Social impact project
1 PREPARATION
1.1 Merge
#- Within variables
Wdata=import("C:/Users/Eason Zhang/Dropbox/social impact project/Final analysis/employeedata.sav")%>%as.data.table()
numerical_variable_names <- names(Wdata)[sapply(Wdata, is.numeric)]
Wdata=group_mean_center(Wdata, numerical_variable_names,by="FactoryAssessedID", add.suffix=".GroC")
added(Wdata, {
wage.SD = sd(wage, na.rm = TRUE)
stress.SD = sd(stress, na.rm = TRUE)
mentalhealth.SD = sd(mentalhealth, na.rm = TRUE)},by=FactoryAssessedID)
#-- Within data check
matching_variables.W <- cc("mentalhealth,stress,wage,migrant10.GroC,
mentalhealth.SD,stress.SD,wage.SD")
contains_variables.W <- names(Wdata) %in% matching_variables.W
matching_variables.W <- names(Wdata)[contains_variables.W]
matching_variables.W
#- Between variables
#-- BA data
BAdata=import("C:/Users/Eason Zhang/Dropbox/social impact project/Final analysis/BA20.1920.finalanalysis.sav")%>%as.data.table()
#-- BM data
BMdata<- import("C:/Users/Eason Zhang/Dropbox/social impact project/Final analysis/managerdata.sav")%>%as.data.table()
BMdata1=BMdata[year == 2019]
BMdata2=BMdata[year == 2020]
BMdata_mean <- BMdata[, .SD, .SDcols = sapply(BMdata, is.numeric)]
BMdata_mean <- BMdata_mean[, lapply(.SD, mean, na.rm = TRUE), by = .(FactoryAssessedID)]
B.Aggregated <- unique(Wdata[, .(FactoryAssessedID, mentalhealth_mean, stress_mean, wage_mean, mentalhealth.SD, stress.SD, wage.SD)], by = "FactoryAssessedID")
#--- Cobmine BM data
setnames(BMdata1, old = names(BMdata1), new = paste0("BM19.", names(BMdata1)))
setnames(BMdata2, old = names(BMdata2), new = paste0("BM20.", names(BMdata2)))
setnames(BMdata_mean, old = names(BMdata_mean), new = paste0("BM.", names(BMdata_mean)))
setnames(BMdata_mean, "BM.FactoryAssessedID", "FactoryAssessedID")
setnames(BMdata1, "BM19.FactoryAssessedID", "FactoryAssessedID")
setnames(BMdata2, "BM20.FactoryAssessedID", "FactoryAssessedID")
Bdata<- Reduce(function(x, y) merge(x, y, by = "FactoryAssessedID", all = TRUE),list(BMdata_mean, BMdata1, BMdata2))
Bdata <- merge(BAdata, Bdata, by = "FactoryAssessedID", all = T)
Bdata2 <- merge(Bdata, B.Aggregated, by = "FactoryAssessedID", all = T)
#-- Between data check
matching_variables.B <- cc("BM.responsiblesourcing,BM19.responsiblesourcing,BM20.responsiblesourcing,BA1920FOApractices,BA20FOApractices,BA1920discriminate4prac,BA20discriminate4prac,BA1920targetedpractices,BA20targetedpractices,FactoryAssessedID, mentalhealth_mean, stress_mean, wage_mean, mentalhealth.SD, stress.SD, wage.SD")
contains_variables.B <- names(Bdata2) %in% matching_variables.B
matching_variables.B <- names(Bdata2)[contains_variables.B]
matching_variables.B
#- Full data
Bdata2=Bdata2[, c("mentalhealth_mean", "stress_mean", "wage_mean",
"mentalhealth.SD", "stress.SD", "wage.SD") := NULL]
BWdata2 <- merge(Bdata2, Wdata, by = "FactoryAssessedID", all = T)
BWdata2$clus <- BWdata2$FactoryAssessedID
#- Export
export(Bdata2,"Bdata3.sav")
export(BWdata2,"BWdata3.sav")
2 OVERVIEW
## Frequency Statistics:
## ───────────────
## N %
## ───────────────
## 1 1730 33.5
## 2 1666 32.3
## 3 1756 34.0
## (NA) 12 0.2
## ───────────────
## Total N = 5,164
## Valid N = 5,152One-round analysis: A. 3; B. 2 C. 1
Two-round analysis: D. 3&2; E. 3&1; F. 2&1
Three-round analysis: G. 3&2&1
3 A. 3rd ROUND DATA
## Frequency Statistics:
## ─────────────
## N %
## ─────────────
## 3 1756 100.0
## ─────────────
## Total N = 1,7563.1 I check for cw2: Cross-level interaction of M
3.1.1 BA1920FOApractices
mentalhealth.BA1920FOApractices=lmer(mentalhealth~BA1920FOApractices*migrant10+migrant10_mean*BA1920FOApractices+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
stress.BA1920FOApractices=lmer(stress~BA1920FOApractices*migrant10+migrant10_mean*BA1920FOApractices+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
wage.BA1920FOApractices=lmer(wage~BA1920FOApractices*migrant10+migrant10_mean*BA1920FOApractices+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
model_summary(list(mentalhealth.BA1920FOApractices, stress.BA1920FOApractices, wage.BA1920FOApractices))##
## Model Summary
##
## ────────────────────────────────────────────────────────────────────────────
## (1) mentalhealth (2) stress (3) wage
## ────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.098 3.745 378.106 *
## (1.701) (2.417) (147.512)
## BA1920FOApractices 0.011 -0.034 -7.288
## (0.086) (0.123) (7.477)
## migrant10 6.805 * -2.403 281.488 *
## (2.750) (2.570) (125.870)
## migrant10_mean -4.163 -0.669 -506.045
## (3.615) (4.352) (286.715)
## BA1920FOApractices:migrant10 -0.346 * 0.157 -15.141 *
## (0.141) (0.132) (6.467)
## BA1920FOApractices:migrant10_mean 0.205 0.009 24.703
## (0.183) (0.222) (14.500)
## ────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.012 0.030 0.040
## Conditional R^2 0.181 0.060 0.360
## AIC 3933.788 6059.325 17848.612
## BIC 3985.815 6113.650 17902.788
## Num. obs. 1343 1690 1665
## Num. groups: clus 66 66 66
## Var: clus (Intercept) 0.021 0.173 695.328
## Var: clus migrant10 0.388 0.298 1264.509
## Cov: clus (Intercept) migrant10 -0.055 -0.223 -288.457
## Var: Residual 0.999 2.026 2389.298
## ────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
3.1.2 BA20FOApractices
mentalhealth.BA20FOApractices=lmer(mentalhealth~BA20FOApractices*migrant10+migrant10_mean*BA20FOApractices+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
stress.BA20FOApractices=lmer(stress~BA20FOApractices*migrant10+migrant10_mean*BA20FOApractices+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
wage.BA20FOApractices=lmer(wage~BA20FOApractices*migrant10+migrant10_mean*BA20FOApractices+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
model_summary(list(mentalhealth.BA20FOApractices, stress.BA20FOApractices, wage.BA20FOApractices))##
## Model Summary
##
## ───────────────────────────────────────────────────────────────────────────
## (1) mentalhealth (2) stress (3) wage
## ───────────────────────────────────────────────────────────────────────────
## (Intercept) 4.988 ** 3.701 337.226 *
## (1.838) (2.855) (168.468)
## BA20FOApractices -0.088 -0.035 -5.244
## (0.092) (0.145) (8.489)
## migrant10 12.968 ** -6.416 * 396.245 *
## (4.925) (3.131) (155.001)
## migrant10_mean -11.243 * 3.366 -432.188
## (4.472) (5.656) (384.388)
## BA20FOApractices:migrant10 -0.650 ** 0.361 * -20.503 **
## (0.247) (0.158) (7.841)
## BA20FOApractices:migrant10_mean 0.566 * -0.191 20.901
## (0.224) (0.285) (19.243)
## ───────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.028 0.052 0.051
## Conditional R^2 0.264 0.094 0.434
## AIC 2246.304 3465.962 10086.196
## BIC 2292.948 3514.735 10134.855
## Num. obs. 784 970 959
## Num. groups: clus 44 44 44
## Var: clus (Intercept) 0.017 0.218 789.517
## Var: clus migrant10 0.601 0.338 1318.768
## Cov: clus (Intercept) migrant10 -0.050 -0.271 -255.259
## Var: Residual 0.909 1.967 1910.987
## ───────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
3.1.3 BA1920discriminate4prac
mentalhealth.BA1920discriminate4prac=lmer(mentalhealth~BA1920discriminate4prac*migrant10+migrant10_mean*BA1920discriminate4prac+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
stress.BA1920discriminate4prac=lmer(stress~BA1920discriminate4prac*migrant10+migrant10_mean*BA1920discriminate4prac+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
wage.BA1920discriminate4prac=lmer(wage~BA1920discriminate4prac*migrant10+migrant10_mean*BA1920discriminate4prac+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
model_summary(list(mentalhealth.BA1920discriminate4prac, stress.BA1920discriminate4prac, wage.BA1920discriminate4prac))##
## Model Summary
##
## ──────────────────────────────────────────────────────────────────────────────────
## (1) mentalhealth (2) stress (3) wage
## ──────────────────────────────────────────────────────────────────────────────────
## (Intercept) 1.126 2.163 406.518 **
## (1.789) (1.778) (141.449)
## BA1920discriminate4prac 0.552 0.219 -43.798
## (0.452) (0.454) (35.815)
## migrant10 -0.138 0.956 29.345
## (1.326) (1.261) (62.273)
## migrant10_mean 2.834 0.550 -280.043
## (2.834) (2.481) (207.741)
## BA1920discriminate4prac:migrant10 0.045 -0.084 -11.093
## (0.342) (0.328) (16.211)
## BA1920discriminate4prac:migrant10_mean -0.752 -0.230 65.974
## (0.716) (0.637) (52.670)
## ──────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.002 0.027 0.032
## Conditional R^2 0.186 0.058 0.368
## AIC 3932.120 6056.879 17846.788
## BIC 3984.147 6111.204 17900.964
## Num. obs. 1343 1690 1665
## Num. groups: clus 66 66 66
## Var: clus (Intercept) 0.031 0.169 714.091
## Var: clus migrant10 0.443 0.282 1391.197
## Cov: clus (Intercept) migrant10 -0.076 -0.211 -316.643
## Var: Residual 0.998 2.028 2389.350
## ──────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.interact_plot(mentalhealth.BA1920discriminate4prac, modx = BA1920discriminate4prac, pred = migrant10)
3.1.4 BA20discriminate4prac
mentalhealth.BA20discriminate4prac=lmer(mentalhealth~BA20discriminate4prac*migrant10+migrant10_mean*BA20discriminate4prac+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
stress.BA20discriminate4prac=lmer(stress~BA20discriminate4prac*migrant10+migrant10_mean*BA20discriminate4prac+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
wage.BA20discriminate4prac=lmer(wage~BA20discriminate4prac*migrant10+migrant10_mean*BA20discriminate4prac+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
model_summary(list(mentalhealth.BA20discriminate4prac, stress.BA20discriminate4prac, wage.BA20discriminate4prac))##
## Model Summary
##
## ────────────────────────────────────────────────────────────────────────────────
## (1) mentalhealth (2) stress (3) wage
## ────────────────────────────────────────────────────────────────────────────────
## (Intercept) 1.263 3.424 578.933 **
## (2.418) (2.209) (183.368)
## BA20discriminate4prac 0.495 -0.124 -87.203
## (0.608) (0.562) (46.228)
## migrant10 1.418 -1.023 109.091
## (2.429) (1.818) (85.307)
## migrant10_mean 0.851 1.269 -557.822
## (4.114) (3.150) (290.183)
## BA20discriminate4prac:migrant10 -0.361 0.445 -30.599
## (0.619) (0.469) (22.017)
## BA20discriminate4prac:migrant10_mean -0.186 -0.398 136.462
## (1.035) (0.807) (73.253)
## ────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.005 0.045 0.056
## Conditional R^2 0.271 0.092 0.431
## AIC 2246.134 3465.777 10080.336
## BIC 2292.778 3514.550 10128.995
## Num. obs. 784 970 959
## Num. groups: clus 44 44 44
## Var: clus (Intercept) 0.034 0.209 765.758
## Var: clus migrant10 0.757 0.405 1515.093
## Cov: clus (Intercept) migrant10 -0.107 -0.288 -356.668
## Var: Residual 0.909 1.972 1911.968
## ────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
3.1.5 BA1920targetedpractices
mentalhealth.BA1920targetedpractices=lmer(mentalhealth~BA1920targetedpractices*migrant10+migrant10_mean*BA1920targetedpractices+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
stress.BA1920targetedpractices=lmer(stress~BA1920targetedpractices*migrant10+migrant10_mean*BA1920targetedpractices+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
wage.BA1920targetedpractices=lmer(wage~BA1920targetedpractices*migrant10+migrant10_mean*BA1920targetedpractices+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
model_summary(list(mentalhealth.BA1920targetedpractices, stress.BA1920targetedpractices, wage.BA1920targetedpractices))##
## Model Summary
##
## ────────────────────────────────────────────────────────────────────────────────────
## (1) mentalhealth (2) stress (3) wage
## ────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 1.592 3.605 ** 380.477 ***
## (1.359) (1.327) (103.053)
## BA1920targetedpractices 0.176 -0.061 -15.080
## (0.138) (0.137) (10.543)
## migrant10 1.267 -0.030 78.992
## (1.285) (1.075) (53.297)
## migrant10_mean 1.524 -0.624 -283.339
## (2.422) (2.015) (168.036)
## BA1920targetedpractices:migrant10 -0.133 0.071 -10.023
## (0.137) (0.116) (5.765)
## BA1920targetedpractices:migrant10_mean -0.166 0.025 27.323
## (0.249) (0.210) (17.305)
## ────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.006 0.027 0.043
## Conditional R^2 0.188 0.057 0.367
## AIC 3936.054 6062.975 17849.806
## BIC 3988.081 6117.300 17903.982
## Num. obs. 1343 1690 1665
## Num. groups: clus 66 66 66
## Var: clus (Intercept) 0.023 0.157 681.623
## Var: clus migrant10 0.444 0.268 1317.940
## Cov: clus (Intercept) migrant10 -0.072 -0.197 -289.455
## Var: Residual 0.998 2.029 2389.709
## ────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.interact_plot(mentalhealth.BA1920targetedpractices, modx = BA1920targetedpractices, pred = migrant10)
3.1.6 BA20targetedpractices
mentalhealth.BA20targetedpractices=lmer(mentalhealth~BA20targetedpractices*migrant10+migrant10_mean*BA20targetedpractices+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
stress.BA20targetedpractices=lmer(stress~BA20targetedpractices*migrant10+migrant10_mean*BA20targetedpractices+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
wage.BA20targetedpractices=lmer(wage~BA20targetedpractices*migrant10+migrant10_mean*BA20targetedpractices+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
model_summary(list(mentalhealth.BA20targetedpractices, stress.BA20targetedpractices, wage.BA20targetedpractices))##
## Model Summary
##
## ──────────────────────────────────────────────────────────────────────────────────
## (1) mentalhealth (2) stress (3) wage
## ──────────────────────────────────────────────────────────────────────────────────
## (Intercept) 2.035 4.195 ** 412.932 ***
## (1.429) (1.435) (113.736)
## BA20targetedpractices 0.122 -0.131 -18.422
## (0.145) (0.149) (11.630)
## migrant10 0.725 -0.779 86.650
## (1.710) (1.206) (57.923)
## migrant10_mean 0.142 -1.169 -346.382
## (2.647) (2.256) (195.737)
## BA20targetedpractices:migrant10 -0.078 0.163 -10.611
## (0.185) (0.132) (6.385)
## BA20targetedpractices:migrant10_mean 0.005 0.088 34.247
## (0.273) (0.239) (20.293)
## ──────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.008 0.047 0.051
## Conditional R^2 0.275 0.093 0.434
## AIC 2252.242 3471.949 10088.206
## BIC 2298.886 3520.722 10136.865
## Num. obs. 784 970 959
## Num. groups: clus 44 44 44
## Var: clus (Intercept) 0.027 0.212 749.745
## Var: clus migrant10 0.785 0.364 1469.150
## Cov: clus (Intercept) migrant10 -0.111 -0.272 -294.527
## Var: Residual 0.909 1.970 1913.017
## ──────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
3.2 II check for ab2: Main effect of M on W (Mplus approach)
3.2.1 BM.responsiblesourcing
BA1920FOApractices.BM.responsiblesourcing=lmer(BA1920FOApractices~BM.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA20FOApractices.BM.responsiblesourcing=lmer(BA20FOApractices~BM.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA1920discriminate4prac.BM.responsiblesourcing=lmer(BA1920discriminate4prac~BM.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA20discriminate4prac.BM.responsiblesourcing=lmer(BA20discriminate4prac~BM.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA1920targetedpractices.BM.responsiblesourcing=lmer(BA1920targetedpractices~BM.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA20targetedpractices.BM.responsiblesourcing=lmer(BA20targetedpractices~BM.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
model_summary(list(BA1920FOApractices.BM.responsiblesourcing, BA20FOApractices.BM.responsiblesourcing, BA1920discriminate4prac.BM.responsiblesourcing, BA20discriminate4prac.BM.responsiblesourcing, BA1920targetedpractices.BM.responsiblesourcing, BA20targetedpractices.BM.responsiblesourcing))##
## Model Summary
##
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (1) BA1920FOApractices (2) BA20FOApractices (3) BA1920discriminate4prac (4) BA20discriminate4prac (5) BA1920targetedpractices (6) BA20targetedpractices
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 19.055 *** 18.910 *** 3.813 *** 3.874 *** 9.121 *** 8.859 ***
## (0.055) (0.065) (0.021) (0.022) (0.060) (0.081)
## BM.responsiblesourcing 0.185 *** 0.333 *** 0.023 ** 0.012 0.081 *** 0.094 ***
## (0.019) (0.023) (0.007) (0.008) (0.021) (0.029)
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.693 0.870 0.190 0.070 0.266 0.247
## Conditional R^2 1.000 1.000 1.000 1.000 1.000 1.000
## AIC -20816.929 -17259.765 -22545.406 -14159.220 -20827.300 -17284.903
## BIC -20796.968 -17241.844 -22525.445 -14141.300 -20807.339 -17266.983
## Num. obs. 1086 652 1086 652 1086 652
## Num. groups: clus 46 31 46 31 46 31
## Var: clus (Intercept) 0.033 0.035 0.005 0.004 0.040 0.057
## Var: Residual 0.000 0.000 0.000 0.000 0.000 0.000
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.3.2.2 BM19.responsiblesourcing
BA1920FOApractices.BM19.responsiblesourcing=lmer(BA1920FOApractices~BM19.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA20FOApractices.BM19.responsiblesourcing=lmer(BA20FOApractices~BM19.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA1920discriminate4prac.BM19.responsiblesourcing=lmer(BA1920discriminate4prac~BM19.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA20discriminate4prac.BM19.responsiblesourcing=lmer(BA20discriminate4prac~BM19.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA1920targetedpractices.BM19.responsiblesourcing=lmer(BA1920targetedpractices~BM19.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA20targetedpractices.BM19.responsiblesourcing=lmer(BA20targetedpractices~BM19.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
model_summary(list(BA1920FOApractices.BM19.responsiblesourcing, BA20FOApractices.BM19.responsiblesourcing, BA1920discriminate4prac.BM19.responsiblesourcing, BA20discriminate4prac.BM19.responsiblesourcing, BA1920targetedpractices.BM19.responsiblesourcing, BA20targetedpractices.BM19.responsiblesourcing))##
## Model Summary
##
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (1) BA1920FOApractices (2) BA20FOApractices (3) BA1920discriminate4prac (4) BA20discriminate4prac (5) BA1920targetedpractices (6) BA20targetedpractices
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 19.154 *** 19.199 *** 3.636 *** 3.608 *** 8.506 *** 8.335 ***
## (0.060) (0.068) (0.023) (0.026) (0.068) (0.083)
## BM19.responsiblesourcing 0.155 *** 0.232 *** 0.089 *** 0.103 *** 0.277 *** 0.292 ***
## (0.018) (0.021) (0.007) (0.008) (0.021) (0.026)
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.674 0.821 0.838 0.893 0.837 0.834
## Conditional R^2 1.000 1.000 1.000 1.000 1.000 1.000
## AIC -13646.961 -14321.831 -21765.239 -15856.399 -19141.811 -9889.651
## BIC -13628.611 -14304.725 -21746.889 -15839.292 -19123.461 -9872.544
## Num. obs. 726 532 726 532 726 532
## Num. groups: clus 33 24 33 24 33 24
## Var: clus (Intercept) 0.035 0.034 0.005 0.004 0.046 0.050
## Var: Residual 0.000 0.000 0.000 0.000 0.000 0.000
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.3.2.3 BM20.responsiblesourcing
BA1920FOApractices.BM20.responsiblesourcing=lmer(BA1920FOApractices~BM20.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA20FOApractices.BM20.responsiblesourcing=lmer(BA20FOApractices~BM20.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA1920discriminate4prac.BM20.responsiblesourcing=lmer(BA1920discriminate4prac~BM20.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA20discriminate4prac.BM20.responsiblesourcing=lmer(BA20discriminate4prac~BM20.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA1920targetedpractices.BM20.responsiblesourcing=lmer(BA1920targetedpractices~BM20.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA20targetedpractices.BM20.responsiblesourcing=lmer(BA20targetedpractices~BM20.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
model_summary(list(BA1920FOApractices.BM20.responsiblesourcing, BA20FOApractices.BM20.responsiblesourcing, BA1920discriminate4prac.BM20.responsiblesourcing, BA20discriminate4prac.BM20.responsiblesourcing, BA1920targetedpractices.BM20.responsiblesourcing, BA20targetedpractices.BM20.responsiblesourcing))##
## Model Summary
##
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (1) BA1920FOApractices (2) BA20FOApractices (3) BA1920discriminate4prac (4) BA20discriminate4prac (5) BA1920targetedpractices (6) BA20targetedpractices
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 19.465 *** 19.410 *** 3.934 *** 3.994 *** 9.499 *** 9.273 ***
## (0.051) (0.070) (0.018) (0.021) (0.055) (0.082)
## BM20.responsiblesourcing 0.032 0.135 *** -0.035 *** -0.055 *** -0.092 *** -0.127 ***
## (0.017) (0.023) (0.006) (0.007) (0.018) (0.027)
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.078 0.500 0.426 0.625 0.373 0.403
## Conditional R^2 1.000 1.000 1.000 1.000 1.000 1.000
## AIC -21942.217 -12184.716 -24583.388 -13828.546 -15297.566 -12491.709
## BIC -21923.365 -12168.114 -24564.537 -13811.943 -15278.714 -12475.107
## Num. obs. 823 469 823 469 823 469
## Num. groups: clus 35 23 35 23 35 23
## Var: clus (Intercept) 0.037 0.049 0.005 0.005 0.044 0.065
## Var: Residual 0.000 0.000 0.000 0.000 0.000 0.000
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.3.3 III check for cw1: Cross-level interaction of W
3.3.1 BM.responsiblesourcing
mentalhealth.BM.responsiblesourcing=lmer(mentalhealth~BM.responsiblesourcing*migrant10+migrant10_mean*BM.responsiblesourcing+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
stress.BM.responsiblesourcing=lmer(stress~BM.responsiblesourcing*migrant10+migrant10_mean*BM.responsiblesourcing+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
wage.BM.responsiblesourcing=lmer(wage~BM.responsiblesourcing*migrant10+migrant10_mean*BM.responsiblesourcing+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
model_summary(list(mentalhealth.BM.responsiblesourcing, stress.BM.responsiblesourcing, wage.BM.responsiblesourcing))##
## Model Summary
##
## ────────────────────────────────────────────────────────────────────────────────────
## (1) mentalhealth (2) stress (3) wage
## ────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.203 *** 3.323 *** 224.175 ***
## (0.204) (0.308) (14.910)
## BM.responsiblesourcing 0.059 -0.075 3.514
## (0.089) (0.127) (6.743)
## migrant10 0.471 0.303 -0.493
## (0.318) (0.326) (13.732)
## migrant10_mean -0.171 -0.707 -23.138
## (0.370) (0.465) (25.382)
## BM.responsiblesourcing:migrant10 -0.189 0.180 -3.903
## (0.104) (0.108) (4.562)
## BM.responsiblesourcing:migrant10_mean 0.002 -0.002 2.060
## (0.141) (0.174) (10.086)
## ────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.026 0.047 0.021
## Conditional R^2 0.224 0.092 0.381
## AIC 2679.246 4040.290 11802.693
## BIC 2727.468 4090.501 11852.796
## Num. obs. 918 1120 1108
## Num. groups: clus 49 49 49
## Var: clus (Intercept) 0.036 0.280 473.151
## Var: clus migrant10 0.547 0.420 1096.093
## Cov: clus (Intercept) migrant10 -0.112 -0.338 37.356
## Var: Residual 0.964 2.035 2233.244
## ────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
3.3.2 BM19.responsiblesourcing
mentalhealth.BM19.responsiblesourcing=lmer(mentalhealth~BM19.responsiblesourcing*migrant10+migrant10_mean*BM19.responsiblesourcing+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
stress.BM19.responsiblesourcing=lmer(stress~BM19.responsiblesourcing*migrant10+migrant10_mean*BM19.responsiblesourcing+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
wage.BM19.responsiblesourcing=lmer(wage~BM19.responsiblesourcing*migrant10+migrant10_mean*BM19.responsiblesourcing+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
model_summary(list(mentalhealth.BM19.responsiblesourcing, stress.BM19.responsiblesourcing, wage.BM19.responsiblesourcing))##
## Model Summary
##
## ─────────────────────────────────────────────────────────────────────────────────────
## (1) mentalhealth (2) stress (3) wage
## ─────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.167 *** 3.852 *** 230.417 ***
## (0.250) (0.360) (19.074)
## BM19.responsiblesourcing 0.027 -0.249 * -1.593
## (0.078) (0.114) (5.653)
## migrant10 0.124 0.251 3.874
## (0.335) (0.316) (12.649)
## migrant10_mean 0.098 -1.036 -24.964
## (0.411) (0.536) (30.287)
## BM19.responsiblesourcing:migrant10 -0.084 0.162 -4.218
## (0.105) (0.098) (3.968)
## BM19.responsiblesourcing:migrant10_mean -0.006 0.179 6.848
## (0.125) (0.172) (8.788)
## ─────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.008 0.052 0.010
## Conditional R^2 0.240 0.089 0.365
## AIC 1848.897 2728.782 7889.363
## BIC 1893.480 2775.035 7935.537
## Num. obs. 638 754 748
## Num. groups: clus 35 35 35
## Var: clus (Intercept) 0.038 0.132 270.071
## Var: clus migrant10 0.737 0.357 964.767
## Cov: clus (Intercept) migrant10 -0.167 -0.217 178.383
## Var: Residual 0.925 2.042 2027.924
## ─────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.interact_plot(mentalhealth.BM19.responsiblesourcing, modx = BM19.responsiblesourcing, pred = migrant10)
3.3.3 BM20.responsiblesourcing
mentalhealth.BM20.responsiblesourcing=lmer(mentalhealth~BM20.responsiblesourcing*migrant10+migrant10_mean*BM20.responsiblesourcing+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
stress.BM20.responsiblesourcing=lmer(stress~BM20.responsiblesourcing*migrant10+migrant10_mean*BM20.responsiblesourcing+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
wage.BM20.responsiblesourcing=lmer(wage~BM20.responsiblesourcing*migrant10+migrant10_mean*BM20.responsiblesourcing+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
model_summary(list(mentalhealth.BM20.responsiblesourcing, stress.BM20.responsiblesourcing, wage.BM20.responsiblesourcing))##
## Model Summary
##
## ─────────────────────────────────────────────────────────────────────────────────────
## (1) mentalhealth (2) stress (3) wage
## ─────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.303 *** 3.022 *** 217.620 ***
## (0.182) (0.248) (12.932)
## BM20.responsiblesourcing 0.053 -0.029 9.386
## (0.089) (0.109) (6.932)
## migrant10 0.200 0.730 * -18.140
## (0.325) (0.295) (14.873)
## migrant10_mean -0.184 -0.774 4.946
## (0.361) (0.423) (23.563)
## BM20.responsiblesourcing:migrant10 -0.097 0.106 0.863
## (0.098) (0.094) (4.601)
## BM20.responsiblesourcing:migrant10_mean -0.036 -0.006 -12.944
## (0.145) (0.155) (10.611)
## ─────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.016 0.065 0.040
## Conditional R^2 0.238 0.099 0.426
## AIC 2003.898 3013.138 8811.311
## BIC 2049.118 3060.424 8858.501
## Num. obs. 680 836 828
## Num. groups: clus 37 37 37
## Var: clus (Intercept) 0.069 0.195 559.372
## Var: clus migrant10 0.654 0.255 1615.160
## Cov: clus (Intercept) migrant10 -0.158 -0.214 -137.613
## Var: Residual 0.974 2.019 2204.179
## ─────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.interact_plot(mentalhealth.BM20.responsiblesourcing, modx = BM20.responsiblesourcing, pred = migrant10)
4 B. 2nd ROUND DATA
## Frequency Statistics:
## ─────────────
## N %
## ─────────────
## 2 1666 100.0
## ─────────────
## Total N = 1,6664.1 I check for cw2: Cross-level interaction of M
4.1.1 BA1920FOApractices
mentalhealth.BA1920FOApractices=lmer(mentalhealth~BA1920FOApractices*migrant10+migrant10_mean*BA1920FOApractices+ (migrant10|clus), na.action = na.exclude, data = dat3, control=lmerControl(optimizer="bobyqa"))
stress.BA1920FOApractices=lmer(stress~BA1920FOApractices*migrant10+migrant10_mean*BA1920FOApractices+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
wage.BA1920FOApractices=lmer(wage~BA1920FOApractices*migrant10+migrant10_mean*BA1920FOApractices+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
model_summary(list(mentalhealth.BA1920FOApractices, stress.BA1920FOApractices, wage.BA1920FOApractices))##
## Model Summary
##
## ─────────────────────────────────────────────────────────────────────────────
## (1) mentalhealth (2) stress (3) wage
## ─────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.098 1.576 413.814 **
## (1.701) (2.241) (139.299)
## BA1920FOApractices 0.011 0.069 -9.525
## (0.086) (0.114) (7.056)
## migrant10 6.805 * -2.863 168.134
## (2.750) (2.522) (106.767)
## migrant10_mean -4.163 5.713 -171.472
## (3.615) (4.487) (269.104)
## BA1920FOApractices:migrant10 -0.346 * 0.165 -8.226
## (0.141) (0.130) (5.491)
## BA1920FOApractices:migrant10_mean 0.205 -0.286 7.941
## (0.183) (0.228) (13.612)
## ─────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.012 0.017 0.027
## Conditional R^2 0.181 0.080 0.301
## AIC 3933.788 5455.984 16367.245
## BIC 3985.815 5509.521 16420.588
## Num. obs. 1343 1562 1532
## Num. groups: clus 66 69 69
## Var: clus (Intercept) 0.021 0.080 571.443
## Var: clus migrant10 0.388 0.283 722.993
## Cov: clus (Intercept) migrant10 -0.055 -0.110 -126.059
## Var: Residual 0.999 1.812 2314.745
## ─────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
4.1.2 BA20FOApractices
mentalhealth.BA20FOApractices=lmer(mentalhealth~BA20FOApractices*migrant10+migrant10_mean*BA20FOApractices+ (migrant10|clus), na.action = na.exclude, data = dat3, control=lmerControl(optimizer="bobyqa"))
stress.BA20FOApractices=lmer(stress~BA20FOApractices*migrant10+migrant10_mean*BA20FOApractices+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
wage.BA20FOApractices=lmer(wage~BA20FOApractices*migrant10+migrant10_mean*BA20FOApractices+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
model_summary(list(mentalhealth.BA20FOApractices, stress.BA20FOApractices, wage.BA20FOApractices))##
## Model Summary
##
## ──────────────────────────────────────────────────────────────────────────
## (1) mentalhealth (2) stress (3) wage
## ──────────────────────────────────────────────────────────────────────────
## (Intercept) 4.988 ** -1.538 401.116 *
## (1.838) (2.571) (175.667)
## BA20FOApractices -0.088 0.222 -9.124
## (0.092) (0.130) (8.849)
## migrant10 12.968 ** -3.315 329.746 *
## (4.925) (3.149) (149.284)
## migrant10_mean -11.243 * 15.740 ** -90.130
## (4.472) (5.819) (389.575)
## BA20FOApractices:migrant10 -0.650 ** 0.186 -16.013 *
## (0.247) (0.160) (7.562)
## BA20FOApractices:migrant10_mean 0.566 * -0.784 ** 4.577
## (0.224) (0.292) (19.527)
## ──────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.028 0.031 0.057
## Conditional R^2 0.264 0.115 0.340
## AIC 2246.304 3191.888 9730.948
## BIC 2292.948 3240.186 9779.005
## Num. obs. 784 925 903
## Num. groups: clus 44 45 45
## Var: clus (Intercept) 0.017 0.081 797.580
## Var: clus migrant10 0.601 0.341 1023.044
## Cov: clus (Intercept) migrant10 -0.050 -0.106 -281.332
## Var: Residual 0.909 1.700 2525.619
## ──────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
4.1.3 BA1920discriminate4prac
mentalhealth.BA1920discriminate4prac=lmer(mentalhealth~BA1920discriminate4prac*migrant10+migrant10_mean*BA1920discriminate4prac+ (migrant10|clus), na.action = na.exclude, data = dat3, control=lmerControl(optimizer="bobyqa"))
stress.BA1920discriminate4prac=lmer(stress~BA1920discriminate4prac*migrant10+migrant10_mean*BA1920discriminate4prac+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
wage.BA1920discriminate4prac=lmer(wage~BA1920discriminate4prac*migrant10+migrant10_mean*BA1920discriminate4prac+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
model_summary(list(mentalhealth.BA1920discriminate4prac, stress.BA1920discriminate4prac, wage.BA1920discriminate4prac))##
## Model Summary
##
## ─────────────────────────────────────────────────────────────────────────────────────
## (1) mentalhealth (2) stress (3) wage
## ─────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 1.126 6.258 *** 692.821 ***
## (1.789) (1.798) (118.860)
## BA1920discriminate4prac 0.552 -0.846 -118.079 ***
## (0.452) (0.457) (30.097)
## migrant10 -0.138 -0.909 28.823
## (1.326) (1.138) (48.367)
## migrant10_mean 2.834 -3.440 -630.601 ***
## (2.834) (2.750) (177.370)
## BA1920discriminate4prac:migrant10 0.045 0.330 -5.418
## (0.342) (0.297) (12.636)
## BA1920discriminate4prac:migrant10_mean -0.752 0.894 155.669 ***
## (0.716) (0.701) (44.962)
## ─────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.002 0.020 0.040
## Conditional R^2 0.186 0.081 0.298
## AIC 3932.120 5447.841 16354.074
## BIC 3984.147 5501.378 16407.417
## Num. obs. 1343 1562 1532
## Num. groups: clus 66 69 69
## Var: clus (Intercept) 0.031 0.069 404.152
## Var: clus migrant10 0.443 0.282 736.508
## Cov: clus (Intercept) migrant10 -0.076 -0.105 -55.068
## Var: Residual 0.998 1.812 2318.477
## ─────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.interact_plot(mentalhealth.BA1920discriminate4prac, modx = BA1920discriminate4prac, pred = migrant10)
4.1.4 BA20discriminate4prac
mentalhealth.BA20discriminate4prac=lmer(mentalhealth~BA20discriminate4prac*migrant10+migrant10_mean*BA20discriminate4prac+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))## Error in lme4::lFormula(formula = mentalhealth ~ BA20discriminate4prac * : 0 (non-NA) casesstress.BA20discriminate4prac=lmer(stress~BA20discriminate4prac*migrant10+migrant10_mean*BA20discriminate4prac+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
wage.BA20discriminate4prac=lmer(wage~BA20discriminate4prac*migrant10+migrant10_mean*BA20discriminate4prac+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
model_summary(list(mentalhealth.BA20discriminate4prac, stress.BA20discriminate4prac, wage.BA20discriminate4prac))##
## Model Summary
##
## ─────────────────────────────────────────────────────────────────────────────────
## (1) mentalhealth (2) stress (3) wage
## ─────────────────────────────────────────────────────────────────────────────────
## (Intercept) 1.263 4.571 993.069 ***
## (2.418) (2.483) (147.409)
## BA20discriminate4prac 0.495 -0.424 -194.485 ***
## (0.608) (0.628) (37.207)
## migrant10 1.418 -0.465 65.784
## (2.429) (1.550) (71.786)
## migrant10_mean 0.851 -0.659 -1085.446 ***
## (4.114) (3.945) (231.653)
## BA20discriminate4prac:migrant10 -0.361 0.215 -13.659
## (0.619) (0.403) (18.658)
## BA20discriminate4prac:migrant10_mean -0.186 0.184 272.705 ***
## (1.035) (1.001) (58.585)
## ─────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.005 0.021 0.101
## Conditional R^2 0.271 0.118 0.317
## AIC 2246.134 3191.183 9712.181
## BIC 2292.778 3239.481 9760.238
## Num. obs. 784 925 903
## Num. groups: clus 44 45 45
## Var: clus (Intercept) 0.034 0.107 443.308
## Var: clus migrant10 0.757 0.396 1075.557
## Cov: clus (Intercept) migrant10 -0.107 -0.134 -247.675
## Var: Residual 0.909 1.700 2536.001
## ─────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
4.1.5 BA1920targetedpractices
mentalhealth.BA1920targetedpractices=lmer(mentalhealth~BA1920targetedpractices*migrant10+migrant10_mean*BA1920targetedpractices+ (migrant10|clus), na.action = na.exclude, data = dat3, control=lmerControl(optimizer="bobyqa"))
stress.BA1920targetedpractices=lmer(stress~BA1920targetedpractices*migrant10+migrant10_mean*BA1920targetedpractices+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
wage.BA1920targetedpractices=lmer(wage~BA1920targetedpractices*migrant10+migrant10_mean*BA1920targetedpractices+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
model_summary(list(mentalhealth.BA1920targetedpractices, stress.BA1920targetedpractices, wage.BA1920targetedpractices))##
## Model Summary
##
## ─────────────────────────────────────────────────────────────────────────────────────
## (1) mentalhealth (2) stress (3) wage
## ─────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 1.592 4.995 *** 535.594 ***
## (1.359) (1.312) (86.763)
## BA1920targetedpractices 0.176 -0.210 -31.706 ***
## (0.138) (0.135) (8.872)
## migrant10 1.267 0.240 114.461 **
## (1.285) (1.067) (42.491)
## migrant10_mean 1.524 -3.781 -438.847 **
## (2.422) (2.271) (144.214)
## BA1920targetedpractices:migrant10 -0.133 0.011 -11.582 *
## (0.137) (0.115) (4.596)
## BA1920targetedpractices:migrant10_mean -0.166 0.401 43.459 **
## (0.249) (0.236) (14.848)
## ─────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.006 0.020 0.061
## Conditional R^2 0.188 0.082 0.287
## AIC 3936.054 5454.658 16353.288
## BIC 3988.081 5508.195 16406.631
## Num. obs. 1343 1562 1532
## Num. groups: clus 66 69 69
## Var: clus (Intercept) 0.023 0.070 410.273
## Var: clus migrant10 0.444 0.302 594.765
## Cov: clus (Intercept) migrant10 -0.072 -0.114 -70.217
## Var: Residual 0.998 1.812 2320.937
## ─────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.interact_plot(mentalhealth.BA1920targetedpractices, modx = BA1920targetedpractices, pred = migrant10)
4.1.6 BA20targetedpractices
mentalhealth.BA20targetedpractices=lmer(mentalhealth~BA20targetedpractices*migrant10+migrant10_mean*BA20targetedpractices+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))## Error in lme4::lFormula(formula = mentalhealth ~ BA20targetedpractices * : 0 (non-NA) casesstress.BA20targetedpractices=lmer(stress~BA20targetedpractices*migrant10+migrant10_mean*BA20targetedpractices+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
wage.BA20targetedpractices=lmer(wage~BA20targetedpractices*migrant10+migrant10_mean*BA20targetedpractices+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
model_summary(list(mentalhealth.BA20targetedpractices, stress.BA20targetedpractices, wage.BA20targetedpractices))##
## Model Summary
##
## ─────────────────────────────────────────────────────────────────────────────────
## (1) mentalhealth (2) stress (3) wage
## ─────────────────────────────────────────────────────────────────────────────────
## (Intercept) 2.035 4.404 ** 586.350 ***
## (1.429) (1.564) (103.082)
## BA20targetedpractices 0.122 -0.153 -37.311 ***
## (0.145) (0.161) (10.541)
## migrant10 0.725 0.769 131.023 *
## (1.710) (1.220) (52.145)
## migrant10_mean 0.142 -3.376 -540.532 **
## (2.647) (2.831) (180.213)
## BA20targetedpractices:migrant10 -0.078 -0.046 -13.069 *
## (0.185) (0.134) (5.747)
## BA20targetedpractices:migrant10_mean 0.005 0.366 55.115 **
## (0.273) (0.295) (18.702)
## ─────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.008 0.022 0.088
## Conditional R^2 0.275 0.123 0.324
## AIC 2252.242 3198.376 9722.335
## BIC 2298.886 3246.674 9770.392
## Num. obs. 784 925 903
## Num. groups: clus 44 45 45
## Var: clus (Intercept) 0.027 0.099 549.614
## Var: clus migrant10 0.785 0.426 887.540
## Cov: clus (Intercept) migrant10 -0.111 -0.135 -172.272
## Var: Residual 0.909 1.699 2533.865
## ─────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
4.2 II check for ab2: Main effect of M on W (Mplus approach)
4.2.1 BM.responsiblesourcing
BA1920FOApractices.BM.responsiblesourcing=lmer(BA1920FOApractices~BM.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA20FOApractices.BM.responsiblesourcing=lmer(BA20FOApractices~BM.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA1920discriminate4prac.BM.responsiblesourcing=lmer(BA1920discriminate4prac~BM.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA20discriminate4prac.BM.responsiblesourcing=lmer(BA20discriminate4prac~BM.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA1920targetedpractices.BM.responsiblesourcing=lmer(BA1920targetedpractices~BM.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA20targetedpractices.BM.responsiblesourcing=lmer(BA20targetedpractices~BM.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
model_summary(list(BA1920FOApractices.BM.responsiblesourcing, BA20FOApractices.BM.responsiblesourcing, BA1920discriminate4prac.BM.responsiblesourcing, BA20discriminate4prac.BM.responsiblesourcing, BA1920targetedpractices.BM.responsiblesourcing, BA20targetedpractices.BM.responsiblesourcing))##
## Model Summary
##
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (1) BA1920FOApractices (2) BA20FOApractices (3) BA1920discriminate4prac (4) BA20discriminate4prac (5) BA1920targetedpractices (6) BA20targetedpractices
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 19.269 *** 18.914 *** 3.791 *** 3.803 *** 9.107 *** 8.937 ***
## (0.056) (0.063) (0.024) (0.025) (0.060) (0.081)
## BM.responsiblesourcing 0.131 *** 0.337 *** 0.026 ** 0.030 *** 0.090 *** 0.096 ***
## (0.019) (0.023) (0.008) (0.009) (0.021) (0.029)
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.492 0.876 0.189 0.273 0.281 0.260
## Conditional R^2 1.000 1.000 1.000 1.000 1.000 1.000
## AIC -27110.283 -12455.622 -30284.873 -13718.358 -27600.067 -17451.794
## BIC -27090.515 -12437.665 -30265.104 -13700.401 -27580.298 -17433.837
## Num. obs. 1035 658 1035 658 1035 658
## Num. groups: clus 47 32 47 32 47 32
## Var: clus (Intercept) 0.036 0.035 0.006 0.005 0.042 0.057
## Var: Residual 0.000 0.000 0.000 0.000 0.000 0.000
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.4.2.2 BM19.responsiblesourcing
BA1920FOApractices.BM19.responsiblesourcing=lmer(BA1920FOApractices~BM19.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA20FOApractices.BM19.responsiblesourcing=lmer(BA20FOApractices~BM19.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA1920discriminate4prac.BM19.responsiblesourcing=lmer(BA1920discriminate4prac~BM19.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA20discriminate4prac.BM19.responsiblesourcing=lmer(BA20discriminate4prac~BM19.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA1920targetedpractices.BM19.responsiblesourcing=lmer(BA1920targetedpractices~BM19.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA20targetedpractices.BM19.responsiblesourcing=lmer(BA20targetedpractices~BM19.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
model_summary(list(BA1920FOApractices.BM19.responsiblesourcing, BA20FOApractices.BM19.responsiblesourcing, BA1920discriminate4prac.BM19.responsiblesourcing, BA20discriminate4prac.BM19.responsiblesourcing, BA1920targetedpractices.BM19.responsiblesourcing, BA20targetedpractices.BM19.responsiblesourcing))##
## Model Summary
##
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (1) BA1920FOApractices (2) BA20FOApractices (3) BA1920discriminate4prac (4) BA20discriminate4prac (5) BA1920targetedpractices (6) BA20targetedpractices
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 19.199 *** 19.197 *** 3.559 *** 3.548 *** 8.491 *** 8.370 ***
## (0.059) (0.065) (0.022) (0.024) (0.066) (0.079)
## BM19.responsiblesourcing 0.162 *** 0.212 *** 0.101 *** 0.109 *** 0.292 *** 0.284 ***
## (0.019) (0.020) (0.007) (0.008) (0.021) (0.025)
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.690 0.802 0.855 0.889 0.849 0.832
## Conditional R^2 1.000 1.000 1.000 1.000 1.000 1.000
## AIC -19787.008 -14859.024 -21835.933 -16463.277 -20336.386 -10386.666
## BIC -19768.581 -14841.777 -21817.506 -16446.030 -20317.959 -10369.419
## Num. obs. 740 551 740 551 740 551
## Num. groups: clus 34 25 34 25 34 25
## Var: clus (Intercept) 0.035 0.033 0.005 0.004 0.045 0.048
## Var: Residual 0.000 0.000 0.000 0.000 0.000 0.000
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.4.2.3 BM20.responsiblesourcing
BA1920FOApractices.BM20.responsiblesourcing=lmer(BA1920FOApractices~BM20.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA20FOApractices.BM20.responsiblesourcing=lmer(BA20FOApractices~BM20.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA1920discriminate4prac.BM20.responsiblesourcing=lmer(BA1920discriminate4prac~BM20.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA20discriminate4prac.BM20.responsiblesourcing=lmer(BA20discriminate4prac~BM20.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA1920targetedpractices.BM20.responsiblesourcing=lmer(BA1920targetedpractices~BM20.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA20targetedpractices.BM20.responsiblesourcing=lmer(BA20targetedpractices~BM20.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
model_summary(list(BA1920FOApractices.BM20.responsiblesourcing, BA20FOApractices.BM20.responsiblesourcing, BA1920discriminate4prac.BM20.responsiblesourcing, BA20discriminate4prac.BM20.responsiblesourcing, BA1920targetedpractices.BM20.responsiblesourcing, BA20targetedpractices.BM20.responsiblesourcing))##
## Model Summary
##
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (1) BA1920FOApractices (2) BA20FOApractices (3) BA1920discriminate4prac (4) BA20discriminate4prac (5) BA1920targetedpractices (6) BA20targetedpractices
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 19.454 *** 19.577 *** 3.953 *** 3.988 *** 9.366 *** 9.156 ***
## (0.053) (0.072) (0.019) (0.021) (0.059) (0.081)
## BM20.responsiblesourcing 0.001 0.104 *** -0.041 *** -0.059 *** -0.026 -0.101 ***
## (0.018) (0.024) (0.006) (0.007) (0.019) (0.026)
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.000 0.373 0.487 0.659 0.039 0.300
## Conditional R^2 1.000 1.000 1.000 1.000 1.000 1.000
## AIC -19838.997 -11811.698 -15676.199 -13301.244 -20214.064 -12217.441
## BIC -19820.490 -11795.279 -15657.692 -13284.825 -20195.558 -12201.022
## Num. obs. 755 448 755 448 755 448
## Num. groups: clus 35 23 35 23 35 23
## Var: clus (Intercept) 0.041 0.052 0.005 0.005 0.049 0.068
## Var: Residual 0.000 0.000 0.000 0.000 0.000 0.000
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.4.3 III check for cw1: Cross-level interaction of W
4.3.1 BM.responsiblesourcing
mentalhealth.BM.responsiblesourcing=lmer(mentalhealth~BM.responsiblesourcing*migrant10+migrant10_mean*BM.responsiblesourcing+ (migrant10|clus), na.action = na.exclude, data = dat3, control=lmerControl(optimizer="bobyqa"))
stress.BM.responsiblesourcing=lmer(stress~BM.responsiblesourcing*migrant10+migrant10_mean*BM.responsiblesourcing+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
wage.BM.responsiblesourcing=lmer(wage~BM.responsiblesourcing*migrant10+migrant10_mean*BM.responsiblesourcing+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
model_summary(list(mentalhealth.BM.responsiblesourcing, stress.BM.responsiblesourcing, wage.BM.responsiblesourcing))##
## Model Summary
##
## ────────────────────────────────────────────────────────────────────────────────────
## (1) mentalhealth (2) stress (3) wage
## ────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.203 *** 3.132 *** 217.909 ***
## (0.204) (0.254) (14.289)
## BM.responsiblesourcing 0.059 -0.145 1.636
## (0.089) (0.113) (6.789)
## migrant10 0.471 0.140 1.157
## (0.318) (0.327) (11.547)
## migrant10_mean -0.171 0.121 6.605
## (0.370) (0.456) (25.055)
## BM.responsiblesourcing:migrant10 -0.189 0.086 2.283
## (0.104) (0.111) (3.890)
## BM.responsiblesourcing:migrant10_mean 0.002 0.080 -4.202
## (0.141) (0.178) (10.220)
## ────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.026 0.024 0.003
## Conditional R^2 0.224 0.103 0.344
## AIC 2679.246 3809.491 11357.598
## BIC 2727.468 3859.403 11407.296
## Num. obs. 918 1087 1064
## Num. groups: clus 49 51 51
## Var: clus (Intercept) 0.036 0.125 468.326
## Var: clus migrant10 0.547 0.505 606.493
## Cov: clus (Intercept) migrant10 -0.112 -0.228 218.227
## Var: Residual 0.964 1.803 2292.253
## ────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
4.3.2 BM19.responsiblesourcing
mentalhealth.BM19.responsiblesourcing=lmer(mentalhealth~BM19.responsiblesourcing*migrant10+migrant10_mean*BM19.responsiblesourcing+ (migrant10|clus), na.action = na.exclude, data = dat3, control=lmerControl(optimizer="bobyqa"))
stress.BM19.responsiblesourcing=lmer(stress~BM19.responsiblesourcing*migrant10+migrant10_mean*BM19.responsiblesourcing+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
wage.BM19.responsiblesourcing=lmer(wage~BM19.responsiblesourcing*migrant10+migrant10_mean*BM19.responsiblesourcing+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
model_summary(list(mentalhealth.BM19.responsiblesourcing, stress.BM19.responsiblesourcing, wage.BM19.responsiblesourcing))##
## Model Summary
##
## ─────────────────────────────────────────────────────────────────────────────────────
## (1) mentalhealth (2) stress (3) wage
## ─────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.167 *** 3.359 *** 290.961 ***
## (0.250) (0.328) (18.305)
## BM19.responsiblesourcing 0.027 -0.178 -20.587 ***
## (0.078) (0.105) (5.769)
## migrant10 0.124 0.266 13.041
## (0.335) (0.306) (11.864)
## migrant10_mean 0.098 -0.406 -104.823 ***
## (0.411) (0.525) (29.407)
## BM19.responsiblesourcing:migrant10 -0.084 0.007 -1.549
## (0.105) (0.096) (3.765)
## BM19.responsiblesourcing:migrant10_mean -0.006 0.237 28.513 **
## (0.125) (0.162) (8.930)
## ─────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.008 0.022 0.044
## Conditional R^2 0.240 0.099 0.268
## AIC 1848.897 2725.104 8179.782
## BIC 1893.480 2771.671 8226.142
## Num. obs. 638 778 762
## Num. groups: clus 35 37 37
## Var: clus (Intercept) 0.038 0.098 276.851
## Var: clus migrant10 0.737 0.466 732.151
## Cov: clus (Intercept) migrant10 -0.167 -0.192 -4.224
## Var: Residual 0.925 1.783 2493.005
## ─────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.interact_plot(mentalhealth.BM19.responsiblesourcing, modx = BM19.responsiblesourcing, pred = migrant10)
4.3.3 BM20.responsiblesourcing
mentalhealth.BM20.responsiblesourcing=lmer(mentalhealth~BM20.responsiblesourcing*migrant10+migrant10_mean*BM20.responsiblesourcing+ (migrant10|clus), na.action = na.exclude, data = dat3, control=lmerControl(optimizer="bobyqa"))
stress.BM20.responsiblesourcing=lmer(stress~BM20.responsiblesourcing*migrant10+migrant10_mean*BM20.responsiblesourcing+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
wage.BM20.responsiblesourcing=lmer(wage~BM20.responsiblesourcing*migrant10+migrant10_mean*BM20.responsiblesourcing+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
model_summary(list(mentalhealth.BM20.responsiblesourcing, stress.BM20.responsiblesourcing, wage.BM20.responsiblesourcing))##
## Model Summary
##
## ─────────────────────────────────────────────────────────────────────────────────────
## (1) mentalhealth (2) stress (3) wage
## ─────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.303 *** 2.947 *** 202.647 ***
## (0.182) (0.230) (12.985)
## BM20.responsiblesourcing 0.053 -0.082 11.009
## (0.089) (0.110) (7.155)
## migrant10 0.200 0.035 -6.286
## (0.325) (0.318) (10.908)
## migrant10_mean -0.184 0.362 33.418
## (0.361) (0.437) (24.249)
## BM20.responsiblesourcing:migrant10 -0.097 0.131 3.246
## (0.098) (0.099) (3.399)
## BM20.responsiblesourcing:migrant10_mean -0.036 -0.024 -17.461
## (0.145) (0.176) (11.082)
## ─────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.016 0.027 0.014
## Conditional R^2 0.238 0.105 0.373
## AIC 2003.898 2731.592 8098.654
## BIC 2049.118 2778.146 8144.974
## Num. obs. 680 777 759
## Num. groups: clus 37 37 37
## Var: clus (Intercept) 0.069 0.154 546.412
## Var: clus migrant10 0.654 0.463 444.378
## Cov: clus (Intercept) migrant10 -0.158 -0.231 345.736
## Var: Residual 0.974 1.802 2303.042
## ─────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.interact_plot(mentalhealth.BM20.responsiblesourcing, modx = BM20.responsiblesourcing, pred = migrant10)
5 C. 1ST ROUND DATA
## Frequency Statistics:
## ─────────────
## N %
## ─────────────
## 1 1730 100.0
## ─────────────
## Total N = 1,7305.1 I check for cw2: Cross-level interaction of M
5.1.1 BA1920FOApractices
mentalhealth.BA1920FOApractices=lmer(mentalhealth~BA1920FOApractices*migrant10+migrant10_mean*BA1920FOApractices+ (migrant10|clus), na.action = na.exclude, data = dat3, control=lmerControl(optimizer="bobyqa"))
stress.BA1920FOApractices=lmer(stress~BA1920FOApractices*migrant10+migrant10_mean*BA1920FOApractices+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
wage.BA1920FOApractices=lmer(wage~BA1920FOApractices*migrant10+migrant10_mean*BA1920FOApractices+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
model_summary(list(mentalhealth.BA1920FOApractices, stress.BA1920FOApractices, wage.BA1920FOApractices))##
## Model Summary
##
## ───────────────────────────────────────────────────────────────────────────
## (1) mentalhealth (2) stress (3) wage
## ───────────────────────────────────────────────────────────────────────────
## (Intercept) 3.098 1.314 267.417
## (1.701) (1.979) (158.601)
## BA1920FOApractices 0.011 0.082 -2.188
## (0.086) (0.100) (8.022)
## migrant10 6.805 * -0.281 208.556
## (2.750) (2.155) (137.299)
## migrant10_mean -4.163 5.817 -94.072
## (3.615) (3.985) (306.881)
## BA1920FOApractices:migrant10 -0.346 * 0.033 -10.381
## (0.141) (0.111) (7.063)
## BA1920FOApractices:migrant10_mean 0.205 -0.292 4.530
## (0.183) (0.202) (15.503)
## ───────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.012 0.016 0.008
## Conditional R^2 0.181 0.057 0.355
## AIC 3933.788 5628.459 17482.631
## BIC 3985.815 5682.292 17536.340
## Num. obs. 1343 1609 1589
## Num. groups: clus 66 69 69
## Var: clus (Intercept) 0.021 0.000 682.012
## Var: clus migrant10 0.388 0.113 1579.610
## Cov: clus (Intercept) migrant10 -0.055 0.000 -67.098
## Var: Residual 0.999 1.850 3163.435
## ───────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
5.1.2 BA20FOApractices
mentalhealth.BA20FOApractices=lmer(mentalhealth~BA20FOApractices*migrant10+migrant10_mean*BA20FOApractices+ (migrant10|clus), na.action = na.exclude, data = dat3, control=lmerControl(optimizer="bobyqa"))
stress.BA20FOApractices=lmer(stress~BA20FOApractices*migrant10+migrant10_mean*BA20FOApractices+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
wage.BA20FOApractices=lmer(wage~BA20FOApractices*migrant10+migrant10_mean*BA20FOApractices+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
model_summary(list(mentalhealth.BA20FOApractices, stress.BA20FOApractices, wage.BA20FOApractices))##
## Model Summary
##
## ──────────────────────────────────────────────────────────────────────────
## (1) mentalhealth (2) stress (3) wage
## ──────────────────────────────────────────────────────────────────────────
## (Intercept) 4.988 ** -1.413 217.005
## (1.838) (2.274) (204.346)
## BA20FOApractices -0.088 0.217 0.338
## (0.092) (0.114) (10.284)
## migrant10 12.968 ** 0.421 310.607
## (4.925) (2.782) (196.096)
## migrant10_mean -11.243 * 13.676 ** 34.888
## (4.472) (5.099) (451.706)
## BA20FOApractices:migrant10 -0.650 ** -0.004 -15.006
## (0.247) (0.141) (9.951)
## BA20FOApractices:migrant10_mean 0.566 * -0.682 ** -1.713
## (0.224) (0.255) (22.621)
## ──────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.028 0.031 0.023
## Conditional R^2 0.264 0.096 0.444
## AIC 2246.304 3232.937 10104.729
## BIC 2292.948 3281.278 10152.973
## Num. obs. 784 929 920
## Num. groups: clus 44 45 45
## Var: clus (Intercept) 0.017 0.000 1075.020
## Var: clus migrant10 0.601 0.213 2518.624
## Cov: clus (Intercept) migrant10 -0.050 0.000 -242.195
## Var: Residual 0.909 1.778 3032.511
## ──────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
5.1.3 BA1920discriminate4prac
mentalhealth.BA1920discriminate4prac=lmer(mentalhealth~BA1920discriminate4prac*migrant10+migrant10_mean*BA1920discriminate4prac+ (migrant10|clus), na.action = na.exclude, data = dat3, control=lmerControl(optimizer="bobyqa"))
stress.BA1920discriminate4prac=lmer(stress~BA1920discriminate4prac*migrant10+migrant10_mean*BA1920discriminate4prac+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
wage.BA1920discriminate4prac=lmer(wage~BA1920discriminate4prac*migrant10+migrant10_mean*BA1920discriminate4prac+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
model_summary(list(mentalhealth.BA1920discriminate4prac, stress.BA1920discriminate4prac, wage.BA1920discriminate4prac))##
## Model Summary
##
## ────────────────────────────────────────────────────────────────────────────────────
## (1) mentalhealth (2) stress (3) wage
## ────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 1.126 4.973 ** 760.172 ***
## (1.789) (1.720) (131.560)
## BA1920discriminate4prac 0.552 -0.513 -135.501 ***
## (0.452) (0.436) (33.330)
## migrant10 -0.138 0.483 92.752
## (1.326) (1.027) (67.572)
## migrant10_mean 2.834 -2.427 -754.546 ***
## (2.834) (2.698) (196.729)
## BA1920discriminate4prac:migrant10 0.045 -0.033 -22.479
## (0.342) (0.268) (17.622)
## BA1920discriminate4prac:migrant10_mean -0.752 0.621 189.133 ***
## (0.716) (0.685) (49.894)
## ────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.002 0.015 0.037
## Conditional R^2 0.186 0.058 0.315
## AIC 3932.120 5623.560 17464.235
## BIC 3984.147 5677.394 17517.943
## Num. obs. 1343 1609 1589
## Num. groups: clus 66 69 69
## Var: clus (Intercept) 0.031 0.000 547.510
## Var: clus migrant10 0.443 0.119 1771.729
## Cov: clus (Intercept) migrant10 -0.076 0.000 -361.182
## Var: Residual 0.998 1.850 3169.321
## ────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.interact_plot(mentalhealth.BA1920discriminate4prac, modx = BA1920discriminate4prac, pred = migrant10)
5.1.4 BA20discriminate4prac
mentalhealth.BA20discriminate4prac=lmer(mentalhealth~BA20discriminate4prac*migrant10+migrant10_mean*BA20discriminate4prac+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))## Error in lme4::lFormula(formula = mentalhealth ~ BA20discriminate4prac * : 0 (non-NA) casesstress.BA20discriminate4prac=lmer(stress~BA20discriminate4prac*migrant10+migrant10_mean*BA20discriminate4prac+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
wage.BA20discriminate4prac=lmer(wage~BA20discriminate4prac*migrant10+migrant10_mean*BA20discriminate4prac+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
model_summary(list(mentalhealth.BA20discriminate4prac, stress.BA20discriminate4prac, wage.BA20discriminate4prac))##
## Model Summary
##
## ─────────────────────────────────────────────────────────────────────────────────
## (1) mentalhealth (2) stress (3) wage
## ─────────────────────────────────────────────────────────────────────────────────
## (Intercept) 1.263 5.075 * 1049.158 ***
## (2.418) (2.203) (175.080)
## BA20discriminate4prac 0.495 -0.539 -208.172 ***
## (0.608) (0.556) (44.227)
## migrant10 1.418 -0.082 84.396
## (2.429) (1.385) (107.744)
## migrant10_mean 0.851 -2.757 -1248.310 ***
## (4.114) (3.635) (271.730)
## BA20discriminate4prac:migrant10 -0.361 0.114 -18.510
## (0.619) (0.360) (27.972)
## BA20discriminate4prac:migrant10_mean -0.186 0.699 314.938 ***
## (1.035) (0.920) (68.779)
## ─────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.005 0.017 0.080
## Conditional R^2 0.271 0.084 0.392
## AIC 2246.134 3234.136 10085.865
## BIC 2292.778 3282.477 10134.109
## Num. obs. 784 929 920
## Num. groups: clus 44 45 45
## Var: clus (Intercept) 0.034 0.000 851.356
## Var: clus migrant10 0.757 0.217 3080.865
## Cov: clus (Intercept) migrant10 -0.107 0.000 -952.715
## Var: Residual 0.909 1.792 3039.760
## ─────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
5.1.5 BA1920targetedpractices
mentalhealth.BA1920targetedpractices=lmer(mentalhealth~BA1920targetedpractices*migrant10+migrant10_mean*BA1920targetedpractices+ (migrant10|clus), na.action = na.exclude, data = dat3, control=lmerControl(optimizer="bobyqa"))
stress.BA1920targetedpractices=lmer(stress~BA1920targetedpractices*migrant10+migrant10_mean*BA1920targetedpractices+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
wage.BA1920targetedpractices=lmer(wage~BA1920targetedpractices*migrant10+migrant10_mean*BA1920targetedpractices+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
model_summary(list(mentalhealth.BA1920targetedpractices, stress.BA1920targetedpractices, wage.BA1920targetedpractices))##
## Model Summary
##
## ────────────────────────────────────────────────────────────────────────────────────
## (1) mentalhealth (2) stress (3) wage
## ────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 1.592 3.727 ** 578.070 ***
## (1.359) (1.265) (94.211)
## BA1920targetedpractices 0.176 -0.080 -36.327 ***
## (0.138) (0.130) (9.645)
## migrant10 1.267 1.430 248.342 ***
## (1.285) (0.924) (53.856)
## migrant10_mean 1.524 -1.222 -546.099 ***
## (2.422) (2.244) (155.992)
## BA1920targetedpractices:migrant10 -0.133 -0.116 -26.255 ***
## (0.137) (0.100) (5.814)
## BA1920targetedpractices:migrant10_mean -0.166 0.132 55.680 ***
## (0.249) (0.232) (16.072)
## ────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.006 0.016 0.092
## Conditional R^2 0.188 0.054 0.291
## AIC 3936.054 5628.616 17450.029
## BIC 3988.081 5682.450 17503.738
## Num. obs. 1343 1609 1589
## Num. groups: clus 66 69 69
## Var: clus (Intercept) 0.023 0.000 534.936
## Var: clus migrant10 0.444 0.105 1140.940
## Cov: clus (Intercept) migrant10 -0.072 0.000 -316.777
## Var: Residual 0.998 1.853 3171.483
## ────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.interact_plot(mentalhealth.BA1920targetedpractices, modx = BA1920targetedpractices, pred = migrant10)
5.1.6 BA20targetedpractices
mentalhealth.BA20targetedpractices=lmer(mentalhealth~BA20targetedpractices*migrant10+migrant10_mean*BA20targetedpractices+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))## Error in lme4::lFormula(formula = mentalhealth ~ BA20targetedpractices * : 0 (non-NA) casesstress.BA20targetedpractices=lmer(stress~BA20targetedpractices*migrant10+migrant10_mean*BA20targetedpractices+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
wage.BA20targetedpractices=lmer(wage~BA20targetedpractices*migrant10+migrant10_mean*BA20targetedpractices+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
model_summary(list(mentalhealth.BA20targetedpractices, stress.BA20targetedpractices, wage.BA20targetedpractices))##
## Model Summary
##
## ──────────────────────────────────────────────────────────────────────────────────
## (1) mentalhealth (2) stress (3) wage
## ──────────────────────────────────────────────────────────────────────────────────
## (Intercept) 2.035 3.842 ** 589.596 ***
## (1.429) (1.412) (115.813)
## BA20targetedpractices 0.122 -0.093 -37.449 **
## (0.145) (0.144) (11.873)
## migrant10 0.725 1.265 269.764 ***
## (1.710) (1.063) (68.005)
## migrant10_mean 0.142 -1.244 -602.852 **
## (2.647) (2.651) (198.921)
## BA20targetedpractices:migrant10 -0.078 -0.101 -28.524 ***
## (0.185) (0.117) (7.503)
## BA20targetedpractices:migrant10_mean 0.005 0.131 62.226 **
## (0.273) (0.275) (20.693)
## ──────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.008 0.019 0.124
## Conditional R^2 0.275 0.082 0.376
## AIC 2252.242 3240.166 10084.446
## BIC 2298.886 3288.508 10132.690
## Num. obs. 784 929 920
## Num. groups: clus 44 45 45
## Var: clus (Intercept) 0.027 0.000 895.483
## Var: clus migrant10 0.785 0.207 1907.209
## Cov: clus (Intercept) migrant10 -0.111 0.000 -679.235
## Var: Residual 0.909 1.792 3041.021
## ──────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
5.2 II check for ab2: Main effect of M on W (Mplus approach)
5.2.1 BM.responsiblesourcing
BA1920FOApractices.BM.responsiblesourcing=lmer(BA1920FOApractices~BM.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA20FOApractices.BM.responsiblesourcing=lmer(BA20FOApractices~BM.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA1920discriminate4prac.BM.responsiblesourcing=lmer(BA1920discriminate4prac~BM.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA20discriminate4prac.BM.responsiblesourcing=lmer(BA20discriminate4prac~BM.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA1920targetedpractices.BM.responsiblesourcing=lmer(BA1920targetedpractices~BM.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA20targetedpractices.BM.responsiblesourcing=lmer(BA20targetedpractices~BM.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
model_summary(list(BA1920FOApractices.BM.responsiblesourcing, BA20FOApractices.BM.responsiblesourcing, BA1920discriminate4prac.BM.responsiblesourcing, BA20discriminate4prac.BM.responsiblesourcing, BA1920targetedpractices.BM.responsiblesourcing, BA20targetedpractices.BM.responsiblesourcing))##
## Model Summary
##
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (1) BA1920FOApractices (2) BA20FOApractices (3) BA1920discriminate4prac (4) BA20discriminate4prac (5) BA1920targetedpractices (6) BA20targetedpractices
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 19.186 *** 18.936 *** 3.768 *** 3.803 *** 8.860 *** 8.943 ***
## (0.054) (0.065) (0.021) (0.025) (0.060) (0.080)
## BM.responsiblesourcing 0.139 *** 0.341 *** 0.032 *** 0.030 *** 0.173 *** 0.071 *
## (0.019) (0.023) (0.007) (0.009) (0.021) (0.028)
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.555 0.885 0.288 0.285 0.616 0.171
## Conditional R^2 1.000 1.000 1.000 1.000 1.000 1.000
## AIC -27133.512 -16193.084 -30248.954 -13279.621 -27219.031 -16428.314
## BIC -27113.739 -16175.320 -30229.181 -13261.857 -27199.259 -16410.550
## Num. obs. 1036 627 1036 627 1036 627
## Num. groups: clus 47 32 47 32 47 32
## Var: clus (Intercept) 0.035 0.036 0.006 0.006 0.043 0.059
## Var: Residual 0.000 0.000 0.000 0.000 0.000 0.000
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.5.2.2 BM19.responsiblesourcing
BA1920FOApractices.BM19.responsiblesourcing=lmer(BA1920FOApractices~BM19.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA20FOApractices.BM19.responsiblesourcing=lmer(BA20FOApractices~BM19.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA1920discriminate4prac.BM19.responsiblesourcing=lmer(BA1920discriminate4prac~BM19.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA20discriminate4prac.BM19.responsiblesourcing=lmer(BA20discriminate4prac~BM19.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA1920targetedpractices.BM19.responsiblesourcing=lmer(BA1920targetedpractices~BM19.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA20targetedpractices.BM19.responsiblesourcing=lmer(BA20targetedpractices~BM19.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
model_summary(list(BA1920FOApractices.BM19.responsiblesourcing, BA20FOApractices.BM19.responsiblesourcing, BA1920discriminate4prac.BM19.responsiblesourcing, BA20discriminate4prac.BM19.responsiblesourcing, BA1920targetedpractices.BM19.responsiblesourcing, BA20targetedpractices.BM19.responsiblesourcing))##
## Model Summary
##
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (1) BA1920FOApractices (2) BA20FOApractices (3) BA1920discriminate4prac (4) BA20discriminate4prac (5) BA1920targetedpractices (6) BA20targetedpractices
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 19.080 *** 19.195 *** 3.562 *** 3.514 *** 8.644 *** 8.390 ***
## (0.059) (0.067) (0.023) (0.027) (0.067) (0.079)
## BM19.responsiblesourcing 0.186 *** 0.199 *** 0.099 *** 0.123 *** 0.232 *** 0.293 ***
## (0.018) (0.021) (0.007) (0.009) (0.021) (0.025)
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.750 0.788 0.853 0.913 0.786 0.846
## Conditional R^2 1.000 1.000 1.000 1.000 1.000 1.000
## AIC -18856.323 -10257.360 -21026.178 -15764.125 -12960.261 -14273.753
## BIC -18838.074 -10240.284 -21007.928 -15747.049 -12942.011 -14256.677
## Num. obs. 708 528 708 528 708 528
## Num. groups: clus 34 25 34 25 34 25
## Var: clus (Intercept) 0.036 0.034 0.005 0.005 0.047 0.050
## Var: Residual 0.000 0.000 0.000 0.000 0.000 0.000
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.5.2.3 BM20.responsiblesourcing
BA1920FOApractices.BM20.responsiblesourcing=lmer(BA1920FOApractices~BM20.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA20FOApractices.BM20.responsiblesourcing=lmer(BA20FOApractices~BM20.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA1920discriminate4prac.BM20.responsiblesourcing=lmer(BA1920discriminate4prac~BM20.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA20discriminate4prac.BM20.responsiblesourcing=lmer(BA20discriminate4prac~BM20.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA1920targetedpractices.BM20.responsiblesourcing=lmer(BA1920targetedpractices~BM20.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA20targetedpractices.BM20.responsiblesourcing=lmer(BA20targetedpractices~BM20.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
model_summary(list(BA1920FOApractices.BM20.responsiblesourcing, BA20FOApractices.BM20.responsiblesourcing, BA1920discriminate4prac.BM20.responsiblesourcing, BA20discriminate4prac.BM20.responsiblesourcing, BA1920targetedpractices.BM20.responsiblesourcing, BA20targetedpractices.BM20.responsiblesourcing))##
## Model Summary
##
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (1) BA1920FOApractices (2) BA20FOApractices (3) BA1920discriminate4prac (4) BA20discriminate4prac (5) BA1920targetedpractices (6) BA20targetedpractices
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 19.505 *** 19.501 *** 3.960 *** 3.982 *** 9.499 *** 9.325 ***
## (0.053) (0.074) (0.019) (0.024) (0.057) (0.084)
## BM20.responsiblesourcing 0.018 0.120 *** -0.040 *** -0.059 *** -0.092 *** -0.138 ***
## (0.018) (0.024) (0.007) (0.008) (0.019) (0.028)
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.023 0.431 0.476 0.648 0.353 0.434
## Conditional R^2 1.000 1.000 1.000 1.000 1.000 1.000
## AIC -19790.401 -11125.324 -22640.193 -12887.404 -14444.461 -11066.676
## BIC -19771.863 -11109.106 -22621.654 -12871.186 -14425.923 -11050.458
## Num. obs. 761 426 761 426 761 426
## Num. groups: clus 35 23 35 23 35 23
## Var: clus (Intercept) 0.040 0.054 0.005 0.005 0.047 0.071
## Var: Residual 0.000 0.000 0.000 0.000 0.000 0.000
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.5.3 III check for cw1: Cross-level interaction of W
5.3.1 BM.responsiblesourcing
mentalhealth.BM.responsiblesourcing=lmer(mentalhealth~BM.responsiblesourcing*migrant10+migrant10_mean*BM.responsiblesourcing+ (migrant10|clus), na.action = na.exclude, data = dat3, control=lmerControl(optimizer="bobyqa"))
stress.BM.responsiblesourcing=lmer(stress~BM.responsiblesourcing*migrant10+migrant10_mean*BM.responsiblesourcing+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
wage.BM.responsiblesourcing=lmer(wage~BM.responsiblesourcing*migrant10+migrant10_mean*BM.responsiblesourcing+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
model_summary(list(mentalhealth.BM.responsiblesourcing, stress.BM.responsiblesourcing, wage.BM.responsiblesourcing))##
## Model Summary
##
## ────────────────────────────────────────────────────────────────────────────────────
## (1) mentalhealth (2) stress (3) wage
## ────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.203 *** 2.899 *** 207.784 ***
## (0.204) (0.223) (18.659)
## BM.responsiblesourcing 0.059 -0.049 9.289
## (0.089) (0.100) (8.656)
## migrant10 0.471 0.391 14.378
## (0.318) (0.247) (16.031)
## migrant10_mean -0.171 0.022 17.580
## (0.370) (0.394) (31.150)
## BM.responsiblesourcing:migrant10 -0.189 0.003 -1.477
## (0.104) (0.082) (5.498)
## BM.responsiblesourcing:migrant10_mean 0.002 0.045 -13.025
## (0.141) (0.155) (12.769)
## ────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.026 0.022 0.010
## Conditional R^2 0.224 0.067 0.390
## AIC 2679.246 3771.629 11720.637
## BIC 2727.468 3821.504 11770.391
## Num. obs. 918 1083 1070
## Num. groups: clus 49 51 51
## Var: clus (Intercept) 0.036 0.000 935.882
## Var: clus migrant10 0.547 0.129 1852.083
## Cov: clus (Intercept) migrant10 -0.112 0.000 -249.933
## Var: Residual 0.964 1.799 2973.152
## ────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
5.3.2 BM19.responsiblesourcing
mentalhealth.BM19.responsiblesourcing=lmer(mentalhealth~BM19.responsiblesourcing*migrant10+migrant10_mean*BM19.responsiblesourcing+ (migrant10|clus), na.action = na.exclude, data = dat3, control=lmerControl(optimizer="bobyqa"))
stress.BM19.responsiblesourcing=lmer(stress~BM19.responsiblesourcing*migrant10+migrant10_mean*BM19.responsiblesourcing+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
wage.BM19.responsiblesourcing=lmer(wage~BM19.responsiblesourcing*migrant10+migrant10_mean*BM19.responsiblesourcing+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
model_summary(list(mentalhealth.BM19.responsiblesourcing, stress.BM19.responsiblesourcing, wage.BM19.responsiblesourcing))##
## Model Summary
##
## ─────────────────────────────────────────────────────────────────────────────────────
## (1) mentalhealth (2) stress (3) wage
## ─────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.167 *** 2.973 *** 234.393 ***
## (0.250) (0.291) (30.742)
## BM19.responsiblesourcing 0.027 -0.071 -2.181
## (0.078) (0.090) (9.434)
## migrant10 0.124 0.474 * -7.885
## (0.335) (0.241) (14.975)
## migrant10_mean 0.098 -0.320 -5.253
## (0.411) (0.488) (47.744)
## BM19.responsiblesourcing:migrant10 -0.084 -0.025 8.276
## (0.105) (0.075) (4.865)
## BM19.responsiblesourcing:migrant10_mean -0.006 0.157 -3.394
## (0.125) (0.149) (14.320)
## ─────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.008 0.028 0.015
## Conditional R^2 0.240 0.066 0.506
## AIC 1848.897 2584.904 7914.924
## BIC 1893.480 2631.038 7960.936
## Num. obs. 638 745 736
## Num. groups: clus 35 37 37
## Var: clus (Intercept) 0.038 0.000 1110.819
## Var: clus migrant10 0.737 0.112 1677.746
## Cov: clus (Intercept) migrant10 -0.167 0.000 149.512
## Var: Residual 0.925 1.757 2388.354
## ─────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.interact_plot(mentalhealth.BM19.responsiblesourcing, modx = BM19.responsiblesourcing, pred = migrant10)
5.3.3 BM20.responsiblesourcing
mentalhealth.BM20.responsiblesourcing=lmer(mentalhealth~BM20.responsiblesourcing*migrant10+migrant10_mean*BM20.responsiblesourcing+ (migrant10|clus), na.action = na.exclude, data = dat3, control=lmerControl(optimizer="bobyqa"))
stress.BM20.responsiblesourcing=lmer(stress~BM20.responsiblesourcing*migrant10+migrant10_mean*BM20.responsiblesourcing+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
wage.BM20.responsiblesourcing=lmer(wage~BM20.responsiblesourcing*migrant10+migrant10_mean*BM20.responsiblesourcing+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
model_summary(list(mentalhealth.BM20.responsiblesourcing, stress.BM20.responsiblesourcing, wage.BM20.responsiblesourcing))##
## Model Summary
##
## ─────────────────────────────────────────────────────────────────────────────────────
## (1) mentalhealth (2) stress (3) wage
## ─────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.303 *** 2.763 *** 209.540 ***
## (0.182) (0.188) (18.534)
## BM20.responsiblesourcing 0.053 0.025 12.321
## (0.089) (0.097) (9.891)
## migrant10 0.200 0.330 42.918 **
## (0.325) (0.281) (16.157)
## migrant10_mean -0.184 0.359 3.785
## (0.361) (0.397) (33.215)
## BM20.responsiblesourcing:migrant10 -0.097 0.014 -10.323 *
## (0.098) (0.088) (5.012)
## BM20.responsiblesourcing:migrant10_mean -0.036 -0.112 -13.505
## (0.145) (0.165) (15.001)
## ─────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.016 0.023 0.045
## Conditional R^2 0.238 0.097 0.410
## AIC 2003.898 2722.512 8503.432
## BIC 2049.118 2769.079 8549.883
## Num. obs. 680 778 769
## Num. groups: clus 37 37 37
## Var: clus (Intercept) 0.069 0.000 1297.561
## Var: clus migrant10 0.654 0.219 1540.307
## Cov: clus (Intercept) migrant10 -0.158 0.000 -197.841
## Var: Residual 0.974 1.794 3335.964
## ─────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.interact_plot(mentalhealth.BM20.responsiblesourcing, modx = BM20.responsiblesourcing, pred = migrant10)
6 D. 3&2 ROUND DATA
## Frequency Statistics:
## ────────────
## N %
## ────────────
## 2 1666 48.7
## 3 1756 51.3
## ────────────
## Total N = 3,4226.1 I check for cw2: Cross-level interaction of M
6.1.1 BA1920FOApractices
mentalhealth.BA1920FOApractices=lmer(mentalhealth~BA1920FOApractices*migrant10+migrant10_mean*BA1920FOApractices+ (migrant10|clus), na.action = na.exclude, data = dat3, control=lmerControl(optimizer="bobyqa"))
stress.BA1920FOApractices=lmer(stress~BA1920FOApractices*migrant10+migrant10_mean*BA1920FOApractices+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
wage.BA1920FOApractices=lmer(wage~BA1920FOApractices*migrant10+migrant10_mean*BA1920FOApractices+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
model_summary(list(mentalhealth.BA1920FOApractices, stress.BA1920FOApractices, wage.BA1920FOApractices))##
## Model Summary
##
## ─────────────────────────────────────────────────────────────────────────────
## (1) mentalhealth (2) stress (3) wage
## ─────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.098 2.280 395.665 **
## (1.701) (1.911) (125.182)
## BA1920FOApractices 0.011 0.034 -8.347
## (0.086) (0.097) (6.347)
## migrant10 6.805 * -2.657 213.942 *
## (2.750) (1.722) (96.474)
## migrant10_mean -4.163 3.681 -324.562
## (3.615) (3.489) (239.204)
## BA1920FOApractices:migrant10 -0.346 * 0.162 -11.207 *
## (0.141) (0.089) (4.961)
## BA1920FOApractices:migrant10_mean 0.205 -0.193 15.607
## (0.183) (0.177) (12.102)
## ─────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.012 0.023 0.025
## Conditional R^2 0.181 0.046 0.265
## AIC 3933.788 11522.959 34368.052
## BIC 3985.815 11583.830 34428.751
## Num. obs. 1343 3252 3197
## Num. groups: clus 66 69 69
## Var: clus (Intercept) 0.021 0.113 545.654
## Var: clus migrant10 0.388 0.109 788.818
## Cov: clus (Intercept) migrant10 -0.055 -0.099 -193.854
## Var: Residual 0.999 1.969 2562.079
## ─────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
6.1.2 BA20FOApractices
mentalhealth.BA20FOApractices=lmer(mentalhealth~BA20FOApractices*migrant10+migrant10_mean*BA20FOApractices+ (migrant10|clus), na.action = na.exclude, data = dat3, control=lmerControl(optimizer="bobyqa"))
stress.BA20FOApractices=lmer(stress~BA20FOApractices*migrant10+migrant10_mean*BA20FOApractices+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
wage.BA20FOApractices=lmer(wage~BA20FOApractices*migrant10+migrant10_mean*BA20FOApractices+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
model_summary(list(mentalhealth.BA20FOApractices, stress.BA20FOApractices, wage.BA20FOApractices))##
## Model Summary
##
## ────────────────────────────────────────────────────────────────────────────
## (1) mentalhealth (2) stress (3) wage
## ────────────────────────────────────────────────────────────────────────────
## (Intercept) 4.988 ** 0.250 375.453 *
## (1.838) (2.222) (154.602)
## BA20FOApractices -0.088 0.131 -7.432
## (0.092) (0.112) (7.794)
## migrant10 12.968 ** -5.354 * 334.453 **
## (4.925) (2.212) (129.744)
## migrant10_mean -11.243 * 12.177 * -249.762
## (4.472) (4.777) (343.684)
## BA20FOApractices:migrant10 -0.650 ** 0.297 ** -16.902 *
## (0.247) (0.112) (6.573)
## BA20FOApractices:migrant10_mean 0.566 * -0.612 * 12.094
## (0.224) (0.240) (17.229)
## ────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.028 0.040 0.037
## Conditional R^2 0.264 0.078 0.312
## AIC 2246.304 6664.082 19962.846
## BIC 2292.948 6719.552 20018.140
## Num. obs. 784 1895 1862
## Num. groups: clus 44 45 45
## Var: clus (Intercept) 0.017 0.111 721.295
## Var: clus migrant10 0.601 0.160 977.860
## Cov: clus (Intercept) migrant10 -0.050 -0.106 -274.438
## Var: Residual 0.909 1.888 2465.032
## ────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
6.1.3 BA1920discriminate4prac
mentalhealth.BA1920discriminate4prac=lmer(mentalhealth~BA1920discriminate4prac*migrant10+migrant10_mean*BA1920discriminate4prac+ (migrant10|clus), na.action = na.exclude, data = dat3, control=lmerControl(optimizer="bobyqa"))
stress.BA1920discriminate4prac=lmer(stress~BA1920discriminate4prac*migrant10+migrant10_mean*BA1920discriminate4prac+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
wage.BA1920discriminate4prac=lmer(wage~BA1920discriminate4prac*migrant10+migrant10_mean*BA1920discriminate4prac+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
model_summary(list(mentalhealth.BA1920discriminate4prac, stress.BA1920discriminate4prac, wage.BA1920discriminate4prac))##
## Model Summary
##
## ─────────────────────────────────────────────────────────────────────────────────────
## (1) mentalhealth (2) stress (3) wage
## ─────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 1.126 4.256 ** 550.732 ***
## (1.789) (1.430) (114.571)
## BA1920discriminate4prac 0.552 -0.340 -80.922 **
## (0.452) (0.365) (29.008)
## migrant10 -0.138 -0.135 24.071
## (1.326) (0.818) (45.037)
## migrant10_mean 2.834 -1.263 -445.929 **
## (2.834) (2.034) (167.554)
## BA1920discriminate4prac:migrant10 0.045 0.161 -7.328
## (0.342) (0.213) (11.757)
## BA1920discriminate4prac:migrant10_mean -0.752 0.305 108.294 *
## (0.716) (0.520) (42.470)
## ─────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.002 0.022 0.029
## Conditional R^2 0.186 0.045 0.266
## AIC 3932.120 11519.844 34360.730
## BIC 3984.147 11580.715 34421.430
## Num. obs. 1343 3252 3197
## Num. groups: clus 66 69 69
## Var: clus (Intercept) 0.031 0.101 484.532
## Var: clus migrant10 0.443 0.119 833.964
## Cov: clus (Intercept) migrant10 -0.076 -0.096 -179.672
## Var: Residual 0.998 1.971 2563.218
## ─────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.interact_plot(mentalhealth.BA1920discriminate4prac, modx = BA1920discriminate4prac, pred = migrant10)
6.1.4 BA20discriminate4prac
mentalhealth.BA20discriminate4prac=lmer(mentalhealth~BA20discriminate4prac*migrant10+migrant10_mean*BA20discriminate4prac+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
stress.BA20discriminate4prac=lmer(stress~BA20discriminate4prac*migrant10+migrant10_mean*BA20discriminate4prac+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
wage.BA20discriminate4prac=lmer(wage~BA20discriminate4prac*migrant10+migrant10_mean*BA20discriminate4prac+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
model_summary(list(mentalhealth.BA20discriminate4prac, stress.BA20discriminate4prac, wage.BA20discriminate4prac))##
## Model Summary
##
## ─────────────────────────────────────────────────────────────────────────────────
## (1) mentalhealth (2) stress (3) wage
## ─────────────────────────────────────────────────────────────────────────────────
## (Intercept) 1.263 3.766 799.836 ***
## (2.418) (1.924) (148.826)
## BA20discriminate4prac 0.495 -0.232 -143.987 ***
## (0.608) (0.488) (37.537)
## migrant10 1.418 -0.848 59.404
## (2.429) (1.202) (64.998)
## migrant10_mean 0.851 0.810 -815.242 ***
## (4.114) (2.896) (230.566)
## BA20discriminate4prac:migrant10 -0.361 0.354 -15.295
## (0.619) (0.312) (16.868)
## BA20discriminate4prac:migrant10_mean -0.186 -0.208 202.446 ***
## (1.035) (0.737) (58.251)
## ─────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.005 0.034 0.063
## Conditional R^2 0.271 0.079 0.304
## AIC 2246.134 6664.171 19950.331
## BIC 2292.778 6719.641 20005.625
## Num. obs. 784 1895 1862
## Num. groups: clus 44 45 45
## Var: clus (Intercept) 0.034 0.134 543.253
## Var: clus migrant10 0.757 0.236 1076.824
## Cov: clus (Intercept) migrant10 -0.107 -0.152 -288.299
## Var: Residual 0.909 1.890 2467.950
## ─────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
6.1.5 BA1920targetedpractices
mentalhealth.BA1920targetedpractices=lmer(mentalhealth~BA1920targetedpractices*migrant10+migrant10_mean*BA1920targetedpractices+ (migrant10|clus), na.action = na.exclude, data = dat3, control=lmerControl(optimizer="bobyqa"))
stress.BA1920targetedpractices=lmer(stress~BA1920targetedpractices*migrant10+migrant10_mean*BA1920targetedpractices+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
wage.BA1920targetedpractices=lmer(wage~BA1920targetedpractices*migrant10+migrant10_mean*BA1920targetedpractices+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
model_summary(list(mentalhealth.BA1920targetedpractices, stress.BA1920targetedpractices, wage.BA1920targetedpractices))##
## Model Summary
##
## ──────────────────────────────────────────────────────────────────────────────────────
## (1) mentalhealth (2) stress (3) wage
## ──────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 1.592 3.761 *** 458.556 ***
## (1.359) (1.071) (82.922)
## BA1920targetedpractices 0.176 -0.085 -23.362 **
## (0.138) (0.111) (8.483)
## migrant10 1.267 0.082 86.780 *
## (1.285) (0.742) (39.337)
## migrant10_mean 1.524 -1.239 -356.275 **
## (2.422) (1.660) (134.737)
## BA1920targetedpractices:migrant10 -0.133 0.043 -9.864 *
## (0.137) (0.080) (4.252)
## BA1920targetedpractices:migrant10_mean -0.166 0.120 34.878 *
## (0.249) (0.173) (13.866)
## ──────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.006 0.022 0.042
## Conditional R^2 0.188 0.046 0.263
## AIC 3936.054 11525.728 34361.078
## BIC 3988.081 11586.599 34421.778
## Num. obs. 1343 3252 3197
## Num. groups: clus 66 69 69
## Var: clus (Intercept) 0.023 0.106 460.780
## Var: clus migrant10 0.444 0.124 740.424
## Cov: clus (Intercept) migrant10 -0.072 -0.102 -156.752
## Var: Residual 0.998 1.970 2563.976
## ──────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.interact_plot(mentalhealth.BA1920targetedpractices, modx = BA1920targetedpractices, pred = migrant10)
6.1.6 BA20targetedpractices
mentalhealth.BA20targetedpractices=lmer(mentalhealth~BA20targetedpractices*migrant10+migrant10_mean*BA20targetedpractices+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
stress.BA20targetedpractices=lmer(stress~BA20targetedpractices*migrant10+migrant10_mean*BA20targetedpractices+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
wage.BA20targetedpractices=lmer(wage~BA20targetedpractices*migrant10+migrant10_mean*BA20targetedpractices+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
model_summary(list(mentalhealth.BA20targetedpractices, stress.BA20targetedpractices, wage.BA20targetedpractices))##
## Model Summary
##
## ──────────────────────────────────────────────────────────────────────────────────
## (1) mentalhealth (2) stress (3) wage
## ──────────────────────────────────────────────────────────────────────────────────
## (Intercept) 2.035 3.394 ** 505.088 ***
## (1.429) (1.238) (97.464)
## BA20targetedpractices 0.122 -0.055 -28.301 **
## (0.145) (0.128) (9.969)
## migrant10 0.725 0.007 92.250 *
## (1.710) (0.911) (46.848)
## migrant10_mean 0.142 -0.787 -444.276 **
## (2.647) (2.055) (167.171)
## BA20targetedpractices:migrant10 -0.078 0.056 -10.159 *
## (0.185) (0.100) (5.164)
## BA20targetedpractices:migrant10_mean 0.005 0.084 44.595 *
## (0.273) (0.216) (17.339)
## ──────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.008 0.033 0.053
## Conditional R^2 0.275 0.080 0.308
## AIC 2252.242 6672.579 19959.455
## BIC 2298.886 6728.048 20014.749
## Num. obs. 784 1895 1862
## Num. groups: clus 44 45 45
## Var: clus (Intercept) 0.027 0.143 582.181
## Var: clus migrant10 0.785 0.251 974.969
## Cov: clus (Intercept) migrant10 -0.111 -0.163 -225.049
## Var: Residual 0.909 1.889 2468.346
## ──────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
6.2 II check for ab2: Main effect of M on W (Mplus approach)
6.2.1 BM.responsiblesourcing
BA1920FOApractices.BM.responsiblesourcing=lmer(BA1920FOApractices~BM.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA20FOApractices.BM.responsiblesourcing=lmer(BA20FOApractices~BM.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA1920discriminate4prac.BM.responsiblesourcing=lmer(BA1920discriminate4prac~BM.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA20discriminate4prac.BM.responsiblesourcing=lmer(BA20discriminate4prac~BM.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA1920targetedpractices.BM.responsiblesourcing=lmer(BA1920targetedpractices~BM.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA20targetedpractices.BM.responsiblesourcing=lmer(BA20targetedpractices~BM.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
model_summary(list(BA1920FOApractices.BM.responsiblesourcing, BA20FOApractices.BM.responsiblesourcing, BA1920discriminate4prac.BM.responsiblesourcing, BA20discriminate4prac.BM.responsiblesourcing, BA1920targetedpractices.BM.responsiblesourcing, BA20targetedpractices.BM.responsiblesourcing))##
## Model Summary
##
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (1) BA1920FOApractices (2) BA20FOApractices (3) BA1920discriminate4prac (4) BA20discriminate4prac (5) BA1920targetedpractices (6) BA20targetedpractices
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 19.120 *** 18.914 *** 3.744 *** 3.803 *** 9.198 *** 9.794 ***
## (0.039) (0.045) (0.016) (0.018) (0.041) (0.064)
## BM.responsiblesourcing 0.142 *** 0.337 *** 0.041 *** 0.029 *** 0.046 *** -0.214 ***
## (0.013) (0.016) (0.006) (0.007) (0.014) (0.023)
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.712 0.933 0.561 0.409 0.178 0.746
## Conditional R^2 1.000 1.000 1.000 1.000 1.000 1.000
## AIC -57120.330 -26048.808 -64413.734 -40334.704 -58670.574 -35298.813
## BIC -57097.691 -26028.097 -64391.095 -40313.993 -58647.935 -35278.102
## Num. obs. 2121 1310 2121 1310 2121 1310
## Num. groups: clus 47 32 47 32 47 32
## Var: clus (Intercept) 0.017 0.017 0.003 0.003 0.021 0.033
## Var: Residual 0.000 0.000 0.000 0.000 0.000 0.000
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.6.2.2 BM19.responsiblesourcing
BA1920FOApractices.BM19.responsiblesourcing=lmer(BA1920FOApractices~BM19.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA20FOApractices.BM19.responsiblesourcing=lmer(BA20FOApractices~BM19.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA1920discriminate4prac.BM19.responsiblesourcing=lmer(BA1920discriminate4prac~BM19.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA20discriminate4prac.BM19.responsiblesourcing=lmer(BA20discriminate4prac~BM19.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA1920targetedpractices.BM19.responsiblesourcing=lmer(BA1920targetedpractices~BM19.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA20targetedpractices.BM19.responsiblesourcing=lmer(BA20targetedpractices~BM19.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
model_summary(list(BA1920FOApractices.BM19.responsiblesourcing, BA20FOApractices.BM19.responsiblesourcing, BA1920discriminate4prac.BM19.responsiblesourcing, BA20discriminate4prac.BM19.responsiblesourcing, BA1920targetedpractices.BM19.responsiblesourcing, BA20targetedpractices.BM19.responsiblesourcing))##
## Model Summary
##
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (1) BA1920FOApractices (2) BA20FOApractices (3) BA1920discriminate4prac (4) BA20discriminate4prac (5) BA1920targetedpractices (6) BA20targetedpractices
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 19.131 *** 19.273 *** 3.628 *** 3.555 *** 8.629 *** 8.435 ***
## (0.041) (0.047) (0.019) (0.017) (0.046) (0.059)
## BM19.responsiblesourcing 0.160 *** 0.180 *** 0.085 *** 0.110 *** 0.262 *** 0.269 ***
## (0.013) (0.014) (0.005) (0.005) (0.014) (0.019)
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.814 0.852 0.892 0.941 0.901 0.897
## Conditional R^2 1.000 1.000 1.000 1.000 1.000 1.000
## AIC -29630.989 -29875.159 -45553.555 -33385.425 -40402.799 -29807.903
## BIC -29609.828 -29855.209 -45532.394 -33365.475 -40381.638 -29787.953
## Num. obs. 1466 1083 1466 1083 1466 1083
## Num. groups: clus 34 25 34 25 34 25
## Var: clus (Intercept) 0.018 0.017 0.003 0.002 0.023 0.024
## Var: Residual 0.000 0.000 0.000 0.000 0.000 0.000
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.6.2.3 BM20.responsiblesourcing
BA1920FOApractices.BM20.responsiblesourcing=lmer(BA1920FOApractices~BM20.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA20FOApractices.BM20.responsiblesourcing=lmer(BA20FOApractices~BM20.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA1920discriminate4prac.BM20.responsiblesourcing=lmer(BA1920discriminate4prac~BM20.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA20discriminate4prac.BM20.responsiblesourcing=lmer(BA20discriminate4prac~BM20.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA1920targetedpractices.BM20.responsiblesourcing=lmer(BA1920targetedpractices~BM20.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA20targetedpractices.BM20.responsiblesourcing=lmer(BA20targetedpractices~BM20.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
model_summary(list(BA1920FOApractices.BM20.responsiblesourcing, BA20FOApractices.BM20.responsiblesourcing, BA1920discriminate4prac.BM20.responsiblesourcing, BA20discriminate4prac.BM20.responsiblesourcing, BA1920targetedpractices.BM20.responsiblesourcing, BA20targetedpractices.BM20.responsiblesourcing))##
## Model Summary
##
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (1) BA1920FOApractices (2) BA20FOApractices (3) BA1920discriminate4prac (4) BA20discriminate4prac (5) BA1920targetedpractices (6) BA20targetedpractices
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 19.522 *** 19.456 *** 3.936 *** 4.000 *** 9.499 *** 9.347 ***
## (0.037) (0.050) (0.013) (0.017) (0.040) (0.058)
## BM20.responsiblesourcing -0.004 0.125 *** -0.031 *** -0.057 *** -0.092 *** -0.155 ***
## (0.012) (0.016) (0.005) (0.006) (0.013) (0.019)
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.002 0.634 0.531 0.779 0.531 0.669
## Conditional R^2 1.000 1.000 1.000 1.000 1.000 1.000
## AIC -42222.421 -17968.597 -49255.084 -28111.442 -31164.529 -25161.019
## BIC -42200.965 -17949.312 -49233.629 -28092.157 -31143.074 -25141.735
## Num. obs. 1578 917 1578 917 1578 917
## Num. groups: clus 35 23 35 23 35 23
## Var: clus (Intercept) 0.019 0.025 0.003 0.003 0.023 0.033
## Var: Residual 0.000 0.000 0.000 0.000 0.000 0.000
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.6.3 III check for cw1: Cross-level interaction of W
6.3.1 BM.responsiblesourcing
mentalhealth.BM.responsiblesourcing=lmer(mentalhealth~BM.responsiblesourcing*migrant10+migrant10_mean*BM.responsiblesourcing+ (migrant10|clus), na.action = na.exclude, data = dat3, control=lmerControl(optimizer="bobyqa"))
stress.BM.responsiblesourcing=lmer(stress~BM.responsiblesourcing*migrant10+migrant10_mean*BM.responsiblesourcing+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
wage.BM.responsiblesourcing=lmer(wage~BM.responsiblesourcing*migrant10+migrant10_mean*BM.responsiblesourcing+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
model_summary(list(mentalhealth.BM.responsiblesourcing, stress.BM.responsiblesourcing, wage.BM.responsiblesourcing))##
## Model Summary
##
## ────────────────────────────────────────────────────────────────────────────────────
## (1) mentalhealth (2) stress (3) wage
## ────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.203 *** 3.200 *** 220.632 ***
## (0.204) (0.221) (11.680)
## BM.responsiblesourcing 0.059 -0.146 2.481
## (0.089) (0.095) (5.440)
## migrant10 0.471 0.131 -1.480
## (0.318) (0.216) (9.262)
## migrant10_mean -0.171 -0.112 -3.776
## (0.370) (0.355) (20.186)
## BM.responsiblesourcing:migrant10 -0.189 0.154 * -0.292
## (0.104) (0.072) (3.114)
## BM.responsiblesourcing:migrant10_mean 0.002 0.047 -1.865
## (0.141) (0.136) (8.149)
## ────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.026 0.032 0.002
## Conditional R^2 0.224 0.060 0.280
## AIC 2679.246 7864.457 23273.775
## BIC 2727.468 7921.451 23330.609
## Num. obs. 918 2207 2172
## Num. groups: clus 49 51 51
## Var: clus (Intercept) 0.036 0.131 336.865
## Var: clus migrant10 0.547 0.156 487.497
## Cov: clus (Intercept) migrant10 -0.112 -0.128 202.472
## Var: Residual 0.964 1.992 2486.939
## ────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
6.3.2 BM19.responsiblesourcing
mentalhealth.BM19.responsiblesourcing=lmer(mentalhealth~BM19.responsiblesourcing*migrant10+migrant10_mean*BM19.responsiblesourcing+ (migrant10|clus), na.action = na.exclude, data = dat3, control=lmerControl(optimizer="bobyqa"))
stress.BM19.responsiblesourcing=lmer(stress~BM19.responsiblesourcing*migrant10+migrant10_mean*BM19.responsiblesourcing+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
wage.BM19.responsiblesourcing=lmer(wage~BM19.responsiblesourcing*migrant10+migrant10_mean*BM19.responsiblesourcing+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
model_summary(list(mentalhealth.BM19.responsiblesourcing, stress.BM19.responsiblesourcing, wage.BM19.responsiblesourcing))##
## Model Summary
##
## ──────────────────────────────────────────────────────────────────────────────────────
## (1) mentalhealth (2) stress (3) wage
## ──────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.167 *** 3.749 *** 260.703 ***
## (0.250) (0.213) (16.197)
## BM19.responsiblesourcing 0.027 -0.272 *** -10.618 *
## (0.078) (0.065) (4.949)
## migrant10 0.124 0.224 7.399
## (0.335) (0.175) (9.139)
## migrant10_mean 0.098 -0.840 * -62.000 *
## (0.411) (0.356) (25.663)
## BM19.responsiblesourcing:migrant10 -0.084 0.079 -2.504
## (0.105) (0.054) (2.900)
## BM19.responsiblesourcing:migrant10_mean -0.006 0.282 ** 16.502 *
## (0.125) (0.106) (7.628)
## ──────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.008 0.033 0.016
## Conditional R^2 0.240 0.040 0.252
## AIC 1848.897 5459.428 16127.482
## BIC 1893.480 5512.771 16180.681
## Num. obs. 638 1532 1510
## Num. groups: clus 35 37 37
## Var: clus (Intercept) 0.038 0.000 250.791
## Var: clus migrant10 0.737 0.022 522.417
## Cov: clus (Intercept) migrant10 -0.167 0.000 122.443
## Var: Residual 0.925 2.006 2409.440
## ──────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.interact_plot(mentalhealth.BM19.responsiblesourcing, modx = BM19.responsiblesourcing, pred = migrant10)
6.3.3 BM20.responsiblesourcing
mentalhealth.BM20.responsiblesourcing=lmer(mentalhealth~BM20.responsiblesourcing*migrant10+migrant10_mean*BM20.responsiblesourcing+ (migrant10|clus), na.action = na.exclude, data = dat3, control=lmerControl(optimizer="bobyqa"))
stress.BM20.responsiblesourcing=lmer(stress~BM20.responsiblesourcing*migrant10+migrant10_mean*BM20.responsiblesourcing+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
wage.BM20.responsiblesourcing=lmer(wage~BM20.responsiblesourcing*migrant10+migrant10_mean*BM20.responsiblesourcing+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
model_summary(list(mentalhealth.BM20.responsiblesourcing, stress.BM20.responsiblesourcing, wage.BM20.responsiblesourcing))##
## Model Summary
##
## ──────────────────────────────────────────────────────────────────────────────────────
## (1) mentalhealth (2) stress (3) wage
## ──────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.303 *** 2.940 *** 211.021 ***
## (0.182) (0.197) (10.468)
## BM20.responsiblesourcing 0.053 -0.064 9.211
## (0.089) (0.088) (5.733)
## migrant10 0.200 0.375 -13.273
## (0.325) (0.228) (9.179)
## migrant10_mean -0.184 -0.129 18.812
## (0.361) (0.329) (19.255)
## BM20.responsiblesourcing:migrant10 -0.097 0.117 2.555
## (0.098) (0.072) (2.864)
## BM20.responsiblesourcing:migrant10_mean -0.036 -0.010 -14.249
## (0.145) (0.127) (8.806)
## ──────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.016 0.041 0.014
## Conditional R^2 0.238 0.078 0.284
## AIC 2003.898 5737.383 17026.667
## BIC 2049.118 5791.242 17080.363
## Num. obs. 680 1613 1587
## Num. groups: clus 37 37 37
## Var: clus (Intercept) 0.069 0.168 380.027
## Var: clus migrant10 0.654 0.241 479.045
## Cov: clus (Intercept) migrant10 -0.158 -0.186 178.599
## Var: Residual 0.974 1.961 2530.237
## ──────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.interact_plot(mentalhealth.BM20.responsiblesourcing, modx = BM20.responsiblesourcing, pred = migrant10)
7 E. 3&1 ROUND DATA
## Frequency Statistics:
## ────────────
## N %
## ────────────
## 1 1730 49.6
## 3 1756 50.4
## ────────────
## Total N = 3,4867.1 I check for cw2: Cross-level interaction of M
7.1.1 BA1920FOApractices
mentalhealth.BA1920FOApractices=lmer(mentalhealth~BA1920FOApractices*migrant10+migrant10_mean*BA1920FOApractices+ (migrant10|clus), na.action = na.exclude, data = dat3, control=lmerControl(optimizer="bobyqa"))
stress.BA1920FOApractices=lmer(stress~BA1920FOApractices*migrant10+migrant10_mean*BA1920FOApractices+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
wage.BA1920FOApractices=lmer(wage~BA1920FOApractices*migrant10+migrant10_mean*BA1920FOApractices+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
model_summary(list(mentalhealth.BA1920FOApractices, stress.BA1920FOApractices, wage.BA1920FOApractices))##
## Model Summary
##
## ─────────────────────────────────────────────────────────────────────────────
## (1) mentalhealth (2) stress (3) wage
## ─────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.098 1.924 349.057 **
## (1.701) (1.849) (131.217)
## BA1920FOApractices 0.011 0.052 -5.974
## (0.086) (0.094) (6.654)
## migrant10 6.805 * -1.733 234.807 *
## (2.750) (1.515) (105.834)
## migrant10_mean -4.163 4.228 -346.433
## (3.615) (3.407) (248.866)
## BA1920FOApractices:migrant10 -0.346 * 0.115 -12.321 *
## (0.141) (0.078) (5.444)
## BA1920FOApractices:migrant10_mean 0.205 -0.224 16.858
## (0.183) (0.173) (12.593)
## ─────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.012 0.022 0.017
## Conditional R^2 0.181 0.041 0.244
## AIC 3933.788 11679.623 35610.101
## BIC 3985.815 11740.637 35670.977
## Num. obs. 1343 3299 3254
## Num. groups: clus 66 69 69
## Var: clus (Intercept) 0.021 0.080 600.269
## Var: clus migrant10 0.388 0.036 1006.672
## Cov: clus (Intercept) migrant10 -0.055 -0.046 -266.819
## Var: Residual 0.999 1.970 3117.852
## ─────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
7.1.2 BA20FOApractices
mentalhealth.BA20FOApractices=lmer(mentalhealth~BA20FOApractices*migrant10+migrant10_mean*BA20FOApractices+ (migrant10|clus), na.action = na.exclude, data = dat3, control=lmerControl(optimizer="bobyqa"))
stress.BA20FOApractices=lmer(stress~BA20FOApractices*migrant10+migrant10_mean*BA20FOApractices+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
wage.BA20FOApractices=lmer(wage~BA20FOApractices*migrant10+migrant10_mean*BA20FOApractices+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
model_summary(list(mentalhealth.BA20FOApractices, stress.BA20FOApractices, wage.BA20FOApractices))##
## Model Summary
##
## ──────────────────────────────────────────────────────────────────────────
## (1) mentalhealth (2) stress (3) wage
## ──────────────────────────────────────────────────────────────────────────
## (Intercept) 4.988 ** 0.601 321.332 *
## (1.838) (2.087) (163.008)
## BA20FOApractices -0.088 0.115 -4.586
## (0.092) (0.105) (8.219)
## migrant10 12.968 ** -3.796 * 322.487 *
## (4.925) (1.703) (139.351)
## migrant10_mean -11.243 * 9.973 * -309.431
## (4.472) (4.516) (359.092)
## BA20FOApractices:migrant10 -0.650 ** 0.219 * -16.268 *
## (0.247) (0.086) (7.066)
## BA20FOApractices:migrant10_mean 0.566 * -0.505 * 15.001
## (0.224) (0.226) (18.002)
## ──────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.028 0.036 0.019
## Conditional R^2 0.264 0.060 0.290
## AIC 2246.304 6705.257 20490.942
## BIC 2292.948 6760.748 20546.326
## Num. obs. 784 1899 1879
## Num. groups: clus 44 45 45
## Var: clus (Intercept) 0.017 0.065 808.236
## Var: clus migrant10 0.601 0.010 1238.253
## Cov: clus (Intercept) migrant10 -0.050 -0.017 -351.200
## Var: Residual 0.909 1.934 2969.807
## ──────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
7.1.3 BA1920discriminate4prac
mentalhealth.BA1920discriminate4prac=lmer(mentalhealth~BA1920discriminate4prac*migrant10+migrant10_mean*BA1920discriminate4prac+ (migrant10|clus), na.action = na.exclude, data = dat3, control=lmerControl(optimizer="bobyqa"))
stress.BA1920discriminate4prac=lmer(stress~BA1920discriminate4prac*migrant10+migrant10_mean*BA1920discriminate4prac+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
wage.BA1920discriminate4prac=lmer(wage~BA1920discriminate4prac*migrant10+migrant10_mean*BA1920discriminate4prac+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
model_summary(list(mentalhealth.BA1920discriminate4prac, stress.BA1920discriminate4prac, wage.BA1920discriminate4prac))##
## Model Summary
##
## ────────────────────────────────────────────────────────────────────────────────────
## (1) mentalhealth (2) stress (3) wage
## ────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 1.126 3.477 * 586.338 ***
## (1.789) (1.464) (117.156)
## BA1920discriminate4prac 0.552 -0.135 -89.870 **
## (0.452) (0.373) (29.691)
## migrant10 -0.138 0.536 47.808
## (1.326) (0.744) (51.453)
## migrant10_mean 2.834 -0.591 -522.816 **
## (2.834) (2.098) (170.652)
## BA1920discriminate4prac:migrant10 0.045 -0.010 -13.741
## (0.342) (0.193) (13.418)
## BA1920discriminate4prac:migrant10_mean -0.752 0.115 128.392 **
## (0.716) (0.535) (43.295)
## ────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.002 0.021 0.027
## Conditional R^2 0.186 0.040 0.237
## AIC 3932.120 11675.869 35600.428
## BIC 3984.147 11736.883 35661.304
## Num. obs. 1343 3299 3254
## Num. groups: clus 66 69 69
## Var: clus (Intercept) 0.031 0.077 564.892
## Var: clus migrant10 0.443 0.045 1102.257
## Cov: clus (Intercept) migrant10 -0.076 -0.048 -345.216
## Var: Residual 0.998 1.970 3118.601
## ────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.interact_plot(mentalhealth.BA1920discriminate4prac, modx = BA1920discriminate4prac, pred = migrant10)
7.1.4 BA20discriminate4prac
mentalhealth.BA20discriminate4prac=lmer(mentalhealth~BA20discriminate4prac*migrant10+migrant10_mean*BA20discriminate4prac+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
stress.BA20discriminate4prac=lmer(stress~BA20discriminate4prac*migrant10+migrant10_mean*BA20discriminate4prac+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
wage.BA20discriminate4prac=lmer(wage~BA20discriminate4prac*migrant10+migrant10_mean*BA20discriminate4prac+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
model_summary(list(mentalhealth.BA20discriminate4prac, stress.BA20discriminate4prac, wage.BA20discriminate4prac))##
## Model Summary
##
## ─────────────────────────────────────────────────────────────────────────────────
## (1) mentalhealth (2) stress (3) wage
## ─────────────────────────────────────────────────────────────────────────────────
## (Intercept) 1.263 4.929 * 858.964 ***
## (2.418) (2.025) (150.399)
## BA20discriminate4prac 0.495 -0.516 -158.327 ***
## (0.608) (0.512) (37.988)
## migrant10 1.418 -0.516 65.895
## (2.429) (0.922) (75.421)
## migrant10_mean 0.851 -2.022 -977.696 ***
## (4.114) (3.111) (230.372)
## BA20discriminate4prac:migrant10 -0.361 0.273 -16.843
## (0.619) (0.239) (19.562)
## BA20discriminate4prac:migrant10_mean -0.186 0.489 243.384 ***
## (1.035) (0.789) (58.298)
## ─────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.005 0.032 0.060
## Conditional R^2 0.271 0.060 0.273
## AIC 2246.134 6704.182 20475.960
## BIC 2292.778 6759.673 20531.345
## Num. obs. 784 1899 1879
## Num. groups: clus 44 45 45
## Var: clus (Intercept) 0.034 0.087 698.904
## Var: clus migrant10 0.757 0.034 1480.482
## Cov: clus (Intercept) migrant10 -0.107 -0.042 -598.164
## Var: Residual 0.909 1.935 2971.240
## ─────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
7.1.5 BA1920targetedpractices
mentalhealth.BA1920targetedpractices=lmer(mentalhealth~BA1920targetedpractices*migrant10+migrant10_mean*BA1920targetedpractices+ (migrant10|clus), na.action = na.exclude, data = dat3, control=lmerControl(optimizer="bobyqa"))
stress.BA1920targetedpractices=lmer(stress~BA1920targetedpractices*migrant10+migrant10_mean*BA1920targetedpractices+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
wage.BA1920targetedpractices=lmer(wage~BA1920targetedpractices*migrant10+migrant10_mean*BA1920targetedpractices+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
model_summary(list(mentalhealth.BA1920targetedpractices, stress.BA1920targetedpractices, wage.BA1920targetedpractices))##
## Model Summary
##
## ─────────────────────────────────────────────────────────────────────────────────────
## (1) mentalhealth (2) stress (3) wage
## ─────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 1.592 3.380 ** 479.872 ***
## (1.359) (1.089) (83.658)
## BA1920targetedpractices 0.176 -0.045 -25.549 **
## (0.138) (0.112) (8.573)
## migrant10 1.267 0.687 149.445 ***
## (1.285) (0.659) (42.932)
## migrant10_mean 1.524 -0.490 -410.408 **
## (2.422) (1.714) (134.502)
## BA1920targetedpractices:migrant10 -0.133 -0.020 -16.752 ***
## (0.137) (0.071) (4.637)
## BA1920targetedpractices:migrant10_mean -0.166 0.038 40.774 **
## (0.249) (0.177) (13.858)
## ─────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.006 0.021 0.056
## Conditional R^2 0.188 0.040 0.230
## AIC 3936.054 11681.398 35591.935
## BIC 3988.081 11742.412 35652.811
## Num. obs. 1343 3299 3254
## Num. groups: clus 66 69 69
## Var: clus (Intercept) 0.023 0.078 536.368
## Var: clus migrant10 0.444 0.048 871.962
## Cov: clus (Intercept) migrant10 -0.072 -0.051 -316.773
## Var: Residual 0.998 1.970 3118.203
## ─────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.interact_plot(mentalhealth.BA1920targetedpractices, modx = BA1920targetedpractices, pred = migrant10)
7.1.6 BA20targetedpractices
mentalhealth.BA20targetedpractices=lmer(mentalhealth~BA20targetedpractices*migrant10+migrant10_mean*BA20targetedpractices+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
stress.BA20targetedpractices=lmer(stress~BA20targetedpractices*migrant10+migrant10_mean*BA20targetedpractices+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
wage.BA20targetedpractices=lmer(wage~BA20targetedpractices*migrant10+migrant10_mean*BA20targetedpractices+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
model_summary(list(mentalhealth.BA20targetedpractices, stress.BA20targetedpractices, wage.BA20targetedpractices))##
## Model Summary
##
## ──────────────────────────────────────────────────────────────────────────────────
## (1) mentalhealth (2) stress (3) wage
## ──────────────────────────────────────────────────────────────────────────────────
## (Intercept) 2.035 4.038 ** 511.145 ***
## (1.429) (1.273) (98.019)
## BA20targetedpractices 0.122 -0.119 -28.673 **
## (0.145) (0.131) (10.054)
## migrant10 0.725 0.265 160.128 **
## (1.710) (0.695) (50.242)
## migrant10_mean 0.142 -1.522 -484.368 **
## (2.647) (2.143) (165.866)
## BA20targetedpractices:migrant10 -0.078 0.030 -17.664 **
## (0.185) (0.076) (5.539)
## BA20targetedpractices:migrant10_mean 0.005 0.149 48.682 **
## (0.273) (0.224) (17.253)
## ──────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.008 0.032 0.072
## Conditional R^2 0.275 0.061 0.272
## AIC 2252.242 6712.523 20478.119
## BIC 2298.886 6768.014 20533.504
## Num. obs. 784 1899 1879
## Num. groups: clus 44 45 45
## Var: clus (Intercept) 0.027 0.096 711.431
## Var: clus migrant10 0.785 0.046 1103.628
## Cov: clus (Intercept) migrant10 -0.111 -0.053 -464.850
## Var: Residual 0.909 1.935 2970.298
## ──────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
7.2 II check for ab2: Main effect of M on W (Mplus approach)
7.2.1 BM.responsiblesourcing
BA1920FOApractices.BM.responsiblesourcing=lmer(BA1920FOApractices~BM.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA20FOApractices.BM.responsiblesourcing=lmer(BA20FOApractices~BM.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA1920discriminate4prac.BM.responsiblesourcing=lmer(BA1920discriminate4prac~BM.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA20discriminate4prac.BM.responsiblesourcing=lmer(BA20discriminate4prac~BM.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA1920targetedpractices.BM.responsiblesourcing=lmer(BA1920targetedpractices~BM.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA20targetedpractices.BM.responsiblesourcing=lmer(BA20targetedpractices~BM.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
model_summary(list(BA1920FOApractices.BM.responsiblesourcing, BA20FOApractices.BM.responsiblesourcing, BA1920discriminate4prac.BM.responsiblesourcing, BA20discriminate4prac.BM.responsiblesourcing, BA1920targetedpractices.BM.responsiblesourcing, BA20targetedpractices.BM.responsiblesourcing))##
## Model Summary
##
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (1) BA1920FOApractices (2) BA20FOApractices (3) BA1920discriminate4prac (4) BA20discriminate4prac (5) BA1920targetedpractices (6) BA20targetedpractices
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 19.025 *** 18.913 *** 3.763 *** 3.803 *** 9.136 *** 8.953 ***
## (0.038) (0.045) (0.015) (0.018) (0.042) (0.058)
## BM.responsiblesourcing 0.206 *** 0.337 *** 0.035 *** 0.033 *** 0.085 *** 0.072 ***
## (0.013) (0.016) (0.005) (0.007) (0.015) (0.021)
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.848 0.935 0.506 0.474 0.440 0.288
## Conditional R^2 1.000 1.000 1.000 1.000 1.000 1.000
## AIC -58225.936 -26065.044 -46726.561 -39413.129 -58056.338 -25293.945
## BIC -58203.295 -26044.428 -46703.920 -39392.514 -58033.698 -25273.330
## Num. obs. 2122 1279 2122 1279 2122 1279
## Num. groups: clus 47 32 47 32 47 32
## Var: clus (Intercept) 0.017 0.018 0.003 0.003 0.021 0.029
## Var: Residual 0.000 0.000 0.000 0.000 0.000 0.000
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.7.2.2 BM19.responsiblesourcing
BA1920FOApractices.BM19.responsiblesourcing=lmer(BA1920FOApractices~BM19.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA20FOApractices.BM19.responsiblesourcing=lmer(BA20FOApractices~BM19.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA1920discriminate4prac.BM19.responsiblesourcing=lmer(BA1920discriminate4prac~BM19.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA20discriminate4prac.BM19.responsiblesourcing=lmer(BA20discriminate4prac~BM19.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA1920targetedpractices.BM19.responsiblesourcing=lmer(BA1920targetedpractices~BM19.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA20targetedpractices.BM19.responsiblesourcing=lmer(BA20targetedpractices~BM19.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
model_summary(list(BA1920FOApractices.BM19.responsiblesourcing, BA20FOApractices.BM19.responsiblesourcing, BA1920discriminate4prac.BM19.responsiblesourcing, BA20discriminate4prac.BM19.responsiblesourcing, BA1920targetedpractices.BM19.responsiblesourcing, BA20targetedpractices.BM19.responsiblesourcing))##
## Model Summary
##
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (1) BA1920FOApractices (2) BA20FOApractices (3) BA1920discriminate4prac (4) BA20discriminate4prac (5) BA1920targetedpractices (6) BA20targetedpractices
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 19.205 *** 19.261 *** 3.591 *** 3.520 *** 8.628 *** 8.417 ***
## (0.041) (0.047) (0.017) (0.018) (0.046) (0.057)
## BM19.responsiblesourcing 0.155 *** 0.157 *** 0.093 *** 0.121 *** 0.232 *** 0.264 ***
## (0.013) (0.015) (0.006) (0.005) (0.014) (0.018)
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.806 0.814 0.909 0.951 0.879 0.895
## Conditional R^2 1.000 1.000 1.000 1.000 1.000 1.000
## AIC -38529.551 -29048.532 -44124.174 -32150.667 -39363.654 -28996.036
## BIC -38508.478 -29028.668 -44103.101 -32130.803 -39342.581 -28976.172
## Num. obs. 1434 1060 1434 1060 1434 1060
## Num. groups: clus 34 25 34 25 34 25
## Var: clus (Intercept) 0.018 0.017 0.003 0.002 0.023 0.025
## Var: Residual 0.000 0.000 0.000 0.000 0.000 0.000
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.7.2.3 BM20.responsiblesourcing
BA1920FOApractices.BM20.responsiblesourcing=lmer(BA1920FOApractices~BM20.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA20FOApractices.BM20.responsiblesourcing=lmer(BA20FOApractices~BM20.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA1920discriminate4prac.BM20.responsiblesourcing=lmer(BA1920discriminate4prac~BM20.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA20discriminate4prac.BM20.responsiblesourcing=lmer(BA20discriminate4prac~BM20.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA1920targetedpractices.BM20.responsiblesourcing=lmer(BA1920targetedpractices~BM20.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA20targetedpractices.BM20.responsiblesourcing=lmer(BA20targetedpractices~BM20.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
model_summary(list(BA1920FOApractices.BM20.responsiblesourcing, BA20FOApractices.BM20.responsiblesourcing, BA1920discriminate4prac.BM20.responsiblesourcing, BA20discriminate4prac.BM20.responsiblesourcing, BA1920targetedpractices.BM20.responsiblesourcing, BA20targetedpractices.BM20.responsiblesourcing))##
## Model Summary
##
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (1) BA1920FOApractices (2) BA20FOApractices (3) BA1920discriminate4prac (4) BA20discriminate4prac (5) BA1920targetedpractices (6) BA20targetedpractices
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 19.569 *** 19.456 *** 3.953 *** 3.995 *** 9.526 *** 9.234 ***
## (0.037) (0.050) (0.013) (0.016) (0.040) (0.058)
## BM20.responsiblesourcing -0.035 ** 0.125 *** -0.041 *** -0.054 *** -0.102 *** -0.119 ***
## (0.012) (0.017) (0.004) (0.005) (0.013) (0.019)
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.159 0.628 0.669 0.760 0.584 0.540
## Conditional R^2 1.000 1.000 1.000 1.000 1.000 1.000
## AIC -42662.644 -18169.262 -34988.734 -27424.606 -43042.544 -24573.634
## BIC -42641.173 -18150.075 -34967.263 -27405.419 -43021.073 -24554.446
## Num. obs. 1584 895 1584 895 1584 895
## Num. groups: clus 35 23 35 23 35 23
## Var: clus (Intercept) 0.020 0.026 0.003 0.003 0.023 0.034
## Var: Residual 0.000 0.000 0.000 0.000 0.000 0.000
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.7.3 III check for cw1: Cross-level interaction of W
7.3.1 BM.responsiblesourcing
mentalhealth.BM.responsiblesourcing=lmer(mentalhealth~BM.responsiblesourcing*migrant10+migrant10_mean*BM.responsiblesourcing+ (migrant10|clus), na.action = na.exclude, data = dat3, control=lmerControl(optimizer="bobyqa"))
stress.BM.responsiblesourcing=lmer(stress~BM.responsiblesourcing*migrant10+migrant10_mean*BM.responsiblesourcing+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
wage.BM.responsiblesourcing=lmer(wage~BM.responsiblesourcing*migrant10+migrant10_mean*BM.responsiblesourcing+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
model_summary(list(mentalhealth.BM.responsiblesourcing, stress.BM.responsiblesourcing, wage.BM.responsiblesourcing))##
## Model Summary
##
## ────────────────────────────────────────────────────────────────────────────────────
## (1) mentalhealth (2) stress (3) wage
## ────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.203 *** 3.108 *** 216.043 ***
## (0.204) (0.221) (14.425)
## BM.responsiblesourcing 0.059 -0.096 7.322
## (0.089) (0.096) (6.657)
## migrant10 0.471 0.307 2.597
## (0.318) (0.175) (11.256)
## migrant10_mean -0.171 -0.313 -3.097
## (0.370) (0.348) (23.820)
## BM.responsiblesourcing:migrant10 -0.189 0.105 -1.673
## (0.104) (0.057) (3.843)
## BM.responsiblesourcing:migrant10_mean 0.002 0.049 -6.640
## (0.141) (0.134) (9.720)
## ────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.026 0.031 0.008
## Conditional R^2 0.224 0.056 0.254
## AIC 2679.246 7803.911 23757.133
## BIC 2727.468 7860.887 23813.995
## Num. obs. 918 2203 2178
## Num. groups: clus 49 51 51
## Var: clus (Intercept) 0.036 0.100 594.608
## Var: clus migrant10 0.547 0.029 869.185
## Cov: clus (Intercept) migrant10 -0.112 -0.050 -149.710
## Var: Residual 0.964 1.958 3001.445
## ────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
7.3.2 BM19.responsiblesourcing
mentalhealth.BM19.responsiblesourcing=lmer(mentalhealth~BM19.responsiblesourcing*migrant10+migrant10_mean*BM19.responsiblesourcing+ (migrant10|clus), na.action = na.exclude, data = dat3, control=lmerControl(optimizer="bobyqa"))
stress.BM19.responsiblesourcing=lmer(stress~BM19.responsiblesourcing*migrant10+migrant10_mean*BM19.responsiblesourcing+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
wage.BM19.responsiblesourcing=lmer(wage~BM19.responsiblesourcing*migrant10+migrant10_mean*BM19.responsiblesourcing+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
model_summary(list(mentalhealth.BM19.responsiblesourcing, stress.BM19.responsiblesourcing, wage.BM19.responsiblesourcing))##
## Model Summary
##
## ──────────────────────────────────────────────────────────────────────────────────────
## (1) mentalhealth (2) stress (3) wage
## ──────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.167 *** 3.479 *** 238.351 ***
## (0.250) (0.215) (22.352)
## BM19.responsiblesourcing 0.027 -0.194 ** -2.469
## (0.078) (0.066) (6.864)
## migrant10 0.124 0.368 * -5.217
## (0.335) (0.178) (10.898)
## migrant10_mean 0.098 -0.772 * -25.204
## (0.411) (0.359) (34.253)
## BM19.responsiblesourcing:migrant10 -0.084 0.059 2.568
## (0.105) (0.055) (3.519)
## BM19.responsiblesourcing:migrant10_mean -0.006 0.220 * 3.042
## (0.125) (0.108) (10.318)
## ──────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.008 0.036 0.007
## Conditional R^2 0.240 0.049 0.292
## AIC 1848.897 5305.701 15964.700
## BIC 1893.480 5358.827 16017.725
## Num. obs. 638 1499 1484
## Num. groups: clus 35 37 37
## Var: clus (Intercept) 0.038 0.000 604.764
## Var: clus migrant10 0.737 0.042 869.512
## Cov: clus (Intercept) migrant10 -0.167 0.000 -101.775
## Var: Residual 0.925 1.950 2566.939
## ──────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.interact_plot(mentalhealth.BM19.responsiblesourcing, modx = BM19.responsiblesourcing, pred = migrant10)
7.3.3 BM20.responsiblesourcing
mentalhealth.BM20.responsiblesourcing=lmer(mentalhealth~BM20.responsiblesourcing*migrant10+migrant10_mean*BM20.responsiblesourcing+ (migrant10|clus), na.action = na.exclude, data = dat3, control=lmerControl(optimizer="bobyqa"))
stress.BM20.responsiblesourcing=lmer(stress~BM20.responsiblesourcing*migrant10+migrant10_mean*BM20.responsiblesourcing+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
wage.BM20.responsiblesourcing=lmer(wage~BM20.responsiblesourcing*migrant10+migrant10_mean*BM20.responsiblesourcing+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
model_summary(list(mentalhealth.BM20.responsiblesourcing, stress.BM20.responsiblesourcing, wage.BM20.responsiblesourcing))##
## Model Summary
##
## ──────────────────────────────────────────────────────────────────────────────────────
## (1) mentalhealth (2) stress (3) wage
## ──────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.303 *** 2.984 *** 215.502 ***
## (0.182) (0.193) (13.558)
## BM20.responsiblesourcing 0.053 -0.082 11.035
## (0.089) (0.091) (7.153)
## migrant10 0.200 0.575 ** 8.478
## (0.325) (0.189) (12.078)
## migrant10_mean -0.184 -0.452 3.181
## (0.361) (0.332) (23.689)
## BM20.responsiblesourcing:migrant10 -0.097 0.043 -4.149
## (0.098) (0.060) (3.763)
## BM20.responsiblesourcing:migrant10_mean -0.036 0.082 -13.791
## (0.145) (0.130) (10.668)
## ──────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.016 0.037 0.027
## Conditional R^2 0.238 0.066 0.274
## AIC 2003.898 5726.992 17529.771
## BIC 2049.118 5780.857 17583.530
## Num. obs. 680 1614 1597
## Num. groups: clus 37 37 37
## Var: clus (Intercept) 0.069 0.116 747.216
## Var: clus migrant10 0.654 0.043 1035.290
## Cov: clus (Intercept) migrant10 -0.158 -0.063 -259.344
## Var: Residual 0.974 1.955 3223.863
## ──────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.interact_plot(mentalhealth.BM20.responsiblesourcing, modx = BM20.responsiblesourcing, pred = migrant10)
8 F. 2&1 ROUND DATA
## Frequency Statistics:
## ────────────
## N %
## ────────────
## 1 1730 50.9
## 2 1666 49.1
## ────────────
## Total N = 3,3968.1 I check for cw2: Cross-level interaction of M
8.1.1 BA1920FOApractices
mentalhealth.BA1920FOApractices=lmer(mentalhealth~BA1920FOApractices*migrant10+migrant10_mean*BA1920FOApractices+ (migrant10|clus), na.action = na.exclude, data = dat3, control=lmerControl(optimizer="bobyqa"))
stress.BA1920FOApractices=lmer(stress~BA1920FOApractices*migrant10+migrant10_mean*BA1920FOApractices+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
wage.BA1920FOApractices=lmer(wage~BA1920FOApractices*migrant10+migrant10_mean*BA1920FOApractices+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
model_summary(list(mentalhealth.BA1920FOApractices, stress.BA1920FOApractices, wage.BA1920FOApractices))##
## Model Summary
##
## ─────────────────────────────────────────────────────────────────────────────
## (1) mentalhealth (2) stress (3) wage
## ─────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.098 1.396 325.573 *
## (1.701) (1.397) (130.955)
## BA1920FOApractices 0.011 0.078 -5.085
## (0.086) (0.071) (6.632)
## migrant10 6.805 * -1.627 211.512 *
## (2.750) (1.566) (96.137)
## migrant10_mean -4.163 5.979 * -129.961
## (3.615) (2.880) (250.374)
## BA1920FOApractices:migrant10 -0.346 * 0.101 -10.561 *
## (0.141) (0.080) (4.946)
## BA1920FOApractices:migrant10_mean 0.205 -0.299 * 6.138
## (0.183) (0.146) (12.658)
## ─────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.012 0.015 0.015
## Conditional R^2 0.181 0.037 0.265
## AIC 3933.788 11087.076 33954.999
## BIC 3985.815 11147.694 34015.458
## Num. obs. 1343 3171 3121
## Num. groups: clus 66 69 69
## Var: clus (Intercept) 0.021 0.000 561.111
## Var: clus migrant10 0.388 0.060 767.134
## Cov: clus (Intercept) migrant10 -0.055 0.000 -80.436
## Var: Residual 0.999 1.885 2916.971
## ─────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
8.1.2 BA20FOApractices
mentalhealth.BA20FOApractices=lmer(mentalhealth~BA20FOApractices*migrant10+migrant10_mean*BA20FOApractices+ (migrant10|clus), na.action = na.exclude, data = dat3, control=lmerControl(optimizer="bobyqa"))
stress.BA20FOApractices=lmer(stress~BA20FOApractices*migrant10+migrant10_mean*BA20FOApractices+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
wage.BA20FOApractices=lmer(wage~BA20FOApractices*migrant10+migrant10_mean*BA20FOApractices+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
model_summary(list(mentalhealth.BA20FOApractices, stress.BA20FOApractices, wage.BA20FOApractices))##
## Model Summary
##
## ─────────────────────────────────────────────────────────────────────────────
## (1) mentalhealth (2) stress (3) wage
## ─────────────────────────────────────────────────────────────────────────────
## (Intercept) 4.988 ** -1.424 293.229
## (1.838) (1.596) (168.762)
## BA20FOApractices -0.088 0.217 ** -3.579
## (0.092) (0.080) (8.502)
## migrant10 12.968 ** -1.490 339.164 **
## (4.925) (2.041) (128.570)
## migrant10_mean -11.243 * 14.715 *** -23.580
## (4.472) (3.672) (373.502)
## BA20FOApractices:migrant10 -0.650 ** 0.092 -16.593 *
## (0.247) (0.103) (6.520)
## BA20FOApractices:migrant10_mean 0.566 * -0.732 *** 1.246
## (0.224) (0.184) (18.717)
## ─────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.028 0.029 0.035
## Conditional R^2 0.264 0.066 0.312
## AIC 2246.304 6422.458 19917.318
## BIC 2292.948 6477.709 19972.401
## Num. obs. 784 1854 1823
## Num. groups: clus 44 45 45
## Var: clus (Intercept) 0.017 0.000 825.860
## Var: clus migrant10 0.601 0.115 960.356
## Cov: clus (Intercept) migrant10 -0.050 0.000 -158.632
## Var: Residual 0.909 1.803 3031.237
## ─────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
8.1.3 BA1920discriminate4prac
mentalhealth.BA1920discriminate4prac=lmer(mentalhealth~BA1920discriminate4prac*migrant10+migrant10_mean*BA1920discriminate4prac+ (migrant10|clus), na.action = na.exclude, data = dat3, control=lmerControl(optimizer="bobyqa"))
stress.BA1920discriminate4prac=lmer(stress~BA1920discriminate4prac*migrant10+migrant10_mean*BA1920discriminate4prac+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
wage.BA1920discriminate4prac=lmer(wage~BA1920discriminate4prac*migrant10+migrant10_mean*BA1920discriminate4prac+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
model_summary(list(mentalhealth.BA1920discriminate4prac, stress.BA1920discriminate4prac, wage.BA1920discriminate4prac))##
## Model Summary
##
## ──────────────────────────────────────────────────────────────────────────────────────
## (1) mentalhealth (2) stress (3) wage
## ──────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 1.126 5.683 *** 721.435 ***
## (1.789) (1.216) (109.609)
## BA1920discriminate4prac 0.552 -0.694 * -125.224 ***
## (0.452) (0.309) (27.761)
## migrant10 -0.138 -0.157 62.781
## (1.326) (0.728) (46.520)
## migrant10_mean 2.834 -3.067 -687.740 ***
## (2.834) (1.912) (161.371)
## BA1920discriminate4prac:migrant10 0.045 0.130 -14.745
## (0.342) (0.190) (12.138)
## BA1920discriminate4prac:migrant10_mean -0.752 0.791 171.004 ***
## (0.716) (0.486) (40.911)
## ──────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.002 0.016 0.038
## Conditional R^2 0.186 0.038 0.240
## AIC 3932.120 11079.835 33937.139
## BIC 3984.147 11140.453 33997.598
## Num. obs. 1343 3171 3121
## Num. groups: clus 66 69 69
## Var: clus (Intercept) 0.031 0.000 430.568
## Var: clus migrant10 0.443 0.063 866.939
## Cov: clus (Intercept) migrant10 -0.076 0.000 -191.607
## Var: Residual 0.998 1.884 2919.711
## ──────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.interact_plot(mentalhealth.BA1920discriminate4prac, modx = BA1920discriminate4prac, pred = migrant10)
8.1.4 BA20discriminate4prac
mentalhealth.BA20discriminate4prac=lmer(mentalhealth~BA20discriminate4prac*migrant10+migrant10_mean*BA20discriminate4prac+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))## Error in lme4::lFormula(formula = mentalhealth ~ BA20discriminate4prac * : 0 (non-NA) casesstress.BA20discriminate4prac=lmer(stress~BA20discriminate4prac*migrant10+migrant10_mean*BA20discriminate4prac+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
wage.BA20discriminate4prac=lmer(wage~BA20discriminate4prac*migrant10+migrant10_mean*BA20discriminate4prac+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
model_summary(list(mentalhealth.BA20discriminate4prac, stress.BA20discriminate4prac, wage.BA20discriminate4prac))##
## Model Summary
##
## ───────────────────────────────────────────────────────────────────────────────────
## (1) mentalhealth (2) stress (3) wage
## ───────────────────────────────────────────────────────────────────────────────────
## (Intercept) 1.263 5.145 *** 1075.026 ***
## (2.418) (1.552) (134.391)
## BA20discriminate4prac 0.495 -0.559 -214.827 ***
## (0.608) (0.392) (33.960)
## migrant10 1.418 -0.192 82.800
## (2.429) (1.016) (71.543)
## migrant10_mean 0.851 -2.262 -1257.344 ***
## (4.114) (2.577) (206.064)
## BA20discriminate4prac:migrant10 -0.361 0.138 -18.649
## (0.619) (0.264) (18.568)
## BA20discriminate4prac:migrant10_mean -0.186 0.584 316.715 ***
## (1.035) (0.652) (52.171)
## ───────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.005 0.017 0.101
## Conditional R^2 0.271 0.059 0.272
## AIC 2246.134 6429.962 19894.235
## BIC 2292.778 6485.213 19949.318
## Num. obs. 784 1854 1823
## Num. groups: clus 44 45 45
## Var: clus (Intercept) 0.034 0.000 563.338
## Var: clus migrant10 0.757 0.132 1324.936
## Cov: clus (Intercept) migrant10 -0.107 0.000 -538.691
## Var: Residual 0.909 1.814 3035.426
## ───────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
8.1.5 BA1920targetedpractices
mentalhealth.BA1920targetedpractices=lmer(mentalhealth~BA1920targetedpractices*migrant10+migrant10_mean*BA1920targetedpractices+ (migrant10|clus), na.action = na.exclude, data = dat3, control=lmerControl(optimizer="bobyqa"))
stress.BA1920targetedpractices=lmer(stress~BA1920targetedpractices*migrant10+migrant10_mean*BA1920targetedpractices+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
wage.BA1920targetedpractices=lmer(wage~BA1920targetedpractices*migrant10+migrant10_mean*BA1920targetedpractices+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
model_summary(list(mentalhealth.BA1920targetedpractices, stress.BA1920targetedpractices, wage.BA1920targetedpractices))##
## Model Summary
##
## ──────────────────────────────────────────────────────────────────────────────────────
## (1) mentalhealth (2) stress (3) wage
## ──────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 1.592 4.534 *** 540.410 ***
## (1.359) (0.899) (79.141)
## BA1920targetedpractices 0.176 -0.162 -32.219 ***
## (0.138) (0.092) (8.102)
## migrant10 1.267 0.844 176.539 ***
## (1.285) (0.672) (36.709)
## migrant10_mean 1.524 -2.738 -465.462 ***
## (2.422) (1.612) (128.494)
## BA1920targetedpractices:migrant10 -0.133 -0.055 -18.509 ***
## (0.137) (0.073) (3.968)
## BA1920targetedpractices:migrant10_mean -0.166 0.291 46.686 ***
## (0.249) (0.167) (13.230)
## ──────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.006 0.015 0.073
## Conditional R^2 0.188 0.038 0.226
## AIC 3936.054 11088.603 33923.399
## BIC 3988.081 11149.221 33983.858
## Num. obs. 1343 3171 3121
## Num. groups: clus 66 69 69
## Var: clus (Intercept) 0.023 0.000 428.172
## Var: clus migrant10 0.444 0.065 535.485
## Cov: clus (Intercept) migrant10 -0.072 0.000 -164.224
## Var: Residual 0.998 1.885 2921.015
## ──────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.interact_plot(mentalhealth.BA1920targetedpractices, modx = BA1920targetedpractices, pred = migrant10)
8.1.6 BA20targetedpractices
mentalhealth.BA20targetedpractices=lmer(mentalhealth~BA20targetedpractices*migrant10+migrant10_mean*BA20targetedpractices+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))## Error in lme4::lFormula(formula = mentalhealth ~ BA20targetedpractices * : 0 (non-NA) casesstress.BA20targetedpractices=lmer(stress~BA20targetedpractices*migrant10+migrant10_mean*BA20targetedpractices+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
wage.BA20targetedpractices=lmer(wage~BA20targetedpractices*migrant10+migrant10_mean*BA20targetedpractices+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
model_summary(list(mentalhealth.BA20targetedpractices, stress.BA20targetedpractices, wage.BA20targetedpractices))##
## Model Summary
##
## ───────────────────────────────────────────────────────────────────────────────────
## (1) mentalhealth (2) stress (3) wage
## ───────────────────────────────────────────────────────────────────────────────────
## (Intercept) 2.035 4.463 *** 571.610 ***
## (1.429) (1.009) (96.845)
## BA20targetedpractices 0.122 -0.156 -35.624 ***
## (0.145) (0.103) (9.923)
## migrant10 0.725 1.029 193.245 ***
## (1.710) (0.793) (44.021)
## migrant10_mean 0.142 -2.799 -541.761 **
## (2.647) (1.919) (164.937)
## BA20targetedpractices:migrant10 -0.078 -0.077 -20.184 ***
## (0.185) (0.087) (4.855)
## BA20targetedpractices:migrant10_mean 0.005 0.299 55.475 **
## (0.273) (0.199) (17.142)
## ───────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.008 0.017 0.099
## Conditional R^2 0.275 0.060 0.269
## AIC 2252.242 6437.285 19896.850
## BIC 2298.886 6492.536 19951.932
## Num. obs. 784 1854 1823
## Num. groups: clus 44 45 45
## Var: clus (Intercept) 0.027 0.000 633.690
## Var: clus migrant10 0.785 0.137 732.197
## Cov: clus (Intercept) migrant10 -0.111 0.000 -305.616
## Var: Residual 0.909 1.814 3034.198
## ───────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
8.2 II check for ab2: Main effect of M on W (Mplus approach)
8.2.1 BM.responsiblesourcing
BA1920FOApractices.BM.responsiblesourcing=lmer(BA1920FOApractices~BM.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA20FOApractices.BM.responsiblesourcing=lmer(BA20FOApractices~BM.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA1920discriminate4prac.BM.responsiblesourcing=lmer(BA1920discriminate4prac~BM.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA20discriminate4prac.BM.responsiblesourcing=lmer(BA20discriminate4prac~BM.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA1920targetedpractices.BM.responsiblesourcing=lmer(BA1920targetedpractices~BM.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA20targetedpractices.BM.responsiblesourcing=lmer(BA20targetedpractices~BM.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
model_summary(list(BA1920FOApractices.BM.responsiblesourcing, BA20FOApractices.BM.responsiblesourcing, BA1920discriminate4prac.BM.responsiblesourcing, BA20discriminate4prac.BM.responsiblesourcing, BA1920targetedpractices.BM.responsiblesourcing, BA20targetedpractices.BM.responsiblesourcing))##
## Model Summary
##
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (1) BA1920FOApractices (2) BA20FOApractices (3) BA1920discriminate4prac (4) BA20discriminate4prac (5) BA1920targetedpractices (6) BA20targetedpractices
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 19.041 *** 18.965 *** 3.753 *** 3.822 *** 9.079 *** 8.894 ***
## (0.039) (0.045) (0.016) (0.017) (0.043) (0.057)
## BM.responsiblesourcing 0.189 *** 0.318 *** 0.041 *** 0.024 *** 0.108 *** 0.051 *
## (0.013) (0.016) (0.006) (0.006) (0.015) (0.020)
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.814 0.929 0.565 0.323 0.546 0.170
## Conditional R^2 1.000 1.000 1.000 1.000 1.000 1.000
## AIC -41249.157 -34409.505 -63344.034 -39113.456 -57225.152 -35036.066
## BIC -41226.614 -34388.871 -63321.491 -39092.822 -57202.609 -35015.432
## Num. obs. 2071 1285 2071 1285 2071 1285
## Num. groups: clus 47 32 47 32 47 32
## Var: clus (Intercept) 0.018 0.018 0.003 0.003 0.021 0.029
## Var: Residual 0.000 0.000 0.000 0.000 0.000 0.000
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.8.2.2 BM19.responsiblesourcing
BA1920FOApractices.BM19.responsiblesourcing=lmer(BA1920FOApractices~BM19.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA20FOApractices.BM19.responsiblesourcing=lmer(BA20FOApractices~BM19.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA1920discriminate4prac.BM19.responsiblesourcing=lmer(BA1920discriminate4prac~BM19.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA20discriminate4prac.BM19.responsiblesourcing=lmer(BA20discriminate4prac~BM19.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA1920targetedpractices.BM19.responsiblesourcing=lmer(BA1920targetedpractices~BM19.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA20targetedpractices.BM19.responsiblesourcing=lmer(BA20targetedpractices~BM19.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
model_summary(list(BA1920FOApractices.BM19.responsiblesourcing, BA20FOApractices.BM19.responsiblesourcing, BA1920discriminate4prac.BM19.responsiblesourcing, BA20discriminate4prac.BM19.responsiblesourcing, BA1920targetedpractices.BM19.responsiblesourcing, BA20targetedpractices.BM19.responsiblesourcing))##
## Model Summary
##
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (1) BA1920FOApractices (2) BA20FOApractices (3) BA1920discriminate4prac (4) BA20discriminate4prac (5) BA1920targetedpractices (6) BA20targetedpractices
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 19.132 *** 19.163 *** 3.544 *** 3.549 *** 8.644 *** 8.458 ***
## (0.041) (0.047) (0.018) (0.017) (0.047) (0.055)
## BM19.responsiblesourcing 0.159 *** 0.203 *** 0.109 *** 0.110 *** 0.232 *** 0.253 ***
## (0.013) (0.015) (0.006) (0.005) (0.015) (0.017)
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.815 0.884 0.932 0.943 0.879 0.890
## Conditional R^2 1.000 1.000 1.000 1.000 1.000 1.000
## AIC -29114.865 -29192.797 -43948.116 -25097.447 -28213.256 -30013.274
## BIC -29093.753 -29172.862 -43927.005 -25077.512 -28192.144 -29993.338
## Num. obs. 1448 1079 1448 1079 1448 1079
## Num. groups: clus 34 25 34 25 34 25
## Var: clus (Intercept) 0.018 0.017 0.003 0.002 0.023 0.025
## Var: Residual 0.000 0.000 0.000 0.000 0.000 0.000
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.8.2.3 BM20.responsiblesourcing
BA1920FOApractices.BM20.responsiblesourcing=lmer(BA1920FOApractices~BM20.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA20FOApractices.BM20.responsiblesourcing=lmer(BA20FOApractices~BM20.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA1920discriminate4prac.BM20.responsiblesourcing=lmer(BA1920discriminate4prac~BM20.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA20discriminate4prac.BM20.responsiblesourcing=lmer(BA20discriminate4prac~BM20.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA1920targetedpractices.BM20.responsiblesourcing=lmer(BA1920targetedpractices~BM20.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA20targetedpractices.BM20.responsiblesourcing=lmer(BA20targetedpractices~BM20.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
model_summary(list(BA1920FOApractices.BM20.responsiblesourcing, BA20FOApractices.BM20.responsiblesourcing, BA1920discriminate4prac.BM20.responsiblesourcing, BA20discriminate4prac.BM20.responsiblesourcing, BA1920targetedpractices.BM20.responsiblesourcing, BA20targetedpractices.BM20.responsiblesourcing))##
## Model Summary
##
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (1) BA1920FOApractices (2) BA20FOApractices (3) BA1920discriminate4prac (4) BA20discriminate4prac (5) BA1920targetedpractices (6) BA20targetedpractices
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 19.480 *** 19.462 *** 3.953 *** 4.006 *** 9.454 *** 9.396 ***
## (0.037) (0.051) (0.014) (0.016) (0.041) (0.059)
## BM20.responsiblesourcing 0.040 ** 0.103 *** -0.041 *** -0.058 *** -0.073 *** -0.157 ***
## (0.012) (0.017) (0.005) (0.005) (0.013) (0.020)
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.196 0.534 0.657 0.784 0.401 0.671
## Conditional R^2 1.000 1.000 1.000 1.000 1.000 1.000
## AIC -40677.287 -23293.336 -33887.879 -26822.025 -41530.006 -23780.858
## BIC -40655.992 -23274.244 -33866.584 -26802.933 -41508.710 -23761.766
## Num. obs. 1516 874 1516 874 1516 874
## Num. groups: clus 35 23 35 23 35 23
## Var: clus (Intercept) 0.020 0.026 0.003 0.003 0.024 0.035
## Var: Residual 0.000 0.000 0.000 0.000 0.000 0.000
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.8.3 III check for cw1: Cross-level interaction of W
8.3.1 BM.responsiblesourcing
mentalhealth.BM.responsiblesourcing=lmer(mentalhealth~BM.responsiblesourcing*migrant10+migrant10_mean*BM.responsiblesourcing+ (migrant10|clus), na.action = na.exclude, data = dat3, control=lmerControl(optimizer="bobyqa"))
stress.BM.responsiblesourcing=lmer(stress~BM.responsiblesourcing*migrant10+migrant10_mean*BM.responsiblesourcing+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
wage.BM.responsiblesourcing=lmer(wage~BM.responsiblesourcing*migrant10+migrant10_mean*BM.responsiblesourcing+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
model_summary(list(mentalhealth.BM.responsiblesourcing, stress.BM.responsiblesourcing, wage.BM.responsiblesourcing))##
## Model Summary
##
## ────────────────────────────────────────────────────────────────────────────────────
## (1) mentalhealth (2) stress (3) wage
## ────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.203 *** 3.093 *** 209.404 ***
## (0.204) (0.156) (14.085)
## BM.responsiblesourcing 0.059 -0.124 7.095
## (0.089) (0.071) (6.591)
## migrant10 0.471 0.225 5.909
## (0.318) (0.187) (9.801)
## migrant10_mean -0.171 0.029 17.345
## (0.370) (0.289) (23.842)
## BM.responsiblesourcing:migrant10 -0.189 0.047 0.466
## (0.104) (0.062) (3.328)
## BM.responsiblesourcing:migrant10_mean 0.002 0.090 -10.780
## (0.141) (0.113) (9.737)
## ────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.026 0.020 0.005
## Conditional R^2 0.224 0.051 0.286
## AIC 2679.246 7575.831 23186.351
## BIC 2727.468 7632.656 23243.009
## Num. obs. 918 2170 2134
## Num. groups: clus 49 51 51
## Var: clus (Intercept) 0.036 0.000 542.087
## Var: clus migrant10 0.547 0.087 557.222
## Cov: clus (Intercept) migrant10 -0.112 0.000 149.871
## Var: Residual 0.964 1.858 2876.733
## ────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
8.3.2 BM19.responsiblesourcing
mentalhealth.BM19.responsiblesourcing=lmer(mentalhealth~BM19.responsiblesourcing*migrant10+migrant10_mean*BM19.responsiblesourcing+ (migrant10|clus), na.action = na.exclude, data = dat3, control=lmerControl(optimizer="bobyqa"))
stress.BM19.responsiblesourcing=lmer(stress~BM19.responsiblesourcing*migrant10+migrant10_mean*BM19.responsiblesourcing+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
wage.BM19.responsiblesourcing=lmer(wage~BM19.responsiblesourcing*migrant10+migrant10_mean*BM19.responsiblesourcing+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
model_summary(list(mentalhealth.BM19.responsiblesourcing, stress.BM19.responsiblesourcing, wage.BM19.responsiblesourcing))##
## Model Summary
##
## ──────────────────────────────────────────────────────────────────────────────────────
## (1) mentalhealth (2) stress (3) wage
## ──────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.167 *** 3.262 *** 261.248 ***
## (0.250) (0.210) (21.513)
## BM19.responsiblesourcing 0.027 -0.152 * -10.342
## (0.078) (0.065) (6.643)
## migrant10 0.124 0.345 4.428
## (0.335) (0.182) (9.876)
## migrant10_mean 0.098 -0.458 -55.069
## (0.411) (0.357) (33.413)
## BM19.responsiblesourcing:migrant10 -0.084 -0.011 1.734
## (0.105) (0.057) (3.158)
## BM19.responsiblesourcing:migrant10_mean -0.006 0.232 * 12.067
## (0.125) (0.108) (10.060)
## ──────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.008 0.022 0.014
## Conditional R^2 0.240 0.052 0.277
## AIC 1848.897 5298.895 16196.138
## BIC 1893.480 5352.180 16249.257
## Num. obs. 638 1523 1498
## Num. groups: clus 35 37 37
## Var: clus (Intercept) 0.038 0.000 534.906
## Var: clus migrant10 0.737 0.087 630.098
## Cov: clus (Intercept) migrant10 -0.167 0.000 34.204
## Var: Residual 0.925 1.824 2726.250
## ──────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.interact_plot(mentalhealth.BM19.responsiblesourcing, modx = BM19.responsiblesourcing, pred = migrant10)
8.3.3 BM20.responsiblesourcing
mentalhealth.BM20.responsiblesourcing=lmer(mentalhealth~BM20.responsiblesourcing*migrant10+migrant10_mean*BM20.responsiblesourcing+ (migrant10|clus), na.action = na.exclude, data = dat3, control=lmerControl(optimizer="bobyqa"))
stress.BM20.responsiblesourcing=lmer(stress~BM20.responsiblesourcing*migrant10+migrant10_mean*BM20.responsiblesourcing+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
wage.BM20.responsiblesourcing=lmer(wage~BM20.responsiblesourcing*migrant10+migrant10_mean*BM20.responsiblesourcing+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
model_summary(list(mentalhealth.BM20.responsiblesourcing, stress.BM20.responsiblesourcing, wage.BM20.responsiblesourcing))##
## Model Summary
##
## ──────────────────────────────────────────────────────────────────────────────────────
## (1) mentalhealth (2) stress (3) wage
## ──────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.303 *** 2.896 *** 202.722 ***
## (0.182) (0.131) (13.658)
## BM20.responsiblesourcing 0.053 -0.033 13.612
## (0.089) (0.068) (7.420)
## migrant10 0.200 0.140 14.066
## (0.325) (0.196) (9.308)
## migrant10_mean -0.184 0.351 25.219
## (0.361) (0.279) (24.502)
## BM20.responsiblesourcing:migrant10 -0.097 0.073 -2.982
## (0.098) (0.062) (2.905)
## BM20.responsiblesourcing:migrant10_mean -0.036 -0.062 -18.431
## (0.145) (0.115) (11.239)
## ──────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.016 0.019 0.024
## Conditional R^2 0.238 0.050 0.317
## AIC 2003.898 5449.057 16698.589
## BIC 2049.118 5502.549 16751.907
## Num. obs. 680 1555 1528
## Num. groups: clus 37 37 37
## Var: clus (Intercept) 0.069 0.000 710.717
## Var: clus migrant10 0.654 0.089 398.168
## Cov: clus (Intercept) migrant10 -0.158 0.000 250.834
## Var: Residual 0.974 1.872 3079.416
## ──────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.interact_plot(mentalhealth.BM20.responsiblesourcing, modx = BM20.responsiblesourcing, pred = migrant10)
9 G. 3&2&1 ROUND DATA
## Frequency Statistics:
## ───────────────
## N %
## ───────────────
## 1 1730 33.5
## 2 1666 32.3
## 3 1756 34.0
## (NA) 12 0.2
## ───────────────
## Total N = 5,164
## Valid N = 5,1529.1 I check for cw2: Cross-level interaction of M
9.1.1 BA1920FOApractices
mentalhealth.BA1920FOApractices=lmer(mentalhealth~BA1920FOApractices*migrant10+migrant10_mean*BA1920FOApractices+ (migrant10|clus), na.action = na.exclude, data = dat3, control=lmerControl(optimizer="bobyqa"))
stress.BA1920FOApractices=lmer(stress~BA1920FOApractices*migrant10+migrant10_mean*BA1920FOApractices+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
wage.BA1920FOApractices=lmer(wage~BA1920FOApractices*migrant10+migrant10_mean*BA1920FOApractices+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
model_summary(list(mentalhealth.BA1920FOApractices, stress.BA1920FOApractices, wage.BA1920FOApractices))##
## Model Summary
##
## ──────────────────────────────────────────────────────────────────────────────
## (1) mentalhealth (2) stress (3) wage
## ──────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.098 2.321 * 364.961 **
## (1.701) (1.133) (121.853)
## BA1920FOApractices 0.011 0.032 -6.839
## (0.086) (0.057) (6.180)
## migrant10 6.805 * -2.010 221.605 *
## (2.750) (1.228) (90.090)
## migrant10_mean -4.163 3.876 -285.516
## (3.615) (2.306) (229.915)
## BA1920FOApractices:migrant10 -0.346 * 0.125 * -11.467 *
## (0.141) (0.063) (4.635)
## BA1920FOApractices:migrant10_mean 0.205 -0.198 13.761
## (0.183) (0.117) (11.634)
## ──────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.012 0.019 0.018
## Conditional R^2 0.181 0.025 0.227
## AIC 3933.788 17153.430 52048.103
## BIC 3985.815 17218.320 52112.838
## Num. obs. 1343 4861 4786
## Num. groups: clus 66 69 69
## Var: clus (Intercept) 0.021 0.000 552.936
## Var: clus migrant10 0.388 0.018 762.851
## Cov: clus (Intercept) migrant10 -0.055 0.000 -209.632
## Var: Residual 0.999 1.970 2949.941
## ──────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
9.1.2 BA20FOApractices
mentalhealth.BA20FOApractices=lmer(mentalhealth~BA20FOApractices*migrant10+migrant10_mean*BA20FOApractices+ (migrant10|clus), na.action = na.exclude, data = dat3, control=lmerControl(optimizer="bobyqa"))
stress.BA20FOApractices=lmer(stress~BA20FOApractices*migrant10+migrant10_mean*BA20FOApractices+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
wage.BA20FOApractices=lmer(wage~BA20FOApractices*migrant10+migrant10_mean*BA20FOApractices+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
model_summary(list(mentalhealth.BA20FOApractices, stress.BA20FOApractices, wage.BA20FOApractices))##
## Model Summary
##
## ─────────────────────────────────────────────────────────────────────────────
## (1) mentalhealth (2) stress (3) wage
## ─────────────────────────────────────────────────────────────────────────────
## (Intercept) 4.988 ** -0.318 343.333 *
## (1.838) (1.313) (153.515)
## BA20FOApractices -0.088 0.161 * -5.840
## (0.092) (0.066) (7.743)
## migrant10 12.968 ** -3.705 * 330.032 **
## (4.925) (1.575) (118.723)
## migrant10_mean -11.243 * 12.872 *** -232.087
## (4.472) (3.019) (337.847)
## BA20FOApractices:migrant10 -0.650 ** 0.210 ** -16.519 **
## (0.247) (0.080) (6.020)
## BA20FOApractices:migrant10_mean 0.566 * -0.644 *** 11.308
## (0.224) (0.151) (16.942)
## ─────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.028 0.033 0.025
## Conditional R^2 0.264 0.047 0.265
## AIC 2246.304 9903.803 30261.834
## BIC 2292.948 9963.262 30321.143
## Num. obs. 784 2824 2782
## Num. groups: clus 44 45 45
## Var: clus (Intercept) 0.017 0.000 755.572
## Var: clus migrant10 0.601 0.047 904.302
## Cov: clus (Intercept) migrant10 -0.050 0.000 -286.989
## Var: Residual 0.909 1.911 2938.176
## ─────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
9.1.3 BA1920discriminate4prac
mentalhealth.BA1920discriminate4prac=lmer(mentalhealth~BA1920discriminate4prac*migrant10+migrant10_mean*BA1920discriminate4prac+ (migrant10|clus), na.action = na.exclude, data = dat3, control=lmerControl(optimizer="bobyqa"))
stress.BA1920discriminate4prac=lmer(stress~BA1920discriminate4prac*migrant10+migrant10_mean*BA1920discriminate4prac+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
wage.BA1920discriminate4prac=lmer(wage~BA1920discriminate4prac*migrant10+migrant10_mean*BA1920discriminate4prac+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
model_summary(list(mentalhealth.BA1920discriminate4prac, stress.BA1920discriminate4prac, wage.BA1920discriminate4prac))##
## Model Summary
##
## ──────────────────────────────────────────────────────────────────────────────────────
## (1) mentalhealth (2) stress (3) wage
## ──────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 1.126 4.949 *** 619.687 ***
## (1.789) (0.963) (107.676)
## BA1920discriminate4prac 0.552 -0.509 * -98.444 ***
## (0.452) (0.245) (27.279)
## migrant10 -0.138 0.062 42.220
## (1.326) (0.582) (43.268)
## migrant10_mean 2.834 -2.286 -550.459 ***
## (2.834) (1.491) (156.228)
## BA1920discriminate4prac:migrant10 0.045 0.094 -11.363
## (0.342) (0.151) (11.288)
## BA1920discriminate4prac:migrant10_mean -0.752 0.575 135.151 ***
## (0.716) (0.379) (39.617)
## ──────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.002 0.018 0.030
## Conditional R^2 0.186 0.026 0.220
## AIC 3932.120 17147.268 52036.676
## BIC 3984.147 17212.158 52101.411
## Num. obs. 1343 4861 4786
## Num. groups: clus 66 69 69
## Var: clus (Intercept) 0.031 0.000 487.449
## Var: clus migrant10 0.443 0.021 830.607
## Cov: clus (Intercept) migrant10 -0.076 0.000 -255.697
## Var: Residual 0.998 1.969 2950.902
## ──────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.interact_plot(mentalhealth.BA1920discriminate4prac, modx = BA1920discriminate4prac, pred = migrant10)
9.1.4 BA20discriminate4prac
mentalhealth.BA20discriminate4prac=lmer(mentalhealth~BA20discriminate4prac*migrant10+migrant10_mean*BA20discriminate4prac+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
stress.BA20discriminate4prac=lmer(stress~BA20discriminate4prac*migrant10+migrant10_mean*BA20discriminate4prac+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
wage.BA20discriminate4prac=lmer(wage~BA20discriminate4prac*migrant10+migrant10_mean*BA20discriminate4prac+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
model_summary(list(mentalhealth.BA20discriminate4prac, stress.BA20discriminate4prac, wage.BA20discriminate4prac))##
## Model Summary
##
## ─────────────────────────────────────────────────────────────────────────────────
## (1) mentalhealth (2) stress (3) wage
## ─────────────────────────────────────────────────────────────────────────────────
## (Intercept) 1.263 4.557 * 920.231 ***
## (2.418) (1.907) (136.065)
## BA20discriminate4prac 0.495 -0.429 -174.347 ***
## (0.608) (0.482) (34.361)
## migrant10 1.418 -0.511 64.779
## (2.429) (0.733) (63.698)
## migrant10_mean 0.851 -1.000 -1028.206 ***
## (4.114) (2.944) (207.739)
## BA20discriminate4prac:migrant10 -0.361 0.255 -15.848
## (0.619) (0.190) (16.524)
## BA20discriminate4prac:migrant10_mean -0.186 0.256 256.597 ***
## (1.035) (0.746) (52.554)
## ─────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.005 0.029 0.073
## Conditional R^2 0.271 0.059 0.249
## AIC 2246.134 9895.508 30244.331
## BIC 2292.778 9954.967 30303.640
## Num. obs. 784 2824 2782
## Num. groups: clus 44 45 45
## Var: clus (Intercept) 0.034 0.079 582.481
## Var: clus migrant10 0.757 0.025 1099.601
## Cov: clus (Intercept) migrant10 -0.107 -0.028 -462.123
## Var: Residual 0.909 1.895 2939.981
## ─────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
9.1.5 BA1920targetedpractices
mentalhealth.BA1920targetedpractices=lmer(mentalhealth~BA1920targetedpractices*migrant10+migrant10_mean*BA1920targetedpractices+ (migrant10|clus), na.action = na.exclude, data = dat3, control=lmerControl(optimizer="bobyqa"))
stress.BA1920targetedpractices=lmer(stress~BA1920targetedpractices*migrant10+migrant10_mean*BA1920targetedpractices+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
wage.BA1920targetedpractices=lmer(wage~BA1920targetedpractices*migrant10+migrant10_mean*BA1920targetedpractices+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
model_summary(list(mentalhealth.BA1920targetedpractices, stress.BA1920targetedpractices, wage.BA1920targetedpractices))##
## Model Summary
##
## ──────────────────────────────────────────────────────────────────────────────────────
## (1) mentalhealth (2) stress (3) wage
## ──────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 1.592 4.018 *** 486.007 ***
## (1.359) (0.708) (77.236)
## BA1920targetedpractices 0.176 -0.110 -26.219 ***
## (0.138) (0.073) (7.912)
## migrant10 1.267 0.478 132.130 ***
## (1.285) (0.522) (35.762)
## migrant10_mean 1.524 -1.543 -396.469 **
## (2.422) (1.233) (123.611)
## BA1920targetedpractices:migrant10 -0.133 -0.006 -14.496 ***
## (0.137) (0.056) (3.864)
## BA1920targetedpractices:migrant10_mean -0.166 0.158 39.162 **
## (0.249) (0.128) (12.728)
## ──────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.006 0.018 0.054
## Conditional R^2 0.188 0.025 0.213
## AIC 3936.054 17155.963 52028.410
## BIC 3988.081 17220.853 52093.145
## Num. obs. 1343 4861 4786
## Num. groups: clus 66 69 69
## Var: clus (Intercept) 0.023 0.000 466.788
## Var: clus migrant10 0.444 0.020 635.638
## Cov: clus (Intercept) migrant10 -0.072 0.000 -228.312
## Var: Residual 0.998 1.970 2951.350
## ──────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.interact_plot(mentalhealth.BA1920targetedpractices, modx = BA1920targetedpractices, pred = migrant10)
9.1.6 BA20targetedpractices
mentalhealth.BA20targetedpractices=lmer(mentalhealth~BA20targetedpractices*migrant10+migrant10_mean*BA20targetedpractices+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
stress.BA20targetedpractices=lmer(stress~BA20targetedpractices*migrant10+migrant10_mean*BA20targetedpractices+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
wage.BA20targetedpractices=lmer(wage~BA20targetedpractices*migrant10+migrant10_mean*BA20targetedpractices+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
model_summary(list(mentalhealth.BA20targetedpractices, stress.BA20targetedpractices, wage.BA20targetedpractices))##
## Model Summary
##
## ──────────────────────────────────────────────────────────────────────────────────
## (1) mentalhealth (2) stress (3) wage
## ──────────────────────────────────────────────────────────────────────────────────
## (Intercept) 2.035 3.500 ** 524.109 ***
## (1.429) (1.200) (91.900)
## BA20targetedpractices 0.122 -0.066 -30.227 **
## (0.145) (0.123) (9.420)
## migrant10 0.725 0.406 141.959 ***
## (1.710) (0.577) (42.236)
## migrant10_mean 0.142 -0.864 -476.885 **
## (2.647) (2.035) (155.287)
## BA20targetedpractices:migrant10 -0.078 0.007 -15.328 ***
## (0.185) (0.064) (4.656)
## BA20targetedpractices:migrant10_mean 0.005 0.094 48.009 **
## (0.273) (0.212) (16.140)
## ──────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.008 0.028 0.070
## Conditional R^2 0.275 0.060 0.248
## AIC 2252.242 9904.829 30249.289
## BIC 2298.886 9964.288 30308.599
## Num. obs. 784 2824 2782
## Num. groups: clus 44 45 45
## Var: clus (Intercept) 0.027 0.088 619.685
## Var: clus migrant10 0.785 0.039 800.754
## Cov: clus (Intercept) migrant10 -0.111 -0.039 -336.537
## Var: Residual 0.909 1.894 2939.984
## ──────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
9.2 II check for ab2: Main effect of M on W (Mplus approach)
9.2.1 BM.responsiblesourcing
BA1920FOApractices.BM.responsiblesourcing=lmer(BA1920FOApractices~BM.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA20FOApractices.BM.responsiblesourcing=lmer(BA20FOApractices~BM.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA1920discriminate4prac.BM.responsiblesourcing=lmer(BA1920discriminate4prac~BM.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA20discriminate4prac.BM.responsiblesourcing=lmer(BA20discriminate4prac~BM.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA1920targetedpractices.BM.responsiblesourcing=lmer(BA1920targetedpractices~BM.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA20targetedpractices.BM.responsiblesourcing=lmer(BA20targetedpractices~BM.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
model_summary(list(BA1920FOApractices.BM.responsiblesourcing, BA20FOApractices.BM.responsiblesourcing, BA1920discriminate4prac.BM.responsiblesourcing, BA20discriminate4prac.BM.responsiblesourcing, BA1920targetedpractices.BM.responsiblesourcing, BA20targetedpractices.BM.responsiblesourcing))##
## Model Summary
##
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (1) BA1920FOApractices (2) BA20FOApractices (3) BA1920discriminate4prac (4) BA20discriminate4prac (5) BA1920targetedpractices (6) BA20targetedpractices
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 18.980 *** 18.914 *** 3.763 *** 3.808 *** 8.920 *** 8.953 ***
## (0.030) (0.037) (0.013) (0.015) (0.035) (0.047)
## BM.responsiblesourcing 0.206 *** 0.336 *** 0.035 *** 0.031 *** 0.125 *** 0.072 ***
## (0.011) (0.013) (0.004) (0.006) (0.012) (0.017)
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.889 0.955 0.596 0.552 0.709 0.375
## Conditional R^2 1.000 1.000 1.000 1.000 1.000 1.000
## AIC -87476.157 -40334.281 -73774.530 -59487.223 -85366.927 -40323.288
## BIC -87451.927 -40312.005 -73750.300 -59464.947 -85342.697 -40301.013
## Num. obs. 3157 1937 3157 1937 3157 1937
## Num. groups: clus 47 32 47 32 47 32
## Var: clus (Intercept) 0.012 0.012 0.002 0.002 0.014 0.019
## Var: Residual 0.000 0.000 0.000 0.000 0.000 0.000
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.9.2.2 BM19.responsiblesourcing
BA1920FOApractices.BM19.responsiblesourcing=lmer(BA1920FOApractices~BM19.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA20FOApractices.BM19.responsiblesourcing=lmer(BA20FOApractices~BM19.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA1920discriminate4prac.BM19.responsiblesourcing=lmer(BA1920discriminate4prac~BM19.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA20discriminate4prac.BM19.responsiblesourcing=lmer(BA20discriminate4prac~BM19.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA1920targetedpractices.BM19.responsiblesourcing=lmer(BA1920targetedpractices~BM19.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA20targetedpractices.BM19.responsiblesourcing=lmer(BA20targetedpractices~BM19.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
model_summary(list(BA1920FOApractices.BM19.responsiblesourcing, BA20FOApractices.BM19.responsiblesourcing, BA1920discriminate4prac.BM19.responsiblesourcing, BA20discriminate4prac.BM19.responsiblesourcing, BA1920targetedpractices.BM19.responsiblesourcing, BA20targetedpractices.BM19.responsiblesourcing))##
## Model Summary
##
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (1) BA1920FOApractices (2) BA20FOApractices (3) BA1920discriminate4prac (4) BA20discriminate4prac (5) BA1920targetedpractices (6) BA20targetedpractices
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 19.131 *** 19.194 *** 3.548 *** 3.549 *** 8.644 *** 8.190 ***
## (0.034) (0.038) (0.013) (0.014) (0.038) (0.047)
## BM19.responsiblesourcing 0.159 *** 0.199 *** 0.103 *** 0.110 *** 0.232 *** 0.346 ***
## (0.010) (0.012) (0.004) (0.004) (0.012) (0.015)
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.868 0.915 0.949 0.961 0.916 0.956
## Conditional R^2 1.000 1.000 1.000 1.000 1.000 1.000
## AIC -46020.905 -34113.776 -48879.709 -37238.857 -44756.585 -44829.119
## BIC -45998.167 -34092.237 -48856.972 -37217.318 -44733.848 -44807.580
## Num. obs. 2174 1611 2174 1611 2174 1611
## Num. groups: clus 34 25 34 25 34 25
## Var: clus (Intercept) 0.012 0.011 0.002 0.002 0.015 0.017
## Var: Residual 0.000 0.000 0.000 0.000 0.000 0.000
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.9.2.3 BM20.responsiblesourcing
BA1920FOApractices.BM20.responsiblesourcing=lmer(BA1920FOApractices~BM20.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA20FOApractices.BM20.responsiblesourcing=lmer(BA20FOApractices~BM20.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA1920discriminate4prac.BM20.responsiblesourcing=lmer(BA1920discriminate4prac~BM20.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA20discriminate4prac.BM20.responsiblesourcing=lmer(BA20discriminate4prac~BM20.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA1920targetedpractices.BM20.responsiblesourcing=lmer(BA1920targetedpractices~BM20.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
BA20targetedpractices.BM20.responsiblesourcing=lmer(BA20targetedpractices~BM20.responsiblesourcing+ (1|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
model_summary(list(BA1920FOApractices.BM20.responsiblesourcing, BA20FOApractices.BM20.responsiblesourcing, BA1920discriminate4prac.BM20.responsiblesourcing, BA20discriminate4prac.BM20.responsiblesourcing, BA1920targetedpractices.BM20.responsiblesourcing, BA20targetedpractices.BM20.responsiblesourcing))##
## Model Summary
##
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (1) BA1920FOApractices (2) BA20FOApractices (3) BA1920discriminate4prac (4) BA20discriminate4prac (5) BA1920targetedpractices (6) BA20targetedpractices
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 19.507 *** 19.481 *** 3.965 *** 3.984 *** 9.409 *** 9.361 ***
## (0.030) (0.042) (0.011) (0.013) (0.033) (0.048)
## BM20.responsiblesourcing 0.015 0.125 *** -0.043 *** -0.043 *** -0.093 *** -0.132 ***
## (0.010) (0.014) (0.004) (0.004) (0.011) (0.015)
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.052 0.720 0.761 0.751 0.626 0.685
## Conditional R^2 1.000 1.000 1.000 1.000 1.000 1.000
## AIC -47300.448 -36078.636 -71743.898 -41904.700 -64425.273 -36714.317
## BIC -47277.418 -36057.826 -71720.868 -41883.889 -64402.243 -36693.506
## Num. obs. 2339 1343 2339 1343 2339 1343
## Num. groups: clus 35 23 35 23 35 23
## Var: clus (Intercept) 0.013 0.017 0.002 0.002 0.016 0.023
## Var: Residual 0.000 0.000 0.000 0.000 0.000 0.000
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.9.3 III check for cw1: Cross-level interaction of W
9.3.1 BM.responsiblesourcing
mentalhealth.BM.responsiblesourcing=lmer(mentalhealth~BM.responsiblesourcing*migrant10+migrant10_mean*BM.responsiblesourcing+ (migrant10|clus), na.action = na.exclude, data = dat3, control=lmerControl(optimizer="bobyqa"))
stress.BM.responsiblesourcing=lmer(stress~BM.responsiblesourcing*migrant10+migrant10_mean*BM.responsiblesourcing+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
wage.BM.responsiblesourcing=lmer(wage~BM.responsiblesourcing*migrant10+migrant10_mean*BM.responsiblesourcing+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
model_summary(list(mentalhealth.BM.responsiblesourcing, stress.BM.responsiblesourcing, wage.BM.responsiblesourcing))##
## Model Summary
##
## ─────────────────────────────────────────────────────────────────────────────────────
## (1) mentalhealth (2) stress (3) wage
## ─────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.203 *** 3.197 *** 215.275 ***
## (0.204) (0.132) (12.571)
## BM.responsiblesourcing 0.059 -0.147 * 6.196
## (0.089) (0.058) (5.851)
## migrant10 0.471 0.193 1.869
## (0.318) (0.148) (8.662)
## migrant10_mean -0.171 -0.157 3.194
## (0.370) (0.238) (21.042)
## BM.responsiblesourcing:migrant10 -0.189 0.101 * -0.459
## (0.104) (0.049) (2.947)
## BM.responsiblesourcing:migrant10_mean 0.002 0.093 -7.043
## (0.141) (0.091) (8.583)
## ─────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.026 0.025 0.004
## Conditional R^2 0.224 0.035 0.232
## AIC 2679.246 11630.794 35180.882
## BIC 2727.468 11691.780 35241.721
## Num. obs. 918 3290 3242
## Num. groups: clus 49 51 51
## Var: clus (Intercept) 0.036 0.000 457.869
## Var: clus migrant10 0.547 0.028 480.311
## Cov: clus (Intercept) migrant10 -0.112 0.000 49.295
## Var: Residual 0.964 1.972 2885.768
## ─────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.
9.3.2 BM19.responsiblesourcing
mentalhealth.BM19.responsiblesourcing=lmer(mentalhealth~BM19.responsiblesourcing*migrant10+migrant10_mean*BM19.responsiblesourcing+ (migrant10|clus), na.action = na.exclude, data = dat3, control=lmerControl(optimizer="bobyqa"))
stress.BM19.responsiblesourcing=lmer(stress~BM19.responsiblesourcing*migrant10+migrant10_mean*BM19.responsiblesourcing+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
wage.BM19.responsiblesourcing=lmer(wage~BM19.responsiblesourcing*migrant10+migrant10_mean*BM19.responsiblesourcing+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
model_summary(list(mentalhealth.BM19.responsiblesourcing, stress.BM19.responsiblesourcing, wage.BM19.responsiblesourcing))##
## Model Summary
##
## ──────────────────────────────────────────────────────────────────────────────────────
## (1) mentalhealth (2) stress (3) wage
## ──────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.167 *** 3.498 *** 256.830 ***
## (0.250) (0.174) (19.185)
## BM19.responsiblesourcing 0.027 -0.208 *** -8.240
## (0.078) (0.053) (5.906)
## migrant10 0.124 0.318 * 2.336
## (0.335) (0.143) (8.592)
## migrant10_mean 0.098 -0.695 * -53.179
## (0.411) (0.292) (29.473)
## BM19.responsiblesourcing:migrant10 -0.084 0.041 0.317
## (0.105) (0.044) (2.753)
## BM19.responsiblesourcing:migrant10_mean -0.006 0.249 ** 11.495
## (0.125) (0.088) (8.876)
## ──────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.008 0.030 0.012
## Conditional R^2 0.240 0.038 0.229
## AIC 1848.897 8026.896 24189.247
## BIC 1893.480 8084.202 24246.416
## Num. obs. 638 2277 2246
## Num. groups: clus 35 37 37
## Var: clus (Intercept) 0.038 0.000 447.763
## Var: clus migrant10 0.737 0.027 520.127
## Cov: clus (Intercept) migrant10 -0.167 0.000 -31.414
## Var: Residual 0.925 1.943 2655.521
## ──────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.interact_plot(mentalhealth.BM19.responsiblesourcing, modx = BM19.responsiblesourcing, pred = migrant10)
9.3.3 BM20.responsiblesourcing
mentalhealth.BM20.responsiblesourcing=lmer(mentalhealth~BM20.responsiblesourcing*migrant10+migrant10_mean*BM20.responsiblesourcing+ (migrant10|clus), na.action = na.exclude, data = dat3, control=lmerControl(optimizer="bobyqa"))
stress.BM20.responsiblesourcing=lmer(stress~BM20.responsiblesourcing*migrant10+migrant10_mean*BM20.responsiblesourcing+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
wage.BM20.responsiblesourcing=lmer(wage~BM20.responsiblesourcing*migrant10+migrant10_mean*BM20.responsiblesourcing+ (migrant10|clus), na.action = na.exclude, data = data, control=lmerControl(optimizer="bobyqa"))
model_summary(list(mentalhealth.BM20.responsiblesourcing, stress.BM20.responsiblesourcing, wage.BM20.responsiblesourcing))##
## Model Summary
##
## ──────────────────────────────────────────────────────────────────────────────────────
## (1) mentalhealth (2) stress (3) wage
## ──────────────────────────────────────────────────────────────────────────────────────
## (Intercept) 3.303 *** 2.969 *** 210.592 ***
## (0.182) (0.168) (11.955)
## BM20.responsiblesourcing 0.053 -0.096 11.624
## (0.089) (0.077) (6.456)
## migrant10 0.200 0.385 * 2.085
## (0.325) (0.169) (8.823)
## migrant10_mean -0.184 -0.150 14.302
## (0.361) (0.281) (21.052)
## BM20.responsiblesourcing:migrant10 -0.097 0.074 -1.315
## (0.098) (0.053) (2.750)
## BM20.responsiblesourcing:migrant10_mean -0.036 0.057 -15.934
## (0.145) (0.111) (9.655)
## ──────────────────────────────────────────────────────────────────────────────────────
## Marginal R^2 0.016 0.033 0.019
## Conditional R^2 0.238 0.059 0.250
## AIC 2003.898 8446.308 25692.881
## BIC 2049.118 8504.103 25750.528
## Num. obs. 680 2391 2356
## Num. groups: clus 37 37 37
## Var: clus (Intercept) 0.069 0.111 575.471
## Var: clus migrant10 0.654 0.087 495.406
## Cov: clus (Intercept) migrant10 -0.158 -0.085 20.366
## Var: Residual 0.974 1.940 3050.200
## ──────────────────────────────────────────────────────────────────────────────────────
## Note. * p < .05, ** p < .01, *** p < .001.interact_plot(mentalhealth.BM20.responsiblesourcing, modx = BM20.responsiblesourcing, pred = migrant10)
