options(Ncores = 12)
library(tidyverse, quietly = T)
library(haven, quietly = T)GSS OLS regression with Environmental module questions
gss <- haven::read_dta(
unz("2021_stata.zip",
filename = "GSS2021.dta")
)library(tidylog, quietly = T)
Attaching package: 'tidylog'The following objects are masked from 'package:dplyr':
add_count, add_tally, anti_join, count, distinct, distinct_all,
distinct_at, distinct_if, filter, filter_all, filter_at, filter_if,
full_join, group_by, group_by_all, group_by_at, group_by_if,
inner_join, left_join, mutate, mutate_all, mutate_at, mutate_if,
relocate, rename, rename_all, rename_at, rename_if, rename_with,
right_join, sample_frac, sample_n, select, select_all, select_at,
select_if, semi_join, slice, slice_head, slice_max, slice_min,
slice_sample, slice_tail, summarise, summarise_all, summarise_at,
summarise_if, summarize, summarize_all, summarize_at, summarize_if,
tally, top_frac, top_n, transmute, transmute_all, transmute_at,
transmute_if, ungroupThe following objects are masked from 'package:tidyr':
drop_na, fill, gather, pivot_longer, pivot_wider, replace_na,
spread, uncountThe following object is masked from 'package:stats':
filtergss_sub0 <- gss %>%
haven::zap_labels() %>%
mutate(
chldidel_fctr1 = factor(case_when(chldidel %in% c(0:1) ~ "1small (0-1)",
chldidel %in% c(2:3) ~ "2normative (2-3)",
chldidel %in% c(4:7) ~ "3large (4+)",
chldidel %in% c(8) ~ "4As many")),
chldidel_fctr1 = relevel(chldidel_fctr1, ref = "2normative (2-3)"),
chldidel_fctr2 = factor(case_when(chldidel %in% c(0:1) ~ "1small (0-1)",
chldidel %in% c(2:3) ~ "2normative (2-3)",
chldidel %in% c(4:7) ~ "3large (4+)")),
chldidel_fctr2 = relevel(chldidel_fctr2, ref = "2normative (2-3)"),
chldidel_rc = if_else(chldidel == 8, NA_real_, chldidel),
grngroup_rc = if_else(grngroup == 2, 0, grngroup),
grnsign_rc = if_else(grnsign == 2, 0, grnsign),
grnmoney_rc = if_else(grnmoney == 2, 0, grnmoney),
grndemo_rc = if_else(grndemo == 2, 0, grndemo),
# Reverse code
grwthelp_rc = 6-grwthelp,
grnexagg_rc = 6-grnexagg,
grnprog_rc = 6-grnprog,
naturdev_rc = 6-naturdev,
impgrn_rc = 6-impgrn,
grnecon_rc = 6-grnecon,
age_cat = case_when(
age %in% c(18:29) ~ "18-29",
age %in% c(30:39) ~ "30-39",
age %in% c(40:49) ~ "40-49",
age %in% c(50:64) ~ "50-64",
age %in% c(65:89) ~ "65-89"
),
race1 = if_else(is.na(racecen1) == F, 1, 0),
race2 = if_else(is.na(racecen2) == F, 1, 0),
race3 = if_else(is.na(racecen3) == F, 1, 0),
race_sum = race1 + race2 + race3,
race_two = if_else(race_sum > 1, 1, 0),
raceth = case_when(
race_two == 0 & racecen1 == 1 ~ "NH White",
race_two == 0 & racecen1 == 2 ~ "NH Black",
race_two == 0 & racecen1 == 3 ~ "AIAN",
race_two == 0 & racecen1 %in% c(4:10) ~ "Asian",
race_two == 0 & racecen1 %in% c(11:14) ~ "NHPI",
race_two == 0 & racecen1 == 15 ~ "Other",
race_two == 0 & racecen1 == 16 ~ "Hispanic",
race_two ==1 ~ "Two or More"
),
# grneffme_rc = case_when(grneffme %in% c(1:2) ~ "1Agree",
# grneffme %in% c(3) ~ "2Neutral",
# grneffme %in% c(3:4) ~ "3Disagree"),
educ = case_when(degree %in% 0 ~ "1Less than HS",
degree %in% 1 ~ "2High School",
degree %in% 2 ~ "3Associate's",
degree %in% 3 ~ "4Bachelor's",
degree %in% 4 ~ "5Graduate"),
sex_rc = if_else(sex == 1, "Male", "Female"),
relitenv_rc = 5-relitenv
) %>%
select(chldidel_fctr1, sex_rc, age_cat, educ, polviews, chldidel_fctr2, scigrn:recycle, impgrn:grnexagg, grncon, helpharm:nobuygrn,
clmtcaus:watergen1, nukegen1, grngroup_rc:relitenv_rc)mutate: converted 'educ' from double to character (43 fewer NA) new variable 'chldidel_fctr1' (factor) with 5 unique values and 33% NA new variable 'chldidel_fctr2' (factor) with 4 unique values and 54% NA new variable 'chldidel_rc' (double) with 9 unique values and 54% NA new variable 'grngroup_rc' (double) with 3 unique values and 55% NA new variable 'grnsign_rc' (double) with 3 unique values and 55% NA new variable 'grnmoney_rc' (double) with 3 unique values and 55% NA new variable 'grndemo_rc' (double) with 3 unique values and 55% NA new variable 'grwthelp_rc' (double) with 6 unique values and 56% NA new variable 'grnexagg_rc' (double) with 6 unique values and 56% NA new variable 'grnprog_rc' (double) with 6 unique values and 56% NA new variable 'naturdev_rc' (double) with 6 unique values and 56% NA new variable 'impgrn_rc' (double) with 6 unique values and 56% NA new variable 'grnecon_rc' (double) with 6 unique values and 55% NA new variable 'age_cat' (character) with 6 unique values and 8% NA new variable 'race1' (double) with 2 unique values and 0% NA new variable 'race2' (double) with 2 unique values and 0% NA new variable 'race3' (double) with 2 unique values and 0% NA new variable 'race_sum' (double) with 4 unique values and 0% NA new variable 'race_two' (double) with 2 unique values and 0% NA new variable 'raceth' (character) with 9 unique values and 1% NA new variable 'sex_rc' (character) with 3 unique values and 2% NA new variable 'relitenv_rc' (double) with 5 unique values and 37% NAselect: dropped 703 variables (year, id, wrkslf, wrkgovt, occ10, …)# Filter data sets one that includes "as many children as you want and another that excludes that category
gss_sub1 <- gss_sub0 %>%
select(-chldidel_fctr2) %>%
filter(complete.cases(.))select: dropped one variable (chldidel_fctr2)filter: removed 3,422 rows (85%), 610 rows remaininggss_sub2 <- gss_sub0 %>%
select(-chldidel_fctr1) %>%
filter(complete.cases(.))select: dropped one variable (chldidel_fctr1)filter: removed 3,594 rows (89%), 438 rows remaininggss_pca1 <- gss_sub0 %>%
select(scigrn, harmsgrn, grnprog_rc, grwthelp_rc, grwtharm, grnprice, grntaxes, grnsol, toodifme,
ihlpgrn, carsgen, recycle, impgrn_rc, othssame, grnexagg_rc, grncon, helpharm, grneffme,
tempgen1, nobuygrn, clmtcaus, clmtwrld, clmtusa, naturdev_rc, indusgen1, chemgen1,
watergen1, nukegen1, grngroup_rc, grnsign_rc, grnmoney_rc, grndemo_rc, grnecon_rc) %>%
filter(complete.cases(.))select: dropped 21 variables (chldidel_fctr1, sex_rc, age_cat, educ, polviews, …)filter: removed 2,752 rows (68%), 1,280 rows remaininggss_pca2 <- gss_sub0 %>%
select(grwthelp_rc, grnexagg_rc, grnprog_rc, naturdev_rc, impgrn_rc, grnecon_rc)%>%
filter(complete.cases(.))select: dropped 48 variables (chldidel_fctr1, sex_rc, age_cat, educ, polviews, …)filter: removed 2,382 rows (59%), 1,650 rows remaininggss_pca3 <- gss_sub0 %>%
select(scigrn, harmsgrn, grwtharm, grnprice, grntaxes, grnsol, toodifme,
ihlpgrn, carsgen, recycle, othssame, grncon, helpharm, grneffme,
tempgen1, nobuygrn, clmtcaus, clmtwrld, clmtusa, indusgen1, chemgen1,
watergen1, nukegen1, grngroup_rc, grnsign_rc, grnmoney_rc, grndemo_rc) %>%
filter(complete.cases(.))select: dropped 27 variables (chldidel_fctr1, sex_rc, age_cat, educ, polviews, …)filter: removed 2,710 rows (67%), 1,322 rows remaining# agree/disagree
pca1 <- gss_sub0 %>%
select(scigrn, grnecon_rc, harmsgrn, grnprog_rc, grwthelp_rc, grwtharm, toodifme, ihlpgrn, impgrn_rc, othssame, grnexagg_rc, helpharm, grneffme) %>%
filter(complete.cases(.))select: dropped 41 variables (chldidel_fctr1, sex_rc, age_cat, educ, polviews, …)filter: removed 2,451 rows (61%), 1,581 rows remaining# dangerous/not dangerous
pca2 <- gss_sub0 %>%
select(carsgen, watergen1, tempgen1, nukegen1, indusgen1, chemgen1) %>%
filter(complete.cases(.)) select: dropped 48 variables (chldidel_fctr1, sex_rc, age_cat, educ, polviews, …)filter: removed 2,377 rows (59%), 1,655 rows remaining# yes/no
pca3 <- gss_sub0 %>%
select(grngroup_rc, grnsign_rc, grnmoney_rc, grndemo_rc) %>%
filter(complete.cases(.))select: dropped 50 variables (chldidel_fctr1, sex_rc, age_cat, educ, polviews, …)filter: removed 2,251 rows (56%), 1,781 rows remaining# willing/unwilling
pca4 <- gss_sub0 %>%
select(grnprice, grntaxes, grnsol, naturdev_rc) %>%
filter(complete.cases(.))select: dropped 50 variables (chldidel_fctr1, sex_rc, age_cat, educ, polviews, …)filter: removed 2,323 rows (58%), 1,709 rows remaining# bad/good and not/very
pca5 <- gss_sub0 %>%
select(clmtwrld, clmtusa, clmtcaus, grncon) %>%
filter(complete.cases(.))select: dropped 50 variables (chldidel_fctr1, sex_rc, age_cat, educ, polviews, …)filter: removed 2,369 rows (59%), 1,663 rows remainingPrincipal Component Analysis
Agree/Disagree response
pc1 <- prcomp(formula = ~., data = pca1, center = T, scale. = T)
summary(pc1)Importance of components:
PC1 PC2 PC3 PC4 PC5 PC6 PC7
Standard deviation 2.0149 1.2525 1.08308 0.9594 0.90727 0.86517 0.83527
Proportion of Variance 0.3123 0.1207 0.09024 0.0708 0.06332 0.05758 0.05367
Cumulative Proportion 0.3123 0.4330 0.52320 0.5940 0.65732 0.71490 0.76857
PC8 PC9 PC10 PC11 PC12 PC13
Standard deviation 0.83080 0.7800 0.74101 0.71911 0.59202 0.54160
Proportion of Variance 0.05309 0.0468 0.04224 0.03978 0.02696 0.02256
Cumulative Proportion 0.82166 0.8685 0.91070 0.95048 0.97744 1.00000biplot(pc1, scale = 0)
pc1_tibble <- pc1$rotation %>%
as_tibble(rownames = "predictor")
pc1_tibble %>%
select(predictor:PC4) %>%
pivot_longer(PC1:PC4, names_to = "component", values_to = "value") %>%
ggplot(aes(x = value, y = predictor)) +
geom_col(fill = "darkgreen", color = "darkgreen", alpha = 0.5) +
facet_wrap(~component) +
labs(y = NULL, x = "Value") +
theme_minimal()select: dropped 9 variables (PC5, PC6, PC7, PC8, PC9, …)pivot_longer: reorganized (PC1, PC2, PC3, PC4) into (component, value) [was 13x5, now 52x3]
Agree/Disagree response with loading values greater than .1
pca1b <- pca1 %>% select(grnecon_rc, harmsgrn, grnprog_rc, grwthelp_rc, ihlpgrn, impgrn_rc, grnexagg_rc, grneffme)select: dropped 5 variables (scigrn, grwtharm, toodifme, othssame, helpharm)pc1b <- prcomp(formula = ~., data = pca1b, center = T, scale. = T)
summary(pc1b)Importance of components:
PC1 PC2 PC3 PC4 PC5 PC6 PC7
Standard deviation 1.8329 1.0311 0.9276 0.86871 0.8324 0.78176 0.59645
Proportion of Variance 0.4199 0.1329 0.1076 0.09433 0.0866 0.07639 0.04447
Cumulative Proportion 0.4199 0.5528 0.6604 0.75471 0.8413 0.91771 0.96218
PC8
Standard deviation 0.55007
Proportion of Variance 0.03782
Cumulative Proportion 1.00000biplot(pc1b, scale = 0)
pc1b_tibble <- pc1b$rotation %>%
as_tibble(rownames = "predictor")
pc1b_tibble %>%
select(predictor:PC3) %>%
pivot_longer(PC1:PC3, names_to = "component", values_to = "value") %>%
ggplot(aes(x = value, y = predictor)) +
geom_col(fill = "darkgreen", color = "darkgreen", alpha = 0.5) +
facet_wrap(~component) +
labs(y = NULL, x = "Value") +
theme_minimal()select: dropped 5 variables (PC4, PC5, PC6, PC7, PC8)pivot_longer: reorganized (PC1, PC2, PC3) into (component, value) [was 8x4, now 24x3]
Dangerous/Not Dangerous response
pc2 <- prcomp(formula = ~., data = pca2, center = T, scale. = T)
summary(pc2)Importance of components:
PC1 PC2 PC3 PC4 PC5 PC6
Standard deviation 1.8455 0.9063 0.8108 0.66938 0.61547 0.53709
Proportion of Variance 0.5677 0.1369 0.1096 0.07468 0.06313 0.04808
Cumulative Proportion 0.5677 0.7046 0.8141 0.88879 0.95192 1.00000biplot(pc2, scale = 0)
pc2_tibble <- pc2$rotation %>%
as_tibble(rownames = "predictor")
pc2_tibble %>%
select(predictor:PC6) %>%
pivot_longer(PC1:PC6, names_to = "component", values_to = "value") %>%
ggplot(aes(x = value, y = predictor)) +
geom_col(fill = "darkgreen", color = "darkgreen", alpha = 0.5) +
facet_wrap(~component) +
labs(y = NULL, x = "Value") +
theme_minimal()select: no changespivot_longer: reorganized (PC1, PC2, PC3, PC4, PC5, …) into (component, value) [was 6x7, now 36x3]
yes/no response
pc3 <- prcomp(formula = ~., data = pca3, center = T, scale. = T)
summary(pc3)Importance of components:
PC1 PC2 PC3 PC4
Standard deviation 1.4123 0.9130 0.8196 0.7071
Proportion of Variance 0.4987 0.2084 0.1679 0.1250
Cumulative Proportion 0.4987 0.7071 0.8750 1.0000biplot(pc3, scale = 0)
pc3_tibble <- pc3$rotation %>%
as_tibble(rownames = "predictor")
pc3_tibble %>%
select(predictor:PC4) %>%
pivot_longer(PC1:PC4, names_to = "component", values_to = "value") %>%
ggplot(aes(x = value, y = predictor)) +
geom_col(fill = "darkgreen", color = "darkgreen", alpha = 0.5) +
facet_wrap(~component) +
labs(y = NULL, x = "Value") +
theme_minimal()select: no changespivot_longer: reorganized (PC1, PC2, PC3, PC4) into (component, value) [was 4x5, now 16x3]
willing/unwilling
pc4 <- prcomp(formula = ~., data = pca4, center = T, scale. = T)
summary(pc4)Importance of components:
PC1 PC2 PC3 PC4
Standard deviation 1.591 0.9520 0.58109 0.47310
Proportion of Variance 0.633 0.2266 0.08442 0.05596
Cumulative Proportion 0.633 0.8596 0.94404 1.00000biplot(pc4, scale = 0)
pc4_tibble <- pc4$rotation %>%
as_tibble(rownames = "predictor")
pc4_tibble %>%
select(predictor:PC4) %>%
pivot_longer(PC1:PC4, names_to = "component", values_to = "value") %>%
ggplot(aes(x = value, y = predictor)) +
geom_col(fill = "darkgreen", color = "darkgreen", alpha = 0.5) +
facet_wrap(~component) +
labs(y = NULL, x = "Value") +
theme_minimal()select: no changespivot_longer: reorganized (PC1, PC2, PC3, PC4) into (component, value) [was 4x5, now 16x3]
bad/good and not/very
pc5 <- prcomp(formula = ~., data = pca5, center = T, scale. = T)
summary(pc5)Importance of components:
PC1 PC2 PC3 PC4
Standard deviation 1.6521 0.8149 0.6734 0.39114
Proportion of Variance 0.6824 0.1660 0.1134 0.03825
Cumulative Proportion 0.6824 0.8484 0.9617 1.00000biplot(pc5, scale = 0)
pc5_tibble <- pc5$rotation %>%
as_tibble(rownames = "predictor")
pc5_tibble %>%
select(predictor:PC4) %>%
pivot_longer(PC1:PC4, names_to = "component", values_to = "value") %>%
ggplot(aes(x = value, y = predictor)) +
geom_col(fill = "darkgreen", color = "darkgreen", alpha = 0.5) +
facet_wrap(~component) +
labs(y = NULL, x = "Value") +
theme_minimal()select: no changespivot_longer: reorganized (PC1, PC2, PC3, PC4) into (component, value) [was 4x5, now 16x3]
All variables
env.pc1 <- prcomp(formula = ~., data = gss_pca1, center = T, scale. = T)
summary(env.pc1)Importance of components:
PC1 PC2 PC3 PC4 PC5 PC6 PC7
Standard deviation 3.240 1.5024 1.29455 1.21955 1.14973 1.04155 1.00713
Proportion of Variance 0.318 0.0684 0.05078 0.04507 0.04006 0.03287 0.03074
Cumulative Proportion 0.318 0.3864 0.43723 0.48230 0.52235 0.55523 0.58596
PC8 PC9 PC10 PC11 PC12 PC13 PC14
Standard deviation 0.98191 0.92850 0.90957 0.90294 0.85435 0.84637 0.81793
Proportion of Variance 0.02922 0.02612 0.02507 0.02471 0.02212 0.02171 0.02027
Cumulative Proportion 0.61518 0.64131 0.66638 0.69108 0.71320 0.73491 0.75518
PC15 PC16 PC17 PC18 PC19 PC20 PC21
Standard deviation 0.81575 0.79849 0.77510 0.7622 0.75259 0.73544 0.70985
Proportion of Variance 0.02016 0.01932 0.01821 0.0176 0.01716 0.01639 0.01527
Cumulative Proportion 0.77535 0.79467 0.81287 0.8305 0.84764 0.86403 0.87930
PC22 PC23 PC24 PC25 PC26 PC27 PC28
Standard deviation 0.69730 0.69426 0.66638 0.63955 0.63045 0.57173 0.55263
Proportion of Variance 0.01473 0.01461 0.01346 0.01239 0.01204 0.00991 0.00925
Cumulative Proportion 0.89403 0.90864 0.92210 0.93449 0.94654 0.95644 0.96570
PC29 PC30 PC31 PC32 PC33
Standard deviation 0.53129 0.51786 0.49345 0.4449 0.37446
Proportion of Variance 0.00855 0.00813 0.00738 0.0060 0.00425
Cumulative Proportion 0.97425 0.98238 0.98975 0.9958 1.00000pca_tibble1 <- env.pc1$rotation %>%
as_tibble(rownames = "predictor") %>%
rename(PC01 = PC1, PC02 = PC2, PC03 = PC3, PC04 = PC4, PC05 = PC5, PC06 = PC6, PC07 = PC7, PC08 = PC8, PC09 = PC9)rename: renamed 9 variables (PC01, PC02, PC03, PC04, PC05, …)pca_tibble1# A tibble: 33 × 34
predictor PC01 PC02 PC03 PC04 PC05 PC06 PC07 PC08
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 scigrn -0.104 0.0494 -0.194 0.0294 -0.365 0.146 -0.165 0.219
2 harmsgrn 0.136 0.268 -0.0999 0.0741 0.131 -0.249 0.130 0.131
3 grnprog_rc 0.215 -0.0706 0.203 0.0392 0.0233 -0.0284 -0.0688 -0.0160
4 grwthelp_rc 0.116 -0.0297 0.186 0.0318 0.295 -0.437 -0.127 -0.383
5 grwtharm 0.0691 0.310 -0.212 0.0655 0.160 -0.491 0.0841 0.204
6 grnprice 0.221 -0.0344 -0.0474 0.237 -0.293 -0.0715 -0.129 -0.183
7 grntaxes 0.218 -0.0343 -0.0310 0.291 -0.285 -0.0432 -0.136 -0.189
8 grnsol 0.221 -0.0281 -0.0319 0.219 -0.211 -0.136 -0.108 -0.139
9 toodifme -0.115 0.281 -0.0310 0.331 0.00334 -0.00117 0.203 -0.0898
10 ihlpgrn 0.141 -0.155 -0.226 -0.0865 -0.253 -0.223 0.165 0.0323
# … with 23 more rows, and 25 more variables: PC09 <dbl>, PC10 <dbl>,
# PC11 <dbl>, PC12 <dbl>, PC13 <dbl>, PC14 <dbl>, PC15 <dbl>, PC16 <dbl>,
# PC17 <dbl>, PC18 <dbl>, PC19 <dbl>, PC20 <dbl>, PC21 <dbl>, PC22 <dbl>,
# PC23 <dbl>, PC24 <dbl>, PC25 <dbl>, PC26 <dbl>, PC27 <dbl>, PC28 <dbl>,
# PC29 <dbl>, PC30 <dbl>, PC31 <dbl>, PC32 <dbl>, PC33 <dbl>pca_tibble1 %>%
select(predictor:PC01) %>%
pivot_longer(PC01, names_to = "component", values_to = "value") %>%
ggplot(aes(x = value, y = predictor)) +
geom_col(fill = "darkgreen", color = "darkgreen", alpha = 0.5) +
#facet_wrap(~component) +
labs(y = NULL, x = "Value") +
theme_minimal()select: dropped 32 variables (PC02, PC03, PC04, PC05, PC06, …)pivot_longer: reorganized (PC01) into (component, value) [was 33x2, now 33x3]
biplot(env.pc1, scale = 0)
Reverse variables
env.pc2 <- prcomp(formula = ~., data = gss_pca2, center = T, scale. = T)
summary(env.pc2)Importance of components:
PC1 PC2 PC3 PC4 PC5 PC6
Standard deviation 1.7708 0.9365 0.8311 0.7843 0.60340 0.56319
Proportion of Variance 0.5226 0.1462 0.1151 0.1025 0.06068 0.05286
Cumulative Proportion 0.5226 0.6688 0.7839 0.8864 0.94714 1.00000pca_tibble2 <- env.pc2$rotation %>%
as_tibble(rownames = "predictor")
pca_tibble2# A tibble: 6 × 7
predictor PC1 PC2 PC3 PC4 PC5 PC6
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 grwthelp_rc 0.283 0.796 -0.489 0.214 -0.00327 -0.0381
2 grnexagg_rc 0.472 -0.222 -0.102 -0.141 -0.616 -0.564
3 grnprog_rc 0.462 -0.0713 -0.0173 -0.422 0.728 -0.272
4 naturdev_rc 0.348 0.323 0.841 0.258 -0.0276 -0.0218
5 impgrn_rc 0.369 -0.451 -0.201 0.753 0.198 0.118
6 grnecon_rc 0.477 -0.0702 -0.0503 -0.349 -0.226 0.769 pca_tibble2 %>%
select(predictor:PC6) %>%
pivot_longer(PC1:PC6, names_to = "component", values_to = "value") %>%
ggplot(aes(x = value, y = predictor)) +
geom_col(fill = "darkgreen", color = "darkgreen", alpha = 0.5) +
facet_wrap(~component, nrow = 1) +
labs(y = NULL, x = "Value") +
theme_minimal()select: no changespivot_longer: reorganized (PC1, PC2, PC3, PC4, PC5, …) into (component, value) [was 6x7, now 36x3]
biplot(env.pc2, scale = 0)
Regular variables
env.pc3 <- prcomp(formula = ~., data = gss_pca3, center = T, scale. = T)
summary(env.pc3)Importance of components:
PC1 PC2 PC3 PC4 PC5 PC6 PC7
Standard deviation 2.8751 1.49077 1.2097 1.19190 1.11527 1.0103 0.97299
Proportion of Variance 0.3061 0.08231 0.0542 0.05262 0.04607 0.0378 0.03506
Cumulative Proportion 0.3061 0.38846 0.4427 0.49528 0.54135 0.5792 0.61421
PC8 PC9 PC10 PC11 PC12 PC13 PC14
Standard deviation 0.92596 0.92439 0.86740 0.84830 0.84216 0.81208 0.79387
Proportion of Variance 0.03176 0.03165 0.02787 0.02665 0.02627 0.02442 0.02334
Cumulative Proportion 0.64597 0.67762 0.70548 0.73214 0.75840 0.78283 0.80617
PC15 PC16 PC17 PC18 PC19 PC20 PC21
Standard deviation 0.78794 0.76463 0.72820 0.71463 0.7010 0.68477 0.63711
Proportion of Variance 0.02299 0.02165 0.01964 0.01891 0.0182 0.01737 0.01503
Cumulative Proportion 0.82917 0.85082 0.87046 0.88937 0.9076 0.92494 0.93997
PC22 PC23 PC24 PC25 PC26 PC27
Standard deviation 0.63172 0.57779 0.52528 0.51568 0.45431 0.37362
Proportion of Variance 0.01478 0.01236 0.01022 0.00985 0.00764 0.00517
Cumulative Proportion 0.95475 0.96712 0.97734 0.98719 0.99483 1.00000pca_tibble3 <- env.pc3$rotation %>%
as_tibble(rownames = "predictor") %>%
rename(PC01 = PC1, PC02 = PC2, PC03 = PC3, PC04 = PC4, PC05 = PC5, PC06 = PC6, PC07 = PC7, PC08 = PC8, PC09 = PC9) rename: renamed 9 variables (PC01, PC02, PC03, PC04, PC05, …)pca_tibble3# A tibble: 27 × 28
predictor PC01 PC02 PC03 PC04 PC05 PC06 PC07 PC08
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 scigrn 0.112 -0.0269 0.157 -0.0401 0.413 -0.189 0.102 0.509
2 harmsgrn -0.157 -0.261 0.161 -0.0205 -0.0948 0.335 0.0862 -0.241
3 grwtharm -0.0848 -0.290 0.294 -0.127 0.00729 0.505 0.187 -0.104
4 grnprice -0.250 0.0597 0.0976 0.283 0.288 -0.0929 0.0885 -0.131
5 grntaxes -0.245 0.0595 0.121 0.331 0.273 -0.120 0.0884 -0.137
6 grnsol -0.250 0.0559 0.0722 0.259 0.204 -0.0225 0.124 -0.160
7 toodifme 0.125 -0.286 0.286 0.206 0.0126 0.0878 -0.333 0.0907
8 ihlpgrn -0.165 0.184 0.0252 -0.113 0.325 0.267 0.0175 0.119
9 carsgen -0.238 -0.215 -0.00592 -0.0746 0.0827 -0.195 -0.103 -0.00779
10 recycle -0.126 0.248 -0.0780 -0.131 0.164 0.326 -0.483 0.210
# … with 17 more rows, and 19 more variables: PC09 <dbl>, PC10 <dbl>,
# PC11 <dbl>, PC12 <dbl>, PC13 <dbl>, PC14 <dbl>, PC15 <dbl>, PC16 <dbl>,
# PC17 <dbl>, PC18 <dbl>, PC19 <dbl>, PC20 <dbl>, PC21 <dbl>, PC22 <dbl>,
# PC23 <dbl>, PC24 <dbl>, PC25 <dbl>, PC26 <dbl>, PC27 <dbl>pca_tibble3 %>%
select(predictor:PC01) %>%
pivot_longer(PC01, names_to = "component", values_to = "value") %>%
ggplot(aes(x = value, y = predictor)) +
geom_col(fill = "darkgreen", color = "darkgreen", alpha = 0.5) +
facet_wrap(~component) +
labs(y = NULL, x = "Value") +
theme_minimal()select: dropped 26 variables (PC02, PC03, PC04, PC05, PC06, …)pivot_longer: reorganized (PC01) into (component, value) [was 27x2, now 27x3]
biplot(env.pc3, scale = 0)
Multinomial Regression
# Load packages for OLS regressgion
require(foreign)
require(nnet)
require(ggplot2)
require(reshape2)# create function to use in `lapply`
ols <- function(x){
test <- multinom(chldidel_fctr1 ~ x + sex_rc + age_cat + educ + raceth, data = gss_sub1)
summary(test)
}# Run lapply to run models individual models for each column
models <- lapply(gss_sub1[,-c(1:5)], ols)exp(coef) and P scores for all models
scigrn
# modern science will solve our environmental problems with little change to our way of life. agree -> disagree
exp1 <- as_tibble(exp(coef(models$scigrn)), rownames = "famsize")
z <- models[["scigrn"]][["coefficients"]]/models[["scigrn"]][["standard.errors"]]
p.1 <- (1 - pnorm(abs(z), 0, 1)) * 2
p.1 <- as_tibble(p.1, rownames = "famsize")
head(exp1)# A tibble: 3 × 18
famsize (Interce…¹ x sex_r…² age_c…³ age_c…⁴ age_c…⁵ age_c…⁶ educ2H…⁷
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1small (0-1) 2.77e-33 1.52 0.638 0.264 0.158 0.264 0.207 7.97e+26
2 3large (4+) 1.75e- 8 0.609 0.607 1.77 0.677 1.46 1.09 3.63e- 1
3 4As many 1.62e+ 0 1.18 0.583 0.438 0.728 0.694 0.358 1.23e+ 0
# … with 9 more variables: `educ3Associate's` <dbl>, `educ4Bachelor's` <dbl>,
# educ5Graduate <dbl>, racethAsian <dbl>, racethHispanic <dbl>,
# `racethNH Black` <dbl>, `racethNH White` <dbl>, racethOther <dbl>,
# `racethTwo or More` <dbl>, and abbreviated variable names ¹`(Intercept)`,
# ²sex_rcMale, ³`age_cat30-39`, ⁴`age_cat40-49`, ⁵`age_cat50-64`,
# ⁶`age_cat65-89`, ⁷`educ2High School`head(p.1)# A tibble: 3 × 18
famsize (Inte…¹ x sex_r…² age_c…³ age_c…⁴ age_c…⁵ age_c…⁶ educ2…⁷ educ3…⁸
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1small… 0 0.0580 0.259 0.0396 0.0130 0.0259 0.00861 0 0
2 3large… 0 0.0138 0.250 0.497 0.684 0.658 0.922 0.178 0.348
3 4As ma… 0.733 0.0874 0.00518 0.0215 0.356 0.272 0.00334 0.691 0.795
# … with 8 more variables: `educ4Bachelor's` <dbl>, educ5Graduate <dbl>,
# racethAsian <dbl>, racethHispanic <dbl>, `racethNH Black` <dbl>,
# `racethNH White` <dbl>, racethOther <dbl>, `racethTwo or More` <dbl>, and
# abbreviated variable names ¹`(Intercept)`, ²sex_rcMale, ³`age_cat30-39`,
# ⁴`age_cat40-49`, ⁵`age_cat50-64`, ⁶`age_cat65-89`, ⁷`educ2High School`,
# ⁸`educ3Associate's`grnecon
# REVERSE we worry to much about the future of the environment and not enough about prices and jobs today. agree -> disagree
exp(coef(models$grnecon_rc)) (Intercept) x sex_rcMale age_cat30-39 age_cat40-49
1small (0-1) 1.491589e-16 0.5948583 0.6562518 0.2912596 0.1889686
3large (4+) 7.035951e-06 1.1971272 0.5842487 1.7774705 0.6360228
4As many 3.273957e+00 0.9963513 0.5766537 0.4405245 0.7465863
age_cat50-64 age_cat65-89 educ2High School educ3Associate's
1small (0-1) 0.3484796 0.2657639 7.674323e+09 6.051445e+09
3large (4+) 1.1709038 0.8956783 4.144858e-01 3.979778e-01
4As many 0.7194849 0.3809765 1.192297e+00 8.619095e-01
educ4Bachelor's educ5Graduate racethAsian racethHispanic
1small (0-1) 3.846515e+09 8.429273e+09 3.175355e-10 1.180305e-08
3large (4+) 3.105807e-01 2.922622e-01 1.655929e+04 1.615044e+04
4As many 1.688739e+00 2.259032e+00 1.272849e-01 3.031569e-01
racethNH Black racethNH White racethOther racethTwo or More
1small (0-1) 2.200841e+06 8.946366e+05 3.115036e+06 1.023093e-09
3large (4+) 6.234437e+04 1.488389e+04 2.765192e-08 4.959772e+04
4As many 4.358709e-01 1.880674e-01 4.790002e-01 1.333439e-01z <- models[["grnecon_rc"]][["coefficients"]]/models[["grnecon_rc"]][["standard.errors"]]
p.2 <- (1 - pnorm(abs(z), 0, 1)) * 2
p.2 (Intercept) x sex_rcMale age_cat30-39 age_cat40-49
1small (0-1) 0.0000000 0.02234298 0.292334508 0.05634589 0.02557649
3large (4+) 0.0000000 0.34943960 0.212378654 0.49124977 0.63492091
4As many 0.3907973 0.96792613 0.004277451 0.02229039 0.39593213
age_cat50-64 age_cat65-89 educ2High School educ3Associate's
1small (0-1) 0.08121453 0.026256756 0.0000000 0.000000
3large (4+) 0.85268518 0.900182622 0.2374983 0.310072
4As many 0.32641706 0.005543457 0.7364370 0.807086
educ4Bachelor's educ5Graduate racethAsian racethHispanic
1small (0-1) 0.0000000 0.0000000 NaN 0.0000000
3large (4+) 0.1424048 0.1525605 0.0000000 0.0000000
4As many 0.3181267 0.1270637 0.1203901 0.3757685
racethNH Black racethNH White racethOther racethTwo or More
1small (0-1) 0.0000000 0.0000000 0.0000000 0.0000000
3large (4+) 0.0000000 0.0000000 0.0000000 0.0000000
4As many 0.5203389 0.1810991 0.6222668 0.1410117harmsgrn
# Almost everything we do in modern life harms the environment. agree -> disagree
exp(coef(models$harmsgrn)) (Intercept) x sex_rcMale age_cat30-39 age_cat40-49
1small (0-1) 4.224040e-16 0.4958716 0.7404823 0.2552821 0.1451424
3large (4+) 7.628734e-06 1.0490816 0.5895079 1.7206618 0.6426233
4As many 2.471364e+00 1.1199861 0.5538230 0.4361625 0.7506481
age_cat50-64 age_cat65-89 educ2High School educ3Associate's
1small (0-1) 0.3362289 0.2724250 9.293000e+09 7.400234e+09
3large (4+) 1.2447807 0.8916691 4.112434e-01 3.833639e-01
4As many 0.6931922 0.3677472 1.230801e+00 8.825464e-01
educ4Bachelor's educ5Graduate racethAsian racethHispanic
1small (0-1) 5.667285e+09 1.504086e+10 3.414784e-10 1.363001e-08
3large (4+) 2.856550e-01 2.662325e-01 2.116025e+04 2.317890e+04
4As many 1.752544e+00 2.309196e+00 1.195102e-01 3.071925e-01
racethNH Black racethNH White racethOther racethTwo or More
1small (0-1) 7.324008e+05 4.248471e+05 2.439336e+06 3.314879e-09
3large (4+) 8.599926e+04 1.926458e+04 6.861324e-08 6.554365e+04
4As many 4.334278e-01 1.832567e-01 4.577131e-01 1.402864e-01z <- models[["harmsgrn"]][["coefficients"]]/models[["harmsgrn"]][["standard.errors"]]
p.3 <- (1 - pnorm(abs(z), 0, 1)) * 2
p.3 (Intercept) x sex_rcMale age_cat30-39 age_cat40-49
1small (0-1) 0.0000000 0.003773592 0.457307711 0.03576658 0.01143194
3large (4+) 0.0000000 0.829506401 0.221633811 0.51617890 0.64236706
4As many 0.5164419 0.265663341 0.002560613 0.02091909 0.40417237
age_cat50-64 age_cat65-89 educ2High School educ3Associate's
1small (0-1) 0.06928048 0.030678520 0.0000000 0.0000000
3large (4+) 0.79634750 0.896649811 0.2358817 0.2948276
4As many 0.27189189 0.004160688 0.6912645 0.8373547
educ4Bachelor's educ5Graduate racethAsian racethHispanic
1small (0-1) 0.0000000 0.0000000 0.0000000 0.0000000
3large (4+) 0.1153809 0.1201452 0.0000000 0.0000000
4As many 0.2836594 0.1142309 0.1099258 0.3813138
racethNH Black racethNH White racethOther racethTwo or More
1small (0-1) 0.0000000 0.0000000 0.0000000 0.000000
3large (4+) 0.0000000 0.0000000 0.0000000 0.000000
4As many 0.5174077 0.1747701 0.6016847 0.151815grnprog
# REVERSE People worry too much about human progress harming the environment. agree -> disagree
exp(coef(models$grnprog_rc)) (Intercept) x sex_rcMale age_cat30-39 age_cat40-49
1small (0-1) 2.625705e-16 0.5656247 0.6753503 0.2667675 0.1721890
3large (4+) 8.340616e-06 1.4414554 0.5727594 1.7879660 0.6254454
4As many 4.474112e+00 0.8682339 0.5856633 0.4418684 0.7635917
age_cat50-64 age_cat65-89 educ2High School educ3Associate's
1small (0-1) 0.3328511 0.2678516 3.851090e+09 2.785591e+09
3large (4+) 1.0888963 0.8100699 4.535007e-01 4.694247e-01
4As many 0.7639062 0.3991875 1.112189e+00 7.906371e-01
educ4Bachelor's educ5Graduate racethAsian racethHispanic
1small (0-1) 1.983871e+09 4.401486e+09 2.817443e-10 7.572545e-09
3large (4+) 3.496691e-01 3.362559e-01 7.917627e+03 6.209392e+03
4As many 1.532035e+00 1.994473e+00 1.347146e-01 3.292513e-01
racethNH Black racethNH White racethOther racethTwo or More
1small (0-1) 2.871851e+06 1.180824e+06 7.814448e+06 1.165909e-09
3large (4+) 2.852809e+04 7.198706e+03 4.730082e-09 2.494040e+04
4As many 4.771210e-01 1.982453e-01 5.565094e-01 1.355278e-01z <- models[["grnprog_rc"]][["coefficients"]]/models[["grnprog_rc"]][["standard.errors"]]
p.4 <- (1 - pnorm(abs(z), 0, 1)) * 2
p.4 (Intercept) x sex_rcMale age_cat30-39 age_cat40-49
1small (0-1) 0.0000000 0.01375229 0.326941208 0.04088438 0.01856842
3large (4+) 0.0000000 0.06666746 0.198996673 0.49077047 0.62404457
4As many 0.2808713 0.14193269 0.005591777 0.02288158 0.43332873
age_cat50-64 age_cat65-89 educ2High School educ3Associate's
1small (0-1) 0.06749978 0.027027612 0.0000000 0.0000000
3large (4+) 0.92052190 0.812375867 0.2989993 0.4118143
4As many 0.42154368 0.008539344 0.8389518 0.7004484
educ4Bachelor's educ5Graduate racethAsian racethHispanic
1small (0-1) 0.0000000 0.0000000 0.0000000 NaN
3large (4+) 0.1905472 0.2107650 0.0000000 0.0000000
4As many 0.4169451 0.1968931 0.1311844 0.4101323
racethNH Black racethNH White racethOther racethTwo or More
1small (0-1) 0.0000000 0.0000000 0.0000000 NaN
3large (4+) 0.0000000 0.0000000 0.0000000 0.0000000
4As many 0.5670242 0.1955704 0.6959536 0.1447649grwthelp
# REVERSE In order to protect the environment, America needs economic growth. agree -> disagree
exp(coef(models$grwthelp_rc)) (Intercept) x sex_rcMale age_cat30-39 age_cat40-49
1small (0-1) 1.260053e-16 0.5430607 0.6546013 0.2695919 0.1573390
3large (4+) 4.617553e-06 1.2911175 0.5887726 1.8049167 0.6287874
4As many 3.835126e+00 0.9380533 0.5781740 0.4399851 0.7455844
age_cat50-64 age_cat65-89 educ2High School educ3Associate's
1small (0-1) 0.3006237 0.2506993 7.798223e+10 5.764105e+10
3large (4+) 1.2679475 0.8791773 4.145603e-01 4.246464e-01
4As many 0.7234713 0.3823197 1.187064e+00 8.604526e-01
educ4Bachelor's educ5Graduate racethAsian racethHispanic
1small (0-1) 4.187493e+10 1.016213e+11 5.009415e-08 2.380109e-07
3large (4+) 2.965433e-01 2.753386e-01 1.649606e+04 1.593059e+04
4As many 1.677394e+00 2.246850e+00 1.324751e-01 3.170769e-01
racethNH Black racethNH White racethOther racethTwo or More
1small (0-1) 5.377014e+05 2.238573e+05 1.782500e+06 2.481897e-08
3large (4+) 6.444831e+04 1.483272e+04 5.615522e-09 5.124543e+04
4As many 4.608156e-01 1.951065e-01 5.179692e-01 1.378266e-01z <- models[["grwthelp_rc"]][["coefficients"]]/models[["grwthelp_rc"]][["standard.errors"]]
p.5 <- (1 - pnorm(abs(z), 0, 1)) * 2
p.5 (Intercept) x sex_rcMale age_cat30-39 age_cat40-49
1small (0-1) 0.0000000 0.002465514 0.294817673 0.04404731 0.01515829
3large (4+) 0.0000000 0.263710489 0.217901923 0.48014585 0.62800992
4As many 0.3349885 0.520038256 0.004465384 0.02215426 0.39233388
age_cat50-64 age_cat65-89 educ2High School educ3Associate's
1small (0-1) 0.04595075 0.020854130 0.0000000 0.0000000
3large (4+) 0.77841849 0.883469605 0.2381980 0.3472772
4As many 0.32941929 0.005652598 0.7430904 0.8051639
educ4Bachelor's educ5Graduate racethAsian racethHispanic
1small (0-1) 0.0000000 0.0000000 0.0000000 0.0000000
3large (4+) 0.1243211 0.1295824 0.0000000 0.0000000
4As many 0.3233121 0.1270691 0.1271529 0.3933988
racethNH Black racethNH White racethOther racethTwo or More
1small (0-1) 0.0000000 0.0000000 0.0000000 0.0000000
3large (4+) 0.0000000 0.0000000 0.0000000 0.0000000
4As many 0.5480847 0.1900609 0.6602535 0.1469857grwtharm
# Economic growth always harms the environment. agree -> disagree
exp(coef(models$grwtharm)) (Intercept) x sex_rcMale age_cat30-39 age_cat40-49
1small (0-1) 9.726542e-24 0.502191 0.7859061 0.2449861 0.1406062
3large (4+) 4.759225e-10 1.037837 0.5887246 1.7170332 0.6358520
4As many 1.396691e+00 1.279725 0.5365683 0.4375902 0.7614996
age_cat50-64 age_cat65-89 educ2High School educ3Associate's
1small (0-1) 0.2710405 0.2578247 1.389103e+19 9.783188e+18
3large (4+) 1.2525300 0.8948375 4.028091e-01 3.773187e-01
4As many 0.7288356 0.3702229 1.125333e+00 8.095382e-01
educ4Bachelor's educ5Graduate racethAsian racethHispanic
1small (0-1) 9.765406e+18 2.599549e+19 9.029557e-10 1.591299e-11
3large (4+) 2.768891e-01 2.580466e-01 3.558215e+08 3.852209e+08
4As many 1.520387e+00 1.973205e+00 1.461497e-01 3.728138e-01
racethNH Black racethNH White racethOther racethTwo or More
1small (0-1) 4.314298e+04 2.143375e+04 1.842519e+05 1.479840e-10
3large (4+) 1.408466e+09 3.161975e+08 8.074955e-13 1.046388e+09
4As many 4.696869e-01 2.059133e-01 4.612188e-01 1.541071e-01z <- models[["grwtharm"]][["coefficients"]]/models[["grwtharm"]][["standard.errors"]]
p.6 <- (1 - pnorm(abs(z), 0, 1)) * 2
p.6 (Intercept) x sex_rcMale age_cat30-39 age_cat40-49
1small (0-1) 0.0000000 0.001867958 0.559750418 0.03444099 0.01048934
3large (4+) 0.0000000 0.885927555 0.228386206 0.51571033 0.63432431
4As many 0.8169575 0.046253156 0.001521546 0.02169676 0.42886905
age_cat50-64 age_cat65-89 educ2High School educ3Associate's
1small (0-1) 0.03093024 0.025190065 0.0000000 0.0000000
3large (4+) 0.78966357 0.899171494 0.2231257 0.2865453
4As many 0.34170243 0.004331753 0.8217800 0.7295133
educ4Bachelor's educ5Graduate racethAsian racethHispanic
1small (0-1) 0.0000000 0.0000000 0.0000000 NaN
3large (4+) 0.1065622 0.1156287 0.0000000 0.0000000
4As many 0.4258146 0.2037289 0.1507265 0.4684369
racethNH Black racethNH White racethOther racethTwo or More
1small (0-1) 0.0000000 0.0000000 0.0000000 NaN
3large (4+) 0.0000000 0.0000000 0.0000000 0.0000000
4As many 0.5612936 0.2097894 0.6069193 0.1751443grnprice
# How willing would you be to pay much higher prices in order to protect the environment. willing -> unwilling
exp(coef(models$grnprice)) (Intercept) x sex_rcMale age_cat30-39 age_cat40-49
1small (0-1) 2.887470e-16 0.5407042 0.6468332 0.3537575 0.2152638
3large (4+) 3.173077e-06 1.3842872 0.5612005 1.4520790 0.5440858
4As many 2.754319e+00 1.0670383 0.5699756 0.4305091 0.7302496
age_cat50-64 age_cat65-89 educ2High School educ3Associate's
1small (0-1) 0.3963213 0.2932477 5.130832e+09 4.124819e+09
3large (4+) 1.0270290 0.7945078 4.361526e-01 3.787007e-01
4As many 0.6933540 0.3744162 1.214293e+00 8.744405e-01
educ4Bachelor's educ5Graduate racethAsian racethHispanic
1small (0-1) 2.765148e+09 5.632775e+09 8.543875e-10 1.068610e-08
3large (4+) 3.183697e-01 3.337756e-01 2.617222e+04 2.708651e+04
4As many 1.751288e+00 2.393420e+00 1.266338e-01 3.052273e-01
racethNH Black racethNH White racethOther racethTwo or More
1small (0-1) 1.497553e+06 9.039968e+05 4.861728e+06 4.479345e-09
3large (4+) 1.138910e+05 2.194574e+04 1.303426e-08 8.797332e+04
4As many 4.388780e-01 1.852636e-01 4.632708e-01 1.357960e-01z <- models[["grnprice"]][["coefficients"]]/models[["grnprice"]][["standard.errors"]]
p.7 <- (1 - pnorm(abs(z), 0, 1)) * 2
p.7 (Intercept) x sex_rcMale age_cat30-39 age_cat40-49
1small (0-1) 0.0000000 0.008449605 0.27934668 0.11168806 0.04018656
3large (4+) 0.0000000 0.093547912 0.18281993 0.65742353 0.52625218
4As many 0.4656066 0.476670460 0.00362228 0.01945061 0.36330564
age_cat50-64 age_cat65-89 educ2High School educ3Associate's
1small (0-1) 0.1267964 0.041480513 0.0000000 0.0000000
3large (4+) 0.9751321 0.794199773 0.2705462 0.2914448
4As many 0.2764421 0.004809313 0.7103823 0.8254080
educ4Bachelor's educ5Graduate racethAsian racethHispanic
1small (0-1) 0.0000000 0.0000000 0.0000000 0.0000000
3large (4+) 0.1520277 0.2069613 0.0000000 0.0000000
4As many 0.2853986 0.1029830 0.1190394 0.3780611
racethNH Black racethNH White racethOther racethTwo or More
1small (0-1) 0.0000000 0.0000000 0.0000000 NaN
3large (4+) 0.0000000 0.0000000 0.0000000 0.0000000
4As many 0.5230863 0.1767995 0.6065974 0.1443935grntaxes
# How willing would you be to pay much higher taxes in order to protect the environment. willing -> unwilling
exp(coef(models$grntaxes)) (Intercept) x sex_rcMale age_cat30-39 age_cat40-49
1small (0-1) 6.300087e-16 0.5317865 0.6261260 0.3910603 0.2511030
3large (4+) 4.696530e-06 1.1184620 0.5956208 1.6387845 0.6009173
4As many 2.887507e+00 1.0396281 0.5735025 0.4333965 0.7292648
age_cat50-64 age_cat65-89 educ2High School educ3Associate's
1small (0-1) 0.4507884 0.3163289 3.174699e+10 2.505312e+10
3large (4+) 1.1849018 0.8769345 4.115715e-01 3.678930e-01
4As many 0.7020218 0.3771067 1.197380e+00 8.630363e-01
educ4Bachelor's educ5Graduate racethAsian racethHispanic
1small (0-1) 1.645222e+10 3.110215e+10 5.651585e-09 1.996443e-08
3large (4+) 2.898040e-01 2.850862e-01 2.902105e+04 3.021904e+04
4As many 1.716251e+00 2.339240e+00 1.286227e-01 3.055778e-01
racethNH Black racethNH White racethOther racethTwo or More
1small (0-1) 1.389522e+05 7.428631e+04 4.217071e+05 5.521375e-09
3large (4+) 1.182415e+05 2.575735e+04 9.296236e-08 8.819903e+04
4As many 4.414187e-01 1.892605e-01 4.810363e-01 1.354329e-01z <- models[["grntaxes"]][["coefficients"]]/models[["grntaxes"]][["standard.errors"]]
p.8 <- (1 - pnorm(abs(z), 0, 1)) * 2
p.8 (Intercept) x sex_rcMale age_cat30-39 age_cat40-49
1small (0-1) 0.0000000 0.002181869 0.245327863 0.15471591 0.06687189
3large (4+) 0.0000000 0.526025125 0.227357627 0.55598061 0.59599561
4As many 0.4457583 0.633498484 0.003937464 0.02074831 0.36495154
age_cat50-64 age_cat65-89 educ2High School educ3Associate's
1small (0-1) 0.1963216 0.058852556 0.0000000 0.0000000
3large (4+) 0.8420981 0.881272273 0.2348409 0.2750960
4As many 0.2947139 0.005138963 0.7303046 0.8087654
educ4Bachelor's educ5Graduate racethAsian racethHispanic
1small (0-1) 0.0000000 0.0000000 0.0000000 0.0000000
3large (4+) 0.1182623 0.1452775 0.0000000 0.0000000
4As many 0.3018952 0.1113681 0.1220189 0.3787447
racethNH Black racethNH White racethOther racethTwo or More
1small (0-1) 0.0000000 0.000000 0.0000000 0.0000000
3large (4+) 0.0000000 0.000000 0.0000000 0.0000000
4As many 0.5263367 0.182519 0.6242659 0.1440661grnsol
# How willing would you be to accep cuts in your standard of living in order to protect the environment. willing -> unwilling
exp(coef(models$grnsol)) (Intercept) x sex_rcMale age_cat30-39 age_cat40-49
1small (0-1) 1.388493e-14 0.4853862 0.6909726 0.3476697 0.2063083
3large (4+) 8.321658e-06 0.9918854 0.5979612 1.7504796 0.6461652
4As many 2.527637e+00 1.1163263 0.5674421 0.4158910 0.7115107
age_cat50-64 age_cat65-89 educ2High School educ3Associate's
1small (0-1) 0.4045772 0.3069063 1.161820e+09 9.859462e+08
3large (4+) 1.2769135 0.9141900 4.029744e-01 3.766043e-01
4As many 0.6704131 0.3598944 1.253315e+00 8.906916e-01
educ4Bachelor's educ5Graduate racethAsian racethHispanic
1small (0-1) 5.989881e+08 1.249362e+09 1.624102e-08 4.009124e-08
3large (4+) 2.795249e-01 2.611416e-01 2.281828e+04 2.437918e+04
4As many 1.835487e+00 2.530180e+00 1.200532e-01 2.874449e-01
racethNH Black racethNH White racethOther racethTwo or More
1small (0-1) 2.171594e+05 1.144063e+05 4.726280e+05 1.081918e-08
3large (4+) 9.104730e+04 2.045271e+04 8.544498e-08 6.688650e+04
4As many 4.035294e-01 1.721484e-01 4.357072e-01 1.293155e-01z <- models[["grnsol"]][["coefficients"]]/models[["grnsol"]][["standard.errors"]]
p.9 <- (1 - pnorm(abs(z), 0, 1)) * 2
p.9 (Intercept) x sex_rcMale age_cat30-39 age_cat40-49
1small (0-1) 0.0000000 0.000997765 0.362733423 0.1062395 0.03590102
3large (4+) 0.0000000 0.966034374 0.231450630 0.5045800 0.64733074
4As many 0.5030664 0.206412220 0.003374246 0.0154376 0.32554621
age_cat50-64 age_cat65-89 educ2High School educ3Associate's
1small (0-1) 0.1360302 0.050785187 0.0000000 0.0000000
3large (4+) 0.7744040 0.919026879 0.2267509 0.2858215
4As many 0.2350685 0.003563561 0.6666165 0.8491839
educ4Bachelor's educ5Graduate racethAsian racethHispanic
1small (0-1) 0.0000000 0.00000000 0.0000000 0.0000000
3large (4+) 0.1119736 0.12359776 0.0000000 0.0000000
4As many 0.2488183 0.08382715 0.1101917 0.3548866
racethNH Black racethNH White racethOther racethTwo or More
1small (0-1) 0.0000000 0.0000000 0.0000000 0.0000000
3large (4+) 0.0000000 0.0000000 0.0000000 0.0000000
4As many 0.4821441 0.1594025 0.5786575 0.1349863toodifme
# It is just too difficult for someone like me to do much about the environment. agree -> disagree
exp(coef(models$toodifme)) (Intercept) x sex_rcMale age_cat30-39 age_cat40-49
1small (0-1) 2.138204e-24 0.9896217 0.6233458 0.2860510 0.1660375
3large (4+) 1.998934e-08 0.7962908 0.5783750 1.7362969 0.6338373
4As many 2.313554e+00 1.1058773 0.5874694 0.4415301 0.7612545
age_cat50-64 age_cat65-89 educ2High School educ3Associate's
1small (0-1) 0.2878339 0.2421976 7.421392e+18 5.493148e+18
3large (4+) 1.2366324 0.9082580 4.639067e-01 4.403905e-01
4As many 0.7318901 0.3806004 1.143789e+00 8.299185e-01
educ4Bachelor's educ5Graduate racethAsian racethHispanic
1small (0-1) 4.533738e+18 1.079146e+19 5.357204e-08 2.630118e-09
3large (4+) 3.370833e-01 3.203364e-01 1.652679e+07 1.736594e+07
4As many 1.594389e+00 2.105670e+00 1.329581e-01 3.109094e-01
racethNH Black racethNH White racethOther racethTwo or More
1small (0-1) 4.998611e+04 2.361047e+04 1.089564e+05 4.824907e-09
3large (4+) 7.107447e+07 1.566020e+07 1.104934e-12 5.518243e+07
4As many 4.441088e-01 1.911835e-01 4.854314e-01 1.310915e-01z <- models[["toodifme"]][["coefficients"]]/models[["toodifme"]][["standard.errors"]]
p.10 <- (1 - pnorm(abs(z), 0, 1)) * 2
p.10 (Intercept) x sex_rcMale age_cat30-39 age_cat40-49
1small (0-1) 0.0000000 0.9577443 0.237651303 0.05111378 0.01588157
3large (4+) 0.0000000 0.2531602 0.204242746 0.50853466 0.63263865
4As many 0.5524968 0.3022130 0.005998815 0.02279264 0.42769749
age_cat50-64 age_cat65-89 educ2High School educ3Associate's
1small (0-1) 0.03557142 0.016382781 0.000000 0.0000000
3large (4+) 0.80128377 0.912661967 0.309801 0.3730052
4As many 0.34782396 0.005438639 0.798209 0.7604585
educ4Bachelor's educ5Graduate racethAsian racethHispanic
1small (0-1) 0.0000000 0.0000000 0.0000000 NaN
3large (4+) 0.1776844 0.1920794 0.0000000 0.0000000
4As many 0.3762306 0.1645729 0.1287364 0.3864947
racethNH Black racethNH White racethOther racethTwo or More
1small (0-1) 0.0000000 0.0000000 0.0000000 0.0000000
3large (4+) 0.0000000 0.0000000 0.0000000 0.0000000
4As many 0.5297546 0.1857789 0.6292102 0.1379987ihlpgrn
# I do what is right for the environment even when it costs more moeny or takes more time. agree -> disagree
exp(coef(models$ihlpgrn)) (Intercept) x sex_rcMale age_cat30-39 age_cat40-49
1small (0-1) 1.011300e-15 0.4865674 0.6922365 0.2577254 0.1618602
3large (4+) 4.320108e-06 1.1315018 0.5883659 1.6860045 0.6367958
4As many 6.774914e+00 0.7887611 0.5918545 0.4203475 0.7290921
age_cat50-64 age_cat65-89 educ2High School educ3Associate's
1small (0-1) 0.2624890 0.1926770 7.022000e+09 5.447446e+09
3large (4+) 1.2477445 0.9154650 4.220865e-01 3.748192e-01
4As many 0.6848065 0.3463746 1.147579e+00 8.256333e-01
educ4Bachelor's educ5Graduate racethAsian racethHispanic
1small (0-1) 4.076348e+09 9.029146e+09 6.237388e-10 3.458703e-09
3large (4+) 2.951977e-01 2.766952e-01 3.112719e+04 3.450472e+04
4As many 1.586244e+00 2.077454e+00 1.179139e-01 2.764974e-01
racethNH Black racethNH White racethOther racethTwo or More
1small (0-1) 5.524423e+05 2.902235e+05 2.177897e+06 8.866057e-10
3large (4+) 1.253099e+05 2.764429e+04 3.524692e-08 9.657787e+04
4As many 4.045233e-01 1.746663e-01 5.675338e-01 1.083866e-01z <- models[["ihlpgrn"]][["coefficients"]]/models[["ihlpgrn"]][["standard.errors"]]
p.11 <- (1 - pnorm(abs(z), 0, 1)) * 2
p.11 (Intercept) x sex_rcMale age_cat30-39 age_cat40-49
1small (0-1) 0.0000000 0.01184123 0.361157714 0.03794503 0.01447337
3large (4+) 0.0000000 0.64325894 0.216985309 0.52959115 0.63520382
4As many 0.1847437 0.06481715 0.006742484 0.01630933 0.35904309
age_cat50-64 age_cat65-89 educ2High School educ3Associate's
1small (0-1) 0.0260111 0.006682843 0.0000000 0.0000000
3large (4+) 0.7933374 0.919912065 0.2478010 0.2842872
4As many 0.2562806 0.002588634 0.7928595 0.7535594
educ4Bachelor's educ5Graduate racethAsian racethHispanic
1small (0-1) 0.0000000 0.0000000 NaN 0.0000000
3large (4+) 0.1247859 0.1339306 0.0000000 0.0000000
4As many 0.3792521 0.1698787 0.1109172 0.3452368
racethNH Black racethNH White racethOther racethTwo or More
1small (0-1) 0.0000000 0.0000000 0.0000000 0.0000000
3large (4+) 0.0000000 0.0000000 0.0000000 0.0000000
4As many 0.4881877 0.1676078 0.7088772 0.1091262recycle
# How often do you make a special effort to sort glass/cans/plastic/newspapers for recycling? always - never
exp(coef(models$recycle)) (Intercept) x sex_rcMale age_cat30-39 age_cat40-49
1small (0-1) 7.428482e-150 0.8863831 0.6373724 0.2845283 0.1625301
3large (4+) 1.156867e-64 1.2600994 0.5675932 1.6794423 0.7170766
4As many 3.785652e+00 0.9351460 0.5815920 0.4397709 0.7301753
age_cat50-64 age_cat65-89 educ2High School educ3Associate's
1small (0-1) 0.2836087 0.2315784 2.551642e+144 1.861463e+144
3large (4+) 1.3006966 1.0137653 4.258416e-01 4.314076e-01
4As many 0.7124836 0.3695855 1.189397e+00 8.424895e-01
educ4Bachelor's educ5Graduate racethAsian racethHispanic
1small (0-1) 1.502433e+144 3.499902e+144 2.925491e-14 2.280096e-26
3large (4+) 3.160552e-01 3.117934e-01 9.659826e+62 1.043665e+63
4As many 1.653051e+00 2.191501e+00 1.250184e-01 2.957041e-01
racethNH Black racethNH White racethOther racethTwo or More
1small (0-1) 5.274779e+04 2.399104e+04 1.061820e+05 7.923279e-24
3large (4+) 3.198210e+63 8.493846e+62 2.510520e-79 2.664080e+63
4As many 4.419159e-01 1.846178e-01 4.734818e-01 1.329777e-01z <- models[["recycle"]][["coefficients"]]/models[["recycle"]][["standard.errors"]]
p.13 <- (1 - pnorm(abs(z), 0, 1)) * 2
p.13 (Intercept) x sex_rcMale age_cat30-39 age_cat40-49
1small (0-1) 0.000000 0.5744233 0.258485124 0.05032684 0.01438545
3large (4+) 0.000000 0.2317694 0.189386592 0.53382935 0.72763428
4As many 0.340067 0.5062859 0.005047102 0.02201506 0.36259700
age_cat50-64 age_cat65-89 educ2High School educ3Associate's
1small (0-1) 0.03346696 0.01426948 0.0000000 0.0000000
3large (4+) 0.75566288 0.98763683 0.2533166 0.3606156
4As many 0.30737961 0.00451484 0.7400918 0.7788005
educ4Bachelor's educ5Graduate racethAsian racethHispanic
1small (0-1) 0.0000000 0.0000000 0.0000000 NaN
3large (4+) 0.1472246 0.1753501 0.0000000 0.0000000
4As many 0.3375982 0.1406989 0.1181344 0.3671607
racethNH Black racethNH White racethOther racethTwo or More
1small (0-1) 0.0000000 0.0000000 0.0000000 0.0000000
3large (4+) 0.0000000 0.0000000 NaN 0.0000000
4As many 0.5281236 0.1776355 0.6179472 0.1414823impgrn
# REVERSE There are more important things to do in life than protect the environment. agree -> disagree
exp(coef(models$impgrn_rc)) (Intercept) x sex_rcMale age_cat30-39 age_cat40-49
1small (0-1) 8.868458e-16 0.6130098 0.6918026 0.2573811 0.1498797
3large (4+) 4.032036e-06 1.6135147 0.5151690 1.8854387 0.6857552
4As many 2.013531e+00 1.2089112 0.5420296 0.4523224 0.7671202
age_cat50-64 age_cat65-89 educ2High School educ3Associate's
1small (0-1) 0.2677717 0.1948480 4.265411e+09 3.158586e+09
3large (4+) 1.3825902 1.0819834 4.088394e-01 3.844160e-01
4As many 0.7361105 0.4090893 1.204930e+00 8.710048e-01
educ4Bachelor's educ5Graduate racethAsian racethHispanic
1small (0-1) 2.636186e+09 6.042445e+09 1.550190e-09 1.204615e-08
3large (4+) 2.611030e-01 2.731759e-01 1.054295e+04 1.497385e+04
4As many 1.651542e+00 2.311171e+00 1.177400e-01 3.294556e-01
racethNH Black racethNH White racethOther racethTwo or More
1small (0-1) 6.128518e+05 2.948229e+05 1.134999e+06 1.872751e-09
3large (4+) 5.037287e+04 1.188678e+04 5.945196e-08 5.495658e+04
4As many 4.293008e-01 1.898056e-01 4.975351e-01 1.495284e-01z <- models[["impgrn_rc"]][["coefficients"]]/models[["impgrn_rc"]][["standard.errors"]]
p.14 <- (1 - pnorm(abs(z), 0, 1)) * 2
p.14 (Intercept) x sex_rcMale age_cat30-39 age_cat40-49
1small (0-1) 0.0000000 0.04228584 0.361168796 0.03676296 0.01170959
3large (4+) 0.0000000 0.02955031 0.130585364 0.45199743 0.69446702
4As many 0.6194456 0.06629390 0.001815176 0.02753579 0.44248428
age_cat50-64 age_cat65-89 educ2High School educ3Associate's
1small (0-1) 0.02805451 0.007013433 0.0000000 0.0000000
3large (4+) 0.70389032 0.929391409 0.2384882 0.3017984
4As many 0.35808624 0.010614722 0.7222072 0.8208749
educ4Bachelor's educ5Graduate racethAsian racethHispanic
1small (0-1) 0.00000000 0.0000000 NaN 0.0000000
3large (4+) 0.09384822 0.1330454 0.0000000 0.0000000
4As many 0.33877769 0.1152089 0.1112334 0.4151598
racethNH Black racethNH White racethOther racethTwo or More
1small (0-1) 0.0000000 0.0000000 0.0000000 0.0000000
3large (4+) 0.0000000 0.0000000 0.0000000 0.0000000
4As many 0.5175177 0.1889637 0.6438723 0.1698127othssame
# There is no point in doing what I can for the environment unless others do the same. agree -> disagree
exp(coef(models$othssame)) (Intercept) x sex_rcMale age_cat30-39 age_cat40-49
1small (0-1) 4.183772e-25 1.1620253 0.6483300 0.2771071 0.1706869
3large (4+) 4.162467e-09 0.8494354 0.5815751 1.8514802 0.6775857
4As many 3.544290e+00 0.9691562 0.5713534 0.4470347 0.7527154
age_cat50-64 age_cat65-89 educ2High School educ3Associate's
1small (0-1) 0.2895453 0.2322999 2.461445e+19 1.809991e+19
3large (4+) 1.3095198 0.9983148 4.332582e-01 4.145347e-01
4As many 0.7227161 0.3871664 1.204922e+00 8.779155e-01
educ4Bachelor's educ5Graduate racethAsian racethHispanic
1small (0-1) 1.472779e+19 3.401537e+19 1.932918e-07 2.615000e-09
3large (4+) 3.055229e-01 2.949652e-01 6.732557e+07 7.521832e+07
4As many 1.716954e+00 2.312128e+00 1.264162e-01 3.046991e-01
racethNH Black racethNH White racethOther racethTwo or More
1small (0-1) 4.178461e+04 2.008285e+04 8.539415e+04 1.838411e-09
3large (4+) 2.819795e+08 6.397046e+07 2.585203e-12 2.119959e+08
4As many 4.386046e-01 1.895180e-01 4.826186e-01 1.339633e-01z <- models[["othssame"]][["coefficients"]]/models[["othssame"]][["standard.errors"]]
p.15 <- (1 - pnorm(abs(z), 0, 1)) * 2
p.15 (Intercept) x sex_rcMale age_cat30-39 age_cat40-49
1small (0-1) 0.0000000 0.4731077 0.278291406 0.04678834 0.01691663
3large (4+) 0.0000000 0.4066498 0.208272210 0.46107753 0.68332832
4As many 0.3655979 0.7435716 0.003878722 0.02559500 0.40885746
age_cat50-64 age_cat65-89 educ2High School educ3Associate's
1small (0-1) 0.03664557 0.014181413 0.0000000 0.0000000
3large (4+) 0.74980142 0.998477568 0.2639935 0.3381202
4As many 0.32773683 0.006689248 0.7211797 0.8307652
educ4Bachelor's educ5Graduate racethAsian racethHispanic
1small (0-1) 0.0000000 0.0000000 0.0000000 0.0000000
3large (4+) 0.1361681 0.1566048 0.0000000 0.0000000
4As many 0.3014430 0.1149062 0.1187182 0.3772595
racethNH Black racethNH White racethOther racethTwo or More
1small (0-1) 0.0000000 0.0000000 0.0000000 0.0000000
3large (4+) 0.0000000 0.0000000 0.0000000 0.0000000
4As many 0.5226816 0.1826125 0.6255039 0.1415468grnexagg
# REVERSE Many of the claims about environmental threats are exaggerated. agree -> disagree
exp(coef(models$grnexagg_rc)) (Intercept) x sex_rcMale age_cat30-39 age_cat40-49
1small (0-1) 3.392108e-16 0.5029606 0.6879699 0.3319110 0.2283103
3large (4+) 4.791195e-06 1.1776906 0.5723498 1.7046942 0.6244793
4As many 3.720785e+00 0.9259039 0.5865379 0.4509189 0.7692632
age_cat50-64 age_cat65-89 educ2High School educ3Associate's
1small (0-1) 0.4039824 0.2841749 4.267149e+09 3.216466e+09
3large (4+) 1.1988899 0.9052333 4.149711e-01 3.979847e-01
4As many 0.7522603 0.3900858 1.173171e+00 8.436304e-01
educ4Bachelor's educ5Graduate racethAsian racethHispanic
1small (0-1) 2.054087e+09 4.279772e+09 7.147768e-10 1.219325e-08
3large (4+) 3.065027e-01 2.919348e-01 2.530250e+04 2.847193e+04
4As many 1.613356e+00 2.127959e+00 1.336731e-01 3.026145e-01
racethNH Black racethNH White racethOther racethTwo or More
1small (0-1) 1.553585e+06 8.087947e+05 3.584551e+06 2.032489e-09
3large (4+) 1.047523e+05 2.331730e+04 1.418758e-08 8.208128e+04
4As many 4.440621e-01 1.937483e-01 4.987588e-01 1.358127e-01z <- models[["grnexagg_rc"]][["coefficients"]]/models[["grnexagg_rc"]][["standard.errors"]]
p.16 <- (1 - pnorm(abs(z), 0, 1)) * 2
p.16 (Intercept) x sex_rcMale age_cat30-39 age_cat40-49
1small (0-1) 0.0000000 0.008592706 0.351497913 0.09044566 0.04913877
3large (4+) 0.0000000 0.362388574 0.197497621 0.52395260 0.62182216
4As many 0.3412862 0.391203030 0.005837872 0.02664750 0.44697765
age_cat50-64 age_cat65-89 educ2High School educ3Associate's
1small (0-1) 0.1371598 0.035595080 0.0000000 0.0000000
3large (4+) 0.8309896 0.909956110 0.2389617 0.3124749
4As many 0.3959214 0.006799262 0.7597318 0.7800786
educ4Bachelor's educ5Graduate racethAsian racethHispanic
1small (0-1) 0.0000000 0.0000000 NaN 0.0000000
3large (4+) 0.1380624 0.1522086 0.0000000 0.0000000
4As many 0.3620278 0.1573251 0.1294435 0.3747319
racethNH Black racethNH White racethOther racethTwo or More
1small (0-1) 0.0000000 0.000000 0.0000000 0.0000000
3large (4+) 0.0000000 0.000000 0.0000000 0.0000000
4As many 0.5291728 0.188847 0.6417667 0.1443975grncon
# How concerned are you about environmental issues? not -> very
exp(coef(models$grncon)) (Intercept) x sex_rcMale age_cat30-39 age_cat40-49
1small (0-1) 3.465794e-31 3.2630456 0.7535393 0.3452532 0.2004138
3large (4+) 1.368746e-09 0.8904979 0.5685281 1.6686335 0.6169582
4As many 1.636041e+00 1.1682362 0.5980646 0.4564590 0.7778807
age_cat50-64 age_cat65-89 educ2High School educ3Associate's
1small (0-1) 0.4048927 0.2409983 1.443563e+23 1.169997e+23
3large (4+) 1.2164394 0.9298440 4.203493e-01 3.923584e-01
4As many 0.7715388 0.3792462 1.151224e+00 8.329414e-01
educ4Bachelor's educ5Graduate racethAsian racethHispanic
1small (0-1) 8.023891e+22 1.499569e+23 6.785239e-05 3.463702e-07
3large (4+) 2.961093e-01 2.835425e-01 2.157429e+08 2.433480e+08
4As many 1.573643e+00 2.010779e+00 1.351051e-01 3.035709e-01
racethNH Black racethNH White racethOther racethTwo or More
1small (0-1) 6.036667e+04 3.119060e+04 9.970299e+04 3.294851e-07
3large (4+) 8.660367e+08 1.900857e+08 2.889861e-13 6.497514e+08
4As many 4.620075e-01 2.003013e-01 4.612200e-01 1.380577e-01z <- models[["grncon"]][["coefficients"]]/models[["grncon"]][["standard.errors"]]
p.17 <- (1 - pnorm(abs(z), 0, 1)) * 2
p.17 (Intercept) x sex_rcMale age_cat30-39 age_cat40-49
1small (0-1) 0.0000000 0.001797804 0.483382324 0.10348739 0.03311761
3large (4+) 0.0000000 0.535741419 0.195462332 0.53925432 0.61298742
4As many 0.7328968 0.112228391 0.008150527 0.02935637 0.46620445
age_cat50-64 age_cat65-89 educ2High School educ3Associate's
1small (0-1) 0.1364841 0.017031457 0.0000000 0.0000000
3large (4+) 0.8170031 0.933968713 0.2448629 0.3051872
4As many 0.4390873 0.005336357 0.7889427 0.7655162
educ4Bachelor's educ5Graduate racethAsian racethHispanic
1small (0-1) 0.0000000 0.0000000 0.0000000 0.0000000
3large (4+) 0.1249771 0.1431250 0.0000000 0.0000000
4As many 0.3903932 0.1945462 0.1329815 0.3782177
racethNH Black racethNH White racethOther racethTwo or More
1small (0-1) 0.0000000 0.0000000 0.0000000 0.0000000
3large (4+) 0.0000000 0.0000000 NaN 0.0000000
4As many 0.5514797 0.2002846 0.6060622 0.1497894helpharm
# I find it hard to know whether the way I live is helpful or harmful to the environment. agree -> disagree
exp(coef(models$helpharm)) (Intercept) x sex_rcMale age_cat30-39 age_cat40-49
1small (0-1) 3.393828e-25 1.4968592 0.6408484 0.2611922 0.1507710
3large (4+) 9.074544e-09 0.7582617 0.6013685 1.7742166 0.7058951
4As many 3.057992e+00 1.0213141 0.5759101 0.4393816 0.7399433
age_cat50-64 age_cat65-89 educ2High School educ3Associate's
1small (0-1) 0.2590538 0.2111605 1.482311e+19 1.089304e+19
3large (4+) 1.3439272 0.9663380 4.285610e-01 4.064786e-01
4As many 0.7128108 0.3777090 1.194776e+00 8.629316e-01
educ4Bachelor's educ5Graduate racethAsian racethHispanic
1small (0-1) 8.521955e+18 1.873463e+19 5.068809e-07 1.112841e-09
3large (4+) 3.053443e-01 3.029447e-01 3.998763e+07 4.195908e+07
4As many 1.686036e+00 2.243688e+00 1.278410e-01 3.011190e-01
racethNH Black racethNH White racethOther racethTwo or More
1small (0-1) 4.186942e+04 2.051771e+04 7.134197e+04 2.450507e-09
3large (4+) 1.668395e+08 3.874452e+07 8.420107e-12 1.255156e+08
4As many 4.359781e-01 1.878506e-01 4.786475e-01 1.332958e-01z <- models[["helpharm"]][["coefficients"]]/models[["helpharm"]][["standard.errors"]]
p.18 <- (1 - pnorm(abs(z), 0, 1)) * 2
p.18 (Intercept) x sex_rcMale age_cat30-39 age_cat40-49
1small (0-1) 0.0000000 0.08194665 0.265241135 0.03891714 0.01121293
3large (4+) 0.0000000 0.20554094 0.237878858 0.49148918 0.71576659
4As many 0.4256246 0.84093186 0.004154825 0.02184254 0.38500179
age_cat50-64 age_cat65-89 educ2High School educ3Associate's
1small (0-1) 0.02440729 0.009690310 0.0000000 0.0000000
3large (4+) 0.72683967 0.968847962 0.2599911 0.3273226
4As many 0.31063385 0.005316213 0.7333858 0.8085943
educ4Bachelor's educ5Graduate racethAsian racethHispanic
1small (0-1) 0.0000000 0.0000000 0.0000000 0.000000
3large (4+) 0.1377211 0.1677467 0.0000000 0.000000
4As many 0.3180991 0.1288991 0.1209977 0.372832
racethNH Black racethNH White racethOther racethTwo or More
1small (0-1) 0.0000000 0.0000000 0.0000000 0.0000000
3large (4+) 0.0000000 0.0000000 0.0000000 0.0000000
4As many 0.5199428 0.1805996 0.6219952 0.1408006grneffme
# Environmental problems have a direct effect on my everyday life. agree -> disagree
exp(coef(models$grneffme)) (Intercept) x sex_rcMale age_cat30-39 age_cat40-49
1small (0-1) 1.741924e-15 0.4917247 0.6506906 0.3053250 0.1537065
3large (4+) 5.202518e-06 1.3702703 0.5680082 1.7187446 0.7074288
4As many 2.840782e+00 1.0464633 0.5707585 0.4388213 0.7512664
age_cat50-64 age_cat65-89 educ2High School educ3Associate's
1small (0-1) 0.2759283 0.2038808 3.420603e+09 3.056734e+09
3large (4+) 1.3318464 1.0086729 4.253640e-01 3.694776e-01
4As many 0.7211977 0.3854634 1.198191e+00 8.590974e-01
educ4Bachelor's educ5Graduate racethAsian racethHispanic
1small (0-1) 1.946234e+09 4.154776e+09 2.278336e-09 1.204400e-08
3large (4+) 2.951666e-01 2.956912e-01 1.414118e+04 1.761953e+04
4As many 1.698213e+00 2.298940e+00 1.288334e-01 3.133767e-01
racethNH Black racethNH White racethOther racethTwo or More
1small (0-1) 5.980323e+05 3.877174e+05 2.241553e+06 1.315956e-08
3large (4+) 5.604464e+04 1.171814e+04 2.690664e-08 4.429575e+04
4As many 4.431477e-01 1.880140e-01 4.734741e-01 1.358416e-01z <- models[["grneffme"]][["coefficients"]]/models[["grneffme"]][["standard.errors"]]
p.19 <- (1 - pnorm(abs(z), 0, 1)) * 2
p.19 (Intercept) x sex_rcMale age_cat30-39 age_cat40-49
1small (0-1) 0.0000000 0.003611611 0.293658871 0.06949659 0.01262753
3large (4+) 0.0000000 0.146863930 0.189198191 0.51696528 0.71782350
4As many 0.4583804 0.649313673 0.003701672 0.02161013 0.40579584
age_cat50-64 age_cat65-89 educ2High School educ3Associate's
1small (0-1) 0.03225309 0.008566329 0.0000000 0.0000000
3large (4+) 0.73643733 0.992202784 0.2540759 0.2782475
4As many 0.32434734 0.006237884 0.7295093 0.8030413
educ4Bachelor's educ5Graduate racethAsian racethHispanic
1small (0-1) 0.0000000 0.0000000 NaN 0.0000000
3large (4+) 0.1242661 0.1544759 0.0000000 0.0000000
4As many 0.3110940 0.1169765 0.1231701 0.3904046
racethNH Black racethNH White racethOther racethTwo or More
1small (0-1) 0.0000000 0.0000000 0.0000000 0.0000000
3large (4+) 0.0000000 0.0000000 0.0000000 0.0000000
4As many 0.5291052 0.1817142 0.6173173 0.1454408nobuygrn
# How often do you avoid buying certain products for environmental reasons? always -> never
exp(coef(models$nobuygrn)) (Intercept) x sex_rcMale age_cat30-39 age_cat40-49
1small (0-1) 1.516942e-14 0.4596242 0.8358117 0.2940947 0.1544023
3large (4+) 1.609741e-06 1.3777459 0.5410845 1.6604298 0.6960769
4As many 2.994703e+00 1.0257848 0.5711266 0.4403524 0.7494235
age_cat50-64 age_cat65-89 educ2High School educ3Associate's
1small (0-1) 0.2861847 0.2182758 2.373021e+09 1.882503e+09
3large (4+) 1.2853876 0.9157181 4.108227e-01 4.011704e-01
4As many 0.7197446 0.3828255 1.192500e+00 8.592503e-01
educ4Bachelor's educ5Graduate racethAsian racethHispanic
1small (0-1) 1.364625e+09 2.687133e+09 9.774437e-10 3.341191e-09
3large (4+) 3.054888e-01 3.051542e-01 5.167766e+04 5.630101e+04
4As many 1.696318e+00 2.280111e+00 1.300312e-01 3.099325e-01
racethNH Black racethNH White racethOther racethTwo or More
1small (0-1) 1.279323e+05 6.367370e+04 2.628061e+05 3.291984e-09
3large (4+) 1.681135e+05 4.171703e+04 1.161456e-07 1.494917e+05
4As many 4.420061e-01 1.903211e-01 4.892736e-01 1.355928e-01z <- models[["nobuygrn"]][["coefficients"]]/models[["nobuygrn"]][["standard.errors"]]
p.23 <- (1 - pnorm(abs(z), 0, 1)) * 2
p.23 (Intercept) x sex_rcMale age_cat30-39 age_cat40-49
1small (0-1) 0.0000000 0.00172803 0.664054122 0.06148212 0.01253371
3large (4+) 0.0000000 0.24026963 0.160843499 0.54226599 0.70416038
4As many 0.4412426 0.83090310 0.004260095 0.02208750 0.40193780
age_cat50-64 age_cat65-89 educ2High School educ3Associate's
1small (0-1) 0.03881278 0.012675128 0.0000000 0.0000000
3large (4+) 0.76652867 0.919899319 0.2358360 0.3205969
4As many 0.32128681 0.005678805 0.7362469 0.8032836
educ4Bachelor's educ5Graduate racethAsian racethHispanic
1small (0-1) 0.0000000 0.0000000 0.0000000 0.0000000
3large (4+) 0.1363959 0.1692455 0.0000000 0.0000000
4As many 0.3121423 0.1207287 0.1250217 0.3861392
racethNH Black racethNH White racethOther racethTwo or More
1small (0-1) 0.0000000 0.0000000 0.0000000 0.0000000
3large (4+) 0.0000000 0.0000000 0.0000000 0.0000000
4As many 0.5270029 0.1844848 0.6324514 0.1450554clmtcaus
# Descirbe opinion about climate. not changing -> anthropogenic change
exp(coef(models$clmtcaus)) (Intercept) x sex_rcMale age_cat30-39 age_cat40-49
1small (0-1) 3.787079e-23 3.0390996 0.6527036 0.3045796 0.2215889
3large (4+) 1.029957e-05 0.5987214 0.5516307 1.6696027 0.5511881
4As many 4.867569e+00 0.8916211 0.5681979 0.4376235 0.7251604
age_cat50-64 age_cat65-89 educ2High School educ3Associate's
1small (0-1) 0.3759650 0.3158484 5.356009e+15 4.258060e+15
3large (4+) 1.0624750 0.8200156 4.154832e-01 3.768220e-01
4As many 0.6905948 0.3691626 1.203536e+00 8.706723e-01
educ4Bachelor's educ5Graduate racethAsian racethHispanic
1small (0-1) 2.791127e+15 6.073030e+15 1.478624e-06 1.036609e-08
3large (4+) 3.115576e-01 3.022653e-01 1.134688e+05 1.296582e+05
4As many 1.744215e+00 2.365195e+00 1.279724e-01 3.120316e-01
racethNH Black racethNH White racethOther racethTwo or More
1small (0-1) 5.561668e+04 2.685367e+04 1.027345e+05 6.363831e-09
3large (4+) 4.522766e+05 9.733136e+04 5.125422e-09 3.574264e+05
4As many 4.355781e-01 1.880209e-01 4.900076e-01 1.349846e-01z <- models[["clmtcaus"]][["coefficients"]]/models[["clmtcaus"]][["standard.errors"]]
p.24 <- (1 - pnorm(abs(z), 0, 1)) * 2
p.24 (Intercept) x sex_rcMale age_cat30-39 age_cat40-49
1small (0-1) 0.000000 0.02005714 0.287681664 0.06627265 0.04392628
3large (4+) 0.000000 0.09570242 0.170896682 0.54030712 0.53487220
4As many 0.282956 0.44896168 0.003494332 0.02122219 0.35282097
age_cat50-64 age_cat65-89 educ2High School educ3Associate's
1small (0-1) 0.1042010 0.05319478 0.0000000 0.0000000
3large (4+) 0.9434821 0.82246747 0.2410562 0.2889101
4As many 0.2703395 0.00441200 0.7233282 0.8202561
educ4Bachelor's educ5Graduate racethAsian racethHispanic
1small (0-1) 0.0000000 0.0000000 0.0000000 0.0000000
3large (4+) 0.1429892 0.1651476 0.0000000 0.0000000
4As many 0.2893698 0.1071584 0.1217018 0.3880645
racethNH Black racethNH White racethOther racethTwo or More
1small (0-1) 0.000000 0.0000000 0.0000000 0.0000000
3large (4+) 0.000000 0.0000000 NaN 0.0000000
4As many 0.520163 0.1815573 0.6336879 0.1439206clmtwrld
# scale of 0-10, how bad or good do you think the impacts of climate change will be for the world as a whole. bad -> good
exp(coef(models$clmtwrld)) (Intercept) x sex_rcMale age_cat30-39 age_cat40-49
1small (0-1) 3.349787e-17 0.6793451 0.6366604 0.3049228 0.1904393
3large (4+) 1.120829e-05 1.0419169 0.6009018 1.7830855 0.6603382
4As many 3.382619e+00 0.9296199 0.5772660 0.4561938 0.7712057
age_cat50-64 age_cat65-89 educ2High School educ3Associate's
1small (0-1) 0.3712584 0.2725738 1.867863e+10 1.349072e+10
3large (4+) 1.2712518 0.9360702 4.032296e-01 3.883297e-01
4As many 0.7771452 0.3975614 1.216548e+00 8.850375e-01
educ4Bachelor's educ5Graduate racethAsian racethHispanic
1small (0-1) 8.364687e+09 1.708193e+10 2.115289e-09 7.789731e-09
3large (4+) 2.905731e-01 2.798575e-01 1.444674e+04 1.554764e+04
4As many 1.618184e+00 2.090951e+00 1.376521e-01 3.203876e-01
racethNH Black racethNH White racethOther racethTwo or More
1small (0-1) 3.662117e+06 1.209879e+06 5.080949e+06 1.339646e-09
3large (4+) 5.282523e+04 1.274692e+04 2.235058e-08 4.101243e+04
4As many 5.196160e-01 2.096165e-01 5.175844e-01 1.516150e-01z <- models[["clmtwrld"]][["coefficients"]]/models[["clmtwrld"]][["standard.errors"]]
p.25 <- (1 - pnorm(abs(z), 0, 1)) * 2
p.25 (Intercept) x sex_rcMale age_cat30-39 age_cat40-49
1small (0-1) 0.0000000 0.001945898 0.262315101 0.06964745 0.02812743
3large (4+) 0.0000000 0.629888186 0.235803287 0.48972453 0.66387516
4As many 0.3734625 0.111104552 0.004397894 0.02896263 0.44997429
age_cat50-64 age_cat65-89 educ2High School educ3Associate's
1small (0-1) 0.1057557 0.031204120 0.0000000 0.0000000
3large (4+) 0.7769575 0.940183311 0.2219093 0.2978795
4As many 0.4517341 0.008071831 0.7089212 0.8418185
educ4Bachelor's educ5Graduate racethAsian racethHispanic
1small (0-1) 0.000000 0.0000000 0.0000000 0.0000000
3large (4+) 0.118629 0.1373334 0.0000000 0.0000000
4As many 0.360045 0.1678721 0.1341614 0.3972446
racethNH Black racethNH White racethOther racethTwo or More
1small (0-1) 0.0000000 0.0000000 0.0000000 NaN
3large (4+) 0.0000000 0.0000000 0.0000000 0.0000000
4As many 0.6121065 0.2100525 0.6586325 0.1675945clmtusa
# scale of 0-10, how bad or good do you think the impacts of climate change will be for America. bad -> good
exp(coef(models$clmtusa)) (Intercept) x sex_rcMale age_cat30-39 age_cat40-49
1small (0-1) 2.344275e-17 0.7224981 0.6569282 0.2869458 0.1854493
3large (4+) 6.693368e-06 1.0940379 0.6045526 1.7630298 0.6309014
4As many 3.503587e+00 0.9395523 0.5759042 0.4464681 0.7640506
age_cat50-64 age_cat65-89 educ2High School educ3Associate's
1small (0-1) 0.3627539 0.2604497 9.486114e+10 7.489918e+10
3large (4+) 1.1944454 0.8851252 4.191320e-01 3.923867e-01
4As many 0.7646466 0.3934857 1.168665e+00 8.685818e-01
educ4Bachelor's educ5Graduate racethAsian racethHispanic
1small (0-1) 4.631380e+10 1.011534e+11 5.003882e-09 6.716041e-08
3large (4+) 3.025015e-01 2.972417e-01 2.115725e+04 2.024824e+04
4As many 1.593902e+00 2.084628e+00 1.357621e-01 3.327167e-01
racethNH Black racethNH White racethOther racethTwo or More
1small (0-1) 8.091476e+05 3.088938e+05 1.274329e+06 1.326029e-08
3large (4+) 7.426533e+04 1.842654e+04 7.683734e-09 5.985285e+04
4As many 4.972965e-01 2.056476e-01 5.211113e-01 1.455970e-01z <- models[["clmtusa"]][["coefficients"]]/models[["clmtusa"]][["standard.errors"]]
p.26 <- (1 - pnorm(abs(z), 0, 1)) * 2
p.26 (Intercept) x sex_rcMale age_cat30-39 age_cat40-49
1small (0-1) 0.0000000 0.004932221 0.29609858 0.05547548 0.02455297
3large (4+) 0.0000000 0.299553624 0.24325105 0.49903845 0.63022821
4As many 0.3604903 0.177033533 0.00420692 0.02467768 0.43403895
age_cat50-64 age_cat65-89 educ2High School educ3Associate's
1small (0-1) 0.09503394 0.025005807 0.0000000 0.0000000
3large (4+) 0.83395766 0.889697881 0.2443021 0.3032378
4As many 0.42267228 0.007412954 0.7662761 0.8174831
educ4Bachelor's educ5Graduate racethAsian racethHispanic
1small (0-1) 0.0000000 0.0000000 0.0000000 0.0000000
3large (4+) 0.1331654 0.1579374 0.0000000 0.0000000
4As many 0.3755793 0.1697862 0.1317308 0.4137704
racethNH Black racethNH White racethOther racethTwo or More
1small (0-1) 0.0000000 0.0000000 0.0000000 0.0000000
3large (4+) 0.0000000 0.0000000 0.0000000 0.0000000
4As many 0.5884925 0.2049549 0.6625041 0.1585612naturdev
# How willing would you be to accept a reduction in the size of America's protected nature areas in order to open them up for economic development? willing -> unwilling
exp(coef(models$naturdev_rc)) (Intercept) x sex_rcMale age_cat30-39 age_cat40-49
1small (0-1) 0.000000e+00 0.6219422 0.6432548 0.2912025 0.1780749
3large (4+) 3.345956e-163 1.0134900 0.6032235 1.7524365 0.6433914
4As many 5.215388e+00 0.7357037 0.5879909 0.4449053 0.7699707
age_cat50-64 age_cat65-89 educ2High School educ3Associate's
1small (0-1) 0.3410358 0.2472957 Inf Inf
3large (4+) 1.2932330 0.9310817 0.4058574 0.3868490
4As many 0.8027366 0.3820376 1.1659451 0.7736107
educ4Bachelor's educ5Graduate racethAsian racethHispanic
1small (0-1) Inf Inf 3.061057e-32 7.050062e-67
3large (4+) 0.2817135 0.267475 5.228259e+161 5.727171e+161
4As many 1.5440802 1.989032 1.630466e-01 3.493867e-01
racethNH Black racethNH White racethOther racethTwo or More
1small (0-1) 9.055542e+04 2.916919e+04 9.938989e+04 3.620427e-61
3large (4+) 2.081342e+162 4.726450e+161 5.025500e-191 1.550768e+162
4As many 6.180028e-01 2.008466e-01 4.181898e-01 1.490188e-01z <- models[["naturdev_rc"]][["coefficients"]]/models[["naturdev_rc"]][["standard.errors"]]
p.27 <- (1 - pnorm(abs(z), 0, 1)) * 2
p.27 (Intercept) x sex_rcMale age_cat30-39 age_cat40-49
1small (0-1) 0.0000000 0.070582537 0.268341934 0.05537428 0.02062269
3large (4+) 0.0000000 0.946700364 0.238514259 0.50104892 0.64370942
4As many 0.2323359 0.005991324 0.006086298 0.02468514 0.44913400
age_cat50-64 age_cat65-89 educ2High School educ3Associate's
1small (0-1) 0.07351513 0.018547835 0.0000000 0.0000000
3large (4+) 0.76084830 0.935137962 0.2266065 0.3015299
4As many 0.51367475 0.005891216 0.7712888 0.6774798
educ4Bachelor's educ5Graduate racethAsian racethHispanic
1small (0-1) 0.0000000 0.0000000 NaN NaN
3large (4+) 0.1101283 0.1242071 0.0000000 0.000000
4As many 0.4123648 0.2009762 0.1711052 0.434611
racethNH Black racethNH White racethOther racethTwo or More
1small (0-1) 0.0000000 0.0000000 0.0000000 NaN
3large (4+) 0.0000000 0.0000000 NaN 0.0000000
4As many 0.7096531 0.1971976 0.5585947 0.1636187carsgen
# Do you think air pollution caused by cars is..dangerous -> not dangerous
exp(coef(models$carsgen)) (Intercept) x sex_rcMale age_cat30-39 age_cat40-49
1small (0-1) 4.828357e-16 0.4930801 0.6660667 0.3110283 0.1898802
3large (4+) 1.023111e-05 0.9599486 0.6000081 1.7378586 0.6450505
4As many 2.311179e+00 1.1723367 0.5638704 0.4370947 0.7367778
age_cat50-64 age_cat65-89 educ2High School educ3Associate's
1small (0-1) 0.3331084 0.2407009 4.229949e+09 3.120567e+09
3large (4+) 1.2597202 0.9023090 4.027397e-01 3.760113e-01
4As many 0.7082804 0.3840097 1.222907e+00 8.844324e-01
educ4Bachelor's educ5Graduate racethAsian racethHispanic
1small (0-1) 2.258998e+09 5.124694e+09 2.604171e-10 8.521154e-09
3large (4+) 2.766221e-01 2.593267e-01 2.028436e+04 2.077306e+04
4As many 1.783436e+00 2.442841e+00 1.160329e-01 3.151535e-01
racethNH Black racethNH White racethOther racethTwo or More
1small (0-1) 1.300263e+06 8.118269e+05 3.156076e+06 3.313390e-09
3large (4+) 8.026750e+04 1.814498e+04 5.844761e-08 5.868359e+04
4As many 4.255729e-01 1.729465e-01 4.593584e-01 1.303053e-01z <- models[["carsgen"]][["coefficients"]]/models[["carsgen"]][["standard.errors"]]
p.12 <- (1 - pnorm(abs(z), 0, 1)) * 2
p.12 (Intercept) x sex_rcMale age_cat30-39 age_cat40-49
1small (0-1) 0.0000000 0.006127942 0.312471193 0.07133669 0.0271372
3large (4+) 0.0000000 0.875336764 0.234853226 0.50645264 0.6451328
4As many 0.5462906 0.181455878 0.003120706 0.02138505 0.3750376
age_cat50-64 age_cat65-89 educ2High School educ3Associate's
1small (0-1) 0.06823255 0.017708095 0.0000000 0.0000000
3large (4+) 0.78422253 0.906554258 0.2225014 0.2838338
4As many 0.29962411 0.005916979 0.7008135 0.8403413
educ4Bachelor's educ5Graduate racethAsian racethHispanic
1small (0-1) 0.0000000 0.00000000 0.0000000 0.0000000
3large (4+) 0.1057333 0.11533554 0.0000000 0.0000000
4As many 0.2703236 0.09418643 0.1034918 0.3897397
racethNH Black racethNH White racethOther racethTwo or More
1small (0-1) 0.0000000 0.0000000 0.0000000 NaN
3large (4+) 0.0000000 0.0000000 0.0000000 0.000000
4As many 0.5060477 0.1585963 0.6019315 0.135229tempgen1
# Do you think a rise in the world's tempaerture caused by climate change is...dangerous -> not dangerous
exp(coef(models$tempgen1)) (Intercept) x sex_rcMale age_cat30-39 age_cat40-49
1small (0-1) 3.781655e-16 0.4075799 0.6555624 0.3319057 0.2424970
3large (4+) 6.252872e-06 1.5115154 0.5789414 1.5704251 0.6004630
4As many 2.835922e+00 1.1018130 0.5718268 0.4370286 0.7291621
age_cat50-64 age_cat65-89 educ2High School educ3Associate's
1small (0-1) 0.4044439 0.3067103 6.162455e+09 4.727751e+09
3large (4+) 1.0298512 0.8386721 3.797074e-01 3.490583e-01
4As many 0.6915633 0.3720785 1.185995e+00 8.590427e-01
educ4Bachelor's educ5Graduate racethAsian racethHispanic
1small (0-1) 3.045432e+09 6.175946e+09 3.961560e-10 2.226476e-08
3large (4+) 3.089434e-01 3.097056e-01 1.304231e+04 1.765978e+04
4As many 1.745925e+00 2.378262e+00 1.194257e-01 3.055495e-01
racethNH Black racethNH White racethOther racethTwo or More
1small (0-1) 1.315089e+06 6.319368e+05 2.476357e+06 7.105459e-09
3large (4+) 5.064118e+04 1.154907e+04 9.730916e-09 4.452610e+04
4As many 4.119444e-01 1.769939e-01 4.584266e-01 1.294618e-01z <- models[["tempgen1"]][["coefficients"]]/models[["tempgen1"]][["standard.errors"]]
p.20 <- (1 - pnorm(abs(z), 0, 1)) * 2
p.20 (Intercept) x sex_rcMale age_cat30-39 age_cat40-49
1small (0-1) 0.0000000 0.002679368 0.294339485 0.09260549 0.05859397
3large (4+) 0.0000000 0.046145595 0.205722600 0.58980272 0.59297788
4As many 0.4502791 0.337205367 0.003759896 0.02107124 0.35982603
age_cat50-64 age_cat65-89 educ2High School educ3Associate's
1small (0-1) 0.1380115 0.05048149 0.0000000 0.0000000
3large (4+) 0.9725789 0.84180431 0.1977562 0.2540908
4As many 0.2704479 0.00455060 0.7441864 0.8029021
educ4Bachelor's educ5Graduate racethAsian racethHispanic
1small (0-1) 0.0000000 0.0000000 NaN 0.0000000
3large (4+) 0.1400551 0.1729833 0.0000000 0.0000000
4As many 0.2872775 0.1036839 0.1103456 0.3795084
racethNH Black racethNH White racethOther racethTwo or More
1small (0-1) 0.0000000 0.0000000 0.0000000 0.0000000
3large (4+) 0.0000000 0.0000000 0.0000000 0.0000000
4As many 0.4932094 0.1670027 0.6022905 0.1361002indusgen1
# Do you think air pollution caused by industry is...dangerous -> not dangerous
exp(coef(models$indusgen1)) (Intercept) x sex_rcMale age_cat30-39 age_cat40-49
1small (0-1) 1.489815e-15 0.3338987 0.7417484 0.3493831 0.2449068
3large (4+) 6.619473e-06 1.0764053 0.5862747 1.7299727 0.6342765
4As many 3.206575e+00 1.0046474 0.5749803 0.4419756 0.7471159
age_cat50-64 age_cat65-89 educ2High School educ3Associate's
1small (0-1) 0.4463347 0.3159870 2.301631e+09 1.43920e+09
3large (4+) 1.2371171 0.8980636 4.109199e-01 3.89416e-01
4As many 0.7203378 0.3819812 1.197316e+00 8.67125e-01
educ4Bachelor's educ5Graduate racethAsian racethHispanic
1small (0-1) 1.248255e+09 2.798899e+09 1.599662e-10 1.669022e-08
3large (4+) 2.860230e-01 2.709219e-01 2.418442e+04 2.709017e+04
4As many 1.696887e+00 2.269770e+00 1.272673e-01 3.035958e-01
racethNH Black racethNH White racethOther racethTwo or More
1small (0-1) 9.975318e+05 5.033987e+05 1.586051e+06 4.573182e-09
3large (4+) 9.677709e+04 2.171513e+04 9.891072e-08 7.386983e+04
4As many 4.349971e-01 1.877906e-01 4.792785e-01 1.332819e-01z <- models[["indusgen1"]][["coefficients"]]/models[["indusgen1"]][["standard.errors"]]
p.28 <- (1 - pnorm(abs(z), 0, 1)) * 2
p.28 (Intercept) x sex_rcMale age_cat30-39 age_cat40-49
1small (0-1) 0.0000000 0.0009568888 0.457987130 0.10912723 0.0624439
3large (4+) 0.0000000 0.7911145299 0.218906530 0.51007236 0.6331784
4As many 0.4007947 0.9699368880 0.004510065 0.02286723 0.3989178
age_cat50-64 age_cat65-89 educ2High School educ3Associate's
1small (0-1) 0.1902502 0.057952607 0.0000000 0.0000000
3large (4+) 0.8020981 0.902544605 0.2332133 0.3040106
4As many 0.3284509 0.005781521 0.7304508 0.8151452
educ4Bachelor's educ5Graduate racethAsian racethHispanic
1small (0-1) 0.0000000 0.0000000 NaN 0.0000000
3large (4+) 0.1138186 0.1263532 0.0000000 0.0000000
4As many 0.3124320 0.1228353 0.1201965 0.3762669
racethNH Black racethNH White racethOther racethTwo or More
1small (0-1) 0.0000000 0.000000 0.0000000 0.0000000
3large (4+) 0.0000000 0.000000 0.0000000 0.0000000
4As many 0.5187913 0.180547 0.6226002 0.1407985chemgen1
# Do you think pesticides and chemicals used in farming are...dangerous -> not dangerous
exp(coef(models$chemgen1)) (Intercept) x sex_rcMale age_cat30-39 age_cat40-49
1small (0-1) 1.048623e-14 0.2866840 0.8985836 0.2217961 0.1294066
3large (4+) 6.054309e-06 1.0454908 0.5901479 1.7391516 0.6419976
4As many 3.724715e+00 0.9436562 0.5896530 0.4366118 0.7375193
age_cat50-64 age_cat65-89 educ2High School educ3Associate's
1small (0-1) 0.2557487 0.1754260 6.645613e+08 4.898277e+08
3large (4+) 1.2730119 0.9104202 4.094068e-01 3.853648e-01
4As many 0.7137020 0.3746651 1.182523e+00 8.609755e-01
educ4Bachelor's educ5Graduate racethAsian racethHispanic
1small (0-1) 4.359720e+08 8.499418e+08 1.849929e-10 7.594242e-09
3large (4+) 2.833198e-01 2.680356e-01 2.785534e+04 3.069092e+04
4As many 1.675805e+00 2.233551e+00 1.264373e-01 2.915579e-01
racethNH Black racethNH White racethOther racethTwo or More
1small (0-1) 1.131407e+06 5.158742e+05 1.029787e+06 1.489922e-09
3large (4+) 1.095681e+05 2.466689e+04 7.515322e-08 8.247557e+04
4As many 4.350991e-01 1.877673e-01 4.577701e-01 1.293875e-01z <- models[["chemgen1"]][["coefficients"]]/models[["chemgen1"]][["standard.errors"]]
p.29 <- (1 - pnorm(abs(z), 0, 1)) * 2
p.29 (Intercept) x sex_rcMale age_cat30-39 age_cat40-49
1small (0-1) 0.0000000 3.419792e-05 0.796382385 0.02570899 0.007248331
3large (4+) 0.0000000 8.563207e-01 0.224695768 0.50587309 0.641656052
4As many 0.3469279 6.014138e-01 0.007202583 0.02108757 0.377035213
age_cat50-64 age_cat65-89 educ2High School educ3Associate's
1small (0-1) 0.02790132 0.005499958 0.0000000 0.0000000
3large (4+) 0.77522289 0.914799818 0.2310913 0.2971323
4As many 0.31010120 0.004905964 0.7485518 0.8059674
educ4Bachelor's educ5Graduate racethAsian racethHispanic
1small (0-1) 0.0000000 0.0000000 NaN 0.0000000
3large (4+) 0.1105121 0.1224883 0.0000000 0.0000000
4As many 0.3237034 0.1299289 0.1185765 0.3605375
racethNH Black racethNH White racethOther racethTwo or More
1small (0-1) 0.0000000 0.000000 0.0000000 0.0000000
3large (4+) 0.0000000 0.000000 0.0000000 0.0000000
4As many 0.5185385 0.180035 0.6014886 0.1350675watergen1
# Do you think pollution of America's rivers, lakes, and streams is...dangerous -> not dangerous
exp(coef(models$watergen1)) (Intercept) x sex_rcMale age_cat30-39 age_cat40-49
1small (0-1) 1.502552e-16 0.3707697 0.7994598 0.2844642 0.1650663
3large (4+) 5.359138e-06 1.4609728 0.5370933 1.7896843 0.6275374
4As many 3.318198e+00 0.9841340 0.5797452 0.4417570 0.7489739
age_cat50-64 age_cat65-89 educ2High School educ3Associate's
1small (0-1) 0.3321961 0.2460329 7.504962e+09 5.685068e+09
3large (4+) 1.2261210 0.9099364 4.684803e-01 4.473741e-01
4As many 0.7211541 0.3815071 1.189775e+00 8.608144e-01
educ4Bachelor's educ5Graduate racethAsian racethHispanic
1small (0-1) 4.437883e+09 1.026713e+10 9.461639e-11 4.887707e-09
3large (4+) 3.260120e-01 3.179914e-01 1.351914e+04 1.718691e+04
4As many 1.683675e+00 2.257960e+00 1.285610e-01 3.033877e-01
racethNH Black racethNH White racethOther racethTwo or More
1small (0-1) 2.798884e+06 1.439250e+06 5.616037e+06 4.398419e-09
3large (4+) 5.738948e+04 1.298104e+04 2.709248e-08 5.308702e+04
4As many 4.393745e-01 1.891584e-01 4.777419e-01 1.333243e-01z <- models[["watergen1"]][["coefficients"]]/models[["watergen1"]][["standard.errors"]]
p.30 <- (1 - pnorm(abs(z), 0, 1)) * 2
p.30 (Intercept) x sex_rcMale age_cat30-39 age_cat40-49
1small (0-1) 0.0000000 0.002174786 0.58371856 0.05422496 0.01719407
3large (4+) 0.0000000 0.140768143 0.15540245 0.48581664 0.62606438
4As many 0.3859139 0.893373510 0.00527789 0.02267089 0.40002822
age_cat50-64 age_cat65-89 educ2High School educ3Associate's
1small (0-1) 0.07037938 0.020354131 0.0000000 0.0000000
3large (4+) 0.80971055 0.914347014 0.3154388 0.3829282
4As many 0.32482263 0.005493057 0.7398931 0.8056394
educ4Bachelor's educ5Graduate racethAsian racethHispanic
1small (0-1) 0.0000000 0.0000000 0.0000000 0.000000
3large (4+) 0.1607249 0.1836815 0.0000000 0.000000
4As many 0.3199230 0.1251423 0.1220808 0.375538
racethNH Black racethNH White racethOther racethTwo or More
1small (0-1) 0.0000000 0.0000000 0.0000000 0.0000000
3large (4+) 0.0000000 0.0000000 0.0000000 0.0000000
4As many 0.5238215 0.1823444 0.6208727 0.1405382nukegen1
# Do you think nuclear power stations are...dangerous -> not dangerous
exp(coef(models$nukegen1)) (Intercept) x sex_rcMale age_cat30-39 age_cat40-49
1small (0-1) 2.352465e-16 0.8278738 0.7028850 0.2694083 0.1436148
3large (4+) 8.750355e-06 0.8766102 0.6450470 1.7402251 0.6299917
4As many 2.624390e+00 1.1411898 0.5215712 0.4592114 0.8077342
age_cat50-64 age_cat65-89 educ2High School educ3Associate's
1small (0-1) 0.2691222 0.2295900 5.810107e+10 4.190069e+10
3large (4+) 1.2743931 0.9304646 4.188275e-01 3.828613e-01
4As many 0.7514086 0.3937872 1.137179e+00 8.416069e-01
educ4Bachelor's educ5Graduate racethAsian racethHispanic
1small (0-1) 3.762328e+10 8.824416e+10 1.446512e-08 1.051891e-07
3large (4+) 3.002714e-01 2.821863e-01 2.699770e+04 2.653017e+04
4As many 1.566567e+00 2.105363e+00 1.196690e-01 3.083423e-01
racethNH Black racethNH White racethOther racethTwo or More
1small (0-1) 8.082630e+04 4.375540e+04 2.079147e+05 3.723312e-08
3large (4+) 1.031436e+05 2.493794e+04 1.323743e-07 7.355151e+04
4As many 4.263057e-01 1.693873e-01 4.412441e-01 1.331857e-01z <- models[["nukegen1"]][["coefficients"]]/models[["nukegen1"]][["standard.errors"]]
p.31 <- (1 - pnorm(abs(z), 0, 1)) * 2
p.31 (Intercept) x sex_rcMale age_cat30-39 age_cat40-49
1small (0-1) 0.0000000 0.3342893 0.399026773 0.04224356 0.01069941
3large (4+) 0.0000000 0.5402816 0.328524472 0.50629217 0.62804135
4As many 0.4873435 0.1762241 0.001628534 0.03080575 0.53989459
age_cat50-64 age_cat65-89 educ2High School educ3Associate's
1small (0-1) 0.02781569 0.013291266 0.0000000 0.0000000
3large (4+) 0.77418349 0.934682411 0.2422797 0.2930830
4As many 0.39211470 0.007443034 0.8062712 0.7769991
educ4Bachelor's educ5Graduate racethAsian racethHispanic
1small (0-1) 0.0000000 0.0000000 0.0000000 0.0000000
3large (4+) 0.1303892 0.1391737 0.0000000 0.0000000
4As many 0.3936221 0.1624419 0.1122462 0.3852339
racethNH Black racethNH White racethOther racethTwo or More
1small (0-1) 0.0000000 0.0000000 0.0000000 0.0000000
3large (4+) 0.0000000 0.0000000 0.0000000 0.0000000
4As many 0.5118525 0.1591284 0.5864899 0.1433474grngroup
# Are you a member of environmental group? yes/no
exp(coef(models$grngroup_rc)) (Intercept) x sex_rcMale age_cat30-39 age_cat40-49
1small (0-1) 5.147327e-17 1.729968e+00 0.6426741 0.2896841 0.1716786
3large (4+) 5.303030e-06 8.319362e-12 0.5837432 1.3446083 0.4721558
4As many 3.244239e+00 8.867717e-01 0.5746232 0.4406068 0.7435100
age_cat50-64 age_cat65-89 educ2High School educ3Associate's
1small (0-1) 0.2898001 0.2420903 3.264080e+11 2.558107e+11
3large (4+) 0.9144252 0.6768892 4.238372e-01 3.468069e-01
4As many 0.7147913 0.3801065 1.198251e+00 8.588292e-01
educ4Bachelor's educ5Graduate racethAsian racethHispanic
1small (0-1) 1.976838e+11 4.368371e+11 1.454551e-08 2.097080e-07
3large (4+) 2.850169e-01 2.975254e-01 4.602213e+04 4.648025e+04
4As many 1.699984e+00 2.297206e+00 1.279692e-01 3.037371e-01
racethNH Black racethNH White racethOther racethTwo or More
1small (0-1) 3.734632e+04 1.954568e+04 8.614188e+04 1.900417e-08
3large (4+) 2.137391e+05 4.668365e+04 5.710863e-08 1.757030e+05
4As many 4.431463e-01 1.908877e-01 4.905208e-01 1.360393e-01z <- models[["grngroup_rc"]][["coefficients"]]/models[["grngroup_rc"]][["standard.errors"]]
p.32 <- (1 - pnorm(abs(z), 0, 1)) * 2
p.32 (Intercept) x sex_rcMale age_cat30-39 age_cat40-49
1small (0-1) 0.00000 0.2574713 0.267904286 0.05432216 0.01734523
3large (4+) 0.00000 0.0000000 0.212656457 0.72513262 0.43995269
4As many 0.39087 0.6686257 0.004032959 0.02225326 0.38830783
age_cat50-64 age_cat65-89 educ2High School educ3Associate's
1small (0-1) 0.03690007 0.016759523 0.0000000 0.0000000
3large (4+) 0.91680054 0.657723313 0.2532157 0.2502787
4As many 0.31142096 0.005324083 0.7291949 0.8025653
educ4Bachelor's educ5Graduate racethAsian racethHispanic
1small (0-1) 0.0000000 0.0000000 0.000000 0.0000000
3large (4+) 0.1160670 0.1576601 0.000000 0.0000000
4As many 0.3097881 0.1169375 0.121167 0.3762743
racethNH Black racethNH White racethOther racethTwo or More
1small (0-1) 0.0000000 0.0000000 0.0000000 0.0000000
3large (4+) 0.0000000 0.0000000 0.0000000 0.0000000
4As many 0.5284036 0.1850459 0.6336047 0.1453241grnsign
# In the last five years, have you signed a petition about an environmental issue? yes/no
exp(coef(models$grnsign_rc)) (Intercept) x sex_rcMale age_cat30-39 age_cat40-49
1small (0-1) 1.954560e-17 2.9111298 0.7002595 0.3120460 0.1715854
3large (4+) 3.921721e-06 1.1532431 0.6024093 1.7624760 0.6559609
4As many 3.684619e+00 0.7509165 0.5630988 0.4301403 0.7371367
age_cat50-64 age_cat65-89 educ2High School educ3Associate's
1small (0-1) 0.3309741 0.2809966 1.022842e+11 7.221996e+10
3large (4+) 1.3001201 0.9331124 4.014921e-01 3.687648e-01
4As many 0.6861691 0.3668475 1.214265e+00 8.946780e-01
educ4Bachelor's educ5Graduate racethAsian racethHispanic
1small (0-1) 5.177375e+10 1.147957e+11 3.641987e-08 3.478899e-08
3large (4+) 2.728692e-01 2.551864e-01 4.680814e+04 4.883017e+04
4As many 1.777266e+00 2.424821e+00 1.129863e-01 2.856573e-01
racethNH Black racethNH White racethOther racethTwo or More
1small (0-1) 2.191827e+05 1.062046e+05 3.409424e+05 1.309395e-08
3large (4+) 1.796019e+05 4.015140e+04 4.249837e-08 1.292357e+05
4As many 4.164567e-01 1.818124e-01 4.975356e-01 1.320657e-01z <- models[["grnsign_rc"]][["coefficients"]]/models[["grnsign_rc"]][["standard.errors"]]
p.33 <- (1 - pnorm(abs(z), 0, 1)) * 2
p.33 (Intercept) x sex_rcMale age_cat30-39 age_cat40-49
1small (0-1) 0.0000000 0.01066559 0.378450682 0.07238981 0.01801118
3large (4+) 0.0000000 0.75395879 0.238188057 0.49614140 0.65802364
4As many 0.3451392 0.17448066 0.003005126 0.01885083 0.37517245
age_cat50-64 age_cat65-89 educ2High School educ3Associate's
1small (0-1) 0.06660941 0.03534601 0.000000 0.0000000
3large (4+) 0.75671445 0.93722030 0.220280 0.2758612
4As many 0.25829876 0.00396158 0.710913 0.8553157
educ4Bachelor's educ5Graduate racethAsian racethHispanic
1small (0-1) 0.0000000 0.00000000 0.0000000 0.0000000
3large (4+) 0.1012412 0.11002180 0.0000000 0.0000000
4As many 0.2734190 0.09686979 0.1028139 0.3547577
racethNH Black racethNH White racethOther racethTwo or More
1small (0-1) 0.0000000 0.000000 0.0000000 0.0000000
3large (4+) 0.0000000 0.000000 0.0000000 0.0000000
4As many 0.4995634 0.174714 0.6416712 0.1408876grnmoney
# In the last five years, have you given money to an environmental group? yes/no
exp(coef(models$grnmoney_rc)) (Intercept) x sex_rcMale age_cat30-39 age_cat40-49
1small (0-1) 2.909811e-18 1.1627443 0.6168749 0.2905121 0.1703828
3large (4+) 7.484683e-07 0.5886060 0.6130271 1.6919027 0.6392001
4As many 3.165584e+00 0.8490575 0.5784438 0.4361144 0.7467384
age_cat50-64 age_cat65-89 educ2High School educ3Associate's
1small (0-1) 0.2907194 0.2437537 5.491008e+12 4.088976e+12
3large (4+) 1.2247782 0.8967046 4.351087e-01 4.313614e-01
4As many 0.7124397 0.3817461 1.223329e+00 8.878872e-01
educ4Bachelor's educ5Graduate racethAsian racethHispanic
1small (0-1) 3.302148e+12 7.680105e+12 1.079013e-08 6.728787e-08
3large (4+) 3.010804e-01 3.003154e-01 2.525572e+05 2.739730e+05
4As many 1.752771e+00 2.388662e+00 1.301450e-01 3.131910e-01
racethNH Black racethNH White racethOther racethTwo or More
1small (0-1) 4.592340e+04 2.165834e+04 9.381663e+04 2.291955e-08
3large (4+) 1.022524e+06 2.353635e+05 8.395163e-09 8.348107e+05
4As many 4.469845e-01 1.963892e-01 5.101287e-01 1.422106e-01z <- models[["grnmoney_rc"]][["coefficients"]]/models[["grnmoney_rc"]][["standard.errors"]]
p.34 <- (1 - pnorm(abs(z), 0, 1)) * 2
p.34 (Intercept) x sex_rcMale age_cat30-39 age_cat40-49
1small (0-1) 0.0000000 0.7179167 0.225683571 0.05418656 0.01686428
3large (4+) 0.0000000 0.3175893 0.254688611 0.52835599 0.63838075
4As many 0.4013178 0.4396823 0.004497515 0.02083018 0.39542223
age_cat50-64 age_cat65-89 educ2High School educ3Associate's
1small (0-1) 0.03698959 0.016827539 0.0000000 0.0000000
3large (4+) 0.81035341 0.901055619 0.2667133 0.3599571
4As many 0.30704284 0.005554543 0.7004502 0.8455009
educ4Bachelor's educ5Graduate racethAsian racethHispanic
1small (0-1) 0.0000000 0.0000000 0.0000000 0.0000000
3large (4+) 0.1312536 0.1629413 0.0000000 0.0000000
4As many 0.2851972 0.1034968 0.1246006 0.3892215
racethNH Black racethNH White racethOther racethTwo or More
1small (0-1) 0.0000000 0.0000000 0.0000000 0.0000000
3large (4+) 0.0000000 0.0000000 0.0000000 0.0000000
4As many 0.5330136 0.1932293 0.6529176 0.1550591grndemo
# In the last five years, have you taken part in a protest or demonstration about an environmental issue? yes/no
exp(coef(models$grndemo_rc)) (Intercept) x sex_rcMale age_cat30-39 age_cat40-49
1small (0-1) 8.117378e-16 4.9290501 0.6505212 0.3666184 0.2039195
3large (4+) 4.413403e-06 1.5502203 0.5947103 1.8187387 0.6802140
4As many 3.499947e+00 0.7940557 0.5746242 0.4379882 0.7411119
age_cat50-64 age_cat65-89 educ2High School educ3Associate's
1small (0-1) 0.3608852 0.3222604 4.119626e+09 2.947022e+09
3large (4+) 1.3234639 0.9651418 3.962080e-01 3.713358e-01
4As many 0.7132938 0.3759934 1.213699e+00 8.779365e-01
educ4Bachelor's educ5Graduate racethAsian racethHispanic
1small (0-1) 2.465157e+09 5.429880e+09 1.708474e-06 5.562636e-06
3large (4+) 2.671367e-01 2.528134e-01 4.138139e+04 4.254750e+04
4As many 1.722068e+00 2.315436e+00 1.175289e-01 2.825877e-01
racethNH Black racethNH White racethOther racethTwo or More
1small (0-1) 1.772557e+05 7.040472e+04 1.873785e+05 8.427337e-07
3large (4+) 1.632017e+05 3.611078e+04 2.317039e-07 1.146578e+05
4As many 3.994573e-01 1.752351e-01 4.600631e-01 1.258811e-01z <- models[["grndemo_rc"]][["coefficients"]]/models[["grndemo_rc"]][["standard.errors"]]
p.35 <- (1 - pnorm(abs(z), 0, 1)) * 2
p.35 (Intercept) x sex_rcMale age_cat30-39 age_cat40-49
1small (0-1) 0.0000000 0.00151929 0.291036391 0.13018384 0.03588296
3large (4+) 0.0000000 0.58553144 0.225364441 0.47450655 0.68713095
4As many 0.3653772 0.57210657 0.004014917 0.02148574 0.38437029
age_cat50-64 age_cat65-89 educ2High School educ3Associate's
1small (0-1) 0.09826195 0.065567682 0.0000000 0.0000000
3large (4+) 0.74076980 0.967944816 0.2146523 0.2776989
4As many 0.30957001 0.005003783 0.7110603 0.8306890
educ4Bachelor's educ5Graduate racethAsian racethHispanic
1small (0-1) 0.00000000 0.0000000 0.0000000 0.0000000
3large (4+) 0.09768299 0.1072108 0.0000000 0.0000000
4As many 0.29884238 0.1137681 0.1097744 0.3517314
racethNH Black racethNH White racethOther racethTwo or More
1small (0-1) 0.0000000 0.0000000 0.0000000 0.000000
3large (4+) 0.0000000 0.0000000 0.0000000 0.000000
4As many 0.4812822 0.1669198 0.6048274 0.132132Variables to explore:
ISSP Environmentalism variables start on p 486 of codebook
| Variable | Question | Answer choices | use? |
|---|---|---|---|
| grnprice | how willing would you be to pay much higher prices in order to protect the environment | 1-5 - very willing to very unwilling | 44.1% RR 1,778 responses |
| grnsol | how willing would you be to accept cuts in your standard of living in order to protect the env? | 1-5 - very willing to very unwilling | 44.1% RR 1,778 responses |
| grntaxes | how willing would you be to pay much higher taxes in order to protect the env? | 1-5 - very willing to very unwilling | 44.0% RR 1,775 responses |
| grngroup | Are you a member of any group that preserves or protects the environment | yes/no | 45.1% 1,820 responses |
| grnsign | In the last 5 years, have you signed a petition about an environmental issue? | yes/no | 44.7% 1,802 responses |
| grnmoney | In the last 5 years, have you given money to an environmental group? | yes/no | 45.0% 1,814 responses |
| grndemo | In the last 5 years, have you taken part in a protest or demonstration about an environmental issue? | yes/no | 45.1% 1,817 responses |
| nobuygrn | how often do you avoid buying certain products for env reasons? | 1 Always , 2 Often, 3 Sometimes, 4 Never | 45.2% RR 1,821 responses |
| recycle | how often do you make a special effort to sort glass/cans/plastic/newspaper for recycling | 1 Always , 2 Often, 3 Sometimes, 4 Never | 42.2% RR 1,700 responses |
| scigrn | modern science will solve our environmental problems with little change to our way of life | 1 agree strongly, 5 disagree strongly | 43.9% 1,772 responses |
| harmsgrn | almost everything we do in life harms the environment | 1 agree strongly, 5 disagree strongly | 44.6% 1,798 |
| ihlpgrn | I do what is right for the env even when it costs more money or takes more time | 1 agree strongly, 5 disagree strongly | 44.2% RR 1,781 responses |
| grncon | how concerned are you about env issues? | 1 not at all concerned - 5 very concerned | 45.2% responses 1,823 responses |
| grneffme | env problems have a direct effect on my everyday life | 1 agree strongly - 5 disagree strongly | 43.8% RR 1,768 responses |
| grwtharm | economic growth harms the environment | 1 agree strongly - 5 disagree strongly | 43.9% 1,771 responses |
grwthelp REVERSE |
in order to protect the environment, America needs economic growth | 1 agree strongly - 5 disagree strongly | 43.5% 1,754 responses |
grnexagg REVERSE |
many of the claims abt env threats are exaggerated | 1 agree strongly - 5 disagree strongly | 44.1% RR 1,777 responses |
grnprog REVERSE |
people worry too much about human progress harming the environment | 1 agree strongly - 5 disagree strongly | 43.9% 1,772 responses |
naturdev REVERSE |
how willing would you be to accept a reduction in the size of America’s protected nature areas in order to open them up for econ development? | 1 very willing - 5 very unwilling | 43.9% 1,771 responses |
impgrn REVERSE |
There are more imp things to do in life than protect the environment | 1 agree strongly - 5 disagree strongly | 44.3% RR 1,788 responses |
grnecon REVERSE |
We worry too much about the future of the environment and not enough about prices and jobs today | 1 agree strongly - 5 disagree strongly | 44.5% RR 1,795 responses |
| helpharm | I find it hard to know whether the way I live is helpful or harmful to the environment | 1 agree strongly - 5 disagree strongly | 43.8% RR 1,761 responses |
| othssame | There is no point in doing what I can for the env unless others do the same | 1 agree strongly - 5 disagree strongly | 44.4% RR 1,791 responses |
| toodifme | It is too difficult for someone like me to do much about the env | 1 agree strongly - 5 disagree strongly | 44.2% 1,783 responses |
| tempgen1 | Do you think that a rise in the world’s temperature change is… | 1 extremely dangerous - 5 not at all dangerous | 43.0% RR 1,734 responses |
| watergen1 | Do you think that pollution of America’s rivers, lakes and streams is | 1 extremely dangerous - 5 not at all dangerous | 44.1% RR 1,780 responses |
| carsgen | Do you think air pollution caused by cars is | 1 extremely dangerous - 5 not at all dangerous | 44.1% 1,778 responses |
| indusgen1 | Do you think air pollution cause by industry is | 1 extremely dangerous - 5 not at all dangerous | 44.1% 1,779 responses |
| chemgen1 | Do you think pesticides an chemicals used in farming are | 1 extremely dangerous - 5 not at all dangerous | 44.0% 1,773 responses |
| nukegen1 | Do you think that nuclear power stations are | 1 extremely dangerous - 5 not at all dangerous | 43.2% 1,743 responses |
| CONFIDENCE QUESTIONS | |||
| confed | Confidence in federal government | 1 A great deal - 3 Hardly any | 65.9% RR 2,658 responses |
| conlegis | Confidence in congress | 1 A great deal - 3 Hardly any | 66.0% RR 2,661 responses |
| conjudge | Confidence in Supreme Court | 1 A great deal - 3 Hardly any | 66.0% RR 2,662 responses |
| consci | Confidence in scientific community | 1 A great deal - 3 Hardly any | 65.8% RR 2,654 responses |
| conmedic | Confidence in medicine | 1 A great deal - 3 Hardly any | 66.0% RR 2,662 responses |
| chldidel | ideal number of children for a family | 1 - 7, 8 As many as you want | 66.8% RR 2,693 responses |
| polviews | where would you place yourself on 7 point scale | 1 Extremely Liberal, 7 Extremely Conservative | 98.3% 3,964 responses |