#Load in Packages
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(lavaan)## This is lavaan 0.6-21
## lavaan is FREE software! Please report any bugs.library(ggplot2)
library(corrplot)## corrplot 0.95 loadedlibrary(ggcorrplot)
library(psych)##
## Attaching package: 'psych'## The following objects are masked from 'package:ggplot2':
##
## %+%, alpha## The following object is masked from 'package:lavaan':
##
## cor2covlibrary(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ forcats 1.0.1 ✔ stringr 1.6.0
## ✔ lubridate 1.9.5 ✔ tibble 3.3.1
## ✔ purrr 1.2.1 ✔ tidyr 1.3.2
## ✔ readr 2.2.0## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ psych::%+%() masks ggplot2::%+%()
## ✖ psych::alpha() masks ggplot2::alpha()
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors#Read in Data
setwd("~/Downloads/foldah")
ANES <- read.csv("ANES_merged.csv")#Recode Missing Codes to NA
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
ANES$CR_slav_w1 <- dplyr::recode(ANES$V162212,
`1` = 5,
`2` = 4,
`3` = 3,
`4` = 2,
`5` = 1)
ANES$CR_wrk_w1 <- ANES$V162214
ANES$CR_slav_w2 <- dplyr::recode(ANES$V202301,
`1` = 5,
`2` = 4,
`3` = 3,
`4` = 2,
`5` = 1)
ANES$CR_wrk_w2 <- ANES$V202303
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$CR_slav_w3)/2)#Critical Action Scale
ANES$CA_rep_w1 <- ifelse(ANES$V162019 == 1, 1, 0)
ANES$CA_pro_w1 <- ifelse(ANES$V162018a == 1, 1, 0)
ANES$CA_org_w1 <- ifelse(ANES$V162195 == 1, 1, 0)
ANES$CA_mtn_w1 <- ifelse(ANES$V162196 == 1, 1, 0)
ANES$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_rep_w3 <- ifelse(ANES$V242036 == 1, 1, 0)
ANES$CA_pro_w3 <- ifelse(ANES$V242029 == 1, 1, 0)
ANES$CA_mtn_w3 <- ifelse(ANES$V242034 == 1, 1, 0)
ANES$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$CA_rep_w3)/3)#External Efficacy Scale
ANES$EPE_care_w1 <- ANES$V162215
ANES$EPE_say_w1 <- ANES$V162216
ANES$EPE_say_w2 <- ANES$V202213
ANES$EPE_care_w2 <- ANES$V202212
ANES$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 Scale
ANES$IPE_comp_w1 <- ANES$V162217
ANES$IPE_und_w1 <- dplyr::recode(ANES$V162218,
`1` = 5,
`2` = 4,
`3` = 3,
`4` = 2,
`5` = 1)
ANES$IPE_comp_w2 <- ANES$V202214
ANES$IPE_und_w2 <- dplyr::recode(ANES$V202215,
`1` = 5,
`2` = 4,
`3` = 3,
`4` = 2,
`5` = 1)
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$IPE_comp_w3)/2)#Demographic Variables
ANES$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$V201600 == 2, 1, 0)
ANES$Age <- ANES$V161267#Mean Split Variables for Moderation Analysis
ANES$in_eff_ms_w2 <- ifelse(ANES$in_eff_w2 > mean(ANES$in_eff_w2, na.rm = TRUE), 1, 0)
ANES$ex_eff_ms_w2 <- ifelse(ANES$ex_eff_w2 > mean(ANES$ex_eff_w2, na.rm = TRUE), 1, 0)#Removing Empty Weight Cases
ANES_clean <- subset(ANES, !is.na(V240106b))#Finding demographics of analytic sampe
mean(ANES_clean$Age[!is.na(ANES_clean$Age)])## [1] 49.16766mean(ANES_clean$Black[!is.na(ANES_clean$Black)])## [1] 0.07846004mean(ANES_clean$White[!is.na(ANES_clean$White)])## [1] 0.7490253mean(ANES_clean$Hispanic[!is.na(ANES_clean$Hispanic)])## [1] 0.08966862mean(ANES_clean$Asian[!is.na(ANES_clean$Asian)])## [1] 0.03021442mean(ANES_clean$Native[!is.na(ANES_clean$Native)])## [1] 0.01998051mean(ANES_clean$Other[!is.na(ANES_clean$Other)])## [1] 0.03265107mean(ANES_clean$Female[!is.na(ANES_clean$Female)])## [1] 0.5406326#Predictors of Missing Weights?
ANES$V240106b_missing <- ifelse(is.na(ANES$V240106b), 1, 0)
model <- lm(V240106b_missing ~ Black + Female + White + Asian + Native + Other + Age, data = ANES)
summary(model)##
## Call:
## lm(formula = V240106b_missing ~ Black + Female + White + Asian +
## Native + Other + Age, data = ANES)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.08189 -0.04860 -0.04500 -0.04101 0.96425
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0548477 0.0200022 2.742 0.00616 **
## Black 0.0077297 0.0224986 0.344 0.73121
## Female -0.0046571 0.0092338 -0.504 0.61406
## White 0.0043850 0.0163980 0.267 0.78918
## Asian 0.0271410 0.0315729 0.860 0.39009
## Native 0.0316348 0.0362262 0.873 0.38262
## Other -0.0132713 0.0298458 -0.445 0.65661
## Age -0.0002295 0.0002860 -0.803 0.42230
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2096 on 2082 degrees of freedom
## (81 observations deleted due to missingness)
## Multiple R-squared: 0.001341, Adjusted R-squared: -0.002016
## F-statistic: 0.3994 on 7 and 2082 DF, p-value: 0.9031#RICLPM IPE + CA
RICLPM1 <- '
# Create between components (random intercepts)
RIipe =~ 1*in_eff_w1 + 1*in_eff_w2 + 1*in_eff_w3
RIca =~ 1*c_act_w1 + 1*c_act_w2 + 1*c_act_w3
# Create within-person centered variables
wipe1 =~ 1*in_eff_w1
wipe2 =~ 1*in_eff_w2
wipe3 =~ 1*in_eff_w3
wca1 =~ 1*c_act_w1
wca2 =~ 1*c_act_w2
wca3 =~ 1*c_act_w3
# Estimate lagged effects between within-person centered variables
wipe2 + wca2 ~ wipe1 + wca1
wipe3 + wca3 ~ wipe2 + wca2
# Estimate covariance between within-person centered variables at first wave
wipe1 ~~ wca1 # Covariance
# Estimate covariances between residuals of within-person centered variables
# (i.e., innovations)
wipe2 ~~ wca2
wipe3 ~~ wca3
# Estimate variance and covariance of random intercepts
RIipe ~~ RIipe
RIca ~~ RIca
RIipe ~~ RIca
# Estimate (residual) variance of within-person centered variables
wipe1 ~~ wipe1 # Variances
wca1 ~~ wca1
wipe2 ~~ wipe2 # Residual variances
wca2 ~~ wca2
wipe3 ~~ wipe3
wca3 ~~ wca3'
RICLPM1fit <- lavaan(RICLPM1,
data = ANES_clean,
missing = 'FIML',
sampling.weights = 'V240106b',
estimator = "MLR",
meanstructure = T,
int.ov.free = T)
summary(RICLPM1fit, fit.measures = T, standardized = T, rsquare = T)## lavaan 0.6-21 ended normally after 73 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 26
##
## Number of observations 2070
## Number of missing patterns 12
## Sampling weights variable V240106b
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 0.486 0.313
## Degrees of freedom 1 1
## P-value (Chi-square) 0.486 0.576
## Scaling correction factor 1.553
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 2871.247 1379.513
## Degrees of freedom 15 15
## P-value 0.000 0.000
## Scaling correction factor 2.081
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000 1.000
## Tucker-Lewis Index (TLI) 1.003 1.008
##
## Robust Comparative Fit Index (CFI) 1.000
## Robust Tucker-Lewis Index (TLI) 1.006
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -5534.689 -5534.689
## Scaling correction factor 2.265
## for the MLR correction
## Loglikelihood unrestricted model (H1) -5534.446 -5534.446
## Scaling correction factor 2.239
## for the MLR correction
##
## Akaike (AIC) 11121.378 11121.378
## Bayesian (BIC) 11267.895 11267.895
## Sample-size adjusted Bayesian (SABIC) 11185.291 11185.291
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000 0.000
## 90 Percent confidence interval - lower 0.000 0.000
## 90 Percent confidence interval - upper 0.051 0.036
## P-value H_0: RMSEA <= 0.050 0.944 0.991
## P-value H_0: RMSEA >= 0.080 0.002 0.000
##
## Robust RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.060
## P-value H_0: Robust RMSEA <= 0.050 0.905
## P-value H_0: Robust RMSEA >= 0.080 0.009
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.003 0.003
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## RIipe =~
## in_eff_w1 1.000 0.548 0.693
## in_eff_w2 1.000 0.548 0.645
## in_eff_w3 1.000 0.548 0.659
## RIca =~
## c_act_w1 1.000 0.112 0.447
## c_act_w2 1.000 0.112 0.469
## c_act_w3 1.000 0.112 0.627
## wipe1 =~
## in_eff_w1 1.000 0.571 0.721
## wipe2 =~
## in_eff_w2 1.000 0.650 0.764
## wipe3 =~
## in_eff_w3 1.000 0.626 0.752
## wca1 =~
## c_act_w1 1.000 0.224 0.894
## wca2 =~
## c_act_w2 1.000 0.211 0.883
## wca3 =~
## c_act_w3 1.000 0.139 0.779
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## wipe2 ~
## wipe1 0.146 0.073 1.987 0.047 0.128 0.128
## wca1 0.403 0.122 3.308 0.001 0.139 0.139
## wca2 ~
## wipe1 0.046 0.015 3.061 0.002 0.126 0.126
## wca1 0.251 0.041 6.056 0.000 0.266 0.266
## wipe3 ~
## wipe2 0.325 0.048 6.769 0.000 0.338 0.338
## wca2 0.358 0.115 3.120 0.002 0.121 0.121
## wca3 ~
## wipe2 0.013 0.010 1.407 0.159 0.063 0.063
## wca2 0.029 0.048 0.603 0.546 0.044 0.044
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## wipe1 ~~
## wca1 0.030 0.006 4.804 0.000 0.233 0.233
## .wipe2 ~~
## .wca2 0.018 0.005 3.690 0.000 0.142 0.142
## .wipe3 ~~
## .wca3 0.011 0.004 2.628 0.009 0.134 0.134
## RIipe ~~
## RIca 0.017 0.005 3.795 0.000 0.283 0.283
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .in_eff_w1 3.142 0.024 132.679 0.000 3.142 3.970
## .in_eff_w2 3.259 0.026 124.842 0.000 3.259 3.831
## .in_eff_w3 3.263 0.025 128.357 0.000 3.263 3.920
## .c_act_w1 0.192 0.007 26.905 0.000 0.192 0.768
## .c_act_w2 0.159 0.007 23.009 0.000 0.159 0.665
## .c_act_w3 0.078 0.005 14.840 0.000 0.078 0.437
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## RIipe 0.301 0.029 10.369 0.000 1.000 1.000
## RIca 0.013 0.002 5.759 0.000 1.000 1.000
## wipe1 0.326 0.028 11.598 0.000 1.000 1.000
## wca1 0.050 0.003 17.213 0.000 1.000 1.000
## .wipe2 0.405 0.032 12.649 0.000 0.956 0.956
## .wca2 0.040 0.003 15.625 0.000 0.898 0.898
## .wipe3 0.335 0.019 17.451 0.000 0.855 0.855
## .wca3 0.019 0.003 6.048 0.000 0.993 0.993
## .in_eff_w1 0.000 0.000 0.000
## .in_eff_w2 0.000 0.000 0.000
## .in_eff_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
## wipe2 0.044
## wca2 0.102
## wipe3 0.145
## wca3 0.007
## in_eff_w1 1.000
## in_eff_w2 1.000
## in_eff_w3 1.000
## c_act_w1 1.000
## c_act_w2 1.000
## c_act_w3 1.000#Extracting covariance matrix
lavInspect(RICLPM1fit, "sampstat")## $cov
## in_f_1 in_f_2 in_f_3 c_ct_1 c_ct_2 c_ct_3
## in_eff_w1 0.627
## in_eff_w2 0.361 0.724
## in_eff_w3 0.330 0.448 0.693
## c_act_w1 0.048 0.043 0.033 0.063
## c_act_w2 0.040 0.044 0.042 0.026 0.057
## c_act_w3 0.018 0.024 0.031 0.013 0.014 0.032
##
## $mean
## in_eff_w1 in_eff_w2 in_eff_w3 c_act_w1 c_act_w2 c_act_w3
## 3.142 3.259 3.263 0.192 0.159 0.078#Extracting residual correlation matrix
resid(RICLPM1fit, type = "cor")## $type
## [1] "cor.bollen"
##
## $cov
## in_f_1 in_f_2 in_f_3 c_ct_1 c_ct_2 c_ct_3
## in_eff_w1 0.000
## in_eff_w2 0.000 0.000
## in_eff_w3 0.002 0.000 0.000
## c_act_w1 0.003 0.004 0.012 0.000
## c_act_w2 -0.001 0.000 0.001 0.000 0.000
## c_act_w3 -0.005 -0.002 0.000 0.001 0.000 0.000
##
## $mean
## in_eff_w1 in_eff_w2 in_eff_w3 c_act_w1 c_act_w2 c_act_w3
## 0 0 0 0 0 0#RICLPM EPE + CA
RICLPM2 <- '
# Create between components (random intercepts)
RIepe =~ 1*ex_eff_w1 + 1*ex_eff_w2 + 1*ex_eff_w3
RIca =~ 1*c_act_w1 + 1*c_act_w2 + 1*c_act_w3
# Create within-person centered variables
wepe1 =~ 1*ex_eff_w1
wepe2 =~ 1*ex_eff_w2
wepe3 =~ 1*ex_eff_w3
wca1 =~ 1*c_act_w1
wca2 =~ 1*c_act_w2
wca3 =~ 1*c_act_w3
# Estimate lagged effects between within-person centered variables
wepe2 + wca2 ~ wepe1 + wca1
wepe3 + wca3 ~ wepe2 + wca2
# Estimate covariance between within-person centered variables at first wave
wepe1 ~~ wca1 # Covariance
# Estimate covariances between residuals of within-person centered variables
# (i.e., innovations)
wepe2 ~~ wca2
wepe3 ~~ wca3
# Estimate variance and covariance of random intercepts
RIepe ~~ RIepe
RIca ~~ RIca
RIepe ~~ RIca
# Estimate (residual) variance of within-person centered variables
wepe1 ~~ wepe1 # Variances
wca1 ~~ wca1
wepe2 ~~ wepe2 # Residual variances
wca2 ~~ wca2
wepe3 ~~ wepe3
wca3 ~~ wca3'
RICLPM2fit <- lavaan(RICLPM2,
data = ANES_clean,
missing = 'FIML',
sampling.weights = 'V240106b',
estimator = "MLR",
meanstructure = T,
int.ov.free = T)
summary(RICLPM2fit, fit.measures = T, standardized = T, rsquare = T)## lavaan 0.6-21 ended normally after 70 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 26
##
## Number of observations 2070
## Number of missing patterns 10
## Sampling weights variable V240106b
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 3.828 2.383
## Degrees of freedom 1 1
## P-value (Chi-square) 0.050 0.123
## Scaling correction factor 1.607
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 1574.648 726.545
## Degrees of freedom 15 15
## P-value 0.000 0.000
## Scaling correction factor 2.167
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.998 0.998
## Tucker-Lewis Index (TLI) 0.973 0.971
##
## Robust Comparative Fit Index (CFI) 0.999
## Robust Tucker-Lewis Index (TLI) 0.980
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -7305.490 -7305.490
## Scaling correction factor 2.243
## for the MLR correction
## Loglikelihood unrestricted model (H1) -7303.576 -7303.576
## Scaling correction factor 2.220
## for the MLR correction
##
## Akaike (AIC) 14662.981 14662.981
## Bayesian (BIC) 14809.499 14809.499
## Sample-size adjusted Bayesian (SABIC) 14726.895 14726.895
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.037 0.026
## 90 Percent confidence interval - lower 0.000 0.000
## 90 Percent confidence interval - upper 0.079 0.060
## P-value H_0: RMSEA <= 0.050 0.625 0.855
## P-value H_0: RMSEA >= 0.080 0.046 0.003
##
## Robust RMSEA 0.032
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.091
## P-value H_0: Robust RMSEA <= 0.050 0.594
## P-value H_0: Robust RMSEA >= 0.080 0.102
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.008 0.008
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## RIepe =~
## ex_eff_w1 1.000 0.543 0.545
## ex_eff_w2 1.000 0.543 0.535
## ex_eff_w3 1.000 0.543 0.571
## RIca =~
## c_act_w1 1.000 0.113 0.451
## c_act_w2 1.000 0.113 0.474
## c_act_w3 1.000 0.113 0.635
## wepe1 =~
## ex_eff_w1 1.000 0.835 0.838
## wepe2 =~
## ex_eff_w2 1.000 0.858 0.845
## wepe3 =~
## ex_eff_w3 1.000 0.781 0.821
## wca1 =~
## c_act_w1 1.000 0.223 0.892
## wca2 =~
## c_act_w2 1.000 0.210 0.881
## wca3 =~
## c_act_w3 1.000 0.137 0.772
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## wepe2 ~
## wepe1 0.053 0.049 1.089 0.276 0.052 0.052
## wca1 0.087 0.158 0.554 0.579 0.023 0.023
## wca2 ~
## wepe1 -0.016 0.010 -1.584 0.113 -0.063 -0.063
## wca1 0.280 0.042 6.602 0.000 0.297 0.297
## wepe3 ~
## wepe2 0.164 0.046 3.588 0.000 0.181 0.181
## wca2 -0.244 0.170 -1.430 0.153 -0.066 -0.066
## wca3 ~
## wepe2 0.007 0.008 0.980 0.327 0.047 0.047
## wca2 0.027 0.049 0.563 0.573 0.042 0.042
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## wepe1 ~~
## wca1 0.013 0.008 1.572 0.116 0.068 0.068
## .wepe2 ~~
## .wca2 0.013 0.007 1.835 0.067 0.076 0.076
## .wepe3 ~~
## .wca3 -0.011 0.005 -2.086 0.037 -0.103 -0.103
## RIepe ~~
## RIca 0.015 0.006 2.508 0.012 0.244 0.244
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .ex_eff_w1 2.563 0.030 86.501 0.000 2.563 2.572
## .ex_eff_w2 2.363 0.030 79.307 0.000 2.363 2.328
## .ex_eff_w3 2.208 0.028 79.045 0.000 2.208 2.322
## .c_act_w1 0.192 0.007 26.917 0.000 0.192 0.766
## .c_act_w2 0.159 0.007 23.008 0.000 0.159 0.665
## .c_act_w3 0.078 0.005 14.844 0.000 0.078 0.438
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## RIepe 0.295 0.033 8.908 0.000 1.000 1.000
## RIca 0.013 0.002 6.064 0.000 1.000 1.000
## wepe1 0.698 0.040 17.350 0.000 1.000 1.000
## wca1 0.050 0.003 17.343 0.000 1.000 1.000
## .wepe2 0.733 0.045 16.121 0.000 0.997 0.997
## .wca2 0.040 0.003 15.284 0.000 0.910 0.910
## .wepe3 0.588 0.036 16.375 0.000 0.965 0.965
## .wca3 0.019 0.003 6.057 0.000 0.996 0.996
## .ex_eff_w1 0.000 0.000 0.000
## .ex_eff_w2 0.000 0.000 0.000
## .ex_eff_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
## wepe2 0.003
## wca2 0.090
## wepe3 0.035
## wca3 0.004
## ex_eff_w1 1.000
## ex_eff_w2 1.000
## ex_eff_w3 1.000
## c_act_w1 1.000
## c_act_w2 1.000
## c_act_w3 1.000#Extracting covariance matrix
lavInspect(RICLPM2fit, "sampstat")## $cov
## ex_f_1 ex_f_2 ex_f_3 c_ct_1 c_ct_2 c_ct_3
## ex_eff_w1 0.994
## ex_eff_w2 0.334 1.030
## ex_eff_w3 0.300 0.411 0.902
## c_act_w1 0.026 0.018 0.004 0.063
## c_act_w2 0.008 0.029 0.005 0.026 0.057
## c_act_w3 0.018 0.021 0.004 0.013 0.014 0.032
##
## $mean
## ex_eff_w1 ex_eff_w2 ex_eff_w3 c_act_w1 c_act_w2 c_act_w3
## 2.563 2.363 2.208 0.192 0.159 0.078#Extracting residual correlation matrix
resid(RICLPM2fit, type = "cor")## $type
## [1] "cor.bollen"
##
## $cov
## ex_f_1 ex_f_2 ex_f_3 c_ct_1 c_ct_2 c_ct_3
## ex_eff_w1 0.000
## ex_eff_w2 0.000 0.000
## ex_eff_w3 -0.003 -0.001 0.000
## c_act_w1 -0.005 -0.008 -0.035 0.000
## c_act_w2 0.004 0.000 -0.004 -0.001 0.000
## c_act_w3 0.018 0.003 -0.002 0.002 0.001 0.000
##
## $mean
## ex_eff_w1 ex_eff_w2 ex_eff_w3 c_act_w1 c_act_w2 c_act_w3
## 0 0 0 0 0 0#RICLPM CR + CA
RICLPM3 <- '
# 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 + wca2 ~ wcr1 + wca1
wcr3 + wca3 ~ wcr2 + wca2
# 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'
RICLPM3fit <- lavaan(RICLPM3,
data = ANES_clean,
missing = 'FIML',
sampling.weights = 'V240106b',
estimator = "MLR",
meanstructure = T,
int.ov.free = T)
summary(RICLPM3fit, fit.measures = T, standardized = T, rsquare = T)## lavaan 0.6-21 ended normally after 76 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 26
##
## Number of observations 2070
## Number of missing patterns 10
## Sampling weights variable V240106b
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 1.978 1.013
## Degrees of freedom 1 1
## P-value (Chi-square) 0.160 0.314
## Scaling correction factor 1.954
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 3830.775 1602.897
## Degrees of freedom 15 15
## P-value 0.000 0.000
## Scaling correction factor 2.390
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000 1.000
## Tucker-Lewis Index (TLI) 0.996 1.000
##
## Robust Comparative Fit Index (CFI) 1.000
## Robust Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -7490.719 -7490.719
## Scaling correction factor 2.304
## for the MLR correction
## Loglikelihood unrestricted model (H1) -7489.729 -7489.729
## Scaling correction factor 2.291
## for the MLR correction
##
## Akaike (AIC) 15033.437 15033.437
## Bayesian (BIC) 15179.955 15179.955
## Sample-size adjusted Bayesian (SABIC) 15097.351 15097.351
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.022 0.002
## 90 Percent confidence interval - lower 0.000 0.000
## 90 Percent confidence interval - upper 0.067 0.044
## P-value H_0: RMSEA <= 0.050 0.808 0.977
## P-value H_0: RMSEA >= 0.080 0.013 0.000
##
## Robust RMSEA 0.006
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.081
## P-value H_0: Robust RMSEA <= 0.050 0.736
## P-value H_0: Robust RMSEA >= 0.080 0.054
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.006 0.006
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## RIcr =~
## c_ref_w1 1.000 0.969 0.790
## c_ref_w2 1.000 0.969 0.779
## c_ref_w3 1.000 0.969 0.802
## RIca =~
## c_act_w1 1.000 0.113 0.452
## c_act_w2 1.000 0.113 0.473
## c_act_w3 1.000 0.113 0.632
## wcr1 =~
## c_ref_w1 1.000 0.751 0.613
## wcr2 =~
## c_ref_w2 1.000 0.781 0.628
## wcr3 =~
## c_ref_w3 1.000 0.722 0.598
## wca1 =~
## c_act_w1 1.000 0.223 0.892
## wca2 =~
## c_act_w2 1.000 0.210 0.881
## wca3 =~
## c_act_w3 1.000 0.138 0.775
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## wcr2 ~
## wcr1 0.121 0.071 1.704 0.088 0.116 0.116
## wca1 0.211 0.150 1.411 0.158 0.060 0.060
## wca2 ~
## wcr1 0.049 0.016 3.088 0.002 0.175 0.175
## wca1 0.260 0.041 6.290 0.000 0.276 0.276
## wcr3 ~
## wcr2 0.174 0.064 2.725 0.006 0.188 0.188
## wca2 0.593 0.211 2.815 0.005 0.173 0.173
## wca3 ~
## wcr2 0.008 0.009 0.891 0.373 0.047 0.047
## wca2 0.025 0.048 0.522 0.602 0.038 0.038
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## wcr1 ~~
## wca1 0.014 0.008 1.733 0.083 0.086 0.086
## .wcr2 ~~
## .wca2 0.034 0.008 4.182 0.000 0.225 0.225
## .wcr3 ~~
## .wca3 0.009 0.005 1.693 0.090 0.093 0.093
## RIcr ~~
## RIca 0.030 0.007 4.281 0.000 0.277 0.277
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .c_ref_w1 3.023 0.037 82.590 0.000 3.023 2.465
## .c_ref_w2 3.234 0.036 89.365 0.000 3.234 2.599
## .c_ref_w3 3.113 0.036 86.139 0.000 3.113 2.576
## .c_act_w1 0.192 0.007 26.911 0.000 0.192 0.769
## .c_act_w2 0.159 0.007 23.008 0.000 0.159 0.665
## .c_act_w3 0.078 0.005 14.840 0.000 0.078 0.437
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## RIcr 0.939 0.053 17.826 0.000 1.000 1.000
## RIca 0.013 0.002 6.115 0.000 1.000 1.000
## wcr1 0.565 0.043 13.166 0.000 1.000 1.000
## wca1 0.050 0.003 17.431 0.000 1.000 1.000
## .wcr2 0.598 0.046 12.986 0.000 0.982 0.982
## .wca2 0.039 0.003 15.441 0.000 0.885 0.885
## .wcr3 0.479 0.035 13.642 0.000 0.919 0.919
## .wca3 0.019 0.003 6.168 0.000 0.995 0.995
## .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 0.115
## wcr3 0.081
## wca3 0.005
## 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#Extracting covariance matrix
lavInspect(RICLPM3fit, "sampstat")## $cov
## c_rf_1 c_rf_2 c_rf_3 c_ct_1 c_ct_2 c_ct_3
## c_ref_w1 1.502
## c_ref_w2 1.009 1.548
## c_ref_w3 0.969 1.070 1.461
## c_act_w1 0.048 0.047 0.048 0.063
## c_act_w2 0.061 0.071 0.064 0.026 0.057
## c_act_w3 0.029 0.035 0.040 0.013 0.014 0.032
##
## $mean
## c_ref_w1 c_ref_w2 c_ref_w3 c_act_w1 c_act_w2 c_act_w3
## 3.023 3.234 3.113 0.192 0.159 0.078#Extracting residual correlation matrix
resid(RICLPM3fit, type = "cor")## $type
## [1] "cor.bollen"
##
## $cov
## c_rf_1 c_rf_2 c_rf_3 c_ct_1 c_ct_2 c_ct_3
## c_ref_w1 0.000
## c_ref_w2 0.000 0.000
## c_ref_w3 0.000 0.000 0.000
## c_act_w1 0.012 0.013 0.024 0.000
## c_act_w2 -0.002 0.000 0.002 0.001 0.000
## c_act_w3 -0.011 -0.005 -0.003 0.002 0.000 0.000
##
## $mean
## c_ref_w1 c_ref_w2 c_ref_w3 c_act_w1 c_act_w2 c_act_w3
## 0 0 0 0 0 0#RICLPM CR + EPE
RICLPM4 <- '
# Create between components (random intercepts)
RIcr =~ 1*c_ref_w1 + 1*c_ref_w2 + 1*c_ref_w3
RIepe =~ 1*ex_eff_w1 + 1*ex_eff_w2 + 1*ex_eff_w3
# Create within-person centered variables
wcr1 =~ 1*c_ref_w1
wcr2 =~ 1*c_ref_w2
wcr3 =~ 1*c_ref_w3
wepe1 =~ 1*ex_eff_w1
wepe2 =~ 1*ex_eff_w2
wepe3 =~ 1*ex_eff_w3
# Estimate lagged effects between within-person centered variables
wcr2 + wepe2 ~ wcr1 + wepe1
wcr3 + wepe3 ~ wcr2 + wepe2
# Estimate covariance between within-person centered variables at first wave
wcr1 ~~ wepe1 # Covariance
# Estimate covariances between residuals of within-person centered variables
# (i.e., innovations)
wcr2 ~~ wepe2
wcr3 ~~ wepe3
# Estimate variance and covariance of random intercepts
RIcr ~~ RIcr
RIepe ~~ RIepe
RIcr ~~ RIepe
# Estimate (residual) variance of within-person centered variables
wcr1 ~~ wcr1 # Variances
wepe1 ~~ wepe1
wcr2 ~~ wcr2 # Residual variances
wepe2 ~~ wepe2
wcr3 ~~ wcr3
wepe3 ~~ wepe3'
RICLPM4fit <- lavaan(RICLPM4,
data = ANES_clean,
missing = 'FIML',
estimator = "ML",
sampling.weights = 'V240106b',
meanstructure = T,
int.ov.free = T)
summary(RICLPM4fit, fit.measures = T, standardized = T, rsquare = T)## lavaan 0.6-21 ended normally after 40 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 26
##
## Number of observations 2070
## Number of missing patterns 15
## Sampling weights variable V240106b
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 1.723 1.059
## Degrees of freedom 1 1
## P-value (Chi-square) 0.189 0.303
## Scaling correction factor 1.627
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 3748.533 1624.049
## Degrees of freedom 15 15
## P-value 0.000 0.000
## Scaling correction factor 2.308
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000 1.000
## Tucker-Lewis Index (TLI) 0.997 0.999
##
## Robust Comparative Fit Index (CFI) 1.000
## Robust Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -16811.657 -16811.657
## Scaling correction factor 1.957
## for the MLR correction
## Loglikelihood unrestricted model (H1) -16810.796 -16810.796
## Scaling correction factor 1.945
## for the MLR correction
##
## Akaike (AIC) 33675.314 33675.314
## Bayesian (BIC) 33821.832 33821.832
## Sample-size adjusted Bayesian (SABIC) 33739.228 33739.228
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.019 0.005
## 90 Percent confidence interval - lower 0.000 0.000
## 90 Percent confidence interval - upper 0.065 0.048
## P-value H_0: RMSEA <= 0.050 0.832 0.960
## P-value H_0: RMSEA >= 0.080 0.010 0.000
##
## Robust RMSEA 0.004
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.076
## P-value H_0: Robust RMSEA <= 0.050 0.770
## P-value H_0: Robust RMSEA >= 0.080 0.038
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.006 0.006
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## RIcr =~
## c_ref_w1 1.000 0.957 0.781
## c_ref_w2 1.000 0.957 0.769
## c_ref_w3 1.000 0.957 0.791
## RIepe =~
## ex_eff_w1 1.000 0.541 0.543
## ex_eff_w2 1.000 0.541 0.533
## ex_eff_w3 1.000 0.541 0.570
## wcr1 =~
## c_ref_w1 1.000 0.765 0.625
## wcr2 =~
## c_ref_w2 1.000 0.795 0.639
## wcr3 =~
## c_ref_w3 1.000 0.740 0.612
## wepe1 =~
## ex_eff_w1 1.000 0.837 0.840
## wepe2 =~
## ex_eff_w2 1.000 0.859 0.846
## wepe3 =~
## ex_eff_w3 1.000 0.780 0.822
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## wcr2 ~
## wcr1 0.158 0.068 2.311 0.021 0.152 0.152
## wepe1 0.071 0.044 1.595 0.111 0.074 0.074
## wepe2 ~
## wcr1 0.309 0.063 4.876 0.000 0.276 0.276
## wepe1 0.051 0.046 1.127 0.260 0.050 0.050
## wcr3 ~
## wcr2 0.179 0.063 2.861 0.004 0.192 0.192
## wepe2 0.212 0.053 3.982 0.000 0.246 0.246
## wepe3 ~
## wcr2 -0.003 0.053 -0.062 0.950 -0.003 -0.003
## wepe2 0.162 0.050 3.215 0.001 0.178 0.178
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## wcr1 ~~
## wepe1 0.017 0.031 0.546 0.585 0.027 0.027
## .wcr2 ~~
## .wepe2 0.166 0.038 4.356 0.000 0.257 0.257
## .wcr3 ~~
## .wepe3 -0.035 0.024 -1.432 0.152 -0.065 -0.065
## RIcr ~~
## RIepe 0.063 0.033 1.890 0.059 0.121 0.121
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .c_ref_w1 3.023 0.037 82.599 0.000 3.023 2.467
## .c_ref_w2 3.235 0.036 89.375 0.000 3.235 2.599
## .c_ref_w3 3.113 0.036 86.157 0.000 3.113 2.573
## .ex_eff_w1 2.563 0.030 86.501 0.000 2.563 2.571
## .ex_eff_w2 2.363 0.030 79.292 0.000 2.363 2.327
## .ex_eff_w3 2.208 0.028 79.089 0.000 2.208 2.326
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## RIcr 0.916 0.052 17.612 0.000 1.000 1.000
## RIepe 0.293 0.033 8.893 0.000 1.000 1.000
## wcr1 0.586 0.044 13.306 0.000 1.000 1.000
## wepe1 0.701 0.040 17.359 0.000 1.000 1.000
## .wcr2 0.614 0.044 13.927 0.000 0.971 0.971
## .wepe2 0.680 0.048 14.288 0.000 0.921 0.921
## .wcr3 0.479 0.034 14.153 0.000 0.875 0.875
## .wepe3 0.590 0.037 16.065 0.000 0.969 0.969
## .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
## .ex_eff_w1 0.000 0.000 0.000
## .ex_eff_w2 0.000 0.000 0.000
## .ex_eff_w3 0.000 0.000 0.000
##
## R-Square:
## Estimate
## wcr2 0.029
## wepe2 0.079
## wcr3 0.125
## wepe3 0.031
## c_ref_w1 1.000
## c_ref_w2 1.000
## c_ref_w3 1.000
## ex_eff_w1 1.000
## ex_eff_w2 1.000
## ex_eff_w3 1.000#Extracting covariance matrix
lavInspect(RICLPM4fit, "sampstat")## $cov
## c_rf_1 c_rf_2 c_rf_3 ex_f_1 ex_f_2 ex_f_3
## c_ref_w1 1.502
## c_ref_w2 1.010 1.549
## c_ref_w3 0.969 1.070 1.461
## ex_eff_w1 0.072 0.104 0.059 0.994
## ex_eff_w2 0.248 0.260 0.252 0.334 1.031
## ex_eff_w3 0.112 0.103 0.066 0.300 0.411 0.902
##
## $mean
## c_ref_w1 c_ref_w2 c_ref_w3 ex_eff_w1 ex_eff_w2 ex_eff_w3
## 3.023 3.235 3.113 2.563 2.363 2.208#Extracting residual correlation matrix
resid(RICLPM4fit, type = "cor")## $type
## [1] "cor.bollen"
##
## $cov
## c_rf_1 c_rf_2 c_rf_3 ex_f_1 ex_f_2 ex_f_3
## c_ref_w1 0.000
## c_ref_w2 0.000 0.000
## c_ref_w3 -0.001 0.000 0.000
## ex_eff_w1 -0.007 -0.009 -0.018 0.000
## ex_eff_w2 0.002 0.000 -0.002 0.000 0.000
## ex_eff_w3 0.018 0.009 0.007 0.001 0.000 0.000
##
## $mean
## c_ref_w1 c_ref_w2 c_ref_w3 ex_eff_w1 ex_eff_w2 ex_eff_w3
## 0 0 0 0 0 0#RICLPM EPE Moderation
RICLPMmod1 <- '
# 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 ~ 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'
RICLPMmodfit1 <- lavaan(RICLPMmod1,
data = ANES_clean,
missing = 'FIML',
estimator = "MLR",
meanstructure = T,
group = "ex_eff_ms_w2",
sampling.weights = "V240106b",
int.ov.free = T)## Warning: lavaan->lav_data_full():
## group variable 'ex_eff_ms_w2' contains missing valuessummary(RICLPMmodfit1, fit.measures = T, standardized = T, rsquare = T)## lavaan 0.6-21 ended normally after 129 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 52
##
## Number of observations per group:
## 0 1105
## 1 957
## Number of missing patterns per group:
## 0 8
## 1 7
## Sampling weights variable V240106b
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 3.044 1.645
## Degrees of freedom 2 2
## P-value (Chi-square) 0.218 0.439
## Scaling correction factor 1.850
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 0 0.846 0.846
## 1 0.799 0.799
##
## Model Test Baseline Model:
##
## Test statistic 3659.303 1581.181
## Degrees of freedom 30 30
## P-value 0.000 0.000
## Scaling correction factor 2.314
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000 1.000
## Tucker-Lewis Index (TLI) 0.996 1.003
##
## Robust Comparative Fit Index (CFI) 1.000
## Robust Tucker-Lewis Index (TLI) 1.003
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -7366.202 -7366.202
## Scaling correction factor 2.223
## for the MLR correction
## Loglikelihood unrestricted model (H1) -7364.680 -7364.680
## Scaling correction factor 2.209
## for the MLR correction
##
## Akaike (AIC) 14836.403 14836.403
## Bayesian (BIC) 15129.238 15129.238
## Sample-size adjusted Bayesian (SABIC) 14964.029 14964.029
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.022 0.000
## 90 Percent confidence interval - lower 0.000 0.000
## 90 Percent confidence interval - upper 0.070 0.045
## P-value H_0: RMSEA <= 0.050 0.785 0.974
## P-value H_0: RMSEA >= 0.080 0.018 0.000
##
## Robust RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.079
## P-value H_0: Robust RMSEA <= 0.050 0.776
## P-value H_0: Robust RMSEA >= 0.080 0.048
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.008 0.008
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread 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 0.964 0.795
## c_ref_w2 1.000 0.964 0.769
## c_ref_w3 1.000 0.964 0.802
## RIca =~
## c_act_w1 1.000 0.084 0.346
## c_act_w2 1.000 0.084 0.382
## c_act_w3 1.000 0.084 0.561
## wcr1 =~
## c_ref_w1 1.000 0.734 0.606
## wcr2 =~
## c_ref_w2 1.000 0.802 0.640
## wcr3 =~
## c_ref_w3 1.000 0.719 0.598
## wca1 =~
## c_act_w1 1.000 0.226 0.938
## wca2 =~
## c_act_w2 1.000 0.202 0.924
## wca3 =~
## c_act_w3 1.000 0.123 0.828
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## wcr2 ~
## wcr1 0.111 0.100 1.103 0.270 0.101 0.101
## wca1 0.171 0.200 0.855 0.392 0.048 0.048
## wcr3 ~
## wcr2 0.164 0.078 2.099 0.036 0.183 0.183
## wca2 0.521 0.297 1.753 0.080 0.146 0.146
## wca2 ~
## wca1 0.272 0.055 4.949 0.000 0.305 0.305
## wca3 ~
## wca2 0.052 0.057 0.900 0.368 0.085 0.085
## wca2 ~
## wcr1 0.027 0.022 1.222 0.222 0.096 0.096
## wca3 ~
## wcr2 -0.003 0.010 -0.277 0.782 -0.018 -0.018
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## wcr1 ~~
## wca1 0.011 0.011 0.984 0.325 0.068 0.068
## .wcr2 ~~
## .wca2 0.025 0.011 2.286 0.022 0.161 0.161
## .wcr3 ~~
## .wca3 0.003 0.005 0.605 0.546 0.036 0.036
## RIcr ~~
## RIca 0.019 0.008 2.365 0.018 0.236 0.236
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .c_ref_w1 2.835 0.050 56.795 0.000 2.835 2.340
## .c_ref_w2 3.036 0.050 60.681 0.000 3.036 2.422
## .c_ref_w3 2.896 0.049 58.990 0.000 2.896 2.409
## .c_act_w1 0.184 0.010 18.674 0.000 0.184 0.764
## .c_act_w2 0.139 0.009 15.632 0.000 0.139 0.638
## .c_act_w3 0.058 0.006 10.114 0.000 0.058 0.392
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## RIcr 0.928 0.071 13.096 0.000 1.000 1.000
## RIca 0.007 0.002 3.461 0.001 1.000 1.000
## wcr1 0.539 0.051 10.505 0.000 1.000 1.000
## wca1 0.051 0.004 11.531 0.000 1.000 1.000
## .wcr2 0.634 0.064 9.882 0.000 0.987 0.987
## .wca2 0.036 0.003 12.129 0.000 0.893 0.893
## .wcr3 0.483 0.045 10.712 0.000 0.935 0.935
## .wca3 0.015 0.003 5.996 0.000 0.993 0.993
## .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 0.013
## wca2 0.107
## wcr3 0.065
## wca3 0.007
## 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 0.916 0.763
## c_ref_w2 1.000 0.916 0.769
## c_ref_w3 1.000 0.916 0.785
## RIca =~
## c_act_w1 1.000 0.138 0.532
## c_act_w2 1.000 0.138 0.534
## c_act_w3 1.000 0.138 0.671
## wcr1 =~
## c_ref_w1 1.000 0.775 0.646
## wcr2 =~
## c_ref_w2 1.000 0.762 0.639
## wcr3 =~
## c_ref_w3 1.000 0.722 0.619
## wca1 =~
## c_act_w1 1.000 0.220 0.846
## wca2 =~
## c_act_w2 1.000 0.219 0.846
## wca3 =~
## c_act_w3 1.000 0.153 0.741
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## wcr2 ~
## wcr1 0.140 0.098 1.424 0.154 0.142 0.142
## wca1 0.269 0.226 1.191 0.234 0.078 0.078
## wcr3 ~
## wcr2 0.191 0.103 1.853 0.064 0.201 0.201
## wca2 0.583 0.283 2.057 0.040 0.177 0.177
## wca2 ~
## wca1 0.240 0.062 3.836 0.000 0.240 0.240
## wca3 ~
## wca2 -0.021 0.076 -0.270 0.787 -0.030 -0.030
## wca2 ~
## wcr1 0.066 0.021 3.142 0.002 0.236 0.236
## wca3 ~
## wcr2 0.022 0.017 1.298 0.194 0.110 0.110
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## wcr1 ~~
## wca1 0.018 0.012 1.488 0.137 0.108 0.108
## .wcr2 ~~
## .wca2 0.043 0.012 3.581 0.000 0.280 0.280
## .wcr3 ~~
## .wca3 0.013 0.009 1.468 0.142 0.129 0.129
## RIcr ~~
## RIca 0.035 0.011 3.146 0.002 0.279 0.279
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .c_ref_w1 3.258 0.052 62.355 0.000 3.258 2.715
## .c_ref_w2 3.476 0.051 68.025 0.000 3.476 2.918
## .c_ref_w3 3.376 0.051 66.310 0.000 3.376 2.895
## .c_act_w1 0.203 0.010 19.474 0.000 0.203 0.783
## .c_act_w2 0.183 0.011 17.000 0.000 0.183 0.708
## .c_act_w3 0.102 0.009 11.104 0.000 0.102 0.493
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## RIcr 0.839 0.079 10.626 0.000 1.000 1.000
## RIca 0.019 0.004 5.294 0.000 1.000 1.000
## wcr1 0.601 0.071 8.458 0.000 1.000 1.000
## wca1 0.048 0.003 14.712 0.000 1.000 1.000
## .wcr2 0.564 0.067 8.428 0.000 0.971 0.971
## .wca2 0.042 0.004 10.318 0.000 0.874 0.874
## .wcr3 0.472 0.051 9.321 0.000 0.906 0.906
## .wca3 0.023 0.006 4.008 0.000 0.989 0.989
## .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 0.029
## wca2 0.126
## wcr3 0.094
## wca3 0.011
## 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#Extracting covariance matrix
lavInspect(RICLPMmodfit1, "sampstat")## $`0`
## $`0`$cov
## c_rf_1 c_rf_2 c_rf_3 c_ct_1 c_ct_2 c_ct_3
## c_ref_w1 1.467
## c_ref_w2 0.990 1.571
## c_ref_w3 0.948 1.050 1.447
## c_act_w1 0.035 0.034 0.037 0.058
## c_act_w2 0.036 0.048 0.045 0.021 0.048
## c_act_w3 0.017 0.018 0.022 0.008 0.009 0.022
##
## $`0`$mean
## c_ref_w1 c_ref_w2 c_ref_w3 c_act_w1 c_act_w2 c_act_w3
## 2.835 3.036 2.896 0.184 0.139 0.058
##
##
## $`1`
## $`1`$cov
## c_rf_1 c_rf_2 c_rf_3 c_ct_1 c_ct_2 c_ct_3
## c_ref_w1 1.438
## c_ref_w2 0.927 1.419
## c_ref_w3 0.880 0.980 1.360
## c_act_w1 0.057 0.055 0.054 0.067
## c_act_w2 0.079 0.088 0.074 0.032 0.067
## c_act_w3 0.032 0.045 0.050 0.019 0.019 0.042
##
## $`1`$mean
## c_ref_w1 c_ref_w2 c_ref_w3 c_act_w1 c_act_w2 c_act_w3
## 3.258 3.476 3.376 0.203 0.183 0.102#Extracting residual correlation matrix
resid(RICLPMmodfit1, type = "cor")## $`0`
## $`0`$type
## [1] "cor.bollen"
##
## $`0`$cov
## c_rf_1 c_rf_2 c_rf_3 c_ct_1 c_ct_2 c_ct_3
## c_ref_w1 0.000
## c_ref_w2 0.000 0.000
## c_ref_w3 0.000 0.000 0.000
## c_act_w1 0.017 0.018 0.031 0.000
## c_act_w2 -0.001 0.000 0.001 0.001 0.000
## c_act_w3 -0.012 -0.006 -0.005 0.003 0.000 0.000
##
## $`0`$mean
## c_ref_w1 c_ref_w2 c_ref_w3 c_act_w1 c_act_w2 c_act_w3
## 0 0 0 0 0 0
##
##
## $`1`
## $`1`$type
## [1] "cor.bollen"
##
## $`1`$cov
## c_rf_1 c_rf_2 c_rf_3 c_ct_1 c_ct_2 c_ct_3
## c_ref_w1 0.000
## c_ref_w2 0.000 0.000
## c_ref_w3 -0.001 0.000 0.000
## c_act_w1 0.011 0.014 0.029 0.000
## c_act_w2 -0.003 0.000 0.003 0.001 0.000
## c_act_w3 -0.016 -0.007 -0.003 0.002 0.000 0.000
##
## $`1`$mean
## c_ref_w1 c_ref_w2 c_ref_w3 c_act_w1 c_act_w2 c_act_w3
## 0 0 0 0 0 0#RICLPM IPE Moderation
RICLPMmod2 <- '
# 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 ~ 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'
RICLPMmodfit2 <- lavaan(RICLPMmod2,
data = ANES_clean,
missing = 'FIML',
estimator = "MLR",
meanstructure = T,
group = "in_eff_ms_w2",
sampling.weights = "V240106b",
int.ov.free = T)## Warning: lavaan->lav_data_full():
## group variable 'in_eff_ms_w2' contains missing valuessummary(RICLPMmodfit2, fit.measures = T, standardized = T, rsquare = T)## lavaan 0.6-21 ended normally after 128 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 52
##
## Number of observations per group:
## 0 862
## 1 1199
## Number of missing patterns per group:
## 0 8
## 1 7
## Sampling weights variable V240106b
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 5.950 3.160
## Degrees of freedom 2 2
## P-value (Chi-square) 0.051 0.206
## Scaling correction factor 1.883
## Yuan-Bentler correction (Mplus variant)
## Test statistic for each group:
## 0 0.163 0.163
## 1 2.997 2.997
##
## Model Test Baseline Model:
##
## Test statistic 3704.261 1590.263
## Degrees of freedom 30 30
## P-value 0.000 0.000
## Scaling correction factor 2.329
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.999 0.999
## Tucker-Lewis Index (TLI) 0.984 0.989
##
## Robust Comparative Fit Index (CFI) 0.999
## Robust Tucker-Lewis Index (TLI) 0.992
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -7319.465 -7319.465
## Scaling correction factor 2.411
## for the MLR correction
## Loglikelihood unrestricted model (H1) -7316.490 -7316.490
## Scaling correction factor 2.392
## for the MLR correction
##
## Akaike (AIC) 14742.930 14742.930
## Bayesian (BIC) 15035.739 15035.739
## Sample-size adjusted Bayesian (SABIC) 14870.531 14870.531
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.044 0.024
## 90 Percent confidence interval - lower 0.000 0.000
## 90 Percent confidence interval - upper 0.086 0.058
## P-value H_0: RMSEA <= 0.050 0.520 0.883
## P-value H_0: RMSEA >= 0.080 0.086 0.002
##
## Robust RMSEA 0.031
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.098
## P-value H_0: Robust RMSEA <= 0.050 0.578
## P-value H_0: Robust RMSEA >= 0.080 0.138
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.010 0.010
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread 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 0.851 0.769
## c_ref_w2 1.000 0.851 0.748
## c_ref_w3 1.000 0.851 0.753
## RIca =~
## c_act_w1 1.000 0.098 0.429
## c_act_w2 1.000 0.098 0.517
## c_act_w3 1.000 0.098 0.615
## wcr1 =~
## c_ref_w1 1.000 0.708 0.640
## wcr2 =~
## c_ref_w2 1.000 0.755 0.664
## wcr3 =~
## c_ref_w3 1.000 0.743 0.658
## wca1 =~
## c_act_w1 1.000 0.207 0.903
## wca2 =~
## c_act_w2 1.000 0.163 0.856
## wca3 =~
## c_act_w3 1.000 0.126 0.789
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## wcr2 ~
## wcr1 0.098 0.114 0.855 0.392 0.092 0.092
## wca1 0.013 0.234 0.056 0.955 0.004 0.004
## wcr3 ~
## wcr2 0.142 0.094 1.505 0.132 0.144 0.144
## wca2 0.077 0.414 0.185 0.853 0.017 0.017
## wca2 ~
## wca1 0.174 0.054 3.252 0.001 0.221 0.221
## wca3 ~
## wca2 -0.111 0.081 -1.376 0.169 -0.144 -0.144
## wca2 ~
## wcr1 0.014 0.022 0.613 0.540 0.059 0.059
## wca3 ~
## wcr2 0.012 0.014 0.826 0.409 0.070 0.070
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## wcr1 ~~
## wca1 0.006 0.011 0.509 0.611 0.038 0.038
## .wcr2 ~~
## .wca2 0.015 0.010 1.445 0.149 0.124 0.124
## .wcr3 ~~
## .wca3 0.015 0.009 1.694 0.090 0.168 0.168
## RIcr ~~
## RIca 0.031 0.010 3.114 0.002 0.366 0.366
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .c_ref_w1 2.911 0.051 57.358 0.000 2.911 2.631
## .c_ref_w2 3.096 0.051 61.182 0.000 3.096 2.722
## .c_ref_w3 3.039 0.053 57.782 0.000 3.039 2.691
## .c_act_w1 0.151 0.010 15.530 0.000 0.151 0.662
## .c_act_w2 0.108 0.008 12.805 0.000 0.108 0.571
## .c_act_w3 0.058 0.008 7.421 0.000 0.058 0.366
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## RIcr 0.723 0.072 10.039 0.000 1.000 1.000
## RIca 0.010 0.002 3.913 0.000 1.000 1.000
## wcr1 0.501 0.058 8.589 0.000 1.000 1.000
## wca1 0.043 0.003 13.134 0.000 1.000 1.000
## .wcr2 0.565 0.067 8.420 0.000 0.992 0.992
## .wca2 0.025 0.003 7.955 0.000 0.946 0.946
## .wcr3 0.540 0.057 9.407 0.000 0.978 0.978
## .wca3 0.015 0.005 3.024 0.002 0.977 0.977
## .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 0.008
## wca2 0.054
## wcr3 0.022
## wca3 0.023
## 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 1.058 0.805
## c_ref_w2 1.000 1.058 0.799
## c_ref_w3 1.000 1.058 0.830
## RIca =~
## c_act_w1 1.000 0.121 0.459
## c_act_w2 1.000 0.121 0.450
## c_act_w3 1.000 0.121 0.625
## wcr1 =~
## c_ref_w1 1.000 0.781 0.594
## wcr2 =~
## c_ref_w2 1.000 0.796 0.601
## wcr3 =~
## c_ref_w3 1.000 0.712 0.558
## wca1 =~
## c_act_w1 1.000 0.233 0.888
## wca2 =~
## c_act_w2 1.000 0.240 0.893
## wca3 =~
## c_act_w3 1.000 0.151 0.781
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## wcr2 ~
## wcr1 0.122 0.091 1.334 0.182 0.119 0.119
## wca1 0.209 0.198 1.054 0.292 0.061 0.061
## wcr3 ~
## wcr2 0.210 0.089 2.367 0.018 0.234 0.234
## wca2 0.788 0.251 3.136 0.002 0.266 0.266
## wca2 ~
## wca1 0.288 0.055 5.248 0.000 0.280 0.280
## wca3 ~
## wca2 0.076 0.060 1.278 0.201 0.121 0.121
## wca2 ~
## wcr1 0.067 0.022 3.028 0.002 0.218 0.218
## wca3 ~
## wcr2 0.005 0.012 0.369 0.712 0.024 0.024
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## wcr1 ~~
## wca1 0.014 0.012 1.212 0.226 0.077 0.077
## .wcr2 ~~
## .wca2 0.046 0.013 3.637 0.000 0.264 0.264
## .wcr3 ~~
## .wca3 0.004 0.006 0.626 0.531 0.037 0.037
## RIcr ~~
## RIca 0.026 0.010 2.680 0.007 0.203 0.203
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .c_ref_w1 3.143 0.052 60.378 0.000 3.143 2.389
## .c_ref_w2 3.373 0.051 65.816 0.000 3.373 2.548
## .c_ref_w3 3.192 0.050 63.867 0.000 3.192 2.502
## .c_act_w1 0.232 0.010 22.937 0.000 0.232 0.883
## .c_act_w2 0.208 0.010 19.882 0.000 0.208 0.775
## .c_act_w3 0.097 0.007 13.752 0.000 0.097 0.502
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## RIcr 1.120 0.073 15.310 0.000 1.000 1.000
## RIca 0.015 0.003 4.719 0.000 1.000 1.000
## wcr1 0.611 0.058 10.560 0.000 1.000 1.000
## wca1 0.055 0.004 12.348 0.000 1.000 1.000
## .wcr2 0.621 0.064 9.644 0.000 0.981 0.981
## .wca2 0.050 0.004 12.898 0.000 0.865 0.865
## .wcr3 0.425 0.041 10.340 0.000 0.838 0.838
## .wca3 0.022 0.003 7.218 0.000 0.983 0.983
## .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 0.019
## wca2 0.135
## wcr3 0.162
## wca3 0.017
## 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#Extracting covariance matrix
lavInspect(RICLPMmodfit2, "sampstat")## $`0`
## $`0`$cov
## c_rf_1 c_rf_2 c_rf_3 c_ct_1 c_ct_2 c_ct_3
## c_ref_w1 1.226
## c_ref_w2 0.773 1.293
## c_ref_w3 0.732 0.805 1.275
## c_act_w1 0.034 0.030 0.027 0.052
## c_act_w2 0.039 0.046 0.035 0.017 0.036
## c_act_w3 0.031 0.036 0.047 0.009 0.007 0.025
##
## $`0`$mean
## c_ref_w1 c_ref_w2 c_ref_w3 c_act_w1 c_act_w2 c_act_w3
## 2.911 3.096 3.039 0.151 0.108 0.058
##
##
## $`1`
## $`1`$cov
## c_rf_1 c_rf_2 c_rf_3 c_ct_1 c_ct_2 c_ct_3
## c_ref_w1 1.725
## c_ref_w2 1.195 1.753
## c_ref_w3 1.169 1.298 1.629
## c_act_w1 0.050 0.051 0.060 0.069
## c_act_w2 0.070 0.081 0.084 0.031 0.072
## c_act_w3 0.022 0.029 0.032 0.016 0.019 0.037
##
## $`1`$mean
## c_ref_w1 c_ref_w2 c_ref_w3 c_act_w1 c_act_w2 c_act_w3
## 3.143 3.373 3.192 0.232 0.208 0.097#Extracting residual correlation matrix
resid(RICLPMmodfit2, type = "cor")## $`0`
## $`0`$type
## [1] "cor.bollen"
##
## $`0`$cov
## c_rf_1 c_rf_2 c_rf_3 c_ct_1 c_ct_2 c_ct_3
## c_ref_w1 0.000
## c_ref_w2 0.000 0.000
## c_ref_w3 0.001 0.000 0.000
## c_act_w1 -0.006 -0.007 -0.015 0.000
## c_act_w2 0.001 0.000 -0.001 -0.001 0.000
## c_act_w3 0.006 0.002 0.001 -0.002 0.000 0.000
##
## $`0`$mean
## c_ref_w1 c_ref_w2 c_ref_w3 c_act_w1 c_act_w2 c_act_w3
## 0 0 0 0 0 0
##
##
## $`1`
## $`1`$type
## [1] "cor.bollen"
##
## $`1`$cov
## c_rf_1 c_rf_2 c_rf_3 c_ct_1 c_ct_2 c_ct_3
## c_ref_w1 0.000
## c_ref_w2 0.000 0.000
## c_ref_w3 -0.001 0.000 0.000
## c_act_w1 0.029 0.032 0.054 0.000
## c_act_w2 -0.003 0.000 0.003 0.001 0.000
## c_act_w3 -0.029 -0.015 -0.011 0.004 0.000 0.000
##
## $`1`$mean
## c_ref_w1 c_ref_w2 c_ref_w3 c_act_w1 c_act_w2 c_act_w3
## 0 0 0 0 0 0