| title: “Lavaan School Study Regression” |
| author: “Levi Brackman” |
| date: “November 12, 2016” |
| output: html_document |
Loading required package: lavaanThis is lavaan 0.5-20lavaan is BETA software! Please report any bugs. ###############################################################################This is semTools 0.4-12All users of R (or SEM) are invited to submit functions or ideas for functions.###############################################################################
Attaching package: 'dplyr'The following objects are masked from 'package:plyr':
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intersect, setdiff, setequal, union
Attaching package: 'purrr'The following objects are masked from 'package:dplyr':
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compactRead-in the dataset
Read in pre and post data from Cranbrook
setwd("/Users/levibrackman/Documents/stats_march_2016/Schools Study")
CranReddam<-read.csv("Three_Group_Means_W-Dummy_July_15_2016.csv")
dim(CranReddam)## [1] 221 49library(dplyr)
#View(CranReddam)
# Correlations with significance levels
library(Hmisc)## Loading required package: lattice## Loading required package: survival## Loading required package: Formula## Loading required package: ggplot2##
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## reliability## The Commander GUI is launched only in interactive sessions#options(scipen=999)
#Creat interaction effect veriable for each group
#View(CranReddam)
#names(CranReddam)
#indProd(CranReddam, "MLQ_1", "GROUP1")
#############################################
#### Creating a function to make the ###
#### interaction terms ###
############################################
#meanallinteraction<- function(dataset, inter, veriable1, group){
#part1<-noquote(paste0(dataset, "$", "GROUP", inter, collapse = ""))
#part2<-noquote(paste0("scale","(",dataset, "$", veriable1,")", collapse = ""))
#part3<- noquote(paste0(dataset, "$", group, collapse = ""))
#final<-noquote(paste0(part1,"<-", part2,"*",part3, collapse = ""))
#final
#}
#meanallinteraction("CranReddam", "APSI_FG_1", "APSI_FG_1", "GROUP1")
#meanallinteraction("CranReddam", "APSI_FN_1", "APSI_FN_1", "GROUP1")
#meanallinteraction("CranReddam", "SelfEsteem_1", "SelfEsteem_1", "GROUP1")
#meanallinteraction("CranReddam", "MLQP_1", "MLQ_1", "GROUP1")
#meanallinteraction("CranReddam", "MLQS_1", "MLQS_1", "GROUP1")
#meanallinteraction("CranReddam", "PERMA_Happy", "PERMA_Happy_1", "GROUP1")
#meanallinteraction("CranReddam", "PERMA_Lonely", "PERMA_Lonely_1", "GROUP1")
#meanallinteraction("CranReddam", "GRIT", "GRIT_1", "GROUP1")
#meanallinteraction("CranReddam", "Res", "Res_1", "GROUP1")
#meanallinteraction("CranReddam", "APSI", "PurposeAPSI_1", "GROUP1")
#meanallinteraction("CranReddam", "PWB", "PurposePWB_1", "GROUP1")
#meanallinteraction("CranReddam", "Optimism", "Optimism_1", "GROUP1")
#meanallinteraction("CranReddam", "LS", "LifeSatisfaction_1", "GROUP1")
#meanallinteraction("CranReddam", "LET", "LifeEngagement_1", "GROUP1")
#meanallinteraction("CranReddam", "PermaEngagement", "Engagement_1", "GROUP1")
#meanallinteraction("CranReddam", "Rrealtionships", "Relationships_1", "GROUP1")
#meanallinteraction("CranReddam", "Nagative", "Negative_1", "GROUP1")
#meanallinteraction("CranReddam", "Acheivement", "Acheivement_1", "GROUP1")
#meanallinteraction("CranReddam", "Positive", "Positive_1", "GROUP1")
#meanallinteraction("CranReddam", "NewPurpose", "NewPurpose_1", "GROUP1")
##Interaction terms
CranReddam$GROUPMLQ_1<-scale(CranReddam$MLQ_1)
CranReddam$GROUPMLQ_2<-scale(CranReddam$MLQ_1)
CranReddam<-indProd(CranReddam, "APSI_FG_1", "GROUP2", match = TRUE, meanC = TRUE, doubleMC = FALSE)
CranReddam<-indProd(CranReddam, "APSI_FN_1", "GROUP2", match = TRUE, meanC = TRUE, doubleMC = FALSE)
CranReddam<-indProd(CranReddam, "MLQ_1", "GROUP2", match = TRUE, meanC = TRUE, doubleMC = FALSE)
CranReddam<-indProd(CranReddam, "MLQS_1", "GROUP2", match = TRUE, meanC = TRUE, doubleMC = FALSE)
CranReddam<-indProd(CranReddam, "Happiness_1", "GROUP2", match = TRUE, meanC = TRUE, doubleMC = FALSE)
CranReddam<-indProd(CranReddam, "Grit_1", "GROUP2", match = TRUE, meanC = TRUE, doubleMC = FALSE)
CranReddam<-indProd(CranReddam, "PurposeAPSI_No36_1", "GROUP2", match = TRUE, meanC = TRUE, doubleMC = FALSE)
CranReddam<-indProd(CranReddam, "PurposePWB_1", "GROUP2", match = TRUE, meanC = TRUE, doubleMC = FALSE)
CranReddam<-indProd(CranReddam, "Optimism_1", "GROUP2", match = TRUE, meanC = TRUE, doubleMC = FALSE)
CranReddam<-indProd(CranReddam, "LifeSatisfaction_1", "GROUP2", match = TRUE, meanC = TRUE, doubleMC = FALSE)
CranReddam<-indProd(CranReddam, "LifeEngagement_1", "GROUP2", match = TRUE, meanC = TRUE, doubleMC = FALSE)
CranReddam<-indProd(CranReddam, "NewPurpose_1", "GROUP2", match = TRUE, meanC = TRUE, doubleMC = FALSE)
CranReddam<-indProd(CranReddam, "EnglishSC_1", "GROUP2", match = TRUE, meanC = TRUE, doubleMC = FALSE)
CranReddam<-indProd(CranReddam, "MathSC_1", "GROUP2", match = TRUE, meanC = TRUE, doubleMC = FALSE)
CranReddam<-indProd(CranReddam, "SciencSC_1", "GROUP2", match = TRUE, meanC = TRUE, doubleMC = FALSE)
CranReddam<-indProd(CranReddam, "GeneralAcademic_1", "GROUP2", match = TRUE, meanC = TRUE, doubleMC = FALSE)
CranReddam<-indProd(CranReddam, "SelfEsteem_1", "GROUP2", match = TRUE, meanC = TRUE, doubleMC = FALSE)
CranReddam<-indProd(CranReddam, "APSI_FG_1", "GROUP1", match = TRUE, meanC = TRUE, doubleMC = FALSE)
CranReddam<-indProd(CranReddam, "APSI_FN_1", "GROUP1", match = TRUE, meanC = TRUE, doubleMC = FALSE)
CranReddam<-indProd(CranReddam, "SelfEsteem_1", "GROUP1", match = TRUE, meanC = TRUE, doubleMC = FALSE)
CranReddam<-indProd(CranReddam, "MLQ_1", "GROUP1", match = TRUE, meanC = TRUE, doubleMC = FALSE)
CranReddam<-indProd(CranReddam, "MLQS_1", "GROUP1", match = TRUE, meanC = TRUE, doubleMC = FALSE)
CranReddam<-indProd(CranReddam, "Happiness_1", "GROUP1", match = TRUE, meanC = TRUE, doubleMC = FALSE)
CranReddam<-indProd(CranReddam, "Grit_1", "GROUP1", match = TRUE, meanC = TRUE, doubleMC = FALSE)
CranReddam<-indProd(CranReddam, "PurposeAPSI_No36_1", "GROUP1", match = TRUE, meanC = TRUE, doubleMC = FALSE)
CranReddam<-indProd(CranReddam, "PurposePWB_1", "GROUP1", match = TRUE, meanC = TRUE, doubleMC = FALSE)
CranReddam<-indProd(CranReddam, "Optimism_1", "GROUP1", match = TRUE, meanC = TRUE, doubleMC = FALSE)
CranReddam<-indProd(CranReddam, "LifeSatisfaction_1", "GROUP1", match = TRUE, meanC = TRUE, doubleMC = FALSE)
CranReddam<-indProd(CranReddam, "LifeEngagement_1", "GROUP1", match = TRUE, meanC = TRUE, doubleMC = FALSE)
CranReddam<-indProd(CranReddam, "NewPurpose_1", "GROUP1", match = TRUE, meanC = TRUE, doubleMC = FALSE)
CranReddam<-indProd(CranReddam, "EnglishSC_1", "GROUP1", match = TRUE, meanC = TRUE, doubleMC = FALSE)
CranReddam<-indProd(CranReddam, "MathSC_1", "GROUP1", match = TRUE, meanC = TRUE, doubleMC = FALSE)
CranReddam<-indProd(CranReddam, "SciencSC_1", "GROUP1", match = TRUE, meanC = TRUE, doubleMC = FALSE)
CranReddam<-indProd(CranReddam, "GeneralAcademic_1", "GROUP1", match = TRUE, meanC = TRUE, doubleMC = FALSE)
#Scale ther rest
CranReddam$SCALED_1_APSI_FN<-scale(CranReddam$APSI_FN_1)
CranReddam$SCALED_1_APSI_FG<-scale(CranReddam$APSI_FG_1)
CranReddam$SCALED_1_SelfEsteem<-scale(CranReddam$SelfEsteem_1)
CranReddam$SCALED_1_MLQP<-scale(CranReddam$MLQ_1)
CranReddam$SCALED_1_MLQS<-scale(CranReddam$MLQS_1)
CranReddam$SCALED_1_Happy<-scale(CranReddam$Happiness_1)
CranReddam$SCALED_1_Res<-scale(CranReddam$Grit_1)
CranReddam$SCALED_1_APSI<-scale(CranReddam$PurposeAPSI_No36_1)
CranReddam$SCALED_1_PWB<-scale(CranReddam$PurposePWB_1)
CranReddam$SCALED_1_Optimism<-scale(CranReddam$Optimism_1)
CranReddam$SCALED_1_LS<-scale(CranReddam$LifeSatisfaction_1)
CranReddam$SCALED_1_LET<-scale(CranReddam$LifeEngagement_1)
CranReddam$SCALED_1_EnglishSC<-scale(CranReddam$EnglishSC_1)
CranReddam$SCALED_1_SciencSC<-scale(CranReddam$SciencSC_1)
CranReddam$SCALED_1_MathSC<-scale(CranReddam$MathSC_1)
CranReddam$SCALED_1_EnglishSC<-scale(CranReddam$EnglishSC_1)
CranReddam$SCALED_1_GeneralAcademic<-scale(CranReddam$GeneralAcademic_1)
CranReddam$SCALED_1_NewPurpose<-scale(CranReddam$NewPurpose_1)
#Scale 2
CranReddam$SCALED_2_APSI_FN<-scale(CranReddam$APSI_FN_2)
CranReddam$SCALED_2_APSI_FG<-scale(CranReddam$APSI_FG_2)
CranReddam$SCALED_2_SelfEsteem<-scale(CranReddam$SelfEsteem_2)
CranReddam$SCALED_2_MLQP<-scale(CranReddam$MLQ_2)
CranReddam$SCALED_2_MLQS<-scale(CranReddam$MLQS_2)
CranReddam$SCALED_2_Happy<-scale(CranReddam$Happiness_2)
CranReddam$SCALED_2_Res<-scale(CranReddam$Grit_2)
CranReddam$SCALED_2_APSI<-scale(CranReddam$PurposeAPSI_No36_2)
CranReddam$SCALED_2_PWB<-scale(CranReddam$PurposePWB_2)
CranReddam$SCALED_2_Optimism<-scale(CranReddam$Optimism_2)
CranReddam$SCALED_2_LS<-scale(CranReddam$LifeSatisfaction_2)
CranReddam$SCALED_2_LET<-scale(CranReddam$LifeEngagement_2)
CranReddam$SCALED_2_EnglishSC<-scale(CranReddam$EnglishSC_2)
CranReddam$SCALED_2_SciencSC<-scale(CranReddam$SciencSC_2)
CranReddam$SCALED_2_MathSC<-scale(CranReddam$MathSC_2)
CranReddam$SCALED_2_EnglishSC<-scale(CranReddam$EnglishSC_2)
CranReddam$SCALED_2_GeneralAcademic<-scale(CranReddam$GeneralAcademic_2)
CranReddam$SCALED_2_NewPurpose<-scale(CranReddam$NewPurpose_2)
library(tibble)##
## Attaching package: 'tibble'## The following object is masked from 'package:dplyr':
##
## tbl_dflibrary(tidyr)
#View(CranReddam)You can also embed plots, for example:
Functions for tables and interactions
#Coefficient Table Function
FitTableYouth<-function(fitname, interactionterm1, interactionterm2, current_scale_name, new_scale_name){
#Function to round numbers in a df
round_df<-function(df, num){nums<-map_lgl(df, is.numeric);df[,nums]<-round(df[,nums],num);(df)}
lolollol<-(parameterEstimates(fitname))
lolollol<-round_df(lolollol, 2)
lolollol$CI<-paste(lolollol$ci.lower, lolollol$ci.upper, sep = ", ")
lolollol<-select(lolollol, rhs, est, se, z, pvalue, CI)
lolollol<-slice(lolollol, c(22, 1:5))
lolollol<-rename(lolollol, Item = rhs, β = est, SE = se, p = pvalue, "90% CI" = CI)
lolollol[1,1] <- sprintf('Intercept', lolollol[1,1])
lolollol[,1]<-gsub("SCALED_1_", "", lolollol[,1])
lolollol[,1]<-gsub("SCALED_2_", "", lolollol[,1])
lolollol[,1]<-gsub(interactionterm1, "Interaction w/ Group 1", lolollol[,1])
lolollol[,1]<-gsub(interactionterm2, "Interaction w/ Group 2", lolollol[,1])
lolollol[,1]<-gsub(current_scale_name, new_scale_name, lolollol[,1])
lolollol
}
#Interaction Plot Function
PlotInterYouth<-function(fitname, PlotTitle){
fit<-(parameterEstimates(fitname))
Treat=1
Control=0
Group0 <-fit[22,4]
Group1<- fit[1,4]
Group2<- fit[2,4]
T1 = fit[3,4]
interaction1 = fit[4,4]
interaction2 = fit[5,4]
TreatHighT2 = Group0 + T1*1 + Group2*Treat + interaction2*1*Treat
TreatMediumT2 = Group0 + T1*0 + Group2*Treat + interaction2*0*Treat
TreatLowT2 = Group0 + T1*-1 + Group2*Treat + interaction2*-1*Treat
ControlHighT2 = Group0 + T1*1 + Group2*Control + interaction2*1*Control
ControlMediumT2 = Group0 + T1*0 + Group2*Control + interaction2*0*Control
ControlLowT2 = Group0 + T1*-1 + Group2*Control + interaction2*-1*Control
#Group 1
RedTreatHighT2 = Group0 + T1*1 + Group1 + interaction1*1
RedTreatMediumT2 = Group0 + T1*0 + Group1 + interaction1*0
RedTreatT2 = Group0 + T1*-1 + Group1 + interaction1*-1
Treat_T2 = c(TreatLowT2, TreatMediumT2, TreatHighT2)
Control_T2 = c(ControlLowT2, ControlMediumT2, ControlHighT2)
RedTreat = c(RedTreatT2, RedTreatMediumT2, RedTreatHighT2)
levels = c("-1","0","1")
fdsd<-data.frame(levels, Treat_T2,Control_T2, RedTreat)
Plot1<-ggplot(fdsd, aes(levels)) +
geom_line(aes(levels, Treat_T2, group = 1, colour = "Treatment 2 (Cran)")) +
geom_line(aes(levels, Control_T2, group = 1, colour = "Control (Redd)")) +
geom_line(aes(levels, RedTreat, group = 1, colour = "Treatment 1 (Redd)")) +
labs(list(title = PlotTitle, x = "Levels Pre in SD", y="Levels Post in SD", colour = "Lines" ))
Plot1
}
fitname<-sem(modelYNewPurpose_interaction, CranReddam, missing='fiml', meanstructure=TRUE,fixed.x=T) ## Found more than one class "Model" in cache; using the first, from namespace 'lavaan'summary(fitname)## lavaan (0.5-20) converged normally after 26 iterations
##
## Number of observations 221
##
## Number of missing patterns 4
##
## Estimator ML
## Minimum Function Test Statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Information Observed
## Standard Errors Standard
##
## Regressions:
## Estimate Std.Err Z-value P(>|z|)
## SCALED_2_NewPurpose ~
## GROUP1 0.354 0.255 1.388 0.165
## GROUP2 0.138 0.190 0.729 0.466
## SCALED_1_NwPrp 0.572 0.071 8.022 0.000
## NwPrp_1.GROUP1 -0.086 0.341 -0.252 0.801
## NwPrp_1.GROUP2 -0.453 0.211 -2.149 0.032
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|)
## SCALED_2_NwPrp -0.132 0.170 -0.776 0.438
##
## Variances:
## Estimate Std.Err Z-value P(>|z|)
## SCALED_2_NwPrp 0.637 0.077 8.298 0.000#SimpleSlopes Function
SimpleSlopesYouth<-function(fitname){
fit<-(parameterEstimates(fitname))
Treat=1
Control=0
Group0 <-fit[22,4]
Group1<- fit[1,4]
Group2<- fit[2,4]
T1 = fit[3,4]
interaction1 = fit[4,4]
interaction2 = fit[5,4]
TreatHighT2 = Group0 + T1*1 + Group2*Treat + interaction2*1*Treat
TreatMediumT2 = Group0 + T1*0 + Group2*Treat + interaction2*0*Treat
TreatLowT2 = Group0 + T1*-1 + Group2*Treat + interaction2*-1*Treat
ControlHighT2 = Group0 + T1*1
ControlMediumT2 = Group0 + T1*0
ControlLowT2 = Group0 + T1*-1
#Group 1
RedTreatHighT2 = Group0 + T1*1 + Group1 + interaction1*1
RedTreatMediumT2 = Group0 + T1*0 + Group1 + interaction1*0
RedTreatT2 = Group0 + T1*-1 + Group1 + interaction1*-1
Treat_T2 = c(TreatLowT2, TreatMediumT2, TreatHighT2)
Control_T2 = c(ControlLowT2, ControlMediumT2, ControlHighT2)
RedTreat = c(RedTreatT2, RedTreatMediumT2, RedTreatHighT2)
levels = c("-1","0","1")
fdsd<-data.frame(levels, Treat_T2,Control_T2, RedTreat)
round_df<-function(df, num){nums<-map_lgl(df, is.numeric);df[,nums]<-round(df[,nums],num);(df)}
round_df(fdsd,2)}
#Results Function - Contains Fits, Coefficiants Table and SimpleSlopes Table
resultsYouth<-function(modelname, fitname, data, interactionterm1, interactionterm2, current_scale_name, new_scale_name){
fitname<-sem(modelname, data, missing='fiml', meanstructure=TRUE,fixed.x=T)
fits<- summary(fitname,fit.measures=TRUE,rsquare=TRUE,standardize=T)
table<-FitTableYouth(fitname, interactionterm1, interactionterm2, current_scale_name, new_scale_name)
slopes<-SimpleSlopesYouth(fitname)
all<-list((table), (slopes))
all
}
#Plots Function
plotsYouth<-function(modelname, fitname, data, new_scale_name){
fitname<-sem(modelname, data, missing='fiml', meanstructure=TRUE,fixed.x=T)
PlotInterYouth(fitname, new_scale_name)
}Fits
#########################################
################# New Purpose ###########
#########################################
resultsYouth(modelname = modelYNewPurpose_interaction, fitname = NewPurposeFit, data = CranReddam, interactionterm1 = "NewPurpose_1.GROUP1", interactionterm2 = "NewPurpose_1.GROUP2", current_scale_name = "NewPurpose", new_scale_name = "New Purpose Scale")## lavaan (0.5-20) converged normally after 26 iterations
##
## Number of observations 221
##
## Number of missing patterns 4
##
## Estimator ML
## Minimum Function Test Statistic 0.000
## Degrees of freedom 0
##
## Model test baseline model:
##
## Minimum Function Test Statistic 60.967
## Degrees of freedom 5
## P-value 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -733.444
## Loglikelihood unrestricted model (H1) -733.444
##
## Number of free parameters 7
## Akaike (AIC) 1480.888
## Bayesian (BIC) 1504.676
## Sample-size adjusted Bayesian (BIC) 1482.492
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent Confidence Interval 0.000 0.000
## P-value RMSEA <= 0.05 1.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.000
##
## Parameter Estimates:
##
## Information Observed
## Standard Errors Standard
##
## Regressions:
## Estimate Std.Err Z-value P(>|z|) Std.lv
## SCALED_2_NewPurpose ~
## GROUP1 0.354 0.255 1.388 0.165 0.354
## GROUP2 0.138 0.190 0.729 0.466 0.138
## SCALED_1_NwPrp 0.572 0.071 8.022 0.000 0.572
## NwPrp_1.GROUP1 -0.086 0.341 -0.252 0.801 -0.086
## NwPrp_1.GROUP2 -0.453 0.211 -2.149 0.032 -0.453
## Std.all
##
## 0.132
## 0.065
## 0.570
## -0.024
## -0.173
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_NwPrp -0.132 0.170 -0.776 0.438 -0.132 -0.132
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_NwPrp 0.637 0.077 8.298 0.000 0.637 0.638
##
## R-Square:
## Estimate
## SCALED_2_NwPrp 0.362## [[1]]
## Item β SE z p 90% CI
## 1 Intercept -0.13 0.17 -0.78 0.44 -0.47, 0.2
## 2 GROUP1 0.35 0.26 1.39 0.17 -0.15, 0.85
## 3 GROUP2 0.14 0.19 0.73 0.47 -0.23, 0.51
## 4 New Purpose Scale 0.57 0.07 8.02 0.00 0.43, 0.71
## 5 Interaction w/ Group 1 -0.09 0.34 -0.25 0.80 -0.75, 0.58
## 6 Interaction w/ Group 2 -0.45 0.21 -2.15 0.03 -0.87, -0.04
##
## [[2]]
## levels Treat_T2 Control_T2 RedTreat
## 1 -1 -0.11 -0.70 -0.26
## 2 0 0.01 -0.13 0.22
## 3 1 0.12 0.44 0.71plotsYouth(modelname = modelYNewPurpose_interaction, fitname = NewPurposeFit, data = CranReddam, new_scale_name = "New Purpose")
#########################################
################# MLQP #################
#########################################
resultsYouth(modelname = modelYMLQP_interaction, fitname = Fit, data = CranReddam, interactionterm1 = "MLQ_1.GROUP1", interactionterm2 = "MLQ_1.GROUP2", current_scale_name = "MLQP", new_scale_name = "MLQ-P T1")## lavaan (0.5-20) converged normally after 25 iterations
##
## Number of observations 221
##
## Number of missing patterns 4
##
## Estimator ML
## Minimum Function Test Statistic 0.000
## Degrees of freedom 0
##
## Model test baseline model:
##
## Minimum Function Test Statistic 68.986
## Degrees of freedom 5
## P-value 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -922.511
## Loglikelihood unrestricted model (H1) -922.511
##
## Number of free parameters 7
## Akaike (AIC) 1859.023
## Bayesian (BIC) 1882.810
## Sample-size adjusted Bayesian (BIC) 1860.626
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent Confidence Interval 0.000 0.000
## P-value RMSEA <= 0.05 1.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.000
##
## Parameter Estimates:
##
## Information Observed
## Standard Errors Standard
##
## Regressions:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_MLQP ~
## GROUP1 0.419 0.256 1.634 0.102 0.419 0.155
## GROUP2 0.221 0.199 1.112 0.266 0.221 0.104
## SCALED_1_MLQP 0.619 0.069 8.967 0.000 0.619 0.612
## MLQ_1.GROUP1 -0.096 0.204 -0.469 0.639 -0.096 -0.044
## MLQ_1.GROUP2 -0.327 0.152 -2.145 0.032 -0.327 -0.192
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_MLQP -0.209 0.181 -1.155 0.248 -0.209 -0.207
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_MLQP 0.601 0.072 8.294 0.000 0.601 0.592
##
## R-Square:
## Estimate
## SCALED_2_MLQP 0.408## [[1]]
## Item β SE z p 90% CI
## 1 Intercept -0.21 0.18 -1.16 0.25 -0.56, 0.15
## 2 GROUP1 0.42 0.26 1.63 0.10 -0.08, 0.92
## 3 GROUP2 0.22 0.20 1.11 0.27 -0.17, 0.61
## 4 MLQ-P T1 0.62 0.07 8.97 0.00 0.48, 0.75
## 5 Interaction w/ Group 1 -0.10 0.20 -0.47 0.64 -0.5, 0.3
## 6 Interaction w/ Group 2 -0.33 0.15 -2.15 0.03 -0.63, -0.03
##
## [[2]]
## levels Treat_T2 Control_T2 RedTreat
## 1 -1 -0.28 -0.83 -0.31
## 2 0 0.01 -0.21 0.21
## 3 1 0.30 0.41 0.73plotsYouth(modelname = modelYMLQP_interaction, fitname = Fit, data = CranReddam, new_scale_name = "MLQ - P (Steger)")
#########################################
################# MLQS #################
#########################################
resultsYouth(modelname = modelYMLQS_interaction, fitname = Fit, data = CranReddam, interactionterm1 = "MLQS_1.GROUP1", interactionterm2 = "MLQS_1.GROUP2", current_scale_name = "MLQS", new_scale_name = "MLQ-S T1")## lavaan (0.5-20) converged normally after 23 iterations
##
## Number of observations 221
##
## Number of missing patterns 4
##
## Estimator ML
## Minimum Function Test Statistic 0.000
## Degrees of freedom 0
##
## Model test baseline model:
##
## Minimum Function Test Statistic 21.829
## Degrees of freedom 5
## P-value 0.001
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -1010.935
## Loglikelihood unrestricted model (H1) -1010.935
##
## Number of free parameters 7
## Akaike (AIC) 2035.870
## Bayesian (BIC) 2059.657
## Sample-size adjusted Bayesian (BIC) 2037.474
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent Confidence Interval 0.000 0.000
## P-value RMSEA <= 0.05 1.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.000
##
## Parameter Estimates:
##
## Information Observed
## Standard Errors Standard
##
## Regressions:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_MLQS ~
## GROUP1 -0.016 0.294 -0.056 0.956 -0.016 -0.006
## GROUP2 -0.294 0.221 -1.332 0.183 -0.294 -0.139
## SCALED_1_MLQS 0.324 0.082 3.956 0.000 0.324 0.322
## MLQS_1.GROUP1 0.398 0.194 2.056 0.040 0.398 0.217
## MLQS_1.GROUP2 0.189 0.163 1.158 0.247 0.189 0.123
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_MLQS 0.225 0.200 1.125 0.261 0.225 0.224
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_MLQS 0.846 0.102 8.293 0.000 0.846 0.842
##
## R-Square:
## Estimate
## SCALED_2_MLQS 0.158## [[1]]
## Item β SE z p 90% CI
## 1 Intercept 0.22 0.20 1.12 0.26 -0.17, 0.62
## 2 GROUP1 -0.02 0.29 -0.06 0.96 -0.59, 0.56
## 3 GROUP2 -0.29 0.22 -1.33 0.18 -0.73, 0.14
## 4 MLQ-S T1 0.32 0.08 3.96 0.00 0.16, 0.48
## 5 Interaction w/ Group 1 0.40 0.19 2.06 0.04 0.02, 0.78
## 6 Interaction w/ Group 2 0.19 0.16 1.16 0.25 -0.13, 0.51
##
## [[2]]
## levels Treat_T2 Control_T2 RedTreat
## 1 -1 -0.58 -0.10 -0.51
## 2 0 -0.07 0.22 0.21
## 3 1 0.44 0.55 0.93plotsYouth(modelname = modelYMLQS_interaction, fitname = Fit, data = CranReddam, new_scale_name = "MLQ - S (Steger)")
#########################################
############## Happiness ###############
#########################################
resultsYouth(modelname = modelYHappiness_interaction, fitname = Fit, data = CranReddam, interactionterm1 = "Happiness_1.GROUP1", interactionterm2 = "Happiness_1.GROUP2", current_scale_name = "Happy", new_scale_name = "Subjective Happiness T1")## lavaan (0.5-20) converged normally after 27 iterations
##
## Number of observations 221
##
## Number of missing patterns 4
##
## Estimator ML
## Minimum Function Test Statistic 0.000
## Degrees of freedom 0
##
## Model test baseline model:
##
## Minimum Function Test Statistic 38.764
## Degrees of freedom 5
## P-value 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -713.819
## Loglikelihood unrestricted model (H1) -713.819
##
## Number of free parameters 7
## Akaike (AIC) 1441.639
## Bayesian (BIC) 1465.426
## Sample-size adjusted Bayesian (BIC) 1443.243
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent Confidence Interval 0.000 0.000
## P-value RMSEA <= 0.05 1.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.000
##
## Parameter Estimates:
##
## Information Observed
## Standard Errors Standard
##
## Regressions:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_Happy ~
## GROUP1 0.015 0.279 0.053 0.958 0.015 0.006
## GROUP2 -0.083 0.215 -0.385 0.700 -0.083 -0.040
## SCALED_1_Happy 0.477 0.074 6.488 0.000 0.477 0.484
## Hppns_1.GROUP1 0.286 0.361 0.793 0.428 0.286 0.086
## Hppns_1.GROUP2 0.265 0.285 0.931 0.352 0.265 0.098
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_Happy 0.059 0.196 0.299 0.765 0.059 0.059
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_Happy 0.730 0.090 8.130 0.000 0.730 0.751
##
## R-Square:
## Estimate
## SCALED_2_Happy 0.249## [[1]]
## Item β SE z p 90% CI
## 1 Intercept 0.06 0.20 0.30 0.77 -0.33, 0.44
## 2 GROUP1 0.01 0.28 0.05 0.96 -0.53, 0.56
## 3 GROUP2 -0.08 0.21 -0.38 0.70 -0.5, 0.34
## 4 Subjective Happiness T1 0.48 0.07 6.49 0.00 0.33, 0.62
## 5 Interaction w/ Group 1 0.29 0.36 0.79 0.43 -0.42, 0.99
## 6 Interaction w/ Group 2 0.27 0.28 0.93 0.35 -0.29, 0.82
##
## [[2]]
## levels Treat_T2 Control_T2 RedTreat
## 1 -1 -0.77 -0.42 -0.69
## 2 0 -0.02 0.06 0.07
## 3 1 0.72 0.54 0.84plotsYouth(modelname = modelYHappiness_interaction, fitname = Fit, data = CranReddam, new_scale_name = "Subjective Happiness")
#########################################
############## Science SC ###############
#########################################
resultsYouth(modelname = modelYScienceSC_interaction, fitname = Fit, data = CranReddam, interactionterm1 = "SciencSC_1.GROUP1", interactionterm2 = "SciencSC_1.GROUP2", current_scale_name = "SciencSC", new_scale_name = "Science Self Concept T1")## lavaan (0.5-20) converged normally after 21 iterations
##
## Number of observations 221
##
## Number of missing patterns 2
##
## Estimator ML
## Minimum Function Test Statistic 0.000
## Degrees of freedom 0
##
## Model test baseline model:
##
## Minimum Function Test Statistic 110.548
## Degrees of freedom 5
## P-value 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -934.264
## Loglikelihood unrestricted model (H1) -934.264
##
## Number of free parameters 7
## Akaike (AIC) 1882.529
## Bayesian (BIC) 1906.316
## Sample-size adjusted Bayesian (BIC) 1884.133
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent Confidence Interval 0.000 0.000
## P-value RMSEA <= 0.05 1.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.000
##
## Parameter Estimates:
##
## Information Observed
## Standard Errors Standard
##
## Regressions:
## Estimate Std.Err Z-value P(>|z|) Std.lv
## SCALED_2_SciencSC ~
## GROUP1 0.054 0.211 0.254 0.800 0.054
## GROUP2 -0.103 0.160 -0.645 0.519 -0.103
## SCALED_1_ScnSC 0.825 0.065 12.632 0.000 0.825
## ScnSC_1.GROUP1 -0.187 0.183 -1.023 0.306 -0.187
## ScnSC_1.GROUP2 -0.086 0.134 -0.644 0.519 -0.086
## Std.all
##
## 0.019
## -0.046
## 0.782
## -0.090
## -0.049
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_ScnSC 0.011 0.144 0.073 0.942 0.011 0.010
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_ScnSC 0.446 0.054 8.307 0.000 0.446 0.402
##
## R-Square:
## Estimate
## SCALED_2_ScnSC 0.598## [[1]]
## Item β SE z p 90% CI
## 1 Intercept 0.01 0.14 0.07 0.94 -0.27, 0.29
## 2 GROUP1 0.05 0.21 0.25 0.80 -0.36, 0.47
## 3 GROUP2 -0.10 0.16 -0.64 0.52 -0.42, 0.21
## 4 Science Self Concept T1 0.82 0.07 12.63 0.00 0.7, 0.95
## 5 Interaction w/ Group 1 -0.19 0.18 -1.02 0.31 -0.55, 0.17
## 6 Interaction w/ Group 2 -0.09 0.13 -0.64 0.52 -0.35, 0.18
##
## [[2]]
## levels Treat_T2 Control_T2 RedTreat
## 1 -1 -0.83 -0.81 -0.57
## 2 0 -0.09 0.01 0.06
## 3 1 0.65 0.84 0.70plotsYouth(modelname = modelYScienceSC_interaction, fitname = Fit, data = CranReddam, new_scale_name = "Science Self Concept (Marsh)") 
#########################################
############## EnglishSC_1 ##############
#########################################
resultsYouth(modelname = modelYEnglishSC_interaction, fitname = Fit, data = CranReddam, interactionterm1 = "EnglishSC_1.GROUP1", interactionterm2 = "EnglishSC_1.GROUP2", current_scale_name = "EnglishSC", new_scale_name = "English Self Concept T1")## lavaan (0.5-20) converged normally after 22 iterations
##
## Number of observations 221
##
## Number of missing patterns 2
##
## Estimator ML
## Minimum Function Test Statistic 0.000
## Degrees of freedom 0
##
## Model test baseline model:
##
## Minimum Function Test Statistic 108.914
## Degrees of freedom 5
## P-value 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -814.083
## Loglikelihood unrestricted model (H1) -814.083
##
## Number of free parameters 7
## Akaike (AIC) 1642.166
## Bayesian (BIC) 1665.953
## Sample-size adjusted Bayesian (BIC) 1643.770
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent Confidence Interval 0.000 0.000
## P-value RMSEA <= 0.05 1.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.000
##
## Parameter Estimates:
##
## Information Observed
## Standard Errors Standard
##
## Regressions:
## Estimate Std.Err Z-value P(>|z|) Std.lv
## SCALED_2_EnglishSC ~
## GROUP1 0.143 0.226 0.633 0.527 0.143
## GROUP2 0.082 0.178 0.461 0.645 0.082
## SCALED_1_EngSC 0.715 0.059 12.171 0.000 0.715
## EngSC_1.GROUP1 -0.083 0.213 -0.390 0.696 -0.083
## EngSC_1.GROUP2 -0.079 0.162 -0.486 0.627 -0.079
## Std.all
##
## 0.055
## 0.040
## 0.731
## -0.034
## -0.039
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_EngSC -0.027 0.163 -0.164 0.870 -0.027 -0.027
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_EngSC 0.451 0.054 8.307 0.000 0.451 0.474
##
## R-Square:
## Estimate
## SCALED_2_EngSC 0.526## [[1]]
## Item β SE z p 90% CI
## 1 Intercept -0.03 0.16 -0.16 0.87 -0.35, 0.29
## 2 GROUP1 0.14 0.23 0.63 0.53 -0.3, 0.59
## 3 GROUP2 0.08 0.18 0.46 0.65 -0.27, 0.43
## 4 English Self Concept T1 0.72 0.06 12.17 0.00 0.6, 0.83
## 5 Interaction w/ Group 1 -0.08 0.21 -0.39 0.70 -0.5, 0.33
## 6 Interaction w/ Group 2 -0.08 0.16 -0.49 0.63 -0.4, 0.24
##
## [[2]]
## levels Treat_T2 Control_T2 RedTreat
## 1 -1 -0.58 -0.74 -0.52
## 2 0 0.06 -0.03 0.12
## 3 1 0.69 0.69 0.75plotsYouth(modelname = modelYEnglishSC_interaction, fitname = Fit, data = CranReddam, new_scale_name = "English Self Concept (Marsh)")
#########################################
################ Math SC ################
#########################################
resultsYouth(modelname = modelYMathSC_interaction, fitname = Fit, data = CranReddam, interactionterm1 = "MathSC_1.GROUP1", interactionterm2 = "MathSC_1.GROUP2", current_scale_name = "MathSC", new_scale_name = "Math Self Concept T1")## lavaan (0.5-20) converged normally after 22 iterations
##
## Number of observations 221
##
## Number of missing patterns 2
##
## Estimator ML
## Minimum Function Test Statistic 0.000
## Degrees of freedom 0
##
## Model test baseline model:
##
## Minimum Function Test Statistic 129.253
## Degrees of freedom 5
## P-value 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -900.720
## Loglikelihood unrestricted model (H1) -900.720
##
## Number of free parameters 7
## Akaike (AIC) 1815.441
## Bayesian (BIC) 1839.228
## Sample-size adjusted Bayesian (BIC) 1817.045
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent Confidence Interval 0.000 0.000
## P-value RMSEA <= 0.05 1.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.000
##
## Parameter Estimates:
##
## Information Observed
## Standard Errors Standard
##
## Regressions:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_MathSC ~
## GROUP1 0.032 0.231 0.139 0.890 0.032 0.012
## GROUP2 -0.120 0.146 -0.823 0.411 -0.120 -0.056
## SCALED_1_MthSC 0.800 0.067 12.007 0.000 0.800 0.787
## MthSC_1.GROUP1 -0.151 0.253 -0.598 0.550 -0.151 -0.069
## MthSC_1.GROUP2 -0.069 0.142 -0.487 0.626 -0.069 -0.039
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_MthSC 0.040 0.131 0.307 0.759 0.040 0.040
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_MthSC 0.389 0.047 8.307 0.000 0.389 0.378
##
## R-Square:
## Estimate
## SCALED_2_MthSC 0.622## [[1]]
## Item β SE z p 90% CI
## 1 Intercept 0.04 0.13 0.31 0.76 -0.22, 0.3
## 2 GROUP1 0.03 0.23 0.14 0.89 -0.42, 0.48
## 3 GROUP2 -0.12 0.15 -0.82 0.41 -0.41, 0.17
## 4 Math Self Concept T1 0.80 0.07 12.01 0.00 0.67, 0.93
## 5 Interaction w/ Group 1 -0.15 0.25 -0.60 0.55 -0.65, 0.34
## 6 Interaction w/ Group 2 -0.07 0.14 -0.49 0.63 -0.35, 0.21
##
## [[2]]
## levels Treat_T2 Control_T2 RedTreat
## 1 -1 -0.81 -0.76 -0.58
## 2 0 -0.08 0.04 0.07
## 3 1 0.65 0.84 0.72plotsYouth(modelname = modelYEnglishSC_interaction, fitname = Fit, data = CranReddam, new_scale_name = "Math Self Concept (Marsh)")
#########################################
########## General Academic SC ##########
#########################################
resultsYouth(modelname = modelYGASC_interaction, fitname = Fit, data = CranReddam, interactionterm1 = "GeneralAcademic_1.GROUP1", interactionterm2 = "GeneralAcademic_1.GROUP2", current_scale_name = "GeneralAcademicSC", new_scale_name = "General Academic Self Concept T1")## lavaan (0.5-20) converged normally after 22 iterations
##
## Number of observations 221
##
## Number of missing patterns 2
##
## Estimator ML
## Minimum Function Test Statistic 0.000
## Degrees of freedom 0
##
## Model test baseline model:
##
## Minimum Function Test Statistic 96.733
## Degrees of freedom 5
## P-value 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -768.443
## Loglikelihood unrestricted model (H1) -768.443
##
## Number of free parameters 7
## Akaike (AIC) 1550.887
## Bayesian (BIC) 1574.674
## Sample-size adjusted Bayesian (BIC) 1552.491
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent Confidence Interval 0.000 0.000
## P-value RMSEA <= 0.05 1.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.000
##
## Parameter Estimates:
##
## Information Observed
## Standard Errors Standard
##
## Regressions:
## Estimate Std.Err Z-value P(>|z|) Std.lv
## SCALED_2_GeneralAcademic ~
## GROUP1 -0.055 0.218 -0.252 0.801 -0.055
## GROUP2 -0.101 0.163 -0.618 0.536 -0.101
## SCALED_1_GnrlA 0.776 0.066 11.742 0.000 0.776
## GnrlA_1.GROUP1 0.437 0.283 1.547 0.122 0.437
## GnrlA_1.GROUP2 0.009 0.207 0.043 0.965 0.009
## Std.all
##
## -0.019
## -0.045
## 0.727
## 0.136
## 0.003
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_GnrlA 0.051 0.147 0.350 0.726 0.051 0.048
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_GnrlA 0.493 0.059 8.307 0.000 0.493 0.434
##
## R-Square:
## Estimate
## SCALED_2_GnrlA 0.566## [[1]]
## Item β SE z p 90% CI
## 1 Intercept 0.05 0.15 0.35 0.73 -0.24, 0.34
## 2 GROUP1 -0.06 0.22 -0.25 0.80 -0.48, 0.37
## 3 GROUP2 -0.10 0.16 -0.62 0.54 -0.42, 0.22
## 4 GeneralAcademic 0.78 0.07 11.74 0.00 0.65, 0.91
## 5 Interaction w/ Group 1 0.44 0.28 1.55 0.12 -0.12, 0.99
## 6 Interaction w/ Group 2 0.01 0.21 0.04 0.97 -0.4, 0.41
##
## [[2]]
## levels Treat_T2 Control_T2 RedTreat
## 1 -1 -0.84 -0.73 -1.22
## 2 0 -0.05 0.05 0.00
## 3 1 0.74 0.83 1.21plotsYouth(modelname = modelYGASC_interaction, fitname = Fit, data = CranReddam, new_scale_name = "General Academic Self Concept")
#########################################
################# LET #################
#########################################
resultsYouth(modelname = modelYLET_interaction, fitname = Fit, data = CranReddam, interactionterm1 = "LET_1.GROUP1", interactionterm2 = "LET_1.GROUP2", current_scale_name = "LET", new_scale_name = "Life Engagement T1")## lavaan (0.5-20) converged normally after 27 iterations
##
## Number of observations 221
##
## Number of missing patterns 4
##
## Estimator ML
## Minimum Function Test Statistic 0.000
## Degrees of freedom 0
##
## Model test baseline model:
##
## Minimum Function Test Statistic 61.733
## Degrees of freedom 5
## P-value 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -648.250
## Loglikelihood unrestricted model (H1) -648.250
##
## Number of free parameters 7
## Akaike (AIC) 1310.500
## Bayesian (BIC) 1334.287
## Sample-size adjusted Bayesian (BIC) 1312.104
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent Confidence Interval 0.000 0.000
## P-value RMSEA <= 0.05 1.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.000
##
## Parameter Estimates:
##
## Information Observed
## Standard Errors Standard
##
## Regressions:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_LET ~
## GROUP1 -0.573 0.253 -2.267 0.023 -0.573 -0.214
## GROUP2 -0.168 0.187 -0.897 0.370 -0.168 -0.079
## SCALED_1_LET 0.601 0.069 8.691 0.000 0.601 0.599
## LfEng_1.GROUP1 -0.104 0.396 -0.263 0.793 -0.104 -0.025
## LfEng_1.GROUP2 -0.420 0.297 -1.411 0.158 -0.420 -0.130
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_LET 0.257 0.167 1.539 0.124 0.257 0.257
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_LET 0.631 0.076 8.274 0.000 0.631 0.630
##
## R-Square:
## Estimate
## SCALED_2_LET 0.370## [[1]]
## Item β SE z p 90% CI
## 1 Intercept 0.26 0.17 1.54 0.12 -0.07, 0.59
## 2 GROUP1 -0.57 0.25 -2.27 0.02 -1.07, -0.08
## 3 GROUP2 -0.17 0.19 -0.90 0.37 -0.53, 0.2
## 4 Life Engagement T1 0.60 0.07 8.69 0.00 0.47, 0.74
## 5 LifeEngagement_1.GROUP1 -0.10 0.40 -0.26 0.79 -0.88, 0.67
## 6 LifeEngagement_1.GROUP2 -0.42 0.30 -1.41 0.16 -1, 0.16
##
## [[2]]
## levels Treat_T2 Control_T2 RedTreat
## 1 -1 -0.09 -0.34 -0.81
## 2 0 0.09 0.26 -0.32
## 3 1 0.27 0.86 0.18plotsYouth(modelname = modelYLET_interaction, fitname = Fit, data = CranReddam, new_scale_name = "Life Engagement Test") 
#########################################
################### LS ###################
#########################################
resultsYouth(modelname = modelYLS_interaction, fitname = Fit, data = CranReddam, interactionterm1 = "LS_1.GROUP1", interactionterm2 = "LS_1.GROUP2", current_scale_name = "LifeSatisfaction", new_scale_name = "Life Satisfaction T1")## lavaan (0.5-20) converged normally after 25 iterations
##
## Number of observations 221
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## Number of missing patterns 4
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## Estimator ML
## Minimum Function Test Statistic 0.000
## Degrees of freedom 0
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## Model test baseline model:
##
## Minimum Function Test Statistic 41.780
## Degrees of freedom 5
## P-value 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
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## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -797.445
## Loglikelihood unrestricted model (H1) -797.445
##
## Number of free parameters 7
## Akaike (AIC) 1608.890
## Bayesian (BIC) 1632.677
## Sample-size adjusted Bayesian (BIC) 1610.493
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## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent Confidence Interval 0.000 0.000
## P-value RMSEA <= 0.05 1.000
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## Standardized Root Mean Square Residual:
##
## SRMR 0.000
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## Parameter Estimates:
##
## Information Observed
## Standard Errors Standard
##
## Regressions:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_LS ~
## GROUP1 0.675 0.267 2.532 0.011 0.675 0.250
## GROUP2 0.108 0.198 0.544 0.587 0.108 0.051
## SCALED_1_LS 0.503 0.078 6.480 0.000 0.503 0.498
## LfSts_1.GROUP1 -0.047 0.284 -0.165 0.869 -0.047 -0.016
## LfSts_1.GROUP2 -0.220 0.184 -1.192 0.233 -0.220 -0.110
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_LS -0.116 0.178 -0.652 0.515 -0.116 -0.115
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_LS 0.712 0.088 8.111 0.000 0.712 0.702
##
## R-Square:
## Estimate
## SCALED_2_LS 0.298## [[1]]
## Item β SE z p 90% CI
## 1 Intercept -0.12 0.18 -0.65 0.51 -0.46, 0.23
## 2 GROUP1 0.68 0.27 2.53 0.01 0.15, 1.2
## 3 GROUP2 0.11 0.20 0.54 0.59 -0.28, 0.5
## 4 LS 0.50 0.08 6.48 0.00 0.35, 0.66
## 5 Life Satisfaction T1_1.GROUP1 -0.05 0.28 -0.17 0.87 -0.6, 0.51
## 6 Life Satisfaction T1_1.GROUP2 -0.22 0.18 -1.19 0.23 -0.58, 0.14
##
## [[2]]
## levels Treat_T2 Control_T2 RedTreat
## 1 -1 -0.29 -0.62 0.10
## 2 0 -0.01 -0.12 0.56
## 3 1 0.28 0.39 1.02plotsYouth(modelname = modelYLS_interaction, fitname = Fit, data = CranReddam, new_scale_name = "Life Satisfaction Test") 
#########################################
############### Optimism #################
#########################################
resultsYouth(modelname = modelYOptimism_interaction, fitname = Fit, data = CranReddam, interactionterm1 = "Optimism_1.GROUP1", interactionterm2 = "Optimism_1.GROUP2", current_scale_name = "Optimism", new_scale_name = "Optimism T1")## lavaan (0.5-20) converged normally after 27 iterations
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## Number of observations 221
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## Number of missing patterns 4
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## Estimator ML
## Minimum Function Test Statistic 0.000
## Degrees of freedom 0
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## Model test baseline model:
##
## Minimum Function Test Statistic 33.192
## Degrees of freedom 5
## P-value 0.000
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## User model versus baseline model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
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## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -636.404
## Loglikelihood unrestricted model (H1) -636.404
##
## Number of free parameters 7
## Akaike (AIC) 1286.807
## Bayesian (BIC) 1310.594
## Sample-size adjusted Bayesian (BIC) 1288.411
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent Confidence Interval 0.000 0.000
## P-value RMSEA <= 0.05 1.000
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## Standardized Root Mean Square Residual:
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## SRMR 0.000
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## Parameter Estimates:
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## Information Observed
## Standard Errors Standard
##
## Regressions:
## Estimate Std.Err Z-value P(>|z|) Std.lv
## SCALED_2_Optimism ~
## GROUP1 -0.077 0.275 -0.279 0.780 -0.077
## GROUP2 -0.061 0.206 -0.294 0.769 -0.061
## SCALED_1_Optms 0.434 0.079 5.511 0.000 0.434
## Optms_1.GROUP1 -1.007 0.413 -2.439 0.015 -1.007
## Optms_1.GROUP2 -0.359 0.325 -1.106 0.269 -0.359
## Std.all
##
## -0.029
## -0.029
## 0.432
## -0.246
## -0.109
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_Optms -0.014 0.184 -0.078 0.938 -0.014 -0.014
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_Optms 0.770 0.094 8.216 0.000 0.770 0.774
##
## R-Square:
## Estimate
## SCALED_2_Optms 0.226## [[1]]
## Item β SE z p 90% CI
## 1 Intercept -0.01 0.18 -0.08 0.94 -0.37, 0.35
## 2 GROUP1 -0.08 0.28 -0.28 0.78 -0.62, 0.46
## 3 GROUP2 -0.06 0.21 -0.29 0.77 -0.47, 0.34
## 4 Optimism T1 0.43 0.08 5.51 0.00 0.28, 0.59
## 5 Interaction w/ Group 1 -1.01 0.41 -2.44 0.01 -1.82, -0.2
## 6 Interaction w/ Group 2 -0.36 0.32 -1.11 0.27 -1, 0.28
##
## [[2]]
## levels Treat_T2 Control_T2 RedTreat
## 1 -1 -0.15 -0.45 0.48
## 2 0 -0.08 -0.01 -0.09
## 3 1 0.00 0.42 -0.66plotsYouth(modelname = modelYOptimism_interaction, fitname = Fit, data = CranReddam, new_scale_name = "Optimism - LOT-R") 
#########################################
################# PWB ###################
#########################################
resultsYouth(modelname = modelYPWB_interaction, fitname = Fit, data = CranReddam, interactionterm1 = "PurposePWB_1.GROUP1", interactionterm2 = "PurposePWB_1.GROUP2", current_scale_name = "PWB", new_scale_name = "Ryff Purpose")## lavaan (0.5-20) converged normally after 27 iterations
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## Number of observations 221
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## Number of missing patterns 4
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## Estimator ML
## Minimum Function Test Statistic 0.000
## Degrees of freedom 0
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## Model test baseline model:
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## Minimum Function Test Statistic 32.483
## Degrees of freedom 5
## P-value 0.000
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## User model versus baseline model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
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## Loglikelihood and Information Criteria:
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## Loglikelihood user model (H0) -669.547
## Loglikelihood unrestricted model (H1) -669.547
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## Number of free parameters 7
## Akaike (AIC) 1353.093
## Bayesian (BIC) 1376.881
## Sample-size adjusted Bayesian (BIC) 1354.697
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## Root Mean Square Error of Approximation:
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## RMSEA 0.000
## 90 Percent Confidence Interval 0.000 0.000
## P-value RMSEA <= 0.05 1.000
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## Standardized Root Mean Square Residual:
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## SRMR 0.000
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## Parameter Estimates:
##
## Information Observed
## Standard Errors Standard
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## Regressions:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_PWB ~
## GROUP1 -0.353 0.300 -1.180 0.238 -0.353 -0.133
## GROUP2 -0.084 0.207 -0.406 0.685 -0.084 -0.040
## SCALED_1_PWB 0.456 0.087 5.262 0.000 0.456 0.458
## PrPWB_1.GROUP1 -0.017 0.466 -0.036 0.972 -0.017 -0.004
## PrPWB_1.GROUP2 -0.315 0.238 -1.322 0.186 -0.315 -0.109
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## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_PWB 0.133 0.185 0.719 0.472 0.133 0.134
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## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_PWB 0.777 0.094 8.237 0.000 0.777 0.788
##
## R-Square:
## Estimate
## SCALED_2_PWB 0.212## [[1]]
## Item β SE z p 90% CI
## 1 Intercept 0.13 0.18 0.72 0.47 -0.23, 0.49
## 2 GROUP1 -0.35 0.30 -1.18 0.24 -0.94, 0.23
## 3 GROUP2 -0.08 0.21 -0.41 0.68 -0.49, 0.32
## 4 Ryff Purpose 0.46 0.09 5.26 0.00 0.29, 0.63
## 5 Interaction w/ Group 1 -0.02 0.47 -0.04 0.97 -0.93, 0.9
## 6 Interaction w/ Group 2 -0.31 0.24 -1.32 0.19 -0.78, 0.15
##
## [[2]]
## levels Treat_T2 Control_T2 RedTreat
## 1 -1 -0.09 -0.32 -0.66
## 2 0 0.05 0.13 -0.22
## 3 1 0.19 0.59 0.22plotsYouth(modelname = modelYPWB_interaction, fitname = Fit, data = CranReddam, new_scale_name = "Ryff Purpose Subscale")
#########################################
################# APSI ###################
#########################################
resultsYouth(modelname = modelYAPSI_interaction, fitname = Fit, data = CranReddam, interactionterm1 = "PurposeAPSI_No36_1.GROUP1", interactionterm2 = "PurposeAPSI_No36_1.GROUP2", current_scale_name = "APSI", new_scale_name = "Sense of Idenitity")## lavaan (0.5-20) converged normally after 26 iterations
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## Number of observations 221
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## Number of missing patterns 4
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## Estimator ML
## Minimum Function Test Statistic 0.000
## Degrees of freedom 0
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## Model test baseline model:
##
## Minimum Function Test Statistic 63.436
## Degrees of freedom 5
## P-value 0.000
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## User model versus baseline model:
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## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
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## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -678.504
## Loglikelihood unrestricted model (H1) -678.504
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## Number of free parameters 7
## Akaike (AIC) 1371.008
## Bayesian (BIC) 1394.795
## Sample-size adjusted Bayesian (BIC) 1372.612
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## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent Confidence Interval 0.000 0.000
## P-value RMSEA <= 0.05 1.000
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## Standardized Root Mean Square Residual:
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## SRMR 0.000
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## Parameter Estimates:
##
## Information Observed
## Standard Errors Standard
##
## Regressions:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_APSI ~
## GROUP1 0.132 0.253 0.520 0.603 0.132 0.050
## GROUP2 0.120 0.190 0.629 0.529 0.120 0.057
## SCALED_1_APSI 0.570 0.069 8.244 0.000 0.570 0.570
## PAPSI_N36_1.GR 0.106 0.333 0.318 0.751 0.106 0.028
## PAPSI_N36_1.GR -0.505 0.229 -2.209 0.027 -0.505 -0.176
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## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_APSI -0.082 0.170 -0.481 0.630 -0.082 -0.082
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## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_APSI 0.619 0.075 8.234 0.000 0.619 0.629
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## R-Square:
## Estimate
## SCALED_2_APSI 0.371## [[1]]
## Item β SE z p 90% CI
## 1 Intercept -0.08 0.17 -0.48 0.63 -0.41, 0.25
## 2 GROUP1 0.13 0.25 0.52 0.60 -0.36, 0.63
## 3 GROUP2 0.12 0.19 0.63 0.53 -0.25, 0.49
## 4 Sense of Idenitity 0.57 0.07 8.24 0.00 0.43, 0.71
## 5 Interaction w/ Group 1 0.11 0.33 0.32 0.75 -0.55, 0.76
## 6 Interaction w/ Group 2 -0.51 0.23 -2.21 0.03 -0.95, -0.06
##
## [[2]]
## levels Treat_T2 Control_T2 RedTreat
## 1 -1 -0.03 -0.65 -0.63
## 2 0 0.04 -0.08 0.05
## 3 1 0.10 0.49 0.73plotsYouth(modelname = modelYAPSI_interaction, fitname = Fit, data = CranReddam, new_scale_name = "APSI - Sense of Identity Subscale")
#########################################
############### Resiliance ##############
#########################################
resultsYouth(modelname = modelYGRIT_interaction, fitname = Fit, data = CranReddam, interactionterm1 = "Grit_1.GROUP1", interactionterm2 = "Grit_1.GROUP2", current_scale_name = "Res", new_scale_name = "Resiliance")## lavaan (0.5-20) converged normally after 21 iterations
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## Number of observations 221
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## Number of missing patterns 4
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## Estimator ML
## Minimum Function Test Statistic 0.000
## Degrees of freedom 0
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## Model test baseline model:
##
## Minimum Function Test Statistic 51.254
## Degrees of freedom 5
## P-value 0.000
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## User model versus baseline model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
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## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -1064.549
## Loglikelihood unrestricted model (H1) -1064.549
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## Number of free parameters 7
## Akaike (AIC) 2143.099
## Bayesian (BIC) 2166.886
## Sample-size adjusted Bayesian (BIC) 2144.702
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## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent Confidence Interval 0.000 0.000
## P-value RMSEA <= 0.05 1.000
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## Standardized Root Mean Square Residual:
##
## SRMR 0.000
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## Parameter Estimates:
##
## Information Observed
## Standard Errors Standard
##
## Regressions:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_Res ~
## GROUP1 -0.332 0.264 -1.257 0.209 -0.332 -0.126
## GROUP2 -0.234 0.195 -1.202 0.229 -0.234 -0.112
## SCALED_1_Res 0.495 0.071 6.962 0.000 0.495 0.498
## Grit_1.GROUP1 0.167 0.131 1.280 0.201 0.167 0.111
## Grit_1.GROUP2 -0.161 0.091 -1.761 0.078 -0.161 -0.146
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## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_Res 0.168 0.175 0.962 0.336 0.168 0.171
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## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_Res 0.674 0.082 8.209 0.000 0.674 0.691
##
## R-Square:
## Estimate
## SCALED_2_Res 0.309## [[1]]
## Item β SE z p 90% CI
## 1 Intercept 0.17 0.18 0.96 0.34 -0.17, 0.51
## 2 GROUP1 -0.33 0.26 -1.26 0.21 -0.85, 0.19
## 3 GROUP2 -0.23 0.19 -1.20 0.23 -0.62, 0.15
## 4 Resiliance 0.49 0.07 6.96 0.00 0.36, 0.63
## 5 Interaction w/ Group 1 0.17 0.13 1.28 0.20 -0.09, 0.42
## 6 Interaction w/ Group 2 -0.16 0.09 -1.76 0.08 -0.34, 0.02
##
## [[2]]
## levels Treat_T2 Control_T2 RedTreat
## 1 -1 -0.40 -0.33 -0.83
## 2 0 -0.07 0.17 -0.16
## 3 1 0.27 0.66 0.50plotsYouth(modelname = modelYGRIT_interaction, fitname = Fit, data = CranReddam, new_scale_name = "Resiliance")
#########################################
############# Self Esteem ###############
#########################################
resultsYouth(modelname = modelYSeleEsteem_interaction, fitname = Fit, data = CranReddam, interactionterm1 = "SelfEsteem_1.GROUP1", interactionterm2 = "SelfEsteem_1.GROUP2", current_scale_name = "Self Esteem", new_scale_name = "Resiliance")## lavaan (0.5-20) converged normally after 26 iterations
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## Number of observations 221
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## Number of missing patterns 4
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## Estimator ML
## Minimum Function Test Statistic 0.000
## Degrees of freedom 0
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## Model test baseline model:
##
## Minimum Function Test Statistic 46.416
## Degrees of freedom 5
## P-value 0.000
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## User model versus baseline model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
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## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -654.169
## Loglikelihood unrestricted model (H1) -654.169
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## Number of free parameters 7
## Akaike (AIC) 1322.338
## Bayesian (BIC) 1346.125
## Sample-size adjusted Bayesian (BIC) 1323.942
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## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent Confidence Interval 0.000 0.000
## P-value RMSEA <= 0.05 1.000
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## Standardized Root Mean Square Residual:
##
## SRMR 0.000
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## Parameter Estimates:
##
## Information Observed
## Standard Errors Standard
##
## Regressions:
## Estimate Std.Err Z-value P(>|z|) Std.lv
## SCALED_2_SelfEsteem ~
## GROUP1 0.218 0.264 0.827 0.408 0.218
## GROUP2 0.083 0.197 0.423 0.672 0.083
## SCALED_1_SlfEs 0.519 0.072 7.178 0.000 0.519
## SlfEs_1.GROUP1 0.394 0.386 1.021 0.307 0.394
## SlfEs_1.GROUP2 -0.059 0.267 -0.220 0.825 -0.059
## Std.all
##
## 0.081
## 0.039
## 0.516
## 0.100
## -0.019
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_SlfEs -0.068 0.177 -0.386 0.699 -0.068 -0.068
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_SlfEs 0.697 0.085 8.199 0.000 0.697 0.696
##
## R-Square:
## Estimate
## SCALED_2_SlfEs 0.304## [[1]]
## Item β SE z p 90% CI
## 1 Intercept -0.07 0.18 -0.39 0.70 -0.41, 0.28
## 2 GROUP1 0.22 0.26 0.83 0.41 -0.3, 0.74
## 3 GROUP2 0.08 0.20 0.42 0.67 -0.3, 0.47
## 4 SelfEsteem 0.52 0.07 7.18 0.00 0.38, 0.66
## 5 Interaction w/ Group 1 0.39 0.39 1.02 0.31 -0.36, 1.15
## 6 Interaction w/ Group 2 -0.06 0.27 -0.22 0.83 -0.58, 0.46
##
## [[2]]
## levels Treat_T2 Control_T2 RedTreat
## 1 -1 -0.45 -0.59 -0.76
## 2 0 0.02 -0.07 0.15
## 3 1 0.48 0.45 1.06plotsYouth(modelname = modelYSeleEsteem_interaction, fitname = Fit, data = CranReddam, new_scale_name = "Global Self Esteem (SDQI)")
#########################################
############### APSI FG #################
#########################################
resultsYouth(modelname = modelYAPSIFG_interaction, fitname = Fit, data = CranReddam, interactionterm1 = "APSI_FG_1.GROUP1", interactionterm2 = "APSI_FG_1.GROUP2", current_scale_name = "APSI_FG", new_scale_name = "APSI Future Goals")## lavaan (0.5-20) converged normally after 27 iterations
##
## Number of observations 221
##
## Number of missing patterns 4
##
## Estimator ML
## Minimum Function Test Statistic 0.000
## Degrees of freedom 0
##
## Model test baseline model:
##
## Minimum Function Test Statistic 42.102
## Degrees of freedom 5
## P-value 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -685.571
## Loglikelihood unrestricted model (H1) -685.571
##
## Number of free parameters 7
## Akaike (AIC) 1385.141
## Bayesian (BIC) 1408.928
## Sample-size adjusted Bayesian (BIC) 1386.745
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent Confidence Interval 0.000 0.000
## P-value RMSEA <= 0.05 1.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.000
##
## Parameter Estimates:
##
## Information Observed
## Standard Errors Standard
##
## Regressions:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_APSI_FG ~
## GROUP1 0.034 0.270 0.125 0.900 0.034 0.013
## GROUP2 -0.060 0.204 -0.295 0.768 -0.060 -0.029
## SCALED_1_APSI_ 0.488 0.073 6.655 0.000 0.488 0.490
## APSI_FG_1.GROU 0.204 0.359 0.568 0.570 0.204 0.054
## APSI_FG_1.GROU -0.452 0.260 -1.741 0.082 -0.452 -0.163
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_APSI_ 0.051 0.183 0.281 0.779 0.051 0.052
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_APSI_ 0.727 0.088 8.266 0.000 0.727 0.742
##
## R-Square:
## Estimate
## SCALED_2_APSI_ 0.258## [[1]]
## Item β SE z p 90% CI
## 1 Intercept 0.05 0.18 0.28 0.78 -0.31, 0.41
## 2 GROUP1 0.03 0.27 0.13 0.90 -0.5, 0.56
## 3 GROUP2 -0.06 0.20 -0.29 0.77 -0.46, 0.34
## 4 APSI Future Goals 0.49 0.07 6.66 0.00 0.34, 0.63
## 5 Interaction w/ Group 1 0.20 0.36 0.57 0.57 -0.5, 0.91
## 6 Interaction w/ Group 2 -0.45 0.26 -1.74 0.08 -0.96, 0.06
##
## [[2]]
## levels Treat_T2 Control_T2 RedTreat
## 1 -1 -0.04 -0.44 -0.61
## 2 0 -0.01 0.05 0.09
## 3 1 0.03 0.54 0.78plotsYouth(modelname = modelYAPSIFG_interaction, fitname = Fit, data = CranReddam, new_scale_name = "APSI Future Goals") 
#########################################
########### APSI Feeling Now ############
#########################################
resultsYouth(modelname = modelYAPSIFN_interaction, fitname = Fit, data = CranReddam, interactionterm1 = "APSI_FN_1.GROUP1", interactionterm2 = "APSI_FN_1.GROUP2", current_scale_name = "APSI_FN", new_scale_name = "APSI Feeling Now")## lavaan (0.5-20) converged normally after 25 iterations
##
## Number of observations 221
##
## Number of missing patterns 4
##
## Estimator ML
## Minimum Function Test Statistic 0.000
## Degrees of freedom 0
##
## Model test baseline model:
##
## Minimum Function Test Statistic 46.266
## Degrees of freedom 5
## P-value 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -782.895
## Loglikelihood unrestricted model (H1) -782.895
##
## Number of free parameters 7
## Akaike (AIC) 1579.790
## Bayesian (BIC) 1603.577
## Sample-size adjusted Bayesian (BIC) 1581.394
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## Root Mean Square Error of Approximation:
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## RMSEA 0.000
## 90 Percent Confidence Interval 0.000 0.000
## P-value RMSEA <= 0.05 1.000
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## Standardized Root Mean Square Residual:
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## SRMR 0.000
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## Parameter Estimates:
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## Information Observed
## Standard Errors Standard
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## Regressions:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_APSI_FN ~
## GROUP1 0.203 0.271 0.749 0.454 0.203 0.076
## GROUP2 0.178 0.200 0.890 0.374 0.178 0.085
## SCALED_1_APSI_ 0.481 0.074 6.518 0.000 0.481 0.482
## APSI_FN_1.GROU -0.215 0.279 -0.769 0.442 -0.215 -0.073
## APSI_FN_1.GROU -0.445 0.198 -2.244 0.025 -0.445 -0.193
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## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_APSI_ -0.151 0.179 -0.841 0.400 -0.151 -0.152
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## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_APSI_ 0.703 0.085 8.240 0.000 0.703 0.712
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## R-Square:
## Estimate
## SCALED_2_APSI_ 0.288## [[1]]
## Item β SE z p 90% CI
## 1 Intercept -0.15 0.18 -0.84 0.40 -0.5, 0.2
## 2 GROUP1 0.20 0.27 0.75 0.45 -0.33, 0.73
## 3 GROUP2 0.18 0.20 0.89 0.37 -0.21, 0.57
## 4 APSI Feeling Now 0.48 0.07 6.52 0.00 0.34, 0.63
## 5 Interaction w/ Group 1 -0.21 0.28 -0.77 0.44 -0.76, 0.33
## 6 Interaction w/ Group 2 -0.44 0.20 -2.24 0.02 -0.83, -0.06
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## [[2]]
## levels Treat_T2 Control_T2 RedTreat
## 1 -1 -0.01 -0.63 -0.21
## 2 0 0.03 -0.15 0.05
## 3 1 0.06 0.33 0.32plotsYouth(modelname = modelYAPSIFN_interaction, fitname = Fit, data = CranReddam, new_scale_name = "APSI Feeling Now") 