Plots and results adults study
Levi Brackman
August 16, 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 objects are masked from 'package:dplyr':
as_data_frame, data_frame, data_frame_, frame_data, glimpse,
knit_print.trunc_mat, tbl_df, tibble, trunc_mat, type_sumRead-in the dataset
Read data
setwd("~/Documents/stats_march_2016/Adult_study Data_Analysis")
adult2<-read.csv("adult2withscaled.csv")
dim(adult2)## [1] 89 137Fits 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 + GROUP2 + SCALED_1_NewPurpose + NewPurpose_mean_1.GROUP1
SCALED_3_NewPurpose ~ GROUP1 + GROUP2 + 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 + GROUP2 + SCALED_1_NewPurpose + NewPurpose_mean_1.GROUP1
SCALED_3_NewPurpose ~ GROUP1 + GROUP2 + 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 + GROUP2 + SCALED_1_MLQP + MLQ_mean_1.GROUP1
SCALED_3_MLQP ~ GROUP1 + GROUP2 + 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 + GROUP2 + SCALED_1_MLQP+ MLQ_mean_1.GROUP1
SCALED_3_MLQP ~ GROUP1 + GROUP2 + 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 + GROUP2 + SCALED_1_MLQS + MLQS_mean_1.GROUP1
SCALED_3_MLQS ~ GROUP1 + GROUP2 + 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 + GROUP2 + SCALED_1_MLQS+ MLQS_mean_1.GROUP1
SCALED_3_MLQS ~ GROUP1 + GROUP2 + 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 + GROUP2 + SCALED_1_PERMA_Happy + PERMA_Happy_mean_1.GROUP1
SCALED_3_PERMA_Happy ~ GROUP1 + GROUP2 + 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 + GROUP2 + SCALED_1_PERMA_Happy + PERMA_Happy_mean_1.GROUP1
SCALED_3_PERMA_Happy ~ GROUP1 + GROUP2 + 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 + GROUP2 + SCALED_1_PERMA_Lonely + PERMA_Lonely_mean_1.GROUP1
SCALED_3_PERMA_Lonely ~ GROUP1 + GROUP2 + 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 + GROUP2 + SCALED_1_PERMA_Lonely + PERMA_Lonely_mean_1.GROUP1
SCALED_3_PERMA_Lonely ~ GROUP1 + GROUP2 + 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 + GROUP2 + SCALED_1_GRIT +GRIT_mean_1.GROUP1
SCALED_3_GRIT ~ GROUP1 + GROUP2 + 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 + GROUP2 + SCALED_1_GRIT +GRIT_mean_1.GROUP1
SCALED_3_GRIT ~ GROUP1 + GROUP2 + 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 + GROUP2 + SCALED_1_Res +Res_mean_1.GROUP1
SCALED_3_Res ~ GROUP1 + GROUP2 + 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 + GROUP2 + SCALED_1_Res +Res_mean_1.GROUP1
SCALED_3_Res ~ GROUP1 + GROUP2 + 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 + GROUP2 + SCALED_1_APSI + PurposeAPSI_mean_1.GROUP1
SCALED_3_APSI ~ GROUP1 + GROUP2 + 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 + GROUP2 + SCALED_1_APSI + PurposeAPSI_mean_1.GROUP1
SCALED_3_APSI ~ GROUP1 + GROUP2 + 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 + GROUP2 + SCALED_1_PWB + PurposePWB_mean_1.GROUP1
SCALED_3_PWB ~ GROUP1 + GROUP2 + 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 + GROUP2 + SCALED_1_PWB + PurposePWB_mean_1.GROUP1
SCALED_3_PWB ~ GROUP1 + GROUP2 + 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 + GROUP2 + SCALED_1_Optimism + Optimism_mean_1.GROUP1
SCALED_3_Optimism ~ GROUP1 + GROUP2 + 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 + GROUP2 + SCALED_1_Optimism + Optimism_mean_1.GROUP1
SCALED_3_Optimism ~ GROUP1 + GROUP2 + 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 + GROUP2 + SCALED_1_LS +LifeSatisfaction_mean_1.GROUP1
SCALED_3_LS ~ GROUP1 + GROUP2 + 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 + GROUP2 + SCALED_1_LS +LifeSatisfaction_mean_1.GROUP1
SCALED_3_LS ~ GROUP1 + GROUP2 + 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 + GROUP2 + SCALED_1_Engagement + Engagement_mean_1.GROUP1
SCALED_3_Engagement ~ GROUP1 + GROUP2 + 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 + GROUP2 + SCALED_1_Engagement + Engagement_mean_1.GROUP1
SCALED_3_Engagement ~ GROUP1 + GROUP2 + 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 + GROUP2 + SCALED_1_Rrealtionships + Relationships_mean_1.GROUP1
SCALED_3_Rrealtionships ~ GROUP1 + GROUP2 + 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 + GROUP2 + SCALED_1_Rrealtionships +Relationships_mean_1.GROUP1
SCALED_3_Rrealtionships ~ GROUP1 + GROUP2 + 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 + GROUP2 + SCALED_1_Nagative + Negative_mean_1.GROUP1
SCALED_3_Nagative ~ GROUP1 + GROUP2 + 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 + GROUP2 + SCALED_1_Nagative + Negative_mean_1.GROUP1
SCALED_3_Nagative ~ GROUP1 + GROUP2 + 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 + GROUP2 + SCALED_1_Acheivement + Acheivement_mean_1.GROUP1
SCALED_3_Acheivement ~ GROUP1 + GROUP2 + 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 + GROUP2 + SCALED_1_ Acheivement + Acheivement_mean_1.GROUP1
SCALED_3_Acheivement ~ GROUP1 + GROUP2 + 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 + GROUP2 + SCALED_1_ Positive + Positive_mean_1.GROUP1
SCALED_3_Positive ~ GROUP1 + GROUP2 + 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 + GROUP2 + SCALED_1_Positive + Positive_mean_1.GROUP1
SCALED_3_Positive ~ GROUP1 + GROUP2 + 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 + GROUP2 + SCALED_1_LET + LifeEngagement_mean_1.GROUP1
SCALED_3_LET ~ GROUP1 + GROUP2 + 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 + GROUP2 + SCALED_1_LET + LifeEngagement_mean_1.GROUP1
SCALED_3_LET ~ GROUP1 + GROUP2 + 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 + GROUP2 + SCALED_1_PERMA_Happy + PERMA_Happy_mean_1.GROUP1
SCALED_3_PERMA_Happy ~ GROUP1 + GROUP2 + 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 + GROUP2 + SCALED_1_PERMA_Happy + PERMA_Happy_mean_1.GROUP1
SCALED_3_PERMA_Happy ~ GROUP1 + GROUP2 + 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 + GROUP2 + SCALED_1_PERMA_Lonely + PERMA_Lonely_mean_1.GROUP1
SCALED_3_PERMA_Lonely ~ GROUP1 + GROUP2 + 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 + GROUP2 + SCALED_1_PERMA_Lonely + PERMA_Lonely_mean_1.GROUP1
SCALED_3_PERMA_Lonely ~ GROUP1 + GROUP2 + 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(22, 1:4, 23, 5:8))
lolollol<-rename(lolollol, Item = rhs, β = est, SE = se, p = pvalue, "90% CI" = CI)
lolollol[1,1] <- sprintf('Intercept', lolollol[1,1])
lolollol[6,1] <- 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, 1:5)
lolollol2<-slice(lolollol, 6:10)
lolollol<-list(lolollol1,lolollol2)
lolollol
}
library(ggplot2)
PlotInter<-function(fitname, PlotTitle){
fit<-(parameterEstimates(fitname))
Treat=1
Control=0
Group0 <-fit[22,4]
Group1<- fit[1,4]
T1 = fit[3,4]
interaction1 = fit[4,4]
Group0_2 = fit[23,4]
Group2 = fit[5,4]
T1_2 = fit[7,4]
interaction2 = fit[8,4]
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 = "Time 2", 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 = "Time 3", x = "Levels Pre in SD", y="Levels Post in SD", lty = "Lines" ))
library(gridExtra)
grid.arrange(Plot1, Plot2, ncol=2, top = PlotTitle)}
SimpleSlopes<-function(fitName){
fit<-(parameterEstimates(fitName))
Treat=1
Control=0
Group0 <-fit[22,4]
Group1<- fit[1,4]
T1 = fit[3,4]
interaction1 = fit[4,4]
Group0_2 = fit[23,4]
Group2 = fit[5,4]
T1_2 = fit[7,4]
interaction2 = fit[8,4]
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){
fitname<-sem(modelname, data, missing='fiml', meanstructure=TRUE,fixed.x=T)
PlotInter(fitname, new_scale_name)
}Fits
#########################################
################# New Purpose ###########
#########################################
library(purrr) ##
## Attaching package: 'purrr'## The following object is masked from 'package:dplyr':
##
## 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 35 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 106.890
## Degrees of freedom 9
## P-value 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -330.990
## Loglikelihood unrestricted model (H1) -330.990
##
## Number of free parameters 13
## Akaike (AIC) 687.980
## Bayesian (BIC) 720.332
## Sample-size adjusted Bayesian (BIC) 679.307
##
## 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 0.699 0.137 5.086 0.000 0.699
## GROUP2 0.055 0.389 0.141 0.888 0.055
## SCALED_1_NwPrp 0.815 0.072 11.367 0.000 0.815
## NwPr__1.GROUP1 -0.530 0.156 -3.396 0.001 -0.530
## SCALED_3_NewPurpose ~
## GROUP1 0.646 0.249 2.597 0.009 0.646
## GROUP2 0.724 0.826 0.877 0.380 0.724
## SCALED_1_NwPrp 0.710 0.137 5.167 0.000 0.710
## NwPr__1.GROUP1 -0.553 0.298 -1.856 0.063 -0.553
## Std.all
##
## 0.342
## 0.011
## 0.794
## -0.237
##
## 0.291
## 0.136
## 0.636
## -0.227
##
## Covariances:
## Estimate Std.Err Z-value P(>|z|) Std.lv
## SCALED_2_NewPurpose ~~
## SCALED_3_NwPrp 0.177 0.074 2.397 0.017 0.177
## Std.all
##
## 0.414
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_NwPrp -0.478 0.093 -5.131 0.000 -0.478 -0.469
## SCALED_3_NwPrp -0.589 0.187 -3.156 0.002 -0.589 -0.533
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_NwPrp 0.285 0.051 5.581 0.000 0.285 0.275
## SCALED_3_NwPrp 0.645 0.134 4.805 0.000 0.645 0.527
##
## R-Square:
## Estimate
## SCALED_2_NwPrp 0.725
## SCALED_3_NwPrp 0.473## [[1]]
## [[1]][[1]]
## Item β SE z p 90% CI
## 1 Intercept -0.48 0.09 -5.13 0.00 -0.66, -0.3
## 2 GROUP1 0.70 0.14 5.09 0.00 0.43, 0.97
## 3 GROUP2 0.05 0.39 0.14 0.89 -0.71, 0.82
## 4 New Purpose 0.81 0.07 11.37 0.00 0.67, 0.96
## 5 Interaction w/ Group -0.53 0.16 -3.40 0.00 -0.84, -0.22
##
## [[1]][[2]]
## Item β SE z p 90% CI
## 1 Intercept -0.59 0.19 -3.16 0.00 -0.96, -0.22
## 2 GROUP1 0.65 0.25 2.60 0.01 0.16, 1.13
## 3 GROUP2 0.72 0.83 0.88 0.38 -0.89, 2.34
## 4 New Purpose 0.71 0.14 5.17 0.00 0.44, 0.98
## 5 Interaction w/ Group -0.55 0.30 -1.86 0.06 -1.14, 0.03
##
##
## [[2]]
## levels Treat_T2 Control_T2 Treat_T3 Control_T3
## 1 -1 -0.06 -1.29 -0.05 -1.30
## 2 0 0.22 -0.48 0.11 -0.59
## 3 1 0.51 0.34 0.27 0.12plots(modelname = modelNewPurpose_interaction, fitname = fitNewPurpose1, data = adult2, interactionterm = "NewPurpose_mean_1.GROUP1", current_scale_name = "NewPurpose", new_scale_name = "New Purpose")##
## Attaching package: 'gridExtra'## The following object is masked from 'package:dplyr':
##
## combine
#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 35 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 106.890
## Degrees of freedom 9
## P-value 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -330.990
## Loglikelihood unrestricted model (H1) -330.990
##
## Number of free parameters 13
## Akaike (AIC) 687.980
## Bayesian (BIC) 720.332
## Sample-size adjusted Bayesian (BIC) 679.307
##
## 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 0.699 0.137 5.086 0.000 0.699
## GROUP2 0.055 0.389 0.141 0.888 0.055
## SCALED_1_NwPrp 0.815 0.072 11.367 0.000 0.815
## NwPr__1.GROUP1 -0.530 0.156 -3.396 0.001 -0.530
## SCALED_3_NewPurpose ~
## GROUP1 0.211 0.275 0.767 0.443 0.211
## GROUP2 0.690 0.857 0.806 0.420 0.690
## SCALED_1_NwPrp 0.203 0.214 0.949 0.343 0.203
## NwPr__1.GROUP1 -0.223 0.297 -0.751 0.452 -0.223
## SCALED_2_NwPrp 0.622 0.228 2.734 0.006 0.622
## Std.all
##
## 0.342
## 0.011
## 0.794
## -0.237
##
## 0.095
## 0.129
## 0.182
## -0.092
## 0.573
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_NwPrp -0.478 0.093 -5.131 0.000 -0.478 -0.469
## SCALED_3_NwPrp -0.292 0.202 -1.444 0.149 -0.292 -0.264
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_NwPrp 0.285 0.051 5.581 0.000 0.285 0.275
## SCALED_3_NwPrp 0.535 0.116 4.627 0.000 0.535 0.437
##
## R-Square:
## Estimate
## SCALED_2_NwPrp 0.725
## SCALED_3_NwPrp 0.563## [[1]]
## [[1]][[1]]
## Item β SE z p 90% CI
## 1 Intercept -0.48 0.09 -5.13 0.00 -0.66, -0.3
## 2 GROUP1 0.70 0.14 5.09 0.00 0.43, 0.97
## 3 GROUP2 0.05 0.39 0.14 0.89 -0.71, 0.82
## 4 New Purpose 0.81 0.07 11.37 0.00 0.67, 0.96
## 5 Interaction w/ Group -0.53 0.16 -3.40 0.00 -0.84, -0.22
##
## [[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 0.21 0.28 0.77 0.44 -0.33, 0.75
## 3 GROUP2 0.69 0.86 0.81 0.42 -0.99, 2.37
## 4 New Purpose 0.20 0.21 0.95 0.34 -0.22, 0.62
## 5 Interaction w/ Group -0.22 0.30 -0.75 0.45 -0.81, 0.36
##
##
## [[2]]
## levels Treat_T2 Control_T2 Treat_T3 Control_T3
## 1 -1 -0.06 -1.29 0.43 -0.49
## 2 0 0.22 -0.48 0.41 -0.29
## 3 1 0.51 0.34 0.39 -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")
#########################################
################# 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 29 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 124.231
## Degrees of freedom 9
## P-value 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -358.423
## Loglikelihood unrestricted model (H1) -358.423
##
## Number of free parameters 13
## Akaike (AIC) 742.845
## Bayesian (BIC) 775.197
## Sample-size adjusted Bayesian (BIC) 734.172
##
## 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 0.777 0.148 5.266 0.000 0.777 0.376
## GROUP2 0.267 0.420 0.635 0.525 0.267 0.054
## SCALED_1_MLQP 0.798 0.076 10.549 0.000 0.798 0.770
## MLQ_m_1.GROUP1 -0.262 0.111 -2.366 0.018 -0.262 -0.174
## SCALED_3_MLQP ~
## GROUP1 0.840 0.188 4.468 0.000 0.840 0.361
## GROUP2 0.653 0.641 1.019 0.308 0.653 0.116
## SCALED_1_MLQP 0.888 0.108 8.215 0.000 0.888 0.761
## MLQ_m_1.GROUP1 -0.553 0.156 -3.549 0.000 -0.553 -0.327
##
## Covariances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_MLQP ~~
## SCALED_3_MLQP 0.182 0.062 2.939 0.003 0.182 0.505
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_MLQP -0.519 0.100 -5.197 0.000 -0.519 -0.503
## SCALED_3_MLQP -0.745 0.136 -5.468 0.000 -0.745 -0.641
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_MLQP 0.330 0.061 5.444 0.000 0.330 0.310
## SCALED_3_MLQP 0.395 0.081 4.850 0.000 0.395 0.292
##
## R-Square:
## Estimate
## SCALED_2_MLQP 0.690
## SCALED_3_MLQP 0.708## [[1]]
## [[1]][[1]]
## Item β SE z p 90% CI
## 1 Intercept -0.52 0.10 -5.20 0.00 -0.71, -0.32
## 2 GROUP1 0.78 0.15 5.27 0.00 0.49, 1.07
## 3 GROUP2 0.27 0.42 0.64 0.53 -0.56, 1.09
## 4 MLQ - P 0.80 0.08 10.55 0.00 0.65, 0.95
## 5 Interaction w/ Group -0.26 0.11 -2.37 0.02 -0.48, -0.04
##
## [[1]][[2]]
## Item β SE z p 90% CI
## 1 Intercept -0.74 0.14 -5.47 0.00 -1.01, -0.48
## 2 GROUP1 0.84 0.19 4.47 0.00 0.47, 1.21
## 3 GROUP2 0.65 0.64 1.02 0.31 -0.6, 1.91
## 4 MLQ - P 0.89 0.11 8.22 0.00 0.68, 1.1
## 5 Interaction w/ Group -0.55 0.16 -3.55 0.00 -0.86, -0.25
##
##
## [[2]]
## levels Treat_T2 Control_T2 Treat_T3 Control_T3
## 1 -1 -0.28 -1.32 -0.30 -1.63
## 2 0 0.26 -0.52 0.03 -0.74
## 3 1 0.79 0.28 0.37 0.14plots(modelname = modelMLQP_interaction, fitname = MLQ, data = adult2, interactionterm = "MLQ_mean_1.GROUP1", new_scale_name = "Meaning in Life Questionnaire - P") 
#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 27 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 124.231
## Degrees of freedom 9
## P-value 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -358.423
## Loglikelihood unrestricted model (H1) -358.423
##
## Number of free parameters 13
## Akaike (AIC) 742.845
## Bayesian (BIC) 775.197
## Sample-size adjusted Bayesian (BIC) 734.172
##
## 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 0.777 0.148 5.266 0.000 0.777 0.376
## GROUP2 0.267 0.420 0.635 0.525 0.267 0.054
## SCALED_1_MLQP 0.798 0.076 10.549 0.000 0.798 0.770
## MLQ_m_1.GROUP1 -0.262 0.111 -2.366 0.018 -0.262 -0.174
## SCALED_3_MLQP ~
## GROUP1 0.412 0.209 1.972 0.049 0.412 0.177
## GROUP2 0.506 0.679 0.745 0.457 0.506 0.090
## SCALED_1_MLQP 0.447 0.153 2.930 0.003 0.447 0.383
## MLQ_m_1.GROUP1 -0.408 0.151 -2.706 0.007 -0.408 -0.241
## SCALED_2_MLQP 0.552 0.150 3.682 0.000 0.552 0.490
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_MLQP -0.519 0.100 -5.197 0.000 -0.519 -0.503
## SCALED_3_MLQP -0.458 0.144 -3.177 0.001 -0.458 -0.394
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_MLQP 0.330 0.061 5.444 0.000 0.330 0.310
## SCALED_3_MLQP 0.294 0.067 4.417 0.000 0.294 0.218
##
## R-Square:
## Estimate
## SCALED_2_MLQP 0.690
## SCALED_3_MLQP 0.782## [[1]]
## [[1]][[1]]
## Item β SE z p 90% CI
## 1 Intercept -0.52 0.10 -5.20 0.00 -0.71, -0.32
## 2 GROUP1 0.78 0.15 5.27 0.00 0.49, 1.07
## 3 GROUP2 0.27 0.42 0.64 0.53 -0.56, 1.09
## 4 MLQ - P 0.80 0.08 10.55 0.00 0.65, 0.95
## 5 Interaction w/ Group -0.26 0.11 -2.37 0.02 -0.48, -0.04
##
## [[1]][[2]]
## Item β SE z p 90% CI
## 1 Intercept -0.46 0.14 -3.18 0.00 -0.74, -0.18
## 2 GROUP1 0.41 0.21 1.97 0.05 0, 0.82
## 3 GROUP2 0.51 0.68 0.74 0.46 -0.83, 1.84
## 4 MLQ - P 0.45 0.15 2.93 0.00 0.15, 0.75
## 5 Interaction w/ Group -0.41 0.15 -2.71 0.01 -0.7, -0.11
##
##
## [[2]]
## levels Treat_T2 Control_T2 Treat_T3 Control_T3
## 1 -1 -0.28 -1.32 0.28 -0.90
## 2 0 0.26 -0.52 0.32 -0.46
## 3 1 0.79 0.28 0.36 -0.01plots(modelname = modelMLQP_interaction_T3, fitname = MLQ, data = adult2, interactionterm = "MLQ_mean_1.GROUP1", new_scale_name = "Meaning in Life Questionnaire - P") 
#########################################
################# 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 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 82.096
## Degrees of freedom 9
## P-value 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -393.798
## Loglikelihood unrestricted model (H1) -393.798
##
## Number of free parameters 13
## Akaike (AIC) 813.597
## Bayesian (BIC) 845.949
## Sample-size adjusted Bayesian (BIC) 804.923
##
## 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 -0.045 0.176 -0.255 0.799 -0.045 -0.023
## GROUP2 -0.194 0.511 -0.379 0.705 -0.194 -0.041
## SCALED_1_MLQS 0.684 0.084 8.136 0.000 0.684 0.701
## MLQS__1.GROUP1 0.138 0.108 1.274 0.203 0.138 0.113
## SCALED_3_MLQS ~
## GROUP1 0.076 0.224 0.340 0.734 0.076 0.038
## GROUP2 1.244 0.803 1.551 0.121 1.244 0.255
## SCALED_1_MLQS 0.578 0.116 4.985 0.000 0.578 0.574
## MLQS__1.GROUP1 0.142 0.150 0.950 0.342 0.142 0.113
##
## Covariances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_MLQS ~~
## SCALED_3_MLQS 0.315 0.081 3.880 0.000 0.315 0.576
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_MLQS 0.044 0.117 0.374 0.709 0.044 0.045
## SCALED_3_MLQS -0.016 0.153 -0.104 0.917 -0.016 -0.016
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_MLQS 0.489 0.086 5.693 0.000 0.489 0.509
## SCALED_3_MLQS 0.612 0.119 5.131 0.000 0.612 0.598
##
## R-Square:
## Estimate
## SCALED_2_MLQS 0.491
## SCALED_3_MLQS 0.402## [[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 0.18 -0.25 0.80 -0.39, 0.3
## 3 GROUP2 -0.19 0.51 -0.38 0.70 -1.2, 0.81
## 4 MLQ - S 0.68 0.08 8.14 0.00 0.52, 0.85
## 5 Interaction w/ Group 0.14 0.11 1.27 0.20 -0.07, 0.35
##
## [[1]][[2]]
## Item β SE z p 90% CI
## 1 Intercept -0.02 0.15 -0.10 0.92 -0.32, 0.28
## 2 GROUP1 0.08 0.22 0.34 0.73 -0.36, 0.52
## 3 GROUP2 1.24 0.80 1.55 0.12 -0.33, 2.82
## 4 MLQ - S 0.58 0.12 4.98 0.00 0.35, 0.81
## 5 Interaction w/ Group 0.14 0.15 0.95 0.34 -0.15, 0.44
##
##
## [[2]]
## levels Treat_T2 Control_T2 Treat_T3 Control_T3
## 1 -1 -0.82 -0.64 -0.78 -0.59
## 2 0 0.00 0.04 -0.06 -0.02
## 3 1 0.82 0.73 0.66 0.56plots(modelname = modelMLQS_interaction, fitname = MLQ, data = adult2, interactionterm = "MLQS_mean_1.GROUP1", new_scale_name = "Meaning in Life Questionnaire - S") 
#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 27 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 82.096
## Degrees of freedom 9
## P-value 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -393.798
## Loglikelihood unrestricted model (H1) -393.798
##
## Number of free parameters 13
## Akaike (AIC) 813.597
## Bayesian (BIC) 845.949
## Sample-size adjusted Bayesian (BIC) 804.923
##
## 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 -0.045 0.176 -0.255 0.799 -0.045 -0.023
## GROUP2 -0.194 0.511 -0.379 0.705 -0.194 -0.041
## SCALED_1_MLQS 0.684 0.084 8.136 0.000 0.684 0.701
## MLQS__1.GROUP1 0.138 0.108 1.274 0.203 0.138 0.113
## SCALED_3_MLQS ~
## GROUP1 0.105 0.197 0.533 0.594 0.105 0.052
## GROUP2 1.369 0.865 1.584 0.113 1.369 0.281
## SCALED_1_MLQS 0.137 0.134 1.022 0.307 0.137 0.136
## MLQS__1.GROUP1 0.054 0.134 0.400 0.689 0.054 0.043
## SCALED_2_MLQS 0.645 0.124 5.214 0.000 0.645 0.624
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_MLQS 0.044 0.117 0.374 0.709 0.044 0.045
## SCALED_3_MLQS -0.044 0.138 -0.319 0.750 -0.044 -0.044
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_MLQS 0.489 0.086 5.693 0.000 0.489 0.509
## SCALED_3_MLQS 0.409 0.086 4.771 0.000 0.409 0.400
##
## R-Square:
## Estimate
## SCALED_2_MLQS 0.491
## SCALED_3_MLQS 0.600## [[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 0.18 -0.25 0.80 -0.39, 0.3
## 3 GROUP2 -0.19 0.51 -0.38 0.70 -1.2, 0.81
## 4 MLQ - S 0.68 0.08 8.14 0.00 0.52, 0.85
## 5 Interaction w/ Group 0.14 0.11 1.27 0.20 -0.07, 0.35
##
## [[1]][[2]]
## Item β SE z p 90% CI
## 1 Intercept -0.04 0.14 -0.32 0.75 -0.32, 0.23
## 2 GROUP1 0.11 0.20 0.53 0.59 -0.28, 0.49
## 3 GROUP2 1.37 0.86 1.58 0.11 -0.33, 3.06
## 4 MLQ - S 0.14 0.13 1.02 0.31 -0.13, 0.4
## 5 Interaction w/ Group 0.05 0.13 0.40 0.69 -0.21, 0.32
##
##
## [[2]]
## levels Treat_T2 Control_T2 Treat_T3 Control_T3
## 1 -1 -0.82 -0.64 -0.28 -0.18
## 2 0 0.00 0.04 -0.09 -0.04
## 3 1 0.82 0.73 0.10 0.09plots(modelname = modelMLQS_interaction_T3, fitname = MLQ, data = adult2, interactionterm = "MLQS_mean_1.GROUP1", new_scale_name = "Meaning in Life Questionnaire - S") 
#########################################
################# 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 30 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 91.525
## Degrees of freedom 9
## P-value 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -397.961
## Loglikelihood unrestricted model (H1) -397.961
##
## Number of free parameters 13
## Akaike (AIC) 821.921
## Bayesian (BIC) 854.273
## Sample-size adjusted Bayesian (BIC) 813.248
##
## 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 0.345 0.176 1.962 0.050 0.345
## GROUP2 0.615 0.500 1.231 0.218 0.615
## SCALED_1_Postv 0.643 0.086 7.450 0.000 0.643
## Pstv__1.GROUP1 -0.158 0.095 -1.672 0.094 -0.158
## SCALED_3_Positive ~
## GROUP1 0.538 0.227 2.370 0.018 0.538
## GROUP2 0.896 0.783 1.144 0.253 0.896
## SCALED_1_Postv 0.665 0.135 4.936 0.000 0.665
## Pstv__1.GROUP1 -0.158 0.153 -1.032 0.302 -0.158
## Std.all
##
## 0.175
## 0.130
## 0.648
## -0.145
##
## 0.249
## 0.172
## 0.610
## -0.131
##
## Covariances:
## Estimate Std.Err Z-value P(>|z|) Std.lv
## SCALED_2_Positive ~~
## SCALED_3_Postv 0.320 0.098 3.263 0.001 0.320
## Std.all
##
## 0.609
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_Postv -0.262 0.115 -2.270 0.023 -0.262 -0.266
## SCALED_3_Postv -0.467 0.153 -3.057 0.002 -0.467 -0.433
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_Postv 0.470 0.090 5.198 0.000 0.470 0.488
## SCALED_3_Postv 0.590 0.124 4.754 0.000 0.590 0.506
##
## R-Square:
## Estimate
## SCALED_2_Postv 0.512
## SCALED_3_Postv 0.494## [[1]]
## [[1]][[1]]
## Item β SE z p 90% CI
## 1 Intercept -0.26 0.12 -2.27 0.02 -0.49, -0.04
## 2 GROUP1 0.34 0.18 1.96 0.05 0, 0.69
## 3 GROUP2 0.62 0.50 1.23 0.22 -0.36, 1.59
## 4 Perma - Positive 0.64 0.09 7.45 0.00 0.47, 0.81
## 5 Interaction w/ Group -0.16 0.09 -1.67 0.09 -0.34, 0.03
##
## [[1]][[2]]
## Item β SE z p 90% CI
## 1 Intercept -0.47 0.15 -3.06 0.00 -0.77, -0.17
## 2 GROUP1 0.54 0.23 2.37 0.02 0.09, 0.98
## 3 GROUP2 0.90 0.78 1.14 0.25 -0.64, 2.43
## 4 Perma - Positive 0.67 0.13 4.94 0.00 0.4, 0.93
## 5 Interaction w/ Group -0.16 0.15 -1.03 0.30 -0.46, 0.14
##
##
## [[2]]
## levels Treat_T2 Control_T2 Treat_T3 Control_T3
## 1 -1 -0.40 -0.90 -0.63 -1.13
## 2 0 0.08 -0.26 -0.12 -0.47
## 3 1 0.57 0.38 0.38 0.20plots(modelname = modelPositive_interaction, fitname = fit, data = adult2, interactionterm = "Positive_mean_1.GROUP1", new_scale_name = "Perma Positive Emotion") 
#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 30 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 91.525
## Degrees of freedom 9
## P-value 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -397.961
## Loglikelihood unrestricted model (H1) -397.961
##
## Number of free parameters 13
## Akaike (AIC) 821.921
## Bayesian (BIC) 854.273
## Sample-size adjusted Bayesian (BIC) 813.248
##
## 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 0.345 0.176 1.962 0.050 0.345
## GROUP2 0.615 0.500 1.231 0.218 0.615
## SCALED_1_Postv 0.643 0.086 7.450 0.000 0.643
## Pstv__1.GROUP1 -0.158 0.095 -1.672 0.094 -0.158
## SCALED_3_Positive ~
## GROUP1 0.303 0.214 1.415 0.157 0.303
## GROUP2 0.477 0.854 0.559 0.576 0.477
## SCALED_1_Postv 0.227 0.142 1.592 0.111 0.227
## Pstv__1.GROUP1 -0.050 0.151 -0.329 0.742 -0.050
## SCALED_2_Postv 0.682 0.150 4.532 0.000 0.682
## Std.all
##
## 0.175
## 0.130
## 0.648
## -0.145
##
## 0.140
## 0.092
## 0.208
## -0.041
## 0.620
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_Postv -0.262 0.115 -2.270 0.023 -0.262 -0.266
## SCALED_3_Postv -0.289 0.145 -1.989 0.047 -0.289 -0.268
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_Postv 0.470 0.090 5.198 0.000 0.470 0.488
## SCALED_3_Postv 0.371 0.092 4.022 0.000 0.371 0.319
##
## R-Square:
## Estimate
## SCALED_2_Postv 0.512
## SCALED_3_Postv 0.681## [[1]]
## [[1]][[1]]
## Item β SE z p 90% CI
## 1 Intercept -0.26 0.12 -2.27 0.02 -0.49, -0.04
## 2 GROUP1 0.34 0.18 1.96 0.05 0, 0.69
## 3 GROUP2 0.62 0.50 1.23 0.22 -0.36, 1.59
## 4 Perma - Positive 0.64 0.09 7.45 0.00 0.47, 0.81
## 5 Interaction w/ Group -0.16 0.09 -1.67 0.09 -0.34, 0.03
##
## [[1]][[2]]
## Item β SE z p 90% CI
## 1 Intercept -0.29 0.15 -1.99 0.05 -0.57, 0
## 2 GROUP1 0.30 0.21 1.41 0.16 -0.12, 0.72
## 3 GROUP2 0.48 0.85 0.56 0.58 -1.2, 2.15
## 4 Perma - Positive 0.23 0.14 1.59 0.11 -0.05, 0.51
## 5 Interaction w/ Group -0.05 0.15 -0.33 0.74 -0.35, 0.25
##
##
## [[2]]
## levels Treat_T2 Control_T2 Treat_T3 Control_T3
## 1 -1 -0.40 -0.90 -0.12 -0.52
## 2 0 0.08 -0.26 0.06 -0.29
## 3 1 0.57 0.38 0.23 -0.06plots(modelname = modelPositive_interaction_T3, fitname = fit, data = adult2, interactionterm = "Positive_mean_1.GROUP1", new_scale_name = "Perma Positive Emotion") 
#########################################
############### 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 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 68.912
## Degrees of freedom 9
## P-value 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -406.748
## Loglikelihood unrestricted model (H1) -406.748
##
## Number of free parameters 13
## Akaike (AIC) 839.497
## Bayesian (BIC) 871.849
## Sample-size adjusted Bayesian (BIC) 830.823
##
## 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 0.400 0.193 2.073 0.038 0.400
## GROUP2 0.204 0.552 0.369 0.712 0.204
## SCALED_1_Achvm 0.623 0.100 6.229 0.000 0.623
## Achv__1.GROUP1 -0.294 0.115 -2.561 0.010 -0.294
## SCALED_3_Acheivement ~
## GROUP1 0.773 0.236 3.281 0.001 0.773
## GROUP2 1.039 0.792 1.313 0.189 1.039
## SCALED_1_Achvm 0.692 0.134 5.147 0.000 0.692
## Achv__1.GROUP1 -0.468 0.153 -3.054 0.002 -0.468
## Std.all
##
## 0.201
## 0.042
## 0.619
## -0.257
##
## 0.343
## 0.191
## 0.609
## -0.362
##
## Covariances:
## Estimate Std.Err Z-value P(>|z|) Std.lv
## SCALED_2_Acheivement ~~
## SCALED_3_Achvm 0.283 0.100 2.819 0.005 0.283
## Std.all
##
## 0.481
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_Achvm -0.288 0.129 -2.227 0.026 -0.288 -0.289
## SCALED_3_Achvm -0.598 0.170 -3.525 0.000 -0.598 -0.531
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_Achvm 0.576 0.103 5.615 0.000 0.576 0.582
## SCALED_3_Achvm 0.601 0.125 4.795 0.000 0.601 0.474
##
## R-Square:
## Estimate
## SCALED_2_Achvm 0.418
## SCALED_3_Achvm 0.526## [[1]]
## [[1]][[1]]
## Item β SE z p 90% CI
## 1 Intercept -0.29 0.13 -2.23 0.03 -0.54, -0.03
## 2 GROUP1 0.40 0.19 2.07 0.04 0.02, 0.78
## 3 GROUP2 0.20 0.55 0.37 0.71 -0.88, 1.29
## 4 Perma - Acheivement 0.62 0.10 6.23 0.00 0.43, 0.82
## 5 Interaction w/ Group -0.29 0.11 -2.56 0.01 -0.52, -0.07
##
## [[1]][[2]]
## Item β SE z p 90% CI
## 1 Intercept -0.60 0.17 -3.52 0.00 -0.93, -0.27
## 2 GROUP1 0.77 0.24 3.28 0.00 0.31, 1.23
## 3 GROUP2 1.04 0.79 1.31 0.19 -0.51, 2.59
## 4 Perma - Acheivement 0.69 0.13 5.15 0.00 0.43, 0.96
## 5 Interaction w/ Group -0.47 0.15 -3.05 0.00 -0.77, -0.17
##
##
## [[2]]
## levels Treat_T2 Control_T2 Treat_T3 Control_T3
## 1 -1 -0.22 -0.91 -0.42 -1.29
## 2 0 0.11 -0.29 -0.20 -0.60
## 3 1 0.44 0.33 0.03 0.09plots(modelname = modelAcheivement_interaction, fitname = fit, data = adult2, interactionterm = "Acheivement_mean_1.GROUP1", new_scale_name = "Perma Acheivement") 
#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 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 68.912
## Degrees of freedom 9
## P-value 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -406.748
## Loglikelihood unrestricted model (H1) -406.748
##
## Number of free parameters 13
## Akaike (AIC) 839.497
## Bayesian (BIC) 871.849
## Sample-size adjusted Bayesian (BIC) 830.823
##
## 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 0.400 0.193 2.073 0.038 0.400
## GROUP2 0.204 0.552 0.369 0.712 0.204
## SCALED_1_Achvm 0.623 0.100 6.229 0.000 0.623
## Achv__1.GROUP1 -0.294 0.115 -2.561 0.010 -0.294
## SCALED_3_Acheivement ~
## GROUP1 0.576 0.224 2.572 0.010 0.576
## GROUP2 0.939 0.834 1.127 0.260 0.939
## SCALED_1_Achvm 0.386 0.158 2.440 0.015 0.386
## Achv__1.GROUP1 -0.324 0.162 -2.002 0.045 -0.324
## SCALED_2_Achvm 0.492 0.147 3.347 0.001 0.492
## Std.all
##
## 0.201
## 0.042
## 0.619
## -0.257
##
## 0.255
## 0.173
## 0.339
## -0.251
## 0.435
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_Achvm -0.288 0.129 -2.227 0.026 -0.288 -0.289
## SCALED_3_Achvm -0.456 0.168 -2.719 0.007 -0.456 -0.405
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_Achvm 0.576 0.103 5.615 0.000 0.576 0.582
## SCALED_3_Achvm 0.462 0.101 4.562 0.000 0.462 0.364
##
## R-Square:
## Estimate
## SCALED_2_Achvm 0.418
## SCALED_3_Achvm 0.636## [[1]]
## [[1]][[1]]
## Item β SE z p 90% CI
## 1 Intercept -0.29 0.13 -2.23 0.03 -0.54, -0.03
## 2 GROUP1 0.40 0.19 2.07 0.04 0.02, 0.78
## 3 GROUP2 0.20 0.55 0.37 0.71 -0.88, 1.29
## 4 Perma - Acheivement 0.62 0.10 6.23 0.00 0.43, 0.82
## 5 Interaction w/ Group -0.29 0.11 -2.56 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 -0.78, -0.13
## 2 GROUP1 0.58 0.22 2.57 0.01 0.14, 1.02
## 3 GROUP2 0.94 0.83 1.13 0.26 -0.69, 2.57
## 4 Perma - Acheivement 0.39 0.16 2.44 0.01 0.08, 0.7
## 5 Interaction w/ Group -0.32 0.16 -2.00 0.05 -0.64, -0.01
##
##
## [[2]]
## levels Treat_T2 Control_T2 Treat_T3 Control_T3
## 1 -1 -0.22 -0.91 -0.12 -0.84
## 2 0 0.11 -0.29 -0.06 -0.46
## 3 1 0.44 0.33 0.01 -0.07plots(modelname = modelAcheivement_interaction_T3, fitname = fit, data = adult2, interactionterm = "Acheivement_mean_1.GROUP1", new_scale_name = "Perma Acheivement") 
#########################################
################ 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 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 83.428
## Degrees of freedom 9
## P-value 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -404.061
## Loglikelihood unrestricted model (H1) -404.061
##
## Number of free parameters 13
## Akaike (AIC) 834.122
## Bayesian (BIC) 866.474
## Sample-size adjusted Bayesian (BIC) 825.448
##
## 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 -0.273 0.193 -1.410 0.158 -0.273
## GROUP2 -0.392 0.556 -0.705 0.481 -0.392
## SCALED_1_Nagtv 0.639 0.100 6.385 0.000 0.639
## Ngtv__1.GROUP1 0.030 0.108 0.283 0.777 0.030
## SCALED_3_Nagative ~
## GROUP1 -0.306 0.195 -1.569 0.117 -0.306
## GROUP2 -0.710 0.698 -1.017 0.309 -0.710
## SCALED_1_Nagtv 0.665 0.097 6.851 0.000 0.665
## Ngtv__1.GROUP1 -0.136 0.105 -1.302 0.193 -0.136
## Std.all
##
## -0.135
## -0.080
## 0.627
## 0.028
##
## -0.156
## -0.150
## 0.672
## -0.127
##
## Covariances:
## Estimate Std.Err Z-value P(>|z|) Std.lv
## SCALED_2_Nagative ~~
## SCALED_3_Nagtv 0.273 0.087 3.132 0.002 0.273
## Std.all
##
## 0.529
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_Nagtv 0.203 0.128 1.586 0.113 0.203 0.201
## SCALED_3_Nagtv 0.184 0.131 1.400 0.161 0.184 0.188
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_Nagtv 0.581 0.103 5.647 0.000 0.581 0.569
## SCALED_3_Nagtv 0.459 0.097 4.720 0.000 0.459 0.477
##
## R-Square:
## Estimate
## SCALED_2_Nagtv 0.431
## SCALED_3_Nagtv 0.523## [[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 -0.27 0.19 -1.41 0.16 -0.65, 0.11
## 3 GROUP2 -0.39 0.56 -0.71 0.48 -1.48, 0.7
## 4 Nagative 0.64 0.10 6.39 0.00 0.44, 0.84
## 5 Interaction w/ Group 0.03 0.11 0.28 0.78 -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 -0.31 0.20 -1.57 0.12 -0.69, 0.08
## 3 GROUP2 -0.71 0.70 -1.02 0.31 -2.08, 0.66
## 4 Nagative 0.67 0.10 6.85 0.00 0.47, 0.86
## 5 Interaction w/ Group -0.14 0.10 -1.30 0.19 -0.34, 0.07
##
##
## [[2]]
## levels Treat_T2 Control_T2 Treat_T3 Control_T3
## 1 -1 -0.74 -0.44 -0.62 -0.48
## 2 0 -0.07 0.20 -0.09 0.18
## 3 1 0.60 0.84 0.44 0.85plots(modelname = modelNegative_interaction, fitname = fit, data = adult2, interactionterm = "Negative_mean_1.GROUP1", new_scale_name = "Perma Negative") 
#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 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 83.428
## Degrees of freedom 9
## P-value 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -404.061
## Loglikelihood unrestricted model (H1) -404.061
##
## Number of free parameters 13
## Akaike (AIC) 834.122
## Bayesian (BIC) 866.474
## Sample-size adjusted Bayesian (BIC) 825.448
##
## 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 -0.273 0.193 -1.410 0.158 -0.273
## GROUP2 -0.392 0.556 -0.705 0.481 -0.392
## SCALED_1_Nagtv 0.639 0.100 6.385 0.000 0.639
## Ngtv__1.GROUP1 0.030 0.108 0.283 0.777 0.030
## SCALED_3_Nagative ~
## GROUP1 -0.178 0.179 -0.997 0.319 -0.178
## GROUP2 -0.526 0.743 -0.708 0.479 -0.526
## SCALED_1_Nagtv 0.365 0.121 3.024 0.002 0.365
## Ngtv__1.GROUP1 -0.150 0.097 -1.555 0.120 -0.150
## SCALED_2_Nagtv 0.470 0.124 3.775 0.000 0.470
## Std.all
##
## -0.135
## -0.080
## 0.627
## 0.028
##
## -0.091
## -0.111
## 0.369
## -0.140
## 0.484
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_Nagtv 0.203 0.128 1.586 0.113 0.203 0.201
## SCALED_3_Nagtv 0.089 0.123 0.720 0.471 0.089 0.090
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_Nagtv 0.581 0.103 5.647 0.000 0.581 0.569
## SCALED_3_Nagtv 0.331 0.072 4.602 0.000 0.331 0.344
##
## R-Square:
## Estimate
## SCALED_2_Nagtv 0.431
## SCALED_3_Nagtv 0.656## [[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 -0.27 0.19 -1.41 0.16 -0.65, 0.11
## 3 GROUP2 -0.39 0.56 -0.71 0.48 -1.48, 0.7
## 4 Nagative 0.64 0.10 6.39 0.00 0.44, 0.84
## 5 Interaction w/ Group 0.03 0.11 0.28 0.78 -0.18, 0.24
##
## [[1]][[2]]
## Item β SE z p 90% CI
## 1 Intercept 0.09 0.12 0.72 0.47 -0.15, 0.33
## 2 GROUP1 -0.18 0.18 -1.00 0.32 -0.53, 0.17
## 3 GROUP2 -0.53 0.74 -0.71 0.48 -1.98, 0.93
## 4 Nagative 0.36 0.12 3.02 0.00 0.13, 0.6
## 5 Interaction w/ Group -0.15 0.10 -1.55 0.12 -0.34, 0.04
##
##
## [[2]]
## levels Treat_T2 Control_T2 Treat_T3 Control_T3
## 1 -1 -0.74 -0.44 -0.40 -0.28
## 2 0 -0.07 0.20 -0.18 0.09
## 3 1 0.60 0.84 0.03 0.45plots(modelname = modelNegative_interaction_T3, fitname = fit, data = adult2, interactionterm = "Negative_mean_1.GROUP1", new_scale_name = "Perma Negative")
#########################################
############### 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 32 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 68.571
## Degrees of freedom 9
## P-value 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -417.315
## Loglikelihood unrestricted model (H1) -417.315
##
## Number of free parameters 13
## Akaike (AIC) 860.630
## Bayesian (BIC) 892.982
## Sample-size adjusted Bayesian (BIC) 851.956
##
## 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 0.224 0.198 1.132 0.258 0.224
## GROUP2 0.811 0.583 1.391 0.164 0.811
## SCALED_1_Rrltn 0.572 0.100 5.706 0.000 0.572
## Rltn__1.GROUP1 0.065 0.101 0.649 0.516 0.065
## SCALED_3_Rrealtionships ~
## GROUP1 0.468 0.254 1.846 0.065 0.468
## GROUP2 0.986 0.950 1.038 0.299 0.986
## SCALED_1_Rrltn 0.429 0.141 3.035 0.002 0.429
## Rltn__1.GROUP1 -0.075 0.140 -0.537 0.591 -0.075
## Std.all
##
## 0.111
## 0.167
## 0.564
## 0.064
##
## 0.217
## 0.190
## 0.396
## -0.069
##
## Covariances:
## Estimate Std.Err Z-value P(>|z|) Std.lv
## SCALED_2_Rrealtionships ~~
## SCALED_3_Rrltn 0.539 0.122 4.433 0.000 0.539
## Std.all
##
## 0.737
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_Rrltn -0.196 0.131 -1.495 0.135 -0.196 -0.194
## SCALED_3_Rrltn -0.325 0.173 -1.884 0.060 -0.325 -0.302
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_Rrltn 0.624 0.110 5.689 0.000 0.624 0.616
## SCALED_3_Rrltn 0.857 0.177 4.836 0.000 0.857 0.739
##
## R-Square:
## Estimate
## SCALED_2_Rrltn 0.384
## SCALED_3_Rrltn 0.261## [[1]]
## [[1]][[1]]
## Item β SE z p 90% CI
## 1 Intercept -0.20 0.13 -1.49 0.13 -0.45, 0.06
## 2 GROUP1 0.22 0.20 1.13 0.26 -0.16, 0.61
## 3 GROUP2 0.81 0.58 1.39 0.16 -0.33, 1.95
## 4 Rrealtionships 0.57 0.10 5.71 0.00 0.38, 0.77
## 5 Interaction w/ Group 0.07 0.10 0.65 0.52 -0.13, 0.26
##
## [[1]][[2]]
## Item β SE z p 90% CI
## 1 Intercept -0.32 0.17 -1.88 0.06 -0.66, 0.01
## 2 GROUP1 0.47 0.25 1.85 0.06 -0.03, 0.97
## 3 GROUP2 0.99 0.95 1.04 0.30 -0.88, 2.85
## 4 Rrealtionships 0.43 0.14 3.04 0.00 0.15, 0.71
## 5 Interaction w/ Group -0.08 0.14 -0.54 0.59 -0.35, 0.2
##
##
## [[2]]
## levels Treat_T2 Control_T2 Treat_T3 Control_T3
## 1 -1 -0.61 -0.77 -0.45 -0.75
## 2 0 0.03 -0.20 -0.10 -0.32
## 3 1 0.67 0.38 0.25 0.10plots(modelname = modelRelationships_interaction, fitname = fit, data = adult2, interactionterm = "Relationships_mean_1.GROUP1", new_scale_name = "Perma Relationships") 
#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 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 68.571
## Degrees of freedom 9
## P-value 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -417.315
## Loglikelihood unrestricted model (H1) -417.315
##
## Number of free parameters 13
## Akaike (AIC) 860.630
## Bayesian (BIC) 892.982
## Sample-size adjusted Bayesian (BIC) 851.956
##
## 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 0.224 0.198 1.132 0.258 0.224
## GROUP2 0.811 0.583 1.391 0.164 0.811
## SCALED_1_Rrltn 0.572 0.100 5.706 0.000 0.572
## Rltn__1.GROUP1 0.065 0.101 0.649 0.516 0.065
## SCALED_3_Rrealtionships ~
## GROUP1 0.274 0.196 1.403 0.161 0.274
## GROUP2 0.285 1.073 0.266 0.790 0.285
## SCALED_1_Rrltn -0.064 0.147 -0.437 0.662 -0.064
## Rltn__1.GROUP1 -0.132 0.118 -1.118 0.263 -0.132
## SCALED_2_Rrltn 0.864 0.132 6.569 0.000 0.864
## Std.all
##
## 0.111
## 0.167
## 0.564
## 0.064
##
## 0.127
## 0.055
## -0.059
## -0.121
## 0.808
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_Rrltn -0.196 0.131 -1.495 0.135 -0.196 -0.194
## SCALED_3_Rrltn -0.156 0.137 -1.135 0.256 -0.156 -0.145
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_Rrltn 0.624 0.110 5.689 0.000 0.624 0.616
## SCALED_3_Rrltn 0.391 0.086 4.536 0.000 0.391 0.338
##
## R-Square:
## Estimate
## SCALED_2_Rrltn 0.384
## SCALED_3_Rrltn 0.662## [[1]]
## [[1]][[1]]
## Item β SE z p 90% CI
## 1 Intercept -0.20 0.13 -1.49 0.13 -0.45, 0.06
## 2 GROUP1 0.22 0.20 1.13 0.26 -0.16, 0.61
## 3 GROUP2 0.81 0.58 1.39 0.16 -0.33, 1.95
## 4 Rrealtionships 0.57 0.10 5.71 0.00 0.38, 0.77
## 5 Interaction w/ Group 0.07 0.10 0.65 0.52 -0.13, 0.26
##
## [[1]][[2]]
## Item β SE z p 90% CI
## 1 Intercept -0.16 0.14 -1.13 0.26 -0.43, 0.11
## 2 GROUP1 0.27 0.20 1.40 0.16 -0.11, 0.66
## 3 GROUP2 0.29 1.07 0.27 0.79 -1.82, 2.39
## 4 Rrealtionships -0.06 0.15 -0.44 0.66 -0.35, 0.22
## 5 Interaction w/ Group -0.13 0.12 -1.12 0.26 -0.36, 0.1
##
##
## [[2]]
## levels Treat_T2 Control_T2 Treat_T3 Control_T3
## 1 -1 -0.61 -0.77 0.26 -0.09
## 2 0 0.03 -0.20 0.07 -0.16
## 3 1 0.67 0.38 -0.13 -0.22plots(modelname = modelRelationships_interaction_T3, fitname = fit, data = adult2, interactionterm = "Relationships_mean_1.GROUP1", new_scale_name = "Perma Relationships") 
#########################################
################# 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 35 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 110.790
## Degrees of freedom 9
## P-value 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -314.097
## Loglikelihood unrestricted model (H1) -314.097
##
## Number of free parameters 13
## Akaike (AIC) 654.194
## Bayesian (BIC) 686.546
## Sample-size adjusted Bayesian (BIC) 645.521
##
## 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 0.278 0.149 1.872 0.061 0.278 0.140
## GROUP2 0.591 0.435 1.357 0.175 0.591 0.123
## SCALED_1_LET 0.830 0.075 11.024 0.000 0.830 0.826
## LfEn__1.GROUP1 -0.039 0.188 -0.207 0.836 -0.039 -0.016
## SCALED_3_LET ~
## GROUP1 0.564 0.227 2.481 0.013 0.564 0.268
## GROUP2 1.632 0.807 2.023 0.043 1.632 0.321
## SCALED_1_LET 0.684 0.119 5.753 0.000 0.684 0.642
## LfEn__1.GROUP1 -0.077 0.297 -0.259 0.795 -0.077 -0.029
##
## Covariances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_LET ~~
## SCALED_3_LET 0.272 0.086 3.145 0.002 0.272 0.600
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_LET -0.244 0.100 -2.440 0.015 -0.244 -0.246
## SCALED_3_LET -0.443 0.163 -2.723 0.006 -0.443 -0.421
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_LET 0.337 0.061 5.556 0.000 0.337 0.343
## SCALED_3_LET 0.608 0.139 4.368 0.000 0.608 0.549
##
## R-Square:
## Estimate
## SCALED_2_LET 0.657
## SCALED_3_LET 0.451## [[1]]
## [[1]][[1]]
## Item β SE z p 90% CI
## 1 Intercept -0.24 0.10 -2.44 0.01 -0.44, -0.05
## 2 GROUP1 0.28 0.15 1.87 0.06 -0.01, 0.57
## 3 GROUP2 0.59 0.44 1.36 0.17 -0.26, 1.44
## 4 LET 0.83 0.08 11.02 0.00 0.68, 0.98
## 5 Interaction w/ Group -0.04 0.19 -0.21 0.84 -0.41, 0.33
##
## [[1]][[2]]
## Item β SE z p 90% CI
## 1 Intercept -0.44 0.16 -2.72 0.01 -0.76, -0.12
## 2 GROUP1 0.56 0.23 2.48 0.01 0.12, 1.01
## 3 GROUP2 1.63 0.81 2.02 0.04 0.05, 3.21
## 4 LET 0.68 0.12 5.75 0.00 0.45, 0.92
## 5 Interaction w/ Group -0.08 0.30 -0.26 0.80 -0.66, 0.51
##
##
## [[2]]
## levels Treat_T2 Control_T2 Treat_T3 Control_T3
## 1 -1 -0.76 -1.07 -0.77 -1.13
## 2 0 0.03 -0.24 -0.17 -0.44
## 3 1 0.83 0.59 0.44 0.24plots(modelname = modelLET_interaction, fitname = fit, data = adult2, interactionterm = "LifeEngagement_mean_1.GROUP1", new_scale_name = "Life Engagement Scale") 
#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 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 110.790
## Degrees of freedom 9
## P-value 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -314.097
## Loglikelihood unrestricted model (H1) -314.097
##
## Number of free parameters 13
## Akaike (AIC) 654.194
## Bayesian (BIC) 686.546
## Sample-size adjusted Bayesian (BIC) 645.521
##
## 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 0.278 0.149 1.872 0.061 0.278 0.140
## GROUP2 0.591 0.435 1.357 0.175 0.591 0.123
## SCALED_1_LET 0.830 0.075 11.024 0.000 0.830 0.826
## LfEn__1.GROUP1 -0.039 0.188 -0.207 0.836 -0.039 -0.016
## SCALED_3_LET ~
## GROUP1 0.340 0.215 1.579 0.114 0.340 0.161
## GROUP2 1.156 0.881 1.313 0.189 1.156 0.228
## SCALED_1_LET 0.016 0.207 0.077 0.939 0.016 0.015
## LfEn__1.GROUP1 -0.046 0.265 -0.172 0.863 -0.046 -0.017
## SCALED_2_LET 0.806 0.208 3.868 0.000 0.806 0.759
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_LET -0.244 0.100 -2.440 0.015 -0.244 -0.246
## SCALED_3_LET -0.247 0.162 -1.520 0.129 -0.247 -0.235
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_LET 0.337 0.061 5.556 0.000 0.337 0.343
## SCALED_3_LET 0.389 0.087 4.467 0.000 0.389 0.351
##
## R-Square:
## Estimate
## SCALED_2_LET 0.657
## SCALED_3_LET 0.649## [[1]]
## [[1]][[1]]
## Item β SE z p 90% CI
## 1 Intercept -0.24 0.10 -2.44 0.01 -0.44, -0.05
## 2 GROUP1 0.28 0.15 1.87 0.06 -0.01, 0.57
## 3 GROUP2 0.59 0.44 1.36 0.17 -0.26, 1.44
## 4 LET 0.83 0.08 11.02 0.00 0.68, 0.98
## 5 Interaction w/ Group -0.04 0.19 -0.21 0.84 -0.41, 0.33
##
## [[1]][[2]]
## Item β SE z p 90% CI
## 1 Intercept -0.25 0.16 -1.52 0.13 -0.57, 0.07
## 2 GROUP1 0.34 0.22 1.58 0.11 -0.08, 0.76
## 3 GROUP2 1.16 0.88 1.31 0.19 -0.57, 2.88
## 4 LET 0.02 0.21 0.08 0.94 -0.39, 0.42
## 5 Interaction w/ Group -0.05 0.27 -0.17 0.86 -0.57, 0.47
##
##
## [[2]]
## levels Treat_T2 Control_T2 Treat_T3 Control_T3
## 1 -1 -0.76 -1.07 0.06 -0.26
## 2 0 0.03 -0.24 0.03 -0.25
## 3 1 0.83 0.59 0.00 -0.23plots(modelname = modelLET_interaction_T3, fitname = fit, data = adult2, interactionterm = "LifeEngagement_mean_1.GROUP1", new_scale_name = "Life Engagement Scale") 
#########################################
################### 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 29 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 110.769
## Degrees of freedom 9
## P-value 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -362.827
## Loglikelihood unrestricted model (H1) -362.827
##
## Number of free parameters 13
## Akaike (AIC) 751.654
## Bayesian (BIC) 784.007
## Sample-size adjusted Bayesian (BIC) 742.981
##
## 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 0.593 0.151 3.919 0.000 0.593 0.309
## GROUP2 0.934 0.428 2.179 0.029 0.934 0.202
## SCALED_1_LS 0.605 0.073 8.275 0.000 0.605 0.627
## LfSt__1.GROUP1 -0.519 0.110 -4.704 0.000 -0.519 -0.360
## SCALED_3_LS ~
## GROUP1 0.566 0.220 2.580 0.010 0.566 0.271
## GROUP2 0.532 0.782 0.680 0.496 0.532 0.106
## SCALED_1_LS 0.581 0.112 5.192 0.000 0.581 0.552
## LfSt__1.GROUP1 -0.506 0.169 -3.002 0.003 -0.506 -0.322
##
## Covariances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_LS ~~
## SCALED_3_LS 0.266 0.078 3.425 0.001 0.266 0.593
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_LS -0.297 0.099 -3.014 0.003 -0.297 -0.311
## SCALED_3_LS -0.374 0.148 -2.525 0.012 -0.374 -0.358
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_LS 0.347 0.063 5.487 0.000 0.347 0.379
## SCALED_3_LS 0.581 0.122 4.753 0.000 0.581 0.534
##
## R-Square:
## Estimate
## SCALED_2_LS 0.621
## SCALED_3_LS 0.466## [[1]]
## [[1]][[1]]
## Item β SE z p 90% CI
## 1 Intercept -0.30 0.10 -3.01 0.00 -0.49, -0.1
## 2 GROUP1 0.59 0.15 3.92 0.00 0.3, 0.89
## 3 GROUP2 0.93 0.43 2.18 0.03 0.09, 1.77
## 4 Life Satisfaction 0.61 0.07 8.27 0.00 0.46, 0.75
## 5 Interaction w/ Group -0.52 0.11 -4.70 0.00 -0.74, -0.3
##
## [[1]][[2]]
## Item β SE z p 90% CI
## 1 Intercept -0.37 0.15 -2.53 0.01 -0.66, -0.08
## 2 GROUP1 0.57 0.22 2.58 0.01 0.14, 1
## 3 GROUP2 0.53 0.78 0.68 0.50 -1, 2.07
## 4 Life Satisfaction 0.58 0.11 5.19 0.00 0.36, 0.8
## 5 Interaction w/ Group -0.51 0.17 -3.00 0.00 -0.84, -0.18
##
##
## [[2]]
## levels Treat_T2 Control_T2 Treat_T3 Control_T3
## 1 -1 0.21 -0.90 0.14 -0.95
## 2 0 0.30 -0.30 0.22 -0.37
## 3 1 0.38 0.31 0.29 0.21plots(modelname = modelLS_interaction, fitname = fit, data = adult2, interactionterm = "LifeSatisfaction_mean_1.GROUP1", new_scale_name = "Life Satisfaction Scale") 
#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 29 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 110.769
## Degrees of freedom 9
## P-value 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -362.827
## Loglikelihood unrestricted model (H1) -362.827
##
## Number of free parameters 13
## Akaike (AIC) 751.654
## Bayesian (BIC) 784.007
## Sample-size adjusted Bayesian (BIC) 742.981
##
## 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 0.593 0.151 3.919 0.000 0.593 0.309
## GROUP2 0.934 0.428 2.179 0.029 0.934 0.202
## SCALED_1_LS 0.605 0.073 8.275 0.000 0.605 0.627
## LfSt__1.GROUP1 -0.519 0.110 -4.704 0.000 -0.519 -0.360
## SCALED_3_LS ~
## GROUP1 0.112 0.213 0.525 0.600 0.112 0.054
## GROUP2 -0.184 0.859 -0.214 0.831 -0.184 -0.036
## SCALED_1_LS 0.117 0.142 0.824 0.410 0.117 0.111
## LfSt__1.GROUP1 -0.108 0.177 -0.607 0.544 -0.108 -0.069
## SCALED_2_LS 0.767 0.172 4.450 0.000 0.767 0.704
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_LS -0.297 0.099 -3.014 0.003 -0.297 -0.311
## SCALED_3_LS -0.145 0.141 -1.028 0.304 -0.145 -0.139
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_LS 0.347 0.063 5.487 0.000 0.347 0.379
## SCALED_3_LS 0.377 0.086 4.396 0.000 0.377 0.347
##
## R-Square:
## Estimate
## SCALED_2_LS 0.621
## SCALED_3_LS 0.653## [[1]]
## [[1]][[1]]
## Item β SE z p 90% CI
## 1 Intercept -0.30 0.10 -3.01 0.00 -0.49, -0.1
## 2 GROUP1 0.59 0.15 3.92 0.00 0.3, 0.89
## 3 GROUP2 0.93 0.43 2.18 0.03 0.09, 1.77
## 4 Life Satisfaction 0.61 0.07 8.27 0.00 0.46, 0.75
## 5 Interaction w/ Group -0.52 0.11 -4.70 0.00 -0.74, -0.3
##
## [[1]][[2]]
## Item β SE z p 90% CI
## 1 Intercept -0.15 0.14 -1.03 0.30 -0.42, 0.13
## 2 GROUP1 0.11 0.21 0.52 0.60 -0.31, 0.53
## 3 GROUP2 -0.18 0.86 -0.21 0.83 -1.87, 1.5
## 4 Life Satisfaction 0.12 0.14 0.82 0.41 -0.16, 0.4
## 5 Interaction w/ Group -0.11 0.18 -0.61 0.54 -0.46, 0.24
##
##
## [[2]]
## levels Treat_T2 Control_T2 Treat_T3 Control_T3
## 1 -1 0.21 -0.90 0.44 -0.26
## 2 0 0.30 -0.30 0.45 -0.15
## 3 1 0.38 0.31 0.46 -0.03plots(modelname = modelLS_interaction_T3, fitname = fit, data = adult2, interactionterm = "LifeSatisfaction_mean_1.GROUP1", new_scale_name = "Life Satisfaction Scale") 
#########################################
############### 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 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 36.618
## Degrees of freedom 9
## P-value 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -425.877
## Loglikelihood unrestricted model (H1) -425.877
##
## Number of free parameters 13
## Akaike (AIC) 877.754
## Bayesian (BIC) 910.107
## Sample-size adjusted Bayesian (BIC) 869.081
##
## 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 0.174 0.215 0.807 0.419 0.174
## GROUP2 0.759 0.630 1.204 0.229 0.759
## SCALED_1_Enggm 0.531 0.111 4.776 0.000 0.531
## Engg__1.GROUP1 -0.129 0.121 -1.070 0.285 -0.129
## SCALED_3_Engagement ~
## GROUP1 0.502 0.286 1.754 0.079 0.502
## GROUP2 0.889 0.991 0.897 0.370 0.889
## SCALED_1_Enggm 0.406 0.195 2.080 0.038 0.406
## Engg__1.GROUP1 -0.221 0.210 -1.054 0.292 -0.221
## Std.all
##
## 0.087
## 0.157
## 0.527
## -0.118
##
## 0.233
## 0.171
## 0.373
## -0.188
##
## Covariances:
## Estimate Std.Err Z-value P(>|z|) Std.lv
## SCALED_2_Engagement ~~
## SCALED_3_Enggm 0.402 0.150 2.677 0.007 0.402
## Std.all
##
## 0.498
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_Enggm -0.172 0.145 -1.184 0.237 -0.172 -0.172
## SCALED_3_Enggm -0.374 0.206 -1.816 0.069 -0.374 -0.347
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_Enggm 0.708 0.130 5.452 0.000 0.708 0.708
## SCALED_3_Enggm 0.917 0.195 4.708 0.000 0.917 0.790
##
## R-Square:
## Estimate
## SCALED_2_Enggm 0.292
## SCALED_3_Enggm 0.210## [[1]]
## [[1]][[1]]
## Item β SE z p 90% CI
## 1 Intercept -0.17 0.15 -1.18 0.24 -0.46, 0.11
## 2 GROUP1 0.17 0.21 0.81 0.42 -0.25, 0.59
## 3 GROUP2 0.76 0.63 1.20 0.23 -0.48, 1.99
## 4 Engagement 0.53 0.11 4.78 0.00 0.31, 0.75
## 5 Interaction w/ Group -0.13 0.12 -1.07 0.28 -0.37, 0.11
##
## [[1]][[2]]
## Item β SE z p 90% CI
## 1 Intercept -0.37 0.21 -1.82 0.07 -0.78, 0.03
## 2 GROUP1 0.50 0.29 1.75 0.08 -0.06, 1.06
## 3 GROUP2 0.89 0.99 0.90 0.37 -1.05, 2.83
## 4 Engagement 0.41 0.20 2.08 0.04 0.02, 0.79
## 5 Interaction w/ Group -0.22 0.21 -1.05 0.29 -0.63, 0.19
##
##
## [[2]]
## levels Treat_T2 Control_T2 Treat_T3 Control_T3
## 1 -1 -0.4 -0.70 -0.39 -0.78
## 2 0 0.0 -0.17 -0.20 -0.37
## 3 1 0.4 0.36 -0.02 0.03plots(modelname = modelEngagement_interaction, fitname = fit, data = adult2, interactionterm = "Engagement_mean_1.GROUP1", new_scale_name = "Perma Engagament") 
#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 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 36.618
## Degrees of freedom 9
## P-value 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -425.877
## Loglikelihood unrestricted model (H1) -425.877
##
## Number of free parameters 13
## Akaike (AIC) 877.754
## Bayesian (BIC) 910.107
## Sample-size adjusted Bayesian (BIC) 869.081
##
## 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 0.174 0.215 0.807 0.419 0.174
## GROUP2 0.759 0.630 1.204 0.229 0.759
## SCALED_1_Enggm 0.531 0.111 4.776 0.000 0.531
## Engg__1.GROUP1 -0.129 0.121 -1.070 0.285 -0.129
## SCALED_3_Engagement ~
## GROUP1 0.404 0.264 1.532 0.125 0.404
## GROUP2 0.458 1.053 0.435 0.664 0.458
## SCALED_1_Enggm 0.104 0.203 0.515 0.607 0.104
## Engg__1.GROUP1 -0.148 0.208 -0.711 0.477 -0.148
## SCALED_2_Enggm 0.567 0.175 3.233 0.001 0.567
## Std.all
##
## 0.087
## 0.157
## 0.527
## -0.118
##
## 0.187
## 0.088
## 0.096
## -0.125
## 0.527
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_Enggm -0.172 0.145 -1.184 0.237 -0.172 -0.172
## SCALED_3_Enggm -0.277 0.195 -1.422 0.155 -0.277 -0.257
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_Enggm 0.708 0.130 5.452 0.000 0.708 0.708
## SCALED_3_Enggm 0.690 0.158 4.353 0.000 0.690 0.594
##
## R-Square:
## Estimate
## SCALED_2_Enggm 0.292
## SCALED_3_Enggm 0.406## [[1]]
## [[1]][[1]]
## Item β SE z p 90% CI
## 1 Intercept -0.17 0.15 -1.18 0.24 -0.46, 0.11
## 2 GROUP1 0.17 0.21 0.81 0.42 -0.25, 0.59
## 3 GROUP2 0.76 0.63 1.20 0.23 -0.48, 1.99
## 4 Engagement 0.53 0.11 4.78 0.00 0.31, 0.75
## 5 Interaction w/ Group -0.13 0.12 -1.07 0.28 -0.37, 0.11
##
## [[1]][[2]]
## Item β SE z p 90% CI
## 1 Intercept -0.28 0.19 -1.42 0.15 -0.66, 0.1
## 2 GROUP1 0.40 0.26 1.53 0.13 -0.11, 0.92
## 3 GROUP2 0.46 1.05 0.44 0.66 -1.61, 2.52
## 4 Engagement 0.10 0.20 0.51 0.61 -0.29, 0.5
## 5 Interaction w/ Group -0.15 0.21 -0.71 0.48 -0.56, 0.26
##
##
## [[2]]
## levels Treat_T2 Control_T2 Treat_T3 Control_T3
## 1 -1 -0.4 -0.70 -0.06 -0.38
## 2 0 0.0 -0.17 -0.10 -0.28
## 3 1 0.4 0.36 -0.15 -0.17plots(modelname = modelEngagement_interaction_T3, fitname = fit, data = adult2, interactionterm = "Engagement_mean_1.GROUP1", new_scale_name = "Perma Engagament") 
#########################################
############### 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 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 39.897
## Degrees of freedom 9
## P-value 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -348.279
## Loglikelihood unrestricted model (H1) -348.279
##
## Number of free parameters 13
## Akaike (AIC) 722.557
## Bayesian (BIC) 754.909
## Sample-size adjusted Bayesian (BIC) 713.883
##
## 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 -0.285 0.233 -1.227 0.220 -0.285
## GROUP2 -0.182 0.661 -0.276 0.783 -0.182
## SCALED_1_Optms 0.356 0.113 3.161 0.002 0.356
## Optm__1.GROUP1 -0.334 0.301 -1.107 0.268 -0.334
## SCALED_3_Optimism ~
## GROUP1 -0.645 0.234 -2.762 0.006 -0.645
## GROUP2 -0.234 0.796 -0.294 0.768 -0.234
## SCALED_1_Optms 0.438 0.119 3.683 0.000 0.438
## Optm__1.GROUP1 -0.707 0.317 -2.233 0.026 -0.707
## Std.all
##
## -0.143
## -0.038
## 0.356
## -0.125
##
## -0.322
## -0.049
## 0.435
## -0.264
##
## Covariances:
## Estimate Std.Err Z-value P(>|z|) Std.lv
## SCALED_2_Optimism ~~
## SCALED_3_Optms 0.247 0.116 2.136 0.033 0.247
## Std.all
##
## 0.350
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_Optms 0.191 0.153 1.248 0.212 0.191 0.193
## SCALED_3_Optms 0.402 0.161 2.498 0.012 0.402 0.402
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_Optms 0.815 0.144 5.668 0.000 0.815 0.826
## SCALED_3_Optms 0.609 0.126 4.847 0.000 0.609 0.610
##
## R-Square:
## Estimate
## SCALED_2_Optms 0.174
## SCALED_3_Optms 0.390## [[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 -0.29 0.23 -1.23 0.22 -0.74, 0.17
## 3 GROUP2 -0.18 0.66 -0.28 0.78 -1.48, 1.11
## 4 Optimism Scale 0.36 0.11 3.16 0.00 0.14, 0.58
## 5 Interaction w/ Group -0.33 0.30 -1.11 0.27 -0.92, 0.26
##
## [[1]][[2]]
## Item β SE z p 90% CI
## 1 Intercept 0.40 0.16 2.50 0.01 0.09, 0.72
## 2 GROUP1 -0.65 0.23 -2.76 0.01 -1.1, -0.19
## 3 GROUP2 -0.23 0.80 -0.29 0.77 -1.79, 1.33
## 4 Optimism Scale 0.44 0.12 3.68 0.00 0.21, 0.67
## 5 Interaction w/ Group -0.71 0.32 -2.23 0.03 -1.33, -0.09
##
##
## [[2]]
## levels Treat_T2 Control_T2 Treat_T3 Control_T3
## 1 -1 -0.12 -0.16 0.39 -0.04
## 2 0 -0.09 0.19 0.12 0.40
## 3 1 -0.07 0.55 -0.15 0.84plots(modelname = modelOPTIMISM_interaction, fitname = fit, data = adult2, interactionterm = "Optimism_mean_1.GROUP1", new_scale_name = "Optimism Scale") 
#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 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 39.897
## Degrees of freedom 9
## P-value 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -348.279
## Loglikelihood unrestricted model (H1) -348.279
##
## Number of free parameters 13
## Akaike (AIC) 722.557
## Bayesian (BIC) 754.909
## Sample-size adjusted Bayesian (BIC) 713.883
##
## 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 -0.285 0.233 -1.227 0.220 -0.285
## GROUP2 -0.182 0.661 -0.276 0.783 -0.182
## SCALED_1_Optms 0.356 0.113 3.161 0.002 0.356
## Optm__1.GROUP1 -0.334 0.301 -1.107 0.268 -0.334
## SCALED_3_Optimism ~
## GROUP1 -0.559 0.230 -2.430 0.015 -0.559
## GROUP2 -0.179 0.820 -0.219 0.827 -0.179
## SCALED_1_Optms 0.331 0.128 2.578 0.010 0.331
## Optm__1.GROUP1 -0.606 0.314 -1.932 0.053 -0.606
## SCALED_2_Optms 0.303 0.129 2.352 0.019 0.303
## Std.all
##
## -0.143
## -0.038
## 0.356
## -0.125
##
## -0.279
## -0.037
## 0.328
## -0.226
## 0.301
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_Optms 0.191 0.153 1.248 0.212 0.191 0.193
## SCALED_3_Optms 0.344 0.162 2.128 0.033 0.344 0.344
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_Optms 0.815 0.144 5.668 0.000 0.815 0.826
## SCALED_3_Optms 0.534 0.114 4.671 0.000 0.534 0.535
##
## R-Square:
## Estimate
## SCALED_2_Optms 0.174
## SCALED_3_Optms 0.465## [[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 -0.29 0.23 -1.23 0.22 -0.74, 0.17
## 3 GROUP2 -0.18 0.66 -0.28 0.78 -1.48, 1.11
## 4 Optimism Scale 0.36 0.11 3.16 0.00 0.14, 0.58
## 5 Interaction w/ Group -0.33 0.30 -1.11 0.27 -0.92, 0.26
##
## [[1]][[2]]
## Item β SE z p 90% CI
## 1 Intercept 0.34 0.16 2.13 0.03 0.03, 0.66
## 2 GROUP1 -0.56 0.23 -2.43 0.02 -1.01, -0.11
## 3 GROUP2 -0.18 0.82 -0.22 0.83 -1.79, 1.43
## 4 Optimism Scale 0.33 0.13 2.58 0.01 0.08, 0.58
## 5 Interaction w/ Group -0.61 0.31 -1.93 0.05 -1.22, 0.01
##
##
## [[2]]
## levels Treat_T2 Control_T2 Treat_T3 Control_T3
## 1 -1 -0.12 -0.16 0.33 0.01
## 2 0 -0.09 0.19 0.06 0.34
## 3 1 -0.07 0.55 -0.22 0.67plots(modelname = modelOPTIMISM_interaction_T3, fitname = fit, data = adult2, interactionterm = "Optimism_mean_1.GROUP1", new_scale_name = "Optimism Scale") 
#########################################
################# 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 35 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 81.933
## Degrees of freedom 9
## P-value 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -346.369
## Loglikelihood unrestricted model (H1) -346.369
##
## Number of free parameters 13
## Akaike (AIC) 718.738
## Bayesian (BIC) 751.090
## Sample-size adjusted Bayesian (BIC) 710.064
##
## 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 0.424 0.181 2.349 0.019 0.424 0.206
## GROUP2 1.052 0.524 2.006 0.045 1.052 0.212
## SCALED_1_PWB 0.735 0.101 7.287 0.000 0.735 0.714
## PPWB__1.GROUP1 -0.316 0.215 -1.474 0.141 -0.316 -0.147
## SCALED_3_PWB ~
## GROUP1 0.706 0.224 3.147 0.002 0.706 0.329
## GROUP2 0.911 0.788 1.156 0.248 0.911 0.176
## SCALED_1_PWB 0.662 0.132 5.006 0.000 0.662 0.617
## PPWB__1.GROUP1 -0.543 0.278 -1.952 0.051 -0.543 -0.242
##
## Covariances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_PWB ~~
## SCALED_3_PWB 0.283 0.093 3.043 0.002 0.283 0.530
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_PWB -0.382 0.125 -3.065 0.002 -0.382 -0.373
## SCALED_3_PWB -0.575 0.162 -3.561 0.000 -0.575 -0.538
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_PWB 0.491 0.089 5.544 0.000 0.491 0.466
## SCALED_3_PWB 0.581 0.123 4.719 0.000 0.581 0.508
##
## R-Square:
## Estimate
## SCALED_2_PWB 0.534
## SCALED_3_PWB 0.492## [[1]]
## [[1]][[1]]
## Item β SE z p 90% CI
## 1 Intercept -0.38 0.12 -3.06 0.00 -0.63, -0.14
## 2 GROUP1 0.42 0.18 2.35 0.02 0.07, 0.78
## 3 GROUP2 1.05 0.52 2.01 0.04 0.02, 2.08
## 4 RPWB 0.74 0.10 7.29 0.00 0.54, 0.93
## 5 Interaction w/ Group -0.32 0.21 -1.47 0.14 -0.74, 0.1
##
## [[1]][[2]]
## Item β SE z p 90% CI
## 1 Intercept -0.58 0.16 -3.56 0.00 -0.89, -0.26
## 2 GROUP1 0.71 0.22 3.15 0.00 0.27, 1.15
## 3 GROUP2 0.91 0.79 1.16 0.25 -0.63, 2.46
## 4 RPWB 0.66 0.13 5.01 0.00 0.4, 0.92
## 5 Interaction w/ Group -0.54 0.28 -1.95 0.05 -1.09, 0
##
##
## [[2]]
## levels Treat_T2 Control_T2 Treat_T3 Control_T3
## 1 -1 -0.38 -1.12 -0.27 -1.24
## 2 0 0.04 -0.38 -0.15 -0.58
## 3 1 0.46 0.35 -0.03 0.09plots(modelname = modelPWB_interaction, fitname = fit, data = adult2, interactionterm = "PurposePWB_mean_1.GROUP1", new_scale_name = "Ryff Purpose Subscale") 
#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 31 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 81.933
## Degrees of freedom 9
## P-value 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -346.369
## Loglikelihood unrestricted model (H1) -346.369
##
## Number of free parameters 13
## Akaike (AIC) 718.738
## Bayesian (BIC) 751.090
## Sample-size adjusted Bayesian (BIC) 710.064
##
## 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 0.424 0.180 2.349 0.019 0.424 0.206
## GROUP2 1.052 0.524 2.006 0.045 1.052 0.212
## SCALED_1_PWB 0.735 0.101 7.287 0.000 0.735 0.714
## PPWB__1.GROUP1 -0.316 0.215 -1.474 0.141 -0.316 -0.147
## SCALED_3_PWB ~
## GROUP1 0.462 0.214 2.161 0.031 0.462 0.215
## GROUP2 0.305 0.852 0.358 0.720 0.305 0.059
## SCALED_1_PWB 0.239 0.169 1.408 0.159 0.239 0.222
## PPWB__1.GROUP1 -0.361 0.265 -1.364 0.172 -0.361 -0.161
## SCALED_2_PWB 0.576 0.155 3.724 0.000 0.576 0.553
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_PWB -0.382 0.125 -3.065 0.002 -0.382 -0.373
## SCALED_3_PWB -0.355 0.160 -2.220 0.026 -0.355 -0.332
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_PWB 0.491 0.089 5.544 0.000 0.491 0.466
## SCALED_3_PWB 0.418 0.092 4.564 0.000 0.418 0.365
##
## R-Square:
## Estimate
## SCALED_2_PWB 0.534
## SCALED_3_PWB 0.635## [[1]]
## [[1]][[1]]
## Item β SE z p 90% CI
## 1 Intercept -0.38 0.12 -3.06 0.00 -0.63, -0.14
## 2 GROUP1 0.42 0.18 2.35 0.02 0.07, 0.78
## 3 GROUP2 1.05 0.52 2.01 0.04 0.02, 2.08
## 4 RPWB 0.74 0.10 7.29 0.00 0.54, 0.93
## 5 Interaction w/ Group -0.32 0.21 -1.47 0.14 -0.74, 0.1
##
## [[1]][[2]]
## Item β SE z p 90% CI
## 1 Intercept -0.36 0.16 -2.22 0.03 -0.67, -0.04
## 2 GROUP1 0.46 0.21 2.16 0.03 0.04, 0.88
## 3 GROUP2 0.31 0.85 0.36 0.72 -1.36, 1.97
## 4 RPWB 0.24 0.17 1.41 0.16 -0.09, 0.57
## 5 Interaction w/ Group -0.36 0.26 -1.36 0.17 -0.88, 0.16
##
##
## [[2]]
## levels Treat_T2 Control_T2 Treat_T3 Control_T3
## 1 -1 -0.38 -1.12 0.19 -0.59
## 2 0 0.04 -0.38 0.07 -0.36
## 3 1 0.46 0.35 -0.05 -0.12plots(modelname = modelPWB_interaction_T3, fitname = fit, data = adult2, interactionterm = "PurposePWB_mean_1.GROUP1", new_scale_name = "Ryff Purpose Subscale") 
#########################################
################# 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 35 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 110.256
## Degrees of freedom 9
## P-value 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -305.729
## Loglikelihood unrestricted model (H1) -305.729
##
## Number of free parameters 13
## Akaike (AIC) 637.459
## Bayesian (BIC) 669.811
## Sample-size adjusted Bayesian (BIC) 628.785
##
## 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 Std.all
## SCALED_2_APSI ~
## GROUP1 0.779 0.142 5.479 0.000 0.779 0.392
## GROUP2 0.790 0.407 1.939 0.052 0.790 0.165
## SCALED_1_APSI 0.816 0.072 11.347 0.000 0.816 0.815
## PAPSI__1.GROUP -0.720 0.198 -3.634 0.000 -0.720 -0.262
## SCALED_3_APSI ~
## GROUP1 0.682 0.234 2.913 0.004 0.682 0.325
## GROUP2 0.370 0.802 0.461 0.645 0.370 0.073
## SCALED_1_APSI 0.671 0.127 5.291 0.000 0.671 0.634
## PAPSI__1.GROUP -0.906 0.351 -2.584 0.010 -0.906 -0.311
##
## Covariances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_APSI ~~
## SCALED_3_APSI 0.212 0.078 2.707 0.007 0.212 0.495
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_APSI -0.509 0.096 -5.274 0.000 -0.509 -0.513
## SCALED_3_APSI -0.542 0.174 -3.112 0.002 -0.542 -0.517
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_APSI 0.305 0.055 5.556 0.000 0.305 0.310
## SCALED_3_APSI 0.600 0.129 4.639 0.000 0.600 0.546
##
## R-Square:
## Estimate
## SCALED_2_APSI 0.690
## SCALED_3_APSI 0.454## [[1]]
## [[1]][[1]]
## Item β SE z p 90% CI
## 1 Intercept -0.51 0.10 -5.27 0.00 -0.7, -0.32
## 2 GROUP1 0.78 0.14 5.48 0.00 0.5, 1.06
## 3 GROUP2 0.79 0.41 1.94 0.05 -0.01, 1.59
## 4 APSI 0.82 0.07 11.35 0.00 0.67, 0.96
## 5 Interaction w/ Group -0.72 0.20 -3.63 0.00 -1.11, -0.33
##
## [[1]][[2]]
## Item β SE z p 90% CI
## 1 Intercept -0.54 0.17 -3.11 0.00 -0.88, -0.2
## 2 GROUP1 0.68 0.23 2.91 0.00 0.22, 1.14
## 3 GROUP2 0.37 0.80 0.46 0.65 -1.2, 1.94
## 4 APSI 0.67 0.13 5.29 0.00 0.42, 0.92
## 5 Interaction w/ Group -0.91 0.35 -2.58 0.01 -1.59, -0.22
##
##
## [[2]]
## levels Treat_T2 Control_T2 Treat_T3 Control_T3
## 1 -1 0.17 -1.32 0.47 -1.21
## 2 0 0.27 -0.51 0.24 -0.54
## 3 1 0.37 0.31 0.00 0.13plots(modelname = modelAPSI_interaction, fitname = fit, data = adult2, interactionterm = "PurposeAPSI_mean_1.GROUP1", new_scale_name = "APSI Sense of Identity") 
#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 35 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 110.256
## Degrees of freedom 9
## P-value 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -305.729
## Loglikelihood unrestricted model (H1) -305.729
##
## Number of free parameters 13
## Akaike (AIC) 637.459
## Bayesian (BIC) 669.811
## Sample-size adjusted Bayesian (BIC) 628.785
##
## 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 Std.all
## SCALED_2_APSI ~
## GROUP1 0.779 0.142 5.479 0.000 0.779 0.392
## GROUP2 0.790 0.407 1.939 0.052 0.790 0.165
## SCALED_1_APSI 0.816 0.072 11.347 0.000 0.816 0.815
## PAPSI__1.GROUP -0.720 0.198 -3.634 0.000 -0.720 -0.262
## SCALED_3_APSI ~
## GROUP1 0.141 0.279 0.505 0.614 0.141 0.067
## GROUP2 -0.179 0.865 -0.207 0.836 -0.179 -0.035
## SCALED_1_APSI 0.104 0.223 0.469 0.639 0.104 0.099
## PAPSI__1.GROUP -0.406 0.358 -1.135 0.256 -0.406 -0.140
## SCALED_2_APSI 0.695 0.216 3.209 0.001 0.695 0.657
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_APSI -0.509 0.096 -5.274 0.000 -0.509 -0.513
## SCALED_3_APSI -0.189 0.206 -0.916 0.360 -0.189 -0.180
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_APSI 0.305 0.055 5.556 0.000 0.305 0.310
## SCALED_3_APSI 0.453 0.100 4.540 0.000 0.453 0.412
##
## R-Square:
## Estimate
## SCALED_2_APSI 0.690
## SCALED_3_APSI 0.588## [[1]]
## [[1]][[1]]
## Item β SE z p 90% CI
## 1 Intercept -0.51 0.10 -5.27 0.00 -0.7, -0.32
## 2 GROUP1 0.78 0.14 5.48 0.00 0.5, 1.06
## 3 GROUP2 0.79 0.41 1.94 0.05 -0.01, 1.59
## 4 APSI 0.82 0.07 11.35 0.00 0.67, 0.96
## 5 Interaction w/ Group -0.72 0.20 -3.63 0.00 -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 0.14 0.28 0.50 0.61 -0.41, 0.69
## 3 GROUP2 -0.18 0.87 -0.21 0.84 -1.87, 1.52
## 4 APSI 0.10 0.22 0.47 0.64 -0.33, 0.54
## 5 Interaction w/ Group -0.41 0.36 -1.13 0.26 -1.11, 0.3
##
##
## [[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 0.37 0.31 0.29 -0.08plots(modelname = modelAPSI_interaction_T3, fitname = fit, data = adult2, interactionterm = "PurposeAPSI_mean_1.GROUP1", new_scale_name = "APSI Sense of Identity") 
#########################################
################# 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 31 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 110.540
## Degrees of freedom 9
## P-value 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -388.563
## Loglikelihood unrestricted model (H1) -388.563
##
## Number of free parameters 13
## Akaike (AIC) 803.126
## Bayesian (BIC) 835.478
## Sample-size adjusted Bayesian (BIC) 794.453
##
## 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 -0.086 0.150 -0.576 0.564 -0.086 -0.040
## GROUP2 -0.227 0.419 -0.541 0.589 -0.227 -0.044
## SCALED_1_Res 0.943 0.084 11.190 0.000 0.943 0.865
## Rs_mn_1.GROUP1 0.236 0.093 2.548 0.011 0.236 0.198
## SCALED_3_Res ~
## GROUP1 0.143 0.204 0.702 0.483 0.143 0.069
## GROUP2 0.366 0.683 0.537 0.592 0.366 0.073
## SCALED_1_Res 0.773 0.135 5.727 0.000 0.773 0.738
## Rs_mn_1.GROUP1 -0.077 0.150 -0.516 0.606 -0.077 -0.068
##
## Covariances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_Res ~~
## SCALED_3_Res 0.104 0.069 1.500 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 -1.125 0.261 -0.109 -0.101
## SCALED_3_Res -0.192 0.135 -1.419 0.156 -0.192 -0.186
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_Res 0.329 0.058 5.701 0.000 0.329 0.284
## SCALED_3_Res 0.448 0.095 4.699 0.000 0.448 0.417
##
## R-Square:
## Estimate
## SCALED_2_Res 0.716
## SCALED_3_Res 0.583## [[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 -0.58 0.56 -0.38, 0.21
## 3 GROUP2 -0.23 0.42 -0.54 0.59 -1.05, 0.59
## 4 Resiliance 0.94 0.08 11.19 0.00 0.78, 1.11
## 5 Interaction w/ Group 0.24 0.09 2.55 0.01 0.05, 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 0.14 0.20 0.70 0.48 -0.26, 0.54
## 3 GROUP2 0.37 0.68 0.54 0.59 -0.97, 1.7
## 4 Resiliance 0.77 0.14 5.73 0.00 0.51, 1.04
## 5 Interaction w/ Group -0.08 0.15 -0.52 0.61 -0.37, 0.22
##
##
## [[2]]
## levels Treat_T2 Control_T2 Treat_T3 Control_T3
## 1 -1 -1.37 -1.05 -0.97 -0.97
## 2 0 -0.20 -0.11 -0.28 -0.19
## 3 1 0.98 0.83 0.42 0.58plots(modelname = modelRes_interaction, fitname = fit, data = adult2, interactionterm = "Res_mean_1.GROUP1", new_scale_name = "Resiliance") 
#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 31 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 110.540
## Degrees of freedom 9
## P-value 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -388.563
## Loglikelihood unrestricted model (H1) -388.563
##
## Number of free parameters 13
## Akaike (AIC) 803.126
## Bayesian (BIC) 835.478
## Sample-size adjusted Bayesian (BIC) 794.453
##
## 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 -0.086 0.150 -0.576 0.564 -0.086 -0.040
## GROUP2 -0.227 0.419 -0.541 0.589 -0.227 -0.044
## SCALED_1_Res 0.943 0.084 11.190 0.000 0.943 0.865
## Rs_mn_1.GROUP1 0.236 0.093 2.548 0.011 0.236 0.198
## SCALED_3_Res ~
## GROUP1 0.171 0.200 0.853 0.394 0.171 0.082
## GROUP2 0.438 0.696 0.630 0.529 0.438 0.088
## SCALED_1_Res 0.475 0.241 1.972 0.049 0.475 0.453
## Rs_mn_1.GROUP1 -0.152 0.150 -1.017 0.309 -0.152 -0.133
## SCALED_2_Res 0.316 0.203 1.556 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 -1.125 0.261 -0.109 -0.101
## SCALED_3_Res -0.158 0.136 -1.164 0.244 -0.158 -0.152
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_Res 0.329 0.058 5.701 0.000 0.329 0.284
## SCALED_3_Res 0.415 0.087 4.794 0.000 0.415 0.387
##
## R-Square:
## Estimate
## SCALED_2_Res 0.716
## SCALED_3_Res 0.613## [[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 -0.58 0.56 -0.38, 0.21
## 3 GROUP2 -0.23 0.42 -0.54 0.59 -1.05, 0.59
## 4 Resiliance 0.94 0.08 11.19 0.00 0.78, 1.11
## 5 Interaction w/ Group 0.24 0.09 2.55 0.01 0.05, 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 0.17 0.20 0.85 0.39 -0.22, 0.56
## 3 GROUP2 0.44 0.70 0.63 0.53 -0.93, 1.8
## 4 Resiliance 0.47 0.24 1.97 0.05 0, 0.95
## 5 Interaction w/ Group -0.15 0.15 -1.02 0.31 -0.45, 0.14
##
##
## [[2]]
## levels Treat_T2 Control_T2 Treat_T3 Control_T3
## 1 -1 -1.37 -1.05 -0.57 -0.63
## 2 0 -0.20 -0.11 -0.24 -0.16
## 3 1 0.98 0.83 0.08 0.32plots(modelname = modelRes_interaction_T3, fitname = fit, data = adult2, interactionterm = "Res_mean_1.GROUP1", new_scale_name = "Resiliance") 
#########################################
################# 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 44 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 28.559
## Degrees of freedom 9
## P-value 0.001
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -261.689
## Loglikelihood unrestricted model (H1) -261.689
##
## Number of free parameters 13
## Akaike (AIC) 549.379
## Bayesian (BIC) 581.731
## Sample-size adjusted Bayesian (BIC) 540.705
##
## 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 -0.433 0.203 -2.130 0.033 -0.433 -0.214
## GROUP2 0.171 0.610 0.280 0.779 0.171 0.035
## SCALED_1_GRIT 0.545 0.109 4.980 0.000 0.545 0.543
## GRIT__1.GROUP1 -0.418 0.801 -0.521 0.602 -0.418 -0.059
## SCALED_3_GRIT ~
## GROUP1 0.048 0.295 0.164 0.870 0.048 0.024
## GROUP2 -0.589 0.988 -0.596 0.551 -0.589 -0.123
## SCALED_1_GRIT 0.200 0.171 1.164 0.244 0.200 0.202
## GRIT__1.GROUP1 0.431 1.199 0.360 0.719 0.431 0.062
##
## Covariances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_GRIT ~~
## SCALED_3_GRIT 0.034 0.121 0.281 0.779 0.034 0.044
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_GRIT 0.168 0.135 1.242 0.214 0.168 0.167
## SCALED_3_GRIT -0.044 0.198 -0.222 0.824 -0.044 -0.044
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_GRIT 0.633 0.114 5.554 0.000 0.633 0.623
## SCALED_3_GRIT 0.936 0.193 4.847 0.000 0.936 0.954
##
## R-Square:
## Estimate
## SCALED_2_GRIT 0.377
## SCALED_3_GRIT 0.046## [[1]]
## [[1]][[1]]
## Item β SE z p 90% CI
## 1 Intercept 0.17 0.14 1.24 0.21 -0.1, 0.43
## 2 GROUP1 -0.43 0.20 -2.13 0.03 -0.83, -0.03
## 3 GROUP2 0.17 0.61 0.28 0.78 -1.03, 1.37
## 4 Grit 0.54 0.11 4.98 0.00 0.33, 0.76
## 5 Interaction w/ Group -0.42 0.80 -0.52 0.60 -1.99, 1.15
##
## [[1]][[2]]
## Item β SE z p 90% CI
## 1 Intercept -0.04 0.20 -0.22 0.82 -0.43, 0.34
## 2 GROUP1 0.05 0.30 0.16 0.87 -0.53, 0.63
## 3 GROUP2 -0.59 0.99 -0.60 0.55 -2.53, 1.35
## 4 Grit 0.20 0.17 1.16 0.24 -0.14, 0.54
## 5 Interaction w/ Group 0.43 1.20 0.36 0.72 -1.92, 2.78
##
##
## [[2]]
## levels Treat_T2 Control_T2 Treat_T3 Control_T3
## 1 -1 -0.39 -0.38 -1.11 -0.24
## 2 0 -0.26 0.17 -0.48 -0.04
## 3 1 -0.14 0.71 0.15 0.16plots(modelname = modelGRIT_interaction, fitname = fit, data = adult2, interactionterm = "GRIT_mean_1.GROUP1", new_scale_name = "Grit Scale (Duckworth)") 
#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 45 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 28.559
## Degrees of freedom 9
## P-value 0.001
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -261.689
## Loglikelihood unrestricted model (H1) -261.689
##
## Number of free parameters 13
## Akaike (AIC) 549.379
## Bayesian (BIC) 581.731
## Sample-size adjusted Bayesian (BIC) 540.705
##
## 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 -0.433 0.203 -2.130 0.033 -0.433 -0.214
## GROUP2 0.171 0.610 0.280 0.779 0.171 0.035
## SCALED_1_GRIT 0.545 0.109 4.980 0.000 0.545 0.543
## GRIT__1.GROUP1 -0.418 0.801 -0.521 0.602 -0.418 -0.059
## SCALED_3_GRIT ~
## GROUP1 0.072 0.299 0.239 0.811 0.072 0.036
## GROUP2 -0.598 0.990 -0.604 0.546 -0.598 -0.125
## SCALED_1_GRIT 0.170 0.210 0.810 0.418 0.170 0.173
## GRIT__1.GROUP1 0.454 1.204 0.377 0.706 0.454 0.065
## SCALED_2_GRIT 0.054 0.191 0.281 0.779 0.054 0.055
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_GRIT 0.168 0.135 1.242 0.214 0.168 0.167
## SCALED_3_GRIT -0.053 0.198 -0.268 0.789 -0.053 -0.054
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_GRIT 0.633 0.114 5.554 0.000 0.633 0.623
## SCALED_3_GRIT 0.934 0.193 4.845 0.000 0.934 0.952
##
## R-Square:
## Estimate
## SCALED_2_GRIT 0.377
## SCALED_3_GRIT 0.048## [[1]]
## [[1]][[1]]
## Item β SE z p 90% CI
## 1 Intercept 0.17 0.14 1.24 0.21 -0.1, 0.43
## 2 GROUP1 -0.43 0.20 -2.13 0.03 -0.83, -0.03
## 3 GROUP2 0.17 0.61 0.28 0.78 -1.03, 1.37
## 4 Grit 0.54 0.11 4.98 0.00 0.33, 0.76
## 5 Interaction w/ Group -0.42 0.80 -0.52 0.60 -1.99, 1.15
##
## [[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 0.07 0.30 0.24 0.81 -0.51, 0.66
## 3 GROUP2 -0.60 0.99 -0.60 0.55 -2.54, 1.34
## 4 Grit 0.17 0.21 0.81 0.42 -0.24, 0.58
## 5 Interaction w/ Group 0.45 1.20 0.38 0.71 -1.91, 2.81
##
##
## [[2]]
## levels Treat_T2 Control_T2 Treat_T3 Control_T3
## 1 -1 -0.39 -0.38 -1.11 -0.22
## 2 0 -0.26 0.17 -0.49 -0.05
## 3 1 -0.14 0.71 0.14 0.12plots(modelname = modelGRIT_interaction_T3, fitname = fit, data = adult2, interactionterm = "GRIT_mean_1.GROUP1", new_scale_name = "Grit Scale (Duckworth)") 
#########################################
################# 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 30 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 62.164
## Degrees of freedom 9
## P-value 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -447.296
## Loglikelihood unrestricted model (H1) -447.296
##
## Number of free parameters 13
## Akaike (AIC) 920.593
## Bayesian (BIC) 952.945
## Sample-size adjusted Bayesian (BIC) 911.919
##
## 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 -0.154 0.208 -0.743 0.457 -0.154
## GROUP2 -0.303 0.596 -0.508 0.611 -0.303
## SCALED_1_PERMA 0.568 0.106 5.384 0.000 0.568
## PERMA_L__1.GRO 0.088 0.081 1.090 0.276 0.088
## SCALED_3_PERMA_Lonely ~
## GROUP1 -0.254 0.218 -1.164 0.244 -0.254
## GROUP2 -1.497 0.765 -1.956 0.050 -1.497
## SCALED_1_PERMA 0.666 0.115 5.775 0.000 0.666
## PERMA_L__1.GRO -0.172 0.090 -1.904 0.057 -0.172
## Std.all
##
## -0.077
## -0.063
## 0.567
## 0.116
##
## -0.121
## -0.296
## 0.634
## -0.216
##
## Covariances:
## Estimate Std.Err Z-value P(>|z|) Std.lv
## SCALED_2_PERMA_Lonely ~~
## SCALED_3_PERMA 0.257 0.088 2.915 0.004 0.257
## Std.all
##
## 0.437
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_PERMA 0.141 0.137 1.031 0.302 0.141 0.141
## SCALED_3_PERMA 0.225 0.147 1.533 0.125 0.225 0.214
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_PERMA 0.660 0.116 5.687 0.000 0.660 0.659
## SCALED_3_PERMA 0.525 0.108 4.874 0.000 0.525 0.477
##
## R-Square:
## Estimate
## SCALED_2_PERMA 0.341
## SCALED_3_PERMA 0.523## [[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 -0.15 0.21 -0.74 0.46 -0.56, 0.25
## 3 GROUP2 -0.30 0.60 -0.51 0.61 -1.47, 0.87
## 4 Perma Lonely 0.57 0.11 5.38 0.00 0.36, 0.78
## 5 Interaction w/ Group 0.09 0.08 1.09 0.28 -0.07, 0.25
##
## [[1]][[2]]
## Item β SE z p 90% CI
## 1 Intercept 0.22 0.15 1.53 0.13 -0.06, 0.51
## 2 GROUP1 -0.25 0.22 -1.16 0.24 -0.68, 0.17
## 3 GROUP2 -1.50 0.77 -1.96 0.05 -3, 0
## 4 Perma Lonely 0.67 0.12 5.78 0.00 0.44, 0.89
## 5 Interaction w/ Group -0.17 0.09 -1.90 0.06 -0.35, 0.01
##
##
## [[2]]
## levels Treat_T2 Control_T2 Treat_T3 Control_T3
## 1 -1 -0.67 -0.43 -0.42 -0.44
## 2 0 -0.01 0.14 0.07 0.22
## 3 1 0.64 0.71 0.56 0.89plots(modelname = modelPERMA_Lonely_interaction, fitname = fit, data = adult2, interactionterm = "PERMA_Lonely_mean_1.GROUP1", new_scale_name = "Perma Lonely") 
#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 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 62.164
## Degrees of freedom 9
## P-value 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -447.296
## Loglikelihood unrestricted model (H1) -447.296
##
## Number of free parameters 13
## Akaike (AIC) 920.593
## Bayesian (BIC) 952.945
## Sample-size adjusted Bayesian (BIC) 911.919
##
## 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 -0.154 0.208 -0.743 0.457 -0.154
## GROUP2 -0.303 0.596 -0.508 0.611 -0.303
## SCALED_1_PERMA 0.568 0.106 5.384 0.000 0.568
## PERMA_L__1.GRO 0.088 0.081 1.090 0.276 0.088
## SCALED_3_PERMA_Lonely ~
## GROUP1 -0.194 0.209 -0.925 0.355 -0.194
## GROUP2 -1.379 0.797 -1.730 0.084 -1.379
## SCALED_1_PERMA 0.445 0.125 3.542 0.000 0.445
## PERMA_L__1.GRO -0.206 0.084 -2.443 0.015 -0.206
## SCALED_2_PERMA 0.390 0.114 3.429 0.001 0.390
## Std.all
##
## -0.077
## -0.063
## 0.567
## 0.116
##
## -0.092
## -0.272
## 0.423
## -0.260
## 0.372
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_PERMA 0.141 0.137 1.031 0.302 0.141 0.141
## SCALED_3_PERMA 0.170 0.142 1.194 0.233 0.170 0.162
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_PERMA 0.660 0.116 5.687 0.000 0.660 0.659
## SCALED_3_PERMA 0.425 0.092 4.637 0.000 0.425 0.386
##
## R-Square:
## Estimate
## SCALED_2_PERMA 0.341
## SCALED_3_PERMA 0.614## [[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 -0.15 0.21 -0.74 0.46 -0.56, 0.25
## 3 GROUP2 -0.30 0.60 -0.51 0.61 -1.47, 0.87
## 4 Perma Lonely 0.57 0.11 5.38 0.00 0.36, 0.78
## 5 Interaction w/ Group 0.09 0.08 1.09 0.28 -0.07, 0.25
##
## [[1]][[2]]
## Item β SE z p 90% CI
## 1 Intercept 0.17 0.14 1.19 0.23 -0.11, 0.45
## 2 GROUP1 -0.19 0.21 -0.92 0.36 -0.6, 0.22
## 3 GROUP2 -1.38 0.80 -1.73 0.08 -2.94, 0.18
## 4 Perma Lonely 0.44 0.13 3.54 0.00 0.2, 0.69
## 5 Interaction w/ Group -0.21 0.08 -2.44 0.01 -0.37, -0.04
##
##
## [[2]]
## levels Treat_T2 Control_T2 Treat_T3 Control_T3
## 1 -1 -0.67 -0.43 -0.22 -0.27
## 2 0 -0.01 0.14 0.02 0.17
## 3 1 0.64 0.71 0.25 0.61plots(modelname = modelPERMA_Lonely_interaction_T3, fitname = fit, data = adult2, interactionterm = "PERMA_Lonely_mean_1.GROUP1", new_scale_name = "Perma Lonely") 
#########################################
################# 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 28 iterations
##
## Number of observations 89
##
## Number of missing patterns 5
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## Estimator ML
## Minimum Function Test Statistic 0.000
## Degrees of freedom 0
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## Model test baseline model:
##
## Minimum Function Test Statistic 82.370
## Degrees of freedom 9
## P-value 0.000
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## User model versus baseline model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
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## Loglikelihood and Information Criteria:
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## Loglikelihood user model (H0) -409.124
## Loglikelihood unrestricted model (H1) -409.124
##
## Number of free parameters 13
## Akaike (AIC) 844.249
## Bayesian (BIC) 876.601
## Sample-size adjusted Bayesian (BIC) 835.575
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent Confidence Interval 0.000 0.000
## P-value RMSEA <= 0.05 1.000
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## Standardized Root Mean Square Residual:
##
## SRMR 0.000
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## Parameter Estimates:
##
## Information Observed
## Standard Errors Standard
##
## Regressions:
## Estimate Std.Err Z-value P(>|z|) Std.lv
## SCALED_2_PERMA_Happy ~
## GROUP1 0.496 0.186 2.657 0.008 0.496
## GROUP2 0.942 0.537 1.752 0.080 0.942
## SCALED_1_PERMA 0.618 0.100 6.210 0.000 0.618
## PERMA_H__1.GRO -0.174 0.103 -1.692 0.091 -0.174
## SCALED_3_PERMA_Happy ~
## GROUP1 0.479 0.221 2.166 0.030 0.479
## GROUP2 0.546 0.786 0.694 0.488 0.546
## SCALED_1_PERMA 0.535 0.124 4.306 0.000 0.535
## PERMA_H__1.GRO -0.282 0.132 -2.141 0.032 -0.282
## Std.all
##
## 0.248
## 0.196
## 0.613
## -0.167
##
## 0.239
## 0.113
## 0.530
## -0.270
##
## Covariances:
## Estimate Std.Err Z-value P(>|z|) Std.lv
## SCALED_2_PERMA_Happy ~~
## SCALED_3_PERMA 0.337 0.097 3.494 0.000 0.337
## Std.all
##
## 0.599
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_PERMA -0.333 0.123 -2.719 0.007 -0.333 -0.334
## SCALED_3_PERMA -0.374 0.152 -2.467 0.014 -0.374 -0.375
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_PERMA 0.531 0.099 5.372 0.000 0.531 0.533
## SCALED_3_PERMA 0.596 0.122 4.897 0.000 0.596 0.597
##
## R-Square:
## Estimate
## SCALED_2_PERMA 0.467
## SCALED_3_PERMA 0.403## [[1]]
## [[1]][[1]]
## Item β SE z p 90% CI
## 1 Intercept -0.33 0.12 -2.72 0.01 -0.57, -0.09
## 2 GROUP1 0.50 0.19 2.66 0.01 0.13, 0.86
## 3 GROUP2 0.94 0.54 1.75 0.08 -0.11, 2
## 4 Perma Happy 0.62 0.10 6.21 0.00 0.42, 0.81
## 5 Interaction w/ Group -0.17 0.10 -1.69 0.09 -0.38, 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 0.48 0.22 2.17 0.03 0.05, 0.91
## 3 GROUP2 0.55 0.79 0.69 0.49 -1, 2.09
## 4 Perma Happy 0.53 0.12 4.31 0.00 0.29, 0.78
## 5 Interaction w/ Group -0.28 0.13 -2.14 0.03 -0.54, -0.02
##
##
## [[2]]
## levels Treat_T2 Control_T2 Treat_T3 Control_T3
## 1 -1 -0.28 -0.95 -0.13 -0.91
## 2 0 0.16 -0.33 0.12 -0.37
## 3 1 0.61 0.29 0.37 0.16plots(modelname = modelPERMA_Happy_interaction, fitname = fit, data = adult2, interactionterm = "PERMA_Happy_mean_1.GROUP1", new_scale_name = "Perma Happy") 
#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 26 iterations
##
## Number of observations 89
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## Number of missing patterns 5
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## Estimator ML
## Minimum Function Test Statistic 0.000
## Degrees of freedom 0
##
## Model test baseline model:
##
## Minimum Function Test Statistic 82.370
## Degrees of freedom 9
## P-value 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -409.124
## Loglikelihood unrestricted model (H1) -409.124
##
## Number of free parameters 13
## Akaike (AIC) 844.249
## Bayesian (BIC) 876.601
## Sample-size adjusted Bayesian (BIC) 835.575
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent Confidence Interval 0.000 0.000
## P-value RMSEA <= 0.05 1.000
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## Standardized Root Mean Square Residual:
##
## SRMR 0.000
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## Parameter Estimates:
##
## Information Observed
## Standard Errors Standard
##
## Regressions:
## Estimate Std.Err Z-value P(>|z|) Std.lv
## SCALED_2_PERMA_Happy ~
## GROUP1 0.496 0.186 2.657 0.008 0.496
## GROUP2 0.942 0.537 1.752 0.080 0.942
## SCALED_1_PERMA 0.618 0.100 6.210 0.000 0.618
## PERMA_H__1.GRO -0.174 0.103 -1.692 0.091 -0.174
## SCALED_3_PERMA_Happy ~
## GROUP1 0.165 0.208 0.794 0.427 0.165
## GROUP2 -0.052 0.860 -0.061 0.952 -0.052
## SCALED_1_PERMA 0.142 0.140 1.017 0.309 0.142
## PERMA_H__1.GRO -0.172 0.135 -1.274 0.203 -0.172
## SCALED_2_PERMA 0.635 0.132 4.801 0.000 0.635
## Std.all
##
## 0.248
## 0.196
## 0.613
## -0.167
##
## 0.082
## -0.011
## 0.141
## -0.164
## 0.634
##
## Intercepts:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_PERMA -0.333 0.123 -2.719 0.007 -0.333 -0.334
## SCALED_3_PERMA -0.163 0.142 -1.143 0.253 -0.163 -0.163
##
## Variances:
## Estimate Std.Err Z-value P(>|z|) Std.lv Std.all
## SCALED_2_PERMA 0.531 0.099 5.372 0.000 0.531 0.533
## SCALED_3_PERMA 0.382 0.090 4.231 0.000 0.382 0.382
##
## R-Square:
## Estimate
## SCALED_2_PERMA 0.467
## SCALED_3_PERMA 0.618## [[1]]
## [[1]][[1]]
## Item β SE z p 90% CI
## 1 Intercept -0.33 0.12 -2.72 0.01 -0.57, -0.09
## 2 GROUP1 0.50 0.19 2.66 0.01 0.13, 0.86
## 3 GROUP2 0.94 0.54 1.75 0.08 -0.11, 2
## 4 Perma Happy 0.62 0.10 6.21 0.00 0.42, 0.81
## 5 Interaction w/ Group -0.17 0.10 -1.69 0.09 -0.38, 0.03
##
## [[1]][[2]]
## Item β SE z p 90% CI
## 1 Intercept -0.16 0.14 -1.14 0.25 -0.44, 0.12
## 2 GROUP1 0.16 0.21 0.79 0.43 -0.24, 0.57
## 3 GROUP2 -0.05 0.86 -0.06 0.95 -1.74, 1.63
## 4 Perma Happy 0.14 0.14 1.02 0.31 -0.13, 0.42
## 5 Interaction w/ Group -0.17 0.13 -1.27 0.20 -0.44, 0.09
##
##
## [[2]]
## levels Treat_T2 Control_T2 Treat_T3 Control_T3
## 1 -1 -0.28 -0.95 0.36 -0.31
## 2 0 0.16 -0.33 0.33 -0.16
## 3 1 0.61 0.29 0.30 -0.02plots(modelname = modelPERMA_Happy_interaction_T3, fitname = fit, data = adult2, interactionterm = "PERMA_Happy_mean_1.GROUP1", new_scale_name = "Perma Happy") 