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')
# 1. run 'analysis-draft.Rmd' to get 'data_for_models.RDS'
# 2. run '01-variable_selection.Rmd' to get 'model_dataframes.RData'
# 3. run this file (modified '02-model_fit.Rmd')
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), ])# 1.1
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 $150k 3.0479 ** 1.4880 6.2432
## 17 PPREG4Northeast 1.0865 0.7805 1.5126
## 18 PPREG4South 1.0792 0.8118 1.4347
## 19 PPREG4West 1.2007 0.8594 1.6775
## 20 workemployed 0.7246 * 0.5549 0.9462
## estimate std.error statistic p.value
## 1 -1.15875 0.3813 -3.03891 2.410e-03
## 2 -0.12446 0.2578 -0.48268 6.294e-01
## 3 0.11411 0.2448 0.46611 6.412e-01
## 4 0.36594 0.2377 1.53979 1.238e-01
## 5 0.79890 0.2284 3.49733 4.820e-04
## 6 1.23304 0.2433 5.06894 4.435e-07
## 7 1.78680 0.3143 5.68561 1.530e-08
## 8 0.12528 0.2196 0.57039 5.685e-01
## 9 0.04576 0.2250 0.20334 8.389e-01
## 10 0.56223 0.2324 2.41912 1.566e-02
## 11 -0.03187 0.3236 -0.09848 9.216e-01
## 12 0.32064 0.3111 1.03078 3.028e-01
## 13 0.54019 0.3157 1.71086 8.729e-02
## 14 0.59079 0.3266 1.80889 7.064e-02
## 15 0.84520 0.3208 2.63487 8.493e-03
## 16 1.11446 0.3658 3.04635 2.352e-03
## 17 0.08301 0.1688 0.49176 6.230e-01
## 18 0.07621 0.1453 0.52457 6.000e-01
## 19 0.18289 0.1706 1.07187 2.839e-01
## 20 -0.32218 0.1361 -2.36646 1.807e-02# 1.1
ns_demo <- svyglm(formula = Q13 ~ ppagecat + PPEDUCAT + income + PPREG4 + work,
design = svy_never_some,
family = quasibinomial(link = "logit"))
print_svy_mod(ns_demo)##
## Call:
## svyglm(formula = Q13 ~ ppagecat + PPEDUCAT + income + PPREG4 +
## work, design = svy_never_some, family = quasibinomial(link = "logit"))
##
## Survey design:
## svydesign(ids = ~1, weights = ~weight, data = never_some[!is.na(never_some$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 $150k 1.7081 0.7678 3.8001
## 17 PPREG4Northeast 0.9458 0.6272 1.4263
## 18 PPREG4South 0.7956 0.5546 1.1414
## 19 PPREG4West 1.5395 * 1.0544 2.2478
## 20 workemployed 0.8580 0.6269 1.1743
## estimate std.error statistic p.value
## 1 -0.86890 0.4384 -1.9819 0.04772
## 2 -0.16824 0.2576 -0.6531 0.51380
## 3 -0.33740 0.2533 -1.3322 0.18303
## 4 -0.23398 0.2472 -0.9467 0.34400
## 5 -0.62973 0.2466 -2.5540 0.01077
## 6 -0.27059 0.2875 -0.9411 0.34684
## 7 -0.87755 0.4835 -1.8149 0.06979
## 8 -0.32996 0.2726 -1.2106 0.22628
## 9 0.42652 0.2647 1.6112 0.10740
## 10 0.55928 0.2681 2.0857 0.03721
## 11 -0.08734 0.3901 -0.2239 0.82290
## 12 0.29968 0.3542 0.8460 0.39772
## 13 0.39828 0.3639 1.0945 0.27393
## 14 0.54574 0.3721 1.4666 0.14274
## 15 0.59808 0.3696 1.6182 0.10587
## 16 0.53540 0.4080 1.3123 0.18965
## 17 -0.05572 0.2096 -0.2658 0.79043
## 18 -0.22860 0.1841 -1.2416 0.21461
## 19 0.43145 0.1931 2.2341 0.02565
## 20 -0.15314 0.1601 -0.9565 0.33901## Some + every vs. never (referent) [Remove]
# nse_demo <- svyglm(formula = Q13 ~ ppagecat + PPEDUCAT + income + PPREG4 + work,
# design = svy_never_someevery,
# family = quasibinomial(link = "logit"))
# print_svy_mod(nse_demo)# 1.2
ne_demo_belief <- svyglm(formula = Q13 ~ ppagecat + PPEDUCAT + income + PPREG4 + work + Q20,
design = svy_never_every,
family = quasibinomial(link = "logit"))
print_svy_mod(ne_demo_belief)##
## Call:
## svyglm(formula = Q13 ~ ppagecat + PPEDUCAT + income + PPREG4 +
## work + Q20, design = svy_never_every, family = quasibinomial(link = "logit"))
##
## Survey design:
## svydesign(ids = ~1, weights = ~weight, data = never_every[!is.na(never_every$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_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 $150k 2.139370 . 0.932648 4.90743
## 17 PPREG4Northeast 0.984228 0.665056 1.45657
## 18 PPREG4South 0.994521 0.710515 1.39205
## 19 PPREG4West 1.097547 0.755449 1.59456
## 20 workemployed 0.779286 0.575469 1.05529
## 21 Q20Somewhat effective 0.144794 *** 0.094745 0.22128
## 22 Q20It varies from season to season 0.070089 *** 0.044083 0.11144
## 23 Q20Not effective 0.005757 *** 0.002056 0.01612
## 24 Q20Don_t know 0.012086 *** 0.006334 0.02306
## estimate std.error statistic p.value
## 1 1.111703 0.4679 2.37588 1.762e-02
## 2 0.257290 0.3169 0.81183 4.170e-01
## 3 0.286100 0.2969 0.96377 3.353e-01
## 4 0.652244 0.2889 2.25779 2.409e-02
## 5 1.071285 0.2825 3.79257 1.543e-04
## 6 1.718542 0.3034 5.66412 1.732e-08
## 7 2.080317 0.3893 5.34429 1.031e-07
## 8 -0.140217 0.2630 -0.53315 5.940e-01
## 9 0.013590 0.2689 0.05054 9.597e-01
## 10 0.485933 0.2711 1.79268 7.320e-02
## 11 -0.285509 0.4013 -0.71154 4.768e-01
## 12 0.177264 0.3849 0.46056 6.452e-01
## 13 0.383067 0.3871 0.98957 3.225e-01
## 14 0.381543 0.3949 0.96621 3.341e-01
## 15 0.544320 0.3923 1.38733 1.655e-01
## 16 0.760511 0.4236 1.79539 7.277e-02
## 17 -0.015898 0.2000 -0.07949 9.366e-01
## 18 -0.005494 0.1716 -0.03202 9.745e-01
## 19 0.093077 0.1906 0.48841 6.253e-01
## 20 -0.249378 0.1547 -1.61211 1.071e-01
## 21 -1.932446 0.2164 -8.93043 1.077e-18
## 22 -2.657984 0.2366 -11.23512 2.657e-28
## 23 -5.157342 0.5254 -9.81662 3.665e-22
## 24 -4.415717 0.3297 -13.39448 5.746e-39# 1.2
ns_demo_belief <- svyglm(formula = Q13 ~ ppagecat + PPEDUCAT + income + PPREG4 + work + Q20,
design = svy_never_some,
family = quasibinomial(link = "logit"))
print_svy_mod(ns_demo_belief)##
## Call:
## svyglm(formula = Q13 ~ ppagecat + PPEDUCAT + income + PPREG4 +
## work + Q20, design = svy_never_some, family = quasibinomial(link = "logit"))
##
## Survey design:
## svydesign(ids = ~1, weights = ~weight, data = never_some[!is.na(never_some$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-12## Some + every vs. never [Remove]
# nse_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)F statistic
Every vs. never
anova(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= 1698Some vs. never
anova(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= 1215Some + every vs. never [Remove]
#anova(nse_demo, nse_demo_belief, test = 'F')AIC/BIC
AIC(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 1.139618#AIC(nse_demo, nse_demo_belief)#BIC(ne_demo, ne_demo_belief, maximal = ne_demo_belief)
#BIC(ns_demo, ns_demo_belief, maximal = ns_demo_belief)
#BIC(nse_demo, nse_demo_belief, maximal = nse_demo_belief)Note: Keep the efficacy (Q20) variable.
Note: Run Q19 and Q21 separately
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_lower or_upper
## 1 (Intercept) 1.790e+00 5.584e-01 5.737e+00
## 2 ppagecat25-34 1.148e+00 6.161e-01 2.140e+00
## 3 ppagecat35-44 1.524e+00 8.372e-01 2.776e+00
## 4 ppagecat45-54 1.719e+00 . 9.571e-01 3.089e+00
## 5 ppagecat55-64 3.773e+00 *** 2.157e+00 6.599e+00
## 6 ppagecat65-74 4.766e+00 *** 2.546e+00 8.921e+00
## 7 ppagecat75+ 1.127e+01 *** 4.673e+00 2.719e+01
## 8 PPEDUCATHigh school 1.645e+00 8.593e-01 3.149e+00
## 9 PPEDUCATSome college 7.692e-01 4.116e-01 1.438e+00
## 10 PPEDUCATBachelor_s degree or higher 1.252e+00 6.543e-01 2.397e+00
## 11 income$10k to $25k 1.535e+00 5.766e-01 4.084e+00
## 12 income$25k to $50k 1.714e+00 6.570e-01 4.473e+00
## 13 income$50k to $75k 1.935e+00 7.429e-01 5.038e+00
## 14 income$75k to $100k 1.698e+00 6.421e-01 4.490e+00
## 15 income$100k to $150k 1.800e+00 6.816e-01 4.753e+00
## 16 incomeover $150k 2.990e+00 * 1.101e+00 8.118e+00
## 17 PPREG4Northeast 1.048e+00 6.736e-01 1.629e+00
## 18 PPREG4South 1.176e+00 7.970e-01 1.735e+00
## 19 PPREG4West 7.888e-01 5.241e-01 1.187e+00
## 20 workemployed 8.201e-01 5.868e-01 1.146e+00
## 21 Q20Somewhat effective 2.853e-01 *** 1.916e-01 4.249e-01
## 22 Q20It varies from season to season 2.660e-01 *** 1.672e-01 4.231e-01
## 23 Q20Not effective 9.681e-02 *** 3.219e-02 2.912e-01
## 24 Q20Don_t know 1.503e-01 *** 6.671e-02 3.386e-01
## 25 Q14Less than $30 4.313e-01 *** 2.999e-01 6.203e-01
## 26 Q14$30 to $60 3.432e-01 *** 1.842e-01 6.395e-01
## 27 Q14More than $60 7.898e+05 *** 1.658e+05 3.762e+06
## 28 Q14Don_t know 2.446e-01 *** 1.339e-01 4.469e-01
## estimate std.error statistic p.value
## 1 0.58212 0.5943 0.9795 3.275e-01
## 2 0.13820 0.3176 0.4351 6.635e-01
## 3 0.42166 0.3058 1.3788 1.682e-01
## 4 0.54191 0.2989 1.8132 7.004e-02
## 5 1.32789 0.2852 4.6554 3.565e-06
## 6 1.56145 0.3199 4.8817 1.182e-06
## 7 2.42238 0.4493 5.3920 8.269e-08
## 8 0.49773 0.3313 1.5024 1.332e-01
## 9 -0.26234 0.3191 -0.8221 4.112e-01
## 10 0.22501 0.3313 0.6793 4.971e-01
## 11 0.42829 0.4994 0.8576 3.913e-01
## 12 0.53901 0.4893 1.1016 2.709e-01
## 13 0.65987 0.4883 1.3513 1.768e-01
## 14 0.52940 0.4962 1.0670 2.862e-01
## 15 0.58769 0.4954 1.1863 2.357e-01
## 16 1.09529 0.5096 2.1494 3.179e-02
## 17 0.04654 0.2253 0.2065 8.364e-01
## 18 0.16207 0.1985 0.8166 4.143e-01
## 19 -0.23730 0.2086 -1.1378 2.554e-01
## 20 -0.19839 0.1707 -1.1621 2.454e-01
## 21 -1.25412 0.2032 -6.1725 8.968e-10
## 22 -1.32438 0.2369 -5.5915 2.741e-08
## 23 -2.33497 0.5618 -4.1559 3.452e-05
## 24 -1.89508 0.4144 -4.5729 5.269e-06
## 25 -0.84095 0.1854 -4.5351 6.289e-06
## 26 -1.06938 0.3175 -3.3679 7.796e-04
## 27 13.57957 0.7964 17.0507 5.582e-59
## 28 -1.40823 0.3075 -4.5793 5.113e-06se_demo_belief_cost <- svyglm(formula = Q13 ~ ppagecat + PPEDUCAT + income + PPREG4 + work + Q20 + Q19,
design = svy_some_every,
family = quasibinomial(link = "logit"))
print_svy_mod(se_demo_belief_cost)##
## Call:
## svyglm(formula = Q13 ~ ppagecat + PPEDUCAT + income + PPREG4 +
## work + Q20 + Q19, design = svy_some_every, family = quasibinomial(link = "logit"))
##
## Survey design:
## svydesign(ids = ~1, weights = ~weight, data = some_every[!is.na(some_every$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_lower or_upper
## 1 (Intercept) 1.8298 0.58037 5.7691
## 2 ppagecat25-34 1.2830 0.69636 2.3639
## 3 ppagecat35-44 1.6723 . 0.93956 2.9766
## 4 ppagecat45-54 1.9862 * 1.13137 3.4869
## 5 ppagecat55-64 4.1990 *** 2.45746 7.1746
## 6 ppagecat65-74 5.2562 *** 2.88894 9.5633
## 7 ppagecat75+ 12.8683 *** 5.29641 31.2652
## 8 PPEDUCATHigh school 1.3155 0.69426 2.4928
## 9 PPEDUCATSome college 0.6581 0.35545 1.2185
## 10 PPEDUCATBachelor_s degree or higher 0.9674 0.51121 1.8307
## 11 income$10k to $25k 1.3347 0.49389 3.6069
## 12 income$25k to $50k 1.4117 0.53065 3.7556
## 13 income$50k to $75k 1.6072 0.60786 4.2497
## 14 income$75k to $100k 1.3218 0.49261 3.5467
## 15 income$100k to $150k 1.5804 0.59023 4.2316
## 16 incomeover $150k 2.1875 0.79957 5.9845
## 17 PPREG4Northeast 1.0442 0.67530 1.6147
## 18 PPREG4South 1.2387 0.84359 1.8190
## 19 PPREG4West 0.7992 0.53778 1.1877
## 20 workemployed 0.8509 0.61444 1.1782
## 21 Q20Somewhat effective 0.2743 *** 0.18459 0.4076
## 22 Q20It varies from season to season 0.2618 *** 0.16472 0.4162
## 23 Q20Not effective 0.1014 *** 0.03275 0.3142
## 24 Q20Don_t know 0.1128 *** 0.05380 0.2367
## 25 Q19No 0.3969 * 0.18253 0.8632
## estimate std.error statistic p.value
## 1 0.60422 0.5859 1.0313 3.026e-01
## 2 0.24922 0.3118 0.7993 4.243e-01
## 3 0.51422 0.2942 1.7481 8.069e-02
## 4 0.68622 0.2871 2.3899 1.700e-02
## 5 1.43484 0.2733 5.2496 1.779e-07
## 6 1.65941 0.3054 5.4341 6.569e-08
## 7 2.55477 0.4529 5.6406 2.078e-08
## 8 0.27425 0.3261 0.8410 4.005e-01
## 9 -0.41839 0.3143 -1.3313 1.833e-01
## 10 -0.03315 0.3254 -0.1019 9.189e-01
## 11 0.28871 0.5072 0.5692 5.693e-01
## 12 0.34480 0.4992 0.6907 4.899e-01
## 13 0.47452 0.4961 0.9565 3.390e-01
## 14 0.27899 0.5036 0.5540 5.797e-01
## 15 0.45767 0.5025 0.9108 3.626e-01
## 16 0.78275 0.5135 1.5244 1.277e-01
## 17 0.04329 0.2224 0.1947 8.457e-01
## 18 0.21409 0.1960 1.0922 2.749e-01
## 19 -0.22413 0.2021 -1.1088 2.677e-01
## 20 -0.16151 0.1661 -0.9724 3.310e-01
## 21 -1.29355 0.2021 -6.4017 2.142e-10
## 22 -1.34003 0.2365 -5.6666 1.792e-08
## 23 -2.28828 0.5769 -3.9666 7.687e-05
## 24 -2.18179 0.3779 -5.7734 9.707e-09
## 25 -0.92399 0.3963 -2.3313 1.989e-02se_demo_belief_cost <- svyglm(formula = Q13 ~ ppagecat + PPEDUCAT + income + PPREG4 + work + Q20 + Q21,
design = svy_some_every,
family = quasibinomial(link = "logit"))
print_svy_mod(se_demo_belief_cost)##
## Call:
## svyglm(formula = Q13 ~ ppagecat + PPEDUCAT + income + PPREG4 +
## work + Q20 + Q21, design = svy_some_every, family = quasibinomial(link = "logit"))
##
## Survey design:
## svydesign(ids = ~1, weights = ~weight, data = some_every[!is.na(some_every$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_lower or_upper
## 1 (Intercept) 1.6231 0.50748 5.1914
## 2 ppagecat25-34 1.1917 0.62972 2.2552
## 3 ppagecat35-44 1.4842 0.81539 2.7017
## 4 ppagecat45-54 2.0554 * 1.14053 3.7041
## 5 ppagecat55-64 3.7993 *** 2.15139 6.7095
## 6 ppagecat65-74 4.6001 *** 2.44772 8.6450
## 7 ppagecat75+ 9.3770 *** 3.80543 23.1059
## 8 PPEDUCATHigh school 1.1613 0.58409 2.3090
## 9 PPEDUCATSome college 0.6153 0.31403 1.2057
## 10 PPEDUCATBachelor_s degree or higher 0.9431 0.47212 1.8840
## 11 income$10k to $25k 2.1368 0.79123 5.7709
## 12 income$25k to $50k 2.3905 . 0.91267 6.2612
## 13 income$50k to $75k 2.5819 . 0.98646 6.7578
## 14 income$75k to $100k 2.1264 0.80237 5.6355
## 15 income$100k to $150k 2.4373 . 0.92072 6.4517
## 16 incomeover $150k 3.5992 * 1.32886 9.7483
## 17 PPREG4Northeast 1.0436 0.66598 1.6355
## 18 PPREG4South 1.1556 0.77903 1.7143
## 19 PPREG4West 0.7685 0.50964 1.1589
## 20 workemployed 0.8213 0.58658 1.1499
## 21 Q20Somewhat effective 0.2825 *** 0.18802 0.4244
## 22 Q20It varies from season to season 0.2591 *** 0.16221 0.4138
## 23 Q20Not effective 0.1169 ** 0.03253 0.4199
## 24 Q20Don_t know 0.1510 *** 0.06677 0.3416
## 25 Q21Yes, but only part of the cost is paid 0.4703 ** 0.29134 0.7591
## 26 Q21No 0.7772 0.27538 2.1934
## 27 Q21Don_t know 0.5500 ** 0.36474 0.8292
## estimate std.error statistic p.value
## 1 0.48435 0.5932 0.8165 4.144e-01
## 2 0.17537 0.3254 0.5389 5.901e-01
## 3 0.39490 0.3056 1.2922 1.965e-01
## 4 0.72047 0.3005 2.3976 1.665e-02
## 5 1.33482 0.2902 4.6003 4.646e-06
## 6 1.52607 0.3219 4.7409 2.371e-06
## 7 2.23826 0.4601 4.8645 1.293e-06
## 8 0.14956 0.3506 0.4265 6.698e-01
## 9 -0.48562 0.3432 -1.4150 1.573e-01
## 10 -0.05857 0.3530 -0.1659 8.682e-01
## 11 0.75933 0.5069 1.4980 1.344e-01
## 12 0.87149 0.4913 1.7740 7.631e-02
## 13 0.94853 0.4909 1.9322 5.356e-02
## 14 0.75445 0.4973 1.5172 1.295e-01
## 15 0.89087 0.4967 1.7937 7.310e-02
## 16 1.28071 0.5084 2.5193 1.188e-02
## 17 0.04272 0.2292 0.1864 8.522e-01
## 18 0.14465 0.2012 0.7190 4.723e-01
## 19 -0.26328 0.2096 -1.2562 2.093e-01
## 20 -0.19688 0.1717 -1.1465 2.518e-01
## 21 -1.26417 0.2077 -6.0870 1.527e-09
## 22 -1.35065 0.2389 -5.6540 1.940e-08
## 23 -2.14665 0.6525 -3.2897 1.031e-03
## 24 -1.89025 0.4164 -4.5391 6.192e-06
## 25 -0.75444 0.2443 -3.0883 2.057e-03
## 26 -0.25206 0.5293 -0.4762 6.340e-01
## 27 -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_lower or_upper
## 1 (Intercept) 2.045e+00 5.948e-01 7.032e+00
## 2 ppagecat25-34 1.180e+00 6.284e-01 2.217e+00
## 3 ppagecat35-44 1.541e+00 8.442e-01 2.813e+00
## 4 ppagecat45-54 1.795e+00 * 1.001e+00 3.219e+00
## 5 ppagecat55-64 3.844e+00 *** 2.196e+00 6.728e+00
## 6 ppagecat65-74 4.735e+00 *** 2.524e+00 8.880e+00
## 7 ppagecat75+ 1.122e+01 *** 4.578e+00 2.752e+01
## 8 PPEDUCATHigh school 1.404e+00 7.140e-01 2.761e+00
## 9 PPEDUCATSome college 6.597e-01 3.447e-01 1.262e+00
## 10 PPEDUCATBachelor_s degree or higher 1.061e+00 5.434e-01 2.072e+00
## 11 income$10k to $25k 1.601e+00 5.837e-01 4.389e+00
## 12 income$25k to $50k 1.762e+00 6.550e-01 4.739e+00
## 13 income$50k to $75k 1.973e+00 7.353e-01 5.296e+00
## 14 income$75k to $100k 1.723e+00 6.329e-01 4.690e+00
## 15 income$100k to $150k 1.851e+00 6.804e-01 5.034e+00
## 16 incomeover $150k 2.979e+00 * 1.065e+00 8.330e+00
## 17 PPREG4Northeast 1.050e+00 6.743e-01 1.635e+00
## 18 PPREG4South 1.210e+00 8.183e-01 1.790e+00
## 19 PPREG4West 8.107e-01 5.378e-01 1.222e+00
## 20 workemployed 8.138e-01 5.820e-01 1.138e+00
## 21 Q20Somewhat effective 2.794e-01 *** 1.866e-01 4.184e-01
## 22 Q20It varies from season to season 2.600e-01 *** 1.629e-01 4.151e-01
## 23 Q20Not effective 1.064e-01 *** 3.453e-02 3.281e-01
## 24 Q20Don_t know 1.436e-01 *** 6.319e-02 3.262e-01
## 25 Q14Less than $30 4.420e-01 *** 3.081e-01 6.339e-01
## 26 Q14$30 to $60 3.731e-01 ** 1.974e-01 7.054e-01
## 27 Q14More than $60 4.236e+05 *** 7.887e+04 2.275e+06
## 28 Q14Don_t know 2.491e-01 *** 1.360e-01 4.563e-01
## 29 Q19No 5.443e-01 2.471e-01 1.199e+00
## estimate std.error statistic p.value
## 1 0.71547 0.6301 1.1355 2.564e-01
## 2 0.16584 0.3216 0.5156 6.062e-01
## 3 0.43247 0.3071 1.4084 1.592e-01
## 4 0.58484 0.2981 1.9620 4.998e-02
## 5 1.34657 0.2856 4.7149 2.680e-06
## 6 1.55488 0.3209 4.8454 1.416e-06
## 7 2.41805 0.4575 5.2849 1.475e-07
## 8 0.33933 0.3450 0.9836 3.255e-01
## 9 -0.41604 0.3311 -1.2564 2.092e-01
## 10 0.05930 0.3414 0.1737 8.621e-01
## 11 0.47042 0.5147 0.9141 3.609e-01
## 12 0.56634 0.5048 1.1218 2.621e-01
## 13 0.67976 0.5037 1.3496 1.774e-01
## 14 0.54403 0.5109 1.0648 2.872e-01
## 15 0.61553 0.5105 1.2056 2.282e-01
## 16 1.09154 0.5247 2.0805 3.768e-02
## 17 0.04867 0.2259 0.2154 8.295e-01
## 18 0.19088 0.1997 0.9557 3.394e-01
## 19 -0.20985 0.2094 -1.0020 3.165e-01
## 20 -0.20609 0.1710 -1.2052 2.284e-01
## 21 -1.27513 0.2060 -6.1890 8.112e-10
## 22 -1.34690 0.2386 -5.6446 2.032e-08
## 23 -2.24024 0.5743 -3.9007 1.009e-04
## 24 -1.94089 0.4187 -4.6351 3.928e-06
## 25 -0.81653 0.1840 -4.4371 9.892e-06
## 26 -0.98585 0.3249 -3.0341 2.461e-03
## 27 12.95651 0.8576 15.1078 1.287e-47
## 28 -1.38986 0.3088 -4.5001 7.402e-06
## 29 -0.60828 0.4028 -1.5100 1.313e-01AIC/BIC
#AIC(ne_demo, ne_demo_belief, ne_demo_belief_social, ne_demo_belief_cost)
#AIC(ns_demo, ns_demo_belief, ns_demo_belief_social, ns_demo_belief_cost)
#AIC(nse_demo, nse_demo_belief, nse_demo_belief_social, nse_demo_belief_cost)#BIC(ne_demo, ne_demo_belief, ne_demo_belief_cost, maximal = ne_demo_belief_cost)
#BIC(ns_demo, ns_demo_belief, ns_demo_belief_cost, maximal = ns_demo_belief_cost)
#BIC(nse_demo, nse_demo_belief, nse_demo_belief_cost, maximal = nse_demo_belief_cost)Note: Flip the response and Q18 variables
library(forcats)
df <- readRDS('./data/subset_recode.RDS')
never_some_flipped <- df[df$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$Q13)##
## Yes, every year Yes, some years No, never
## Yes 0 61 49
## No 0 362 770Note: The reference response is “Yes, Some years.” The model is modeling NOT getting a vaccine.
# Barriers only
ns_demo_belief_barriers <- svyglm(formula = Q13 ~ ppagecat + PPEDUCAT + income + PPREG4 + work + Q18_1 + Q18_2 + Q18_3 + Q18_4 + Q18_5 + Q18_6 + Q18_7 + Q18_8 + Q18_9,
design = svy_never_some_flipped,
family = quasibinomial(link = "logit"))
print_svy_mod(ns_demo_belief_barriers)##
## Call:
## svyglm(formula = Q13 ~ ppagecat + PPEDUCAT + income + PPREG4 +
## work + Q18_1 + Q18_2 + Q18_3 + Q18_4 + Q18_5 + Q18_6 + Q18_7 +
## Q18_8 + Q18_9, design = svy_never_some_flipped, family = quasibinomial(link = "logit"))
##
## Survey design:
## svydesign(ids = ~1, weights = ~weight, data = never_some_flipped[!is.na(never_some_flipped$weight),
## ])
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.19736 0.50842 2.355 0.018677
## ppagecat25-34 0.17987 0.26934 0.668 0.504378
## ppagecat35-44 0.25292 0.26279 0.962 0.336017
## ppagecat45-54 0.19071 0.25866 0.737 0.461068
## ppagecat55-64 0.47785 0.26031 1.836 0.066644
## ppagecat65-74 0.18985 0.30659 0.619 0.535879
## ppagecat75+ 0.64755 0.50074 1.293 0.196189
## PPEDUCATHigh school 0.27488 0.28997 0.948 0.343333
## PPEDUCATSome college -0.54848 0.28280 -1.939 0.052679
## PPEDUCATBachelor_s degree or higher -0.66525 0.28435 -2.340 0.019467
## income$10k to $25k 0.09054 0.44696 0.203 0.839503
## income$25k to $50k -0.41818 0.39852 -1.049 0.294244
## income$50k to $75k -0.36400 0.40667 -0.895 0.370919
## income$75k to $100k -0.59596 0.41198 -1.447 0.148274
## income$100k to $150k -0.68699 0.41275 -1.664 0.096288
## incomeover $150k -0.66702 0.45003 -1.482 0.138551
## PPREG4Northeast 0.04156 0.21954 0.189 0.849875
## PPREG4South 0.25413 0.19581 1.298 0.194589
## PPREG4West -0.40488 0.20738 -1.952 0.051124
## workemployed 0.24199 0.17176 1.409 0.159142
## Q18_1Yes -1.07341 0.23909 -4.489 7.82e-06
## Q18_2Yes -0.68288 0.16001 -4.268 2.13e-05
## Q18_3Yes 0.62076 0.17873 3.473 0.000532
## Q18_4Yes 0.33707 0.43162 0.781 0.434989
## Q18_5Yes 0.73334 0.19208 3.818 0.000141
## Q18_6Yes -0.50720 0.33316 -1.522 0.128178
## Q18_7Yes 0.34853 0.18043 1.932 0.053627
## Q18_8Yes -0.71194 0.14725 -4.835 1.50e-06
## Q18_9Yes -0.31360 0.49476 -0.634 0.526295
##
## (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
## 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 1.030493)
##
## Number of Fisher Scoring iterations: 4
##
## term or sig or_lower or_upper
## 1 (Intercept) 3.3114 * 1.2225 8.9697
## 2 ppagecat25-34 1.1971 0.7061 2.0295
## 3 ppagecat35-44 1.2878 0.7694 2.1554
## 4 ppagecat45-54 1.2101 0.7289 2.0091
## 5 ppagecat55-64 1.6126 . 0.9682 2.6860
## 6 ppagecat65-74 1.2091 0.6629 2.2051
## 7 ppagecat75+ 1.9109 0.7161 5.0988
## 8 PPEDUCATHigh school 1.3164 0.7457 2.3239
## 9 PPEDUCATSome college 0.5778 . 0.3320 1.0058
## 10 PPEDUCATBachelor_s degree or higher 0.5141 * 0.2945 0.8977
## 11 income$10k to $25k 1.0948 0.4559 2.6289
## 12 income$25k to $50k 0.6582 0.3014 1.4375
## 13 income$50k to $75k 0.6949 0.3131 1.5420
## 14 income$75k to $100k 0.5510 0.2457 1.2356
## 15 income$100k to $150k 0.5031 . 0.2240 1.1298
## 16 incomeover $150k 0.5132 0.2124 1.2399
## 17 PPREG4Northeast 1.0424 0.6779 1.6030
## 18 PPREG4South 1.2893 0.8784 1.8925
## 19 PPREG4West 0.6671 . 0.4443 1.0016
## 20 workemployed 1.2738 0.9097 1.7836
## 21 Q18_1Yes 0.3418 *** 0.2139 0.5462
## 22 Q18_2Yes 0.5052 *** 0.3692 0.6913
## 23 Q18_3Yes 1.8603 *** 1.3106 2.6407
## 24 Q18_4Yes 1.4008 0.6012 3.2643
## 25 Q18_5Yes 2.0820 *** 1.4288 3.0338
## 26 Q18_6Yes 0.6022 0.3134 1.1570
## 27 Q18_7Yes 1.4170 . 0.9949 2.0181
## 28 Q18_8Yes 0.4907 *** 0.3677 0.6549
## 29 Q18_9Yes 0.7308 0.2771 1.9273
## estimate std.error statistic p.value
## 1 1.19736 0.5084 2.3551 1.868e-02
## 2 0.17987 0.2693 0.6678 5.044e-01
## 3 0.25292 0.2628 0.9624 3.360e-01
## 4 0.19071 0.2587 0.7373 4.611e-01
## 5 0.47785 0.2603 1.8357 6.664e-02
## 6 0.18985 0.3066 0.6192 5.359e-01
## 7 0.64755 0.5007 1.2932 1.962e-01
## 8 0.27488 0.2900 0.9480 3.433e-01
## 9 -0.54848 0.2828 -1.9394 5.268e-02
## 10 -0.66525 0.2843 -2.3396 1.947e-02
## 11 0.09054 0.4470 0.2026 8.395e-01
## 12 -0.41818 0.3985 -1.0493 2.942e-01
## 13 -0.36400 0.4067 -0.8951 3.709e-01
## 14 -0.59596 0.4120 -1.4466 1.483e-01
## 15 -0.68699 0.4128 -1.6644 9.629e-02
## 16 -0.66702 0.4500 -1.4822 1.386e-01
## 17 0.04156 0.2195 0.1893 8.499e-01
## 18 0.25413 0.1958 1.2978 1.946e-01
## 19 -0.40488 0.2074 -1.9524 5.112e-02
## 20 0.24199 0.1718 1.4088 1.591e-01
## 21 -1.07341 0.2391 -4.4895 7.820e-06
## 22 -0.68288 0.1600 -4.2676 2.129e-05
## 23 0.62076 0.1787 3.4732 5.324e-04
## 24 0.33707 0.4316 0.7809 4.350e-01
## 25 0.73334 0.1921 3.8179 1.414e-04
## 26 -0.50720 0.3332 -1.5224 1.282e-01
## 27 0.34853 0.1804 1.9317 5.363e-02
## 28 -0.71194 0.1473 -4.8347 1.504e-06
## 29 -0.31360 0.4948 -0.6339 5.263e-01Note: p-value < 0.05 for Q18_1, 2, 3, 5, 8 Only including the above Q18 subset
# Barriers + efficacy
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,
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_lower or_upper
## 1 (Intercept) 0.8874 0.3374 2.3340
## 2 ppagecat25-34 0.9347 0.5397 1.6189
## 3 ppagecat35-44 1.2120 0.7158 2.0520
## 4 ppagecat45-54 1.1254 0.6842 1.8510
## 5 ppagecat55-64 1.4714 0.8745 2.4758
## 6 ppagecat65-74 0.9620 0.5226 1.7708
## 7 ppagecat75+ 1.5223 0.5618 4.1254
## 8 PPEDUCATHigh school 1.4946 0.8334 2.6803
## 9 PPEDUCATSome college 0.5432 * 0.3038 0.9712
## 10 PPEDUCATBachelor_s degree or higher 0.5962 . 0.3324 1.0696
## 11 income$10k to $25k 1.2113 0.5115 2.8686
## 12 income$25k to $50k 0.8045 0.3796 1.7051
## 13 income$50k to $75k 0.8171 0.3776 1.7680
## 14 income$75k to $100k 0.6644 0.3050 1.4471
## 15 income$100k to $150k 0.6700 0.3098 1.4489
## 16 incomeover $150k 0.6414 0.2765 1.4879
## 17 PPREG4Northeast 1.0799 0.6946 1.6790
## 18 PPREG4South 1.3090 0.8811 1.9446
## 19 PPREG4West 0.6170 * 0.4038 0.9427
## 20 workemployed 1.3092 0.9156 1.8721
## 21 Q20Somewhat effective 1.9656 * 1.1273 3.4272
## 22 Q20It varies from season to season 4.2466 *** 2.3043 7.8260
## 23 Q20Not effective 10.5001 *** 4.6986 23.4649
## 24 Q20Don_t know 10.6070 *** 5.3870 20.8851
## 25 Q18_1Yes 0.3812 *** 0.2307 0.6297
## 26 Q18_2Yes 0.4175 *** 0.2968 0.5874
## 27 Q18_3Yes 2.0205 *** 1.4053 2.9049
## 28 Q18_4Yes 1.5091 0.5651 4.0301
## 29 Q18_5Yes 2.0702 *** 1.4046 3.0512
## 30 Q18_6Yes 0.5788 0.2847 1.1765
## 31 Q18_7Yes 1.4099 . 0.9749 2.0391
## 32 Q18_8Yes 0.5169 *** 0.3824 0.6988
## 33 Q18_9Yes 0.6314 0.2772 1.4383
## estimate std.error statistic p.value
## 1 -0.11943 0.4934 -0.2421 8.088e-01
## 2 -0.06752 0.2803 -0.2409 8.096e-01
## 3 0.19226 0.2687 0.7156 4.744e-01
## 4 0.11810 0.2539 0.4652 6.419e-01
## 5 0.38623 0.2655 1.4548 1.460e-01
## 6 -0.03875 0.3113 -0.1245 9.010e-01
## 7 0.42025 0.5086 0.8263 4.088e-01
## 8 0.40185 0.2980 1.3485 1.777e-01
## 9 -0.61032 0.2965 -2.0584 3.976e-02
## 10 -0.51711 0.2981 -1.7345 8.309e-02
## 11 0.19170 0.4399 0.4358 6.630e-01
## 12 -0.21758 0.3833 -0.5677 5.703e-01
## 13 -0.20197 0.3938 -0.5129 6.081e-01
## 14 -0.40893 0.3972 -1.0295 3.034e-01
## 15 -0.40049 0.3935 -1.0177 3.090e-01
## 16 -0.44416 0.4293 -1.0345 3.011e-01
## 17 0.07685 0.2252 0.3413 7.330e-01
## 18 0.26925 0.2019 1.3333 1.827e-01
## 19 -0.48288 0.2163 -2.2329 2.574e-02
## 20 0.26944 0.1825 1.4767 1.400e-01
## 21 0.67578 0.2836 2.3825 1.735e-02
## 22 1.44612 0.3119 4.6364 3.931e-06
## 23 2.35138 0.4103 5.7313 1.258e-08
## 24 2.36151 0.3457 6.8316 1.329e-11
## 25 -0.96454 0.2561 -3.7656 1.742e-04
## 26 -0.87339 0.1742 -5.0147 6.106e-07
## 27 0.70333 0.1852 3.7967 1.539e-04
## 28 0.41153 0.5012 0.8212 4.117e-01
## 29 0.72765 0.1979 3.6770 2.464e-04
## 30 -0.54682 0.3619 -1.5109 1.311e-01
## 31 0.34355 0.1882 1.8251 6.823e-02
## 32 -0.65987 0.1538 -4.2909 1.922e-05
## 33 -0.45983 0.4200 -1.0947 2.739e-01# Barriers + efficacy + cost
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_lower or_upper
## 1 (Intercept) 0.6990 0.2689 1.8174
## 2 ppagecat25-34 0.9232 0.5274 1.6161
## 3 ppagecat35-44 1.1975 0.7040 2.0371
## 4 ppagecat45-54 1.0882 0.6613 1.7906
## 5 ppagecat55-64 1.3995 0.8351 2.3454
## 6 ppagecat65-74 1.0364 0.5653 1.9002
## 7 ppagecat75+ 1.6177 0.6028 4.3412
## 8 PPEDUCATHigh school 1.6858 . 0.9222 3.0817
## 9 PPEDUCATSome college 0.6396 0.3498 1.1697
## 10 PPEDUCATBachelor_s degree or higher 0.6941 0.3768 1.2784
## 11 income$10k to $25k 1.2437 0.5329 2.9026
## 12 income$25k to $50k 0.9096 0.4331 1.9103
## 13 income$50k to $75k 0.9050 0.4143 1.9765
## 14 income$75k to $100k 0.7523 0.3439 1.6457
## 15 income$100k to $150k 0.7574 0.3505 1.6367
## 16 incomeover $150k 0.7233 0.3143 1.6644
## 17 PPREG4Northeast 1.0498 0.6763 1.6296
## 18 PPREG4South 1.2725 0.8559 1.8919
## 19 PPREG4West 0.6175 * 0.4057 0.9398
## 20 workemployed 1.3506 . 0.9446 1.9310
## 21 Q20Somewhat effective 1.8622 * 1.0711 3.2376
## 22 Q20It varies from season to season 4.0548 *** 2.2106 7.4376
## 23 Q20Not effective 10.3612 *** 4.5768 23.4565
## 24 Q20Don_t know 9.0067 *** 4.6250 17.5397
## 25 Q18_1Yes 0.3436 *** 0.2096 0.5633
## 26 Q18_2Yes 0.4117 *** 0.2927 0.5791
## 27 Q18_3Yes 1.9460 *** 1.3485 2.8082
## 28 Q18_5Yes 1.9798 *** 1.3340 2.9381
## 29 Q18_7Yes 1.4778 * 1.0133 2.1553
## 30 Q18_8Yes 0.5203 *** 0.3849 0.7034
## 31 Q19No 2.0415 * 1.0882 3.8300
## estimate std.error statistic p.value
## 1 -0.35805 0.4875 -0.7345 4.628e-01
## 2 -0.07992 0.2857 -0.2798 7.797e-01
## 3 0.18026 0.2711 0.6650 5.062e-01
## 4 0.08450 0.2541 0.3325 7.395e-01
## 5 0.33610 0.2634 1.2758 2.023e-01
## 6 0.03575 0.3093 0.1156 9.080e-01
## 7 0.48101 0.5036 0.9550 3.397e-01
## 8 0.52222 0.3078 1.6967 9.002e-02
## 9 -0.44687 0.3080 -1.4510 1.470e-01
## 10 -0.36520 0.3116 -1.1719 2.415e-01
## 11 0.21810 0.4324 0.5044 6.141e-01
## 12 -0.09471 0.3786 -0.2502 8.025e-01
## 13 -0.09986 0.3986 -0.2506 8.022e-01
## 14 -0.28458 0.3994 -0.7126 4.763e-01
## 15 -0.27784 0.3931 -0.7068 4.799e-01
## 16 -0.32393 0.4252 -0.7618 4.463e-01
## 17 0.04862 0.2244 0.2167 8.285e-01
## 18 0.24102 0.2023 1.1912 2.338e-01
## 19 -0.48215 0.2143 -2.2496 2.466e-02
## 20 0.30055 0.1824 1.6478 9.966e-02
## 21 0.62174 0.2822 2.2033 2.776e-02
## 22 1.39990 0.3095 4.5229 6.703e-06
## 23 2.33807 0.4169 5.6086 2.528e-08
## 24 2.19797 0.3401 6.4636 1.481e-10
## 25 -1.06830 0.2523 -4.2349 2.460e-05
## 26 -0.88746 0.1741 -5.0977 3.990e-07
## 27 0.66577 0.1871 3.5577 3.886e-04
## 28 0.68298 0.2014 3.3910 7.190e-04
## 29 0.39058 0.1925 2.0288 4.269e-02
## 30 -0.65330 0.1538 -4.2479 2.324e-05
## 31 0.71368 0.3210 2.2232 2.639e-02se_model <- svyglm(formula = Q13 ~ ppagecat + PPEDUCAT + income + PPREG4 + work + Q20 + Q16 + Q17 + Q14 + Q19,
design = svy_some_every,
family = quasibinomial(link = "logit"))
print_svy_mod(se_model)##
## Call:
## svyglm(formula = Q13 ~ ppagecat + PPEDUCAT + income + PPREG4 +
## work + Q20 + Q16 + Q17 + 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) 1.06432 0.73541 1.447 0.148073
## ppagecat25-34 0.04911 0.34067 0.144 0.885405
## ppagecat35-44 0.38432 0.32288 1.190 0.234157
## ppagecat45-54 0.49010 0.30884 1.587 0.112788
## ppagecat55-64 1.23356 0.30277 4.074 4.90e-05
## ppagecat65-74 1.49569 0.33795 4.426 1.04e-05
## ppagecat75+ 2.28943 0.46587 4.914 1.01e-06
## PPEDUCATHigh school 0.32713 0.35195 0.930 0.352804
## PPEDUCATSome college -0.44513 0.33508 -1.328 0.184273
## PPEDUCATBachelor_s degree or higher 0.06551 0.34380 0.191 0.848918
## income$10k to $25k 0.61096 0.57232 1.068 0.285943
## income$25k to $50k 0.79661 0.55673 1.431 0.152712
## income$50k to $75k 0.97487 0.55691 1.751 0.080270
## income$75k to $100k 0.82447 0.56319 1.464 0.143461
## income$100k to $150k 0.86339 0.55486 1.556 0.119943
## incomeover $150k 1.27704 0.57375 2.226 0.026203
## PPREG4Northeast -0.06033 0.23427 -0.258 0.796807
## PPREG4South 0.09253 0.20608 0.449 0.653510
## PPREG4West -0.28856 0.21826 -1.322 0.186371
## workemployed -0.19167 0.17401 -1.101 0.270888
## Q20Somewhat effective -1.27741 0.20840 -6.130 1.17e-09
## Q20It varies from season to season -1.35081 0.24239 -5.573 3.05e-08
## Q20Not effective -1.82663 0.66778 -2.735 0.006318
## Q20Don_t know -1.92842 0.44562 -4.328 1.63e-05
## Q16No, no effect -0.62779 0.18903 -3.321 0.000922
## Q16No, less likely -1.50790 0.31746 -4.750 2.27e-06
## Q17Protect myself and others 0.23212 0.15872 1.462 0.143872
## Q17Protect others -0.97782 0.49324 -1.982 0.047646
## Q14Less than $30 -0.86413 0.18359 -4.707 2.79e-06
## Q14$30 to $60 -0.94222 0.35006 -2.692 0.007204
## Q14More than $60 13.53184 1.08139 12.513 < 2e-16
## Q14Don_t know -1.40061 0.32203 -4.349 1.47e-05
## Q19No -0.34048 0.41335 -0.824 0.410260
##
## (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 ***
## Q16No, no effect ***
## Q16No, less likely ***
## Q17Protect myself and others
## Q17Protect others *
## 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 0.980685)
##
## Number of Fisher Scoring iterations: 13
##
## term or sig or_lower or_upper
## 1 (Intercept) 2.899e+00 6.859e-01 1.225e+01
## 2 ppagecat25-34 1.050e+00 5.387e-01 2.048e+00
## 3 ppagecat35-44 1.469e+00 7.799e-01 2.765e+00
## 4 ppagecat45-54 1.632e+00 8.912e-01 2.990e+00
## 5 ppagecat55-64 3.433e+00 *** 1.897e+00 6.215e+00
## 6 ppagecat65-74 4.462e+00 *** 2.301e+00 8.654e+00
## 7 ppagecat75+ 9.869e+00 *** 3.960e+00 2.459e+01
## 8 PPEDUCATHigh school 1.387e+00 6.958e-01 2.765e+00
## 9 PPEDUCATSome college 6.407e-01 3.322e-01 1.236e+00
## 10 PPEDUCATBachelor_s degree or higher 1.068e+00 5.443e-01 2.095e+00
## 11 income$10k to $25k 1.842e+00 6.000e-01 5.656e+00
## 12 income$25k to $50k 2.218e+00 7.448e-01 6.605e+00
## 13 income$50k to $75k 2.651e+00 . 8.899e-01 7.896e+00
## 14 income$75k to $100k 2.281e+00 7.562e-01 6.878e+00
## 15 income$100k to $150k 2.371e+00 7.992e-01 7.035e+00
## 16 incomeover $150k 3.586e+00 * 1.165e+00 1.104e+01
## 17 PPREG4Northeast 9.415e-01 5.948e-01 1.490e+00
## 18 PPREG4South 1.097e+00 7.324e-01 1.643e+00
## 19 PPREG4West 7.493e-01 4.885e-01 1.149e+00
## 20 workemployed 8.256e-01 5.870e-01 1.161e+00
## 21 Q20Somewhat effective 2.788e-01 *** 1.853e-01 4.194e-01
## 22 Q20It varies from season to season 2.590e-01 *** 1.611e-01 4.166e-01
## 23 Q20Not effective 1.610e-01 ** 4.348e-02 5.958e-01
## 24 Q20Don_t know 1.454e-01 *** 6.070e-02 3.482e-01
## 25 Q16No, no effect 5.338e-01 *** 3.685e-01 7.732e-01
## 26 Q16No, less likely 2.214e-01 *** 1.188e-01 4.124e-01
## 27 Q17Protect myself and others 1.261e+00 9.241e-01 1.722e+00
## 28 Q17Protect others 3.761e-01 * 1.430e-01 9.890e-01
## 29 Q14Less than $30 4.214e-01 *** 2.941e-01 6.039e-01
## 30 Q14$30 to $60 3.898e-01 ** 1.963e-01 7.741e-01
## 31 Q14More than $60 7.530e+05 *** 9.043e+04 6.270e+06
## 32 Q14Don_t know 2.464e-01 *** 1.311e-01 4.633e-01
## 33 Q19No 7.114e-01 3.164e-01 1.599e+00
## estimate std.error statistic p.value
## 1 1.06432 0.7354 1.4473 1.481e-01
## 2 0.04911 0.3407 0.1441 8.854e-01
## 3 0.38432 0.3229 1.1903 2.342e-01
## 4 0.49010 0.3088 1.5869 1.128e-01
## 5 1.23356 0.3028 4.0742 4.903e-05
## 6 1.49569 0.3379 4.4258 1.043e-05
## 7 2.28943 0.4659 4.9143 1.007e-06
## 8 0.32713 0.3519 0.9295 3.528e-01
## 9 -0.44513 0.3351 -1.3284 1.843e-01
## 10 0.06551 0.3438 0.1905 8.489e-01
## 11 0.61096 0.5723 1.0675 2.859e-01
## 12 0.79661 0.5567 1.4309 1.527e-01
## 13 0.97487 0.5569 1.7505 8.027e-02
## 14 0.82447 0.5632 1.4639 1.435e-01
## 15 0.86339 0.5549 1.5561 1.199e-01
## 16 1.27704 0.5737 2.2258 2.620e-02
## 17 -0.06033 0.2343 -0.2575 7.968e-01
## 18 0.09253 0.2061 0.4490 6.535e-01
## 19 -0.28856 0.2183 -1.3221 1.864e-01
## 20 -0.19167 0.1740 -1.1015 2.709e-01
## 21 -1.27741 0.2084 -6.1295 1.174e-09
## 22 -1.35081 0.2424 -5.5730 3.053e-08
## 23 -1.82663 0.6678 -2.7354 6.318e-03
## 24 -1.92842 0.4456 -4.3275 1.626e-05
## 25 -0.62779 0.1890 -3.3210 9.223e-04
## 26 -1.50790 0.3175 -4.7498 2.267e-06
## 27 0.23212 0.1587 1.4624 1.439e-01
## 28 -0.97782 0.4932 -1.9824 4.765e-02
## 29 -0.86413 0.1836 -4.7067 2.793e-06
## 30 -0.94222 0.3501 -2.6916 7.204e-03
## 31 13.53184 1.0814 12.5134 5.742e-34
## 32 -1.40061 0.3220 -4.3494 1.474e-05
## 33 -0.34048 0.4133 -0.8237 4.103e-01
2. Social influence (Q15, Q16, Q17)
(Dv, Q20, Q15,16,17)
Every vs. some (referent) ???
F statistic
> Note: There is no ‘never’ group
** AIC/BIC **
> Note: There is no ‘never’ group