#clean the environment
rm(list = ls())
#Libraries
library(readr)
library(stringr)
library(plyr)
library(dplyr)
library(tidyverse)
library(lme4)
library(car)
library(emmeans)
library(ggthemes)
library(data.table)
library(corrplot)
library(Hmisc)
library(lavaan)
library(lavaanPlot)
library(semPlot)
library(effectsize)#main dataset
df <- read_delim("../src/expolect_S.csv",
delim = ";", escape_double = FALSE, trim_ws = TRUE)
#variable type----
df$subject <- as.factor(df$subject)
df$grade <- as.factor(df$grade)
df$sex <- as.factor(df$sex)
df$laterality <- as.factor(df$laterality)
df$DEM_VT <- as.numeric(df$DEM_VT)
df$DEM_dsVT <- as.numeric(df$DEM_dsVT)
df$DEM_HT <- as.numeric(df$DEM_HT)
df$DEM_dsHT <- as.numeric(df$DEM_dsHT)
df$DEM_dsHE <- as.numeric(df$DEM_dsHE)
#scale reading time
df$s_LPAR_P_time <- scale(df$LPAR_P_time)
df$s_LPLA_P_time <- scale(df$LPLA_P_time)
df$s_LDEM_P_time <- scale(df$LDEM_P_time)
#print preview table
knitr::kable(head(df),digits=2,format = "html")| EXCL_CRITERIA | EXCL_PAR | subject | grade | cycle | age | sex | laterality | DEM_VT | DEM_dsVT | DEM_HT | DEM_dsHT | DEM_HE | DEM_dsHE | AL_age | AL_CM | AL_dsCM | AL_CTL | AL_dsCTL | PA_SUP | PA_dsSUP | mum_diploma | LPAR_P_freq | LPAR_P_time | LPLA_P_freq | LPLA_P_time | LDEM_P_freq | LDEM_P_time | Book_nb_P | s_LPAR_P_time | s_LPLA_P_time | s_LDEM_P_time |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 1 | 1 | 2 | 76 | M | D | 58.56 | -0.65 | 73.85 | -1.12 | 25 | 1.22 | 87 | 86.05 | 0.12 | 111 | 1.24 | 12 | 0.86 | 3 | 3 | 30 | 5 | 120 | 5 | 240 | 15 | -0.4706694 | -0.2216449 | 2.02510766 |
| 0 | 0 | 2 | 1 | 2 | 78 | F | D | 69.58 | -0.13 | 124.75 | 0.55 | 22 | 0.86 | 78 | 66.67 | -2.04 | 26 | -1.26 | 8 | -0.46 | 7 | 5 | 420 | 5 | 180 | 1 | 0 | 100 | 4.3142466 | 0.1359304 | -0.72528072 |
| 0 | 0 | 3 | 1 | 2 | 77 | F | D | 147.00 | 3.56 | 228.72 | 3.96 | 11 | -0.47 | 78 | 77.78 | -0.80 | 21 | -1.41 | 11 | 0.53 | 4 | 3 | 60 | 5 | 210 | 4 | 60 | 45 | -0.1025990 | 0.3147181 | -0.03768362 |
| 0 | 0 | 4 | 1 | 2 | 83 | F | D | 76.51 | 0.20 | 179.59 | 2.34 | 35 | 2.42 | <78 | 62.96 | -2.45 | 17 | -1.53 | 5 | -1.45 | 2 | 4 | 60 | 5 | 60 | 4 | 20 | NA | -0.1025990 | -0.5792202 | -0.49608169 |
| 0 | 0 | 5 | 1 | 2 | 86 | M | D | 58.78 | 0.59 | 98.46 | 1.21 | 17 | 1.20 | 86 | 80.88 | -0.46 | 110 | 1.21 | 12 | 0.86 | 7 | 5 | 150 | 5 | 120 | 4 | 60 | 100 | 1.0016124 | -0.2216449 | -0.03768362 |
| 0 | 0 | 6 | 1 | 2 | 80 | M | D | 47.91 | -1.16 | 134.49 | 0.86 | 11 | -0.47 | 78 | 73.17 | -1.31 | 30 | -1.15 | 7 | -0.79 | 3 | 5 | 30 | 3 | 60 | 5 | 60 | 30 | -0.4706694 | -0.5792202 | -0.03768362 |
#select dataframe from our analysis
df.1 <- df %>% filter(EXCL_PAR==0)The dataset contains the following variables :
EXCL_CRIT (binary): specify participants who must be excluded based on the following specific exclusion criteria : […]. Based on this criteria 0 participants must be excluded.
EXCL_PAR (binary): […]
subject (factor: 001-359): […]
grade (factor: 1-5): […]
cycle (factor: 2-3): […]
age (numeric): […]
sex (factor : M/F): […]
laterality (factor: D/G): […]
DEM_VT (numeric): […]
DEM_dsVT (numeric): […]
DEM_HT (numeric): […]
DEM_dsHT (numeric): […]
DEM_HE (numeric): […]
DEM_dsHE (numeric): […]
AL_age (numeric): […]
AL_CM (numeric): […]
AL_dsCM (numeric): […]
AL_CTL (numeric): […]
AL_dsCTL (numeric): […]
PA_SUP (numeric): […]
PA_dsSUP (numeric): […]
mum_diploma (factor: 1-8): […]
LPAR_P_freq (discrete: 1-5): […]
LPAR_P_time (numeric): […]
LPLA_P_freq (discrete: 1-5): […]
LPLA_P_time (numeric): […]
LDEM_P_freq (discrete: 1-5): […]
LDEM_P_time (numeric): […]
Book_nb_P (numeric): […]
[…]
sum.by.grade <- df.1 %>%
group_by(grade) %>%
summarise(nb = n(),
m.VT = mean(DEM_VT), m.HT = mean(DEM_HT), mean.HE = mean(DEM_HE),
m.ctl = mean(AL_CTL), m.cm = mean(AL_CM),
m.LPAR = mean(LPAR_P_time), m.LPLA= mean(LPLA_P_time),
m.LDEM = mean(LDEM_P_time))
knitr::kable(sum.by.grade,digits=2,format = "html")| grade | nb | m.VT | m.HT | mean.HE | m.ctl | m.cm | m.LPAR | m.LPLA | m.LDEM |
|---|---|---|---|---|---|---|---|---|---|
| 1 | 34 | 64.89 | 131.93 | 20.12 | 80.68 | 82.31 | 103.24 | 115.44 | 74.32 |
| 2 | 38 | 52.67 | 85.97 | 9.37 | 167.30 | 89.01 | 102.11 | 129.87 | 44.95 |
| 3 | 30 | 46.41 | 66.13 | 6.13 | 224.66 | 95.04 | 54.67 | 147.63 | 56.17 |
| 4 | 27 | 42.41 | 58.92 | 3.67 | 283.11 | 95.84 | 52.59 | 188.33 | 62.78 |
| 5 | 70 | 41.14 | 52.15 | 2.71 | 299.59 | 95.60 | 49.21 | 179.79 | 66.64 |
sum.by.cycle <- df.1 %>%
group_by(cycle) %>%
summarise(nb = n(),
m.VT = mean(DEM_VT), m.HT = mean(DEM_HT), mean.HE = mean(DEM_HE),
m.ctl = mean(AL_CTL), m.cm = mean(AL_CM),
m.LPAR = mean(LPAR_P_time), m.LPLA= mean(LPLA_P_time),
m.LDEM = mean(LDEM_P_time))
knitr::kable(sum.by.cycle,digits=2,format = "html")| cycle | nb | m.VT | m.HT | mean.HE | m.ctl | m.cm | m.LPAR | m.LPLA | m.LDEM |
|---|---|---|---|---|---|---|---|---|---|
| 2 | 102 | 54.9 | 95.46 | 12.00 | 155.3 | 88.55 | 88.53 | 130.28 | 58.04 |
| 3 | 97 | 41.5 | 54.03 | 2.98 | 295.0 | 95.67 | 50.15 | 182.16 | 65.57 |
#sub-dataframe
df.corW <- df.1 %>% select(DEM_dsHT, DEM_dsVT, AL_dsCM, AL_dsCTL, PA_dsSUP, mum_diploma,
LPAR_P_time, LPLA_P_time, LDEM_P_time)
#correlation matrices
mat.parW <- rcorr(as.matrix(df.corW))$r
pmat.parW <- rcorr(as.matrix(df.corW))$p
#plot
corrplot(mat.parW, method="color", type="lower",
p.mat=pmat.parW,sig.level = 0.01, insig = "blank",
addCoef.col ='black', number.cex = 0.5)
#sub-dataframe
df.corC2 <- df.1 %>% filter(cycle==2) %>%
select(DEM_dsHT, DEM_dsVT, AL_dsCM, AL_dsCTL, PA_dsSUP, mum_diploma,
LPAR_P_time, LPLA_P_time, LDEM_P_time)
df.corC3 <- df.1 %>% filter(cycle==3) %>%
select(DEM_dsHT, DEM_dsVT, AL_dsCM, AL_dsCTL, PA_dsSUP, mum_diploma,
LPAR_P_time, LPLA_P_time, LDEM_P_time)
#correlation matrices
mat.parC2 <- rcorr(as.matrix(df.corC2))$r
pmat.parC2 <- rcorr(as.matrix(df.corC2))$p
mat.parC3 <- rcorr(as.matrix(df.corC3))$r
pmat.parC3 <- rcorr(as.matrix(df.corC3))$p
#plot
corrplot(mat.parC2, method="color", type="lower",
p.mat=pmat.parC2,sig.level = 0.01, insig = "blank",
addCoef.col ='black', number.cex = 0.5)
corrplot(mat.parC3, method="color", type="lower",
p.mat=pmat.parC2,sig.level = 0.01, insig = "blank",
addCoef.col ='black', number.cex = 0.5)
[…] Explain the procedure […]
m.DEM.UD <- ' # direct effect
AL_dsCTL ~ c * s_LPLA_P_time
AL_dsCTL ~ d * PA_dsSUP
# mediator
DEM_dsHT ~ a * s_LPLA_P_time
AL_dsCTL ~ b * DEM_dsHT
# indirect effect (a*b)
ab := a*b
# total effect
total := d + c + (a*b)
'
m.DEM.UD.fit <- sem(m.DEM.UD, data=df.1, se = "bootstrap", bootstrap = 1000)
summary(m.DEM.UD.fit, fit.measures=TRUE, rsquare=TRUE)lavaan 0.6.16 ended normally after 2 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 6
Number of observations 199
Model Test User Model:
Test statistic 1.239
Degrees of freedom 1
P-value (Chi-square) 0.266
Model Test Baseline Model:
Test statistic 127.985
Degrees of freedom 5
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.998
Tucker-Lewis Index (TLI) 0.990
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -636.527
Loglikelihood unrestricted model (H1) -635.907
Akaike (AIC) 1285.053
Bayesian (BIC) 1304.813
Sample-size adjusted Bayesian (SABIC) 1285.805
Root Mean Square Error of Approximation:
RMSEA 0.035
90 Percent confidence interval - lower 0.000
90 Percent confidence interval - upper 0.195
P-value H_0: RMSEA <= 0.050 0.376
P-value H_0: RMSEA >= 0.080 0.481
Standardized Root Mean Square Residual:
SRMR 0.027
Parameter Estimates:
Standard errors Bootstrap
Number of requested bootstrap draws 1000
Number of successful bootstrap draws 1000
Regressions:
Estimate Std.Err z-value P(>|z|)
AL_dsCTL ~
s_LPLA_P_t (c) 0.333 0.082 4.077 0.000
PA_dsSUP (d) 0.220 0.072 3.030 0.002
DEM_dsHT ~
s_LPLA_P_t (a) -0.360 0.096 -3.751 0.000
AL_dsCTL ~
DEM_dsHT (b) -0.400 0.030 -13.237 0.000
Variances:
Estimate Std.Err z-value P(>|z|)
.AL_dsCTL 0.880 0.084 10.442 0.000
.DEM_dsHT 2.339 0.309 7.572 0.000
R-Square:
Estimate
AL_dsCTL 0.435
DEM_dsHT 0.052
Defined Parameters:
Estimate Std.Err z-value P(>|z|)
ab 0.144 0.041 3.530 0.000
total 0.696 0.120 5.817 0.000lavaanPlot(model = m.DEM.UD.fit, node_options = list(shape = "box", fontname = "Helvetica"), edge_options = list(color = "grey"), coefs = TRUE, sig = .05, stars="regress")interpret(m.DEM.UD.fit) Name Value Threshold Interpretation
1 GFI 0.99401237 0.95 satisfactory
2 AGFI 0.94012374 0.90 satisfactory
3 NFI 0.99031921 0.90 satisfactory
4 NNFI 0.99028356 0.90 satisfactory
5 CFI 0.99805671 0.90 satisfactory
6 RMSEA 0.03465517 0.05 satisfactory
7 SRMR 0.02728922 0.08 satisfactory
8 RFI 0.95159606 0.90 satisfactory
9 PNFI 0.19806384 0.50 poor
10 IFI 0.99811793 0.90 satisfactorym.DEM.BD <- ' # direct effect
AL_dsCTL ~ c * s_LPLA_P_time
s_LPLA_P_time ~ c*AL_dsCTL
AL_dsCTL ~ d * PA_dsSUP
# mediator
DEM_dsHT ~ a * s_LPLA_P_time
AL_dsCTL ~ b * DEM_dsHT
# indirect effect (a*b)
ab := a*b
# total effect
total := d + c + (a*b)
'
m.DEM.BD.fit <- sem(m.DEM.BD, data=df.1, se = "bootstrap", bootstrap = 1000)
summary(m.DEM.BD.fit, fit.measures=TRUE, rsquare=TRUE)lavaan 0.6.16 ended normally after 12 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 8
Number of equality constraints 1
Number of observations 199
Model Test User Model:
Test statistic 5.166
Degrees of freedom 2
P-value (Chi-square) 0.076
Model Test Baseline Model:
Test statistic 134.305
Degrees of freedom 6
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.975
Tucker-Lewis Index (TLI) 0.926
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -916.264
Loglikelihood unrestricted model (H1) -913.681
Akaike (AIC) 1846.527
Bayesian (BIC) 1869.581
Sample-size adjusted Bayesian (SABIC) 1847.404
Root Mean Square Error of Approximation:
RMSEA 0.089
90 Percent confidence interval - lower 0.000
90 Percent confidence interval - upper 0.188
P-value H_0: RMSEA <= 0.050 0.178
P-value H_0: RMSEA >= 0.080 0.656
Standardized Root Mean Square Residual:
SRMR 0.061
Parameter Estimates:
Standard errors Bootstrap
Number of requested bootstrap draws 1000
Number of successful bootstrap draws 1000
Regressions:
Estimate Std.Err z-value P(>|z|)
AL_dsCTL ~
s_LPLA_P_t (c) 0.173 0.034 5.094 0.000
s_LPLA_P_time ~
AL_dsCTL (c) 0.173 0.034 5.094 0.000
AL_dsCTL ~
PA_dsSUP (d) 0.247 0.073 3.373 0.001
DEM_dsHT ~
s_LPLA_P_t (a) -0.183 0.100 -1.828 0.068
AL_dsCTL ~
DEM_dsHT (b) -0.409 0.032 -12.685 0.000
Variances:
Estimate Std.Err z-value P(>|z|)
.AL_dsCTL 0.903 0.088 10.284 0.000
.s_LPLA_P_time 0.858 0.165 5.193 0.000
.DEM_dsHT 2.370 0.320 7.396 0.000
R-Square:
Estimate
AL_dsCTL 0.412
s_LPLA_P_time 0.125
DEM_dsHT 0.039
Defined Parameters:
Estimate Std.Err z-value P(>|z|)
ab 0.075 0.041 1.815 0.070
total 0.495 0.090 5.480 0.000lavaanPlot(model = m.DEM.BD.fit, node_options = list(shape = "box", fontname = "Helvetica"), edge_options = list(color = "grey"), coefs = TRUE, sig = .05, stars="regress")interpret(m.DEM.BD.fit) Name Value Threshold Interpretation
1 GFI 0.98329406 0.95 satisfactory
2 AGFI 0.91647031 0.90 satisfactory
3 NFI 0.96153324 0.90 satisfactory
4 NNFI 0.92596662 0.90 satisfactory
5 CFI 0.97532221 0.90 satisfactory
6 RMSEA 0.08919372 0.05 poor
7 SRMR 0.06093312 0.08 satisfactory
8 RFI 0.88459973 0.90 poor
9 PNFI 0.32051108 0.50 poor
10 IFI 0.97606829 0.90 satisfactorym.PA.UD <- ' # direct effect
AL_dsCTL ~ c * s_LPLA_P_time
AL_dsCTL ~ d * DEM_dsHT
# mediator
PA_dsSUP ~ a * s_LPLA_P_time
AL_dsCTL ~ b * PA_dsSUP
# indirect effect (a*b)
ab := a*b
# total effect
total := d + c + (a*b)
'
m.PA.UD.fit <- sem(m.PA.UD, data=df.1, se = "bootstrap", bootstrap = 1000)
summary(m.PA.UD.fit, fit.measures=TRUE, rsquare=TRUE)lavaan 0.6.16 ended normally after 2 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 6
Number of observations 199
Model Test User Model:
Test statistic 1.239
Degrees of freedom 1
P-value (Chi-square) 0.266
Model Test Baseline Model:
Test statistic 123.718
Degrees of freedom 5
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.998
Tucker-Lewis Index (TLI) 0.990
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -540.620
Loglikelihood unrestricted model (H1) -540.001
Akaike (AIC) 1093.240
Bayesian (BIC) 1113.000
Sample-size adjusted Bayesian (SABIC) 1093.992
Root Mean Square Error of Approximation:
RMSEA 0.035
90 Percent confidence interval - lower 0.000
90 Percent confidence interval - upper 0.195
P-value H_0: RMSEA <= 0.050 0.376
P-value H_0: RMSEA >= 0.080 0.481
Standardized Root Mean Square Residual:
SRMR 0.027
Parameter Estimates:
Standard errors Bootstrap
Number of requested bootstrap draws 1000
Number of successful bootstrap draws 1000
Regressions:
Estimate Std.Err z-value P(>|z|)
AL_dsCTL ~
s_LPLA_P_t (c) 0.333 0.083 4.021 0.000
DEM_dsHT (d) -0.400 0.032 -12.671 0.000
PA_dsSUP ~
s_LPLA_P_t (a) 0.171 0.057 3.012 0.003
AL_dsCTL ~
PA_dsSUP (b) 0.220 0.072 3.036 0.002
Variances:
Estimate Std.Err z-value P(>|z|)
.AL_dsCTL 0.880 0.086 10.197 0.000
.PA_dsSUP 0.892 0.069 12.932 0.000
R-Square:
Estimate
AL_dsCTL 0.435
PA_dsSUP 0.031
Defined Parameters:
Estimate Std.Err z-value P(>|z|)
ab 0.038 0.019 1.937 0.053
total -0.029 0.095 -0.310 0.757lavaanPlot(model = m.PA.UD.fit, node_options = list(shape = "box", fontname = "Helvetica"), edge_options = list(color = "grey"), coefs = TRUE, sig = .05, stars="regress")interpret(m.PA.UD.fit) Name Value Threshold Interpretation
1 GFI 0.99501765 0.95 satisfactory
2 AGFI 0.95017648 0.90 satisfactory
3 NFI 0.98998535 0.90 satisfactory
4 NNFI 0.98993436 0.90 satisfactory
5 CFI 0.99798687 0.90 satisfactory
6 RMSEA 0.03465517 0.05 satisfactory
7 SRMR 0.02728922 0.08 satisfactory
8 RFI 0.94992676 0.90 satisfactory
9 PNFI 0.19799707 0.50 poor
10 IFI 0.99805249 0.90 satisfactorym.PA.BD <- ' # direct effect
AL_dsCTL ~ c * s_LPLA_P_time
s_LPLA_P_time ~ c*AL_dsCTL
AL_dsCTL ~ d * DEM_dsHT
# mediator
PA_dsSUP ~ a * s_LPLA_P_time
AL_dsCTL ~ b * PA_dsSUP
# indirect effect (a*b)
ab := a*b
# total effect
total := d + c + (a*b)
'
m.PA.BD.fit <- sem(m.PA.BD, data=df.1, se = "bootstrap", bootstrap = 1000)
summary(m.PA.BD.fit, fit.measures=TRUE, rsquare=TRUE)lavaan 0.6.16 ended normally after 10 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 8
Number of equality constraints 1
Number of observations 199
Model Test User Model:
Test statistic 3.818
Degrees of freedom 2
P-value (Chi-square) 0.148
Model Test Baseline Model:
Test statistic 134.305
Degrees of freedom 6
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.986
Tucker-Lewis Index (TLI) 0.957
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -817.550
Loglikelihood unrestricted model (H1) -815.641
Akaike (AIC) 1649.099
Bayesian (BIC) 1672.152
Sample-size adjusted Bayesian (SABIC) 1649.976
Root Mean Square Error of Approximation:
RMSEA 0.068
90 Percent confidence interval - lower 0.000
90 Percent confidence interval - upper 0.171
P-value H_0: RMSEA <= 0.050 0.286
P-value H_0: RMSEA >= 0.080 0.523
Standardized Root Mean Square Residual:
SRMR 0.050
Parameter Estimates:
Standard errors Bootstrap
Number of requested bootstrap draws 1000
Number of successful bootstrap draws 1000
Regressions:
Estimate Std.Err z-value P(>|z|)
AL_dsCTL ~
s_LPLA_P_t (c) 0.183 0.033 5.479 0.000
s_LPLA_P_time ~
AL_dsCTL (c) 0.183 0.033 5.479 0.000
AL_dsCTL ~
DEM_dsHT (d) -0.421 0.031 -13.811 0.000
PA_dsSUP ~
s_LPLA_P_t (a) 0.133 0.063 2.108 0.035
AL_dsCTL ~
PA_dsSUP (b) 0.218 0.073 2.979 0.003
Variances:
Estimate Std.Err z-value P(>|z|)
.AL_dsCTL 0.901 0.087 10.370 0.000
.s_LPLA_P_time 0.853 0.166 5.135 0.000
.PA_dsSUP 0.893 0.070 12.716 0.000
R-Square:
Estimate
AL_dsCTL 0.414
s_LPLA_P_time 0.124
PA_dsSUP 0.030
Defined Parameters:
Estimate Std.Err z-value P(>|z|)
ab 0.029 0.017 1.675 0.094
total -0.209 0.045 -4.673 0.000lavaanPlot(model = m.PA.BD.fit, node_options = list(shape = "box", fontname = "Helvetica"), edge_options = list(color = "grey"), coefs = TRUE, sig = .05, stars="regress")interpret(m.PA.BD.fit) Name Value Threshold Interpretation
1 GFI 0.98851019 0.95 satisfactory
2 AGFI 0.94255096 0.90 satisfactory
3 NFI 0.97157390 0.90 satisfactory
4 NNFI 0.95749718 0.90 satisfactory
5 CFI 0.98583239 0.90 satisfactory
6 RMSEA 0.06758173 0.05 poor
7 SRMR 0.05015564 0.08 satisfactory
8 RFI 0.91472169 0.90 satisfactory
9 PNFI 0.32385797 0.50 poor
10 IFI 0.98626072 0.90 satisfactorym.DEM.PA.UD <- '# direct effect
AL_dsCTL ~ c * s_LPLA_P_time
# mediator
DEM_dsHT ~ a1 * s_LPLA_P_time
AL_dsCTL ~ b1 * DEM_dsHT
PA_dsSUP ~ a2 * s_LPLA_P_time
AL_dsCTL ~ b2 * PA_dsSUP
# indirect effect (a*b)
a1b1 := a1*b1
a2b2 := a2*b2
# total effect
total := c + (a1*b1) + (a2*b2)
'
m.DEM.PA.UD.fit <- sem(m.DEM.PA.UD, data=df.1, se = "bootstrap", bootstrap = 1000)
summary(m.DEM.PA.UD.fit, fit.measures=TRUE, rsquare=TRUE)lavaan 0.6.16 ended normally after 2 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 8
Number of observations 199
Model Test User Model:
Test statistic 1.239
Degrees of freedom 1
P-value (Chi-square) 0.266
Model Test Baseline Model:
Test statistic 134.305
Degrees of freedom 6
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.998
Tucker-Lewis Index (TLI) 0.989
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -907.534
Loglikelihood unrestricted model (H1) -906.915
Akaike (AIC) 1831.069
Bayesian (BIC) 1857.415
Sample-size adjusted Bayesian (SABIC) 1832.071
Root Mean Square Error of Approximation:
RMSEA 0.035
90 Percent confidence interval - lower 0.000
90 Percent confidence interval - upper 0.195
P-value H_0: RMSEA <= 0.050 0.376
P-value H_0: RMSEA >= 0.080 0.481
Standardized Root Mean Square Residual:
SRMR 0.027
Parameter Estimates:
Standard errors Bootstrap
Number of requested bootstrap draws 1000
Number of successful bootstrap draws 1000
Regressions:
Estimate Std.Err z-value P(>|z|)
AL_dsCTL ~
s_LPLA_P_ (c) 0.333 0.079 4.213 0.000
DEM_dsHT ~
s_LPLA_P_ (a1) -0.360 0.092 -3.924 0.000
AL_dsCTL ~
DEM_dsHT (b1) -0.400 0.031 -12.823 0.000
PA_dsSUP ~
s_LPLA_P_ (a2) 0.171 0.059 2.898 0.004
AL_dsCTL ~
PA_dsSUP (b2) 0.220 0.072 3.058 0.002
Variances:
Estimate Std.Err z-value P(>|z|)
.AL_dsCTL 0.880 0.083 10.608 0.000
.DEM_dsHT 2.339 0.304 7.685 0.000
.PA_dsSUP 0.892 0.068 13.042 0.000
R-Square:
Estimate
AL_dsCTL 0.435
DEM_dsHT 0.052
PA_dsSUP 0.031
Defined Parameters:
Estimate Std.Err z-value P(>|z|)
a1b1 0.144 0.039 3.664 0.000
a2b2 0.038 0.020 1.896 0.058
total 0.514 0.092 5.569 0.000lavaanPlot(model = m.DEM.PA.UD.fit, node_options = list(shape = "box", fontname = "Helvetica"), edge_options = list(color = "grey"), coefs = TRUE, sig = .05, stars="regress")interpret(m.DEM.PA.UD.fit) Name Value Threshold Interpretation
1 GFI 0.99592155 0.95 satisfactory
2 AGFI 0.95921553 0.90 satisfactory
3 NFI 0.99077480 0.90 satisfactory
4 NNFI 0.98882377 0.90 satisfactory
5 CFI 0.99813730 0.90 satisfactory
6 RMSEA 0.03465517 0.05 satisfactory
7 SRMR 0.02728922 0.08 satisfactory
8 RFI 0.94464879 0.90 satisfactory
9 PNFI 0.16512913 0.50 poor
10 IFI 0.99820716 0.90 satisfactorym.DEM.PA.BD <- '# direct effect
AL_dsCTL ~ c * s_LPLA_P_time
s_LPLA_P_time ~ c*AL_dsCTL
# mediator
DEM_dsHT ~ a1 * s_LPLA_P_time
AL_dsCTL ~ b1 * DEM_dsHT
PA_dsSUP ~ a2 * s_LPLA_P_time
AL_dsCTL ~ b2 * PA_dsSUP
# indirect effect (a*b)
a1b1 := a1*b1
a2b2 := a2*b2
# total effect
total := c + (a1*b1) + (a2*b2)
'
m.DEM.PA.BD.fit <- sem(m.DEM.PA.BD, data=df.1, se = "bootstrap", bootstrap = 1000)
summary(m.DEM.PA.BD.fit, fit.measures=TRUE, rsquare=TRUE)lavaan 0.6.16 ended normally after 13 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 10
Number of equality constraints 1
Number of observations 199
Model Test User Model:
Test statistic 1.374
Degrees of freedom 1
P-value (Chi-square) 0.241
Model Test Baseline Model:
Test statistic 134.305
Degrees of freedom 6
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.997
Tucker-Lewis Index (TLI) 0.983
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -1188.535
Loglikelihood unrestricted model (H1) -1187.849
Akaike (AIC) 2395.071
Bayesian (BIC) 2424.711
Sample-size adjusted Bayesian (SABIC) 2396.198
Root Mean Square Error of Approximation:
RMSEA 0.043
90 Percent confidence interval - lower 0.000
90 Percent confidence interval - upper 0.200
P-value H_0: RMSEA <= 0.050 0.351
P-value H_0: RMSEA >= 0.080 0.507
Standardized Root Mean Square Residual:
SRMR 0.029
Parameter Estimates:
Standard errors Bootstrap
Number of requested bootstrap draws 1000
Number of successful bootstrap draws 1000
Regressions:
Estimate Std.Err z-value P(>|z|)
AL_dsCTL ~
s_LPLA_P_ (c) 0.166 0.033 5.048 0.000
s_LPLA_P_time ~
AL_dsCTL (c) 0.166 0.033 5.048 0.000
DEM_dsHT ~
s_LPLA_P_ (a1) -0.188 0.098 -1.918 0.055
AL_dsCTL ~
DEM_dsHT (b1) -0.411 0.033 -12.426 0.000
PA_dsSUP ~
s_LPLA_P_ (a2) 0.135 0.061 2.211 0.027
AL_dsCTL ~
PA_dsSUP (b2) 0.225 0.073 3.058 0.002
Variances:
Estimate Std.Err z-value P(>|z|)
.AL_dsCTL 0.906 0.086 10.529 0.000
.s_LPLA_P_time 0.861 0.166 5.188 0.000
.DEM_dsHT 2.368 0.320 7.398 0.000
.PA_dsSUP 0.893 0.068 13.131 0.000
R-Square:
Estimate
AL_dsCTL 0.417
s_LPLA_P_time 0.127
DEM_dsHT 0.040
PA_dsSUP 0.030
Defined Parameters:
Estimate Std.Err z-value P(>|z|)
a1b1 0.077 0.041 1.874 0.061
a2b2 0.030 0.017 1.758 0.079
total 0.274 0.050 5.446 0.000lavaanPlot(model = m.DEM.PA.BD.fit, node_options = list(shape = "box", fontname = "Helvetica"), edge_options = list(color = "grey"), coefs = TRUE, sig = .05, stars="regress")interpret(m.DEM.PA.BD.fit) Name Value Threshold Interpretation
1 GFI 0.99656963 0.95 satisfactory
2 AGFI 0.96569632 0.90 satisfactory
3 NFI 0.98977132 0.90 satisfactory
4 NNFI 0.98252137 0.90 satisfactory
5 CFI 0.99708689 0.90 satisfactory
6 RMSEA 0.04333853 0.05 satisfactory
7 SRMR 0.02896112 0.08 satisfactory
8 RFI 0.93862794 0.90 satisfactory
9 PNFI 0.16496189 0.50 poor
10 IFI 0.99719616 0.90 satisfactoryanova(m.DEM.UD.fit, m.DEM.BD.fit,m.PA.UD.fit,m.PA.BD.fit, m.DEM.PA.UD.fit, m.DEM.PA.BD.fit)Warning in lavTestLRT(object = object, ..., model.names = NAMES): lavaan WARNING:
Some restricted models fit better than less restricted models;
either these models are not nested, or the less restricted model
failed to reach a global optimum. Smallest difference =
-1.34851480908035Warning in lavTestLRT(object = object, ..., model.names = NAMES): lavaan
WARNING: some models have the same degrees of freedom
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff Pr(>Chisq)
m.DEM.UD.fit 1 1285.0 1304.8 1.2390
m.PA.UD.fit 1 1093.2 1113.0 1.2390 0.0000 0.00000 0
m.DEM.PA.UD.fit 1 1831.1 1857.4 1.2390 0.0000 0.00000 0
m.DEM.PA.BD.fit 1 2395.1 2424.7 1.3738 0.1348 0.00000 0
m.DEM.BD.fit 2 1846.5 1869.6 5.1663 3.7925 0.11846 1 0.05148 .
m.PA.BD.fit 2 1649.1 1672.2 3.8178 -1.3485 0.00000 0
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1m.DEM.UD.cm <- ' # direct effect
AL_dsCM ~ c * s_LPLA_P_time
AL_dsCM ~ d * PA_dsSUP
# mediator
DEM_dsHT ~ a * s_LPLA_P_time
AL_dsCM ~ b * DEM_dsHT
# indirect effect (a*b)
ab := a*b
# total effect
total := d + c + (a*b)
'
m.DEM.UD.cm.fit <- sem(m.DEM.UD.cm, data=df.1, se = "bootstrap", bootstrap = 1000)
summary(m.DEM.UD.cm.fit, fit.measures=TRUE, rsquare=TRUE)lavaan 0.6.16 ended normally after 1 iteration
Estimator ML
Optimization method NLMINB
Number of model parameters 6
Number of observations 199
Model Test User Model:
Test statistic 1.239
Degrees of freedom 1
P-value (Chi-square) 0.266
Model Test Baseline Model:
Test statistic 83.973
Degrees of freedom 5
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.997
Tucker-Lewis Index (TLI) 0.985
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -647.161
Loglikelihood unrestricted model (H1) -646.542
Akaike (AIC) 1306.322
Bayesian (BIC) 1326.082
Sample-size adjusted Bayesian (SABIC) 1307.074
Root Mean Square Error of Approximation:
RMSEA 0.035
90 Percent confidence interval - lower 0.000
90 Percent confidence interval - upper 0.195
P-value H_0: RMSEA <= 0.050 0.376
P-value H_0: RMSEA >= 0.080 0.481
Standardized Root Mean Square Residual:
SRMR 0.027
Parameter Estimates:
Standard errors Bootstrap
Number of requested bootstrap draws 1000
Number of successful bootstrap draws 1000
Regressions:
Estimate Std.Err z-value P(>|z|)
AL_dsCM ~
s_LPLA_P_t (c) 0.100 0.051 1.968 0.049
PA_dsSUP (d) 0.142 0.074 1.907 0.057
DEM_dsHT ~
s_LPLA_P_t (a) -0.360 0.096 -3.755 0.000
AL_dsCM ~
DEM_dsHT (b) -0.376 0.094 -3.999 0.000
Variances:
Estimate Std.Err z-value P(>|z|)
.AL_dsCM 0.979 0.218 4.498 0.000
.DEM_dsHT 2.339 0.300 7.808 0.000
R-Square:
Estimate
AL_dsCM 0.298
DEM_dsHT 0.052
Defined Parameters:
Estimate Std.Err z-value P(>|z|)
ab 0.136 0.059 2.313 0.021
total 0.377 0.080 4.704 0.000lavaanPlot(model = m.DEM.UD.cm.fit, node_options = list(shape = "box", fontname = "Helvetica"), edge_options = list(color = "grey"), coefs = TRUE, sig = .05, stars="regress")interpret(m.DEM.UD.cm.fit) Name Value Threshold Interpretation
1 GFI 0.99384930 0.95 satisfactory
2 AGFI 0.93849297 0.90 satisfactory
3 NFI 0.98524532 0.90 satisfactory
4 NNFI 0.98486855 0.90 satisfactory
5 CFI 0.99697371 0.90 satisfactory
6 RMSEA 0.03465517 0.05 satisfactory
7 SRMR 0.02695806 0.08 satisfactory
8 RFI 0.92622658 0.90 satisfactory
9 PNFI 0.19704906 0.50 poor
10 IFI 0.99711960 0.90 satisfactorym.DEM.BD.cm <- ' # direct effect
AL_dsCM ~ c * s_LPLA_P_time
s_LPLA_P_time ~ c*AL_dsCM
AL_dsCM ~ d * PA_dsSUP
# mediator
DEM_dsHT ~ a * s_LPLA_P_time
AL_dsCM ~ b * DEM_dsHT
# indirect effect (a*b)
ab := a*b
# total effect
total := d + c + (a*b)
'
m.DEM.BD.cm.fit <- sem(m.DEM.BD.cm, data=df.1, se = "bootstrap", bootstrap = 1000)
summary(m.DEM.BD.cm.fit, fit.measures=TRUE, rsquare=TRUE)lavaan 0.6.16 ended normally after 10 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 8
Number of equality constraints 1
Number of observations 199
Model Test User Model:
Test statistic 7.053
Degrees of freedom 2
P-value (Chi-square) 0.029
Model Test Baseline Model:
Test statistic 90.294
Degrees of freedom 6
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.940
Tucker-Lewis Index (TLI) 0.820
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -927.841
Loglikelihood unrestricted model (H1) -924.315
Akaike (AIC) 1869.683
Bayesian (BIC) 1892.736
Sample-size adjusted Bayesian (SABIC) 1870.559
Root Mean Square Error of Approximation:
RMSEA 0.113
90 Percent confidence interval - lower 0.031
90 Percent confidence interval - upper 0.208
P-value H_0: RMSEA <= 0.050 0.090
P-value H_0: RMSEA >= 0.080 0.791
Standardized Root Mean Square Residual:
SRMR 0.068
Parameter Estimates:
Standard errors Bootstrap
Number of requested bootstrap draws 1000
Number of successful bootstrap draws 1000
Regressions:
Estimate Std.Err z-value P(>|z|)
AL_dsCM ~
s_LPLA_P_t (c) 0.055 0.023 2.348 0.019
s_LPLA_P_time ~
AL_dsCM (c) 0.055 0.023 2.348 0.019
AL_dsCM ~
PA_dsSUP (d) 0.150 0.076 1.986 0.047
DEM_dsHT ~
s_LPLA_P_t (a) -0.311 0.106 -2.941 0.003
AL_dsCM ~
DEM_dsHT (b) -0.375 0.092 -4.081 0.000
Variances:
Estimate Std.Err z-value P(>|z|)
.AL_dsCM 0.981 0.212 4.630 0.000
.s_LPLA_P_time 0.962 0.179 5.377 0.000
.DEM_dsHT 2.341 0.301 7.774 0.000
R-Square:
Estimate
AL_dsCM 0.292
s_LPLA_P_time 0.023
DEM_dsHT 0.051
Defined Parameters:
Estimate Std.Err z-value P(>|z|)
ab 0.117 0.054 2.148 0.032
total 0.322 0.075 4.286 0.000lavaanPlot(model = m.DEM.BD.cm.fit, node_options = list(shape = "box", fontname = "Helvetica"), edge_options = list(color = "grey"), coefs = TRUE, sig = .05, stars="regress")interpret(m.DEM.BD.cm.fit) Name Value Threshold Interpretation
1 GFI 0.97732932 0.95 satisfactory
2 AGFI 0.88664662 0.90 poor
3 NFI 0.92189056 0.90 satisfactory
4 NNFI 0.82017206 0.90 poor
5 CFI 0.94005735 0.90 satisfactory
6 RMSEA 0.11267396 0.05 poor
7 SRMR 0.06796922 0.08 satisfactory
8 RFI 0.76567168 0.90 poor
9 PNFI 0.30729685 0.50 poor
10 IFI 0.94277296 0.90 satisfactorym.PA.UD.cm <- ' # direct effect
AL_dsCM ~ c * s_LPLA_P_time
AL_dsCM ~ d * DEM_dsHT
# mediator
PA_dsSUP ~ a * s_LPLA_P_time
AL_dsCM ~ b * PA_dsSUP
# indirect effect (a*b)
ab := a*b
# total effect
total := d + c + (a*b)
'
m.PA.UD.cm.fit <- sem(m.PA.UD.cm, data=df.1, se = "bootstrap", bootstrap = 1000)
summary(m.PA.UD.cm.fit, fit.measures=TRUE, rsquare=TRUE)lavaan 0.6.16 ended normally after 1 iteration
Estimator ML
Optimization method NLMINB
Number of model parameters 6
Number of observations 199
Model Test User Model:
Test statistic 1.239
Degrees of freedom 1
P-value (Chi-square) 0.266
Model Test Baseline Model:
Test statistic 79.706
Degrees of freedom 5
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.997
Tucker-Lewis Index (TLI) 0.984
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -551.254
Loglikelihood unrestricted model (H1) -550.635
Akaike (AIC) 1114.509
Bayesian (BIC) 1134.269
Sample-size adjusted Bayesian (SABIC) 1115.260
Root Mean Square Error of Approximation:
RMSEA 0.035
90 Percent confidence interval - lower 0.000
90 Percent confidence interval - upper 0.195
P-value H_0: RMSEA <= 0.050 0.376
P-value H_0: RMSEA >= 0.080 0.481
Standardized Root Mean Square Residual:
SRMR 0.027
Parameter Estimates:
Standard errors Bootstrap
Number of requested bootstrap draws 1000
Number of successful bootstrap draws 1000
Regressions:
Estimate Std.Err z-value P(>|z|)
AL_dsCM ~
s_LPLA_P_t (c) 0.100 0.052 1.917 0.055
DEM_dsHT (d) -0.376 0.098 -3.837 0.000
PA_dsSUP ~
s_LPLA_P_t (a) 0.171 0.060 2.828 0.005
AL_dsCM ~
PA_dsSUP (b) 0.142 0.077 1.846 0.065
Variances:
Estimate Std.Err z-value P(>|z|)
.AL_dsCM 0.979 0.222 4.407 0.000
.PA_dsSUP 0.892 0.068 13.110 0.000
R-Square:
Estimate
AL_dsCM 0.298
PA_dsSUP 0.031
Defined Parameters:
Estimate Std.Err z-value P(>|z|)
ab 0.024 0.017 1.411 0.158
total -0.253 0.115 -2.192 0.028lavaanPlot(model = m.PA.UD.cm.fit, node_options = list(shape = "box", fontname = "Helvetica"), edge_options = list(color = "grey"), coefs = TRUE, sig = .05, stars="regress")interpret(m.PA.UD.cm.fit) Name Value Threshold Interpretation
1 GFI 0.99428574 0.95 satisfactory
2 AGFI 0.94285742 0.90 satisfactory
3 NFI 0.98445550 0.90 satisfactory
4 NNFI 0.98400436 0.90 satisfactory
5 CFI 0.99680087 0.90 satisfactory
6 RMSEA 0.03465517 0.05 satisfactory
7 SRMR 0.02695806 0.08 satisfactory
8 RFI 0.92227751 0.90 satisfactory
9 PNFI 0.19689110 0.50 poor
10 IFI 0.99696346 0.90 satisfactorym.PA.BD.cm <- ' # direct effect
AL_dsCM ~ c * s_LPLA_P_time
s_LPLA_P_time ~ c*AL_dsCM
AL_dsCM ~ d * DEM_dsHT
# mediator
PA_dsSUP ~ a * s_LPLA_P_time
AL_dsCM ~ b * PA_dsSUP
# indirect effect (a*b)
ab := a*b
# total effect
total := d + c + (a*b)
'
m.PA.BD.cm.fit <- sem(m.PA.BD.cm, data=df.1, se = "bootstrap", bootstrap = 1000)
summary(m.PA.BD.cm.fit, fit.measures=TRUE, rsquare=TRUE)lavaan 0.6.16 ended normally after 7 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 8
Number of equality constraints 1
Number of observations 199
Model Test User Model:
Test statistic 8.633
Degrees of freedom 2
P-value (Chi-square) 0.013
Model Test Baseline Model:
Test statistic 90.294
Degrees of freedom 6
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.921
Tucker-Lewis Index (TLI) 0.764
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -830.591
Loglikelihood unrestricted model (H1) -826.275
Akaike (AIC) 1675.183
Bayesian (BIC) 1698.236
Sample-size adjusted Bayesian (SABIC) 1676.060
Root Mean Square Error of Approximation:
RMSEA 0.129
90 Percent confidence interval - lower 0.050
90 Percent confidence interval - upper 0.223
P-value H_0: RMSEA <= 0.050 0.050
P-value H_0: RMSEA >= 0.080 0.866
Standardized Root Mean Square Residual:
SRMR 0.071
Parameter Estimates:
Standard errors Bootstrap
Number of requested bootstrap draws 1000
Number of successful bootstrap draws 1000
Regressions:
Estimate Std.Err z-value P(>|z|)
AL_dsCM ~
s_LPLA_P_t (c) 0.077 0.021 3.760 0.000
s_LPLA_P_time ~
AL_dsCM (c) 0.077 0.021 3.760 0.000
AL_dsCM ~
DEM_dsHT (d) -0.380 0.093 -4.078 0.000
PA_dsSUP ~
s_LPLA_P_t (a) 0.162 0.060 2.693 0.007
AL_dsCM ~
PA_dsSUP (b) 0.132 0.078 1.685 0.092
Variances:
Estimate Std.Err z-value P(>|z|)
.AL_dsCM 0.980 0.222 4.418 0.000
.s_LPLA_P_time 0.955 0.175 5.441 0.000
.PA_dsSUP 0.892 0.069 12.997 0.000
R-Square:
Estimate
AL_dsCM 0.291
s_LPLA_P_time 0.023
PA_dsSUP 0.031
Defined Parameters:
Estimate Std.Err z-value P(>|z|)
ab 0.021 0.016 1.364 0.173
total -0.282 0.097 -2.907 0.004lavaanPlot(model = m.PA.BD.cm.fit, node_options = list(shape = "box", fontname = "Helvetica"), edge_options = list(color = "grey"), coefs = TRUE, sig = .05, stars="regress")interpret(m.PA.BD.cm.fit) Name Value Threshold Interpretation
1 GFI 0.97366311 0.95 satisfactory
2 AGFI 0.86831556 0.90 poor
3 NFI 0.90438923 0.90 satisfactory
4 NNFI 0.76393084 0.90 poor
5 CFI 0.92131028 0.90 satisfactory
6 RMSEA 0.12909654 0.05 poor
7 SRMR 0.07096947 0.08 satisfactory
8 RFI 0.71316768 0.90 poor
9 PNFI 0.30146308 0.50 poor
10 IFI 0.92487519 0.90 satisfactorym.DEM.PA.UD.cm <- '# direct effect
AL_dsCM ~ c * s_LPLA_P_time
# mediator
DEM_dsHT ~ a1 * s_LPLA_P_time
AL_dsCM ~ b1 * DEM_dsHT
PA_dsSUP ~ a2 * s_LPLA_P_time
AL_dsCM ~ b2 * PA_dsSUP
# indirect effect (a*b)
a1b1 := a1*b1
a2b2 := a2*b2
# total effect
total := c + (a1*b1) + (a2*b2)
'
m.DEM.PA.UD.cm.fit <- sem(m.DEM.PA.UD.cm, data=df.1, se = "bootstrap", bootstrap = 1000)
summary(m.DEM.PA.UD.cm.fit, fit.measures=TRUE, rsquare=TRUE)lavaan 0.6.16 ended normally after 1 iteration
Estimator ML
Optimization method NLMINB
Number of model parameters 8
Number of observations 199
Model Test User Model:
Test statistic 1.239
Degrees of freedom 1
P-value (Chi-square) 0.266
Model Test Baseline Model:
Test statistic 90.294
Degrees of freedom 6
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.997
Tucker-Lewis Index (TLI) 0.983
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -918.169
Loglikelihood unrestricted model (H1) -917.549
Akaike (AIC) 1852.338
Bayesian (BIC) 1878.684
Sample-size adjusted Bayesian (SABIC) 1853.340
Root Mean Square Error of Approximation:
RMSEA 0.035
90 Percent confidence interval - lower 0.000
90 Percent confidence interval - upper 0.195
P-value H_0: RMSEA <= 0.050 0.376
P-value H_0: RMSEA >= 0.080 0.481
Standardized Root Mean Square Residual:
SRMR 0.027
Parameter Estimates:
Standard errors Bootstrap
Number of requested bootstrap draws 1000
Number of successful bootstrap draws 1000
Regressions:
Estimate Std.Err z-value P(>|z|)
AL_dsCM ~
s_LPLA_P_ (c) 0.100 0.051 1.967 0.049
DEM_dsHT ~
s_LPLA_P_ (a1) -0.360 0.097 -3.725 0.000
AL_dsCM ~
DEM_dsHT (b1) -0.376 0.093 -4.047 0.000
PA_dsSUP ~
s_LPLA_P_ (a2) 0.171 0.061 2.807 0.005
AL_dsCM ~
PA_dsSUP (b2) 0.142 0.075 1.895 0.058
Variances:
Estimate Std.Err z-value P(>|z|)
.AL_dsCM 0.979 0.215 4.549 0.000
.DEM_dsHT 2.339 0.290 8.059 0.000
.PA_dsSUP 0.892 0.068 13.155 0.000
R-Square:
Estimate
AL_dsCM 0.298
DEM_dsHT 0.052
PA_dsSUP 0.031
Defined Parameters:
Estimate Std.Err z-value P(>|z|)
a1b1 0.136 0.058 2.321 0.020
a2b2 0.024 0.017 1.470 0.142
total 0.259 0.065 4.005 0.000lavaanPlot(model = m.DEM.PA.UD.cm.fit, node_options = list(shape = "box", fontname = "Helvetica"), edge_options = list(color = "grey"), coefs = TRUE, sig = .05, stars="regress")interpret(m.DEM.PA.UD.cm.fit) Name Value Threshold Interpretation
1 GFI 0.99587394 0.95 satisfactory
2 AGFI 0.95873943 0.90 satisfactory
3 NFI 0.98627814 0.90 satisfactory
4 NNFI 0.98298836 0.90 satisfactory
5 CFI 0.99716473 0.90 satisfactory
6 RMSEA 0.03465517 0.05 satisfactory
7 SRMR 0.02695806 0.08 satisfactory
8 RFI 0.91766884 0.90 satisfactory
9 PNFI 0.16437969 0.50 poor
10 IFI 0.99732349 0.90 satisfactorym.DEM.PA.BD.cm <- '# direct effect
AL_dsCM ~ c * s_LPLA_P_time
s_LPLA_P_time ~ c*AL_dsCM
# mediator
DEM_dsHT ~ a1 * s_LPLA_P_time
AL_dsCM ~ b1 * DEM_dsHT
PA_dsSUP ~ a2 * s_LPLA_P_time
AL_dsCM ~ b2 * PA_dsSUP
# indirect effect (a*b)
a1b1 := a1*b1
a2b2 := a2*b2
# total effect
total := c + (a1*b1) + (a2*b2)
'
m.DEM.PA.BD.cm.fit <- sem(m.DEM.PA.BD.cm, data=df.1, se = "bootstrap", bootstrap = 1000)
summary(m.DEM.PA.BD.cm.fit, fit.measures=TRUE, rsquare=TRUE)lavaan 0.6.16 ended normally after 11 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 10
Number of equality constraints 1
Number of observations 199
Model Test User Model:
Test statistic 1.245
Degrees of freedom 1
P-value (Chi-square) 0.264
Model Test Baseline Model:
Test statistic 90.294
Degrees of freedom 6
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.997
Tucker-Lewis Index (TLI) 0.983
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -1199.105
Loglikelihood unrestricted model (H1) -1198.483
Akaike (AIC) 2416.211
Bayesian (BIC) 2445.851
Sample-size adjusted Bayesian (SABIC) 2417.338
Root Mean Square Error of Approximation:
RMSEA 0.035
90 Percent confidence interval - lower 0.000
90 Percent confidence interval - upper 0.196
P-value H_0: RMSEA <= 0.050 0.375
P-value H_0: RMSEA >= 0.080 0.483
Standardized Root Mean Square Residual:
SRMR 0.027
Parameter Estimates:
Standard errors Bootstrap
Number of requested bootstrap draws 1000
Number of successful bootstrap draws 1000
Regressions:
Estimate Std.Err z-value P(>|z|)
AL_dsCM ~
s_LPLA_P_ (c) 0.049 0.024 2.045 0.041
s_LPLA_P_time ~
AL_dsCM (c) 0.049 0.024 2.045 0.041
DEM_dsHT ~
s_LPLA_P_ (a1) -0.315 0.107 -2.949 0.003
AL_dsCM ~
DEM_dsHT (b1) -0.377 0.095 -3.967 0.000
PA_dsSUP ~
s_LPLA_P_ (a2) 0.164 0.059 2.776 0.005
AL_dsCM ~
PA_dsSUP (b2) 0.142 0.072 1.976 0.048
Variances:
Estimate Std.Err z-value P(>|z|)
.AL_dsCM 0.981 0.217 4.513 0.000
.s_LPLA_P_time 0.964 0.179 5.373 0.000
.DEM_dsHT 2.341 0.303 7.715 0.000
.PA_dsSUP 0.892 0.069 12.858 0.000
R-Square:
Estimate
AL_dsCM 0.296
s_LPLA_P_time 0.022
DEM_dsHT 0.051
PA_dsSUP 0.031
Defined Parameters:
Estimate Std.Err z-value P(>|z|)
a1b1 0.119 0.057 2.071 0.038
a2b2 0.023 0.016 1.493 0.135
total 0.192 0.048 4.013 0.000lavaanPlot(model = m.DEM.PA.BD.cm.fit, node_options = list(shape = "box", fontname = "Helvetica"), edge_options = list(color = "grey"), coefs = TRUE, sig = .05, stars="regress")interpret(m.DEM.PA.BD.cm.fit) Name Value Threshold Interpretation
1 GFI 0.99689088 0.95 satisfactory
2 AGFI 0.96890876 0.90 satisfactory
3 NFI 0.98621009 0.90 satisfactory
4 NNFI 0.98255101 0.90 satisfactory
5 CFI 0.99709183 0.90 satisfactory
6 RMSEA 0.03509782 0.05 satisfactory
7 SRMR 0.02704097 0.08 satisfactory
8 RFI 0.91726055 0.90 satisfactory
9 PNFI 0.16436835 0.50 poor
10 IFI 0.99725468 0.90 satisfactoryanova(m.DEM.UD.cm.fit, m.DEM.BD.cm.fit, m.PA.UD.cm.fit, m.PA.BD.cm.fit, m.DEM.PA.UD.cm.fit, m.DEM.PA.BD.cm.fit)Warning in lavTestLRT(object = object, ..., model.names = NAMES): lavaan
WARNING: some models have the same degrees of freedom
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
m.DEM.UD.cm.fit 1 1306.3 1326.1 1.2390
m.PA.UD.cm.fit 1 1114.5 1134.3 1.2390 0.0000 0.00000 0
m.DEM.PA.UD.cm.fit 1 1852.3 1878.7 1.2390 0.0000 0.00000 0
m.DEM.PA.BD.cm.fit 1 2416.2 2445.8 1.2451 0.0061 0.00000 0
m.DEM.BD.cm.fit 2 1869.7 1892.7 7.0528 5.8076 0.15543 1
m.PA.BD.cm.fit 2 1675.2 1698.2 8.6330 1.5803 0.00000 0
Pr(>Chisq)
m.DEM.UD.cm.fit
m.PA.UD.cm.fit
m.DEM.PA.UD.cm.fit
m.DEM.PA.BD.cm.fit
m.DEM.BD.cm.fit 0.01596 *
m.PA.BD.cm.fit
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Work in progress
df.c2 <- df.1 %>% filter(cycle=="2")
m.DEM.UD.c2 <- ' # direct effect
AL_dsCTL ~ c * s_LPLA_P_time
AL_dsCTL ~ d * PA_dsSUP
# mediator
DEM_dsHT ~ a * s_LPLA_P_time
AL_dsCTL ~ b * DEM_dsHT
# indirect effect (a*b)
ab := a*b
# total effect
total := d + c + (a*b)
'
m.DEM.UD.c2.fit <- sem(m.DEM.UD.c2, data=df.c2, se = "bootstrap", bootstrap = 1000)
summary(m.DEM.UD.c2.fit, fit.measures=TRUE, rsquare=TRUE)lavaan 0.6.16 ended normally after 1 iteration
Estimator ML
Optimization method NLMINB
Number of model parameters 6
Number of observations 102
Model Test User Model:
Test statistic 0.551
Degrees of freedom 1
P-value (Chi-square) 0.458
Model Test Baseline Model:
Test statistic 78.386
Degrees of freedom 5
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 1.000
Tucker-Lewis Index (TLI) 1.031
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -340.370
Loglikelihood unrestricted model (H1) -340.095
Akaike (AIC) 692.741
Bayesian (BIC) 708.491
Sample-size adjusted Bayesian (SABIC) 689.539
Root Mean Square Error of Approximation:
RMSEA 0.000
90 Percent confidence interval - lower 0.000
90 Percent confidence interval - upper 0.235
P-value H_0: RMSEA <= 0.050 0.512
P-value H_0: RMSEA >= 0.080 0.413
Standardized Root Mean Square Residual:
SRMR 0.026
Parameter Estimates:
Standard errors Bootstrap
Number of requested bootstrap draws 1000
Number of successful bootstrap draws 1000
Regressions:
Estimate Std.Err z-value P(>|z|)
AL_dsCTL ~
s_LPLA_P_t (c) 0.276 0.175 1.575 0.115
PA_dsSUP (d) 0.411 0.109 3.757 0.000
DEM_dsHT ~
s_LPLA_P_t (a) -0.557 0.192 -2.905 0.004
AL_dsCTL ~
DEM_dsHT (b) -0.418 0.041 -10.167 0.000
Variances:
Estimate Std.Err z-value P(>|z|)
.AL_dsCTL 0.946 0.122 7.759 0.000
.DEM_dsHT 2.869 0.493 5.821 0.000
R-Square:
Estimate
AL_dsCTL 0.503
DEM_dsHT 0.080
Defined Parameters:
Estimate Std.Err z-value P(>|z|)
ab 0.233 0.079 2.955 0.003
total 0.919 0.216 4.251 0.000lavaanPlot(model = m.DEM.UD.c2.fit, node_options = list(shape = "box", fontname = "Helvetica"), edge_options = list(color = "grey"), coefs = TRUE, sig = .05, stars="regress")interpret(m.DEM.UD.c2.fit) Name Value Threshold Interpretation
1 GFI 0.99491520 0.95 satisfactory
2 AGFI 0.94915200 0.90 satisfactory
3 NFI 0.99297267 0.90 satisfactory
4 NNFI 1.03060189 0.90 satisfactory
5 CFI 1.00000000 0.90 satisfactory
6 RMSEA 0.00000000 0.05 satisfactory
7 SRMR 0.02552154 0.08 satisfactory
8 RFI 0.96486334 0.90 satisfactory
9 PNFI 0.19859453 0.50 poor
10 IFI 1.00580402 0.90 satisfactorym.DEM.BD.c2 <- ' # direct effect
AL_dsCTL ~ c * s_LPLA_P_time
s_LPLA_P_time ~ c*AL_dsCTL
AL_dsCTL ~ d * PA_dsSUP
# mediator
DEM_dsHT ~ a * s_LPLA_P_time
AL_dsCTL ~ b * DEM_dsHT
# indirect effect (a*b)
ab := a*b
# total effect
total := d + c + (a*b)
'
m.DEM.BD.c2.fit <- sem(m.DEM.BD.c2, data=df.c2, se = "bootstrap", bootstrap = 1000)
summary(m.DEM.BD.c2.fit, fit.measures=TRUE, rsquare=TRUE)lavaan 0.6.16 ended normally after 13 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 8
Number of equality constraints 1
Number of observations 102
Model Test User Model:
Test statistic 7.289
Degrees of freedom 2
P-value (Chi-square) 0.026
Model Test Baseline Model:
Test statistic 88.752
Degrees of freedom 6
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.936
Tucker-Lewis Index (TLI) 0.808
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -472.354
Loglikelihood unrestricted model (H1) -468.710
Akaike (AIC) 958.709
Bayesian (BIC) 977.084
Sample-size adjusted Bayesian (SABIC) 954.973
Root Mean Square Error of Approximation:
RMSEA 0.161
90 Percent confidence interval - lower 0.047
90 Percent confidence interval - upper 0.294
P-value H_0: RMSEA <= 0.050 0.053
P-value H_0: RMSEA >= 0.080 0.897
Standardized Root Mean Square Residual:
SRMR 0.078
Parameter Estimates:
Standard errors Bootstrap
Number of requested bootstrap draws 1000
Number of successful bootstrap draws 1000
Regressions:
Estimate Std.Err z-value P(>|z|)
AL_dsCTL ~
s_LPLA_P_t (c) 0.146 0.062 2.341 0.019
s_LPLA_P_time ~
AL_dsCTL (c) 0.146 0.062 2.341 0.019
AL_dsCTL ~
PA_dsSUP (d) 0.450 0.104 4.340 0.000
DEM_dsHT ~
s_LPLA_P_t (a) -0.326 0.151 -2.167 0.030
AL_dsCTL ~
DEM_dsHT (b) -0.421 0.040 -10.568 0.000
Variances:
Estimate Std.Err z-value P(>|z|)
.AL_dsCTL 0.958 0.120 8.014 0.000
.s_LPLA_P_time 0.697 0.253 2.752 0.006
.DEM_dsHT 2.912 0.522 5.578 0.000
R-Square:
Estimate
AL_dsCTL 0.482
s_LPLA_P_time 0.125
DEM_dsHT 0.066
Defined Parameters:
Estimate Std.Err z-value P(>|z|)
ab 0.137 0.064 2.141 0.032
total 0.733 0.135 5.419 0.000lavaanPlot(model = m.DEM.BD.c2.fit, node_options = list(shape = "box", fontname = "Helvetica"), edge_options = list(color = "grey"), coefs = TRUE, sig = .05, stars="regress")interpret(m.DEM.BD.c2.fit) Name Value Threshold Interpretation
1 GFI 0.95648076 0.95 satisfactory
2 AGFI 0.78240380 0.90 poor
3 NFI 0.91787052 0.90 satisfactory
4 NNFI 0.80825306 0.90 poor
5 CFI 0.93608435 0.90 satisfactory
6 RMSEA 0.16101879 0.05 poor
7 SRMR 0.07778028 0.08 satisfactory
8 RFI 0.75361155 0.90 poor
9 PNFI 0.30595684 0.50 poor
10 IFI 0.93903142 0.90 satisfactorym.PA.UD.c2 <- ' # direct effect
AL_dsCTL ~ c * s_LPLA_P_time
AL_dsCTL ~ d * DEM_dsHT
# mediator
PA_dsSUP ~ a * s_LPLA_P_time
AL_dsCTL ~ b * PA_dsSUP
# indirect effect (a*b)
ab := a*b
# total effect
total := d + c + (a*b)
'
m.PA.UD.c2.fit <- sem(m.PA.UD.c2, data=df.c2, se = "bootstrap", bootstrap = 1000)
summary(m.PA.UD.c2.fit, fit.measures=TRUE, rsquare=TRUE)lavaan 0.6.16 ended normally after 1 iteration
Estimator ML
Optimization method NLMINB
Number of model parameters 6
Number of observations 102
Model Test User Model:
Test statistic 0.551
Degrees of freedom 1
P-value (Chi-square) 0.458
Model Test Baseline Model:
Test statistic 80.218
Degrees of freedom 5
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 1.000
Tucker-Lewis Index (TLI) 1.030
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -272.184
Loglikelihood unrestricted model (H1) -271.909
Akaike (AIC) 556.369
Bayesian (BIC) 572.118
Sample-size adjusted Bayesian (SABIC) 553.167
Root Mean Square Error of Approximation:
RMSEA 0.000
90 Percent confidence interval - lower 0.000
90 Percent confidence interval - upper 0.235
P-value H_0: RMSEA <= 0.050 0.512
P-value H_0: RMSEA >= 0.080 0.413
Standardized Root Mean Square Residual:
SRMR 0.026
Parameter Estimates:
Standard errors Bootstrap
Number of requested bootstrap draws 1000
Number of successful bootstrap draws 1000
Regressions:
Estimate Std.Err z-value P(>|z|)
AL_dsCTL ~
s_LPLA_P_t (c) 0.276 0.188 1.464 0.143
DEM_dsHT (d) -0.418 0.043 -9.645 0.000
PA_dsSUP ~
s_LPLA_P_t (a) 0.316 0.084 3.769 0.000
AL_dsCTL ~
PA_dsSUP (b) 0.411 0.111 3.713 0.000
Variances:
Estimate Std.Err z-value P(>|z|)
.AL_dsCTL 0.946 0.121 7.830 0.000
.PA_dsSUP 0.754 0.074 10.240 0.000
R-Square:
Estimate
AL_dsCTL 0.503
PA_dsSUP 0.097
Defined Parameters:
Estimate Std.Err z-value P(>|z|)
ab 0.130 0.053 2.450 0.014
total -0.012 0.214 -0.057 0.954lavaanPlot(model = m.PA.UD.c2.fit, node_options = list(shape = "box", fontname = "Helvetica"), edge_options = list(color = "grey"), coefs = TRUE, sig = .05, stars="regress")interpret(m.PA.UD.c2.fit) Name Value Threshold Interpretation
1 GFI 0.99590818 0.95 satisfactory
2 AGFI 0.95908175 0.90 satisfactory
3 NFI 0.99313312 0.90 satisfactory
4 NNFI 1.02985670 0.90 satisfactory
5 CFI 1.00000000 0.90 satisfactory
6 RMSEA 0.00000000 0.05 satisfactory
7 SRMR 0.02552154 0.08 satisfactory
8 RFI 0.96566562 0.90 satisfactory
9 PNFI 0.19862662 0.50 poor
10 IFI 1.00566983 0.90 satisfactorym.PA.BD.c2 <- ' # direct effect
AL_dsCTL ~ c * s_LPLA_P_time
s_LPLA_P_time ~ c*AL_dsCTL
AL_dsCTL ~ d * DEM_dsHT
# mediator
PA_dsSUP ~ a * s_LPLA_P_time
AL_dsCTL ~ b * PA_dsSUP
# indirect effect (a*b)
ab := a*b
# total effect
total := d + c + (a*b)
'
m.PA.BD.c2.fit <- sem(m.PA.BD.c2, data=df.c2, se = "bootstrap", bootstrap = 1000)
summary(m.PA.BD.c2.fit, fit.measures=TRUE, rsquare=TRUE)lavaan 0.6.16 ended normally after 10 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 8
Number of equality constraints 1
Number of observations 102
Model Test User Model:
Test statistic 3.361
Degrees of freedom 2
P-value (Chi-square) 0.186
Model Test Baseline Model:
Test statistic 88.752
Degrees of freedom 6
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.984
Tucker-Lewis Index (TLI) 0.951
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -403.120
Loglikelihood unrestricted model (H1) -401.440
Akaike (AIC) 820.240
Bayesian (BIC) 838.615
Sample-size adjusted Bayesian (SABIC) 816.504
Root Mean Square Error of Approximation:
RMSEA 0.082
90 Percent confidence interval - lower 0.000
90 Percent confidence interval - upper 0.229
P-value H_0: RMSEA <= 0.050 0.264
P-value H_0: RMSEA >= 0.080 0.622
Standardized Root Mean Square Residual:
SRMR 0.048
Parameter Estimates:
Standard errors Bootstrap
Number of requested bootstrap draws 1000
Number of successful bootstrap draws 1000
Regressions:
Estimate Std.Err z-value P(>|z|)
AL_dsCTL ~
s_LPLA_P_t (c) 0.154 0.061 2.538 0.011
s_LPLA_P_time ~
AL_dsCTL (c) 0.154 0.061 2.538 0.011
AL_dsCTL ~
DEM_dsHT (d) -0.436 0.037 -11.711 0.000
PA_dsSUP ~
s_LPLA_P_t (a) 0.256 0.079 3.243 0.001
AL_dsCTL ~
PA_dsSUP (b) 0.400 0.106 3.788 0.000
Variances:
Estimate Std.Err z-value P(>|z|)
.AL_dsCTL 0.957 0.121 7.895 0.000
.s_LPLA_P_time 0.693 0.239 2.901 0.004
.PA_dsSUP 0.756 0.076 9.977 0.000
R-Square:
Estimate
AL_dsCTL 0.485
s_LPLA_P_time 0.127
PA_dsSUP 0.092
Defined Parameters:
Estimate Std.Err z-value P(>|z|)
ab 0.102 0.043 2.372 0.018
total -0.179 0.069 -2.591 0.010lavaanPlot(model = m.PA.BD.c2.fit, node_options = list(shape = "box", fontname = "Helvetica"), edge_options = list(color = "grey"), coefs = TRUE, sig = .05, stars="regress")interpret(m.PA.BD.c2.fit) Name Value Threshold Interpretation
1 GFI 0.98123792 0.95 satisfactory
2 AGFI 0.90618962 0.90 satisfactory
3 NFI 0.96213230 0.90 satisfactory
4 NNFI 0.95066617 0.90 satisfactory
5 CFI 0.98355539 0.90 satisfactory
6 RMSEA 0.08167418 0.05 poor
7 SRMR 0.04832082 0.08 satisfactory
8 RFI 0.88639690 0.90 poor
9 PNFI 0.32071077 0.50 poor
10 IFI 0.98431363 0.90 satisfactorym.DEM.PA.UD.c2 <- '# direct effect
AL_dsCTL ~ c * s_LPLA_P_time
# mediator
DEM_dsHT ~ a1 * s_LPLA_P_time
AL_dsCTL ~ b1 * DEM_dsHT
PA_dsSUP ~ a2 * s_LPLA_P_time
AL_dsCTL ~ b2 * PA_dsSUP
# indirect effect (a*b)
a1b1 := a1*b1
a2b2 := a2*b2
# total effect
total := c + (a1*b1) + (a2*b2)
'
m.DEM.PA.UD.c2.fit <- sem(m.DEM.PA.UD.c2, data=df.c2, se = "bootstrap", bootstrap = 1000)
summary(m.DEM.PA.UD.c2.fit, fit.measures=TRUE, rsquare=TRUE)lavaan 0.6.16 ended normally after 2 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 8
Number of observations 102
Model Test User Model:
Test statistic 0.551
Degrees of freedom 1
P-value (Chi-square) 0.458
Model Test Baseline Model:
Test statistic 88.752
Degrees of freedom 6
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 1.000
Tucker-Lewis Index (TLI) 1.033
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -470.672
Loglikelihood unrestricted model (H1) -470.397
Akaike (AIC) 957.345
Bayesian (BIC) 978.345
Sample-size adjusted Bayesian (SABIC) 953.076
Root Mean Square Error of Approximation:
RMSEA 0.000
90 Percent confidence interval - lower 0.000
90 Percent confidence interval - upper 0.235
P-value H_0: RMSEA <= 0.050 0.512
P-value H_0: RMSEA >= 0.080 0.413
Standardized Root Mean Square Residual:
SRMR 0.026
Parameter Estimates:
Standard errors Bootstrap
Number of requested bootstrap draws 1000
Number of successful bootstrap draws 1000
Regressions:
Estimate Std.Err z-value P(>|z|)
AL_dsCTL ~
s_LPLA_P_ (c) 0.276 0.192 1.439 0.150
DEM_dsHT ~
s_LPLA_P_ (a1) -0.557 0.181 -3.080 0.002
AL_dsCTL ~
DEM_dsHT (b1) -0.418 0.043 -9.662 0.000
PA_dsSUP ~
s_LPLA_P_ (a2) 0.316 0.081 3.897 0.000
AL_dsCTL ~
PA_dsSUP (b2) 0.411 0.108 3.801 0.000
Variances:
Estimate Std.Err z-value P(>|z|)
.AL_dsCTL 0.946 0.119 7.930 0.000
.DEM_dsHT 2.869 0.502 5.714 0.000
.PA_dsSUP 0.754 0.076 9.911 0.000
R-Square:
Estimate
AL_dsCTL 0.503
DEM_dsHT 0.080
PA_dsSUP 0.097
Defined Parameters:
Estimate Std.Err z-value P(>|z|)
a1b1 0.233 0.071 3.279 0.001
a2b2 0.130 0.050 2.572 0.010
total 0.638 0.231 2.765 0.006lavaanPlot(model = m.DEM.PA.UD.c2.fit, node_options = list(shape = "box", fontname = "Helvetica"), edge_options = list(color = "grey"), coefs = TRUE, sig = .05, stars="regress")interpret(m.DEM.PA.UD.c2.fit) Name Value Threshold Interpretation
1 GFI 0.99653224 0.95 satisfactory
2 AGFI 0.96532241 0.90 satisfactory
3 NFI 0.99379338 0.90 satisfactory
4 NNFI 1.03256635 0.90 satisfactory
5 CFI 1.00000000 0.90 satisfactory
6 RMSEA 0.00000000 0.05 satisfactory
7 SRMR 0.02552154 0.08 satisfactory
8 RFI 0.96276026 0.90 satisfactory
9 PNFI 0.16563223 0.50 poor
10 IFI 1.00511846 0.90 satisfactorym.DEM.PA.BD.c2 <- '# direct effect
AL_dsCTL ~ c * s_LPLA_P_time
s_LPLA_P_time ~ c*AL_dsCTL
# mediator
DEM_dsHT ~ a1 * s_LPLA_P_time
AL_dsCTL ~ b1 * DEM_dsHT
PA_dsSUP ~ a2 * s_LPLA_P_time
AL_dsCTL ~ b2 * PA_dsSUP
# indirect effect (a*b)
a1b1 := a1*b1
a2b2 := a2*b2
# total effect
total := c + (a1*b1) + (a2*b2)
'
m.DEM.PA.BD.c2.fit <- sem(m.DEM.PA.BD.c2, data=df.c2, se = "bootstrap", bootstrap = 1000)Warning in lav_model_nvcov_bootstrap(lavmodel = lavmodel, lavsamplestats =
lavsamplestats, : lavaan WARNING: 1 bootstrap runs failed or did not converge.summary(m.DEM.PA.BD.c2.fit, fit.measures=TRUE, rsquare=TRUE)lavaan 0.6.16 ended normally after 14 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 10
Number of equality constraints 1
Number of observations 102
Model Test User Model:
Test statistic 0.476
Degrees of freedom 1
P-value (Chi-square) 0.490
Model Test Baseline Model:
Test statistic 88.752
Degrees of freedom 6
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 1.000
Tucker-Lewis Index (TLI) 1.038
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -604.432
Loglikelihood unrestricted model (H1) -604.194
Akaike (AIC) 1226.865
Bayesian (BIC) 1250.489
Sample-size adjusted Bayesian (SABIC) 1222.062
Root Mean Square Error of Approximation:
RMSEA 0.000
90 Percent confidence interval - lower 0.000
90 Percent confidence interval - upper 0.230
P-value H_0: RMSEA <= 0.050 0.543
P-value H_0: RMSEA >= 0.080 0.386
Standardized Root Mean Square Residual:
SRMR 0.024
Parameter Estimates:
Standard errors Bootstrap
Number of requested bootstrap draws 1000
Number of successful bootstrap draws 999
Regressions:
Estimate Std.Err z-value P(>|z|)
AL_dsCTL ~
s_LPLA_P_ (c) 0.121 0.069 1.751 0.080
s_LPLA_P_time ~
AL_dsCTL (c) 0.121 0.069 1.751 0.080
DEM_dsHT ~
s_LPLA_P_ (a1) -0.363 0.168 -2.158 0.031
AL_dsCTL ~
DEM_dsHT (b1) -0.426 0.041 -10.514 0.000
PA_dsSUP ~
s_LPLA_P_ (a2) 0.266 0.083 3.197 0.001
AL_dsCTL ~
PA_dsSUP (b2) 0.419 0.109 3.860 0.000
Variances:
Estimate Std.Err z-value P(>|z|)
.AL_dsCTL 0.964 0.118 8.163 0.000
.s_LPLA_P_time 0.710 0.257 2.764 0.006
.DEM_dsHT 2.900 0.521 5.560 0.000
.PA_dsSUP 0.756 0.075 10.012 0.000
R-Square:
Estimate
AL_dsCTL 0.493
s_LPLA_P_time 0.120
DEM_dsHT 0.071
PA_dsSUP 0.094
Defined Parameters:
Estimate Std.Err z-value P(>|z|)
a1b1 0.155 0.074 2.078 0.038
a2b2 0.112 0.050 2.252 0.024
total 0.387 0.090 4.311 0.000lavaanPlot(model = m.DEM.PA.BD.c2.fit, node_options = list(shape = "box", fontname = "Helvetica"), edge_options = list(color = "grey"), coefs = TRUE, sig = .05, stars="regress")interpret(m.DEM.PA.BD.c2.fit) Name Value Threshold Interpretation
1 GFI 0.99768132 0.95 satisfactory
2 AGFI 0.97681320 0.90 satisfactory
3 NFI 0.99464175 0.90 satisfactory
4 NNFI 1.03802565 0.90 satisfactory
5 CFI 1.00000000 0.90 satisfactory
6 RMSEA 0.00000000 0.05 satisfactory
7 SRMR 0.02389854 0.08 satisfactory
8 RFI 0.96785049 0.90 satisfactory
9 PNFI 0.16577362 0.50 poor
10 IFI 1.00597650 0.90 satisfactoryanova(m.DEM.UD.c2.fit, m.DEM.BD.c2.fit,m.PA.UD.c2.fit,m.PA.BD.c2.fit, m.DEM.PA.UD.c2.fit, m.DEM.PA.BD.c2.fit)Warning in lavTestLRT(object = object, ..., model.names = NAMES): lavaan WARNING:
Some restricted models fit better than less restricted models;
either these models are not nested, or the less restricted model
failed to reach a global optimum. Smallest difference =
-3.9283015008133Warning in lavTestLRT(object = object, ..., model.names = NAMES): lavaan
WARNING: some models have the same degrees of freedom
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
m.DEM.UD.c2.fit 1 692.74 708.49 0.5508
m.PA.UD.c2.fit 1 556.37 572.12 0.5508 0.0000 0.00000 0
m.DEM.PA.UD.c2.fit 1 957.34 978.34 0.5508 0.0000 0.00000 0
m.DEM.PA.BD.c2.fit 1 1226.86 1250.49 0.4756 -0.0753 0.00000 0
m.DEM.BD.c2.fit 2 958.71 977.08 7.2891 6.8136 0.23874 1
m.PA.BD.c2.fit 2 820.24 838.61 3.3608 -3.9283 0.00000 0
Pr(>Chisq)
m.DEM.UD.c2.fit
m.PA.UD.c2.fit
m.DEM.PA.UD.c2.fit
m.DEM.PA.BD.c2.fit
m.DEM.BD.c2.fit 0.009047 **
m.PA.BD.c2.fit
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1m.DEM.UD.cm.c2 <- ' # direct effect
AL_dsCM ~ c * s_LPLA_P_time
AL_dsCM ~ d * PA_dsSUP
# mediator
DEM_dsHT ~ a * s_LPLA_P_time
AL_dsCM ~ b * DEM_dsHT
# indirect effect (a*b)
ab := a*b
# total effect
total := d + c + (a*b)
'
m.DEM.UD.cm.c2.fit <- sem(m.DEM.UD.cm.c2, data=df.c2, se = "bootstrap", bootstrap = 1000)
summary(m.DEM.UD.cm.c2.fit, fit.measures=TRUE, rsquare=TRUE)lavaan 0.6.16 ended normally after 1 iteration
Estimator ML
Optimization method NLMINB
Number of model parameters 6
Number of observations 102
Model Test User Model:
Test statistic 0.551
Degrees of freedom 1
P-value (Chi-square) 0.458
Model Test Baseline Model:
Test statistic 58.083
Degrees of freedom 5
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 1.000
Tucker-Lewis Index (TLI) 1.042
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -360.265
Loglikelihood unrestricted model (H1) -359.990
Akaike (AIC) 732.531
Bayesian (BIC) 748.281
Sample-size adjusted Bayesian (SABIC) 729.329
Root Mean Square Error of Approximation:
RMSEA 0.000
90 Percent confidence interval - lower 0.000
90 Percent confidence interval - upper 0.235
P-value H_0: RMSEA <= 0.050 0.512
P-value H_0: RMSEA >= 0.080 0.413
Standardized Root Mean Square Residual:
SRMR 0.025
Parameter Estimates:
Standard errors Bootstrap
Number of requested bootstrap draws 1000
Number of successful bootstrap draws 1000
Regressions:
Estimate Std.Err z-value P(>|z|)
AL_dsCM ~
s_LPLA_P_t (c) -0.040 0.095 -0.417 0.676
PA_dsSUP (d) 0.340 0.130 2.609 0.009
DEM_dsHT ~
s_LPLA_P_t (a) -0.557 0.186 -2.987 0.003
AL_dsCM ~
DEM_dsHT (b) -0.499 0.126 -3.950 0.000
Variances:
Estimate Std.Err z-value P(>|z|)
.AL_dsCM 1.397 0.346 4.037 0.000
.DEM_dsHT 2.869 0.512 5.607 0.000
R-Square:
Estimate
AL_dsCM 0.391
DEM_dsHT 0.080
Defined Parameters:
Estimate Std.Err z-value P(>|z|)
ab 0.278 0.141 1.966 0.049
total 0.578 0.160 3.603 0.000lavaanPlot(model = m.DEM.UD.cm.c2.fit, node_options = list(shape = "box", fontname = "Helvetica"), edge_options = list(color = "grey"), coefs = TRUE, sig = .05, stars="regress")interpret(m.DEM.UD.cm.c2.fit) Name Value Threshold Interpretation
1 GFI 0.99467549 0.95 satisfactory
2 AGFI 0.94675488 0.90 satisfactory
3 NFI 0.99051616 0.90 satisfactory
4 NNFI 1.04230685 0.90 satisfactory
5 CFI 1.00000000 0.90 satisfactory
6 RMSEA 0.00000000 0.05 satisfactory
7 SRMR 0.02543241 0.08 satisfactory
8 RFI 0.95258079 0.90 satisfactory
9 PNFI 0.19810323 0.50 poor
10 IFI 1.00786845 0.90 satisfactorym.DEM.BD.cm.c2 <- ' # direct effect
AL_dsCM ~ c * s_LPLA_P_time
s_LPLA_P_time ~ c*AL_dsCM
AL_dsCM ~ d * PA_dsSUP
# mediator
DEM_dsHT ~ a * s_LPLA_P_time
AL_dsCM ~ b * DEM_dsHT
# indirect effect (a*b)
ab := a*b
# total effect
total := d + c + (a*b)
'
m.DEM.BD.cm.c2.fit <- sem(m.DEM.BD.cm.c2, data=df.c2, se = "bootstrap", bootstrap = 1000)
summary(m.DEM.BD.cm.c2.fit, fit.measures=TRUE, rsquare=TRUE)lavaan 0.6.16 ended normally after 11 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 8
Number of equality constraints 1
Number of observations 102
Model Test User Model:
Test statistic 10.924
Degrees of freedom 2
P-value (Chi-square) 0.004
Model Test Baseline Model:
Test statistic 68.448
Degrees of freedom 6
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.857
Tucker-Lewis Index (TLI) 0.571
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -494.067
Loglikelihood unrestricted model (H1) -488.605
Akaike (AIC) 1002.134
Bayesian (BIC) 1020.509
Sample-size adjusted Bayesian (SABIC) 998.398
Root Mean Square Error of Approximation:
RMSEA 0.209
90 Percent confidence interval - lower 0.100
90 Percent confidence interval - upper 0.338
P-value H_0: RMSEA <= 0.050 0.011
P-value H_0: RMSEA >= 0.080 0.972
Standardized Root Mean Square Residual:
SRMR 0.098
Parameter Estimates:
Standard errors Bootstrap
Number of requested bootstrap draws 1000
Number of successful bootstrap draws 1000
Regressions:
Estimate Std.Err z-value P(>|z|)
AL_dsCM ~
s_LPLA_P_t (c) 0.013 0.028 0.456 0.648
s_LPLA_P_time ~
AL_dsCM (c) 0.013 0.028 0.456 0.648
AL_dsCM ~
PA_dsSUP (d) 0.324 0.133 2.441 0.015
DEM_dsHT ~
s_LPLA_P_t (a) -0.534 0.205 -2.610 0.009
AL_dsCM ~
DEM_dsHT (b) -0.489 0.127 -3.837 0.000
Variances:
Estimate Std.Err z-value P(>|z|)
.AL_dsCM 1.399 0.341 4.099 0.000
.s_LPLA_P_time 0.800 0.256 3.125 0.002
.DEM_dsHT 2.870 0.491 5.840 0.000
R-Square:
Estimate
AL_dsCM 0.378
s_LPLA_P_time 0.008
DEM_dsHT 0.080
Defined Parameters:
Estimate Std.Err z-value P(>|z|)
ab 0.261 0.144 1.818 0.069
total 0.598 0.167 3.588 0.000lavaanPlot(model = m.DEM.BD.cm.c2.fit, node_options = list(shape = "box", fontname = "Helvetica"), edge_options = list(color = "grey"), coefs = TRUE, sig = .05, stars="regress")interpret(m.DEM.BD.cm.c2.fit) Name Value Threshold Interpretation
1 GFI 0.93666223 0.95 poor
2 AGFI 0.68331117 0.90 poor
3 NFI 0.84040037 0.90 poor
4 NNFI 0.57127822 0.90 poor
5 CFI 0.85709274 0.90 poor
6 RMSEA 0.20915622 0.05 poor
7 SRMR 0.09752428 0.08 poor
8 RFI 0.52120112 0.90 poor
9 PNFI 0.28013346 0.50 poor
10 IFI 0.86569541 0.90 poorm.PA.UD.cm.c2 <- ' # direct effect
AL_dsCM ~ c * s_LPLA_P_time
AL_dsCM ~ d * DEM_dsHT
# mediator
PA_dsSUP ~ a * s_LPLA_P_time
AL_dsCM ~ b * PA_dsSUP
# indirect effect (a*b)
ab := a*b
# total effect
total := d + c + (a*b)
'
m.PA.UD.cm.c2.fit <- sem(m.PA.UD.cm.c2, data=df.c2, se = "bootstrap", bootstrap = 1000)
summary(m.PA.UD.cm.c2.fit, fit.measures=TRUE, rsquare=TRUE)lavaan 0.6.16 ended normally after 2 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 6
Number of observations 102
Model Test User Model:
Test statistic 0.551
Degrees of freedom 1
P-value (Chi-square) 0.458
Model Test Baseline Model:
Test statistic 59.914
Degrees of freedom 5
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 1.000
Tucker-Lewis Index (TLI) 1.041
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -292.079
Loglikelihood unrestricted model (H1) -291.804
Akaike (AIC) 596.158
Bayesian (BIC) 611.908
Sample-size adjusted Bayesian (SABIC) 592.957
Root Mean Square Error of Approximation:
RMSEA 0.000
90 Percent confidence interval - lower 0.000
90 Percent confidence interval - upper 0.235
P-value H_0: RMSEA <= 0.050 0.512
P-value H_0: RMSEA >= 0.080 0.413
Standardized Root Mean Square Residual:
SRMR 0.025
Parameter Estimates:
Standard errors Bootstrap
Number of requested bootstrap draws 1000
Number of successful bootstrap draws 1000
Regressions:
Estimate Std.Err z-value P(>|z|)
AL_dsCM ~
s_LPLA_P_t (c) -0.040 0.096 -0.416 0.678
DEM_dsHT (d) -0.499 0.125 -3.995 0.000
PA_dsSUP ~
s_LPLA_P_t (a) 0.316 0.084 3.748 0.000
AL_dsCM ~
PA_dsSUP (b) 0.340 0.127 2.682 0.007
Variances:
Estimate Std.Err z-value P(>|z|)
.AL_dsCM 1.397 0.330 4.230 0.000
.PA_dsSUP 0.754 0.073 10.263 0.000
R-Square:
Estimate
AL_dsCM 0.391
PA_dsSUP 0.097
Defined Parameters:
Estimate Std.Err z-value P(>|z|)
ab 0.107 0.054 2.001 0.045
total -0.431 0.168 -2.571 0.010lavaanPlot(model = m.PA.UD.cm.c2.fit, node_options = list(shape = "box", fontname = "Helvetica"), edge_options = list(color = "grey"), coefs = TRUE, sig = .05, stars="regress")interpret(m.PA.UD.cm.c2.fit) Name Value Threshold Interpretation
1 GFI 0.99536813 0.95 satisfactory
2 AGFI 0.95368131 0.90 satisfactory
3 NFI 0.99080609 0.90 satisfactory
4 NNFI 1.04089574 0.90 satisfactory
5 CFI 1.00000000 0.90 satisfactory
6 RMSEA 0.00000000 0.05 satisfactory
7 SRMR 0.02543241 0.08 satisfactory
8 RFI 0.95403043 0.90 satisfactory
9 PNFI 0.19816122 0.50 poor
10 IFI 1.00762382 0.90 satisfactorym.PA.BD.cm.c2 <- ' # direct effect
AL_dsCM ~ c * s_LPLA_P_time
s_LPLA_P_time ~ c*AL_dsCM
AL_dsCM ~ d * DEM_dsHT
# mediator
PA_dsSUP ~ a * s_LPLA_P_time
AL_dsCM ~ b * PA_dsSUP
# indirect effect (a*b)
ab := a*b
# total effect
total := d + c + (a*b)
'
m.PA.BD.cm.c2.fit <- sem(m.PA.BD.cm.c2, data=df.c2, se = "bootstrap", bootstrap = 1000)
summary(m.PA.BD.cm.c2.fit, fit.measures=TRUE, rsquare=TRUE)lavaan 0.6.16 ended normally after 10 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 8
Number of equality constraints 1
Number of observations 102
Model Test User Model:
Test statistic 8.081
Degrees of freedom 2
P-value (Chi-square) 0.018
Model Test Baseline Model:
Test statistic 68.448
Degrees of freedom 6
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.903
Tucker-Lewis Index (TLI) 0.708
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -425.375
Loglikelihood unrestricted model (H1) -421.335
Akaike (AIC) 864.750
Bayesian (BIC) 883.125
Sample-size adjusted Bayesian (SABIC) 861.014
Root Mean Square Error of Approximation:
RMSEA 0.173
90 Percent confidence interval - lower 0.061
90 Percent confidence interval - upper 0.304
P-value H_0: RMSEA <= 0.050 0.038
P-value H_0: RMSEA >= 0.080 0.922
Standardized Root Mean Square Residual:
SRMR 0.077
Parameter Estimates:
Standard errors Bootstrap
Number of requested bootstrap draws 1000
Number of successful bootstrap draws 1000
Regressions:
Estimate Std.Err z-value P(>|z|)
AL_dsCM ~
s_LPLA_P_t (c) 0.047 0.026 1.827 0.068
s_LPLA_P_time ~
AL_dsCM (c) 0.047 0.026 1.827 0.068
AL_dsCM ~
DEM_dsHT (d) -0.487 0.121 -4.031 0.000
PA_dsSUP ~
s_LPLA_P_t (a) 0.303 0.082 3.717 0.000
AL_dsCM ~
PA_dsSUP (b) 0.290 0.135 2.152 0.031
Variances:
Estimate Std.Err z-value P(>|z|)
.AL_dsCM 1.402 0.336 4.170 0.000
.s_LPLA_P_time 0.786 0.247 3.183 0.001
.PA_dsSUP 0.754 0.071 10.543 0.000
R-Square:
Estimate
AL_dsCM 0.376
s_LPLA_P_time 0.019
PA_dsSUP 0.096
Defined Parameters:
Estimate Std.Err z-value P(>|z|)
ab 0.088 0.051 1.710 0.087
total -0.352 0.133 -2.645 0.008lavaanPlot(model = m.PA.BD.cm.c2.fit, node_options = list(shape = "box", fontname = "Helvetica"), edge_options = list(color = "grey"), coefs = TRUE, sig = .05, stars="regress")interpret(m.PA.BD.cm.c2.fit) Name Value Threshold Interpretation
1 GFI 0.9556183 0.95 satisfactory
2 AGFI 0.7780914 0.90 poor
3 NFI 0.8819406 0.90 poor
4 NNFI 0.7078726 0.90 poor
5 CFI 0.9026242 0.90 satisfactory
6 RMSEA 0.1726510 0.05 poor
7 SRMR 0.0770163 0.08 satisfactory
8 RFI 0.6458219 0.90 poor
9 PNFI 0.2939802 0.50 poor
10 IFI 0.9084860 0.90 satisfactorym.DEM.PA.UD.cm.c2 <- '# direct effect
AL_dsCM ~ c * s_LPLA_P_time
# mediator
DEM_dsHT ~ a1 * s_LPLA_P_time
AL_dsCM ~ b1 * DEM_dsHT
PA_dsSUP ~ a2 * s_LPLA_P_time
AL_dsCM ~ b2 * PA_dsSUP
# indirect effect (a*b)
a1b1 := a1*b1
a2b2 := a2*b2
# total effect
total := c + (a1*b1) + (a2*b2)
'
m.DEM.PA.UD.cm.c2.fit <- sem(m.DEM.PA.UD.cm.c2, data=df.c2, se = "bootstrap", bootstrap = 1000)
summary(m.DEM.PA.UD.cm.c2.fit, fit.measures=TRUE, rsquare=TRUE)lavaan 0.6.16 ended normally after 1 iteration
Estimator ML
Optimization method NLMINB
Number of model parameters 8
Number of observations 102
Model Test User Model:
Test statistic 0.551
Degrees of freedom 1
P-value (Chi-square) 0.458
Model Test Baseline Model:
Test statistic 68.448
Degrees of freedom 6
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 1.000
Tucker-Lewis Index (TLI) 1.043
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -490.567
Loglikelihood unrestricted model (H1) -490.292
Akaike (AIC) 997.135
Bayesian (BIC) 1018.135
Sample-size adjusted Bayesian (SABIC) 992.866
Root Mean Square Error of Approximation:
RMSEA 0.000
90 Percent confidence interval - lower 0.000
90 Percent confidence interval - upper 0.235
P-value H_0: RMSEA <= 0.050 0.512
P-value H_0: RMSEA >= 0.080 0.413
Standardized Root Mean Square Residual:
SRMR 0.025
Parameter Estimates:
Standard errors Bootstrap
Number of requested bootstrap draws 1000
Number of successful bootstrap draws 1000
Regressions:
Estimate Std.Err z-value P(>|z|)
AL_dsCM ~
s_LPLA_P_ (c) -0.040 0.099 -0.401 0.688
DEM_dsHT ~
s_LPLA_P_ (a1) -0.557 0.184 -3.022 0.003
AL_dsCM ~
DEM_dsHT (b1) -0.499 0.131 -3.813 0.000
PA_dsSUP ~
s_LPLA_P_ (a2) 0.316 0.079 3.986 0.000
AL_dsCM ~
PA_dsSUP (b2) 0.340 0.139 2.440 0.015
Variances:
Estimate Std.Err z-value P(>|z|)
.AL_dsCM 1.397 0.350 3.994 0.000
.DEM_dsHT 2.869 0.499 5.750 0.000
.PA_dsSUP 0.754 0.072 10.481 0.000
R-Square:
Estimate
AL_dsCM 0.391
DEM_dsHT 0.080
PA_dsSUP 0.097
Defined Parameters:
Estimate Std.Err z-value P(>|z|)
a1b1 0.278 0.143 1.948 0.051
a2b2 0.107 0.057 1.879 0.060
total 0.346 0.139 2.492 0.013lavaanPlot(model = m.DEM.PA.UD.cm.c2.fit, node_options = list(shape = "box", fontname = "Helvetica"), edge_options = list(color = "grey"), coefs = TRUE, sig = .05, stars="regress")interpret(m.DEM.PA.UD.cm.c2.fit) Name Value Threshold Interpretation
1 GFI 0.99649949 0.95 satisfactory
2 AGFI 0.96499486 0.90 satisfactory
3 NFI 0.99195230 0.90 satisfactory
4 NNFI 1.04315467 0.90 satisfactory
5 CFI 1.00000000 0.90 satisfactory
6 RMSEA 0.00000000 0.05 satisfactory
7 SRMR 0.02543241 0.08 satisfactory
8 RFI 0.95171383 0.90 satisfactory
9 PNFI 0.16532538 0.50 poor
10 IFI 1.00665926 0.90 satisfactorym.DEM.PA.BD.cm.c2 <- '# direct effect
AL_dsCM ~ c * s_LPLA_P_time
s_LPLA_P_time ~ c*AL_dsCM
# mediator
DEM_dsHT ~ a1 * s_LPLA_P_time
AL_dsCM ~ b1 * DEM_dsHT
PA_dsSUP ~ a2 * s_LPLA_P_time
AL_dsCM ~ b2 * PA_dsSUP
# indirect effect (a*b)
a1b1 := a1*b1
a2b2 := a2*b2
# total effect
total := c + (a1*b1) + (a2*b2)
'
m.DEM.PA.BD.cm.c2.fit <- sem(m.DEM.PA.BD.cm.c2, data=df.c2, se = "bootstrap", bootstrap = 1000)
summary(m.DEM.PA.BD.cm.c2.fit, fit.measures=TRUE, rsquare=TRUE)lavaan 0.6.16 ended normally after 12 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 10
Number of equality constraints 1
Number of observations 102
Model Test User Model:
Test statistic 0.550
Degrees of freedom 1
P-value (Chi-square) 0.458
Model Test Baseline Model:
Test statistic 68.448
Degrees of freedom 6
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 1.000
Tucker-Lewis Index (TLI) 1.043
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -624.364
Loglikelihood unrestricted model (H1) -624.089
Akaike (AIC) 1266.729
Bayesian (BIC) 1290.354
Sample-size adjusted Bayesian (SABIC) 1261.926
Root Mean Square Error of Approximation:
RMSEA 0.000
90 Percent confidence interval - lower 0.000
90 Percent confidence interval - upper 0.235
P-value H_0: RMSEA <= 0.050 0.513
P-value H_0: RMSEA >= 0.080 0.413
Standardized Root Mean Square Residual:
SRMR 0.025
Parameter Estimates:
Standard errors Bootstrap
Number of requested bootstrap draws 1000
Number of successful bootstrap draws 1000
Regressions:
Estimate Std.Err z-value P(>|z|)
AL_dsCM ~
s_LPLA_P_ (c) -0.015 0.037 -0.404 0.686
s_LPLA_P_time ~
AL_dsCM (c) -0.015 0.037 -0.404 0.686
DEM_dsHT ~
s_LPLA_P_ (a1) -0.583 0.231 -2.526 0.012
AL_dsCM ~
DEM_dsHT (b1) -0.499 0.128 -3.888 0.000
PA_dsSUP ~
s_LPLA_P_ (a2) 0.321 0.090 3.573 0.000
AL_dsCM ~
PA_dsSUP (b2) 0.340 0.134 2.532 0.011
Variances:
Estimate Std.Err z-value P(>|z|)
.AL_dsCM 1.397 0.343 4.072 0.000
.s_LPLA_P_time 0.816 0.258 3.159 0.002
.DEM_dsHT 2.870 0.510 5.628 0.000
.PA_dsSUP 0.754 0.075 10.104 0.000
R-Square:
Estimate
AL_dsCM 0.391
s_LPLA_P_time -0.011
DEM_dsHT 0.080
PA_dsSUP 0.097
Defined Parameters:
Estimate Std.Err z-value P(>|z|)
a1b1 0.291 0.168 1.731 0.084
a2b2 0.109 0.065 1.683 0.092
total 0.385 0.169 2.277 0.023lavaanPlot(model = m.DEM.PA.BD.cm.c2.fit, node_options = list(shape = "box", fontname = "Helvetica"), edge_options = list(color = "grey"), coefs = TRUE, sig = .05, stars="regress")interpret(m.DEM.PA.BD.cm.c2.fit) Name Value Threshold Interpretation
1 GFI 0.99731913 0.95 satisfactory
2 AGFI 0.97319132 0.90 satisfactory
3 NFI 0.99196685 0.90 satisfactory
4 NNFI 1.04325030 0.90 satisfactory
5 CFI 1.00000000 0.90 satisfactory
6 RMSEA 0.00000000 0.05 satisfactory
7 SRMR 0.02541498 0.08 satisfactory
8 RFI 0.95180107 0.90 satisfactory
9 PNFI 0.16532781 0.50 poor
10 IFI 1.00667402 0.90 satisfactoryanova(m.DEM.UD.cm.c2.fit, m.DEM.BD.cm.c2.fit, m.PA.UD.cm.c2.fit, m.PA.BD.cm.c2.fit, m.DEM.PA.UD.cm.c2.fit, m.DEM.PA.BD.cm.c2.fit)Warning in lavTestLRT(object = object, ..., model.names = NAMES): lavaan WARNING:
Some restricted models fit better than less restricted models;
either these models are not nested, or the less restricted model
failed to reach a global optimum. Smallest difference =
-2.8433420749944Warning in lavTestLRT(object = object, ..., model.names = NAMES): lavaan
WARNING: some models have the same degrees of freedom
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
m.DEM.UD.cm.c2.fit 1 732.53 748.28 0.5508
m.PA.UD.cm.c2.fit 1 596.16 611.91 0.5508 0.0000 0.00000 0
m.DEM.PA.UD.cm.c2.fit 1 997.13 1018.13 0.5508 0.0000 0.00000 0
m.DEM.PA.BD.cm.c2.fit 1 1266.73 1290.35 0.5499 -0.0010 0.00000 0
m.DEM.BD.cm.c2.fit 2 1002.13 1020.51 10.9242 10.3744 0.30316 1
m.PA.BD.cm.c2.fit 2 864.75 883.12 8.0809 -2.8433 0.00000 0
Pr(>Chisq)
m.DEM.UD.cm.c2.fit
m.PA.UD.cm.c2.fit
m.DEM.PA.UD.cm.c2.fit
m.DEM.PA.BD.cm.c2.fit
m.DEM.BD.cm.c2.fit 0.001278 **
m.PA.BD.cm.c2.fit
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1df.c3 <- df.1 %>% filter(cycle=="3")
m.DEM.UD.c3 <- ' # direct effect
AL_dsCTL ~ c * s_LPLA_P_time
AL_dsCTL ~ d * PA_dsSUP
# mediator
DEM_dsHT ~ a * s_LPLA_P_time
AL_dsCTL ~ b * DEM_dsHT
# indirect effect (a*b)
ab := a*b
# total effect
total := d + c + (a*b)
'
m.DEM.UD.c3.fit <- sem(m.DEM.UD.c3, data=df.c3, se = "bootstrap", bootstrap = 1000)
summary(m.DEM.UD.c3.fit, fit.measures=TRUE, rsquare=TRUE)lavaan 0.6.16 ended normally after 2 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 6
Number of observations 97
Model Test User Model:
Test statistic 5.476
Degrees of freedom 1
P-value (Chi-square) 0.019
Model Test Baseline Model:
Test statistic 58.636
Degrees of freedom 5
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.917
Tucker-Lewis Index (TLI) 0.583
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -288.172
Loglikelihood unrestricted model (H1) -285.434
Akaike (AIC) 588.344
Bayesian (BIC) 603.792
Sample-size adjusted Bayesian (SABIC) 584.846
Root Mean Square Error of Approximation:
RMSEA 0.215
90 Percent confidence interval - lower 0.069
90 Percent confidence interval - upper 0.405
P-value H_0: RMSEA <= 0.050 0.035
P-value H_0: RMSEA >= 0.080 0.939
Standardized Root Mean Square Residual:
SRMR 0.081
Parameter Estimates:
Standard errors Bootstrap
Number of requested bootstrap draws 1000
Number of successful bootstrap draws 1000
Regressions:
Estimate Std.Err z-value P(>|z|)
AL_dsCTL ~
s_LPLA_P_t (c) 0.339 0.071 4.740 0.000
PA_dsSUP (d) 0.062 0.103 0.603 0.546
DEM_dsHT ~
s_LPLA_P_t (a) -0.215 0.124 -1.737 0.082
AL_dsCTL ~
DEM_dsHT (b) -0.406 0.066 -6.122 0.000
Variances:
Estimate Std.Err z-value P(>|z|)
.AL_dsCTL 0.757 0.117 6.456 0.000
.DEM_dsHT 1.725 0.268 6.423 0.000
R-Square:
Estimate
AL_dsCTL 0.397
DEM_dsHT 0.029
Defined Parameters:
Estimate Std.Err z-value P(>|z|)
ab 0.088 0.055 1.602 0.109
total 0.488 0.129 3.794 0.000lavaanPlot(model = m.DEM.UD.c3.fit, node_options = list(shape = "box", fontname = "Helvetica"), edge_options = list(color = "grey"), coefs = TRUE, sig = .05, stars="regress")interpret(m.DEM.UD.c3.fit) Name Value Threshold Interpretation
1 GFI 0.94903789 0.95 poor
2 AGFI 0.49037890 0.90 poor
3 NFI 0.90661055 0.90 satisfactory
4 NNFI 0.58274416 0.90 poor
5 CFI 0.91654883 0.90 satisfactory
6 RMSEA 0.21481265 0.05 poor
7 SRMR 0.08081569 0.08 poor
8 RFI 0.53305277 0.90 poor
9 PNFI 0.18132211 0.50 poor
10 IFI 0.92234040 0.90 satisfactorym.DEM.BD.c3 <- ' # direct effect
AL_dsCTL ~ c * s_LPLA_P_time
s_LPLA_P_time ~ c*AL_dsCTL
AL_dsCTL ~ d * PA_dsSUP
# mediator
DEM_dsHT ~ a * s_LPLA_P_time
AL_dsCTL ~ b * DEM_dsHT
# indirect effect (a*b)
ab := a*b
# total effect
total := d + c + (a*b)
'
m.DEM.BD.c3.fit <- sem(m.DEM.BD.c3, data=df.c3, se = "bootstrap", bootstrap = 1000)
summary(m.DEM.BD.c3.fit, fit.measures=TRUE, rsquare=TRUE)lavaan 0.6.16 ended normally after 11 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 8
Number of equality constraints 1
Number of observations 97
Model Test User Model:
Test statistic 6.181
Degrees of freedom 2
P-value (Chi-square) 0.045
Model Test Baseline Model:
Test statistic 59.502
Degrees of freedom 6
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.922
Tucker-Lewis Index (TLI) 0.766
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -431.421
Loglikelihood unrestricted model (H1) -428.331
Akaike (AIC) 876.843
Bayesian (BIC) 894.866
Sample-size adjusted Bayesian (SABIC) 872.762
Root Mean Square Error of Approximation:
RMSEA 0.147
90 Percent confidence interval - lower 0.018
90 Percent confidence interval - upper 0.285
P-value H_0: RMSEA <= 0.050 0.082
P-value H_0: RMSEA >= 0.080 0.855
Standardized Root Mean Square Residual:
SRMR 0.092
Parameter Estimates:
Standard errors Bootstrap
Number of requested bootstrap draws 1000
Number of successful bootstrap draws 1000
Regressions:
Estimate Std.Err z-value P(>|z|)
AL_dsCTL ~
s_LPLA_P_t (c) 0.195 0.039 5.055 0.000
s_LPLA_P_time ~
AL_dsCTL (c) 0.195 0.039 5.055 0.000
AL_dsCTL ~
PA_dsSUP (d) 0.073 0.106 0.689 0.491
DEM_dsHT ~
s_LPLA_P_t (a) -0.084 0.152 -0.551 0.581
AL_dsCTL ~
DEM_dsHT (b) -0.416 0.068 -6.116 0.000
Variances:
Estimate Std.Err z-value P(>|z|)
.AL_dsCTL 0.779 0.122 6.386 0.000
.s_LPLA_P_time 0.984 0.207 4.743 0.000
.DEM_dsHT 1.744 0.288 6.047 0.000
R-Square:
Estimate
AL_dsCTL 0.376
s_LPLA_P_time 0.124
DEM_dsHT 0.018
Defined Parameters:
Estimate Std.Err z-value P(>|z|)
ab 0.035 0.065 0.535 0.593
total 0.303 0.129 2.347 0.019lavaanPlot(model = m.DEM.BD.c3.fit, node_options = list(shape = "box", fontname = "Helvetica"), edge_options = list(color = "grey"), coefs = TRUE, sig = .05, stars="regress")interpret(m.DEM.BD.c3.fit) Name Value Threshold Interpretation
1 GFI 0.96052796 0.95 satisfactory
2 AGFI 0.80263979 0.90 poor
3 NFI 0.89611747 0.90 poor
4 NNFI 0.76554748 0.90 poor
5 CFI 0.92184916 0.90 satisfactory
6 RMSEA 0.14680886 0.05 poor
7 SRMR 0.09239585 0.08 poor
8 RFI 0.68835242 0.90 poor
9 PNFI 0.29870582 0.50 poor
10 IFI 0.92728552 0.90 satisfactorym.PA.UD.c3 <- ' # direct effect
AL_dsCTL ~ c * s_LPLA_P_time
AL_dsCTL ~ d * DEM_dsHT
# mediator
PA_dsSUP ~ a * s_LPLA_P_time
AL_dsCTL ~ b * PA_dsSUP
# indirect effect (a*b)
ab := a*b
# total effect
total := d + c + (a*b)
'
m.PA.UD.c3.fit <- sem(m.PA.UD.c3, data=df.c3, se = "bootstrap", bootstrap = 1000)
summary(m.PA.UD.c3.fit, fit.measures=TRUE, rsquare=TRUE)lavaan 0.6.16 ended normally after 2 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 6
Number of observations 97
Model Test User Model:
Test statistic 5.476
Degrees of freedom 1
P-value (Chi-square) 0.019
Model Test Baseline Model:
Test statistic 56.609
Degrees of freedom 5
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.913
Tucker-Lewis Index (TLI) 0.566
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -261.387
Loglikelihood unrestricted model (H1) -258.649
Akaike (AIC) 534.773
Bayesian (BIC) 550.222
Sample-size adjusted Bayesian (SABIC) 531.276
Root Mean Square Error of Approximation:
RMSEA 0.215
90 Percent confidence interval - lower 0.069
90 Percent confidence interval - upper 0.405
P-value H_0: RMSEA <= 0.050 0.035
P-value H_0: RMSEA >= 0.080 0.939
Standardized Root Mean Square Residual:
SRMR 0.081
Parameter Estimates:
Standard errors Bootstrap
Number of requested bootstrap draws 1000
Number of successful bootstrap draws 1000
Regressions:
Estimate Std.Err z-value P(>|z|)
AL_dsCTL ~
s_LPLA_P_t (c) 0.339 0.069 4.939 0.000
DEM_dsHT (d) -0.406 0.065 -6.270 0.000
PA_dsSUP ~
s_LPLA_P_t (a) 0.089 0.095 0.932 0.351
AL_dsCTL ~
PA_dsSUP (b) 0.062 0.102 0.610 0.542
Variances:
Estimate Std.Err z-value P(>|z|)
.AL_dsCTL 0.757 0.112 6.730 0.000
.PA_dsSUP 0.993 0.098 10.166 0.000
R-Square:
Estimate
AL_dsCTL 0.397
PA_dsSUP 0.009
Defined Parameters:
Estimate Std.Err z-value P(>|z|)
ab 0.006 0.014 0.397 0.691
total -0.062 0.098 -0.636 0.525lavaanPlot(model = m.PA.UD.c3.fit, node_options = list(shape = "box", fontname = "Helvetica"), edge_options = list(color = "grey"), coefs = TRUE, sig = .05, stars="regress")interpret(m.PA.UD.c3.fit) Name Value Threshold Interpretation
1 GFI 0.95665389 0.95 satisfactory
2 AGFI 0.56653887 0.90 poor
3 NFI 0.90326625 0.90 satisfactory
4 NNFI 0.56635450 0.90 poor
5 CFI 0.91327090 0.90 satisfactory
6 RMSEA 0.21481265 0.05 poor
7 SRMR 0.08081569 0.08 poor
8 RFI 0.51633126 0.90 poor
9 PNFI 0.18065325 0.50 poor
10 IFI 0.91950938 0.90 satisfactorym.PA.BD.c3 <- ' # direct effect
AL_dsCTL ~ c * s_LPLA_P_time
s_LPLA_P_time ~ c*AL_dsCTL
AL_dsCTL ~ d * DEM_dsHT
# mediator
PA_dsSUP ~ a * s_LPLA_P_time
AL_dsCTL ~ b * PA_dsSUP
# indirect effect (a*b)
ab := a*b
# total effect
total := d + c + (a*b)
'
m.PA.BD.c3.fit <- sem(m.PA.BD.c3, data=df.c3, se = "bootstrap", bootstrap = 1000)
summary(m.PA.BD.c3.fit, fit.measures=TRUE, rsquare=TRUE)lavaan 0.6.16 ended normally after 9 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 8
Number of equality constraints 1
Number of observations 97
Model Test User Model:
Test statistic 5.915
Degrees of freedom 2
P-value (Chi-square) 0.052
Model Test Baseline Model:
Test statistic 59.502
Degrees of freedom 6
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.927
Tucker-Lewis Index (TLI) 0.780
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -403.489
Loglikelihood unrestricted model (H1) -400.532
Akaike (AIC) 820.978
Bayesian (BIC) 839.001
Sample-size adjusted Bayesian (SABIC) 816.898
Root Mean Square Error of Approximation:
RMSEA 0.142
90 Percent confidence interval - lower 0.000
90 Percent confidence interval - upper 0.281
P-value H_0: RMSEA <= 0.050 0.091
P-value H_0: RMSEA >= 0.080 0.841
Standardized Root Mean Square Residual:
SRMR 0.087
Parameter Estimates:
Standard errors Bootstrap
Number of requested bootstrap draws 1000
Number of successful bootstrap draws 1000
Regressions:
Estimate Std.Err z-value P(>|z|)
AL_dsCTL ~
s_LPLA_P_t (c) 0.202 0.038 5.338 0.000
s_LPLA_P_time ~
AL_dsCTL (c) 0.202 0.038 5.338 0.000
AL_dsCTL ~
DEM_dsHT (d) -0.426 0.066 -6.486 0.000
PA_dsSUP ~
s_LPLA_P_t (a) 0.078 0.094 0.832 0.405
AL_dsCTL ~
PA_dsSUP (b) 0.056 0.111 0.510 0.610
Variances:
Estimate Std.Err z-value P(>|z|)
.AL_dsCTL 0.777 0.119 6.542 0.000
.s_LPLA_P_time 0.980 0.196 5.007 0.000
.PA_dsSUP 0.993 0.100 9.953 0.000
R-Square:
Estimate
AL_dsCTL 0.376
s_LPLA_P_time 0.122
PA_dsSUP 0.009
Defined Parameters:
Estimate Std.Err z-value P(>|z|)
ab 0.004 0.014 0.310 0.756
total -0.220 0.075 -2.920 0.004lavaanPlot(model = m.PA.BD.c3.fit, node_options = list(shape = "box", fontname = "Helvetica"), edge_options = list(color = "grey"), coefs = TRUE, sig = .05, stars="regress")interpret(m.PA.BD.c3.fit) Name Value Threshold Interpretation
1 GFI 0.96446286 0.95 satisfactory
2 AGFI 0.82231430 0.90 poor
3 NFI 0.90059253 0.90 satisfactory
4 NNFI 0.78047820 0.90 poor
5 CFI 0.92682607 0.90 satisfactory
6 RMSEA 0.14205733 0.05 poor
7 SRMR 0.08656915 0.08 poor
8 RFI 0.70177759 0.90 poor
9 PNFI 0.30019751 0.50 poor
10 IFI 0.93191622 0.90 satisfactorym.DEM.PA.UD.c3 <- '# direct effect
AL_dsCTL ~ c * s_LPLA_P_time
# mediator
DEM_dsHT ~ a1 * s_LPLA_P_time
AL_dsCTL ~ b1 * DEM_dsHT
PA_dsSUP ~ a2 * s_LPLA_P_time
AL_dsCTL ~ b2 * PA_dsSUP
# indirect effect (a*b)
a1b1 := a1*b1
a2b2 := a2*b2
# total effect
total := c + (a1*b1) + (a2*b2)
'
m.DEM.PA.UD.c3.fit <- sem(m.DEM.PA.UD.c3, data=df.c3, se = "bootstrap", bootstrap = 1000)
summary(m.DEM.PA.UD.c3.fit, fit.measures=TRUE, rsquare=TRUE)lavaan 0.6.16 ended normally after 1 iteration
Estimator ML
Optimization method NLMINB
Number of model parameters 8
Number of observations 97
Model Test User Model:
Test statistic 5.476
Degrees of freedom 1
P-value (Chi-square) 0.019
Model Test Baseline Model:
Test statistic 59.502
Degrees of freedom 6
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.916
Tucker-Lewis Index (TLI) 0.498
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -425.453
Loglikelihood unrestricted model (H1) -422.715
Akaike (AIC) 866.906
Bayesian (BIC) 887.504
Sample-size adjusted Bayesian (SABIC) 862.243
Root Mean Square Error of Approximation:
RMSEA 0.215
90 Percent confidence interval - lower 0.069
90 Percent confidence interval - upper 0.405
P-value H_0: RMSEA <= 0.050 0.035
P-value H_0: RMSEA >= 0.080 0.939
Standardized Root Mean Square Residual:
SRMR 0.081
Parameter Estimates:
Standard errors Bootstrap
Number of requested bootstrap draws 1000
Number of successful bootstrap draws 1000
Regressions:
Estimate Std.Err z-value P(>|z|)
AL_dsCTL ~
s_LPLA_P_ (c) 0.339 0.066 5.141 0.000
DEM_dsHT ~
s_LPLA_P_ (a1) -0.215 0.119 -1.813 0.070
AL_dsCTL ~
DEM_dsHT (b1) -0.406 0.067 -6.085 0.000
PA_dsSUP ~
s_LPLA_P_ (a2) 0.089 0.089 0.997 0.319
AL_dsCTL ~
PA_dsSUP (b2) 0.062 0.100 0.618 0.536
Variances:
Estimate Std.Err z-value P(>|z|)
.AL_dsCTL 0.757 0.116 6.502 0.000
.DEM_dsHT 1.725 0.274 6.294 0.000
.PA_dsSUP 0.993 0.100 9.950 0.000
R-Square:
Estimate
AL_dsCTL 0.397
DEM_dsHT 0.029
PA_dsSUP 0.009
Defined Parameters:
Estimate Std.Err z-value P(>|z|)
a1b1 0.088 0.052 1.678 0.093
a2b2 0.006 0.014 0.388 0.698
total 0.432 0.076 5.696 0.000lavaanPlot(model = m.DEM.PA.UD.c3.fit, node_options = list(shape = "box", fontname = "Helvetica"), edge_options = list(color = "grey"), coefs = TRUE, sig = .05, stars="regress")interpret(m.DEM.PA.UD.c3.fit) Name Value Threshold Interpretation
1 GFI 0.96501930 0.95 satisfactory
2 AGFI 0.65019298 0.90 poor
3 NFI 0.90796974 0.90 satisfactory
4 NNFI 0.49803887 0.90 poor
5 CFI 0.91633981 0.90 satisfactory
6 RMSEA 0.21481265 0.05 poor
7 SRMR 0.08081569 0.08 poor
8 RFI 0.44781842 0.90 poor
9 PNFI 0.15132829 0.50 poor
10 IFI 0.92348997 0.90 satisfactorym.DEM.PA.BD.c3 <- '# direct effect
AL_dsCTL ~ c * s_LPLA_P_time
s_LPLA_P_time ~ c*AL_dsCTL
# mediator
DEM_dsHT ~ a1 * s_LPLA_P_time
AL_dsCTL ~ b1 * DEM_dsHT
PA_dsSUP ~ a2 * s_LPLA_P_time
AL_dsCTL ~ b2 * PA_dsSUP
# indirect effect (a*b)
a1b1 := a1*b1
a2b2 := a2*b2
# total effect
total := c + (a1*b1) + (a2*b2)
'
m.DEM.PA.BD.c3.fit <- sem(m.DEM.PA.BD.c3, data=df.c3, se = "bootstrap", bootstrap = 1000)
summary(m.DEM.PA.BD.c3.fit, fit.measures=TRUE, rsquare=TRUE)lavaan 0.6.16 ended normally after 12 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 10
Number of equality constraints 1
Number of observations 97
Model Test User Model:
Test statistic 5.534
Degrees of freedom 1
P-value (Chi-square) 0.019
Model Test Baseline Model:
Test statistic 59.502
Degrees of freedom 6
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.915
Tucker-Lewis Index (TLI) 0.492
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -568.812
Loglikelihood unrestricted model (H1) -566.045
Akaike (AIC) 1155.623
Bayesian (BIC) 1178.796
Sample-size adjusted Bayesian (SABIC) 1150.377
Root Mean Square Error of Approximation:
RMSEA 0.216
90 Percent confidence interval - lower 0.071
90 Percent confidence interval - upper 0.406
P-value H_0: RMSEA <= 0.050 0.034
P-value H_0: RMSEA >= 0.080 0.940
Standardized Root Mean Square Residual:
SRMR 0.082
Parameter Estimates:
Standard errors Bootstrap
Number of requested bootstrap draws 1000
Number of successful bootstrap draws 1000
Regressions:
Estimate Std.Err z-value P(>|z|)
AL_dsCTL ~
s_LPLA_P_ (c) 0.194 0.039 4.957 0.000
s_LPLA_P_time ~
AL_dsCTL (c) 0.194 0.039 4.957 0.000
DEM_dsHT ~
s_LPLA_P_ (a1) -0.083 0.145 -0.576 0.564
AL_dsCTL ~
DEM_dsHT (b1) -0.419 0.066 -6.344 0.000
PA_dsSUP ~
s_LPLA_P_ (a2) 0.078 0.096 0.811 0.417
AL_dsCTL ~
PA_dsSUP (b2) 0.060 0.112 0.538 0.591
Variances:
Estimate Std.Err z-value P(>|z|)
.AL_dsCTL 0.779 0.118 6.585 0.000
.s_LPLA_P_time 0.984 0.208 4.729 0.000
.DEM_dsHT 1.744 0.283 6.170 0.000
.PA_dsSUP 0.993 0.099 10.069 0.000
R-Square:
Estimate
AL_dsCTL 0.378
s_LPLA_P_time 0.125
DEM_dsHT 0.018
PA_dsSUP 0.009
Defined Parameters:
Estimate Std.Err z-value P(>|z|)
a1b1 0.035 0.063 0.556 0.578
a2b2 0.005 0.015 0.306 0.760
total 0.234 0.067 3.462 0.001lavaanPlot(model = m.DEM.PA.BD.c3.fit, node_options = list(shape = "box", fontname = "Helvetica"), edge_options = list(color = "grey"), coefs = TRUE, sig = .05, stars="regress")interpret(m.DEM.PA.BD.c3.fit) Name Value Threshold Interpretation
1 GFI 0.97300681 0.95 satisfactory
2 AGFI 0.73006814 0.90 poor
3 NFI 0.90700036 0.90 satisfactory
4 NNFI 0.49157033 0.90 poor
5 CFI 0.91526172 0.90 satisfactory
6 RMSEA 0.21619232 0.05 poor
7 SRMR 0.08171611 0.08 poor
8 RFI 0.44200214 0.90 poor
9 PNFI 0.15116673 0.50 poor
10 IFI 0.92250402 0.90 satisfactoryanova(m.DEM.UD.c3.fit, m.DEM.BD.c3.fit,m.PA.UD.c3.fit,m.PA.BD.c3.fit, m.DEM.PA.UD.c3.fit, m.DEM.PA.BD.c3.fit)Warning in lavTestLRT(object = object, ..., model.names = NAMES): lavaan WARNING:
Some restricted models fit better than less restricted models;
either these models are not nested, or the less restricted model
failed to reach a global optimum. Smallest difference =
-0.266276183108956Warning in lavTestLRT(object = object, ..., model.names = NAMES): lavaan
WARNING: some models have the same degrees of freedom
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
m.DEM.UD.c3.fit 1 588.34 603.79 5.4760
m.PA.UD.c3.fit 1 534.77 550.22 5.4760 0.00000 0 0
m.DEM.PA.UD.c3.fit 1 866.91 887.50 5.4760 0.00000 0 0
m.DEM.PA.BD.c3.fit 1 1155.62 1178.80 5.5337 0.05768 0 0
m.DEM.BD.c3.fit 2 876.84 894.87 6.1813 0.64756 0 1
m.PA.BD.c3.fit 2 820.98 839.00 5.9150 -0.26628 0 0
Pr(>Chisq)
m.DEM.UD.c3.fit
m.PA.UD.c3.fit
m.DEM.PA.UD.c3.fit
m.DEM.PA.BD.c3.fit
m.DEM.BD.c3.fit 0.421
m.PA.BD.c3.fit df.c3 <- df.1 %>% filter(cycle=="3")
m.DEM.UD.cm.c3 <- ' # direct effect
AL_dsCM ~ c * s_LPLA_P_time
AL_dsCM ~ d * PA_dsSUP
# mediator
DEM_dsHT ~ a * s_LPLA_P_time
AL_dsCM ~ b * DEM_dsHT
# indirect effect (a*b)
ab := a*b
# total effect
total := d + c + (a*b)
'
m.DEM.UD.cm.c3.fit <- sem(m.DEM.UD.cm.c3, data=df.c3, se = "bootstrap", bootstrap = 1000)
summary(m.DEM.UD.cm.c3.fit, fit.measures=TRUE, rsquare=TRUE)lavaan 0.6.16 ended normally after 1 iteration
Estimator ML
Optimization method NLMINB
Number of model parameters 6
Number of observations 97
Model Test User Model:
Test statistic 5.476
Degrees of freedom 1
P-value (Chi-square) 0.019
Model Test Baseline Model:
Test statistic 32.955
Degrees of freedom 5
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.840
Tucker-Lewis Index (TLI) 0.199
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -253.911
Loglikelihood unrestricted model (H1) -251.173
Akaike (AIC) 519.822
Bayesian (BIC) 535.270
Sample-size adjusted Bayesian (SABIC) 516.324
Root Mean Square Error of Approximation:
RMSEA 0.215
90 Percent confidence interval - lower 0.069
90 Percent confidence interval - upper 0.405
P-value H_0: RMSEA <= 0.050 0.035
P-value H_0: RMSEA >= 0.080 0.939
Standardized Root Mean Square Residual:
SRMR 0.078
Parameter Estimates:
Standard errors Bootstrap
Number of requested bootstrap draws 1000
Number of successful bootstrap draws 1000
Regressions:
Estimate Std.Err z-value P(>|z|)
AL_dsCM ~
s_LPLA_P_t (c) 0.124 0.055 2.273 0.023
PA_dsSUP (d) 0.076 0.064 1.187 0.235
DEM_dsHT ~
s_LPLA_P_t (a) -0.215 0.128 -1.680 0.093
AL_dsCM ~
DEM_dsHT (b) -0.187 0.060 -3.108 0.002
Variances:
Estimate Std.Err z-value P(>|z|)
.AL_dsCM 0.373 0.054 6.852 0.000
.DEM_dsHT 1.725 0.279 6.192 0.000
R-Square:
Estimate
AL_dsCM 0.210
DEM_dsHT 0.029
Defined Parameters:
Estimate Std.Err z-value P(>|z|)
ab 0.040 0.029 1.399 0.162
total 0.241 0.082 2.918 0.004lavaanPlot(model = m.DEM.UD.cm.c3.fit, node_options = list(shape = "box", fontname = "Helvetica"), edge_options = list(color = "grey"), coefs = TRUE, sig = .05, stars="regress")interpret(m.DEM.UD.cm.c3.fit) Name Value Threshold Interpretation
1 GFI 0.94817641 0.95 poor
2 AGFI 0.48176410 0.90 poor
3 NFI 0.83383123 0.90 poor
4 NNFI 0.19941176 0.90 poor
5 CFI 0.83988235 0.90 poor
6 RMSEA 0.21481265 0.05 poor
7 SRMR 0.07783175 0.08 satisfactory
8 RFI 0.16915617 0.90 poor
9 PNFI 0.16676625 0.50 poor
10 IFI 0.85992554 0.90 poorm.DEM.BD.cm.c3 <- ' # direct effect
AL_dsCM ~ c * s_LPLA_P_time
s_LPLA_P_time ~ c*AL_dsCM
AL_dsCM ~ d * PA_dsSUP
# mediator
DEM_dsHT ~ a * s_LPLA_P_time
AL_dsCM ~ b * DEM_dsHT
# indirect effect (a*b)
ab := a*b
# total effect
total := d + c + (a*b)
'
m.DEM.BD.cm.c3.fit <- sem(m.DEM.BD.cm.c3, data=df.c3, se = "bootstrap", bootstrap = 1000)
summary(m.DEM.BD.cm.c3.fit, fit.measures=TRUE, rsquare=TRUE)lavaan 0.6.16 ended normally after 11 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 8
Number of equality constraints 1
Number of observations 97
Model Test User Model:
Test statistic 6.227
Degrees of freedom 2
P-value (Chi-square) 0.044
Model Test Baseline Model:
Test statistic 33.821
Degrees of freedom 6
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.848
Tucker-Lewis Index (TLI) 0.544
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -397.183
Loglikelihood unrestricted model (H1) -394.070
Akaike (AIC) 808.366
Bayesian (BIC) 826.389
Sample-size adjusted Bayesian (SABIC) 804.286
Root Mean Square Error of Approximation:
RMSEA 0.148
90 Percent confidence interval - lower 0.020
90 Percent confidence interval - upper 0.286
P-value H_0: RMSEA <= 0.050 0.080
P-value H_0: RMSEA >= 0.080 0.857
Standardized Root Mean Square Residual:
SRMR 0.089
Parameter Estimates:
Standard errors Bootstrap
Number of requested bootstrap draws 1000
Number of successful bootstrap draws 1000
Regressions:
Estimate Std.Err z-value P(>|z|)
AL_dsCM ~
s_LPLA_P_t (c) 0.094 0.039 2.390 0.017
s_LPLA_P_time ~
AL_dsCM (c) 0.094 0.039 2.390 0.017
AL_dsCM ~
PA_dsSUP (d) 0.079 0.064 1.242 0.214
DEM_dsHT ~
s_LPLA_P_t (a) -0.188 0.129 -1.463 0.143
AL_dsCM ~
DEM_dsHT (b) -0.187 0.056 -3.315 0.001
Variances:
Estimate Std.Err z-value P(>|z|)
.AL_dsCM 0.374 0.056 6.718 0.000
.s_LPLA_P_time 1.093 0.248 4.409 0.000
.DEM_dsHT 1.725 0.283 6.104 0.000
R-Square:
Estimate
AL_dsCM 0.204
s_LPLA_P_time 0.028
DEM_dsHT 0.029
Defined Parameters:
Estimate Std.Err z-value P(>|z|)
ab 0.035 0.027 1.303 0.193
total 0.208 0.073 2.841 0.004lavaanPlot(model = m.DEM.BD.cm.c3.fit, node_options = list(shape = "box", fontname = "Helvetica"), edge_options = list(color = "grey"), coefs = TRUE, sig = .05, stars="regress")interpret(m.DEM.BD.cm.c3.fit) Name Value Threshold Interpretation
1 GFI 0.96024178 0.95 satisfactory
2 AGFI 0.80120892 0.90 poor
3 NFI 0.81588498 0.90 poor
4 NNFI 0.54419983 0.90 poor
5 CFI 0.84806661 0.90 poor
6 RMSEA 0.14760750 0.05 poor
7 SRMR 0.08887802 0.08 poor
8 RFI 0.44765494 0.90 poor
9 PNFI 0.27196166 0.50 poor
10 IFI 0.86716540 0.90 poorm.PA.UD.cm.c3 <- ' # direct effect
AL_dsCM ~ c * s_LPLA_P_time
AL_dsCM ~ d * DEM_dsHT
# mediator
PA_dsSUP ~ a * s_LPLA_P_time
AL_dsCM ~ b * PA_dsSUP
# indirect effect (a*b)
ab := a*b
# total effect
total := d + c + (a*b)
'
m.PA.UD.cm.c3.fit <- sem(m.PA.UD.cm.c3, data=df.c3, se = "bootstrap", bootstrap = 1000)
summary(m.PA.UD.cm.c3.fit, fit.measures=TRUE, rsquare=TRUE)lavaan 0.6.16 ended normally after 1 iteration
Estimator ML
Optimization method NLMINB
Number of model parameters 6
Number of observations 97
Model Test User Model:
Test statistic 5.476
Degrees of freedom 1
P-value (Chi-square) 0.019
Model Test Baseline Model:
Test statistic 30.927
Degrees of freedom 5
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.827
Tucker-Lewis Index (TLI) 0.137
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -227.126
Loglikelihood unrestricted model (H1) -224.388
Akaike (AIC) 466.251
Bayesian (BIC) 481.700
Sample-size adjusted Bayesian (SABIC) 462.754
Root Mean Square Error of Approximation:
RMSEA 0.215
90 Percent confidence interval - lower 0.069
90 Percent confidence interval - upper 0.405
P-value H_0: RMSEA <= 0.050 0.035
P-value H_0: RMSEA >= 0.080 0.939
Standardized Root Mean Square Residual:
SRMR 0.078
Parameter Estimates:
Standard errors Bootstrap
Number of requested bootstrap draws 1000
Number of successful bootstrap draws 1000
Regressions:
Estimate Std.Err z-value P(>|z|)
AL_dsCM ~
s_LPLA_P_t (c) 0.124 0.055 2.242 0.025
DEM_dsHT (d) -0.187 0.060 -3.135 0.002
PA_dsSUP ~
s_LPLA_P_t (a) 0.089 0.093 0.958 0.338
AL_dsCM ~
PA_dsSUP (b) 0.076 0.063 1.196 0.232
Variances:
Estimate Std.Err z-value P(>|z|)
.AL_dsCM 0.373 0.055 6.848 0.000
.PA_dsSUP 0.993 0.102 9.734 0.000
R-Square:
Estimate
AL_dsCM 0.210
PA_dsSUP 0.009
Defined Parameters:
Estimate Std.Err z-value P(>|z|)
ab 0.007 0.011 0.641 0.522
total -0.056 0.092 -0.605 0.545lavaanPlot(model = m.PA.UD.cm.c3.fit, node_options = list(shape = "box", fontname = "Helvetica"), edge_options = list(color = "grey"), coefs = TRUE, sig = .05, stars="regress")interpret(m.PA.UD.cm.c3.fit) Name Value Threshold Interpretation
1 GFI 0.94941909 0.95 poor
2 AGFI 0.49419085 0.90 poor
3 NFI 0.82293940 0.90 poor
4 NNFI 0.13681594 0.90 poor
5 CFI 0.82736319 0.90 poor
6 RMSEA 0.21481265 0.05 poor
7 SRMR 0.07783175 0.08 satisfactory
8 RFI 0.11469701 0.90 poor
9 PNFI 0.16458788 0.50 poor
10 IFI 0.85043731 0.90 poorm.PA.BD.cm.c3 <- ' # direct effect
AL_dsCM ~ c * s_LPLA_P_time
s_LPLA_P_time ~ c*AL_dsCM
AL_dsCM ~ d * DEM_dsHT
# mediator
PA_dsSUP ~ a * s_LPLA_P_time
AL_dsCM ~ b * PA_dsSUP
# indirect effect (a*b)
ab := a*b
# total effect
total := d + c + (a*b)
'
m.PA.BD.cm.c3.fit <- sem(m.PA.BD.cm.c3, data=df.c3, se = "bootstrap", bootstrap = 1000)
summary(m.PA.BD.cm.c3.fit, fit.measures=TRUE, rsquare=TRUE)lavaan 0.6.16 ended normally after 11 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 8
Number of equality constraints 1
Number of observations 97
Model Test User Model:
Test statistic 7.659
Degrees of freedom 2
P-value (Chi-square) 0.022
Model Test Baseline Model:
Test statistic 33.821
Degrees of freedom 6
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.797
Tucker-Lewis Index (TLI) 0.390
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -370.100
Loglikelihood unrestricted model (H1) -366.271
Akaike (AIC) 754.200
Bayesian (BIC) 772.223
Sample-size adjusted Bayesian (SABIC) 750.120
Root Mean Square Error of Approximation:
RMSEA 0.171
90 Percent confidence interval - lower 0.056
90 Percent confidence interval - upper 0.306
P-value H_0: RMSEA <= 0.050 0.044
P-value H_0: RMSEA >= 0.080 0.913
Standardized Root Mean Square Residual:
SRMR 0.097
Parameter Estimates:
Standard errors Bootstrap
Number of requested bootstrap draws 1000
Number of successful bootstrap draws 1000
Regressions:
Estimate Std.Err z-value P(>|z|)
AL_dsCM ~
s_LPLA_P_t (c) 0.099 0.038 2.617 0.009
s_LPLA_P_time ~
AL_dsCM (c) 0.099 0.038 2.617 0.009
AL_dsCM ~
DEM_dsHT (d) -0.191 0.058 -3.316 0.001
PA_dsSUP ~
s_LPLA_P_t (a) 0.082 0.095 0.868 0.385
AL_dsCM ~
PA_dsSUP (b) 0.074 0.066 1.115 0.265
Variances:
Estimate Std.Err z-value P(>|z|)
.AL_dsCM 0.374 0.055 6.812 0.000
.s_LPLA_P_time 1.091 0.235 4.639 0.000
.PA_dsSUP 0.993 0.102 9.744 0.000
R-Square:
Estimate
AL_dsCM 0.197
s_LPLA_P_time 0.025
PA_dsSUP 0.009
Defined Parameters:
Estimate Std.Err z-value P(>|z|)
ab 0.006 0.011 0.580 0.562
total -0.085 0.074 -1.154 0.248lavaanPlot(model = m.PA.BD.cm.c3.fit, node_options = list(shape = "box", fontname = "Helvetica"), edge_options = list(color = "grey"), coefs = TRUE, sig = .05, stars="regress")interpret(m.PA.BD.cm.c3.fit) Name Value Threshold Interpretation
1 GFI 0.95249864 0.95 satisfactory
2 AGFI 0.76249319 0.90 poor
3 NFI 0.77354438 0.90 poor
4 NNFI 0.38978349 0.90 poor
5 CFI 0.79659450 0.90 poor
6 RMSEA 0.17079028 0.05 poor
7 SRMR 0.09709379 0.08 poor
8 RFI 0.32063315 0.90 poor
9 PNFI 0.25784813 0.50 poor
10 IFI 0.82216359 0.90 poorm.DEM.PA.UD.cm.c3 <- '# direct effect
AL_dsCM ~ c * s_LPLA_P_time
# mediator
DEM_dsHT ~ a1 * s_LPLA_P_time
AL_dsCM ~ b1 * DEM_dsHT
PA_dsSUP ~ a2 * s_LPLA_P_time
AL_dsCM ~ b2 * PA_dsSUP
# indirect effect (a*b)
a1b1 := a1*b1
a2b2 := a2*b2
# total effect
total := c + (a1*b1) + (a2*b2)
'
m.DEM.PA.UD.cm.c3.fit <- sem(m.DEM.PA.UD.cm.c3, data=df.c3, se = "bootstrap", bootstrap = 1000)
summary(m.DEM.PA.UD.cm.c3.fit, fit.measures=TRUE, rsquare=TRUE)lavaan 0.6.16 ended normally after 1 iteration
Estimator ML
Optimization method NLMINB
Number of model parameters 8
Number of observations 97
Model Test User Model:
Test statistic 5.476
Degrees of freedom 1
P-value (Chi-square) 0.019
Model Test Baseline Model:
Test statistic 33.821
Degrees of freedom 6
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.839
Tucker-Lewis Index (TLI) 0.035
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -391.192
Loglikelihood unrestricted model (H1) -388.454
Akaike (AIC) 798.384
Bayesian (BIC) 818.982
Sample-size adjusted Bayesian (SABIC) 793.721
Root Mean Square Error of Approximation:
RMSEA 0.215
90 Percent confidence interval - lower 0.069
90 Percent confidence interval - upper 0.405
P-value H_0: RMSEA <= 0.050 0.035
P-value H_0: RMSEA >= 0.080 0.939
Standardized Root Mean Square Residual:
SRMR 0.078
Parameter Estimates:
Standard errors Bootstrap
Number of requested bootstrap draws 1000
Number of successful bootstrap draws 1000
Regressions:
Estimate Std.Err z-value P(>|z|)
AL_dsCM ~
s_LPLA_P_ (c) 0.124 0.057 2.173 0.030
DEM_dsHT ~
s_LPLA_P_ (a1) -0.215 0.131 -1.641 0.101
AL_dsCM ~
DEM_dsHT (b1) -0.187 0.057 -3.307 0.001
PA_dsSUP ~
s_LPLA_P_ (a2) 0.089 0.092 0.969 0.333
AL_dsCM ~
PA_dsSUP (b2) 0.076 0.067 1.135 0.257
Variances:
Estimate Std.Err z-value P(>|z|)
.AL_dsCM 0.373 0.054 6.898 0.000
.DEM_dsHT 1.725 0.279 6.174 0.000
.PA_dsSUP 0.993 0.097 10.191 0.000
R-Square:
Estimate
AL_dsCM 0.210
DEM_dsHT 0.029
PA_dsSUP 0.009
Defined Parameters:
Estimate Std.Err z-value P(>|z|)
a1b1 0.040 0.030 1.360 0.174
a2b2 0.007 0.011 0.593 0.553
total 0.171 0.058 2.946 0.003lavaanPlot(model = m.DEM.PA.UD.cm.c3.fit, node_options = list(shape = "box", fontname = "Helvetica"), edge_options = list(color = "grey"), coefs = TRUE, sig = .05, stars="regress")interpret(m.DEM.PA.UD.cm.c3.fit) Name Value Threshold Interpretation
1 GFI 0.96460666 0.95 satisfactory
2 AGFI 0.64606656 0.90 poor
3 NFI 0.83808607 0.90 poor
4 NNFI 0.03466652 0.90 poor
5 CFI 0.83911109 0.90 poor
6 RMSEA 0.21481265 0.05 poor
7 SRMR 0.07783175 0.08 satisfactory
8 RFI 0.02851644 0.90 poor
9 PNFI 0.13968101 0.50 poor
10 IFI 0.86362150 0.90 poorm.DEM.PA.BD.cm.c3 <- '# direct effect
AL_dsCM ~ c * s_LPLA_P_time
s_LPLA_P_time ~ c*AL_dsCM
# mediator
DEM_dsHT ~ a1 * s_LPLA_P_time
AL_dsCM ~ b1 * DEM_dsHT
PA_dsSUP ~ a2 * s_LPLA_P_time
AL_dsCM ~ b2 * PA_dsSUP
# indirect effect (a*b)
a1b1 := a1*b1
a2b2 := a2*b2
# total effect
total := c + (a1*b1) + (a2*b2)
'
m.DEM.PA.BD.cm.c3.fit <- sem(m.DEM.PA.BD.cm.c3, data=df.c3, se = "bootstrap", bootstrap = 1000)
summary(m.DEM.PA.BD.cm.c3.fit, fit.measures=TRUE, rsquare=TRUE)lavaan 0.6.16 ended normally after 11 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 10
Number of equality constraints 1
Number of observations 97
Model Test User Model:
Test statistic 5.483
Degrees of freedom 1
P-value (Chi-square) 0.019
Model Test Baseline Model:
Test statistic 33.821
Degrees of freedom 6
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.839
Tucker-Lewis Index (TLI) 0.033
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -534.525
Loglikelihood unrestricted model (H1) -531.784
Akaike (AIC) 1087.051
Bayesian (BIC) 1110.223
Sample-size adjusted Bayesian (SABIC) 1081.804
Root Mean Square Error of Approximation:
RMSEA 0.215
90 Percent confidence interval - lower 0.070
90 Percent confidence interval - upper 0.405
P-value H_0: RMSEA <= 0.050 0.035
P-value H_0: RMSEA >= 0.080 0.939
Standardized Root Mean Square Residual:
SRMR 0.078
Parameter Estimates:
Standard errors Bootstrap
Number of requested bootstrap draws 1000
Number of successful bootstrap draws 1000
Regressions:
Estimate Std.Err z-value P(>|z|)
AL_dsCM ~
s_LPLA_P_ (c) 0.093 0.041 2.285 0.022
s_LPLA_P_time ~
AL_dsCM (c) 0.093 0.041 2.285 0.022
DEM_dsHT ~
s_LPLA_P_ (a1) -0.188 0.128 -1.471 0.141
AL_dsCM ~
DEM_dsHT (b1) -0.187 0.058 -3.210 0.001
PA_dsSUP ~
s_LPLA_P_ (a2) 0.083 0.094 0.880 0.379
AL_dsCM ~
PA_dsSUP (b2) 0.076 0.066 1.155 0.248
Variances:
Estimate Std.Err z-value P(>|z|)
.AL_dsCM 0.374 0.055 6.756 0.000
.s_LPLA_P_time 1.093 0.240 4.547 0.000
.DEM_dsHT 1.725 0.269 6.409 0.000
.PA_dsSUP 0.993 0.102 9.743 0.000
R-Square:
Estimate
AL_dsCM 0.207
s_LPLA_P_time 0.028
DEM_dsHT 0.029
PA_dsSUP 0.009
Defined Parameters:
Estimate Std.Err z-value P(>|z|)
a1b1 0.035 0.028 1.275 0.202
a2b2 0.006 0.011 0.567 0.571
total 0.134 0.046 2.948 0.003lavaanPlot(model = m.DEM.PA.BD.cm.c3.fit, node_options = list(shape = "box", fontname = "Helvetica"), edge_options = list(color = "grey"), coefs = TRUE, sig = .05, stars="regress")interpret(m.DEM.PA.BD.cm.c3.fit) Name Value Threshold Interpretation
1 GFI 0.97325566 0.95 satisfactory
2 AGFI 0.73255663 0.90 poor
3 NFI 0.83788896 0.90 poor
4 NNFI 0.03322875 0.90 poor
5 CFI 0.83887146 0.90 poor
6 RMSEA 0.21497257 0.05 poor
7 SRMR 0.07791088 0.08 satisfactory
8 RFI 0.02733373 0.90 poor
9 PNFI 0.13964816 0.50 poor
10 IFI 0.86341837 0.90 pooranova(m.DEM.UD.cm.c3.fit, m.DEM.BD.cm.c3.fit, m.PA.UD.cm.c3.fit, m.PA.BD.cm.c3.fit, m.DEM.PA.UD.cm.c3.fit, m.DEM.PA.BD.cm.c3.fit)Warning in lavTestLRT(object = object, ..., model.names = NAMES): lavaan
WARNING: some models have the same degrees of freedom
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
m.DEM.UD.cm.c3.fit 1 519.82 535.27 5.4760
m.PA.UD.cm.c3.fit 1 466.25 481.70 5.4760 0.00000 0 0
m.DEM.PA.UD.cm.c3.fit 1 798.38 818.98 5.4760 0.00000 0 0
m.DEM.PA.BD.cm.c3.fit 1 1087.05 1110.22 5.4827 0.00667 0 0
m.DEM.BD.cm.c3.fit 2 808.37 826.39 6.2269 0.74419 0 1
m.PA.BD.cm.c3.fit 2 754.20 772.22 7.6588 1.43198 0 0
Pr(>Chisq)
m.DEM.UD.cm.c3.fit
m.PA.UD.cm.c3.fit
m.DEM.PA.UD.cm.c3.fit
m.DEM.PA.BD.cm.c3.fit
m.DEM.BD.cm.c3.fit 0.3883
m.PA.BD.cm.c3.fit