plots_results_w-o-ITT
Levi Brackman
November 20, 2016
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':
arrange, count, desc, failwith, id, mutate, rename, summarise,
summarizeThe following objects are masked from 'package:stats':
filter, lagThe following objects are masked from 'package:base':
intersect, setdiff, setequal, union
Attaching package: 'tibble'The following object is masked from 'package:dplyr':
tbl_dfRead-in the dataset
Read data
setwd("~/Documents/stats_march_2016/Adult_study Data_Analysis")
adult2<-read.csv("adult2withscaled_W-OI-TT.csv")
# View(adult2)
#dim(adult2)Fits Create indevidual dataframes
#View(adult2)
NewPurpose<-as_data_frame(select(adult2, SCALED_1_NewPurpose, SCALED_2_NewPurpose, SCALED_3_NewPurpose, NewPurpose_mean_1.GROUP1))
MLQ<-as_data_frame(select(adult2, SCALED_1_MLQP, SCALED_2_MLQP, SCALED_3_MLQP, MLQ_mean_1.GROUP1))
MLQS<-as_data_frame(select(adult2, SCALED_1_MLQS, SCALED_2_MLQS, SCALED_3_MLQS, MLQS_mean_1.GROUP1))
PermaHappy<-as_data_frame(select(adult2, SCALED_1_PERMA_Happy, SCALED_2_PERMA_Happy, SCALED_3_PERMA_Happy, PERMA_Happy_mean_1.GROUP1))
PermaLonely<-as_data_frame(select(adult2, SCALED_1_PERMA_Lonely, SCALED_2_PERMA_Lonely, SCALED_3_PERMA_Lonely, PERMA_Lonely_mean_1.GROUP1))
GRIT<-as_data_frame(select(adult2, SCALED_1_GRIT, SCALED_2_GRIT, SCALED_3_GRIT, GRIT_mean_1.GROUP1))
Resiliance<-as_data_frame(select(adult2, SCALED_1_Res, SCALED_2_Res, SCALED_3_Res, Res_mean_1.GROUP1))
APSI<-as_data_frame(select(adult2, SCALED_1_APSI, SCALED_2_APSI, SCALED_3_APSI, PurposeAPSI_mean_1.GROUP1))
PWB<-as_data_frame(select(adult2, SCALED_1_PWB, SCALED_2_PWB, SCALED_3_PWB, PurposePWB_mean_1.GROUP1))
Optimism<-as_data_frame(select(adult2, SCALED_1_Optimism, SCALED_2_Optimism, SCALED_3_Optimism, Optimism_mean_1.GROUP1))
LifeSatisfaction<-as_data_frame(select(adult2, SCALED_1_LS, SCALED_2_LS, SCALED_3_LS, LifeSatisfaction_mean_1.GROUP1))
LifeEngagement<-as_data_frame(select(adult2, SCALED_1_LET, SCALED_2_LET, SCALED_3_LET, LifeEngagement_mean_1.GROUP1))
PermaEngagement<-as_data_frame(select(adult2, SCALED_1_Engagement, SCALED_2_Engagement, SCALED_3_Engagement, Engagement_mean_1.GROUP1))
PermaRelationships<-as_data_frame(select(adult2, SCALED_1_Rrealtionships, SCALED_2_Rrealtionships, SCALED_3_Rrealtionships, Relationships_mean_1.GROUP1))
PermaNegetive<-as_data_frame(select(adult2, SCALED_1_Nagative, SCALED_2_Nagative, SCALED_3_Nagative, Negative_mean_1.GROUP1))
PermaAcheivement<-as_data_frame(select(adult2, SCALED_1_Acheivement, SCALED_2_Acheivement, SCALED_3_Acheivement, Acheivement_mean_1.GROUP1))
PermaPositive<-as_data_frame(select(adult2, SCALED_1_Positive, SCALED_2_Positive, SCALED_3_Positive, Positive_mean_1.GROUP1))
#create nexted dataframe
#by_scale<-nest(NewPurpose, MLQ, MLQS, PermaHappy, PermaLonely, PermaPositive, PermaAcheivement, PermaRelationships, PermaEngagement, PermaNegetive, LifeEngagement, LifeSatisfaction, Optimism, PWB, APSI, Resiliance, GRIT)
##Time 1 to time 2 and time 1 to time 3 NewPurpose
modelNewPurpose_interaction <- '
# Regression model
SCALED_2_NewPurpose ~ GROUP1 + SCALED_1_NewPurpose + NewPurpose_mean_1.GROUP1
SCALED_3_NewPurpose ~ GROUP1 + SCALED_1_NewPurpose + NewPurpose_mean_1.GROUP1'
#Time 1 to time 2 and time 1 to time 3 taking time 2 into account NewPurpose
modelNewPurpose_interaction_T3 <- '
# Regression model
SCALED_2_NewPurpose ~ GROUP1 + SCALED_1_NewPurpose + NewPurpose_mean_1.GROUP1
SCALED_3_NewPurpose ~ GROUP1 + SCALED_1_NewPurpose + NewPurpose_mean_1.GROUP1 + SCALED_2_ NewPurpose '
#Time 1 to time 2 and time 1 to time 3 MLQP
modelMLQP_interaction <- '
# Regression model
SCALED_2_MLQP ~ GROUP1 + SCALED_1_MLQP + MLQ_mean_1.GROUP1
SCALED_3_MLQP ~ GROUP1 + SCALED_1_MLQP + MLQ_mean_1.GROUP1'
#Time 1 to time 2 and time 1 to time 3 taking time 2 into account MLQP
modelMLQP_interaction_T3 <- '
# Regression model
SCALED_2_MLQP ~ GROUP1 + SCALED_1_MLQP+ MLQ_mean_1.GROUP1
SCALED_3_MLQP ~ GROUP1 + SCALED_1_MLQP + MLQ_mean_1.GROUP1 + SCALED_2_MLQP'
#Time 1 to time 2 and time 1 to time 3 MLQS
modelMLQS_interaction <- '
# Regression model
SCALED_2_MLQS ~ GROUP1 + SCALED_1_MLQS + MLQS_mean_1.GROUP1
SCALED_3_MLQS ~ GROUP1 + SCALED_1_MLQS + MLQS_mean_1.GROUP1'
#Time 1 to time 2 and time 1 to time 3 taking time 2 into account MLQS
modelMLQS_interaction_T3 <- '
# Regression model
SCALED_2_MLQS ~ GROUP1 + SCALED_1_MLQS+ MLQS_mean_1.GROUP1
SCALED_3_MLQS ~ GROUP1 + SCALED_1_MLQS + MLQS_mean_1.GROUP1 + SCALED_2_MLQS'
#Time 1 to time 2 and time 1 to time 3 Perman Happy
modelHappy_interaction <- '
# Regression model
SCALED_2_PERMA_Happy ~ GROUP1 + SCALED_1_PERMA_Happy + PERMA_Happy_mean_1.GROUP1
SCALED_3_PERMA_Happy ~ GROUP1 + SCALED_1_PERMA_Happy + PERMA_Happy_mean_1.GROUP1'
#Time 1 to time 2 and time 1 to time 3 taking time 2 into account Perma Happy
modelHappy_interaction_T3 <- '
# Regression model
SCALED_2_PERMA_Happy ~ GROUP1 + SCALED_1_PERMA_Happy + PERMA_Happy_mean_1.GROUP1
SCALED_3_PERMA_Happy ~ GROUP1 + SCALED_1_PERMA_Happy + PERMA_Happy_mean_1.GROUP1 + SCALED_2_PERMA_Happy '
#Time 1 to time 2 and time 1 to time 3 Perma Lonely
modelLonely_interaction <- '
# Regression model
SCALED_2_PERMA_Lonely ~ GROUP1 + SCALED_1_PERMA_Lonely + PERMA_Lonely_mean_1.GROUP1
SCALED_3_PERMA_Lonely ~ GROUP1 + SCALED_1_PERMA_Lonely + PERMA_Lonely_mean_1.GROUP1'
#Time 1 to time 2 and time 1 to time 3 taking time 2 into account Perma Lonely
modelLonely_interaction_T3 <- '
# Regression model
SCALED_2_PERMA_Lonely ~ GROUP1 + SCALED_1_PERMA_Lonely + PERMA_Lonely_mean_1.GROUP1
SCALED_3_PERMA_Lonely ~ GROUP1 + SCALED_1_PERMA_Lonely + PERMA_Lonely_mean_1.GROUP1 + SCALED_2_PERMA_Lonely '
#Time 1 to time 2 and time 1 to time 3 Grit
modelGRIT_interaction <- '
# Regression model
SCALED_2_GRIT ~ GROUP1 + SCALED_1_GRIT +GRIT_mean_1.GROUP1
SCALED_3_GRIT ~ GROUP1 + SCALED_1_GRIT +GRIT_mean_1.GROUP1'
#Time 1 to time 2 and time 1 to time 3 taking time 2 into account Grit
modelGRIT_interaction_T3 <- '
# Regression model
SCALED_2_GRIT ~ GROUP1 + SCALED_1_GRIT +GRIT_mean_1.GROUP1
SCALED_3_GRIT ~ GROUP1 + SCALED_1_GRIT +GRIT_mean_1.GROUP1 + SCALED_2_GRIT '
#Time 1 to time 2 and time 1 to time 3 Resiliance
modelRes_interaction <- '
# Regression model
SCALED_2_Res ~ GROUP1 + SCALED_1_Res +Res_mean_1.GROUP1
SCALED_3_Res ~ GROUP1 + SCALED_1_Res +Res_mean_1.GROUP1'
#Time 1 to time 2 and time 1 to time 3 taking time 2 into account Resiliance
modelRes_interaction_T3 <- '
# Regression model
SCALED_2_Res ~ GROUP1 + SCALED_1_Res +Res_mean_1.GROUP1
SCALED_3_Res ~ GROUP1 + SCALED_1_Res +Res_mean_1.GROUP1 + SCALED_2_Res '
#Time 1 to time 2 and time 1 to time 3 APSI
modelAPSI_interaction <- '
# Regression model
SCALED_2_APSI ~ GROUP1 + SCALED_1_APSI + PurposeAPSI_mean_1.GROUP1
SCALED_3_APSI ~ GROUP1 + SCALED_1_APSI + PurposeAPSI_mean_1.GROUP1'
#Time 1 to time 2 and time 1 to time 3 taking time 2 into account APSI
modelAPSI_interaction_T3 <- '
# Regression model
SCALED_2_APSI ~ GROUP1 + SCALED_1_APSI + PurposeAPSI_mean_1.GROUP1
SCALED_3_APSI ~ GROUP1 + SCALED_1_APSI + PurposeAPSI_mean_1.GROUP1 + SCALED_2_APSI '
#Time 1 to time 2 and time 1 to time 3 PWB
modelPWB_interaction <- '
# Regression model
SCALED_2_PWB ~ GROUP1 + SCALED_1_PWB + PurposePWB_mean_1.GROUP1
SCALED_3_PWB ~ GROUP1 + SCALED_1_PWB + PurposePWB_mean_1.GROUP1'
#Time 1 to time 2 and time 1 to time 3 taking time 2 into account PWB
modelPWB_interaction_T3 <- '
# Regression model
SCALED_2_PWB ~ GROUP1 + SCALED_1_PWB + PurposePWB_mean_1.GROUP1
SCALED_3_PWB ~ GROUP1 + SCALED_1_PWB + PurposePWB_mean_1.GROUP1 + SCALED_2_PWB '
#Time 1 to time 2 and time 1 to time 3 OPTIMISM
modelOPTIMISM_interaction <- '
# Regression model
SCALED_2_Optimism ~ GROUP1 + SCALED_1_Optimism + Optimism_mean_1.GROUP1
SCALED_3_Optimism ~ GROUP1 + SCALED_1_Optimism + Optimism_mean_1.GROUP1'
#Time 1 to time 2 and time 1 to time 3 taking time 2 into account OPTIMISM
modelOPTIMISM_interaction_T3 <- '
# Regression model
SCALED_2_Optimism ~ GROUP1 + SCALED_1_Optimism + Optimism_mean_1.GROUP1
SCALED_3_Optimism ~ GROUP1 + SCALED_1_Optimism + Optimism_mean_1.GROUP1 + SCALED_2_Optimism'
#Time 1 to time 2 and time 1 to time 3 LS
modelLS_interaction <- '
# Regression model
SCALED_2_LS ~ GROUP1 + SCALED_1_LS +LifeSatisfaction_mean_1.GROUP1
SCALED_3_LS ~ GROUP1 + SCALED_1_LS +LifeSatisfaction_mean_1.GROUP1'
#Time 1 to time 2 and time 1 to time 3 taking time 2 into account LS
modelLS_interaction_T3 <- '
# Regression model
SCALED_2_LS ~ GROUP1 + SCALED_1_LS +LifeSatisfaction_mean_1.GROUP1
SCALED_3_LS ~ GROUP1 + SCALED_1_LS +LifeSatisfaction_mean_1.GROUP1 + SCALED_2_LS '
#Time 1 to time 2 and time 1 to time 3 Engagement
modelEngagement_interaction <- '
# Regression model
SCALED_2_Engagement ~ GROUP1 + SCALED_1_Engagement + Engagement_mean_1.GROUP1
SCALED_3_Engagement ~ GROUP1 + SCALED_1_Engagement + Engagement_mean_1.GROUP1'
#Time 1 to time 2 and time 1 to time 3 taking time 2 into account Engagement
modelEngagement_interaction_T3 <- '
# Regression model
SCALED_2_Engagement ~ GROUP1 + SCALED_1_Engagement + Engagement_mean_1.GROUP1
SCALED_3_Engagement ~ GROUP1 + SCALED_1_Engagement + Engagement_mean_1.GROUP1 + SCALED_2_Engagement'
#Time 1 to time 2 and time 1 to time 3 RELATIONSHIPS
modelRelationships_interaction <- '
# Regression model
SCALED_2_Rrealtionships ~ GROUP1 + SCALED_1_Rrealtionships + Relationships_mean_1.GROUP1
SCALED_3_Rrealtionships ~ GROUP1 + SCALED_1_Rrealtionships + Relationships_mean_1.GROUP1'
#Time 1 to time 2 and time 1 to time 3 taking time 2 into account RELATIONSHIPS
modelRelationships_interaction_T3 <- '
# Regression model
SCALED_2_Rrealtionships ~ GROUP1 + SCALED_1_Rrealtionships +Relationships_mean_1.GROUP1
SCALED_3_Rrealtionships ~ GROUP1 + SCALED_1_Rrealtionships +Relationships_mean_1.GROUP1 + SCALED_2_Rrealtionships '
#Time 1 to time 2 and time 1 to time 3 NAGATIVE
modelNegative_interaction <- '
# Regression model
SCALED_2_Nagative ~ GROUP1 + SCALED_1_Nagative + Negative_mean_1.GROUP1
SCALED_3_Nagative ~ GROUP1 + SCALED_1_Nagative + Negative_mean_1.GROUP1'
#Time 1 to time 2 and time 1 to time 3 taking time 2 into account NAGATIVE
modelNegative_interaction_T3 <- '
# Regression model
SCALED_2_Nagative ~ GROUP1 + SCALED_1_Nagative + Negative_mean_1.GROUP1
SCALED_3_Nagative ~ GROUP1 + SCALED_1_Nagative + Negative_mean_1.GROUP1 + SCALED_2_Nagative '
#Time 1 to time 2 and time 1 to time 3 Acheivement
modelAcheivement_interaction <- '
# Regression model
SCALED_2_Acheivement ~ GROUP1 + SCALED_1_Acheivement + Acheivement_mean_1.GROUP1
SCALED_3_Acheivement ~ GROUP1 + SCALED_1_Acheivement + Acheivement_mean_1.GROUP1'
#Time 1 to time 2 and time 1 to time 3 taking time 2 into account Acheivement
modelAcheivement_interaction_T3 <- '
# Regression model
SCALED_2_Acheivement ~ GROUP1 + SCALED_1_ Acheivement + Acheivement_mean_1.GROUP1
SCALED_3_Acheivement ~ GROUP1 + SCALED_1_ Acheivement + Acheivement_mean_1.GROUP1 + SCALED_2_Acheivement '
#Time 1 to time 2 and time 1 to time 3 Positive
modelPositive_interaction <- '
# Regression model
SCALED_2_Positive ~ GROUP1 + SCALED_1_ Positive + Positive_mean_1.GROUP1
SCALED_3_Positive ~ GROUP1 + SCALED_1_Positive + Positive_mean_1.GROUP1'
#Time 1 to time 2 and time 1 to time 3 taking time 2 into account Positive
modelPositive_interaction_T3 <- '
# Regression model
SCALED_2_Positive ~ GROUP1 + SCALED_1_Positive + Positive_mean_1.GROUP1
SCALED_3_Positive ~ GROUP1 + SCALED_1_Positive + Positive_mean_1.GROUP1 + SCALED_2_Positive'
#Time 1 to time 2 and time 1 to time 3 LET
modelLET_interaction <- '
# Regression model
SCALED_2_LET ~ GROUP1 + SCALED_1_LET + LifeEngagement_mean_1.GROUP1
SCALED_3_LET ~ GROUP1 + SCALED_1_LET + LifeEngagement_mean_1.GROUP1'
#Time 1 to time 2 and time 1 to time 3 taking time 2 into account LET
modelLET_interaction_T3 <- '
# Regression model
SCALED_2_LET ~ GROUP1 + SCALED_1_LET + LifeEngagement_mean_1.GROUP1
SCALED_3_LET ~ GROUP1 + SCALED_1_LET+ LifeEngagement_mean_1.GROUP1 + SCALED_2_LET '
#Time 1 to time 2 and time 1 to time 3 Perma Happy
modelPERMA_Happy_interaction <- '
# Regression model
SCALED_2_PERMA_Happy ~ GROUP1 + SCALED_1_PERMA_Happy + PERMA_Happy_mean_1.GROUP1
SCALED_3_PERMA_Happy ~ GROUP1 + SCALED_1_PERMA_Happy + PERMA_Happy_mean_1.GROUP1'
#Time 1 to time 2 and time 1 to time 3 taking time 2 into account Perma Happy
modelPERMA_Happy_interaction_T3 <- '
# Regression model
SCALED_2_PERMA_Happy ~ GROUP1 + SCALED_1_PERMA_Happy + PERMA_Happy_mean_1.GROUP1
SCALED_3_PERMA_Happy ~ GROUP1 + SCALED_1_PERMA_Happy + PERMA_Happy_mean_1.GROUP1 + SCALED_2_PERMA_Happy '
#Time 1 to time 2 and time 1 to time 3 Perma Lonely
modelPERMA_Lonely_interaction <- '
# Regression model
SCALED_2_PERMA_Lonely ~ GROUP1 + SCALED_1_PERMA_Lonely + PERMA_Lonely_mean_1.GROUP1
SCALED_3_PERMA_Lonely ~ GROUP1 + SCALED_1_PERMA_Lonely + PERMA_Lonely_mean_1.GROUP1'
#Time 1 to time 2 and time 1 to time 3 taking time 2 into account Perma Lonely
modelPERMA_Lonely_interaction_T3 <- '
# Regression model
SCALED_2_PERMA_Lonely ~ GROUP1 + SCALED_1_PERMA_Lonely + PERMA_Lonely_mean_1.GROUP1
SCALED_3_PERMA_Lonely ~ GROUP1 + SCALED_1_PERMA_Lonely + PERMA_Lonely_mean_1.GROUP1 + SCALED_2_PERMA_Lonely ' Fits
#####Functions for fit creation
fitsetc<- function(scale, fitone, fittwo, modelname1, modelname2, dataset, xname1, intercation, xname2, yname, xlabname, ylabnaem){
fits1<-noquote(paste0(fitone,"<-","sem","(", modelname1,",", dataset,",", "missing='fiml',", "meanstructure=TRUE",",", "fixed.x=T",")", collapse=NULL))
sum1<-noquote(paste0("summary","(", fitone,",", "fit.measures=TRUE", ",","rsquare=TRUE,", "standardize=T",")",collapse=NULL))
paramas1<- noquote(paste0("parameterEstimates","(", fitone,")", collapse=NULL))
interprobe1<-noquote(paste0("inter", scale,"1","<-","probe2WayMC","(",fitone,",", "nameX=c(", xname1,",", intercation,",",xname2,"),",
"nameY=", yname,",", "modVar=", intercation,",", "valProbe=c(1,2)",")",collapse=NULL))
PlotProbes1<- noquote(paste0("plotProbe","(","inter",scale,"1", ",","xlim=c(1,9)","," ,"xlab=", xlabname,",", "ylab=", ylabnaem,")",collapse=NULL))
fits2<-noquote(paste0(fittwo,"<-","sem","(", modelname2,",", dataset,",", "missing='fiml',", "meanstructure=TRUE",",", "fixed.x=T",")", collapse=NULL))
sum2<-noquote(paste0("summary","(", fittwo,",", "fit.measures=TRUE", ",","rsquare=TRUE,", "standardize=T",")",collapse=NULL))
paramas2<- noquote(paste0("parameterEstimates","(", fitone,")", collapse=NULL))
interprobe2<-noquote(paste0("inter", scale,"1","<-","probe2WayMC","(",fitone,",", "nameX=c(", xname1,",", intercation,",",xname2,"),",
"nameY=", yname,",", "modVar=", intercation,",", "valProbe=c(1,2)",")",collapse=NULL))
PlotProbes2<- noquote(paste0("plotProbe","(","inter",scale,"1", ",","xlim=c(1,9)","," ,"xlab=", xlabname,",", "ylab=", ylabnaem,")",collapse=NULL))
all<-list(c(fits1, sum1, paramas1, interprobe1, PlotProbes1, fits2, sum2, paramas2, interprobe2, PlotProbes2))
all
}
#Model fitsFunctions for tables and interactions
FitTable<-function(fitname, interactionterm, 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(16, 1:3, 17, 4:6))
lolollol<-rename(lolollol, Item = rhs, β = est, SE = se, p = pvalue, "90% CI" = CI)
lolollol[1,1] <- sprintf('Intercept', lolollol[1,1])
lolollol[5,1] <- sprintf('Intercept', lolollol[1,1])
lolollol[,1]<-gsub("SCALED_1_", "", lolollol[,1])
lolollol[,1]<-gsub(interactionterm, "Interaction w/ Group", lolollol[,1])
lolollol[,1]<-gsub(current_scale_name, new_scale_name, lolollol[,1])
lolollol1<-slice(lolollol, 1:4)
lolollol2<-slice(lolollol, 5:8)
lolollol<-list(lolollol1,lolollol2)
lolollol
}
library(ggplot2)
PlotInter<-function(fitname, PlotTitle, time2, time3){
fit<-(parameterEstimates(fitname))
Treat=1
Control=0
Group0 <-fit[16,4]
Group1<- fit[1,4]
T1 = fit[2,4]
interaction1 = fit[3,4]
Group0_2 = fit[17,4]
Group2 = fit[4,4]
T1_2 = fit[5,4]
interaction2 = fit[6,4]
library(ggplot2)
TreatHighT2 = Group0 + T1*1 + Group1*Treat - interaction1*1*Treat
TreatMediumT2 = Group0 + T1*0 + Group1*Treat - interaction1*0*Treat
TreatLowT2 = Group0 + T1*-1 + Group1*Treat - interaction1*-1*Treat
ControlHighT2 = Group0 + T1*1 + Group1*Control - interaction1*1*Control
ControlMediumT2 = Group0 + T1*0 + Group1*Control - interaction1*0*Control
ControlLowT2 = Group0 + T1*-1 + Group1*Control - interaction1*-1*Control
#Same thing for time three as IV
TreatHighT3 = Group0_2 + T1_2*1 + Group1*Treat - interaction2*1*Treat
TreatMediumT3 = Group0_2 + T1_2*0 + Group1*Treat - interaction2*0*Treat
TreatLowT3 = Group0_2 + T1_2*-1 + Group1*Treat - interaction2*-1*Treat
ControlHighT3 = Group0_2 + T1_2*1 + Group1*Control - interaction2*1*Control
ControlMediumT3 = Group0_2 + T1_2*0 + Group1*Control - interaction2*0*Control
ControlLowT3 = Group0_2 + T1_2*-1 + Group1*Control - interaction2*-1*Control
Treat_T2 = c(TreatLowT2, TreatMediumT2, TreatHighT2)
Control_T2 = c(ControlLowT2, ControlMediumT2, ControlHighT2)
Treat_T3 = c(TreatLowT3, TreatMediumT3, TreatHighT3)
Control_T3 = c(ControlLowT3, ControlMediumT3, ControlHighT3)
levels = c("-1","0","1")
fdsd<-data.frame(levels, Treat_T2,Control_T2,Treat_T3,Control_T3)
Plot1<-ggplot(fdsd, aes(levels)) +
geom_line(aes(levels, Treat_T2, group = 1, lty="Treatment")) +
geom_line(aes(levels, Control_T2, group = 1, lty="Control")) +
labs(list(title = paste(PlotTitle, time2), x = "Levels Pre in SD", y="Levels Post in SD", lty = "Lines" ))
Plot2<-ggplot(fdsd, aes(levels)) +
geom_line(aes(levels, Treat_T3, group = 1, lty="Treatment")) +
geom_line(aes(levels, Control_T3, group = 1, lty="Control")) +
labs(list(title = paste(PlotTitle, time3), x = "Levels Pre in SD", y="Levels Post in SD", lty = "Lines" ))
library(gridExtra)
g<-grid.arrange(Plot1, Plot2, ncol=2, top = PlotTitle)
ggsave(file=paste(PlotTitle, time2,".PNG", sep = ""), Plot1)
ggsave(file=paste(PlotTitle, time3,".PNG", sep = ""), Plot2)}
SimpleSlopes<-function(fitName){
fit<-(parameterEstimates(fitName))
Treat=1
Control=0
Group0 <-fit[16,4]
Group1<- fit[1,4]
T1 = fit[2,4]
interaction1 = fit[3,4]
Group0_2 = fit[17,4]
Group2 = fit[4,4]
T1_2 = fit[5,4]
interaction2 = fit[6,4]
library(ggplot2)
TreatHighT2 = Group0 + T1*1 + Group1*Treat + interaction1*1*Treat
TreatMediumT2 = Group0 + T1*0 + Group1*Treat + interaction1*0*Treat
TreatLowT2 = Group0 + T1*-1 + Group1*Treat + interaction1*-1*Treat
ControlHighT2 = Group0 + T1*1 + Group1*Control + interaction1*1*Control
ControlMediumT2 = Group0 + T1*0 + Group1*Control + interaction1*0*Control
ControlLowT2 = Group0 + T1*-1 + Group1*Control + interaction1*-1*Control
#Same thing for time three as IV
TreatHighT3 = Group0_2 + T1_2*1 + Group1*Treat + interaction2*1*Treat
TreatMediumT3 = Group0_2 + T1_2*0 + Group1*Treat + interaction2*0*Treat
TreatLowT3 = Group0_2 + T1_2*-1 + Group1*Treat + interaction2*-1*Treat
ControlHighT3 = Group0_2 + T1_2*1 + Group1*Control + interaction2*1*Control
ControlMediumT3 = Group0_2 + T1_2*0 + Group1*Control + interaction2*0*Control
ControlLowT3 = Group0_2 + T1_2*-1 + Group1*Control + interaction2*-1*Control
Treat_T2 = c(TreatLowT2, TreatMediumT2, TreatHighT2)
Control_T2 = c(ControlLowT2, ControlMediumT2, ControlHighT2)
Treat_T3 = c(TreatLowT3, TreatMediumT3, TreatHighT3)
Control_T3 = c(ControlLowT3, ControlMediumT3, ControlHighT3)
levels = c("-1","0","1")
fdsd<-data.frame(levels, Treat_T2,Control_T2,Treat_T3,Control_T3)
round_df<-function(df, num){nums<-map_lgl(df, is.numeric);df[,nums]<-round(df[,nums],num);(df)}
round_df(fdsd,2)}
results<-function(modelname, fitname, data, interactionterm, 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<-FitTable(fitname, interactionterm, current_scale_name, new_scale_name)
slopes<-SimpleSlopes(fitname)
all<-list((table), (slopes))
all
}
plots<-function(modelname, fitname, data, interactionterm, current_scale_name, new_scale_name, time2, time3){
fitname<-sem(modelname, data, missing='fiml', meanstructure=TRUE,fixed.x=T)
PlotInter(fitname, new_scale_name, time2, time3)
}Fits
#########################################
################# New Purpose ###########
#########################################
library(purrr) ##
## Attaching package: 'purrr'## The following objects are masked from 'package:dplyr':
##
## contains, order_by## The following object is masked from 'package:plyr':
##
## compactresults(modelname = modelNewPurpose_interaction, fitname = fitNewPurpose1, data = adult2, interactionterm = "NewPurpose_mean_1.GROUP1", current_scale_name = "NewPurpose", new_scale_name = "New Purpose")## lavaan (0.5-20) converged normally after 26 iterations
##
## Number of observations 89
##
## Number of missing patterns 5
##
## Estimator ML
## Minimum Function Test Statistic 0.000
## Degrees of freedom 0
##
## Model test baseline model:
##
## Minimum Function Test Statistic 104.105
## Degrees of freedom 7
## 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) -348.605
## Loglikelihood unrestricted model (H1) -348.605
##
## Number of free parameters 11
## Akaike (AIC) 719.210
## Bayesian (BIC) 746.585
## Sample-size adjusted Bayesian (BIC) 711.871
##
## 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 0.658 0.137 4.786 0.000 0.658
## SCALED_1_NwPrp 0.814 0.073 11.127 0.000 0.814
## NwPr__1.GROUP1 -0.527 0.159 -3.309 0.001 -0.527
## SCALED_3_NewPurpose ~
## GROUP1 0.624 0.246 2.539 0.011 0.624
## SCALED_1_NwPrp 0.708 0.137 5.155 0.000 0.708
## NwPr__1.GROUP1 -0.554 0.299 -1.853 0.064 -0.554
## Std.all
##
## 0.325
## 0.800
## -0.238
##
## 0.282
## 0.636
## -0.228
##
## Covariances:
## Estimate Std.Err Z-value P(>|z|) Std.lv
## SCALED_2_NewPurpose ~~
## SCALED_3_NwPrp 0.185 0.077 2.396 0.017 0.185
## Std.all
##
## 0.421
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_NwPrp -0.478 0.095 -5.027 0.000 -0.478 -0.473
## SCALED_3_NwPrp -0.589 0.187 -3.149 0.002 -0.589 -0.533
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_NwPrp 0.297 0.053 5.575 0.000 0.297 0.291
## SCALED_3_NwPrp 0.649 0.136 4.776 0.000 0.649 0.531
##
## R-Square:
## Estimate
## SCALED_2_NwPrp 0.709
## SCALED_3_NwPrp 0.469## [[1]]
## [[1]][[1]]
## Item β SE z p 90% CI
## 1 Intercept -0.48 0.09 -5.03 0 -0.66, -0.29
## 2 GROUP1 0.66 0.14 4.79 0 0.39, 0.93
## 3 New Purpose 0.81 0.07 11.13 0 0.67, 0.96
## 4 Interaction w/ Group -0.53 0.16 -3.31 0 -0.84, -0.21
##
## [[1]][[2]]
## Item β SE z p 90% CI
## 1 Intercept -0.59 0.19 -3.15 0.00 -0.96, -0.22
## 2 GROUP1 0.62 0.25 2.54 0.01 0.14, 1.11
## 3 New Purpose 0.71 0.14 5.15 0.00 0.44, 0.98
## 4 Interaction w/ Group -0.55 0.30 -1.85 0.06 -1.14, 0.03
##
##
## [[2]]
## levels Treat_T2 Control_T2 Treat_T3 Control_T3
## 1 -1 -0.11 -1.29 -0.09 -1.30
## 2 0 0.18 -0.48 0.07 -0.59
## 3 1 0.47 0.34 0.22 0.12plots(modelname = modelNewPurpose_interaction, fitname = fitNewPurpose1, data = adult2, interactionterm = "NewPurpose_mean_1.GROUP1", current_scale_name = "NewPurpose", new_scale_name = "New Purpose", time2 = "T1-T2", time3 = "T1-T3")##
## Attaching package: 'gridExtra'## The following object is masked from 'package:dplyr':
##
## combine## Saving 7 x 5 in image
## Saving 7 x 5 in image
#Time 3 as a result of time 1 and 2
results(modelname = modelNewPurpose_interaction_T3, fitname = fitNewPurpose1, data = adult2, interactionterm = "NewPurpose_mean_1.GROUP1", current_scale_name = "NewPurpose", new_scale_name = "New Purpose")## lavaan (0.5-20) converged normally after 26 iterations
##
## Number of observations 89
##
## Number of missing patterns 5
##
## Estimator ML
## Minimum Function Test Statistic 0.000
## Degrees of freedom 0
##
## Model test baseline model:
##
## Minimum Function Test Statistic 104.105
## Degrees of freedom 7
## 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) -348.605
## Loglikelihood unrestricted model (H1) -348.605
##
## Number of free parameters 11
## Akaike (AIC) 719.210
## Bayesian (BIC) 746.585
## Sample-size adjusted Bayesian (BIC) 711.871
##
## 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 0.658 0.137 4.786 0.000 0.658
## SCALED_1_NwPrp 0.814 0.073 11.127 0.000 0.814
## NwPr__1.GROUP1 -0.527 0.159 -3.309 0.001 -0.527
## SCALED_3_NewPurpose ~
## GROUP1 0.215 0.273 0.787 0.431 0.215
## SCALED_1_NwPrp 0.202 0.213 0.947 0.343 0.202
## NwPr__1.GROUP1 -0.225 0.297 -0.759 0.448 -0.225
## SCALED_2_NwPrp 0.622 0.227 2.736 0.006 0.622
## Std.all
##
## 0.325
## 0.800
## -0.238
##
## 0.097
## 0.181
## -0.093
## 0.568
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_NwPrp -0.478 0.095 -5.027 0.000 -0.478 -0.473
## SCALED_3_NwPrp -0.292 0.202 -1.443 0.149 -0.292 -0.264
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_NwPrp 0.297 0.053 5.575 0.000 0.297 0.291
## SCALED_3_NwPrp 0.534 0.116 4.619 0.000 0.534 0.437
##
## R-Square:
## Estimate
## SCALED_2_NwPrp 0.709
## SCALED_3_NwPrp 0.563## [[1]]
## [[1]][[1]]
## Item β SE z p 90% CI
## 1 Intercept -0.48 0.09 -5.03 0 -0.66, -0.29
## 2 GROUP1 0.66 0.14 4.79 0 0.39, 0.93
## 3 New Purpose 0.81 0.07 11.13 0 0.67, 0.96
## 4 Interaction w/ Group -0.53 0.16 -3.31 0 -0.84, -0.21
##
## [[1]][[2]]
## Item β SE z p 90% CI
## 1 Intercept -0.29 0.20 -1.44 0.15 -0.69, 0.1
## 2 GROUP1 0.22 0.27 0.79 0.43 -0.32, 0.75
## 3 New Purpose 0.20 0.21 0.95 0.34 -0.22, 0.62
## 4 Interaction w/ Group -0.23 0.30 -0.76 0.45 -0.81, 0.36
##
##
## [[2]]
## levels Treat_T2 Control_T2 Treat_T3 Control_T3
## 1 -1 -0.11 -1.29 0.39 -0.49
## 2 0 0.18 -0.48 0.37 -0.29
## 3 1 0.47 0.34 0.34 -0.09#plots
plots(modelname = modelNewPurpose_interaction_T3, fitname = fitNewPurpose1, data = adult2, interactionterm = "NewPurpose_mean_1.GROUP1", current_scale_name = "NewPurpose", new_scale_name = "New Purpose", time2 = "T1-T2", time3 = "T1-T2-T3")## Saving 7 x 5 in image
## Saving 7 x 5 in image
#########################################
################# MLQP #################
#########################################
results(modelname = modelMLQP_interaction, fitname = MLQ1, data = adult2, interactionterm = "MLQ_mean_1.GROUP1", current_scale_name = "MLQP", new_scale_name = "MLQ - P")## lavaan (0.5-20) converged normally after 22 iterations
##
## Number of observations 89
##
## Number of missing patterns 5
##
## Estimator ML
## Minimum Function Test Statistic 0.000
## Degrees of freedom 0
##
## Model test baseline model:
##
## Minimum Function Test Statistic 122.649
## Degrees of freedom 7
## 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) -375.161
## Loglikelihood unrestricted model (H1) -375.161
##
## Number of free parameters 11
## Akaike (AIC) 772.322
## Bayesian (BIC) 799.697
## Sample-size adjusted Bayesian (BIC) 764.983
##
## 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 0.743 0.146 5.081 0.000 0.743 0.360
## SCALED_1_MLQP 0.802 0.076 10.507 0.000 0.802 0.774
## MLQ_m_1.GROUP1 -0.264 0.112 -2.359 0.018 -0.264 -0.176
## SCALED_3_MLQP ~
## GROUP1 0.814 0.185 4.403 0.000 0.814 0.351
## SCALED_1_MLQP 0.886 0.108 8.231 0.000 0.886 0.762
## MLQ_m_1.GROUP1 -0.550 0.155 -3.545 0.000 -0.550 -0.326
##
## Covariances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_MLQP ~~
## SCALED_3_MLQP 0.187 0.063 2.946 0.003 0.187 0.510
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_MLQP -0.521 0.101 -5.160 0.000 -0.521 -0.505
## SCALED_3_MLQP -0.743 0.136 -5.451 0.000 -0.743 -0.642
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_MLQP 0.338 0.062 5.437 0.000 0.338 0.317
## SCALED_3_MLQP 0.397 0.082 4.835 0.000 0.397 0.296
##
## R-Square:
## Estimate
## SCALED_2_MLQP 0.683
## SCALED_3_MLQP 0.704## [[1]]
## [[1]][[1]]
## Item β SE z p 90% CI
## 1 Intercept -0.52 0.10 -5.16 0.00 -0.72, -0.32
## 2 GROUP1 0.74 0.15 5.08 0.00 0.46, 1.03
## 3 MLQ - P 0.80 0.08 10.51 0.00 0.65, 0.95
## 4 Interaction w/ Group -0.26 0.11 -2.36 0.02 -0.48, -0.04
##
## [[1]][[2]]
## Item β SE z p 90% CI
## 1 Intercept -0.74 0.14 -5.45 0 -1.01, -0.48
## 2 GROUP1 0.81 0.18 4.40 0 0.45, 1.18
## 3 MLQ - P 0.89 0.11 8.23 0 0.68, 1.1
## 4 Interaction w/ Group -0.55 0.16 -3.54 0 -0.85, -0.25
##
##
## [[2]]
## levels Treat_T2 Control_T2 Treat_T3 Control_T3
## 1 -1 -0.32 -1.32 -0.34 -1.63
## 2 0 0.22 -0.52 0.00 -0.74
## 3 1 0.76 0.28 0.34 0.14plots(modelname = modelMLQP_interaction, fitname = MLQ, data = adult2, interactionterm = "MLQ_mean_1.GROUP1", new_scale_name = "Meaning in Life Questionnaire - P", time2 = "T1-T2", time3 = "T1-T3") ## Saving 7 x 5 in image
## Saving 7 x 5 in image
#Time 3 as a result of time 1 and 2
results(modelname = modelMLQP_interaction_T3, fitname = MLQ1, data = adult2, interactionterm = "MLQ_mean_1.GROUP1", current_scale_name = "MLQP", new_scale_name = "MLQ - P")## lavaan (0.5-20) converged normally after 19 iterations
##
## Number of observations 89
##
## Number of missing patterns 5
##
## Estimator ML
## Minimum Function Test Statistic 0.000
## Degrees of freedom 0
##
## Model test baseline model:
##
## Minimum Function Test Statistic 122.649
## Degrees of freedom 7
## 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) -375.161
## Loglikelihood unrestricted model (H1) -375.161
##
## Number of free parameters 11
## Akaike (AIC) 772.322
## Bayesian (BIC) 799.697
## Sample-size adjusted Bayesian (BIC) 764.983
##
## 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 0.743 0.146 5.081 0.000 0.743 0.360
## SCALED_1_MLQP 0.802 0.076 10.507 0.000 0.802 0.774
## MLQ_m_1.GROUP1 -0.264 0.112 -2.359 0.018 -0.264 -0.176
## SCALED_3_MLQP ~
## GROUP1 0.403 0.206 1.957 0.050 0.403 0.174
## SCALED_1_MLQP 0.443 0.152 2.918 0.004 0.443 0.381
## MLQ_m_1.GROUP1 -0.404 0.150 -2.695 0.007 -0.404 -0.239
## SCALED_2_MLQP 0.553 0.150 3.698 0.000 0.553 0.493
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_MLQP -0.521 0.101 -5.160 0.000 -0.521 -0.505
## SCALED_3_MLQP -0.455 0.144 -3.160 0.002 -0.455 -0.393
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_MLQP 0.338 0.062 5.437 0.000 0.338 0.317
## SCALED_3_MLQP 0.294 0.067 4.409 0.000 0.294 0.219
##
## R-Square:
## Estimate
## SCALED_2_MLQP 0.683
## SCALED_3_MLQP 0.781## [[1]]
## [[1]][[1]]
## Item β SE z p 90% CI
## 1 Intercept -0.52 0.10 -5.16 0.00 -0.72, -0.32
## 2 GROUP1 0.74 0.15 5.08 0.00 0.46, 1.03
## 3 MLQ - P 0.80 0.08 10.51 0.00 0.65, 0.95
## 4 Interaction w/ Group -0.26 0.11 -2.36 0.02 -0.48, -0.04
##
## [[1]][[2]]
## Item β SE z p 90% CI
## 1 Intercept -0.45 0.14 -3.16 0.00 -0.74, -0.17
## 2 GROUP1 0.40 0.21 1.96 0.05 0, 0.81
## 3 MLQ - P 0.44 0.15 2.92 0.00 0.15, 0.74
## 4 Interaction w/ Group -0.40 0.15 -2.70 0.01 -0.7, -0.11
##
##
## [[2]]
## levels Treat_T2 Control_T2 Treat_T3 Control_T3
## 1 -1 -0.32 -1.32 0.25 -0.90
## 2 0 0.22 -0.52 0.29 -0.45
## 3 1 0.76 0.28 0.33 -0.01plots(modelname = modelMLQP_interaction_T3, fitname = MLQ, data = adult2, interactionterm = "MLQ_mean_1.GROUP1", new_scale_name = "Meaning in Life Questionnaire - P", time2 = "T1-T2", time3 = "T1-T2-T3") ## Saving 7 x 5 in image
## Saving 7 x 5 in image
#########################################
################# MLQS #################
#########################################
results(modelname = modelMLQS_interaction, fitname = MLQ1, data = adult2, interactionterm = "MLQS_mean_1.GROUP1", current_scale_name = "MLQS", new_scale_name = "MLQ - S")## lavaan (0.5-20) converged normally after 25 iterations
##
## Number of observations 89
##
## Number of missing patterns 5
##
## Estimator ML
## Minimum Function Test Statistic 0.000
## Degrees of freedom 0
##
## Model test baseline model:
##
## Minimum Function Test Statistic 79.943
## Degrees of freedom 7
## 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) -410.746
## Loglikelihood unrestricted model (H1) -410.746
##
## Number of free parameters 11
## Akaike (AIC) 843.492
## Bayesian (BIC) 870.867
## Sample-size adjusted Bayesian (BIC) 836.153
##
## 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 -0.036 0.173 -0.207 0.836 -0.036 -0.018
## SCALED_1_MLQS 0.676 0.084 8.014 0.000 0.676 0.694
## MLQS__1.GROUP1 0.148 0.108 1.365 0.172 0.148 0.121
## SCALED_3_MLQS ~
## GROUP1 0.125 0.224 0.557 0.578 0.125 0.063
## SCALED_1_MLQS 0.558 0.117 4.758 0.000 0.558 0.571
## MLQS__1.GROUP1 0.157 0.152 1.029 0.304 0.157 0.128
##
## Covariances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_MLQS ~~
## SCALED_3_MLQS 0.327 0.084 3.896 0.000 0.327 0.583
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_MLQS 0.043 0.118 0.366 0.714 0.043 0.044
## SCALED_3_MLQS -0.023 0.156 -0.147 0.883 -0.023 -0.023
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_MLQS 0.494 0.087 5.655 0.000 0.494 0.518
## SCALED_3_MLQS 0.638 0.124 5.142 0.000 0.638 0.663
##
## R-Square:
## Estimate
## SCALED_2_MLQS 0.482
## SCALED_3_MLQS 0.337## [[1]]
## [[1]][[1]]
## Item β SE z p 90% CI
## 1 Intercept 0.04 0.12 0.37 0.71 -0.19, 0.27
## 2 GROUP1 -0.04 0.17 -0.21 0.84 -0.38, 0.3
## 3 MLQ - S 0.68 0.08 8.01 0.00 0.51, 0.84
## 4 Interaction w/ Group 0.15 0.11 1.36 0.17 -0.06, 0.36
##
## [[1]][[2]]
## Item β SE z p 90% CI
## 1 Intercept -0.02 0.16 -0.15 0.88 -0.33, 0.28
## 2 GROUP1 0.12 0.22 0.56 0.58 -0.31, 0.56
## 3 MLQ - S 0.56 0.12 4.76 0.00 0.33, 0.79
## 4 Interaction w/ Group 0.16 0.15 1.03 0.30 -0.14, 0.46
##
##
## [[2]]
## levels Treat_T2 Control_T2 Treat_T3 Control_T3
## 1 -1 -0.82 -0.63 -0.77 -0.58
## 2 0 0.01 0.04 -0.06 -0.02
## 3 1 0.83 0.72 0.66 0.54plots(modelname = modelMLQS_interaction, fitname = MLQ, data = adult2, interactionterm = "MLQS_mean_1.GROUP1", new_scale_name = "Meaning in Life Questionnaire - S", time2 = "T1-T2", time3 = "T1-T3") ## Saving 7 x 5 in image
## Saving 7 x 5 in image
#Time 3 as a result of time 1 and 2
results(modelname = modelMLQS_interaction_T3, fitname = MLQ1, data = adult2, interactionterm = "MLQS_mean_1.GROUP1", current_scale_name = "MLQS", new_scale_name = "MLQ - S")## lavaan (0.5-20) converged normally after 17 iterations
##
## Number of observations 89
##
## Number of missing patterns 5
##
## Estimator ML
## Minimum Function Test Statistic 0.000
## Degrees of freedom 0
##
## Model test baseline model:
##
## Minimum Function Test Statistic 79.943
## Degrees of freedom 7
## 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) -410.746
## Loglikelihood unrestricted model (H1) -410.746
##
## Number of free parameters 11
## Akaike (AIC) 843.492
## Bayesian (BIC) 870.867
## Sample-size adjusted Bayesian (BIC) 836.153
##
## 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 -0.036 0.173 -0.207 0.836 -0.036 -0.018
## SCALED_1_MLQS 0.676 0.084 8.014 0.000 0.676 0.694
## MLQS__1.GROUP1 0.148 0.108 1.365 0.172 0.148 0.121
## SCALED_3_MLQS ~
## GROUP1 0.148 0.198 0.750 0.453 0.148 0.076
## SCALED_1_MLQS 0.111 0.134 0.827 0.409 0.111 0.114
## MLQS__1.GROUP1 0.059 0.136 0.433 0.665 0.059 0.048
## SCALED_2_MLQS 0.662 0.125 5.311 0.000 0.662 0.659
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_MLQS 0.043 0.118 0.366 0.714 0.043 0.044
## SCALED_3_MLQS -0.052 0.140 -0.367 0.713 -0.052 -0.053
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_MLQS 0.494 0.087 5.655 0.000 0.494 0.518
## SCALED_3_MLQS 0.421 0.089 4.722 0.000 0.421 0.437
##
## R-Square:
## Estimate
## SCALED_2_MLQS 0.482
## SCALED_3_MLQS 0.563## [[1]]
## [[1]][[1]]
## Item β SE z p 90% CI
## 1 Intercept 0.04 0.12 0.37 0.71 -0.19, 0.27
## 2 GROUP1 -0.04 0.17 -0.21 0.84 -0.38, 0.3
## 3 MLQ - S 0.68 0.08 8.01 0.00 0.51, 0.84
## 4 Interaction w/ Group 0.15 0.11 1.36 0.17 -0.06, 0.36
##
## [[1]][[2]]
## Item β SE z p 90% CI
## 1 Intercept -0.05 0.14 -0.37 0.71 -0.33, 0.22
## 2 GROUP1 0.15 0.20 0.75 0.45 -0.24, 0.54
## 3 MLQ - S 0.11 0.13 0.83 0.41 -0.15, 0.37
## 4 Interaction w/ Group 0.06 0.14 0.43 0.67 -0.21, 0.33
##
##
## [[2]]
## levels Treat_T2 Control_T2 Treat_T3 Control_T3
## 1 -1 -0.82 -0.63 -0.26 -0.16
## 2 0 0.01 0.04 -0.09 -0.05
## 3 1 0.83 0.72 0.08 0.06plots(modelname = modelMLQS_interaction_T3, fitname = MLQ, data = adult2, interactionterm = "MLQS_mean_1.GROUP1", new_scale_name = "Meaning in Life Questionnaire - S", time2 = "T1-T2", time3 = "T1-T2-T3") ## Saving 7 x 5 in image
## Saving 7 x 5 in image
#########################################
################# Positive #################
#########################################
results(modelname = modelPositive_interaction, fitname = fit, data = adult2, interactionterm = "Positive_mean_1.GROUP1", current_scale_name = "Positive", new_scale_name = "Perma - Positive")## lavaan (0.5-20) converged normally after 20 iterations
##
## Number of observations 89
##
## Number of missing patterns 5
##
## Estimator ML
## Minimum Function Test Statistic 0.000
## Degrees of freedom 0
##
## Model test baseline model:
##
## Minimum Function Test Statistic 91.013
## Degrees of freedom 7
## 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) -414.232
## Loglikelihood unrestricted model (H1) -414.232
##
## Number of free parameters 11
## Akaike (AIC) 850.463
## Bayesian (BIC) 877.838
## Sample-size adjusted Bayesian (BIC) 843.124
##
## 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_Positive ~
## GROUP1 0.370 0.173 2.146 0.032 0.370
## SCALED_1_Postv 0.638 0.086 7.398 0.000 0.638
## Pstv__1.GROUP1 -0.159 0.095 -1.673 0.094 -0.159
## SCALED_3_Positive ~
## GROUP1 0.565 0.223 2.530 0.011 0.565
## SCALED_1_Postv 0.660 0.135 4.903 0.000 0.660
## Pstv__1.GROUP1 -0.160 0.153 -1.044 0.296 -0.160
## Std.all
##
## 0.188
## 0.643
## -0.146
##
## 0.262
## 0.606
## -0.133
##
## Covariances:
## Estimate Std.Err Z-value P(>|z|) Std.lv
## SCALED_2_Positive ~~
## SCALED_3_Postv 0.324 0.099 3.283 0.001 0.324
## Std.all
##
## 0.612
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_Postv -0.262 0.116 -2.263 0.024 -0.262 -0.267
## SCALED_3_Postv -0.467 0.153 -3.048 0.002 -0.467 -0.433
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_Postv 0.474 0.091 5.200 0.000 0.474 0.491
## SCALED_3_Postv 0.593 0.125 4.751 0.000 0.593 0.511
##
## R-Square:
## Estimate
## SCALED_2_Postv 0.509
## SCALED_3_Postv 0.489## [[1]]
## [[1]][[1]]
## Item β SE z p 90% CI
## 1 Intercept -0.26 0.12 -2.26 0.02 -0.49, -0.04
## 2 GROUP1 0.37 0.17 2.15 0.03 0.03, 0.71
## 3 Perma - Positive 0.64 0.09 7.40 0.00 0.47, 0.81
## 4 Interaction w/ Group -0.16 0.10 -1.67 0.09 -0.35, 0.03
##
## [[1]][[2]]
## Item β SE z p 90% CI
## 1 Intercept -0.47 0.15 -3.05 0.00 -0.77, -0.17
## 2 GROUP1 0.57 0.22 2.53 0.01 0.13, 1
## 3 Perma - Positive 0.66 0.13 4.90 0.00 0.4, 0.92
## 4 Interaction w/ Group -0.16 0.15 -1.04 0.30 -0.46, 0.14
##
##
## [[2]]
## levels Treat_T2 Control_T2 Treat_T3 Control_T3
## 1 -1 -0.37 -0.90 -0.6 -1.13
## 2 0 0.11 -0.26 -0.1 -0.47
## 3 1 0.59 0.38 0.4 0.19plots(modelname = modelPositive_interaction, fitname = fit, data = adult2, interactionterm = "Positive_mean_1.GROUP1", new_scale_name = "Perma Positive Emotion", time2 = "T1-T2", time3 = "T1-T3") ## Saving 7 x 5 in image
## Saving 7 x 5 in image
#Time 3 as a result of time 1 and 2
results(modelname = modelPositive_interaction_T3, fitname = fit, data = adult2, interactionterm = "Positive_mean_1.GROUP1", current_scale_name = "Positive", new_scale_name = "Perma - Positive")## lavaan (0.5-20) converged normally after 20 iterations
##
## Number of observations 89
##
## Number of missing patterns 5
##
## Estimator ML
## Minimum Function Test Statistic 0.000
## Degrees of freedom 0
##
## Model test baseline model:
##
## Minimum Function Test Statistic 91.013
## Degrees of freedom 7
## 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) -414.232
## Loglikelihood unrestricted model (H1) -414.232
##
## Number of free parameters 11
## Akaike (AIC) 850.463
## Bayesian (BIC) 877.838
## Sample-size adjusted Bayesian (BIC) 843.124
##
## 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_Positive ~
## GROUP1 0.370 0.173 2.146 0.032 0.370
## SCALED_1_Postv 0.638 0.086 7.398 0.000 0.638
## Pstv__1.GROUP1 -0.159 0.095 -1.673 0.094 -0.159
## SCALED_3_Positive ~
## GROUP1 0.312 0.213 1.463 0.144 0.312
## SCALED_1_Postv 0.223 0.142 1.571 0.116 0.223
## Pstv__1.GROUP1 -0.051 0.151 -0.336 0.737 -0.051
## SCALED_2_Postv 0.684 0.150 4.568 0.000 0.684
## Std.all
##
## 0.188
## 0.643
## -0.145
##
## 0.145
## 0.204
## -0.042
## 0.623
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_Postv -0.262 0.116 -2.263 0.024 -0.262 -0.267
## SCALED_3_Postv -0.288 0.145 -1.981 0.048 -0.288 -0.267
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_Postv 0.474 0.091 5.200 0.000 0.474 0.491
## SCALED_3_Postv 0.371 0.092 4.022 0.000 0.371 0.320
##
## R-Square:
## Estimate
## SCALED_2_Postv 0.509
## SCALED_3_Postv 0.680## [[1]]
## [[1]][[1]]
## Item β SE z p 90% CI
## 1 Intercept -0.26 0.12 -2.26 0.02 -0.49, -0.04
## 2 GROUP1 0.37 0.17 2.15 0.03 0.03, 0.71
## 3 Perma - Positive 0.64 0.09 7.40 0.00 0.47, 0.81
## 4 Interaction w/ Group -0.16 0.10 -1.67 0.09 -0.35, 0.03
##
## [[1]][[2]]
## Item β SE z p 90% CI
## 1 Intercept -0.29 0.15 -1.98 0.05 -0.57, 0
## 2 GROUP1 0.31 0.21 1.46 0.14 -0.11, 0.73
## 3 Perma - Positive 0.22 0.14 1.57 0.12 -0.06, 0.5
## 4 Interaction w/ Group -0.05 0.15 -0.34 0.74 -0.35, 0.25
##
##
## [[2]]
## levels Treat_T2 Control_T2 Treat_T3 Control_T3
## 1 -1 -0.37 -0.90 -0.09 -0.51
## 2 0 0.11 -0.26 0.08 -0.29
## 3 1 0.59 0.38 0.25 -0.06plots(modelname = modelPositive_interaction_T3, fitname = fit, data = adult2, interactionterm = "Positive_mean_1.GROUP1", new_scale_name = "Perma Positive Emotion", time2 = "T1-T2", time3 = "T1-T2-T3") ## Saving 7 x 5 in image
## Saving 7 x 5 in image
#########################################
############### Achievement###############
#########################################
results(modelname = modelAcheivement_interaction, fitname = fit, data = adult2, interactionterm = "Acheivement_mean_1.GROUP1", current_scale_name = "Acheivement", new_scale_name = "Perma - Acheivement")## lavaan (0.5-20) converged normally after 19 iterations
##
## Number of observations 89
##
## Number of missing patterns 5
##
## Estimator ML
## Minimum Function Test Statistic 0.000
## Degrees of freedom 0
##
## Model test baseline model:
##
## Minimum Function Test Statistic 68.667
## Degrees of freedom 7
## 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) -422.718
## Loglikelihood unrestricted model (H1) -422.718
##
## Number of free parameters 11
## Akaike (AIC) 867.436
## Bayesian (BIC) 894.811
## Sample-size adjusted Bayesian (BIC) 860.097
##
## 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_Acheivement ~
## GROUP1 0.390 0.189 2.066 0.039 0.390
## SCALED_1_Achvm 0.622 0.100 6.216 0.000 0.622
## Achv__1.GROUP1 -0.292 0.115 -2.543 0.011 -0.292
## SCALED_3_Acheivement ~
## GROUP1 0.779 0.232 3.350 0.001 0.779
## SCALED_1_Achvm 0.692 0.135 5.140 0.000 0.692
## Achv__1.GROUP1 -0.469 0.154 -3.052 0.002 -0.469
## Std.all
##
## 0.196
## 0.618
## -0.255
##
## 0.347
## 0.610
## -0.364
##
## Covariances:
## Estimate Std.Err Z-value P(>|z|) Std.lv
## SCALED_2_Acheivement ~~
## SCALED_3_Achvm 0.285 0.101 2.823 0.005 0.285
## Std.all
##
## 0.482
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_Achvm -0.287 0.129 -2.222 0.026 -0.287 -0.289
## SCALED_3_Achvm -0.598 0.170 -3.523 0.000 -0.598 -0.533
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_Achvm 0.577 0.103 5.613 0.000 0.577 0.583
## SCALED_3_Achvm 0.603 0.126 4.795 0.000 0.603 0.479
##
## R-Square:
## Estimate
## SCALED_2_Achvm 0.417
## SCALED_3_Achvm 0.521## Warning in lavaan(slotOptions = object@Options, slotParTable =
## object@ParTable, : lavaan WARNING: model has NOT converged!
## Warning in lavaan(slotOptions = object@Options, slotParTable =
## object@ParTable, : lavaan WARNING: model has NOT converged!## [[1]]
## [[1]][[1]]
## Item β SE z p 90% CI
## 1 Intercept -0.29 0.13 -2.22 0.03 -0.54, -0.03
## 2 GROUP1 0.39 0.19 2.07 0.04 0.02, 0.76
## 3 Perma - Acheivement 0.62 0.10 6.22 0.00 0.43, 0.82
## 4 Interaction w/ Group -0.29 0.11 -2.54 0.01 -0.52, -0.07
##
## [[1]][[2]]
## Item β SE z p 90% CI
## 1 Intercept -0.60 0.17 -3.52 0 -0.93, -0.27
## 2 GROUP1 0.78 0.23 3.35 0 0.32, 1.23
## 3 Perma - Acheivement 0.69 0.13 5.14 0 0.43, 0.96
## 4 Interaction w/ Group -0.47 0.15 -3.05 0 -0.77, -0.17
##
##
## [[2]]
## levels Treat_T2 Control_T2 Treat_T3 Control_T3
## 1 -1 -0.23 -0.91 -0.43 -1.29
## 2 0 0.10 -0.29 -0.21 -0.60
## 3 1 0.43 0.33 0.02 0.09plots(modelname = modelAcheivement_interaction, fitname = fit, data = adult2, interactionterm = "Acheivement_mean_1.GROUP1", new_scale_name = "Perma Acheivement", time2 = "T1-T2", time3 = "T1-T3") ## Warning in lavaan(slotOptions = object@Options, slotParTable =
## object@ParTable, : lavaan WARNING: model has NOT converged!## Saving 7 x 5 in image
## Saving 7 x 5 in image
#Time 3 as a result of time 1 and 2
results(modelname = modelAcheivement_interaction_T3, fitname = fit, data = adult2, interactionterm = "Acheivement_mean_1.GROUP1", current_scale_name = "Acheivement", new_scale_name = "Perma - Acheivement")## lavaan (0.5-20) converged normally after 17 iterations
##
## Number of observations 89
##
## Number of missing patterns 5
##
## Estimator ML
## Minimum Function Test Statistic 0.000
## Degrees of freedom 0
##
## Model test baseline model:
##
## Minimum Function Test Statistic 68.667
## Degrees of freedom 7
## 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) -422.718
## Loglikelihood unrestricted model (H1) -422.718
##
## Number of free parameters 11
## Akaike (AIC) 867.436
## Bayesian (BIC) 894.811
## Sample-size adjusted Bayesian (BIC) 860.097
##
## 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_Acheivement ~
## GROUP1 0.390 0.189 2.066 0.039 0.390
## SCALED_1_Achvm 0.622 0.100 6.216 0.000 0.622
## Achv__1.GROUP1 -0.292 0.115 -2.543 0.011 -0.292
## SCALED_3_Acheivement ~
## GROUP1 0.586 0.223 2.630 0.009 0.586
## SCALED_1_Achvm 0.386 0.159 2.433 0.015 0.386
## Achv__1.GROUP1 -0.325 0.162 -2.008 0.045 -0.325
## SCALED_2_Achvm 0.493 0.147 3.355 0.001 0.493
## Std.all
##
## 0.196
## 0.618
## -0.255
##
## 0.261
## 0.340
## -0.252
## 0.437
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_Achvm -0.287 0.129 -2.222 0.026 -0.287 -0.289
## SCALED_3_Achvm -0.457 0.168 -2.718 0.007 -0.457 -0.407
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_Achvm 0.577 0.103 5.613 0.000 0.577 0.583
## SCALED_3_Achvm 0.463 0.102 4.560 0.000 0.463 0.367
##
## R-Square:
## Estimate
## SCALED_2_Achvm 0.417
## SCALED_3_Achvm 0.633## [[1]]
## [[1]][[1]]
## Item β SE z p 90% CI
## 1 Intercept -0.29 0.13 -2.22 0.03 -0.54, -0.03
## 2 GROUP1 0.39 0.19 2.07 0.04 0.02, 0.76
## 3 Perma - Acheivement 0.62 0.10 6.22 0.00 0.43, 0.82
## 4 Interaction w/ Group -0.29 0.11 -2.54 0.01 -0.52, -0.07
##
## [[1]][[2]]
## Item β SE z p 90% CI
## 1 Intercept -0.46 0.17 -2.72 0.01 -0.79, -0.13
## 2 GROUP1 0.59 0.22 2.63 0.01 0.15, 1.02
## 3 Perma - Acheivement 0.39 0.16 2.43 0.01 0.07, 0.7
## 4 Interaction w/ Group -0.33 0.16 -2.01 0.04 -0.64, -0.01
##
##
## [[2]]
## levels Treat_T2 Control_T2 Treat_T3 Control_T3
## 1 -1 -0.23 -0.91 -0.13 -0.84
## 2 0 0.10 -0.29 -0.07 -0.46
## 3 1 0.43 0.33 -0.01 -0.07plots(modelname = modelAcheivement_interaction_T3, fitname = fit, data = adult2, interactionterm = "Acheivement_mean_1.GROUP1", new_scale_name = "Perma Acheivement", time2 = "T1-T2", time3 = "T1-T2-T3") ## Saving 7 x 5 in image
## Saving 7 x 5 in image
#########################################
################ Negative ################
#########################################
results(modelname = modelNegative_interaction, fitname = fit, data = adult2, interactionterm = "Negative_mean_1.GROUP1", current_scale_name = "Negative", new_scale_name = "Perma - Negative")## lavaan (0.5-20) converged normally after 17 iterations
##
## Number of observations 89
##
## Number of missing patterns 5
##
## Estimator ML
## Minimum Function Test Statistic 0.000
## Degrees of freedom 0
##
## Model test baseline model:
##
## Minimum Function Test Statistic 82.989
## Degrees of freedom 7
## 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) -420.562
## Loglikelihood unrestricted model (H1) -420.562
##
## Number of free parameters 11
## Akaike (AIC) 863.124
## Bayesian (BIC) 890.499
## Sample-size adjusted Bayesian (BIC) 855.785
##
## 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_Nagative ~
## GROUP1 -0.289 0.190 -1.525 0.127 -0.289
## SCALED_1_Nagtv 0.635 0.100 6.358 0.000 0.635
## Ngtv__1.GROUP1 0.033 0.108 0.307 0.759 0.033
## SCALED_3_Nagative ~
## GROUP1 -0.328 0.193 -1.702 0.089 -0.328
## SCALED_1_Nagtv 0.657 0.097 6.803 0.000 0.657
## Ngtv__1.GROUP1 -0.131 0.105 -1.255 0.210 -0.131
## Std.all
##
## -0.143
## 0.623
## 0.030
##
## -0.168
## 0.666
## -0.123
##
## Covariances:
## Estimate Std.Err Z-value P(>|z|) Std.lv
## SCALED_2_Nagative ~~
## SCALED_3_Nagtv 0.276 0.088 3.154 0.002 0.276
## Std.all
##
## 0.532
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_Nagtv 0.203 0.128 1.587 0.113 0.203 0.201
## SCALED_3_Nagtv 0.184 0.132 1.396 0.163 0.184 0.188
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_Nagtv 0.583 0.103 5.643 0.000 0.583 0.571
## SCALED_3_Nagtv 0.463 0.098 4.724 0.000 0.463 0.485
##
## R-Square:
## Estimate
## SCALED_2_Nagtv 0.429
## SCALED_3_Nagtv 0.515## [[1]]
## [[1]][[1]]
## Item β SE z p 90% CI
## 1 Intercept 0.20 0.13 1.59 0.11 -0.05, 0.45
## 2 GROUP1 -0.29 0.19 -1.52 0.13 -0.66, 0.08
## 3 Nagative 0.63 0.10 6.36 0.00 0.44, 0.83
## 4 Interaction w/ Group 0.03 0.11 0.31 0.76 -0.18, 0.24
##
## [[1]][[2]]
## Item β SE z p 90% CI
## 1 Intercept 0.18 0.13 1.40 0.16 -0.07, 0.44
## 2 GROUP1 -0.33 0.19 -1.70 0.09 -0.71, 0.05
## 3 Nagative 0.66 0.10 6.80 0.00 0.47, 0.85
## 4 Interaction w/ Group -0.13 0.10 -1.25 0.21 -0.34, 0.07
##
##
## [[2]]
## levels Treat_T2 Control_T2 Treat_T3 Control_T3
## 1 -1 -0.75 -0.43 -0.63 -0.47
## 2 0 -0.09 0.20 -0.10 0.18
## 3 1 0.58 0.84 0.42 0.84plots(modelname = modelNegative_interaction, fitname = fit, data = adult2, interactionterm = "Negative_mean_1.GROUP1", new_scale_name = "Perma Negative", time2 = "T1-T2", time3 = "T1-T3") ## Saving 7 x 5 in image
## Saving 7 x 5 in image
#Time 3 as a result of time 1 and 2
results(modelname = modelNegative_interaction_T3, fitname = fit, data = adult2, interactionterm = "Negative_mean_1.GROUP1", current_scale_name = "Negative", new_scale_name = "Perma - Negative")## lavaan (0.5-20) converged normally after 19 iterations
##
## Number of observations 89
##
## Number of missing patterns 5
##
## Estimator ML
## Minimum Function Test Statistic 0.000
## Degrees of freedom 0
##
## Model test baseline model:
##
## Minimum Function Test Statistic 82.989
## Degrees of freedom 7
## 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) -420.562
## Loglikelihood unrestricted model (H1) -420.562
##
## Number of free parameters 11
## Akaike (AIC) 863.124
## Bayesian (BIC) 890.499
## Sample-size adjusted Bayesian (BIC) 855.785
##
## 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_Nagative ~
## GROUP1 -0.289 0.190 -1.525 0.127 -0.289
## SCALED_1_Nagtv 0.635 0.100 6.358 0.000 0.635
## Ngtv__1.GROUP1 0.033 0.108 0.307 0.759 0.033
## SCALED_3_Nagative ~
## GROUP1 -0.191 0.178 -1.072 0.284 -0.191
## SCALED_1_Nagtv 0.356 0.120 2.977 0.003 0.356
## Ngtv__1.GROUP1 -0.147 0.097 -1.518 0.129 -0.147
## SCALED_2_Nagtv 0.474 0.124 3.817 0.000 0.474
## Std.all
##
## -0.143
## 0.623
## 0.030
##
## -0.098
## 0.361
## -0.138
## 0.490
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_Nagtv 0.203 0.128 1.587 0.113 0.203 0.201
## SCALED_3_Nagtv 0.088 0.123 0.711 0.477 0.088 0.090
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_Nagtv 0.583 0.103 5.643 0.000 0.583 0.571
## SCALED_3_Nagtv 0.332 0.072 4.596 0.000 0.332 0.347
##
## R-Square:
## Estimate
## SCALED_2_Nagtv 0.429
## SCALED_3_Nagtv 0.653## [[1]]
## [[1]][[1]]
## Item β SE z p 90% CI
## 1 Intercept 0.20 0.13 1.59 0.11 -0.05, 0.45
## 2 GROUP1 -0.29 0.19 -1.52 0.13 -0.66, 0.08
## 3 Nagative 0.63 0.10 6.36 0.00 0.44, 0.83
## 4 Interaction w/ Group 0.03 0.11 0.31 0.76 -0.18, 0.24
##
## [[1]][[2]]
## Item β SE z p 90% CI
## 1 Intercept 0.09 0.12 0.71 0.48 -0.15, 0.33
## 2 GROUP1 -0.19 0.18 -1.07 0.28 -0.54, 0.16
## 3 Nagative 0.36 0.12 2.98 0.00 0.12, 0.59
## 4 Interaction w/ Group -0.15 0.10 -1.52 0.13 -0.34, 0.04
##
##
## [[2]]
## levels Treat_T2 Control_T2 Treat_T3 Control_T3
## 1 -1 -0.75 -0.43 -0.41 -0.27
## 2 0 -0.09 0.20 -0.20 0.09
## 3 1 0.58 0.84 0.01 0.44plots(modelname = modelNegative_interaction_T3, fitname = fit, data = adult2, interactionterm = "Negative_mean_1.GROUP1", new_scale_name = "Perma Negative", time2 = "T1-T2", time3 = "T1-T2-T3")## Saving 7 x 5 in image
## Saving 7 x 5 in image
#########################################
############### Relationships ##############
#########################################
results(modelname = modelRelationships_interaction, fitname = fit, data = adult2, interactionterm = "Relationships_mean_1.GROUP1", current_scale_name = "Relationships", new_scale_name = "Perma - Relationships")## lavaan (0.5-20) converged normally after 23 iterations
##
## Number of observations 89
##
## Number of missing patterns 5
##
## Estimator ML
## Minimum Function Test Statistic 0.000
## Degrees of freedom 0
##
## Model test baseline model:
##
## Minimum Function Test Statistic 67.342
## Degrees of freedom 7
## 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) -434.963
## Loglikelihood unrestricted model (H1) -434.963
##
## Number of free parameters 11
## Akaike (AIC) 891.927
## Bayesian (BIC) 919.302
## Sample-size adjusted Bayesian (BIC) 884.588
##
## 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_Rrealtionships ~
## GROUP1 0.271 0.195 1.386 0.166 0.271
## SCALED_1_Rrltn 0.579 0.100 5.764 0.000 0.579
## Rltn__1.GROUP1 0.053 0.101 0.527 0.598 0.053
## SCALED_3_Rrealtionships ~
## GROUP1 0.515 0.251 2.048 0.041 0.515
## SCALED_1_Rrltn 0.430 0.141 3.055 0.002 0.430
## Rltn__1.GROUP1 -0.081 0.140 -0.576 0.565 -0.081
## Std.all
##
## 0.135
## 0.574
## 0.052
##
## 0.240
## 0.398
## -0.075
##
## Covariances:
## Estimate Std.Err Z-value P(>|z|) Std.lv
## SCALED_2_Rrealtionships ~~
## SCALED_3_Rrltn 0.551 0.124 4.453 0.000 0.551
## Std.all
##
## 0.741
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_Rrltn -0.196 0.132 -1.485 0.138 -0.196 -0.195
## SCALED_3_Rrltn -0.323 0.173 -1.863 0.062 -0.323 -0.301
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_Rrltn 0.635 0.112 5.689 0.000 0.635 0.632
## SCALED_3_Rrltn 0.869 0.180 4.830 0.000 0.869 0.755
##
## R-Square:
## Estimate
## SCALED_2_Rrltn 0.368
## SCALED_3_Rrltn 0.245## [[1]]
## [[1]][[1]]
## Item β SE z p 90% CI
## 1 Intercept -0.20 0.13 -1.48 0.14 -0.45, 0.06
## 2 GROUP1 0.27 0.20 1.39 0.17 -0.11, 0.65
## 3 Rrealtionships 0.58 0.10 5.76 0.00 0.38, 0.78
## 4 Interaction w/ Group 0.05 0.10 0.53 0.60 -0.14, 0.25
##
## [[1]][[2]]
## Item β SE z p 90% CI
## 1 Intercept -0.32 0.17 -1.86 0.06 -0.66, 0.02
## 2 GROUP1 0.51 0.25 2.05 0.04 0.02, 1.01
## 3 Rrealtionships 0.43 0.14 3.05 0.00 0.15, 0.71
## 4 Interaction w/ Group -0.08 0.14 -0.58 0.56 -0.36, 0.19
##
##
## [[2]]
## levels Treat_T2 Control_T2 Treat_T3 Control_T3
## 1 -1 -0.56 -0.77 -0.40 -0.75
## 2 0 0.07 -0.20 -0.05 -0.32
## 3 1 0.71 0.38 0.30 0.11plots(modelname = modelRelationships_interaction, fitname = fit, data = adult2, interactionterm = "Relationships_mean_1.GROUP1", new_scale_name = "Perma Relationships", time2 = "T1-T2", time3 = "T1-T3") ## Saving 7 x 5 in image
## Saving 7 x 5 in image
#Time 3 as a result of time 1 and 2
results(modelname = modelRelationships_interaction_T3, fitname = fit, data = adult2, interactionterm = "Relationships_mean_1.GROUP1", current_scale_name = "Relationships", new_scale_name = "Perma - Relationships")## lavaan (0.5-20) converged normally after 18 iterations
##
## Number of observations 89
##
## Number of missing patterns 5
##
## Estimator ML
## Minimum Function Test Statistic 0.000
## Degrees of freedom 0
##
## Model test baseline model:
##
## Minimum Function Test Statistic 67.342
## Degrees of freedom 7
## 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) -434.963
## Loglikelihood unrestricted model (H1) -434.963
##
## Number of free parameters 11
## Akaike (AIC) 891.927
## Bayesian (BIC) 919.302
## Sample-size adjusted Bayesian (BIC) 884.588
##
## 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_Rrealtionships ~
## GROUP1 0.271 0.195 1.386 0.166 0.271
## SCALED_1_Rrltn 0.579 0.100 5.764 0.000 0.579
## Rltn__1.GROUP1 0.053 0.101 0.527 0.598 0.053
## SCALED_3_Rrealtionships ~
## GROUP1 0.280 0.195 1.434 0.152 0.280
## SCALED_1_Rrltn -0.071 0.146 -0.488 0.625 -0.071
## Rltn__1.GROUP1 -0.127 0.118 -1.074 0.283 -0.127
## SCALED_2_Rrltn 0.867 0.131 6.617 0.000 0.867
## Std.all
##
## 0.135
## 0.574
## 0.052
##
## 0.130
## -0.066
## -0.117
## 0.810
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_Rrltn -0.196 0.132 -1.485 0.138 -0.196 -0.195
## SCALED_3_Rrltn -0.153 0.137 -1.115 0.265 -0.153 -0.143
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_Rrltn 0.635 0.112 5.689 0.000 0.635 0.632
## SCALED_3_Rrltn 0.392 0.086 4.533 0.000 0.392 0.340
##
## R-Square:
## Estimate
## SCALED_2_Rrltn 0.368
## SCALED_3_Rrltn 0.660## [[1]]
## [[1]][[1]]
## Item β SE z p 90% CI
## 1 Intercept -0.20 0.13 -1.48 0.14 -0.45, 0.06
## 2 GROUP1 0.27 0.20 1.39 0.17 -0.11, 0.65
## 3 Rrealtionships 0.58 0.10 5.76 0.00 0.38, 0.78
## 4 Interaction w/ Group 0.05 0.10 0.53 0.60 -0.14, 0.25
##
## [[1]][[2]]
## Item β SE z p 90% CI
## 1 Intercept -0.15 0.14 -1.11 0.26 -0.42, 0.12
## 2 GROUP1 0.28 0.20 1.43 0.15 -0.1, 0.66
## 3 Rrealtionships -0.07 0.15 -0.49 0.63 -0.36, 0.22
## 4 Interaction w/ Group -0.13 0.12 -1.07 0.28 -0.36, 0.1
##
##
## [[2]]
## levels Treat_T2 Control_T2 Treat_T3 Control_T3
## 1 -1 -0.56 -0.77 0.32 -0.08
## 2 0 0.07 -0.20 0.12 -0.15
## 3 1 0.71 0.38 -0.08 -0.22plots(modelname = modelRelationships_interaction_T3, fitname = fit, data = adult2, interactionterm = "Relationships_mean_1.GROUP1", new_scale_name = "Perma Relationships", time2 = "T1-T2", time3 = "T1-T2-T3") ## Saving 7 x 5 in image
## Saving 7 x 5 in image
#########################################
################# LET #################
#########################################
results(modelname = modelLET_interaction, fitname = fit, data = adult2, interactionterm = "LifeEngagement_mean_1.GROUP1", current_scale_name = "Relationships", new_scale_name = "LET")## lavaan (0.5-20) converged normally after 26 iterations
##
## Number of observations 89
##
## Number of missing patterns 5
##
## Estimator ML
## Minimum Function Test Statistic 0.000
## Degrees of freedom 0
##
## Model test baseline model:
##
## Minimum Function Test Statistic 108.504
## Degrees of freedom 7
## 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) -333.567
## Loglikelihood unrestricted model (H1) -333.567
##
## Number of free parameters 11
## Akaike (AIC) 689.134
## Bayesian (BIC) 716.509
## Sample-size adjusted Bayesian (BIC) 681.795
##
## 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 0.311 0.148 2.103 0.035 0.311 0.156
## SCALED_1_LET 0.819 0.075 10.903 0.000 0.819 0.815
## LfEn__1.GROUP1 -0.011 0.187 -0.061 0.951 -0.011 -0.005
## SCALED_3_LET ~
## GROUP1 0.608 0.230 2.644 0.008 0.608 0.289
## SCALED_1_LET 0.666 0.121 5.509 0.000 0.666 0.625
## LfEn__1.GROUP1 -0.031 0.302 -0.104 0.917 -0.031 -0.012
##
## Covariances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_LET ~~
## SCALED_3_LET 0.289 0.089 3.256 0.001 0.289 0.616
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_LET -0.240 0.101 -2.385 0.017 -0.240 -0.242
## SCALED_3_LET -0.430 0.166 -2.592 0.010 -0.430 -0.409
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_LET 0.344 0.062 5.532 0.000 0.344 0.349
## SCALED_3_LET 0.640 0.146 4.386 0.000 0.640 0.579
##
## R-Square:
## Estimate
## SCALED_2_LET 0.651
## SCALED_3_LET 0.421## [[1]]
## [[1]][[1]]
## Item β SE z p 90% CI
## 1 Intercept -0.24 0.10 -2.38 0.02 -0.44, -0.04
## 2 GROUP1 0.31 0.15 2.10 0.04 0.02, 0.6
## 3 LET 0.82 0.08 10.90 0.00 0.67, 0.97
## 4 Interaction w/ Group -0.01 0.19 -0.06 0.95 -0.38, 0.36
##
## [[1]][[2]]
## Item β SE z p 90% CI
## 1 Intercept -0.43 0.17 -2.59 0.01 -0.76, -0.1
## 2 GROUP1 0.61 0.23 2.64 0.01 0.16, 1.06
## 3 LET 0.67 0.12 5.51 0.00 0.43, 0.9
## 4 Interaction w/ Group -0.03 0.30 -0.10 0.92 -0.62, 0.56
##
##
## [[2]]
## levels Treat_T2 Control_T2 Treat_T3 Control_T3
## 1 -1 -0.74 -1.06 -0.75 -1.10
## 2 0 0.07 -0.24 -0.12 -0.43
## 3 1 0.88 0.58 0.52 0.24plots(modelname = modelLET_interaction, fitname = fit, data = adult2, interactionterm = "LifeEngagement_mean_1.GROUP1", new_scale_name = "Life Engagement Scale", time2 = "T1-T2", time3 = "T1-T3") ## Saving 7 x 5 in image
## Saving 7 x 5 in image
#Time 3 as a result of time 1 and 2
results(modelname = modelLET_interaction_T3, fitname = fit, data = adult2, interactionterm = "LifeEngagement_mean_1.GROUP1", current_scale_name = "Relationships", new_scale_name = "LET")## lavaan (0.5-20) converged normally after 25 iterations
##
## Number of observations 89
##
## Number of missing patterns 5
##
## Estimator ML
## Minimum Function Test Statistic 0.000
## Degrees of freedom 0
##
## Model test baseline model:
##
## Minimum Function Test Statistic 108.504
## Degrees of freedom 7
## 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) -333.567
## Loglikelihood unrestricted model (H1) -333.567
##
## Number of free parameters 11
## Akaike (AIC) 689.134
## Bayesian (BIC) 716.509
## Sample-size adjusted Bayesian (BIC) 681.795
##
## 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 0.311 0.148 2.103 0.035 0.311 0.156
## SCALED_1_LET 0.819 0.075 10.903 0.000 0.819 0.815
## LfEn__1.GROUP1 -0.011 0.187 -0.061 0.951 -0.011 -0.005
## SCALED_3_LET ~
## GROUP1 0.347 0.219 1.585 0.113 0.347 0.165
## SCALED_1_LET -0.023 0.204 -0.113 0.910 -0.023 -0.022
## LfEn__1.GROUP1 -0.022 0.267 -0.081 0.935 -0.022 -0.008
## SCALED_2_LET 0.841 0.206 4.077 0.000 0.841 0.794
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_LET -0.240 0.101 -2.385 0.017 -0.240 -0.242
## SCALED_3_LET -0.228 0.163 -1.400 0.162 -0.228 -0.217
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_LET 0.344 0.062 5.532 0.000 0.344 0.349
## SCALED_3_LET 0.397 0.090 4.431 0.000 0.397 0.359
##
## R-Square:
## Estimate
## SCALED_2_LET 0.651
## SCALED_3_LET 0.641## [[1]]
## [[1]][[1]]
## Item β SE z p 90% CI
## 1 Intercept -0.24 0.10 -2.38 0.02 -0.44, -0.04
## 2 GROUP1 0.31 0.15 2.10 0.04 0.02, 0.6
## 3 LET 0.82 0.08 10.90 0.00 0.67, 0.97
## 4 Interaction w/ Group -0.01 0.19 -0.06 0.95 -0.38, 0.36
##
## [[1]][[2]]
## Item β SE z p 90% CI
## 1 Intercept -0.23 0.16 -1.40 0.16 -0.55, 0.09
## 2 GROUP1 0.35 0.22 1.58 0.11 -0.08, 0.78
## 3 LET -0.02 0.20 -0.11 0.91 -0.42, 0.38
## 4 Interaction w/ Group -0.02 0.27 -0.08 0.94 -0.55, 0.5
##
##
## [[2]]
## levels Treat_T2 Control_T2 Treat_T3 Control_T3
## 1 -1 -0.74 -1.06 0.13 -0.21
## 2 0 0.07 -0.24 0.08 -0.23
## 3 1 0.88 0.58 0.04 -0.25plots(modelname = modelLET_interaction_T3, fitname = fit, data = adult2, interactionterm = "LifeEngagement_mean_1.GROUP1", new_scale_name = "Life Engagement Scale", time2 = "T1-T2", time3 = "T1-T2-T3") ## Saving 7 x 5 in image
## Saving 7 x 5 in image
#########################################
################### LS ###################
#########################################
results(modelname = modelLS_interaction, fitname = fit, data = adult2, interactionterm = "LifeSatisfaction_mean_1.GROUP1", current_scale_name = "LS", new_scale_name = "Life Satisfaction")## lavaan (0.5-20) converged normally after 20 iterations
##
## Number of observations 89
##
## Number of missing patterns 5
##
## Estimator ML
## Minimum Function Test Statistic 0.000
## Degrees of freedom 0
##
## Model test baseline model:
##
## Minimum Function Test Statistic 110.111
## Degrees of freedom 7
## 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) -379.126
## Loglikelihood unrestricted model (H1) -379.126
##
## Number of free parameters 11
## Akaike (AIC) 780.251
## Bayesian (BIC) 807.626
## Sample-size adjusted Bayesian (BIC) 772.912
##
## 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_LS ~
## GROUP1 0.616 0.149 4.146 0.000 0.616 0.324
## SCALED_1_LS 0.600 0.073 8.196 0.000 0.600 0.626
## LfSt__1.GROUP1 -0.520 0.111 -4.689 0.000 -0.520 -0.364
## SCALED_3_LS ~
## GROUP1 0.581 0.216 2.690 0.007 0.581 0.278
## SCALED_1_LS 0.578 0.112 5.181 0.000 0.578 0.548
## LfSt__1.GROUP1 -0.504 0.167 -3.018 0.003 -0.504 -0.320
##
## Covariances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_LS ~~
## SCALED_3_LS 0.269 0.078 3.445 0.001 0.269 0.596
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_LS -0.298 0.099 -3.004 0.003 -0.298 -0.314
## SCALED_3_LS -0.373 0.148 -2.523 0.012 -0.373 -0.357
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_LS 0.351 0.064 5.488 0.000 0.351 0.389
## SCALED_3_LS 0.583 0.123 4.746 0.000 0.583 0.534
##
## R-Square:
## Estimate
## SCALED_2_LS 0.611
## SCALED_3_LS 0.466## [[1]]
## [[1]][[1]]
## Item β SE z p 90% CI
## 1 Intercept -0.30 0.10 -3.00 0 -0.49, -0.1
## 2 GROUP1 0.62 0.15 4.15 0 0.32, 0.91
## 3 Life Satisfaction 0.60 0.07 8.20 0 0.46, 0.74
## 4 Interaction w/ Group -0.52 0.11 -4.69 0 -0.74, -0.3
##
## [[1]][[2]]
## Item β SE z p 90% CI
## 1 Intercept -0.37 0.15 -2.52 0.01 -0.66, -0.08
## 2 GROUP1 0.58 0.22 2.69 0.01 0.16, 1
## 3 Life Satisfaction 0.58 0.11 5.18 0.00 0.36, 0.8
## 4 Interaction w/ Group -0.50 0.17 -3.02 0.00 -0.83, -0.18
##
##
## [[2]]
## levels Treat_T2 Control_T2 Treat_T3 Control_T3
## 1 -1 0.24 -0.9 0.17 -0.95
## 2 0 0.32 -0.3 0.24 -0.37
## 3 1 0.40 0.3 0.32 0.20plots(modelname = modelLS_interaction, fitname = fit, data = adult2, interactionterm = "LifeSatisfaction_mean_1.GROUP1", new_scale_name = "Life Satisfaction Scale", time2 = "T1-T2", time3 = "T1-T3") ## Saving 7 x 5 in image
## Saving 7 x 5 in image
#Time 3 as a result of time 1 and 2
results(modelname = modelLS_interaction_T3, fitname = fit, data = adult2, interactionterm = "LifeSatisfaction_mean_1.GROUP1", current_scale_name = "LS", new_scale_name = "Life Satisfaction")## lavaan (0.5-20) converged normally after 21 iterations
##
## Number of observations 89
##
## Number of missing patterns 5
##
## Estimator ML
## Minimum Function Test Statistic 0.000
## Degrees of freedom 0
##
## Model test baseline model:
##
## Minimum Function Test Statistic 110.111
## Degrees of freedom 7
## 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) -379.126
## Loglikelihood unrestricted model (H1) -379.126
##
## Number of free parameters 11
## Akaike (AIC) 780.251
## Bayesian (BIC) 807.626
## Sample-size adjusted Bayesian (BIC) 772.912
##
## 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_LS ~
## GROUP1 0.616 0.149 4.146 0.000 0.616 0.324
## SCALED_1_LS 0.600 0.073 8.196 0.000 0.600 0.626
## LfSt__1.GROUP1 -0.520 0.111 -4.689 0.000 -0.520 -0.364
## SCALED_3_LS ~
## GROUP1 0.108 0.211 0.511 0.610 0.108 0.052
## SCALED_1_LS 0.117 0.141 0.828 0.408 0.117 0.111
## LfSt__1.GROUP1 -0.105 0.176 -0.593 0.553 -0.105 -0.066
## SCALED_2_LS 0.768 0.171 4.480 0.000 0.768 0.698
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_LS -0.298 0.099 -3.004 0.003 -0.298 -0.314
## SCALED_3_LS -0.144 0.141 -1.024 0.306 -0.144 -0.138
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_LS 0.351 0.064 5.488 0.000 0.351 0.389
## SCALED_3_LS 0.376 0.085 4.399 0.000 0.376 0.344
##
## R-Square:
## Estimate
## SCALED_2_LS 0.611
## SCALED_3_LS 0.656## [[1]]
## [[1]][[1]]
## Item β SE z p 90% CI
## 1 Intercept -0.30 0.10 -3.00 0 -0.49, -0.1
## 2 GROUP1 0.62 0.15 4.15 0 0.32, 0.91
## 3 Life Satisfaction 0.60 0.07 8.20 0 0.46, 0.74
## 4 Interaction w/ Group -0.52 0.11 -4.69 0 -0.74, -0.3
##
## [[1]][[2]]
## Item β SE z p 90% CI
## 1 Intercept -0.14 0.14 -1.02 0.31 -0.42, 0.13
## 2 GROUP1 0.11 0.21 0.51 0.61 -0.31, 0.52
## 3 Life Satisfaction 0.12 0.14 0.83 0.41 -0.16, 0.39
## 4 Interaction w/ Group -0.10 0.18 -0.59 0.55 -0.45, 0.24
##
##
## [[2]]
## levels Treat_T2 Control_T2 Treat_T3 Control_T3
## 1 -1 0.24 -0.9 0.46 -0.26
## 2 0 0.32 -0.3 0.47 -0.14
## 3 1 0.40 0.3 0.48 -0.03plots(modelname = modelLS_interaction_T3, fitname = fit, data = adult2, interactionterm = "LifeSatisfaction_mean_1.GROUP1", new_scale_name = "Life Satisfaction Scale", time2 = "T1-T2", time3 = "T1-T2-T3") ## Saving 7 x 5 in image
## Saving 7 x 5 in image
#########################################
############### Engagement ##############
#########################################
results(modelname = modelEngagement_interaction, fitname = fit, data = adult2, interactionterm = "Engagement_mean_1.GROUP1", current_scale_name = "Engagament", new_scale_name = "Perma Engagament")## lavaan (0.5-20) converged normally after 22 iterations
##
## Number of observations 89
##
## Number of missing patterns 5
##
## Estimator ML
## Minimum Function Test Statistic 0.000
## Degrees of freedom 0
##
## Model test baseline model:
##
## Minimum Function Test Statistic 35.618
## Degrees of freedom 7
## 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) -443.353
## Loglikelihood unrestricted model (H1) -443.353
##
## Number of free parameters 11
## Akaike (AIC) 908.705
## Bayesian (BIC) 936.080
## Sample-size adjusted Bayesian (BIC) 901.366
##
## 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_Engagement ~
## GROUP1 0.213 0.213 0.999 0.318 0.213
## SCALED_1_Enggm 0.519 0.111 4.665 0.000 0.519
## Engg__1.GROUP1 -0.114 0.121 -0.944 0.345 -0.114
## SCALED_3_Engagement ~
## GROUP1 0.533 0.285 1.868 0.062 0.533
## SCALED_1_Enggm 0.394 0.195 2.026 0.043 0.394
## Engg__1.GROUP1 -0.204 0.210 -0.970 0.332 -0.204
## Std.all
##
## 0.107
## 0.516
## -0.104
##
## 0.248
## 0.363
## -0.173
##
## Covariances:
## Estimate Std.Err Z-value P(>|z|) Std.lv
## SCALED_2_Engagement ~~
## SCALED_3_Enggm 0.412 0.152 2.717 0.007 0.412
## Std.all
##
## 0.505
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_Enggm -0.168 0.146 -1.152 0.249 -0.168 -0.169
## SCALED_3_Enggm -0.368 0.206 -1.784 0.074 -0.368 -0.342
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_Enggm 0.719 0.132 5.451 0.000 0.719 0.721
## SCALED_3_Enggm 0.925 0.197 4.699 0.000 0.925 0.799
##
## R-Square:
## Estimate
## SCALED_2_Enggm 0.279
## SCALED_3_Enggm 0.201## [[1]]
## [[1]][[1]]
## Item β SE z p 90% CI
## 1 Intercept -0.17 0.15 -1.15 0.25 -0.45, 0.12
## 2 GROUP1 0.21 0.21 1.00 0.32 -0.21, 0.63
## 3 Engagement 0.52 0.11 4.66 0.00 0.3, 0.74
## 4 Interaction w/ Group -0.11 0.12 -0.94 0.34 -0.35, 0.12
##
## [[1]][[2]]
## Item β SE z p 90% CI
## 1 Intercept -0.37 0.21 -1.78 0.07 -0.77, 0.04
## 2 GROUP1 0.53 0.29 1.87 0.06 -0.03, 1.09
## 3 Engagement 0.39 0.19 2.03 0.04 0.01, 0.78
## 4 Interaction w/ Group -0.20 0.21 -0.97 0.33 -0.61, 0.21
##
##
## [[2]]
## levels Treat_T2 Control_T2 Treat_T3 Control_T3
## 1 -1 -0.36 -0.69 -0.35 -0.76
## 2 0 0.04 -0.17 -0.15 -0.37
## 3 1 0.45 0.35 0.04 0.03plots(modelname = modelEngagement_interaction, fitname = fit, data = adult2, interactionterm = "Engagement_mean_1.GROUP1", new_scale_name = "Perma Engagament", time2 = "T1-T2", time3 = "T1-T3") ## Saving 7 x 5 in image
## Saving 7 x 5 in image
#Time 3 as a result of time 1 and 2
results(modelname = modelEngagement_interaction_T3, fitname = fit, data = adult2, interactionterm = "Engagement_mean_1.GROUP1", current_scale_name = "Engagament", new_scale_name = "Perma Engagament")## lavaan (0.5-20) converged normally after 22 iterations
##
## Number of observations 89
##
## Number of missing patterns 5
##
## Estimator ML
## Minimum Function Test Statistic 0.000
## Degrees of freedom 0
##
## Model test baseline model:
##
## Minimum Function Test Statistic 35.618
## Degrees of freedom 7
## 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) -443.353
## Loglikelihood unrestricted model (H1) -443.353
##
## Number of free parameters 11
## Akaike (AIC) 908.705
## Bayesian (BIC) 936.080
## Sample-size adjusted Bayesian (BIC) 901.366
##
## 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_Engagement ~
## GROUP1 0.213 0.213 0.999 0.318 0.213
## SCALED_1_Enggm 0.519 0.111 4.665 0.000 0.519
## Engg__1.GROUP1 -0.114 0.121 -0.944 0.345 -0.114
## SCALED_3_Engagement ~
## GROUP1 0.411 0.263 1.561 0.118 0.411
## SCALED_1_Enggm 0.096 0.201 0.479 0.632 0.096
## Engg__1.GROUP1 -0.138 0.207 -0.666 0.505 -0.138
## SCALED_2_Enggm 0.573 0.174 3.295 0.001 0.573
## Std.all
##
## 0.107
## 0.516
## -0.104
##
## 0.191
## 0.089
## -0.117
## 0.532
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_Enggm -0.168 0.146 -1.152 0.249 -0.168 -0.169
## SCALED_3_Enggm -0.271 0.194 -1.397 0.162 -0.271 -0.252
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_Enggm 0.719 0.132 5.451 0.000 0.719 0.721
## SCALED_3_Enggm 0.689 0.158 4.347 0.000 0.689 0.595
##
## R-Square:
## Estimate
## SCALED_2_Enggm 0.279
## SCALED_3_Enggm 0.405## Warning in lavaan(slotOptions = object@Options, slotParTable =
## object@ParTable, : lavaan WARNING: model has NOT converged!
## Warning in lavaan(slotOptions = object@Options, slotParTable =
## object@ParTable, : lavaan WARNING: model has NOT converged!## [[1]]
## [[1]][[1]]
## Item β SE z p 90% CI
## 1 Intercept -0.17 0.15 -1.15 0.25 -0.45, 0.12
## 2 GROUP1 0.21 0.21 1.00 0.32 -0.21, 0.63
## 3 Engagement 0.52 0.11 4.67 0.00 0.3, 0.74
## 4 Interaction w/ Group -0.11 0.12 -0.94 0.34 -0.35, 0.12
##
## [[1]][[2]]
## Item β SE z p 90% CI
## 1 Intercept -0.27 0.19 -1.40 0.16 -0.65, 0.11
## 2 GROUP1 0.41 0.26 1.56 0.12 -0.1, 0.93
## 3 Engagement 0.10 0.20 0.48 0.63 -0.3, 0.49
## 4 Interaction w/ Group -0.14 0.21 -0.67 0.51 -0.54, 0.27
##
##
## [[2]]
## levels Treat_T2 Control_T2 Treat_T3 Control_T3
## 1 -1 -0.36 -0.69 -0.02 -0.37
## 2 0 0.04 -0.17 -0.06 -0.27
## 3 1 0.45 0.35 -0.10 -0.18plots(modelname = modelEngagement_interaction_T3, fitname = fit, data = adult2, interactionterm = "Engagement_mean_1.GROUP1", new_scale_name = "Perma Engagament", time2 = "T1-T2", time3 = "T1-T2-T3") ## Warning in lavaan(slotOptions = object@Options, slotParTable =
## object@ParTable, : lavaan WARNING: model has NOT converged!## Saving 7 x 5 in image
## Saving 7 x 5 in image
#########################################
############### Optimism #################
#########################################
results(modelname = modelOPTIMISM_interaction, fitname = fit, data = adult2, interactionterm = "Optimism_mean_1.GROUP1", current_scale_name = "Optimism", new_scale_name = "Optimism Scale")## lavaan (0.5-20) converged normally after 21 iterations
##
## Number of observations 89
##
## Number of missing patterns 5
##
## Estimator ML
## Minimum Function Test Statistic 0.000
## Degrees of freedom 0
##
## Model test baseline model:
##
## Minimum Function Test Statistic 39.594
## Degrees of freedom 7
## 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) -364.308
## Loglikelihood unrestricted model (H1) -364.308
##
## Number of free parameters 11
## Akaike (AIC) 750.615
## Bayesian (BIC) 777.990
## Sample-size adjusted Bayesian (BIC) 743.276
##
## 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_Optimism ~
## GROUP1 -0.273 0.228 -1.200 0.230 -0.273
## SCALED_1_Optms 0.358 0.112 3.201 0.001 0.358
## Optm__1.GROUP1 -0.332 0.302 -1.100 0.271 -0.332
## SCALED_3_Optimism ~
## GROUP1 -0.625 0.231 -2.705 0.007 -0.625
## SCALED_1_Optms 0.440 0.119 3.688 0.000 0.440
## Optm__1.GROUP1 -0.699 0.317 -2.203 0.028 -0.699
## Std.all
##
## -0.138
## 0.358
## -0.124
##
## -0.313
## 0.437
## -0.261
##
## Covariances:
## Estimate Std.Err Z-value P(>|z|) Std.lv
## SCALED_2_Optimism ~~
## SCALED_3_Optms 0.248 0.116 2.134 0.033 0.248
## Std.all
##
## 0.351
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_Optms 0.192 0.154 1.248 0.212 0.192 0.193
## SCALED_3_Optms 0.401 0.161 2.488 0.013 0.401 0.402
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_Optms 0.816 0.144 5.665 0.000 0.816 0.826
## SCALED_3_Optms 0.613 0.126 4.847 0.000 0.613 0.615
##
## R-Square:
## Estimate
## SCALED_2_Optms 0.174
## SCALED_3_Optms 0.385## [[1]]
## [[1]][[1]]
## Item β SE z p 90% CI
## 1 Intercept 0.19 0.15 1.25 0.21 -0.11, 0.49
## 2 GROUP1 -0.27 0.23 -1.20 0.23 -0.72, 0.17
## 3 Optimism Scale 0.36 0.11 3.20 0.00 0.14, 0.58
## 4 Interaction w/ Group -0.33 0.30 -1.10 0.27 -0.92, 0.26
##
## [[1]][[2]]
## Item β SE z p 90% CI
## 1 Intercept 0.40 0.16 2.49 0.01 0.09, 0.72
## 2 GROUP1 -0.62 0.23 -2.71 0.01 -1.08, -0.17
## 3 Optimism Scale 0.44 0.12 3.69 0.00 0.21, 0.67
## 4 Interaction w/ Group -0.70 0.32 -2.20 0.03 -1.32, -0.08
##
##
## [[2]]
## levels Treat_T2 Control_T2 Treat_T3 Control_T3
## 1 -1 -0.11 -0.17 0.39 -0.04
## 2 0 -0.08 0.19 0.13 0.40
## 3 1 -0.06 0.55 -0.13 0.84plots(modelname = modelOPTIMISM_interaction, fitname = fit, data = adult2, interactionterm = "Optimism_mean_1.GROUP1", new_scale_name = "Optimism Scale", time2 = "T1-T2", time3 = "T1-T3") ## Saving 7 x 5 in image
## Saving 7 x 5 in image
#Time 3 as a result of time 1 and 2
results(modelname = modelOPTIMISM_interaction_T3, fitname = fit, data = adult2, interactionterm = "Optimism_mean_1.GROUP1", current_scale_name = "Optimism", new_scale_name = "Optimism Scale")## lavaan (0.5-20) converged normally after 23 iterations
##
## Number of observations 89
##
## Number of missing patterns 5
##
## Estimator ML
## Minimum Function Test Statistic 0.000
## Degrees of freedom 0
##
## Model test baseline model:
##
## Minimum Function Test Statistic 39.594
## Degrees of freedom 7
## 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) -364.308
## Loglikelihood unrestricted model (H1) -364.308
##
## Number of free parameters 11
## Akaike (AIC) 750.615
## Bayesian (BIC) 777.990
## Sample-size adjusted Bayesian (BIC) 743.276
##
## 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_Optimism ~
## GROUP1 -0.273 0.228 -1.200 0.230 -0.273
## SCALED_1_Optms 0.358 0.112 3.201 0.001 0.358
## Optm__1.GROUP1 -0.332 0.302 -1.100 0.271 -0.332
## SCALED_3_Optimism ~
## GROUP1 -0.542 0.228 -2.379 0.017 -0.542
## SCALED_1_Optms 0.331 0.129 2.573 0.010 0.331
## Optm__1.GROUP1 -0.598 0.314 -1.903 0.057 -0.598
## SCALED_2_Optms 0.304 0.129 2.352 0.019 0.304
## Std.all
##
## -0.138
## 0.358
## -0.124
##
## -0.271
## 0.329
## -0.223
## 0.303
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_Optms 0.192 0.154 1.248 0.212 0.192 0.193
## SCALED_3_Optms 0.343 0.162 2.117 0.034 0.343 0.343
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_Optms 0.816 0.144 5.665 0.000 0.816 0.826
## SCALED_3_Optms 0.537 0.115 4.667 0.000 0.537 0.539
##
## R-Square:
## Estimate
## SCALED_2_Optms 0.174
## SCALED_3_Optms 0.461## [[1]]
## [[1]][[1]]
## Item β SE z p 90% CI
## 1 Intercept 0.19 0.15 1.25 0.21 -0.11, 0.49
## 2 GROUP1 -0.27 0.23 -1.20 0.23 -0.72, 0.17
## 3 Optimism Scale 0.36 0.11 3.20 0.00 0.14, 0.58
## 4 Interaction w/ Group -0.33 0.30 -1.10 0.27 -0.92, 0.26
##
## [[1]][[2]]
## Item β SE z p 90% CI
## 1 Intercept 0.34 0.16 2.12 0.03 0.03, 0.66
## 2 GROUP1 -0.54 0.23 -2.38 0.02 -0.99, -0.1
## 3 Optimism Scale 0.33 0.13 2.57 0.01 0.08, 0.58
## 4 Interaction w/ Group -0.60 0.31 -1.90 0.06 -1.21, 0.02
##
##
## [[2]]
## levels Treat_T2 Control_T2 Treat_T3 Control_T3
## 1 -1 -0.11 -0.17 0.34 0.01
## 2 0 -0.08 0.19 0.07 0.34
## 3 1 -0.06 0.55 -0.20 0.67plots(modelname = modelOPTIMISM_interaction_T3, fitname = fit, data = adult2, interactionterm = "Optimism_mean_1.GROUP1", new_scale_name = "Optimism Scale", time2 = "T1-T2", time3 = "T1-T2-T3") ## Saving 7 x 5 in image
## Saving 7 x 5 in image
#########################################
################# PWB ###################
#########################################
results(modelname = modelPWB_interaction, fitname = fit, data = adult2, interactionterm = "PurposePWB_mean_1.GROUP1", current_scale_name = "PWB", new_scale_name = "RPWB")## lavaan (0.5-20) converged normally after 26 iterations
##
## Number of observations 89
##
## Number of missing patterns 5
##
## Estimator ML
## Minimum Function Test Statistic 0.000
## Degrees of freedom 0
##
## Model test baseline model:
##
## Minimum Function Test Statistic 80.220
## Degrees of freedom 7
## 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) -364.655
## Loglikelihood unrestricted model (H1) -364.655
##
## Number of free parameters 11
## Akaike (AIC) 751.310
## Bayesian (BIC) 778.685
## Sample-size adjusted Bayesian (BIC) 743.971
##
## 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_PWB ~
## GROUP1 0.461 0.181 2.551 0.011 0.461 0.225
## SCALED_1_PWB 0.721 0.102 7.081 0.000 0.721 0.700
## PPWB__1.GROUP1 -0.287 0.217 -1.327 0.184 -0.287 -0.134
## SCALED_3_PWB ~
## GROUP1 0.736 0.223 3.302 0.001 0.736 0.343
## SCALED_1_PWB 0.657 0.133 4.954 0.000 0.657 0.610
## PPWB__1.GROUP1 -0.526 0.279 -1.887 0.059 -0.526 -0.234
##
## Covariances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_PWB ~~
## SCALED_3_PWB 0.294 0.095 3.098 0.002 0.294 0.540
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_PWB -0.373 0.126 -2.954 0.003 -0.373 -0.364
## SCALED_3_PWB -0.573 0.162 -3.541 0.000 -0.573 -0.535
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_PWB 0.505 0.091 5.550 0.000 0.505 0.481
## SCALED_3_PWB 0.587 0.125 4.702 0.000 0.587 0.510
##
## R-Square:
## Estimate
## SCALED_2_PWB 0.519
## SCALED_3_PWB 0.490## [[1]]
## [[1]][[1]]
## Item β SE z p 90% CI
## 1 Intercept -0.37 0.13 -2.95 0.00 -0.62, -0.13
## 2 GROUP1 0.46 0.18 2.55 0.01 0.11, 0.81
## 3 RPWB 0.72 0.10 7.08 0.00 0.52, 0.92
## 4 Interaction w/ Group -0.29 0.22 -1.33 0.18 -0.71, 0.14
##
## [[1]][[2]]
## Item β SE z p 90% CI
## 1 Intercept -0.57 0.16 -3.54 0.00 -0.89, -0.26
## 2 GROUP1 0.74 0.22 3.30 0.00 0.3, 1.17
## 3 RPWB 0.66 0.13 4.95 0.00 0.4, 0.92
## 4 Interaction w/ Group -0.53 0.28 -1.89 0.06 -1.07, 0.02
##
##
## [[2]]
## levels Treat_T2 Control_T2 Treat_T3 Control_T3
## 1 -1 -0.35 -1.09 -0.24 -1.23
## 2 0 0.09 -0.37 -0.11 -0.57
## 3 1 0.52 0.35 0.02 0.08plots(modelname = modelPWB_interaction, fitname = fit, data = adult2, interactionterm = "PurposePWB_mean_1.GROUP1", new_scale_name = "Ryff Purpose Subscale", time2 = "T1-T2", time3 = "T1-T3") ## Saving 7 x 5 in image
## Saving 7 x 5 in image
#Time 3 as a result of time 1 and 2
results(modelname = modelPWB_interaction_T3, fitname = fit, data = adult2, interactionterm = "PurposePWB_mean_1.GROUP1", current_scale_name = "PWB", new_scale_name = "RPWB")## lavaan (0.5-20) converged normally after 22 iterations
##
## Number of observations 89
##
## Number of missing patterns 5
##
## Estimator ML
## Minimum Function Test Statistic 0.000
## Degrees of freedom 0
##
## Model test baseline model:
##
## Minimum Function Test Statistic 80.220
## Degrees of freedom 7
## 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) -364.655
## Loglikelihood unrestricted model (H1) -364.655
##
## Number of free parameters 11
## Akaike (AIC) 751.310
## Bayesian (BIC) 778.685
## Sample-size adjusted Bayesian (BIC) 743.971
##
## 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_PWB ~
## GROUP1 0.461 0.181 2.551 0.011 0.461 0.225
## SCALED_1_PWB 0.721 0.102 7.081 0.000 0.721 0.700
## PPWB__1.GROUP1 -0.287 0.217 -1.327 0.184 -0.287 -0.134
## SCALED_3_PWB ~
## GROUP1 0.468 0.213 2.202 0.028 0.468 0.218
## SCALED_1_PWB 0.238 0.168 1.420 0.156 0.238 0.221
## PPWB__1.GROUP1 -0.359 0.263 -1.362 0.173 -0.359 -0.159
## SCALED_2_PWB 0.581 0.153 3.811 0.000 0.581 0.556
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_PWB -0.373 0.126 -2.954 0.003 -0.373 -0.364
## SCALED_3_PWB -0.356 0.159 -2.237 0.025 -0.356 -0.332
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_PWB 0.505 0.091 5.550 0.000 0.505 0.481
## SCALED_3_PWB 0.416 0.091 4.565 0.000 0.416 0.362
##
## R-Square:
## Estimate
## SCALED_2_PWB 0.519
## SCALED_3_PWB 0.638## [[1]]
## [[1]][[1]]
## Item β SE z p 90% CI
## 1 Intercept -0.37 0.13 -2.95 0.00 -0.62, -0.13
## 2 GROUP1 0.46 0.18 2.55 0.01 0.11, 0.81
## 3 RPWB 0.72 0.10 7.08 0.00 0.52, 0.92
## 4 Interaction w/ Group -0.29 0.22 -1.33 0.18 -0.71, 0.14
##
## [[1]][[2]]
## Item β SE z p 90% CI
## 1 Intercept -0.36 0.16 -2.24 0.03 -0.67, -0.04
## 2 GROUP1 0.47 0.21 2.20 0.03 0.05, 0.89
## 3 RPWB 0.24 0.17 1.42 0.16 -0.09, 0.57
## 4 Interaction w/ Group -0.36 0.26 -1.36 0.17 -0.87, 0.16
##
##
## [[2]]
## levels Treat_T2 Control_T2 Treat_T3 Control_T3
## 1 -1 -0.35 -1.09 0.23 -0.59
## 2 0 0.09 -0.37 0.10 -0.36
## 3 1 0.52 0.35 -0.02 -0.12plots(modelname = modelPWB_interaction_T3, fitname = fit, data = adult2, interactionterm = "PurposePWB_mean_1.GROUP1", new_scale_name = "Ryff Purpose Subscale", time2 = "T1-T2", time3 = "T1-T2-T3") ## Saving 7 x 5 in image
## Saving 7 x 5 in image
#########################################
################# APSI ###################
#########################################
results(modelname = modelAPSI_interaction, fitname = fit, data = adult2, interactionterm = "PurposeAPSI_mean_1.GROUP1", current_scale_name = "APSI", new_scale_name = "APSI")## lavaan (0.5-20) converged normally after 26 iterations
##
## Number of observations 89
##
## Number of missing patterns 5
##
## Estimator ML
## Minimum Function Test Statistic 0.000
## Degrees of freedom 0
##
## Model test baseline model:
##
## Minimum Function Test Statistic 109.932
## Degrees of freedom 7
## 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) -322.678
## Loglikelihood unrestricted model (H1) -322.678
##
## Number of free parameters 11
## Akaike (AIC) 667.356
## Bayesian (BIC) 694.731
## Sample-size adjusted Bayesian (BIC) 660.016
##
## 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 ~
## GROUP1 0.777 0.140 5.555 0.000 0.777 0.392
## SCALED_1_APSI 0.816 0.072 11.380 0.000 0.816 0.815
## PAPSI__1.GROUP -0.721 0.197 -3.651 0.000 -0.721 -0.262
## SCALED_3_APSI ~
## GROUP1 0.673 0.233 2.891 0.004 0.673 0.321
## SCALED_1_APSI 0.673 0.127 5.306 0.000 0.673 0.637
## PAPSI__1.GROUP -0.912 0.351 -2.601 0.009 -0.912 -0.314
##
## Covariances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_APSI ~~
## SCALED_3_APSI 0.213 0.079 2.707 0.007 0.213 0.496
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_APSI -0.509 0.096 -5.275 0.000 -0.509 -0.513
## SCALED_3_APSI -0.544 0.174 -3.119 0.002 -0.544 -0.520
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_APSI 0.305 0.055 5.552 0.000 0.305 0.311
## SCALED_3_APSI 0.603 0.130 4.635 0.000 0.603 0.551
##
## R-Square:
## Estimate
## SCALED_2_APSI 0.689
## SCALED_3_APSI 0.449## [[1]]
## [[1]][[1]]
## Item β SE z p 90% CI
## 1 Intercept -0.51 0.10 -5.27 0 -0.7, -0.32
## 2 GROUP1 0.78 0.14 5.56 0 0.5, 1.05
## 3 APSI 0.82 0.07 11.38 0 0.68, 0.96
## 4 Interaction w/ Group -0.72 0.20 -3.65 0 -1.11, -0.33
##
## [[1]][[2]]
## Item β SE z p 90% CI
## 1 Intercept -0.54 0.17 -3.12 0.00 -0.89, -0.2
## 2 GROUP1 0.67 0.23 2.89 0.00 0.22, 1.13
## 3 APSI 0.67 0.13 5.31 0.00 0.42, 0.92
## 4 Interaction w/ Group -0.91 0.35 -2.60 0.01 -1.6, -0.22
##
##
## [[2]]
## levels Treat_T2 Control_T2 Treat_T3 Control_T3
## 1 -1 0.17 -1.32 0.47 -1.22
## 2 0 0.27 -0.51 0.23 -0.54
## 3 1 0.36 0.31 -0.01 0.13plots(modelname = modelAPSI_interaction, fitname = fit, data = adult2, interactionterm = "PurposeAPSI_mean_1.GROUP1", new_scale_name = "APSI Sense of Identity", time2 = "T1-T2", time3 = "T1-T3") ## Saving 7 x 5 in image
## Saving 7 x 5 in image
#Time 3 as a result of time 1 and 2
results(modelname = modelAPSI_interaction_T3, fitname = fit, data = adult2, interactionterm = "PurposeAPSI_mean_1.GROUP1", current_scale_name = "APSI", new_scale_name = "APSI")## lavaan (0.5-20) converged normally after 28 iterations
##
## Number of observations 89
##
## Number of missing patterns 5
##
## Estimator ML
## Minimum Function Test Statistic 0.000
## Degrees of freedom 0
##
## Model test baseline model:
##
## Minimum Function Test Statistic 109.932
## Degrees of freedom 7
## 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) -322.678
## Loglikelihood unrestricted model (H1) -322.678
##
## Number of free parameters 11
## Akaike (AIC) 667.356
## Bayesian (BIC) 694.731
## Sample-size adjusted Bayesian (BIC) 660.016
##
## 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 ~
## GROUP1 0.777 0.140 5.555 0.000 0.777 0.392
## SCALED_1_APSI 0.816 0.072 11.380 0.000 0.816 0.815
## PAPSI__1.GROUP -0.721 0.197 -3.651 0.000 -0.721 -0.262
## SCALED_3_APSI ~
## GROUP1 0.131 0.279 0.470 0.639 0.131 0.063
## SCALED_1_APSI 0.104 0.223 0.468 0.640 0.104 0.099
## PAPSI__1.GROUP -0.410 0.358 -1.143 0.253 -0.410 -0.141
## SCALED_2_APSI 0.697 0.217 3.214 0.001 0.697 0.660
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_APSI -0.509 0.096 -5.275 0.000 -0.509 -0.513
## SCALED_3_APSI -0.189 0.207 -0.917 0.359 -0.189 -0.181
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_APSI 0.305 0.055 5.552 0.000 0.305 0.311
## SCALED_3_APSI 0.454 0.100 4.536 0.000 0.454 0.415
##
## R-Square:
## Estimate
## SCALED_2_APSI 0.689
## SCALED_3_APSI 0.585## [[1]]
## [[1]][[1]]
## Item β SE z p 90% CI
## 1 Intercept -0.51 0.10 -5.27 0 -0.7, -0.32
## 2 GROUP1 0.78 0.14 5.56 0 0.5, 1.05
## 3 APSI 0.82 0.07 11.38 0 0.68, 0.96
## 4 Interaction w/ Group -0.72 0.20 -3.65 0 -1.11, -0.33
##
## [[1]][[2]]
## Item β SE z p 90% CI
## 1 Intercept -0.19 0.21 -0.92 0.36 -0.59, 0.22
## 2 GROUP1 0.13 0.28 0.47 0.64 -0.42, 0.68
## 3 APSI 0.10 0.22 0.47 0.64 -0.33, 0.54
## 4 Interaction w/ Group -0.41 0.36 -1.14 0.25 -1.11, 0.29
##
##
## [[2]]
## levels Treat_T2 Control_T2 Treat_T3 Control_T3
## 1 -1 0.17 -1.32 0.89 -0.29
## 2 0 0.27 -0.51 0.59 -0.19
## 3 1 0.36 0.31 0.28 -0.08plots(modelname = modelAPSI_interaction_T3, fitname = fit, data = adult2, interactionterm = "PurposeAPSI_mean_1.GROUP1", new_scale_name = "APSI Sense of Identity", time2 = "T1-T2", time3 = "T1-T2-T3") ## Saving 7 x 5 in image
## Saving 7 x 5 in image
#########################################
################# Res #################
#########################################
results(modelname = modelRes_interaction, fitname = fit, data = adult2, interactionterm = "Res_mean_1.GROUP1", current_scale_name = "Res", new_scale_name = "Resiliance")## lavaan (0.5-20) converged normally after 22 iterations
##
## Number of observations 89
##
## Number of missing patterns 5
##
## Estimator ML
## Minimum Function Test Statistic 0.000
## Degrees of freedom 0
##
## Model test baseline model:
##
## Minimum Function Test Statistic 110.299
## Degrees of freedom 7
## 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) -404.539
## Loglikelihood unrestricted model (H1) -404.539
##
## Number of free parameters 11
## Akaike (AIC) 831.077
## Bayesian (BIC) 858.452
## Sample-size adjusted Bayesian (BIC) 823.738
##
## 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_Res ~
## GROUP1 -0.095 0.147 -0.645 0.519 -0.095 -0.044
## SCALED_1_Res 0.943 0.084 11.182 0.000 0.943 0.865
## Rs_mn_1.GROUP1 0.240 0.092 2.602 0.009 0.240 0.201
## SCALED_3_Res ~
## GROUP1 0.152 0.201 0.754 0.451 0.152 0.073
## SCALED_1_Res 0.771 0.135 5.711 0.000 0.771 0.736
## Rs_mn_1.GROUP1 -0.081 0.150 -0.539 0.590 -0.081 -0.071
##
## Covariances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_Res ~~
## SCALED_3_Res 0.104 0.070 1.497 0.134 0.104 0.271
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_Res -0.109 0.097 -1.122 0.262 -0.109 -0.101
## SCALED_3_Res -0.192 0.136 -1.419 0.156 -0.192 -0.186
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_Res 0.330 0.058 5.700 0.000 0.330 0.284
## SCALED_3_Res 0.449 0.096 4.699 0.000 0.449 0.419
##
## R-Square:
## Estimate
## SCALED_2_Res 0.716
## SCALED_3_Res 0.581## [[1]]
## [[1]][[1]]
## Item β SE z p 90% CI
## 1 Intercept -0.11 0.10 -1.12 0.26 -0.3, 0.08
## 2 GROUP1 -0.09 0.15 -0.65 0.52 -0.38, 0.19
## 3 Resiliance 0.94 0.08 11.18 0.00 0.78, 1.11
## 4 Interaction w/ Group 0.24 0.09 2.60 0.01 0.06, 0.42
##
## [[1]][[2]]
## Item β SE z p 90% CI
## 1 Intercept -0.19 0.14 -1.42 0.16 -0.46, 0.07
## 2 GROUP1 0.15 0.20 0.75 0.45 -0.24, 0.55
## 3 Resiliance 0.77 0.13 5.71 0.00 0.51, 1.04
## 4 Interaction w/ Group -0.08 0.15 -0.54 0.59 -0.37, 0.21
##
##
## [[2]]
## levels Treat_T2 Control_T2 Treat_T3 Control_T3
## 1 -1 -1.39 -1.05 -0.98 -0.96
## 2 0 -0.20 -0.11 -0.29 -0.19
## 3 1 0.98 0.83 0.40 0.58plots(modelname = modelRes_interaction, fitname = fit, data = adult2, interactionterm = "Res_mean_1.GROUP1", new_scale_name = "Resiliance", , time2 = "T1-T2", time3 = "T1-T3") ## Saving 7 x 5 in image
## Saving 7 x 5 in image
#Time 3 as a result of time 1 and 2
results(modelname = modelRes_interaction_T3, fitname = fit, data = adult2, interactionterm = "Res_mean_1.GROUP1", current_scale_name = "Res", new_scale_name = "Resiliance")## lavaan (0.5-20) converged normally after 22 iterations
##
## Number of observations 89
##
## Number of missing patterns 5
##
## Estimator ML
## Minimum Function Test Statistic 0.000
## Degrees of freedom 0
##
## Model test baseline model:
##
## Minimum Function Test Statistic 110.299
## Degrees of freedom 7
## 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) -404.539
## Loglikelihood unrestricted model (H1) -404.539
##
## Number of free parameters 11
## Akaike (AIC) 831.077
## Bayesian (BIC) 858.452
## Sample-size adjusted Bayesian (BIC) 823.738
##
## 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_Res ~
## GROUP1 -0.095 0.147 -0.645 0.519 -0.095 -0.044
## SCALED_1_Res 0.943 0.084 11.182 0.000 0.943 0.865
## Rs_mn_1.GROUP1 0.240 0.092 2.602 0.009 0.240 0.201
## SCALED_3_Res ~
## GROUP1 0.182 0.198 0.919 0.358 0.182 0.088
## SCALED_1_Res 0.472 0.241 1.961 0.050 0.472 0.451
## Rs_mn_1.GROUP1 -0.156 0.149 -1.049 0.294 -0.156 -0.137
## SCALED_2_Res 0.316 0.204 1.553 0.120 0.316 0.329
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_Res -0.109 0.097 -1.122 0.262 -0.109 -0.101
## SCALED_3_Res -0.158 0.136 -1.164 0.244 -0.158 -0.153
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_Res 0.330 0.058 5.700 0.000 0.330 0.284
## SCALED_3_Res 0.416 0.087 4.794 0.000 0.416 0.388
##
## R-Square:
## Estimate
## SCALED_2_Res 0.716
## SCALED_3_Res 0.612## [[1]]
## [[1]][[1]]
## Item β SE z p 90% CI
## 1 Intercept -0.11 0.10 -1.12 0.26 -0.3, 0.08
## 2 GROUP1 -0.09 0.15 -0.65 0.52 -0.38, 0.19
## 3 Resiliance 0.94 0.08 11.18 0.00 0.78, 1.11
## 4 Interaction w/ Group 0.24 0.09 2.60 0.01 0.06, 0.42
##
## [[1]][[2]]
## Item β SE z p 90% CI
## 1 Intercept -0.16 0.14 -1.16 0.24 -0.42, 0.11
## 2 GROUP1 0.18 0.20 0.92 0.36 -0.21, 0.57
## 3 Resiliance 0.47 0.24 1.96 0.05 0, 0.94
## 4 Interaction w/ Group -0.16 0.15 -1.05 0.29 -0.45, 0.14
##
##
## [[2]]
## levels Treat_T2 Control_T2 Treat_T3 Control_T3
## 1 -1 -1.39 -1.05 -0.57 -0.63
## 2 0 -0.20 -0.11 -0.25 -0.16
## 3 1 0.98 0.83 0.06 0.31plots(modelname = modelRes_interaction_T3, fitname = fit, data = adult2, interactionterm = "Res_mean_1.GROUP1", new_scale_name = "Resiliance", time2 = "T1-T2", time3 = "T1-T2-T3") ## Saving 7 x 5 in image
## Saving 7 x 5 in image
#########################################
################# GRIT #################
#########################################
results(modelname = modelGRIT_interaction, fitname = fit, data = adult2, interactionterm = "GRIT_mean_1.GROUP1", current_scale_name = "GRIT", new_scale_name = "Grit")## lavaan (0.5-20) converged normally after 33 iterations
##
## Number of observations 89
##
## Number of missing patterns 5
##
## Estimator ML
## Minimum Function Test Statistic 0.000
## Degrees of freedom 0
##
## Model test baseline model:
##
## Minimum Function Test Statistic 27.606
## Degrees of freedom 7
## 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) -281.905
## Loglikelihood unrestricted model (H1) -281.905
##
## Number of free parameters 11
## Akaike (AIC) 585.810
## Bayesian (BIC) 613.185
## Sample-size adjusted Bayesian (BIC) 578.471
##
## 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_GRIT ~
## GROUP1 -0.406 0.201 -2.018 0.044 -0.406 -0.203
## SCALED_1_GRIT 0.561 0.108 5.186 0.000 0.561 0.563
## GRIT__1.GROUP1 -0.597 0.782 -0.763 0.445 -0.597 -0.085
## SCALED_3_GRIT ~
## GROUP1 0.018 0.292 0.062 0.950 0.018 0.009
## SCALED_1_GRIT 0.207 0.171 1.213 0.225 0.207 0.210
## GRIT__1.GROUP1 0.470 1.197 0.393 0.694 0.470 0.067
##
## Covariances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_GRIT ~~
## SCALED_3_GRIT 0.034 0.124 0.278 0.781 0.034 0.044
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_GRIT 0.173 0.136 1.272 0.203 0.173 0.172
## SCALED_3_GRIT -0.044 0.199 -0.220 0.826 -0.044 -0.044
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_GRIT 0.638 0.115 5.565 0.000 0.638 0.635
## SCALED_3_GRIT 0.945 0.195 4.847 0.000 0.945 0.959
##
## R-Square:
## Estimate
## SCALED_2_GRIT 0.365
## SCALED_3_GRIT 0.041## [[1]]
## [[1]][[1]]
## Item β SE z p 90% CI
## 1 Intercept 0.17 0.14 1.27 0.20 -0.09, 0.44
## 2 GROUP1 -0.41 0.20 -2.02 0.04 -0.8, -0.01
## 3 Grit 0.56 0.11 5.19 0.00 0.35, 0.77
## 4 Interaction w/ Group -0.60 0.78 -0.76 0.45 -2.13, 0.94
##
## [[1]][[2]]
## Item β SE z p 90% CI
## 1 Intercept -0.04 0.20 -0.22 0.83 -0.43, 0.35
## 2 GROUP1 0.02 0.29 0.06 0.95 -0.55, 0.59
## 3 Grit 0.21 0.17 1.21 0.23 -0.13, 0.54
## 4 Interaction w/ Group 0.47 1.20 0.39 0.69 -1.88, 2.82
##
##
## [[2]]
## levels Treat_T2 Control_T2 Treat_T3 Control_T3
## 1 -1 -0.20 -0.39 -1.13 -0.25
## 2 0 -0.23 0.17 -0.45 -0.04
## 3 1 -0.27 0.73 0.23 0.16plots(modelname = modelGRIT_interaction, fitname = fit, data = adult2, interactionterm = "GRIT_mean_1.GROUP1", new_scale_name = "Grit Scale (Duckworth)", , time2 = "T1-T2", time3 = "T1-T3") ## Saving 7 x 5 in image
## Saving 7 x 5 in image
#Time 3 as a result of time 1 and 2
results(modelname = modelGRIT_interaction_T3, fitname = fit, data = adult2, interactionterm = "GRIT_mean_1.GROUP1", current_scale_name = "GRIT", new_scale_name = "Grit")## lavaan (0.5-20) converged normally after 34 iterations
##
## Number of observations 89
##
## Number of missing patterns 5
##
## Estimator ML
## Minimum Function Test Statistic 0.000
## Degrees of freedom 0
##
## Model test baseline model:
##
## Minimum Function Test Statistic 27.606
## Degrees of freedom 7
## 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) -281.905
## Loglikelihood unrestricted model (H1) -281.905
##
## Number of free parameters 11
## Akaike (AIC) 585.810
## Bayesian (BIC) 613.185
## Sample-size adjusted Bayesian (BIC) 578.471
##
## 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_GRIT ~
## GROUP1 -0.406 0.201 -2.018 0.044 -0.406 -0.203
## SCALED_1_GRIT 0.561 0.108 5.186 0.000 0.561 0.563
## GRIT__1.GROUP1 -0.597 0.782 -0.763 0.445 -0.597 -0.085
## SCALED_3_GRIT ~
## GROUP1 0.040 0.297 0.135 0.892 0.040 0.020
## SCALED_1_GRIT 0.177 0.212 0.837 0.403 0.177 0.180
## GRIT__1.GROUP1 0.502 1.204 0.417 0.676 0.502 0.072
## SCALED_2_GRIT 0.054 0.194 0.279 0.780 0.054 0.055
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_GRIT 0.173 0.136 1.272 0.203 0.173 0.172
## SCALED_3_GRIT -0.053 0.199 -0.267 0.790 -0.053 -0.054
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_GRIT 0.638 0.115 5.565 0.000 0.638 0.635
## SCALED_3_GRIT 0.943 0.195 4.845 0.000 0.943 0.957
##
## R-Square:
## Estimate
## SCALED_2_GRIT 0.365
## SCALED_3_GRIT 0.043## [[1]]
## [[1]][[1]]
## Item β SE z p 90% CI
## 1 Intercept 0.17 0.14 1.27 0.20 -0.09, 0.44
## 2 GROUP1 -0.41 0.20 -2.02 0.04 -0.8, -0.01
## 3 Grit 0.56 0.11 5.19 0.00 0.35, 0.77
## 4 Interaction w/ Group -0.60 0.78 -0.76 0.45 -2.13, 0.94
##
## [[1]][[2]]
## Item β SE z p 90% CI
## 1 Intercept -0.05 0.20 -0.27 0.79 -0.44, 0.34
## 2 GROUP1 0.04 0.30 0.14 0.89 -0.54, 0.62
## 3 Grit 0.18 0.21 0.84 0.40 -0.24, 0.59
## 4 Interaction w/ Group 0.50 1.20 0.42 0.68 -1.86, 2.86
##
##
## [[2]]
## levels Treat_T2 Control_T2 Treat_T3 Control_T3
## 1 -1 -0.20 -0.39 -1.14 -0.23
## 2 0 -0.23 0.17 -0.46 -0.05
## 3 1 -0.27 0.73 0.22 0.12plots(modelname = modelGRIT_interaction_T3, fitname = fit, data = adult2, interactionterm = "GRIT_mean_1.GROUP1", new_scale_name = "Grit Scale (Duckworth)", time2 = "T1-T2", time3 = "T1-T2-T3") ## Saving 7 x 5 in image
## Saving 7 x 5 in image
#########################################
################# Lonely #################
#########################################
results(modelname = modelPERMA_Lonely_interaction, fitname = fit, data = adult2, interactionterm = "PERMA_Lonely_mean_1.GROUP1", current_scale_name = "PERMA_Lonely", new_scale_name = "Perma Lonely")## lavaan (0.5-20) converged normally after 21 iterations
##
## Number of observations 89
##
## Number of missing patterns 5
##
## Estimator ML
## Minimum Function Test Statistic 0.000
## Degrees of freedom 0
##
## Model test baseline model:
##
## Minimum Function Test Statistic 59.551
## Degrees of freedom 7
## 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) -464.622
## Loglikelihood unrestricted model (H1) -464.622
##
## Number of free parameters 11
## Akaike (AIC) 951.243
## Bayesian (BIC) 978.618
## Sample-size adjusted Bayesian (BIC) 943.904
##
## 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_PERMA_Lonely ~
## GROUP1 -0.181 0.205 -0.887 0.375 -0.181
## SCALED_1_PERMA 0.555 0.105 5.279 0.000 0.555
## PERMA_L__1.GRO 0.094 0.081 1.162 0.245 0.094
## SCALED_3_PERMA_Lonely ~
## GROUP1 -0.295 0.222 -1.330 0.184 -0.295
## SCALED_1_PERMA 0.630 0.116 5.420 0.000 0.630
## PERMA_L__1.GRO -0.152 0.092 -1.654 0.098 -0.152
## Std.all
##
## -0.091
## 0.556
## 0.124
##
## -0.146
## 0.623
## -0.199
##
## Covariances:
## Estimate Std.Err Z-value P(>|z|) Std.lv
## SCALED_2_PERMA_Lonely ~~
## SCALED_3_PERMA 0.270 0.092 2.921 0.003 0.270
## Std.all
##
## 0.444
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_PERMA 0.141 0.137 1.026 0.305 0.141 0.141
## SCALED_3_PERMA 0.213 0.150 1.417 0.157 0.213 0.211
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_PERMA 0.666 0.118 5.655 0.000 0.666 0.668
## SCALED_3_PERMA 0.555 0.114 4.880 0.000 0.555 0.544
##
## R-Square:
## Estimate
## SCALED_2_PERMA 0.332
## SCALED_3_PERMA 0.456## [[1]]
## [[1]][[1]]
## Item β SE z p 90% CI
## 1 Intercept 0.14 0.14 1.03 0.30 -0.13, 0.41
## 2 GROUP1 -0.18 0.20 -0.89 0.38 -0.58, 0.22
## 3 Perma Lonely 0.56 0.11 5.28 0.00 0.35, 0.76
## 4 Interaction w/ Group 0.09 0.08 1.16 0.25 -0.06, 0.25
##
## [[1]][[2]]
## Item β SE z p 90% CI
## 1 Intercept 0.21 0.15 1.42 0.16 -0.08, 0.51
## 2 GROUP1 -0.29 0.22 -1.33 0.18 -0.73, 0.14
## 3 Perma Lonely 0.63 0.12 5.42 0.00 0.4, 0.86
## 4 Interaction w/ Group -0.15 0.09 -1.65 0.10 -0.33, 0.03
##
##
## [[2]]
## levels Treat_T2 Control_T2 Treat_T3 Control_T3
## 1 -1 -0.69 -0.41 -0.45 -0.42
## 2 0 -0.04 0.14 0.03 0.21
## 3 1 0.61 0.70 0.51 0.84plots(modelname = modelPERMA_Lonely_interaction, fitname = fit, data = adult2, interactionterm = "PERMA_Lonely_mean_1.GROUP1", new_scale_name = "Perma Lonely", time2 = "T1-T2", time3 = "T1-T3") ## Saving 7 x 5 in image
## Saving 7 x 5 in image
#Time 3 as a result of time 1 and 2
results(modelname = modelPERMA_Lonely_interaction_T3, fitname = fit, data = adult2, interactionterm = "PERMA_Lonely_mean_1.GROUP1", current_scale_name = "PERMA_Lonely", new_scale_name = "Perma Lonely")## lavaan (0.5-20) converged normally after 16 iterations
##
## Number of observations 89
##
## Number of missing patterns 5
##
## Estimator ML
## Minimum Function Test Statistic 0.000
## Degrees of freedom 0
##
## Model test baseline model:
##
## Minimum Function Test Statistic 59.551
## Degrees of freedom 7
## 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) -464.622
## Loglikelihood unrestricted model (H1) -464.622
##
## Number of free parameters 11
## Akaike (AIC) 951.243
## Bayesian (BIC) 978.618
## Sample-size adjusted Bayesian (BIC) 943.904
##
## 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_PERMA_Lonely ~
## GROUP1 -0.181 0.205 -0.887 0.375 -0.181
## SCALED_1_PERMA 0.555 0.105 5.279 0.000 0.555
## PERMA_L__1.GRO 0.094 0.081 1.162 0.245 0.094
## SCALED_3_PERMA_Lonely ~
## GROUP1 -0.221 0.214 -1.031 0.302 -0.221
## SCALED_1_PERMA 0.405 0.126 3.224 0.001 0.405
## PERMA_L__1.GRO -0.191 0.086 -2.214 0.027 -0.191
## SCALED_2_PERMA 0.406 0.117 3.473 0.001 0.406
## Std.all
##
## -0.091
## 0.556
## 0.124
##
## -0.109
## 0.400
## -0.249
## 0.401
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_PERMA 0.141 0.137 1.026 0.305 0.141 0.141
## SCALED_3_PERMA 0.156 0.146 1.071 0.284 0.156 0.154
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_PERMA 0.666 0.118 5.655 0.000 0.666 0.668
## SCALED_3_PERMA 0.445 0.097 4.597 0.000 0.445 0.437
##
## R-Square:
## Estimate
## SCALED_2_PERMA 0.332
## SCALED_3_PERMA 0.563## [[1]]
## [[1]][[1]]
## Item β SE z p 90% CI
## 1 Intercept 0.14 0.14 1.03 0.30 -0.13, 0.41
## 2 GROUP1 -0.18 0.20 -0.89 0.38 -0.58, 0.22
## 3 Perma Lonely 0.56 0.11 5.28 0.00 0.35, 0.76
## 4 Interaction w/ Group 0.09 0.08 1.16 0.25 -0.06, 0.25
##
## [[1]][[2]]
## Item β SE z p 90% CI
## 1 Intercept 0.16 0.15 1.07 0.28 -0.13, 0.44
## 2 GROUP1 -0.22 0.21 -1.03 0.30 -0.64, 0.2
## 3 Perma Lonely 0.40 0.13 3.22 0.00 0.16, 0.65
## 4 Interaction w/ Group -0.19 0.09 -2.21 0.03 -0.36, -0.02
##
##
## [[2]]
## levels Treat_T2 Control_T2 Treat_T3 Control_T3
## 1 -1 -0.69 -0.41 -0.24 -0.25
## 2 0 -0.04 0.14 -0.03 0.16
## 3 1 0.61 0.70 0.19 0.56plots(modelname = modelPERMA_Lonely_interaction_T3, fitname = fit, data = adult2, interactionterm = "PERMA_Lonely_mean_1.GROUP1", new_scale_name = "Perma Lonely", time2 = "T1-T2", time3 = "T1-T2-T3") ## Saving 7 x 5 in image
## Saving 7 x 5 in image
#########################################
################# Happy #################
#########################################
results(modelname = modelPERMA_Happy_interaction, fitname = fit, data = adult2, interactionterm = "PERMA_Happy_mean_1.GROUP1", current_scale_name = "PERMA_Happy", new_scale_name = "Perma Happy")## lavaan (0.5-20) converged normally after 19 iterations
##
## Number of observations 89
##
## Number of missing patterns 5
##
## Estimator ML
## Minimum Function Test Statistic 0.000
## Degrees of freedom 0
##
## Model test baseline model:
##
## Minimum Function Test Statistic 81.680
## Degrees of freedom 7
## 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) -425.527
## Loglikelihood unrestricted model (H1) -425.527
##
## Number of free parameters 11
## Akaike (AIC) 873.054
## Bayesian (BIC) 900.429
## Sample-size adjusted Bayesian (BIC) 865.715
##
## 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_PERMA_Happy ~
## GROUP1 0.526 0.184 2.861 0.004 0.526
## SCALED_1_PERMA 0.606 0.099 6.116 0.000 0.606
## PERMA_H__1.GRO -0.171 0.103 -1.649 0.099 -0.171
## SCALED_3_PERMA_Happy ~
## GROUP1 0.500 0.218 2.294 0.022 0.500
## SCALED_1_PERMA 0.531 0.124 4.280 0.000 0.531
## PERMA_H__1.GRO -0.278 0.132 -2.112 0.035 -0.278
## Std.all
##
## 0.265
## 0.605
## -0.164
##
## 0.250
## 0.526
## -0.266
##
## Covariances:
## Estimate Std.Err Z-value P(>|z|) Std.lv
## SCALED_2_PERMA_Happy ~~
## SCALED_3_PERMA 0.343 0.097 3.533 0.000 0.343
## Std.all
##
## 0.605
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_PERMA -0.332 0.123 -2.691 0.007 -0.332 -0.335
## SCALED_3_PERMA -0.375 0.152 -2.469 0.014 -0.375 -0.375
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_PERMA 0.539 0.100 5.381 0.000 0.539 0.547
## SCALED_3_PERMA 0.598 0.122 4.889 0.000 0.598 0.599
##
## R-Square:
## Estimate
## SCALED_2_PERMA 0.453
## SCALED_3_PERMA 0.401## [[1]]
## [[1]][[1]]
## Item β SE z p 90% CI
## 1 Intercept -0.33 0.12 -2.69 0.01 -0.57, -0.09
## 2 GROUP1 0.53 0.18 2.86 0.00 0.17, 0.89
## 3 Perma Happy 0.61 0.10 6.12 0.00 0.41, 0.8
## 4 Interaction w/ Group -0.17 0.10 -1.65 0.10 -0.37, 0.03
##
## [[1]][[2]]
## Item β SE z p 90% CI
## 1 Intercept -0.37 0.15 -2.47 0.01 -0.67, -0.08
## 2 GROUP1 0.50 0.22 2.29 0.02 0.07, 0.93
## 3 Perma Happy 0.53 0.12 4.28 0.00 0.29, 0.77
## 4 Interaction w/ Group -0.28 0.13 -2.11 0.03 -0.54, -0.02
##
##
## [[2]]
## levels Treat_T2 Control_T2 Treat_T3 Control_T3
## 1 -1 -0.24 -0.94 -0.10 -0.91
## 2 0 0.19 -0.33 0.15 -0.37
## 3 1 0.63 0.27 0.40 0.16plots(modelname = modelPERMA_Happy_interaction, fitname = fit, data = adult2, interactionterm = "PERMA_Happy_mean_1.GROUP1", new_scale_name = "Perma Happy", time2 = "T1-T2", time3 = "T1-T3") ## Saving 7 x 5 in image
## Saving 7 x 5 in image
#Time 3 as a result of time 1 and 2
results(modelname = modelPERMA_Happy_interaction_T3, fitname = fit, data = adult2, interactionterm = "PERMA_Happy_mean_1.GROUP1", current_scale_name = "PERMA_Happy", new_scale_name = "Perma Happy")## lavaan (0.5-20) converged normally after 17 iterations
##
## Number of observations 89
##
## Number of missing patterns 5
##
## Estimator ML
## Minimum Function Test Statistic 0.000
## Degrees of freedom 0
##
## Model test baseline model:
##
## Minimum Function Test Statistic 81.680
## Degrees of freedom 7
## 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) -425.527
## Loglikelihood unrestricted model (H1) -425.527
##
## Number of free parameters 11
## Akaike (AIC) 873.054
## Bayesian (BIC) 900.429
## Sample-size adjusted Bayesian (BIC) 865.715
##
## 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_PERMA_Happy ~
## GROUP1 0.526 0.184 2.861 0.004 0.526
## SCALED_1_PERMA 0.606 0.099 6.116 0.000 0.606
## PERMA_H__1.GRO -0.171 0.103 -1.649 0.099 -0.171
## SCALED_3_PERMA_Happy ~
## GROUP1 0.165 0.206 0.801 0.423 0.165
## SCALED_1_PERMA 0.145 0.139 1.042 0.297 0.145
## PERMA_H__1.GRO -0.170 0.135 -1.261 0.207 -0.170
## SCALED_2_PERMA 0.637 0.131 4.864 0.000 0.637
## Std.all
##
## 0.265
## 0.605
## -0.164
##
## 0.082
## 0.144
## -0.162
## 0.633
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_PERMA -0.332 0.123 -2.691 0.007 -0.332 -0.335
## SCALED_3_PERMA -0.163 0.142 -1.150 0.250 -0.163 -0.163
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_PERMA 0.539 0.100 5.381 0.000 0.539 0.547
## SCALED_3_PERMA 0.379 0.090 4.237 0.000 0.379 0.380
##
## R-Square:
## Estimate
## SCALED_2_PERMA 0.453
## SCALED_3_PERMA 0.620## [[1]]
## [[1]][[1]]
## Item β SE z p 90% CI
## 1 Intercept -0.33 0.12 -2.69 0.01 -0.57, -0.09
## 2 GROUP1 0.53 0.18 2.86 0.00 0.17, 0.89
## 3 Perma Happy 0.61 0.10 6.12 0.00 0.41, 0.8
## 4 Interaction w/ Group -0.17 0.10 -1.65 0.10 -0.37, 0.03
##
## [[1]][[2]]
## Item β SE z p 90% CI
## 1 Intercept -0.16 0.14 -1.15 0.25 -0.44, 0.12
## 2 GROUP1 0.16 0.21 0.80 0.42 -0.24, 0.57
## 3 Perma Happy 0.14 0.14 1.04 0.30 -0.13, 0.42
## 4 Interaction w/ Group -0.17 0.13 -1.26 0.21 -0.43, 0.09
##
##
## [[2]]
## levels Treat_T2 Control_T2 Treat_T3 Control_T3
## 1 -1 -0.24 -0.94 0.39 -0.31
## 2 0 0.19 -0.33 0.36 -0.16
## 3 1 0.63 0.27 0.34 -0.02plots(modelname = modelPERMA_Happy_interaction_T3, fitname = fit, data = adult2, interactionterm = "PERMA_Happy_mean_1.GROUP1", new_scale_name = "Perma Happy", time2 = "T1-T2", time3 = "T1-T2-T3") ## Saving 7 x 5 in image
## Saving 7 x 5 in image