Loading Packages
library(tidyverse)
library(sjstats)
library(jmRtools)
library(MuMIn)
library(psych)
library(car)Loading Data
SciMo_esm <- read_csv("/Volumes/educ/CEPSE/Projects/SchmidtLab/PSE/PSE_Raw Data Files/SciMo_Raw Data File/scimo_esm_11.19.18.csv")## Parsed with column specification:
## cols(
## .default = col_double(),
## stud_ID = col_character()
## )## See spec(...) for full column specifications.SciMo_student_survey <- read_csv("/Volumes/educ/CEPSE/Projects/SchmidtLab/PSE/PSE_Raw Data Files/SciMo_Raw Data File/scimo_student-survey_07.30.18.csv")## Parsed with column specification:
## cols(
## .default = col_double(),
## stud_ID = col_character(),
## ThirdQuar_2010 = col_character(),
## FourthQuar_2010 = col_character(),
## ThirdQuar_2011 = col_logical()
## )
## See spec(...) for full column specifications.SciMo_video_value <- read_csv("/Volumes/educ/CEPSE/Projects/SchmidtLab/PSE/PSE_Raw Data Files/SciMo_Raw Data File/scimo_value_final_07.30.18.csv")## Parsed with column specification:
## cols(
## .default = col_double(),
## date = col_character()
## )
## See spec(...) for full column specifications.Fixing Joining Variables
#Fixing response_date in ESM dataset
SciMo_esm$year <- ifelse(SciMo_esm$year == 8, 2008, 2009)
SciMo_esm$response_date <- as.Date(with(SciMo_esm, paste(year, month, day, sep="-")), "%Y-%m-%d")
SciMo_esm$response_date <- format(as.Date(SciMo_esm$response_date, format = "%Y-%m-%d"), "%Y-%m-%d")
#Renaming variables in video value dataset (right side is what they were named; new mame on left)
SciMo_video_value <- rename(SciMo_video_value,
response_date = date,
teacher_ID = teacher)
#Formating as response date variable as a date for video value dataset
SciMo_video_value$response_date <- format(as.Date(SciMo_video_value$response_date, format = "%m/%d/%Y"), "%Y-%m-%d")Creating New Signal Variables in ESM Dataset
SciMo_esm$signal_value <- ifelse(SciMo_esm$pager == 1 & SciMo_esm$signal == 1, 1,
ifelse(SciMo_esm$pager == 2 & SciMo_esm$signal == 1, 2,
ifelse(SciMo_esm$pager == 1 & SciMo_esm$signal == 2, 3,
ifelse(SciMo_esm$pager == 2 & SciMo_esm$signal == 4, 4,
ifelse(SciMo_esm$pager == 3 & SciMo_esm$signal == 1, 1,
ifelse(SciMo_esm$pager == 4 & SciMo_esm$signal == 2, 2,
ifelse(SciMo_esm$pager == 3 & SciMo_esm$signal == 2, 3, 4)))))))Creating Predictor Variables
#Creating dummy female variable
SciMo_student_survey$female <- ifelse(SciMo_student_survey$gender == 2, 1, 0)
#Creating minority dummy variable - 1 = minority status, 0 = white/asian
SciMo_student_survey$minority <- ifelse(SciMo_student_survey$race == 4 | SciMo_student_survey$race == 1, 0, 1)
#Creating grade variables
SciMo_esm <- mutate(SciMo_esm, ninth = ifelse(SciMo_esm$grade == 1, 1, 0))
SciMo_esm <- mutate(SciMo_esm, tenth = ifelse(SciMo_esm$grade == 2, 1, 0))
SciMo_esm <- mutate(SciMo_esm, eleventh = ifelse(SciMo_esm$grade == 3, 1, 0))
SciMo_esm <- mutate(SciMo_esm, twelfth = ifelse(SciMo_esm$grade == 4, 1, 0))
#Creating dummy grade variable, 0 = 9th grade; 1 = 10th, 11th, 12th grade
SciMo_esm <- mutate(SciMo_esm, grade_dum = ifelse(SciMo_esm$grade == 1, 0, 1))
#Creating Overall Value Sum in Video Value Dataset
SciMo_video_value$ov_sum <- (SciMo_video_value$high_utility_sum + SciMo_video_value$high_attainment_sum + SciMo_video_value$high_intrinsic_sum)
#Renaming utility value variable
SciMo_video_value <- rename(SciMo_video_value, uv_sum = high_utility_sum)
#Creating Perceived Competence Variable
SciMo_student_survey$per_comp <- composite_mean_maker(SciMo_student_survey, confident1, capable1)
#Renaming instructional practice variable
SciMo_esm <- rename(SciMo_esm, activity = instructional_practice)
#Creating class activity variable, same as in JRST paper
# SciMo_esm$act_re <- car::recode(SciMo_esm$activity,
# "1 = 'Lecture';
# c(2, 3) = 'Individual Work';
# c(4, 5) = 'Group Work';
# c(6, 8, 7) = 'Quiz and Test';
# c(9) = 'Discussion';
# c(11, 10) = 'Presentation';
# c(12, 13) = 'Video';
# c(15, 14, 16) = 'Laboratory';
# c(17) = 'Non-instructional';
# c(18) = NA")
#
# SciMo_esm$act_re <- ifelse((SciMo_esm$act_re == "Discussion" | SciMo_esm$act_re == "Non-instructional" | SciMo_esm$act_re == "Presentation" | SciMo_esm$act_re == "Video" | SciMo_esm$act_re == "Group Work"), "Other", SciMo_esm$act_re)
SciMo_esm$act_aa <- ifelse((SciMo_esm$activity == 10 | SciMo_esm$activity == 11 | SciMo_esm$activity ==14 | SciMo_esm$activity ==15 | SciMo_esm$activity ==16), 1, 0)
SciMo_esm$act_seat <- ifelse((SciMo_esm$activity == 4 | SciMo_esm$activity == 5 | SciMo_esm$activity == 2 | SciMo_esm$activity == 3), 1, 0)
SciMo_esm$act_lec <- ifelse((SciMo_esm$activity == 1 | SciMo_esm$activity == 12 | SciMo_esm$activity == 13), 1, 0)
SciMo_esm$act_test <- ifelse((SciMo_esm$activity == 6 | SciMo_esm$activity == 7 | SciMo_esm$activity == 8), 1, 0)
SciMo_esm$act_oth <- ifelse((SciMo_esm$activity == 9 | SciMo_esm$activity == 17 | SciMo_esm$activity == 18), 1, 0)#Creating class activity variable, same as in JRST paper
#SciMo_esm$act_re <- car::recode(SciMo_esm$activity,
# "1 = 'Lecture';
# c(2, 3) = 'Individual Work';
# c(4, 5) = 'Group Work';
# c(6, 8, 7) = 'Quiz and Test';
# c(9) = 'Discussion';
# c(11, 10) = 'Presentation';
# c(12, 13) = 'Video';
# c(15, 14, 16) = 'Laboratory';
# c(17) = 'Non-instructional';
# c(18) = NA")
#table(SciMo_esm$act_re)
# SciMo_esm$act_re <- ifelse((SciMo_esm$act_re == "Discussion" | SciMo_esm$act_re == "Non-instructional" | SciMo_esm$act_re == "Presentation" | SciMo_esm$act_re == "Video" | SciMo_esm$act_re == "Group Work"), "Other", SciMo_esm$act_re)#Creating class activity variable, same as in JRST paper
SciMo_esm$act_re <- car::recode(SciMo_esm$activity,
"c(1, 12, 13, 9) = 'Lecture';
c(2, 3) = 'Individual Work';
c(4, 5) = 'Group Work';
c(6, 8, 7) = 'Quiz and Test';
c(11, 10) = 'Presentation';
c(15, 14, 16) = 'Laboratory';
c(17) = 'Non-instructional';
c(18) = NA")
table(SciMo_esm$act_re)##
## Group Work Individual Work Laboratory Lecture
## 271 671 1023 791
## Non-instructional Presentation Quiz and Test
## 322 313 688#Creating dummy coded variables
SciMo_esm$act_lec <- ifelse((SciMo_esm$activity == 1 | SciMo_esm$activity == 12 | SciMo_esm$activity == 13 | SciMo_esm$activity == 9), 1, 0)
SciMo_esm$act_indwork <- ifelse((SciMo_esm$activity == 2 | SciMo_esm$activity == 3), 1, 0)
SciMo_esm$act_groupwork <- ifelse((SciMo_esm$activity == 4 | SciMo_esm$activity == 5), 1, 0)
SciMo_esm$act_test <- ifelse((SciMo_esm$activity == 6 | SciMo_esm$activity == 7 | SciMo_esm$activity == 8), 1, 0)
SciMo_esm$act_pres <- ifelse((SciMo_esm$activity == 11 | SciMo_esm$activity == 10), 1, 0)
SciMo_esm$act_lab <- ifelse((SciMo_esm$activity == 15 | SciMo_esm$activity == 14 | SciMo_esm$activity == 16), 1, 0)
SciMo_esm$act_nonins <- ifelse((SciMo_esm$activity == 17), 1, 0)Multigroup SEM constrained model
library(lavaan)
MG_EMO <- '
#measurement model
cont =~ control + succeed + learning + exp_y + exp_t
value =~ imp_y + imp_fut
#regressions
happy ~ cont + value
excited ~ cont + value
frustrated ~ cont + value
bored ~ cont + value
#residual correlations
happy ~~ excited + frustrated + bored
excited ~~ frustrated + bored
frustrated ~~ bored
cont ~~ value
'
fit<-sem(MG_EMO, data=SciMo_esm, cluster = "uniqueid", group = "act_re", se = "robust.cluster.sem", group.equal = c("regressions")) #meanstructure = TRUE)
summary(fit, fit.measures=TRUE, standardized = TRUE)## lavaan 0.6-3 ended normally after 161 iterations
##
## Optimization method NLMINB
## Number of free parameters 352
## Number of equality constraints 56
##
## Used Total
## Number of observations per group
## Individual Work 633 671
## Number of clusters [uniqueid] 198
## Quiz and Test 629 688
## Number of clusters [uniqueid] 179
## NaN 43 51
## Number of clusters [uniqueid] 22
## Non-instructional 302 322
## Number of clusters [uniqueid] 159
## Lecture 746 791
## Number of clusters [uniqueid] 202
## Laboratory 941 1023
## Number of clusters [uniqueid] 141
## Group Work 249 271
## Number of clusters [uniqueid] 127
## Presentation 295 313
## Number of clusters [uniqueid] 70
##
## Estimator ML Robust
## Model Fit Test Statistic 905.383 618.733
## Degrees of freedom 320 320
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.463
## for the Yuan-Bentler correction (Mplus variant)
##
## Chi-square for each group:
##
## Individual Work 106.167 72.554
## Quiz and Test 94.897 64.852
## NaN 68.158 46.579
## Non-instructional 91.984 62.861
## Lecture 148.934 101.780
## Laboratory 194.760 133.098
## Group Work 108.747 74.317
## Presentation 91.738 62.693
##
## Model test baseline model:
##
## Minimum Function Test Statistic 17567.287 11181.798
## Degrees of freedom 440 440
## P-value 0.000 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 0.966 0.972
## Tucker-Lewis Index (TLI) 0.953 0.962
##
## Robust Comparative Fit Index (CFI) 0.974
## Robust Tucker-Lewis Index (TLI) 0.964
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -50826.991 -50826.991
## Scaling correction factor 1.566
## for the MLR correction
## Loglikelihood unrestricted model (H1) -50374.299 -50374.299
## Scaling correction factor 1.655
## for the MLR correction
##
## Number of free parameters 296 296
## Akaike (AIC) 102245.982 102245.982
## Bayesian (BIC) 104096.783 104096.783
## Sample-size adjusted Bayesian (BIC) 103156.233 103156.233
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.062 0.044
## 90 Percent Confidence Interval 0.057 0.067 0.040 0.048
## P-value RMSEA <= 0.05 0.000 0.989
##
## Robust RMSEA 0.053
## 90 Percent Confidence Interval 0.047 0.060
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.047 0.047
##
## Parameter Estimates:
##
## Information Expected
## Information saturated (h1) model Structured
## Standard Errors Robust.cluster.sem
##
##
## Group 1 [Individual Work]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## cont =~
## control 1.000 0.730 0.714
## succeed 1.032 0.079 13.056 0.000 0.753 0.798
## learning 0.974 0.070 13.955 0.000 0.712 0.727
## exp_y 1.179 0.074 15.964 0.000 0.861 0.891
## exp_t 1.140 0.075 15.199 0.000 0.832 0.839
## value =~
## imp_y 1.000 0.960 0.960
## imp_fut 0.753 0.073 10.377 0.000 0.722 0.723
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## happy ~
## cont (.p8.) 0.561 0.035 16.156 0.000 0.410 0.390
## value (.p9.) 0.102 0.026 3.875 0.000 0.098 0.093
## excited ~
## cont (.10.) 0.285 0.037 7.690 0.000 0.208 0.202
## value (.11.) 0.161 0.028 5.663 0.000 0.154 0.150
## frustrated ~
## cont (.12.) -0.239 0.030 -7.973 0.000 -0.175 -0.209
## value (.13.) 0.050 0.023 2.160 0.031 0.048 0.057
## bored ~
## cont (.14.) -0.247 0.046 -5.421 0.000 -0.180 -0.164
## value (.15.) -0.272 0.036 -7.527 0.000 -0.261 -0.236
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .happy ~~
## .excited 0.477 0.058 8.200 0.000 0.477 0.517
## .frustrated -0.064 0.038 -1.679 0.093 -0.064 -0.083
## .bored -0.175 0.055 -3.195 0.001 -0.175 -0.179
## .excited ~~
## .frustrated 0.033 0.044 0.741 0.458 0.033 0.041
## .bored -0.142 0.056 -2.553 0.011 -0.142 -0.140
## .frustrated ~~
## .bored 0.157 0.042 3.741 0.000 0.157 0.184
## cont ~~
## value 0.319 0.053 6.008 0.000 0.454 0.454
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .control 1.635 0.066 24.606 0.000 1.635 1.599
## .succeed 1.750 0.056 31.244 0.000 1.750 1.853
## .learning 1.659 0.056 29.650 0.000 1.659 1.695
## .exp_y 1.712 0.064 26.555 0.000 1.712 1.771
## .exp_t 1.701 0.064 26.398 0.000 1.701 1.716
## .imp_y 1.166 0.071 16.468 0.000 1.166 1.167
## .imp_fut 0.968 0.071 13.577 0.000 0.968 0.970
## .happy 1.479 0.076 19.432 0.000 1.479 1.408
## .excited 0.817 0.071 11.443 0.000 0.817 0.795
## .frustrated 0.613 0.051 11.975 0.000 0.613 0.734
## .bored 1.461 0.074 19.620 0.000 1.461 1.324
## cont 0.000 0.000 0.000
## value 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .control 0.512 0.064 7.943 0.000 0.512 0.490
## .succeed 0.324 0.033 9.970 0.000 0.324 0.363
## .learning 0.451 0.048 9.436 0.000 0.451 0.471
## .exp_y 0.193 0.026 7.526 0.000 0.193 0.206
## .exp_t 0.291 0.044 6.538 0.000 0.291 0.295
## .imp_y 0.078 0.077 1.011 0.312 0.078 0.078
## .imp_fut 0.476 0.059 8.122 0.000 0.476 0.477
## .happy 0.889 0.056 15.933 0.000 0.889 0.806
## .excited 0.960 0.074 12.934 0.000 0.960 0.909
## .frustrated 0.672 0.054 12.359 0.000 0.672 0.964
## .bored 1.074 0.065 16.631 0.000 1.074 0.882
## cont 0.533 0.076 7.036 0.000 1.000 1.000
## value 0.921 0.100 9.205 0.000 1.000 1.000
##
##
## Group 2 [Quiz and Test]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## cont =~
## control 1.000 0.721 0.680
## succeed 0.956 0.056 16.952 0.000 0.689 0.771
## learning 0.795 0.073 10.914 0.000 0.574 0.561
## exp_y 1.084 0.058 18.628 0.000 0.782 0.862
## exp_t 1.049 0.061 17.273 0.000 0.757 0.772
## value =~
## imp_y 1.000 0.907 0.880
## imp_fut 0.934 0.096 9.708 0.000 0.847 0.754
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## happy ~
## cont (.p8.) 0.561 0.035 16.156 0.000 0.405 0.370
## value (.p9.) 0.102 0.026 3.875 0.000 0.093 0.085
## excited ~
## cont (.10.) 0.285 0.037 7.690 0.000 0.205 0.205
## value (.11.) 0.161 0.028 5.663 0.000 0.146 0.145
## frustrated ~
## cont (.12.) -0.239 0.030 -7.973 0.000 -0.173 -0.189
## value (.13.) 0.050 0.023 2.160 0.031 0.045 0.049
## bored ~
## cont (.14.) -0.247 0.046 -5.421 0.000 -0.178 -0.153
## value (.15.) -0.272 0.036 -7.527 0.000 -0.246 -0.212
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .happy ~~
## .excited 0.499 0.056 8.853 0.000 0.499 0.524
## .frustrated -0.144 0.047 -3.051 0.002 -0.144 -0.161
## .bored -0.127 0.062 -2.049 0.040 -0.127 -0.116
## .excited ~~
## .frustrated -0.003 0.044 -0.078 0.938 -0.003 -0.004
## .bored -0.006 0.057 -0.101 0.919 -0.006 -0.006
## .frustrated ~~
## .bored 0.284 0.052 5.434 0.000 0.284 0.286
## cont ~~
## value 0.291 0.050 5.805 0.000 0.446 0.446
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .control 1.696 0.066 25.614 0.000 1.696 1.600
## .succeed 1.900 0.049 38.727 0.000 1.900 2.125
## .learning 1.521 0.057 26.815 0.000 1.521 1.489
## .exp_y 1.812 0.053 34.451 0.000 1.812 1.997
## .exp_t 1.763 0.062 28.652 0.000 1.763 1.800
## .imp_y 1.752 0.063 27.731 0.000 1.752 1.701
## .imp_fut 1.402 0.079 17.678 0.000 1.402 1.249
## .happy 1.380 0.073 18.782 0.000 1.380 1.262
## .excited 0.725 0.062 11.600 0.000 0.725 0.724
## .frustrated 0.693 0.055 12.678 0.000 0.693 0.760
## .bored 1.364 0.075 18.245 0.000 1.364 1.174
## cont 0.000 0.000 0.000
## value 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .control 0.604 0.063 9.545 0.000 0.604 0.537
## .succeed 0.324 0.031 10.486 0.000 0.324 0.405
## .learning 0.715 0.067 10.665 0.000 0.715 0.685
## .exp_y 0.212 0.032 6.651 0.000 0.212 0.257
## .exp_t 0.387 0.051 7.557 0.000 0.387 0.404
## .imp_y 0.239 0.081 2.931 0.003 0.239 0.225
## .imp_fut 0.544 0.094 5.798 0.000 0.544 0.432
## .happy 0.991 0.059 16.790 0.000 0.991 0.828
## .excited 0.914 0.070 13.074 0.000 0.914 0.910
## .frustrated 0.807 0.065 12.333 0.000 0.807 0.970
## .bored 1.219 0.058 21.167 0.000 1.219 0.903
## cont 0.521 0.065 8.056 0.000 1.000 1.000
## value 0.822 0.103 7.976 0.000 1.000 1.000
##
##
## Group 3 [NaN]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## cont =~
## control 1.000 0.742 0.784
## succeed 0.857 0.117 7.342 0.000 0.636 0.791
## learning 0.957 0.162 5.904 0.000 0.710 0.741
## exp_y 1.176 0.155 7.587 0.000 0.873 0.950
## exp_t 1.140 0.142 8.019 0.000 0.846 0.917
## value =~
## imp_y 1.000 0.966 1.144
## imp_fut 0.491 0.220 2.235 0.025 0.474 0.567
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## happy ~
## cont (.p8.) 0.561 0.035 16.156 0.000 0.416 0.423
## value (.p9.) 0.102 0.026 3.875 0.000 0.099 0.100
## excited ~
## cont (.10.) 0.285 0.037 7.690 0.000 0.211 0.264
## value (.11.) 0.161 0.028 5.663 0.000 0.155 0.194
## frustrated ~
## cont (.12.) -0.239 0.030 -7.973 0.000 -0.177 -0.260
## value (.13.) 0.050 0.023 2.160 0.031 0.048 0.070
## bored ~
## cont (.14.) -0.247 0.046 -5.421 0.000 -0.183 -0.173
## value (.15.) -0.272 0.036 -7.527 0.000 -0.262 -0.248
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .happy ~~
## .excited 0.331 0.183 1.808 0.071 0.331 0.504
## .frustrated -0.096 0.092 -1.042 0.297 -0.096 -0.166
## .bored -0.083 0.112 -0.738 0.460 -0.083 -0.094
## .excited ~~
## .frustrated -0.041 0.075 -0.552 0.581 -0.041 -0.084
## .bored -0.207 0.104 -1.994 0.046 -0.207 -0.278
## .frustrated ~~
## .bored 0.076 0.072 1.052 0.293 0.076 0.115
## cont ~~
## value 0.122 0.126 0.968 0.333 0.171 0.171
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .control 1.302 0.184 7.095 0.000 1.302 1.377
## .succeed 1.651 0.139 11.920 0.000 1.651 2.055
## .learning 1.674 0.177 9.482 0.000 1.674 1.748
## .exp_y 1.605 0.192 8.337 0.000 1.605 1.747
## .exp_t 1.721 0.187 9.221 0.000 1.721 1.864
## .imp_y 0.930 0.170 5.487 0.000 0.930 1.101
## .imp_fut 0.628 0.157 3.990 0.000 0.628 0.751
## .happy 1.488 0.163 9.147 0.000 1.488 1.512
## .excited 0.419 0.133 3.153 0.002 0.419 0.524
## .frustrated 0.465 0.114 4.090 0.000 0.465 0.681
## .bored 1.395 0.192 7.254 0.000 1.395 1.319
## cont 0.000 0.000 0.000
## value 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .control 0.344 0.105 3.284 0.001 0.344 0.385
## .succeed 0.242 0.063 3.826 0.000 0.242 0.374
## .learning 0.413 0.081 5.072 0.000 0.413 0.450
## .exp_y 0.082 0.045 1.844 0.065 0.082 0.098
## .exp_t 0.136 0.050 2.700 0.007 0.136 0.160
## .imp_y -0.220 0.322 -0.682 0.495 -0.220 -0.308
## .imp_fut 0.474 0.149 3.182 0.001 0.474 0.678
## .happy 0.772 0.224 3.453 0.001 0.772 0.797
## .excited 0.558 0.149 3.737 0.000 0.558 0.875
## .frustrated 0.436 0.122 3.562 0.000 0.436 0.934
## .bored 1.000 0.163 6.128 0.000 1.000 0.894
## cont 0.551 0.132 4.169 0.000 1.000 1.000
## value 0.933 0.401 2.328 0.020 1.000 1.000
##
##
## Group 4 [Non-instructional]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## cont =~
## control 1.000 0.685 0.697
## succeed 1.031 0.086 12.009 0.000 0.706 0.780
## learning 0.799 0.090 8.863 0.000 0.547 0.562
## exp_y 1.203 0.093 12.879 0.000 0.824 0.867
## exp_t 1.175 0.099 11.820 0.000 0.805 0.849
## value =~
## imp_y 1.000 0.857 0.884
## imp_fut 0.777 0.121 6.413 0.000 0.666 0.666
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## happy ~
## cont (.p8.) 0.561 0.035 16.156 0.000 0.385 0.379
## value (.p9.) 0.102 0.026 3.875 0.000 0.088 0.086
## excited ~
## cont (.10.) 0.285 0.037 7.690 0.000 0.195 0.209
## value (.11.) 0.161 0.028 5.663 0.000 0.138 0.147
## frustrated ~
## cont (.12.) -0.239 0.030 -7.973 0.000 -0.164 -0.185
## value (.13.) 0.050 0.023 2.160 0.031 0.042 0.048
## bored ~
## cont (.14.) -0.247 0.046 -5.421 0.000 -0.169 -0.145
## value (.15.) -0.272 0.036 -7.527 0.000 -0.233 -0.200
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .happy ~~
## .excited 0.395 0.054 7.315 0.000 0.395 0.484
## .frustrated -0.147 0.051 -2.863 0.004 -0.147 -0.184
## .bored -0.002 0.070 -0.033 0.974 -0.002 -0.002
## .excited ~~
## .frustrated -0.044 0.043 -1.011 0.312 -0.044 -0.056
## .bored 0.036 0.063 0.564 0.573 0.036 0.036
## .frustrated ~~
## .bored 0.284 0.064 4.463 0.000 0.284 0.292
## cont ~~
## value 0.283 0.056 5.075 0.000 0.481 0.481
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .control 1.709 0.072 23.732 0.000 1.709 1.737
## .succeed 1.772 0.065 27.191 0.000 1.772 1.958
## .learning 1.391 0.066 20.949 0.000 1.391 1.429
## .exp_y 1.702 0.070 24.428 0.000 1.702 1.790
## .exp_t 1.791 0.068 26.239 0.000 1.791 1.888
## .imp_y 1.142 0.069 16.665 0.000 1.142 1.179
## .imp_fut 0.907 0.075 12.047 0.000 0.907 0.908
## .happy 1.497 0.071 21.180 0.000 1.497 1.476
## .excited 0.768 0.073 10.499 0.000 0.768 0.821
## .frustrated 0.579 0.058 9.966 0.000 0.579 0.655
## .bored 1.414 0.085 16.645 0.000 1.414 1.214
## cont 0.000 0.000 0.000
## value 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .control 0.498 0.062 8.090 0.000 0.498 0.515
## .succeed 0.320 0.045 7.117 0.000 0.320 0.391
## .learning 0.647 0.068 9.565 0.000 0.647 0.684
## .exp_y 0.225 0.038 5.847 0.000 0.225 0.249
## .exp_t 0.252 0.051 4.960 0.000 0.252 0.280
## .imp_y 0.205 0.102 2.005 0.045 0.205 0.219
## .imp_fut 0.555 0.079 7.006 0.000 0.555 0.556
## .happy 0.840 0.077 10.960 0.000 0.840 0.817
## .excited 0.792 0.072 11.071 0.000 0.792 0.905
## .frustrated 0.761 0.081 9.455 0.000 0.761 0.972
## .bored 1.235 0.076 16.321 0.000 1.235 0.911
## cont 0.469 0.069 6.806 0.000 1.000 1.000
## value 0.734 0.116 6.309 0.000 1.000 1.000
##
##
## Group 5 [Lecture]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## cont =~
## control 1.000 0.678 0.646
## succeed 1.027 0.057 18.121 0.000 0.696 0.785
## learning 0.993 0.060 16.505 0.000 0.673 0.721
## exp_y 1.074 0.066 16.207 0.000 0.728 0.812
## exp_t 1.135 0.062 18.234 0.000 0.770 0.856
## value =~
## imp_y 1.000 0.910 0.957
## imp_fut 0.765 0.067 11.464 0.000 0.696 0.694
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## happy ~
## cont (.p8.) 0.561 0.035 16.156 0.000 0.380 0.375
## value (.p9.) 0.102 0.026 3.875 0.000 0.093 0.092
## excited ~
## cont (.10.) 0.285 0.037 7.690 0.000 0.193 0.197
## value (.11.) 0.161 0.028 5.663 0.000 0.146 0.149
## frustrated ~
## cont (.12.) -0.239 0.030 -7.973 0.000 -0.162 -0.194
## value (.13.) 0.050 0.023 2.160 0.031 0.045 0.054
## bored ~
## cont (.14.) -0.247 0.046 -5.421 0.000 -0.168 -0.152
## value (.15.) -0.272 0.036 -7.527 0.000 -0.247 -0.225
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .happy ~~
## .excited 0.411 0.042 9.684 0.000 0.411 0.478
## .frustrated -0.117 0.038 -3.115 0.002 -0.117 -0.155
## .bored -0.158 0.048 -3.321 0.001 -0.158 -0.166
## .excited ~~
## .frustrated 0.010 0.035 0.279 0.780 0.010 0.013
## .bored -0.045 0.052 -0.877 0.380 -0.045 -0.046
## .frustrated ~~
## .bored 0.223 0.042 5.276 0.000 0.223 0.261
## cont ~~
## value 0.282 0.037 7.570 0.000 0.458 0.458
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .control 1.664 0.063 26.407 0.000 1.664 1.586
## .succeed 2.013 0.046 43.394 0.000 2.013 2.272
## .learning 1.981 0.049 40.565 0.000 1.981 2.123
## .exp_y 1.909 0.048 39.632 0.000 1.909 2.129
## .exp_t 1.956 0.050 39.176 0.000 1.956 2.174
## .imp_y 1.247 0.056 22.159 0.000 1.247 1.310
## .imp_fut 0.903 0.062 14.483 0.000 0.903 0.900
## .happy 1.619 0.062 25.986 0.000 1.619 1.598
## .excited 0.818 0.058 14.055 0.000 0.818 0.833
## .frustrated 0.525 0.046 11.409 0.000 0.525 0.629
## .bored 1.357 0.066 20.561 0.000 1.357 1.232
## cont 0.000 0.000 0.000
## value 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .control 0.641 0.051 12.698 0.000 0.641 0.583
## .succeed 0.301 0.023 12.977 0.000 0.301 0.383
## .learning 0.418 0.034 12.115 0.000 0.418 0.480
## .exp_y 0.274 0.040 6.901 0.000 0.274 0.341
## .exp_t 0.217 0.026 8.389 0.000 0.217 0.268
## .imp_y 0.077 0.066 1.171 0.242 0.077 0.085
## .imp_fut 0.523 0.051 10.203 0.000 0.523 0.519
## .happy 0.841 0.046 18.308 0.000 0.841 0.819
## .excited 0.879 0.055 16.034 0.000 0.879 0.912
## .frustrated 0.677 0.061 11.157 0.000 0.677 0.969
## .bored 1.086 0.056 19.384 0.000 1.086 0.895
## cont 0.459 0.057 8.087 0.000 1.000 1.000
## value 0.828 0.073 11.284 0.000 1.000 1.000
##
##
## Group 6 [Laboratory]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## cont =~
## control 1.000 0.770 0.753
## succeed 1.016 0.040 25.108 0.000 0.782 0.812
## learning 0.979 0.045 21.784 0.000 0.754 0.789
## exp_y 1.149 0.044 25.944 0.000 0.885 0.888
## exp_t 1.061 0.044 24.195 0.000 0.817 0.839
## value =~
## imp_y 1.000 0.917 0.937
## imp_fut 0.721 0.054 13.225 0.000 0.661 0.703
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## happy ~
## cont (.p8.) 0.561 0.035 16.156 0.000 0.432 0.406
## value (.p9.) 0.102 0.026 3.875 0.000 0.094 0.088
## excited ~
## cont (.10.) 0.285 0.037 7.690 0.000 0.219 0.211
## value (.11.) 0.161 0.028 5.663 0.000 0.147 0.141
## frustrated ~
## cont (.12.) -0.239 0.030 -7.973 0.000 -0.184 -0.201
## value (.13.) 0.050 0.023 2.160 0.031 0.045 0.050
## bored ~
## cont (.14.) -0.247 0.046 -5.421 0.000 -0.190 -0.160
## value (.15.) -0.272 0.036 -7.527 0.000 -0.249 -0.210
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .happy ~~
## .excited 0.446 0.058 7.651 0.000 0.446 0.478
## .frustrated -0.008 0.051 -0.149 0.882 -0.008 -0.009
## .bored -0.112 0.061 -1.844 0.065 -0.112 -0.106
## .excited ~~
## .frustrated 0.077 0.050 1.523 0.128 0.077 0.086
## .bored 0.026 0.062 0.426 0.670 0.026 0.024
## .frustrated ~~
## .bored 0.210 0.056 3.730 0.000 0.210 0.208
## cont ~~
## value 0.392 0.051 7.728 0.000 0.556 0.556
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .control 1.615 0.071 22.828 0.000 1.615 1.581
## .succeed 1.799 0.061 29.492 0.000 1.799 1.868
## .learning 1.579 0.061 25.884 0.000 1.579 1.652
## .exp_y 1.674 0.068 24.740 0.000 1.674 1.679
## .exp_t 1.762 0.064 27.680 0.000 1.762 1.811
## .imp_y 1.140 0.065 17.488 0.000 1.140 1.164
## .imp_fut 0.952 0.063 15.219 0.000 0.952 1.013
## .happy 1.499 0.073 20.548 0.000 1.499 1.409
## .excited 0.961 0.068 14.025 0.000 0.961 0.923
## .frustrated 0.635 0.052 12.277 0.000 0.635 0.693
## .bored 1.224 0.077 15.939 0.000 1.224 1.032
## cont 0.000 0.000 0.000
## value 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .control 0.451 0.053 8.545 0.000 0.451 0.432
## .succeed 0.316 0.034 9.151 0.000 0.316 0.340
## .learning 0.345 0.035 9.717 0.000 0.345 0.377
## .exp_y 0.211 0.029 7.180 0.000 0.211 0.212
## .exp_t 0.280 0.044 6.420 0.000 0.280 0.296
## .imp_y 0.118 0.048 2.451 0.014 0.118 0.123
## .imp_fut 0.446 0.046 9.792 0.000 0.446 0.505
## .happy 0.892 0.057 15.595 0.000 0.892 0.788
## .excited 0.977 0.067 14.565 0.000 0.977 0.902
## .frustrated 0.813 0.071 11.521 0.000 0.813 0.968
## .bored 1.256 0.065 19.222 0.000 1.256 0.893
## cont 0.593 0.061 9.716 0.000 1.000 1.000
## value 0.841 0.075 11.280 0.000 1.000 1.000
##
##
## Group 7 [Group Work]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## cont =~
## control 1.000 0.739 0.733
## succeed 0.959 0.072 13.243 0.000 0.709 0.771
## learning 0.993 0.080 12.348 0.000 0.734 0.765
## exp_y 1.105 0.082 13.526 0.000 0.817 0.834
## exp_t 1.046 0.093 11.216 0.000 0.773 0.801
## value =~
## imp_y 1.000 1.031 1.014
## imp_fut 0.615 0.092 6.670 0.000 0.634 0.626
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## happy ~
## cont (.p8.) 0.561 0.035 16.156 0.000 0.415 0.408
## value (.p9.) 0.102 0.026 3.875 0.000 0.105 0.103
## excited ~
## cont (.10.) 0.285 0.037 7.690 0.000 0.211 0.202
## value (.11.) 0.161 0.028 5.663 0.000 0.165 0.159
## frustrated ~
## cont (.12.) -0.239 0.030 -7.973 0.000 -0.177 -0.202
## value (.13.) 0.050 0.023 2.160 0.031 0.051 0.059
## bored ~
## cont (.14.) -0.247 0.046 -5.421 0.000 -0.183 -0.161
## value (.15.) -0.272 0.036 -7.527 0.000 -0.280 -0.247
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .happy ~~
## .excited 0.407 0.065 6.267 0.000 0.407 0.460
## .frustrated -0.094 0.055 -1.697 0.090 -0.094 -0.122
## .bored -0.106 0.083 -1.279 0.201 -0.106 -0.111
## .excited ~~
## .frustrated 0.056 0.057 0.986 0.324 0.056 0.066
## .bored -0.117 0.078 -1.489 0.137 -0.117 -0.111
## .frustrated ~~
## .bored 0.246 0.069 3.586 0.000 0.246 0.270
## cont ~~
## value 0.403 0.060 6.718 0.000 0.528 0.528
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .control 1.807 0.081 22.349 0.000 1.807 1.793
## .succeed 1.867 0.072 25.898 0.000 1.867 2.030
## .learning 1.667 0.079 21.168 0.000 1.667 1.737
## .exp_y 1.747 0.084 20.852 0.000 1.747 1.783
## .exp_t 1.847 0.076 24.371 0.000 1.847 1.913
## .imp_y 1.185 0.081 14.615 0.000 1.185 1.166
## .imp_fut 1.004 0.082 12.198 0.000 1.004 0.992
## .happy 1.570 0.089 17.711 0.000 1.570 1.542
## .excited 0.976 0.085 11.426 0.000 0.976 0.938
## .frustrated 0.594 0.076 7.777 0.000 0.594 0.680
## .bored 1.329 0.096 13.865 0.000 1.329 1.170
## cont 0.000 0.000 0.000
## value 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .control 0.470 0.068 6.863 0.000 0.470 0.462
## .succeed 0.343 0.058 5.919 0.000 0.343 0.406
## .learning 0.382 0.057 6.690 0.000 0.382 0.414
## .exp_y 0.293 0.049 6.004 0.000 0.293 0.305
## .exp_t 0.334 0.071 4.739 0.000 0.334 0.359
## .imp_y -0.029 0.128 -0.230 0.818 -0.029 -0.029
## .imp_fut 0.623 0.083 7.470 0.000 0.623 0.608
## .happy 0.808 0.079 10.164 0.000 0.808 0.779
## .excited 0.973 0.083 11.674 0.000 0.973 0.900
## .frustrated 0.739 0.093 7.940 0.000 0.739 0.968
## .bored 1.125 0.086 13.041 0.000 1.125 0.871
## cont 0.547 0.081 6.778 0.000 1.000 1.000
## value 1.063 0.141 7.537 0.000 1.000 1.000
##
##
## Group 8 [Presentation]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## cont =~
## control 1.000 0.686 0.657
## succeed 1.061 0.099 10.752 0.000 0.728 0.832
## learning 1.003 0.120 8.335 0.000 0.688 0.769
## exp_y 1.242 0.120 10.319 0.000 0.852 0.936
## exp_t 1.223 0.118 10.353 0.000 0.839 0.911
## value =~
## imp_y 1.000 0.936 0.981
## imp_fut 0.560 0.097 5.791 0.000 0.525 0.598
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## happy ~
## cont (.p8.) 0.561 0.035 16.156 0.000 0.385 0.434
## value (.p9.) 0.102 0.026 3.875 0.000 0.096 0.108
## excited ~
## cont (.10.) 0.285 0.037 7.690 0.000 0.195 0.199
## value (.11.) 0.161 0.028 5.663 0.000 0.150 0.153
## frustrated ~
## cont (.12.) -0.239 0.030 -7.973 0.000 -0.164 -0.192
## value (.13.) 0.050 0.023 2.160 0.031 0.046 0.054
## bored ~
## cont (.14.) -0.247 0.046 -5.421 0.000 -0.170 -0.160
## value (.15.) -0.272 0.036 -7.527 0.000 -0.254 -0.240
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .happy ~~
## .excited 0.356 0.062 5.767 0.000 0.356 0.494
## .frustrated -0.057 0.053 -1.076 0.282 -0.057 -0.087
## .bored 0.060 0.058 1.041 0.298 0.060 0.078
## .excited ~~
## .frustrated 0.151 0.058 2.605 0.009 0.151 0.192
## .bored 0.155 0.071 2.182 0.029 0.155 0.167
## .frustrated ~~
## .bored 0.264 0.071 3.736 0.000 0.264 0.315
## cont ~~
## value 0.309 0.084 3.697 0.000 0.481 0.481
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .control 1.712 0.110 15.590 0.000 1.712 1.640
## .succeed 2.000 0.089 22.365 0.000 2.000 2.285
## .learning 2.027 0.090 22.615 0.000 2.027 2.267
## .exp_y 2.037 0.102 19.885 0.000 2.037 2.237
## .exp_t 2.037 0.101 20.113 0.000 2.037 2.210
## .imp_y 1.285 0.095 13.581 0.000 1.285 1.346
## .imp_fut 0.780 0.094 8.320 0.000 0.780 0.889
## .happy 1.614 0.096 16.840 0.000 1.614 1.818
## .excited 0.905 0.110 8.240 0.000 0.905 0.921
## .frustrated 0.481 0.073 6.569 0.000 0.481 0.564
## .bored 1.207 0.105 11.524 0.000 1.207 1.138
## cont 0.000 0.000 0.000
## value 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .control 0.619 0.083 7.416 0.000 0.619 0.568
## .succeed 0.236 0.034 6.934 0.000 0.236 0.308
## .learning 0.326 0.048 6.724 0.000 0.326 0.408
## .exp_y 0.103 0.027 3.734 0.000 0.103 0.124
## .exp_t 0.145 0.033 4.465 0.000 0.145 0.171
## .imp_y 0.034 0.114 0.299 0.765 0.034 0.038
## .imp_fut 0.493 0.063 7.849 0.000 0.493 0.642
## .happy 0.595 0.066 9.059 0.000 0.595 0.755
## .excited 0.876 0.092 9.519 0.000 0.876 0.908
## .frustrated 0.708 0.106 6.679 0.000 0.708 0.970
## .bored 0.989 0.084 11.730 0.000 0.989 0.880
## cont 0.471 0.128 3.687 0.000 1.000 1.000
## value 0.876 0.121 7.251 0.000 1.000 1.000library(semTools)## ## ################################################################################# This is semTools 0.5-1## All users of R (or SEM) are invited to submit functions or ideas for functions.## #################################################################################
## Attaching package: 'semTools'## The following object is masked from 'package:psych':
##
## skew## The following object is masked from 'package:readr':
##
## clipboardmeasurementInvariance(model = MG_EMO, data = SciMo_esm, group = "act_re")##
## Measurement invariance models:
##
## Model 1 : fit.configural
## Model 2 : fit.loadings
## Model 3 : fit.intercepts
## Model 4 : fit.means
##
## Chi Square Difference Test
##
## Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
## fit.configural 264 102248 104449 795.82
## fit.loadings 299 102225 104207 842.24 46.42 35 0.09389 .
## fit.intercepts 362 102465 104053 1208.21 365.97 63 < 2e-16 ***
## fit.means 376 102686 104187 1457.89 249.68 14 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Fit measures:
##
## cfi rmsea cfi.delta rmsea.delta
## fit.configural 0.969 0.065 NA NA
## fit.loadings 0.968 0.062 0.001 0.003
## fit.intercepts 0.951 0.070 0.018 0.008
## fit.means 0.937 0.077 0.014 0.008Multigroup SEM items fixed to 1
library(lavaan)
MG_EMO3 <- '
#measurement model
cont =~ control + succeed + learning + exp_y + exp_t
value =~ imp_y + imp_fut
#intercepts
control ~ 1
imp_y ~ 1
#variances fixed to one
cont ~~ 1*cont
value ~~ 1*value
#regressions
happy ~ cont + value
excited ~ cont + value
frustrated ~ cont + value
bored ~ cont + value
#residual correlations
happy ~~ excited + frustrated + bored
excited ~~ frustrated + bored
frustrated ~~ bored
cont ~~ value
'
fit6<-sem(MG_EMO3, data=SciMo_esm, cluster = "uniqueid", group = "act_re", se = "robust.cluster.sem") #meanstructure = TRUE)
summary(fit6, fit.measures=TRUE, standardized = TRUE)## lavaan 0.6-3 ended normally after 126 iterations
##
## Optimization method NLMINB
## Number of free parameters 336
##
## Used Total
## Number of observations per group
## Individual Work 633 671
## Number of clusters [uniqueid] 198
## Quiz and Test 629 688
## Number of clusters [uniqueid] 179
## NaN 43 51
## Number of clusters [uniqueid] 22
## Non-instructional 302 322
## Number of clusters [uniqueid] 159
## Lecture 746 791
## Number of clusters [uniqueid] 202
## Laboratory 941 1023
## Number of clusters [uniqueid] 141
## Group Work 249 271
## Number of clusters [uniqueid] 127
## Presentation 295 313
## Number of clusters [uniqueid] 70
##
## Estimator ML Robust
## Model Fit Test Statistic 1091.391 709.664
## Degrees of freedom 280 280
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.538
## for the Yuan-Bentler correction (Mplus variant)
##
## Chi-square for each group:
##
## Individual Work 145.454 94.580
## Quiz and Test 131.823 85.716
## NaN 52.765 34.309
## Non-instructional 116.780 75.935
## Lecture 209.144 135.993
## Laboratory 200.651 130.471
## Group Work 119.918 77.975
## Presentation 114.856 74.684
##
## Model test baseline model:
##
## Minimum Function Test Statistic 17567.287 11181.798
## Degrees of freedom 440 440
## P-value 0.000 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 0.953 0.960
## Tucker-Lewis Index (TLI) 0.926 0.937
##
## Robust Comparative Fit Index (CFI) 0.961
## Robust Tucker-Lewis Index (TLI) 0.938
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -50919.995 -50919.995
## Scaling correction factor 1.752
## for the MLR correction
## Loglikelihood unrestricted model (H1) -50374.299 -50374.299
## Scaling correction factor 1.655
## for the MLR correction
##
## Number of free parameters 336 336
## Akaike (AIC) 102511.990 102511.990
## Bayesian (BIC) 104612.899 104612.899
## Sample-size adjusted Bayesian (BIC) 103545.248 103545.248
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.078 0.057
## 90 Percent Confidence Interval 0.073 0.083 0.052 0.061
## P-value RMSEA <= 0.05 0.000 0.005
##
## Robust RMSEA 0.070
## 90 Percent Confidence Interval 0.064 0.077
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.136 0.136
##
## Parameter Estimates:
##
## Information Expected
## Information saturated (h1) model Structured
## Standard Errors Robust.cluster.sem
##
##
## Group 1 [Individual Work]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## cont =~
## control 1.000 1.000 0.813
## succeed 0.864 0.034 25.588 0.000 0.864 0.834
## learning 0.818 0.031 26.719 0.000 0.818 0.773
## exp_y 0.987 0.030 32.600 0.000 0.987 0.912
## exp_t 0.954 0.034 28.083 0.000 0.954 0.869
## value =~
## imp_y 1.000 1.000 0.968
## imp_fut 0.744 0.046 16.048 0.000 0.744 0.731
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## happy ~
## cont 0.527 0.051 10.357 0.000 0.527 0.482
## value 0.057 0.063 0.902 0.367 0.057 0.052
## excited ~
## cont 0.190 0.060 3.150 0.002 0.190 0.186
## value 0.146 0.070 2.084 0.037 0.146 0.142
## frustrated ~
## cont -0.217 0.044 -4.877 0.000 -0.217 -0.256
## value -0.008 0.047 -0.177 0.860 -0.008 -0.010
## bored ~
## cont -0.221 0.085 -2.592 0.010 -0.221 -0.193
## value -0.331 0.094 -3.522 0.000 -0.331 -0.290
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .happy ~~
## .excited 0.479 0.058 8.222 0.000 0.479 0.521
## .frustrated -0.063 0.038 -1.661 0.097 -0.063 -0.081
## .bored -0.175 0.053 -3.278 0.001 -0.175 -0.180
## .excited ~~
## .frustrated 0.031 0.044 0.712 0.476 0.031 0.039
## .bored -0.145 0.054 -2.683 0.007 -0.145 -0.144
## .frustrated ~~
## .bored 0.151 0.042 3.587 0.000 0.151 0.179
## cont ~~
## value 0.507 0.064 7.980 0.000 0.507 0.507
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .control 1.635 0.066 24.606 0.000 1.635 1.329
## .imp_y 1.166 0.071 16.468 0.000 1.166 1.128
## .succeed 1.750 0.056 31.244 0.000 1.750 1.690
## .learning 1.659 0.056 29.650 0.000 1.659 1.567
## .exp_y 1.712 0.064 26.555 0.000 1.712 1.583
## .exp_t 1.701 0.064 26.398 0.000 1.701 1.551
## .imp_fut 0.968 0.071 13.577 0.000 0.968 0.952
## .happy 1.479 0.076 19.432 0.000 1.479 1.354
## .excited 0.817 0.071 11.443 0.000 0.817 0.799
## .frustrated 0.613 0.051 11.975 0.000 0.613 0.724
## .bored 1.461 0.074 19.620 0.000 1.461 1.281
## cont 0.000 0.000 0.000
## value 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## cont 1.000 1.000 1.000
## value 1.000 1.000 1.000
## .control 0.514 0.073 7.058 0.000 0.514 0.340
## .succeed 0.326 0.032 10.266 0.000 0.326 0.304
## .learning 0.450 0.047 9.646 0.000 0.450 0.402
## .exp_y 0.197 0.025 7.917 0.000 0.197 0.168
## .exp_t 0.294 0.044 6.760 0.000 0.294 0.245
## .imp_y 0.068 0.046 1.482 0.138 0.068 0.063
## .imp_fut 0.482 0.049 9.922 0.000 0.482 0.465
## .happy 0.882 0.058 15.297 0.000 0.882 0.739
## .excited 0.960 0.074 12.987 0.000 0.960 0.918
## .frustrated 0.668 0.053 12.593 0.000 0.668 0.932
## .bored 1.068 0.068 15.622 0.000 1.068 0.822
##
##
## Group 2 [Quiz and Test]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## cont =~
## control 1.000 1.000 0.792
## succeed 0.781 0.031 25.375 0.000 0.781 0.808
## learning 0.650 0.045 14.509 0.000 0.650 0.609
## exp_y 0.878 0.030 29.424 0.000 0.878 0.881
## exp_t 0.852 0.031 27.318 0.000 0.852 0.805
## value =~
## imp_y 1.000 1.000 0.928
## imp_fut 0.851 0.051 16.773 0.000 0.851 0.739
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## happy ~
## cont 0.494 0.057 8.644 0.000 0.494 0.444
## value 0.014 0.067 0.203 0.839 0.014 0.012
## excited ~
## cont 0.268 0.050 5.350 0.000 0.268 0.269
## value 0.043 0.054 0.800 0.424 0.043 0.043
## frustrated ~
## cont -0.242 0.053 -4.578 0.000 -0.242 -0.263
## value 0.090 0.055 1.650 0.099 0.090 0.098
## bored ~
## cont -0.251 0.067 -3.745 0.000 -0.251 -0.212
## value -0.240 0.074 -3.231 0.001 -0.240 -0.203
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .happy ~~
## .excited 0.494 0.057 8.712 0.000 0.494 0.522
## .frustrated -0.139 0.048 -2.887 0.004 -0.139 -0.156
## .bored -0.127 0.061 -2.073 0.038 -0.127 -0.116
## .excited ~~
## .frustrated 0.001 0.043 0.015 0.988 0.001 0.001
## .bored -0.009 0.056 -0.155 0.877 -0.009 -0.008
## .frustrated ~~
## .bored 0.282 0.052 5.398 0.000 0.282 0.285
## cont ~~
## value 0.493 0.048 10.238 0.000 0.493 0.493
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .control 1.696 0.066 25.614 0.000 1.696 1.344
## .imp_y 1.752 0.063 27.731 0.000 1.752 1.626
## .succeed 1.900 0.049 38.727 0.000 1.900 1.967
## .learning 1.521 0.057 26.815 0.000 1.521 1.427
## .exp_y 1.812 0.053 34.451 0.000 1.812 1.820
## .exp_t 1.763 0.062 28.652 0.000 1.763 1.667
## .imp_fut 1.402 0.079 17.678 0.000 1.402 1.219
## .happy 1.380 0.073 18.782 0.000 1.380 1.242
## .excited 0.725 0.062 11.600 0.000 0.725 0.727
## .frustrated 0.693 0.055 12.678 0.000 0.693 0.753
## .bored 1.364 0.075 18.245 0.000 1.364 1.153
## cont 0.000 0.000 0.000
## value 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## cont 1.000 1.000 1.000
## value 1.000 1.000 1.000
## .control 0.593 0.068 8.693 0.000 0.593 0.372
## .succeed 0.323 0.030 10.794 0.000 0.323 0.347
## .learning 0.715 0.066 10.845 0.000 0.715 0.629
## .exp_y 0.222 0.031 7.099 0.000 0.222 0.224
## .exp_t 0.394 0.051 7.707 0.000 0.394 0.352
## .imp_y 0.161 0.049 3.282 0.001 0.161 0.139
## .imp_fut 0.600 0.071 8.400 0.000 0.600 0.453
## .happy 0.984 0.060 16.361 0.000 0.984 0.797
## .excited 0.909 0.071 12.890 0.000 0.909 0.914
## .frustrated 0.802 0.066 12.144 0.000 0.802 0.946
## .bored 1.220 0.059 20.675 0.000 1.220 0.872
##
##
## Group 3 [NaN]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## cont =~
## control 1.000 1.000 0.868
## succeed 0.709 0.088 8.068 0.000 0.709 0.821
## learning 0.794 0.101 7.825 0.000 0.794 0.778
## exp_y 0.973 0.103 9.476 0.000 0.973 0.958
## exp_t 0.945 0.097 9.737 0.000 0.945 0.931
## value =~
## imp_y 1.000 1.000 1.150
## imp_fut 0.481 0.114 4.228 0.000 0.481 0.569
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## happy ~
## cont 0.172 0.125 1.378 0.168 0.172 0.209
## value -0.097 0.139 -0.696 0.487 -0.097 -0.118
## excited ~
## cont -0.051 0.122 -0.418 0.676 -0.051 -0.074
## value 0.067 0.141 0.472 0.637 0.067 0.097
## frustrated ~
## cont -0.200 0.069 -2.914 0.004 -0.200 -0.286
## value 0.256 0.119 2.148 0.032 0.256 0.366
## bored ~
## cont -0.218 0.197 -1.107 0.268 -0.218 -0.204
## value -0.264 0.133 -1.991 0.046 -0.264 -0.247
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .happy ~~
## .excited 0.225 0.137 1.638 0.102 0.225 0.409
## .frustrated -0.046 0.064 -0.728 0.467 -0.046 -0.091
## .bored -0.067 0.100 -0.668 0.504 -0.067 -0.084
## .excited ~~
## .frustrated -0.006 0.051 -0.111 0.912 -0.006 -0.013
## .bored -0.204 0.107 -1.905 0.057 -0.204 -0.297
## .frustrated ~~
## .bored 0.054 0.073 0.739 0.460 0.054 0.084
## cont ~~
## value 0.194 0.155 1.255 0.210 0.194 0.194
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .control 1.302 0.184 7.095 0.000 1.302 1.130
## .imp_y 0.930 0.170 5.487 0.000 0.930 1.070
## .succeed 1.651 0.139 11.920 0.000 1.651 1.913
## .learning 1.674 0.177 9.482 0.000 1.674 1.640
## .exp_y 1.605 0.192 8.337 0.000 1.605 1.580
## .exp_t 1.721 0.187 9.221 0.000 1.721 1.696
## .imp_fut 0.628 0.157 3.990 0.000 0.628 0.743
## .happy 1.488 0.163 9.147 0.000 1.488 1.814
## .excited 0.419 0.133 3.153 0.002 0.419 0.606
## .frustrated 0.465 0.114 4.090 0.000 0.465 0.665
## .bored 1.395 0.192 7.254 0.000 1.395 1.305
## cont 0.000 0.000 0.000
## value 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## cont 1.000 1.000 1.000
## value 1.000 1.000 1.000
## .control 0.329 0.113 2.909 0.004 0.329 0.247
## .succeed 0.243 0.062 3.892 0.000 0.243 0.326
## .learning 0.412 0.079 5.183 0.000 0.412 0.395
## .exp_y 0.085 0.042 2.015 0.044 0.085 0.083
## .exp_t 0.137 0.050 2.741 0.006 0.137 0.133
## .imp_y -0.244 0.109 -2.250 0.024 -0.244 -0.323
## .imp_fut 0.484 0.110 4.406 0.000 0.484 0.676
## .happy 0.641 0.147 4.345 0.000 0.641 0.952
## .excited 0.471 0.127 3.718 0.000 0.471 0.988
## .frustrated 0.403 0.092 4.371 0.000 0.403 0.825
## .bored 1.004 0.162 6.196 0.000 1.004 0.878
##
##
## Group 4 [Non-instructional]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## cont =~
## control 1.000 1.000 0.819
## succeed 0.833 0.042 19.621 0.000 0.833 0.830
## learning 0.639 0.051 12.461 0.000 0.639 0.621
## exp_y 0.961 0.040 23.881 0.000 0.961 0.894
## exp_t 0.936 0.043 21.743 0.000 0.936 0.877
## value =~
## imp_y 1.000 1.000 0.952
## imp_fut 0.681 0.064 10.680 0.000 0.681 0.659
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## happy ~
## cont 0.411 0.071 5.775 0.000 0.411 0.395
## value 0.129 0.067 1.928 0.054 0.129 0.124
## excited ~
## cont 0.204 0.078 2.610 0.009 0.204 0.212
## value 0.218 0.082 2.660 0.008 0.218 0.226
## frustrated ~
## cont -0.160 0.063 -2.541 0.011 -0.160 -0.182
## value 0.103 0.072 1.434 0.152 0.103 0.117
## bored ~
## cont -0.314 0.082 -3.846 0.000 -0.314 -0.268
## value -0.110 0.095 -1.154 0.249 -0.110 -0.093
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .happy ~~
## .excited 0.395 0.055 7.162 0.000 0.395 0.485
## .frustrated -0.150 0.051 -2.928 0.003 -0.150 -0.187
## .bored -0.006 0.068 -0.082 0.935 -0.006 -0.005
## .excited ~~
## .frustrated -0.048 0.042 -1.139 0.255 -0.048 -0.062
## .bored 0.027 0.061 0.439 0.661 0.027 0.027
## .frustrated ~~
## .bored 0.278 0.062 4.481 0.000 0.278 0.288
## cont ~~
## value 0.539 0.053 10.180 0.000 0.539 0.539
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .control 1.709 0.072 23.732 0.000 1.709 1.399
## .imp_y 1.142 0.069 16.665 0.000 1.142 1.088
## .succeed 1.772 0.065 27.191 0.000 1.772 1.765
## .learning 1.391 0.066 20.949 0.000 1.391 1.352
## .exp_y 1.702 0.070 24.428 0.000 1.702 1.583
## .exp_t 1.791 0.068 26.239 0.000 1.791 1.679
## .imp_fut 0.907 0.075 12.047 0.000 0.907 0.878
## .happy 1.497 0.071 21.180 0.000 1.497 1.437
## .excited 0.768 0.073 10.499 0.000 0.768 0.798
## .frustrated 0.579 0.058 9.966 0.000 0.579 0.658
## .bored 1.414 0.085 16.645 0.000 1.414 1.205
## cont 0.000 0.000 0.000
## value 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## cont 1.000 1.000 1.000
## value 1.000 1.000 1.000
## .control 0.493 0.073 6.786 0.000 0.493 0.330
## .succeed 0.314 0.042 7.444 0.000 0.314 0.311
## .learning 0.649 0.067 9.761 0.000 0.649 0.614
## .exp_y 0.232 0.037 6.245 0.000 0.232 0.201
## .exp_t 0.263 0.050 5.289 0.000 0.263 0.231
## .imp_y 0.103 0.067 1.551 0.121 0.103 0.094
## .imp_fut 0.604 0.057 10.527 0.000 0.604 0.566
## .happy 0.843 0.077 10.887 0.000 0.843 0.776
## .excited 0.790 0.074 10.701 0.000 0.790 0.852
## .frustrated 0.758 0.081 9.326 0.000 0.758 0.976
## .bored 1.228 0.074 16.686 0.000 1.228 0.892
##
##
## Group 5 [Lecture]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## cont =~
## control 1.000 1.000 0.778
## succeed 0.806 0.029 27.725 0.000 0.806 0.827
## learning 0.780 0.028 27.499 0.000 0.780 0.770
## exp_y 0.837 0.026 31.956 0.000 0.837 0.845
## exp_t 0.885 0.027 32.431 0.000 0.885 0.882
## value =~
## imp_y 1.000 1.000 1.010
## imp_fut 0.686 0.045 15.260 0.000 0.686 0.672
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## happy ~
## cont 0.508 0.044 11.524 0.000 0.508 0.484
## value 0.023 0.050 0.448 0.654 0.023 0.021
## excited ~
## cont 0.255 0.050 5.113 0.000 0.255 0.258
## value 0.102 0.050 2.043 0.041 0.102 0.103
## frustrated ~
## cont -0.292 0.043 -6.779 0.000 -0.292 -0.339
## value 0.044 0.040 1.107 0.268 0.044 0.051
## bored ~
## cont -0.336 0.056 -5.975 0.000 -0.336 -0.297
## value -0.178 0.058 -3.067 0.002 -0.178 -0.157
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .happy ~~
## .excited 0.408 0.042 9.773 0.000 0.408 0.477
## .frustrated -0.109 0.037 -2.908 0.004 -0.109 -0.146
## .bored -0.150 0.049 -3.093 0.002 -0.150 -0.159
## .excited ~~
## .frustrated 0.013 0.035 0.379 0.705 0.013 0.017
## .bored -0.044 0.051 -0.849 0.396 -0.044 -0.045
## .frustrated ~~
## .bored 0.210 0.042 5.046 0.000 0.210 0.248
## cont ~~
## value 0.499 0.035 14.078 0.000 0.499 0.499
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .control 1.664 0.063 26.407 0.000 1.664 1.295
## .imp_y 1.247 0.056 22.159 0.000 1.247 1.259
## .succeed 2.013 0.046 43.394 0.000 2.013 2.065
## .learning 1.981 0.049 40.565 0.000 1.981 1.956
## .exp_y 1.909 0.048 39.632 0.000 1.909 1.926
## .exp_t 1.956 0.050 39.176 0.000 1.956 1.948
## .imp_fut 0.903 0.062 14.483 0.000 0.903 0.885
## .happy 1.619 0.062 25.986 0.000 1.619 1.542
## .excited 0.818 0.058 14.055 0.000 0.818 0.826
## .frustrated 0.525 0.046 11.409 0.000 0.525 0.611
## .bored 1.357 0.066 20.561 0.000 1.357 1.198
## cont 0.000 0.000 0.000
## value 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## cont 1.000 1.000 1.000
## value 1.000 1.000 1.000
## .control 0.651 0.055 11.826 0.000 0.651 0.394
## .succeed 0.301 0.023 13.333 0.000 0.301 0.317
## .learning 0.417 0.034 12.445 0.000 0.417 0.407
## .exp_y 0.281 0.039 7.285 0.000 0.281 0.286
## .exp_t 0.224 0.025 8.929 0.000 0.224 0.222
## .imp_y -0.019 0.042 -0.454 0.650 -0.019 -0.019
## .imp_fut 0.571 0.042 13.641 0.000 0.571 0.548
## .happy 0.833 0.046 18.069 0.000 0.833 0.755
## .excited 0.879 0.054 16.298 0.000 0.879 0.897
## .frustrated 0.665 0.059 11.258 0.000 0.665 0.900
## .bored 1.077 0.058 18.644 0.000 1.077 0.841
##
##
## Group 6 [Laboratory]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## cont =~
## control 1.000 1.000 0.832
## succeed 0.874 0.029 30.291 0.000 0.874 0.840
## learning 0.843 0.031 27.630 0.000 0.843 0.820
## exp_y 0.990 0.024 42.040 0.000 0.990 0.907
## exp_t 0.915 0.029 31.302 0.000 0.915 0.866
## value =~
## imp_y 1.000 1.000 0.959
## imp_fut 0.692 0.043 15.977 0.000 0.692 0.714
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## happy ~
## cont 0.402 0.055 7.322 0.000 0.402 0.367
## value 0.216 0.059 3.642 0.000 0.216 0.197
## excited ~
## cont 0.239 0.064 3.720 0.000 0.239 0.223
## value 0.222 0.066 3.373 0.001 0.222 0.208
## frustrated ~
## cont -0.066 0.048 -1.382 0.167 -0.066 -0.073
## value 0.052 0.057 0.905 0.366 0.052 0.058
## bored ~
## cont -0.055 0.074 -0.744 0.457 -0.055 -0.048
## value -0.245 0.079 -3.103 0.002 -0.245 -0.214
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .happy ~~
## .excited 0.442 0.058 7.561 0.000 0.442 0.476
## .frustrated -0.012 0.050 -0.233 0.816 -0.012 -0.014
## .bored -0.116 0.059 -1.955 0.051 -0.116 -0.111
## .excited ~~
## .frustrated 0.071 0.051 1.391 0.164 0.071 0.080
## .bored 0.019 0.061 0.314 0.753 0.019 0.017
## .frustrated ~~
## .bored 0.194 0.054 3.615 0.000 0.194 0.195
## cont ~~
## value 0.600 0.035 17.218 0.000 0.600 0.600
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .control 1.615 0.071 22.828 0.000 1.615 1.345
## .imp_y 1.140 0.065 17.488 0.000 1.140 1.094
## .succeed 1.799 0.061 29.492 0.000 1.799 1.729
## .learning 1.579 0.061 25.884 0.000 1.579 1.536
## .exp_y 1.674 0.068 24.740 0.000 1.674 1.533
## .exp_t 1.762 0.064 27.680 0.000 1.762 1.668
## .imp_fut 0.952 0.063 15.219 0.000 0.952 0.982
## .happy 1.499 0.073 20.548 0.000 1.499 1.369
## .excited 0.961 0.068 14.025 0.000 0.961 0.898
## .frustrated 0.635 0.052 12.277 0.000 0.635 0.709
## .bored 1.224 0.077 15.939 0.000 1.224 1.066
## cont 0.000 0.000 0.000
## value 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## cont 1.000 1.000 1.000
## value 1.000 1.000 1.000
## .control 0.443 0.055 8.023 0.000 0.443 0.307
## .succeed 0.318 0.034 9.313 0.000 0.318 0.294
## .learning 0.346 0.035 9.863 0.000 0.346 0.328
## .exp_y 0.211 0.029 7.206 0.000 0.211 0.177
## .exp_t 0.279 0.043 6.467 0.000 0.279 0.250
## .imp_y 0.087 0.043 2.033 0.042 0.087 0.080
## .imp_fut 0.460 0.043 10.616 0.000 0.460 0.490
## .happy 0.886 0.059 15.086 0.000 0.886 0.739
## .excited 0.974 0.068 14.358 0.000 0.974 0.852
## .frustrated 0.801 0.067 11.910 0.000 0.801 0.996
## .bored 1.239 0.059 20.983 0.000 1.239 0.940
##
##
## Group 7 [Group Work]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## cont =~
## control 1.000 1.000 0.826
## succeed 0.810 0.041 19.713 0.000 0.810 0.809
## learning 0.833 0.043 19.336 0.000 0.833 0.799
## exp_y 0.937 0.042 22.565 0.000 0.937 0.867
## exp_t 0.889 0.048 18.634 0.000 0.889 0.839
## value =~
## imp_y 1.000 1.000 0.968
## imp_fut 0.677 0.069 9.867 0.000 0.677 0.664
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## happy ~
## cont 0.415 0.095 4.367 0.000 0.415 0.392
## value 0.209 0.092 2.273 0.023 0.209 0.197
## excited ~
## cont 0.267 0.087 3.066 0.002 0.267 0.250
## value 0.203 0.109 1.861 0.063 0.203 0.190
## frustrated ~
## cont -0.282 0.078 -3.627 0.000 -0.282 -0.313
## value -0.029 0.072 -0.406 0.685 -0.029 -0.033
## bored ~
## cont -0.235 0.108 -2.179 0.029 -0.235 -0.198
## value -0.390 0.115 -3.403 0.001 -0.390 -0.328
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .happy ~~
## .excited 0.402 0.067 6.045 0.000 0.402 0.457
## .frustrated -0.087 0.056 -1.537 0.124 -0.087 -0.114
## .bored -0.094 0.083 -1.138 0.255 -0.094 -0.100
## .excited ~~
## .frustrated 0.063 0.054 1.163 0.245 0.063 0.076
## .bored -0.103 0.075 -1.366 0.172 -0.103 -0.100
## .frustrated ~~
## .bored 0.231 0.073 3.174 0.002 0.231 0.259
## cont ~~
## value 0.584 0.050 11.578 0.000 0.584 0.584
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .control 1.807 0.081 22.349 0.000 1.807 1.493
## .imp_y 1.185 0.081 14.615 0.000 1.185 1.147
## .succeed 1.867 0.072 25.898 0.000 1.867 1.866
## .learning 1.667 0.079 21.168 0.000 1.667 1.599
## .exp_y 1.747 0.084 20.852 0.000 1.747 1.616
## .exp_t 1.847 0.076 24.371 0.000 1.847 1.745
## .imp_fut 1.004 0.082 12.198 0.000 1.004 0.983
## .happy 1.570 0.089 17.711 0.000 1.570 1.484
## .excited 0.976 0.085 11.426 0.000 0.976 0.914
## .frustrated 0.594 0.076 7.777 0.000 0.594 0.661
## .bored 1.329 0.096 13.865 0.000 1.329 1.116
## cont 0.000 0.000 0.000
## value 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## cont 1.000 1.000 1.000
## value 1.000 1.000 1.000
## .control 0.465 0.072 6.456 0.000 0.465 0.317
## .succeed 0.346 0.058 5.936 0.000 0.346 0.345
## .learning 0.393 0.058 6.806 0.000 0.393 0.362
## .exp_y 0.291 0.048 6.026 0.000 0.291 0.249
## .exp_t 0.331 0.069 4.829 0.000 0.331 0.295
## .imp_y 0.068 0.074 0.914 0.361 0.068 0.063
## .imp_fut 0.583 0.074 7.900 0.000 0.583 0.560
## .happy 0.803 0.081 9.893 0.000 0.803 0.717
## .excited 0.964 0.086 11.167 0.000 0.964 0.846
## .frustrated 0.718 0.092 7.822 0.000 0.718 0.889
## .bored 1.104 0.098 11.258 0.000 1.104 0.778
##
##
## Group 8 [Presentation]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## cont =~
## control 1.000 1.000 0.781
## succeed 0.850 0.047 18.087 0.000 0.850 0.869
## learning 0.802 0.053 15.049 0.000 0.802 0.815
## exp_y 0.991 0.048 20.495 0.000 0.991 0.950
## exp_t 0.978 0.049 19.772 0.000 0.978 0.931
## value =~
## imp_y 1.000 1.000 0.977
## imp_fut 0.568 0.070 8.157 0.000 0.568 0.631
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## happy ~
## cont 0.508 0.071 7.183 0.000 0.508 0.534
## value 0.088 0.065 1.367 0.172 0.088 0.093
## excited ~
## cont 0.337 0.084 4.006 0.000 0.337 0.322
## value 0.214 0.084 2.566 0.010 0.214 0.205
## frustrated ~
## cont -0.180 0.096 -1.878 0.060 -0.180 -0.211
## value 0.094 0.093 1.014 0.311 0.094 0.111
## bored ~
## cont -0.182 0.102 -1.786 0.074 -0.182 -0.168
## value -0.303 0.107 -2.822 0.005 -0.303 -0.280
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .happy ~~
## .excited 0.347 0.063 5.529 0.000 0.347 0.489
## .frustrated -0.058 0.051 -1.137 0.256 -0.058 -0.090
## .bored 0.061 0.056 1.089 0.276 0.061 0.080
## .excited ~~
## .frustrated 0.147 0.055 2.672 0.008 0.147 0.189
## .bored 0.159 0.068 2.320 0.020 0.159 0.173
## .frustrated ~~
## .bored 0.266 0.070 3.776 0.000 0.266 0.319
## cont ~~
## value 0.558 0.055 10.067 0.000 0.558 0.558
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .control 1.712 0.110 15.590 0.000 1.712 1.337
## .imp_y 1.285 0.095 13.581 0.000 1.285 1.255
## .succeed 2.000 0.089 22.365 0.000 2.000 2.044
## .learning 2.027 0.090 22.615 0.000 2.027 2.059
## .exp_y 2.037 0.102 19.885 0.000 2.037 1.952
## .exp_t 2.037 0.101 20.113 0.000 2.037 1.940
## .imp_fut 0.780 0.094 8.320 0.000 0.780 0.865
## .happy 1.614 0.096 16.840 0.000 1.614 1.697
## .excited 0.905 0.110 8.240 0.000 0.905 0.865
## .frustrated 0.481 0.073 6.569 0.000 0.481 0.564
## .bored 1.207 0.105 11.524 0.000 1.207 1.114
## cont 0.000 0.000 0.000
## value 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## cont 1.000 1.000 1.000
## value 1.000 1.000 1.000
## .control 0.639 0.094 6.831 0.000 0.639 0.390
## .succeed 0.234 0.034 6.905 0.000 0.234 0.245
## .learning 0.326 0.048 6.819 0.000 0.326 0.336
## .exp_y 0.106 0.027 3.886 0.000 0.106 0.098
## .exp_t 0.146 0.032 4.557 0.000 0.146 0.133
## .imp_y 0.048 0.067 0.712 0.476 0.048 0.045
## .imp_fut 0.488 0.055 8.866 0.000 0.488 0.602
## .happy 0.589 0.068 8.650 0.000 0.589 0.651
## .excited 0.856 0.086 9.932 0.000 0.856 0.781
## .frustrated 0.706 0.108 6.555 0.000 0.706 0.969
## .bored 0.987 0.086 11.488 0.000 0.987 0.841library(semTools)
measurementInvariance(model = MG_EMO, data = SciMo_esm, group = "act_re")##
## Measurement invariance models:
##
## Model 1 : fit.configural
## Model 2 : fit.loadings
## Model 3 : fit.intercepts
## Model 4 : fit.means
##
## Chi Square Difference Test
##
## Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
## fit.configural 264 102248 104449 795.82
## fit.loadings 299 102225 104207 842.24 46.42 35 0.09389 .
## fit.intercepts 362 102465 104053 1208.21 365.97 63 < 2e-16 ***
## fit.means 376 102686 104187 1457.89 249.68 14 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Fit measures:
##
## cfi rmsea cfi.delta rmsea.delta
## fit.configural 0.969 0.065 NA NA
## fit.loadings 0.968 0.062 0.001 0.003
## fit.intercepts 0.951 0.070 0.018 0.008
## fit.means 0.937 0.077 0.014 0.008Multigroup SEM model
library(lavaan)
MG_EMO2 <- '
#measurement model
cont =~ control + succeed + learning + exp_y + exp_t
value =~ imp_y + imp_fut
#regressions
happy ~ cont + value
excited ~ cont + value
frustrated ~ cont + value
bored ~ cont + value
#residual correlations
happy ~~ excited + frustrated + bored
excited ~~ frustrated + bored
frustrated ~~ bored
cont ~~ value
'
fit4<-sem(MG_EMO2, data=SciMo_esm, cluster = "uniqueid", group = "act_re", se = "robust.cluster.sem")
summary(fit4, fit.measures=TRUE, standardized = TRUE)## lavaan 0.6-3 ended normally after 165 iterations
##
## Optimization method NLMINB
## Number of free parameters 352
##
## Used Total
## Number of observations per group
## Individual Work 633 671
## Number of clusters [uniqueid] 198
## Quiz and Test 629 688
## Number of clusters [uniqueid] 179
## NaN 43 51
## Number of clusters [uniqueid] 22
## Non-instructional 302 322
## Number of clusters [uniqueid] 159
## Lecture 746 791
## Number of clusters [uniqueid] 202
## Laboratory 941 1023
## Number of clusters [uniqueid] 141
## Group Work 249 271
## Number of clusters [uniqueid] 127
## Presentation 295 313
## Number of clusters [uniqueid] 70
##
## Estimator ML Robust
## Model Fit Test Statistic 795.825 560.718
## Degrees of freedom 264 264
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.419
## for the Yuan-Bentler correction (Mplus variant)
##
## Chi-square for each group:
##
## Individual Work 96.076 67.692
## Quiz and Test 88.762 62.540
## NaN 50.699 35.721
## Non-instructional 85.714 60.392
## Lecture 134.137 94.510
## Laboratory 156.486 110.256
## Group Work 99.856 70.356
## Presentation 84.094 59.251
##
## Model test baseline model:
##
## Minimum Function Test Statistic 17567.287 11181.798
## Degrees of freedom 440 440
## P-value 0.000 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 0.969 0.972
## Tucker-Lewis Index (TLI) 0.948 0.954
##
## Robust Comparative Fit Index (CFI) 0.975
## Robust Tucker-Lewis Index (TLI) 0.958
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -50772.212 -50772.212
## Scaling correction factor 1.831
## for the MLR correction
## Loglikelihood unrestricted model (H1) -50374.299 -50374.299
## Scaling correction factor 1.655
## for the MLR correction
##
## Number of free parameters 352 352
## Akaike (AIC) 102248.423 102248.423
## Bayesian (BIC) 104449.376 104449.376
## Sample-size adjusted Bayesian (BIC) 103330.884 103330.884
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.065 0.048
## 90 Percent Confidence Interval 0.060 0.070 0.044 0.053
## P-value RMSEA <= 0.05 0.000 0.708
##
## Robust RMSEA 0.058
## 90 Percent Confidence Interval 0.051 0.064
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.031 0.031
##
## Parameter Estimates:
##
## Information Expected
## Information saturated (h1) model Structured
## Standard Errors Robust.cluster.sem
##
##
## Group 1 [Individual Work]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## cont =~
## control 1.000 0.716 0.707
## succeed 1.053 0.087 12.102 0.000 0.753 0.798
## learning 0.995 0.076 13.105 0.000 0.712 0.727
## exp_y 1.203 0.084 14.245 0.000 0.861 0.891
## exp_t 1.163 0.084 13.885 0.000 0.832 0.839
## value =~
## imp_y 1.000 0.960 0.961
## imp_fut 0.751 0.076 9.858 0.000 0.721 0.722
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## happy ~
## cont 0.634 0.078 8.138 0.000 0.454 0.429
## value 0.061 0.064 0.951 0.342 0.059 0.056
## excited ~
## cont 0.229 0.093 2.462 0.014 0.164 0.162
## value 0.148 0.072 2.051 0.040 0.142 0.140
## frustrated ~
## cont -0.262 0.070 -3.726 0.000 -0.188 -0.224
## value -0.009 0.048 -0.188 0.851 -0.009 -0.010
## bored ~
## cont -0.266 0.128 -2.079 0.038 -0.191 -0.170
## value -0.335 0.100 -3.344 0.001 -0.321 -0.286
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .happy ~~
## .excited 0.480 0.058 8.250 0.000 0.480 0.521
## .frustrated -0.063 0.038 -1.681 0.093 -0.063 -0.082
## .bored -0.175 0.053 -3.289 0.001 -0.175 -0.180
## .excited ~~
## .frustrated 0.031 0.043 0.707 0.480 0.031 0.038
## .bored -0.145 0.054 -2.705 0.007 -0.145 -0.143
## .frustrated ~~
## .bored 0.151 0.042 3.591 0.000 0.151 0.179
## cont ~~
## value 0.312 0.053 5.862 0.000 0.454 0.454
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .control 1.635 0.066 24.606 0.000 1.635 1.615
## .succeed 1.750 0.056 31.244 0.000 1.750 1.853
## .learning 1.659 0.056 29.650 0.000 1.659 1.695
## .exp_y 1.712 0.064 26.555 0.000 1.712 1.771
## .exp_t 1.701 0.064 26.398 0.000 1.701 1.716
## .imp_y 1.166 0.071 16.468 0.000 1.166 1.167
## .imp_fut 0.968 0.071 13.577 0.000 0.968 0.970
## .happy 1.479 0.076 19.432 0.000 1.479 1.398
## .excited 0.817 0.071 11.443 0.000 0.817 0.806
## .frustrated 0.613 0.051 11.975 0.000 0.613 0.730
## .bored 1.461 0.074 19.620 0.000 1.461 1.300
## cont 0.000 0.000 0.000
## value 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .control 0.513 0.064 8.019 0.000 0.513 0.500
## .succeed 0.324 0.032 9.984 0.000 0.324 0.364
## .learning 0.451 0.048 9.412 0.000 0.451 0.471
## .exp_y 0.193 0.026 7.552 0.000 0.193 0.207
## .exp_t 0.291 0.044 6.558 0.000 0.291 0.295
## .imp_y 0.076 0.081 0.933 0.351 0.076 0.076
## .imp_fut 0.477 0.062 7.687 0.000 0.477 0.479
## .happy 0.885 0.058 15.258 0.000 0.885 0.791
## .excited 0.960 0.074 13.019 0.000 0.960 0.933
## .frustrated 0.668 0.053 12.587 0.000 0.668 0.948
## .bored 1.067 0.070 15.290 0.000 1.067 0.845
## cont 0.512 0.080 6.413 0.000 1.000 1.000
## value 0.922 0.101 9.143 0.000 1.000 1.000
##
##
## Group 2 [Quiz and Test]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## cont =~
## control 1.000 0.722 0.681
## succeed 0.955 0.059 16.280 0.000 0.690 0.771
## learning 0.796 0.076 10.516 0.000 0.575 0.562
## exp_y 1.082 0.059 18.294 0.000 0.781 0.860
## exp_t 1.047 0.063 16.665 0.000 0.756 0.771
## value =~
## imp_y 1.000 0.920 0.892
## imp_fut 0.910 0.096 9.485 0.000 0.838 0.746
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## happy ~
## cont 0.599 0.087 6.876 0.000 0.432 0.398
## value 0.019 0.074 0.250 0.803 0.017 0.016
## excited ~
## cont 0.325 0.076 4.278 0.000 0.235 0.238
## value 0.047 0.060 0.781 0.435 0.043 0.044
## frustrated ~
## cont -0.294 0.082 -3.588 0.000 -0.212 -0.232
## value 0.089 0.061 1.451 0.147 0.082 0.090
## bored ~
## cont -0.291 0.100 -2.922 0.003 -0.210 -0.180
## value -0.264 0.082 -3.220 0.001 -0.243 -0.208
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .happy ~~
## .excited 0.494 0.057 8.698 0.000 0.494 0.522
## .frustrated -0.139 0.048 -2.899 0.004 -0.139 -0.157
## .bored -0.128 0.061 -2.100 0.036 -0.128 -0.117
## .excited ~~
## .frustrated 0.000 0.043 0.008 0.994 0.000 0.000
## .bored -0.009 0.056 -0.164 0.870 -0.009 -0.009
## .frustrated ~~
## .bored 0.283 0.052 5.417 0.000 0.283 0.286
## cont ~~
## value 0.296 0.052 5.712 0.000 0.446 0.446
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .control 1.696 0.066 25.614 0.000 1.696 1.600
## .succeed 1.900 0.049 38.727 0.000 1.900 2.125
## .learning 1.521 0.057 26.815 0.000 1.521 1.489
## .exp_y 1.812 0.053 34.451 0.000 1.812 1.997
## .exp_t 1.763 0.062 28.652 0.000 1.763 1.800
## .imp_y 1.752 0.063 27.731 0.000 1.752 1.698
## .imp_fut 1.402 0.079 17.678 0.000 1.402 1.249
## .happy 1.380 0.073 18.782 0.000 1.380 1.270
## .excited 0.725 0.062 11.600 0.000 0.725 0.734
## .frustrated 0.693 0.055 12.678 0.000 0.693 0.757
## .bored 1.364 0.075 18.245 0.000 1.364 1.167
## cont 0.000 0.000 0.000
## value 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .control 0.603 0.064 9.427 0.000 0.603 0.536
## .succeed 0.324 0.031 10.517 0.000 0.324 0.405
## .learning 0.714 0.067 10.635 0.000 0.714 0.684
## .exp_y 0.214 0.032 6.734 0.000 0.214 0.260
## .exp_t 0.389 0.051 7.566 0.000 0.389 0.405
## .imp_y 0.218 0.087 2.503 0.012 0.218 0.205
## .imp_fut 0.559 0.094 5.961 0.000 0.559 0.444
## .happy 0.986 0.060 16.315 0.000 0.986 0.836
## .excited 0.909 0.071 12.884 0.000 0.909 0.932
## .frustrated 0.803 0.066 12.132 0.000 0.803 0.957
## .bored 1.218 0.060 20.304 0.000 1.218 0.891
## cont 0.521 0.067 7.782 0.000 1.000 1.000
## value 0.846 0.109 7.778 0.000 1.000 1.000
##
##
## Group 3 [NaN]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## cont =~
## control 1.000 0.819 0.818
## succeed 0.774 0.125 6.186 0.000 0.633 0.788
## learning 0.868 0.155 5.590 0.000 0.710 0.742
## exp_y 1.066 0.153 6.966 0.000 0.873 0.950
## exp_t 1.034 0.141 7.326 0.000 0.846 0.917
## value =~
## imp_y 1.000 0.880 1.040
## imp_fut 0.597 0.218 2.731 0.006 0.525 0.628
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## happy ~
## cont 0.190 0.174 1.095 0.274 0.156 0.190
## value -0.130 0.205 -0.633 0.527 -0.114 -0.140
## excited ~
## cont -0.063 0.175 -0.359 0.719 -0.051 -0.074
## value 0.100 0.192 0.523 0.601 0.088 0.128
## frustrated ~
## cont -0.226 0.089 -2.529 0.011 -0.185 -0.266
## value 0.317 0.117 2.723 0.006 0.279 0.403
## bored ~
## cont -0.228 0.256 -0.891 0.373 -0.187 -0.176
## value -0.347 0.174 -1.988 0.047 -0.305 -0.288
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .happy ~~
## .excited 0.229 0.137 1.676 0.094 0.229 0.420
## .frustrated -0.038 0.056 -0.671 0.502 -0.038 -0.076
## .bored -0.078 0.099 -0.791 0.429 -0.078 -0.099
## .excited ~~
## .frustrated -0.014 0.051 -0.282 0.778 -0.014 -0.034
## .bored -0.195 0.098 -1.995 0.046 -0.195 -0.288
## .frustrated ~~
## .bored 0.075 0.085 0.886 0.375 0.075 0.123
## cont ~~
## value 0.120 0.147 0.814 0.416 0.166 0.166
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .control 1.302 0.184 7.095 0.000 1.302 1.301
## .succeed 1.651 0.139 11.920 0.000 1.651 2.055
## .learning 1.674 0.177 9.482 0.000 1.674 1.748
## .exp_y 1.605 0.192 8.337 0.000 1.605 1.747
## .exp_t 1.721 0.187 9.221 0.000 1.721 1.864
## .imp_y 0.930 0.170 5.487 0.000 0.930 1.099
## .imp_fut 0.628 0.157 3.990 0.000 0.628 0.751
## .happy 1.488 0.163 9.147 0.000 1.488 1.820
## .excited 0.419 0.133 3.153 0.002 0.419 0.607
## .frustrated 0.465 0.114 4.090 0.000 0.465 0.670
## .bored 1.395 0.192 7.254 0.000 1.395 1.317
## cont 0.000 0.000 0.000
## value 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .control 0.331 0.110 3.016 0.003 0.331 0.331
## .succeed 0.244 0.063 3.868 0.000 0.244 0.379
## .learning 0.412 0.080 5.127 0.000 0.412 0.450
## .exp_y 0.082 0.045 1.847 0.065 0.082 0.098
## .exp_t 0.136 0.051 2.660 0.008 0.136 0.160
## .imp_y -0.059 0.204 -0.289 0.772 -0.059 -0.082
## .imp_fut 0.423 0.123 3.432 0.001 0.423 0.605
## .happy 0.637 0.146 4.373 0.000 0.637 0.953
## .excited 0.467 0.124 3.774 0.000 0.467 0.981
## .frustrated 0.386 0.096 4.010 0.000 0.386 0.803
## .bored 0.976 0.166 5.884 0.000 0.976 0.869
## cont 0.670 0.169 3.964 0.000 1.000 1.000
## value 0.775 0.292 2.650 0.008 1.000 1.000
##
##
## Group 4 [Non-instructional]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## cont =~
## control 1.000 0.691 0.700
## succeed 1.024 0.089 11.492 0.000 0.708 0.782
## learning 0.793 0.090 8.813 0.000 0.548 0.563
## exp_y 1.190 0.099 12.066 0.000 0.823 0.865
## exp_t 1.164 0.104 11.183 0.000 0.805 0.848
## value =~
## imp_y 1.000 0.837 0.865
## imp_fut 0.812 0.120 6.785 0.000 0.680 0.681
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## happy ~
## cont 0.502 0.124 4.047 0.000 0.347 0.344
## value 0.148 0.086 1.715 0.086 0.124 0.123
## excited ~
## cont 0.226 0.131 1.728 0.084 0.156 0.165
## value 0.273 0.109 2.497 0.013 0.229 0.242
## frustrated ~
## cont -0.218 0.102 -2.139 0.032 -0.151 -0.172
## value 0.141 0.095 1.487 0.137 0.118 0.134
## bored ~
## cont -0.385 0.133 -2.889 0.004 -0.266 -0.230
## value -0.121 0.124 -0.973 0.330 -0.101 -0.087
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .happy ~~
## .excited 0.392 0.056 7.018 0.000 0.392 0.483
## .frustrated -0.152 0.051 -2.956 0.003 -0.152 -0.190
## .bored -0.005 0.068 -0.068 0.946 -0.005 -0.005
## .excited ~~
## .frustrated -0.053 0.041 -1.290 0.197 -0.053 -0.070
## .bored 0.029 0.061 0.473 0.636 0.029 0.030
## .frustrated ~~
## .bored 0.280 0.063 4.469 0.000 0.280 0.290
## cont ~~
## value 0.281 0.057 4.966 0.000 0.485 0.485
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .control 1.709 0.072 23.732 0.000 1.709 1.731
## .succeed 1.772 0.065 27.191 0.000 1.772 1.958
## .learning 1.391 0.066 20.949 0.000 1.391 1.429
## .exp_y 1.702 0.070 24.428 0.000 1.702 1.790
## .exp_t 1.791 0.068 26.239 0.000 1.791 1.888
## .imp_y 1.142 0.069 16.665 0.000 1.142 1.180
## .imp_fut 0.907 0.075 12.047 0.000 0.907 0.908
## .happy 1.497 0.071 21.180 0.000 1.497 1.483
## .excited 0.768 0.073 10.499 0.000 0.768 0.813
## .frustrated 0.579 0.058 9.966 0.000 0.579 0.659
## .bored 1.414 0.085 16.645 0.000 1.414 1.224
## cont 0.000 0.000 0.000
## value 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .control 0.497 0.063 7.893 0.000 0.497 0.509
## .succeed 0.318 0.044 7.137 0.000 0.318 0.388
## .learning 0.646 0.068 9.557 0.000 0.646 0.683
## .exp_y 0.227 0.039 5.875 0.000 0.227 0.251
## .exp_t 0.253 0.051 4.989 0.000 0.253 0.281
## .imp_y 0.236 0.101 2.331 0.020 0.236 0.252
## .imp_fut 0.535 0.076 7.007 0.000 0.535 0.536
## .happy 0.840 0.078 10.722 0.000 0.840 0.825
## .excited 0.782 0.076 10.323 0.000 0.782 0.875
## .frustrated 0.754 0.081 9.283 0.000 0.754 0.975
## .bored 1.228 0.074 16.508 0.000 1.228 0.920
## cont 0.478 0.077 6.219 0.000 1.000 1.000
## value 0.701 0.108 6.506 0.000 1.000 1.000
##
##
## Group 5 [Lecture]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## cont =~
## control 1.000 0.654 0.632
## succeed 1.066 0.062 17.109 0.000 0.697 0.787
## learning 1.030 0.064 16.150 0.000 0.674 0.722
## exp_y 1.111 0.070 15.793 0.000 0.727 0.810
## exp_t 1.175 0.067 17.435 0.000 0.768 0.854
## value =~
## imp_y 1.000 0.949 0.997
## imp_fut 0.705 0.080 8.812 0.000 0.669 0.667
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## happy ~
## cont 0.662 0.074 8.960 0.000 0.433 0.426
## value 0.030 0.053 0.565 0.572 0.028 0.028
## excited ~
## cont 0.331 0.085 3.900 0.000 0.216 0.221
## value 0.108 0.055 1.945 0.052 0.102 0.105
## frustrated ~
## cont -0.385 0.075 -5.168 0.000 -0.252 -0.297
## value 0.044 0.041 1.059 0.290 0.042 0.049
## bored ~
## cont -0.433 0.096 -4.499 0.000 -0.283 -0.255
## value -0.188 0.067 -2.809 0.005 -0.179 -0.161
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .happy ~~
## .excited 0.409 0.042 9.781 0.000 0.409 0.478
## .frustrated -0.110 0.038 -2.928 0.003 -0.110 -0.147
## .bored -0.152 0.049 -3.113 0.002 -0.152 -0.160
## .excited ~~
## .frustrated 0.013 0.035 0.363 0.717 0.013 0.017
## .bored -0.044 0.051 -0.855 0.393 -0.044 -0.045
## .frustrated ~~
## .bored 0.211 0.042 5.056 0.000 0.211 0.249
## cont ~~
## value 0.276 0.036 7.667 0.000 0.444 0.444
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .control 1.664 0.063 26.407 0.000 1.664 1.608
## .succeed 2.013 0.046 43.394 0.000 2.013 2.272
## .learning 1.981 0.049 40.565 0.000 1.981 2.123
## .exp_y 1.909 0.048 39.632 0.000 1.909 2.129
## .exp_t 1.956 0.050 39.176 0.000 1.956 2.174
## .imp_y 1.247 0.056 22.159 0.000 1.247 1.309
## .imp_fut 0.903 0.062 14.483 0.000 0.903 0.900
## .happy 1.619 0.062 25.986 0.000 1.619 1.592
## .excited 0.818 0.058 14.055 0.000 0.818 0.836
## .frustrated 0.525 0.046 11.409 0.000 0.525 0.619
## .bored 1.357 0.066 20.561 0.000 1.357 1.221
## cont 0.000 0.000 0.000
## value 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .control 0.643 0.049 13.091 0.000 0.643 0.601
## .succeed 0.299 0.023 12.907 0.000 0.299 0.381
## .learning 0.417 0.034 12.119 0.000 0.417 0.479
## .exp_y 0.276 0.040 6.955 0.000 0.276 0.343
## .exp_t 0.218 0.026 8.540 0.000 0.218 0.270
## .imp_y 0.006 0.095 0.059 0.953 0.006 0.006
## .imp_fut 0.559 0.060 9.338 0.000 0.559 0.556
## .happy 0.835 0.047 17.918 0.000 0.835 0.807
## .excited 0.879 0.054 16.260 0.000 0.879 0.920
## .frustrated 0.665 0.059 11.266 0.000 0.665 0.923
## .bored 1.078 0.059 18.337 0.000 1.078 0.873
## cont 0.428 0.053 8.120 0.000 1.000 1.000
## value 0.901 0.099 9.074 0.000 1.000 1.000
##
##
## Group 6 [Laboratory]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## cont =~
## control 1.000 0.794 0.765
## succeed 0.984 0.043 22.764 0.000 0.781 0.811
## learning 0.949 0.049 19.330 0.000 0.753 0.788
## exp_y 1.116 0.044 25.481 0.000 0.886 0.889
## exp_t 1.030 0.045 23.042 0.000 0.818 0.841
## value =~
## imp_y 1.000 0.909 0.927
## imp_fut 0.734 0.058 12.738 0.000 0.667 0.710
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## happy ~
## cont 0.440 0.076 5.776 0.000 0.350 0.328
## value 0.237 0.067 3.559 0.000 0.215 0.202
## excited ~
## cont 0.259 0.087 2.982 0.003 0.206 0.195
## value 0.240 0.074 3.222 0.001 0.218 0.207
## frustrated ~
## cont -0.078 0.061 -1.268 0.205 -0.062 -0.069
## value 0.058 0.064 0.919 0.358 0.053 0.059
## bored ~
## cont -0.050 0.100 -0.502 0.616 -0.040 -0.035
## value -0.265 0.091 -2.899 0.004 -0.241 -0.211
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .happy ~~
## .excited 0.439 0.059 7.494 0.000 0.439 0.474
## .frustrated -0.013 0.050 -0.252 0.801 -0.013 -0.015
## .bored -0.113 0.059 -1.916 0.055 -0.113 -0.108
## .excited ~~
## .frustrated 0.070 0.051 1.374 0.169 0.070 0.079
## .bored 0.021 0.061 0.351 0.726 0.021 0.019
## .frustrated ~~
## .bored 0.195 0.054 3.635 0.000 0.195 0.196
## cont ~~
## value 0.403 0.050 7.980 0.000 0.558 0.558
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .control 1.615 0.071 22.828 0.000 1.615 1.556
## .succeed 1.799 0.061 29.492 0.000 1.799 1.868
## .learning 1.579 0.061 25.884 0.000 1.579 1.652
## .exp_y 1.674 0.068 24.740 0.000 1.674 1.679
## .exp_t 1.762 0.064 27.680 0.000 1.762 1.811
## .imp_y 1.140 0.065 17.488 0.000 1.140 1.164
## .imp_fut 0.952 0.063 15.219 0.000 0.952 1.013
## .happy 1.499 0.073 20.548 0.000 1.499 1.407
## .excited 0.961 0.068 14.025 0.000 0.961 0.911
## .frustrated 0.635 0.052 12.277 0.000 0.635 0.709
## .bored 1.224 0.077 15.939 0.000 1.224 1.071
## cont 0.000 0.000 0.000
## value 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .control 0.448 0.054 8.258 0.000 0.448 0.415
## .succeed 0.318 0.035 9.195 0.000 0.318 0.342
## .learning 0.346 0.036 9.667 0.000 0.346 0.378
## .exp_y 0.208 0.029 7.162 0.000 0.208 0.210
## .exp_t 0.277 0.043 6.422 0.000 0.277 0.293
## .imp_y 0.135 0.050 2.694 0.007 0.135 0.140
## .imp_fut 0.438 0.048 9.128 0.000 0.438 0.496
## .happy 0.884 0.059 14.967 0.000 0.884 0.778
## .excited 0.971 0.068 14.238 0.000 0.971 0.874
## .frustrated 0.800 0.067 11.927 0.000 0.800 0.996
## .bored 1.236 0.059 20.837 0.000 1.236 0.946
## cont 0.630 0.064 9.783 0.000 1.000 1.000
## value 0.826 0.073 11.291 0.000 1.000 1.000
##
##
## Group 7 [Group Work]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## cont =~
## control 1.000 0.721 0.724
## succeed 0.982 0.076 12.951 0.000 0.708 0.770
## learning 1.017 0.085 11.959 0.000 0.733 0.764
## exp_y 1.134 0.089 12.708 0.000 0.817 0.834
## exp_t 1.076 0.101 10.610 0.000 0.776 0.803
## value =~
## imp_y 1.000 1.018 1.002
## imp_fut 0.630 0.105 6.019 0.000 0.642 0.634
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## happy ~
## cont 0.522 0.151 3.459 0.001 0.376 0.366
## value 0.182 0.090 2.036 0.042 0.186 0.181
## excited ~
## cont 0.353 0.137 2.585 0.010 0.254 0.242
## value 0.170 0.103 1.645 0.100 0.173 0.164
## frustrated ~
## cont -0.339 0.122 -2.778 0.005 -0.245 -0.276
## value -0.029 0.065 -0.444 0.657 -0.029 -0.033
## bored ~
## cont -0.307 0.175 -1.755 0.079 -0.221 -0.188
## value -0.358 0.122 -2.927 0.003 -0.365 -0.311
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .happy ~~
## .excited 0.405 0.067 6.063 0.000 0.405 0.458
## .frustrated -0.087 0.057 -1.529 0.126 -0.087 -0.114
## .bored -0.099 0.084 -1.178 0.239 -0.099 -0.105
## .excited ~~
## .frustrated 0.064 0.055 1.163 0.245 0.064 0.077
## .bored -0.109 0.076 -1.432 0.152 -0.109 -0.105
## .frustrated ~~
## .bored 0.231 0.072 3.188 0.001 0.231 0.258
## cont ~~
## value 0.390 0.057 6.787 0.000 0.531 0.531
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .control 1.807 0.081 22.349 0.000 1.807 1.815
## .succeed 1.867 0.072 25.898 0.000 1.867 2.030
## .learning 1.667 0.079 21.168 0.000 1.667 1.737
## .exp_y 1.747 0.084 20.852 0.000 1.747 1.783
## .exp_t 1.847 0.076 24.371 0.000 1.847 1.913
## .imp_y 1.185 0.081 14.615 0.000 1.185 1.165
## .imp_fut 1.004 0.082 12.198 0.000 1.004 0.992
## .happy 1.570 0.089 17.711 0.000 1.570 1.528
## .excited 0.976 0.085 11.426 0.000 0.976 0.927
## .frustrated 0.594 0.076 7.777 0.000 0.594 0.670
## .bored 1.329 0.096 13.865 0.000 1.329 1.133
## cont 0.000 0.000 0.000
## value 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .control 0.471 0.068 6.922 0.000 0.471 0.476
## .succeed 0.344 0.058 5.909 0.000 0.344 0.407
## .learning 0.383 0.057 6.667 0.000 0.383 0.416
## .exp_y 0.292 0.049 5.980 0.000 0.292 0.304
## .exp_t 0.330 0.070 4.713 0.000 0.330 0.354
## .imp_y -0.003 0.146 -0.022 0.983 -0.003 -0.003
## .imp_fut 0.612 0.093 6.608 0.000 0.612 0.598
## .happy 0.806 0.082 9.802 0.000 0.806 0.763
## .excited 0.967 0.085 11.341 0.000 0.967 0.873
## .frustrated 0.719 0.092 7.802 0.000 0.719 0.913
## .bored 1.110 0.098 11.285 0.000 1.110 0.806
## cont 0.520 0.082 6.337 0.000 1.000 1.000
## value 1.037 0.157 6.595 0.000 1.000 1.000
##
##
## Group 8 [Presentation]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## cont =~
## control 1.000 0.664 0.645
## succeed 1.097 0.105 10.481 0.000 0.728 0.832
## learning 1.036 0.122 8.499 0.000 0.688 0.769
## exp_y 1.284 0.121 10.580 0.000 0.852 0.936
## exp_t 1.265 0.120 10.554 0.000 0.839 0.911
## value =~
## imp_y 1.000 0.877 0.920
## imp_fut 0.636 0.095 6.664 0.000 0.558 0.637
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## happy ~
## cont 0.634 0.110 5.753 0.000 0.421 0.465
## value 0.120 0.079 1.525 0.127 0.106 0.117
## excited ~
## cont 0.404 0.142 2.843 0.004 0.268 0.263
## value 0.264 0.108 2.435 0.015 0.231 0.228
## frustrated ~
## cont -0.247 0.151 -1.636 0.102 -0.164 -0.193
## value 0.117 0.112 1.046 0.296 0.103 0.121
## bored ~
## cont -0.211 0.161 -1.309 0.190 -0.140 -0.131
## value -0.353 0.127 -2.777 0.005 -0.310 -0.291
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .happy ~~
## .excited 0.343 0.063 5.429 0.000 0.343 0.486
## .frustrated -0.060 0.050 -1.200 0.230 -0.060 -0.094
## .bored 0.067 0.056 1.203 0.229 0.067 0.089
## .excited ~~
## .frustrated 0.142 0.055 2.606 0.009 0.142 0.184
## .bored 0.169 0.069 2.460 0.014 0.169 0.186
## .frustrated ~~
## .bored 0.271 0.071 3.836 0.000 0.271 0.327
## cont ~~
## value 0.290 0.077 3.755 0.000 0.498 0.498
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .control 1.712 0.110 15.590 0.000 1.712 1.663
## .succeed 2.000 0.089 22.365 0.000 2.000 2.285
## .learning 2.027 0.090 22.615 0.000 2.027 2.267
## .exp_y 2.037 0.102 19.885 0.000 2.037 2.237
## .exp_t 2.037 0.101 20.113 0.000 2.037 2.210
## .imp_y 1.285 0.095 13.581 0.000 1.285 1.348
## .imp_fut 0.780 0.094 8.320 0.000 0.780 0.889
## .happy 1.614 0.096 16.840 0.000 1.614 1.782
## .excited 0.905 0.110 8.240 0.000 0.905 0.890
## .frustrated 0.481 0.073 6.569 0.000 0.481 0.566
## .bored 1.207 0.105 11.524 0.000 1.207 1.133
## cont 0.000 0.000 0.000
## value 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .control 0.619 0.082 7.542 0.000 0.619 0.584
## .succeed 0.236 0.034 6.925 0.000 0.236 0.308
## .learning 0.326 0.049 6.726 0.000 0.326 0.408
## .exp_y 0.103 0.028 3.732 0.000 0.103 0.124
## .exp_t 0.145 0.032 4.494 0.000 0.145 0.171
## .imp_y 0.139 0.094 1.484 0.138 0.139 0.153
## .imp_fut 0.457 0.061 7.437 0.000 0.457 0.594
## .happy 0.588 0.070 8.452 0.000 0.588 0.716
## .excited 0.848 0.085 9.954 0.000 0.848 0.819
## .frustrated 0.704 0.108 6.537 0.000 0.704 0.971
## .bored 0.975 0.087 11.167 0.000 0.975 0.860
## cont 0.441 0.111 3.972 0.000 1.000 1.000
## value 0.770 0.109 7.091 0.000 1.000 1.000MLSEM w/ activities as predictors
library(lavaan)
ML_EMO <- '
level:1
#measurement model
cont =~ succeed + control + learning + exp_y + exp_t
value =~ imp_y + imp_fut
#regressions
happy ~ cont + value + act_lec + act_indwork + act_groupwork + act_pres + act_lab + act_nonins
excited ~ cont + value + act_lec + act_indwork + act_groupwork + act_pres + act_lab + act_nonins
frustrated ~ cont + value + act_lec + act_indwork + act_groupwork + act_pres + act_lab + act_nonins
bored ~ cont + value + act_lec + act_indwork + act_groupwork + act_pres + act_lab + act_nonins
cont ~ act_lec + act_indwork + act_groupwork + act_pres + act_lab + act_nonins
value ~ act_lec + act_indwork + act_groupwork + act_pres + act_lab + act_nonins
#residual correlations
happy ~~ excited + frustrated + bored
excited ~~ frustrated + bored
frustrated ~~ bored
cont ~~ value
act_lec ~~ act_indwork + act_groupwork + act_pres + act_lab + act_nonins
act_indwork ~~ act_groupwork + act_pres + act_lab + act_nonins
act_groupwork ~~ act_pres + act_lab + act_nonins
act_pres ~~ act_lab + act_nonins
act_lab ~~ act_nonins
level:2
#measurement model
cont =~ succeed + control + learning + exp_y + exp_t
value =~ imp_y + imp_fut
#regressions
happy ~ cont + value + act_lec + act_indwork + act_groupwork + act_pres + act_lab + act_nonins
excited ~ cont + value + act_lec + act_indwork + act_groupwork + act_pres + act_lab + act_nonins
frustrated ~ cont + value + act_lec + act_indwork + act_groupwork + act_pres + act_lab + act_nonins
bored ~ cont + value + act_lec + act_indwork + act_groupwork + act_pres + act_lab + act_nonins
cont ~ act_lec + act_indwork + act_groupwork + act_pres + act_lab + act_nonins
value ~ act_lec + act_indwork + act_groupwork + act_pres + act_lab + act_nonins
#residual correlations
happy ~~ excited + frustrated + bored
excited ~~ frustrated + bored
frustrated ~~ bored
cont ~~ value
act_lec ~~ act_indwork + act_groupwork + act_pres + act_lab + act_nonins
act_indwork ~~ act_groupwork + act_pres + act_lab + act_nonins
act_groupwork ~~ act_pres + act_lab + act_nonins
act_pres ~~ act_lab + act_nonins
act_lab ~~ act_nonins
'
fit2<-sem(ML_EMO, data=SciMo_esm, cluster = "uniqueid")
summary(fit2, fit.measures=TRUE, standardized = TRUE)## lavaan 0.6-3 ended normally after 662 iterations
##
## Optimization method NLMINB
## Number of free parameters 197
##
## Used Total
## Number of observations 3800 4135
## Number of clusters [uniqueid] 244
##
## Estimator ML
## Model Fit Test Statistic 538.183
## Degrees of freedom 126
## P-value (Chi-square) 0.000
##
## Model test baseline model:
##
## Minimum Function Test Statistic 16158.750
## Degrees of freedom 272
## P-value 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 0.974
## Tucker-Lewis Index (TLI) 0.944
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -50123.847
## Loglikelihood unrestricted model (H1) -49854.756
##
## Number of free parameters 197
## Akaike (AIC) 100641.694
## Bayesian (BIC) 101871.517
## Sample-size adjusted Bayesian (BIC) 101245.544
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.029
## 90 Percent Confidence Interval 0.027 0.032
## P-value RMSEA <= 0.05 1.000
##
## Standardized Root Mean Square Residual (corr metric):
##
## SRMR (within covariance matrix) 0.018
## SRMR (between covariance matrix) 0.047
##
## Parameter Estimates:
##
## Information Observed
## Observed information based on Hessian
## Standard Errors Standard
##
##
## Level 1 [within]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## cont =~
## succeed 1.000 0.562 0.722
## control 0.885 0.026 34.293 0.000 0.498 0.617
## learning 0.930 0.027 34.931 0.000 0.523 0.626
## exp_y 1.086 0.025 43.513 0.000 0.611 0.820
## exp_t 1.092 0.025 43.040 0.000 0.614 0.801
## value =~
## imp_y 1.000 0.704 0.888
## imp_fut 0.636 0.034 18.710 0.000 0.448 0.600
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## happy ~
## cont 0.507 0.027 18.461 0.000 0.285 0.366
## value 0.048 0.023 2.119 0.034 0.034 0.043
## act_lec 0.176 0.044 3.975 0.000 0.176 0.087
## act_indwork 0.154 0.044 3.479 0.001 0.154 0.069
## act_groupwork 0.222 0.059 3.757 0.000 0.222 0.070
## act_pres 0.070 0.062 1.117 0.264 0.070 0.021
## act_lab 0.241 0.045 5.393 0.000 0.241 0.113
## act_nonins 0.149 0.056 2.678 0.007 0.149 0.051
## excited ~
## cont 0.332 0.028 11.762 0.000 0.187 0.234
## value 0.120 0.024 4.963 0.000 0.085 0.106
## act_lec 0.154 0.047 3.294 0.001 0.154 0.075
## act_indwork 0.154 0.047 3.306 0.001 0.154 0.068
## act_groupwork 0.201 0.062 3.217 0.001 0.201 0.062
## act_pres 0.248 0.066 3.760 0.000 0.248 0.074
## act_lab 0.249 0.047 5.288 0.000 0.249 0.115
## act_nonins 0.124 0.059 2.114 0.034 0.124 0.042
## frustrated ~
## cont -0.228 0.027 -8.465 0.000 -0.128 -0.172
## value 0.077 0.023 3.323 0.001 0.054 0.072
## act_lec -0.114 0.045 -2.541 0.011 -0.114 -0.059
## act_indwork -0.074 0.045 -1.658 0.097 -0.074 -0.035
## act_groupwork -0.094 0.060 -1.566 0.117 -0.094 -0.031
## act_pres -0.140 0.063 -2.204 0.027 -0.140 -0.045
## act_lab -0.131 0.045 -2.885 0.004 -0.131 -0.064
## act_nonins -0.097 0.056 -1.721 0.085 -0.097 -0.035
## bored ~
## cont -0.189 0.032 -5.993 0.000 -0.106 -0.121
## value -0.114 0.027 -4.212 0.000 -0.080 -0.091
## act_lec -0.002 0.053 -0.035 0.972 -0.002 -0.001
## act_indwork -0.058 0.053 -1.093 0.274 -0.058 -0.023
## act_groupwork -0.104 0.071 -1.471 0.141 -0.104 -0.029
## act_pres -0.046 0.075 -0.616 0.538 -0.046 -0.012
## act_lab -0.194 0.053 -3.635 0.000 -0.194 -0.081
## act_nonins 0.054 0.067 0.808 0.419 0.054 0.016
## cont ~
## act_lec 0.118 0.036 3.313 0.001 0.210 0.081
## act_indwork -0.037 0.036 -1.030 0.303 -0.065 -0.023
## act_groupwork 0.109 0.048 2.263 0.024 0.194 0.047
## act_pres 0.157 0.051 3.057 0.002 0.279 0.067
## act_lab 0.031 0.036 0.876 0.381 0.056 0.020
## act_nonins -0.046 0.045 -1.003 0.316 -0.081 -0.022
## value ~
## act_lec -0.416 0.045 -9.260 0.000 -0.591 -0.229
## act_indwork -0.498 0.045 -11.045 0.000 -0.707 -0.248
## act_groupwork -0.462 0.061 -7.598 0.000 -0.657 -0.161
## act_pres -0.355 0.065 -5.471 0.000 -0.503 -0.121
## act_lab -0.543 0.045 -12.069 0.000 -0.771 -0.283
## act_nonins -0.512 0.057 -8.943 0.000 -0.727 -0.195
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .happy ~~
## .excited 0.211 0.010 21.064 0.000 0.211 0.388
## .frustrated -0.093 0.009 -10.201 0.000 -0.093 -0.177
## .bored -0.074 0.011 -6.987 0.000 -0.074 -0.120
## .excited ~~
## .frustrated -0.048 0.009 -5.085 0.000 -0.048 -0.086
## .bored -0.055 0.011 -4.912 0.000 -0.055 -0.083
## .frustrated ~~
## .bored 0.106 0.011 9.749 0.000 0.106 0.167
## .cont ~~
## .value 0.147 0.009 17.207 0.000 0.386 0.386
## act_lec ~~
## act_indwork -0.034 0.002 -14.275 0.000 -0.034 -0.247
## act_groupwork -0.009 0.002 -5.640 0.000 -0.009 -0.095
## act_pres -0.027 0.002 -16.389 0.000 -0.027 -0.287
## act_lab -0.032 0.002 -13.008 0.000 -0.032 -0.224
## act_nonins -0.018 0.002 -10.206 0.000 -0.018 -0.174
## act_indwork ~~
## act_groupwork -0.009 0.001 -6.175 0.000 -0.009 -0.104
## act_pres -0.011 0.001 -7.701 0.000 -0.011 -0.130
## act_lab -0.022 0.002 -10.129 0.000 -0.022 -0.172
## act_nonins -0.009 0.002 -5.898 0.000 -0.009 -0.099
## act_groupwork ~~
## act_pres -0.003 0.001 -2.945 0.003 -0.003 -0.049
## act_lab -0.025 0.002 -15.761 0.000 -0.025 -0.274
## act_nonins -0.005 0.001 -4.581 0.000 -0.005 -0.077
## act_pres ~~
## act_lab -0.004 0.001 -2.592 0.010 -0.004 -0.044
## act_nonins -0.011 0.001 -10.127 0.000 -0.011 -0.172
## act_lab ~~
## act_nonins -0.017 0.002 -10.426 0.000 -0.017 -0.178
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .succeed 0.000 0.000 0.000
## .control 0.000 0.000 0.000
## .learning 0.000 0.000 0.000
## .exp_y 0.000 0.000 0.000
## .exp_t 0.000 0.000 0.000
## .imp_y 0.000 0.000 0.000
## .imp_fut 0.000 0.000 0.000
## .happy 0.000 0.000 0.000
## .excited 0.000 0.000 0.000
## .frustrated 0.000 0.000 0.000
## .bored 0.000 0.000 0.000
## act_lec 0.000 0.000 0.000
## act_indwork 0.000 0.000 0.000
## act_groupwork 0.000 0.000 0.000
## act_pres 0.000 0.000 0.000
## act_lab 0.000 0.000 0.000
## act_nonins 0.000 0.000 0.000
## .cont 0.000 0.000 0.000
## .value 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .succeed 0.290 0.008 34.531 0.000 0.290 0.478
## .control 0.402 0.011 38.023 0.000 0.402 0.619
## .learning 0.425 0.011 37.797 0.000 0.425 0.609
## .exp_y 0.182 0.006 28.351 0.000 0.182 0.327
## .exp_t 0.210 0.007 30.201 0.000 0.210 0.358
## .imp_y 0.133 0.025 5.418 0.000 0.133 0.212
## .imp_fut 0.357 0.013 27.436 0.000 0.357 0.640
## .happy 0.512 0.012 41.063 0.000 0.512 0.844
## .excited 0.578 0.014 41.655 0.000 0.578 0.909
## .frustrated 0.537 0.013 41.866 0.000 0.537 0.969
## .bored 0.750 0.018 41.979 0.000 0.750 0.964
## act_lec 0.150 0.004 42.052 0.000 0.150 1.000
## act_indwork 0.123 0.003 42.181 0.000 0.123 1.000
## act_groupwork 0.060 0.001 42.208 0.000 0.060 1.000
## act_pres 0.057 0.001 42.023 0.000 0.057 1.000
## act_lab 0.134 0.003 42.094 0.000 0.134 1.000
## act_nonins 0.072 0.002 42.278 0.000 0.072 1.000
## .cont 0.312 0.013 23.384 0.000 0.988 0.988
## .value 0.464 0.028 16.651 0.000 0.935 0.935
##
##
## Level 2 [uniqueid]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## cont =~
## succeed 1.000 0.471 0.952
## control 1.072 0.071 15.074 0.000 0.505 0.791
## learning 0.980 0.052 18.673 0.000 0.462 0.886
## exp_y 1.174 0.052 22.727 0.000 0.553 0.934
## exp_t 1.099 0.053 20.716 0.000 0.518 0.899
## value =~
## imp_y 1.000 0.645 1.042
## imp_fut 0.863 0.067 12.816 0.000 0.557 0.807
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## happy ~
## cont 0.702 0.166 4.227 0.000 0.331 0.479
## value 0.093 0.100 0.930 0.352 0.060 0.087
## act_lec -1.284 2.494 -0.515 0.607 -1.284 -0.164
## act_indwork -0.454 1.651 -0.275 0.783 -0.454 -0.082
## act_groupwork 3.095 3.846 0.805 0.421 3.095 0.170
## act_pres -0.154 1.162 -0.133 0.894 -0.154 -0.027
## act_lab -1.126 1.814 -0.621 0.535 -1.126 -0.372
## act_nonins 2.190 3.006 0.729 0.466 2.190 0.115
## excited ~
## cont 0.364 0.183 1.988 0.047 0.172 0.277
## value 0.167 0.110 1.519 0.129 0.108 0.174
## act_lec -1.895 2.780 -0.681 0.496 -1.895 -0.271
## act_indwork 0.278 1.751 0.159 0.874 0.278 0.056
## act_groupwork 6.153 4.273 1.440 0.150 6.153 0.378
## act_pres 0.552 1.244 0.444 0.657 0.552 0.106
## act_lab -0.856 1.957 -0.437 0.662 -0.856 -0.316
## act_nonins 1.990 3.440 0.578 0.563 1.990 0.117
## frustrated ~
## cont -0.296 0.156 -1.900 0.057 -0.140 -0.293
## value 0.086 0.097 0.886 0.376 0.055 0.116
## act_lec 3.017 2.443 1.235 0.217 3.017 0.560
## act_indwork 1.393 1.709 0.815 0.415 1.393 0.365
## act_groupwork -1.160 3.991 -0.291 0.771 -1.160 -0.093
## act_pres 0.788 1.204 0.654 0.513 0.788 0.197
## act_lab 2.022 1.920 1.053 0.292 2.022 0.969
## act_nonins -3.365 2.919 -1.153 0.249 -3.365 -0.256
## bored ~
## cont -0.219 0.166 -1.315 0.189 -0.103 -0.145
## value -0.400 0.107 -3.732 0.000 -0.258 -0.363
## act_lec 0.358 2.471 0.145 0.885 0.358 0.044
## act_indwork 1.819 1.688 1.077 0.281 1.819 0.319
## act_groupwork -0.985 3.728 -0.264 0.792 -0.985 -0.053
## act_pres 0.920 1.183 0.777 0.437 0.920 0.153
## act_lab 0.814 1.802 0.451 0.652 0.814 0.261
## act_nonins -1.766 2.939 -0.601 0.548 -1.766 -0.090
## cont ~
## act_lec 0.827 1.945 0.425 0.671 1.754 0.155
## act_indwork -0.056 1.289 -0.043 0.965 -0.118 -0.015
## act_groupwork -3.897 2.864 -1.361 0.174 -8.267 -0.314
## act_pres 0.007 0.907 0.008 0.994 0.015 0.002
## act_lab 0.551 1.350 0.409 0.683 1.170 0.267
## act_nonins -0.151 2.449 -0.062 0.951 -0.321 -0.012
## value ~
## act_lec 0.450 2.163 0.208 0.835 0.697 0.062
## act_indwork -0.849 1.545 -0.550 0.583 -1.316 -0.164
## act_groupwork -1.409 3.394 -0.415 0.678 -2.184 -0.083
## act_pres -1.805 1.081 -1.669 0.095 -2.797 -0.332
## act_lab -0.637 1.597 -0.399 0.690 -0.987 -0.225
## act_nonins 0.414 2.650 0.156 0.876 0.641 0.023
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .happy ~~
## .excited 0.212 0.042 5.093 0.000 0.212 0.670
## .frustrated 0.033 0.030 1.090 0.276 0.033 0.130
## .bored -0.020 0.032 -0.633 0.527 -0.020 -0.057
## .excited ~~
## .frustrated 0.104 0.032 3.224 0.001 0.104 0.433
## .bored 0.034 0.034 0.994 0.320 0.034 0.103
## .frustrated ~~
## .bored 0.111 0.033 3.379 0.001 0.111 0.414
## .cont ~~
## .value 0.173 0.028 6.229 0.000 0.601 0.601
## act_lec ~~
## act_indwork 0.001 0.001 0.673 0.501 0.001 0.080
## act_groupwork -0.004 0.001 -5.601 0.000 -0.004 -1.129
## act_pres 0.011 0.001 8.611 0.000 0.011 1.059
## act_lab -0.017 0.002 -6.814 0.000 -0.017 -0.818
## act_nonins 0.002 0.001 3.708 0.000 0.002 0.758
## act_indwork ~~
## act_groupwork -0.002 0.001 -2.782 0.005 -0.002 -0.431
## act_pres -0.002 0.001 -1.337 0.181 -0.002 -0.120
## act_lab -0.019 0.003 -6.893 0.000 -0.019 -0.679
## act_nonins -0.004 0.001 -4.767 0.000 -0.004 -0.853
## act_groupwork ~~
## act_pres -0.002 0.001 -3.351 0.001 -0.002 -0.476
## act_lab 0.008 0.001 6.702 0.000 0.008 0.960
## act_nonins -0.000 0.000 -0.375 0.708 -0.000 -0.096
## act_pres ~~
## act_lab -0.015 0.002 -6.462 0.000 -0.015 -0.561
## act_nonins 0.005 0.001 6.795 0.000 0.005 1.124
## act_lab ~~
## act_nonins -0.002 0.001 -1.777 0.076 -0.002 -0.267
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .succeed 1.839 0.673 2.732 0.006 1.839 3.714
## .control 1.634 0.722 2.264 0.024 1.634 2.556
## .learning 1.663 0.660 2.520 0.012 1.663 3.189
## .exp_y 1.753 0.790 2.218 0.027 1.753 2.960
## .exp_t 1.787 0.740 2.414 0.016 1.787 3.100
## .imp_y 1.686 0.807 2.089 0.037 1.686 2.722
## .imp_fut 1.349 0.700 1.927 0.054 1.349 1.953
## .happy 1.775 0.904 1.963 0.050 1.775 2.569
## .excited 0.835 0.898 0.930 0.353 0.835 1.347
## .frustrated -0.397 0.866 -0.459 0.646 -0.397 -0.834
## .bored 0.726 0.980 0.741 0.459 0.726 1.019
## act_lec 0.196 0.008 23.190 0.000 0.196 2.217
## act_indwork 0.168 0.010 16.984 0.000 0.168 1.345
## act_groupwork 0.065 0.005 13.870 0.000 0.065 1.702
## act_pres 0.077 0.009 8.934 0.000 0.077 0.645
## act_lab 0.246 0.016 15.467 0.000 0.246 1.076
## act_nonins 0.079 0.005 15.990 0.000 0.079 2.170
## .cont 0.000 0.000 0.000
## .value 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .succeed 0.023 0.005 4.161 0.000 0.023 0.093
## .control 0.153 0.018 8.492 0.000 0.153 0.375
## .learning 0.058 0.009 6.367 0.000 0.058 0.215
## .exp_y 0.045 0.008 5.798 0.000 0.045 0.127
## .exp_t 0.064 0.009 7.169 0.000 0.064 0.191
## .imp_y -0.033 0.025 -1.329 0.184 -0.033 -0.086
## .imp_fut 0.167 0.026 6.532 0.000 0.167 0.349
## .happy 0.337 0.044 7.607 0.000 0.337 0.705
## .excited 0.298 0.052 5.775 0.000 0.298 0.776
## .frustrated 0.193 0.040 4.829 0.000 0.193 0.851
## .bored 0.369 0.045 8.258 0.000 0.369 0.728
## act_lec 0.008 0.002 4.909 0.000 0.008 1.000
## act_indwork 0.016 0.002 7.233 0.000 0.016 1.000
## act_groupwork 0.001 0.000 3.042 0.002 0.001 1.000
## act_pres 0.014 0.002 8.676 0.000 0.014 1.000
## act_lab 0.052 0.006 9.327 0.000 0.052 1.000
## act_nonins 0.001 0.001 2.495 0.013 0.001 1.000
## .cont 0.206 0.031 6.724 0.000 0.925 0.925
## .value 0.402 0.047 8.521 0.000 0.966 0.966lavInspect(fit2, "icc")## succeed control learning exp_y exp_t
## 0.289 0.384 0.277 0.389 0.364
## imp_y imp_fut happy excited frustrated
## 0.379 0.460 0.440 0.376 0.291
## bored act_lec act_indwork act_groupwork act_pres
## 0.395 0.062 0.117 0.030 0.210
## act_lab act_nonins
## 0.281 0.029lavInspect(fit2, "h1")## $within
## $within$cov
## succed contrl lernng exp_y exp_t imp_y imp_ft happy
## succeed 0.605
## control 0.299 0.650
## learning 0.326 0.241 0.699
## exp_y 0.329 0.298 0.317 0.554
## exp_t 0.335 0.302 0.304 0.391 0.587
## imp_y 0.149 0.151 0.156 0.152 0.156 0.629
## imp_fut 0.099 0.097 0.127 0.083 0.079 0.315 0.558
## happy 0.189 0.170 0.154 0.176 0.168 0.085 0.059 0.606
## excited 0.136 0.125 0.113 0.129 0.129 0.098 0.057 0.282
## frustrated -0.066 -0.062 -0.054 -0.069 -0.060 0.010 0.013 -0.127
## bored -0.088 -0.078 -0.078 -0.072 -0.083 -0.081 -0.038 -0.119
## act_lec 0.017 -0.006 0.044 0.008 0.014 -0.005 -0.007 0.013
## act_indwork -0.017 -0.002 -0.005 -0.012 -0.014 -0.023 -0.010 -0.003
## act_groupwork 0.005 0.009 0.003 0.003 0.006 -0.003 0.000 0.006
## act_pres 0.004 -0.002 0.013 0.009 0.006 0.006 -0.002 -0.002
## act_lab -0.001 0.003 -0.004 -0.004 0.000 -0.026 -0.021 0.013
## act_nonins -0.008 -0.001 -0.025 -0.007 -0.004 -0.008 -0.005 -0.004
## excitd frstrt bored act_lc act_nd act_gr act_pr act_lb
## succeed
## control
## learning
## exp_y
## exp_t
## imp_y
## imp_fut
## happy
## excited 0.636
## frustrated -0.072 0.554
## bored -0.093 0.118 0.778
## act_lec 0.003 -0.008 0.007 0.148
## act_indwork -0.004 0.002 0.003 -0.034 0.123
## act_groupwork 0.003 -0.001 -0.002 -0.009 -0.009 0.059
## act_pres 0.008 -0.003 -0.003 -0.026 -0.010 -0.003 0.057
## act_lab 0.014 -0.010 -0.020 -0.031 -0.022 -0.024 -0.004 0.134
## act_nonins -0.007 0.001 0.011 -0.018 -0.010 -0.005 -0.010 -0.017
## act_nn
## succeed
## control
## learning
## exp_y
## exp_t
## imp_y
## imp_fut
## happy
## excited
## frustrated
## bored
## act_lec
## act_indwork
## act_groupwork
## act_pres
## act_lab
## act_nonins 0.071
##
## $within$mean
## succeed control learning exp_y exp_t
## 0.113 0.101 0.102 0.108 0.110
## imp_y imp_fut happy excited frustrated
## 0.077 0.060 0.091 0.051 0.036
## bored act_lec act_indwork act_groupwork act_pres
## 0.081 0.012 0.010 0.004 0.005
## act_lab act_nonins
## 0.015 0.004
##
##
## $uniqueid
## $uniqueid$cov
## succed contrl lernng exp_y exp_t imp_y imp_ft happy
## succeed 0.247
## control 0.232 0.406
## learning 0.218 0.239 0.268
## exp_y 0.259 0.281 0.251 0.352
## exp_t 0.252 0.253 0.221 0.301 0.335
## imp_y 0.177 0.223 0.221 0.198 0.168 0.384
## imp_fut 0.129 0.196 0.177 0.141 0.121 0.358 0.475
## happy 0.167 0.198 0.185 0.184 0.168 0.160 0.146 0.477
## excited 0.088 0.098 0.118 0.082 0.081 0.119 0.118 0.297
## frustrated -0.059 -0.032 -0.032 -0.044 -0.055 -0.023 0.002 -0.017
## bored -0.130 -0.149 -0.152 -0.136 -0.111 -0.204 -0.174 -0.129
## act_lec 0.012 0.004 0.014 0.016 0.013 -0.002 -0.014 0.008
## act_indwork -0.003 -0.005 -0.001 -0.001 -0.006 0.004 0.004 -0.002
## act_groupwork -0.004 0.000 -0.005 -0.006 -0.004 -0.003 -0.001 -0.002
## act_pres 0.007 0.006 0.014 0.011 0.011 -0.005 -0.016 0.011
## act_lab -0.015 -0.013 -0.021 -0.022 -0.013 -0.008 0.008 -0.018
## act_nonins 0.000 0.005 0.001 0.000 0.002 -0.003 -0.002 0.004
## excitd frstrt bored act_lc act_nd act_gr act_pr act_lb
## succeed
## control
## learning
## exp_y
## exp_t
## imp_y
## imp_fut
## happy
## excited 0.383
## frustrated 0.075 0.227
## bored -0.036 0.144 0.508
## act_lec -0.009 -0.007 -0.001 0.010
## act_indwork -0.002 0.001 0.019 0.001 0.016
## act_groupwork 0.005 0.001 0.001 -0.003 -0.002 0.002
## act_pres -0.002 -0.006 -0.006 0.010 -0.002 -0.002 0.015
## act_lab 0.012 0.019 -0.010 -0.017 -0.019 0.008 -0.015 0.052
## act_nonins 0.001 -0.003 -0.005 0.003 -0.003 0.000 0.004 -0.002
## act_nn
## succeed
## control
## learning
## exp_y
## exp_t
## imp_y
## imp_fut
## happy
## excited
## frustrated
## bored
## act_lec
## act_indwork
## act_groupwork
## act_pres
## act_lab
## act_nonins 0.002
##
## $uniqueid$mean
## succeed control learning exp_y exp_t
## 1.751 1.560 1.584 1.674 1.704
## imp_y imp_fut happy excited frustrated
## 1.201 0.936 1.419 0.788 0.567
## bored act_lec act_indwork act_groupwork act_pres
## 1.247 0.185 0.158 0.060 0.072
## act_lab act_nonins
## 0.230 0.075MSEM take 2 no between level model
library(lavaan)
ML_EMO2 <- '
level:1
#measurement model
cont =~ control + succeed + learning + exp_y + exp_t
value =~ imp_y + imp_fut
#regressions
happy ~ cont + value + act_lec + act_indwork + act_groupwork + act_pres + act_lab + act_nonins
excited ~ cont + value + act_lec + act_indwork + act_groupwork + act_pres + act_lab + act_nonins
frustrated ~ cont + value + act_lec + act_indwork + act_groupwork + act_pres + act_lab + act_nonins
bored ~ cont + value + act_lec + act_indwork + act_groupwork + act_pres + act_lab + act_nonins
cont ~ act_lec + act_indwork + act_groupwork + act_pres + act_lab + act_nonins
value ~ act_lec + act_indwork + act_groupwork + act_pres + act_lab + act_nonins
#residual correlations
happy ~~ excited + frustrated + bored
excited ~~ frustrated + bored
frustrated ~~ bored
cont ~~ value
act_lec ~~ act_indwork + act_groupwork + act_pres + act_lab + act_nonins
act_indwork ~~ act_groupwork + act_pres + act_lab + act_nonins
act_groupwork ~~ act_pres + act_lab + act_nonins
act_pres ~~ act_lab + act_nonins
act_lab ~~ act_nonins
level:2
#saturated model
control ~~ control + succeed + learning + exp_y + exp_t
succeed ~~ succeed + learning + exp_y + exp_t
learning ~~ learning + exp_y + exp_t
exp_y ~~ exp_y + exp_t
exp_t ~~ exp_t
'
fit3<-sem(ML_EMO2, data=SciMo_esm, cluster = "uniqueid")
summary(fit3, fit.measures=TRUE, standardized = TRUE)## lavaan 0.6-3 ended normally after 160 iterations
##
## Optimization method NLMINB
## Number of free parameters 122
##
## Used Total
## Number of observations 3800 4135
## Number of clusters [uniqueid] 244
##
## Estimator ML
## Model Fit Test Statistic 502.839
## Degrees of freedom 63
## P-value (Chi-square) 0.000
##
## Model test baseline model:
##
## Minimum Function Test Statistic 17142.821
## Degrees of freedom 146
## P-value 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 0.974
## Tucker-Lewis Index (TLI) 0.940
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -54389.959
## Loglikelihood unrestricted model (H1) -54138.540
##
## Number of free parameters 122
## Akaike (AIC) 109023.918
## Bayesian (BIC) 109785.535
## Sample-size adjusted Bayesian (BIC) 109397.876
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.043
## 90 Percent Confidence Interval 0.039 0.046
## P-value RMSEA <= 0.05 1.000
##
## Standardized Root Mean Square Residual (corr metric):
##
## SRMR (within covariance matrix) 0.021
## SRMR (between covariance matrix) 0.016
##
## Parameter Estimates:
##
## Information Observed
## Observed information based on Hessian
## Standard Errors Standard
##
##
## Level 1 [within]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## cont =~
## control 1.000 0.566 0.667
## succeed 1.112 0.031 35.700 0.000 0.630 0.761
## learning 1.066 0.033 32.253 0.000 0.604 0.681
## exp_y 1.205 0.032 37.952 0.000 0.682 0.847
## exp_t 1.207 0.032 37.335 0.000 0.684 0.829
## value =~
## imp_y 1.000 0.931 0.923
## imp_fut 0.784 0.025 31.018 0.000 0.730 0.718
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## happy ~
## cont 0.892 0.043 20.762 0.000 0.505 0.485
## value 0.048 0.024 1.992 0.046 0.044 0.042
## act_lec 0.179 0.053 3.408 0.001 0.179 0.068
## act_indwork 0.176 0.053 3.291 0.001 0.176 0.063
## act_groupwork 0.164 0.071 2.311 0.021 0.164 0.039
## act_pres 0.132 0.069 1.904 0.057 0.132 0.034
## act_lab 0.165 0.050 3.274 0.001 0.165 0.068
## act_nonins 0.197 0.066 2.971 0.003 0.197 0.051
## excited ~
## cont 0.570 0.043 13.161 0.000 0.323 0.318
## value 0.102 0.024 4.243 0.000 0.095 0.093
## act_lec 0.091 0.054 1.707 0.088 0.091 0.036
## act_indwork 0.182 0.055 3.326 0.001 0.182 0.067
## act_groupwork 0.273 0.073 3.765 0.000 0.273 0.067
## act_pres 0.152 0.070 2.178 0.029 0.152 0.040
## act_lab 0.307 0.051 6.019 0.000 0.307 0.131
## act_nonins 0.138 0.068 2.035 0.042 0.138 0.037
## frustrated ~
## cont -0.298 0.038 -7.792 0.000 -0.169 -0.192
## value 0.060 0.022 2.758 0.006 0.056 0.063
## act_lec -0.104 0.049 -2.129 0.033 -0.104 -0.047
## act_indwork -0.058 0.050 -1.164 0.244 -0.058 -0.025
## act_groupwork -0.044 0.066 -0.663 0.508 -0.044 -0.012
## act_pres -0.135 0.063 -2.134 0.033 -0.135 -0.041
## act_lab -0.023 0.046 -0.499 0.618 -0.023 -0.011
## act_nonins -0.090 0.062 -1.461 0.144 -0.090 -0.028
## bored ~
## cont -0.318 0.049 -6.482 0.000 -0.180 -0.158
## value -0.266 0.028 -9.418 0.000 -0.248 -0.218
## act_lec -0.107 0.061 -1.752 0.080 -0.107 -0.037
## act_indwork -0.065 0.062 -1.044 0.297 -0.065 -0.021
## act_groupwork -0.155 0.083 -1.869 0.062 -0.155 -0.034
## act_pres -0.240 0.079 -3.038 0.002 -0.240 -0.057
## act_lab -0.297 0.058 -5.127 0.000 -0.297 -0.113
## act_nonins -0.122 0.077 -1.576 0.115 -0.122 -0.029
## cont ~
## act_lec 0.097 0.038 2.554 0.011 0.171 0.068
## act_indwork -0.054 0.038 -1.428 0.153 -0.096 -0.036
## act_groupwork 0.061 0.049 1.230 0.219 0.107 0.027
## act_pres 0.143 0.050 2.836 0.005 0.252 0.067
## act_lab -0.017 0.037 -0.465 0.642 -0.030 -0.013
## act_nonins -0.056 0.047 -1.200 0.230 -0.099 -0.027
## value ~
## act_lec -0.519 0.054 -9.654 0.000 -0.557 -0.221
## act_indwork -0.574 0.056 -10.272 0.000 -0.617 -0.230
## act_groupwork -0.551 0.074 -7.473 0.000 -0.592 -0.146
## act_pres -0.512 0.070 -7.317 0.000 -0.549 -0.147
## act_lab -0.599 0.051 -11.668 0.000 -0.643 -0.278
## act_nonins -0.606 0.069 -8.786 0.000 -0.651 -0.176
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .happy ~~
## .excited 0.385 0.017 22.575 0.000 0.385 0.458
## .frustrated -0.073 0.014 -5.406 0.000 -0.073 -0.095
## .bored -0.098 0.017 -5.697 0.000 -0.098 -0.101
## .excited ~~
## .frustrated 0.049 0.013 3.648 0.000 0.049 0.061
## .bored -0.004 0.017 -0.240 0.811 -0.004 -0.004
## .frustrated ~~
## .bored 0.231 0.016 14.517 0.000 0.231 0.249
## .cont ~~
## .value 0.265 0.014 19.245 0.000 0.519 0.519
## act_lec ~~
## act_indwork -0.033 0.002 -13.300 0.000 -0.033 -0.221
## act_groupwork -0.013 0.002 -8.000 0.000 -0.013 -0.131
## act_pres -0.015 0.002 -8.749 0.000 -0.015 -0.143
## act_lab -0.049 0.003 -16.816 0.000 -0.049 -0.284
## act_nonins -0.016 0.002 -8.859 0.000 -0.016 -0.145
## act_indwork ~~
## act_groupwork -0.011 0.002 -7.247 0.000 -0.011 -0.118
## act_pres -0.013 0.002 -7.929 0.000 -0.013 -0.130
## act_lab -0.041 0.003 -15.315 0.000 -0.041 -0.256
## act_nonins -0.013 0.002 -8.029 0.000 -0.013 -0.131
## act_groupwork ~~
## act_pres -0.005 0.001 -4.722 0.000 -0.005 -0.077
## act_lab -0.016 0.002 -9.259 0.000 -0.016 -0.152
## act_nonins -0.005 0.001 -4.782 0.000 -0.005 -0.078
## act_pres ~~
## act_lab -0.019 0.002 -10.121 0.000 -0.019 -0.166
## act_nonins -0.006 0.001 -5.236 0.000 -0.006 -0.085
## act_lab ~~
## act_nonins -0.020 0.002 -10.247 0.000 -0.020 -0.169
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .control 0.000 0.000 0.000
## .succeed 0.000 0.000 0.000
## .learning 0.000 0.000 0.000
## .exp_y 0.000 0.000 0.000
## .exp_t 0.000 0.000 0.000
## .imp_y 1.751 0.040 43.944 0.000 1.751 1.735
## .imp_fut 1.375 0.035 39.724 0.000 1.375 1.353
## .happy 1.379 0.043 32.435 0.000 1.379 1.322
## .excited 0.724 0.041 17.779 0.000 0.724 0.714
## .frustrated 0.689 0.035 19.763 0.000 0.689 0.785
## .bored 1.356 0.045 29.842 0.000 1.356 1.192
## act_lec 0.196 0.006 30.463 0.000 0.196 0.494
## act_indwork 0.167 0.006 27.557 0.000 0.167 0.447
## act_groupwork 0.066 0.004 16.323 0.000 0.066 0.265
## act_pres 0.078 0.004 17.882 0.000 0.078 0.290
## act_lab 0.248 0.007 35.364 0.000 0.248 0.574
## act_nonins 0.079 0.004 18.112 0.000 0.079 0.294
## .cont 0.000 0.000 0.000
## .value 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .control 0.401 0.011 38.005 0.000 0.401 0.556
## .succeed 0.289 0.008 34.654 0.000 0.289 0.422
## .learning 0.422 0.011 37.692 0.000 0.422 0.537
## .exp_y 0.183 0.006 28.615 0.000 0.183 0.282
## .exp_t 0.212 0.007 30.455 0.000 0.212 0.312
## .imp_y 0.151 0.025 6.112 0.000 0.151 0.148
## .imp_fut 0.500 0.019 26.502 0.000 0.500 0.484
## .happy 0.805 0.021 37.659 0.000 0.805 0.740
## .excited 0.878 0.021 41.041 0.000 0.878 0.854
## .frustrated 0.745 0.017 42.845 0.000 0.745 0.967
## .bored 1.154 0.027 42.609 0.000 1.154 0.890
## act_lec 0.158 0.004 43.589 0.000 0.158 1.000
## act_indwork 0.139 0.003 43.589 0.000 0.139 1.000
## act_groupwork 0.061 0.001 43.589 0.000 0.061 1.000
## act_pres 0.072 0.002 43.589 0.000 0.072 1.000
## act_lab 0.186 0.004 43.589 0.000 0.186 1.000
## act_nonins 0.073 0.002 43.589 0.000 0.073 1.000
## .cont 0.317 0.017 18.606 0.000 0.987 0.987
## .value 0.822 0.033 25.211 0.000 0.948 0.948
##
##
## Level 2 [uniqueid]:
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .control ~~
## .succeed 0.116 0.018 6.570 0.000 0.116 0.581
## .learning 0.107 0.017 6.124 0.000 0.107 0.553
## .exp_y 0.154 0.022 7.064 0.000 0.154 0.605
## .exp_t 0.134 0.021 6.300 0.000 0.134 0.524
## .succeed ~~
## .learning 0.102 0.014 7.261 0.000 0.102 0.731
## .exp_y 0.148 0.018 8.313 0.000 0.148 0.806
## .exp_t 0.152 0.018 8.411 0.000 0.152 0.818
## .learning ~~
## .exp_y 0.124 0.017 7.310 0.000 0.124 0.698
## .exp_t 0.102 0.016 6.243 0.000 0.102 0.568
## .exp_y ~~
## .exp_t 0.194 0.022 8.760 0.000 0.194 0.825
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .control 1.646 0.039 42.251 0.000 1.646 3.130
## .succeed 1.847 0.032 58.285 0.000 1.847 4.851
## .learning 1.672 0.031 53.609 0.000 1.672 4.558
## .exp_y 1.764 0.037 47.239 0.000 1.764 3.655
## .exp_t 1.796 0.038 47.587 0.000 1.796 3.683
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .control 0.277 0.029 9.510 0.000 0.277 1.000
## .succeed 0.145 0.017 8.741 0.000 0.145 1.000
## .learning 0.135 0.017 8.090 0.000 0.135 1.000
## .exp_y 0.233 0.024 9.601 0.000 0.233 1.000
## .exp_t 0.238 0.025 9.587 0.000 0.238 1.000Preparing dataset for Mplus Multilevel SEM
#scimo_emo <- select(SciMo_esm,
# uniqueid, signal_value, teacher_ID,
# control, succeed, learning, exp_y, exp_t,
# imp_y, imp_fut,
# happy, excited, frustrated, bored,
# act_lec, act_indwork, act_groupwork, act_pres,
# act_lab, act_nonins, act_test)#Preparing data for dat file for Mplus
#MTH_132_Mplus_long$stud_id <- as.numeric(sub("_","", MTH_132_Mplus_long$stud_id))
#scimo_emo[is.na(scimo_emo)] <- -999Writing data to csv file for mplus
#write_csv(scimo_emo, "/Volumes/educ/CEPSE/Projects/SchmidtLab/Beymer_Projects/Publications/Scimo SEM #Emotions/Mplus/scimo_emo_11_19_19.csv")