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# "The Cultural Divide in Europe: Migration, Multiculturalism, and Political Trust"
# by Lauren M. McLaren
# World Politics, Volume 64, Issue 2April 2012 , pp. 199-241
# DOI: https://doi.org/10.1017/S0043887112000032
#
# Data analysis code
# June 26, 2020
#
# Marta Kolczynska, mkolczynska@gmail.com
# 1. SETUP ----------
sessionInfo()
# R version 3.6.3 (2020-02-29)
# Platform: x86_64-w64-mingw32/x64 (64-bit)
# Running under: Windows 10 x64 (build 18362)
#
# Matrix products: default
#
# locale:
# [1] LC_COLLATE=English_United States.1252 LC_CTYPE=English_United States.1252 LC_MONETARY=English_United States.1252
# [4] LC_NUMERIC=C LC_TIME=English_United States.1252
#
# attached base packages:
# [1] stats graphics grDevices utils datasets methods base
#
# loaded via a namespace (and not attached):
# [1] compiler_3.6.3 tools_3.6.3 packrat_0.5.0
## 1.1. Packages ----------
library(lme4) # for estimating multi-level models
library(mice) # for imputation and analyzing imputed data
# 2. Reading in the data ----------
# complete-case survey data
data_clean_5pct <- readRDS("data_clean_5pct.rds")
# imputed data
data_imp_5pct <- readRDS("data_imp_5pct.rds")
# 3. Analyses -----------
## 3.1 Main analysis (complete cases, weights) -----------
m1 <- lmer(trstprl_rev ~ imm_concern + happy_rev + stflife_rev + sclmeet_rev + distrust_soc +
stfeco_rev + hincfel + stfhlth_rev + stfedu_rev +
vote_gov + vote_frparty + lrscale + hhinc_std + agea + educ + female +
vote_share_fr + socexp + lt_imm_cntry + wgi + gdppc + unemp + (1 | cntry),
weights = pspwght,
data = data_clean_5pct)
summary(m1)
# Linear mixed model fit by REML ['lmerMod']
# Formula: trstprl_rev ~ imm_concern + happy_rev + stflife_rev + sclmeet_rev +
# distrust_soc + stfeco_rev + hincfel + stfhlth_rev + stfedu_rev +
# vote_gov + vote_frparty + lrscale + hhinc_std + agea + educ +
# female + vote_share_fr + socexp + lt_imm_cntry + wgi + gdppc + unemp + (1 | cntry)
# Data: data_clean_5pct
# Weights: pspwght
#
# REML criterion at convergence: 3694.5
#
# Scaled residuals:
# Min 1Q Median 3Q Max
# -3.5752 -0.6246 -0.0678 0.5711 3.5389
#
# Random effects:
# Groups Name Variance Std.Dev.
# cntry (Intercept) 0.0292 0.1709
# Residual 3.3620 1.8336
# Number of obs: 858, groups: cntry, 13
#
# Fixed effects:
# Estimate Std. Error t value
# (Intercept) 3.246e+00 9.812e-01 3.308
# imm_concern 1.797e-01 3.864e-02 4.651
# happy_rev -8.221e-02 5.449e-02 -1.509
# stflife_rev 1.577e-01 5.239e-02 3.009
# sclmeet_rev 8.809e-02 4.717e-02 1.868
# distrust_soc 6.500e-02 4.605e-02 1.412
# stfeco_rev 2.367e-01 3.805e-02 6.221
# hincfel 3.787e-02 1.001e-01 0.378
# stfhlth_rev 1.117e-02 3.419e-02 0.327
# stfedu_rev 1.281e-01 3.599e-02 3.560
# vote_gov1 -4.757e-01 1.401e-01 -3.394
# vote_frparty1 2.782e-01 3.601e-01 0.773
# lrscale -3.453e-02 3.403e-02 -1.015
# hhinc_std -1.384e-01 8.175e-02 -1.693
# agea -4.726e-03 3.932e-03 -1.202
# educ -1.021e-01 5.105e-02 -2.000
# female -2.457e-02 1.321e-01 -0.186
# vote_share_fr -6.641e-02 3.264e-02 -2.034
# socexp -9.360e-05 9.134e-05 -1.025
# lt_imm_cntry -9.512e-01 6.520e-01 -1.459
# wgi -4.637e-01 5.646e-01 -0.821
# gdppc 7.341e-05 2.792e-05 2.630
# unemp -1.136e-01 5.120e-02 -2.219
#
# Correlation matrix not shown by default, as p = 23 > 12.
# Use print(x, correlation=TRUE) or
# vcov(x) if you need it
#
# fit warnings:
# Some predictor variables are on very different scales: consider rescaling
## 3.2 Auxiliary analysis 1 (complete cases, no weights) -----------
m2 <- lmer(trstprl_rev ~ imm_concern + happy_rev + stflife_rev + sclmeet_rev + distrust_soc +
stfeco_rev + hincfel + stfhlth_rev + stfedu_rev +
vote_gov + vote_frparty + lrscale + hhinc_std + agea + educ + female +
vote_share_fr + socexp + lt_imm_cntry + wgi + gdppc + unemp + (1 | cntry),
data = data_clean_5pct)
summary(m2)
# Linear mixed model fit by REML ['lmerMod']
# Formula: trstprl_rev ~ imm_concern + happy_rev + stflife_rev + sclmeet_rev +
# distrust_soc + stfeco_rev + hincfel + stfhlth_rev + stfedu_rev +
# vote_gov + vote_frparty + lrscale + hhinc_std + agea + educ +
# female + vote_share_fr + socexp + lt_imm_cntry + wgi + gdppc + unemp + (1 | cntry)
# Data: data_clean_5pct
#
# REML criterion at convergence: 3627.7
#
# Scaled residuals:
# Min 1Q Median 3Q Max
# -3.6483 -0.6477 -0.0737 0.6150 2.9536
#
# Random effects:
# Groups Name Variance Std.Dev.
# cntry (Intercept) 0.00 0.000
# Residual 3.58 1.892
# Number of obs: 858, groups: cntry, 13
#
# Fixed effects:
# Estimate Std. Error t value
# (Intercept) 2.980e+00 8.623e-01 3.455
# imm_concern 2.145e-01 3.925e-02 5.465
# happy_rev -1.045e-01 5.506e-02 -1.897
# stflife_rev 1.505e-01 5.354e-02 2.811
# sclmeet_rev 8.803e-02 4.746e-02 1.855
# distrust_soc 8.275e-02 4.640e-02 1.784
# stfeco_rev 2.582e-01 3.798e-02 6.796
# hincfel 3.160e-02 1.001e-01 0.316
# stfhlth_rev 5.227e-02 3.535e-02 1.479
# stfedu_rev 9.396e-02 3.623e-02 2.593
# vote_gov1 -3.669e-01 1.404e-01 -2.613
# vote_frparty1 2.402e-01 3.846e-01 0.624
# lrscale -5.029e-02 3.483e-02 -1.444
# hhinc_std -1.134e-01 8.351e-02 -1.358
# agea -5.627e-03 4.071e-03 -1.382
# educ -1.100e-01 5.108e-02 -2.153
# female -2.732e-02 1.330e-01 -0.205
# vote_share_fr -7.272e-02 2.740e-02 -2.654
# socexp -1.531e-04 7.600e-05 -2.015
# lt_imm_cntry -1.306e+00 5.447e-01 -2.397
# wgi -1.699e-02 4.757e-01 -0.036
# gdppc 8.837e-05 2.309e-05 3.827
# unemp -1.448e-01 4.246e-02 -3.410
#
# Correlation matrix not shown by default, as p = 23 > 12.
# Use print(x, correlation=TRUE) or
# vcov(x) if you need it
#
# fit warnings:
# Some predictor variables are on very different scales: consider rescaling
# convergence code: 0
# boundary (singular) fit: see ?isSingular
## 3.3 Auxiliary analysis 2 (imputed data, weights) -----------
fitimp1 <- with(data_imp_5pct,
lmer(trstprl_rev ~ imm_concern + happy_rev + stflife_rev + sclmeet_rev + distrust_soc +
stfeco_rev + hincfel + stfhlth_rev + stfedu_rev +
vote_gov + vote_frparty + lrscale + hhinc_std + agea + educ + female +
vote_share_fr + socexp + lt_imm_cntry + wgi + gdppc + unemp + (1 | cntry),
weights = pspwght))
summary(pool(fitimp1))
# term estimate std.error statistic df p.value
# 1 (Intercept) 2.816689e+00 8.221700e-01 3.4259212 451.61567 6.687248e-04
# 2 imm_concern 1.423741e-01 3.119772e-02 4.5636050 1208.19658 5.541157e-06
# 3 happy_rev -4.983350e-02 4.560246e-02 -1.0927809 805.54346 2.748167e-01
# 4 stflife_rev 7.047185e-02 4.111709e-02 1.7139311 818.63266 8.691993e-02
# 5 sclmeet_rev 3.297687e-02 3.890915e-02 0.8475351 472.48439 3.971261e-01
# 6 distrust_soc 1.626593e-01 3.786510e-02 4.2957577 629.47191 2.016327e-05
# 7 stfeco_rev 2.771359e-01 3.316246e-02 8.3569146 239.35237 5.329071e-15
# 8 hincfel -5.732207e-02 8.306497e-02 -0.6900872 1107.58966 4.902839e-01
# 9 stfhlth_rev 5.563212e-02 2.951422e-02 1.8849260 372.91831 6.021716e-02
# 10 stfedu_rev 1.259361e-01 3.149656e-02 3.9984089 291.20678 8.089455e-05
# 11 vote_gov1 -2.859426e-01 1.236809e-01 -2.3119386 781.13063 2.104033e-02
# 12 vote_frparty1 1.669440e-01 3.400873e-01 0.4908858 1172.40909 6.235991e-01
# 13 lrscale -5.498991e-02 3.573257e-02 -1.5389295 34.40401 1.329694e-01
# 14 hhinc_std -1.738076e-01 7.689969e-02 -2.2601861 64.11253 2.721389e-02
# 15 agea -2.954197e-03 3.197596e-03 -0.9238808 500.03315 3.559940e-01
# 16 educ -8.500129e-02 4.405474e-02 -1.9294472 419.87255 5.434886e-02
# 17 female 6.804313e-02 1.115148e-01 0.6101711 653.26185 5.419607e-01
# 18 vote_share_fr -7.867001e-02 3.095496e-02 -2.5414351 1235.93419 1.116120e-02
# 19 socexp -3.232296e-05 8.388477e-05 -0.3853257 1203.17571 7.000642e-01
# 20 lt_imm_cntry -9.573876e-01 4.922548e-01 -1.9449023 1237.44329 5.201302e-02
# 21 wgi -4.402953e-01 4.968003e-01 -0.8862622 1246.29015 3.756472e-01
# 22 gdppc 6.614430e-05 2.555400e-05 2.5884131 1231.41218 9.755476e-03
# 23 unemp -1.224877e-01 4.030609e-02 -3.0389375 1159.40209 2.427313e-03
## 3.4 Auxiliary analysis 3 (imputed data, no weights) -----------
fitimp2 <- with(data_imp_5pct,
lmer(trstprl_rev ~ imm_concern + happy_rev + stflife_rev + sclmeet_rev + distrust_soc +
stfeco_rev + hincfel + stfhlth_rev + stfedu_rev +
vote_gov + vote_frparty + lrscale + hhinc_std + agea + educ + female +
vote_share_fr + socexp + lt_imm_cntry + wgi + gdppc + unemp + (1 | cntry)))
summary(pool(fitimp2))
# term estimate std.error statistic df p.value
# 1 (Intercept) 2.670425e+00 6.820727e-01 3.915162454 931.25755 9.693099e-05
# 2 imm_concern 1.733889e-01 3.111443e-02 5.572619399 1244.66947 3.073233e-08
# 3 happy_rev -3.984772e-02 4.427729e-02 -0.899958569 1176.99260 3.683264e-01
# 4 stflife_rev 6.032247e-02 4.131462e-02 1.460075785 1138.43822 1.445451e-01
# 5 sclmeet_rev 6.519698e-02 3.776982e-02 1.726165822 776.34841 8.471544e-02
# 6 distrust_soc 1.442623e-01 3.720122e-02 3.877892535 988.38166 1.123306e-04
# 7 stfeco_rev 2.832967e-01 3.073871e-02 9.216286016 1210.79162 0.000000e+00
# 8 hincfel -1.648051e-02 8.131428e-02 -0.202676669 1070.52447 8.394263e-01
# 9 stfhlth_rev 8.005235e-02 2.902248e-02 2.758287879 948.94275 5.922195e-03
# 10 stfedu_rev 1.087282e-01 3.101416e-02 3.505760697 352.97420 5.140006e-04
# 11 vote_gov1 -2.670967e-01 1.193788e-01 -2.237388784 1161.03361 2.545008e-02
# 12 vote_frparty1 1.000650e-01 3.494422e-01 0.286356421 1237.24931 7.746531e-01
# 13 lrscale -6.169859e-02 3.015253e-02 -2.046216038 501.96215 4.125558e-02
# 14 hhinc_std -1.551995e-01 7.601284e-02 -2.041754293 89.44537 4.412153e-02
# 15 agea -4.243024e-03 3.134274e-03 -1.353750000 1205.77576 1.760698e-01
# 16 educ -9.205973e-02 4.230064e-02 -2.176320097 889.41254 2.979383e-02
# 17 female 8.929983e-04 1.105279e-01 0.008079392 750.23635 9.935558e-01
# 18 vote_share_fr -7.701945e-02 2.459950e-02 -3.130935119 1222.91623 1.783804e-03
# 19 socexp -7.656541e-05 6.729134e-05 -1.137819684 1202.30676 2.554225e-01
# 20 lt_imm_cntry -1.076957e+00 3.907036e-01 -2.756455645 1210.87103 5.930883e-03
# 21 wgi -2.017787e-01 3.978470e-01 -0.507176717 1244.09435 6.121207e-01
# 22 gdppc 7.336745e-05 1.999235e-05 3.669775968 1243.82138 2.530379e-04
# 23 unemp -1.387186e-01 3.162545e-02 -4.386295851 1180.15849 1.255900e-05
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