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), ])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_lower or_upper
## 1 (Intercept) 0.3139 ** 0.1487 0.6627
## 2 ppagecat25-34 0.8830 0.5327 1.4636
## 3 ppagecat35-44 1.1209 0.6937 1.8111
## 4 ppagecat45-54 1.4419 0.9050 2.2973
## 5 ppagecat55-64 2.2231 *** 1.4207 3.4786
## 6 ppagecat65-74 3.4316 *** 2.1303 5.5279
## 7 ppagecat75+ 5.9703 *** 3.2247 11.0537
## 8 PPEDUCATHigh school 1.1335 0.7370 1.7433
## 9 PPEDUCATSome college 1.0468 0.6735 1.6271
## 10 PPEDUCATBachelor_s degree or higher 1.7546 * 1.1126 2.7670
## 11 income$10k to $25k 0.9686 0.5137 1.8264
## 12 income$25k to $50k 1.3780 0.7490 2.5354
## 13 income$50k to $75k 1.7163 . 0.9243 3.1869
## 14 income$75k to $100k 1.8054 . 0.9518 3.4244
## 15 income$100k to $150k 2.3284 ** 1.2417 4.3663
## 16 incomeover $150k 3.0479 ** 1.4880 6.2432
## 17 PPREG4Northeast 1.0865 0.7805 1.5126
## 18 PPREG4South 1.0792 0.8118 1.4347
## 19 PPREG4West 1.2007 0.8594 1.6775
## 20 workemployed 0.7246 * 0.5549 0.9462
## estimate std.error statistic p.value
## 1 -1.15875 0.3813 -3.03891 2.410e-03
## 2 -0.12446 0.2578 -0.48268 6.294e-01
## 3 0.11411 0.2448 0.46611 6.412e-01
## 4 0.36594 0.2377 1.53979 1.238e-01
## 5 0.79890 0.2284 3.49733 4.820e-04
## 6 1.23304 0.2433 5.06894 4.435e-07
## 7 1.78680 0.3143 5.68561 1.530e-08
## 8 0.12528 0.2196 0.57039 5.685e-01
## 9 0.04576 0.2250 0.20334 8.389e-01
## 10 0.56223 0.2324 2.41912 1.566e-02
## 11 -0.03187 0.3236 -0.09848 9.216e-01
## 12 0.32064 0.3111 1.03078 3.028e-01
## 13 0.54019 0.3157 1.71086 8.729e-02
## 14 0.59079 0.3266 1.80889 7.064e-02
## 15 0.84520 0.3208 2.63487 8.493e-03
## 16 1.11446 0.3658 3.04635 2.352e-03
## 17 0.08301 0.1688 0.49176 6.230e-01
## 18 0.07621 0.1453 0.52457 6.000e-01
## 19 0.18289 0.1706 1.07187 2.839e-01
## 20 -0.32218 0.1361 -2.36646 1.807e-02ns_demo <- svyglm(formula = Q13 ~ ppagecat + PPEDUCAT + income + PPREG4 + work,
design = svy_never_some,
family = quasibinomial(link = "logit"))
print_svy_mod(ns_demo)##
## Call:
## svyglm(formula = Q13 ~ ppagecat + PPEDUCAT + income + PPREG4 +
## work, design = svy_never_some, family = quasibinomial(link = "logit"))
##
## Survey design:
## svydesign(ids = ~1, weights = ~weight, data = never_some[!is.na(never_some$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_lower or_upper
## 1 (Intercept) 0.4194 * 0.1776 0.9905
## 2 ppagecat25-34 0.8451 0.5101 1.4002
## 3 ppagecat35-44 0.7136 0.4344 1.1723
## 4 ppagecat45-54 0.7914 0.4875 1.2846
## 5 ppagecat55-64 0.5327 * 0.3286 0.8638
## 6 ppagecat65-74 0.7629 0.4343 1.3404
## 7 ppagecat75+ 0.4158 . 0.1612 1.0727
## 8 PPEDUCATHigh school 0.7190 0.4214 1.2266
## 9 PPEDUCATSome college 1.5319 0.9118 2.5738
## 10 PPEDUCATBachelor_s degree or higher 1.7494 * 1.0343 2.9590
## 11 income$10k to $25k 0.9164 0.4266 1.9686
## 12 income$25k to $50k 1.3494 0.6739 2.7020
## 13 income$50k to $75k 1.4893 0.7298 3.0388
## 14 income$75k to $100k 1.7259 0.8323 3.5790
## 15 income$100k to $150k 1.8186 0.8813 3.7527
## 16 incomeover $150k 1.7081 0.7678 3.8001
## 17 PPREG4Northeast 0.9458 0.6272 1.4263
## 18 PPREG4South 0.7956 0.5546 1.1414
## 19 PPREG4West 1.5395 * 1.0544 2.2478
## 20 workemployed 0.8580 0.6269 1.1743
## estimate std.error statistic p.value
## 1 -0.86890 0.4384 -1.9819 0.04772
## 2 -0.16824 0.2576 -0.6531 0.51380
## 3 -0.33740 0.2533 -1.3322 0.18303
## 4 -0.23398 0.2472 -0.9467 0.34400
## 5 -0.62973 0.2466 -2.5540 0.01077
## 6 -0.27059 0.2875 -0.9411 0.34684
## 7 -0.87755 0.4835 -1.8149 0.06979
## 8 -0.32996 0.2726 -1.2106 0.22628
## 9 0.42652 0.2647 1.6112 0.10740
## 10 0.55928 0.2681 2.0857 0.03721
## 11 -0.08734 0.3901 -0.2239 0.82290
## 12 0.29968 0.3542 0.8460 0.39772
## 13 0.39828 0.3639 1.0945 0.27393
## 14 0.54574 0.3721 1.4666 0.14274
## 15 0.59808 0.3696 1.6182 0.10587
## 16 0.53540 0.4080 1.3123 0.18965
## 17 -0.05572 0.2096 -0.2658 0.79043
## 18 -0.22860 0.1841 -1.2416 0.21461
## 19 0.43145 0.1931 2.2341 0.02565
## 20 -0.15314 0.1601 -0.9565 0.33901nse_demo <- svyglm(formula = Q13 ~ ppagecat + PPEDUCAT + income + PPREG4 + work,
design = svy_never_someevery,
family = quasibinomial(link = "logit"))
print_svy_mod(nse_demo)##
## Call:
## svyglm(formula = Q13 ~ ppagecat + PPEDUCAT + income + PPREG4 +
## work, design = svy_never_someevery, family = quasibinomial(link = "logit"))
##
## Survey design:
## svydesign(ids = ~1, weights = ~weight, data = never_someevery[!is.na(never_someevery$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_lower or_upper
## 1 (Intercept) 0.7046 0.3747 1.3248
## 2 ppagecat25-34 0.8848 0.5847 1.3388
## 3 ppagecat35-44 0.9044 0.6060 1.3495
## 4 ppagecat45-54 1.1015 0.7453 1.6279
## 5 ppagecat55-64 1.3271 0.9079 1.9398
## 6 ppagecat65-74 2.0096 ** 1.3203 3.0588
## 7 ppagecat75+ 3.0804 *** 1.7378 5.4602
## 8 PPEDUCATHigh school 0.9850 0.6723 1.4432
## 9 PPEDUCATSome college 1.2418 0.8408 1.8341
## 10 PPEDUCATBachelor_s degree or higher 1.7347 ** 1.1600 2.5942
## 11 income$10k to $25k 0.9827 0.5685 1.6986
## 12 income$25k to $50k 1.3752 0.8177 2.3127
## 13 income$50k to $75k 1.6286 . 0.9597 2.7638
## 14 income$75k to $100k 1.7810 * 1.0302 3.0789
## 15 income$100k to $150k 2.1200 ** 1.2363 3.6354
## 16 incomeover $150k 2.5545 ** 1.3802 4.7279
## 17 PPREG4Northeast 1.0247 0.7610 1.3798
## 18 PPREG4South 0.9700 0.7483 1.2573
## 19 PPREG4West 1.3534 * 1.0070 1.8190
## 20 workemployed 0.7672 * 0.6039 0.9747
## estimate std.error statistic p.value
## 1 -0.35017 0.3221 -1.08699 0.2771645
## 2 -0.12243 0.2113 -0.57929 0.5624560
## 3 -0.10054 0.2042 -0.49232 0.6225456
## 4 0.09667 0.1993 0.48506 0.6276867
## 5 0.28297 0.1937 1.46107 0.1441437
## 6 0.69794 0.2143 3.25650 0.0011456
## 7 1.12506 0.2921 3.85214 0.0001205
## 8 -0.01512 0.1949 -0.07760 0.9381533
## 9 0.21655 0.1990 1.08835 0.2765655
## 10 0.55083 0.2053 2.68262 0.0073612
## 11 -0.01750 0.2792 -0.06266 0.9500436
## 12 0.31860 0.2652 1.20125 0.2297891
## 13 0.48772 0.2698 1.80747 0.0708304
## 14 0.57717 0.2793 2.06657 0.0388953
## 15 0.75141 0.2752 2.73076 0.0063709
## 16 0.93784 0.3141 2.98576 0.0028610
## 17 0.02439 0.1518 0.16062 0.8724041
## 18 -0.03050 0.1324 -0.23037 0.8178240
## 19 0.30264 0.1508 2.00630 0.0449507
## 20 -0.26498 0.1221 -2.16945 0.0301590ne_demo_belief <- svyglm(formula = Q13 ~ ppagecat + PPEDUCAT + income + PPREG4 + work + Q20,
design = svy_never_every,
family = quasibinomial(link = "logit"))
print_svy_mod(ne_demo_belief)##
## Call:
## svyglm(formula = Q13 ~ ppagecat + PPEDUCAT + income + PPREG4 +
## work + Q20, design = svy_never_every, family = quasibinomial(link = "logit"))
##
## Survey design:
## svydesign(ids = ~1, weights = ~weight, data = never_every[!is.na(never_every$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_lower or_upper
## 1 (Intercept) 3.039529 * 1.214818 7.60504
## 2 ppagecat25-34 1.293420 0.694974 2.40719
## 3 ppagecat35-44 1.331226 0.743984 2.38199
## 4 ppagecat45-54 1.919844 * 1.089837 3.38197
## 5 ppagecat55-64 2.919129 *** 1.678071 5.07804
## 6 ppagecat65-74 5.576390 *** 3.076712 10.10693
## 7 ppagecat75+ 8.007009 *** 3.733581 17.17177
## 8 PPEDUCATHigh school 0.869170 0.519083 1.45537
## 9 PPEDUCATSome college 1.013682 0.598470 1.71696
## 10 PPEDUCATBachelor_s degree or higher 1.625692 . 0.955659 2.76550
## 11 income$10k to $25k 0.751632 0.342336 1.65028
## 12 income$25k to $50k 1.193946 0.561512 2.53869
## 13 income$50k to $75k 1.466777 0.686840 3.13237
## 14 income$75k to $100k 1.464543 0.675412 3.17567
## 15 income$100k to $150k 1.723436 0.798768 3.71852
## 16 incomeover $150k 2.139370 . 0.932648 4.90743
## 17 PPREG4Northeast 0.984228 0.665056 1.45657
## 18 PPREG4South 0.994521 0.710515 1.39205
## 19 PPREG4West 1.097547 0.755449 1.59456
## 20 workemployed 0.779286 0.575469 1.05529
## 21 Q20Somewhat effective 0.144794 *** 0.094745 0.22128
## 22 Q20It varies from season to season 0.070089 *** 0.044083 0.11144
## 23 Q20Not effective 0.005757 *** 0.002056 0.01612
## 24 Q20Don_t know 0.012086 *** 0.006334 0.02306
## estimate std.error statistic p.value
## 1 1.111703 0.4679 2.37588 1.762e-02
## 2 0.257290 0.3169 0.81183 4.170e-01
## 3 0.286100 0.2969 0.96377 3.353e-01
## 4 0.652244 0.2889 2.25779 2.409e-02
## 5 1.071285 0.2825 3.79257 1.543e-04
## 6 1.718542 0.3034 5.66412 1.732e-08
## 7 2.080317 0.3893 5.34429 1.031e-07
## 8 -0.140217 0.2630 -0.53315 5.940e-01
## 9 0.013590 0.2689 0.05054 9.597e-01
## 10 0.485933 0.2711 1.79268 7.320e-02
## 11 -0.285509 0.4013 -0.71154 4.768e-01
## 12 0.177264 0.3849 0.46056 6.452e-01
## 13 0.383067 0.3871 0.98957 3.225e-01
## 14 0.381543 0.3949 0.96621 3.341e-01
## 15 0.544320 0.3923 1.38733 1.655e-01
## 16 0.760511 0.4236 1.79539 7.277e-02
## 17 -0.015898 0.2000 -0.07949 9.366e-01
## 18 -0.005494 0.1716 -0.03202 9.745e-01
## 19 0.093077 0.1906 0.48841 6.253e-01
## 20 -0.249378 0.1547 -1.61211 1.071e-01
## 21 -1.932446 0.2164 -8.93043 1.077e-18
## 22 -2.657984 0.2366 -11.23512 2.657e-28
## 23 -5.157342 0.5254 -9.81662 3.665e-22
## 24 -4.415717 0.3297 -13.39448 5.746e-39ns_demo_belief <- svyglm(formula = Q13 ~ ppagecat + PPEDUCAT + income + PPREG4 + work + Q20,
design = svy_never_some,
family = quasibinomial(link = "logit"))
print_svy_mod(ns_demo_belief)##
## Call:
## svyglm(formula = Q13 ~ ppagecat + PPEDUCAT + income + PPREG4 +
## work + Q20, design = svy_never_some, family = quasibinomial(link = "logit"))
##
## Survey design:
## svydesign(ids = ~1, weights = ~weight, data = never_some[!is.na(never_some$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_lower or_upper
## 1 (Intercept) 1.40854 0.55820 3.5543
## 2 ppagecat25-34 1.09336 0.63460 1.8838
## 3 ppagecat35-44 0.78610 0.46824 1.3197
## 4 ppagecat45-54 0.88946 0.54459 1.4527
## 5 ppagecat55-64 0.61537 . 0.37328 1.0145
## 6 ppagecat65-74 0.99549 0.55588 1.7828
## 7 ppagecat75+ 0.54786 0.21359 1.4053
## 8 PPEDUCATHigh school 0.65784 0.37926 1.1410
## 9 PPEDUCATSome college 1.66720 . 0.97079 2.8632
## 10 PPEDUCATBachelor_s degree or higher 1.52990 0.88538 2.6436
## 11 income$10k to $25k 0.85388 0.37778 1.9300
## 12 income$25k to $50k 1.14478 0.55209 2.3738
## 13 income$50k to $75k 1.30711 0.61061 2.7981
## 14 income$75k to $100k 1.47205 0.68259 3.1746
## 15 income$100k to $150k 1.42447 0.67025 3.0274
## 16 incomeover $150k 1.45400 0.64117 3.2973
## 17 PPREG4Northeast 0.91947 0.60225 1.4038
## 18 PPREG4South 0.78959 0.54207 1.1501
## 19 PPREG4West 1.60997 * 1.08240 2.3947
## 20 workemployed 0.82980 0.59540 1.1565
## 21 Q20Somewhat effective 0.51980 * 0.30460 0.8870
## 22 Q20It varies from season to season 0.25537 *** 0.14354 0.4543
## 23 Q20Not effective 0.10434 *** 0.04885 0.2229
## 24 Q20Don_t know 0.09902 *** 0.05190 0.1889
## estimate std.error statistic p.value
## 1 0.342553 0.4722 0.72538 4.684e-01
## 2 0.089256 0.2776 0.32158 7.478e-01
## 3 -0.240676 0.2643 -0.91050 3.627e-01
## 4 -0.117146 0.2503 -0.46804 6.398e-01
## 5 -0.485524 0.2551 -1.90363 5.719e-02
## 6 -0.004515 0.2973 -0.01519 9.879e-01
## 7 -0.601732 0.4806 -1.25205 2.108e-01
## 8 -0.418800 0.2810 -1.49046 1.364e-01
## 9 0.511144 0.2759 1.85257 6.419e-02
## 10 0.425203 0.2791 1.52373 1.278e-01
## 11 -0.157969 0.4161 -0.37968 7.042e-01
## 12 0.135212 0.3721 0.36340 7.164e-01
## 13 0.267820 0.3883 0.68968 4.905e-01
## 14 0.386658 0.3921 0.98612 3.243e-01
## 15 0.353797 0.3846 0.91980 3.579e-01
## 16 0.374320 0.4177 0.89605 3.704e-01
## 17 -0.083956 0.2159 -0.38890 6.974e-01
## 18 -0.236242 0.1919 -1.23110 2.185e-01
## 19 0.476215 0.2026 2.35089 1.889e-02
## 20 -0.186572 0.1694 -1.10163 2.708e-01
## 21 -0.654320 0.2727 -2.39974 1.656e-02
## 22 -1.365035 0.2939 -4.64383 3.792e-06
## 23 -2.260054 0.3872 -5.83707 6.809e-09
## 24 -2.312482 0.3296 -7.01677 3.764e-12nse_demo_belief <- svyglm(formula = Q13 ~ ppagecat + PPEDUCAT + income + PPREG4 + work + Q20,
design = svy_never_someevery,
family = quasibinomial(link = "logit"))
print_svy_mod(nse_demo_belief)##
## Call:
## svyglm(formula = Q13 ~ ppagecat + PPEDUCAT + income + PPREG4 +
## work + Q20, design = svy_never_someevery, family = quasibinomial(link = "logit"))
##
## Survey design:
## svydesign(ids = ~1, weights = ~weight, data = never_someevery[!is.na(never_someevery$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_lower or_upper
## 1 (Intercept) 4.76236 *** 2.22795 10.17977
## 2 ppagecat25-34 1.24075 0.75926 2.02758
## 3 ppagecat35-44 1.00171 0.63172 1.58842
## 4 ppagecat45-54 1.22518 0.78786 1.90524
## 5 ppagecat55-64 1.42589 0.92648 2.19451
## 6 ppagecat65-74 2.49837 *** 1.55790 4.00658
## 7 ppagecat75+ 3.14512 *** 1.65540 5.97546
## 8 PPEDUCATHigh school 0.86521 0.56072 1.33506
## 9 PPEDUCATSome college 1.41637 0.90388 2.21941
## 10 PPEDUCATBachelor_s degree or higher 1.66267 * 1.05535 2.61946
## 11 income$10k to $25k 0.91332 0.47673 1.74975
## 12 income$25k to $50k 1.25430 0.68485 2.29727
## 13 income$50k to $75k 1.47589 0.79252 2.74854
## 14 income$75k to $100k 1.53683 0.81683 2.89146
## 15 income$100k to $150k 1.62777 0.86896 3.04921
## 16 incomeover $150k 1.96496 . 0.99680 3.87343
## 17 PPREG4Northeast 0.98168 0.70351 1.36986
## 18 PPREG4South 0.92886 0.69586 1.23987
## 19 PPREG4West 1.40211 * 1.01255 1.94155
## 20 workemployed 0.79723 . 0.61011 1.04175
## 21 Q20Somewhat effective 0.21246 *** 0.14009 0.32222
## 22 Q20It varies from season to season 0.10344 *** 0.06627 0.16144
## 23 Q20Not effective 0.02333 *** 0.01217 0.04472
## 24 Q20Don_t know 0.02781 *** 0.01656 0.04672
## estimate std.error statistic p.value
## 1 1.560743 0.3876 4.026875 5.851e-05
## 2 0.215717 0.2506 0.860890 3.894e-01
## 3 0.001712 0.2352 0.007279 9.942e-01
## 4 0.203090 0.2253 0.901562 3.674e-01
## 5 0.354798 0.2200 1.612865 1.069e-01
## 6 0.915637 0.2410 3.799799 1.489e-04
## 7 1.145851 0.3275 3.499279 4.761e-04
## 8 -0.144782 0.2213 -0.654219 5.130e-01
## 9 0.348094 0.2292 1.519013 1.289e-01
## 10 0.508423 0.2319 2.192314 2.847e-02
## 11 -0.090667 0.3317 -0.273338 7.846e-01
## 12 0.226580 0.3087 0.733872 4.631e-01
## 13 0.389262 0.3172 1.226997 2.200e-01
## 14 0.429722 0.3225 1.332596 1.828e-01
## 15 0.487213 0.3202 1.521402 1.283e-01
## 16 0.675470 0.3463 1.950757 5.122e-02
## 17 -0.018486 0.1700 -0.108742 9.134e-01
## 18 -0.073798 0.1473 -0.500840 6.165e-01
## 19 0.337980 0.1661 2.035126 4.196e-02
## 20 -0.226606 0.1365 -1.660333 9.700e-02
## 21 -1.549009 0.2125 -7.289800 4.360e-13
## 22 -2.268787 0.2271 -9.989061 5.436e-23
## 23 -3.758014 0.3319 -11.321652 6.800e-29
## 24 -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.
ne_demo_belief_cost <- svyglm(formula = Q13 ~ ppagecat + PPEDUCAT + income + PPREG4 + work + Q20 + Q14 + Q19,
design = svy_never_every,
family = quasibinomial(link = "logit"))## Warning: glm.fit: algorithm did not convergeprint_svy_mod(ne_demo_belief_social)##
## Call:
## svyglm(formula = Q13 ~ ppagecat + PPEDUCAT + income + PPREG4 +
## work + Q20 + Q17, 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) -26.918869 0.287361 -93.676 <2e-16
## ppagecat25-34 0.043153 0.203678 0.212 0.832
## ppagecat35-44 0.087126 0.190868 0.456 0.648
## ppagecat45-54 0.184541 0.180518 1.022 0.307
## ppagecat55-64 0.191401 0.170839 1.120 0.263
## ppagecat65-74 0.213515 0.175366 1.218 0.224
## ppagecat75+ 0.235870 0.188101 1.254 0.210
## PPEDUCATHigh school 0.165529 0.155034 1.068 0.286
## PPEDUCATSome college 0.195378 0.160797 1.215 0.225
## PPEDUCATBachelor_s degree or higher 0.254270 0.160346 1.586 0.113
## income$10k to $25k 0.043147 0.252872 0.171 0.865
## income$25k to $50k 0.015673 0.241878 0.065 0.948
## income$50k to $75k 0.057215 0.243267 0.235 0.814
## income$75k to $100k 0.024112 0.247778 0.097 0.923
## income$100k to $150k 0.033550 0.243329 0.138 0.890
## incomeover $150k 0.006603 0.257373 0.026 0.980
## PPREG4Northeast -0.004500 0.109306 -0.041 0.967
## PPREG4South -0.022113 0.094810 -0.233 0.816
## PPREG4West -0.022002 0.106567 -0.206 0.836
## workemployed -0.024834 0.088264 -0.281 0.779
## Q20Somewhat effective 0.010332 0.080464 0.128 0.898
## Q20It varies from season to season -0.002182 0.111120 -0.020 0.984
## Q20Not effective -0.013460 0.436733 -0.031 0.975
## Q20Don_t know -0.044878 0.252487 -0.178 0.859
## Q17Protect myself and others -0.005260 0.080146 -0.066 0.948
## Q17Protect others -0.005066 0.437928 -0.012 0.991
##
## (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
## Q17Protect myself and others
## Q17Protect others
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 7.246607e-15)
##
## Number of Fisher Scoring iterations: 25
##
## term or sig or_lower or_upper
## 1 (Intercept) 2.038e-12 *** 1.161e-12 3.580e-12
## 2 ppagecat25-34 1.044e+00 7.004e-01 1.556e+00
## 3 ppagecat35-44 1.091e+00 7.505e-01 1.586e+00
## 4 ppagecat45-54 1.203e+00 8.443e-01 1.713e+00
## 5 ppagecat55-64 1.211e+00 8.664e-01 1.693e+00
## 6 ppagecat65-74 1.238e+00 8.779e-01 1.746e+00
## 7 ppagecat75+ 1.266e+00 8.756e-01 1.830e+00
## 8 PPEDUCATHigh school 1.180e+00 8.708e-01 1.599e+00
## 9 PPEDUCATSome college 1.216e+00 8.871e-01 1.666e+00
## 10 PPEDUCATBachelor_s degree or higher 1.290e+00 9.418e-01 1.766e+00
## 11 income$10k to $25k 1.044e+00 6.360e-01 1.714e+00
## 12 income$25k to $50k 1.016e+00 6.323e-01 1.632e+00
## 13 income$50k to $75k 1.059e+00 6.573e-01 1.706e+00
## 14 income$75k to $100k 1.024e+00 6.303e-01 1.665e+00
## 15 income$100k to $150k 1.034e+00 6.419e-01 1.666e+00
## 16 incomeover $150k 1.007e+00 6.078e-01 1.667e+00
## 17 PPREG4Northeast 9.955e-01 8.035e-01 1.233e+00
## 18 PPREG4South 9.781e-01 8.123e-01 1.178e+00
## 19 PPREG4West 9.782e-01 7.938e-01 1.205e+00
## 20 workemployed 9.755e-01 8.205e-01 1.160e+00
## 21 Q20Somewhat effective 1.010e+00 8.630e-01 1.183e+00
## 22 Q20It varies from season to season 9.978e-01 8.025e-01 1.241e+00
## 23 Q20Not effective 9.866e-01 4.192e-01 2.322e+00
## 24 Q20Don_t know 9.561e-01 5.829e-01 1.568e+00
## 25 Q17Protect myself and others 9.948e-01 8.501e-01 1.164e+00
## 26 Q17Protect others 9.949e-01 4.217e-01 2.347e+00
## estimate std.error statistic p.value
## 1 -26.918869 0.28736 -93.67604 0.0000
## 2 0.043153 0.20368 0.21187 0.8323
## 3 0.087126 0.19087 0.45647 0.6482
## 4 0.184541 0.18052 1.02229 0.3069
## 5 0.191401 0.17084 1.12036 0.2629
## 6 0.213515 0.17537 1.21754 0.2237
## 7 0.235870 0.18810 1.25395 0.2102
## 8 0.165529 0.15503 1.06769 0.2860
## 9 0.195378 0.16080 1.21506 0.2247
## 10 0.254270 0.16035 1.58575 0.1132
## 11 0.043147 0.25287 0.17063 0.8646
## 12 0.015673 0.24188 0.06480 0.9484
## 13 0.057215 0.24327 0.23519 0.8141
## 14 0.024112 0.24778 0.09731 0.9225
## 15 0.033550 0.24333 0.13788 0.8904
## 16 0.006603 0.25737 0.02565 0.9795
## 17 -0.004500 0.10931 -0.04117 0.9672
## 18 -0.022113 0.09481 -0.23323 0.8156
## 19 -0.022002 0.10657 -0.20646 0.8365
## 20 -0.024834 0.08826 -0.28136 0.7785
## 21 0.010332 0.08046 0.12840 0.8979
## 22 -0.002182 0.11112 -0.01964 0.9843
## 23 -0.013460 0.43673 -0.03082 0.9754
## 24 -0.044878 0.25249 -0.17774 0.8590
## 25 -0.005260 0.08015 -0.06563 0.9477
## 26 -0.005066 0.43793 -0.01157 0.9908ns_demo_belief_cost <- svyglm(formula = Q13 ~ ppagecat + PPEDUCAT + income + PPREG4 + work + Q20 + Q14 + Q19,
design = svy_never_some,
family = quasibinomial(link = "logit"))## Warning: glm.fit: algorithm did not convergeprint_svy_mod(ne_demo_belief_social)##
## Call:
## svyglm(formula = Q13 ~ ppagecat + PPEDUCAT + income + PPREG4 +
## work + Q20 + Q17, 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) -26.918869 0.287361 -93.676 <2e-16
## ppagecat25-34 0.043153 0.203678 0.212 0.832
## ppagecat35-44 0.087126 0.190868 0.456 0.648
## ppagecat45-54 0.184541 0.180518 1.022 0.307
## ppagecat55-64 0.191401 0.170839 1.120 0.263
## ppagecat65-74 0.213515 0.175366 1.218 0.224
## ppagecat75+ 0.235870 0.188101 1.254 0.210
## PPEDUCATHigh school 0.165529 0.155034 1.068 0.286
## PPEDUCATSome college 0.195378 0.160797 1.215 0.225
## PPEDUCATBachelor_s degree or higher 0.254270 0.160346 1.586 0.113
## income$10k to $25k 0.043147 0.252872 0.171 0.865
## income$25k to $50k 0.015673 0.241878 0.065 0.948
## income$50k to $75k 0.057215 0.243267 0.235 0.814
## income$75k to $100k 0.024112 0.247778 0.097 0.923
## income$100k to $150k 0.033550 0.243329 0.138 0.890
## incomeover $150k 0.006603 0.257373 0.026 0.980
## PPREG4Northeast -0.004500 0.109306 -0.041 0.967
## PPREG4South -0.022113 0.094810 -0.233 0.816
## PPREG4West -0.022002 0.106567 -0.206 0.836
## workemployed -0.024834 0.088264 -0.281 0.779
## Q20Somewhat effective 0.010332 0.080464 0.128 0.898
## Q20It varies from season to season -0.002182 0.111120 -0.020 0.984
## Q20Not effective -0.013460 0.436733 -0.031 0.975
## Q20Don_t know -0.044878 0.252487 -0.178 0.859
## Q17Protect myself and others -0.005260 0.080146 -0.066 0.948
## Q17Protect others -0.005066 0.437928 -0.012 0.991
##
## (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
## Q17Protect myself and others
## Q17Protect others
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 7.246607e-15)
##
## Number of Fisher Scoring iterations: 25
##
## term or sig or_lower or_upper
## 1 (Intercept) 2.038e-12 *** 1.161e-12 3.580e-12
## 2 ppagecat25-34 1.044e+00 7.004e-01 1.556e+00
## 3 ppagecat35-44 1.091e+00 7.505e-01 1.586e+00
## 4 ppagecat45-54 1.203e+00 8.443e-01 1.713e+00
## 5 ppagecat55-64 1.211e+00 8.664e-01 1.693e+00
## 6 ppagecat65-74 1.238e+00 8.779e-01 1.746e+00
## 7 ppagecat75+ 1.266e+00 8.756e-01 1.830e+00
## 8 PPEDUCATHigh school 1.180e+00 8.708e-01 1.599e+00
## 9 PPEDUCATSome college 1.216e+00 8.871e-01 1.666e+00
## 10 PPEDUCATBachelor_s degree or higher 1.290e+00 9.418e-01 1.766e+00
## 11 income$10k to $25k 1.044e+00 6.360e-01 1.714e+00
## 12 income$25k to $50k 1.016e+00 6.323e-01 1.632e+00
## 13 income$50k to $75k 1.059e+00 6.573e-01 1.706e+00
## 14 income$75k to $100k 1.024e+00 6.303e-01 1.665e+00
## 15 income$100k to $150k 1.034e+00 6.419e-01 1.666e+00
## 16 incomeover $150k 1.007e+00 6.078e-01 1.667e+00
## 17 PPREG4Northeast 9.955e-01 8.035e-01 1.233e+00
## 18 PPREG4South 9.781e-01 8.123e-01 1.178e+00
## 19 PPREG4West 9.782e-01 7.938e-01 1.205e+00
## 20 workemployed 9.755e-01 8.205e-01 1.160e+00
## 21 Q20Somewhat effective 1.010e+00 8.630e-01 1.183e+00
## 22 Q20It varies from season to season 9.978e-01 8.025e-01 1.241e+00
## 23 Q20Not effective 9.866e-01 4.192e-01 2.322e+00
## 24 Q20Don_t know 9.561e-01 5.829e-01 1.568e+00
## 25 Q17Protect myself and others 9.948e-01 8.501e-01 1.164e+00
## 26 Q17Protect others 9.949e-01 4.217e-01 2.347e+00
## estimate std.error statistic p.value
## 1 -26.918869 0.28736 -93.67604 0.0000
## 2 0.043153 0.20368 0.21187 0.8323
## 3 0.087126 0.19087 0.45647 0.6482
## 4 0.184541 0.18052 1.02229 0.3069
## 5 0.191401 0.17084 1.12036 0.2629
## 6 0.213515 0.17537 1.21754 0.2237
## 7 0.235870 0.18810 1.25395 0.2102
## 8 0.165529 0.15503 1.06769 0.2860
## 9 0.195378 0.16080 1.21506 0.2247
## 10 0.254270 0.16035 1.58575 0.1132
## 11 0.043147 0.25287 0.17063 0.8646
## 12 0.015673 0.24188 0.06480 0.9484
## 13 0.057215 0.24327 0.23519 0.8141
## 14 0.024112 0.24778 0.09731 0.9225
## 15 0.033550 0.24333 0.13788 0.8904
## 16 0.006603 0.25737 0.02565 0.9795
## 17 -0.004500 0.10931 -0.04117 0.9672
## 18 -0.022113 0.09481 -0.23323 0.8156
## 19 -0.022002 0.10657 -0.20646 0.8365
## 20 -0.024834 0.08826 -0.28136 0.7785
## 21 0.010332 0.08046 0.12840 0.8979
## 22 -0.002182 0.11112 -0.01964 0.9843
## 23 -0.013460 0.43673 -0.03082 0.9754
## 24 -0.044878 0.25249 -0.17774 0.8590
## 25 -0.005260 0.08015 -0.06563 0.9477
## 26 -0.005066 0.43793 -0.01157 0.9908nse_demo_belief_cost <- svyglm(formula = Q13 ~ ppagecat + PPEDUCAT + income + PPREG4 + work + Q20 + Q14 + Q19,
design = svy_never_someevery,
family = quasibinomial(link = "logit"))## Warning: glm.fit: algorithm did not convergeprint_svy_mod(ne_demo_belief_social)##
## Call:
## svyglm(formula = Q13 ~ ppagecat + PPEDUCAT + income + PPREG4 +
## work + Q20 + Q17, 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) -26.918869 0.287361 -93.676 <2e-16
## ppagecat25-34 0.043153 0.203678 0.212 0.832
## ppagecat35-44 0.087126 0.190868 0.456 0.648
## ppagecat45-54 0.184541 0.180518 1.022 0.307
## ppagecat55-64 0.191401 0.170839 1.120 0.263
## ppagecat65-74 0.213515 0.175366 1.218 0.224
## ppagecat75+ 0.235870 0.188101 1.254 0.210
## PPEDUCATHigh school 0.165529 0.155034 1.068 0.286
## PPEDUCATSome college 0.195378 0.160797 1.215 0.225
## PPEDUCATBachelor_s degree or higher 0.254270 0.160346 1.586 0.113
## income$10k to $25k 0.043147 0.252872 0.171 0.865
## income$25k to $50k 0.015673 0.241878 0.065 0.948
## income$50k to $75k 0.057215 0.243267 0.235 0.814
## income$75k to $100k 0.024112 0.247778 0.097 0.923
## income$100k to $150k 0.033550 0.243329 0.138 0.890
## incomeover $150k 0.006603 0.257373 0.026 0.980
## PPREG4Northeast -0.004500 0.109306 -0.041 0.967
## PPREG4South -0.022113 0.094810 -0.233 0.816
## PPREG4West -0.022002 0.106567 -0.206 0.836
## workemployed -0.024834 0.088264 -0.281 0.779
## Q20Somewhat effective 0.010332 0.080464 0.128 0.898
## Q20It varies from season to season -0.002182 0.111120 -0.020 0.984
## Q20Not effective -0.013460 0.436733 -0.031 0.975
## Q20Don_t know -0.044878 0.252487 -0.178 0.859
## Q17Protect myself and others -0.005260 0.080146 -0.066 0.948
## Q17Protect others -0.005066 0.437928 -0.012 0.991
##
## (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
## Q17Protect myself and others
## Q17Protect others
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 7.246607e-15)
##
## Number of Fisher Scoring iterations: 25
##
## term or sig or_lower or_upper
## 1 (Intercept) 2.038e-12 *** 1.161e-12 3.580e-12
## 2 ppagecat25-34 1.044e+00 7.004e-01 1.556e+00
## 3 ppagecat35-44 1.091e+00 7.505e-01 1.586e+00
## 4 ppagecat45-54 1.203e+00 8.443e-01 1.713e+00
## 5 ppagecat55-64 1.211e+00 8.664e-01 1.693e+00
## 6 ppagecat65-74 1.238e+00 8.779e-01 1.746e+00
## 7 ppagecat75+ 1.266e+00 8.756e-01 1.830e+00
## 8 PPEDUCATHigh school 1.180e+00 8.708e-01 1.599e+00
## 9 PPEDUCATSome college 1.216e+00 8.871e-01 1.666e+00
## 10 PPEDUCATBachelor_s degree or higher 1.290e+00 9.418e-01 1.766e+00
## 11 income$10k to $25k 1.044e+00 6.360e-01 1.714e+00
## 12 income$25k to $50k 1.016e+00 6.323e-01 1.632e+00
## 13 income$50k to $75k 1.059e+00 6.573e-01 1.706e+00
## 14 income$75k to $100k 1.024e+00 6.303e-01 1.665e+00
## 15 income$100k to $150k 1.034e+00 6.419e-01 1.666e+00
## 16 incomeover $150k 1.007e+00 6.078e-01 1.667e+00
## 17 PPREG4Northeast 9.955e-01 8.035e-01 1.233e+00
## 18 PPREG4South 9.781e-01 8.123e-01 1.178e+00
## 19 PPREG4West 9.782e-01 7.938e-01 1.205e+00
## 20 workemployed 9.755e-01 8.205e-01 1.160e+00
## 21 Q20Somewhat effective 1.010e+00 8.630e-01 1.183e+00
## 22 Q20It varies from season to season 9.978e-01 8.025e-01 1.241e+00
## 23 Q20Not effective 9.866e-01 4.192e-01 2.322e+00
## 24 Q20Don_t know 9.561e-01 5.829e-01 1.568e+00
## 25 Q17Protect myself and others 9.948e-01 8.501e-01 1.164e+00
## 26 Q17Protect others 9.949e-01 4.217e-01 2.347e+00
## estimate std.error statistic p.value
## 1 -26.918869 0.28736 -93.67604 0.0000
## 2 0.043153 0.20368 0.21187 0.8323
## 3 0.087126 0.19087 0.45647 0.6482
## 4 0.184541 0.18052 1.02229 0.3069
## 5 0.191401 0.17084 1.12036 0.2629
## 6 0.213515 0.17537 1.21754 0.2237
## 7 0.235870 0.18810 1.25395 0.2102
## 8 0.165529 0.15503 1.06769 0.2860
## 9 0.195378 0.16080 1.21506 0.2247
## 10 0.254270 0.16035 1.58575 0.1132
## 11 0.043147 0.25287 0.17063 0.8646
## 12 0.015673 0.24188 0.06480 0.9484
## 13 0.057215 0.24327 0.23519 0.8141
## 14 0.024112 0.24778 0.09731 0.9225
## 15 0.033550 0.24333 0.13788 0.8904
## 16 0.006603 0.25737 0.02565 0.9795
## 17 -0.004500 0.10931 -0.04117 0.9672
## 18 -0.022113 0.09481 -0.23323 0.8156
## 19 -0.022002 0.10657 -0.20646 0.8365
## 20 -0.024834 0.08826 -0.28136 0.7785
## 21 0.010332 0.08046 0.12840 0.8979
## 22 -0.002182 0.11112 -0.01964 0.9843
## 23 -0.013460 0.43673 -0.03082 0.9754
## 24 -0.044878 0.25249 -0.17774 0.8590
## 25 -0.005260 0.08015 -0.06563 0.9477
## 26 -0.005066 0.43793 -0.01157 0.9908AIC(ne_demo, ne_demo_belief, ne_demo_belief_social, ne_demo_belief_cost)## eff.p AIC deltabar
## [1,] 2.271370e+01 2.227893e+03 1.195458e+00
## [2,] 2.696941e+01 1.712290e+03 1.172583e+00
## [3,] 2.095767e-10 5.283537e-09 8.383069e-12
## [4,] 2.337024e-10 5.347225e-09 8.346514e-12AIC(ns_demo, ns_demo_belief, ns_demo_belief_social, ns_demo_belief_cost)## eff.p AIC deltabar
## [1,] 2.181199e+01 1.555697e+03 1.147999e+00
## [2,] 2.621122e+01 1.438047e+03 1.139618e+00
## [3,] 2.281033e-10 2.823317e-09 8.448270e-12
## [4,] 2.282095e-10 2.821180e-09 8.452205e-12AIC(nse_demo, nse_demo_belief, nse_demo_belief_social, nse_demo_belief_cost)## eff.p AIC deltabar
## [1,] 2.288354e+01 2.793109e+03 1.204397e+00
## [2,] 2.777803e+01 2.336361e+03 1.207741e+00
## [3,] 2.465347e-10 7.699311e-09 8.501197e-12
## [4,] 2.379679e-10 7.754839e-09 8.498852e-12BIC(ne_demo, ne_demo_belief, ne_demo_belief_cost, maximal = ne_demo_belief_cost)## p BIC neff
## [1,] 20 -80.43742 2.073680e+14
## [2,] 24 51.23692 2.091341e+14
## [3,] 29 197.36595 NaNBIC(ns_demo, ns_demo_belief, ns_demo_belief_cost, maximal = ns_demo_belief_cost)## p BIC neff
## [1,] 20 -61.08918 1.443675e+14
## [2,] 24 69.06733 1.443412e+14
## [3,] 28 169.12713 NaNBIC(nse_demo, nse_demo_belief, nse_demo_belief_cost, maximal = nse_demo_belief_cost)## p BIC neff
## [1,] 20 -75.55857 2.487591e+14
## [2,] 24 56.73697 2.492236e+14
## [3,] 29 208.44206 NaNDrop the cost variables.
Flip the response and Q18 variables
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$Q13)##
## Yes, every year Yes, some years No, never
## Yes 0 61 49
## No 0 362 770The reference response is “Yes, Sometimes.”
The model is modeling NOT getting a vaccine.
ns_demo_belief_barriers <- svyglm(formula = Q13 ~ ppagecat + PPEDUCAT + income + PPREG4 + work + Q20 + Q18_1 + Q18_2 + Q18_3 + Q18_4 + Q18_5 + Q18_6 + Q18_7 + Q18_8 + Q18_9 + Q18_10,
design = svy_never_some_flipped,
family = quasibinomial(link = "logit"))
print_svy_mod(ns_demo_belief_barriers)##
## Call:
## svyglm(formula = Q13 ~ ppagecat + PPEDUCAT + income + PPREG4 +
## work + Q20 + Q18_1 + Q18_2 + Q18_3 + Q18_4 + Q18_5 + Q18_6 +
## Q18_7 + Q18_8 + Q18_9 + Q18_10, design = svy_never_some_flipped,
## family = quasibinomial(link = "logit"))
##
## Survey design:
## svydesign(ids = ~1, weights = ~weight, data = never_some_flipped[!is.na(never_some_flipped$weight),
## ])
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.15571 0.49617 -0.314 0.753709
## ppagecat25-34 -0.05572 0.28031 -0.199 0.842454
## ppagecat35-44 0.20061 0.26920 0.745 0.456284
## ppagecat45-54 0.11955 0.25431 0.470 0.638372
## ppagecat55-64 0.38560 0.26598 1.450 0.147386
## ppagecat65-74 -0.04164 0.31259 -0.133 0.894048
## ppagecat75+ 0.40487 0.51011 0.794 0.427535
## PPEDUCATHigh school 0.38895 0.29808 1.305 0.192186
## PPEDUCATSome college -0.62718 0.29713 -2.111 0.034997
## PPEDUCATBachelor_s degree or higher -0.54476 0.30054 -1.813 0.070140
## income$10k to $25k 0.20248 0.44083 0.459 0.646089
## income$25k to $50k -0.19751 0.38378 -0.515 0.606904
## income$50k to $75k -0.18459 0.39508 -0.467 0.640413
## income$75k to $100k -0.38757 0.39873 -0.972 0.331238
## income$100k to $150k -0.37769 0.39423 -0.958 0.338233
## incomeover $150k -0.42697 0.43006 -0.993 0.320999
## PPREG4Northeast 0.08237 0.22547 0.365 0.714925
## PPREG4South 0.27317 0.20194 1.353 0.176404
## PPREG4West -0.48089 0.21630 -2.223 0.026385
## workemployed 0.26621 0.18238 1.460 0.144654
## Q20Somewhat effective 0.67247 0.28389 2.369 0.018003
## Q20It varies from season to season 1.43950 0.31246 4.607 4.52e-06
## Q20Not effective 2.33496 0.41198 5.668 1.81e-08
## Q20Don_t know 2.34607 0.34525 6.795 1.70e-11
## Q18_1Yes -0.95155 0.25579 -3.720 0.000208
## Q18_2Yes -0.86311 0.17560 -4.915 1.01e-06
## Q18_3Yes 0.71456 0.18633 3.835 0.000132
## Q18_4Yes 0.41757 0.49844 0.838 0.402333
## Q18_5Yes 0.74214 0.20046 3.702 0.000223
## Q18_6Yes -0.53782 0.36478 -1.474 0.140636
## Q18_7Yes 0.35296 0.18891 1.868 0.061949
## Q18_8Yes -0.63482 0.15856 -4.004 6.62e-05
## Q18_9Yes -0.45353 0.42010 -1.080 0.280546
## Q18_10Yes 0.14691 0.23884 0.615 0.538611
##
## (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
## Q18_10Yes
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 1.009234)
##
## Number of Fisher Scoring iterations: 4
##
## term or sig or_lower or_upper
## 1 (Intercept) 0.8558 0.3236 2.2632
## 2 ppagecat25-34 0.9458 0.5460 1.6383
## 3 ppagecat35-44 1.2222 0.7211 2.0714
## 4 ppagecat45-54 1.1270 0.6846 1.8552
## 5 ppagecat55-64 1.4705 0.8731 2.4767
## 6 ppagecat65-74 0.9592 0.5198 1.7701
## 7 ppagecat75+ 1.4991 0.5516 4.0742
## 8 PPEDUCATHigh school 1.4754 0.8226 2.6464
## 9 PPEDUCATSome college 0.5341 * 0.2983 0.9562
## 10 PPEDUCATBachelor_s degree or higher 0.5800 . 0.3218 1.0453
## 11 income$10k to $25k 1.2244 0.5161 2.9052
## 12 income$25k to $50k 0.8208 0.3869 1.7414
## 13 income$50k to $75k 0.8314 0.3833 1.8035
## 14 income$75k to $100k 0.6787 0.3107 1.4828
## 15 income$100k to $150k 0.6854 0.3165 1.4844
## 16 incomeover $150k 0.6525 0.2809 1.5158
## 17 PPREG4Northeast 1.0859 0.6980 1.6893
## 18 PPREG4South 1.3141 0.8846 1.9522
## 19 PPREG4West 0.6182 * 0.4046 0.9447
## 20 workemployed 1.3050 0.9128 1.8658
## 21 Q20Somewhat effective 1.9591 * 1.1231 3.4174
## 22 Q20It varies from season to season 4.2186 *** 2.2866 7.7829
## 23 Q20Not effective 10.3290 *** 4.6065 23.1603
## 24 Q20Don_t know 10.4445 *** 5.3089 20.5481
## 25 Q18_1Yes 0.3861 *** 0.2339 0.6375
## 26 Q18_2Yes 0.4218 *** 0.2990 0.5952
## 27 Q18_3Yes 2.0433 *** 1.4181 2.9440
## 28 Q18_4Yes 1.5183 0.5716 4.0330
## 29 Q18_5Yes 2.1004 *** 1.4180 3.1113
## 30 Q18_6Yes 0.5840 0.2857 1.1938
## 31 Q18_7Yes 1.4233 . 0.9828 2.0611
## 32 Q18_8Yes 0.5300 *** 0.3884 0.7232
## 33 Q18_9Yes 0.6354 0.2789 1.4475
## 34 Q18_10Yes 1.1582 0.7253 1.8497
## estimate std.error statistic p.value
## 1 -0.15571 0.4962 -0.3138 7.537e-01
## 2 -0.05572 0.2803 -0.1988 8.425e-01
## 3 0.20061 0.2692 0.7452 4.563e-01
## 4 0.11955 0.2543 0.4701 6.384e-01
## 5 0.38560 0.2660 1.4498 1.474e-01
## 6 -0.04164 0.3126 -0.1332 8.940e-01
## 7 0.40487 0.5101 0.7937 4.275e-01
## 8 0.38895 0.2981 1.3049 1.922e-01
## 9 -0.62718 0.2971 -2.1108 3.500e-02
## 10 -0.54476 0.3005 -1.8126 7.014e-02
## 11 0.20248 0.4408 0.4593 6.461e-01
## 12 -0.19751 0.3838 -0.5146 6.069e-01
## 13 -0.18459 0.3951 -0.4672 6.404e-01
## 14 -0.38757 0.3987 -0.9720 3.312e-01
## 15 -0.37769 0.3942 -0.9580 3.382e-01
## 16 -0.42697 0.4301 -0.9928 3.210e-01
## 17 0.08237 0.2255 0.3653 7.149e-01
## 18 0.27317 0.2019 1.3527 1.764e-01
## 19 -0.48089 0.2163 -2.2232 2.639e-02
## 20 0.26621 0.1824 1.4596 1.447e-01
## 21 0.67247 0.2839 2.3688 1.800e-02
## 22 1.43950 0.3125 4.6069 4.521e-06
## 23 2.33496 0.4120 5.6676 1.810e-08
## 24 2.34607 0.3453 6.7952 1.696e-11
## 25 -0.95155 0.2558 -3.7200 2.084e-04
## 26 -0.86311 0.1756 -4.9152 1.009e-06
## 27 0.71456 0.1863 3.8348 1.322e-04
## 28 0.41757 0.4984 0.8378 4.023e-01
## 29 0.74214 0.2005 3.7021 2.234e-04
## 30 -0.53782 0.3648 -1.4744 1.406e-01
## 31 0.35296 0.1889 1.8684 6.195e-02
## 32 -0.63482 0.1586 -4.0036 6.620e-05
## 33 -0.45353 0.4201 -1.0796 2.805e-01
## 34 0.14691 0.2388 0.6151 5.386e-01ns_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)##
## 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 + Q18_10 + PPEDUCAT * Q20, 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
## (Intercept) -1.24535
## ppagecat25-34 -0.04565
## ppagecat35-44 0.14054
## ppagecat45-54 0.12964
## ppagecat55-64 0.39507
## ppagecat65-74 -0.01901
## ppagecat75+ 0.41384
## PPEDUCATHigh school 2.08719
## PPEDUCATSome college 0.97342
## PPEDUCATBachelor_s degree or higher 0.35122
## income$10k to $25k 0.08492
## income$25k to $50k -0.33254
## income$50k to $75k -0.28240
## income$75k to $100k -0.48585
## income$100k to $150k -0.48552
## incomeover $150k -0.58160
## PPREG4Northeast 0.09268
## PPREG4South 0.27413
## PPREG4West -0.46402
## workemployed 0.26623
## Q20Somewhat effective 1.94282
## Q20It varies from season to season 3.25230
## Q20Not effective 3.72719
## Q20Don_t know 3.96031
## Q18_1Yes -0.97314
## Q18_2Yes -0.82595
## Q18_3Yes 0.71408
## Q18_4Yes 0.31629
## Q18_5Yes 0.73819
## Q18_6Yes -0.56564
## Q18_7Yes 0.35928
## Q18_8Yes -0.64133
## Q18_9Yes -0.63690
## Q18_10Yes 0.13628
## PPEDUCATHigh school:Q20Somewhat effective -1.85645
## PPEDUCATSome college:Q20Somewhat effective -1.63406
## PPEDUCATBachelor_s degree or higher:Q20Somewhat effective -0.93506
## PPEDUCATHigh school:Q20It varies from season to season -2.49493
## PPEDUCATSome college:Q20It varies from season to season -2.11361
## PPEDUCATBachelor_s degree or higher:Q20It varies from season to season -1.57214
## PPEDUCATHigh school:Q20Not effective -1.85675
## PPEDUCATSome college:Q20Not effective -1.82470
## PPEDUCATBachelor_s degree or higher:Q20Not effective -1.23807
## PPEDUCATHigh school:Q20Don_t know -1.94038
## PPEDUCATSome college:Q20Don_t know -2.40289
## PPEDUCATBachelor_s degree or higher:Q20Don_t know -0.68133
## Std. Error
## (Intercept) 0.78226
## ppagecat25-34 0.28002
## ppagecat35-44 0.26722
## ppagecat45-54 0.25737
## ppagecat55-64 0.26862
## ppagecat65-74 0.31441
## ppagecat75+ 0.52460
## PPEDUCATHigh school 0.84701
## PPEDUCATSome college 0.90210
## PPEDUCATBachelor_s degree or higher 0.93068
## income$10k to $25k 0.47523
## income$25k to $50k 0.41631
## income$50k to $75k 0.42902
## income$75k to $100k 0.43008
## income$100k to $150k 0.42944
## incomeover $150k 0.46243
## PPREG4Northeast 0.22304
## PPREG4South 0.19979
## PPREG4West 0.21539
## workemployed 0.18469
## Q20Somewhat effective 0.80650
## Q20It varies from season to season 1.19109
## Q20Not effective 1.37768
## Q20Don_t know 0.99239
## Q18_1Yes 0.25771
## Q18_2Yes 0.17405
## Q18_3Yes 0.18796
## Q18_4Yes 0.48925
## Q18_5Yes 0.20229
## Q18_6Yes 0.36759
## Q18_7Yes 0.19008
## Q18_8Yes 0.16341
## Q18_9Yes 0.45224
## Q18_10Yes 0.23909
## PPEDUCATHigh school:Q20Somewhat effective 0.94091
## PPEDUCATSome college:Q20Somewhat effective 0.98433
## PPEDUCATBachelor_s degree or higher:Q20Somewhat effective 0.99411
## PPEDUCATHigh school:Q20It varies from season to season 1.30637
## PPEDUCATSome college:Q20It varies from season to season 1.33730
## PPEDUCATBachelor_s degree or higher:Q20It varies from season to season 1.33773
## PPEDUCATHigh school:Q20Not effective 1.54536
## PPEDUCATSome college:Q20Not effective 1.54471
## PPEDUCATBachelor_s degree or higher:Q20Not effective 1.56037
## PPEDUCATHigh school:Q20Don_t know 1.21627
## PPEDUCATSome college:Q20Don_t know 1.16222
## PPEDUCATBachelor_s degree or higher:Q20Don_t know 1.33625
## t value
## (Intercept) -1.592
## ppagecat25-34 -0.163
## ppagecat35-44 0.526
## ppagecat45-54 0.504
## ppagecat55-64 1.471
## ppagecat65-74 -0.060
## ppagecat75+ 0.789
## PPEDUCATHigh school 2.464
## PPEDUCATSome college 1.079
## PPEDUCATBachelor_s degree or higher 0.377
## income$10k to $25k 0.179
## income$25k to $50k -0.799
## income$50k to $75k -0.658
## income$75k to $100k -1.130
## income$100k to $150k -1.131
## incomeover $150k -1.258
## PPREG4Northeast 0.416
## PPREG4South 1.372
## PPREG4West -2.154
## workemployed 1.442
## Q20Somewhat effective 2.409
## Q20It varies from season to season 2.731
## Q20Not effective 2.705
## Q20Don_t know 3.991
## Q18_1Yes -3.776
## Q18_2Yes -4.745
## Q18_3Yes 3.799
## Q18_4Yes 0.646
## Q18_5Yes 3.649
## Q18_6Yes -1.539
## Q18_7Yes 1.890
## Q18_8Yes -3.925
## Q18_9Yes -1.408
## Q18_10Yes 0.570
## PPEDUCATHigh school:Q20Somewhat effective -1.973
## PPEDUCATSome college:Q20Somewhat effective -1.660
## PPEDUCATBachelor_s degree or higher:Q20Somewhat effective -0.941
## PPEDUCATHigh school:Q20It varies from season to season -1.910
## PPEDUCATSome college:Q20It varies from season to season -1.581
## PPEDUCATBachelor_s degree or higher:Q20It varies from season to season -1.175
## PPEDUCATHigh school:Q20Not effective -1.201
## PPEDUCATSome college:Q20Not effective -1.181
## PPEDUCATBachelor_s degree or higher:Q20Not effective -0.793
## PPEDUCATHigh school:Q20Don_t know -1.595
## PPEDUCATSome college:Q20Don_t know -2.068
## PPEDUCATBachelor_s degree or higher:Q20Don_t know -0.510
## Pr(>|t|)
## (Intercept) 0.111652
## ppagecat25-34 0.870518
## ppagecat35-44 0.599023
## ppagecat45-54 0.614552
## ppagecat55-64 0.141631
## ppagecat65-74 0.951795
## ppagecat75+ 0.430353
## PPEDUCATHigh school 0.013873
## PPEDUCATSome college 0.280781
## PPEDUCATBachelor_s degree or higher 0.705955
## income$10k to $25k 0.858212
## income$25k to $50k 0.424584
## income$50k to $75k 0.510511
## income$75k to $100k 0.258841
## income$100k to $150k 0.258454
## incomeover $150k 0.208742
## PPREG4Northeast 0.677816
## PPREG4South 0.170296
## PPREG4West 0.031416
## workemployed 0.149689
## Q20Somewhat effective 0.016149
## Q20It varies from season to season 0.006416
## Q20Not effective 0.006919
## Q20Don_t know 6.99e-05
## Q18_1Yes 0.000167
## Q18_2Yes 2.33e-06
## Q18_3Yes 0.000153
## Q18_4Yes 0.518093
## Q18_5Yes 0.000274
## Q18_6Yes 0.124118
## Q18_7Yes 0.058984
## Q18_8Yes 9.18e-05
## Q18_9Yes 0.159296
## Q18_10Yes 0.568787
## PPEDUCATHigh school:Q20Somewhat effective 0.048722
## PPEDUCATSome college:Q20Somewhat effective 0.097164
## PPEDUCATBachelor_s degree or higher:Q20Somewhat effective 0.347099
## PPEDUCATHigh school:Q20It varies from season to season 0.056396
## PPEDUCATSome college:Q20It varies from season to season 0.114256
## PPEDUCATBachelor_s degree or higher:Q20It varies from season to season 0.240139
## PPEDUCATHigh school:Q20Not effective 0.229796
## PPEDUCATSome college:Q20Not effective 0.237736
## PPEDUCATBachelor_s degree or higher:Q20Not effective 0.427675
## PPEDUCATHigh school:Q20Don_t know 0.110896
## PPEDUCATSome college:Q20Don_t know 0.038901
## PPEDUCATBachelor_s degree or higher:Q20Don_t know 0.610228
##
## (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
## Q18_10Yes
## PPEDUCATHigh school:Q20Somewhat effective *
## PPEDUCATSome college:Q20Somewhat effective .
## PPEDUCATBachelor_s degree or higher:Q20Somewhat effective
## PPEDUCATHigh school:Q20It varies from season to season .
## PPEDUCATSome college:Q20It varies from season to season
## PPEDUCATBachelor_s degree or higher:Q20It varies from season to season
## PPEDUCATHigh school:Q20Not effective
## PPEDUCATSome college:Q20Not effective
## PPEDUCATBachelor_s degree or higher:Q20Not effective
## PPEDUCATHigh school:Q20Don_t know
## PPEDUCATSome college:Q20Don_t know *
## PPEDUCATBachelor_s degree or higher:Q20Don_t know
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 1.019612)
##
## Number of Fisher Scoring iterations: 5
##
## term
## 1 (Intercept)
## 2 ppagecat25-34
## 3 ppagecat35-44
## 4 ppagecat45-54
## 5 ppagecat55-64
## 6 ppagecat65-74
## 7 ppagecat75+
## 8 PPEDUCATHigh school
## 9 PPEDUCATSome college
## 10 PPEDUCATBachelor_s degree or higher
## 11 income$10k to $25k
## 12 income$25k to $50k
## 13 income$50k to $75k
## 14 income$75k to $100k
## 15 income$100k to $150k
## 16 incomeover $150k
## 17 PPREG4Northeast
## 18 PPREG4South
## 19 PPREG4West
## 20 workemployed
## 21 Q20Somewhat effective
## 22 Q20It varies from season to season
## 23 Q20Not effective
## 24 Q20Don_t know
## 25 Q18_1Yes
## 26 Q18_2Yes
## 27 Q18_3Yes
## 28 Q18_4Yes
## 29 Q18_5Yes
## 30 Q18_6Yes
## 31 Q18_7Yes
## 32 Q18_8Yes
## 33 Q18_9Yes
## 34 Q18_10Yes
## 35 PPEDUCATHigh school:Q20Somewhat effective
## 36 PPEDUCATSome college:Q20Somewhat effective
## 37 PPEDUCATBachelor_s degree or higher:Q20Somewhat effective
## 38 PPEDUCATHigh school:Q20It varies from season to season
## 39 PPEDUCATSome college:Q20It varies from season to season
## 40 PPEDUCATBachelor_s degree or higher:Q20It varies from season to season
## 41 PPEDUCATHigh school:Q20Not effective
## 42 PPEDUCATSome college:Q20Not effective
## 43 PPEDUCATBachelor_s degree or higher:Q20Not effective
## 44 PPEDUCATHigh school:Q20Don_t know
## 45 PPEDUCATSome college:Q20Don_t know
## 46 PPEDUCATBachelor_s degree or higher:Q20Don_t know
## or sig or_lower or_upper estimate std.error statistic p.value
## 1 0.28784 0.062126 1.3336 -1.24535 0.7823 -1.59199 1.117e-01
## 2 0.95537 0.551844 1.6540 -0.04565 0.2800 -0.16304 8.705e-01
## 3 1.15090 0.681676 1.9431 0.14054 0.2672 0.52595 5.990e-01
## 4 1.13842 0.687426 1.8853 0.12964 0.2574 0.50372 6.146e-01
## 5 1.48449 0.876838 2.5132 0.39507 0.2686 1.47072 1.416e-01
## 6 0.98117 0.529798 1.8171 -0.01901 0.3144 -0.06047 9.518e-01
## 7 1.51261 0.540972 4.2294 0.41384 0.5246 0.78886 4.304e-01
## 8 8.06221 * 1.532728 42.4075 2.08719 0.8470 2.46419 1.387e-02
## 9 2.64698 0.451716 15.5109 0.97342 0.9021 1.07906 2.808e-01
## 10 1.42080 0.229259 8.8052 0.35122 0.9307 0.37738 7.060e-01
## 11 1.08863 0.428897 2.7632 0.08492 0.4752 0.17869 8.582e-01
## 12 0.71710 0.317107 1.6216 -0.33254 0.4163 -0.79877 4.246e-01
## 13 0.75397 0.325213 1.7480 -0.28240 0.4290 -0.65824 5.105e-01
## 14 0.61517 0.264792 1.4292 -0.48585 0.4301 -1.12967 2.588e-01
## 15 0.61538 0.265214 1.4279 -0.48552 0.4294 -1.13059 2.585e-01
## 16 0.55900 0.225831 1.3837 -0.58160 0.4624 -1.25771 2.087e-01
## 17 1.09712 0.708590 1.6987 0.09268 0.2230 0.41555 6.778e-01
## 18 1.31539 0.889175 1.9459 0.27413 0.1998 1.37208 1.703e-01
## 19 0.62875 * 0.412226 0.9590 -0.46402 0.2154 -2.15430 3.142e-02
## 20 1.30504 0.908693 1.8743 0.26623 0.1847 1.44156 1.497e-01
## 21 6.97838 * 1.436309 33.9048 1.94282 0.8065 2.40895 1.615e-02
## 22 25.84980 ** 2.503697 266.8901 3.25230 1.1911 2.73053 6.416e-03
## 23 41.56222 ** 2.792482 618.5959 3.72719 1.3777 2.70540 6.919e-03
## 24 52.47339 *** 7.502364 367.0119 3.96031 0.9924 3.99067 6.990e-05
## 25 0.37789 *** 0.228035 0.6262 -0.97314 0.2577 -3.77609 1.672e-04
## 26 0.43782 *** 0.311272 0.6158 -0.82595 0.1741 -4.74543 2.332e-06
## 27 2.04230 *** 1.412945 2.9520 0.71408 0.1880 3.79910 1.525e-04
## 28 1.37203 0.525903 3.5795 0.31629 0.4892 0.64648 5.181e-01
## 29 2.09214 *** 1.407341 3.1101 0.73819 0.2023 3.64919 2.744e-04
## 30 0.56799 0.276342 1.1675 -0.56564 0.3676 -1.53880 1.241e-01
## 31 1.43230 . 0.986805 2.0789 0.35928 0.1901 1.89012 5.898e-02
## 32 0.52659 *** 0.382276 0.7254 -0.64133 0.1634 -3.92459 9.184e-05
## 33 0.52893 0.217993 1.2834 -0.63690 0.4522 -1.40832 1.593e-01
## 34 1.14600 0.717248 1.8311 0.13628 0.2391 0.57000 5.688e-01
## 35 0.15623 * 0.024708 0.9878 -1.85645 0.9409 -1.97304 4.872e-02
## 36 0.19514 . 0.028344 1.3434 -1.63406 0.9843 -1.66006 9.716e-02
## 37 0.39256 0.055937 2.7549 -0.93506 0.9941 -0.94060 3.471e-01
## 38 0.08250 . 0.006375 1.0678 -2.49493 1.3064 -1.90982 5.640e-02
## 39 0.12080 0.008785 1.6611 -2.11361 1.3373 -1.58051 1.143e-01
## 40 0.20760 0.015084 2.8571 -1.57214 1.3377 -1.17522 2.401e-01
## 41 0.15618 0.007554 3.2290 -1.85675 1.5454 -1.20150 2.298e-01
## 42 0.16127 0.007810 3.3299 -1.82470 1.5447 -1.18126 2.377e-01
## 43 0.28994 0.013617 6.1734 -1.23807 1.5604 -0.79345 4.277e-01
## 44 0.14365 0.013243 1.5581 -1.94038 1.2163 -1.59536 1.109e-01
## 45 0.09046 * 0.009271 0.8825 -2.40289 1.1622 -2.06751 3.890e-02
## 46 0.50594 0.036869 6.9429 -0.68133 1.3362 -0.50988 6.102e-01ns_demo_belief_barriers <- svyglm(formula = Q13 ~ ppagecat + PPEDUCAT + income + PPREG4 + work + Q20 + Q18_3 + Q18_5,
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_3 + Q18_5, 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) -0.44850 0.46464 -0.965 0.3346
## ppagecat25-34 -0.08400 0.27558 -0.305 0.7605
## ppagecat35-44 0.25725 0.26481 0.971 0.3315
## ppagecat45-54 0.14775 0.25284 0.584 0.5591
## ppagecat55-64 0.36674 0.25692 1.427 0.1537
## ppagecat65-74 -0.04172 0.29734 -0.140 0.8884
## ppagecat75+ 0.53306 0.47312 1.127 0.2601
## PPEDUCATHigh school 0.34449 0.28692 1.201 0.2301
## PPEDUCATSome college -0.60558 0.28350 -2.136 0.0329
## PPEDUCATBachelor_s degree or higher -0.56715 0.28490 -1.991 0.0467
## income$10k to $25k 0.10318 0.41689 0.248 0.8046
## income$25k to $50k -0.19997 0.36641 -0.546 0.5853
## income$50k to $75k -0.30427 0.38219 -0.796 0.4261
## income$75k to $100k -0.40667 0.38708 -1.051 0.2936
## income$100k to $150k -0.44277 0.37882 -1.169 0.2427
## incomeover $150k -0.43668 0.41435 -1.054 0.2921
## PPREG4Northeast 0.08980 0.22000 0.408 0.6832
## PPREG4South 0.25150 0.19614 1.282 0.2000
## PPREG4West -0.45239 0.20602 -2.196 0.0283
## workemployed 0.24251 0.17214 1.409 0.1591
## Q20Somewhat effective 0.57081 0.27207 2.098 0.0361
## Q20It varies from season to season 1.25443 0.29480 4.255 2.25e-05
## Q20Not effective 2.14509 0.38404 5.586 2.87e-08
## Q20Don_t know 2.32963 0.32722 7.119 1.85e-12
## Q18_3Yes 0.72527 0.18241 3.976 7.42e-05
## Q18_5Yes 0.75888 0.18701 4.058 5.26e-05
##
## (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_3Yes ***
## Q18_5Yes ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 0.9904011)
##
## Number of Fisher Scoring iterations: 4
##
## term or sig or_lower or_upper
## 1 (Intercept) 0.6386 0.2569 1.5875
## 2 ppagecat25-34 0.9194 0.5357 1.5779
## 3 ppagecat35-44 1.2934 0.7697 2.1734
## 4 ppagecat45-54 1.1592 0.7062 1.9028
## 5 ppagecat55-64 1.4430 0.8721 2.3876
## 6 ppagecat65-74 0.9591 0.5355 1.7178
## 7 ppagecat75+ 1.7041 0.6742 4.3076
## 8 PPEDUCATHigh school 1.4113 0.8042 2.4765
## 9 PPEDUCATSome college 0.5458 * 0.3131 0.9513
## 10 PPEDUCATBachelor_s degree or higher 0.5671 * 0.3245 0.9913
## 11 income$10k to $25k 1.1087 0.4897 2.5100
## 12 income$25k to $50k 0.8188 0.3993 1.6790
## 13 income$50k to $75k 0.7377 0.3488 1.5602
## 14 income$75k to $100k 0.6659 0.3118 1.4219
## 15 income$100k to $150k 0.6423 0.3057 1.3495
## 16 incomeover $150k 0.6462 0.2868 1.4556
## 17 PPREG4Northeast 1.0940 0.7108 1.6837
## 18 PPREG4South 1.2860 0.8755 1.8888
## 19 PPREG4West 0.6361 * 0.4248 0.9526
## 20 workemployed 1.2744 0.9095 1.7859
## 21 Q20Somewhat effective 1.7697 * 1.0383 3.0164
## 22 Q20It varies from season to season 3.5058 *** 1.9672 6.2479
## 23 Q20Not effective 8.5428 *** 4.0244 18.1345
## 24 Q20Don_t know 10.2741 *** 5.4101 19.5112
## 25 Q18_3Yes 2.0653 *** 1.4445 2.9529
## 26 Q18_5Yes 2.1359 *** 1.4805 3.0815
## estimate std.error statistic p.value
## 1 -0.44850 0.4646 -0.9653 3.346e-01
## 2 -0.08400 0.2756 -0.3048 7.605e-01
## 3 0.25725 0.2648 0.9714 3.315e-01
## 4 0.14775 0.2528 0.5844 5.591e-01
## 5 0.36674 0.2569 1.4275 1.537e-01
## 6 -0.04172 0.2973 -0.1403 8.884e-01
## 7 0.53306 0.4731 1.1267 2.601e-01
## 8 0.34449 0.2869 1.2006 2.301e-01
## 9 -0.60558 0.2835 -2.1361 3.287e-02
## 10 -0.56715 0.2849 -1.9907 4.674e-02
## 11 0.10318 0.4169 0.2475 8.046e-01
## 12 -0.19997 0.3664 -0.5458 5.853e-01
## 13 -0.30427 0.3822 -0.7961 4.261e-01
## 14 -0.40667 0.3871 -1.0506 2.936e-01
## 15 -0.44277 0.3788 -1.1688 2.427e-01
## 16 -0.43668 0.4143 -1.0539 2.921e-01
## 17 0.08980 0.2200 0.4082 6.832e-01
## 18 0.25150 0.1961 1.2823 2.000e-01
## 19 -0.45239 0.2060 -2.1958 2.829e-02
## 20 0.24251 0.1721 1.4089 1.591e-01
## 21 0.57081 0.2721 2.0980 3.611e-02
## 22 1.25443 0.2948 4.2551 2.250e-05
## 23 2.14509 0.3840 5.5856 2.873e-08
## 24 2.32963 0.3272 7.1194 1.852e-12
## 25 0.72527 0.1824 3.9760 7.423e-05
## 26 0.75888 0.1870 4.0581 5.265e-05
3 Social influence and herd immunity (Q15, 16, 17)
3.1 every vs. never
3.2 sometimes vs. never
3.3 sometimes+every vs. never
3.4 sometimes vs every
3.5 F statistic
3.6 AIC/BIC
Drop the Social influence and herd immunity variables.