setwd("C:/Users/user/Desktop/NTNU/longd")
dta.l <- read.csv("./dta_collapse_parent.csv")
names(dta.l) [1] "baby_id" "baby_sex" "fa_T1" "fa_T2"
[5] "fa_T3" "ma_T1" "ma_T2" "ma_T3"
[9] "fatherinvo01_T1" "fatherinvo01_T2" "fatherinvo01_T3" "fatherinvo02_T1"
[13] "fatherinvo02_T2" "fatherinvo02_T3" "fatherinvo03_T1" "fatherinvo03_T2"
[17] "fatherinvo03_T3" "fatherinvo04_T1" "fatherinvo04_T2" "fatherinvo04_T3"
[21] "fatherinvo05_T1" "fatherinvo05_T2" "fatherinvo05_T3" "motherinvo01_T1"
[25] "motherinvo01_T2" "motherinvo01_T3" "motherinvo02_T1" "motherinvo02_T2"
[29] "motherinvo02_T3" "motherinvo03_T1" "motherinvo03_T2" "motherinvo03_T3"
[33] "motherinvo04_T1" "motherinvo04_T2" "motherinvo04_T3" "motherinvo05_T1"
[37] "motherinvo05_T2" "motherinvo05_T3" "famedu01_T1" "famedu01_T2"
[41] "famedu01_T3" "famedu02_T1" "famedu02_T2" "famedu02_T3"
[45] "famedu03_T1" "famedu03_T2" "famedu03_T3" "famedu04_T1"
[49] "famedu04_T2" "famedu04_T3" "famedu05_T1" "famedu05_T2"
[53] "famedu05_T3" "famedu06_T1" "famedu06_T2" "famedu06_T3"
[57] "famedu07_T1" "famedu07_T2" "famedu07_T3" "famedu08_T1"
[61] "famedu08_T2" "famedu08_T3" "famedu09_T1" "famedu09_T2"
[65] "famedu09_T3" "socc02_T1" "socc02_T2" "socc02_T3"
[69] "socc03_T1" "socc03_T2" "socc03_T3" "socc09_T1"
[73] "socc09_T2" "socc09_T3" "socc10_T1"
[ reached getOption("max.print") -- omitted 17 entries ]library(lavaan)個體內(WFX, WFY)指的是同一波次的不同items的變異
個體間(RIX, RIY)則是指不同waves的同一個item的變異
RICLPM1 <- '
################
# BETWEEN PART #
################
# Create between factors (random intercepts) for each indicator separately
RIX1 =~ 1*fatherinvo01_T1 + 1*fatherinvo01_T2 + 1*fatherinvo01_T3
RIX2 =~ 1*fatherinvo02_T1 + 1*fatherinvo02_T2 + 1*fatherinvo02_T3
RIX3 =~ 1*fatherinvo03_T1 + 1*fatherinvo03_T2 + 1*fatherinvo03_T3
RIX4 =~ 1*fatherinvo04_T1 + 1*fatherinvo04_T2 + 1*fatherinvo04_T3
RIX5 =~ 1*fatherinvo05_T1 + 1*fatherinvo05_T2 + 1*fatherinvo05_T3
RIY1 =~ 1*socc02_T1 + 1*socc02_T2 + 1*socc02_T3
RIY2 =~ 1*socc03_T1 + 1*socc03_T2 + 1*socc03_T3
RIY3 =~ 1*socc09_T1 + 1*socc09_T2 + 1*socc09_T3
RIY4 =~ 1*socc10_T1 + 1*socc10_T2 + 1*socc10_T3
##################################
# WITHIN PART: MEASUREMENT MODEL #
##################################
# Factor models for X at 3 waves
WFX1 =~ fatherinvo01_T1 + fatherinvo02_T1 + fatherinvo03_T1 + fatherinvo04_T1 + fatherinvo05_T1
WFX2 =~ fatherinvo01_T2 + fatherinvo02_T2 + fatherinvo03_T2 + fatherinvo04_T2 + fatherinvo05_T2
WFX3 =~ fatherinvo01_T3 + fatherinvo02_T3 + fatherinvo03_T3 + fatherinvo04_T3 + fatherinvo05_T3
# Factor models for Y at 3 waves
WFY1 =~ socc02_T1 + socc03_T1 + socc09_T1 + socc10_T1
WFY2 =~ socc02_T2 + socc03_T2 + socc09_T2 + socc10_T2
WFY3 =~ socc02_T3 + socc03_T3 + socc09_T3 + socc10_T3
#########################
# WITHIN PART: DYNAMICS #
#########################
# Specify lagged effects between within-person centered latent variables
WFX2 + WFY2 ~ WFX1 + WFY1
WFX3 + WFY3 ~ WFX2 + WFY2
# Estimate correlations within same wave
WFX1 ~~ WFY1
WFX2 ~~ WFY2
WFX3 ~~ WFY3
##########################
# ADDITIONAL CONSTRAINTS #
##########################
# Constrain covariance of between factors and exogenous within factors to 0
#確保 爸爸投入的五個題目 安全依附的四個題目 在三波次之間 共變=0
RIX1 + RIX2 + RIX3 + RIX4 + RIX5 + RIY1 + RIY2 + RIY3 + RIY4 ~~ 0*WFY1 + 0*WFX1
'
rst.1 <- cfa(RICLPM1, data = dta.l, ordered = names(dta.l)[9:77], estimator = 'wlsmv')
summary(rst.1, fit.measures = T)lavaan 0.6-12 ended normally after 137 iterations
Estimator DWLS
Optimization method NLMINB
Number of model parameters 173
Number of observations 1575
Model Test User Model:
Standard Robust
Test Statistic 165.689 304.804
Degrees of freedom 268 268
P-value (Chi-square) 1.000 0.060
Scaling correction factor 0.913
Shift parameter 123.367
simple second-order correction
Model Test Baseline Model:
Test statistic 255314.800 84895.576
Degrees of freedom 351 351
P-value 0.000 0.000
Scaling correction factor 3.016
User Model versus Baseline Model:
Comparative Fit Index (CFI) 1.000 1.000
Tucker-Lewis Index (TLI) 1.001 0.999
Robust Comparative Fit Index (CFI) NA
Robust Tucker-Lewis Index (TLI) NA
Root Mean Square Error of Approximation:
RMSEA 0.000 0.009
90 Percent confidence interval - lower 0.000 0.000
90 Percent confidence interval - upper 0.000 0.014
P-value RMSEA <= 0.05 1.000 1.000
Robust RMSEA NA
90 Percent confidence interval - lower 0.000
90 Percent confidence interval - upper NA
Standardized Root Mean Square Residual:
SRMR 0.017 0.017
Parameter Estimates:
Standard errors Robust.sem
Information Expected
Information saturated (h1) model Unstructured
Latent Variables:
Estimate Std.Err z-value P(>|z|)
RIX1 =~
fatherinv01_T1 1.000
fatherinv01_T2 1.000
fatherinv01_T3 1.000
RIX2 =~
fatherinv02_T1 1.000
fatherinv02_T2 1.000
fatherinv02_T3 1.000
RIX3 =~
fatherinv03_T1 1.000
fatherinv03_T2 1.000
fatherinv03_T3 1.000
RIX4 =~
fatherinv04_T1 1.000
fatherinv04_T2 1.000
[ 達到了 getOption("max.print") -- 省略最後 54 列 ]]
Regressions:
Estimate Std.Err z-value P(>|z|)
WFX2 ~
WFX1 0.246 0.067 3.680 0.000
WFY1 -0.007 0.110 -0.066 0.948
WFY2 ~
WFX1 0.070 0.074 0.952 0.341
WFY1 0.385 0.144 2.670 0.008
WFX3 ~
WFX2 0.315 0.052 6.062 0.000
WFY2 0.034 0.063 0.541 0.589
WFY3 ~
WFX2 -0.024 0.049 -0.492 0.623
WFY2 0.519 0.074 6.984 0.000
Covariances:
Estimate Std.Err z-value P(>|z|)
WFX1 ~~
WFY1 0.037 0.024 1.573 0.116
.WFX2 ~~
.WFY2 0.023 0.018 1.249 0.212
.WFX3 ~~
.WFY3 0.032 0.013 2.503 0.012
RIX1 ~~
WFY1 0.000
WFX1 0.000
RIX2 ~~
WFY1 0.000
WFX1 0.000
RIX3 ~~
WFY1 0.000
WFX1 0.000
[ 達到了 getOption("max.print") -- 省略最後 62 列 ]]
Intercepts:
Estimate Std.Err z-value P(>|z|)
.fatherinv01_T1 0.000
.fatherinv01_T2 0.000
.fatherinv01_T3 0.000
.fatherinv02_T1 0.000
.fatherinv02_T2 0.000
.fatherinv02_T3 0.000
.fatherinv03_T1 0.000
.fatherinv03_T2 0.000
.fatherinv03_T3 0.000
.fatherinv04_T1 0.000
.fatherinv04_T2 0.000
.fatherinv04_T3 0.000
.fatherinv05_T1 0.000
.fatherinv05_T2 0.000
.fatherinv05_T3 0.000
[ 達到了 getOption("max.print") -- 省略最後 27 列 ]]
Thresholds:
Estimate Std.Err z-value P(>|z|)
fthrnv01_T1|t1 -1.313 0.044 -29.989 0.000
fthrnv01_T1|t2 -0.579 0.034 -17.238 0.000
fthrnv01_T1|t3 0.196 0.032 6.168 0.000
fthrnv01_T2|t1 -1.269 0.043 -29.637 0.000
fthrnv01_T2|t2 -0.566 0.033 -16.894 0.000
fthrnv01_T2|t3 0.128 0.032 4.055 0.000
fthrnv01_T3|t1 -1.265 0.043 -29.606 0.000
fthrnv01_T3|t2 -0.581 0.034 -17.287 0.000
fthrnv01_T3|t3 0.103 0.032 3.249 0.001
fthrnv02_T1|t1 -1.458 0.047 -30.759 0.000
fthrnv02_T1|t2 -0.531 0.033 -15.958 0.000
fthrnv02_T1|t3 0.343 0.032 10.634 0.000
fthrnv02_T2|t1 -1.468 0.048 -30.788 0.000
fthrnv02_T2|t2 -0.522 0.033 -15.711 0.000
fthrnv02_T2|t3 0.295 0.032 9.181 0.000
[ 達到了 getOption("max.print") -- 省略最後 75 列 ]]
Variances:
Estimate Std.Err z-value P(>|z|)
.fatherinv01_T1 0.187
.fatherinv01_T2 0.145
.fatherinv01_T3 0.183
.fatherinv02_T1 0.095
.fatherinv02_T2 0.081
.fatherinv02_T3 0.085
.fatherinv03_T1 0.120
.fatherinv03_T2 0.137
.fatherinv03_T3 0.106
.fatherinv04_T1 0.147
.fatherinv04_T2 0.134
.fatherinv04_T3 0.131
.fatherinv05_T1 0.147
.fatherinv05_T2 0.113
.fatherinv05_T3 0.133
[ 達到了 getOption("max.print") -- 省略最後 27 列 ]]
Scales y*:
Estimate Std.Err z-value P(>|z|)
fatherinv01_T1 1.000
fatherinv01_T2 1.000
fatherinv01_T3 1.000
fatherinv02_T1 1.000
fatherinv02_T2 1.000
fatherinv02_T3 1.000
fatherinv03_T1 1.000
fatherinv03_T2 1.000
fatherinv03_T3 1.000
fatherinv04_T1 1.000
fatherinv04_T2 1.000
fatherinv04_T3 1.000
fatherinv05_T1 1.000
fatherinv05_T2 1.000
fatherinv05_T3 1.000
[ 達到了 getOption("max.print") -- 省略最後 12 列 ]]RICLPM2 <- '
################
# BETWEEN PART #
################
# Create between factors (random intercepts) for each indicator separately
RIX1 =~ 1*motherinvo01_T1 + 1*motherinvo01_T2 + 1*motherinvo01_T3
RIX2 =~ 1*motherinvo02_T1 + 1*motherinvo02_T2 + 1*motherinvo02_T3
RIX3 =~ 1*motherinvo03_T1 + 1*motherinvo03_T2 + 1*motherinvo03_T3
RIX4 =~ 1*motherinvo04_T1 + 1*motherinvo04_T2 + 1*motherinvo04_T3
RIX5 =~ 1*motherinvo05_T1 + 1*motherinvo05_T2 + 1*motherinvo05_T3
RIY1 =~ 1*socc02_T1 + 1*socc02_T2 + 1*socc02_T3
RIY2 =~ 1*socc03_T1 + 1*socc03_T2 + 1*socc03_T3
RIY3 =~ 1*socc09_T1 + 1*socc09_T2 + 1*socc09_T3
RIY4 =~ 1*socc10_T1 + 1*socc10_T2 + 1*socc10_T3
##################################
# WITHIN PART: MEASUREMENT MODEL #
##################################
# Factor models for X at 3 waves
WFX1 =~ motherinvo01_T1 + motherinvo02_T1 + motherinvo03_T1 + motherinvo04_T1 + motherinvo05_T1
WFX2 =~ motherinvo01_T2 + motherinvo02_T2 + motherinvo03_T2 + motherinvo04_T2 + motherinvo05_T2
WFX3 =~ motherinvo01_T3 + motherinvo02_T3 + motherinvo03_T3 + motherinvo04_T3 + motherinvo05_T3
# Factor models for Y at 3 waves
WFY1 =~ socc02_T1 + socc03_T1 + socc09_T1 + socc10_T1
WFY2 =~ socc02_T2 + socc03_T2 + socc09_T2 + socc10_T2
WFY3 =~ socc02_T3 + socc03_T3 + socc09_T3 + socc10_T3
#########################
# WITHIN PART: DYNAMICS #
#########################
# Specify lagged effects between within-person centered latent variables
WFX2 + WFY2 ~ WFX1 + WFY1
WFX3 + WFY3 ~ WFX2 + WFY2
# Estimate correlations within same wave
WFX1 ~~ WFY1
WFX2 ~~ WFY2
WFX3 ~~ WFY3
##########################
# ADDITIONAL CONSTRAINTS #
##########################
# Constrain covariance of between factors and exogenous within factors to 0
RIX1 + RIX2 + RIX3 + RIX4 + RIX5 + RIY1 + RIY2 + RIY3 + RIY4 ~~ 0*WFY1 + 0*WFX1
'
rst.2 <- cfa(RICLPM2, data = dta.l, ordered = names(dta.l)[9:77], estimator = 'wlsmv')
summary(rst.2, fit.measures = T)lavaan 0.6-12 ended normally after 121 iterations
Estimator DWLS
Optimization method NLMINB
Number of model parameters 173
Number of observations 1575
Model Test User Model:
Standard Robust
Test Statistic 139.991 271.808
Degrees of freedom 268 268
P-value (Chi-square) 1.000 0.424
Scaling correction factor 0.931
Shift parameter 121.447
simple second-order correction
Model Test Baseline Model:
Test statistic 220659.560 79179.169
Degrees of freedom 351 351
P-value 0.000 0.000
Scaling correction factor 2.795
User Model versus Baseline Model:
Comparative Fit Index (CFI) 1.000 1.000
Tucker-Lewis Index (TLI) 1.001 1.000
Robust Comparative Fit Index (CFI) NA
Robust Tucker-Lewis Index (TLI) NA
Root Mean Square Error of Approximation:
RMSEA 0.000 0.003
90 Percent confidence interval - lower 0.000 0.000
90 Percent confidence interval - upper 0.000 0.011
P-value RMSEA <= 0.05 1.000 1.000
Robust RMSEA NA
90 Percent confidence interval - lower 0.000
90 Percent confidence interval - upper NA
Standardized Root Mean Square Residual:
SRMR 0.022 0.022
Parameter Estimates:
Standard errors Robust.sem
Information Expected
Information saturated (h1) model Unstructured
Latent Variables:
Estimate Std.Err z-value P(>|z|)
RIX1 =~
motherinv01_T1 1.000
motherinv01_T2 1.000
motherinv01_T3 1.000
RIX2 =~
motherinv02_T1 1.000
motherinv02_T2 1.000
motherinv02_T3 1.000
RIX3 =~
motherinv03_T1 1.000
motherinv03_T2 1.000
motherinv03_T3 1.000
RIX4 =~
motherinv04_T1 1.000
motherinv04_T2 1.000
[ 達到了 getOption("max.print") -- 省略最後 54 列 ]]
Regressions:
Estimate Std.Err z-value P(>|z|)
WFX2 ~
WFX1 0.173 0.075 2.288 0.022
WFY1 0.099 0.148 0.673 0.501
WFY2 ~
WFX1 0.020 0.062 0.319 0.750
WFY1 0.364 0.153 2.376 0.017
WFX3 ~
WFX2 0.199 0.075 2.643 0.008
WFY2 0.129 0.088 1.466 0.143
WFY3 ~
WFX2 -0.018 0.051 -0.365 0.715
WFY2 0.466 0.071 6.539 0.000
Covariances:
Estimate Std.Err z-value P(>|z|)
WFX1 ~~
WFY1 0.070 0.036 1.955 0.051
.WFX2 ~~
.WFY2 0.032 0.025 1.279 0.201
.WFX3 ~~
.WFY3 0.053 0.019 2.748 0.006
RIX1 ~~
WFY1 0.000
WFX1 0.000
RIX2 ~~
WFY1 0.000
WFX1 0.000
RIX3 ~~
WFY1 0.000
WFX1 0.000
[ 達到了 getOption("max.print") -- 省略最後 62 列 ]]
Intercepts:
Estimate Std.Err z-value P(>|z|)
.motherinv01_T1 0.000
.motherinv01_T2 0.000
.motherinv01_T3 0.000
.motherinv02_T1 0.000
.motherinv02_T2 0.000
.motherinv02_T3 0.000
.motherinv03_T1 0.000
.motherinv03_T2 0.000
.motherinv03_T3 0.000
.motherinv04_T1 0.000
.motherinv04_T2 0.000
.motherinv04_T3 0.000
.motherinv05_T1 0.000
.motherinv05_T2 0.000
.motherinv05_T3 0.000
[ 達到了 getOption("max.print") -- 省略最後 27 列 ]]
Thresholds:
Estimate Std.Err z-value P(>|z|)
mthrnv01_T1|t1 -2.669 0.137 -19.489 0.000
mthrnv01_T1|t2 -1.902 0.064 -29.598 0.000
mthrnv01_T1|t3 -1.026 0.038 -26.693 0.000
mthrnv01_T2|t1 -2.729 0.147 -18.530 0.000
mthrnv01_T2|t2 -1.975 0.068 -28.970 0.000
mthrnv01_T2|t3 -1.065 0.039 -27.271 0.000
mthrnv01_T3|t1 -2.729 0.147 -18.530 0.000
mthrnv01_T3|t2 -1.975 0.068 -28.970 0.000
mthrnv01_T3|t3 -1.056 0.039 -27.149 0.000
mthrnv02_T1|t1 -2.669 0.137 -19.489 0.000
mthrnv02_T1|t2 -1.856 0.062 -29.943 0.000
mthrnv02_T1|t3 -0.842 0.036 -23.370 0.000
mthrnv02_T2|t1 -2.802 0.161 -17.387 0.000
mthrnv02_T2|t2 -1.964 0.068 -29.072 0.000
mthrnv02_T2|t3 -0.924 0.037 -24.960 0.000
[ 達到了 getOption("max.print") -- 省略最後 75 列 ]]
Variances:
Estimate Std.Err z-value P(>|z|)
.motherinv01_T1 0.048
.motherinv01_T2 0.103
.motherinv01_T3 0.083
.motherinv02_T1 0.042
.motherinv02_T2 0.024
.motherinv02_T3 0.060
.motherinv03_T1 0.066
.motherinv03_T2 0.029
.motherinv03_T3 0.062
.motherinv04_T1 0.073
.motherinv04_T2 0.018
.motherinv04_T3 0.081
.motherinv05_T1 0.063
.motherinv05_T2 0.128
.motherinv05_T3 0.130
[ 達到了 getOption("max.print") -- 省略最後 27 列 ]]
Scales y*:
Estimate Std.Err z-value P(>|z|)
motherinv01_T1 1.000
motherinv01_T2 1.000
motherinv01_T3 1.000
motherinv02_T1 1.000
motherinv02_T2 1.000
motherinv02_T3 1.000
motherinv03_T1 1.000
motherinv03_T2 1.000
motherinv03_T3 1.000
motherinv04_T1 1.000
motherinv04_T2 1.000
motherinv04_T3 1.000
motherinv05_T1 1.000
motherinv05_T2 1.000
motherinv05_T3 1.000
[ 達到了 getOption("max.print") -- 省略最後 12 列 ]]RICLPM3 <- '
################
# BETWEEN PART #
################
# Create between factors (random intercepts) for each indicator separately
RIX1 =~ 1*famedu01_T1 + 1*famedu01_T2 + 1*famedu01_T3
RIX2 =~ 1*famedu02_T1 + 1*famedu02_T2 + 1*famedu02_T3
RIX3 =~ 1*famedu03_T1 + 1*famedu03_T2 + 1*famedu03_T3
RIY1 =~ 1*socc02_T1 + 1*socc02_T2 + 1*socc02_T3
RIY2 =~ 1*socc03_T1 + 1*socc03_T2 + 1*socc03_T3
RIY3 =~ 1*socc09_T1 + 1*socc09_T2 + 1*socc09_T3
RIY4 =~ 1*socc10_T1 + 1*socc10_T2 + 1*socc10_T3
##################################
# WITHIN PART: MEASUREMENT MODEL #
##################################
# Factor models for X at 3 waves
WFX1 =~ famedu01_T1 + famedu02_T1 + famedu03_T1
WFX2 =~ famedu01_T2 + famedu02_T2 + famedu03_T2
WFX3 =~ famedu01_T3 + famedu02_T3 + famedu03_T3
# Factor models for Y at 3 waves
WFY1 =~ socc02_T1 + socc03_T1 + socc09_T1 + socc10_T1
WFY2 =~ socc02_T2 + socc03_T2 + socc09_T2 + socc10_T2
WFY3 =~ socc02_T3 + socc03_T3 + socc09_T3 + socc10_T3
#########################
# WITHIN PART: DYNAMICS #
#########################
# Specify lagged effects between within-person centered latent variables
WFX2 + WFY2 ~ WFX1 + WFY1
WFX3 + WFY3 ~ WFX2 + WFY2
# Estimate correlations within same wave
WFX1 ~~ WFY1
WFX2 ~~ WFY2
WFX3 ~~ WFY3
##########################
# ADDITIONAL CONSTRAINTS #
##########################
# Constrain covariance of between factors and exogenous within factors to 0
RIX1 + RIX2 + RIX3 + RIY1 + RIY2 + RIY3 + RIY4 ~~ 0*WFY1 + 0*WFX1
'
rst.3 <- cfa(RICLPM3, data = dta.l, ordered = names(dta.l)[9:77], estimator = 'wlsmv')
summary(rst.3, fit.measures = T)lavaan 0.6-12 ended normally after 114 iterations
Estimator DWLS
Optimization method NLMINB
Number of model parameters 132
Number of observations 1575
Model Test User Model:
Standard Robust
Test Statistic 108.422 168.520
Degrees of freedom 150 150
P-value (Chi-square) 0.996 0.143
Scaling correction factor 0.802
Shift parameter 33.289
simple second-order correction
Model Test Baseline Model:
Test statistic 26186.682 14975.234
Degrees of freedom 210 210
P-value 0.000 0.000
Scaling correction factor 1.759
User Model versus Baseline Model:
Comparative Fit Index (CFI) 1.000 0.999
Tucker-Lewis Index (TLI) 1.002 0.998
Robust Comparative Fit Index (CFI) NA
Robust Tucker-Lewis Index (TLI) NA
Root Mean Square Error of Approximation:
RMSEA 0.000 0.009
90 Percent confidence interval - lower 0.000 0.000
90 Percent confidence interval - upper 0.000 0.015
P-value RMSEA <= 0.05 1.000 1.000
Robust RMSEA NA
90 Percent confidence interval - lower 0.000
90 Percent confidence interval - upper NA
Standardized Root Mean Square Residual:
SRMR 0.020 0.020
Parameter Estimates:
Standard errors Robust.sem
Information Expected
Information saturated (h1) model Unstructured
Latent Variables:
Estimate Std.Err z-value P(>|z|)
RIX1 =~
famedu01_T1 1.000
famedu01_T2 1.000
famedu01_T3 1.000
RIX2 =~
famedu02_T1 1.000
famedu02_T2 1.000
famedu02_T3 1.000
RIX3 =~
famedu03_T1 1.000
famedu03_T2 1.000
famedu03_T3 1.000
RIY1 =~
socc02_T1 1.000
socc02_T2 1.000
[ 達到了 getOption("max.print") -- 省略最後 40 列 ]]
Regressions:
Estimate Std.Err z-value P(>|z|)
WFX2 ~
WFX1 0.083 0.137 0.608 0.543
WFY1 -0.177 0.120 -1.478 0.139
WFY2 ~
WFX1 -0.378 0.129 -2.942 0.003
WFY1 0.393 0.134 2.940 0.003
WFX3 ~
WFX2 0.339 0.064 5.303 0.000
WFY2 -0.114 0.066 -1.733 0.083
WFY3 ~
WFX2 -0.104 0.056 -1.849 0.064
WFY2 0.492 0.070 7.021 0.000
Covariances:
Estimate Std.Err z-value P(>|z|)
WFX1 ~~
WFY1 0.001 0.023 0.035 0.972
.WFX2 ~~
.WFY2 -0.016 0.021 -0.747 0.455
.WFX3 ~~
.WFY3 0.014 0.014 1.011 0.312
RIX1 ~~
WFY1 0.000
WFX1 0.000
RIX2 ~~
WFY1 0.000
WFX1 0.000
RIX3 ~~
WFY1 0.000
WFX1 0.000
[ 達到了 getOption("max.print") -- 省略最後 39 列 ]]
Intercepts:
Estimate Std.Err z-value P(>|z|)
.famedu01_T1 0.000
.famedu01_T2 0.000
.famedu01_T3 0.000
.famedu02_T1 0.000
.famedu02_T2 0.000
.famedu02_T3 0.000
.famedu03_T1 0.000
.famedu03_T2 0.000
.famedu03_T3 0.000
.socc02_T1 0.000
.socc02_T2 0.000
.socc02_T3 0.000
.socc03_T1 0.000
.socc03_T2 0.000
.socc03_T3 0.000
[ 達到了 getOption("max.print") -- 省略最後 19 列 ]]
Thresholds:
Estimate Std.Err z-value P(>|z|)
famedu01_T1|t1 -1.431 0.047 -30.661 0.000
famedu01_T1|t2 0.211 0.032 6.620 0.000
famedu01_T1|t3 1.662 0.054 30.845 0.000
famedu01_T2|t1 -1.496 0.048 -30.861 0.000
famedu01_T2|t2 0.055 0.032 1.738 0.082
famedu01_T2|t3 1.715 0.056 30.689 0.000
famedu01_T3|t1 -1.536 0.050 -30.926 0.000
famedu01_T3|t2 -0.025 0.032 -0.781 0.435
famedu01_T3|t3 1.608 0.052 30.932 0.000
famedu02_T1|t1 -1.418 0.046 -30.606 0.000
famedu02_T1|t2 0.141 0.032 4.457 0.000
famedu02_T1|t3 1.722 0.056 30.663 0.000
famedu02_T2|t1 -1.482 0.048 -30.827 0.000
famedu02_T2|t2 0.130 0.032 4.105 0.000
famedu02_T2|t3 1.821 0.060 30.168 0.000
[ 達到了 getOption("max.print") -- 省略最後 57 列 ]]
Variances:
Estimate Std.Err z-value P(>|z|)
.famedu01_T1 0.413
.famedu01_T2 0.255
.famedu01_T3 0.232
.famedu02_T1 0.249
.famedu02_T2 0.153
.famedu02_T3 0.177
.famedu03_T1 0.479
.famedu03_T2 0.464
.famedu03_T3 0.392
.socc02_T1 0.455
.socc02_T2 0.355
.socc02_T3 0.429
.socc03_T1 0.670
.socc03_T2 0.570
.socc03_T3 0.552
[ 達到了 getOption("max.print") -- 省略最後 19 列 ]]
Scales y*:
Estimate Std.Err z-value P(>|z|)
famedu01_T1 1.000
famedu01_T2 1.000
famedu01_T3 1.000
famedu02_T1 1.000
famedu02_T2 1.000
famedu02_T3 1.000
famedu03_T1 1.000
famedu03_T2 1.000
famedu03_T3 1.000
socc02_T1 1.000
socc02_T2 1.000
socc02_T3 1.000
socc03_T1 1.000
socc03_T2 1.000
socc03_T3 1.000
[ 達到了 getOption("max.print") -- 省略最後 6 列 ]]