#|cache=T
#|autodept=T
#|cache.comments=TReliability
On this page, reliability is discussed in more detail.
Setup of Analysis
# load packages
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This is semTools 0.5-6
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alphaWarning: package 'ggcorrplot' was built under R version 4.2.1set.seed(12345) # for replicability# Setup of Analysis
datw1 <- readr::read_csv("data/UBC_Data_Wave1_Clean.csv");Rows: 4058 Columns: 86
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (17): HSP ID, help, Gender_Other, Culture, Culture_Other, First_Lanuage,...
dbl (69): Gender, Age, Year_School, CG1, CG2, CG3, CI1, CI2, CI3, SS1, SS2, ...
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.datw1$wave = 1
datw2 <- readr::read_csv("data/UBC_Data_Wave2_Clean.csv")Rows: 4415 Columns: 117
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (32): HSP ID, help, Gender_Other, Culture, Culture_Other, First_Lanuage,...
dbl (85): duration, Gender, Age, Year_School, CG1, CG2, CG3, CI1, CI2, CI3, ...
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.datw2$wave = 2
mydata <- full_join(datw1, datw2)Joining, by = c("HSP ID", "help", "Gender", "Gender_Other", "Age", "Culture",
"Culture_Other", "First_Lanuage", "First_Lanuage_O", "First_Lanuage_2",
"English_Years", "English_Fluent", "Nationality", "Years_Canada",
"Old_Move_Canada", "Religion", "Religion_Other", "Handedness", "Political",
"Year_School", "CG1", "CG2", "CG3", "CI1", "CI2", "CI3", "SS1", "SS2", "SS3",
"SO1", "SO2", "SO3", "DM1", "DM2", "DM3", "DP1", "DP2", "DP3", "DG1", "DG2",
"DG3", "HSP ID_decline", "help_decline", "Gender_decline",
"Gender_Other_decline", "Age_decline", "Culture_decline",
"Culture_Other_decline", "First_Lanuage_decline", "First_Lanuage_O_decline",
"First_Lanuage_2_decline", "English_Years_decline", "English_Fluent_decline",
"Nationality_decline", "Years_Canada_decline", "Old_Move_Canada_decline",
"Religion_decline", "Religion_Other_decline", "Handedness_decline",
"Political_decline", "Year_School_decline", "CG1_decline", "CG2_decline",
"CG3_decline", "CI1_decline", "CI2_decline", "CI3_decline", "SS1_decline",
"SS2_decline", "SS3_decline", "SO1_decline", "SO2_decline", "SO3_decline",
"DM1_decline", "DM2_decline", "DM3_decline", "DP1_decline", "DP2_decline",
"DP3_decline", "DG1_decline", "DG2_decline", "DG3_decline",
"item_decline_total", "Age_Group", "keep_case", "wave")ITEMS <- c(paste0("CG",1:3), paste0("CI",1:3), paste0("SS",1:3), paste0("SO",1:3), paste0("DM",1:3), paste0("DP",1:3), paste0("DG",1:3))#|include=F
temp <- mydata %>%
dplyr::select(all_of(c("HSP ID","wave", "Gender", "Age", "Year_School", ITEMS))) %>%
group_by(`HSP ID`)%>%
mutate(
person_response_total = n()
) %>%
arrange(desc(person_response_total), `HSP ID`)
# View(temp)
# for test-retest reliability analysis
# remove HSP ID#
# 10310726 - age 19 - error in entry of ID number
# 19860731 - age 22 - ""
# 32040619 - wave 1 & age 19- ""
# 38851108 - two wave one data only
# 39550606 - ""
n.prev = nrow(mydata)
temp.mydata <- mydata %>%
dplyr::select(
all_of(c("HSP ID","wave", "Gender", "Age",
"Year_School", ITEMS))
) %>%
# remove specific rows with known issues
filter(
!(`HSP ID` == 10310726 & Age == 19),
!(`HSP ID` == 19860731 & Age == 22),
!(`HSP ID` == 32040619 & Age == 19 & wave == 1),
!(`HSP ID` == 38851108),
!(`HSP ID` == 39550606)
)
nrow(temp.mydata) - n.prev[1] -7# Lost 7 responses
# Next, check the counts for missing at each wave
temp.mydata$n_miss_items <- rowSums(is.na(temp.mydata[,ITEMS]))
# overall
table(temp.mydata$n_miss_items)
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
7943 177 57 21 21 13 12 4 6 12 3 7 4 8 7 8
16 17 18 19 20 21
6 5 5 2 14 131 # by wave with complete missing at top (n_miss_items = 21)
temp.mydata %>%
group_by(wave)%>%
count(n_miss_items) %>%
pivot_wider(
id_cols = "n_miss_items",
names_from = "wave",
names_prefix = "Count Wave ",
values_from = "n",
values_fill = 0
) %>%
arrange(desc(n_miss_items))%>%
kable(format="html") %>%
kable_styling(full_width = T)| n_miss_items | Count Wave 1 | Count Wave 2 |
|---|---|---|
| 21 | 80 | 51 |
| 20 | 8 | 6 |
| 19 | 1 | 1 |
| 18 | 3 | 2 |
| 17 | 4 | 1 |
| 16 | 4 | 2 |
| 15 | 5 | 3 |
| 14 | 3 | 4 |
| 13 | 6 | 2 |
| 12 | 3 | 1 |
| 11 | 1 | 6 |
| 10 | 1 | 2 |
| 9 | 2 | 10 |
| 8 | 4 | 2 |
| 7 | 1 | 3 |
| 6 | 6 | 6 |
| 5 | 11 | 2 |
| 4 | 12 | 9 |
| 3 | 8 | 13 |
| 2 | 29 | 28 |
| 1 | 114 | 63 |
| 0 | 3745 | 4198 |
# Exclude individuals with missing at either wave
n.prev = nrow(temp.mydata)
temp.mydata <- temp.mydata %>%
filter(n_miss_items == 0)
nrow(temp.mydata) - n.prev[1] -523# lost 523 cases
# make sure to exclude each wave for those cases
n.prev = nrow(temp.mydata)
temp.mydata <- temp.mydata %>%
group_by(`HSP ID`)%>%
mutate(
respond_both_waves = ifelse(n()==2,1,0)
) %>%
filter(respond_both_waves==1)
nrow(temp.mydata) - n.prev[1] -5473# lost 5473
# Lastly, create two dataframe for use in the analyses
# (1) long format with "Wave indicator variable" for multi-group
analysis.dat <- temp.mydata %>%
dplyr::select(all_of(c("HSP ID","wave", "Gender", "Age", "Year_School", ITEMS)))
# (2) wide format for longitudinal IFA models
analysis.dat.wide <- analysis.dat %>%
pivot_wider(
id_cols = "HSP ID",
names_from = "wave",
values_from = all_of(ITEMS)
)Model Specification
full_model <- "
## ================================================= ##
## Wave 1
# Factor Loadings
# NOTE: you need to enter the first item of each factor twice in order to set the label (X.lambda.1) so that lavaan estimates the loading instead of fixing it to 1
C1 =~ NA*CG1_1 + C.lambda.1*CG1_1 + C.lambda.2*CG2_1 + C.lambda.3*CG3_1 + C.lambda.4*CI1_1 + C.lambda.5*CI2_1 + C.lambda.6*CI3_1
S1 =~ NA*SS1_1 + S.lambda.1*SS1_1 + S.lambda.2*SS2_1 + S.lambda.3*SS3_1 + S.lambda.4*SO1_1 + S.lambda.5*SO2_1 + S.lambda.6*SO3_1
D1 =~ NA*DM1_1 + D.lambda.1*DM1_1 + D.lambda.2*DM2_1 + D.lambda.3*DM3_1 + D.lambda.4*DP1_1 + D.lambda.5*DP2_1 + D.lambda.6*DP3_1 + D.lambda.7*DG1_1 + D.lambda.8*DG2_1 + D.lambda.9*DG3_1
# Factor covariances
C1 ~~ S1 + D1
S1 ~~ D1
# Factor variances
C1 ~~ 1*C1
S1 ~~ 1*S1
D1 ~~ 1*D1
# Factor means/intercepts
C1 ~ 0*1
S1 ~ 0*1
D1 ~ 0*1
# Thresholds
CG1_1 | CG1.thr1*t1 + CG1.thr2*t2 + CG1.thr3*t3 + CG1.thr4*t4 + CG1.thr5*t5 + CG1.thr6*t6
CG2_1 | CG2.thr1*t1 + CG2.thr2*t2 + CG2.thr3*t3 + CG2.thr4*t4 + CG2.thr5*t5 + CG2.thr6*t6
CG3_1 | CG3.thr1*t1 + CG3.thr2*t2 + CG3.thr3*t3 + CG3.thr4*t4 + CG3.thr5*t5 + CG3.thr6*t6
CI1_1 | CI1.thr1*t1 + CI1.thr2*t2 + CI1.thr3*t3 + CI1.thr4*t4 + CI1.thr5*t5 + CI1.thr6*t6
CI2_1 | CI2.thr1*t1 + CI2.thr2*t2 + CI2.thr3*t3 + CI2.thr4*t4 + CI2.thr5*t5 + CI2.thr6*t6
CI3_1 | CI3.thr1*t1 + CI3.thr2*t2 + CI3.thr3*t3 + CI3.thr4*t4 + CI3.thr5*t5 + CI3.thr6*t6
SS1_1 | SS1.thr1*t1 + SS1.thr2*t2 + SS1.thr3*t3 + SS1.thr4*t4 + SS1.thr5*t5 + SS1.thr6*t6
SS2_1 | SS2.thr1*t1 + SS2.thr2*t2 + SS2.thr3*t3 + SS2.thr4*t4 + SS2.thr5*t5 + SS2.thr6*t6
SS3_1 | SS3.thr1*t1 + SS3.thr2*t2 + SS3.thr3*t3 + SS3.thr4*t4 + SS3.thr5*t5 + SS3.thr6*t6
SO1_1 | SO1.thr1*t1 + SO1.thr2*t2 + SO1.thr3*t3 + SO1.thr4*t4 + SO1.thr5*t5 + SO1.thr6*t6
SO2_1 | SO2.thr1*t1 + SO2.thr2*t2 + SO2.thr3*t3 + SO2.thr4*t4 + SO2.thr5*t5 + SO2.thr6*t6
SO3_1 | SO3.thr1*t1 + SO3.thr2*t2 + SO3.thr3*t3 + SO3.thr4*t4 + SO3.thr5*t5 + SO3.thr6*t6
DM1_1 | DM1.thr1*t1 + DM1.thr2*t2 + DM1.thr3*t3 + DM1.thr4*t4 + DM1.thr5*t5 + DM1.thr6*t6
DM2_1 | DM2.thr1*t1 + DM2.thr2*t2 + DM2.thr3*t3 + DM2.thr4*t4 + DM2.thr5*t5 + DM2.thr6*t6
DM3_1 | DM3.thr1*t1 + DM3.thr2*t2 + DM3.thr3*t3 + DM3.thr4*t4 + DM3.thr5*t5 + DM3.thr6*t6
DP1_1 | DP1.thr1*t1 + DP1.thr2*t2 + DP1.thr3*t3 + DP1.thr4*t4 + DP1.thr5*t5 + DP1.thr6*t6
DP2_1 | DP2.thr1*t1 + DP2.thr2*t2 + DP2.thr3*t3 + DP2.thr4*t4 + DP2.thr5*t5 + DP2.thr6*t6
DP3_1 | DP3.thr1*t1 + DP3.thr2*t2 + DP3.thr3*t3 + DP3.thr4*t4 + DP3.thr5*t5 + DP3.thr6*t6
DG1_1 | DG1.thr1*t1 + DG1.thr2*t2 + DG1.thr3*t3 + DG1.thr4*t4 + DG1.thr5*t5 + DG1.thr6*t6
DG2_1 | DG2.thr1*t1 + DG2.thr2*t2 + DG2.thr3*t3 + DG2.thr4*t4 + DG2.thr5*t5 + DG2.thr6*t6
DG3_1 | DG3.thr1*t1 + DG3.thr2*t2 + DG3.thr3*t3 + DG3.thr4*t4 + DG3.thr5*t5 + DG3.thr6*t6
# latent response intercepts - fixed to 0
CG1_1 ~ 0*1
CG2_1 ~ 0*1
CG3_1 ~ 0*1
CI1_1 ~ 0*1
CI2_1 ~ 0*1
CI3_1 ~ 0*1
SS1_1 ~ 0*1
SS2_1 ~ 0*1
SS3_1 ~ 0*1
SO1_1 ~ 0*1
SO2_1 ~ 0*1
SO3_1 ~ 0*1
DM1_1 ~ 0*1
DM2_1 ~ 0*1
DM3_1 ~ 0*1
DP1_1 ~ 0*1
DP2_1 ~ 0*1
DP3_1 ~ 0*1
DG1_1 ~ 0*1
DG2_1 ~ 0*1
DG3_1 ~ 0*1
# latent response scales - fixed to 1
CG1_1 ~~ 1*CG1_1
CG2_1 ~~ 1*CG2_1
CG3_1 ~~ 1*CG3_1
CI1_1 ~~ 1*CI1_1
CI2_1 ~~ 1*CI2_1
CI3_1 ~~ 1*CI3_1
SS1_1 ~~ 1*SS1_1
SS2_1 ~~ 1*SS2_1
SS3_1 ~~ 1*SS3_1
SO1_1 ~~ 1*SO1_1
SO2_1 ~~ 1*SO2_1
SO3_1 ~~ 1*SO3_1
DM1_1 ~~ 1*DM1_1
DM2_1 ~~ 1*DM2_1
DM3_1 ~~ 1*DM3_1
DP1_1 ~~ 1*DP1_1
DP2_1 ~~ 1*DP2_1
DP3_1 ~~ 1*DP3_1
DG1_1 ~~ 1*DG1_1
DG2_1 ~~ 1*DG2_1
DG3_1 ~~ 1*DG3_1
## ================================================= ##
## Wave 2
# Factor Loadings
# NOTE: you need to enter the first item of each factor twice in order to set the label (X.lambda.1) so that lavaan estimates the loading instead of fixing it to 1
C2 =~ NA*CG1_2 + C.lambda.1*CG1_2 + C.lambda.2*CG2_2 + C.lambda.3*CG3_2 + C.lambda.4*CI1_2 + C.lambda.5*CI2_2 + C.lambda.6*CI3_2
S2 =~ NA*SS1_2 + S.lambda.1*SS1_2 + S.lambda.2*SS2_2 + S.lambda.3*SS3_2 + S.lambda.4*SO1_2 + S.lambda.5*SO2_2 + S.lambda.6*SO3_2
D2 =~ NA*DM1_2 + D.lambda.1*DM1_2 + D.lambda.2*DM2_2 + D.lambda.3*DM3_2 + D.lambda.4*DP1_2 + D.lambda.5*DP2_2 + D.lambda.6*DP3_2 + D.lambda.7*DG1_2 + D.lambda.8*DG2_2 + D.lambda.9*DG3_2
# Factor covariances
C2 ~~ S2 + D2
S2 ~~ D2
# Factor Variances - freely estimate
C2 ~~ NA*C2 + phi.c*C2
S2 ~~ NA*S2 + phi.s*S2
D2 ~~ NA*D2 + phi.d*D2
# Factor means/intercepts - freely estimate
C2 ~ NA*1
S2 ~ NA*1
D2 ~ NA*1
# Thresholds
CG1_2 | CG1.thr1*t1 + CG1.thr2*t2 + CG1.thr3*t3 + CG1.thr4*t4 + CG1.thr5*t5 + CG1.thr6*t6
CG2_2 | CG2.thr1*t1 + CG2.thr2*t2 + CG2.thr3*t3 + CG2.thr4*t4 + CG2.thr5*t5 + CG2.thr6*t6
CG3_2 | CG3.thr1*t1 + CG3.thr2*t2 + CG3.thr3*t3 + CG3.thr4*t4 + CG3.thr5*t5 + CG3.thr6*t6
CI1_2 | CI1.thr1*t1 + CI1.thr2*t2 + CI1.thr3*t3 + CI1.thr4*t4 + CI1.thr5*t5 + CI1.thr6*t6
CI2_2 | CI2.thr1*t1 + CI2.thr2*t2 + CI2.thr3*t3 + CI2.thr4*t4 + CI2.thr5*t5 + CI2.thr6*t6
CI3_2 | CI3.thr1*t1 + CI3.thr2*t2 + CI3.thr3*t3 + CI3.thr4*t4 + CI3.thr5*t5 + CI3.thr6*t6
SS1_2 | SS1.thr1*t1 + SS1.thr2*t2 + SS1.thr3*t3 + SS1.thr4*t4 + SS1.thr5*t5 + SS1.thr6*t6
SS2_2 | SS2.thr1*t1 + SS2.thr2*t2 + SS2.thr3*t3 + SS2.thr4*t4 + SS2.thr5*t5 + SS2.thr6*t6
SS3_2 | SS3.thr1*t1 + SS3.thr2*t2 + SS3.thr3*t3 + SS3.thr4*t4 + SS3.thr5*t5 + SS3.thr6*t6
SO1_2 | SO1.thr1*t1 + SO1.thr2*t2 + SO1.thr3*t3 + SO1.thr4*t4 + SO1.thr5*t5 + SO1.thr6*t6
SO2_2 | SO2.thr1*t1 + SO2.thr2*t2 + SO2.thr3*t3 + SO2.thr4*t4 + SO2.thr5*t5 + SO2.thr6*t6
SO3_2 | SO3.thr1*t1 + SO3.thr2*t2 + SO3.thr3*t3 + SO3.thr4*t4 + SO3.thr5*t5 + SO3.thr6*t6
DM1_2 | DM1.thr1*t1 + DM1.thr2*t2 + DM1.thr3*t3 + DM1.thr4*t4 + DM1.thr5*t5 + DM1.thr6*t6
DM2_2 | DM2.thr1*t1 + DM2.thr2*t2 + DM2.thr3*t3 + DM2.thr4*t4 + DM2.thr5*t5 + DM2.thr6*t6
DM3_2 | DM3.thr1*t1 + DM3.thr2*t2 + DM3.thr3*t3 + DM3.thr4*t4 + DM3.thr5*t5 + DM3.thr6*t6
DP1_2 | DP1.thr1*t1 + DP1.thr2*t2 + DP1.thr3*t3 + DP1.thr4*t4 + DP1.thr5*t5 + DP1.thr6*t6
DP2_2 | DP2.thr1*t1 + DP2.thr2*t2 + DP2.thr3*t3 + DP2.thr4*t4 + DP2.thr5*t5 + DP2.thr6*t6
DP3_2 | DP3.thr1*t1 + DP3.thr2*t2 + DP3.thr3*t3 + DP3.thr4*t4 + DP3.thr5*t5 + DP3.thr6*t6
DG1_2 | DG1.thr1*t1 + DG1.thr2*t2 + DG1.thr3*t3 + DG1.thr4*t4 + DG1.thr5*t5 + DG1.thr6*t6
DG2_2 | DG2.thr1*t1 + DG2.thr2*t2 + DG2.thr3*t3 + DG2.thr4*t4 + DG2.thr5*t5 + DG2.thr6*t6
DG3_2 | DG3.thr1*t1 + DG3.thr2*t2 + DG3.thr3*t3 + DG3.thr4*t4 + DG3.thr5*t5 + DG3.thr6*t6
# latent response intercepts
CG1_2 ~ 0*1
CG2_2 ~ 0*1
CG3_2 ~ 0*1
CI1_2 ~ 0*1
CI2_2 ~ 0*1
CI3_2 ~ 0*1
SS1_2 ~ 0*1
SS2_2 ~ 0*1
SS3_2 ~ 0*1
SO1_2 ~ 0*1
SO2_2 ~ 0*1
SO3_2 ~ 0*1
DM1_2 ~ 0*1
DM2_2 ~ 0*1
DM3_2 ~ 0*1
DP1_2 ~ 0*1
DP2_2 ~ 0*1
DP3_2 ~ 0*1
DG1_2 ~ 0*1
DG2_2 ~ 0*1
DG3_2 ~ 0*1
# latent response variances
CG1_2 ~~ 1*CG1_2
CG2_2 ~~ 1*CG2_2
CG3_2 ~~ 1*CG3_2
CI1_2 ~~ 1*CI1_2
CI2_2 ~~ 1*CI2_2
CI3_2 ~~ 1*CI3_2
SS1_2 ~~ 1*SS1_2
SS2_2 ~~ 1*SS2_2
SS3_2 ~~ 1*SS3_2
SO1_2 ~~ 1*SO1_2
SO2_2 ~~ 1*SO2_2
SO3_2 ~~ 1*SO3_2
DM1_2 ~~ 1*DM1_2
DM2_2 ~~ 1*DM2_2
DM3_2 ~~ 1*DM3_2
DP1_2 ~~ 1*DP1_2
DP2_2 ~~ 1*DP2_2
DP3_2 ~~ 1*DP3_2
DG1_2 ~~ 1*DG1_2
DG2_2 ~~ 1*DG2_2
DG3_2 ~~ 1*DG3_2
## ================================================= ##
# cross wave factor covariances
C1 ~~ C2 + S2 + D2
S1 ~~ C2 + S2 + D2
D1 ~~ C2 + S2 + D2
# cross wave item residual covariances
CG1_1 ~~ CG1_2
CG2_1 ~~ CG2_2
CG3_1 ~~ CG3_2
CI1_1 ~~ CI1_2
CI2_1 ~~ CI2_2
CI3_1 ~~ CI3_2
SS1_1 ~~ SS1_2
SS2_1 ~~ SS2_2
SS3_1 ~~ SS3_2
SO1_1 ~~ SO1_2
SO2_1 ~~ SO2_2
SO3_1 ~~ SO3_2
DM1_1 ~~ DM1_2
DM2_1 ~~ DM2_2
DM3_1 ~~ DM3_2
DP1_1 ~~ DP1_2
DP2_1 ~~ DP2_2
DP3_1 ~~ DP3_2
DG1_1 ~~ DG1_2
DG2_1 ~~ DG2_2
DG3_1 ~~ DG3_2
# define omega for estimation
omega.c := (C.lambda.1 + C.lambda.2 + C.lambda.3 + C.lambda.4 + C.lambda.5 + C.lambda.6)^2/( (C.lambda.1 + C.lambda.2 + C.lambda.3 + C.lambda.4 + C.lambda.5 + C.lambda.6)^2 + 6)
omega.s := (S.lambda.1 + S.lambda.2 + S.lambda.3 + S.lambda.4 + S.lambda.5 + S.lambda.6)^2/((S.lambda.1 + S.lambda.2 + S.lambda.3 + S.lambda.4 + S.lambda.5 + S.lambda.6)^2 + 6)
omega.d := (D.lambda.1 + D.lambda.2 + D.lambda.3 + D.lambda.4 + D.lambda.5 + D.lambda.6 + D.lambda.7 + D.lambda.8 + D.lambda.9)^2/((D.lambda.1 + D.lambda.2 + D.lambda.3 + D.lambda.4 + D.lambda.5 + D.lambda.6 + D.lambda.7 + D.lambda.8 + D.lambda.9)^2+9)
omega.c2 := (C.lambda.1 + C.lambda.2 + C.lambda.3 + C.lambda.4 + C.lambda.5 + C.lambda.6)^2*phi.c/( (C.lambda.1 + C.lambda.2 + C.lambda.3 + C.lambda.4 + C.lambda.5 + C.lambda.6)^2*phi.c + 6)
omega.s2 := (S.lambda.1 + S.lambda.2 + S.lambda.3 + S.lambda.4 + S.lambda.5 + S.lambda.6)^2*phi.s/((S.lambda.1 + S.lambda.2 + S.lambda.3 + S.lambda.4 + S.lambda.5 + S.lambda.6)^2*phi.s + 6)
omega.d2 := (D.lambda.1 + D.lambda.2 + D.lambda.3 + D.lambda.4 + D.lambda.5 + D.lambda.6 + D.lambda.7 + D.lambda.8 + D.lambda.9)^2*phi.d/((D.lambda.1 + D.lambda.2 + D.lambda.3 + D.lambda.4 + D.lambda.5 + D.lambda.6 + D.lambda.7 + D.lambda.8 + D.lambda.9)^2*phi.d+9)
# maximal reliability
max.c := 1/(1 + (C.lambda.1^2 + C.lambda.2^2 + C.lambda.3^2 + C.lambda.4^2 + C.lambda.5^2 + C.lambda.6^2)^(-1))
max.S := 1/(1 + (S.lambda.1^2 + S.lambda.2^2 + S.lambda.3^2 + S.lambda.4^2 + S.lambda.5^2 + S.lambda.6^2)^(-1))
max.D := 1/(1 + (D.lambda.1^2 + D.lambda.2^2 + D.lambda.3^2 + D.lambda.4^2 + D.lambda.5^2 + D.lambda.6^2 + D.lambda.7^2 + D.lambda.8^2 + D.lambda.9^2)^(-1))
"Obtain Reliability Estimates
# fit model
fit <- cfa(
model=full_model,
data = analysis.dat.wide,
ordered = T
)
summary(fit, standardized=T, fit.measures=T,ci = T, rsquare=T)lavaan 0.6-12 ended normally after 117 iterations
Estimator DWLS
Optimization method NLMINB
Number of model parameters 336
Number of equality constraints 147
Number of observations 1235
Model Test User Model:
Standard Robust
Test Statistic 8487.827 7368.276
Degrees of freedom 924 924
P-value (Chi-square) 0.000 0.000
Scaling correction factor 1.241
Shift parameter 530.733
simple second-order correction
Model Test Baseline Model:
Test statistic 611392.010 90588.712
Degrees of freedom 861 861
P-value 0.000 0.000
Scaling correction factor 6.804
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.988 0.928
Tucker-Lewis Index (TLI) 0.988 0.933
Robust Comparative Fit Index (CFI) NA
Robust Tucker-Lewis Index (TLI) NA
Root Mean Square Error of Approximation:
RMSEA 0.081 0.075
90 Percent confidence interval - lower 0.080 0.074
90 Percent confidence interval - upper 0.083 0.077
P-value RMSEA <= 0.05 0.000 0.000
Robust RMSEA NA
90 Percent confidence interval - lower NA
90 Percent confidence interval - upper NA
Standardized Root Mean Square Residual:
SRMR 0.057 0.057
Parameter Estimates:
Standard errors Robust.sem
Information Expected
Information saturated (h1) model Unstructured
Latent Variables:
Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
C1 =~
CG1_1 (C..1) 0.853 0.008 112.145 0.000 0.838 0.867
CG2_1 (C..2) 0.853 0.007 116.637 0.000 0.839 0.867
CG3_1 (C..3) 0.778 0.010 77.872 0.000 0.758 0.797
CI1_1 (C..4) 0.897 0.007 122.476 0.000 0.883 0.911
CI2_1 (C..5) 0.660 0.015 44.640 0.000 0.631 0.689
CI3_1 (C..6) 0.756 0.012 61.506 0.000 0.732 0.780
S1 =~
SS1_1 (S..1) 0.794 0.011 74.504 0.000 0.773 0.815
SS2_1 (S..2) 0.698 0.014 51.029 0.000 0.671 0.725
SS3_1 (S..3) 0.882 0.009 100.022 0.000 0.864 0.899
SO1_1 (S..4) 0.644 0.015 44.086 0.000 0.615 0.672
SO2_1 (S..5) 0.735 0.012 60.840 0.000 0.712 0.759
SO3_1 (S..6) 0.722 0.013 56.613 0.000 0.697 0.746
D1 =~
DM1_1 (D..1) 0.782 0.010 76.422 0.000 0.762 0.802
DM2_1 (D..2) 0.834 0.008 106.572 0.000 0.818 0.849
DM3_1 (D..3) 0.873 0.006 141.273 0.000 0.861 0.885
DP1_1 (D..4) 0.888 0.006 146.907 0.000 0.876 0.900
DP2_1 (D..5) 0.851 0.008 108.859 0.000 0.836 0.867
DP3_1 (D..6) 0.720 0.011 64.683 0.000 0.698 0.742
DG1_1 (D..7) 0.763 0.010 74.408 0.000 0.743 0.783
DG2_1 (D..8) 0.696 0.012 57.764 0.000 0.673 0.720
DG3_1 (D..9) 0.677 0.012 54.317 0.000 0.653 0.702
C2 =~
CG1_2 (C..1) 0.853 0.008 112.145 0.000 0.838 0.867
CG2_2 (C..2) 0.853 0.007 116.637 0.000 0.839 0.867
CG3_2 (C..3) 0.778 0.010 77.872 0.000 0.758 0.797
CI1_2 (C..4) 0.897 0.007 122.476 0.000 0.883 0.911
CI2_2 (C..5) 0.660 0.015 44.640 0.000 0.631 0.689
CI3_2 (C..6) 0.756 0.012 61.506 0.000 0.732 0.780
S2 =~
SS1_2 (S..1) 0.794 0.011 74.504 0.000 0.773 0.815
SS2_2 (S..2) 0.698 0.014 51.029 0.000 0.671 0.725
SS3_2 (S..3) 0.882 0.009 100.022 0.000 0.864 0.899
SO1_2 (S..4) 0.644 0.015 44.086 0.000 0.615 0.672
SO2_2 (S..5) 0.735 0.012 60.840 0.000 0.712 0.759
SO3_2 (S..6) 0.722 0.013 56.613 0.000 0.697 0.746
D2 =~
DM1_2 (D..1) 0.782 0.010 76.422 0.000 0.762 0.802
DM2_2 (D..2) 0.834 0.008 106.572 0.000 0.818 0.849
DM3_2 (D..3) 0.873 0.006 141.273 0.000 0.861 0.885
DP1_2 (D..4) 0.888 0.006 146.907 0.000 0.876 0.900
DP2_2 (D..5) 0.851 0.008 108.859 0.000 0.836 0.867
DP3_2 (D..6) 0.720 0.011 64.683 0.000 0.698 0.742
DG1_2 (D..7) 0.763 0.010 74.408 0.000 0.743 0.783
DG2_2 (D..8) 0.696 0.012 57.764 0.000 0.673 0.720
DG3_2 (D..9) 0.677 0.012 54.317 0.000 0.653 0.702
Std.lv Std.all
0.853 0.853
0.853 0.853
0.778 0.778
0.897 0.897
0.660 0.660
0.756 0.756
0.794 0.794
0.698 0.698
0.882 0.882
0.644 0.644
0.735 0.735
0.722 0.722
0.782 0.782
0.834 0.834
0.873 0.873
0.888 0.888
0.851 0.851
0.720 0.720
0.763 0.763
0.696 0.696
0.677 0.677
0.889 0.889
0.889 0.889
0.811 0.811
0.935 0.935
0.688 0.688
0.789 0.789
0.815 0.815
0.717 0.717
0.905 0.905
0.661 0.661
0.755 0.755
0.741 0.741
0.811 0.811
0.864 0.864
0.906 0.906
0.920 0.920
0.883 0.883
0.747 0.747
0.791 0.791
0.722 0.722
0.702 0.702
Covariances:
Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
C1 ~~
S1 0.747 0.013 58.756 0.000 0.722 0.772
D1 0.703 0.013 53.038 0.000 0.677 0.729
S1 ~~
D1 0.784 0.012 65.242 0.000 0.760 0.807
C2 ~~
S2 0.810 0.019 42.703 0.000 0.773 0.848
D2 0.818 0.017 49.063 0.000 0.785 0.851
S2 ~~
D2 0.881 0.018 50.276 0.000 0.847 0.916
C1 ~~
C2 0.693 0.017 40.460 0.000 0.660 0.727
S2 0.581 0.021 28.193 0.000 0.541 0.622
D2 0.573 0.020 28.878 0.000 0.534 0.612
S1 ~~
C2 0.569 0.021 26.814 0.000 0.528 0.611
S2 0.730 0.018 41.213 0.000 0.695 0.764
D2 0.610 0.020 30.358 0.000 0.570 0.649
D1 ~~
C2 0.564 0.020 28.009 0.000 0.525 0.604
S2 0.637 0.019 33.592 0.000 0.600 0.674
D2 0.691 0.016 42.287 0.000 0.659 0.723
.CG1_1 ~~
.CG1_2 0.086 0.010 8.526 0.000 0.066 0.106
.CG2_1 ~~
.CG2_2 0.052 0.011 4.841 0.000 0.031 0.073
.CG3_1 ~~
.CG3_2 0.108 0.012 9.356 0.000 0.086 0.131
.CI1_1 ~~
.CI1_2 0.047 0.010 4.810 0.000 0.028 0.066
.CI2_1 ~~
.CI2_2 0.171 0.016 10.491 0.000 0.139 0.202
.CI3_1 ~~
.CI3_2 0.151 0.014 10.452 0.000 0.123 0.180
.SS1_1 ~~
.SS1_2 0.138 0.013 10.624 0.000 0.113 0.164
.SS2_1 ~~
.SS2_2 0.219 0.016 13.762 0.000 0.188 0.250
.SS3_1 ~~
.SS3_2 0.017 0.011 1.563 0.118 -0.004 0.038
.SO1_1 ~~
.SO1_2 0.190 0.018 10.787 0.000 0.156 0.225
.SO2_1 ~~
.SO2_2 0.199 0.014 14.125 0.000 0.172 0.227
.SO3_1 ~~
.SO3_2 0.206 0.017 12.483 0.000 0.174 0.239
.DM1_1 ~~
.DM1_2 0.141 0.014 10.020 0.000 0.114 0.169
.DM2_1 ~~
.DM2_2 0.082 0.013 6.289 0.000 0.057 0.108
.DM3_1 ~~
.DM3_2 0.031 0.012 2.668 0.008 0.008 0.054
.DP1_1 ~~
.DP1_2 0.018 0.010 1.843 0.065 -0.001 0.038
.DP2_1 ~~
.DP2_2 0.080 0.011 7.240 0.000 0.058 0.101
.DP3_1 ~~
.DP3_2 0.155 0.016 9.416 0.000 0.123 0.187
.DG1_1 ~~
.DG1_2 0.170 0.015 11.310 0.000 0.141 0.200
.DG2_1 ~~
.DG2_2 0.192 0.017 11.338 0.000 0.159 0.225
.DG3_1 ~~
.DG3_2 0.196 0.019 10.344 0.000 0.159 0.233
Std.lv Std.all
0.747 0.747
0.703 0.703
0.784 0.784
0.757 0.757
0.757 0.757
0.828 0.828
0.665 0.665
0.566 0.566
0.553 0.553
0.546 0.546
0.710 0.710
0.588 0.588
0.541 0.541
0.620 0.620
0.666 0.666
0.086 0.360
0.052 0.217
0.108 0.294
0.047 0.300
0.171 0.313
0.151 0.376
0.138 0.393
0.219 0.438
0.017 0.084
0.190 0.331
0.199 0.448
0.206 0.444
0.141 0.387
0.082 0.296
0.031 0.151
0.018 0.102
0.080 0.324
0.155 0.335
0.170 0.430
0.192 0.386
0.196 0.374
Intercepts:
Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
C1 0.000 0.000 0.000
S1 0.000 0.000 0.000
D1 0.000 0.000 0.000
.CG1_1 0.000 0.000 0.000
.CG2_1 0.000 0.000 0.000
.CG3_1 0.000 0.000 0.000
.CI1_1 0.000 0.000 0.000
.CI2_1 0.000 0.000 0.000
.CI3_1 0.000 0.000 0.000
.SS1_1 0.000 0.000 0.000
.SS2_1 0.000 0.000 0.000
.SS3_1 0.000 0.000 0.000
.SO1_1 0.000 0.000 0.000
.SO2_1 0.000 0.000 0.000
.SO3_1 0.000 0.000 0.000
.DM1_1 0.000 0.000 0.000
.DM2_1 0.000 0.000 0.000
.DM3_1 0.000 0.000 0.000
.DP1_1 0.000 0.000 0.000
.DP2_1 0.000 0.000 0.000
.DP3_1 0.000 0.000 0.000
.DG1_1 0.000 0.000 0.000
.DG2_1 0.000 0.000 0.000
.DG3_1 0.000 0.000 0.000
C2 -0.090 0.025 -3.605 0.000 -0.139 -0.041
S2 -0.109 0.025 -4.360 0.000 -0.157 -0.060
D2 -0.124 0.024 -5.136 0.000 -0.171 -0.076
.CG1_2 0.000 0.000 0.000
.CG2_2 0.000 0.000 0.000
.CG3_2 0.000 0.000 0.000
.CI1_2 0.000 0.000 0.000
.CI2_2 0.000 0.000 0.000
.CI3_2 0.000 0.000 0.000
.SS1_2 0.000 0.000 0.000
.SS2_2 0.000 0.000 0.000
.SS3_2 0.000 0.000 0.000
.SO1_2 0.000 0.000 0.000
.SO2_2 0.000 0.000 0.000
.SO3_2 0.000 0.000 0.000
.DM1_2 0.000 0.000 0.000
.DM2_2 0.000 0.000 0.000
.DM3_2 0.000 0.000 0.000
.DP1_2 0.000 0.000 0.000
.DP2_2 0.000 0.000 0.000
.DP3_2 0.000 0.000 0.000
.DG1_2 0.000 0.000 0.000
.DG2_2 0.000 0.000 0.000
.DG3_2 0.000 0.000 0.000
Std.lv Std.all
0.000 0.000
0.000 0.000
0.000 0.000
0.000 0.000
0.000 0.000
0.000 0.000
0.000 0.000
0.000 0.000
0.000 0.000
0.000 0.000
0.000 0.000
0.000 0.000
0.000 0.000
0.000 0.000
0.000 0.000
0.000 0.000
0.000 0.000
0.000 0.000
0.000 0.000
0.000 0.000
0.000 0.000
0.000 0.000
0.000 0.000
0.000 0.000
-0.087 -0.087
-0.106 -0.106
-0.119 -0.119
0.000 0.000
0.000 0.000
0.000 0.000
0.000 0.000
0.000 0.000
0.000 0.000
0.000 0.000
0.000 0.000
0.000 0.000
0.000 0.000
0.000 0.000
0.000 0.000
0.000 0.000
0.000 0.000
0.000 0.000
0.000 0.000
0.000 0.000
0.000 0.000
0.000 0.000
0.000 0.000
0.000 0.000
Thresholds:
Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
CG1_1| (CG1.1) -1.358 0.042 -32.168 0.000 -1.441 -1.276
CG1_1| (CG1.2) -0.573 0.034 -16.957 0.000 -0.639 -0.506
CG1_1| (CG1.3) -0.244 0.032 -7.530 0.000 -0.308 -0.181
CG1_1| (CG1.4) 0.344 0.033 10.558 0.000 0.280 0.408
CG1_1| (CG1.5) 1.015 0.038 26.955 0.000 0.941 1.089
CG1_1| (CG1.6) 1.762 0.056 31.292 0.000 1.651 1.872
CG2_1| (CG2.1) -1.419 0.044 -32.487 0.000 -1.505 -1.334
CG2_1| (CG2.2) -0.615 0.034 -18.237 0.000 -0.681 -0.549
CG2_1| (CG2.3) -0.253 0.032 -7.843 0.000 -0.317 -0.190
CG2_1| (CG2.4) 0.357 0.032 11.261 0.000 0.295 0.420
CG2_1| (CG2.5) 1.059 0.037 28.644 0.000 0.986 1.131
CG2_1| (CG2.6) 1.788 0.056 31.784 0.000 1.678 1.898
CG3_1| (CG3.1) -1.838 0.053 -34.591 0.000 -1.942 -1.734
CG3_1| (CG3.2) -1.085 0.037 -29.504 0.000 -1.157 -1.013
CG3_1| (CG3.3) -0.709 0.034 -21.065 0.000 -0.775 -0.643
CG3_1| (CG3.4) -0.146 0.031 -4.733 0.000 -0.206 -0.086
CG3_1| (CG3.5) 0.688 0.034 20.364 0.000 0.622 0.755
CG3_1| (CG3.6) 1.574 0.050 31.577 0.000 1.476 1.672
CI1_1| (CI1.1) -1.520 0.046 -33.033 0.000 -1.610 -1.430
CI1_1| (CI1.2) -0.822 0.035 -23.353 0.000 -0.891 -0.753
CI1_1| (CI1.3) -0.396 0.033 -12.045 0.000 -0.461 -0.332
CI1_1| (CI1.4) 0.138 0.032 4.269 0.000 0.075 0.202
CI1_1| (CI1.5) 0.805 0.035 23.123 0.000 0.736 0.873
CI1_1| (CI1.6) 1.661 0.052 32.095 0.000 1.559 1.762
CI2_1| (CI2.1) -2.160 0.067 -32.123 0.000 -2.291 -2.028
CI2_1| (CI2.2) -1.531 0.043 -35.392 0.000 -1.615 -1.446
CI2_1| (CI2.3) -1.051 0.036 -29.408 0.000 -1.122 -0.981
CI2_1| (CI2.4) -0.536 0.031 -17.013 0.000 -0.597 -0.474
CI2_1| (CI2.5) 0.426 0.031 13.575 0.000 0.365 0.488
CI2_1| (CI2.6) 1.595 0.047 33.782 0.000 1.502 1.687
CI3_1| (CI3.1) -1.939 0.058 -33.207 0.000 -2.053 -1.824
CI3_1| (CI3.2) -1.223 0.039 -31.007 0.000 -1.300 -1.146
CI3_1| (CI3.3) -0.796 0.034 -23.718 0.000 -0.862 -0.731
CI3_1| (CI3.4) -0.215 0.031 -6.879 0.000 -0.276 -0.154
CI3_1| (CI3.5) 0.535 0.033 16.444 0.000 0.472 0.599
CI3_1| (CI3.6) 1.384 0.044 31.461 0.000 1.298 1.471
SS1_1| (SS1.1) -1.771 0.052 -34.018 0.000 -1.874 -1.669
SS1_1| (SS1.2) -1.047 0.037 -28.043 0.000 -1.120 -0.974
SS1_1| (SS1.3) -0.521 0.033 -15.681 0.000 -0.586 -0.456
SS1_1| (SS1.4) 0.027 0.032 0.852 0.394 -0.035 0.089
SS1_1| (SS1.5) 0.744 0.034 21.797 0.000 0.677 0.811
SS1_1| (SS1.6) 1.617 0.050 32.420 0.000 1.519 1.715
SS2_1| (SS2.1) -1.900 0.059 -32.377 0.000 -2.016 -1.785
SS2_1| (SS2.2) -1.405 0.043 -32.841 0.000 -1.488 -1.321
SS2_1| (SS2.3) -0.914 0.036 -25.726 0.000 -0.983 -0.844
SS2_1| (SS2.4) -0.316 0.032 -9.921 0.000 -0.378 -0.253
SS2_1| (SS2.5) 0.295 0.032 9.274 0.000 0.233 0.358
SS2_1| (SS2.6) 1.195 0.040 29.930 0.000 1.117 1.274
SS3_1| (SS3.1) -1.716 0.050 -34.578 0.000 -1.814 -1.619
SS3_1| (SS3.2) -0.898 0.036 -24.892 0.000 -0.968 -0.827
SS3_1| (SS3.3) -0.386 0.032 -11.909 0.000 -0.450 -0.323
SS3_1| (SS3.4) 0.194 0.032 6.040 0.000 0.131 0.257
SS3_1| (SS3.5) 0.838 0.036 23.374 0.000 0.767 0.908
SS3_1| (SS3.6) 1.585 0.050 31.736 0.000 1.487 1.683
SO1_1| (SO1.1) -2.090 0.063 -32.938 0.000 -2.214 -1.966
SO1_1| (SO1.2) -1.382 0.042 -33.290 0.000 -1.464 -1.301
SO1_1| (SO1.3) -0.920 0.035 -26.375 0.000 -0.988 -0.851
SO1_1| (SO1.4) -0.230 0.030 -7.617 0.000 -0.289 -0.171
SO1_1| (SO1.5) 0.505 0.032 15.788 0.000 0.442 0.568
SO1_1| (SO1.6) 1.591 0.047 33.733 0.000 1.499 1.684
SO2_1| (SO2.1) -1.587 0.046 -34.746 0.000 -1.676 -1.497
SO2_1| (SO2.2) -0.881 0.036 -24.768 0.000 -0.950 -0.811
SO2_1| (SO2.3) -0.316 0.032 -9.937 0.000 -0.378 -0.253
SO2_1| (SO2.4) 0.201 0.032 6.320 0.000 0.139 0.263
SO2_1| (SO2.5) 0.956 0.036 26.555 0.000 0.886 1.027
SO2_1| (SO2.6) 1.854 0.059 31.537 0.000 1.738 1.969
SO3_1| (SO3.1) -1.540 0.046 -33.601 0.000 -1.630 -1.451
SO3_1| (SO3.2) -0.783 0.035 -22.341 0.000 -0.851 -0.714
SO3_1| (SO3.3) -0.283 0.031 -9.011 0.000 -0.345 -0.222
SO3_1| (SO3.4) 0.368 0.032 11.619 0.000 0.306 0.430
SO3_1| (SO3.5) 1.097 0.038 29.051 0.000 1.023 1.171
SO3_1| (SO3.6) 2.069 0.069 29.849 0.000 1.933 2.205
DM1_1| (DM1.1) -1.430 0.043 -33.531 0.000 -1.513 -1.346
DM1_1| (DM1.2) -0.717 0.035 -20.444 0.000 -0.785 -0.648
DM1_1| (DM1.3) -0.329 0.032 -10.235 0.000 -0.393 -0.266
DM1_1| (DM1.4) 0.468 0.032 14.473 0.000 0.405 0.532
DM1_1| (DM1.5) 1.153 0.039 29.717 0.000 1.077 1.229
DM1_1| (DM1.6) 1.851 0.059 31.282 0.000 1.735 1.967
DM2_1| (DM2.1) -1.455 0.043 -33.501 0.000 -1.540 -1.369
DM2_1| (DM2.2) -0.749 0.034 -21.898 0.000 -0.816 -0.682
DM2_1| (DM2.3) -0.333 0.032 -10.475 0.000 -0.395 -0.270
DM2_1| (DM2.4) 0.125 0.031 4.090 0.000 0.065 0.185
DM2_1| (DM2.5) 0.800 0.035 22.626 0.000 0.731 0.869
DM2_1| (DM2.6) 1.538 0.047 32.921 0.000 1.446 1.629
DM3_1| (DM3.1) -1.598 0.047 -34.151 0.000 -1.690 -1.506
DM3_1| (DM3.2) -0.874 0.035 -24.669 0.000 -0.943 -0.805
DM3_1| (DM3.3) -0.488 0.032 -15.070 0.000 -0.551 -0.424
DM3_1| (DM3.4) -0.064 0.031 -2.058 0.040 -0.125 -0.003
DM3_1| (DM3.5) 0.664 0.033 19.899 0.000 0.598 0.729
DM3_1| (DM3.6) 1.494 0.046 32.315 0.000 1.403 1.584
DP1_1| (DP1.1) -1.789 0.054 -33.393 0.000 -1.894 -1.684
DP1_1| (DP1.2) -1.174 0.038 -30.571 0.000 -1.249 -1.099
DP1_1| (DP1.3) -0.790 0.034 -23.349 0.000 -0.856 -0.723
DP1_1| (DP1.4) -0.351 0.031 -11.167 0.000 -0.412 -0.289
DP1_1| (DP1.5) 0.491 0.033 14.986 0.000 0.427 0.555
DP1_1| (DP1.6) 1.432 0.045 31.788 0.000 1.344 1.520
DP2_1| (DP2.1) -1.617 0.047 -34.243 0.000 -1.710 -1.524
DP2_1| (DP2.2) -0.916 0.036 -25.501 0.000 -0.986 -0.845
DP2_1| (DP2.3) -0.472 0.033 -14.326 0.000 -0.537 -0.408
DP2_1| (DP2.4) 0.021 0.032 0.644 0.519 -0.042 0.083
DP2_1| (DP2.5) 0.709 0.034 20.722 0.000 0.642 0.776
DP2_1| (DP2.6) 1.604 0.050 32.078 0.000 1.506 1.702
DP3_1| (DP3.1) -2.129 0.063 -33.556 0.000 -2.253 -2.004
DP3_1| (DP3.2) -1.568 0.043 -36.067 0.000 -1.654 -1.483
DP3_1| (DP3.3) -1.174 0.037 -32.025 0.000 -1.246 -1.102
DP3_1| (DP3.4) -0.540 0.031 -17.189 0.000 -0.602 -0.479
DP3_1| (DP3.5) 0.267 0.031 8.531 0.000 0.206 0.329
DP3_1| (DP3.6) 1.316 0.042 31.142 0.000 1.233 1.398
DG1_1| (DG1.1) -1.923 0.055 -35.272 0.000 -2.030 -1.816
DG1_1| (DG1.2) -1.210 0.038 -31.858 0.000 -1.285 -1.136
DG1_1| (DG1.3) -0.694 0.033 -21.058 0.000 -0.758 -0.629
DG1_1| (DG1.4) -0.291 0.032 -9.187 0.000 -0.353 -0.229
DG1_1| (DG1.5) 0.508 0.033 15.588 0.000 0.444 0.571
DG1_1| (DG1.6) 1.355 0.042 32.312 0.000 1.273 1.437
DG2_1| (DG2.1) -2.220 0.073 -30.362 0.000 -2.363 -2.077
DG2_1| (DG2.2) -1.691 0.049 -34.678 0.000 -1.787 -1.596
DG2_1| (DG2.3) -1.310 0.039 -33.276 0.000 -1.387 -1.233
DG2_1| (DG2.4) -0.884 0.033 -26.502 0.000 -0.949 -0.819
DG2_1| (DG2.5) -0.097 0.031 -3.141 0.002 -0.157 -0.036
DG2_1| (DG2.6) 0.904 0.035 25.712 0.000 0.835 0.973
DG3_1| (DG3.1) -2.091 0.061 -34.109 0.000 -2.211 -1.971
DG3_1| (DG3.2) -1.501 0.044 -34.090 0.000 -1.587 -1.414
DG3_1| (DG3.3) -1.036 0.035 -29.201 0.000 -1.105 -0.966
DG3_1| (DG3.4) -0.662 0.032 -20.616 0.000 -0.725 -0.599
DG3_1| (DG3.5) 0.149 0.031 4.874 0.000 0.089 0.209
DG3_1| (DG3.6) 1.030 0.037 28.209 0.000 0.958 1.102
CG1_2| (CG1.1) -1.358 0.042 -32.168 0.000 -1.441 -1.276
CG1_2| (CG1.2) -0.573 0.034 -16.957 0.000 -0.639 -0.506
CG1_2| (CG1.3) -0.244 0.032 -7.530 0.000 -0.308 -0.181
CG1_2| (CG1.4) 0.344 0.033 10.558 0.000 0.280 0.408
CG1_2| (CG1.5) 1.015 0.038 26.955 0.000 0.941 1.089
CG1_2| (CG1.6) 1.762 0.056 31.292 0.000 1.651 1.872
CG2_2| (CG2.1) -1.419 0.044 -32.487 0.000 -1.505 -1.334
CG2_2| (CG2.2) -0.615 0.034 -18.237 0.000 -0.681 -0.549
CG2_2| (CG2.3) -0.253 0.032 -7.843 0.000 -0.317 -0.190
CG2_2| (CG2.4) 0.357 0.032 11.261 0.000 0.295 0.420
CG2_2| (CG2.5) 1.059 0.037 28.644 0.000 0.986 1.131
CG2_2| (CG2.6) 1.788 0.056 31.784 0.000 1.678 1.898
CG3_2| (CG3.1) -1.838 0.053 -34.591 0.000 -1.942 -1.734
CG3_2| (CG3.2) -1.085 0.037 -29.504 0.000 -1.157 -1.013
CG3_2| (CG3.3) -0.709 0.034 -21.065 0.000 -0.775 -0.643
CG3_2| (CG3.4) -0.146 0.031 -4.733 0.000 -0.206 -0.086
CG3_2| (CG3.5) 0.688 0.034 20.364 0.000 0.622 0.755
CG3_2| (CG3.6) 1.574 0.050 31.577 0.000 1.476 1.672
CI1_2| (CI1.1) -1.520 0.046 -33.033 0.000 -1.610 -1.430
CI1_2| (CI1.2) -0.822 0.035 -23.353 0.000 -0.891 -0.753
CI1_2| (CI1.3) -0.396 0.033 -12.045 0.000 -0.461 -0.332
CI1_2| (CI1.4) 0.138 0.032 4.269 0.000 0.075 0.202
CI1_2| (CI1.5) 0.805 0.035 23.123 0.000 0.736 0.873
CI1_2| (CI1.6) 1.661 0.052 32.095 0.000 1.559 1.762
CI2_2| (CI2.1) -2.160 0.067 -32.123 0.000 -2.291 -2.028
CI2_2| (CI2.2) -1.531 0.043 -35.392 0.000 -1.615 -1.446
CI2_2| (CI2.3) -1.051 0.036 -29.408 0.000 -1.122 -0.981
CI2_2| (CI2.4) -0.536 0.031 -17.013 0.000 -0.597 -0.474
CI2_2| (CI2.5) 0.426 0.031 13.575 0.000 0.365 0.488
CI2_2| (CI2.6) 1.595 0.047 33.782 0.000 1.502 1.687
CI3_2| (CI3.1) -1.939 0.058 -33.207 0.000 -2.053 -1.824
CI3_2| (CI3.2) -1.223 0.039 -31.007 0.000 -1.300 -1.146
CI3_2| (CI3.3) -0.796 0.034 -23.718 0.000 -0.862 -0.731
CI3_2| (CI3.4) -0.215 0.031 -6.879 0.000 -0.276 -0.154
CI3_2| (CI3.5) 0.535 0.033 16.444 0.000 0.472 0.599
CI3_2| (CI3.6) 1.384 0.044 31.461 0.000 1.298 1.471
SS1_2| (SS1.1) -1.771 0.052 -34.018 0.000 -1.874 -1.669
SS1_2| (SS1.2) -1.047 0.037 -28.043 0.000 -1.120 -0.974
SS1_2| (SS1.3) -0.521 0.033 -15.681 0.000 -0.586 -0.456
SS1_2| (SS1.4) 0.027 0.032 0.852 0.394 -0.035 0.089
SS1_2| (SS1.5) 0.744 0.034 21.797 0.000 0.677 0.811
SS1_2| (SS1.6) 1.617 0.050 32.420 0.000 1.519 1.715
SS2_2| (SS2.1) -1.900 0.059 -32.377 0.000 -2.016 -1.785
SS2_2| (SS2.2) -1.405 0.043 -32.841 0.000 -1.488 -1.321
SS2_2| (SS2.3) -0.914 0.036 -25.726 0.000 -0.983 -0.844
SS2_2| (SS2.4) -0.316 0.032 -9.921 0.000 -0.378 -0.253
SS2_2| (SS2.5) 0.295 0.032 9.274 0.000 0.233 0.358
SS2_2| (SS2.6) 1.195 0.040 29.930 0.000 1.117 1.274
SS3_2| (SS3.1) -1.716 0.050 -34.578 0.000 -1.814 -1.619
SS3_2| (SS3.2) -0.898 0.036 -24.892 0.000 -0.968 -0.827
SS3_2| (SS3.3) -0.386 0.032 -11.909 0.000 -0.450 -0.323
SS3_2| (SS3.4) 0.194 0.032 6.040 0.000 0.131 0.257
SS3_2| (SS3.5) 0.838 0.036 23.374 0.000 0.767 0.908
SS3_2| (SS3.6) 1.585 0.050 31.736 0.000 1.487 1.683
SO1_2| (SO1.1) -2.090 0.063 -32.938 0.000 -2.214 -1.966
SO1_2| (SO1.2) -1.382 0.042 -33.290 0.000 -1.464 -1.301
SO1_2| (SO1.3) -0.920 0.035 -26.375 0.000 -0.988 -0.851
SO1_2| (SO1.4) -0.230 0.030 -7.617 0.000 -0.289 -0.171
SO1_2| (SO1.5) 0.505 0.032 15.788 0.000 0.442 0.568
SO1_2| (SO1.6) 1.591 0.047 33.733 0.000 1.499 1.684
SO2_2| (SO2.1) -1.587 0.046 -34.746 0.000 -1.676 -1.497
SO2_2| (SO2.2) -0.881 0.036 -24.768 0.000 -0.950 -0.811
SO2_2| (SO2.3) -0.316 0.032 -9.937 0.000 -0.378 -0.253
SO2_2| (SO2.4) 0.201 0.032 6.320 0.000 0.139 0.263
SO2_2| (SO2.5) 0.956 0.036 26.555 0.000 0.886 1.027
SO2_2| (SO2.6) 1.854 0.059 31.537 0.000 1.738 1.969
SO3_2| (SO3.1) -1.540 0.046 -33.601 0.000 -1.630 -1.451
SO3_2| (SO3.2) -0.783 0.035 -22.341 0.000 -0.851 -0.714
SO3_2| (SO3.3) -0.283 0.031 -9.011 0.000 -0.345 -0.222
SO3_2| (SO3.4) 0.368 0.032 11.619 0.000 0.306 0.430
SO3_2| (SO3.5) 1.097 0.038 29.051 0.000 1.023 1.171
SO3_2| (SO3.6) 2.069 0.069 29.849 0.000 1.933 2.205
DM1_2| (DM1.1) -1.430 0.043 -33.531 0.000 -1.513 -1.346
DM1_2| (DM1.2) -0.717 0.035 -20.444 0.000 -0.785 -0.648
DM1_2| (DM1.3) -0.329 0.032 -10.235 0.000 -0.393 -0.266
DM1_2| (DM1.4) 0.468 0.032 14.473 0.000 0.405 0.532
DM1_2| (DM1.5) 1.153 0.039 29.717 0.000 1.077 1.229
DM1_2| (DM1.6) 1.851 0.059 31.282 0.000 1.735 1.967
DM2_2| (DM2.1) -1.455 0.043 -33.501 0.000 -1.540 -1.369
DM2_2| (DM2.2) -0.749 0.034 -21.898 0.000 -0.816 -0.682
DM2_2| (DM2.3) -0.333 0.032 -10.475 0.000 -0.395 -0.270
DM2_2| (DM2.4) 0.125 0.031 4.090 0.000 0.065 0.185
DM2_2| (DM2.5) 0.800 0.035 22.626 0.000 0.731 0.869
DM2_2| (DM2.6) 1.538 0.047 32.921 0.000 1.446 1.629
DM3_2| (DM3.1) -1.598 0.047 -34.151 0.000 -1.690 -1.506
DM3_2| (DM3.2) -0.874 0.035 -24.669 0.000 -0.943 -0.805
DM3_2| (DM3.3) -0.488 0.032 -15.070 0.000 -0.551 -0.424
DM3_2| (DM3.4) -0.064 0.031 -2.058 0.040 -0.125 -0.003
DM3_2| (DM3.5) 0.664 0.033 19.899 0.000 0.598 0.729
DM3_2| (DM3.6) 1.494 0.046 32.315 0.000 1.403 1.584
DP1_2| (DP1.1) -1.789 0.054 -33.393 0.000 -1.894 -1.684
DP1_2| (DP1.2) -1.174 0.038 -30.571 0.000 -1.249 -1.099
DP1_2| (DP1.3) -0.790 0.034 -23.349 0.000 -0.856 -0.723
DP1_2| (DP1.4) -0.351 0.031 -11.167 0.000 -0.412 -0.289
DP1_2| (DP1.5) 0.491 0.033 14.986 0.000 0.427 0.555
DP1_2| (DP1.6) 1.432 0.045 31.788 0.000 1.344 1.520
DP2_2| (DP2.1) -1.617 0.047 -34.243 0.000 -1.710 -1.524
DP2_2| (DP2.2) -0.916 0.036 -25.501 0.000 -0.986 -0.845
DP2_2| (DP2.3) -0.472 0.033 -14.326 0.000 -0.537 -0.408
DP2_2| (DP2.4) 0.021 0.032 0.644 0.519 -0.042 0.083
DP2_2| (DP2.5) 0.709 0.034 20.722 0.000 0.642 0.776
DP2_2| (DP2.6) 1.604 0.050 32.078 0.000 1.506 1.702
DP3_2| (DP3.1) -2.129 0.063 -33.556 0.000 -2.253 -2.004
DP3_2| (DP3.2) -1.568 0.043 -36.067 0.000 -1.654 -1.483
DP3_2| (DP3.3) -1.174 0.037 -32.025 0.000 -1.246 -1.102
DP3_2| (DP3.4) -0.540 0.031 -17.189 0.000 -0.602 -0.479
DP3_2| (DP3.5) 0.267 0.031 8.531 0.000 0.206 0.329
DP3_2| (DP3.6) 1.316 0.042 31.142 0.000 1.233 1.398
DG1_2| (DG1.1) -1.923 0.055 -35.272 0.000 -2.030 -1.816
DG1_2| (DG1.2) -1.210 0.038 -31.858 0.000 -1.285 -1.136
DG1_2| (DG1.3) -0.694 0.033 -21.058 0.000 -0.758 -0.629
DG1_2| (DG1.4) -0.291 0.032 -9.187 0.000 -0.353 -0.229
DG1_2| (DG1.5) 0.508 0.033 15.588 0.000 0.444 0.571
DG1_2| (DG1.6) 1.355 0.042 32.312 0.000 1.273 1.437
DG2_2| (DG2.1) -2.220 0.073 -30.362 0.000 -2.363 -2.077
DG2_2| (DG2.2) -1.691 0.049 -34.678 0.000 -1.787 -1.596
DG2_2| (DG2.3) -1.310 0.039 -33.276 0.000 -1.387 -1.233
DG2_2| (DG2.4) -0.884 0.033 -26.502 0.000 -0.949 -0.819
DG2_2| (DG2.5) -0.097 0.031 -3.141 0.002 -0.157 -0.036
DG2_2| (DG2.6) 0.904 0.035 25.712 0.000 0.835 0.973
DG3_2| (DG3.1) -2.091 0.061 -34.109 0.000 -2.211 -1.971
DG3_2| (DG3.2) -1.501 0.044 -34.090 0.000 -1.587 -1.414
DG3_2| (DG3.3) -1.036 0.035 -29.201 0.000 -1.105 -0.966
DG3_2| (DG3.4) -0.662 0.032 -20.616 0.000 -0.725 -0.599
DG3_2| (DG3.5) 0.149 0.031 4.874 0.000 0.089 0.209
DG3_2| (DG3.6) 1.030 0.037 28.209 0.000 0.958 1.102
Std.lv Std.all
-1.358 -1.358
-0.573 -0.573
-0.244 -0.244
0.344 0.344
1.015 1.015
1.762 1.762
-1.419 -1.419
-0.615 -0.615
-0.253 -0.253
0.357 0.357
1.059 1.059
1.788 1.788
-1.838 -1.838
-1.085 -1.085
-0.709 -0.709
-0.146 -0.146
0.688 0.688
1.574 1.574
-1.520 -1.520
-0.822 -0.822
-0.396 -0.396
0.138 0.138
0.805 0.805
1.661 1.661
-2.160 -2.160
-1.531 -1.531
-1.051 -1.051
-0.536 -0.536
0.426 0.426
1.595 1.595
-1.939 -1.939
-1.223 -1.223
-0.796 -0.796
-0.215 -0.215
0.535 0.535
1.384 1.384
-1.771 -1.771
-1.047 -1.047
-0.521 -0.521
0.027 0.027
0.744 0.744
1.617 1.617
-1.900 -1.900
-1.405 -1.405
-0.914 -0.914
-0.316 -0.316
0.295 0.295
1.195 1.195
-1.716 -1.716
-0.898 -0.898
-0.386 -0.386
0.194 0.194
0.838 0.838
1.585 1.585
-2.090 -2.090
-1.382 -1.382
-0.920 -0.920
-0.230 -0.230
0.505 0.505
1.591 1.591
-1.587 -1.587
-0.881 -0.881
-0.316 -0.316
0.201 0.201
0.956 0.956
1.854 1.854
-1.540 -1.540
-0.783 -0.783
-0.283 -0.283
0.368 0.368
1.097 1.097
2.069 2.069
-1.430 -1.430
-0.717 -0.717
-0.329 -0.329
0.468 0.468
1.153 1.153
1.851 1.851
-1.455 -1.455
-0.749 -0.749
-0.333 -0.333
0.125 0.125
0.800 0.800
1.538 1.538
-1.598 -1.598
-0.874 -0.874
-0.488 -0.488
-0.064 -0.064
0.664 0.664
1.494 1.494
-1.789 -1.789
-1.174 -1.174
-0.790 -0.790
-0.351 -0.351
0.491 0.491
1.432 1.432
-1.617 -1.617
-0.916 -0.916
-0.472 -0.472
0.021 0.021
0.709 0.709
1.604 1.604
-2.129 -2.129
-1.568 -1.568
-1.174 -1.174
-0.540 -0.540
0.267 0.267
1.316 1.316
-1.923 -1.923
-1.210 -1.210
-0.694 -0.694
-0.291 -0.291
0.508 0.508
1.355 1.355
-2.220 -2.220
-1.691 -1.691
-1.310 -1.310
-0.884 -0.884
-0.097 -0.097
0.904 0.904
-2.091 -2.091
-1.501 -1.501
-1.036 -1.036
-0.662 -0.662
0.149 0.149
1.030 1.030
-1.358 -1.358
-0.573 -0.573
-0.244 -0.244
0.344 0.344
1.015 1.015
1.762 1.762
-1.419 -1.419
-0.615 -0.615
-0.253 -0.253
0.357 0.357
1.059 1.059
1.788 1.788
-1.838 -1.838
-1.085 -1.085
-0.709 -0.709
-0.146 -0.146
0.688 0.688
1.574 1.574
-1.520 -1.520
-0.822 -0.822
-0.396 -0.396
0.138 0.138
0.805 0.805
1.661 1.661
-2.160 -2.160
-1.531 -1.531
-1.051 -1.051
-0.536 -0.536
0.426 0.426
1.595 1.595
-1.939 -1.939
-1.223 -1.223
-0.796 -0.796
-0.215 -0.215
0.535 0.535
1.384 1.384
-1.771 -1.771
-1.047 -1.047
-0.521 -0.521
0.027 0.027
0.744 0.744
1.617 1.617
-1.900 -1.900
-1.405 -1.405
-0.914 -0.914
-0.316 -0.316
0.295 0.295
1.195 1.195
-1.716 -1.716
-0.898 -0.898
-0.386 -0.386
0.194 0.194
0.838 0.838
1.585 1.585
-2.090 -2.090
-1.382 -1.382
-0.920 -0.920
-0.230 -0.230
0.505 0.505
1.591 1.591
-1.587 -1.587
-0.881 -0.881
-0.316 -0.316
0.201 0.201
0.956 0.956
1.854 1.854
-1.540 -1.540
-0.783 -0.783
-0.283 -0.283
0.368 0.368
1.097 1.097
2.069 2.069
-1.430 -1.430
-0.717 -0.717
-0.329 -0.329
0.468 0.468
1.153 1.153
1.851 1.851
-1.455 -1.455
-0.749 -0.749
-0.333 -0.333
0.125 0.125
0.800 0.800
1.538 1.538
-1.598 -1.598
-0.874 -0.874
-0.488 -0.488
-0.064 -0.064
0.664 0.664
1.494 1.494
-1.789 -1.789
-1.174 -1.174
-0.790 -0.790
-0.351 -0.351
0.491 0.491
1.432 1.432
-1.617 -1.617
-0.916 -0.916
-0.472 -0.472
0.021 0.021
0.709 0.709
1.604 1.604
-2.129 -2.129
-1.568 -1.568
-1.174 -1.174
-0.540 -0.540
0.267 0.267
1.316 1.316
-1.923 -1.923
-1.210 -1.210
-0.694 -0.694
-0.291 -0.291
0.508 0.508
1.355 1.355
-2.220 -2.220
-1.691 -1.691
-1.310 -1.310
-0.884 -0.884
-0.097 -0.097
0.904 0.904
-2.091 -2.091
-1.501 -1.501
-1.036 -1.036
-0.662 -0.662
0.149 0.149
1.030 1.030
Variances:
Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
C1 1.000 1.000 1.000
S1 1.000 1.000 1.000
D1 1.000 1.000 1.000
.CG1_1 0.273 0.273 0.273
.CG2_1 0.272 0.272 0.272
.CG3_1 0.395 0.395 0.395
.CI1_1 0.195 0.195 0.195
.CI2_1 0.565 0.565 0.565
.CI3_1 0.428 0.428 0.428
.SS1_1 0.370 0.370 0.370
.SS2_1 0.513 0.513 0.513
.SS3_1 0.223 0.223 0.223
.SO1_1 0.586 0.586 0.586
.SO2_1 0.459 0.459 0.459
.SO3_1 0.479 0.479 0.479
.DM1_1 0.388 0.388 0.388
.DM2_1 0.305 0.305 0.305
.DM3_1 0.237 0.237 0.237
.DP1_1 0.212 0.212 0.212
.DP2_1 0.275 0.275 0.275
.DP3_1 0.481 0.481 0.481
.DG1_1 0.418 0.418 0.418
.DG2_1 0.515 0.515 0.515
.DG3_1 0.541 0.541 0.541
C2 (ph.c) 1.087 0.015 73.976 0.000 1.058 1.116
S2 (ph.s) 1.055 0.022 47.835 0.000 1.011 1.098
D2 (ph.d) 1.075 0.015 74.017 0.000 1.047 1.104
.CG1_2 0.210 0.210 0.210
.CG2_2 0.209 0.209 0.209
.CG3_2 0.343 0.343 0.343
.CI1_2 0.125 0.125 0.125
.CI2_2 0.527 0.527 0.527
.CI3_2 0.378 0.378 0.378
.SS1_2 0.335 0.335 0.335
.SS2_2 0.486 0.486 0.486
.SS3_2 0.180 0.180 0.180
.SO1_2 0.563 0.563 0.563
.SO2_2 0.430 0.430 0.430
.SO3_2 0.451 0.451 0.451
.DM1_2 0.342 0.342 0.342
.DM2_2 0.253 0.253 0.253
.DM3_2 0.180 0.180 0.180
.DP1_2 0.153 0.153 0.153
.DP2_2 0.220 0.220 0.220
.DP3_2 0.442 0.442 0.442
.DG1_2 0.375 0.375 0.375
.DG2_2 0.479 0.479 0.479
.DG3_2 0.507 0.507 0.507
Std.lv Std.all
1.000 1.000
1.000 1.000
1.000 1.000
0.273 0.273
0.272 0.272
0.395 0.395
0.195 0.195
0.565 0.565
0.428 0.428
0.370 0.370
0.513 0.513
0.223 0.223
0.586 0.586
0.459 0.459
0.479 0.479
0.388 0.388
0.305 0.305
0.237 0.237
0.212 0.212
0.275 0.275
0.481 0.481
0.418 0.418
0.515 0.515
0.541 0.541
1.000 1.000
1.000 1.000
1.000 1.000
0.210 0.210
0.209 0.209
0.343 0.343
0.125 0.125
0.527 0.527
0.378 0.378
0.335 0.335
0.486 0.486
0.180 0.180
0.563 0.563
0.430 0.430
0.451 0.451
0.342 0.342
0.253 0.253
0.180 0.180
0.153 0.153
0.220 0.220
0.442 0.442
0.375 0.375
0.479 0.479
0.507 0.507
Scales y*:
Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
CG1_1 1.000 1.000 1.000
CG2_1 1.000 1.000 1.000
CG3_1 1.000 1.000 1.000
CI1_1 1.000 1.000 1.000
CI2_1 1.000 1.000 1.000
CI3_1 1.000 1.000 1.000
SS1_1 1.000 1.000 1.000
SS2_1 1.000 1.000 1.000
SS3_1 1.000 1.000 1.000
SO1_1 1.000 1.000 1.000
SO2_1 1.000 1.000 1.000
SO3_1 1.000 1.000 1.000
DM1_1 1.000 1.000 1.000
DM2_1 1.000 1.000 1.000
DM3_1 1.000 1.000 1.000
DP1_1 1.000 1.000 1.000
DP2_1 1.000 1.000 1.000
DP3_1 1.000 1.000 1.000
DG1_1 1.000 1.000 1.000
DG2_1 1.000 1.000 1.000
DG3_1 1.000 1.000 1.000
CG1_2 1.000 1.000 1.000
CG2_2 1.000 1.000 1.000
CG3_2 1.000 1.000 1.000
CI1_2 1.000 1.000 1.000
CI2_2 1.000 1.000 1.000
CI3_2 1.000 1.000 1.000
SS1_2 1.000 1.000 1.000
SS2_2 1.000 1.000 1.000
SS3_2 1.000 1.000 1.000
SO1_2 1.000 1.000 1.000
SO2_2 1.000 1.000 1.000
SO3_2 1.000 1.000 1.000
DM1_2 1.000 1.000 1.000
DM2_2 1.000 1.000 1.000
DM3_2 1.000 1.000 1.000
DP1_2 1.000 1.000 1.000
DP2_2 1.000 1.000 1.000
DP3_2 1.000 1.000 1.000
DG1_2 1.000 1.000 1.000
DG2_2 1.000 1.000 1.000
DG3_2 1.000 1.000 1.000
Std.lv Std.all
1.000 1.000
1.000 1.000
1.000 1.000
1.000 1.000
1.000 1.000
1.000 1.000
1.000 1.000
1.000 1.000
1.000 1.000
1.000 1.000
1.000 1.000
1.000 1.000
1.000 1.000
1.000 1.000
1.000 1.000
1.000 1.000
1.000 1.000
1.000 1.000
1.000 1.000
1.000 1.000
1.000 1.000
1.000 1.000
1.000 1.000
1.000 1.000
1.000 1.000
1.000 1.000
1.000 1.000
1.000 1.000
1.000 1.000
1.000 1.000
1.000 1.000
1.000 1.000
1.000 1.000
1.000 1.000
1.000 1.000
1.000 1.000
1.000 1.000
1.000 1.000
1.000 1.000
1.000 1.000
1.000 1.000
1.000 1.000
R-Square:
Estimate
CG1_1 0.727
CG2_1 0.728
CG3_1 0.605
CI1_1 0.805
CI2_1 0.435
CI3_1 0.572
SS1_1 0.630
SS2_1 0.487
SS3_1 0.777
SO1_1 0.414
SO2_1 0.541
SO3_1 0.521
DM1_1 0.612
DM2_1 0.695
DM3_1 0.763
DP1_1 0.788
DP2_1 0.725
DP3_1 0.519
DG1_1 0.582
DG2_1 0.485
DG3_1 0.459
CG1_2 0.790
CG2_2 0.791
CG3_2 0.657
CI1_2 0.875
CI2_2 0.473
CI3_2 0.622
SS1_2 0.665
SS2_2 0.514
SS3_2 0.820
SO1_2 0.437
SO2_2 0.570
SO3_2 0.549
DM1_2 0.658
DM2_2 0.747
DM3_2 0.820
DP1_2 0.847
DP2_2 0.780
DP3_2 0.558
DG1_2 0.625
DG2_2 0.521
DG3_2 0.493
Defined Parameters:
Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
omega.c 0.793 0.003 305.660 0.000 0.788 0.798
omega.s 0.769 0.004 210.035 0.000 0.762 0.777
omega.d 0.848 0.002 423.573 0.000 0.844 0.852
omega.c2 0.806 0.002 390.830 0.000 0.802 0.811
omega.s2 0.779 0.003 240.646 0.000 0.772 0.785
omega.d2 0.857 0.002 521.947 0.000 0.854 0.860
max.c 0.795 0.002 320.057 0.000 0.790 0.800
max.S 0.771 0.004 217.987 0.000 0.764 0.778
max.D 0.849 0.002 439.987 0.000 0.845 0.853
Std.lv Std.all
0.793 0.793
0.769 0.769
0.848 0.848
0.793 0.793
0.769 0.769
0.848 0.848
0.795 0.795
0.771 0.771
0.849 0.849Traditional Sum-Score Approach
Naive computation of reliability.
analysis.dat.wide <- analysis.dat.wide %>%
mutate(
C1 = CG1_1 + CG2_1 + CG3_1 + CI1_1 + CI2_1 + CI3_1,
C2 = CG1_2 + CG2_2 + CG3_2 + CI1_2 + CI2_2 + CI3_2,
S1 = SO1_1 + SO2_1 + SO3_1 + SS1_1 + SS2_1 + SS3_1,
S2 = SO1_2 + SO2_2 + SO3_2 + SS1_2 + SS2_2 + SS3_2,
D1 = DG1_1 + DG2_1 + DG3_1 + DP1_1 + DP2_1 + DP3_1 + DM1_1 + DM2_1 + DM3_1,
D2 = DG1_2 + DG2_2 + DG3_2 + DP1_2 + DP2_2 + DP3_2 + DM1_2 + DM2_2 + DM3_2
)
ggplot(analysis.dat.wide, aes(x=C1, y=C2))+
geom_jitter()+
theme_classic()
ggplot(analysis.dat.wide, aes(x=S1, y=S2))+
geom_jitter()+
theme_classic()
ggplot(analysis.dat.wide, aes(x=D1, y=D2))+
geom_jitter()+
theme_classic()
cor(analysis.dat.wide[,c("C1","C2","S1","S2","D1","D2")]) C1 C2 S1 S2 D1 D2
C1 1.0000000 0.6492757 0.6606195 0.5153272 0.6522519 0.5188590
C2 0.6492757 1.0000000 0.4933733 0.6758117 0.5120393 0.7055878
S1 0.6606195 0.4933733 1.0000000 0.6734827 0.7121317 0.5457071
S2 0.5153272 0.6758117 0.6734827 1.0000000 0.5781363 0.7706349
D1 0.6522519 0.5120393 0.7121317 0.5781363 1.0000000 0.6669784
D2 0.5188590 0.7055878 0.5457071 0.7706349 0.6669784 1.0000000# est: .65, .67, .66