Create dataset
library(tidyverse)
library(car)
# Download the data
anesraw <- readr::read_csv("Archival Studies/ANES/ANES 2020 data.csv") %>% #%>% # Convert "don't knows" into NAs
as.data.frame()%>%
rename(
state = V203001,
congdistnum = V203002) %>%
unite("congdist", state, congdistnum, sep = "-", remove = FALSE) %>% # Creates the "congdist" column
mutate_all(~ case_when(. < 0 ~ NA, TRUE ~ .))%>%
mutate_all(~ case_when(. == 99 ~ NA, TRUE ~ .)) %>%
rowwise() %>%
mutate(V201225x = as.numeric(V201225x))Select items
Select moral diversity items
# Items we selected as being "Pre-Moral Diversity"
premd_items <- itemstouse %>%
dplyr::filter(`Type of variable` == "MD" )%>%
dplyr::filter(PrePost == "Pre") %>%
dplyr::select(VariableName) %>%
dplyr::pull()
# Items we selected as being "Post-Moral Diversity"
postmd_items <- itemstouse %>%
dplyr::filter(`Type of variable` == "MD" )%>%
dplyr::filter(PrePost == "Post") %>%
dplyr::select(VariableName) %>%
dplyr::pull()Looseness creation
Alpha values of original items we selected
source("UsefulR/corstars.R")
itemstouse <- readxl::read_excel("Archival Studies/ANES/ANESAnalysis.xlsx") %>% filter(is.na(EXCLUDE))
looseness_items <- itemstouse %>% filter(`Type of variable` == "looseness") %>% select(VariableName) %>% dplyr::pull(.)
loosedataset <- anesraw %>%
select(looseness_items)
loosedatacor <- corstars(loosedataset)
psych::alpha(loosedataset)## Some items ( V201379 V201405x V201414x V201415 V201416 V201429 V201626 V202225 V202242x V202256 V202259x V202325 V202331x V202336x V202337 ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option##
## Reliability analysis
## Call: psych::alpha(x = loosedataset)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## -0.33 -0.13 0.28 -0.0038 -0.11 0.022 3.1 0.26 -0.048
##
## 95% confidence boundaries
## lower alpha upper
## Feldt -0.37 -0.33 -0.28
## Duhachek -0.37 -0.33 -0.28
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## V201366 -0.411 -0.170 0.25 -0.0050 -0.145 0.024 0.082 -0.042
## V201368 -0.335 -0.132 0.27 -0.0040 -0.117 0.022 0.084 -0.055
## V201369 -0.338 -0.143 0.27 -0.0043 -0.125 0.022 0.085 -0.042
## V201378 -0.385 -0.143 0.27 -0.0043 -0.125 0.023 0.080 -0.042
## V201379 -0.320 -0.131 0.29 -0.0040 -0.116 0.022 0.085 -0.055
## V201393 -0.341 -0.104 0.30 -0.0032 -0.094 0.022 0.081 -0.055
## V201405x -0.286 -0.121 0.29 -0.0037 -0.108 0.021 0.082 -0.042
## V201408x -0.348 -0.071 0.31 -0.0023 -0.066 0.023 0.079 -0.055
## V201411x -0.366 -0.084 0.30 -0.0027 -0.077 0.023 0.077 -0.042
## V201414x -0.301 -0.140 0.27 -0.0043 -0.123 0.021 0.082 -0.042
## V201415 -0.315 -0.153 0.25 -0.0046 -0.133 0.022 0.081 -0.042
## V201416 -0.282 -0.126 0.27 -0.0039 -0.112 0.021 0.080 -0.042
## V201417 -0.389 -0.155 0.26 -0.0046 -0.134 0.023 0.080 -0.042
## V201420x -0.436 -0.149 0.27 -0.0045 -0.130 0.024 0.080 -0.049
## V201423x -0.415 -0.157 0.26 -0.0047 -0.136 0.024 0.080 -0.042
## V201426x -0.390 -0.132 0.26 -0.0040 -0.116 0.023 0.074 -0.042
## V201429 -0.038 -0.037 0.32 -0.0012 -0.035 0.016 0.075 -0.042
## V201430 -0.362 -0.147 0.28 -0.0044 -0.128 0.023 0.084 -0.058
## V201626 -0.257 -0.079 0.31 -0.0025 -0.073 0.020 0.079 -0.042
## V202225 -0.349 -0.154 0.27 -0.0046 -0.133 0.022 0.085 -0.078
## V202231x -0.413 -0.171 0.26 -0.0051 -0.146 0.024 0.082 -0.055
## V202242x -0.197 -0.079 0.31 -0.0025 -0.073 0.019 0.081 -0.042
## V202248x -0.366 -0.121 0.28 -0.0037 -0.108 0.023 0.080 -0.042
## V202255x -0.248 -0.058 0.31 -0.0019 -0.055 0.021 0.079 -0.064
## V202256 -0.222 -0.099 0.29 -0.0031 -0.090 0.020 0.079 -0.055
## V202259x -0.211 -0.123 0.28 -0.0038 -0.110 0.019 0.078 -0.042
## V202325 -0.328 -0.142 0.27 -0.0043 -0.124 0.022 0.081 -0.042
## V202331x -0.300 -0.135 0.28 -0.0041 -0.119 0.021 0.084 -0.042
## V202336x -0.233 -0.118 0.28 -0.0037 -0.106 0.020 0.077 -0.042
## V202337 -0.282 -0.102 0.30 -0.0032 -0.093 0.021 0.080 -0.042
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## V201366 8223 0.34413 0.272 0.2586 0.1379 3.7 1.32
## V201368 8257 0.14908 0.196 0.1168 0.0222 4.6 0.74
## V201369 8233 0.17538 0.217 0.1418 0.0182 4.1 0.95
## V201378 8204 0.30775 0.219 0.1682 0.1040 4.1 1.20
## V201379 8171 0.00906 0.192 0.0294 -0.0394 1.4 0.48
## V201393 8255 0.22175 0.133 -0.0016 0.0178 3.2 1.12
## V201405x 8242 0.09294 0.172 0.0442 -0.0905 2.4 1.61
## V201408x 8166 0.31725 0.057 -0.0938 -0.0075 3.5 2.04
## V201411x 7967 0.34915 0.088 -0.0226 0.0154 3.5 2.01
## V201414x 8161 0.00076 0.212 0.1458 -0.0870 1.5 0.89
## V201415 8152 -0.05389 0.239 0.2548 -0.0785 1.2 0.40
## V201416 8161 -0.10955 0.181 0.1683 -0.1729 1.4 0.71
## V201417 8197 0.32388 0.242 0.2345 0.1658 2.8 0.89
## V201420x 8225 0.41360 0.231 0.1931 0.0858 4.5 2.16
## V201423x 8131 0.36149 0.247 0.2471 0.1390 5.0 1.37
## V201426x 8240 0.41545 0.194 0.2411 0.0192 4.3 2.47
## V201429 7713 -0.13933 -0.026 -0.1941 -0.3878 3.5 2.31
## V201430 8245 0.22784 0.227 0.1114 0.1038 2.0 0.80
## V201626 8104 -0.07289 0.077 -0.0834 -0.1828 2.6 1.11
## V202225 7407 0.17425 0.240 0.1460 0.0536 1.6 0.87
## V202231x 7235 0.33213 0.275 0.2388 0.1968 2.3 1.02
## V202242x 7390 0.04847 0.075 -0.1027 -0.2129 2.7 1.89
## V202248x 7384 0.25469 0.170 0.0707 0.0402 6.1 1.54
## V202255x 7348 0.14311 0.028 -0.1183 -0.1362 3.8 1.97
## V202256 7375 0.03729 0.123 0.0528 -0.1923 4.3 1.67
## V202259x 7384 0.13178 0.176 0.1347 -0.1733 3.6 2.21
## V202325 7384 0.10591 0.216 0.1572 -0.0046 1.5 0.79
## V202331x 7384 0.18436 0.202 0.0767 -0.0676 2.2 1.77
## V202336x 7364 0.09784 0.165 0.1231 -0.1616 2.8 1.88
## V202337 7375 -0.00491 0.130 0.0017 -0.1364 1.9 0.96
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## V201366 0.10 0.07 0.22 0.21 0.39 0.00 0.00 0.01
## V201368 0.01 0.01 0.07 0.23 0.68 0.00 0.00 0.00
## V201369 0.02 0.03 0.20 0.36 0.39 0.00 0.00 0.01
## V201378 0.04 0.08 0.21 0.12 0.55 0.00 0.00 0.01
## V201379 0.62 0.38 0.00 0.00 0.00 0.00 0.00 0.01
## V201393 0.10 0.12 0.43 0.21 0.14 0.00 0.00 0.00
## V201405x 0.44 0.23 0.04 0.21 0.01 0.04 0.03 0.00
## V201408x 0.26 0.16 0.08 0.06 0.16 0.28 0.00 0.01
## V201411x 0.30 0.11 0.05 0.10 0.21 0.22 0.00 0.04
## V201414x 0.72 0.16 0.05 0.07 0.00 0.00 0.00 0.01
## V201415 0.80 0.20 0.00 0.00 0.00 0.00 0.00 0.02
## V201416 0.68 0.19 0.13 0.00 0.00 0.00 0.00 0.01
## V201417 0.13 0.14 0.55 0.17 0.00 0.00 0.00 0.01
## V201420x 0.15 0.09 0.03 0.28 0.03 0.14 0.28 0.01
## V201423x 0.05 0.05 0.02 0.11 0.29 0.48 0.00 0.02
## V201426x 0.26 0.08 0.02 0.18 0.02 0.09 0.35 0.00
## V201429 0.34 0.10 0.08 0.13 0.09 0.08 0.18 0.07
## V201430 0.33 0.36 0.31 0.00 0.00 0.00 0.00 0.00
## V201626 0.22 0.25 0.26 0.27 0.00 0.00 0.00 0.02
## V202225 0.70 0.04 0.26 0.00 0.00 0.00 0.00 0.11
## V202231x 0.29 0.27 0.31 0.13 0.00 0.00 0.00 0.13
## V202242x 0.34 0.29 0.06 0.17 0.01 0.05 0.08 0.11
## V202248x 0.03 0.02 0.01 0.16 0.02 0.12 0.64 0.11
## V202255x 0.22 0.16 0.03 0.05 0.29 0.25 0.00 0.11
## V202256 0.05 0.12 0.15 0.29 0.15 0.12 0.13 0.11
## V202259x 0.26 0.16 0.03 0.25 0.02 0.09 0.18 0.11
## V202325 0.65 0.16 0.19 0.00 0.00 0.00 0.00 0.11
## V202331x 0.56 0.16 0.02 0.16 0.01 0.03 0.05 0.11
## V202336x 0.37 0.20 0.04 0.25 0.01 0.06 0.07 0.11
## V202337 0.52 0.07 0.42 0.00 0.00 0.00 0.00 0.11Items that it said I should recode
V201379 V201405x V201414x V201415 V201416 V201429 V201626 V202225 V202242x V202256 V202259x V202325 V202331x V202336x V202337
anesraw$r_V201379 <- car::recode(anesraw$V201379, "1 =2 ; 2 = 1") # Would you prefer a government official who compromises to get things done, or who sticks to their principles no matter what? (1 = Compromises; 2 = Sticks to principles)
anesraw$r_V201405x <- car::recode(anesraw$V201405x, "1 = 7; 2 = 6; 3 = 5; 4 = 4; 5 = 3; 6 = 2; 7 = 1") # Do you favor, oppose, or neither favor nor oppose requiring employers tooffer paid leave to parents of new children? 1 = Favor; 7 = Oppose
anesraw$r_V201414x <- car::recode(anesraw$V201414x, "1 = 4; 2 = 3; 3 = 2; 4 = 1") # Do you favor or oppose laws to protect gays and lesbians against job discrimination? (1 = Favor Strongly; 4 = Oppose Strongly)
anesraw$r_V201415 <- car::recode(anesraw$V201415, "1 = 2; 2 = 1") # Do you think gay or lesbian couples should be legally permitted to adopt children? (1 = Yes, 2 = No)
anesraw$r_V201416 <- car::recode(anesraw$V201416, "1 = 3; 2 = 2; 3 = 1") # Which comes closest to your view? You can just tell me the number of your choice. ( 1 = Gay + Lesbian should be allowed to marry; 3 = there should be no legal recognition of gay or lesbian couples' relationships)
anesraw$r_V201429 <- car::recode(anesraw$V201429, "1 = 7; 2 = 6; 3 = 5; 4 = 4; 5 = 3; 6 = 2; 7 = 1") # What is the best way to deal with the problem of urban unrest and rioting? ( 1= Solve problems of racism and police violence; 7 = Use all available force to maintain law and order)
anesraw$r_V201626 <- car::recode(anesraw$V201626, "1 = 4; 2 = 3; 3 = 2; 4 = 1") # Some people think that the way people talk needs to change with the times to be more sensitive to people from different backgrounds. Others think that this has already gone too far and many people are just too easily offended ( 1 = The way people talk needs to change a lot; 4 = People are much too easily offended)
anesraw$r_V202225 <- car::recode(anesraw$V202225, "1 = 1; 2 = 3; 3 = 2") # Do you favor, oppose, or neither favor nor oppose placing limits on political campaign spending? (1 = Favor; 3 = Neither favor)
anesraw$r_V202242x <- car::recode(anesraw$V202242x, "1 = 7; 2 = 6; 3 = 5; 4 = 4; 5 = 3; 6 = 2; 7 = 1") # Do you favor, oppose, or neither favor nor oppose providing a path to citizenship for unauthorized immigrants who obey the law, pay a fine, and pass security checks? (1 = Favor; 7 = Oppose)
anesraw$r_V202256 <- car::recode(anesraw$V202256, "1 = 7; 2 = 6; 3 = 5; 4 = 4; 5 = 3; 6 = 2; 7 = 1") # Would it be good for society to have more government regulation, about the same amount of regulation as there is now, or less government regulation? ( 1 = More, 7 = Less)
anesraw$r_V202259x <- car::recode(anesraw$V202259x, "1 = 7; 2 = 6; 3 = 5; 4 = 4; 5 = 3; 6 = 2; 7 = 1") # Next, do you favor, oppose, or neither favor nor oppose the governmenttrying to reduce the difference in incomes between the richest and poorest households? (1 = Favor, 7 = Oppose)
anesraw$r_V202325 <- car::recode(anesraw$V202325, "1 = 2; 2 = 3; 3 = 1") # Do you favor, oppose, or neither favor nor oppose increasing incometaxes on people making over one million dollars per year? (1 = Favor, 3 = Neither)
anesraw$r_V202331x <- car::recode(anesraw$V202331x, "1 = 7; 2 = 6; 3 = 5; 4 = 4; 5 = 3; 6 = 2; 7 = 1") # Do you favor, oppose, or neither favor nor oppose requiring children to be vaccinated in order to attend public schools? (1 = Favor; 7 = Oppose)
anesraw$r_V202336x <- car::recode(anesraw$V202336x, "1 = 7; 2 = 6; 3 = 5; 4 = 4; 5 = 3; 6 = 2; 7 = 1") # Do you favor, oppose, or neither favor nor oppose increased government regulation on businesses that produce a great deal of greenhouse emissions linked to climate change? (1 = Favor, 7 = Oppose)
anesraw$r_V202337 <- car::recode(anesraw$V202337, "1 = 2; 2 = 3; 3 = 1") # Do you think the federal government should make it more difficult for people to buy a gun than it is now, make it easier for people to buy a gun, or keep these rules about the same as they are now? (1 = Difficult, 3 = Keep Same)
## Updating the dataset with the recoded items
notrecoded_looseness_recodeditems <- c( "V201379", "V201405x", "V201414x", "V201415", "V201416", "V201429", "V201626", "V202225", "V202242x", "V202256", "V202259x", "V202325", "V202331x", "V202336x", "V202337")
recoded_looseness_recodeditems <- c("r_V201379", "r_V201405x", "r_V201414x", "r_V201415", "r_V201416", "r_V201429", "r_V201626", "r_V202225", "r_V202242x", "r_V202256", "r_V202259x", "r_V202325", "r_V202331x", "r_V202336x", "r_V202337")
looseness_items <- itemstouse %>% filter(`Type of variable` == "looseness") %>% select(VariableName) %>% dplyr::pull(.)
# Remove variables I recoded
updatedloosenessitems <- looseness_items[! looseness_items %in% notrecoded_looseness_recodeditems]
# Add recoded variables
updatedloosenessitems_withrecode <- append(updatedloosenessitems, recoded_looseness_recodeditems)
# Take reliabilities
loosedataset_recode <- anesraw %>% dplyr::select(updatedloosenessitems_withrecode)Recoded reliability
## Some items ( r_V202225 r_V202325 ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option##
## Reliability analysis
## Call: psych::alpha(x = loosedataset_recode)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.9 0.89 0.91 0.21 8.1 0.0013 3.6 0.75 0.22
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.9 0.9 0.9
## Duhachek 0.9 0.9 0.9
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## V201366 0.90 0.89 0.91 0.21 7.9 0.0013 0.027 0.22
## V201368 0.90 0.89 0.91 0.22 8.2 0.0013 0.026 0.23
## V201369 0.90 0.89 0.91 0.22 8.2 0.0013 0.026 0.23
## V201378 0.90 0.89 0.90 0.21 7.7 0.0014 0.027 0.21
## V201393 0.90 0.89 0.90 0.21 7.7 0.0014 0.027 0.21
## V201408x 0.89 0.88 0.90 0.21 7.6 0.0014 0.026 0.21
## V201411x 0.89 0.88 0.90 0.21 7.5 0.0014 0.025 0.21
## V201417 0.90 0.89 0.90 0.21 7.7 0.0014 0.026 0.21
## V201420x 0.90 0.89 0.90 0.21 7.7 0.0014 0.027 0.21
## V201423x 0.90 0.88 0.90 0.21 7.7 0.0014 0.026 0.21
## V201426x 0.89 0.88 0.90 0.20 7.3 0.0015 0.024 0.21
## V201430 0.90 0.89 0.91 0.22 8.1 0.0013 0.027 0.23
## V202231x 0.90 0.89 0.91 0.21 7.9 0.0013 0.027 0.21
## V202248x 0.90 0.89 0.90 0.21 7.7 0.0014 0.027 0.21
## V202255x 0.89 0.88 0.90 0.21 7.7 0.0014 0.026 0.21
## r_V201379 0.90 0.89 0.91 0.22 8.2 0.0013 0.027 0.23
## r_V201405x 0.90 0.89 0.91 0.21 7.9 0.0013 0.027 0.21
## r_V201414x 0.90 0.89 0.91 0.21 7.8 0.0013 0.027 0.22
## r_V201415 0.90 0.89 0.90 0.21 7.8 0.0013 0.027 0.22
## r_V201416 0.90 0.89 0.90 0.21 7.7 0.0013 0.026 0.21
## r_V201429 0.89 0.88 0.90 0.20 7.4 0.0015 0.024 0.21
## r_V201626 0.90 0.88 0.90 0.21 7.7 0.0014 0.026 0.21
## r_V202225 0.90 0.90 0.91 0.23 8.9 0.0013 0.020 0.23
## r_V202242x 0.90 0.89 0.90 0.21 7.7 0.0014 0.027 0.21
## r_V202256 0.89 0.88 0.90 0.21 7.6 0.0014 0.026 0.21
## r_V202259x 0.89 0.88 0.90 0.21 7.6 0.0014 0.026 0.21
## r_V202325 0.90 0.90 0.91 0.23 8.6 0.0013 0.024 0.23
## r_V202331x 0.90 0.89 0.91 0.22 8.1 0.0013 0.027 0.23
## r_V202336x 0.89 0.88 0.90 0.21 7.5 0.0014 0.025 0.21
## r_V202337 0.90 0.89 0.91 0.22 8.1 0.0013 0.027 0.23
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## V201366 8223 0.454 0.48 0.452 0.399 3.7 1.32
## V201368 8257 0.243 0.28 0.246 0.204 4.6 0.74
## V201369 8233 0.255 0.29 0.253 0.211 4.1 0.95
## V201378 8204 0.562 0.57 0.551 0.519 4.1 1.20
## V201393 8255 0.550 0.55 0.524 0.515 3.2 1.12
## V201408x 8166 0.643 0.63 0.619 0.587 3.5 2.04
## V201411x 7967 0.695 0.69 0.685 0.647 3.5 2.01
## V201417 8197 0.564 0.57 0.558 0.536 2.8 0.89
## V201420x 8225 0.605 0.57 0.553 0.537 4.5 2.16
## V201423x 8131 0.598 0.60 0.584 0.551 5.0 1.37
## V201426x 8240 0.814 0.79 0.805 0.774 4.3 2.47
## V201430 8245 0.331 0.36 0.311 0.299 2.0 0.80
## V202231x 7235 0.451 0.47 0.438 0.415 2.3 1.02
## V202248x 7384 0.570 0.56 0.540 0.521 6.1 1.54
## V202255x 7348 0.638 0.60 0.592 0.583 3.8 1.97
## r_V201379 8171 0.211 0.26 0.209 0.185 1.6 0.48
## r_V201405x 8242 0.477 0.46 0.431 0.418 5.6 1.61
## r_V201414x 8161 0.456 0.50 0.470 0.419 3.5 0.89
## r_V201415 8152 0.450 0.51 0.493 0.433 1.8 0.40
## r_V201416 8161 0.508 0.55 0.547 0.480 2.6 0.71
## r_V201429 7713 0.770 0.75 0.758 0.733 4.5 2.31
## r_V201626 8104 0.606 0.60 0.585 0.577 2.4 1.11
## r_V202225 7407 -0.150 -0.12 -0.188 -0.173 1.3 0.55
## r_V202242x 7390 0.564 0.54 0.523 0.503 5.3 1.89
## r_V202256 7375 0.643 0.61 0.606 0.598 3.7 1.67
## r_V202259x 7384 0.675 0.63 0.623 0.618 4.4 2.21
## r_V202325 7384 -0.011 0.04 -0.027 -0.038 2.0 0.59
## r_V202331x 7384 0.341 0.34 0.293 0.266 5.8 1.77
## r_V202336x 7364 0.697 0.67 0.667 0.652 5.2 1.88
## r_V202337 7375 0.298 0.33 0.280 0.274 1.7 0.60
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## V201366 0.10 0.07 0.22 0.21 0.39 0.00 0.00 0.01
## V201368 0.01 0.01 0.07 0.23 0.68 0.00 0.00 0.00
## V201369 0.02 0.03 0.20 0.36 0.39 0.00 0.00 0.01
## V201378 0.04 0.08 0.21 0.12 0.55 0.00 0.00 0.01
## V201393 0.10 0.12 0.43 0.21 0.14 0.00 0.00 0.00
## V201408x 0.26 0.16 0.08 0.06 0.16 0.28 0.00 0.01
## V201411x 0.30 0.11 0.05 0.10 0.21 0.22 0.00 0.04
## V201417 0.13 0.14 0.55 0.17 0.00 0.00 0.00 0.01
## V201420x 0.15 0.09 0.03 0.28 0.03 0.14 0.28 0.01
## V201423x 0.05 0.05 0.02 0.11 0.29 0.48 0.00 0.02
## V201426x 0.26 0.08 0.02 0.18 0.02 0.09 0.35 0.00
## V201430 0.33 0.36 0.31 0.00 0.00 0.00 0.00 0.00
## V202231x 0.29 0.27 0.31 0.13 0.00 0.00 0.00 0.13
## V202248x 0.03 0.02 0.01 0.16 0.02 0.12 0.64 0.11
## V202255x 0.22 0.16 0.03 0.05 0.29 0.25 0.00 0.11
## r_V201379 0.38 0.62 0.00 0.00 0.00 0.00 0.00 0.01
## r_V201405x 0.03 0.04 0.01 0.21 0.04 0.23 0.44 0.00
## r_V201414x 0.07 0.05 0.16 0.72 0.00 0.00 0.00 0.01
## r_V201415 0.20 0.80 0.00 0.00 0.00 0.00 0.00 0.02
## r_V201416 0.13 0.19 0.68 0.00 0.00 0.00 0.00 0.01
## r_V201429 0.18 0.08 0.09 0.13 0.08 0.10 0.34 0.07
## r_V201626 0.27 0.26 0.25 0.22 0.00 0.00 0.00 0.02
## r_V202225 0.70 0.26 0.04 0.00 0.00 0.00 0.00 0.11
## r_V202242x 0.08 0.05 0.01 0.17 0.06 0.29 0.34 0.11
## r_V202256 0.13 0.12 0.15 0.29 0.15 0.12 0.05 0.11
## r_V202259x 0.18 0.09 0.02 0.25 0.03 0.16 0.26 0.11
## r_V202325 0.19 0.65 0.16 0.00 0.00 0.00 0.00 0.11
## r_V202331x 0.05 0.03 0.01 0.16 0.02 0.16 0.56 0.11
## r_V202336x 0.07 0.06 0.01 0.25 0.04 0.20 0.37 0.11
## r_V202337 0.42 0.52 0.07 0.00 0.00 0.00 0.00 0.11Remake dataset with updated items
# Remove r_V20225 and r_202325; add V20225 + V202325
updatedloosenessitems_withrecode_v2 <- append(updatedloosenessitems_withrecode[! updatedloosenessitems_withrecode %in% c("r_V202225", "r_V202325")], c("V202225", "V202325"))
loosedataset_recode2 <- anesraw %>% dplyr::select(updatedloosenessitems_withrecode_v2)
# Turns out removing them didn't matter. Still negatively correlated
psych::alpha(loosedataset_recode2)## Some items ( V202225 V202325 ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option##
## Reliability analysis
## Call: psych::alpha(x = loosedataset_recode2)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.89 0.88 0.9 0.2 7.4 0.0014 3.6 0.74 0.22
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.89 0.89 0.9
## Duhachek 0.89 0.89 0.9
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## V201366 0.89 0.88 0.90 0.20 7.2 0.0014 0.038 0.22
## V201368 0.89 0.88 0.90 0.21 7.5 0.0014 0.037 0.23
## V201369 0.89 0.88 0.90 0.20 7.5 0.0014 0.038 0.23
## V201378 0.89 0.88 0.90 0.20 7.0 0.0014 0.038 0.21
## V201393 0.89 0.88 0.90 0.20 7.1 0.0014 0.038 0.21
## V201408x 0.89 0.87 0.90 0.19 6.9 0.0015 0.037 0.21
## V201411x 0.89 0.87 0.90 0.19 6.8 0.0015 0.036 0.21
## V201417 0.89 0.88 0.90 0.19 7.0 0.0014 0.037 0.21
## V201420x 0.89 0.88 0.90 0.19 7.0 0.0014 0.037 0.21
## V201423x 0.89 0.87 0.90 0.19 7.0 0.0014 0.037 0.21
## V201426x 0.88 0.87 0.89 0.19 6.7 0.0016 0.034 0.21
## V201430 0.89 0.88 0.90 0.20 7.4 0.0014 0.038 0.23
## V202231x 0.89 0.88 0.90 0.20 7.2 0.0014 0.038 0.21
## V202248x 0.89 0.88 0.90 0.20 7.0 0.0014 0.037 0.21
## V202255x 0.89 0.87 0.90 0.19 7.0 0.0015 0.036 0.21
## r_V201379 0.89 0.88 0.91 0.21 7.5 0.0014 0.038 0.23
## r_V201405x 0.89 0.88 0.90 0.20 7.2 0.0014 0.038 0.21
## r_V201414x 0.89 0.88 0.90 0.20 7.1 0.0014 0.038 0.22
## r_V201415 0.89 0.88 0.90 0.20 7.1 0.0014 0.038 0.22
## r_V201416 0.89 0.88 0.90 0.20 7.0 0.0014 0.037 0.21
## r_V201429 0.88 0.87 0.89 0.19 6.7 0.0015 0.035 0.21
## r_V201626 0.89 0.87 0.90 0.19 7.0 0.0014 0.037 0.21
## r_V202242x 0.89 0.88 0.90 0.20 7.1 0.0014 0.037 0.21
## r_V202256 0.89 0.87 0.90 0.19 7.0 0.0015 0.036 0.21
## r_V202259x 0.89 0.87 0.90 0.19 6.9 0.0015 0.036 0.21
## r_V202331x 0.90 0.88 0.90 0.20 7.4 0.0013 0.038 0.23
## r_V202336x 0.89 0.87 0.90 0.19 6.9 0.0015 0.036 0.21
## r_V202337 0.89 0.88 0.90 0.20 7.4 0.0014 0.038 0.23
## V202225 0.90 0.89 0.91 0.22 8.1 0.0013 0.033 0.23
## V202325 0.90 0.90 0.91 0.23 8.6 0.0013 0.024 0.23
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## V201366 8223 0.45 0.471 0.44 0.39 3.7 1.32
## V201368 8257 0.24 0.274 0.24 0.20 4.6 0.74
## V201369 8233 0.25 0.288 0.25 0.21 4.1 0.95
## V201378 8204 0.56 0.568 0.55 0.51 4.1 1.20
## V201393 8255 0.55 0.544 0.52 0.51 3.2 1.12
## V201408x 8166 0.64 0.630 0.62 0.59 3.5 2.04
## V201411x 7967 0.70 0.690 0.69 0.65 3.5 2.01
## V201417 8197 0.57 0.580 0.57 0.54 2.8 0.89
## V201420x 8225 0.61 0.574 0.55 0.54 4.5 2.16
## V201423x 8131 0.60 0.605 0.59 0.55 5.0 1.37
## V201426x 8240 0.81 0.789 0.80 0.77 4.3 2.47
## V201430 8245 0.33 0.361 0.32 0.30 2.0 0.80
## V202231x 7235 0.45 0.472 0.44 0.42 2.3 1.02
## V202248x 7384 0.57 0.560 0.54 0.52 6.1 1.54
## V202255x 7348 0.64 0.596 0.59 0.58 3.8 1.97
## r_V201379 8171 0.21 0.262 0.21 0.18 1.6 0.48
## r_V201405x 8242 0.48 0.463 0.43 0.42 5.6 1.61
## r_V201414x 8161 0.46 0.501 0.48 0.42 3.5 0.89
## r_V201415 8152 0.45 0.512 0.50 0.43 1.8 0.40
## r_V201416 8161 0.51 0.557 0.55 0.48 2.6 0.71
## r_V201429 7713 0.77 0.749 0.76 0.73 4.5 2.31
## r_V201626 8104 0.61 0.601 0.59 0.58 2.4 1.11
## r_V202242x 7390 0.56 0.545 0.52 0.50 5.3 1.89
## r_V202256 7375 0.64 0.613 0.61 0.60 3.7 1.67
## r_V202259x 7384 0.67 0.626 0.62 0.61 4.4 2.21
## r_V202331x 7384 0.34 0.331 0.29 0.26 5.8 1.77
## r_V202336x 7364 0.69 0.668 0.66 0.65 5.2 1.88
## r_V202337 7375 0.29 0.316 0.27 0.27 1.7 0.60
## V202225 7407 -0.11 -0.095 -0.16 -0.15 1.6 0.87
## V202325 7384 -0.46 -0.418 -0.49 -0.49 1.5 0.79
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## V201366 0.10 0.07 0.22 0.21 0.39 0.00 0.00 0.01
## V201368 0.01 0.01 0.07 0.23 0.68 0.00 0.00 0.00
## V201369 0.02 0.03 0.20 0.36 0.39 0.00 0.00 0.01
## V201378 0.04 0.08 0.21 0.12 0.55 0.00 0.00 0.01
## V201393 0.10 0.12 0.43 0.21 0.14 0.00 0.00 0.00
## V201408x 0.26 0.16 0.08 0.06 0.16 0.28 0.00 0.01
## V201411x 0.30 0.11 0.05 0.10 0.21 0.22 0.00 0.04
## V201417 0.13 0.14 0.55 0.17 0.00 0.00 0.00 0.01
## V201420x 0.15 0.09 0.03 0.28 0.03 0.14 0.28 0.01
## V201423x 0.05 0.05 0.02 0.11 0.29 0.48 0.00 0.02
## V201426x 0.26 0.08 0.02 0.18 0.02 0.09 0.35 0.00
## V201430 0.33 0.36 0.31 0.00 0.00 0.00 0.00 0.00
## V202231x 0.29 0.27 0.31 0.13 0.00 0.00 0.00 0.13
## V202248x 0.03 0.02 0.01 0.16 0.02 0.12 0.64 0.11
## V202255x 0.22 0.16 0.03 0.05 0.29 0.25 0.00 0.11
## r_V201379 0.38 0.62 0.00 0.00 0.00 0.00 0.00 0.01
## r_V201405x 0.03 0.04 0.01 0.21 0.04 0.23 0.44 0.00
## r_V201414x 0.07 0.05 0.16 0.72 0.00 0.00 0.00 0.01
## r_V201415 0.20 0.80 0.00 0.00 0.00 0.00 0.00 0.02
## r_V201416 0.13 0.19 0.68 0.00 0.00 0.00 0.00 0.01
## r_V201429 0.18 0.08 0.09 0.13 0.08 0.10 0.34 0.07
## r_V201626 0.27 0.26 0.25 0.22 0.00 0.00 0.00 0.02
## r_V202242x 0.08 0.05 0.01 0.17 0.06 0.29 0.34 0.11
## r_V202256 0.13 0.12 0.15 0.29 0.15 0.12 0.05 0.11
## r_V202259x 0.18 0.09 0.02 0.25 0.03 0.16 0.26 0.11
## r_V202331x 0.05 0.03 0.01 0.16 0.02 0.16 0.56 0.11
## r_V202336x 0.07 0.06 0.01 0.25 0.04 0.20 0.37 0.11
## r_V202337 0.42 0.52 0.07 0.00 0.00 0.00 0.00 0.11
## V202225 0.70 0.04 0.26 0.00 0.00 0.00 0.00 0.11
## V202325 0.65 0.16 0.19 0.00 0.00 0.00 0.00 0.11Final items we use in the analyses
preloosenessitems <- c("V201369","V201378","r_V201379","V201393","V201403","V201405x","V201406","V201408x","V201411x","r_V201414x","r_V201415","r_V201416","V201417","V201420x","V201423x","V201426x","r_V201429","V201430","r_V201626")
postloosenessitems <- c("r_V202225","V202231x","V202240","r_V202242x","V202248x","V202255x","r_V202256","r_V202259x","r_V202325","r_V202331x","r_V202336x","r_V202337","V202613","V202621")
allloosenessitems <- append(preloosenessitems, postloosenessitems)Final items we use in the analyses
preloosenessitems <- c("V201369","V201378","r_V201379","V201393","V201403","V201405x","V201406","V201408x","V201411x","r_V201414x","r_V201415","r_V201416","V201417","V201420x","V201423x","V201426x","r_V201429","V201430","r_V201626")
postloosenessitems <- c("r_V202225","V202231x","V202240","r_V202242x","V202248x","V202255x","r_V202256","r_V202259x","r_V202325","r_V202331x","r_V202336x","r_V202337","V202613","V202621")
allloosenessitems <- append(preloosenessitems, postloosenessitems)Congressional District
Build the dataset
Create dataframe of pre- and post- moral diversity SDs
# Pre MD
sds_premd_congressionaldistrict1 <- congressionaldistrict1 %>%
select(congdist, premd_items) %>%
as.data.frame()%>%
mutate_if(is.numeric, .funs = ~scale(.x, center = T, scale = T)[, 1]) %>%
group_by(congdist) %>%
summarise_at(vars(premd_items), funs(sd(., na.rm = TRUE)))
# Post MD
sds_postmd_congressionaldistrict1 <- congressionaldistrict1 %>%
select(congdist, postmd_items) %>%
as.data.frame()%>%
mutate_if(is.numeric, .funs = ~scale(.x, center = T, scale = T)[, 1]) %>%
group_by(congdist) %>%
summarise_at(vars(postmd_items), funs(sd(., na.rm = TRUE)))PCA
# To create PCA
congdist_premddata <- sds_premd_congressionaldistrict1 %>% tibble::column_to_rownames("congdist")
congdist_postmddata <- sds_postmd_congressionaldistrict1%>% tibble::column_to_rownames("congdist")
congdist_allmddata <- sds_all <- sds_premd_congressionaldistrict1 %>% left_join(sds_postmd_congressionaldistrict1) %>% tibble::column_to_rownames("congdist")
# Pre MD - Impute missing values
for(i in 1:ncol(congdist_premddata)) {
congdist_premddata[is.na(congdist_premddata[, i]), i] <- mean(congdist_premddata[, i], na.rm = TRUE)
}
# Post MD - Impute missing values
for(i in 1:ncol(congdist_postmddata)) {
congdist_postmddata[is.na(congdist_postmddata[, i]), i] <- mean(congdist_postmddata[, i], na.rm = TRUE)
}
# All MD - Impute missing values
for(i in 1:ncol(sds_all)) {
congdist_allmddata[is.na(congdist_allmddata[, i]), i] <- mean(congdist_allmddata[, i], na.rm = TRUE)
}
# Create PCAs
congdist_premdpc <- as.data.frame(prcomp(congdist_premddata, scale. = TRUE)$x[,1]) %>% rownames_to_column("congdist")
congdist_postmdpc <- as.data.frame(prcomp(congdist_postmddata, scale. = TRUE)$x[,1]) %>% rownames_to_column("congdist")
congdist_allmdpc <- as.data.frame(prcomp(congdist_allmddata, scale. = TRUE)$x[,1]) %>% rownames_to_column("congdist")Combine Moral Diversity PCAs
Looseness
# Combine all of the looseness items
congdist_avgdlooseness <- congressionaldistrict1 %>%
dplyr::select(congdist, preloosenessitems, postloosenessitems) %>%
as.data.frame() %>%
mutate_if(is.numeric, .funs = ~scale(.x, center = T, scale = T)[, 1]) %>%
dplyr::mutate(
preloosemean = mean(c_across(preloosenessitems), na.rm = TRUE),
postloosemean = mean(c_across(postloosenessitems), na.rm = TRUE),
allloosenessmean = mean(c_across(allloosenessitems), na.rm = TRUE))%>%
dplyr::select(congdist, allloosenessitems, preloosemean, postloosemean, allloosenessmean) %>%
select(congdist, preloosemean, postloosemean, allloosenessmean) %>%
dplyr::group_by(congdist) %>%
dplyr::summarize_if(
is.numeric, mean
)Censorship
# Select the censorship items I plan to use
itemstouse <- readxl::read_excel("Archival Studies/ANES/ANESAnalysis.xlsx") %>% filter(is.na(EXCLUDE))
# Create a vector of the variable names
censorshipnames <- itemstouse %>%
dplyr::filter(`Type of variable` == "censor") %>%
select(VariableName) %>%
dplyr::pull()
# Create a dataframe with the all censorship items
congdist_censorshiptotal <- congressionaldistrict1 %>%
as.data.frame() %>%
select(congdist, censorshipnames, V201627) %>%
dplyr::mutate(across(where(is.numeric), scale)) %>%
dplyr::rowwise() %>%
dplyr::mutate(censorshipmean = mean(c_across(censorshipnames), na.rm = TRUE)) %>%
dplyr::group_by(congdist) %>%
dplyr::summarize_if(
is.numeric, mean, na.rm = TRUE
)State
Build the dataset
Create dataframe of pre- and post- moral diversity SDs
# Pre MD
sds_premd_state1 <- state1 %>%
select(state, premd_items) %>%
as.data.frame()%>%
mutate_if(is.numeric, .funs = ~scale(.x, center = T, scale = T)[, 1]) %>%
group_by(state) %>%
summarise_at(vars(premd_items), funs(sd(., na.rm = TRUE)))
# Post MD
sds_postmd_state1 <- state1 %>%
select(state, postmd_items) %>%
as.data.frame()%>%
mutate_if(is.numeric, .funs = ~scale(.x, center = T, scale = T)[, 1]) %>%
group_by(state) %>%
summarise_at(vars(postmd_items), funs(sd(., na.rm = TRUE)))PCA
I did not run the chunks below because I the signs flipped. However, the absolute values are identical.
# To create PCA
state_premddata <- sds_premd_state1 %>% tibble::column_to_rownames("state")
state_postmddata <- sds_postmd_state1%>% tibble::column_to_rownames("state")
state_allmddata <- sds_all <- sds_premd_state1 %>% left_join(sds_postmd_state1) %>% tibble::column_to_rownames("state")
# Pre MD - Impute missing values
for(i in 1:ncol(state_premddata)) {
state_premddata[is.na(state_premddata[, i]), i] <- mean(state_premddata[, i], na.rm = TRUE)
}
# Post MD - Impute missing values
for(i in 1:ncol(state_postmddata)) {
state_postmddata[is.na(state_postmddata[, i]), i] <- mean(state_postmddata[, i], na.rm = TRUE)
}
# All MD - Impute missing values
for(i in 1:ncol(sds_all)) {
state_allmddata[is.na(state_allmddata[, i]), i] <- mean(state_allmddata[, i], na.rm = TRUE)
}
# Create PCAs
state_premdpc <- as.data.frame(prcomp(state_premddata, scale. = TRUE)$x[,1]) %>% rownames_to_column("state")
state_postmdpc <- as.data.frame(prcomp(state_postmddata, scale. = TRUE)$x[,1]) %>% rownames_to_column("state")
state_allmdpc <- as.data.frame(prcomp(state_allmddata, scale. = TRUE)$x[,1]) %>% rownames_to_column("state")Looseness
# Combine all of the looseness items
state_avgdlooseness <- state1 %>%
dplyr::select(state, preloosenessitems, postloosenessitems) %>%
as.data.frame() %>%
mutate_if(is.numeric, .funs = ~scale(.x, center = T, scale = T)[, 1]) %>%
dplyr::mutate(
preloosemean = mean(c_across(preloosenessitems), na.rm = TRUE),
postloosemean = mean(c_across(postloosenessitems), na.rm = TRUE),
allloosenessmean = mean(c_across(allloosenessitems), na.rm = TRUE))%>%
dplyr::select(state, allloosenessitems, preloosemean, postloosemean, allloosenessmean)%>%
as.data.frame() %>%
select(state, preloosemean, postloosemean, allloosenessmean) %>%
dplyr::group_by(state) %>%
dplyr::summarize_if(
is.numeric, mean
)Censorship
# Select the censorship items I plan to use
itemstouse <- readxl::read_excel("Archival Studies/ANES/ANESAnalysis.xlsx") %>% filter(is.na(EXCLUDE))
# Create a vector of the variable names
censorshipnames <- itemstouse %>%
dplyr::filter(`Type of variable` == "censor") %>%
select(VariableName) %>%
dplyr::pull()
# Create a dataframe with the all censorship items
state_censorshiptotal <- state1 %>%
as.data.frame() %>%
select(state, censorshipnames, V201627) %>%
dplyr::mutate(across(where(is.numeric), scale)) %>%
dplyr::rowwise() %>%
dplyr::mutate(censorshipmean = mean(c_across(censorshipnames), na.rm = TRUE)) %>%
dplyr::group_by(state) %>%
dplyr::summarize_if(
is.numeric, mean, na.rm = TRUE
)Do we need to nest in state for each censorship item?
No: the highest ICC is ~ .30
library(lme4)
var_list <- c("V201353", "V201356x", "V201359x", "V201362x", "V201375x")
map_dfr(var_list,
function(x){
formula_mlm = as.formula(paste0(x,"~ allmdpc + (1|state)"));
model_fit = lmer(formula_mlm,data=congdist_anesdataraw);
re_variances = VarCorr(model_fit,comp="Variance") %>%
data.frame() %>%
dplyr::mutate(variable = x);
return(re_variances)
}) %>%
dplyr::select(variable,grp,vcov) %>%
pivot_wider(names_from="grp",values_from="vcov") %>%
dplyr::mutate(icc = state/(state+Residual))Scale reliabilities
Correlation between PRE MD and POST MD
##
## Pearson's product-moment correlation
##
## data: premoraldiversity and postmoraldiversity
## t = -8.6, df = 434, p-value <0.0000000000000002
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.4600 -0.2995
## sample estimates:
## cor
## -0.3826Correlation between PRE Looseness and POST Looseness
##
## Pearson's product-moment correlation
##
## data: preloosemean and postloosemean
## t = NA, df = 434, p-value = NA
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## NA NA
## sample estimates:
## cor
## NAReliabilities of censorship
with(congressionaldistrict1, psych::alpha(data.frame(cbind(V201353, V201356x, V201362x, V201375x))))## Some items ( V201353 V201375x ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option##
## Reliability analysis
## Call: psych::alpha(x = data.frame(cbind(V201353, V201356x, V201362x,
## V201375x)))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## -0.48 -0.59 -0.22 -0.1 -0.37 0.026 3.8 0.78 -0.25
##
## 95% confidence boundaries
## lower alpha upper
## Feldt -0.53 -0.48 -0.43
## Duhachek -0.53 -0.48 -0.43
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## V201353 -0.280 -0.30 -0.050 -0.084 -0.23 0.024 0.109 -0.24
## V201356x -0.432 -0.33 -0.106 -0.090 -0.25 0.027 0.075 -0.24
## V201362x -0.737 -0.57 -0.193 -0.137 -0.36 0.034 0.099 -0.31
## V201375x -0.011 -0.36 -0.072 -0.097 -0.26 0.015 0.117 -0.26
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## V201353 8134 0.094 0.38 NaN -0.238 2.6 1.0
## V201356x 8257 0.562 0.40 NaN -0.141 4.3 2.1
## V201362x 8251 0.596 0.48 NaN -0.043 3.0 2.0
## V201375x 8214 0.338 0.41 NaN -0.289 5.1 1.9
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## V201353 0.14 0.34 0.32 0.14 0.05 0.00 0.00 0.02
## V201356x 0.15 0.09 0.02 0.37 0.02 0.09 0.27 0.00
## V201362x 0.30 0.21 0.06 0.25 0.02 0.06 0.10 0.00
## V201375x 0.07 0.07 0.02 0.28 0.03 0.17 0.36 0.01Reliabilities of Pre-looseness
## Some items ( V201403 V201405x ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option##
## Reliability analysis
## Call: psych::alpha(x = data.frame(congressionaldistrict1[preloosenessitems]))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.81 0.82 0.87 0.19 4.5 0.0023 3.1 0.67 0.23
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.81 0.81 0.82
## Duhachek 0.81 0.81 0.82
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## V201369 0.81 0.82 0.88 0.21 4.7 0.0023 0.063 0.26
## V201378 0.80 0.81 0.87 0.19 4.2 0.0025 0.062 0.21
## r_V201379 0.81 0.82 0.88 0.20 4.6 0.0023 0.064 0.26
## V201393 0.80 0.81 0.87 0.19 4.2 0.0025 0.062 0.23
## V201403 0.82 0.84 0.88 0.23 5.4 0.0022 0.047 0.25
## V201405x 0.85 0.85 0.88 0.24 5.5 0.0018 0.041 0.25
## V201406 0.80 0.80 0.85 0.18 4.1 0.0024 0.057 0.21
## V201408x 0.79 0.80 0.84 0.18 4.0 0.0026 0.055 0.21
## V201411x 0.78 0.80 0.86 0.18 3.9 0.0027 0.058 0.21
## r_V201414x 0.80 0.81 0.87 0.19 4.2 0.0024 0.062 0.22
## r_V201415 0.81 0.81 0.86 0.19 4.2 0.0024 0.061 0.23
## r_V201416 0.80 0.80 0.86 0.19 4.1 0.0024 0.060 0.21
## V201417 0.80 0.80 0.86 0.19 4.1 0.0025 0.061 0.21
## V201420x 0.80 0.81 0.86 0.19 4.2 0.0025 0.061 0.22
## V201423x 0.79 0.80 0.86 0.19 4.1 0.0025 0.060 0.21
## V201426x 0.77 0.79 0.85 0.18 3.8 0.0030 0.055 0.21
## r_V201429 0.78 0.80 0.85 0.18 3.9 0.0028 0.056 0.21
## V201430 0.81 0.82 0.87 0.20 4.4 0.0024 0.064 0.25
## r_V201626 0.80 0.81 0.86 0.19 4.1 0.0025 0.060 0.21
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## V201369 8233 0.23 0.25 0.16 0.15 4.1 0.95
## V201378 8204 0.56 0.55 0.51 0.48 4.1 1.20
## r_V201379 8171 0.22 0.30 0.22 0.19 1.6 0.48
## V201393 8255 0.53 0.52 0.47 0.46 3.2 1.12
## V201403 8245 -0.16 -0.13 -0.19 -0.22 1.5 0.82
## V201405x 8242 -0.24 -0.22 -0.28 -0.36 2.4 1.61
## V201406 8168 0.61 0.62 0.65 0.59 1.5 0.50
## V201408x 8166 0.69 0.69 0.73 0.59 3.5 2.04
## V201411x 7967 0.73 0.71 0.70 0.65 3.5 2.01
## r_V201414x 8161 0.46 0.52 0.48 0.40 3.5 0.89
## r_V201415 8152 0.47 0.56 0.54 0.45 1.8 0.40
## r_V201416 8161 0.54 0.61 0.60 0.50 2.6 0.71
## V201417 8197 0.59 0.59 0.56 0.54 2.8 0.89
## V201420x 8225 0.62 0.56 0.52 0.50 4.5 2.16
## V201423x 8131 0.61 0.60 0.57 0.53 5.0 1.37
## V201426x 8240 0.83 0.77 0.78 0.75 4.3 2.47
## r_V201429 7713 0.78 0.72 0.73 0.70 4.5 2.31
## V201430 8245 0.37 0.40 0.33 0.32 2.0 0.80
## r_V201626 8104 0.60 0.58 0.54 0.54 2.4 1.11
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## V201369 0.02 0.03 0.20 0.36 0.39 0.00 0.00 0.01
## V201378 0.04 0.08 0.21 0.12 0.55 0.00 0.00 0.01
## r_V201379 0.38 0.62 0.00 0.00 0.00 0.00 0.00 0.01
## V201393 0.10 0.12 0.43 0.21 0.14 0.00 0.00 0.00
## V201403 0.71 0.08 0.21 0.00 0.00 0.00 0.00 0.00
## V201405x 0.44 0.23 0.04 0.21 0.01 0.04 0.03 0.00
## V201406 0.50 0.50 0.00 0.00 0.00 0.00 0.00 0.01
## V201408x 0.26 0.16 0.08 0.06 0.16 0.28 0.00 0.01
## V201411x 0.30 0.11 0.05 0.10 0.21 0.22 0.00 0.04
## r_V201414x 0.07 0.05 0.16 0.72 0.00 0.00 0.00 0.01
## r_V201415 0.20 0.80 0.00 0.00 0.00 0.00 0.00 0.02
## r_V201416 0.13 0.19 0.68 0.00 0.00 0.00 0.00 0.01
## V201417 0.13 0.14 0.55 0.17 0.00 0.00 0.00 0.01
## V201420x 0.15 0.09 0.03 0.28 0.03 0.14 0.28 0.01
## V201423x 0.05 0.05 0.02 0.11 0.29 0.48 0.00 0.02
## V201426x 0.26 0.08 0.02 0.18 0.02 0.09 0.35 0.00
## r_V201429 0.18 0.08 0.09 0.13 0.08 0.10 0.34 0.07
## V201430 0.33 0.36 0.31 0.00 0.00 0.00 0.00 0.00
## r_V201626 0.27 0.26 0.25 0.22 0.00 0.00 0.00 0.02Reliabilities of Post-looseness
## Some items ( r_V202225 V202240 r_V202325 ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option##
## Reliability analysis
## Call: psych::alpha(x = data.frame(congressionaldistrict1[postloosenessitems]))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.71 0.58 0.67 0.1 1.4 0.0038 3.1 0.63 0.13
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.7 0.71 0.72
## Duhachek 0.7 0.71 0.72
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## r_V202225 0.73 0.63 0.71 0.136 1.73 0.0037 0.057 0.175
## V202231x 0.69 0.54 0.65 0.095 1.16 0.0039 0.064 0.094
## V202240 0.75 0.67 0.71 0.158 2.06 0.0035 0.040 0.175
## r_V202242x 0.69 0.55 0.62 0.101 1.24 0.0040 0.049 0.092
## V202248x 0.68 0.52 0.63 0.091 1.10 0.0041 0.057 0.092
## V202255x 0.64 0.47 0.58 0.075 0.89 0.0047 0.052 0.092
## r_V202256 0.64 0.48 0.58 0.076 0.91 0.0046 0.051 0.092
## r_V202259x 0.64 0.49 0.60 0.080 0.96 0.0047 0.052 0.092
## r_V202325 0.72 0.62 0.70 0.128 1.62 0.0037 0.063 0.179
## r_V202331x 0.71 0.56 0.66 0.105 1.29 0.0036 0.063 0.119
## r_V202336x 0.64 0.49 0.59 0.079 0.95 0.0047 0.050 0.092
## r_V202337 0.71 0.56 0.66 0.102 1.25 0.0039 0.065 0.140
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## r_V202225 7407 -0.10936 0.057 -0.145 -0.171 1.3 0.55
## V202231x 7235 0.42830 0.496 0.393 0.326 2.3 1.02
## V202240 7395 -0.31294 -0.182 -0.346 -0.390 1.5 0.77
## r_V202242x 7390 0.54585 0.435 0.421 0.367 5.3 1.89
## V202248x 7384 0.57860 0.544 0.483 0.444 6.1 1.54
## V202255x 7348 0.74771 0.717 0.756 0.619 3.8 1.97
## r_V202256 7375 0.73632 0.703 0.737 0.629 3.7 1.67
## r_V202259x 7384 0.74026 0.660 0.662 0.587 4.4 2.21
## r_V202325 7384 0.00011 0.139 -0.065 -0.067 2.0 0.59
## r_V202331x 7384 0.43424 0.389 0.259 0.248 5.8 1.77
## r_V202336x 7364 0.74351 0.670 0.681 0.622 5.2 1.88
## r_V202337 7375 0.30419 0.423 0.294 0.241 1.7 0.60
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## r_V202225 0.70 0.26 0.04 0.00 0.00 0.00 0.00 0.11
## V202231x 0.29 0.27 0.31 0.13 0.00 0.00 0.00 0.13
## V202240 0.69 0.14 0.17 0.00 0.00 0.00 0.00 0.11
## r_V202242x 0.08 0.05 0.01 0.17 0.06 0.29 0.34 0.11
## V202248x 0.03 0.02 0.01 0.16 0.02 0.12 0.64 0.11
## V202255x 0.22 0.16 0.03 0.05 0.29 0.25 0.00 0.11
## r_V202256 0.13 0.12 0.15 0.29 0.15 0.12 0.05 0.11
## r_V202259x 0.18 0.09 0.02 0.25 0.03 0.16 0.26 0.11
## r_V202325 0.19 0.65 0.16 0.00 0.00 0.00 0.00 0.11
## r_V202331x 0.05 0.03 0.01 0.16 0.02 0.16 0.56 0.11
## r_V202336x 0.07 0.06 0.01 0.25 0.04 0.20 0.37 0.11
## r_V202337 0.42 0.52 0.07 0.00 0.00 0.00 0.00 0.11Reliabilities of all looseness
## Some items ( V201403 V201405x r_V202225 V202240 r_V202325 ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option##
## Reliability analysis
## Call: psych::alpha(x = data.frame(congressionaldistrict1[allloosenessitems]))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.87 0.85 0.89 0.15 5.7 0.0016 3.1 0.61 0.18
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.87 0.87 0.87
## Duhachek 0.87 0.87 0.87
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## V201369 0.87 0.85 0.90 0.16 5.7 0.0016 0.054 0.21
## V201378 0.86 0.84 0.89 0.15 5.3 0.0017 0.053 0.18
## r_V201379 0.87 0.85 0.90 0.16 5.7 0.0016 0.054 0.21
## V201393 0.86 0.84 0.89 0.15 5.3 0.0017 0.052 0.18
## V201403 0.88 0.86 0.90 0.17 6.3 0.0016 0.047 0.21
## V201405x 0.89 0.87 0.90 0.18 6.4 0.0014 0.044 0.21
## V201406 0.87 0.84 0.88 0.15 5.2 0.0017 0.051 0.18
## V201408x 0.86 0.84 0.88 0.15 5.2 0.0017 0.050 0.18
## V201411x 0.86 0.84 0.88 0.15 5.1 0.0018 0.050 0.18
## r_V201414x 0.87 0.84 0.89 0.15 5.4 0.0017 0.053 0.18
## r_V201415 0.87 0.84 0.89 0.15 5.4 0.0016 0.053 0.18
## r_V201416 0.87 0.84 0.89 0.15 5.3 0.0017 0.052 0.18
## V201417 0.86 0.84 0.89 0.15 5.3 0.0017 0.052 0.18
## V201420x 0.86 0.84 0.89 0.15 5.3 0.0017 0.052 0.18
## V201423x 0.86 0.84 0.89 0.15 5.3 0.0017 0.052 0.18
## V201426x 0.85 0.83 0.88 0.14 5.0 0.0019 0.048 0.18
## r_V201429 0.85 0.84 0.88 0.14 5.1 0.0018 0.049 0.18
## V201430 0.87 0.85 0.89 0.16 5.5 0.0016 0.054 0.20
## r_V201626 0.86 0.84 0.89 0.15 5.3 0.0017 0.052 0.18
## r_V202225 0.87 0.86 0.90 0.17 6.1 0.0016 0.051 0.21
## V202231x 0.87 0.84 0.89 0.15 5.4 0.0017 0.054 0.18
## V202240 0.88 0.86 0.90 0.18 6.4 0.0016 0.046 0.21
## r_V202242x 0.86 0.84 0.89 0.15 5.4 0.0017 0.051 0.18
## V202248x 0.86 0.84 0.89 0.15 5.3 0.0017 0.052 0.18
## V202255x 0.86 0.84 0.89 0.15 5.2 0.0017 0.051 0.18
## r_V202256 0.86 0.84 0.89 0.15 5.2 0.0017 0.051 0.18
## r_V202259x 0.86 0.84 0.89 0.15 5.2 0.0018 0.051 0.18
## r_V202325 0.87 0.86 0.90 0.17 6.0 0.0016 0.053 0.21
## r_V202331x 0.87 0.85 0.89 0.16 5.6 0.0016 0.054 0.20
## r_V202336x 0.86 0.84 0.89 0.15 5.2 0.0018 0.050 0.18
## r_V202337 0.87 0.85 0.89 0.16 5.6 0.0016 0.054 0.20
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## V201369 8233 0.21 0.226 0.167 0.166 4.1 0.95
## V201378 8204 0.55 0.547 0.523 0.502 4.1 1.20
## r_V201379 8171 0.21 0.264 0.206 0.184 1.6 0.48
## V201393 8255 0.55 0.548 0.524 0.514 3.2 1.12
## V201403 8245 -0.22 -0.195 -0.240 -0.271 1.5 0.82
## V201405x 8242 -0.31 -0.291 -0.339 -0.396 2.4 1.61
## V201406 8168 0.59 0.614 0.640 0.580 1.5 0.50
## V201408x 8166 0.66 0.682 0.713 0.605 3.5 2.04
## V201411x 7967 0.70 0.699 0.698 0.650 3.5 2.01
## r_V201414x 8161 0.44 0.478 0.450 0.400 3.5 0.89
## r_V201415 8152 0.45 0.514 0.500 0.431 1.8 0.40
## r_V201416 8161 0.51 0.561 0.555 0.476 2.6 0.71
## V201417 8197 0.56 0.564 0.548 0.530 2.8 0.89
## V201420x 8225 0.60 0.559 0.538 0.524 4.5 2.16
## V201423x 8131 0.59 0.579 0.564 0.535 5.0 1.37
## V201426x 8240 0.82 0.791 0.809 0.775 4.3 2.47
## r_V201429 7713 0.76 0.742 0.753 0.724 4.5 2.31
## V201430 8245 0.35 0.376 0.328 0.310 2.0 0.80
## r_V201626 8104 0.60 0.598 0.582 0.572 2.4 1.11
## r_V202225 7407 -0.13 -0.073 -0.147 -0.151 1.3 0.55
## V202231x 7235 0.46 0.484 0.449 0.423 2.3 1.02
## V202240 7395 -0.33 -0.274 -0.330 -0.358 1.5 0.77
## r_V202242x 7390 0.54 0.491 0.487 0.469 5.3 1.89
## V202248x 7384 0.56 0.534 0.509 0.499 6.1 1.54
## V202255x 7348 0.65 0.608 0.605 0.587 3.8 1.97
## r_V202256 7375 0.65 0.618 0.614 0.600 3.7 1.67
## r_V202259x 7384 0.67 0.620 0.612 0.606 4.4 2.21
## r_V202325 7384 -0.02 0.029 -0.047 -0.051 2.0 0.59
## r_V202331x 7384 0.35 0.336 0.288 0.264 5.8 1.77
## r_V202336x 7364 0.69 0.652 0.647 0.636 5.2 1.88
## r_V202337 7375 0.30 0.332 0.280 0.274 1.7 0.60
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## V201369 0.02 0.03 0.20 0.36 0.39 0.00 0.00 0.01
## V201378 0.04 0.08 0.21 0.12 0.55 0.00 0.00 0.01
## r_V201379 0.38 0.62 0.00 0.00 0.00 0.00 0.00 0.01
## V201393 0.10 0.12 0.43 0.21 0.14 0.00 0.00 0.00
## V201403 0.71 0.08 0.21 0.00 0.00 0.00 0.00 0.00
## V201405x 0.44 0.23 0.04 0.21 0.01 0.04 0.03 0.00
## V201406 0.50 0.50 0.00 0.00 0.00 0.00 0.00 0.01
## V201408x 0.26 0.16 0.08 0.06 0.16 0.28 0.00 0.01
## V201411x 0.30 0.11 0.05 0.10 0.21 0.22 0.00 0.04
## r_V201414x 0.07 0.05 0.16 0.72 0.00 0.00 0.00 0.01
## r_V201415 0.20 0.80 0.00 0.00 0.00 0.00 0.00 0.02
## r_V201416 0.13 0.19 0.68 0.00 0.00 0.00 0.00 0.01
## V201417 0.13 0.14 0.55 0.17 0.00 0.00 0.00 0.01
## V201420x 0.15 0.09 0.03 0.28 0.03 0.14 0.28 0.01
## V201423x 0.05 0.05 0.02 0.11 0.29 0.48 0.00 0.02
## V201426x 0.26 0.08 0.02 0.18 0.02 0.09 0.35 0.00
## r_V201429 0.18 0.08 0.09 0.13 0.08 0.10 0.34 0.07
## V201430 0.33 0.36 0.31 0.00 0.00 0.00 0.00 0.00
## r_V201626 0.27 0.26 0.25 0.22 0.00 0.00 0.00 0.02
## r_V202225 0.70 0.26 0.04 0.00 0.00 0.00 0.00 0.11
## V202231x 0.29 0.27 0.31 0.13 0.00 0.00 0.00 0.13
## V202240 0.69 0.14 0.17 0.00 0.00 0.00 0.00 0.11
## r_V202242x 0.08 0.05 0.01 0.17 0.06 0.29 0.34 0.11
## V202248x 0.03 0.02 0.01 0.16 0.02 0.12 0.64 0.11
## V202255x 0.22 0.16 0.03 0.05 0.29 0.25 0.00 0.11
## r_V202256 0.13 0.12 0.15 0.29 0.15 0.12 0.05 0.11
## r_V202259x 0.18 0.09 0.02 0.25 0.03 0.16 0.26 0.11
## r_V202325 0.19 0.65 0.16 0.00 0.00 0.00 0.00 0.11
## r_V202331x 0.05 0.03 0.01 0.16 0.02 0.16 0.56 0.11
## r_V202336x 0.07 0.06 0.01 0.25 0.04 0.20 0.37 0.11
## r_V202337 0.42 0.52 0.07 0.00 0.00 0.00 0.00 0.11Congressional district Linear Regressions
DV: Looseness
Pre MD –> Pre looseness
##
## Call:
## lm(formula = preloosemean ~ premoraldiversity, data = congdist_anesdataraw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0000000000000000000000000000000096 -0.0000000000000000000000000000000046 -0.0000000000000000000000000000000031 -0.0000000000000000000000000000000018 0.0000000000000000000000000000013891
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.00000000000000003429670765708743018 0.00000000000000000000000000000000320 10722980050021052.00 <0.0000000000000002 ***
## premoraldiversity 0.00000000000000000000000000000000148 0.00000000000000000000000000000000240 0.62 0.54
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0000000000000000000000000000000668 on 434 degrees of freedom
## Multiple R-squared: 0.499, Adjusted R-squared: 0.498
## F-statistic: 433 on 1 and 434 DF, p-value: <0.0000000000000002Pre MD –> Post looseness
##
## Call:
## lm(formula = postloosemean ~ premoraldiversity, data = congdist_anesdataraw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.000000000000000000000000000030817 0.000000000000000000000000000000041 0.000000000000000000000000000000069 0.000000000000000000000000000000101 0.000000000000000000000000000000212
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0000000000000002807191227500628565 0.0000000000000000000000000000000710 -3956192494309554.50 <0.0000000000000002 ***
## premoraldiversity -0.0000000000000000000000000000000329 0.0000000000000000000000000000000532 -0.62 0.54
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.00000000000000000000000000000148 on 434 degrees of freedom
## Multiple R-squared: 0.5, Adjusted R-squared: 0.499
## F-statistic: 434 on 1 and 434 DF, p-value: <0.0000000000000002Post MD –> Post looseness
##
## Call:
## lm(formula = postloosemean ~ postmoraldiversity, data = congdist_anesdataraw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.000000000000000000000000000030829 0.000000000000000000000000000000050 0.000000000000000000000000000000073 0.000000000000000000000000000000090 0.000000000000000000000000000000163
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0000000000000002807191227500628565 0.0000000000000000000000000000000710 -3955419925683549.00 <0.0000000000000002 ***
## postmoraldiversity -0.0000000000000000000000000000000158 0.0000000000000000000000000000000342 -0.46 0.65
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.00000000000000000000000000000148 on 434 degrees of freedom
## Multiple R-squared: 0.5, Adjusted R-squared: 0.499
## F-statistic: 434 on 1 and 434 DF, p-value: <0.0000000000000002All MD –> All looseness
##
## Call:
## lm(formula = allloosenessmean ~ allmdpc, data = congdist_anesdataraw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.000000000000000000000000000000114 -0.000000000000000000000000000000076 -0.000000000000000000000000000000067 -0.000000000000000000000000000000054 0.000000000000000000000000000028384
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.00000000000000015965810324423840777 0.00000000000000000000000000000006533 -2443795319904543.50 <0.0000000000000002 ***
## allmdpc -0.00000000000000000000000000000000789 0.00000000000000000000000000000002949 -0.27 0.79
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.00000000000000000000000000000136 on 434 degrees of freedom
## Multiple R-squared: 0.5, Adjusted R-squared: 0.499
## F-statistic: 434 on 1 and 434 DF, p-value: <0.0000000000000002Pre MD –> Censorship
##
## Call:
## lm(formula = censorshipmean ~ premoraldiversity, data = congdist_anesdataraw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.4067 -0.0707 -0.0057 0.0710 0.4198
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.000103 0.005045 -0.02 0.9837
## premoraldiversity 0.011619 0.003781 3.07 0.0023 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.105 on 434 degrees of freedom
## Multiple R-squared: 0.0213, Adjusted R-squared: 0.019
## F-statistic: 9.44 on 1 and 434 DF, p-value: 0.00225Post MD –> Censorship
##
## Call:
## lm(formula = censorshipmean ~ postmoraldiversity, data = congdist_anesdataraw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.4123 -0.0705 -0.0055 0.0658 0.4062
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.000103 0.005020 -0.02 0.98363
## postmoraldiversity -0.008998 0.002419 -3.72 0.00023 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.105 on 434 degrees of freedom
## Multiple R-squared: 0.0309, Adjusted R-squared: 0.0287
## F-statistic: 13.8 on 1 and 434 DF, p-value: 0.000226DV: Censorship
Note: I used the combined T1 + T2 moral diversity measures
| Item | Sig? | Dir. |
|---|---|---|
| How often people denied right to vote (higher values = more often) | Y | + |
| FAVOR OR OPPOSE VOTE BY MAIL. | Y. | - |
| Favor or oppose requiring ID when voting (higher value = oppose) | Y | +. |
| Favor or oppose allowing felons to vote (higher = oppose) | Y | - |
| Favor or oppose restricting journalist access (higher = favor) | Y | +. |
| Self-censor | N | NA |
Mean
Interpret this with caution, given the low alpha of the combined measures
##
## Call:
## lm(formula = censorshipmean ~ allmdpc, data = congdist_anesdataraw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.4038 -0.0709 -0.0048 0.0662 0.4003
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.000103 0.004974 -0.02 0.98
## allmdpc 0.010568 0.002245 4.71 0.0000034 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.104 on 434 degrees of freedom
## Multiple R-squared: 0.0486, Adjusted R-squared: 0.0464
## F-statistic: 22.2 on 1 and 434 DF, p-value: 0.00000338How often people denied right to vote (higher values = more often)
##
## Call:
## lm(formula = V201353 ~ allmdpc, data = congdist_anesdataraw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.7323 -0.1893 -0.0335 0.1823 0.9197
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.00713 0.01373 0.52 0.6
## allmdpc 0.04366 0.00620 7.04 0.0000000000073 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.287 on 434 degrees of freedom
## Multiple R-squared: 0.103, Adjusted R-squared: 0.101
## F-statistic: 49.6 on 1 and 434 DF, p-value: 0.00000000000734FAVOR OR OPPOSE VOTE BY MAIL (higher value = oppose)
##
## Call:
## lm(formula = V201356x ~ allmdpc, data = congdist_anesdataraw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.9723 -0.2351 0.0116 0.2372 1.1748
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.00277 0.01586 -0.17 0.86
## allmdpc -0.07556 0.00716 -10.56 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.331 on 434 degrees of freedom
## Multiple R-squared: 0.204, Adjusted R-squared: 0.203
## F-statistic: 111 on 1 and 434 DF, p-value: <0.0000000000000002Favor or oppose requiring ID when voting (higher value = oppose)
##
## Call:
## lm(formula = V201359x ~ allmdpc, data = congdist_anesdataraw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.7396 -0.1971 -0.0269 0.1992 1.0112
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.000414 0.014245 0.03 0.98
## allmdpc 0.077455 0.006430 12.05 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.297 on 434 degrees of freedom
## Multiple R-squared: 0.251, Adjusted R-squared: 0.249
## F-statistic: 145 on 1 and 434 DF, p-value: <0.0000000000000002Favor or oppose allowing felons to vote (higher = oppose)
##
## Call:
## lm(formula = V201362x ~ allmdpc, data = congdist_anesdataraw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.8736 -0.1817 -0.0096 0.1817 0.9119
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.00206 0.01376 0.15 0.88
## allmdpc -0.04326 0.00621 -6.97 0.000000000012 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.287 on 434 degrees of freedom
## Multiple R-squared: 0.101, Adjusted R-squared: 0.0985
## F-statistic: 48.5 on 1 and 434 DF, p-value: 0.0000000000121Favor or oppose restricting journalist access (higher values = oppose)
##
## Call:
## lm(formula = V201375x ~ allmdpc, data = congdist_anesdataraw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.1744 -0.1805 -0.0033 0.1799 0.8216
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.00840 0.01253 -0.67 0.5
## allmdpc 0.05154 0.00566 9.11 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.262 on 434 degrees of freedom
## Multiple R-squared: 0.161, Adjusted R-squared: 0.159
## F-statistic: 83 on 1 and 434 DF, p-value: <0.0000000000000002How often self-censor (higher = more often) # Not significant
##
## Call:
## lm(formula = V201627 ~ allmdpc, data = congdist_anesdataraw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.668 -0.181 0.007 0.172 0.798
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.00184 0.01197 -0.15 0.88
## allmdpc 0.00158 0.00540 0.29 0.77
##
## Residual standard error: 0.25 on 434 degrees of freedom
## Multiple R-squared: 0.000198, Adjusted R-squared: -0.00211
## F-statistic: 0.086 on 1 and 434 DF, p-value: 0.77State-level
Pre- and Post- comparisons
Pre MD –> Pre looseness
##
## Call:
## lm(formula = preloosemean ~ premoraldiversity, data = state_anesdataraw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.00000000000000000000000000000001926 -0.00000000000000000000000000000000643 -0.00000000000000000000000000000000399 -0.00000000000000000000000000000000059 0.00000000000000000000000000000020342
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.00000000000000003429670765708739321 0.00000000000000000000000000000000415 8261308721096011.00 <0.0000000000000002 ***
## premoraldiversity 0.00000000000000000000000000000000496 0.00000000000000000000000000000000507 0.98 0.33
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0000000000000000000000000000000296 on 49 degrees of freedom
## Multiple R-squared: 0.501, Adjusted R-squared: 0.49
## F-statistic: 49.1 on 1 and 49 DF, p-value: 0.00000000641Pre MD –> Post looseness
##
## Call:
## lm(formula = postloosemean ~ premoraldiversity, data = state_anesdataraw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0000000000000000000000000000007674 0.0000000000000000000000000000000022 0.0000000000000000000000000000000151 0.0000000000000000000000000000000242 0.0000000000000000000000000000000727
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0000000000000002807191227500614267 0.0000000000000000000000000000000157 -17923840549010830.00 <0.0000000000000002 ***
## premoraldiversity -0.0000000000000000000000000000000187 0.0000000000000000000000000000000191 -0.98 0.33
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.000000000000000000000000000000112 on 49 degrees of freedom
## Multiple R-squared: 0.516, Adjusted R-squared: 0.507
## F-statistic: 52.3 on 1 and 49 DF, p-value: 0.00000000289Post MD –> Post looseness
##
## Call:
## lm(formula = postloosemean ~ postmoraldiversity, data = state_anesdataraw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0000000000000000000000000000007738 0.0000000000000000000000000000000063 0.0000000000000000000000000000000153 0.0000000000000000000000000000000239 0.0000000000000000000000000000000458
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0000000000000002807191227500614267 0.0000000000000000000000000000000158 -17728379537046776.00 <0.0000000000000002 ***
## postmoraldiversity -0.0000000000000000000000000000000104 0.0000000000000000000000000000000141 -0.74 0.47
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.000000000000000000000000000000112 on 49 degrees of freedom
## Multiple R-squared: 0.512, Adjusted R-squared: 0.502
## F-statistic: 51.3 on 1 and 49 DF, p-value: 0.00000000371All MD –> All looseness
##
## Call:
## lm(formula = allloosenessmean ~ allmdpc, data = state_anesdataraw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0000000000000000000000000000005368 0.0000000000000000000000000000000079 0.0000000000000000000000000000000108 0.0000000000000000000000000000000135 0.0000000000000000000000000000000191
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.00000000000000015965810324423993619 0.00000000000000000000000000000001092 -14626751543888870.00 <0.0000000000000002 ***
## allmdpc 0.00000000000000000000000000000000295 0.00000000000000000000000000000000836 0.35 0.73
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0000000000000000000000000000000776 on 49 degrees of freedom
## Multiple R-squared: 0.509, Adjusted R-squared: 0.499
## F-statistic: 50.8 on 1 and 49 DF, p-value: 0.00000000419Pre MD –> Censorship
##
## Call:
## lm(formula = censorshipmean ~ premoraldiversity, data = state_anesdataraw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.11811 -0.04323 0.00453 0.04316 0.10939
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.01011 0.00863 -1.17 0.24693
## premoraldiversity 0.03761 0.01054 3.57 0.00081 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0616 on 49 degrees of freedom
## Multiple R-squared: 0.206, Adjusted R-squared: 0.19
## F-statistic: 12.7 on 1 and 49 DF, p-value: 0.000814Post MD –> Censorship
##
## Call:
## lm(formula = censorshipmean ~ postmoraldiversity, data = state_anesdataraw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.13075 -0.04303 -0.00238 0.04612 0.11765
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.00601 0.00900 -0.67 0.5072
## postmoraldiversity -0.02337 0.00803 -2.91 0.0054 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0638 on 49 degrees of freedom
## Multiple R-squared: 0.147, Adjusted R-squared: 0.13
## F-statistic: 8.47 on 1 and 49 DF, p-value: 0.00541All MD –> Censorship
| Item | Sig? | Dir. |
|---|---|---|
| How often people denied right to vote | Y | - |
| FAVOR OR OPPOSE VOTE BY MAIL. (higher value = oppose | Y | + |
| Favor or oppose requiring ID when voting (higher value = oppose) | Y | - |
| Favor or oppose allowing felons to vote (higher = oppose) | Y | + |
| Favor or oppose restricting journalist access (higher = favor) | Y | - |
| Self-censor | Y | + |
Mean
Interpret this with caution, given the low alpha of the combined measures
##
## Call:
## lm(formula = censorshipmean ~ allmdpc, data = state_anesdataraw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.12914 -0.03894 -0.00128 0.04701 0.11984
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.00621 0.00879 -0.71 0.4830
## allmdpc 0.02238 0.00673 3.33 0.0017 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0624 on 49 degrees of freedom
## Multiple R-squared: 0.184, Adjusted R-squared: 0.168
## F-statistic: 11.1 on 1 and 49 DF, p-value: 0.00167How often people denied right to vote (higher values = more often)
##
## Call:
## lm(formula = V201353 ~ allmdpc, data = state_anesdataraw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.5417 -0.0852 -0.0218 0.0949 0.3867
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0348 0.0234 -1.49 0.1439
## allmdpc 0.0758 0.0180 4.22 0.0001 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.167 on 49 degrees of freedom
## Multiple R-squared: 0.267, Adjusted R-squared: 0.252
## F-statistic: 17.8 on 1 and 49 DF, p-value: 0.000104FAVOR OR OPPOSE VOTE BY MAIL (higher value = oppose)
##
## Call:
## lm(formula = V201356x ~ allmdpc, data = state_anesdataraw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.6665 -0.1198 0.0115 0.1552 0.4740
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.00327 0.03222 -0.10 0.92
## allmdpc -0.13382 0.02468 -5.42 0.0000018 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.229 on 49 degrees of freedom
## Multiple R-squared: 0.375, Adjusted R-squared: 0.362
## F-statistic: 29.4 on 1 and 49 DF, p-value: 0.00000179Favor or oppose requiring ID when voting (higher value = oppose)
##
## Call:
## lm(formula = V201359x ~ allmdpc, data = state_anesdataraw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.388 -0.104 -0.012 0.123 0.397
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.00375 0.02497 0.15 0.88
## allmdpc 0.13549 0.01912 7.08 0.0000000049 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.177 on 49 degrees of freedom
## Multiple R-squared: 0.506, Adjusted R-squared: 0.496
## F-statistic: 50.2 on 1 and 49 DF, p-value: 0.00000000491Favor or oppose allowing felons to vote (higher = oppose)
##
## Call:
## lm(formula = V201362x ~ allmdpc, data = state_anesdataraw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.4408 -0.0736 -0.0375 0.0700 0.4954
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0218 0.0247 0.88 0.3817
## allmdpc -0.0612 0.0190 -3.23 0.0022 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.176 on 49 degrees of freedom
## Multiple R-squared: 0.176, Adjusted R-squared: 0.159
## F-statistic: 10.4 on 1 and 49 DF, p-value: 0.00222Favor or oppose restricting journalist access (higher = oppose)
##
## Call:
## lm(formula = V201375x ~ allmdpc, data = state_anesdataraw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.4062 -0.0740 -0.0228 0.1056 0.3869
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0191 0.0218 -0.88 0.39
## allmdpc 0.0985 0.0167 5.91 0.00000032 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.155 on 49 degrees of freedom
## Multiple R-squared: 0.416, Adjusted R-squared: 0.404
## F-statistic: 34.9 on 1 and 49 DF, p-value: 0.000000323How often self-censor (higher = more often)
##
## Call:
## lm(formula = V201627 ~ allmdpc, data = state_anesdataraw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.3188 -0.0548 0.0195 0.0700 0.1940
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.00926 0.01511 -0.61 0.543
## allmdpc -0.02086 0.01157 -1.80 0.078 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.107 on 49 degrees of freedom
## Multiple R-squared: 0.0622, Adjusted R-squared: 0.0431
## F-statistic: 3.25 on 1 and 49 DF, p-value: 0.0775