Stepping_Stone_3
Arlene
2023-11-26
# List of packages
packages <- c("tidyverse", "modelsummary", "forcats", "RColorBrewer",
"fst", "viridis", "knitr", "rmarkdown", "ggridges", "viridis", "questionr", "flextable", "infer", "broom", "effects", "sandwich")
# Install packages if they aren't installed already
new_packages <- packages[!(packages %in% installed.packages()[,"Package"])]
if(length(new_packages)) install.packages(new_packages)
# Load the packages
lapply(packages, library, character.only = TRUE)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
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## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
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## [16] "lubridate" "forcats" "stringr" "dplyr" "purrr"
## [21] "readr" "tidyr" "tibble" "ggplot2" "tidyverse"
## [26] "stats" "graphics" "grDevices" "utils" "datasets"
## [31] "methods" "base"ess <- read_fst("All-ESS-Data.fst")table(ess$essround)##
## 1 2 3 4 5 6 7 8 9 10
## 42359 47537 43000 56752 52458 54673 40185 44387 49519 59685ess$year <- NA
replacements <- c(2002, 2004, 2006, 2008, 2010, 2012, 2014, 2016, 2018, 2020)
for(i in 1:10){
ess$year[ess$essround == i] <- replacements[i]
}sweden_data <- ess[ess$cntry == "SE", ]sweden_data_subset <- sweden_data %>%
filter(essround %in% c("8")) %>%
mutate(
current_union_member = case_when(
mbtru == 1 ~ "Yes",
mbtru %in% c(7, 8, 9) ~ NA_character_,
TRUE ~ "No"
),
educ_level = case_when(
essround < 5 & edulvla == 5 ~ "BA",
essround >= 5 & edulvlb > 600 ~ "BA",
TRUE ~ "No BA"
),
voted_right_of_center = case_when(
prtvtbse %in% c(2, 5, 3, 10) ~ 1,
prtvtbse %in% c(1, 4, 6, 7, 8, 9, 11) ~ 0,
prtvtbse %in% c(66, 77, 88, 99) ~ NA_real_,
TRUE ~ prtvtbse
),
lr_scaled_parties = case_when(
prtvtbse == 1 ~ "Centre",
prtvtbse == 6 ~ "Centre-left",
prtvtbse %in% c(2, 5) ~ "Centre-right",
prtvtbse %in% c(3, 10) ~ "Right",
prtvtbse %in% c(4, 7, 8) ~ "Left",
prtvtbse %in% c(9, 11) ~ NA_character_,
TRUE ~ NA_character_
),
cohort = ifelse(yrbrn < 1930 | yrbrn > 2000, NA, yrbrn),
gen = case_when(
yrbrn %in% 1900:1945 ~ "1",
yrbrn %in% 1946:1964 ~ "2",
yrbrn %in% 1965:1979 ~ "3",
yrbrn %in% 1980:1996 ~ "4",
TRUE ~ as.character(cohort)
),
gen = factor(gen, levels = c("1", "2", "3", "4"),
labels = c("Interwar", "Baby Boomers", "Gen X", "Millennials")),
)
table(sweden_data_subset$current_union_member)##
## No Yes
## 788 757table(sweden_data_subset$lr_scaled_parties)##
## Centre Centre-left Centre-right Left Right
## 81 419 408 204 138Hypothesis Testing
Step 1: Chi-squared statistic calculation
test_stat <- sweden_data_subset %>%
specify(explanatory = current_union_member,
response = lr_scaled_parties) %>%
hypothesize(null = "independence") %>%
calculate(stat = "Chisq") ## Warning: Removed 303 rows containing missing values.print(test_stat$stat)## X-squared
## 45.73123Using the Chi-squared test, we produce a value of 45.73 – the high magnitude of this value shows that the null hypothesis (that current union membership cannot be used to explain left-right party alignment) is unlikely to be true.
Step 2: Null distribution simulation
null_distribution <- sweden_data_subset %>%
specify(explanatory = current_union_member,
response = lr_scaled_parties) %>%
hypothesize(null = "independence") %>%
generate(reps = 1000, type = "permute") %>%
calculate(stat = "Chisq")## Warning: Removed 303 rows containing missing values.print(null_distribution$stat)## X-squared X-squared X-squared X-squared X-squared X-squared
## 3.21484537 2.28545917 3.30018773 3.26542362 4.30195727 12.15253416
## X-squared X-squared X-squared X-squared X-squared X-squared
## 2.96942158 2.19802335 5.29342100 7.10866067 5.76377852 10.56341546
## X-squared X-squared X-squared X-squared X-squared X-squared
## 1.34037889 6.62051642 0.96095007 1.86570638 3.98592812 0.75487293
## X-squared X-squared X-squared X-squared X-squared X-squared
## 2.55360120 1.61764789 4.35505433 2.32773475 4.24787711 4.47532995
## X-squared X-squared X-squared X-squared X-squared X-squared
## 2.10925714 5.66249918 4.44228529 1.87228402 5.23382856 2.04858534
## X-squared X-squared X-squared X-squared X-squared X-squared
## 3.49661947 10.40134581 12.00329951 1.09674644 3.30942436 3.27509885
## X-squared X-squared X-squared X-squared X-squared X-squared
## 0.61597575 4.39998519 3.34580006 1.94151154 5.91428521 1.49775610
## X-squared X-squared X-squared X-squared X-squared X-squared
## 3.88080464 1.71584576 1.88445308 4.80275160 1.05117225 4.11001200
## X-squared X-squared X-squared X-squared X-squared X-squared
## 4.77209577 5.19031574 4.05958663 4.64625567 1.53553078 10.78801784
## X-squared X-squared X-squared X-squared X-squared X-squared
## 3.07604442 3.57139382 3.76721656 2.13470721 8.57421292 0.65909505
## X-squared X-squared X-squared X-squared X-squared X-squared
## 4.14479457 4.47658086 1.63316462 2.78893931 1.15432112 6.42937528
## X-squared X-squared X-squared X-squared X-squared X-squared
## 6.46339774 1.99787368 0.45559466 2.67184297 2.80174674 4.64640285
## X-squared X-squared X-squared X-squared X-squared X-squared
## 15.56575252 2.69090596 2.90687344 3.07712267 1.66708087 5.54821488
## X-squared X-squared X-squared X-squared X-squared X-squared
## 1.17966060 4.43466135 6.95399046 6.95787144 3.59750066 2.16585762
## X-squared X-squared X-squared X-squared X-squared X-squared
## 2.43325652 3.02794866 4.57118484 4.36537795 5.46200368 5.36857769
## X-squared X-squared X-squared X-squared X-squared X-squared
## 3.79372832 2.63834434 0.97520600 3.21076515 3.65059400 4.24339413
## X-squared X-squared X-squared X-squared X-squared X-squared
## 2.02773396 4.30581050 3.44642655 4.53757548 13.60941621 3.21700006
## X-squared X-squared X-squared X-squared X-squared X-squared
## 5.88672043 4.42226457 2.23801663 3.57363589 8.02806830 4.48487037
## X-squared X-squared X-squared X-squared X-squared X-squared
## 3.23735676 3.65548809 1.88675620 3.64888845 4.93089945 6.82462443
## X-squared X-squared X-squared X-squared X-squared X-squared
## 4.62437828 2.20715949 5.16414590 4.14324790 10.79172078 5.96488106
## X-squared X-squared X-squared X-squared X-squared X-squared
## 3.46520233 1.36872616 8.28364428 13.30441355 3.20088553 1.52072617
## X-squared X-squared X-squared X-squared X-squared X-squared
## 7.83831460 0.75514977 4.02321324 1.72378149 2.04264216 1.34716760
## X-squared X-squared X-squared X-squared X-squared X-squared
## 2.11338178 1.66474001 1.65538066 6.89190490 2.16179658 3.81592153
## X-squared X-squared X-squared X-squared X-squared X-squared
## 2.49093344 3.41221452 3.13259208 0.68052152 7.38365523 3.46722860
## X-squared X-squared X-squared X-squared X-squared X-squared
## 1.14602959 3.21492955 2.25208804 3.70549021 0.75632742 0.40263898
## X-squared X-squared X-squared X-squared X-squared X-squared
## 3.11604276 4.92292954 3.20637623 1.59124025 4.86768612 1.68035014
## X-squared X-squared X-squared X-squared X-squared X-squared
## 5.33973171 1.51968745 1.29190462 8.85874829 7.14146959 2.56979642
## X-squared X-squared X-squared X-squared X-squared X-squared
## 4.30322138 6.86230802 4.78300468 8.16103094 3.50156899 8.08053602
## X-squared X-squared X-squared X-squared X-squared X-squared
## 8.35484132 3.88641953 4.56046233 8.07167692 1.32171062 3.17505964
## X-squared X-squared X-squared X-squared X-squared X-squared
## 2.77191278 5.64225930 2.88099853 4.44181899 0.89934865 4.06519953
## X-squared X-squared X-squared X-squared X-squared X-squared
## 3.96535581 4.30876406 3.61693193 5.27528218 1.16627440 3.40104316
## X-squared X-squared X-squared X-squared X-squared X-squared
## 4.47367343 1.84244639 2.69361092 10.36251399 3.94083111 3.94238074
## X-squared X-squared X-squared X-squared X-squared X-squared
## 0.60229916 3.30964418 4.77763273 2.14245888 5.69027579 8.67033974
## X-squared X-squared X-squared X-squared X-squared X-squared
## 4.08989771 1.23551672 6.00985894 4.27371371 3.33506118 6.97873026
## X-squared X-squared X-squared X-squared X-squared X-squared
## 2.28976493 2.55155554 0.24645748 0.98507512 3.04263730 5.04983268
## X-squared X-squared X-squared X-squared X-squared X-squared
## 1.00051308 2.12575076 9.47340650 3.66114764 3.05819193 1.37426299
## X-squared X-squared X-squared X-squared X-squared X-squared
## 2.21732610 3.85196899 1.18161533 1.15689425 3.30889861 9.04767133
## X-squared X-squared X-squared X-squared X-squared X-squared
## 3.94678057 6.71567811 0.74370010 1.97909758 3.50594404 1.28126057
## X-squared X-squared X-squared X-squared X-squared X-squared
## 7.80781427 4.81582191 1.71107688 1.12609771 1.27631615 2.38497644
## X-squared X-squared X-squared X-squared X-squared X-squared
## 1.66748494 4.58997381 7.97992436 0.98315748 0.07077937 2.44796412
## X-squared X-squared X-squared X-squared X-squared X-squared
## 0.45073023 4.83602312 3.59554704 3.93404668 1.52453803 0.75390371
## X-squared X-squared X-squared X-squared X-squared X-squared
## 9.63289013 1.37707594 2.86307478 1.90976920 2.58138411 3.71983094
## X-squared X-squared X-squared X-squared X-squared X-squared
## 11.53283277 7.50186420 3.91794195 1.04775275 0.73253448 2.86334942
## X-squared X-squared X-squared X-squared X-squared X-squared
## 2.51637404 3.23900663 1.09211080 1.03933210 6.95543020 0.68420683
## X-squared X-squared X-squared X-squared X-squared X-squared
## 1.87456824 3.92084552 4.38403700 1.86850192 12.13832918 12.65413871
## X-squared X-squared X-squared X-squared X-squared X-squared
## 3.36266984 4.47896809 4.40250684 13.83254305 1.43703162 2.54560606
## X-squared X-squared X-squared X-squared X-squared X-squared
## 7.70204066 4.17028570 7.13480177 3.24006432 4.97078550 5.74717162
## X-squared X-squared X-squared X-squared X-squared X-squared
## 1.68108858 6.52882940 3.57737424 2.81258210 4.29005631 2.24490704
## X-squared X-squared X-squared X-squared X-squared X-squared
## 11.04695183 4.20003761 5.28994248 1.66550229 2.97026158 3.64894482
## X-squared X-squared X-squared X-squared X-squared X-squared
## 2.48079456 6.48285500 3.10356282 4.66001815 9.73896139 3.27474783
## X-squared X-squared X-squared X-squared X-squared X-squared
## 2.56780621 4.86030430 4.26314394 3.01525568 3.44629693 4.27858599
## X-squared X-squared X-squared X-squared X-squared X-squared
## 5.75063897 11.68607255 12.88615692 5.57156627 5.53973346 6.92295958
## X-squared X-squared X-squared X-squared X-squared X-squared
## 5.20712276 3.03610775 2.15093828 0.29947935 3.60232651 4.63890953
## X-squared X-squared X-squared X-squared X-squared X-squared
## 3.70467976 2.12066484 1.84822279 4.95651097 6.31385550 3.02311803
## X-squared X-squared X-squared X-squared X-squared X-squared
## 2.43616213 9.65414280 6.31327974 1.76187149 3.65639806 1.52912116
## X-squared X-squared X-squared X-squared X-squared X-squared
## 3.03365791 5.07033291 6.24262038 3.95036626 1.93552216 4.32297351
## X-squared X-squared X-squared X-squared X-squared X-squared
## 3.55904865 3.26773602 10.51612509 1.15905506 2.61551135 4.61141649
## X-squared X-squared X-squared X-squared X-squared X-squared
## 3.06933193 1.27339365 0.82365440 4.30508068 1.47347691 5.49864512
## X-squared X-squared X-squared X-squared X-squared X-squared
## 4.51577186 1.53795158 9.31440941 8.17442773 5.27099768 2.95767346
## X-squared X-squared X-squared X-squared X-squared X-squared
## 3.32564789 4.89143184 4.06668554 5.70865447 5.89650027 6.56277510
## X-squared X-squared X-squared X-squared X-squared X-squared
## 6.63654936 3.55694596 0.41801444 2.69993365 2.99356097 3.98245317
## X-squared X-squared X-squared X-squared X-squared X-squared
## 5.94460324 2.19289763 2.98799879 4.31835452 7.21895535 2.51044868
## X-squared X-squared X-squared X-squared X-squared X-squared
## 6.35234119 1.81076852 4.17347260 1.09362045 1.84901492 4.57072389
## X-squared X-squared X-squared X-squared X-squared X-squared
## 6.06418447 6.20830113 2.63017458 3.80071621 1.87900976 2.94367262
## X-squared X-squared X-squared X-squared X-squared X-squared
## 1.30628203 2.09400843 10.40738062 7.85109335 4.41427720 3.84098514
## X-squared X-squared X-squared X-squared X-squared X-squared
## 0.92983542 1.80087408 6.50193296 0.24462929 3.52947811 1.88967504
## X-squared X-squared X-squared X-squared X-squared X-squared
## 5.38833683 1.53732824 3.55552592 3.69043203 3.07895097 3.36698608
## X-squared X-squared X-squared X-squared X-squared X-squared
## 1.81161224 0.78382411 0.28776568 1.79405591 0.97679969 2.23133570
## X-squared X-squared X-squared X-squared X-squared X-squared
## 1.62971564 8.18036053 7.32624578 1.75566923 2.03453969 4.70901313
## X-squared X-squared X-squared X-squared X-squared X-squared
## 3.36483333 3.47142110 2.80125476 3.57636710 4.25634884 2.56304793
## X-squared X-squared X-squared X-squared X-squared X-squared
## 0.57485637 2.07180811 4.81969126 2.34567292 2.00180411 4.43337138
## X-squared X-squared X-squared X-squared X-squared X-squared
## 4.69140467 1.60660623 3.00086646 2.64838265 1.80665940 3.15369868
## X-squared X-squared X-squared X-squared X-squared X-squared
## 8.66358717 11.05460684 3.12727149 6.64799593 7.67762609 1.58162955
## X-squared X-squared X-squared X-squared X-squared X-squared
## 1.42768244 1.64719630 3.57626512 0.65639536 1.88809680 3.98691914
## X-squared X-squared X-squared X-squared X-squared X-squared
## 2.10318721 2.02079975 2.22272513 6.15837098 0.90314276 2.78387859
## X-squared X-squared X-squared X-squared X-squared X-squared
## 5.78297742 1.28656421 6.24457531 2.76445983 2.62261334 1.12038641
## X-squared X-squared X-squared X-squared X-squared X-squared
## 1.20178568 3.63158137 4.11490114 2.38831275 8.54872261 2.10042443
## X-squared X-squared X-squared X-squared X-squared X-squared
## 2.34712498 1.32525507 5.80312032 6.34655022 2.92236016 5.77323436
## X-squared X-squared X-squared X-squared X-squared X-squared
## 4.80795358 7.86626253 7.39581623 3.30845536 1.68361280 1.48366604
## X-squared X-squared X-squared X-squared X-squared X-squared
## 1.63132730 8.12566295 0.76484225 1.96154449 0.94597602 7.12609086
## X-squared X-squared X-squared X-squared X-squared X-squared
## 4.53880300 2.18901977 1.96246512 1.36164758 11.77440702 2.19027773
## X-squared X-squared X-squared X-squared X-squared X-squared
## 1.49692929 11.27690765 6.43539934 3.60969184 4.68744590 1.93824506
## X-squared X-squared X-squared X-squared X-squared X-squared
## 1.87686841 3.05632742 1.32300618 8.89395536 3.90905288 2.85783142
## X-squared X-squared X-squared X-squared X-squared X-squared
## 9.08809869 3.36523204 0.63954784 2.45339897 1.34752573 4.79934421
## X-squared X-squared X-squared X-squared X-squared X-squared
## 4.63145818 3.83097497 10.14375706 4.21226102 2.15030971 3.74956807
## X-squared X-squared X-squared X-squared X-squared X-squared
## 4.61262594 3.64417260 10.13958094 9.14449874 5.33382623 3.68047016
## X-squared X-squared X-squared X-squared X-squared X-squared
## 9.40277428 5.77090281 7.94013997 1.63437091 2.35381521 2.37506708
## X-squared X-squared X-squared X-squared X-squared X-squared
## 7.00251819 0.90018718 4.58881171 3.27585755 2.03731717 3.38343607
## X-squared X-squared X-squared X-squared X-squared X-squared
## 0.40058577 4.35227862 3.96854805 4.61232721 13.69748919 11.82384848
## X-squared X-squared X-squared X-squared X-squared X-squared
## 6.30602981 5.96654691 2.35569947 4.15870521 1.07087762 4.09604199
## X-squared X-squared X-squared X-squared X-squared X-squared
## 4.70478984 5.67910989 2.24585052 0.41609039 4.79701492 1.72088669
## X-squared X-squared X-squared X-squared X-squared X-squared
## 2.84590126 6.12379473 0.99264838 2.37820960 5.13824549 0.28919437
## X-squared X-squared X-squared X-squared X-squared X-squared
## 12.85201484 2.18224376 2.84635040 9.57611870 1.22223131 3.47036692
## X-squared X-squared X-squared X-squared X-squared X-squared
## 8.53144128 3.67769320 5.84710168 0.84837762 2.65758780 7.71379143
## X-squared X-squared X-squared X-squared X-squared X-squared
## 1.44363150 7.10665755 4.18226388 4.80193781 3.14085043 3.24753625
## X-squared X-squared X-squared X-squared X-squared X-squared
## 1.96246512 5.17445691 7.78389761 3.83543120 5.03579929 6.57512415
## X-squared X-squared X-squared X-squared X-squared X-squared
## 7.57681024 1.28661906 4.42672825 0.97526001 0.94954194 3.65140957
## X-squared X-squared X-squared X-squared X-squared X-squared
## 0.52089028 9.28568027 3.04263271 2.08323284 2.05628056 5.09578102
## X-squared X-squared X-squared X-squared X-squared X-squared
## 1.27297472 4.91927689 4.01844624 1.99697802 9.67013378 1.80508061
## X-squared X-squared X-squared X-squared X-squared X-squared
## 3.27723873 1.61260929 2.55344053 0.70876397 12.13321922 3.77512657
## X-squared X-squared X-squared X-squared X-squared X-squared
## 1.22801690 1.75505485 1.27372671 2.20971513 2.11144257 2.70627576
## X-squared X-squared X-squared X-squared X-squared X-squared
## 7.74572311 7.14347804 4.33105237 1.69025194 2.70846554 2.90798398
## X-squared X-squared X-squared X-squared X-squared X-squared
## 0.74111839 5.38244227 6.46271958 8.67137091 4.67165588 8.36662233
## X-squared X-squared X-squared X-squared X-squared X-squared
## 6.49285728 1.15533064 3.16671078 3.30276136 4.17347478 0.22379787
## X-squared X-squared X-squared X-squared X-squared X-squared
## 5.02887987 0.38119509 2.60639853 5.97255103 2.95339869 4.35614507
## X-squared X-squared X-squared X-squared X-squared X-squared
## 2.87515864 8.34551551 1.57834127 3.76527580 1.45085888 1.61227748
## X-squared X-squared X-squared X-squared X-squared X-squared
## 8.66514852 4.01122686 3.53174824 0.93381802 4.04492992 2.66378965
## X-squared X-squared X-squared X-squared X-squared X-squared
## 0.93739353 13.09152255 2.49625390 1.10208493 2.38757568 3.90298370
## X-squared X-squared X-squared X-squared X-squared X-squared
## 14.56390658 13.57268651 2.28750502 1.19305263 3.28528296 5.99688719
## X-squared X-squared X-squared X-squared X-squared X-squared
## 6.60065574 5.66618659 6.62525159 1.10110995 1.10553948 2.42800662
## X-squared X-squared X-squared X-squared X-squared X-squared
## 4.04880007 1.54877499 3.34728712 6.64528172 0.75714792 1.78100458
## X-squared X-squared X-squared X-squared X-squared X-squared
## 1.70242578 10.84625395 2.02633392 2.71061621 7.71286659 1.43584627
## X-squared X-squared X-squared X-squared X-squared X-squared
## 9.73708406 2.08284504 2.83638768 2.87935668 1.32513932 3.18156819
## X-squared X-squared X-squared X-squared X-squared X-squared
## 5.58934396 4.54152747 6.18130687 5.38345863 2.20095823 3.85868606
## X-squared X-squared X-squared X-squared X-squared X-squared
## 4.07098929 2.50896685 3.58904721 1.03229493 1.38255832 11.42056169
## X-squared X-squared X-squared X-squared X-squared X-squared
## 2.08968340 2.39475835 13.61400401 5.95959797 4.19590694 5.60752498
## X-squared X-squared X-squared X-squared X-squared X-squared
## 1.85708509 5.27376596 2.90401575 5.35909637 5.37176623 5.09349246
## X-squared X-squared X-squared X-squared X-squared X-squared
## 2.22456264 4.34729474 0.78050640 0.68799660 4.52791195 5.79271790
## X-squared X-squared X-squared X-squared X-squared X-squared
## 2.35765222 1.71424083 1.88132892 5.21398205 6.87930917 7.88865873
## X-squared X-squared X-squared X-squared X-squared X-squared
## 4.10601486 7.22292415 6.08612613 2.37459552 2.70686259 1.84583117
## X-squared X-squared X-squared X-squared X-squared X-squared
## 3.23045898 5.44154000 2.08968734 1.20902685 10.88286429 10.10350588
## X-squared X-squared X-squared X-squared X-squared X-squared
## 1.59873436 5.24584402 6.21778320 5.98541634 1.41823061 3.01236503
## X-squared X-squared X-squared X-squared X-squared X-squared
## 2.70739797 3.18250030 4.82309892 2.62962130 1.61974763 0.56777404
## X-squared X-squared X-squared X-squared X-squared X-squared
## 4.65209261 2.97003349 4.86578271 2.77317673 6.77412266 3.98665868
## X-squared X-squared X-squared X-squared X-squared X-squared
## 2.63415699 2.55261916 11.96705958 2.38467202 1.01211498 3.17429933
## X-squared X-squared X-squared X-squared X-squared X-squared
## 3.67180959 7.83369495 2.75779499 1.38569031 3.73822307 1.82734405
## X-squared X-squared X-squared X-squared X-squared X-squared
## 1.01222824 6.57342365 4.95707640 7.52394239 0.71566982 1.49513202
## X-squared X-squared X-squared X-squared X-squared X-squared
## 2.03486605 5.21476640 4.94823407 3.57342733 1.33865379 0.76727378
## X-squared X-squared X-squared X-squared X-squared X-squared
## 4.27924100 1.60872116 5.73254889 3.24757470 6.90885219 1.99537914
## X-squared X-squared X-squared X-squared X-squared X-squared
## 4.48492849 1.77838520 3.32565539 1.57180336 1.20169786 6.97019049
## X-squared X-squared X-squared X-squared X-squared X-squared
## 3.57231069 5.56819504 1.17688877 2.37487370 4.75082367 1.26792468
## X-squared X-squared X-squared X-squared X-squared X-squared
## 4.31163021 3.24813854 3.21823515 6.60710917 17.25189954 7.29318984
## X-squared X-squared X-squared X-squared X-squared X-squared
## 1.70462731 2.82678537 7.84510963 6.68480093 0.65726368 4.06928759
## X-squared X-squared X-squared X-squared X-squared X-squared
## 6.01569290 3.65750607 8.38213145 0.80423313 0.97529783 9.92838087
## X-squared X-squared X-squared X-squared X-squared X-squared
## 3.64061145 1.82262050 6.64354154 5.89108790 3.04590553 9.32552651
## X-squared X-squared X-squared X-squared X-squared X-squared
## 1.31135112 3.07121253 2.81534004 11.27282789 4.25454137 1.34962556
## X-squared X-squared X-squared X-squared X-squared X-squared
## 3.71124832 5.86809523 6.18306693 3.37100669 7.13367952 8.43066056
## X-squared X-squared X-squared X-squared X-squared X-squared
## 3.07209422 2.08239225 4.26236677 3.35364545 5.84715302 3.02927191
## X-squared X-squared X-squared X-squared X-squared X-squared
## 5.05480675 4.27346987 0.49168501 9.73862613 1.30515645 7.19571603
## X-squared X-squared X-squared X-squared X-squared X-squared
## 2.46539843 3.25662107 7.37209857 1.55434835 4.88945982 2.97003349
## X-squared X-squared X-squared X-squared X-squared X-squared
## 0.72609464 4.09049622 2.35438816 1.42673305 6.66183326 3.85941099
## X-squared X-squared X-squared X-squared X-squared X-squared
## 3.55270353 10.83531726 1.44564176 1.91881530 2.32991350 1.78271363
## X-squared X-squared X-squared X-squared X-squared X-squared
## 2.47988268 3.87965151 3.14036425 3.93444140 5.79379961 5.15544100
## X-squared X-squared X-squared X-squared X-squared X-squared
## 9.20985011 2.84907248 4.50468338 9.03853193 3.56794496 2.98367281
## X-squared X-squared X-squared X-squared X-squared X-squared
## 5.45843828 4.79721942 2.70368103 5.26931871 0.72753930 3.31829193
## X-squared X-squared X-squared X-squared X-squared X-squared
## 2.63449069 2.22840345 1.05792367 6.37611586 2.99076951 6.96255242
## X-squared X-squared X-squared X-squared X-squared X-squared
## 3.37218943 0.91210332 1.49882485 3.56895461 3.79610748 5.42979285
## X-squared X-squared X-squared X-squared X-squared X-squared
## 0.57001718 14.17513901 10.00946201 3.18160590 2.42591167 3.24316486
## X-squared X-squared X-squared X-squared X-squared X-squared
## 3.69388269 3.40939311 9.33046614 6.63658306 8.31087463 1.56684481
## X-squared X-squared X-squared X-squared X-squared X-squared
## 2.45601290 4.21826520 4.67705307 5.01007381 2.77625760 11.81777978
## X-squared X-squared X-squared X-squared X-squared X-squared
## 6.27449248 10.60230890 2.47495254 7.54069785 3.99856378 5.48332409
## X-squared X-squared X-squared X-squared X-squared X-squared
## 5.12088458 6.18504953 4.77857198 3.66043251 2.28066781 2.28792249
## X-squared X-squared X-squared X-squared X-squared X-squared
## 3.05916209 2.93428048 2.71550908 9.73303519 1.72295413 2.73717008
## X-squared X-squared X-squared X-squared X-squared X-squared
## 9.48396963 1.48728871 6.80719881 6.19799990 7.41409760 4.68274640
## X-squared X-squared X-squared X-squared X-squared X-squared
## 2.94611466 1.94641130 0.99495732 0.15247733 2.54039093 2.63752470
## X-squared X-squared X-squared X-squared X-squared X-squared
## 7.30131629 4.43475588 4.52916460 3.10863484 1.57666882 8.17135954
## X-squared X-squared X-squared X-squared X-squared X-squared
## 1.32588340 2.71369070 11.12249360 1.82676474 3.77719790 2.48560110
## X-squared X-squared X-squared X-squared X-squared X-squared
## 3.52577805 1.69599004 0.85908714 11.00113416 2.14612315 6.71470706
## X-squared X-squared X-squared X-squared X-squared X-squared
## 2.31582273 3.37374267 3.21058495 3.96559454 2.69346417 6.87804724
## X-squared X-squared X-squared X-squared X-squared X-squared
## 0.20695791 1.91933730 5.79822178 2.04844795 5.26457449 0.98448974
## X-squared X-squared X-squared X-squared X-squared X-squared
## 3.74157712 11.55915768 1.50843353 3.48991659 1.44635130 6.92345566
## X-squared X-squared X-squared X-squared X-squared X-squared
## 1.10481045 3.64466837 13.38062719 3.11879593 2.95026421 1.63125583
## X-squared X-squared X-squared X-squared X-squared X-squared
## 1.62366393 4.57633233 2.10751290 1.51801457 6.50780282 0.94698171
## X-squared X-squared X-squared X-squared X-squared X-squared
## 11.96739770 3.09519908 1.93597351 4.80871603 5.68745787 0.82543448
## X-squared X-squared X-squared X-squared X-squared X-squared
## 5.55464681 16.69733848 5.72209139 1.58976267 5.14195867 1.85466039
## X-squared X-squared X-squared X-squared X-squared X-squared
## 3.64512232 9.69318167 3.23803181 3.18027768 4.57966796 4.30037937
## X-squared X-squared X-squared X-squared
## 9.49683065 4.29912114 4.20418790 2.48823944Step 3: P-value calculation
p_val <- null_distribution %>%
get_pvalue(obs_stat = test_stat, direction = "greater") ## Warning: Please be cautious in reporting a p-value of 0. This result is an
## approximation based on the number of `reps` chosen in the `generate()` step.
## See `?get_p_value()` for more information.p_val## # A tibble: 1 × 1
## p_value
## <dbl>
## 1 0We produce a p value approximately 0, which strongly suggests to us that we can reject our null hypothesis.
Step 4: Visualization
conf_int <- null_distribution %>%
get_confidence_interval(level = 0.95, type = "percentile")
null_distribution %>%
visualize() +
shade_p_value(obs_stat = test_stat, direction = "greater") +
shade_confidence_interval(endpoints = conf_int)## Warning in min(diff(unique_loc)): no non-missing arguments to min; returning
## Inf
null_distribution## Response: lr_scaled_parties (factor)
## Explanatory: current_union_member (factor)
## Null Hypothesis: independence
## # A tibble: 1,000 × 2
## replicate stat
## <int> <dbl>
## 1 1 3.21
## 2 2 2.29
## 3 3 3.30
## 4 4 3.27
## 5 5 4.30
## 6 6 12.2
## 7 7 2.97
## 8 8 2.20
## 9 9 5.29
## 10 10 7.11
## # ℹ 990 more rowsThe 95% confidence interval is shaded in green, but is not necessary for distinguishing our results. We can observe that the red line sourced from the chi-squared statistic falls entirely outside the null distribution, and shows that we are able to reject the null hypothesis.
We thus conclude that union membership has a strong relationship with how respondents support parties on the left-right scale.
Modelling
model1 <- glm(voted_right_of_center ~ current_union_member, data = sweden_data_subset, family = binomial)
model2 <- glm(voted_right_of_center ~ current_union_member + gen, data = sweden_data_subset, family = binomial)
model3 <- glm(voted_right_of_center ~ current_union_member + educ_level, data = sweden_data_subset, family = binomial)
modelsummary(
list(model1, model2, model3),
fmt = 1,
estimate = c( "{estimate} ({std.error}){stars}",
"{estimate} ({std.error}){stars}",
"{estimate} ({std.error}){stars}"),
statistic = NULL,
coef_omit = "Intercept")| (1) | (2) | (3) | |
|---|---|---|---|
| current_union_memberYes | −0.7 (0.1)*** | −0.8 (0.1)*** | −0.7 (0.1)*** |
| genBaby Boomers | 0.0 (0.2) | ||
| genGen X | 0.3 (0.2) | ||
| genMillennials | 0.0 (0.2) | ||
| educ_levelNo BA | −0.2 (0.1)+ | ||
| Num.Obs. | 1260 | 1259 | 1260 |
| AIC | 1690.9 | 1689.0 | 1689.6 |
| BIC | 1701.1 | 1714.7 | 1705.0 |
| Log.Lik. | −843.435 | −839.497 | −841.794 |
| RMSE | 0.49 | 0.49 | 0.49 |
exp(coef(model1))## (Intercept) current_union_memberYes
## 1.0912281 0.4986914