13 ~ ppagecat + PPEDUCAT + income + PPREG4 + work + Q20,
design = svy_never_every,
family = quasibinomial(link = "logit"))
print_svy_mod(ne_demo_belief)##
## Call:
## svyglm(formula = Q13 ~ ppagecat + PPEDUCAT + income + PPREG4 +
## work + Q20, design = svy_never_every, family = quasibinomial(link = "logit"))
##
## Survey design:
## svydesign(ids = ~1, weights = ~weight, data = never_every[!is.na(never_every$weight),
## ])
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.111703 0.467912 2.376 0.017618
## ppagecat25-34 0.257290 0.316924 0.812 0.417000
## ppagecat35-44 0.286100 0.296855 0.964 0.335298
## ppagecat45-54 0.652244 0.288885 2.258 0.024085
## ppagecat55-64 1.071285 0.282470 3.793 0.000154
## ppagecat65-74 1.718542 0.303408 5.664 1.73e-08
## ppagecat75+ 2.080317 0.389260 5.344 1.03e-07
## PPEDUCATHigh school -0.140217 0.262998 -0.533 0.594001
## PPEDUCATSome college 0.013590 0.268861 0.051 0.959694
## PPEDUCATBachelor_s degree or higher 0.485933 0.271065 1.793 0.073202
## income$10k to $25k -0.285509 0.401252 -0.712 0.476845
## income$25k to $50k 0.177264 0.384890 0.461 0.645175
## income$50k to $75k 0.383067 0.387103 0.990 0.322523
## income$75k to $100k 0.381543 0.394885 0.966 0.334076
## income$100k to $150k 0.544320 0.392350 1.387 0.165522
## incomeover $150k 0.760511 0.423591 1.795 0.072769
## PPREG4Northeast -0.015898 0.199993 -0.079 0.936649
## PPREG4South -0.005494 0.171567 -0.032 0.974459
## PPREG4West 0.093077 0.190572 0.488 0.625322
## workemployed -0.249378 0.154690 -1.612 0.107124
## Q20Somewhat effective -1.932446 0.216389 -8.930 < 2e-16
## Q20It varies from season to season -2.657984 0.236578 -11.235 < 2e-16
## Q20Not effective -5.157342 0.525368 -9.817 < 2e-16
## Q20Don_t know -4.415717 0.329667 -13.394 < 2e-16
##
## (Intercept) *
## ppagecat25-34
## ppagecat35-44
## ppagecat45-54 *
## ppagecat55-64 ***
## ppagecat65-74 ***
## ppagecat75+ ***
## PPEDUCATHigh school
## PPEDUCATSome college
## PPEDUCATBachelor_s degree or higher .
## income$10k to $25k
## income$25k to $50k
## income$50k to $75k
## income$75k to $100k
## income$100k to $150k
## incomeover $150k .
## PPREG4Northeast
## PPREG4South
## PPREG4West
## workemployed
## Q20Somewhat effective ***
## Q20It varies from season to season ***
## Q20Not effective ***
## Q20Don_t know ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 1.012814)
##
## Number of Fisher Scoring iterations: 5
##
## term or sig or_std_err or_lower
## 1 (Intercept) 3.039529 * 1.597 -0.08992
## 2 ppagecat25-34 1.293420 1.373 -1.39746
## 3 ppagecat35-44 1.331226 1.346 -1.30619
## 4 ppagecat45-54 1.919844 * 1.335 -0.69664
## 5 ppagecat55-64 2.919129 *** 1.326 0.31938
## 6 ppagecat65-74 5.576390 *** 1.354 2.92163
## 7 ppagecat75+ 8.007009 *** 1.476 5.11427
## 8 PPEDUCATHigh school 0.869170 1.301 -1.68044
## 9 PPEDUCATSome college 1.013682 1.308 -1.55093
## 10 PPEDUCATBachelor_s degree or higher 1.625692 . 1.311 -0.94457
## 11 income$10k to $25k 0.751632 1.494 -2.17601
## 12 income$25k to $50k 1.193946 1.469 -1.68618
## 13 income$50k to $75k 1.466777 1.473 -1.41973
## 14 income$75k to $100k 1.464543 1.484 -1.44452
## 15 income$100k to $150k 1.723436 1.480 -1.17826
## 16 incomeover $150k 2.139370 . 1.527 -0.85441
## 17 PPREG4Northeast 0.984228 1.221 -1.40970
## 18 PPREG4South 0.994521 1.187 -1.33232
## 19 PPREG4West 1.097547 1.210 -1.27394
## 20 workemployed 0.779286 1.167 -1.50862
## 21 Q20Somewhat effective 0.144794 *** 1.242 -2.28871
## 22 Q20It varies from season to season 0.070089 *** 1.267 -2.41305
## 23 Q20Not effective 0.005757 *** 1.691 -3.30876
## 24 Q20Don_t know 0.012086 *** 1.391 -2.71330
## or_upper estimate std.error statistic p.value
## 1 6.169 1.111703 0.4679 2.37588 1.762e-02
## 2 3.984 0.257290 0.3169 0.81183 4.170e-01
## 3 3.969 0.286100 0.2969 0.96377 3.353e-01
## 4 4.536 0.652244 0.2889 2.25779 2.409e-02
## 5 5.519 1.071285 0.2825 3.79257 1.543e-04
## 6 8.231 1.718542 0.3034 5.66412 1.732e-08
## 7 10.900 2.080317 0.3893 5.34429 1.031e-07
## 8 3.419 -0.140217 0.2630 -0.53315 5.940e-01
## 9 3.578 0.013590 0.2689 0.05054 9.597e-01
## 10 4.196 0.485933 0.2711 1.79268 7.320e-02
## 11 3.679 -0.285509 0.4013 -0.71154 4.768e-01
## 12 4.074 0.177264 0.3849 0.46056 6.452e-01
## 13 4.353 0.383067 0.3871 0.98957 3.225e-01
## 14 4.374 0.381543 0.3949 0.96621 3.341e-01
## 15 4.625 0.544320 0.3923 1.38733 1.655e-01
## 16 5.133 0.760511 0.4236 1.79539 7.277e-02
## 17 3.378 -0.015898 0.2000 -0.07949 9.366e-01
## 18 3.321 -0.005494 0.1716 -0.03202 9.745e-01
## 19 3.469 0.093077 0.1906 0.48841 6.253e-01
## 20 3.067 -0.249378 0.1547 -1.61211 1.071e-01
## 21 2.578 -1.932446 0.2164 -8.93043 1.077e-18
## 22 2.553 -2.657984 0.2366 -11.23512 2.657e-28
## 23 3.320 -5.157342 0.5254 -9.81662 3.665e-22
## 24 2.737 -4.415717 0.3297 -13.39448 5.746e-39ns_demo_belief <- svyglm(formula = Q13 ~ ppagecat + PPEDUCAT + income + PPREG4 + work + Q20,
design = svy_never_some,
family = quasibinomial(link = "logit"))
print_svy_mod(ns_demo_belief)##
## Call:
## svyglm(formula = Q13 ~ ppagecat + PPEDUCAT + income + PPREG4 +
## work + Q20, design = svy_never_some, family = quasibinomial(link = "logit"))
##
## Survey design:
## svydesign(ids = ~1, weights = ~weight, data = never_some[!is.na(never_some$weight),
## ])
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.342553 0.472241 0.725 0.4684
## ppagecat25-34 0.089256 0.277557 0.322 0.7478
## ppagecat35-44 -0.240676 0.264334 -0.910 0.3627
## ppagecat45-54 -0.117146 0.250290 -0.468 0.6398
## ppagecat55-64 -0.485524 0.255052 -1.904 0.0572
## ppagecat65-74 -0.004515 0.297290 -0.015 0.9879
## ppagecat75+ -0.601732 0.480598 -1.252 0.2108
## PPEDUCATHigh school -0.418800 0.280988 -1.490 0.1364
## PPEDUCATSome college 0.511144 0.275911 1.853 0.0642
## PPEDUCATBachelor_s degree or higher 0.425203 0.279053 1.524 0.1278
## income$10k to $25k -0.157969 0.416059 -0.380 0.7042
## income$25k to $50k 0.135212 0.372071 0.363 0.7164
## income$50k to $75k 0.267820 0.388324 0.690 0.4905
## income$75k to $100k 0.386658 0.392101 0.986 0.3243
## income$100k to $150k 0.353797 0.384644 0.920 0.3579
## incomeover $150k 0.374320 0.417745 0.896 0.3704
## PPREG4Northeast -0.083956 0.215883 -0.389 0.6974
## PPREG4South -0.236242 0.191895 -1.231 0.2185
## PPREG4West 0.476215 0.202568 2.351 0.0189
## workemployed -0.186572 0.169359 -1.102 0.2708
## Q20Somewhat effective -0.654320 0.272663 -2.400 0.0166
## Q20It varies from season to season -1.365035 0.293946 -4.644 3.79e-06
## Q20Not effective -2.260054 0.387190 -5.837 6.81e-09
## Q20Don_t know -2.312482 0.329565 -7.017 3.76e-12
##
## (Intercept)
## ppagecat25-34
## ppagecat35-44
## ppagecat45-54
## ppagecat55-64 .
## ppagecat65-74
## ppagecat75+
## PPEDUCATHigh school
## PPEDUCATSome college .
## PPEDUCATBachelor_s degree or higher
## income$10k to $25k
## income$25k to $50k
## income$50k to $75k
## income$75k to $100k
## income$100k to $150k
## incomeover $150k
## PPREG4Northeast
## PPREG4South
## PPREG4West *
## workemployed
## Q20Somewhat effective *
## Q20It varies from season to season ***
## Q20Not effective ***
## Q20Don_t know ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 0.9903779)
##
## Number of Fisher Scoring iterations: 4
##
## term or sig or_std_err or_lower
## 1 (Intercept) 1.40854 1.604 -1.7345
## 2 ppagecat25-34 1.09336 1.320 -1.4936
## 3 ppagecat35-44 0.78610 1.303 -1.7669
## 4 ppagecat45-54 0.88946 1.284 -1.6280
## 5 ppagecat55-64 0.61537 . 1.291 -1.9141
## 6 ppagecat65-74 0.99549 1.346 -1.6431
## 7 ppagecat75+ 0.54786 1.617 -2.6215
## 8 PPEDUCATHigh school 0.65784 1.324 -1.9381
## 9 PPEDUCATSome college 1.66720 . 1.318 -0.9156
## 10 PPEDUCATBachelor_s degree or higher 1.52990 1.322 -1.0610
## 11 income$10k to $25k 0.85388 1.516 -2.1174
## 12 income$25k to $50k 1.14478 1.451 -1.6987
## 13 income$50k to $75k 1.30711 1.475 -1.5829
## 14 income$75k to $100k 1.47205 1.480 -1.4289
## 15 income$100k to $150k 1.42447 1.469 -1.4550
## 16 incomeover $150k 1.45400 1.519 -1.5223
## 17 PPREG4Northeast 0.91947 1.241 -1.5128
## 18 PPREG4South 0.78959 1.212 -1.5850
## 19 PPREG4West 1.60997 * 1.225 -0.7901
## 20 workemployed 0.82980 1.185 -1.4919
## 21 Q20Somewhat effective 0.51980 * 1.313 -2.0546
## 22 Q20It varies from season to season 0.25537 *** 1.342 -2.3744
## 23 Q20Not effective 0.10434 *** 1.473 -2.7824
## 24 Q20Don_t know 0.09902 *** 1.390 -2.6261
## or_upper estimate std.error statistic p.value
## 1 4.552 0.342553 0.4722 0.72538 4.684e-01
## 2 3.680 0.089256 0.2776 0.32158 7.478e-01
## 3 3.339 -0.240676 0.2643 -0.91050 3.627e-01
## 4 3.407 -0.117146 0.2503 -0.46804 6.398e-01
## 5 3.145 -0.485524 0.2551 -1.90363 5.719e-02
## 6 3.634 -0.004515 0.2973 -0.01519 9.879e-01
## 7 3.717 -0.601732 0.4806 -1.25205 2.108e-01
## 8 3.254 -0.418800 0.2810 -1.49046 1.364e-01
## 9 4.250 0.511144 0.2759 1.85257 6.419e-02
## 10 4.121 0.425203 0.2791 1.52373 1.278e-01
## 11 3.825 -0.157969 0.4161 -0.37968 7.042e-01
## 12 3.988 0.135212 0.3721 0.36340 7.164e-01
## 13 4.197 0.267820 0.3883 0.68968 4.905e-01
## 14 4.373 0.386658 0.3921 0.98612 3.243e-01
## 15 4.304 0.353797 0.3846 0.91980 3.579e-01
## 16 4.430 0.374320 0.4177 0.89605 3.704e-01
## 17 3.352 -0.083956 0.2159 -0.38890 6.974e-01
## 18 3.164 -0.236242 0.1919 -1.23110 2.185e-01
## 19 4.010 0.476215 0.2026 2.35089 1.889e-02
## 20 3.152 -0.186572 0.1694 -1.10163 2.708e-01
## 21 3.094 -0.654320 0.2727 -2.39974 1.656e-02
## 22 2.885 -1.365035 0.2939 -4.64383 3.792e-06
## 23 2.991 -2.260054 0.3872 -5.83707 6.809e-09
## 24 2.824 -2.312482 0.3296 -7.01677 3.764e-12nse_demo_belief <- svyglm(formula = Q13 ~ ppagecat + PPEDUCAT + income + PPREG4 + work + Q20,
design = svy_never_someevery,
family = quasibinomial(link = "logit"))
print_svy_mod(nse_demo_belief)##
## Call:
## svyglm(formula = Q13 ~ ppagecat + PPEDUCAT + income + PPREG4 +
## work + Q20, design = svy_never_someevery, family = quasibinomial(link = "logit"))
##
## Survey design:
## svydesign(ids = ~1, weights = ~weight, data = never_someevery[!is.na(never_someevery$weight),
## ])
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.560743 0.387582 4.027 5.85e-05
## ppagecat25-34 0.215717 0.250575 0.861 0.389396
## ppagecat35-44 0.001712 0.235219 0.007 0.994193
## ppagecat45-54 0.203090 0.225265 0.902 0.367392
## ppagecat55-64 0.354798 0.219980 1.613 0.106923
## ppagecat65-74 0.915637 0.240970 3.800 0.000149
## ppagecat75+ 1.145851 0.327454 3.499 0.000476
## PPEDUCATHigh school -0.144782 0.221305 -0.654 0.513042
## PPEDUCATSome college 0.348094 0.229158 1.519 0.128908
## PPEDUCATBachelor_s degree or higher 0.508423 0.231912 2.192 0.028465
## income$10k to $25k -0.090667 0.331705 -0.273 0.784620
## income$25k to $50k 0.226580 0.308746 0.734 0.463108
## income$50k to $75k 0.389262 0.317248 1.227 0.219960
## income$75k to $100k 0.429722 0.322469 1.333 0.182808
## income$100k to $150k 0.487213 0.320240 1.521 0.128308
## incomeover $150k 0.675470 0.346260 1.951 0.051218
## PPREG4Northeast -0.018486 0.169996 -0.109 0.913417
## PPREG4South -0.073798 0.147349 -0.501 0.616536
## PPREG4West 0.337980 0.166073 2.035 0.041962
## workemployed -0.226606 0.136482 -1.660 0.096995
## Q20Somewhat effective -1.549009 0.212490 -7.290 4.36e-13
## Q20It varies from season to season -2.268787 0.227127 -9.989 < 2e-16
## Q20Not effective -3.758014 0.331932 -11.322 < 2e-16
## Q20Don_t know -3.582236 0.264587 -13.539 < 2e-16
##
## (Intercept) ***
## ppagecat25-34
## ppagecat35-44
## ppagecat45-54
## ppagecat55-64
## ppagecat65-74 ***
## ppagecat75+ ***
## PPEDUCATHigh school
## PPEDUCATSome college
## PPEDUCATBachelor_s degree or higher *
## income$10k to $25k
## income$25k to $50k
## income$50k to $75k
## income$75k to $100k
## income$100k to $150k
## incomeover $150k .
## PPREG4Northeast
## PPREG4South
## PPREG4West *
## workemployed .
## Q20Somewhat effective ***
## Q20It varies from season to season ***
## Q20Not effective ***
## Q20Don_t know ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 0.9815631)
##
## Number of Fisher Scoring iterations: 4
##
## term or sig or_std_err or_lower
## 1 (Intercept) 4.76236 *** 1.473 1.874467
## 2 ppagecat25-34 1.24075 1.285 -1.277385
## 3 ppagecat35-44 1.00171 1.265 -1.478050
## 4 ppagecat45-54 1.22518 1.253 -1.230019
## 5 ppagecat55-64 1.42589 1.246 -1.016369
## 6 ppagecat65-74 2.49837 *** 1.272 0.004301
## 7 ppagecat75+ 3.14512 *** 1.387 0.425754
## 8 PPEDUCATHigh school 0.86521 1.248 -1.580289
## 9 PPEDUCATSome college 1.41637 1.258 -1.048414
## 10 PPEDUCATBachelor_s degree or higher 1.66267 * 1.261 -0.808909
## 11 income$10k to $25k 0.91332 1.393 -1.817628
## 12 income$25k to $50k 1.25430 1.362 -1.414660
## 13 income$50k to $75k 1.47589 1.373 -1.215861
## 14 income$75k to $100k 1.53683 1.381 -1.169015
## 15 income$100k to $150k 1.62777 1.377 -1.072044
## 16 incomeover $150k 1.96496 . 1.414 -0.806034
## 17 PPREG4Northeast 0.98168 1.185 -1.341504
## 18 PPREG4South 0.92886 1.159 -1.342308
## 19 PPREG4West 1.40211 * 1.181 -0.911980
## 20 workemployed 0.79723 . 1.146 -1.449385
## 21 Q20Somewhat effective 0.21246 *** 1.237 -2.211579
## 22 Q20It varies from season to season 0.10344 *** 1.255 -2.356342
## 23 Q20Not effective 0.02333 *** 1.394 -2.708239
## 24 Q20Don_t know 0.02781 *** 1.303 -2.525857
## or_upper estimate std.error statistic p.value
## 1 7.650 1.560743 0.3876 4.026875 5.851e-05
## 2 3.759 0.215717 0.2506 0.860890 3.894e-01
## 3 3.481 0.001712 0.2352 0.007279 9.942e-01
## 4 3.680 0.203090 0.2253 0.901562 3.674e-01
## 5 3.868 0.354798 0.2200 1.612865 1.069e-01
## 6 4.992 0.915637 0.2410 3.799799 1.489e-04
## 7 5.864 1.145851 0.3275 3.499279 4.761e-04
## 8 3.311 -0.144782 0.2213 -0.654219 5.130e-01
## 9 3.881 0.348094 0.2292 1.519013 1.289e-01
## 10 4.134 0.508423 0.2319 2.192314 2.847e-02
## 11 3.644 -0.090667 0.3317 -0.273338 7.846e-01
## 12 3.923 0.226580 0.3087 0.733872 4.631e-01
## 13 4.168 0.389262 0.3172 1.226997 2.200e-01
## 14 4.243 0.429722 0.3225 1.332596 1.828e-01
## 15 4.328 0.487213 0.3202 1.521402 1.283e-01
## 16 4.736 0.675470 0.3463 1.950757 5.122e-02
## 17 3.305 -0.018486 0.1700 -0.108742 9.134e-01
## 18 3.200 -0.073798 0.1473 -0.500840 6.165e-01
## 19 3.716 0.337980 0.1661 2.035126 4.196e-02
## 20 3.044 -0.226606 0.1365 -1.660333 9.700e-02
## 21 2.636 -1.549009 0.2125 -7.289800 4.360e-13
## 22 2.563 -2.268787 0.2271 -9.989061 5.436e-23
## 23 2.755 -3.758014 0.3319 -11.321652 6.800e-29
## 24 2.581 -3.582236 0.2646 -13.538952 4.072e-40anova(ne_demo, ne_demo_belief, test = 'F')## Working (Rao-Scott+F) LRT for Q20
## in svyglm(formula = Q13 ~ ppagecat + PPEDUCAT + income + PPREG4 +
## work + Q20, design = svy_never_every, family = quasibinomial(link = "logit"))
## Working 2logLR = 424.2936 p= < 2.22e-16
## (scale factors: 1.2 0.98 0.96 0.89 ); denominator df= 1698anova(ns_demo, ns_demo_belief, test = 'F')## Working (Rao-Scott+F) LRT for Q20
## in svyglm(formula = Q13 ~ ppagecat + PPEDUCAT + income + PPREG4 +
## work + Q20, design = svy_never_some, family = quasibinomial(link = "logit"))
## Working 2logLR = 105.0894 p= < 2.22e-16
## (scale factors: 1.1 1 0.96 0.88 ); denominator df= 1215anova(nse_demo, nse_demo_belief, test = 'F')## Working (Rao-Scott+F) LRT for Q20
## in svyglm(formula = Q13 ~ ppagecat + PPEDUCAT + income + PPREG4 +
## work + Q20, design = svy_never_someevery, family = quasibinomial(link = "logit"))
## Working 2logLR = 360.4446 p= < 2.22e-16
## (scale factors: 1.2 0.98 0.96 0.86 ); denominator df= 2120AIC(ne_demo, ne_demo_belief)## eff.p AIC deltabar
## [1,] 22.71370 2227.893 1.195458
## [2,] 26.96941 1712.290 1.172583AIC(ns_demo, ns_demo_belief)## eff.p AIC deltabar
## [1,] 21.81199 1555.697 1.147999
## [2,] 26.21122 1438.047 1.139618AIC(nse_demo, nse_demo_belief)## eff.p AIC deltabar
## [1,] 22.88354 2793.109 1.204397
## [2,] 27.77803 2336.361 1.207741BIC(ne_demo, ne_demo_belief, maximal = ne_demo_belief)## p BIC neff
## [1,] 20 2054.776 1404.979
## [2,] 24 1837.181 NaNBIC(ns_demo, ns_demo_belief, maximal = ns_demo_belief)## p BIC neff
## [1,] 20 1620.012 1037.038
## [2,] 24 1556.554 NaNBIC(nse_demo, nse_demo_belief, maximal = nse_demo_belief)## p BIC neff
## [1,] 20 2698.113 1673.793
## [2,] 24 2464.895 NaNKeep the belief variable.
se_demo_belief_cost <- svyglm(formula = Q13 ~ ppagecat + PPEDUCAT + income + PPREG4 + work + Q20 + Q14,
design = svy_some_every,
family = quasibinomial(link = "logit"))
print_svy_mod(se_demo_belief_cost)##
## Call:
## svyglm(formula = Q13 ~ ppagecat + PPEDUCAT + income + PPREG4 +
## work + Q20 + Q14, design = svy_some_every, family = quasibinomial(link = "logit"))
##
## Survey design:
## svydesign(ids = ~1, weights = ~weight, data = some_every[!is.na(some_every$weight),
## ])
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.58212 0.59429 0.980 0.32750
## ppagecat25-34 0.13820 0.31759 0.435 0.66353
## ppagecat35-44 0.42166 0.30581 1.379 0.16819
## ppagecat45-54 0.54191 0.29888 1.813 0.07004
## ppagecat55-64 1.32789 0.28523 4.655 3.56e-06
## ppagecat65-74 1.56145 0.31986 4.882 1.18e-06
## ppagecat75+ 2.42238 0.44925 5.392 8.27e-08
## PPEDUCATHigh school 0.49773 0.33130 1.502 0.13325
## PPEDUCATSome college -0.26234 0.31912 -0.822 0.41118
## PPEDUCATBachelor_s degree or higher 0.22501 0.33125 0.679 0.49710
## income$10k to $25k 0.42829 0.49943 0.858 0.39129
## income$25k to $50k 0.53901 0.48931 1.102 0.27085
## income$50k to $75k 0.65987 0.48832 1.351 0.17683
## income$75k to $100k 0.52940 0.49617 1.067 0.28618
## income$100k to $150k 0.58769 0.49541 1.186 0.23573
## incomeover $150k 1.09529 0.50959 2.149 0.03179
## PPREG4Northeast 0.04654 0.22535 0.207 0.83640
## PPREG4South 0.16207 0.19846 0.817 0.41429
## PPREG4West -0.23730 0.20856 -1.138 0.25543
## workemployed -0.19839 0.17071 -1.162 0.24541
## Q20Somewhat effective -1.25412 0.20318 -6.173 8.97e-10
## Q20It varies from season to season -1.32438 0.23686 -5.592 2.74e-08
## Q20Not effective -2.33497 0.56184 -4.156 3.45e-05
## Q20Don_t know -1.89508 0.41441 -4.573 5.27e-06
## Q14Less than $30 -0.84095 0.18543 -4.535 6.29e-06
## Q14$30 to $60 -1.06938 0.31752 -3.368 0.00078
## Q14More than $60 13.57957 0.79642 17.051 < 2e-16
## Q14Don_t know -1.40823 0.30752 -4.579 5.11e-06
##
## (Intercept)
## ppagecat25-34
## ppagecat35-44
## ppagecat45-54 .
## ppagecat55-64 ***
## ppagecat65-74 ***
## ppagecat75+ ***
## PPEDUCATHigh school
## PPEDUCATSome college
## PPEDUCATBachelor_s degree or higher
## income$10k to $25k
## income$25k to $50k
## income$50k to $75k
## income$75k to $100k
## income$100k to $150k
## incomeover $150k *
## PPREG4Northeast
## PPREG4South
## PPREG4West
## workemployed
## Q20Somewhat effective ***
## Q20It varies from season to season ***
## Q20Not effective ***
## Q20Don_t know ***
## Q14Less than $30 ***
## Q14$30 to $60 ***
## Q14More than $60 ***
## Q14Don_t know ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 0.9593007)
##
## Number of Fisher Scoring iterations: 13
##
## term or sig or_std_err or_lower
## 1 (Intercept) 1.790e+00 1.812 -1.761e+00
## 2 ppagecat25-34 1.148e+00 1.374 -1.544e+00
## 3 ppagecat35-44 1.524e+00 1.358 -1.137e+00
## 4 ppagecat45-54 1.719e+00 . 1.348 -9.235e-01
## 5 ppagecat55-64 3.773e+00 *** 1.330 1.166e+00
## 6 ppagecat65-74 4.766e+00 *** 1.377 2.067e+00
## 7 ppagecat75+ 1.127e+01 *** 1.567 8.201e+00
## 8 PPEDUCATHigh school 1.645e+00 1.393 -1.085e+00
## 9 PPEDUCATSome college 7.692e-01 1.376 -1.928e+00
## 10 PPEDUCATBachelor_s degree or higher 1.252e+00 1.393 -1.477e+00
## 11 income$10k to $25k 1.535e+00 1.648 -1.695e+00
## 12 income$25k to $50k 1.714e+00 1.631 -1.483e+00
## 13 income$50k to $75k 1.935e+00 1.630 -1.259e+00
## 14 income$75k to $100k 1.698e+00 1.642 -1.521e+00
## 15 income$100k to $150k 1.800e+00 1.641 -1.417e+00
## 16 incomeover $150k 2.990e+00 * 1.665 -2.726e-01
## 17 PPREG4Northeast 1.048e+00 1.253 -1.408e+00
## 18 PPREG4South 1.176e+00 1.220 -1.214e+00
## 19 PPREG4West 7.888e-01 1.232 -1.626e+00
## 20 workemployed 8.201e-01 1.186 -1.505e+00
## 21 Q20Somewhat effective 2.853e-01 *** 1.225 -2.116e+00
## 22 Q20It varies from season to season 2.660e-01 *** 1.267 -2.218e+00
## 23 Q20Not effective 9.681e-02 *** 1.754 -3.341e+00
## 24 Q20Don_t know 1.503e-01 *** 1.513 -2.816e+00
## 25 Q14Less than $30 4.313e-01 *** 1.204 -1.928e+00
## 26 Q14$30 to $60 3.432e-01 *** 1.374 -2.349e+00
## 27 Q14More than $60 7.898e+05 *** 2.218 7.898e+05
## 28 Q14Don_t know 2.446e-01 *** 1.360 -2.421e+00
## or_upper estimate std.error statistic p.value
## 1 5.341e+00 0.58212 0.5943 0.9795 3.275e-01
## 2 3.841e+00 0.13820 0.3176 0.4351 6.635e-01
## 3 4.186e+00 0.42166 0.3058 1.3788 1.682e-01
## 4 4.362e+00 0.54191 0.2989 1.8132 7.004e-02
## 5 6.380e+00 1.32789 0.2852 4.6554 3.565e-06
## 6 7.465e+00 1.56145 0.3199 4.8817 1.182e-06
## 7 1.434e+01 2.42238 0.4493 5.3920 8.269e-08
## 8 4.375e+00 0.49773 0.3313 1.5024 1.332e-01
## 9 3.466e+00 -0.26234 0.3191 -0.8221 4.112e-01
## 10 3.982e+00 0.22501 0.3313 0.6793 4.971e-01
## 11 4.764e+00 0.42829 0.4994 0.8576 3.913e-01
## 12 4.911e+00 0.53901 0.4893 1.1016 2.709e-01
## 13 5.129e+00 0.65987 0.4883 1.3513 1.768e-01
## 14 4.917e+00 0.52940 0.4962 1.0670 2.862e-01
## 15 5.017e+00 0.58769 0.4954 1.1863 2.357e-01
## 16 6.253e+00 1.09529 0.5096 2.1494 3.179e-02
## 17 3.503e+00 0.04654 0.2253 0.2065 8.364e-01
## 18 3.566e+00 0.16207 0.1985 0.8166 4.143e-01
## 19 3.203e+00 -0.23730 0.2086 -1.1378 2.554e-01
## 20 3.145e+00 -0.19839 0.1707 -1.1621 2.454e-01
## 21 2.687e+00 -1.25412 0.2032 -6.1725 8.968e-10
## 22 2.750e+00 -1.32438 0.2369 -5.5915 2.741e-08
## 23 3.534e+00 -2.33497 0.5618 -4.1559 3.452e-05
## 24 3.117e+00 -1.89508 0.4144 -4.5729 5.269e-06
## 25 2.791e+00 -0.84095 0.1854 -4.5351 6.289e-06
## 26 3.036e+00 -1.06938 0.3175 -3.3679 7.796e-04
## 27 7.898e+05 13.57957 0.7964 17.0507 5.582e-59
## 28 2.910e+00 -1.40823 0.3075 -4.5793 5.113e-06se_demo_belief_cost <- svyglm(formula = Q13 ~ ppagecat + PPEDUCAT + income + PPREG4 + work + Q20 + Q19,
design = svy_some_every,
family = quasibinomial(link = "logit"))
print_svy_mod(se_demo_belief_cost)##
## Call:
## svyglm(formula = Q13 ~ ppagecat + PPEDUCAT + income + PPREG4 +
## work + Q20 + Q19, design = svy_some_every, family = quasibinomial(link = "logit"))
##
## Survey design:
## svydesign(ids = ~1, weights = ~weight, data = some_every[!is.na(some_every$weight),
## ])
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.60422 0.58587 1.031 0.3026
## ppagecat25-34 0.24922 0.31179 0.799 0.4243
## ppagecat35-44 0.51422 0.29416 1.748 0.0807
## ppagecat45-54 0.68622 0.28714 2.390 0.0170
## ppagecat55-64 1.43484 0.27332 5.250 1.78e-07
## ppagecat65-74 1.65941 0.30537 5.434 6.57e-08
## ppagecat75+ 2.55477 0.45293 5.641 2.08e-08
## PPEDUCATHigh school 0.27425 0.32610 0.841 0.4005
## PPEDUCATSome college -0.41839 0.31428 -1.331 0.1833
## PPEDUCATBachelor_s degree or higher -0.03315 0.32542 -0.102 0.9189
## income$10k to $25k 0.28871 0.50722 0.569 0.5693
## income$25k to $50k 0.34480 0.49921 0.691 0.4899
## income$50k to $75k 0.47452 0.49609 0.957 0.3390
## income$75k to $100k 0.27899 0.50358 0.554 0.5797
## income$100k to $150k 0.45767 0.50251 0.911 0.3626
## incomeover $150k 0.78275 0.51349 1.524 0.1277
## PPREG4Northeast 0.04329 0.22239 0.195 0.8457
## PPREG4South 0.21409 0.19601 1.092 0.2749
## PPREG4West -0.22413 0.20213 -1.109 0.2677
## workemployed -0.16151 0.16609 -0.972 0.3310
## Q20Somewhat effective -1.29355 0.20206 -6.402 2.14e-10
## Q20It varies from season to season -1.34003 0.23648 -5.667 1.79e-08
## Q20Not effective -2.28828 0.57688 -3.967 7.69e-05
## Q20Don_t know -2.18179 0.37790 -5.773 9.71e-09
## Q19No -0.92399 0.39634 -2.331 0.0199
##
## (Intercept)
## ppagecat25-34
## ppagecat35-44 .
## ppagecat45-54 *
## ppagecat55-64 ***
## ppagecat65-74 ***
## ppagecat75+ ***
## PPEDUCATHigh school
## PPEDUCATSome college
## PPEDUCATBachelor_s degree or higher
## income$10k to $25k
## income$25k to $50k
## income$50k to $75k
## income$75k to $100k
## income$100k to $150k
## incomeover $150k
## PPREG4Northeast
## PPREG4South
## PPREG4West
## workemployed
## Q20Somewhat effective ***
## Q20It varies from season to season ***
## Q20Not effective ***
## Q20Don_t know ***
## Q19No *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 0.9861006)
##
## Number of Fisher Scoring iterations: 5
##
## term or sig or_std_err or_lower
## 1 (Intercept) 1.8298 1.797 -1.6914
## 2 ppagecat25-34 1.2830 1.366 -1.3941
## 3 ppagecat35-44 1.6723 . 1.342 -0.9580
## 4 ppagecat45-54 1.9862 * 1.333 -0.6257
## 5 ppagecat55-64 4.1990 *** 1.314 1.6229
## 6 ppagecat65-74 5.2562 *** 1.357 2.5963
## 7 ppagecat75+ 12.8683 *** 1.573 9.7854
## 8 PPEDUCATHigh school 1.3155 1.386 -1.4001
## 9 PPEDUCATSome college 0.6581 1.369 -2.0257
## 10 PPEDUCATBachelor_s degree or higher 0.9674 1.385 -1.7465
## 11 income$10k to $25k 1.3347 1.661 -1.9202
## 12 income$25k to $50k 1.4117 1.647 -1.8172
## 13 income$50k to $75k 1.6072 1.642 -1.6116
## 14 income$75k to $100k 1.3218 1.655 -1.9213
## 15 income$100k to $150k 1.5804 1.653 -1.6592
## 16 incomeover $150k 2.1875 1.671 -1.0879
## 17 PPREG4Northeast 1.0442 1.249 -1.4039
## 18 PPREG4South 1.2387 1.217 -1.1457
## 19 PPREG4West 0.7992 1.224 -1.5998
## 20 workemployed 0.8509 1.181 -1.4633
## 21 Q20Somewhat effective 0.2743 *** 1.224 -2.1246
## 22 Q20It varies from season to season 0.2618 *** 1.267 -2.2210
## 23 Q20Not effective 0.1014 *** 1.780 -3.3883
## 24 Q20Don_t know 0.1128 *** 1.459 -2.7472
## 25 Q19No 0.3969 * 1.486 -2.5164
## or_upper estimate std.error statistic p.value
## 1 5.351 0.60422 0.5859 1.0313 3.026e-01
## 2 3.960 0.24922 0.3118 0.7993 4.243e-01
## 3 4.303 0.51422 0.2942 1.7481 8.069e-02
## 4 4.598 0.68622 0.2871 2.3899 1.700e-02
## 5 6.775 1.43484 0.2733 5.2496 1.779e-07
## 6 7.916 1.65941 0.3054 5.4341 6.569e-08
## 7 15.951 2.55477 0.4529 5.6406 2.078e-08
## 8 4.031 0.27425 0.3261 0.8410 4.005e-01
## 9 3.342 -0.41839 0.3143 -1.3313 1.833e-01
## 10 3.681 -0.03315 0.3254 -0.1019 9.189e-01
## 11 4.590 0.28871 0.5072 0.5692 5.693e-01
## 12 4.641 0.34480 0.4992 0.6907 4.899e-01
## 13 4.826 0.47452 0.4961 0.9565 3.390e-01
## 14 4.565 0.27899 0.5036 0.5540 5.797e-01
## 15 4.820 0.45767 0.5025 0.9108 3.626e-01
## 16 5.463 0.78275 0.5135 1.5244 1.277e-01
## 17 3.492 0.04329 0.2224 0.1947 8.457e-01
## 18 3.623 0.21409 0.1960 1.0922 2.749e-01
## 19 3.198 -0.22413 0.2021 -1.1088 2.677e-01
## 20 3.165 -0.16151 0.1661 -0.9724 3.310e-01
## 21 2.673 -1.29355 0.2021 -6.4017 2.142e-10
## 22 2.745 -1.34003 0.2365 -5.6666 1.792e-08
## 23 3.591 -2.28828 0.5769 -3.9666 7.687e-05
## 24 2.973 -2.18179 0.3779 -5.7734 9.707e-09
## 25 3.310 -0.92399 0.3963 -2.3313 1.989e-02se_demo_belief_cost <- svyglm(formula = Q13 ~ ppagecat + PPEDUCAT + income + PPREG4 + work + Q20 + Q21,
design = svy_some_every,
family = quasibinomial(link = "logit"))
print_svy_mod(se_demo_belief_cost)##
## Call:
## svyglm(formula = Q13 ~ ppagecat + PPEDUCAT + income + PPREG4 +
## work + Q20 + Q21, design = svy_some_every, family = quasibinomial(link = "logit"))
##
## Survey design:
## svydesign(ids = ~1, weights = ~weight, data = some_every[!is.na(some_every$weight),
## ])
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 0.48435 0.59319 0.817
## ppagecat25-34 0.17537 0.32544 0.539
## ppagecat35-44 0.39490 0.30561 1.292
## ppagecat45-54 0.72047 0.30050 2.398
## ppagecat55-64 1.33482 0.29016 4.600
## ppagecat65-74 1.52607 0.32190 4.741
## ppagecat75+ 2.23826 0.46012 4.865
## PPEDUCATHigh school 0.14956 0.35064 0.427
## PPEDUCATSome college -0.48562 0.34319 -1.415
## PPEDUCATBachelor_s degree or higher -0.05857 0.35303 -0.166
## income$10k to $25k 0.75933 0.50689 1.498
## income$25k to $50k 0.87149 0.49126 1.774
## income$50k to $75k 0.94853 0.49090 1.932
## income$75k to $100k 0.75445 0.49726 1.517
## income$100k to $150k 0.89087 0.49667 1.794
## incomeover $150k 1.28071 0.50836 2.519
## PPREG4Northeast 0.04272 0.22919 0.186
## PPREG4South 0.14465 0.20120 0.719
## PPREG4West -0.26328 0.20958 -1.256
## workemployed -0.19688 0.17172 -1.147
## Q20Somewhat effective -1.26417 0.20768 -6.087
## Q20It varies from season to season -1.35065 0.23889 -5.654
## Q20Not effective -2.14665 0.65254 -3.290
## Q20Don_t know -1.89025 0.41644 -4.539
## Q21Yes, but only part of the cost is paid -0.75444 0.24429 -3.088
## Q21No -0.25206 0.52935 -0.476
## Q21Don_t know -0.59791 0.20952 -2.854
## Pr(>|t|)
## (Intercept) 0.41436
## ppagecat25-34 0.59007
## ppagecat35-44 0.19654
## ppagecat45-54 0.01665 *
## ppagecat55-64 4.65e-06 ***
## ppagecat65-74 2.37e-06 ***
## ppagecat75+ 1.29e-06 ***
## PPEDUCATHigh school 0.66979
## PPEDUCATSome college 0.15731
## PPEDUCATBachelor_s degree or higher 0.86825
## income$10k to $25k 0.13438
## income$25k to $50k 0.07631 .
## income$50k to $75k 0.05356 .
## income$75k to $100k 0.12947
## income$100k to $150k 0.07310 .
## incomeover $150k 0.01188 *
## PPREG4Northeast 0.85218
## PPREG4South 0.47230
## PPREG4West 0.20928
## workemployed 0.25181
## Q20Somewhat effective 1.53e-09 ***
## Q20It varies from season to season 1.94e-08 ***
## Q20Not effective 0.00103 **
## Q20Don_t know 6.19e-06 ***
## Q21Yes, but only part of the cost is paid 0.00206 **
## Q21No 0.63403
## Q21Don_t know 0.00439 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 0.9785475)
##
## Number of Fisher Scoring iterations: 5
##
## term or sig or_std_err
## 1 (Intercept) 1.6231 1.810
## 2 ppagecat25-34 1.1917 1.385
## 3 ppagecat35-44 1.4842 1.357
## 4 ppagecat45-54 2.0554 * 1.351
## 5 ppagecat55-64 3.7993 *** 1.337
## 6 ppagecat65-74 4.6001 *** 1.380
## 7 ppagecat75+ 9.3770 *** 1.584
## 8 PPEDUCATHigh school 1.1613 1.420
## 9 PPEDUCATSome college 0.6153 1.409
## 10 PPEDUCATBachelor_s degree or higher 0.9431 1.423
## 11 income$10k to $25k 2.1368 1.660
## 12 income$25k to $50k 2.3905 . 1.634
## 13 income$50k to $75k 2.5819 . 1.634
## 14 income$75k to $100k 2.1264 1.644
## 15 income$100k to $150k 2.4373 . 1.643
## 16 incomeover $150k 3.5992 * 1.663
## 17 PPREG4Northeast 1.0436 1.258
## 18 PPREG4South 1.1556 1.223
## 19 PPREG4West 0.7685 1.233
## 20 workemployed 0.8213 1.187
## 21 Q20Somewhat effective 0.2825 *** 1.231
## 22 Q20It varies from season to season 0.2591 *** 1.270
## 23 Q20Not effective 0.1169 ** 1.920
## 24 Q20Don_t know 0.1510 *** 1.517
## 25 Q21Yes, but only part of the cost is paid 0.4703 ** 1.277
## 26 Q21No 0.7772 1.698
## 27 Q21Don_t know 0.5500 ** 1.233
## or_lower or_upper estimate std.error statistic p.value
## 1 -1.9240 5.170 0.48435 0.5932 0.8165 4.144e-01
## 2 -1.5222 3.906 0.17537 0.3254 0.5389 5.901e-01
## 3 -1.1764 4.145 0.39490 0.3056 1.2922 1.965e-01
## 4 -0.5916 4.702 0.72047 0.3005 2.3976 1.665e-02
## 5 1.1795 6.419 1.33482 0.2902 4.6003 4.646e-06
## 6 1.8958 7.304 1.52607 0.3219 4.7409 2.371e-06
## 7 6.2718 12.482 2.23826 0.4601 4.8645 1.293e-06
## 8 -1.6218 3.944 0.14956 0.3506 0.4265 6.698e-01
## 9 -2.1472 3.378 -0.48562 0.3432 -1.4150 1.573e-01
## 10 -1.8467 3.733 -0.05857 0.3530 -0.1659 8.682e-01
## 11 -1.1170 5.391 0.75933 0.5069 1.4980 1.344e-01
## 12 -0.8129 5.594 0.87149 0.4913 1.7740 7.631e-02
## 13 -0.6203 5.784 0.94853 0.4909 1.9322 5.356e-02
## 14 -1.0962 5.349 0.75445 0.4973 1.5172 1.295e-01
## 15 -0.7835 5.658 0.89087 0.4967 1.7937 7.310e-02
## 16 0.3406 6.858 1.28071 0.5084 2.5193 1.188e-02
## 17 -1.4212 3.509 0.04272 0.2292 0.1864 8.522e-01
## 18 -1.2412 3.552 0.14465 0.2012 0.7190 4.723e-01
## 19 -1.6485 3.186 -0.26328 0.2096 -1.2562 2.093e-01
## 20 -1.5059 3.148 -0.19688 0.1717 -1.1465 2.518e-01
## 21 -2.1299 2.695 -1.26417 0.2077 -6.0870 1.527e-09
## 22 -2.2298 2.748 -1.35065 0.2389 -5.6540 1.940e-08
## 23 -3.6471 3.881 -2.14665 0.6525 -3.2897 1.031e-03
## 24 -2.8214 3.123 -1.89025 0.4164 -4.5391 6.192e-06
## 25 -2.0321 2.973 -0.75444 0.2443 -3.0883 2.057e-03
## 26 -2.5505 4.105 -0.25206 0.5293 -0.4762 6.340e-01
## 27 -1.8669 2.967 -0.59791 0.2095 -2.8538 4.391e-03se_demo_belief_cost <- svyglm(formula = Q13 ~ ppagecat + PPEDUCAT + income + PPREG4 + work + Q20 + Q14 + Q19,
design = svy_some_every,
family = quasibinomial(link = "logit"))
print_svy_mod(se_demo_belief_cost)##
## Call:
## svyglm(formula = Q13 ~ ppagecat + PPEDUCAT + income + PPREG4 +
## work + Q20 + Q14 + Q19, design = svy_some_every, family = quasibinomial(link = "logit"))
##
## Survey design:
## svydesign(ids = ~1, weights = ~weight, data = some_every[!is.na(some_every$weight),
## ])
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.71547 0.63011 1.135 0.256391
## ppagecat25-34 0.16584 0.32165 0.516 0.606232
## ppagecat35-44 0.43247 0.30705 1.408 0.159243
## ppagecat45-54 0.58484 0.29808 1.962 0.049976
## ppagecat55-64 1.34657 0.28560 4.715 2.68e-06
## ppagecat65-74 1.55488 0.32090 4.845 1.42e-06
## ppagecat75+ 2.41805 0.45754 5.285 1.47e-07
## PPEDUCATHigh school 0.33933 0.34500 0.984 0.325508
## PPEDUCATSome college -0.41604 0.33113 -1.256 0.209192
## PPEDUCATBachelor_s degree or higher 0.05930 0.34143 0.174 0.862137
## income$10k to $25k 0.47042 0.51465 0.914 0.360855
## income$25k to $50k 0.56634 0.50483 1.122 0.262143
## income$50k to $75k 0.67976 0.50369 1.350 0.177390
## income$75k to $100k 0.54403 0.51092 1.065 0.287162
## income$100k to $150k 0.61553 0.51055 1.206 0.228181
## incomeover $150k 1.09154 0.52466 2.080 0.037679
## PPREG4Northeast 0.04867 0.22589 0.215 0.829454
## PPREG4South 0.19088 0.19972 0.956 0.339381
## PPREG4West -0.20985 0.20943 -1.002 0.316532
## workemployed -0.20609 0.17100 -1.205 0.228354
## Q20Somewhat effective -1.27513 0.20603 -6.189 8.11e-10
## Q20It varies from season to season -1.34690 0.23862 -5.645 2.03e-08
## Q20Not effective -2.24024 0.57432 -3.901 0.000101
## Q20Don_t know -1.94089 0.41874 -4.635 3.93e-06
## Q14Less than $30 -0.81653 0.18402 -4.437 9.89e-06
## Q14$30 to $60 -0.98585 0.32493 -3.034 0.002461
## Q14More than $60 12.95651 0.85760 15.108 < 2e-16
## Q14Don_t know -1.38986 0.30885 -4.500 7.40e-06
## Q19No -0.60828 0.40283 -1.510 0.131278
##
## (Intercept)
## ppagecat25-34
## ppagecat35-44
## ppagecat45-54 *
## ppagecat55-64 ***
## ppagecat65-74 ***
## ppagecat75+ ***
## PPEDUCATHigh school
## PPEDUCATSome college
## PPEDUCATBachelor_s degree or higher
## income$10k to $25k
## income$25k to $50k
## income$50k to $75k
## income$75k to $100k
## income$100k to $150k
## incomeover $150k *
## PPREG4Northeast
## PPREG4South
## PPREG4West
## workemployed
## Q20Somewhat effective ***
## Q20It varies from season to season ***
## Q20Not effective ***
## Q20Don_t know ***
## Q14Less than $30 ***
## Q14$30 to $60 **
## Q14More than $60 ***
## Q14Don_t know ***
## Q19No
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 0.9602307)
##
## Number of Fisher Scoring iterations: 13
##
## term or sig or_std_err or_lower
## 1 (Intercept) 2.045e+00 1.878 -1.635e+00
## 2 ppagecat25-34 1.180e+00 1.379 -1.523e+00
## 3 ppagecat35-44 1.541e+00 1.359 -1.123e+00
## 4 ppagecat45-54 1.795e+00 * 1.347 -8.460e-01
## 5 ppagecat55-64 3.844e+00 *** 1.331 1.236e+00
## 6 ppagecat65-74 4.735e+00 *** 1.378 2.033e+00
## 7 ppagecat75+ 1.122e+01 *** 1.580 8.127e+00
## 8 PPEDUCATHigh school 1.404e+00 1.412 -1.363e+00
## 9 PPEDUCATSome college 6.597e-01 1.393 -2.070e+00
## 10 PPEDUCATBachelor_s degree or higher 1.061e+00 1.407 -1.697e+00
## 11 income$10k to $25k 1.601e+00 1.673 -1.679e+00
## 12 income$25k to $50k 1.762e+00 1.657 -1.485e+00
## 13 income$50k to $75k 1.973e+00 1.655 -1.270e+00
## 14 income$75k to $100k 1.723e+00 1.667 -1.544e+00
## 15 income$100k to $150k 1.851e+00 1.666 -1.415e+00
## 16 incomeover $150k 2.979e+00 * 1.690 -3.333e-01
## 17 PPREG4Northeast 1.050e+00 1.253 -1.407e+00
## 18 PPREG4South 1.210e+00 1.221 -1.183e+00
## 19 PPREG4West 8.107e-01 1.233 -1.606e+00
## 20 workemployed 8.138e-01 1.186 -1.512e+00
## 21 Q20Somewhat effective 2.794e-01 *** 1.229 -2.129e+00
## 22 Q20It varies from season to season 2.600e-01 *** 1.269 -2.228e+00
## 23 Q20Not effective 1.064e-01 *** 1.776 -3.374e+00
## 24 Q20Don_t know 1.436e-01 *** 1.520 -2.836e+00
## 25 Q14Less than $30 4.420e-01 *** 1.202 -1.914e+00
## 26 Q14$30 to $60 3.731e-01 ** 1.384 -2.339e+00
## 27 Q14More than $60 4.236e+05 *** 2.358 4.236e+05
## 28 Q14Don_t know 2.491e-01 *** 1.362 -2.420e+00
## 29 Q19No 5.443e-01 1.496 -2.388e+00
## or_upper estimate std.error statistic p.value
## 1 5.726e+00 0.71547 0.6301 1.1355 2.564e-01
## 2 3.884e+00 0.16584 0.3216 0.5156 6.062e-01
## 3 4.206e+00 0.43247 0.3071 1.4084 1.592e-01
## 4 4.435e+00 0.58484 0.2981 1.9620 4.998e-02
## 5 6.452e+00 1.34657 0.2856 4.7149 2.680e-06
## 6 7.436e+00 1.55488 0.3209 4.8454 1.416e-06
## 7 1.432e+01 2.41805 0.4575 5.2849 1.475e-07
## 8 4.172e+00 0.33933 0.3450 0.9836 3.255e-01
## 9 3.389e+00 -0.41604 0.3311 -1.2564 2.092e-01
## 10 3.819e+00 0.05930 0.3414 0.1737 8.621e-01
## 11 4.880e+00 0.47042 0.5147 0.9141 3.609e-01
## 12 5.009e+00 0.56634 0.5048 1.1218 2.621e-01
## 13 5.217e+00 0.67976 0.5037 1.3496 1.774e-01
## 14 4.990e+00 0.54403 0.5109 1.0648 2.872e-01
## 15 5.116e+00 0.61553 0.5105 1.2056 2.282e-01
## 16 6.291e+00 1.09154 0.5247 2.0805 3.768e-02
## 17 3.507e+00 0.04867 0.2259 0.2154 8.295e-01
## 18 3.604e+00 0.19088 0.1997 0.9557 3.394e-01
## 19 3.227e+00 -0.20985 0.2094 -1.0020 3.165e-01
## 20 3.139e+00 -0.20609 0.1710 -1.2052 2.284e-01
## 21 2.688e+00 -1.27513 0.2060 -6.1890 8.112e-10
## 22 2.748e+00 -1.34690 0.2386 -5.6446 2.032e-08
## 23 3.587e+00 -2.24024 0.5743 -3.9007 1.009e-04
## 24 3.123e+00 -1.94089 0.4187 -4.6351 3.928e-06
## 25 2.798e+00 -0.81653 0.1840 -4.4371 9.892e-06
## 26 3.086e+00 -0.98585 0.3249 -3.0341 2.461e-03
## 27 4.236e+05 12.95651 0.8576 15.1078 1.287e-47
## 28 2.918e+00 -1.38986 0.3088 -4.5001 7.402e-06
## 29 3.477e+00 -0.60828 0.4028 -1.5100 1.313e-01#AIC(ne_demo, ne_demo_belief, ne_demo_belief_social, ne_demo_belief_cost)
#AIC(ns_demo, ns_demo_belief, ns_demo_belief_social, ns_demo_belief_cost)
#AIC(nse_demo, nse_demo_belief, nse_demo_belief_social, nse_demo_belief_cost)#BIC(ne_demo, ne_demo_belief, ne_demo_belief_cost, maximal = ne_demo_belief_cost)
#BIC(ns_demo, ns_demo_belief, ns_demo_belief_cost, maximal = ns_demo_belief_cost)
#BIC(nse_demo, nse_demo_belief, nse_demo_belief_cost, maximal = nse_demo_belief_cost)library(forcats)
df <- readRDS('./data/subset_recode.RDS')
never_some_flipped <- df[df$Q13 %in% c('Yes, some years', 'No, never'), ]
never_some_flipped$Q13 <- droplevels(never_some$Q13)
never_some_flipped$Q13 <- fct_relevel(never_some$Q13, 'Yes, some years', after = 0L)
table(never_some_flipped$Q13, useNA = 'always')##
## Yes, some years No, never <NA>
## 423 819 0levels(never_some_flipped$Q13)## [1] "Yes, some years" "No, never"testthat::expect_equal(levels(never_some_flipped$Q13)[1], "Yes, some years")table(never_some_flipped$Q13, never_some_flipped$Q18_1, useNA = 'always')##
## Yes No <NA>
## Yes, some years 61 362 0
## No, never 49 770 0
## <NA> 0 0 0make_no_ref <- function(dat) {
dat <- fct_relevel(dat, "No", after = 0L)
testthat::expect_equal(levels(dat)[1], "No")
return(dat)
}never_some_flipped$Q18_1 <- sapply(never_some_flipped$Q18_1, make_no_ref)
never_some_flipped$Q18_2 <- sapply(never_some_flipped$Q18_2, make_no_ref)
never_some_flipped$Q18_3 <- sapply(never_some_flipped$Q18_3, make_no_ref)
never_some_flipped$Q18_4 <- sapply(never_some_flipped$Q18_4, make_no_ref)
never_some_flipped$Q18_5 <- sapply(never_some_flipped$Q18_5, make_no_ref)
never_some_flipped$Q18_6 <- sapply(never_some_flipped$Q18_6, make_no_ref)
never_some_flipped$Q18_7 <- sapply(never_some_flipped$Q18_7, make_no_ref)
never_some_flipped$Q18_8 <- sapply(never_some_flipped$Q18_8, make_no_ref)
never_some_flipped$Q18_9 <- sapply(never_some_flipped$Q18_9, make_no_ref)
never_some_flipped$Q18_10 <- sapply(never_some_flipped$Q18_10, make_no_ref)
svy_never_some_flipped <- svydesign(ids = ~1, weights = ~weight, data = never_some_flipped[!is.na(never_some_flipped$weight), ])lapply(never_some_flipped[, 37:46], table)## $Q18_1
##
## No Yes
## 1132 110
##
## $Q18_2
##
## No Yes
## 903 339
##
## $Q18_3
##
## No Yes
## 964 278
##
## $Q18_4
##
## No Yes
## 1199 43
##
## $Q18_5
##
## No Yes
## 958 284
##
## $Q18_6
##
## No Yes
## 1184 58
##
## $Q18_7
##
## No Yes
## 976 266
##
## $Q18_8
##
## No Yes
## 878 364
##
## $Q18_9
##
## No Yes
## 1216 26
##
## $Q18_10
##
## No Yes
## 1064 178table(df$Q18_1, df$Q13)##
## Yes, every year Yes, some years No, never
## Yes 0 61 49
## No 0 362 770ns_demo_belief_barriers <- svyglm(formula = Q13 ~ ppagecat + PPEDUCAT + income + PPREG4 + work + Q20 + Q18_1 + Q18_2 + Q18_3 + Q18_4 + Q18_5 + Q18_6 + Q18_7 + Q18_8 + Q18_9,
design = svy_never_some_flipped,
family = quasibinomial(link = "logit"))
print_svy_mod(ns_demo_belief_barriers)##
## Call:
## svyglm(formula = Q13 ~ ppagecat + PPEDUCAT + income + PPREG4 +
## work + Q20 + Q18_1 + Q18_2 + Q18_3 + Q18_4 + Q18_5 + Q18_6 +
## Q18_7 + Q18_8 + Q18_9, design = svy_never_some_flipped, family = quasibinomial(link = "logit"))
##
## Survey design:
## svydesign(ids = ~1, weights = ~weight, data = never_some_flipped[!is.na(never_some_flipped$weight),
## ])
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.11943 0.49337 -0.242 0.808762
## ppagecat25-34 -0.06752 0.28025 -0.241 0.809648
## ppagecat35-44 0.19226 0.26866 0.716 0.474357
## ppagecat45-54 0.11810 0.25388 0.465 0.641882
## ppagecat55-64 0.38623 0.26548 1.455 0.145981
## ppagecat65-74 -0.03875 0.31132 -0.124 0.900955
## ppagecat75+ 0.42025 0.50862 0.826 0.408825
## PPEDUCATHigh school 0.40185 0.29800 1.349 0.177744
## PPEDUCATSome college -0.61032 0.29650 -2.058 0.039762
## PPEDUCATBachelor_s degree or higher -0.51711 0.29814 -1.734 0.083092
## income$10k to $25k 0.19170 0.43985 0.436 0.663048
## income$25k to $50k -0.21758 0.38326 -0.568 0.570328
## income$50k to $75k -0.20197 0.39379 -0.513 0.608123
## income$75k to $100k -0.40893 0.39720 -1.030 0.303442
## income$100k to $150k -0.40049 0.39353 -1.018 0.309032
## incomeover $150k -0.44416 0.42933 -1.035 0.301094
## PPREG4Northeast 0.07685 0.22517 0.341 0.732954
## PPREG4South 0.26925 0.20194 1.333 0.182682
## PPREG4West -0.48288 0.21626 -2.233 0.025738
## workemployed 0.26944 0.18246 1.477 0.140007
## Q20Somewhat effective 0.67578 0.28365 2.382 0.017352
## Q20It varies from season to season 1.44612 0.31190 4.636 3.93e-06
## Q20Not effective 2.35138 0.41027 5.731 1.26e-08
## Q20Don_t know 2.36151 0.34568 6.832 1.33e-11
## Q18_1Yes -0.96454 0.25615 -3.766 0.000174
## Q18_2Yes -0.87339 0.17417 -5.015 6.11e-07
## Q18_3Yes 0.70333 0.18525 3.797 0.000154
## Q18_4Yes 0.41153 0.50115 0.821 0.411708
## Q18_5Yes 0.72765 0.19790 3.677 0.000246
## Q18_6Yes -0.54682 0.36191 -1.511 0.131073
## Q18_7Yes 0.34355 0.18823 1.825 0.068231
## Q18_8Yes -0.65987 0.15379 -4.291 1.92e-05
## Q18_9Yes -0.45983 0.42004 -1.095 0.273854
##
## (Intercept)
## ppagecat25-34
## ppagecat35-44
## ppagecat45-54
## ppagecat55-64
## ppagecat65-74
## ppagecat75+
## PPEDUCATHigh school
## PPEDUCATSome college *
## PPEDUCATBachelor_s degree or higher .
## income$10k to $25k
## income$25k to $50k
## income$50k to $75k
## income$75k to $100k
## income$100k to $150k
## incomeover $150k
## PPREG4Northeast
## PPREG4South
## PPREG4West *
## workemployed
## Q20Somewhat effective *
## Q20It varies from season to season ***
## Q20Not effective ***
## Q20Don_t know ***
## Q18_1Yes ***
## Q18_2Yes ***
## Q18_3Yes ***
## Q18_4Yes
## Q18_5Yes ***
## Q18_6Yes
## Q18_7Yes .
## Q18_8Yes ***
## Q18_9Yes
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 1.007906)
##
## Number of Fisher Scoring iterations: 4
##
## term or sig or_std_err or_lower
## 1 (Intercept) 0.8874 1.638 -2.3227
## 2 ppagecat25-34 0.9347 1.323 -1.6593
## 3 ppagecat35-44 1.2120 1.308 -1.3521
## 4 ppagecat45-54 1.1254 1.289 -1.4011
## 5 ppagecat55-64 1.4714 1.304 -1.0845
## 6 ppagecat65-74 0.9620 1.365 -1.7139
## 7 ppagecat75+ 1.5223 1.663 -1.7371
## 8 PPEDUCATHigh school 1.4946 1.347 -1.1458
## 9 PPEDUCATSome college 0.5432 * 1.345 -2.0933
## 10 PPEDUCATBachelor_s degree or higher 0.5962 . 1.347 -2.0446
## 11 income$10k to $25k 1.2113 1.552 -1.8316
## 12 income$25k to $50k 0.8045 1.467 -2.0710
## 13 income$50k to $75k 0.8171 1.483 -2.0887
## 14 income$75k to $100k 0.6644 1.488 -2.2514
## 15 income$100k to $150k 0.6700 1.482 -2.2351
## 16 incomeover $150k 0.6414 1.536 -2.3697
## 17 PPREG4Northeast 1.0799 1.253 -1.3751
## 18 PPREG4South 1.3090 1.224 -1.0896
## 19 PPREG4West 0.6170 * 1.241 -1.8162
## 20 workemployed 1.3092 1.200 -1.0431
## 21 Q20Somewhat effective 1.9656 * 1.328 -0.6372
## 22 Q20It varies from season to season 4.2466 *** 1.366 1.5692
## 23 Q20Not effective 10.5001 *** 1.507 7.5459
## 24 Q20Don_t know 10.6070 *** 1.413 7.8376
## 25 Q18_1Yes 0.3812 *** 1.292 -2.1510
## 26 Q18_2Yes 0.4175 *** 1.190 -1.9154
## 27 Q18_3Yes 2.0205 *** 1.204 -0.3384
## 28 Q18_4Yes 1.5091 1.651 -1.7261
## 29 Q18_5Yes 2.0702 *** 1.219 -0.3187
## 30 Q18_6Yes 0.5788 1.436 -2.2359
## 31 Q18_7Yes 1.4099 . 1.207 -0.9560
## 32 Q18_8Yes 0.5169 *** 1.166 -1.7689
## 33 Q18_9Yes 0.6314 1.522 -2.3518
## or_upper estimate std.error statistic p.value
## 1 4.098 -0.11943 0.4934 -0.2421 8.088e-01
## 2 3.529 -0.06752 0.2803 -0.2409 8.096e-01
## 3 3.776 0.19226 0.2687 0.7156 4.744e-01
## 4 3.652 0.11810 0.2539 0.4652 6.419e-01
## 5 4.027 0.38623 0.2655 1.4548 1.460e-01
## 6 3.638 -0.03875 0.3113 -0.1245 9.010e-01
## 7 4.782 0.42025 0.5086 0.8263 4.088e-01
## 8 4.135 0.40185 0.2980 1.3485 1.777e-01
## 9 3.180 -0.61032 0.2965 -2.0584 3.976e-02
## 10 3.237 -0.51711 0.2981 -1.7345 8.309e-02
## 11 4.254 0.19170 0.4399 0.4358 6.630e-01
## 12 3.680 -0.21758 0.3833 -0.5677 5.703e-01
## 13 3.723 -0.20197 0.3938 -0.5129 6.081e-01
## 14 3.580 -0.40893 0.3972 -1.0295 3.034e-01
## 15 3.575 -0.40049 0.3935 -1.0177 3.090e-01
## 16 3.652 -0.44416 0.4293 -1.0345 3.011e-01
## 17 3.535 0.07685 0.2252 0.3413 7.330e-01
## 18 3.708 0.26925 0.2019 1.3333 1.827e-01
## 19 3.050 -0.48288 0.2163 -2.2329 2.574e-02
## 20 3.662 0.26944 0.1825 1.4767 1.400e-01
## 21 4.568 0.67578 0.2836 2.3825 1.735e-02
## 22 6.924 1.44612 0.3119 4.6364 3.931e-06
## 23 13.454 2.35138 0.4103 5.7313 1.258e-08
## 24 13.376 2.36151 0.3457 6.8316 1.329e-11
## 25 2.913 -0.96454 0.2561 -3.7656 1.742e-04
## 26 2.750 -0.87339 0.1742 -5.0147 6.106e-07
## 27 4.379 0.70333 0.1852 3.7967 1.539e-04
## 28 4.744 0.41153 0.5012 0.8212 4.117e-01
## 29 4.459 0.72765 0.1979 3.6770 2.464e-04
## 30 3.393 -0.54682 0.3619 -1.5109 1.311e-01
## 31 3.776 0.34355 0.1882 1.8251 6.823e-02
## 32 2.803 -0.65987 0.1538 -4.2909 1.922e-05
## 33 3.615 -0.45983 0.4200 -1.0947 2.739e-01#ns_demo_belief_barriers <- svyglm(formula = Q13 ~ ppagecat + PPEDUCAT + income + PPREG4 + work + Q20 + Q18_1 + Q18_2 + #Q18_3 + Q18_4 + Q18_5 + Q18_6 + Q18_7 + Q18_8 + Q18_9 + Q18_10 + PPEDUCAT*Q20,
# design = svy_never_some_flipped,
# family = quasibinomial(link = "logit"))
#print_svy_mod(ns_demo_belief_barriers)ns_demo_belief_barriers <- svyglm(formula = Q13 ~ ppagecat + PPEDUCAT + income + PPREG4 + work + Q20 + Q18_1 + Q18_2 + Q18_3 + Q18_5 + Q18_7 + Q18_8 + Q19,
design = svy_never_some_flipped,
family = quasibinomial(link = "logit"))
print_svy_mod(ns_demo_belief_barriers)##
## Call:
## svyglm(formula = Q13 ~ ppagecat + PPEDUCAT + income + PPREG4 +
## work + Q20 + Q18_1 + Q18_2 + Q18_3 + Q18_5 + Q18_7 + Q18_8 +
## Q19, design = svy_never_some_flipped, family = quasibinomial(link = "logit"))
##
## Survey design:
## svydesign(ids = ~1, weights = ~weight, data = never_some_flipped[!is.na(never_some_flipped$weight),
## ])
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.35805 0.48747 -0.735 0.462784
## ppagecat25-34 -0.07992 0.28568 -0.280 0.779709
## ppagecat35-44 0.18026 0.27106 0.665 0.506158
## ppagecat45-54 0.08450 0.25410 0.333 0.739532
## ppagecat55-64 0.33610 0.26344 1.276 0.202263
## ppagecat65-74 0.03575 0.30929 0.116 0.907993
## ppagecat75+ 0.48101 0.50365 0.955 0.339746
## PPEDUCATHigh school 0.52222 0.30779 1.697 0.090015
## PPEDUCATSome college -0.44687 0.30797 -1.451 0.147040
## PPEDUCATBachelor_s degree or higher -0.36520 0.31164 -1.172 0.241481
## income$10k to $25k 0.21810 0.43240 0.504 0.614067
## income$25k to $50k -0.09471 0.37856 -0.250 0.802489
## income$50k to $75k -0.09986 0.39857 -0.251 0.802202
## income$75k to $100k -0.28458 0.39937 -0.713 0.476253
## income$100k to $150k -0.27784 0.39312 -0.707 0.479857
## incomeover $150k -0.32393 0.42521 -0.762 0.446324
## PPREG4Northeast 0.04862 0.22435 0.217 0.828482
## PPREG4South 0.24102 0.20234 1.191 0.233821
## PPREG4West -0.48215 0.21433 -2.250 0.024657
## workemployed 0.30055 0.18240 1.648 0.099662
## Q20Somewhat effective 0.62174 0.28218 2.203 0.027760
## Q20It varies from season to season 1.39990 0.30952 4.523 6.70e-06
## Q20Not effective 2.33807 0.41687 5.609 2.53e-08
## Q20Don_t know 2.19797 0.34005 6.464 1.48e-10
## Q18_1Yes -1.06830 0.25226 -4.235 2.46e-05
## Q18_2Yes -0.88746 0.17409 -5.098 3.99e-07
## Q18_3Yes 0.66577 0.18713 3.558 0.000389
## Q18_5Yes 0.68298 0.20141 3.391 0.000719
## Q18_7Yes 0.39058 0.19251 2.029 0.042693
## Q18_8Yes -0.65330 0.15379 -4.248 2.32e-05
## Q19No 0.71368 0.32102 2.223 0.026387
##
## (Intercept)
## ppagecat25-34
## ppagecat35-44
## ppagecat45-54
## ppagecat55-64
## ppagecat65-74
## ppagecat75+
## PPEDUCATHigh school .
## PPEDUCATSome college
## PPEDUCATBachelor_s degree or higher
## income$10k to $25k
## income$25k to $50k
## income$50k to $75k
## income$75k to $100k
## income$100k to $150k
## incomeover $150k
## PPREG4Northeast
## PPREG4South
## PPREG4West *
## workemployed .
## Q20Somewhat effective *
## Q20It varies from season to season ***
## Q20Not effective ***
## Q20Don_t know ***
## Q18_1Yes ***
## Q18_2Yes ***
## Q18_3Yes ***
## Q18_5Yes ***
## Q18_7Yes *
## Q18_8Yes ***
## Q19No *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 1.015909)
##
## Number of Fisher Scoring iterations: 4
##
## term or sig or_std_err or_lower
## 1 (Intercept) 0.6990 1.628 -2.4922
## 2 ppagecat25-34 0.9232 1.331 -1.6849
## 3 ppagecat35-44 1.1975 1.311 -1.3727
## 4 ppagecat45-54 1.0882 1.289 -1.4388
## 5 ppagecat55-64 1.3995 1.301 -1.1513
## 6 ppagecat65-74 1.0364 1.362 -1.6340
## 7 ppagecat75+ 1.6177 1.655 -1.6256
## 8 PPEDUCATHigh school 1.6858 . 1.360 -0.9806
## 9 PPEDUCATSome college 0.6396 1.361 -2.0273
## 10 PPEDUCATBachelor_s degree or higher 0.6941 1.366 -1.9826
## 11 income$10k to $25k 1.2437 1.541 -1.7765
## 12 income$25k to $50k 0.9096 1.460 -1.9523
## 13 income$50k to $75k 0.9050 1.490 -2.0148
## 14 income$75k to $100k 0.7523 1.491 -2.1698
## 15 income$100k to $150k 0.7574 1.482 -2.1465
## 16 incomeover $150k 0.7233 1.530 -2.2753
## 17 PPREG4Northeast 1.0498 1.252 -1.4031
## 18 PPREG4South 1.2725 1.224 -1.1270
## 19 PPREG4West 0.6175 * 1.239 -1.8110
## 20 workemployed 1.3506 . 1.200 -1.0016
## 21 Q20Somewhat effective 1.8622 * 1.326 -0.7368
## 22 Q20It varies from season to season 4.0548 *** 1.363 1.3838
## 23 Q20Not effective 10.3612 *** 1.517 7.3875
## 24 Q20Don_t know 9.0067 *** 1.405 6.2528
## 25 Q18_1Yes 0.3436 *** 1.287 -2.1788
## 26 Q18_2Yes 0.4117 *** 1.190 -1.9210
## 27 Q18_3Yes 1.9460 *** 1.206 -0.4174
## 28 Q18_5Yes 1.9798 *** 1.223 -0.4176
## 29 Q18_7Yes 1.4778 * 1.212 -0.8983
## 30 Q18_8Yes 0.5203 *** 1.166 -1.7655
## 31 Q19No 2.0415 * 1.379 -0.6604
## or_upper estimate std.error statistic p.value
## 1 3.890 -0.35805 0.4875 -0.7345 4.628e-01
## 2 3.531 -0.07992 0.2857 -0.2798 7.797e-01
## 3 3.768 0.18026 0.2711 0.6650 5.062e-01
## 4 3.615 0.08450 0.2541 0.3325 7.395e-01
## 5 3.950 0.33610 0.2634 1.2758 2.023e-01
## 6 3.707 0.03575 0.3093 0.1156 9.080e-01
## 7 4.861 0.48101 0.5036 0.9550 3.397e-01
## 8 4.352 0.52222 0.3078 1.6967 9.002e-02
## 9 3.307 -0.44687 0.3080 -1.4510 1.470e-01
## 10 3.371 -0.36520 0.3116 -1.1719 2.415e-01
## 11 4.264 0.21810 0.4324 0.5044 6.141e-01
## 12 3.772 -0.09471 0.3786 -0.2502 8.025e-01
## 13 3.825 -0.09986 0.3986 -0.2506 8.022e-01
## 14 3.674 -0.28458 0.3994 -0.7126 4.763e-01
## 15 3.661 -0.27784 0.3931 -0.7068 4.799e-01
## 16 3.722 -0.32393 0.4252 -0.7618 4.463e-01
## 17 3.503 0.04862 0.2244 0.2167 8.285e-01
## 18 3.672 0.24102 0.2023 1.1912 2.338e-01
## 19 3.046 -0.48215 0.2143 -2.2496 2.466e-02
## 20 3.703 0.30055 0.1824 1.6478 9.966e-02
## 21 4.461 0.62174 0.2822 2.2033 2.776e-02
## 22 6.726 1.39990 0.3095 4.5229 6.703e-06
## 23 13.335 2.33807 0.4169 5.6086 2.528e-08
## 24 11.761 2.19797 0.3401 6.4636 1.481e-10
## 25 2.866 -1.06830 0.2523 -4.2349 2.460e-05
## 26 2.744 -0.88746 0.1741 -5.0977 3.990e-07
## 27 4.309 0.66577 0.1871 3.5577 3.886e-04
## 28 4.377 0.68298 0.2014 3.3910 7.190e-04
## 29 3.854 0.39058 0.1925 2.0288 4.269e-02
## 30 2.806 -0.65330 0.1538 -4.2479 2.324e-05
## 31 4.743 0.71368 0.3210 2.2232 2.639e-02
3 Social influence and herd immunity (Q15 + Q16 + Q17)
3.1 Every vs. some
Q15, Q16, Q17
3.1.1 F statistic
3.1.2 AIC/BIC