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## as.Date, as.Date.numeric#read in data
setwd("~/Downloads/foldah")
ANES <- read.csv("ANES_merged.csv")#clean data
ANES <- ANES %>%
mutate(across(
.cols = starts_with("V2"),
.fns = ~replace(., . %in% c(-9, -8, -7, -6, -5, -4, -2, -1), NA)
))
ANES <- ANES %>%
mutate(across(
.cols = starts_with("V1"),
.fns = ~replace(., . %in% c(-9, -8, -7, -6, -5, -4, -2, -1), NA)
))#Critical Reflection Scale
#reverse coding measure of belief in slavery's legacy
ANES$CR_slav_w1 <- dplyr::recode(ANES$V162212,
`1` = 5,
`2` = 4,
`3` = 3,
`4` = 2,
`5` = 1)
#renaming measure of if black people would be equal if they work their way up
ANES$CR_wrk_w1 <- ANES$V162214
#same for 2020
ANES$CR_slav_w2 <- dplyr::recode(ANES$V202301,
`1` = 5,
`2` = 4,
`3` = 3,
`4` = 2,
`5` = 1)
ANES$CR_wrk_w2 <- ANES$V202303
#same for 2024
ANES$CR_slav_w3 <- dplyr::recode(ANES$V242301,
`1` = 5,
`2` = 4,
`3` = 3,
`4` = 2,
`5` = 1)
ANES$CR_wrk_w3 <-ANES$V242303
ANES$c_ref_w1 <- ((ANES$CR_wrk_w1 + ANES$CR_slav_w1)/2)
ANES$c_ref_w2 <- ((ANES$CR_wrk_w2 + ANES$CR_slav_w2)/2)
ANES$c_ref_w3 <- ((ANES$CR_wrk_w3 + ANES$CR_slav_w3)/2)#Critical Action Scale
#Contact rep?
ANES$CA_rep_w1 <- ifelse(ANES$V162019 == 1, 1, 0)
#Attend protest?
ANES$CA_pro_w1 <- ifelse(ANES$V162018a == 1, 1, 0)
#Participate in community organizing?
ANES$CA_org_w1 <- ifelse(ANES$V162195 == 1, 1, 0)
ANES$CA_mtn_w1 <- ifelse(ANES$V162196 == 1, 1, 0)
#Petition?
ANES$CA_pet_w1 <- ifelse(ANES$V162018b == 1, 1, 0)
#boycott?
ANES$CA_byc_w1 <- ifelse(ANES$V162141 == 1, 0, 1)
#same for 2020
ANES$CA_rep_w2 <- ifelse(ANES$V202030 == 1, 1, 0)
ANES$CA_pro_w2 <- ifelse(ANES$V202025 == 1, 1, 0)
ANES$CA_org_w2 <- ifelse(ANES$V202031 == 1, 1, 0)
ANES$CA_mtn_w2 <- ifelse(ANES$V202032 == 1, 1, 0)
ANES$CA_pet_w2 <- ifelse(ANES$V202026 == 1, 1, 0)
ANES$CA_byc_w2 <- ifelse(ANES$V242037 == 1, 0, 1)
#same for 2024
ANES$CA_rep_w3 <- ifelse(ANES$V242036 == 1, 1, 0)
ANES$CA_pro_w3 <- ifelse(ANES$V242029 == 1, 1, 0)
ANES$CA_org_w3 <- ifelse(ANES$V242034 == 1, 1, 0)
ANES$CA_mtn_w3 <- ifelse(ANES$V242034 == 1, 1, 0)
ANES$CA_pet_w3 <- ifelse(ANES$V242030 == 1, 1, 0)
ANES$CA_byc_w3 <- ifelse(ANES$V242037 == 1, 1, 0)
ANES$c_act_w1 <- ((ANES$CA_org_w1 + ANES$CA_mtn_w1 + ANES$CA_pro_w1 + ANES$CA_rep_w1)/4)
ANES$c_act_w2 <- ((ANES$CA_org_w2 + ANES$CA_mtn_w2 + ANES$CA_pro_w2 + ANES$CA_rep_w2)/4)
ANES$c_act_w3 <- ((ANES$CA_mtn_w3 + ANES$CA_pro_w3 + ANES$CA_rep_w3)/3)#External Efficacy Scale
#Ptx care what you think?
ANES$EPE_care_w1 <- ANES$V162215
#Do you have a say in ptx?
ANES$EPE_say_w1 <- ANES$V162216
#Same for 2020
ANES$EPE_say_w2 <- ANES$V202213
ANES$EPE_care_w2 <- ANES$V202212
#Same for 2024
ANES$EPE_say_w3 <- ANES$V242201
ANES$EPE_care_w3 <- ANES$V242200
ANES$ex_eff_w1 <- ((ANES$EPE_say_w1 + ANES$EPE_care_w1)/2)
ANES$ex_eff_w2 <- ((ANES$EPE_say_w2 + ANES$EPE_care_w2)/2)
ANES$ex_eff_w3 <- ((ANES$EPE_say_w3 + ANES$EPE_care_w3)/2)#Internal Efficacy
#Are ptx complicated?
ANES$IPE_comp_w1 <- ANES$V162217
#Do you understand ptx?
ANES$IPE_und_w1 <- dplyr::recode(ANES$V162218,
`1` = 5,
`2` = 4,
`3` = 3,
`4` = 2,
`5` = 1)
#Same for 2020
ANES$IPE_comp_w2 <- ANES$V202214
ANES$IPE_und_w2 <- dplyr::recode(ANES$V202215,
`1` = 5,
`2` = 4,
`3` = 3,
`4` = 2,
`5` = 1)
#Same for 2024
ANES$IPE_comp_w3 <- ANES$V242202
ANES$IPE_und_w3 <- dplyr::recode(ANES$V242203,
`1` = 5,
`2` = 4,
`3` = 3,
`4` = 2,
`5` = 1)
ANES$in_eff_w1 <- ((ANES$IPE_und_w1 + ANES$IPE_comp_w1)/2)
ANES$in_eff_w2 <- ((ANES$IPE_und_w2 + ANES$IPE_comp_w2)/2)
ANES$in_eff_w3 <- ((ANES$IPE_und_w3 + ANES$IPE_comp_w3)/2)
ANES$in_eff_trait <- ((ANES$in_eff_w1 + ANES$in_eff_w2 + ANES$in_eff_w3)/3)#Demographic Variables
ANES$Black <- ifelse(ANES$V201549x == 2, 1, 0)
ANES$White <- ifelse(ANES$V201549x == 1, 1, 0)
ANES$Hispanic <- ifelse(ANES$V201549x == 3, 1, 0)
ANES$Asian <- ifelse(ANES$V201549x == 4, 1, 0)
ANES$Native <- ifelse(ANES$V201549x == 5, 1, 0)
ANES$Other <- ifelse(ANES$V201549x == 6, 1, 0)
ANES$Female <- ifelse(ANES$V201600 == 2, 1, 0)#Mean split
ANES$in_eff_ms_w1 <- ifelse(ANES$in_eff_w1 > mean(ANES$in_eff_w1, na.rm = TRUE), 1, 0)
ANES$in_eff_ms_w2 <- ifelse(ANES$in_eff_w2 > mean(ANES$in_eff_w2, na.rm = TRUE), 1, 0)
ANES$in_eff_ms_w3 <- ifelse(ANES$in_eff_w3 > mean(ANES$in_eff_w3, na.rm = TRUE), 1, 0)
ANES$in_eff_trait_ms <- ifelse(ANES$in_eff_trait > mean(ANES$in_eff_trait, na.rm = TRUE), 1, 0)
ANES$ex_eff_trait <- ((ANES$ex_eff_w1 + ANES$ex_eff_w2 + ANES$ex_eff_w3)/3)
ANES$ex_eff_trait_ms <- ifelse(ANES$ex_eff_trait > mean(ANES$ex_eff_trait, na.rm = TRUE), 1, 0)
ANES_grouped <- subset(ANES, !is.na(in_eff_trait_ms))
ANES_grouped2 <- subset(ANES, !is.na(ex_eff_trait_ms))#composite summaries
summary(ANES$in_eff_w1)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.000 2.500 3.000 3.221 3.500 5.000 10summary(ANES$in_eff_w2)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.00 3.00 3.50 3.38 4.00 5.00 9summary(ANES$in_eff_w3)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.000 3.000 3.500 3.354 4.000 5.000 113summary(ANES$ex_eff_w1)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.000 2.000 2.500 2.595 3.500 5.000 7summary(ANES$ex_eff_w2)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.000 1.500 2.000 2.413 3.000 5.000 9summary(ANES$ex_eff_w3)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.000 1.500 2.000 2.244 3.000 5.000 112summary(ANES$c_ref_w1)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.000 2.000 3.000 3.042 4.000 5.000 11summary(ANES$c_ref_w2)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.000 2.500 3.000 3.283 4.500 5.000 16summary(ANES$c_ref_w3)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.000 2.000 3.000 3.168 4.000 5.000 125summary(ANES$c_act_w1)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.0000 0.0000 0.0000 0.2134 0.5000 1.0000 7summary(ANES$c_act_w2)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.0000 0.0000 0.0000 0.1715 0.2500 1.0000 2summary(ANES$c_act_w3)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.0000 0.0000 0.0000 0.0812 0.0000 1.0000 102#RICLPM IPE Mod (this one worked)
RICLPMmod <- '
# Create between components (random intercepts)
RIcr =~ 1*c_ref_w1 + 1*c_ref_w2 + 1*c_ref_w3
RIca =~ 1*c_act_w1 + 1*c_act_w2 + 1*c_act_w3
# Create within-person centered variables
wcr1 =~ 1*c_ref_w1
wcr2 =~ 1*c_ref_w2
wcr3 =~ 1*c_ref_w3
wca1 =~ 1*c_act_w1
wca2 =~ 1*c_act_w2
wca3 =~ 1*c_act_w3
# Estimate lagged effects between within-person centered variables
wcr2 ~ wcr1 + wca1
wcr3 ~ c(g1, g2)*wca2
wcr3 ~ wcr2
wca2 ~ wca1
wca3 ~ wca2
wca2 ~ c(g1, g2)*wcr1
wca3 ~ wcr2
# Estimate covariance between within-person centered variables at first wave
wcr1 ~~ wca1 # Covariance
# Estimate covariances between residuals of within-person centered variables
# (i.e., innovations)
wcr2 ~~ wca2
wcr3 ~~ wca3
# Estimate variance and covariance of random intercepts
RIcr ~~ RIcr
RIca ~~ RIca
RIcr ~~ RIca
# Estimate (residual) variance of within-person centered variables
wcr1 ~~ wcr1 # Variances
wca1 ~~ wca1
wcr2 ~~ wcr2 # Residual variances
wca2 ~~ wca2
wcr3 ~~ wcr3
wca3 ~~ wca3'
RICLPMmodfit <- lavaan(RICLPMmod,
data = ANES_grouped,
missing = 'ML',
estimator = "ML",
meanstructure = T,
group = "in_eff_trait_ms",
int.ov.free = T)
summary(RICLPMmodfit, fit.measures = T, standardized = T, rsquare = T)## lavaan 0.6-19 ended normally after 122 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 52
## Number of equality constraints 2
##
## Number of observations per group:
## 0 948
## 1 1096
## Number of missing patterns per group:
## 0 7
## 1 6
##
## Model Test User Model:
##
## Test statistic 29.345
## Degrees of freedom 4
## P-value (Chi-square) 0.000
## Test statistic for each group:
## 0 3.544
## 1 25.801
##
## Model Test Baseline Model:
##
## Test statistic 4076.133
## Degrees of freedom 30
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.994
## Tucker-Lewis Index (TLI) 0.953
##
## Robust Comparative Fit Index (CFI) 0.994
## Robust Tucker-Lewis Index (TLI) 0.953
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -7183.169
## Loglikelihood unrestricted model (H1) -7168.497
##
## Akaike (AIC) 14466.339
## Bayesian (BIC) 14747.472
## Sample-size adjusted Bayesian (SABIC) 14588.618
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.079
## 90 Percent confidence interval - lower 0.054
## 90 Percent confidence interval - upper 0.107
## P-value H_0: RMSEA <= 0.050 0.031
## P-value H_0: RMSEA >= 0.080 0.506
##
## Robust RMSEA 0.079
## 90 Percent confidence interval - lower 0.054
## 90 Percent confidence interval - upper 0.107
## P-value H_0: Robust RMSEA <= 0.050 0.031
## P-value H_0: Robust RMSEA >= 0.080 0.509
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.023
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
##
## Group 1 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## RIcr =~
## c_ref_w1 1.000 0.833 0.746
## c_ref_w2 1.000 0.833 0.693
## c_ref_w3 1.000 0.833 0.739
## RIca =~
## c_act_w1 1.000 0.091 0.399
## c_act_w2 1.000 0.091 0.457
## c_act_w3 1.000 0.091 0.648
## wcr1 =~
## c_ref_w1 1.000 0.744 0.666
## wcr2 =~
## c_ref_w2 1.000 0.866 0.721
## wcr3 =~
## c_ref_w3 1.000 0.759 0.674
## wca1 =~
## c_act_w1 1.000 0.209 0.917
## wca2 =~
## c_act_w2 1.000 0.177 0.889
## wca3 =~
## c_act_w3 1.000 0.107 0.762
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## wcr2 ~
## wcr1 0.264 0.083 3.200 0.001 0.227 0.227
## wca1 0.270 0.173 1.565 0.118 0.065 0.065
## wcr3 ~
## wca2 (g1) 0.020 0.011 1.796 0.072 0.005 0.005
## wcr2 0.350 0.048 7.368 0.000 0.400 0.400
## wca2 ~
## wca1 0.228 0.036 6.350 0.000 0.269 0.269
## wca3 ~
## wca2 -0.017 0.052 -0.321 0.748 -0.028 -0.028
## wca2 ~
## wcr1 (g1) 0.020 0.011 1.796 0.072 0.084 0.084
## wca3 ~
## wcr2 0.006 0.008 0.774 0.439 0.048 0.048
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## wcr1 ~~
## wca1 0.005 0.007 0.681 0.496 0.033 0.033
## .wcr2 ~~
## .wca2 0.026 0.006 4.276 0.000 0.183 0.183
## .wcr3 ~~
## .wca3 0.005 0.004 1.458 0.145 0.073 0.073
## RIcr ~~
## RIca 0.019 0.005 3.791 0.000 0.248 0.248
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .c_ref_w1 2.880 0.036 79.384 0.000 2.880 2.580
## .c_ref_w2 3.094 0.039 79.200 0.000 3.094 2.575
## .c_ref_w3 3.028 0.037 82.623 0.000 3.028 2.688
## .c_act_w1 0.159 0.007 21.435 0.000 0.159 0.697
## .c_act_w2 0.113 0.006 17.538 0.000 0.113 0.570
## .c_act_w3 0.051 0.005 11.210 0.000 0.051 0.364
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## RIcr 0.693 0.058 11.888 0.000 1.000 1.000
## RIca 0.008 0.001 5.856 0.000 1.000 1.000
## wcr1 0.553 0.053 10.481 0.000 1.000 1.000
## wca1 0.044 0.002 18.862 0.000 1.000 1.000
## .wcr2 0.708 0.055 12.887 0.000 0.943 0.943
## .wca2 0.029 0.002 16.158 0.000 0.919 0.919
## .wcr3 0.483 0.030 15.852 0.000 0.839 0.839
## .wca3 0.011 0.001 7.595 0.000 0.998 0.998
## .c_ref_w1 0.000 0.000 0.000
## .c_ref_w2 0.000 0.000 0.000
## .c_ref_w3 0.000 0.000 0.000
## .c_act_w1 0.000 0.000 0.000
## .c_act_w2 0.000 0.000 0.000
## .c_act_w3 0.000 0.000 0.000
##
## R-Square:
## Estimate
## wcr2 0.057
## wca2 0.081
## wcr3 0.161
## wca3 0.002
## c_ref_w1 1.000
## c_ref_w2 1.000
## c_ref_w3 1.000
## c_act_w1 1.000
## c_act_w2 1.000
## c_act_w3 1.000
##
##
## Group 2 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## RIcr =~
## c_ref_w1 1.000 1.082 0.826
## c_ref_w2 1.000 1.082 0.828
## c_ref_w3 1.000 1.082 0.854
## RIca =~
## c_act_w1 1.000 0.135 0.498
## c_act_w2 1.000 0.135 0.510
## c_act_w3 1.000 0.135 0.660
## wcr1 =~
## c_ref_w1 1.000 0.738 0.564
## wcr2 =~
## c_ref_w2 1.000 0.732 0.560
## wcr3 =~
## c_ref_w3 1.000 0.660 0.521
## wca1 =~
## c_act_w1 1.000 0.235 0.867
## wca2 =~
## c_act_w2 1.000 0.228 0.860
## wca3 =~
## c_act_w3 1.000 0.154 0.752
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## wcr2 ~
## wcr1 0.129 0.081 1.600 0.110 0.130 0.130
## wca1 -0.099 0.140 -0.707 0.479 -0.032 -0.032
## wcr3 ~
## wca2 (g2) 0.016 0.015 1.045 0.296 0.005 0.005
## wcr2 0.218 0.070 3.124 0.002 0.242 0.242
## wca2 ~
## wca1 0.223 0.042 5.361 0.000 0.230 0.230
## wca3 ~
## wca2 0.045 0.045 1.009 0.313 0.067 0.067
## wca2 ~
## wcr1 (g2) 0.016 0.015 1.045 0.296 0.051 0.051
## wca3 ~
## wcr2 -0.016 0.012 -1.326 0.185 -0.078 -0.078
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## wcr1 ~~
## wca1 0.009 0.008 1.086 0.278 0.051 0.051
## .wcr2 ~~
## .wca2 0.011 0.008 1.458 0.145 0.070 0.070
## .wcr3 ~~
## .wca3 -0.005 0.005 -0.893 0.372 -0.047 -0.047
## RIcr ~~
## RIca 0.052 0.008 6.926 0.000 0.357 0.357
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .c_ref_w1 3.194 0.040 80.659 0.000 3.194 2.438
## .c_ref_w2 3.469 0.039 87.876 0.000 3.469 2.655
## .c_ref_w3 3.294 0.038 85.966 0.000 3.294 2.599
## .c_act_w1 0.263 0.008 32.071 0.000 0.263 0.969
## .c_act_w2 0.223 0.008 27.846 0.000 0.223 0.841
## .c_act_w3 0.107 0.006 17.363 0.000 0.107 0.525
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## RIcr 1.171 0.068 17.332 0.000 1.000 1.000
## RIca 0.018 0.002 8.242 0.000 1.000 1.000
## wcr1 0.545 0.048 11.455 0.000 1.000 1.000
## wca1 0.055 0.003 18.863 0.000 1.000 1.000
## .wcr2 0.527 0.061 8.642 0.000 0.982 0.982
## .wca2 0.049 0.003 17.131 0.000 0.943 0.943
## .wcr3 0.410 0.033 12.470 0.000 0.941 0.941
## .wca3 0.023 0.002 10.921 0.000 0.990 0.990
## .c_ref_w1 0.000 0.000 0.000
## .c_ref_w2 0.000 0.000 0.000
## .c_ref_w3 0.000 0.000 0.000
## .c_act_w1 0.000 0.000 0.000
## .c_act_w2 0.000 0.000 0.000
## .c_act_w3 0.000 0.000 0.000
##
## R-Square:
## Estimate
## wcr2 0.018
## wca2 0.057
## wcr3 0.059
## wca3 0.010
## c_ref_w1 1.000
## c_ref_w2 1.000
## c_ref_w3 1.000
## c_act_w1 1.000
## c_act_w2 1.000
## c_act_w3 1.000constraints <- '
wca2 ~ c(g1, g2)*wcr1
g1 == g2
'
lavTestWald(RICLPMmodfit, constraints = constraints)## $stat
## [1] 0.05142862
##
## $df
## [1] 1
##
## $p.value
## [1] 0.8205958
##
## $se
## [1] "standard"constraints2 <- '
wcr2 ~ c(g1, g2)*wca2
g1 == g2
'
lavTestWald(RICLPMmodfit, constraints = constraints2)## $stat
## [1] 0.05142862
##
## $df
## [1] 1
##
## $p.value
## [1] 0.8205958
##
## $se
## [1] "standard"#RICLPM EPE Mod (doing it the same way as the IPE one in case consistency is important – i prefer just using the latent variable version)
RICLPMmod9 <- '
# Create between components (random intercepts)
RIcr =~ 1*c_ref_w1 + 1*c_ref_w2 + 1*c_ref_w3
RIca =~ 1*c_act_w1 + 1*c_act_w2 + 1*c_act_w3
# Create within-person centered variables
wcr1 =~ 1*c_ref_w1
wcr2 =~ 1*c_ref_w2
wcr3 =~ 1*c_ref_w3
wca1 =~ 1*c_act_w1
wca2 =~ 1*c_act_w2
wca3 =~ 1*c_act_w3
# Estimate lagged effects between within-person centered variables
wcr2 ~ wcr1 + wca1
wcr3 ~ wcr2 + wca2
wca2 ~ wca1
wca3 ~ wca2
wca2 ~ c(g1, g2)*wcr1
wca3 ~ wcr2
# Estimate covariance between within-person centered variables at first wave
wcr1 ~~ wca1 # Covariance
# Estimate covariances between residuals of within-person centered variables
# (i.e., innovations)
wcr2 ~~ wca2
wcr3 ~~ wca3
# Estimate variance and covariance of random intercepts
RIcr ~~ RIcr
RIca ~~ RIca
RIcr ~~ RIca
# Estimate (residual) variance of within-person centered variables
wcr1 ~~ wcr1 # Variances
wca1 ~~ wca1
wcr2 ~~ wcr2 # Residual variances
wca2 ~~ wca2
wcr3 ~~ wcr3
wca3 ~~ wca3'
RICLPMmodfit9 <- lavaan(RICLPMmod9,
data = ANES_grouped2,
missing = 'ML',
estimator = "ML",
meanstructure = T,
group = "ex_eff_trait_ms",
int.ov.free = T)
summary(RICLPMmodfit9, fit.measures = T, standardized = T, rsquare = T)## lavaan 0.6-19 ended normally after 129 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 52
##
## Number of observations per group:
## 1 968
## 0 1078
## Number of missing patterns per group:
## 1 6
## 0 8
##
## Model Test User Model:
##
## Test statistic 4.544
## Degrees of freedom 2
## P-value (Chi-square) 0.103
## Test statistic for each group:
## 1 1.153
## 0 3.391
##
## Model Test Baseline Model:
##
## Test statistic 4246.786
## Degrees of freedom 30
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.999
## Tucker-Lewis Index (TLI) 0.991
##
## Robust Comparative Fit Index (CFI) 0.999
## Robust Tucker-Lewis Index (TLI) 0.991
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -7328.011
## Loglikelihood unrestricted model (H1) -7325.739
##
## Akaike (AIC) 14760.021
## Bayesian (BIC) 15052.451
## Sample-size adjusted Bayesian (SABIC) 14887.243
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.035
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.079
## P-value H_0: RMSEA <= 0.050 0.643
## P-value H_0: RMSEA >= 0.080 0.048
##
## Robust RMSEA 0.035
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.080
## P-value H_0: Robust RMSEA <= 0.050 0.642
## P-value H_0: Robust RMSEA >= 0.080 0.048
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.009
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## RIcr =~
## c_ref_w1 1.000 0.985 0.807
## c_ref_w2 1.000 0.985 0.799
## c_ref_w3 1.000 0.985 0.823
## RIca =~
## c_act_w1 1.000 0.139 0.527
## c_act_w2 1.000 0.139 0.538
## c_act_w3 1.000 0.139 0.717
## wcr1 =~
## c_ref_w1 1.000 0.720 0.590
## wcr2 =~
## c_ref_w2 1.000 0.741 0.601
## wcr3 =~
## c_ref_w3 1.000 0.680 0.568
## wca1 =~
## c_act_w1 1.000 0.224 0.850
## wca2 =~
## c_act_w2 1.000 0.217 0.843
## wca3 =~
## c_act_w3 1.000 0.135 0.697
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## wcr2 ~
## wcr1 0.219 0.077 2.838 0.005 0.213 0.213
## wca1 0.268 0.154 1.746 0.081 0.081 0.081
## wcr3 ~
## wcr2 0.169 0.070 2.401 0.016 0.184 0.184
## wca2 0.815 0.202 4.027 0.000 0.260 0.260
## wca2 ~
## wca1 0.237 0.042 5.592 0.000 0.243 0.243
## wca3 ~
## wca2 0.016 0.051 0.305 0.760 0.025 0.025
## wca2 ~
## wcr1 (g1) 0.081 0.017 4.742 0.000 0.270 0.270
## wca3 ~
## wcr2 -0.009 0.013 -0.670 0.503 -0.048 -0.048
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## wcr1 ~~
## wca1 0.014 0.008 1.758 0.079 0.087 0.087
## .wcr2 ~~
## .wca2 0.041 0.008 5.032 0.000 0.284 0.284
## .wcr3 ~~
## .wca3 -0.001 0.005 -0.225 0.822 -0.014 -0.014
## RIcr ~~
## RIca 0.045 0.008 5.901 0.000 0.331 0.331
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .c_ref_w1 3.226 0.039 82.260 0.000 3.226 2.645
## .c_ref_w2 3.479 0.040 87.816 0.000 3.479 2.823
## .c_ref_w3 3.320 0.039 86.166 0.000 3.320 2.774
## .c_act_w1 0.226 0.008 26.710 0.000 0.226 0.859
## .c_act_w2 0.188 0.008 22.652 0.000 0.188 0.728
## .c_act_w3 0.094 0.006 15.149 0.000 0.094 0.487
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## RIcr 0.970 0.060 16.155 0.000 1.000 1.000
## RIca 0.019 0.002 9.013 0.000 1.000 1.000
## wcr1 0.518 0.045 11.423 0.000 1.000 1.000
## wca1 0.050 0.003 17.838 0.000 1.000 1.000
## .wcr2 0.519 0.051 10.192 0.000 0.945 0.945
## .wca2 0.040 0.003 15.473 0.000 0.857 0.857
## .wcr3 0.401 0.032 12.391 0.000 0.866 0.866
## .wca3 0.018 0.002 8.668 0.000 0.998 0.998
## .c_ref_w1 0.000 0.000 0.000
## .c_ref_w2 0.000 0.000 0.000
## .c_ref_w3 0.000 0.000 0.000
## .c_act_w1 0.000 0.000 0.000
## .c_act_w2 0.000 0.000 0.000
## .c_act_w3 0.000 0.000 0.000
##
## R-Square:
## Estimate
## wcr2 0.055
## wca2 0.143
## wcr3 0.134
## wca3 0.002
## c_ref_w1 1.000
## c_ref_w2 1.000
## c_ref_w3 1.000
## c_act_w1 1.000
## c_act_w2 1.000
## c_act_w3 1.000
##
##
## Group 2 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## RIcr =~
## c_ref_w1 1.000 0.952 0.779
## c_ref_w2 1.000 0.952 0.742
## c_ref_w3 1.000 0.952 0.780
## RIca =~
## c_act_w1 1.000 0.097 0.386
## c_act_w2 1.000 0.097 0.418
## c_act_w3 1.000 0.097 0.596
## wcr1 =~
## c_ref_w1 1.000 0.767 0.627
## wcr2 =~
## c_ref_w2 1.000 0.861 0.671
## wcr3 =~
## c_ref_w3 1.000 0.764 0.626
## wca1 =~
## c_act_w1 1.000 0.232 0.922
## wca2 =~
## c_act_w2 1.000 0.211 0.908
## wca3 =~
## c_act_w3 1.000 0.131 0.803
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## wcr2 ~
## wcr1 0.191 0.082 2.336 0.020 0.170 0.170
## wca1 0.233 0.158 1.477 0.140 0.063 0.063
## wcr3 ~
## wcr2 0.329 0.049 6.698 0.000 0.371 0.371
## wca2 0.485 0.170 2.854 0.004 0.134 0.134
## wca2 ~
## wca1 0.268 0.036 7.386 0.000 0.295 0.295
## wca3 ~
## wca2 0.077 0.044 1.766 0.077 0.124 0.124
## wca2 ~
## wcr1 (g2) 0.034 0.014 2.362 0.018 0.123 0.123
## wca3 ~
## wcr2 0.010 0.008 1.271 0.204 0.067 0.067
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## wcr1 ~~
## wca1 0.018 0.009 2.119 0.034 0.103 0.103
## .wcr2 ~~
## .wca2 0.034 0.008 4.274 0.000 0.204 0.204
## .wcr3 ~~
## .wca3 0.010 0.004 2.313 0.021 0.108 0.108
## RIcr ~~
## RIca 0.015 0.006 2.363 0.018 0.166 0.166
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .c_ref_w1 2.888 0.037 77.489 0.000 2.888 2.362
## .c_ref_w2 3.127 0.039 79.903 0.000 3.127 2.435
## .c_ref_w3 3.036 0.037 81.597 0.000 3.036 2.487
## .c_act_w1 0.204 0.008 26.708 0.000 0.204 0.814
## .c_act_w2 0.158 0.007 22.393 0.000 0.158 0.682
## .c_act_w3 0.069 0.005 13.928 0.000 0.069 0.424
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## RIcr 0.907 0.063 14.407 0.000 1.000 1.000
## RIca 0.009 0.002 5.150 0.000 1.000 1.000
## wcr1 0.588 0.053 11.100 0.000 1.000 1.000
## wca1 0.054 0.003 19.084 0.000 1.000 1.000
## .wcr2 0.716 0.061 11.716 0.000 0.965 0.965
## .wca2 0.039 0.002 18.027 0.000 0.890 0.890
## .wcr3 0.480 0.030 16.115 0.000 0.821 0.821
## .wca3 0.017 0.002 10.024 0.000 0.976 0.976
## .c_ref_w1 0.000 0.000 0.000
## .c_ref_w2 0.000 0.000 0.000
## .c_ref_w3 0.000 0.000 0.000
## .c_act_w1 0.000 0.000 0.000
## .c_act_w2 0.000 0.000 0.000
## .c_act_w3 0.000 0.000 0.000
##
## R-Square:
## Estimate
## wcr2 0.035
## wca2 0.110
## wcr3 0.179
## wca3 0.024
## c_ref_w1 1.000
## c_ref_w2 1.000
## c_ref_w3 1.000
## c_act_w1 1.000
## c_act_w2 1.000
## c_act_w3 1.000constraints <- '
wca2 ~ c(g1, g2)*wcr1
g1 == g2
'
lavTestWald(RICLPMmodfit9, constraints = constraints)## $stat
## [1] 4.534277
##
## $df
## [1] 1
##
## $p.value
## [1] 0.0332225
##
## $se
## [1] "standard"#Moderation Model EPE (original – latent variables using modsem)
model_mod <- '
# Latent variables
CR1 =~ 1*CR_slav_w1 + CR_wrk_w1
CR2 =~ 1*CR_slav_w2 + CR_wrk_w2
CR3 =~ 1*CR_slav_w3 + CR_wrk_w3
CA1 =~ 1*CA_pro_w1 + CA_rep_w1 + CA_org_w1 + CA_mtn_w1
CA2 =~ 1*CA_pro_w2 + CA_rep_w2 + CA_org_w2 + CA_mtn_w2
CA3 =~ 1*CA_pro_w3 + CA_rep_w3 + CA_mtn_w3
EPE1 =~ 1*EPE_say_w1 + EPE_care_w1
EPE2 =~ 1*EPE_say_w2 + EPE_care_w2
# Regressions
CA2 ~ CA1
CA2 ~ CR1
CR2 ~ CR1
CR2 ~ CA1
CA3 ~ CA2
CA3 ~ CR2
CR3 ~ CR2
CR3 ~ CA2
CA2 ~ CR1:EPE1
CA2 ~ EPE1
CA3 ~ CR2:EPE2
CA3 ~ EPE2
CA_rep_w1 ~~ CA_rep_w2
CA_rep_w2 ~~ CA_rep_w3
CA_rep_w1 ~~ CA_rep_w3
'
fit_mod <- modsem(model_mod, data = ANES, estimator = "WLSMV", ordered = c("CA_pro_w1", "CA_rep_w1", "CA_org_w1", "CA_mtn_w1", "CA_pro_w2", "CA_rep_w2", "CA_org_w2", "CA_mtn_w2", "CA_pro_w3", "CA_rep_w3", "CA_mtn_w3"), method = "dblcent")
summary(fit_mod, fit.measures = T, standardized = T, rsquare = T)## modsem (version 1.0.6, approach = dblcent):
## lavaan 0.6-19 ended normally after 84 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 115
##
## Used Total
## Number of observations 2020 2171
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 871.368 1003.409
## Degrees of freedom 338 338
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.006
## Shift parameter 136.900
## simple second-order correction
##
## Model Test Baseline Model:
##
## Test statistic 23046.648 9374.685
## Degrees of freedom 406 406
## P-value 0.000 0.000
## Scaling correction factor 2.524
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.976 0.926
## Tucker-Lewis Index (TLI) 0.972 0.911
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.028 0.031
## 90 Percent confidence interval - lower 0.026 0.029
## 90 Percent confidence interval - upper 0.030 0.033
## P-value H_0: RMSEA <= 0.050 1.000 1.000
## P-value H_0: RMSEA >= 0.080 0.000 0.000
##
## Robust RMSEA NA
## 90 Percent confidence interval - lower NA
## 90 Percent confidence interval - upper NA
## P-value H_0: Robust RMSEA <= 0.050 NA
## P-value H_0: Robust RMSEA >= 0.080 NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.052 0.052
##
## Parameter Estimates:
##
## Parameterization Delta
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## CR1 =~
## CR_slav_w1 1.000 0.970 0.675
## CR_wrk_w1 1.059 0.055 19.345 0.000 1.028 0.757
## CR2 =~
## CR_slav_w2 1.000 1.110 0.764
## CR_wrk_w2 1.047 0.052 20.172 0.000 1.162 0.842
## CR3 =~
## CR_slav_w3 1.000 1.028 0.726
## CR_wrk_w3 1.037 0.051 20.148 0.000 1.065 0.800
## CA1 =~
## CA_pro_w1 1.000 0.709 0.709
## CA_rep_w1 0.757 0.083 9.116 0.000 0.537 0.537
## CA_org_w1 1.247 0.109 11.481 0.000 0.884 0.884
## CA_mtn_w1 1.191 0.103 11.509 0.000 0.845 0.845
## CA2 =~
## CA_pro_w2 1.000 0.724 0.724
## CA_rep_w2 0.782 0.063 12.439 0.000 0.566 0.566
## CA_org_w2 0.993 0.066 15.108 0.000 0.719 0.719
## CA_mtn_w2 1.016 0.071 14.240 0.000 0.736 0.736
## CA3 =~
## CA_pro_w3 1.000 0.656 0.656
## CA_rep_w3 0.870 0.123 7.060 0.000 0.571 0.571
## CA_mtn_w3 1.040 0.144 7.227 0.000 0.682 0.682
## EPE1 =~
## EPE_say_w1 1.000 0.998 0.820
## EPE_care_w1 0.724 0.060 12.036 0.000 0.723 0.675
## EPE2 =~
## EPE_say_w2 1.000 1.151 0.941
## EPE_care_w2 0.620 0.047 13.172 0.000 0.714 0.647
## CR1EPE1 =~
## CR_slv_1EPE__1 1.000 1.014 0.544
## CR_wrk_1EPE__1 0.930 0.056 16.732 0.000 0.943 0.534
## CR_slv_1EPE__1 0.896 0.058 15.479 0.000 0.908 0.561
## CR_wrk_1EPE__1 0.858 0.072 11.846 0.000 0.869 0.570
## CR2EPE2 =~
## CR_slv_2EPE__2 1.000 1.138 0.620
## CR_wrk_2EPE__2 0.969 0.043 22.472 0.000 1.103 0.648
## CR_slv_2EPE__2 0.837 0.055 15.302 0.000 0.953 0.584
## CR_wrk_2EPE__2 0.778 0.061 12.759 0.000 0.885 0.580
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## CA2 ~
## CA1 0.669 0.068 9.829 0.000 0.655 0.655
## CR1 0.195 0.027 7.340 0.000 0.261 0.261
## CR2 ~
## CR1 1.119 0.077 14.539 0.000 0.979 0.979
## CA1 -0.015 0.042 -0.361 0.718 -0.010 -0.010
## CA3 ~
## CA2 0.720 0.097 7.415 0.000 0.795 0.795
## CR2 -0.016 0.033 -0.486 0.627 -0.027 -0.027
## CR3 ~
## CR2 0.873 0.044 19.756 0.000 0.943 0.943
## CA2 0.076 0.034 2.243 0.025 0.053 0.053
## CA2 ~
## CR1EPE1 0.116 0.027 4.285 0.000 0.162 0.162
## EPE1 0.008 0.023 0.363 0.717 0.011 0.011
## CA3 ~
## CR2EPE2 0.037 0.033 1.136 0.256 0.065 0.065
## EPE2 0.053 0.026 2.025 0.043 0.094 0.094
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv
## .CA_rep_w1 ~~
## .CA_rep_w2 0.364 0.039 9.392 0.000 0.364
## .CA_rep_w2 ~~
## .CA_rep_w3 0.378 0.042 9.038 0.000 0.378
## .CA_rep_w1 ~~
## .CA_rep_w3 0.355 0.044 8.065 0.000 0.355
## .CR_wrk_w1EPE_say_w1 ~~
## .CR_slv_1EPE__1 0.000 0.000
## .CR_slav_w1EPE_say_w1 ~~
## .CR_wrk_1EPE__1 0.000 0.000
## .CR_slv_1EPE__1 0.740 0.066 11.294 0.000 0.740
## .CR_slav_w1EPE_care_w1 ~~
## .CR_wrk_1EPE__1 0.609 0.057 10.695 0.000 0.609
## .CR_slav_w1EPE_say_w1 ~~
## .CR_wrk_1EPE__1 0.882 0.074 11.880 0.000 0.882
## .CR_wrk_w1EPE_say_w1 ~~
## .CR_wrk_1EPE__1 0.761 0.062 12.314 0.000 0.761
## .CR_wrk_w2EPE_say_w2 ~~
## .CR_slv_2EPE__2 0.000 0.000
## .CR_slav_w2EPE_say_w2 ~~
## .CR_wrk_2EPE__2 0.000 0.000
## .CR_slv_2EPE__2 0.740 0.057 12.889 0.000 0.740
## .CR_slav_w2EPE_care_w2 ~~
## .CR_wrk_2EPE__2 0.679 0.056 12.151 0.000 0.679
## .CR_slav_w2EPE_say_w2 ~~
## .CR_wrk_2EPE__2 0.694 0.080 8.623 0.000 0.694
## .CR_wrk_w2EPE_say_w2 ~~
## .CR_wrk_2EPE__2 0.538 0.048 11.279 0.000 0.538
## CR1 ~~
## CA1 0.173 0.030 5.832 0.000 0.251
## EPE1 0.091 0.027 3.320 0.001 0.094
## EPE2 0.334 0.037 9.084 0.000 0.299
## CR1EPE1 -0.021 0.032 -0.652 0.515 -0.021
## CR2EPE2 -0.023 0.032 -0.730 0.466 -0.021
## CA1 ~~
## EPE1 0.112 0.025 4.378 0.000 0.158
## EPE2 0.123 0.027 4.571 0.000 0.150
## CR1EPE1 0.028 0.029 0.969 0.332 0.039
## CR2EPE2 0.094 0.031 3.019 0.003 0.116
## EPE1 ~~
## EPE2 0.532 0.046 11.602 0.000 0.462
## CR1EPE1 0.051 0.030 1.698 0.090 0.051
## CR2EPE2 0.141 0.038 3.732 0.000 0.124
## EPE2 ~~
## CR1EPE1 0.103 0.037 2.799 0.005 0.088
## CR2EPE2 0.105 0.032 3.244 0.001 0.080
## CR1EPE1 ~~
## CR2EPE2 0.653 0.062 10.513 0.000 0.566
## .CR3 ~~
## .CA3 0.031 0.018 1.733 0.083 0.311
## Std.all
##
## 0.524
##
## 0.558
##
## 0.512
##
## 0.000
##
## 0.000
## 0.353
##
## 0.363
##
## 0.378
##
## 0.407
##
## 0.000
##
## 0.000
## 0.388
##
## 0.412
##
## 0.371
##
## 0.333
##
## 0.251
## 0.094
## 0.299
## -0.021
## -0.021
##
## 0.158
## 0.150
## 0.039
## 0.116
##
## 0.462
## 0.051
## 0.124
##
## 0.088
## 0.080
##
## 0.566
##
## 0.311
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .CR_slav_w1 3.042 0.032 93.784 0.000 3.042 2.114
## .CR_wrk_w1 3.059 0.030 101.229 0.000 3.059 2.253
## .CR_slav_w2 3.228 0.035 93.377 0.000 3.228 2.224
## .CR_wrk_w2 3.369 0.032 105.100 0.000 3.369 2.444
## .CR_slav_w3 3.066 0.032 95.994 0.000 3.066 2.167
## .CR_wrk_w3 3.279 0.030 109.233 0.000 3.279 2.462
## .EPE_say_w1 2.779 0.028 100.564 0.000 2.779 2.282
## .EPE_care_w1 2.440 0.026 93.515 0.000 2.440 2.279
## .EPE_say_w2 2.540 0.030 83.998 0.000 2.540 2.075
## .EPE_care_w2 2.292 0.029 79.002 0.000 2.292 2.078
## .CR_slv_1EPE__1 -0.022 0.041 -0.540 0.589 -0.022 -0.012
## .CR_wrk_1EPE__1 -0.024 0.039 -0.609 0.543 -0.024 -0.014
## .CR_slv_1EPE__1 -0.022 0.036 -0.610 0.542 -0.022 -0.014
## .CR_wrk_1EPE__1 -0.028 0.034 -0.827 0.408 -0.028 -0.018
## .CR_slv_2EPE__2 -0.011 0.041 -0.266 0.791 -0.011 -0.006
## .CR_wrk_2EPE__2 -0.004 0.038 -0.115 0.909 -0.004 -0.003
## .CR_slv_2EPE__2 -0.006 0.037 -0.171 0.864 -0.006 -0.004
## .CR_wrk_2EPE__2 0.007 0.034 0.214 0.831 0.007 0.005
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## CA_pro_w1|t1 1.767 0.051 34.506 0.000 1.767 1.767
## CA_rep_w1|t1 1.125 0.035 31.823 0.000 1.125 1.125
## CA_org_w1|t1 0.336 0.028 11.811 0.000 0.336 0.336
## CA_mtn_w1|t1 0.454 0.029 15.690 0.000 0.454 0.454
## CA_pro_w2|t1 1.365 0.040 34.356 0.000 1.365 1.365
## CA_rep_w2|t1 0.920 0.033 28.195 0.000 0.920 0.920
## CA_org_w2|t1 0.730 0.031 23.727 0.000 0.730 0.730
## CA_mtn_w2|t1 0.876 0.032 27.238 0.000 0.876 0.876
## CA_pro_w3|t1 1.864 0.055 33.850 0.000 1.864 1.864
## CA_rep_w3|t1 1.235 0.037 33.214 0.000 1.235 1.235
## CA_mtn_w3|t1 1.251 0.037 33.386 0.000 1.251 1.251
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .CR_slav_w1 1.128 0.056 20.285 0.000 1.128 0.545
## .CR_wrk_w1 0.786 0.047 16.769 0.000 0.786 0.427
## .CR_slav_w2 0.876 0.041 21.424 0.000 0.876 0.416
## .CR_wrk_w2 0.552 0.034 16.369 0.000 0.552 0.290
## .CR_slav_w3 0.945 0.040 23.496 0.000 0.945 0.472
## .CR_wrk_w3 0.638 0.033 19.084 0.000 0.638 0.360
## .CA_pro_w1 0.497 0.497 0.497
## .CA_rep_w1 0.711 0.711 0.711
## .CA_org_w1 0.218 0.218 0.218
## .CA_mtn_w1 0.286 0.286 0.286
## .CA_pro_w2 0.476 0.476 0.476
## .CA_rep_w2 0.679 0.679 0.679
## .CA_org_w2 0.483 0.483 0.483
## .CA_mtn_w2 0.459 0.459 0.459
## .CA_pro_w3 0.570 0.570 0.570
## .CA_rep_w3 0.674 0.674 0.674
## .CA_mtn_w3 0.535 0.535 0.535
## .EPE_say_w1 0.486 0.075 6.488 0.000 0.486 0.328
## .EPE_care_w1 0.623 0.043 14.571 0.000 0.623 0.544
## .EPE_say_w2 0.173 0.085 2.024 0.043 0.173 0.115
## .EPE_care_w2 0.707 0.039 18.324 0.000 0.707 0.581
## .CR_slv_1EPE__1 2.445 0.107 22.871 0.000 2.445 0.704
## .CR_wrk_1EPE__1 2.226 0.098 22.676 0.000 2.226 0.714
## .CR_slv_1EPE__1 1.796 0.085 21.224 0.000 1.796 0.685
## .CR_wrk_1EPE__1 1.571 0.076 20.750 0.000 1.571 0.675
## .CR_slv_2EPE__2 2.074 0.108 19.224 0.000 2.074 0.616
## .CR_wrk_2EPE__2 1.683 0.093 18.028 0.000 1.683 0.580
## .CR_slv_2EPE__2 1.752 0.075 23.285 0.000 1.752 0.659
## .CR_wrk_2EPE__2 1.549 0.066 23.476 0.000 1.549 0.664
## CR1 0.942 0.100 9.381 0.000 1.000 1.000
## .CR2 0.058 0.046 1.249 0.212 0.047 0.047
## .CR3 0.071 0.021 3.405 0.001 0.068 0.068
## CA1 0.503 0.082 6.110 0.000 1.000 1.000
## .CA2 0.200 0.031 6.526 0.000 0.381 0.381
## .CA3 0.140 0.048 2.892 0.004 0.326 0.326
## EPE1 0.997 0.104 9.549 0.000 1.000 1.000
## EPE2 1.326 0.126 10.562 0.000 1.000 1.000
## CR1EPE1 1.028 0.112 9.137 0.000 1.000 1.000
## CR2EPE2 1.295 0.123 10.548 0.000 1.000 1.000
##
## R-Square:
## Estimate
## CR_slav_w1 0.455
## CR_wrk_w1 0.573
## CR_slav_w2 0.584
## CR_wrk_w2 0.710
## CR_slav_w3 0.528
## CR_wrk_w3 0.640
## CA_pro_w1 0.503
## CA_rep_w1 0.289
## CA_org_w1 0.782
## CA_mtn_w1 0.714
## CA_pro_w2 0.524
## CA_rep_w2 0.321
## CA_org_w2 0.517
## CA_mtn_w2 0.541
## CA_pro_w3 0.430
## CA_rep_w3 0.326
## CA_mtn_w3 0.465
## EPE_say_w1 0.672
## EPE_care_w1 0.456
## EPE_say_w2 0.885
## EPE_care_w2 0.419
## CR_slv_1EPE__1 0.296
## CR_wrk_1EPE__1 0.286
## CR_slv_1EPE__1 0.315
## CR_wrk_1EPE__1 0.325
## CR_slv_2EPE__2 0.384
## CR_wrk_2EPE__2 0.420
## CR_slv_2EPE__2 0.341
## CR_wrk_2EPE__2 0.336
## CR2 0.953
## CR3 0.932
## CA2 0.619
## CA3 0.674(mean(ANES$EPE_say_w1, na.rm = TRUE) + mean(ANES$EPE_care_w1, na.rm = TRUE))/2## [1] 2.595843ANES$EPE_comp_w1 <- ((ANES$EPE_say_w1 + ANES$EPE_care_w1)/2)
mean(ANES$EPE_comp_w1, na.rm = TRUE)## [1] 2.595425sd(ANES$EPE_comp_w1, na.rm = TRUE)## [1] 1.011959#simple slopes
simple_slopes(x = "CR1", z = "EPE1", y = "CA2", vals_z = c(-1.01, 0, 1.01), model = fit_mod)##
## Predicted CA2, given EPE1 = -1.01:
## -------------------------------------------------------------------------
## CR1 | Predicted CA2 | Std.Error | z.value | p.value | Conf.Interval
## -------------------------------------------------------------------------
## -2.91 | -0.2356 | 0.105 | -2.23 | 0.025 | [-0.442, -0.0289]
## -1.94 | -0.1598 | 0.080 | -1.99 | 0.047 | [-0.317, -0.0023]
## -0.97 | -0.0841 | 0.063 | -1.34 | 0.180 | [-0.207, 0.0388]
## 0.00 | -0.0083 | 0.059 | -0.14 | 0.889 | [-0.125, 0.1082]
## 0.97 | 0.0675 | 0.073 | 0.93 | 0.353 | [-0.075, 0.2098]
## 1.94 | 0.1433 | 0.096 | 1.50 | 0.134 | [-0.044, 0.3307]
## 2.91 | 0.2191 | 0.123 | 1.78 | 0.075 | [-0.022, 0.4602]
##
##
## Predicted CA2, given EPE1 = 0:
## -------------------------------------------------------------------------
## CR1 | Predicted CA2 | Std.Error | z.value | p.value | Conf.Interval
## -------------------------------------------------------------------------
## -2.91 | -0.5678 | 0.084 | -6.73 | 0.000 | [-0.733, -0.4025]
## -1.94 | -0.3785 | 0.066 | -5.70 | 0.000 | [-0.509, -0.2484]
## -0.97 | -0.1893 | 0.055 | -3.43 | 0.001 | [-0.297, -0.0812]
## 0.00 | 0.0000 | 0.055 | 0.00 | 1.000 | [-0.107, 0.1073]
## 0.97 | 0.1893 | 0.065 | 2.89 | 0.004 | [ 0.061, 0.3176]
## 1.94 | 0.3785 | 0.083 | 4.55 | 0.000 | [ 0.216, 0.5414]
## 2.91 | 0.5678 | 0.104 | 5.45 | 0.000 | [ 0.364, 0.7720]
##
##
## Predicted CA2, given EPE1 = 1.01:
## -------------------------------------------------------------------------
## CR1 | Predicted CA2 | Std.Error | z.value | p.value | Conf.Interval
## -------------------------------------------------------------------------
## -2.91 | -0.8999 | 0.124 | -7.27 | 0.000 | [-1.143, -0.6572]
## -1.94 | -0.5972 | 0.090 | -6.65 | 0.000 | [-0.773, -0.4212]
## -0.97 | -0.2944 | 0.064 | -4.59 | 0.000 | [-0.420, -0.1687]
## 0.00 | 0.0083 | 0.059 | 0.14 | 0.889 | [-0.108, 0.1243]
## 0.97 | 0.3110 | 0.079 | 3.94 | 0.000 | [ 0.156, 0.4656]
## 1.94 | 0.6137 | 0.111 | 5.54 | 0.000 | [ 0.397, 0.8309]
## 2.91 | 0.9164 | 0.147 | 6.23 | 0.000 | [ 0.628, 1.2049]