library(foreign)
spss<-read.spss("D:/CFA/MyDATAZACH.sav", to.data.frame=TRUE)
## re-encoding from CP950
## Warning in read.spss("D:/CFA/MyDATAZACH.sav", to.data.frame = TRUE): Undeclared
## level(s) 3 added in variable: Gender
dim(spss)
## [1] 870 153
names(spss)
## [1] "ID" "School" "grade" "Class" "Number" "Gender"
## [7] "EN1T1" "EN2T1" "EN3T1" "EN4T1" "EN5T1" "FR1T1"
## [13] "FR2T1" "FR3T1" "SE1T1" "SE2T1" "SE3T1" "SE4T1"
## [19] "SE5T1" "ENR1T1" "ENR2T1" "ENR3T1" "ENR4T1" "FRR1T1"
## [25] "FRR2T1" "FRR3T1" "FRR4T1" "BO1T1" "BO2T1" "BO3T1"
## [31] "BO4T1" "CB1T1" "CB2T1" "CB3T1" "AC1T1" "AC2T1"
## [37] "AC3T1" "AC4T1" "AC5T1" "AC6T1" "AC7T1" "AC8T1"
## [43] "AC9T1" "PC1T1" "PC2T1" "PC3T1" "PC4T1" "ACHT1"
## [49] "EN1T2" "EN2T2" "EN3T2" "EN4T2" "EN5T2" "FR1T2"
## [55] "FR2T2" "FR3T2" "SE1T2" "SE2T2" "SE3T2" "SE4T2"
## [61] "SE5T2" "ENR1T2" "ENR2T2" "ENR3T2" "ENR4T2" "FRR1T2"
## [67] "FRR2T2" "FRR3T2" "FRR4T2" "BO1T2" "BO2T2" "BO3T2"
## [73] "BO4T2" "CB1T2" "CB2T2" "CB3T2" "AC1T2" "AC2T2"
## [79] "AC3T2" "AC4T2" "AC5T2" "AC6T2" "AC7T2" "AC8T2"
## [85] "AC9T2" "PC1T2" "PC2T2" "PC3T2" "PC4T2" "ACHT2"
## [91] "EN1T3" "EN2T3" "EN3T3" "EN4T3" "EN5T3" "FR1T3"
## [97] "FR2T3" "FR3T3" "SE1T3" "SE2T3" "SE3T3" "SE4T3"
## [103] "SE5T3" "ENR1T3" "ENR2T3" "ENR3T3" "ENR4T3" "FRR1T3"
## [109] "FRR2T3" "FRR3T3" "FRR4T3" "BO1T3" "BO2T3" "BO3T3"
## [115] "BO4T3" "CB1T3" "CB2T3" "CB3T3" "AC1T3" "AC2T3"
## [121] "AC3T3" "AC4T3" "AC5T3" "AC6T3" "AC7T3" "AC8T3"
## [127] "AC9T3" "PC1T3" "PC2T3" "PC3T3" "PC4T3" "ACHT3"
## [133] "filter_." "ZACHT1" "ZACHT2" "ZACHT3" "ZZACHT1" "ZZACHT2"
## [139] "ZZACHT3" "ZEN1T1" "ZEN2T1" "ZEN3T1" "ZEN4T1" "ZEN5T1"
## [145] "MAC1T1" "MAC2T1" "MAC3T1" "MAC1T2" "MAC2T2" "MAC3T2"
## [151] "MAC1T3" "MAC2T3" "MAC3T3"
library(lavaan)
## This is lavaan 0.6-18
## lavaan is FREE software! Please report any bugs.
Lvmodel <- ' enjoy =~ EN1T1 + EN2T1 + EN3T1 + EN4T1 + EN5T1 '
modelfit <- cfa(Lvmodel, data = spss)
summary(modelfit, fit.measure = TRUE)
## lavaan 0.6-18 ended normally after 21 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 10
##
## Number of observations 870
##
## Model Test User Model:
##
## Test statistic 352.472
## Degrees of freedom 5
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 3539.065
## Degrees of freedom 10
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.902
## Tucker-Lewis Index (TLI) 0.803
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -6041.403
## Loglikelihood unrestricted model (H1) -5865.167
##
## Akaike (AIC) 12102.806
## Bayesian (BIC) 12150.491
## Sample-size adjusted Bayesian (SABIC) 12118.733
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.283
## 90 Percent confidence interval - lower 0.258
## 90 Percent confidence interval - upper 0.308
## P-value H_0: RMSEA <= 0.050 0.000
## P-value H_0: RMSEA >= 0.080 1.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.072
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## enjoy =~
## EN1T1 1.000
## EN2T1 1.062 0.024 44.708 0.000
## EN3T1 0.893 0.024 37.478 0.000
## EN4T1 0.935 0.032 29.088 0.000
## EN5T1 0.877 0.039 22.481 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .EN1T1 0.287 0.021 13.766 0.000
## .EN2T1 0.261 0.021 12.205 0.000
## .EN3T1 0.404 0.024 17.001 0.000
## .EN4T1 0.930 0.049 19.070 0.000
## .EN5T1 1.540 0.077 19.917 0.000
## enjoy 1.434 0.083 17.305 0.000
Lvmodel2 <- ' Frustration =~ FR1T1 + FR2T1 + FR3T1 '
modelfit2 <- cfa(Lvmodel2, data = spss)
summary(modelfit2, fit.measure = TRUE)
## lavaan 0.6-18 ended normally after 23 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 6
##
## Number of observations 870
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Model Test Baseline Model:
##
## Test statistic 845.429
## Degrees of freedom 3
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -4357.260
## Loglikelihood unrestricted model (H1) -4357.260
##
## Akaike (AIC) 8726.520
## Bayesian (BIC) 8755.131
## Sample-size adjusted Bayesian (SABIC) 8736.076
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.000
## P-value H_0: RMSEA <= 0.050 NA
## P-value H_0: RMSEA >= 0.080 NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## Frustration =~
## FR1T1 1.000
## FR2T1 1.073 0.062 17.285 0.000
## FR3T1 0.776 0.049 15.947 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .FR1T1 1.006 0.084 12.029 0.000
## .FR2T1 0.344 0.080 4.275 0.000
## .FR3T1 1.737 0.093 18.698 0.000
## Frustration 1.382 0.124 11.119 0.000
Lvmodel4 <- ' frustrationregular =~ FRR1T1 + FRR2T1 + FRR3T1 + FRR4T1 '
modelfit4 <- cfa(Lvmodel4, data = spss)
summary(modelfit4, fit.measure = TRUE)
## lavaan 0.6-18 ended normally after 25 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 8
##
## Number of observations 870
##
## Model Test User Model:
##
## Test statistic 8.105
## Degrees of freedom 2
## P-value (Chi-square) 0.017
##
## Model Test Baseline Model:
##
## Test statistic 1390.039
## Degrees of freedom 6
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.996
## Tucker-Lewis Index (TLI) 0.987
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -5358.255
## Loglikelihood unrestricted model (H1) -5354.203
##
## Akaike (AIC) 10732.510
## Bayesian (BIC) 10770.658
## Sample-size adjusted Bayesian (SABIC) 10745.252
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.059
## 90 Percent confidence interval - lower 0.021
## 90 Percent confidence interval - upper 0.104
## P-value H_0: RMSEA <= 0.050 0.291
## P-value H_0: RMSEA >= 0.080 0.256
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.020
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## frustrationregular =~
## FRR1T1 1.000
## FRR2T1 0.997 0.035 28.444 0.000
## FRR3T1 0.754 0.035 21.263 0.000
## FRR4T1 0.328 0.043 7.617 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .FRR1T1 0.369 0.043 8.624 0.000
## .FRR2T1 0.329 0.042 7.874 0.000
## .FRR3T1 1.072 0.056 19.033 0.000
## .FRR4T1 2.014 0.097 20.680 0.000
## frustratinrglr 1.431 0.093 15.381 0.000
Lvmodel5 <- ' AC =~ AC1T1 + AC2T1 + AC3T1 + AC4T1 + AC5T1+ AC6T1+ AC7T1+ AC8T1 + AC9T1 '
modelfit5 <- cfa(Lvmodel5, data = spss)
summary(modelfit5, fit.measure = TRUE)
## lavaan 0.6-18 ended normally after 24 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 18
##
## Number of observations 870
##
## Model Test User Model:
##
## Test statistic 492.431
## Degrees of freedom 27
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 3573.480
## Degrees of freedom 36
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.868
## Tucker-Lewis Index (TLI) 0.825
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -12602.613
## Loglikelihood unrestricted model (H1) -12356.398
##
## Akaike (AIC) 25241.226
## Bayesian (BIC) 25327.059
## Sample-size adjusted Bayesian (SABIC) 25269.896
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.141
## 90 Percent confidence interval - lower 0.130
## 90 Percent confidence interval - upper 0.152
## P-value H_0: RMSEA <= 0.050 0.000
## P-value H_0: RMSEA >= 0.080 1.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.064
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## AC =~
## AC1T1 1.000
## AC2T1 0.842 0.047 17.804 0.000
## AC3T1 0.593 0.036 16.379 0.000
## AC4T1 1.054 0.042 25.169 0.000
## AC5T1 1.019 0.046 22.233 0.000
## AC6T1 0.722 0.041 17.721 0.000
## AC7T1 0.882 0.046 19.325 0.000
## AC8T1 0.691 0.042 16.629 0.000
## AC9T1 1.000 0.044 22.519 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .AC1T1 0.870 0.051 17.154 0.000
## .AC2T1 1.641 0.084 19.479 0.000
## .AC3T1 1.024 0.052 19.762 0.000
## .AC4T1 0.734 0.046 15.976 0.000
## .AC5T1 1.180 0.065 18.029 0.000
## .AC6T1 1.225 0.063 19.497 0.000
## .AC7T1 1.417 0.074 19.100 0.000
## .AC8T1 1.337 0.068 19.716 0.000
## .AC9T1 1.081 0.060 17.887 0.000
## AC 1.328 0.101 13.149 0.000
install.packages("lavaan")
## Warning: 正在使用 'lavaan' 這個程式套件，因此不會被安裝
library(lavaan)
library(lavaan)
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.1
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
# 定義測量模型
model <- '
enjoy1 =~ EN1T1 + EN2T1 + EN3T1 + EN4T1 + EN5T1
enjoy2 =~ EN1T2 + EN2T2 + EN3T2 + EN4T2 + EN5T2
enjoy3 =~ EN1T3 + EN2T3 + EN3T3 + EN4T3 + EN5T3
'
spss_long <- spss %>%
pivot_longer(cols = starts_with("EN"), names_to = c("Variable", "TimePoint"), names_sep = "T") %>%
mutate(TimePoint = paste0("T", TimePoint)) %>%
pivot_wider(names_from = Variable, values_from = value)
# 查看新数据框
str(spss_long)
## tibble [2,610 × 136] (S3: tbl_df/tbl/data.frame)
## $ ID : num [1:2610] 120201 120201 120201 120202 120202 ...
## $ School : Factor w/ 11 levels "興華","嘉義",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ grade : num [1:2610] 2 2 2 2 2 2 2 2 2 2 ...
## $ Class : num [1:2610] 2 2 2 2 2 2 2 2 2 2 ...
## $ Number : num [1:2610] 1 1 1 2 2 2 3 3 3 4 ...
## $ Gender : Factor w/ 3 levels "female","male",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ FR1T1 : num [1:2610] 2 2 2 5 5 5 2 2 2 4 ...
## $ FR2T1 : num [1:2610] 1 1 1 2 2 2 2 2 2 3 ...
## $ FR3T1 : num [1:2610] 4 4 4 6 6 6 3 3 3 3 ...
## $ SE1T1 : num [1:2610] 3 3 3 3 3 3 5 5 5 2 ...
## $ SE2T1 : num [1:2610] 1 1 1 6 6 6 5 5 5 1 ...
## $ SE3T1 : num [1:2610] 3 3 3 5 5 5 4 4 4 4 ...
## $ SE4T1 : num [1:2610] 3 3 3 4 4 4 2 2 2 4 ...
## $ SE5T1 : num [1:2610] 3 3 3 4 4 4 2 2 2 3 ...
## $ FRR1T1 : num [1:2610] 3 3 3 6 6 6 5 5 5 3 ...
## $ FRR2T1 : num [1:2610] 4 4 4 4 4 4 5 5 5 3 ...
## $ FRR3T1 : num [1:2610] 3 3 3 6 6 6 5 5 5 3 ...
## $ FRR4T1 : num [1:2610] 3 3 3 6 6 6 6 6 6 3 ...
## $ BO1T1 : num [1:2610] 1 1 1 3 3 3 2 2 2 3 ...
## $ BO2T1 : num [1:2610] 1 1 1 3 3 3 2 2 2 3 ...
## $ BO3T1 : num [1:2610] 1 1 1 3 3 3 2 2 2 4 ...
## $ BO4T1 : num [1:2610] 1 1 1 3 3 3 2 2 2 5 ...
## $ CB1T1 : num [1:2610] 1 1 1 1 1 1 2 2 2 1 ...
## $ CB2T1 : num [1:2610] 1 1 1 3 3 3 2 2 2 4 ...
## $ CB3T1 : num [1:2610] 1 1 1 5 5 5 3 3 3 3 ...
## $ AC1T1 : num [1:2610] 3 3 3 4 4 4 4 4 4 4 ...
## $ AC2T1 : num [1:2610] 4 4 4 6 6 6 5 5 5 4 ...
## $ AC3T1 : num [1:2610] 1 1 1 1 1 1 1 1 1 5 ...
## $ AC4T1 : num [1:2610] 2 2 2 4 4 4 4 4 4 5 ...
## $ AC5T1 : num [1:2610] 1 1 1 5 5 5 5 5 5 4 ...
## $ AC6T1 : num [1:2610] 1 1 1 3 3 3 4 4 4 1 ...
## $ AC7T1 : num [1:2610] 1 1 1 5 5 5 4 4 4 3 ...
## $ AC8T1 : num [1:2610] 1 1 1 1 1 1 3 3 3 1 ...
## $ AC9T1 : num [1:2610] 1 1 1 1 1 1 4 4 4 2 ...
## $ PC1T1 : num [1:2610] 1 1 1 4 4 4 3 3 3 4 ...
## $ PC2T1 : num [1:2610] 1 1 1 4 4 4 4 4 4 3 ...
## $ PC3T1 : num [1:2610] 1 1 1 3 3 3 4 4 4 4 ...
## $ PC4T1 : num [1:2610] 1 1 1 3 3 3 4 4 4 5 ...
## $ ACHT1 : num [1:2610] 83 83 83 64 64 64 72 72 72 21 ...
## $ FR1T2 : num [1:2610] 1 1 1 1 1 1 1 1 1 4 ...
## $ FR2T2 : num [1:2610] 1 1 1 1 1 1 2 2 2 3 ...
## $ FR3T2 : num [1:2610] 3 3 3 6 6 6 1 1 1 1 ...
## $ SE1T2 : num [1:2610] 1 1 1 1 1 1 5 5 5 3 ...
## $ SE2T2 : num [1:2610] 1 1 1 6 6 6 6 6 6 1 ...
## $ SE3T2 : num [1:2610] 3 3 3 3 3 3 5 5 5 5 ...
## $ SE4T2 : num [1:2610] 4 4 4 4 4 4 3 3 3 6 ...
## $ SE5T2 : num [1:2610] 3 3 3 3 3 3 3 3 3 5 ...
## $ FRR1T2 : num [1:2610] 3 3 3 6 6 6 6 6 6 4 ...
## $ FRR2T2 : num [1:2610] 4 4 4 6 6 6 6 6 6 4 ...
## $ FRR3T2 : num [1:2610] 3 3 3 4 4 4 3 3 3 2 ...
## $ FRR4T2 : num [1:2610] 5 5 5 4 4 4 5 5 5 4 ...
## $ BO1T2 : num [1:2610] 1 1 1 1 1 1 3 3 3 5 ...
## $ BO2T2 : num [1:2610] 1 1 1 1 1 1 3 3 3 4 ...
## $ BO3T2 : num [1:2610] 1 1 1 1 1 1 3 3 3 6 ...
## $ BO4T2 : num [1:2610] 1 1 1 1 1 1 2 2 2 5 ...
## $ CB1T2 : num [1:2610] 1 1 1 2 2 2 1 1 1 1 ...
## $ CB2T2 : num [1:2610] 1 1 1 2 2 2 1 1 1 4 ...
## $ CB3T2 : num [1:2610] 1 1 1 1 1 1 4 4 4 4 ...
## $ AC1T2 : num [1:2610] 2 2 2 3 3 3 4 4 4 4 ...
## $ AC2T2 : num [1:2610] 2 2 2 3 3 3 5 5 5 4 ...
## $ AC3T2 : num [1:2610] 1 1 1 3 3 3 1 1 1 1 ...
## $ AC4T2 : num [1:2610] 2 2 2 2 2 2 1 1 1 4 ...
## $ AC5T2 : num [1:2610] 2 2 2 2 2 2 3 3 3 4 ...
## $ AC6T2 : num [1:2610] 2 2 2 1 1 1 3 3 3 1 ...
## $ AC7T2 : num [1:2610] 1 1 1 4 4 4 4 4 4 4 ...
## $ AC8T2 : num [1:2610] 1 1 1 1 1 1 3 3 3 1 ...
## $ AC9T2 : num [1:2610] 2 2 2 1 1 1 4 4 4 4 ...
## $ PC1T2 : num [1:2610] 1 1 1 2 2 2 4 4 4 4 ...
## $ PC2T2 : num [1:2610] 1 1 1 2 2 2 4 4 4 4 ...
## $ PC3T2 : num [1:2610] 1 1 1 2 2 2 4 4 4 4 ...
## $ PC4T2 : num [1:2610] 1 1 1 2 2 2 4 4 4 4 ...
## $ ACHT2 : num [1:2610] 88 88 88 78 78 78 73 73 73 25 ...
## $ FR1T3 : num [1:2610] 3 3 3 3 3 3 1 1 1 4 ...
## $ FR2T3 : num [1:2610] 3 3 3 1 1 1 1 1 1 4 ...
## $ FR3T3 : num [1:2610] 3 3 3 6 6 6 1 1 1 4 ...
## $ SE1T3 : num [1:2610] 3 3 3 4 4 4 4 4 4 5 ...
## $ SE2T3 : num [1:2610] 3 3 3 2 2 2 5 5 5 6 ...
## $ SE3T3 : num [1:2610] 4 4 4 5 5 5 3 3 3 5 ...
## $ SE4T3 : num [1:2610] 4 4 4 4 4 4 3 3 3 5 ...
## $ SE5T3 : num [1:2610] 4 4 4 4 4 4 3 3 3 4 ...
## $ FRR1T3 : num [1:2610] 3 3 3 6 6 6 5 5 5 4 ...
## $ FRR2T3 : num [1:2610] 3 3 3 6 6 6 4 4 4 4 ...
## $ FRR3T3 : num [1:2610] 4 4 4 4 4 4 5 5 5 3 ...
## $ FRR4T3 : num [1:2610] 4 4 4 6 6 6 6 6 6 3 ...
## $ BO1T3 : num [1:2610] 2 2 2 1 1 1 3 3 3 4 ...
## $ BO2T3 : num [1:2610] 2 2 2 1 1 1 3 3 3 3 ...
## $ BO3T3 : num [1:2610] 2 2 2 1 1 1 3 3 3 5 ...
## $ BO4T3 : num [1:2610] 2 2 2 1 1 1 3 3 3 4 ...
## $ CB1T3 : num [1:2610] 1 1 1 1 1 1 1 1 1 1 ...
## $ CB2T3 : num [1:2610] 2 2 2 4 4 4 4 4 4 3 ...
## $ CB3T3 : num [1:2610] 2 2 2 4 4 4 3 3 3 3 ...
## $ AC1T3 : num [1:2610] 1 1 1 4 4 4 4 4 4 4 ...
## $ AC2T3 : num [1:2610] 1 1 1 4 4 4 4 4 4 5 ...
## $ AC3T3 : num [1:2610] 1 1 1 1 1 1 1 1 1 1 ...
## $ AC4T3 : num [1:2610] 1 1 1 4 4 4 3 3 3 3 ...
## $ AC5T3 : num [1:2610] 1 1 1 4 4 4 3 3 3 3 ...
## $ AC6T3 : num [1:2610] 1 1 1 2 2 2 3 3 3 2 ...
## $ AC7T3 : num [1:2610] 1 1 1 4 4 4 5 5 5 3 ...
## $ AC8T3 : num [1:2610] 1 1 1 1 1 1 1 1 1 1 ...
## [list output truncated]
summary(spss_long)
## ID School grade Class
## Min. : 120201 嘉義 :438 Min. :1.000 Min. : 1.000
## 1st Qu.: 220817 瑞豐 :375 1st Qu.:1.000 1st Qu.: 2.000
## Median : 530524 玉山 :357 Median :2.000 Median : 4.000
## Mean : 548365 鳳甲 :270 Mean :1.775 Mean : 4.894
## 3rd Qu.: 720511 民生 :258 3rd Qu.:2.000 3rd Qu.: 6.000
## Max. :1120542 鳳山 :255 Max. :3.000 Max. :19.000
## (Other):657
## Number Gender FR1T1 FR2T1 FR3T1
## Min. : 1.00 female:1524 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.: 6.00 male :1083 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000
## Median :13.00 3 : 3 Median :3.000 Median :3.000 Median :3.000
## Mean :13.06 Mean :3.166 Mean :2.834 Mean :3.032
## 3rd Qu.:19.00 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :42.00 Max. :6.000 Max. :6.000 Max. :6.000
##
## SE1T1 SE2T1 SE3T1 SE4T1 SE5T1
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.00
## 1st Qu.:2.000 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:3.000 1st Qu.:3.00
## Median :3.000 Median :5.000 Median :4.000 Median :4.000 Median :4.00
## Mean :3.094 Mean :4.709 Mean :4.225 Mean :3.503 Mean :3.86
## 3rd Qu.:4.000 3rd Qu.:6.000 3rd Qu.:5.000 3rd Qu.:4.000 3rd Qu.:5.00
## Max. :6.000 Max. :6.000 Max. :6.000 Max. :6.000 Max. :6.00
##
## FRR1T1 FRR2T1 FRR3T1 FRR4T1
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000
## Median :4.000 Median :4.000 Median :4.000 Median :4.000
## Mean :3.714 Mean :3.738 Mean :3.629 Mean :3.787
## 3rd Qu.:4.000 3rd Qu.:5.000 3rd Qu.:4.000 3rd Qu.:5.000
## Max. :6.000 Max. :6.000 Max. :6.000 Max. :6.000
##
## BO1T1 BO2T1 BO3T1 BO4T1
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000
## Median :3.000 Median :3.000 Median :3.000 Median :3.000
## Mean :3.457 Mean :3.431 Mean :3.102 Mean :3.231
## 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :6.000 Max. :6.000 Max. :6.000 Max. :6.000
##
## CB1T1 CB2T1 CB3T1 AC1T1
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:2.000 1st Qu.:2.000
## Median :1.000 Median :2.000 Median :3.000 Median :4.000
## Mean :1.898 Mean :2.326 Mean :3.085 Mean :3.457
## 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :6.000 Max. :6.000 Max. :6.000 Max. :6.000
##
## AC2T1 AC3T1 AC4T1 AC5T1 AC6T1
## Min. :1.000 Min. :1.000 Min. :1.00 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:1.000 1st Qu.:2.00 1st Qu.:2.000 1st Qu.:1.000
## Median :3.000 Median :1.000 Median :3.00 Median :3.000 Median :2.000
## Mean :3.331 Mean :1.926 Mean :3.09 Mean :3.159 Mean :2.187
## 3rd Qu.:5.000 3rd Qu.:3.000 3rd Qu.:4.00 3rd Qu.:4.000 3rd Qu.:3.000
## Max. :6.000 Max. :6.000 Max. :6.00 Max. :6.000 Max. :6.000
##
## AC7T1 AC8T1 AC9T1 PC1T1
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:1.000 1st Qu.:2.000 1st Qu.:1.000
## Median :3.000 Median :2.000 Median :3.000 Median :2.000
## Mean :3.051 Mean :2.107 Mean :3.259 Mean :2.595
## 3rd Qu.:4.000 3rd Qu.:3.000 3rd Qu.:4.000 3rd Qu.:3.000
## Max. :6.000 Max. :6.000 Max. :6.000 Max. :6.000
##
## PC2T1 PC3T1 PC4T1 ACHT1
## Min. :1.000 Min. :1.000 Min. :1.000 Min. : 0.00
## 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.: 38.00
## Median :2.000 Median :2.000 Median :2.000 Median : 63.00
## Mean :2.684 Mean :2.599 Mean :2.683 Mean : 58.25
## 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.: 79.00
## Max. :6.000 Max. :6.000 Max. :6.000 Max. :100.00
##
## FR1T2 FR2T2 FR3T2 SE1T2 SE2T2
## Min. :1.000 Min. :1.00 Min. :1.00 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.00 1st Qu.:2.00 1st Qu.:2.000 1st Qu.:4.000
## Median :3.000 Median :3.00 Median :3.00 Median :3.000 Median :5.000
## Mean :3.271 Mean :2.96 Mean :3.14 Mean :3.201 Mean :4.586
## 3rd Qu.:4.000 3rd Qu.:4.00 3rd Qu.:4.00 3rd Qu.:4.000 3rd Qu.:6.000
## Max. :6.000 Max. :6.00 Max. :6.00 Max. :6.000 Max. :6.000
##
## SE3T2 SE4T2 SE5T2 FRR1T2 FRR2T2
## Min. :1.00 Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:4.00 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000
## Median :4.00 Median :4.000 Median :4.000 Median :4.000 Median :4.000
## Mean :4.24 Mean :3.644 Mean :3.933 Mean :3.785 Mean :3.795
## 3rd Qu.:5.00 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000
## Max. :6.00 Max. :6.000 Max. :6.000 Max. :6.000 Max. :6.000
##
## FRR3T2 FRR4T2 BO1T2 BO2T2 BO3T2
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.0 Min. :1.000
## 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:2.000 1st Qu.:2.0 1st Qu.:2.000
## Median :4.000 Median :4.000 Median :3.000 Median :3.0 Median :3.000
## Mean :3.686 Mean :3.755 Mean :3.487 Mean :3.5 Mean :3.149
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.0 3rd Qu.:4.000
## Max. :6.000 Max. :6.000 Max. :6.000 Max. :6.0 Max. :6.000
##
## BO4T2 CB1T2 CB2T2 CB3T2
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:2.000
## Median :3.000 Median :1.000 Median :2.000 Median :3.000
## Mean :3.263 Mean :2.051 Mean :2.609 Mean :3.114
## 3rd Qu.:4.000 3rd Qu.:3.000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :6.000 Max. :6.000 Max. :6.000 Max. :6.000
##
## AC1T2 AC2T2 AC3T2 AC4T2 AC5T2
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.00 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:1.000 1st Qu.:2.00 1st Qu.:2.000
## Median :4.000 Median :4.000 Median :2.000 Median :3.00 Median :3.000
## Mean :3.418 Mean :3.421 Mean :2.121 Mean :3.18 Mean :3.245
## 3rd Qu.:4.000 3rd Qu.:5.000 3rd Qu.:3.000 3rd Qu.:4.00 3rd Qu.:4.000
## Max. :6.000 Max. :6.000 Max. :6.000 Max. :6.00 Max. :6.000
##
## AC6T2 AC7T2 AC8T2 AC9T2
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:2.000 1st Qu.:1.000 1st Qu.:2.000
## Median :2.000 Median :3.000 Median :2.000 Median :3.000
## Mean :2.343 Mean :3.126 Mean :2.298 Mean :3.292
## 3rd Qu.:3.000 3rd Qu.:4.000 3rd Qu.:3.000 3rd Qu.:4.000
## Max. :6.000 Max. :6.000 Max. :6.000 Max. :6.000
##
## PC1T2 PC2T2 PC3T2 PC4T2
## Min. :1.000 Min. :1.00 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:1.00 1st Qu.:1.000 1st Qu.:1.000
## Median :2.000 Median :3.00 Median :2.000 Median :3.000
## Mean :2.701 Mean :2.79 Mean :2.682 Mean :2.778
## 3rd Qu.:4.000 3rd Qu.:4.00 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :6.000 Max. :6.00 Max. :6.000 Max. :6.000
##
## ACHT2 FR1T3 FR2T3 FR3T3
## Min. : 0.00 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.: 35.00 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000
## Median : 58.00 Median :3.000 Median :3.000 Median :3.000
## Mean : 56.59 Mean :3.347 Mean :3.064 Mean :3.155
## 3rd Qu.: 80.00 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :100.00 Max. :6.000 Max. :6.000 Max. :6.000
##
## SE1T3 SE2T3 SE3T3 SE4T3 SE5T3
## Min. :1.000 Min. :1.000 Min. :1.00 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:3.000 1st Qu.:3.00 1st Qu.:3.000 1st Qu.:3.000
## Median :3.000 Median :4.000 Median :4.00 Median :4.000 Median :4.000
## Mean :3.271 Mean :4.295 Mean :4.14 Mean :3.579 Mean :3.809
## 3rd Qu.:4.000 3rd Qu.:6.000 3rd Qu.:5.00 3rd Qu.:4.000 3rd Qu.:5.000
## Max. :6.000 Max. :6.000 Max. :6.00 Max. :6.000 Max. :6.000
##
## FRR1T3 FRR2T3 FRR3T3 FRR4T3
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000
## Median :4.000 Median :4.000 Median :4.000 Median :4.000
## Mean :3.751 Mean :3.732 Mean :3.645 Mean :3.792
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:4.000 3rd Qu.:5.000
## Max. :6.000 Max. :6.000 Max. :6.000 Max. :6.000
##
## BO1T3 BO2T3 BO3T3 BO4T3
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000
## Median :3.000 Median :3.000 Median :3.000 Median :3.000
## Mean :3.518 Mean :3.483 Mean :3.222 Mean :3.345
## 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :6.000 Max. :6.000 Max. :6.000 Max. :6.000
##
## CB1T3 CB2T3 CB3T3 AC1T3 AC2T3
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.00
## 1st Qu.:1.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.00
## Median :2.000 Median :3.000 Median :3.000 Median :4.000 Median :4.00
## Mean :2.295 Mean :2.855 Mean :3.176 Mean :3.469 Mean :3.48
## 3rd Qu.:3.000 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:5.00
## Max. :6.000 Max. :6.000 Max. :6.000 Max. :6.000 Max. :6.00
##
## AC3T3 AC4T3 AC5T3 AC6T3 AC7T3
## Min. :1.000 Min. :1.00 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:2.00 1st Qu.:2.000 1st Qu.:1.000 1st Qu.:2.000
## Median :2.000 Median :3.00 Median :3.000 Median :2.000 Median :3.000
## Mean :2.341 Mean :3.29 Mean :3.317 Mean :2.517 Mean :3.293
## 3rd Qu.:3.000 3rd Qu.:4.00 3rd Qu.:4.000 3rd Qu.:3.000 3rd Qu.:4.000
## Max. :6.000 Max. :6.00 Max. :6.000 Max. :6.000 Max. :6.000
##
## AC8T3 AC9T3 PC1T3 PC2T3
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000
## Median :2.000 Median :3.000 Median :3.000 Median :3.000
## Mean :2.526 Mean :3.333 Mean :2.807 Mean :2.908
## 3rd Qu.:3.000 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :6.000 Max. :6.000 Max. :6.000 Max. :6.000
##
## PC3T3 PC4T3 ACHT3 filter_.
## Min. :1.000 Min. :1.000 Min. : 0.00 Not Selected: 0
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.: 38.00 Selected :2610
## Median :3.000 Median :3.000 Median : 60.00
## Mean :2.782 Mean :2.886 Mean : 58.11
## 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.: 80.00
## Max. :6.000 Max. :6.000 Max. :100.00
##
## ZACHT1 ZACHT2 ZACHT3
## Min. :-3.0131555 Min. :-3.187241 Min. :-3.013155
## 1st Qu.:-0.6833303 1st Qu.:-0.661663 1st Qu.:-0.676073
## Median :-0.0100814 Median : 0.004321 Median : 0.004320
## Mean :-0.0006499 Mean :-0.001995 Mean :-0.001895
## 3rd Qu.: 0.6598713 3rd Qu.: 0.685150 3rd Qu.: 0.665252
## Max. : 2.5187201 Max. : 2.232721 Max. : 2.268986
##
## ZZACHT1 ZZACHT2 ZZACHT3 ZEN1T1
## Min. :-3.030778 Min. :-3.215978 Min. :-3.036674 Min. :-1.23948
## 1st Qu.:-0.686821 1st Qu.:-0.666033 1st Qu.:-0.679867 1st Qu.:-0.59499
## Median :-0.009489 Median : 0.006376 Median : 0.006268 Median :-0.01296
## Mean : 0.000000 Mean : 0.000000 Mean : 0.000000 Mean : 0.01476
## 3rd Qu.: 0.664528 3rd Qu.: 0.693775 3rd Qu.: 0.672778 3rd Qu.: 0.78127
## Max. : 2.534652 Max. : 2.256276 Max. : 2.290046 Max. : 2.19917
##
## ZEN2T1 ZEN3T1 ZEN4T1 ZEN5T1
## Min. :-1.2875 Min. :-1.14012 Min. :-1.27763 Min. :-1.367392
## 1st Qu.:-0.6653 1st Qu.:-1.14012 1st Qu.:-0.64561 1st Qu.:-0.763799
## Median :-0.1329 Median : 0.20889 Median :-0.15034 Median : 0.179542
## Mean : 0.0125 Mean : 0.02014 Mean : 0.01331 Mean : 0.003013
## 3rd Qu.: 0.6157 3rd Qu.: 0.20889 3rd Qu.: 0.48498 3rd Qu.: 0.758028
## Max. : 2.0604 Max. : 2.37553 Max. : 1.90644 Max. : 1.500124
##
## MAC1T1 MAC2T1 MAC3T1 MAC1T2
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000
## Median :3.000 Median :2.667 Median :2.667 Median :3.000
## Mean :2.905 Mean :2.812 Mean :2.805 Mean :2.987
## 3rd Qu.:3.667 3rd Qu.:3.667 3rd Qu.:3.667 3rd Qu.:3.667
## Max. :6.000 Max. :6.000 Max. :6.000 Max. :6.000
##
## MAC2T2 MAC3T2 MAC1T3 MAC2T3
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.333 1st Qu.:2.000
## Median :3.000 Median :3.000 Median :3.167 Median :3.000
## Mean :2.923 Mean :2.906 Mean :3.097 Mean :3.041
## 3rd Qu.:3.667 3rd Qu.:3.667 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :6.000 Max. :6.000 Max. :6.000 Max. :6.000
##
## MAC3T3 TimePoint EN1 EN2
## Min. :1.000 Length:2610 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 Class :character 1st Qu.:2.000 1st Qu.:2.000
## Median :3.000 Mode :character Median :3.000 Median :3.000
## Mean :3.051 Mean :2.925 Mean :3.045
## 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :6.000 Max. :6.000 Max. :6.000
##
## EN3 EN4 EN5 ENR1 ENR2
## Min. :1.00 Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.00 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:3.000 1st Qu.:3.000
## Median :3.00 Median :3.000 Median :4.000 Median :4.000 Median :4.000
## Mean :2.71 Mean :3.177 Mean :3.539 Mean :3.799 Mean :3.821
## 3rd Qu.:4.00 3rd Qu.:4.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000
## Max. :6.00 Max. :6.000 Max. :6.000 Max. :6.000 Max. :6.000
##
## ENR3 ENR4
## Min. :1.00 Min. :1.000
## 1st Qu.:3.00 1st Qu.:3.000
## Median :4.00 Median :4.000
## Mean :3.61 Mean :3.771
## 3rd Qu.:4.00 3rd Qu.:5.000
## Max. :6.00 Max. :6.000
##
model <- '
enjoy =~ EN1 + EN2 + EN3 + EN4 + EN5
'
# 不约束模型（配置不变性）
fit_configural <- cfa(model, data = spss_long, group = "TimePoint", control = list(iter.max = 10000, reltol = 1e-8))
summary(fit_configural, fit.measures = TRUE)
## lavaan 0.6-18 ended normally after 42 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 45
##
## Number of observations per group:
## T1 870
## T2 870
## T3 870
##
## Model Test User Model:
##
## Test statistic 898.046
## Degrees of freedom 15
## P-value (Chi-square) 0.000
## Test statistic for each group:
## T1 352.472
## T2 287.290
## T3 258.284
##
## Model Test Baseline Model:
##
## Test statistic 11859.620
## Degrees of freedom 30
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.925
## Tucker-Lewis Index (TLI) 0.851
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -17413.561
## Loglikelihood unrestricted model (H1) -16964.538
##
## Akaike (AIC) 34917.122
## Bayesian (BIC) 35181.141
## Sample-size adjusted Bayesian (SABIC) 35038.163
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.260
## 90 Percent confidence interval - lower 0.246
## 90 Percent confidence interval - upper 0.275
## P-value H_0: RMSEA <= 0.050 0.000
## P-value H_0: RMSEA >= 0.080 1.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.053
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
##
## Group 1 [T1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## enjoy =~
## EN1 1.000
## EN2 1.062 0.024 44.708 0.000
## EN3 0.893 0.024 37.478 0.000
## EN4 0.935 0.032 29.088 0.000
## EN5 0.877 0.039 22.481 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .EN1 2.910 0.044 65.441 0.000
## .EN2 3.071 0.046 66.110 0.000
## .EN3 2.653 0.042 62.929 0.000
## .EN4 3.166 0.050 63.178 0.000
## .EN5 3.547 0.055 64.349 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .EN1 0.287 0.021 13.766 0.000
## .EN2 0.261 0.021 12.205 0.000
## .EN3 0.404 0.024 17.001 0.000
## .EN4 0.930 0.049 19.070 0.000
## .EN5 1.540 0.077 19.917 0.000
## enjoy 1.434 0.083 17.305 0.000
##
##
## Group 2 [T2]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## enjoy =~
## EN1 1.000
## EN2 1.088 0.024 45.889 0.000
## EN3 0.909 0.023 38.737 0.000
## EN4 0.997 0.032 31.322 0.000
## EN5 0.878 0.037 23.772 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .EN1 2.907 0.045 64.624 0.000
## .EN2 3.049 0.047 65.059 0.000
## .EN3 2.693 0.042 63.981 0.000
## .EN4 3.179 0.051 62.192 0.000
## .EN5 3.499 0.053 65.933 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .EN1 0.326 0.021 15.296 0.000
## .EN2 0.214 0.019 11.202 0.000
## .EN3 0.357 0.021 16.778 0.000
## .EN4 0.848 0.045 18.861 0.000
## .EN5 1.344 0.068 19.895 0.000
## enjoy 1.434 0.084 17.033 0.000
##
##
## Group 3 [T3]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## enjoy =~
## EN1 1.000
## EN2 1.046 0.017 60.579 0.000
## EN3 0.927 0.019 48.773 0.000
## EN4 0.948 0.025 37.523 0.000
## EN5 0.866 0.033 26.373 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .EN1 2.959 0.045 65.118 0.000
## .EN2 3.014 0.046 65.067 0.000
## .EN3 2.783 0.044 63.934 0.000
## .EN4 3.187 0.049 65.316 0.000
## .EN5 3.570 0.053 67.154 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .EN1 0.212 0.014 14.611 0.000
## .EN2 0.134 0.013 10.632 0.000
## .EN3 0.287 0.017 17.109 0.000
## .EN4 0.648 0.034 19.189 0.000
## .EN5 1.269 0.063 20.157 0.000
## enjoy 1.584 0.086 18.395 0.000
if(!fit_configural@Fit@converged) stop("Configural model did not converge")
# 度量不变性模型
fit_metric <- cfa(model, data = spss_long, group = "TimePoint", group.equal = "loadings", control = list(iter.max = 1000, reltol = 1e-8))
summary(fit_metric, fit.measures = TRUE)
## lavaan 0.6-18 ended normally after 40 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 45
## Number of equality constraints 8
##
## Number of observations per group:
## T1 870
## T2 870
## T3 870
##
## Model Test User Model:
##
## Test statistic 905.190
## Degrees of freedom 23
## P-value (Chi-square) 0.000
## Test statistic for each group:
## T1 353.800
## T2 290.311
## T3 261.079
##
## Model Test Baseline Model:
##
## Test statistic 11859.620
## Degrees of freedom 30
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.925
## Tucker-Lewis Index (TLI) 0.903
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -17417.133
## Loglikelihood unrestricted model (H1) -16964.538
##
## Akaike (AIC) 34908.266
## Bayesian (BIC) 35125.349
## Sample-size adjusted Bayesian (SABIC) 35007.789
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.210
## 90 Percent confidence interval - lower 0.198
## 90 Percent confidence interval - upper 0.222
## P-value H_0: RMSEA <= 0.050 0.000
## P-value H_0: RMSEA >= 0.080 1.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.054
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
##
## Group 1 [T1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## enjoy =~
## EN1 1.000
## EN2 (.p2.) 1.061 0.012 88.123 0.000
## EN3 (.p3.) 0.912 0.013 72.627 0.000
## EN4 (.p4.) 0.959 0.017 56.925 0.000
## EN5 (.p5.) 0.873 0.021 42.079 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .EN1 2.910 0.044 65.694 0.000
## .EN2 3.071 0.046 66.423 0.000
## .EN3 2.653 0.043 62.285 0.000
## .EN4 3.166 0.051 62.537 0.000
## .EN5 3.547 0.055 64.663 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .EN1 0.290 0.020 14.285 0.000
## .EN2 0.265 0.021 12.881 0.000
## .EN3 0.400 0.024 17.015 0.000
## .EN4 0.925 0.049 19.062 0.000
## .EN5 1.537 0.077 19.970 0.000
## enjoy 1.418 0.075 18.889 0.000
##
##
## Group 2 [T2]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## enjoy =~
## EN1 1.000
## EN2 (.p2.) 1.061 0.012 88.123 0.000
## EN3 (.p3.) 0.912 0.013 72.627 0.000
## EN4 (.p4.) 0.959 0.017 56.925 0.000
## EN5 (.p5.) 0.873 0.021 42.079 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .EN1 2.907 0.045 64.001 0.000
## .EN2 3.049 0.046 65.619 0.000
## .EN3 2.693 0.043 63.246 0.000
## .EN4 3.179 0.050 63.104 0.000
## .EN5 3.499 0.053 65.679 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .EN1 0.321 0.021 15.334 0.000
## .EN2 0.221 0.018 12.029 0.000
## .EN3 0.353 0.021 16.757 0.000
## .EN4 0.853 0.045 19.040 0.000
## .EN5 1.347 0.068 19.912 0.000
## enjoy 1.474 0.078 18.907 0.000
##
##
## Group 3 [T3]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## enjoy =~
## EN1 1.000
## EN2 (.p2.) 1.061 0.012 88.123 0.000
## EN3 (.p3.) 0.912 0.013 72.627 0.000
## EN4 (.p4.) 0.959 0.017 56.925 0.000
## EN5 (.p5.) 0.873 0.021 42.079 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .EN1 2.959 0.045 65.358 0.000
## .EN2 3.014 0.047 64.590 0.000
## .EN3 2.783 0.043 64.990 0.000
## .EN4 3.187 0.049 65.034 0.000
## .EN5 3.570 0.053 67.110 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .EN1 0.214 0.014 14.901 0.000
## .EN2 0.130 0.012 10.448 0.000
## .EN3 0.292 0.017 17.426 0.000
## .EN4 0.648 0.034 19.208 0.000
## .EN5 1.267 0.063 20.173 0.000
## enjoy 1.568 0.081 19.259 0.000
if(!fit_metric@Fit@converged) stop("Metric model did not converge")
# 截距不变性模型
fit_scalar <- cfa(model, data = spss_long, group = "TimePoint", group.equal = c("loadings", "intercepts"), control = list(iter.max = 1000, reltol = 1e-8))
summary(fit_scalar, fit.measures = TRUE)
## lavaan 0.6-18 ended normally after 51 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 47
## Number of equality constraints 18
##
## Number of observations per group:
## T1 870
## T2 870
## T3 870
##
## Model Test User Model:
##
## Test statistic 941.281
## Degrees of freedom 31
## P-value (Chi-square) 0.000
## Test statistic for each group:
## T1 367.461
## T2 294.312
## T3 279.508
##
## Model Test Baseline Model:
##
## Test statistic 11859.620
## Degrees of freedom 30
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.923
## Tucker-Lewis Index (TLI) 0.926
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -17435.178
## Loglikelihood unrestricted model (H1) -16964.538
##
## Akaike (AIC) 34928.356
## Bayesian (BIC) 35098.502
## Sample-size adjusted Bayesian (SABIC) 35006.361
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.184
## 90 Percent confidence interval - lower 0.174
## 90 Percent confidence interval - upper 0.194
## P-value H_0: RMSEA <= 0.050 0.000
## P-value H_0: RMSEA >= 0.080 1.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.055
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
##
## Group 1 [T1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## enjoy =~
## EN1 1.000
## EN2 (.p2.) 1.059 0.012 87.801 0.000
## EN3 (.p3.) 0.911 0.013 72.448 0.000
## EN4 (.p4.) 0.959 0.017 56.964 0.000
## EN5 (.p5.) 0.873 0.021 42.098 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .EN1 (.12.) 2.922 0.042 68.860 0.000
## .EN2 (.13.) 3.028 0.045 67.784 0.000
## .EN3 (.14.) 2.710 0.039 68.910 0.000
## .EN4 (.15.) 3.172 0.043 73.375 0.000
## .EN5 (.16.) 3.533 0.043 82.759 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .EN1 0.289 0.020 14.230 0.000
## .EN2 0.269 0.021 12.963 0.000
## .EN3 0.404 0.024 17.029 0.000
## .EN4 0.924 0.049 19.045 0.000
## .EN5 1.536 0.077 19.962 0.000
## enjoy 1.418 0.075 18.884 0.000
##
##
## Group 2 [T2]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## enjoy =~
## EN1 1.000
## EN2 (.p2.) 1.059 0.012 87.801 0.000
## EN3 (.p3.) 0.911 0.013 72.448 0.000
## EN4 (.p4.) 0.959 0.017 56.964 0.000
## EN5 (.p5.) 0.873 0.021 42.098 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .EN1 (.12.) 2.922 0.042 68.860 0.000
## .EN2 (.13.) 3.028 0.045 67.784 0.000
## .EN3 (.14.) 2.710 0.039 68.910 0.000
## .EN4 (.15.) 3.172 0.043 73.375 0.000
## .EN5 (.16.) 3.533 0.043 82.759 0.000
## enjoy -0.000 0.059 -0.007 0.994
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .EN1 0.321 0.021 15.318 0.000
## .EN2 0.223 0.018 12.076 0.000
## .EN3 0.353 0.021 16.745 0.000
## .EN4 0.853 0.045 19.033 0.000
## .EN5 1.347 0.068 19.909 0.000
## enjoy 1.475 0.078 18.903 0.000
##
##
## Group 3 [T3]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## enjoy =~
## EN1 1.000
## EN2 (.p2.) 1.059 0.012 87.801 0.000
## EN3 (.p3.) 0.911 0.013 72.448 0.000
## EN4 (.p4.) 0.959 0.017 56.964 0.000
## EN5 (.p5.) 0.873 0.021 42.098 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .EN1 (.12.) 2.922 0.042 68.860 0.000
## .EN2 (.13.) 3.028 0.045 67.784 0.000
## .EN3 (.14.) 2.710 0.039 68.910 0.000
## .EN4 (.15.) 3.172 0.043 73.375 0.000
## .EN5 (.16.) 3.533 0.043 82.759 0.000
## enjoy 0.018 0.060 0.305 0.761
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .EN1 0.214 0.014 14.808 0.000
## .EN2 0.134 0.013 10.613 0.000
## .EN3 0.295 0.017 17.428 0.000
## .EN4 0.646 0.034 19.180 0.000
## .EN5 1.267 0.063 20.162 0.000
## enjoy 1.570 0.082 19.258 0.000
if(!fit_scalar@Fit@converged) stop("Scalar model did not converge")
# 严格不变性模型
fit_strict <- cfa(model, data = spss_long, group = "TimePoint", group.equal = c("loadings", "intercepts", "residuals"), control = list(iter.max = 1000, reltol = 1e-8))
summary(fit_strict, fit.measures = TRUE)
## lavaan 0.6-18 ended normally after 46 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 47
## Number of equality constraints 28
##
## Number of observations per group:
## T1 870
## T2 870
## T3 870
##
## Model Test User Model:
##
## Test statistic 1074.001
## Degrees of freedom 41
## P-value (Chi-square) 0.000
## Test statistic for each group:
## T1 398.826
## T2 301.737
## T3 373.438
##
## Model Test Baseline Model:
##
## Test statistic 11859.620
## Degrees of freedom 30
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.913
## Tucker-Lewis Index (TLI) 0.936
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -17501.538
## Loglikelihood unrestricted model (H1) -16964.538
##
## Akaike (AIC) 35041.077
## Bayesian (BIC) 35152.552
## Sample-size adjusted Bayesian (SABIC) 35092.183
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.170
## 90 Percent confidence interval - lower 0.161
## 90 Percent confidence interval - upper 0.179
## P-value H_0: RMSEA <= 0.050 0.000
## P-value H_0: RMSEA >= 0.080 1.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.058
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
##
## Group 1 [T1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## enjoy =~
## EN1 1.000
## EN2 (.p2.) 1.063 0.012 85.432 0.000
## EN3 (.p3.) 0.909 0.013 71.269 0.000
## EN4 (.p4.) 0.959 0.017 55.925 0.000
## EN5 (.p5.) 0.873 0.021 41.766 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .EN1 (.12.) 2.920 0.043 68.678 0.000
## .EN2 (.13.) 3.039 0.045 67.914 0.000
## .EN3 (.14.) 2.704 0.039 68.848 0.000
## .EN4 (.15.) 3.172 0.043 73.253 0.000
## .EN5 (.16.) 3.534 0.043 82.649 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .EN1 (.p6.) 0.273 0.011 25.039 0.000
## .EN2 (.p7.) 0.207 0.010 20.098 0.000
## .EN3 (.p8.) 0.352 0.012 29.290 0.000
## .EN4 (.p9.) 0.811 0.025 32.930 0.000
## .EN5 (.10.) 1.386 0.040 34.597 0.000
## enjoy 1.431 0.075 18.970 0.000
##
##
## Group 2 [T2]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## enjoy =~
## EN1 1.000
## EN2 (.p2.) 1.063 0.012 85.432 0.000
## EN3 (.p3.) 0.909 0.013 71.269 0.000
## EN4 (.p4.) 0.959 0.017 55.925 0.000
## EN5 (.p5.) 0.873 0.021 41.766 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .EN1 (.12.) 2.920 0.043 68.678 0.000
## .EN2 (.13.) 3.039 0.045 67.914 0.000
## .EN3 (.14.) 2.704 0.039 68.848 0.000
## .EN4 (.15.) 3.172 0.043 73.253 0.000
## .EN5 (.16.) 3.534 0.043 82.649 0.000
## enjoy -0.003 0.059 -0.044 0.965
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .EN1 (.p6.) 0.273 0.011 25.039 0.000
## .EN2 (.p7.) 0.207 0.010 20.098 0.000
## .EN3 (.p8.) 0.352 0.012 29.290 0.000
## .EN4 (.p9.) 0.811 0.025 32.930 0.000
## .EN5 (.10.) 1.386 0.040 34.597 0.000
## enjoy 1.478 0.078 18.999 0.000
##
##
## Group 3 [T3]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## enjoy =~
## EN1 1.000
## EN2 (.p2.) 1.063 0.012 85.432 0.000
## EN3 (.p3.) 0.909 0.013 71.269 0.000
## EN4 (.p4.) 0.959 0.017 55.925 0.000
## EN5 (.p5.) 0.873 0.021 41.766 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .EN1 (.12.) 2.920 0.043 68.678 0.000
## .EN2 (.13.) 3.039 0.045 67.914 0.000
## .EN3 (.14.) 2.704 0.039 68.848 0.000
## .EN4 (.15.) 3.172 0.043 73.253 0.000
## .EN5 (.16.) 3.534 0.043 82.649 0.000
## enjoy 0.020 0.060 0.327 0.744
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .EN1 (.p6.) 0.273 0.011 25.039 0.000
## .EN2 (.p7.) 0.207 0.010 20.098 0.000
## .EN3 (.p8.) 0.352 0.012 29.290 0.000
## .EN4 (.p9.) 0.811 0.025 32.930 0.000
## .EN5 (.10.) 1.386 0.040 34.597 0.000
## enjoy 1.550 0.081 19.038 0.000
if(!fit_strict@Fit@converged) stop("Strict model did not converge")
# 比较模型
anova(fit_configural, fit_metric, fit_scalar, fit_strict)
##
## Chi-Squared Difference Test
##
## Df AIC BIC Chisq Chisq diff RMSEA Df diff Pr(>Chisq)
## fit_configural 15 34917 35181 898.05
## fit_metric 23 34908 35125 905.19 7.144 0.000000 8 0.5211
## fit_scalar 31 34928 35099 941.28 36.090 0.063529 8 1.691e-05
## fit_strict 41 35041 35153 1074.00 132.720 0.118768 10 < 2.2e-16
##
## fit_configural
## fit_metric
## fit_scalar ***
## fit_strict ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
library(haven)
selected_data <- spss[, c("MAC1T1", "MAC2T1", "MAC3T1")]
# 检查选择的变量
print(head(selected_data))
## MAC1T1 MAC2T1 MAC3T1
## 1 2.666667 1.333333 1.000000
## 2 3.666667 4.000000 2.333333
## 3 3.333333 4.333333 3.666667
## 4 4.333333 3.333333 2.000000
## 5 4.000000 4.000000 3.666667
## 6 3.666667 1.666667 2.000000
# 计算协方差矩阵
cov_matrix <- cov(selected_data, use = "pairwise.complete.obs")
# 打印协方差矩阵
print(cov_matrix)
## MAC1T1 MAC2T1 MAC3T1
## MAC1T1 1.2511704 1.020317 0.9335632
## MAC2T1 1.0203170 1.510035 1.0300044
## MAC3T1 0.9335632 1.030004 1.5022574
install.packages("lavaan")
## Warning: 正在使用 'lavaan' 這個程式套件，因此不會被安裝
library(lavaan)
library(lavaan)
library(tidyverse)
# 定義測量模型
model2 <- '
fr1 =~ FR1T1 + FR2T1 + FR3T1
fr2 =~ FR1T2 + FR2T2 + FR3T2
fr3 =~ FR1T3 + FR2T3 + FR3T3
'
spss_long2 <- spss %>%
pivot_longer(cols = starts_with("FR"), names_to = c("Variable", "TimePoint"), names_sep = "T") %>%
mutate(TimePoint = paste0("T", TimePoint)) %>%
pivot_wider(names_from = Variable, values_from = value)
# 查看新数据框
str(spss_long2)
## tibble [2,610 × 140] (S3: tbl_df/tbl/data.frame)
## $ ID : num [1:2610] 120201 120201 120201 120202 120202 ...
## $ School : Factor w/ 11 levels "興華","嘉義",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ grade : num [1:2610] 2 2 2 2 2 2 2 2 2 2 ...
## $ Class : num [1:2610] 2 2 2 2 2 2 2 2 2 2 ...
## $ Number : num [1:2610] 1 1 1 2 2 2 3 3 3 4 ...
## $ Gender : Factor w/ 3 levels "female","male",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ EN1T1 : num [1:2610] 4 4 4 4 4 4 3 3 3 3 ...
## $ EN2T1 : num [1:2610] 5 5 5 5 5 5 3 3 3 4 ...
## $ EN3T1 : num [1:2610] 4 4 4 4 4 4 3 3 3 2 ...
## $ EN4T1 : num [1:2610] 6 6 6 6 6 6 3 3 3 3 ...
## $ EN5T1 : num [1:2610] 6 6 6 6 6 6 3 3 3 3 ...
## $ SE1T1 : num [1:2610] 3 3 3 3 3 3 5 5 5 2 ...
## $ SE2T1 : num [1:2610] 1 1 1 6 6 6 5 5 5 1 ...
## $ SE3T1 : num [1:2610] 3 3 3 5 5 5 4 4 4 4 ...
## $ SE4T1 : num [1:2610] 3 3 3 4 4 4 2 2 2 4 ...
## $ SE5T1 : num [1:2610] 3 3 3 4 4 4 2 2 2 3 ...
## $ ENR1T1 : num [1:2610] 4 4 4 6 6 6 4 4 4 3 ...
## $ ENR2T1 : num [1:2610] 3 3 3 6 6 6 4 4 4 3 ...
## $ ENR3T1 : num [1:2610] 4 4 4 4 4 4 4 4 4 3 ...
## $ ENR4T1 : num [1:2610] 3 3 3 6 6 6 4 4 4 4 ...
## $ BO1T1 : num [1:2610] 1 1 1 3 3 3 2 2 2 3 ...
## $ BO2T1 : num [1:2610] 1 1 1 3 3 3 2 2 2 3 ...
## $ BO3T1 : num [1:2610] 1 1 1 3 3 3 2 2 2 4 ...
## $ BO4T1 : num [1:2610] 1 1 1 3 3 3 2 2 2 5 ...
## $ CB1T1 : num [1:2610] 1 1 1 1 1 1 2 2 2 1 ...
## $ CB2T1 : num [1:2610] 1 1 1 3 3 3 2 2 2 4 ...
## $ CB3T1 : num [1:2610] 1 1 1 5 5 5 3 3 3 3 ...
## $ AC1T1 : num [1:2610] 3 3 3 4 4 4 4 4 4 4 ...
## $ AC2T1 : num [1:2610] 4 4 4 6 6 6 5 5 5 4 ...
## $ AC3T1 : num [1:2610] 1 1 1 1 1 1 1 1 1 5 ...
## $ AC4T1 : num [1:2610] 2 2 2 4 4 4 4 4 4 5 ...
## $ AC5T1 : num [1:2610] 1 1 1 5 5 5 5 5 5 4 ...
## $ AC6T1 : num [1:2610] 1 1 1 3 3 3 4 4 4 1 ...
## $ AC7T1 : num [1:2610] 1 1 1 5 5 5 4 4 4 3 ...
## $ AC8T1 : num [1:2610] 1 1 1 1 1 1 3 3 3 1 ...
## $ AC9T1 : num [1:2610] 1 1 1 1 1 1 4 4 4 2 ...
## $ PC1T1 : num [1:2610] 1 1 1 4 4 4 3 3 3 4 ...
## $ PC2T1 : num [1:2610] 1 1 1 4 4 4 4 4 4 3 ...
## $ PC3T1 : num [1:2610] 1 1 1 3 3 3 4 4 4 4 ...
## $ PC4T1 : num [1:2610] 1 1 1 3 3 3 4 4 4 5 ...
## $ ACHT1 : num [1:2610] 83 83 83 64 64 64 72 72 72 21 ...
## $ EN1T2 : num [1:2610] 4 4 4 4 4 4 2 2 2 3 ...
## $ EN2T2 : num [1:2610] 4 4 4 6 6 6 3 3 3 2 ...
## $ EN3T2 : num [1:2610] 3 3 3 4 4 4 4 4 4 2 ...
## $ EN4T2 : num [1:2610] 5 5 5 6 6 6 1 1 1 2 ...
## $ EN5T2 : num [1:2610] 5 5 5 6 6 6 1 1 1 4 ...
## $ SE1T2 : num [1:2610] 1 1 1 1 1 1 5 5 5 3 ...
## $ SE2T2 : num [1:2610] 1 1 1 6 6 6 6 6 6 1 ...
## $ SE3T2 : num [1:2610] 3 3 3 3 3 3 5 5 5 5 ...
## $ SE4T2 : num [1:2610] 4 4 4 4 4 4 3 3 3 6 ...
## $ SE5T2 : num [1:2610] 3 3 3 3 3 3 3 3 3 5 ...
## $ ENR1T2 : num [1:2610] 5 5 5 6 6 6 4 4 4 3 ...
## $ ENR2T2 : num [1:2610] 3 3 3 6 6 6 4 4 4 3 ...
## $ ENR3T2 : num [1:2610] 4 4 4 4 4 4 3 3 3 3 ...
## $ ENR4T2 : num [1:2610] 4 4 4 6 6 6 5 5 5 2 ...
## $ BO1T2 : num [1:2610] 1 1 1 1 1 1 3 3 3 5 ...
## $ BO2T2 : num [1:2610] 1 1 1 1 1 1 3 3 3 4 ...
## $ BO3T2 : num [1:2610] 1 1 1 1 1 1 3 3 3 6 ...
## $ BO4T2 : num [1:2610] 1 1 1 1 1 1 2 2 2 5 ...
## $ CB1T2 : num [1:2610] 1 1 1 2 2 2 1 1 1 1 ...
## $ CB2T2 : num [1:2610] 1 1 1 2 2 2 1 1 1 4 ...
## $ CB3T2 : num [1:2610] 1 1 1 1 1 1 4 4 4 4 ...
## $ AC1T2 : num [1:2610] 2 2 2 3 3 3 4 4 4 4 ...
## $ AC2T2 : num [1:2610] 2 2 2 3 3 3 5 5 5 4 ...
## $ AC3T2 : num [1:2610] 1 1 1 3 3 3 1 1 1 1 ...
## $ AC4T2 : num [1:2610] 2 2 2 2 2 2 1 1 1 4 ...
## $ AC5T2 : num [1:2610] 2 2 2 2 2 2 3 3 3 4 ...
## $ AC6T2 : num [1:2610] 2 2 2 1 1 1 3 3 3 1 ...
## $ AC7T2 : num [1:2610] 1 1 1 4 4 4 4 4 4 4 ...
## $ AC8T2 : num [1:2610] 1 1 1 1 1 1 3 3 3 1 ...
## $ AC9T2 : num [1:2610] 2 2 2 1 1 1 4 4 4 4 ...
## $ PC1T2 : num [1:2610] 1 1 1 2 2 2 4 4 4 4 ...
## $ PC2T2 : num [1:2610] 1 1 1 2 2 2 4 4 4 4 ...
## $ PC3T2 : num [1:2610] 1 1 1 2 2 2 4 4 4 4 ...
## $ PC4T2 : num [1:2610] 1 1 1 2 2 2 4 4 4 4 ...
## $ ACHT2 : num [1:2610] 88 88 88 78 78 78 73 73 73 25 ...
## $ EN1T3 : num [1:2610] 4 4 4 6 6 6 3 3 3 4 ...
## $ EN2T3 : num [1:2610] 4 4 4 6 6 6 3 3 3 3 ...
## $ EN3T3 : num [1:2610] 3 3 3 5 5 5 3 3 3 3 ...
## $ EN4T3 : num [1:2610] 4 4 4 6 6 6 4 4 4 3 ...
## $ EN5T3 : num [1:2610] 4 4 4 6 6 6 4 4 4 5 ...
## $ SE1T3 : num [1:2610] 3 3 3 4 4 4 4 4 4 5 ...
## $ SE2T3 : num [1:2610] 3 3 3 2 2 2 5 5 5 6 ...
## $ SE3T3 : num [1:2610] 4 4 4 5 5 5 3 3 3 5 ...
## $ SE4T3 : num [1:2610] 4 4 4 4 4 4 3 3 3 5 ...
## $ SE5T3 : num [1:2610] 4 4 4 4 4 4 3 3 3 4 ...
## $ ENR1T3 : num [1:2610] 4 4 4 6 6 6 4 4 4 4 ...
## $ ENR2T3 : num [1:2610] 4 4 4 6 6 6 4 4 4 3 ...
## $ ENR3T3 : num [1:2610] 4 4 4 5 5 5 4 4 4 4 ...
## $ ENR4T3 : num [1:2610] 4 4 4 6 6 6 4 4 4 3 ...
## $ BO1T3 : num [1:2610] 2 2 2 1 1 1 3 3 3 4 ...
## $ BO2T3 : num [1:2610] 2 2 2 1 1 1 3 3 3 3 ...
## $ BO3T3 : num [1:2610] 2 2 2 1 1 1 3 3 3 5 ...
## $ BO4T3 : num [1:2610] 2 2 2 1 1 1 3 3 3 4 ...
## $ CB1T3 : num [1:2610] 1 1 1 1 1 1 1 1 1 1 ...
## $ CB2T3 : num [1:2610] 2 2 2 4 4 4 4 4 4 3 ...
## $ CB3T3 : num [1:2610] 2 2 2 4 4 4 3 3 3 3 ...
## $ AC1T3 : num [1:2610] 1 1 1 4 4 4 4 4 4 4 ...
## $ AC2T3 : num [1:2610] 1 1 1 4 4 4 4 4 4 5 ...
## [list output truncated]
summary(spss_long2)
## ID School grade Class
## Min. : 120201 嘉義 :438 Min. :1.000 Min. : 1.000
## 1st Qu.: 220817 瑞豐 :375 1st Qu.:1.000 1st Qu.: 2.000
## Median : 530524 玉山 :357 Median :2.000 Median : 4.000
## Mean : 548365 鳳甲 :270 Mean :1.775 Mean : 4.894
## 3rd Qu.: 720511 民生 :258 3rd Qu.:2.000 3rd Qu.: 6.000
## Max. :1120542 鳳山 :255 Max. :3.000 Max. :19.000
## (Other):657
## Number Gender EN1T1 EN2T1 EN3T1
## Min. : 1.00 female:1524 Min. :1.00 Min. :1.000 Min. :1.000
## 1st Qu.: 6.00 male :1083 1st Qu.:2.00 1st Qu.:2.000 1st Qu.:1.000
## Median :13.00 3 : 3 Median :3.00 Median :3.000 Median :3.000
## Mean :13.06 Mean :2.91 Mean :3.071 Mean :2.653
## 3rd Qu.:19.00 3rd Qu.:4.00 3rd Qu.:4.000 3rd Qu.:3.000
## Max. :42.00 Max. :6.00 Max. :6.000 Max. :6.000
##
## EN4T1 EN5T1 SE1T1 SE2T1
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:4.000
## Median :3.000 Median :4.000 Median :3.000 Median :5.000
## Mean :3.166 Mean :3.547 Mean :3.094 Mean :4.709
## 3rd Qu.:4.000 3rd Qu.:5.000 3rd Qu.:4.000 3rd Qu.:6.000
## Max. :6.000 Max. :6.000 Max. :6.000 Max. :6.000
##
## SE3T1 SE4T1 SE5T1 ENR1T1 ENR2T1
## Min. :1.000 Min. :1.000 Min. :1.00 Min. :1.000 Min. :1.000
## 1st Qu.:4.000 1st Qu.:3.000 1st Qu.:3.00 1st Qu.:3.000 1st Qu.:3.000
## Median :4.000 Median :4.000 Median :4.00 Median :4.000 Median :4.000
## Mean :4.225 Mean :3.503 Mean :3.86 Mean :3.741 Mean :3.753
## 3rd Qu.:5.000 3rd Qu.:4.000 3rd Qu.:5.00 3rd Qu.:5.000 3rd Qu.:5.000
## Max. :6.000 Max. :6.000 Max. :6.00 Max. :6.000 Max. :6.000
##
## ENR3T1 ENR4T1 BO1T1 BO2T1 BO3T1
## Min. :1.000 Min. :1.00 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:3.00 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000
## Median :4.000 Median :4.00 Median :3.000 Median :3.000 Median :3.000
## Mean :3.545 Mean :3.72 Mean :3.457 Mean :3.431 Mean :3.102
## 3rd Qu.:4.000 3rd Qu.:5.00 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :6.000 Max. :6.00 Max. :6.000 Max. :6.000 Max. :6.000
##
## BO4T1 CB1T1 CB2T1 CB3T1
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:2.000
## Median :3.000 Median :1.000 Median :2.000 Median :3.000
## Mean :3.231 Mean :1.898 Mean :2.326 Mean :3.085
## 3rd Qu.:4.000 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:4.000
## Max. :6.000 Max. :6.000 Max. :6.000 Max. :6.000
##
## AC1T1 AC2T1 AC3T1 AC4T1 AC5T1
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.00 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:1.000 1st Qu.:2.00 1st Qu.:2.000
## Median :4.000 Median :3.000 Median :1.000 Median :3.00 Median :3.000
## Mean :3.457 Mean :3.331 Mean :1.926 Mean :3.09 Mean :3.159
## 3rd Qu.:4.000 3rd Qu.:5.000 3rd Qu.:3.000 3rd Qu.:4.00 3rd Qu.:4.000
## Max. :6.000 Max. :6.000 Max. :6.000 Max. :6.00 Max. :6.000
##
## AC6T1 AC7T1 AC8T1 AC9T1
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:2.000 1st Qu.:1.000 1st Qu.:2.000
## Median :2.000 Median :3.000 Median :2.000 Median :3.000
## Mean :2.187 Mean :3.051 Mean :2.107 Mean :3.259
## 3rd Qu.:3.000 3rd Qu.:4.000 3rd Qu.:3.000 3rd Qu.:4.000
## Max. :6.000 Max. :6.000 Max. :6.000 Max. :6.000
##
## PC1T1 PC2T1 PC3T1 PC4T1
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000
## Median :2.000 Median :2.000 Median :2.000 Median :2.000
## Mean :2.595 Mean :2.684 Mean :2.599 Mean :2.683
## 3rd Qu.:3.000 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :6.000 Max. :6.000 Max. :6.000 Max. :6.000
##
## ACHT1 EN1T2 EN2T2 EN3T2
## Min. : 0.00 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.: 38.00 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000
## Median : 63.00 Median :3.000 Median :3.000 Median :3.000
## Mean : 58.25 Mean :2.907 Mean :3.049 Mean :2.693
## 3rd Qu.: 79.00 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:3.000
## Max. :100.00 Max. :6.000 Max. :6.000 Max. :6.000
##
## EN4T2 EN5T2 SE1T2 SE2T2 SE3T2
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.00
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:4.000 1st Qu.:4.00
## Median :3.000 Median :4.000 Median :3.000 Median :5.000 Median :4.00
## Mean :3.179 Mean :3.499 Mean :3.201 Mean :4.586 Mean :4.24
## 3rd Qu.:4.000 3rd Qu.:5.000 3rd Qu.:4.000 3rd Qu.:6.000 3rd Qu.:5.00
## Max. :6.000 Max. :6.000 Max. :6.000 Max. :6.000 Max. :6.00
##
## SE4T2 SE5T2 ENR1T2 ENR2T2
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000
## Median :4.000 Median :4.000 Median :4.000 Median :4.000
## Mean :3.644 Mean :3.933 Mean :3.828 Mean :3.856
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000
## Max. :6.000 Max. :6.000 Max. :6.000 Max. :6.000
##
## ENR3T2 ENR4T2 BO1T2 BO2T2 BO3T2
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.0 Min. :1.000
## 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:2.000 1st Qu.:2.0 1st Qu.:2.000
## Median :4.000 Median :4.000 Median :3.000 Median :3.0 Median :3.000
## Mean :3.629 Mean :3.818 Mean :3.487 Mean :3.5 Mean :3.149
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.0 3rd Qu.:4.000
## Max. :6.000 Max. :6.000 Max. :6.000 Max. :6.0 Max. :6.000
##
## BO4T2 CB1T2 CB2T2 CB3T2
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:2.000
## Median :3.000 Median :1.000 Median :2.000 Median :3.000
## Mean :3.263 Mean :2.051 Mean :2.609 Mean :3.114
## 3rd Qu.:4.000 3rd Qu.:3.000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :6.000 Max. :6.000 Max. :6.000 Max. :6.000
##
## AC1T2 AC2T2 AC3T2 AC4T2 AC5T2
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.00 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:1.000 1st Qu.:2.00 1st Qu.:2.000
## Median :4.000 Median :4.000 Median :2.000 Median :3.00 Median :3.000
## Mean :3.418 Mean :3.421 Mean :2.121 Mean :3.18 Mean :3.245
## 3rd Qu.:4.000 3rd Qu.:5.000 3rd Qu.:3.000 3rd Qu.:4.00 3rd Qu.:4.000
## Max. :6.000 Max. :6.000 Max. :6.000 Max. :6.00 Max. :6.000
##
## AC6T2 AC7T2 AC8T2 AC9T2
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:2.000 1st Qu.:1.000 1st Qu.:2.000
## Median :2.000 Median :3.000 Median :2.000 Median :3.000
## Mean :2.343 Mean :3.126 Mean :2.298 Mean :3.292
## 3rd Qu.:3.000 3rd Qu.:4.000 3rd Qu.:3.000 3rd Qu.:4.000
## Max. :6.000 Max. :6.000 Max. :6.000 Max. :6.000
##
## PC1T2 PC2T2 PC3T2 PC4T2
## Min. :1.000 Min. :1.00 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:1.00 1st Qu.:1.000 1st Qu.:1.000
## Median :2.000 Median :3.00 Median :2.000 Median :3.000
## Mean :2.701 Mean :2.79 Mean :2.682 Mean :2.778
## 3rd Qu.:4.000 3rd Qu.:4.00 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :6.000 Max. :6.00 Max. :6.000 Max. :6.000
##
## ACHT2 EN1T3 EN2T3 EN3T3
## Min. : 0.00 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.: 35.00 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000
## Median : 58.00 Median :3.000 Median :3.000 Median :3.000
## Mean : 56.59 Mean :2.959 Mean :3.014 Mean :2.783
## 3rd Qu.: 80.00 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :100.00 Max. :6.000 Max. :6.000 Max. :6.000
##
## EN4T3 EN5T3 SE1T3 SE2T3 SE3T3
## Min. :1.000 Min. :1.00 Min. :1.000 Min. :1.000 Min. :1.00
## 1st Qu.:2.000 1st Qu.:3.00 1st Qu.:2.000 1st Qu.:3.000 1st Qu.:3.00
## Median :3.000 Median :4.00 Median :3.000 Median :4.000 Median :4.00
## Mean :3.187 Mean :3.57 Mean :3.271 Mean :4.295 Mean :4.14
## 3rd Qu.:4.000 3rd Qu.:5.00 3rd Qu.:4.000 3rd Qu.:6.000 3rd Qu.:5.00
## Max. :6.000 Max. :6.00 Max. :6.000 Max. :6.000 Max. :6.00
##
## SE4T3 SE5T3 ENR1T3 ENR2T3
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000
## Median :4.000 Median :4.000 Median :4.000 Median :4.000
## Mean :3.579 Mean :3.809 Mean :3.829 Mean :3.854
## 3rd Qu.:4.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000
## Max. :6.000 Max. :6.000 Max. :6.000 Max. :6.000
##
## ENR3T3 ENR4T3 BO1T3 BO2T3
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:2.000
## Median :4.000 Median :4.000 Median :3.000 Median :3.000
## Mean :3.656 Mean :3.775 Mean :3.518 Mean :3.483
## 3rd Qu.:4.000 3rd Qu.:5.000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :6.000 Max. :6.000 Max. :6.000 Max. :6.000
##
## BO3T3 BO4T3 CB1T3 CB2T3
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:1.000 1st Qu.:2.000
## Median :3.000 Median :3.000 Median :2.000 Median :3.000
## Mean :3.222 Mean :3.345 Mean :2.295 Mean :2.855
## 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:3.000 3rd Qu.:4.000
## Max. :6.000 Max. :6.000 Max. :6.000 Max. :6.000
##
## CB3T3 AC1T3 AC2T3 AC3T3 AC4T3
## Min. :1.000 Min. :1.000 Min. :1.00 Min. :1.000 Min. :1.00
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.00 1st Qu.:1.000 1st Qu.:2.00
## Median :3.000 Median :4.000 Median :4.00 Median :2.000 Median :3.00
## Mean :3.176 Mean :3.469 Mean :3.48 Mean :2.341 Mean :3.29
## 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:5.00 3rd Qu.:3.000 3rd Qu.:4.00
## Max. :6.000 Max. :6.000 Max. :6.00 Max. :6.000 Max. :6.00
##
## AC5T3 AC6T3 AC7T3 AC8T3
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:1.000 1st Qu.:2.000 1st Qu.:1.000
## Median :3.000 Median :2.000 Median :3.000 Median :2.000
## Mean :3.317 Mean :2.517 Mean :3.293 Mean :2.526
## 3rd Qu.:4.000 3rd Qu.:3.000 3rd Qu.:4.000 3rd Qu.:3.000
## Max. :6.000 Max. :6.000 Max. :6.000 Max. :6.000
##
## AC9T3 PC1T3 PC2T3 PC3T3
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000
## Median :3.000 Median :3.000 Median :3.000 Median :3.000
## Mean :3.333 Mean :2.807 Mean :2.908 Mean :2.782
## 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :6.000 Max. :6.000 Max. :6.000 Max. :6.000
##
## PC4T3 ACHT3 filter_. ZACHT1
## Min. :1.000 Min. : 0.00 Not Selected: 0 Min. :-3.0131555
## 1st Qu.:2.000 1st Qu.: 38.00 Selected :2610 1st Qu.:-0.6833303
## Median :3.000 Median : 60.00 Median :-0.0100814
## Mean :2.886 Mean : 58.11 Mean :-0.0006499
## 3rd Qu.:4.000 3rd Qu.: 80.00 3rd Qu.: 0.6598713
## Max. :6.000 Max. :100.00 Max. : 2.5187201
##
## ZACHT2 ZACHT3 ZZACHT1
## Min. :-3.187241 Min. :-3.013155 Min. :-3.030778
## 1st Qu.:-0.661663 1st Qu.:-0.676073 1st Qu.:-0.686821
## Median : 0.004321 Median : 0.004320 Median :-0.009489
## Mean :-0.001995 Mean :-0.001895 Mean : 0.000000
## 3rd Qu.: 0.685150 3rd Qu.: 0.665252 3rd Qu.: 0.664528
## Max. : 2.232721 Max. : 2.268986 Max. : 2.534652
##
## ZZACHT2 ZZACHT3 ZEN1T1 ZEN2T1
## Min. :-3.215978 Min. :-3.036674 Min. :-1.23948 Min. :-1.2875
## 1st Qu.:-0.666033 1st Qu.:-0.679867 1st Qu.:-0.59499 1st Qu.:-0.6653
## Median : 0.006376 Median : 0.006268 Median :-0.01296 Median :-0.1329
## Mean : 0.000000 Mean : 0.000000 Mean : 0.01476 Mean : 0.0125
## 3rd Qu.: 0.693775 3rd Qu.: 0.672778 3rd Qu.: 0.78127 3rd Qu.: 0.6157
## Max. : 2.256276 Max. : 2.290046 Max. : 2.19917 Max. : 2.0604
##
## ZEN3T1 ZEN4T1 ZEN5T1 MAC1T1
## Min. :-1.14012 Min. :-1.27763 Min. :-1.367392 Min. :1.000
## 1st Qu.:-1.14012 1st Qu.:-0.64561 1st Qu.:-0.763799 1st Qu.:2.000
## Median : 0.20889 Median :-0.15034 Median : 0.179542 Median :3.000
## Mean : 0.02014 Mean : 0.01331 Mean : 0.003013 Mean :2.905
## 3rd Qu.: 0.20889 3rd Qu.: 0.48498 3rd Qu.: 0.758028 3rd Qu.:3.667
## Max. : 2.37553 Max. : 1.90644 Max. : 1.500124 Max. :6.000
##
## MAC2T1 MAC3T1 MAC1T2 MAC2T2
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000
## Median :2.667 Median :2.667 Median :3.000 Median :3.000
## Mean :2.812 Mean :2.805 Mean :2.987 Mean :2.923
## 3rd Qu.:3.667 3rd Qu.:3.667 3rd Qu.:3.667 3rd Qu.:3.667
## Max. :6.000 Max. :6.000 Max. :6.000 Max. :6.000
##
## MAC3T2 MAC1T3 MAC2T3 MAC3T3
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.333 1st Qu.:2.000 1st Qu.:2.000
## Median :3.000 Median :3.167 Median :3.000 Median :3.000
## Mean :2.906 Mean :3.097 Mean :3.041 Mean :3.051
## 3rd Qu.:3.667 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :6.000 Max. :6.000 Max. :6.000 Max. :6.000
##
## TimePoint FR1 FR2 FR3
## Length:2610 Min. :1.000 Min. :1.000 Min. :1.000
## Class :character 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000
## Mode :character Median :3.000 Median :3.000 Median :3.000
## Mean :3.261 Mean :2.953 Mean :3.109
## 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :6.000 Max. :6.000 Max. :6.000
##
## FRR1 FRR2 FRR3 FRR4
## Min. :1.00 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:3.00 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000
## Median :4.00 Median :4.000 Median :4.000 Median :4.000
## Mean :3.75 Mean :3.755 Mean :3.653 Mean :3.778
## 3rd Qu.:5.00 3rd Qu.:5.000 3rd Qu.:4.000 3rd Qu.:5.000
## Max. :6.00 Max. :6.000 Max. :6.000 Max. :6.000
##
model2 <- '
frustruction =~ FR1 + FR2 + FR3
'
# 不约束模型（配置不变性）
fit_configural2 <- cfa(model2, data = spss_long2, group = "TimePoint", control = list(iter.max = 10000, reltol = 1e-8))
summary(fit_configural2, fit.measures = TRUE)
## lavaan 0.6-18 ended normally after 69 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 27
##
## Number of observations per group:
## T1 870
## T2 870
## T3 870
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
## Test statistic for each group:
## T1 0.000
## T2 0.000
## T3 0.000
##
## Model Test Baseline Model:
##
## Test statistic 2676.040
## Degrees of freedom 9
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -12858.826
## Loglikelihood unrestricted model (H1) -12858.826
##
## Akaike (AIC) 25771.653
## Bayesian (BIC) 25930.064
## Sample-size adjusted Bayesian (SABIC) 25844.278
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.000
## P-value H_0: RMSEA <= 0.050 NA
## P-value H_0: RMSEA >= 0.080 NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
##
## Group 1 [T1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## frustruction =~
## FR1 1.000
## FR2 1.073 0.062 17.285 0.000
## FR3 0.776 0.049 15.947 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .FR1 3.166 0.052 60.415 0.000
## .FR2 2.834 0.047 60.090 0.000
## .FR3 3.032 0.054 55.799 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .FR1 1.006 0.084 12.029 0.000
## .FR2 0.344 0.080 4.275 0.000
## .FR3 1.737 0.093 18.698 0.000
## frustruction 1.382 0.124 11.119 0.000
##
##
## Group 2 [T2]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## frustruction =~
## FR1 1.000
## FR2 1.090 0.068 15.965 0.000
## FR3 0.616 0.046 13.359 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .FR1 3.271 0.051 64.154 0.000
## .FR2 2.960 0.048 61.213 0.000
## .FR3 3.140 0.054 58.663 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .FR1 0.817 0.090 9.041 0.000
## .FR2 0.316 0.098 3.225 0.001
## .FR3 1.945 0.098 19.799 0.000
## frustruction 1.445 0.130 11.124 0.000
##
##
## Group 3 [T3]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## frustruction =~
## FR1 1.000
## FR2 0.992 0.053 18.802 0.000
## FR3 0.618 0.042 14.652 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .FR1 3.347 0.049 67.865 0.000
## .FR2 3.064 0.046 66.188 0.000
## .FR3 3.155 0.051 62.133 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .FR1 0.595 0.077 7.732 0.000
## .FR2 0.369 0.072 5.087 0.000
## .FR3 1.662 0.084 19.731 0.000
## frustruction 1.521 0.121 12.595 0.000
if(!fit_configural@Fit@converged) stop("Configural model did not converge")
# 度量不变性模型
fit_metric2 <- cfa(model2, data = spss_long2, group = "TimePoint", group.equal = "loadings", control = list(iter.max = 1000, reltol = 1e-8))
summary(fit_metric2, fit.measures = TRUE)
## lavaan 0.6-18 ended normally after 47 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 27
## Number of equality constraints 4
##
## Number of observations per group:
## T1 870
## T2 870
## T3 870
##
## Model Test User Model:
##
## Test statistic 9.647
## Degrees of freedom 4
## P-value (Chi-square) 0.047
## Test statistic for each group:
## T1 5.501
## T2 2.388
## T3 1.758
##
## Model Test Baseline Model:
##
## Test statistic 2676.040
## Degrees of freedom 9
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.998
## Tucker-Lewis Index (TLI) 0.995
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -12863.650
## Loglikelihood unrestricted model (H1) -12858.826
##
## Akaike (AIC) 25773.299
## Bayesian (BIC) 25908.243
## Sample-size adjusted Bayesian (SABIC) 25835.165
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.040
## 90 Percent confidence interval - lower 0.004
## 90 Percent confidence interval - upper 0.074
## P-value H_0: RMSEA <= 0.050 0.636
## P-value H_0: RMSEA >= 0.080 0.022
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.023
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
##
## Group 1 [T1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## frustruction =~
## FR1 1.000
## FR2 (.p2.) 1.047 0.035 30.351 0.000
## FR3 (.p3.) 0.667 0.026 25.515 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .FR1 3.166 0.053 59.592 0.000
## .FR2 2.834 0.047 60.014 0.000
## .FR3 3.032 0.053 57.432 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .FR1 0.995 0.074 13.476 0.000
## .FR2 0.339 0.063 5.374 0.000
## .FR3 1.776 0.091 19.614 0.000
## frustruction 1.460 0.101 14.513 0.000
##
##
## Group 2 [T2]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## frustruction =~
## FR1 1.000
## FR2 (.p2.) 1.047 0.035 30.351 0.000
## FR3 (.p3.) 0.667 0.026 25.515 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .FR1 3.271 0.051 64.199 0.000
## .FR2 2.960 0.048 61.475 0.000
## .FR3 3.140 0.055 57.573 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .FR1 0.769 0.066 11.563 0.000
## .FR2 0.382 0.063 6.066 0.000
## .FR3 1.926 0.097 19.769 0.000
## frustruction 1.490 0.100 14.886 0.000
##
##
## Group 3 [T3]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## frustruction =~
## FR1 1.000
## FR2 (.p2.) 1.047 0.035 30.351 0.000
## FR3 (.p3.) 0.667 0.026 25.515 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .FR1 3.347 0.049 68.530 0.000
## .FR2 3.064 0.046 66.017 0.000
## .FR3 3.155 0.051 61.416 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .FR1 0.653 0.059 11.106 0.000
## .FR2 0.314 0.056 5.576 0.000
## .FR3 1.664 0.084 19.774 0.000
## frustruction 1.422 0.095 15.049 0.000
if(!fit_metric@Fit@converged) stop("Metric model did not converge")
# 截距不变性模型
fit_scalar2 <- cfa(model2, data = spss_long2, group = "TimePoint", group.equal = c("loadings", "intercepts"), control = list(iter.max = 1000, reltol = 1e-8))
summary(fit_scalar2, fit.measures = TRUE)
## lavaan 0.6-18 ended normally after 52 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 29
## Number of equality constraints 10
##
## Number of observations per group:
## T1 870
## T2 870
## T3 870
##
## Model Test User Model:
##
## Test statistic 10.738
## Degrees of freedom 8
## P-value (Chi-square) 0.217
## Test statistic for each group:
## T1 5.743
## T2 2.623
## T3 2.372
##
## Model Test Baseline Model:
##
## Test statistic 2676.040
## Degrees of freedom 9
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.999
## Tucker-Lewis Index (TLI) 0.999
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -12864.195
## Loglikelihood unrestricted model (H1) -12858.826
##
## Akaike (AIC) 25766.390
## Bayesian (BIC) 25877.865
## Sample-size adjusted Bayesian (SABIC) 25817.497
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.020
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.047
## P-value H_0: RMSEA <= 0.050 0.968
## P-value H_0: RMSEA >= 0.080 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.024
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
##
## Group 1 [T1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## frustruction =~
## FR1 1.000
## FR2 (.p2.) 1.051 0.034 30.574 0.000
## FR3 (.p3.) 0.667 0.026 25.582 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .FR1 (.p8.) 3.151 0.047 67.411 0.000
## .FR2 (.p9.) 2.839 0.046 61.111 0.000
## .FR3 (.10.) 3.036 0.039 78.153 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .FR1 0.999 0.074 13.555 0.000
## .FR2 0.335 0.063 5.314 0.000
## .FR3 1.777 0.091 19.623 0.000
## frustruction 1.455 0.100 14.539 0.000
##
##
## Group 2 [T2]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## frustruction =~
## FR1 1.000
## FR2 (.p2.) 1.051 0.034 30.574 0.000
## FR3 (.p3.) 0.667 0.026 25.582 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .FR1 (.p8.) 3.151 0.047 67.411 0.000
## .FR2 (.p9.) 2.839 0.046 61.111 0.000
## .FR3 (.10.) 3.036 0.039 78.153 0.000
## frstrct 0.119 0.062 1.903 0.057
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .FR1 0.772 0.066 11.649 0.000
## .FR2 0.378 0.063 6.008 0.000
## .FR3 1.927 0.097 19.775 0.000
## frustruction 1.485 0.100 14.910 0.000
##
##
## Group 3 [T3]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## frustruction =~
## FR1 1.000
## FR2 (.p2.) 1.051 0.034 30.574 0.000
## FR3 (.p3.) 0.667 0.026 25.582 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .FR1 (.p8.) 3.151 0.047 67.411 0.000
## .FR2 (.p9.) 2.839 0.046 61.111 0.000
## .FR3 (.10.) 3.036 0.039 78.153 0.000
## frstrct 0.208 0.062 3.371 0.001
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .FR1 0.657 0.059 11.200 0.000
## .FR2 0.311 0.056 5.520 0.000
## .FR3 1.665 0.084 19.780 0.000
## frustruction 1.417 0.094 15.072 0.000
if(!fit_scalar@Fit@converged) stop("Scalar model did not converge")
# 严格不变性模型
fit_strict2 <- cfa(model2, data = spss_long2, group = "TimePoint", group.equal = c("loadings", "intercepts", "residuals"), control = list(iter.max = 1000, reltol = 1e-8))
summary(fit_strict2, fit.measures = TRUE)
## lavaan 0.6-18 ended normally after 46 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 29
## Number of equality constraints 16
##
## Number of observations per group:
## T1 870
## T2 870
## T3 870
##
## Model Test User Model:
##
## Test statistic 41.348
## Degrees of freedom 14
## P-value (Chi-square) 0.000
## Test statistic for each group:
## T1 17.749
## T2 5.539
## T3 18.060
##
## Model Test Baseline Model:
##
## Test statistic 2676.040
## Degrees of freedom 9
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.990
## Tucker-Lewis Index (TLI) 0.993
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -12879.500
## Loglikelihood unrestricted model (H1) -12858.826
##
## Akaike (AIC) 25785.001
## Bayesian (BIC) 25861.273
## Sample-size adjusted Bayesian (SABIC) 25819.968
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.047
## 90 Percent confidence interval - lower 0.031
## 90 Percent confidence interval - upper 0.064
## P-value H_0: RMSEA <= 0.050 0.572
## P-value H_0: RMSEA >= 0.080 0.001
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.029
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
##
## Group 1 [T1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## frustruction =~
## FR1 1.000
## FR2 (.p2.) 1.056 0.035 30.194 0.000
## FR3 (.p3.) 0.669 0.026 25.501 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .FR1 (.p8.) 3.153 0.046 68.061 0.000
## .FR2 (.p9.) 2.839 0.047 60.925 0.000
## .FR3 (.10.) 3.037 0.039 78.111 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .FR1 (.p4.) 0.813 0.048 17.025 0.000
## .FR2 (.p5.) 0.338 0.048 7.052 0.000
## .FR3 (.p6.) 1.790 0.053 33.736 0.000
## frstrct 1.451 0.097 14.939 0.000
##
##
## Group 2 [T2]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## frustruction =~
## FR1 1.000
## FR2 (.p2.) 1.056 0.035 30.194 0.000
## FR3 (.p3.) 0.669 0.026 25.501 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .FR1 (.p8.) 3.153 0.046 68.061 0.000
## .FR2 (.p9.) 2.839 0.047 60.925 0.000
## .FR3 (.10.) 3.037 0.039 78.111 0.000
## frstrct 0.118 0.062 1.889 0.059
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .FR1 (.p4.) 0.813 0.048 17.025 0.000
## .FR2 (.p5.) 0.338 0.048 7.052 0.000
## .FR3 (.p6.) 1.790 0.053 33.736 0.000
## frstrct 1.489 0.100 14.945 0.000
##
##
## Group 3 [T3]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## frustruction =~
## FR1 1.000
## FR2 (.p2.) 1.056 0.035 30.194 0.000
## FR3 (.p3.) 0.669 0.026 25.501 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .FR1 (.p8.) 3.153 0.046 68.061 0.000
## .FR2 (.p9.) 2.839 0.047 60.925 0.000
## .FR3 (.10.) 3.037 0.039 78.111 0.000
## frstrct 0.207 0.061 3.368 0.001
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .FR1 (.p4.) 0.813 0.048 17.025 0.000
## .FR2 (.p5.) 0.338 0.048 7.052 0.000
## .FR3 (.p6.) 1.790 0.053 33.736 0.000
## frstrct 1.382 0.093 14.926 0.000
if(!fit_strict@Fit@converged) stop("Strict model did not converge")
# 比较模型
anova(fit_configural2, fit_metric2, fit_scalar2, fit_strict2)
##
## Chi-Squared Difference Test
##
## Df AIC BIC Chisq Chisq diff RMSEA Df diff Pr(>Chisq)
## fit_configural2 0 25772 25930 0.0000
## fit_metric2 4 25773 25908 9.6468 9.6468 0.040282 4 0.04682
## fit_scalar2 8 25766 25878 10.7378 1.0910 0.000000 4 0.89570
## fit_strict2 14 25785 25861 41.3479 30.6101 0.068663 6 3.008e-05
##
## fit_configural2
## fit_metric2 *
## fit_scalar2
## fit_strict2 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
library(lavaan)
library(lavaan)
library(tidyverse)
# 定義測量模型
model3 <- '
AC1 =~ AC1T1 + AC2T1 + AC3T1 + AC4T1 + AC5T1 + AC6T1 + AC7T1 + AC8T1 + AC9T1
AC2 =~ AC1T2 + AC2T2 + AC3T2 + AC4T2 + AC5T2 + AC6T2 + AC7T2 + AC8T2 + AC9T2
AC3 =~ AC1T3 + AC2T3 + AC3T3 + AC4T3 + AC5T3 + AC6T3 + AC7T3 + AC8T3 + AC9T3
'
spss_long3 <- spss %>%
pivot_longer(cols = starts_with("AC"), names_to = c("Variable", "TimePoint"), names_sep = "T") %>%
mutate(TimePoint = paste0("T", TimePoint)) %>%
pivot_wider(names_from = Variable, values_from = value)
# 查看新数据框
str(spss_long3)
## tibble [2,610 × 134] (S3: tbl_df/tbl/data.frame)
## $ ID : num [1:2610] 120201 120201 120201 120202 120202 ...
## $ School : Factor w/ 11 levels "興華","嘉義",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ grade : num [1:2610] 2 2 2 2 2 2 2 2 2 2 ...
## $ Class : num [1:2610] 2 2 2 2 2 2 2 2 2 2 ...
## $ Number : num [1:2610] 1 1 1 2 2 2 3 3 3 4 ...
## $ Gender : Factor w/ 3 levels "female","male",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ EN1T1 : num [1:2610] 4 4 4 4 4 4 3 3 3 3 ...
## $ EN2T1 : num [1:2610] 5 5 5 5 5 5 3 3 3 4 ...
## $ EN3T1 : num [1:2610] 4 4 4 4 4 4 3 3 3 2 ...
## $ EN4T1 : num [1:2610] 6 6 6 6 6 6 3 3 3 3 ...
## $ EN5T1 : num [1:2610] 6 6 6 6 6 6 3 3 3 3 ...
## $ FR1T1 : num [1:2610] 2 2 2 5 5 5 2 2 2 4 ...
## $ FR2T1 : num [1:2610] 1 1 1 2 2 2 2 2 2 3 ...
## $ FR3T1 : num [1:2610] 4 4 4 6 6 6 3 3 3 3 ...
## $ SE1T1 : num [1:2610] 3 3 3 3 3 3 5 5 5 2 ...
## $ SE2T1 : num [1:2610] 1 1 1 6 6 6 5 5 5 1 ...
## $ SE3T1 : num [1:2610] 3 3 3 5 5 5 4 4 4 4 ...
## $ SE4T1 : num [1:2610] 3 3 3 4 4 4 2 2 2 4 ...
## $ SE5T1 : num [1:2610] 3 3 3 4 4 4 2 2 2 3 ...
## $ ENR1T1 : num [1:2610] 4 4 4 6 6 6 4 4 4 3 ...
## $ ENR2T1 : num [1:2610] 3 3 3 6 6 6 4 4 4 3 ...
## $ ENR3T1 : num [1:2610] 4 4 4 4 4 4 4 4 4 3 ...
## $ ENR4T1 : num [1:2610] 3 3 3 6 6 6 4 4 4 4 ...
## $ FRR1T1 : num [1:2610] 3 3 3 6 6 6 5 5 5 3 ...
## $ FRR2T1 : num [1:2610] 4 4 4 4 4 4 5 5 5 3 ...
## $ FRR3T1 : num [1:2610] 3 3 3 6 6 6 5 5 5 3 ...
## $ FRR4T1 : num [1:2610] 3 3 3 6 6 6 6 6 6 3 ...
## $ BO1T1 : num [1:2610] 1 1 1 3 3 3 2 2 2 3 ...
## $ BO2T1 : num [1:2610] 1 1 1 3 3 3 2 2 2 3 ...
## $ BO3T1 : num [1:2610] 1 1 1 3 3 3 2 2 2 4 ...
## $ BO4T1 : num [1:2610] 1 1 1 3 3 3 2 2 2 5 ...
## $ CB1T1 : num [1:2610] 1 1 1 1 1 1 2 2 2 1 ...
## $ CB2T1 : num [1:2610] 1 1 1 3 3 3 2 2 2 4 ...
## $ CB3T1 : num [1:2610] 1 1 1 5 5 5 3 3 3 3 ...
## $ PC1T1 : num [1:2610] 1 1 1 4 4 4 3 3 3 4 ...
## $ PC2T1 : num [1:2610] 1 1 1 4 4 4 4 4 4 3 ...
## $ PC3T1 : num [1:2610] 1 1 1 3 3 3 4 4 4 4 ...
## $ PC4T1 : num [1:2610] 1 1 1 3 3 3 4 4 4 5 ...
## $ EN1T2 : num [1:2610] 4 4 4 4 4 4 2 2 2 3 ...
## $ EN2T2 : num [1:2610] 4 4 4 6 6 6 3 3 3 2 ...
## $ EN3T2 : num [1:2610] 3 3 3 4 4 4 4 4 4 2 ...
## $ EN4T2 : num [1:2610] 5 5 5 6 6 6 1 1 1 2 ...
## $ EN5T2 : num [1:2610] 5 5 5 6 6 6 1 1 1 4 ...
## $ FR1T2 : num [1:2610] 1 1 1 1 1 1 1 1 1 4 ...
## $ FR2T2 : num [1:2610] 1 1 1 1 1 1 2 2 2 3 ...
## $ FR3T2 : num [1:2610] 3 3 3 6 6 6 1 1 1 1 ...
## $ SE1T2 : num [1:2610] 1 1 1 1 1 1 5 5 5 3 ...
## $ SE2T2 : num [1:2610] 1 1 1 6 6 6 6 6 6 1 ...
## $ SE3T2 : num [1:2610] 3 3 3 3 3 3 5 5 5 5 ...
## $ SE4T2 : num [1:2610] 4 4 4 4 4 4 3 3 3 6 ...
## $ SE5T2 : num [1:2610] 3 3 3 3 3 3 3 3 3 5 ...
## $ ENR1T2 : num [1:2610] 5 5 5 6 6 6 4 4 4 3 ...
## $ ENR2T2 : num [1:2610] 3 3 3 6 6 6 4 4 4 3 ...
## $ ENR3T2 : num [1:2610] 4 4 4 4 4 4 3 3 3 3 ...
## $ ENR4T2 : num [1:2610] 4 4 4 6 6 6 5 5 5 2 ...
## $ FRR1T2 : num [1:2610] 3 3 3 6 6 6 6 6 6 4 ...
## $ FRR2T2 : num [1:2610] 4 4 4 6 6 6 6 6 6 4 ...
## $ FRR3T2 : num [1:2610] 3 3 3 4 4 4 3 3 3 2 ...
## $ FRR4T2 : num [1:2610] 5 5 5 4 4 4 5 5 5 4 ...
## $ BO1T2 : num [1:2610] 1 1 1 1 1 1 3 3 3 5 ...
## $ BO2T2 : num [1:2610] 1 1 1 1 1 1 3 3 3 4 ...
## $ BO3T2 : num [1:2610] 1 1 1 1 1 1 3 3 3 6 ...
## $ BO4T2 : num [1:2610] 1 1 1 1 1 1 2 2 2 5 ...
## $ CB1T2 : num [1:2610] 1 1 1 2 2 2 1 1 1 1 ...
## $ CB2T2 : num [1:2610] 1 1 1 2 2 2 1 1 1 4 ...
## $ CB3T2 : num [1:2610] 1 1 1 1 1 1 4 4 4 4 ...
## $ PC1T2 : num [1:2610] 1 1 1 2 2 2 4 4 4 4 ...
## $ PC2T2 : num [1:2610] 1 1 1 2 2 2 4 4 4 4 ...
## $ PC3T2 : num [1:2610] 1 1 1 2 2 2 4 4 4 4 ...
## $ PC4T2 : num [1:2610] 1 1 1 2 2 2 4 4 4 4 ...
## $ EN1T3 : num [1:2610] 4 4 4 6 6 6 3 3 3 4 ...
## $ EN2T3 : num [1:2610] 4 4 4 6 6 6 3 3 3 3 ...
## $ EN3T3 : num [1:2610] 3 3 3 5 5 5 3 3 3 3 ...
## $ EN4T3 : num [1:2610] 4 4 4 6 6 6 4 4 4 3 ...
## $ EN5T3 : num [1:2610] 4 4 4 6 6 6 4 4 4 5 ...
## $ FR1T3 : num [1:2610] 3 3 3 3 3 3 1 1 1 4 ...
## $ FR2T3 : num [1:2610] 3 3 3 1 1 1 1 1 1 4 ...
## $ FR3T3 : num [1:2610] 3 3 3 6 6 6 1 1 1 4 ...
## $ SE1T3 : num [1:2610] 3 3 3 4 4 4 4 4 4 5 ...
## $ SE2T3 : num [1:2610] 3 3 3 2 2 2 5 5 5 6 ...
## $ SE3T3 : num [1:2610] 4 4 4 5 5 5 3 3 3 5 ...
## $ SE4T3 : num [1:2610] 4 4 4 4 4 4 3 3 3 5 ...
## $ SE5T3 : num [1:2610] 4 4 4 4 4 4 3 3 3 4 ...
## $ ENR1T3 : num [1:2610] 4 4 4 6 6 6 4 4 4 4 ...
## $ ENR2T3 : num [1:2610] 4 4 4 6 6 6 4 4 4 3 ...
## $ ENR3T3 : num [1:2610] 4 4 4 5 5 5 4 4 4 4 ...
## $ ENR4T3 : num [1:2610] 4 4 4 6 6 6 4 4 4 3 ...
## $ FRR1T3 : num [1:2610] 3 3 3 6 6 6 5 5 5 4 ...
## $ FRR2T3 : num [1:2610] 3 3 3 6 6 6 4 4 4 4 ...
## $ FRR3T3 : num [1:2610] 4 4 4 4 4 4 5 5 5 3 ...
## $ FRR4T3 : num [1:2610] 4 4 4 6 6 6 6 6 6 3 ...
## $ BO1T3 : num [1:2610] 2 2 2 1 1 1 3 3 3 4 ...
## $ BO2T3 : num [1:2610] 2 2 2 1 1 1 3 3 3 3 ...
## $ BO3T3 : num [1:2610] 2 2 2 1 1 1 3 3 3 5 ...
## $ BO4T3 : num [1:2610] 2 2 2 1 1 1 3 3 3 4 ...
## $ CB1T3 : num [1:2610] 1 1 1 1 1 1 1 1 1 1 ...
## $ CB2T3 : num [1:2610] 2 2 2 4 4 4 4 4 4 3 ...
## $ CB3T3 : num [1:2610] 2 2 2 4 4 4 3 3 3 3 ...
## $ PC1T3 : num [1:2610] 1 1 1 1 1 1 5 5 5 5 ...
## [list output truncated]
summary(spss_long3)
## ID School grade Class
## Min. : 120201 嘉義 :438 Min. :1.000 Min. : 1.000
## 1st Qu.: 220817 瑞豐 :375 1st Qu.:1.000 1st Qu.: 2.000
## Median : 530524 玉山 :357 Median :2.000 Median : 4.000
## Mean : 548365 鳳甲 :270 Mean :1.775 Mean : 4.894
## 3rd Qu.: 720511 民生 :258 3rd Qu.:2.000 3rd Qu.: 6.000
## Max. :1120542 鳳山 :255 Max. :3.000 Max. :19.000
## (Other):657
## Number Gender EN1T1 EN2T1 EN3T1
## Min. : 1.00 female:1524 Min. :1.00 Min. :1.000 Min. :1.000
## 1st Qu.: 6.00 male :1083 1st Qu.:2.00 1st Qu.:2.000 1st Qu.:1.000
## Median :13.00 3 : 3 Median :3.00 Median :3.000 Median :3.000
## Mean :13.06 Mean :2.91 Mean :3.071 Mean :2.653
## 3rd Qu.:19.00 3rd Qu.:4.00 3rd Qu.:4.000 3rd Qu.:3.000
## Max. :42.00 Max. :6.00 Max. :6.000 Max. :6.000
##
## EN4T1 EN5T1 FR1T1 FR2T1
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000
## Median :3.000 Median :4.000 Median :3.000 Median :3.000
## Mean :3.166 Mean :3.547 Mean :3.166 Mean :2.834
## 3rd Qu.:4.000 3rd Qu.:5.000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :6.000 Max. :6.000 Max. :6.000 Max. :6.000
##
## FR3T1 SE1T1 SE2T1 SE3T1
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:4.000 1st Qu.:4.000
## Median :3.000 Median :3.000 Median :5.000 Median :4.000
## Mean :3.032 Mean :3.094 Mean :4.709 Mean :4.225
## 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:6.000 3rd Qu.:5.000
## Max. :6.000 Max. :6.000 Max. :6.000 Max. :6.000
##
## SE4T1 SE5T1 ENR1T1 ENR2T1 ENR3T1
## Min. :1.000 Min. :1.00 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:3.00 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000
## Median :4.000 Median :4.00 Median :4.000 Median :4.000 Median :4.000
## Mean :3.503 Mean :3.86 Mean :3.741 Mean :3.753 Mean :3.545
## 3rd Qu.:4.000 3rd Qu.:5.00 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:4.000
## Max. :6.000 Max. :6.00 Max. :6.000 Max. :6.000 Max. :6.000
##
## ENR4T1 FRR1T1 FRR2T1 FRR3T1 FRR4T1
## Min. :1.00 Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:3.00 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000
## Median :4.00 Median :4.000 Median :4.000 Median :4.000 Median :4.000
## Mean :3.72 Mean :3.714 Mean :3.738 Mean :3.629 Mean :3.787
## 3rd Qu.:5.00 3rd Qu.:4.000 3rd Qu.:5.000 3rd Qu.:4.000 3rd Qu.:5.000
## Max. :6.00 Max. :6.000 Max. :6.000 Max. :6.000 Max. :6.000
##
## BO1T1 BO2T1 BO3T1 BO4T1
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000
## Median :3.000 Median :3.000 Median :3.000 Median :3.000
## Mean :3.457 Mean :3.431 Mean :3.102 Mean :3.231
## 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :6.000 Max. :6.000 Max. :6.000 Max. :6.000
##
## CB1T1 CB2T1 CB3T1 PC1T1
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:2.000 1st Qu.:1.000
## Median :1.000 Median :2.000 Median :3.000 Median :2.000
## Mean :1.898 Mean :2.326 Mean :3.085 Mean :2.595
## 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:4.000 3rd Qu.:3.000
## Max. :6.000 Max. :6.000 Max. :6.000 Max. :6.000
##
## PC2T1 PC3T1 PC4T1 EN1T2
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:2.000
## Median :2.000 Median :2.000 Median :2.000 Median :3.000
## Mean :2.684 Mean :2.599 Mean :2.683 Mean :2.907
## 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :6.000 Max. :6.000 Max. :6.000 Max. :6.000
##
## EN2T2 EN3T2 EN4T2 EN5T2
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000
## Median :3.000 Median :3.000 Median :3.000 Median :4.000
## Mean :3.049 Mean :2.693 Mean :3.179 Mean :3.499
## 3rd Qu.:4.000 3rd Qu.:3.000 3rd Qu.:4.000 3rd Qu.:5.000
## Max. :6.000 Max. :6.000 Max. :6.000 Max. :6.000
##
## FR1T2 FR2T2 FR3T2 SE1T2 SE2T2
## Min. :1.000 Min. :1.00 Min. :1.00 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.00 1st Qu.:2.00 1st Qu.:2.000 1st Qu.:4.000
## Median :3.000 Median :3.00 Median :3.00 Median :3.000 Median :5.000
## Mean :3.271 Mean :2.96 Mean :3.14 Mean :3.201 Mean :4.586
## 3rd Qu.:4.000 3rd Qu.:4.00 3rd Qu.:4.00 3rd Qu.:4.000 3rd Qu.:6.000
## Max. :6.000 Max. :6.00 Max. :6.00 Max. :6.000 Max. :6.000
##
## SE3T2 SE4T2 SE5T2 ENR1T2 ENR2T2
## Min. :1.00 Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:4.00 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000
## Median :4.00 Median :4.000 Median :4.000 Median :4.000 Median :4.000
## Mean :4.24 Mean :3.644 Mean :3.933 Mean :3.828 Mean :3.856
## 3rd Qu.:5.00 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000
## Max. :6.00 Max. :6.000 Max. :6.000 Max. :6.000 Max. :6.000
##
## ENR3T2 ENR4T2 FRR1T2 FRR2T2
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000
## Median :4.000 Median :4.000 Median :4.000 Median :4.000
## Mean :3.629 Mean :3.818 Mean :3.785 Mean :3.795
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000
## Max. :6.000 Max. :6.000 Max. :6.000 Max. :6.000
##
## FRR3T2 FRR4T2 BO1T2 BO2T2 BO3T2
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.0 Min. :1.000
## 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:2.000 1st Qu.:2.0 1st Qu.:2.000
## Median :4.000 Median :4.000 Median :3.000 Median :3.0 Median :3.000
## Mean :3.686 Mean :3.755 Mean :3.487 Mean :3.5 Mean :3.149
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.0 3rd Qu.:4.000
## Max. :6.000 Max. :6.000 Max. :6.000 Max. :6.0 Max. :6.000
##
## BO4T2 CB1T2 CB2T2 CB3T2
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:2.000
## Median :3.000 Median :1.000 Median :2.000 Median :3.000
## Mean :3.263 Mean :2.051 Mean :2.609 Mean :3.114
## 3rd Qu.:4.000 3rd Qu.:3.000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :6.000 Max. :6.000 Max. :6.000 Max. :6.000
##
## PC1T2 PC2T2 PC3T2 PC4T2 EN1T3
## Min. :1.000 Min. :1.00 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:1.00 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:2.000
## Median :2.000 Median :3.00 Median :2.000 Median :3.000 Median :3.000
## Mean :2.701 Mean :2.79 Mean :2.682 Mean :2.778 Mean :2.959
## 3rd Qu.:4.000 3rd Qu.:4.00 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :6.000 Max. :6.00 Max. :6.000 Max. :6.000 Max. :6.000
##
## EN2T3 EN3T3 EN4T3 EN5T3 FR1T3
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.00 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:3.00 1st Qu.:2.000
## Median :3.000 Median :3.000 Median :3.000 Median :4.00 Median :3.000
## Mean :3.014 Mean :2.783 Mean :3.187 Mean :3.57 Mean :3.347
## 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:5.00 3rd Qu.:4.000
## Max. :6.000 Max. :6.000 Max. :6.000 Max. :6.00 Max. :6.000
##
## FR2T3 FR3T3 SE1T3 SE2T3 SE3T3
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.00
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:3.000 1st Qu.:3.00
## Median :3.000 Median :3.000 Median :3.000 Median :4.000 Median :4.00
## Mean :3.064 Mean :3.155 Mean :3.271 Mean :4.295 Mean :4.14
## 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:6.000 3rd Qu.:5.00
## Max. :6.000 Max. :6.000 Max. :6.000 Max. :6.000 Max. :6.00
##
## SE4T3 SE5T3 ENR1T3 ENR2T3
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000
## Median :4.000 Median :4.000 Median :4.000 Median :4.000
## Mean :3.579 Mean :3.809 Mean :3.829 Mean :3.854
## 3rd Qu.:4.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000
## Max. :6.000 Max. :6.000 Max. :6.000 Max. :6.000
##
## ENR3T3 ENR4T3 FRR1T3 FRR2T3
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000
## Median :4.000 Median :4.000 Median :4.000 Median :4.000
## Mean :3.656 Mean :3.775 Mean :3.751 Mean :3.732
## 3rd Qu.:4.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000
## Max. :6.000 Max. :6.000 Max. :6.000 Max. :6.000
##
## FRR3T3 FRR4T3 BO1T3 BO2T3
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:2.000
## Median :4.000 Median :4.000 Median :3.000 Median :3.000
## Mean :3.645 Mean :3.792 Mean :3.518 Mean :3.483
## 3rd Qu.:4.000 3rd Qu.:5.000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :6.000 Max. :6.000 Max. :6.000 Max. :6.000
##
## BO3T3 BO4T3 CB1T3 CB2T3
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:1.000 1st Qu.:2.000
## Median :3.000 Median :3.000 Median :2.000 Median :3.000
## Mean :3.222 Mean :3.345 Mean :2.295 Mean :2.855
## 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:3.000 3rd Qu.:4.000
## Max. :6.000 Max. :6.000 Max. :6.000 Max. :6.000
##
## CB3T3 PC1T3 PC2T3 PC3T3
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000
## Median :3.000 Median :3.000 Median :3.000 Median :3.000
## Mean :3.176 Mean :2.807 Mean :2.908 Mean :2.782
## 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :6.000 Max. :6.000 Max. :6.000 Max. :6.000
##
## PC4T3 filter_. ZACHT1 ZACHT2
## Min. :1.000 Not Selected: 0 Min. :-3.0131555 Min. :-3.187241
## 1st Qu.:2.000 Selected :2610 1st Qu.:-0.6833303 1st Qu.:-0.661663
## Median :3.000 Median :-0.0100814 Median : 0.004321
## Mean :2.886 Mean :-0.0006499 Mean :-0.001995
## 3rd Qu.:4.000 3rd Qu.: 0.6598713 3rd Qu.: 0.685150
## Max. :6.000 Max. : 2.5187201 Max. : 2.232721
##
## ZACHT3 ZZACHT1 ZZACHT2
## Min. :-3.013155 Min. :-3.030778 Min. :-3.215978
## 1st Qu.:-0.676073 1st Qu.:-0.686821 1st Qu.:-0.666033
## Median : 0.004320 Median :-0.009489 Median : 0.006376
## Mean :-0.001895 Mean : 0.000000 Mean : 0.000000
## 3rd Qu.: 0.665252 3rd Qu.: 0.664528 3rd Qu.: 0.693775
## Max. : 2.268986 Max. : 2.534652 Max. : 2.256276
##
## ZZACHT3 ZEN1T1 ZEN2T1 ZEN3T1
## Min. :-3.036674 Min. :-1.23948 Min. :-1.2875 Min. :-1.14012
## 1st Qu.:-0.679867 1st Qu.:-0.59499 1st Qu.:-0.6653 1st Qu.:-1.14012
## Median : 0.006268 Median :-0.01296 Median :-0.1329 Median : 0.20889
## Mean : 0.000000 Mean : 0.01476 Mean : 0.0125 Mean : 0.02014
## 3rd Qu.: 0.672778 3rd Qu.: 0.78127 3rd Qu.: 0.6157 3rd Qu.: 0.20889
## Max. : 2.290046 Max. : 2.19917 Max. : 2.0604 Max. : 2.37553
##
## ZEN4T1 ZEN5T1 MAC1T1 MAC2T1
## Min. :-1.27763 Min. :-1.367392 Min. :1.000 Min. :1.000
## 1st Qu.:-0.64561 1st Qu.:-0.763799 1st Qu.:2.000 1st Qu.:2.000
## Median :-0.15034 Median : 0.179542 Median :3.000 Median :2.667
## Mean : 0.01331 Mean : 0.003013 Mean :2.905 Mean :2.812
## 3rd Qu.: 0.48498 3rd Qu.: 0.758028 3rd Qu.:3.667 3rd Qu.:3.667
## Max. : 1.90644 Max. : 1.500124 Max. :6.000 Max. :6.000
##
## MAC3T1 MAC1T2 MAC2T2 MAC3T2
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000
## Median :2.667 Median :3.000 Median :3.000 Median :3.000
## Mean :2.805 Mean :2.987 Mean :2.923 Mean :2.906
## 3rd Qu.:3.667 3rd Qu.:3.667 3rd Qu.:3.667 3rd Qu.:3.667
## Max. :6.000 Max. :6.000 Max. :6.000 Max. :6.000
##
## MAC1T3 MAC2T3 MAC3T3 TimePoint
## Min. :1.000 Min. :1.000 Min. :1.000 Length:2610
## 1st Qu.:2.333 1st Qu.:2.000 1st Qu.:2.000 Class :character
## Median :3.167 Median :3.000 Median :3.000 Mode :character
## Mean :3.097 Mean :3.041 Mean :3.051
## 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :6.000 Max. :6.000 Max. :6.000
##
## AC1 AC2 AC3 AC4 AC5
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.00
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:1.000 1st Qu.:2.000 1st Qu.:2.00
## Median :4.000 Median :4.000 Median :2.000 Median :3.000 Median :3.00
## Mean :3.448 Mean :3.411 Mean :2.129 Mean :3.187 Mean :3.24
## 3rd Qu.:4.000 3rd Qu.:5.000 3rd Qu.:3.000 3rd Qu.:4.000 3rd Qu.:4.00
## Max. :6.000 Max. :6.000 Max. :6.000 Max. :6.000 Max. :6.00
##
## AC6 AC7 AC8 AC9
## Min. :1.000 Min. :1.000 Min. :1.00 Min. :1.000
## 1st Qu.:1.000 1st Qu.:2.000 1st Qu.:1.00 1st Qu.:2.000
## Median :2.000 Median :3.000 Median :2.00 Median :3.000
## Mean :2.349 Mean :3.157 Mean :2.31 Mean :3.295
## 3rd Qu.:3.000 3rd Qu.:4.000 3rd Qu.:3.00 3rd Qu.:4.000
## Max. :6.000 Max. :6.000 Max. :6.00 Max. :6.000
##
## ACH
## Min. : 0.00
## 1st Qu.: 36.00
## Median : 60.00
## Mean : 57.65
## 3rd Qu.: 80.00
## Max. :100.00
##
model3 <- '
Cheating =~ AC1 + AC2 + AC3 +AC4 + AC5 + AC6 + AC7 + AC8 + AC9
'
# 不约束模型（配置不变性）
fit_configural3 <- cfa(model3, data = spss_long3, group = "TimePoint", control = list(iter.max = 10000, reltol = 1e-8))
summary(fit_configural3, fit.measures = TRUE)
## lavaan 0.6-18 ended normally after 55 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 81
##
## Number of observations per group:
## T1 870
## T2 870
## T3 870
##
## Model Test User Model:
##
## Test statistic 1867.907
## Degrees of freedom 81
## P-value (Chi-square) 0.000
## Test statistic for each group:
## T1 492.431
## T2 590.244
## T3 785.232
##
## Model Test Baseline Model:
##
## Test statistic 12999.284
## Degrees of freedom 108
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.861
## Tucker-Lewis Index (TLI) 0.815
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -36879.385
## Loglikelihood unrestricted model (H1) -35945.432
##
## Akaike (AIC) 73920.770
## Bayesian (BIC) 74396.006
## Sample-size adjusted Bayesian (SABIC) 74138.646
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.159
## 90 Percent confidence interval - lower 0.153
## 90 Percent confidence interval - upper 0.166
## P-value H_0: RMSEA <= 0.050 0.000
## P-value H_0: RMSEA >= 0.080 1.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.062
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
##
## Group 1 [T1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## Cheating =~
## AC1 1.000
## AC2 0.842 0.047 17.804 0.000
## AC3 0.593 0.036 16.379 0.000
## AC4 1.054 0.042 25.169 0.000
## AC5 1.019 0.046 22.233 0.000
## AC6 0.722 0.041 17.721 0.000
## AC7 0.882 0.046 19.325 0.000
## AC8 0.691 0.042 16.629 0.000
## AC9 1.000 0.044 22.519 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .AC1 3.457 0.050 68.793 0.000
## .AC2 3.331 0.054 61.141 0.000
## .AC3 1.926 0.041 46.527 0.000
## .AC4 3.090 0.050 61.326 0.000
## .AC5 3.159 0.054 58.243 0.000
## .AC6 2.187 0.047 46.589 0.000
## .AC7 3.051 0.053 57.482 0.000
## .AC8 2.107 0.048 44.261 0.000
## .AC9 3.259 0.053 61.944 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .AC1 0.870 0.051 17.154 0.000
## .AC2 1.641 0.084 19.479 0.000
## .AC3 1.024 0.052 19.762 0.000
## .AC4 0.734 0.046 15.976 0.000
## .AC5 1.180 0.065 18.029 0.000
## .AC6 1.225 0.063 19.497 0.000
## .AC7 1.417 0.074 19.100 0.000
## .AC8 1.337 0.068 19.716 0.000
## .AC9 1.081 0.060 17.887 0.000
## Cheating 1.328 0.101 13.149 0.000
##
##
## Group 2 [T2]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## Cheating =~
## AC1 1.000
## AC2 0.817 0.040 20.300 0.000
## AC3 0.643 0.034 18.987 0.000
## AC4 1.028 0.034 29.846 0.000
## AC5 0.920 0.039 23.885 0.000
## AC6 0.728 0.036 20.036 0.000
## AC7 0.833 0.038 21.668 0.000
## AC8 0.705 0.037 18.871 0.000
## AC9 0.951 0.038 25.209 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .AC1 3.418 0.051 66.841 0.000
## .AC2 3.421 0.053 64.092 0.000
## .AC3 2.121 0.044 47.792 0.000
## .AC4 3.180 0.050 63.104 0.000
## .AC5 3.245 0.053 61.270 0.000
## .AC6 2.343 0.048 48.746 0.000
## .AC7 3.126 0.052 60.488 0.000
## .AC8 2.298 0.049 47.052 0.000
## .AC9 3.292 0.053 62.559 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .AC1 0.736 0.044 16.745 0.000
## .AC2 1.451 0.074 19.481 0.000
## .AC3 1.076 0.055 19.708 0.000
## .AC4 0.583 0.038 15.379 0.000
## .AC5 1.137 0.061 18.622 0.000
## .AC6 1.193 0.061 19.530 0.000
## .AC7 1.255 0.065 19.201 0.000
## .AC8 1.311 0.066 19.726 0.000
## .AC9 1.015 0.056 18.174 0.000
## Cheating 1.539 0.107 14.453 0.000
##
##
## Group 3 [T3]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## Cheating =~
## AC1 1.000
## AC2 0.886 0.038 23.592 0.000
## AC3 0.668 0.034 19.639 0.000
## AC4 1.064 0.032 33.106 0.000
## AC5 0.946 0.035 27.164 0.000
## AC6 0.769 0.036 21.478 0.000
## AC7 0.893 0.036 24.642 0.000
## AC8 0.746 0.037 20.182 0.000
## AC9 0.991 0.035 28.480 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .AC1 3.469 0.050 69.891 0.000
## .AC2 3.480 0.052 66.793 0.000
## .AC3 2.341 0.045 51.780 0.000
## .AC4 3.290 0.050 65.815 0.000
## .AC5 3.317 0.050 65.788 0.000
## .AC6 2.517 0.049 51.892 0.000
## .AC7 3.293 0.051 64.652 0.000
## .AC8 2.526 0.049 51.107 0.000
## .AC9 3.333 0.051 65.118 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .AC1 0.639 0.037 17.156 0.000
## .AC2 1.183 0.061 19.304 0.000
## .AC3 1.107 0.056 19.916 0.000
## .AC4 0.472 0.031 15.182 0.000
## .AC5 0.866 0.047 18.428 0.000
## .AC6 1.158 0.059 19.665 0.000
## .AC7 1.057 0.055 19.088 0.000
## .AC8 1.287 0.065 19.847 0.000
## .AC9 0.803 0.045 17.975 0.000
## Cheating 1.504 0.100 14.987 0.000
if(!fit_configural@Fit@converged) stop("Configural model did not converge")
# 度量不变性模型
fit_metric3 <- cfa(model3, data = spss_long3, group = "TimePoint", group.equal = "loadings", control = list(iter.max = 1000, reltol = 1e-8))
summary(fit_metric3, fit.measures = TRUE)
## lavaan 0.6-18 ended normally after 44 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 81
## Number of equality constraints 16
##
## Number of observations per group:
## T1 870
## T2 870
## T3 870
##
## Model Test User Model:
##
## Test statistic 1878.893
## Degrees of freedom 97
## P-value (Chi-square) 0.000
## Test statistic for each group:
## T1 498.659
## T2 592.251
## T3 787.984
##
## Model Test Baseline Model:
##
## Test statistic 12999.284
## Degrees of freedom 108
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.862
## Tucker-Lewis Index (TLI) 0.846
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -36884.878
## Loglikelihood unrestricted model (H1) -35945.432
##
## Akaike (AIC) 73899.757
## Bayesian (BIC) 74281.119
## Sample-size adjusted Bayesian (SABIC) 74074.595
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.145
## 90 Percent confidence interval - lower 0.140
## 90 Percent confidence interval - upper 0.151
## P-value H_0: RMSEA <= 0.050 0.000
## P-value H_0: RMSEA >= 0.080 1.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.064
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
##
## Group 1 [T1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## Cheating =~
## AC1 1.000
## AC2 (.p2.) 0.852 0.024 35.884 0.000
## AC3 (.p3.) 0.637 0.020 31.788 0.000
## AC4 (.p4.) 1.049 0.020 51.192 0.000
## AC5 (.p5.) 0.956 0.022 42.484 0.000
## AC6 (.p6.) 0.742 0.022 34.297 0.000
## AC7 (.p7.) 0.871 0.023 38.097 0.000
## AC8 (.p8.) 0.717 0.022 32.232 0.000
## AC9 (.p9.) 0.980 0.022 44.237 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .AC1 3.457 0.050 68.620 0.000
## .AC2 3.331 0.055 60.807 0.000
## .AC3 1.926 0.042 45.551 0.000
## .AC4 3.090 0.050 61.279 0.000
## .AC5 3.159 0.053 59.789 0.000
## .AC6 2.187 0.047 46.207 0.000
## .AC7 3.051 0.053 57.690 0.000
## .AC8 2.107 0.048 43.821 0.000
## .AC9 3.259 0.052 62.424 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .AC1 0.872 0.050 17.410 0.000
## .AC2 1.640 0.084 19.537 0.000
## .AC3 1.014 0.052 19.657 0.000
## .AC4 0.741 0.045 16.399 0.000
## .AC5 1.207 0.065 18.592 0.000
## .AC6 1.214 0.062 19.498 0.000
## .AC7 1.420 0.074 19.262 0.000
## .AC8 1.325 0.067 19.696 0.000
## .AC9 1.086 0.060 18.204 0.000
## Cheating 1.337 0.080 16.680 0.000
##
##
## Group 2 [T2]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## Cheating =~
## AC1 1.000
## AC2 (.p2.) 0.852 0.024 35.884 0.000
## AC3 (.p3.) 0.637 0.020 31.788 0.000
## AC4 (.p4.) 1.049 0.020 51.192 0.000
## AC5 (.p5.) 0.956 0.022 42.484 0.000
## AC6 (.p6.) 0.742 0.022 34.297 0.000
## AC7 (.p7.) 0.871 0.023 38.097 0.000
## AC8 (.p8.) 0.717 0.022 32.232 0.000
## AC9 (.p9.) 0.980 0.022 44.237 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .AC1 3.418 0.051 67.668 0.000
## .AC2 3.421 0.054 63.611 0.000
## .AC3 2.121 0.044 48.272 0.000
## .AC4 3.180 0.050 63.098 0.000
## .AC5 3.245 0.053 60.783 0.000
## .AC6 2.343 0.048 48.774 0.000
## .AC7 3.126 0.052 59.973 0.000
## .AC8 2.298 0.049 47.116 0.000
## .AC9 3.292 0.053 62.321 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .AC1 0.745 0.043 17.185 0.000
## .AC2 1.445 0.074 19.497 0.000
## .AC3 1.081 0.054 19.842 0.000
## .AC4 0.588 0.037 15.737 0.000
## .AC5 1.132 0.061 18.667 0.000
## .AC6 1.195 0.061 19.609 0.000
## .AC7 1.246 0.065 19.208 0.000
## .AC8 1.312 0.066 19.798 0.000
## .AC9 1.010 0.055 18.268 0.000
## Cheating 1.475 0.087 16.989 0.000
##
##
## Group 3 [T3]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## Cheating =~
## AC1 1.000
## AC2 (.p2.) 0.852 0.024 35.884 0.000
## AC3 (.p3.) 0.637 0.020 31.788 0.000
## AC4 (.p4.) 1.049 0.020 51.192 0.000
## AC5 (.p5.) 0.956 0.022 42.484 0.000
## AC6 (.p6.) 0.742 0.022 34.297 0.000
## AC7 (.p7.) 0.871 0.023 38.097 0.000
## AC8 (.p8.) 0.717 0.022 32.232 0.000
## AC9 (.p9.) 0.980 0.022 44.237 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .AC1 3.469 0.050 69.264 0.000
## .AC2 3.480 0.052 67.533 0.000
## .AC3 2.341 0.045 52.314 0.000
## .AC4 3.290 0.050 65.852 0.000
## .AC5 3.317 0.051 64.867 0.000
## .AC6 2.517 0.048 52.255 0.000
## .AC7 3.293 0.051 64.954 0.000
## .AC8 2.526 0.049 51.512 0.000
## .AC9 3.333 0.051 64.939 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .AC1 0.633 0.037 17.174 0.000
## .AC2 1.186 0.061 19.446 0.000
## .AC3 1.115 0.056 20.025 0.000
## .AC4 0.467 0.030 15.359 0.000
## .AC5 0.860 0.047 18.394 0.000
## .AC6 1.166 0.059 19.774 0.000
## .AC7 1.062 0.055 19.211 0.000
## .AC8 1.296 0.065 19.949 0.000
## .AC9 0.803 0.044 18.080 0.000
## Cheating 1.549 0.090 17.215 0.000
if(!fit_metric@Fit@converged) stop("Metric model did not converge")
# 截距不变性模型
fit_scalar3 <- cfa(model3, data = spss_long3, group = "TimePoint", group.equal = c("loadings", "intercepts"), control = list(iter.max = 1000, reltol = 1e-8))
summary(fit_scalar3, fit.measures = TRUE)
## lavaan 0.6-18 ended normally after 56 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 83
## Number of equality constraints 34
##
## Number of observations per group:
## T1 870
## T2 870
## T3 870
##
## Model Test User Model:
##
## Test statistic 1979.931
## Degrees of freedom 113
## P-value (Chi-square) 0.000
## Test statistic for each group:
## T1 550.566
## T2 593.605
## T3 835.759
##
## Model Test Baseline Model:
##
## Test statistic 12999.284
## Degrees of freedom 108
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.855
## Tucker-Lewis Index (TLI) 0.862
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -36935.397
## Loglikelihood unrestricted model (H1) -35945.432
##
## Akaike (AIC) 73968.794
## Bayesian (BIC) 74256.282
## Sample-size adjusted Bayesian (SABIC) 74100.595
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.138
## 90 Percent confidence interval - lower 0.133
## 90 Percent confidence interval - upper 0.143
## P-value H_0: RMSEA <= 0.050 0.000
## P-value H_0: RMSEA >= 0.080 1.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.067
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
##
## Group 1 [T1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## Cheating =~
## AC1 1.000
## AC2 (.p2.) 0.856 0.024 35.816 0.000
## AC3 (.p3.) 0.648 0.020 31.920 0.000
## AC4 (.p4.) 1.054 0.021 50.926 0.000
## AC5 (.p5.) 0.960 0.023 42.356 0.000
## AC6 (.p6.) 0.751 0.022 34.395 0.000
## AC7 (.p7.) 0.878 0.023 38.103 0.000
## AC8 (.p8.) 0.728 0.022 32.362 0.000
## AC9 (.p9.) 0.982 0.022 43.959 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .AC1 (.20.) 3.338 0.044 75.902 0.000
## .AC2 (.21.) 3.324 0.042 79.550 0.000
## .AC3 (.22.) 2.061 0.033 62.190 0.000
## .AC4 (.23.) 3.081 0.045 67.858 0.000
## .AC5 (.24.) 3.142 0.044 71.739 0.000
## .AC6 (.25.) 2.276 0.037 61.238 0.000
## .AC7 (.26.) 3.073 0.042 73.681 0.000
## .AC8 (.27.) 2.239 0.037 60.415 0.000
## .AC9 (.28.) 3.190 0.044 72.194 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .AC1 0.894 0.051 17.515 0.000
## .AC2 1.640 0.084 19.531 0.000
## .AC3 1.032 0.053 19.643 0.000
## .AC4 0.742 0.045 16.383 0.000
## .AC5 1.207 0.065 18.580 0.000
## .AC6 1.221 0.063 19.479 0.000
## .AC7 1.417 0.074 19.241 0.000
## .AC8 1.342 0.068 19.681 0.000
## .AC9 1.094 0.060 18.225 0.000
## Cheating 1.320 0.079 16.622 0.000
##
##
## Group 2 [T2]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## Cheating =~
## AC1 1.000
## AC2 (.p2.) 0.856 0.024 35.816 0.000
## AC3 (.p3.) 0.648 0.020 31.920 0.000
## AC4 (.p4.) 1.054 0.021 50.926 0.000
## AC5 (.p5.) 0.960 0.023 42.356 0.000
## AC6 (.p6.) 0.751 0.022 34.395 0.000
## AC7 (.p7.) 0.878 0.023 38.103 0.000
## AC8 (.p8.) 0.728 0.022 32.362 0.000
## AC9 (.p9.) 0.982 0.022 43.959 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .AC1 (.20.) 3.338 0.044 75.902 0.000
## .AC2 (.21.) 3.324 0.042 79.550 0.000
## .AC3 (.22.) 2.061 0.033 62.190 0.000
## .AC4 (.23.) 3.081 0.045 67.858 0.000
## .AC5 (.24.) 3.142 0.044 71.739 0.000
## .AC6 (.25.) 2.276 0.037 61.238 0.000
## .AC7 (.26.) 3.073 0.042 73.681 0.000
## .AC8 (.27.) 2.239 0.037 60.415 0.000
## .AC9 (.28.) 3.190 0.044 72.194 0.000
## Cheatng 0.092 0.059 1.545 0.122
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .AC1 0.749 0.043 17.243 0.000
## .AC2 1.446 0.074 19.498 0.000
## .AC3 1.078 0.054 19.814 0.000
## .AC4 0.589 0.037 15.747 0.000
## .AC5 1.133 0.061 18.670 0.000
## .AC6 1.192 0.061 19.587 0.000
## .AC7 1.247 0.065 19.197 0.000
## .AC8 1.309 0.066 19.772 0.000
## .AC9 1.012 0.055 18.288 0.000
## Cheating 1.458 0.086 16.948 0.000
##
##
## Group 3 [T3]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## Cheating =~
## AC1 1.000
## AC2 (.p2.) 0.856 0.024 35.816 0.000
## AC3 (.p3.) 0.648 0.020 31.920 0.000
## AC4 (.p4.) 1.054 0.021 50.926 0.000
## AC5 (.p5.) 0.960 0.023 42.356 0.000
## AC6 (.p6.) 0.751 0.022 34.395 0.000
## AC7 (.p7.) 0.878 0.023 38.103 0.000
## AC8 (.p8.) 0.728 0.022 32.362 0.000
## AC9 (.p9.) 0.982 0.022 43.959 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .AC1 (.20.) 3.338 0.044 75.902 0.000
## .AC2 (.21.) 3.324 0.042 79.550 0.000
## .AC3 (.22.) 2.061 0.033 62.190 0.000
## .AC4 (.23.) 3.081 0.045 67.858 0.000
## .AC5 (.24.) 3.142 0.044 71.739 0.000
## .AC6 (.25.) 2.276 0.037 61.238 0.000
## .AC7 (.26.) 3.073 0.042 73.681 0.000
## .AC8 (.27.) 2.239 0.037 60.415 0.000
## .AC9 (.28.) 3.190 0.044 72.194 0.000
## Cheatng 0.206 0.060 3.437 0.001
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .AC1 0.642 0.037 17.251 0.000
## .AC2 1.187 0.061 19.441 0.000
## .AC3 1.136 0.057 20.017 0.000
## .AC4 0.467 0.030 15.337 0.000
## .AC5 0.860 0.047 18.384 0.000
## .AC6 1.172 0.059 19.759 0.000
## .AC7 1.063 0.055 19.194 0.000
## .AC8 1.314 0.066 19.939 0.000
## .AC9 0.808 0.045 18.102 0.000
## Cheating 1.532 0.089 17.165 0.000
if(!fit_scalar@Fit@converged) stop("Scalar model did not converge")
# 严格不变性模型
fit_strict3 <- cfa(model3, data = spss_long3, group = "TimePoint", group.equal = c("loadings", "intercepts", "residuals"), control = list(iter.max = 1000, reltol = 1e-8))
summary(fit_strict3, fit.measures = TRUE)
## lavaan 0.6-18 ended normally after 49 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 83
## Number of equality constraints 52
##
## Number of observations per group:
## T1 870
## T2 870
## T3 870
##
## Model Test User Model:
##
## Test statistic 2105.400
## Degrees of freedom 131
## P-value (Chi-square) 0.000
## Test statistic for each group:
## T1 597.104
## T2 595.863
## T3 912.433
##
## Model Test Baseline Model:
##
## Test statistic 12999.284
## Degrees of freedom 108
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.847
## Tucker-Lewis Index (TLI) 0.874
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -36998.132
## Loglikelihood unrestricted model (H1) -35945.432
##
## Akaike (AIC) 74058.264
## Bayesian (BIC) 74240.144
## Sample-size adjusted Bayesian (SABIC) 74141.648
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.132
## 90 Percent confidence interval - lower 0.127
## 90 Percent confidence interval - upper 0.137
## P-value H_0: RMSEA <= 0.050 0.000
## P-value H_0: RMSEA >= 0.080 1.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.070
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
##
## Group 1 [T1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## Cheating =~
## AC1 1.000
## AC2 (.p2.) 0.853 0.024 35.186 0.000
## AC3 (.p3.) 0.650 0.020 31.914 0.000
## AC4 (.p4.) 1.054 0.021 49.956 0.000
## AC5 (.p5.) 0.965 0.023 41.813 0.000
## AC6 (.p6.) 0.752 0.022 34.289 0.000
## AC7 (.p7.) 0.878 0.023 37.623 0.000
## AC8 (.p8.) 0.728 0.023 32.235 0.000
## AC9 (.p9.) 0.983 0.023 43.379 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .AC1 (.20.) 3.350 0.044 76.096 0.000
## .AC2 (.21.) 3.327 0.042 79.587 0.000
## .AC3 (.22.) 2.066 0.033 61.986 0.000
## .AC4 (.23.) 3.083 0.045 67.948 0.000
## .AC5 (.24.) 3.146 0.044 71.408 0.000
## .AC6 (.25.) 2.275 0.037 61.019 0.000
## .AC7 (.26.) 3.071 0.042 73.478 0.000
## .AC8 (.27.) 2.239 0.037 60.245 0.000
## .AC9 (.28.) 3.198 0.044 72.156 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .AC1 (.10.) 0.762 0.026 29.646 0.000
## .AC2 (.11.) 1.427 0.042 33.631 0.000
## .AC3 (.12.) 1.081 0.032 34.220 0.000
## .AC4 (.13.) 0.599 0.022 26.958 0.000
## .AC5 (.14.) 1.065 0.033 31.830 0.000
## .AC6 (.15.) 1.193 0.035 33.807 0.000
## .AC7 (.16.) 1.242 0.038 33.084 0.000
## .AC8 (.17.) 1.322 0.039 34.168 0.000
## .AC9 (.18.) 0.970 0.031 31.218 0.000
## Cheatng 1.341 0.080 16.791 0.000
##
##
## Group 2 [T2]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## Cheating =~
## AC1 1.000
## AC2 (.p2.) 0.853 0.024 35.186 0.000
## AC3 (.p3.) 0.650 0.020 31.914 0.000
## AC4 (.p4.) 1.054 0.021 49.956 0.000
## AC5 (.p5.) 0.965 0.023 41.813 0.000
## AC6 (.p6.) 0.752 0.022 34.289 0.000
## AC7 (.p7.) 0.878 0.023 37.623 0.000
## AC8 (.p8.) 0.728 0.023 32.235 0.000
## AC9 (.p9.) 0.983 0.023 43.379 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .AC1 (.20.) 3.350 0.044 76.096 0.000
## .AC2 (.21.) 3.327 0.042 79.587 0.000
## .AC3 (.22.) 2.066 0.033 61.986 0.000
## .AC4 (.23.) 3.083 0.045 67.948 0.000
## .AC5 (.24.) 3.146 0.044 71.408 0.000
## .AC6 (.25.) 2.275 0.037 61.019 0.000
## .AC7 (.26.) 3.071 0.042 73.478 0.000
## .AC8 (.27.) 2.239 0.037 60.245 0.000
## .AC9 (.28.) 3.198 0.044 72.156 0.000
## Cheatng 0.087 0.059 1.467 0.142
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .AC1 (.10.) 0.762 0.026 29.646 0.000
## .AC2 (.11.) 1.427 0.042 33.631 0.000
## .AC3 (.12.) 1.081 0.032 34.220 0.000
## .AC4 (.13.) 0.599 0.022 26.958 0.000
## .AC5 (.14.) 1.065 0.033 31.830 0.000
## .AC6 (.15.) 1.193 0.035 33.807 0.000
## .AC7 (.16.) 1.242 0.038 33.084 0.000
## .AC8 (.17.) 1.322 0.039 34.168 0.000
## .AC9 (.18.) 0.970 0.031 31.218 0.000
## Cheatng 1.456 0.086 16.887 0.000
##
##
## Group 3 [T3]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## Cheating =~
## AC1 1.000
## AC2 (.p2.) 0.853 0.024 35.186 0.000
## AC3 (.p3.) 0.650 0.020 31.914 0.000
## AC4 (.p4.) 1.054 0.021 49.956 0.000
## AC5 (.p5.) 0.965 0.023 41.813 0.000
## AC6 (.p6.) 0.752 0.022 34.289 0.000
## AC7 (.p7.) 0.878 0.023 37.623 0.000
## AC8 (.p8.) 0.728 0.023 32.235 0.000
## AC9 (.p9.) 0.983 0.023 43.379 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .AC1 (.20.) 3.350 0.044 76.096 0.000
## .AC2 (.21.) 3.327 0.042 79.587 0.000
## .AC3 (.22.) 2.066 0.033 61.986 0.000
## .AC4 (.23.) 3.083 0.045 67.948 0.000
## .AC5 (.24.) 3.146 0.044 71.408 0.000
## .AC6 (.25.) 2.275 0.037 61.019 0.000
## .AC7 (.26.) 3.071 0.042 73.478 0.000
## .AC8 (.27.) 2.239 0.037 60.245 0.000
## .AC9 (.28.) 3.198 0.044 72.156 0.000
## Cheatng 0.207 0.060 3.444 0.001
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .AC1 (.10.) 0.762 0.026 29.646 0.000
## .AC2 (.11.) 1.427 0.042 33.631 0.000
## .AC3 (.12.) 1.081 0.032 34.220 0.000
## .AC4 (.13.) 0.599 0.022 26.958 0.000
## .AC5 (.14.) 1.065 0.033 31.830 0.000
## .AC6 (.15.) 1.193 0.035 33.807 0.000
## .AC7 (.16.) 1.242 0.038 33.084 0.000
## .AC8 (.17.) 1.322 0.039 34.168 0.000
## .AC9 (.18.) 0.970 0.031 31.218 0.000
## Cheatng 1.512 0.089 16.929 0.000
if(!fit_strict@Fit@converged) stop("Strict model did not converge")
# 比较模型
anova(fit_configural3, fit_metric3, fit_scalar3, fit_strict3)
##
## Chi-Squared Difference Test
##
## Df AIC BIC Chisq Chisq diff RMSEA Df diff Pr(>Chisq)
## fit_configural3 81 73921 74396 1867.9
## fit_metric3 97 73900 74281 1878.9 10.986 0.000000 16 0.8103
## fit_scalar3 113 73969 74256 1979.9 101.037 0.078160 16 2.213e-14
## fit_strict3 131 74058 74240 2105.4 125.470 0.082841 18 < 2.2e-16
##
## fit_configural3
## fit_metric3
## fit_scalar3 ***
## fit_strict3 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1