#Reading in the data
library(haven)
## Warning: package 'haven' was built under R version 4.3.3
csek12 <- as.data.frame(read_sav("RegRep_K12 Sex Ed_De-Id Data_Clean_11-24-23.sav"))
#Making variables nominal
csek12$Vote <- factor(csek12$Vote, levels = c(1:3))
csek12$Gender_3cat <- factor(csek12$Gender_3cat, levels = c(1:3))
csek12$Gender_5cat <- factor(csek12$Gender_5cat, levels = c(1:5))
csek12$Race_cat <- factor(csek12$Race_cat, levels = c(1:7))
csek12$SO <- factor(csek12$SO, levels = c(1:6))
csek12$Geo <- factor(csek12$Geo, levels = c(1:4))
csek12$Parent <- factor(csek12$Parent, levels = c(1:3))
#setting "I don't know" responses to NA
csek12$PolAff <- ifelse(csek12$Politic == 8, NA, csek12$Politic)
#setting "I would abstain (not vote)" responses to NA
csek12$Vote1 <- ifelse(csek12$Vote == 3, NA, csek12$Vote)
#Descriptive statistics
library(psych)
## Warning: package 'psych' was built under R version 4.3.3
library(summarytools)
describe(csek12$Age)
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 298 45.23 15.68 43 45 20.76 19 77 58 0.1 -1.19 0.91
describe(csek12$Income)
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 298 4.98 2.86 4 4.98 2.97 1 10 9 0.41 -1.02 0.17
describe(csek12$Edu)
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 298 6.24 1.34 7 6.24 1.48 2 9 7 -0.28 -0.7 0.08
describe(csek12$Important_Middle)
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 298 1.86 1.14 1.5 1.86 0.74 1 5 4 1.38 0.92 0.07
describe(csek12$Important_High)
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 298 1.3 0.84 1 1.3 0 1 5 4 3.39 11.4 0.05
describe(csek12$Politic)
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 298 5 1.99 6 5 1.48 1 8 7 -0.56 -0.9 0.12
freq(csek12$Gender_5cat)
## Frequencies
## csek12$Gender_5cat
## Type: Factor
##
## Freq % Valid % Valid Cum. % Total % Total Cum.
## ----------- ------ --------- -------------- --------- --------------
## 1 145 50.00 50.00 48.66 48.66
## 2 0 0.00 50.00 0.00 48.66
## 3 134 46.21 96.21 44.97 93.62
## 4 3 1.03 97.24 1.01 94.63
## 5 8 2.76 100.00 2.68 97.32
## <NA> 8 2.68 100.00
## Total 298 100.00 100.00 100.00 100.00
freq(csek12$Gender_3cat)
## Frequencies
## csek12$Gender_3cat
## Type: Factor
##
## Freq % Valid % Valid Cum. % Total % Total Cum.
## ----------- ------ --------- -------------- --------- --------------
## 1 145 50.00 50.00 48.66 48.66
## 2 137 47.24 97.24 45.97 94.63
## 3 8 2.76 100.00 2.68 97.32
## <NA> 8 2.68 100.00
## Total 298 100.00 100.00 100.00 100.00
freq(csek12$Race_cat)
## Frequencies
## csek12$Race_cat
## Type: Factor
##
## Freq % Valid % Valid Cum. % Total % Total Cum.
## ----------- ------ --------- -------------- --------- --------------
## 1 19 6.42 6.42 6.38 6.38
## 2 35 11.82 18.24 11.74 18.12
## 3 13 4.39 22.64 4.36 22.48
## 4 0 0.00 22.64 0.00 22.48
## 5 1 0.34 22.97 0.34 22.82
## 6 201 67.91 90.88 67.45 90.27
## 7 27 9.12 100.00 9.06 99.33
## <NA> 2 0.67 100.00
## Total 298 100.00 100.00 100.00 100.00
freq(csek12$Edu)
## Frequencies
## csek12$Edu
## Label: What is the highest level of education you have completed?
## Type: Numeric
##
## Freq % Valid % Valid Cum. % Total % Total Cum.
## ----------- ------ --------- -------------- --------- --------------
## 2 1 0.34 0.34 0.34 0.34
## 4 35 11.74 12.08 11.74 12.08
## 5 66 22.15 34.23 22.15 34.23
## 6 33 11.07 45.30 11.07 45.30
## 7 122 40.94 86.24 40.94 86.24
## 8 33 11.07 97.32 11.07 97.32
## 9 8 2.68 100.00 2.68 100.00
## <NA> 0 0.00 100.00
## Total 298 100.00 100.00 100.00 100.00
freq(csek12$SO)
## Frequencies
## csek12$SO
## Type: Factor
##
## Freq % Valid % Valid Cum. % Total % Total Cum.
## ----------- ------ --------- -------------- --------- --------------
## 1 234 78.52 78.52 78.52 78.52
## 2 11 3.69 82.21 3.69 82.21
## 3 35 11.74 93.96 11.74 93.96
## 4 1 0.34 94.30 0.34 94.30
## 5 15 5.03 99.33 5.03 99.33
## 6 2 0.67 100.00 0.67 100.00
## <NA> 0 0.00 100.00
## Total 298 100.00 100.00 100.00 100.00
freq(csek12$Geo)
## Frequencies
## csek12$Geo
## Type: Factor
##
## Freq % Valid % Valid Cum. % Total % Total Cum.
## ----------- ------ --------- -------------- --------- --------------
## 1 33 11.15 11.15 11.07 11.07
## 2 63 21.28 32.43 21.14 32.21
## 3 134 45.27 77.70 44.97 77.18
## 4 66 22.30 100.00 22.15 99.33
## <NA> 2 0.67 100.00
## Total 298 100.00 100.00 100.00 100.00
freq(csek12$Politic)
## Frequencies
## csek12$Politic
## Label: How would you describe your political affiliations?
## Type: Numeric
##
## Freq % Valid % Valid Cum. % Total % Total Cum.
## ----------- ------ --------- -------------- --------- --------------
## 1 21 7.05 7.05 7.05 7.05
## 2 26 8.72 15.77 8.72 15.77
## 3 22 7.38 23.15 7.38 23.15
## 4 49 16.44 39.60 16.44 39.60
## 5 29 9.73 49.33 9.73 49.33
## 6 53 17.79 67.11 17.79 67.11
## 7 93 31.21 98.32 31.21 98.32
## 8 5 1.68 100.00 1.68 100.00
## <NA> 0 0.00 100.00
## Total 298 100.00 100.00 100.00 100.00
freq(csek12$Parent)
## Frequencies
## csek12$Parent
## Type: Factor
##
## Freq % Valid % Valid Cum. % Total % Total Cum.
## ----------- ------ --------- -------------- --------- --------------
## 1 75 25.17 25.17 25.17 25.17
## 2 66 22.15 47.32 22.15 47.32
## 3 157 52.68 100.00 52.68 100.00
## <NA> 0 0.00 100.00
## Total 298 100.00 100.00 100.00 100.00
freq(csek12$Vote)
## Frequencies
## csek12$Vote
## Type: Factor
##
## Freq % Valid % Valid Cum. % Total % Total Cum.
## ----------- ------ --------- -------------- --------- --------------
## 1 36 12.08 12.08 12.08 12.08
## 2 243 81.54 93.62 81.54 93.62
## 3 19 6.38 100.00 6.38 100.00
## <NA> 0 0.00 100.00
## Total 298 100.00 100.00 100.00 100.00
CSEK12_ItemDescriptives <- describe(csek12[,c(5:45)])
write.csv(CSEK12_ItemDescriptives, "CSEK12_ItemDescriptives.csv")
SexPos_ItemDescriptives <- describe(csek12[,c(46:54, 85)])
write.csv(SexPos_ItemDescriptives, "SexPos_ItemDescriptives.csv")
SexNeg_ItemDescriptives <- describe(csek12[,c(58:65, 86)])
write.csv(SexNeg_ItemDescriptives, "SexNeg_ItemDescriptives.csv")
#Interitem correlations
library(performance)
## Warning: package 'performance' was built under R version 4.3.3
library(Hmisc)
## Warning: package 'Hmisc' was built under R version 4.3.3
##
## Attaching package: 'Hmisc'
## The following objects are masked from 'package:summarytools':
##
## label, label<-
## The following object is masked from 'package:psych':
##
## describe
## The following objects are masked from 'package:base':
##
## format.pval, units
CSEK12_Items <- as.data.frame(csek12[,c(5:13, 15:36, 38:45)])
summary(CSEK12_Items)
## Q2_1 Q2_2 Q2_3 Q2_4 Q2_5
## Min. :1.000 Min. :1.000 Min. :1.00 Min. :1.000 Min. :1.000
## 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:4.00 1st Qu.:4.000 1st Qu.:4.000
## Median :5.000 Median :5.000 Median :5.00 Median :5.000 Median :5.000
## Mean :4.275 Mean :4.208 Mean :4.44 Mean :4.282 Mean :4.289
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.00 3rd Qu.:5.000 3rd Qu.:5.000
## Max. :5.000 Max. :5.000 Max. :5.00 Max. :5.000 Max. :5.000
##
## Q2_6 Q2_7 Q2_8 Q2_9
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:4.000
## Median :5.000 Median :5.000 Median :5.000 Median :5.000
## Mean :4.369 Mean :4.456 Mean :4.349 Mean :4.453
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
##
## Q3_1 Q3_2 Q3_3 Q3_4 Q3_5
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.00
## 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:4.00
## Median :5.000 Median :5.000 Median :5.000 Median :5.000 Median :5.00
## Mean :4.369 Mean :4.403 Mean :4.483 Mean :4.386 Mean :4.46
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.00
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.00
##
## Q3_6 Q3_7 Q3_8 Q3_9
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:3.000 1st Qu.:3.000
## Median :5.000 Median :5.000 Median :5.000 Median :4.500
## Mean :4.492 Mean :4.228 Mean :4.074 Mean :3.815
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
## NA's :1
## Q3_10 Q4_1 Q4_2 Q4_3
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:4.000
## Median :5.000 Median :5.000 Median :5.000 Median :5.000
## Mean :4.248 Mean :4.419 Mean :4.322 Mean :4.463
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
##
## Q4_4 Q4_5 Q4_6 Q4_7 Q4_8
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.00
## 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:5.00
## Median :5.000 Median :5.000 Median :5.000 Median :5.000 Median :5.00
## Mean :4.463 Mean :4.497 Mean :4.406 Mean :4.228 Mean :4.54
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.00
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.00
##
## Q4_9 Q4_10 Q5_1 Q5_2
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:4.000
## Median :5.000 Median :5.000 Median :5.000 Median :5.000
## Mean :4.205 Mean :4.406 Mean :4.322 Mean :4.369
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
##
## Q5_4 Q5_5 Q5_6 Q5_7 Q5_8
## Min. :1.000 Min. :1.000 Min. :1.00 Min. :1.000 Min. :1.000
## 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:4.00 1st Qu.:4.000 1st Qu.:4.000
## Median :5.000 Median :5.000 Median :5.00 Median :5.000 Median :5.000
## Mean :4.503 Mean :4.419 Mean :4.51 Mean :4.423 Mean :4.362
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.00 3rd Qu.:5.000 3rd Qu.:5.000
## Max. :5.000 Max. :5.000 Max. :5.00 Max. :5.000 Max. :5.000
##
## Q5_9 Q5_10 Q5_11
## Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:4.000
## Median :5.000 Median :5.000 Median :5.000
## Mean :4.557 Mean :4.379 Mean :4.477
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000
## Max. :5.000 Max. :5.000 Max. :5.000
##
View(psych::describe(CSEK12_Items))
intercor_CSEK12 <- item_intercor(CSEK12_Items) #mean interitem correlation = 0.72
itemcor_CSEK12 <- cor(CSEK12_Items, use = "complete.obs")
psych::alpha(CSEK12_Items)
##
## Reliability analysis
## Call: psych::alpha(x = CSEK12_Items)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.99 0.99 0.99 0.72 101 0.00087 4.4 0.88 0.74
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.99 0.99 0.99
## Duhachek 0.99 0.99 0.99
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## Q2_1 0.99 0.99 0.99 0.72 98 0.00090 0.0099 0.74
## Q2_2 0.99 0.99 0.99 0.73 101 0.00087 0.0093 0.75
## Q2_3 0.99 0.99 0.99 0.72 98 0.00090 0.0098 0.74
## Q2_4 0.99 0.99 0.99 0.72 98 0.00089 0.0099 0.74
## Q2_5 0.99 0.99 0.99 0.72 98 0.00090 0.0098 0.74
## Q2_6 0.99 0.99 0.99 0.72 99 0.00089 0.0099 0.75
## Q2_7 0.99 0.99 0.99 0.72 98 0.00089 0.0099 0.74
## Q2_8 0.99 0.99 0.99 0.72 99 0.00089 0.0100 0.75
## Q2_9 0.99 0.99 0.99 0.72 100 0.00088 0.0098 0.75
## Q3_1 0.99 0.99 0.99 0.72 97 0.00090 0.0097 0.74
## Q3_2 0.99 0.99 0.99 0.72 97 0.00090 0.0097 0.74
## Q3_3 0.99 0.99 0.99 0.72 98 0.00090 0.0098 0.74
## Q3_4 0.99 0.99 0.99 0.72 97 0.00091 0.0096 0.74
## Q3_5 0.99 0.99 0.99 0.72 98 0.00090 0.0098 0.74
## Q3_6 0.99 0.99 0.99 0.72 98 0.00089 0.0098 0.74
## Q3_7 0.99 0.99 0.99 0.72 99 0.00089 0.0097 0.74
## Q3_8 0.99 0.99 0.99 0.73 101 0.00085 0.0091 0.75
## Q3_9 0.99 0.99 0.99 0.73 102 0.00084 0.0089 0.75
## Q3_10 0.99 0.99 0.99 0.72 99 0.00089 0.0099 0.75
## Q4_1 0.99 0.99 0.99 0.72 97 0.00091 0.0096 0.74
## Q4_2 0.99 0.99 0.99 0.72 97 0.00090 0.0098 0.74
## Q4_3 0.99 0.99 0.99 0.72 96 0.00091 0.0094 0.74
## Q4_4 0.99 0.99 0.99 0.72 97 0.00091 0.0096 0.74
## Q4_5 0.99 0.99 0.99 0.72 98 0.00090 0.0098 0.74
## Q4_6 0.99 0.99 0.99 0.72 97 0.00091 0.0096 0.74
## Q4_7 0.99 0.99 0.99 0.72 98 0.00090 0.0098 0.74
## Q4_8 0.99 0.99 0.99 0.72 98 0.00089 0.0098 0.74
## Q4_9 0.99 0.99 0.99 0.73 104 0.00084 0.0072 0.75
## Q4_10 0.99 0.99 0.99 0.72 97 0.00090 0.0097 0.74
## Q5_1 0.99 0.99 0.99 0.72 98 0.00090 0.0098 0.74
## Q5_2 0.99 0.99 0.99 0.72 98 0.00089 0.0098 0.74
## Q5_4 0.99 0.99 0.99 0.72 97 0.00090 0.0096 0.74
## Q5_5 0.99 0.99 0.99 0.72 99 0.00089 0.0099 0.75
## Q5_6 0.99 0.99 0.99 0.72 98 0.00090 0.0098 0.74
## Q5_7 0.99 0.99 0.99 0.72 97 0.00090 0.0096 0.74
## Q5_8 0.99 0.99 0.99 0.73 102 0.00086 0.0087 0.75
## Q5_9 0.99 0.99 0.99 0.72 97 0.00090 0.0098 0.74
## Q5_10 0.99 0.99 0.99 0.72 100 0.00088 0.0097 0.75
## Q5_11 0.99 0.99 0.99 0.72 97 0.00090 0.0096 0.74
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## Q2_1 298 0.86 0.86 0.86 0.86 4.3 1.05
## Q2_2 298 0.74 0.74 0.73 0.72 4.2 1.11
## Q2_3 298 0.88 0.88 0.88 0.87 4.4 0.95
## Q2_4 298 0.86 0.86 0.85 0.85 4.3 1.06
## Q2_5 298 0.87 0.87 0.86 0.86 4.3 1.07
## Q2_6 298 0.83 0.83 0.83 0.82 4.4 1.06
## Q2_7 298 0.86 0.86 0.85 0.85 4.5 0.95
## Q2_8 298 0.84 0.85 0.84 0.84 4.3 0.97
## Q2_9 298 0.80 0.80 0.80 0.79 4.5 0.89
## Q3_1 298 0.91 0.90 0.90 0.90 4.4 1.03
## Q3_2 298 0.91 0.91 0.91 0.91 4.4 1.01
## Q3_3 298 0.88 0.89 0.88 0.88 4.5 0.92
## Q3_4 298 0.93 0.93 0.93 0.92 4.4 1.04
## Q3_5 298 0.89 0.89 0.89 0.88 4.5 0.97
## Q3_6 297 0.84 0.85 0.84 0.84 4.5 0.97
## Q3_7 298 0.84 0.83 0.83 0.82 4.2 1.18
## Q3_8 298 0.74 0.73 0.73 0.72 4.1 1.34
## Q3_9 298 0.72 0.71 0.70 0.70 3.8 1.44
## Q3_10 298 0.83 0.83 0.83 0.82 4.2 1.06
## Q4_1 298 0.92 0.92 0.92 0.92 4.4 1.00
## Q4_2 298 0.89 0.89 0.89 0.88 4.3 1.06
## Q4_3 298 0.94 0.94 0.94 0.94 4.5 0.97
## Q4_4 298 0.92 0.92 0.92 0.92 4.5 1.00
## Q4_5 298 0.87 0.87 0.87 0.86 4.5 0.97
## Q4_6 298 0.92 0.92 0.92 0.92 4.4 1.03
## Q4_7 298 0.87 0.87 0.87 0.86 4.2 1.17
## Q4_8 298 0.87 0.87 0.87 0.86 4.5 0.98
## Q4_9 298 0.60 0.60 0.59 0.58 4.2 1.09
## Q4_10 298 0.90 0.90 0.90 0.90 4.4 1.00
## Q5_1 298 0.88 0.88 0.88 0.87 4.3 1.03
## Q5_2 298 0.86 0.86 0.86 0.85 4.4 1.07
## Q5_4 298 0.91 0.92 0.92 0.91 4.5 0.94
## Q5_5 298 0.83 0.84 0.83 0.82 4.4 0.98
## Q5_6 298 0.88 0.88 0.88 0.87 4.5 0.90
## Q5_7 298 0.91 0.91 0.91 0.91 4.4 1.04
## Q5_8 298 0.68 0.69 0.67 0.66 4.4 1.06
## Q5_9 298 0.89 0.89 0.89 0.89 4.6 0.88
## Q5_10 298 0.78 0.78 0.78 0.77 4.4 0.96
## Q5_11 298 0.91 0.92 0.92 0.91 4.5 0.99
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## Q2_1 0.03 0.06 0.06 0.29 0.56 0
## Q2_2 0.04 0.04 0.15 0.20 0.57 0
## Q2_3 0.03 0.02 0.09 0.20 0.66 0
## Q2_4 0.04 0.04 0.12 0.21 0.59 0
## Q2_5 0.04 0.05 0.10 0.21 0.60 0
## Q2_6 0.04 0.05 0.06 0.21 0.64 0
## Q2_7 0.03 0.03 0.06 0.22 0.66 0
## Q2_8 0.03 0.03 0.10 0.24 0.59 0
## Q2_9 0.02 0.02 0.08 0.23 0.64 0
## Q3_1 0.04 0.03 0.09 0.21 0.63 0
## Q3_2 0.03 0.03 0.09 0.18 0.66 0
## Q3_3 0.02 0.03 0.07 0.20 0.68 0
## Q3_4 0.04 0.03 0.08 0.19 0.65 0
## Q3_5 0.03 0.02 0.08 0.18 0.68 0
## Q3_6 0.03 0.01 0.10 0.14 0.72 0
## Q3_7 0.06 0.05 0.12 0.15 0.62 0
## Q3_8 0.10 0.04 0.11 0.16 0.58 0
## Q3_9 0.13 0.08 0.14 0.15 0.50 0
## Q3_10 0.04 0.05 0.10 0.26 0.56 0
## Q4_1 0.04 0.03 0.06 0.22 0.65 0
## Q4_2 0.04 0.04 0.10 0.20 0.62 0
## Q4_3 0.03 0.03 0.08 0.17 0.69 0
## Q4_4 0.04 0.03 0.05 0.19 0.69 0
## Q4_5 0.03 0.04 0.05 0.16 0.71 0
## Q4_6 0.04 0.04 0.07 0.19 0.66 0
## Q4_7 0.05 0.05 0.11 0.18 0.60 0
## Q4_8 0.04 0.03 0.05 0.13 0.76 0
## Q4_9 0.04 0.03 0.17 0.19 0.56 0
## Q4_10 0.04 0.03 0.08 0.20 0.65 0
## Q5_1 0.04 0.03 0.12 0.21 0.61 0
## Q5_2 0.04 0.04 0.08 0.18 0.65 0
## Q5_4 0.03 0.02 0.06 0.18 0.70 0
## Q5_5 0.03 0.02 0.09 0.20 0.65 0
## Q5_6 0.03 0.02 0.07 0.19 0.69 0
## Q5_7 0.04 0.03 0.07 0.18 0.68 0
## Q5_8 0.04 0.04 0.09 0.18 0.65 0
## Q5_9 0.02 0.01 0.08 0.15 0.73 0
## Q5_10 0.02 0.03 0.12 0.20 0.62 0
## Q5_11 0.04 0.03 0.05 0.19 0.69 0
write.csv(itemcor_CSEK12, "CSEK12_ItemCorrelations.csv")
SexPos_Items <- csek12[,c(46:53)]
item_intercor(SexPos_Items) #0.76
## [1] 0.7637165
itemcor_SexPos <- cor(SexPos_Items)
psych::describe(csek12$Sex_Pos)
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 298 38.88 8.34 40 39.97 8.9 8 48 40 -1.15 1.57 0.48
SexNeg_Items <- csek12[,c(58:65)]
item_intercor(SexNeg_Items) #0.54
## [1] 0.5388472
itemcor_SexNeg <- cor(SexNeg_Items)
psych::describe(csek12$Sex_Neg)
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 298 11.22 4.55 10 10.31 2.97 8 48 40 3.25 16.66 0.26
#Missingness (only 1 missing value in scale; Q3_6)
library(finalfit)
## Warning: package 'finalfit' was built under R version 4.3.3
library(naniar)
## Warning: package 'naniar' was built under R version 4.3.3
library(misty)
## Warning: package 'misty' was built under R version 4.3.3
## |-------------------------------------|
## | misty 0.6.5 (2024-06-29) |
## | Miscellaneous Functions T. Yanagida |
## |-------------------------------------|
##
## Attaching package: 'misty'
## The following object is masked from 'package:Hmisc':
##
## na.pattern
## The following object is masked from 'package:summarytools':
##
## freq
library(dplyr)
## Warning: package 'dplyr' was built under R version 4.3.3
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:Hmisc':
##
## src, summarize
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
missing_plot(CSEK12_Items)
missing_pattern(CSEK12_Items)
## Q2_1 Q2_2 Q2_3 Q2_4 Q2_5 Q2_6 Q2_7 Q2_8 Q2_9 Q3_1 Q3_2 Q3_3 Q3_4 Q3_5 Q3_7
## 297 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## Q3_8 Q3_9 Q3_10 Q4_1 Q4_2 Q4_3 Q4_4 Q4_5 Q4_6 Q4_7 Q4_8 Q4_9 Q4_10 Q5_1
## 297 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## Q5_2 Q5_4 Q5_5 Q5_6 Q5_7 Q5_8 Q5_9 Q5_10 Q5_11 Q3_6
## 297 1 1 1 1 1 1 1 1 1 1 0
## 1 1 1 1 1 1 1 1 1 1 0 1
## 0 0 0 0 0 0 0 0 0 1 1
explanatory = c("Age", "Gender_5cat", "Gender_3cat", "Race_cat", "Edu", "Income", "Geo", "SO", "Politic", "Parent", "Vote", "Important_Middle", "Important_High", "Sex_Pos", "Sex_Neg")
dependent = "Q3_6"
csek12 %>% missing_compare(dependent, explanatory)
## Warning: There was 1 warning in `dplyr::summarise()`.
## ℹ In argument: `chisq.test(Gender_5cat, Q3_6)$p.value`.
## Caused by warning in `chisq.test()`:
## ! Chi-squared approximation may be incorrect
## Warning: There was 1 warning in `dplyr::summarise()`.
## ℹ In argument: `chisq.test(Gender_3cat, Q3_6)$p.value`.
## Caused by warning in `chisq.test()`:
## ! Chi-squared approximation may be incorrect
## Warning: There was 1 warning in `dplyr::summarise()`.
## ℹ In argument: `chisq.test(Race_cat, Q3_6)$p.value`.
## Caused by warning in `chisq.test()`:
## ! Chi-squared approximation may be incorrect
## Warning: There was 1 warning in `dplyr::summarise()`.
## ℹ In argument: `chisq.test(Geo, Q3_6)$p.value`.
## Caused by warning in `chisq.test()`:
## ! Chi-squared approximation may be incorrect
## Warning: There was 1 warning in `dplyr::summarise()`.
## ℹ In argument: `chisq.test(SO, Q3_6)$p.value`.
## Caused by warning in `chisq.test()`:
## ! Chi-squared approximation may be incorrect
## Warning: There was 1 warning in `dplyr::summarise()`.
## ℹ In argument: `chisq.test(Parent, Q3_6)$p.value`.
## Caused by warning in `chisq.test()`:
## ! Chi-squared approximation may be incorrect
## Warning: There was 1 warning in `dplyr::summarise()`.
## ℹ In argument: `chisq.test(Vote, Q3_6)$p.value`.
## Caused by warning in `chisq.test()`:
## ! Chi-squared approximation may be incorrect
## Missing data analysis: How do you feel about comprehensive sex ed. in K-12 schools if … - The class reduces STDs.
## What is your age?
## Gender_5cat
##
##
##
##
## Gender_3cat
##
##
## Race_cat
##
##
##
##
##
##
## What is the highest level of education you have completed?
## What is your household income before taxes?
## Geo
##
##
##
## SO
##
##
##
##
##
## How would you describe your political affiliations?
## Parent
##
##
## Vote
##
##
## How important do you think it is to have sex education in middle school?
## How important do you think it is to have sex education in high school?
## Sex positivity scale composite score. Range 8-48. Higher scores = more sex positivity.
## Sex negtivity scale composite score. Range 8-48. Higher scores = more sex negativity.
## Not missing Missing p
## Mean (SD) 45.2 (15.7) 56.0 (NA) 0.492
## 1 144 (99.3) 1 (0.7) 0.800
## 2 0 (NaN) 0 (NaN)
## 3 134 (100.0) 0 (0.0)
## 4 3 (100.0) 0 (0.0)
## 5 8 (100.0) 0 (0.0)
## 1 144 (99.3) 1 (0.7) 0.605
## 2 137 (100.0) 0 (0.0)
## 3 8 (100.0) 0 (0.0)
## 1 19 (100.0) 0 (0.0) 0.187
## 2 34 (97.1) 1 (2.9)
## 3 13 (100.0) 0 (0.0)
## 4 0 (NaN) 0 (NaN)
## 5 1 (100.0) 0 (0.0)
## 6 201 (100.0) 0 (0.0)
## 7 27 (100.0) 0 (0.0)
## Mean (SD) 6.2 (1.3) 5.0 (NA) 0.353
## Mean (SD) 5.0 (2.9) 3.0 (NA) 0.489
## 1 33 (100.0) 0 (0.0) 0.750
## 2 63 (100.0) 0 (0.0)
## 3 133 (99.3) 1 (0.7)
## 4 66 (100.0) 0 (0.0)
## 1 233 (99.6) 1 (0.4) 0.998
## 2 11 (100.0) 0 (0.0)
## 3 35 (100.0) 0 (0.0)
## 4 1 (100.0) 0 (0.0)
## 5 15 (100.0) 0 (0.0)
## 6 2 (100.0) 0 (0.0)
## Mean (SD) 5.0 (2.0) 4.0 (NA) 0.617
## 1 75 (100.0) 0 (0.0) 0.637
## 2 66 (100.0) 0 (0.0)
## 3 156 (99.4) 1 (0.6)
## 1 36 (100.0) 0 (0.0) 0.893
## 2 242 (99.6) 1 (0.4)
## 3 19 (100.0) 0 (0.0)
## Mean (SD) 1.9 (1.1) 2.0 (NA) 0.902
## Mean (SD) 1.3 (0.8) 1.0 (NA) 0.720
## Mean (SD) 38.9 (8.4) 38.0 (NA) 0.916
## Mean (SD) 11.2 (4.6) 10.0 (NA) 0.789
explanatory1 = c("Age", "Race_cat", "Edu", "Income", "Geo", "SO", "Politic", "Parent", "Vote", "Important_Middle", "Important_High", "Sex_Pos", "Sex_Neg")
dependent1a = "Gender_5cat"
dependent1b = "Gender_3cat"
csek12 %>% missing_compare(dependent1a, explanatory1)
## Warning: There was 1 warning in `dplyr::summarise()`.
## ℹ In argument: `chisq.test(Race_cat, Gender_5cat)$p.value`.
## Caused by warning in `chisq.test()`:
## ! Chi-squared approximation may be incorrect
## Warning: There was 1 warning in `dplyr::summarise()`.
## ℹ In argument: `chisq.test(Geo, Gender_5cat)$p.value`.
## Caused by warning in `chisq.test()`:
## ! Chi-squared approximation may be incorrect
## Warning: There was 1 warning in `dplyr::summarise()`.
## ℹ In argument: `chisq.test(SO, Gender_5cat)$p.value`.
## Caused by warning in `chisq.test()`:
## ! Chi-squared approximation may be incorrect
## Warning: There was 1 warning in `dplyr::summarise()`.
## ℹ In argument: `chisq.test(Parent, Gender_5cat)$p.value`.
## Caused by warning in `chisq.test()`:
## ! Chi-squared approximation may be incorrect
## Warning: There was 1 warning in `dplyr::summarise()`.
## ℹ In argument: `chisq.test(Vote, Gender_5cat)$p.value`.
## Caused by warning in `chisq.test()`:
## ! Chi-squared approximation may be incorrect
## Missing data analysis: Gender_5cat
## What is your age?
## Race_cat
##
##
##
##
##
##
## What is the highest level of education you have completed?
## What is your household income before taxes?
## Geo
##
##
##
## SO
##
##
##
##
##
## How would you describe your political affiliations?
## Parent
##
##
## Vote
##
##
## How important do you think it is to have sex education in middle school?
## How important do you think it is to have sex education in high school?
## Sex positivity scale composite score. Range 8-48. Higher scores = more sex positivity.
## Sex negtivity scale composite score. Range 8-48. Higher scores = more sex negativity.
## Not missing Missing p
## Mean (SD) 45.1 (15.6) 49.6 (18.6) 0.422
## 1 19 (100.0) 0 (0.0) 0.109
## 2 34 (97.1) 1 (2.9)
## 3 11 (84.6) 2 (15.4)
## 4 0 (NaN) 0 (NaN)
## 5 1 (100.0) 0 (0.0)
## 6 197 (98.0) 4 (2.0)
## 7 26 (96.3) 1 (3.7)
## Mean (SD) 6.3 (1.3) 5.9 (1.2) 0.433
## Mean (SD) 5.0 (2.9) 5.0 (2.8) 0.987
## 1 31 (93.9) 2 (6.1) 0.357
## 2 63 (100.0) 0 (0.0)
## 3 130 (97.0) 4 (3.0)
## 4 64 (97.0) 2 (3.0)
## 1 227 (97.0) 7 (3.0) 0.971
## 2 11 (100.0) 0 (0.0)
## 3 34 (97.1) 1 (2.9)
## 4 1 (100.0) 0 (0.0)
## 5 15 (100.0) 0 (0.0)
## 6 2 (100.0) 0 (0.0)
## Mean (SD) 5.0 (2.0) 3.2 (2.3) 0.012
## 1 72 (96.0) 3 (4.0) 0.642
## 2 64 (97.0) 2 (3.0)
## 3 154 (98.1) 3 (1.9)
## 1 33 (91.7) 3 (8.3) 0.071
## 2 238 (97.9) 5 (2.1)
## 3 19 (100.0) 0 (0.0)
## Mean (SD) 1.8 (1.1) 2.4 (1.5) 0.195
## Mean (SD) 1.3 (0.8) 1.6 (1.4) 0.272
## Mean (SD) 39.0 (8.1) 33.9 (14.5) 0.086
## Mean (SD) 11.2 (4.5) 11.9 (6.0) 0.680
csek12 %>% missing_compare(dependent1b, explanatory1)
## Warning: There was 1 warning in `dplyr::summarise()`.
## ℹ In argument: `chisq.test(Race_cat, Gender_3cat)$p.value`.
## Caused by warning in `chisq.test()`:
## ! Chi-squared approximation may be incorrect
## Warning: There was 1 warning in `dplyr::summarise()`.
## ℹ In argument: `chisq.test(Geo, Gender_3cat)$p.value`.
## Caused by warning in `chisq.test()`:
## ! Chi-squared approximation may be incorrect
## Warning: There was 1 warning in `dplyr::summarise()`.
## ℹ In argument: `chisq.test(SO, Gender_3cat)$p.value`.
## Caused by warning in `chisq.test()`:
## ! Chi-squared approximation may be incorrect
## Warning: There was 1 warning in `dplyr::summarise()`.
## ℹ In argument: `chisq.test(Parent, Gender_3cat)$p.value`.
## Caused by warning in `chisq.test()`:
## ! Chi-squared approximation may be incorrect
## Warning: There was 1 warning in `dplyr::summarise()`.
## ℹ In argument: `chisq.test(Vote, Gender_3cat)$p.value`.
## Caused by warning in `chisq.test()`:
## ! Chi-squared approximation may be incorrect
## Missing data analysis: Gender_3cat
## What is your age?
## Race_cat
##
##
##
##
##
##
## What is the highest level of education you have completed?
## What is your household income before taxes?
## Geo
##
##
##
## SO
##
##
##
##
##
## How would you describe your political affiliations?
## Parent
##
##
## Vote
##
##
## How important do you think it is to have sex education in middle school?
## How important do you think it is to have sex education in high school?
## Sex positivity scale composite score. Range 8-48. Higher scores = more sex positivity.
## Sex negtivity scale composite score. Range 8-48. Higher scores = more sex negativity.
## Not missing Missing p
## Mean (SD) 45.1 (15.6) 49.6 (18.6) 0.422
## 1 19 (100.0) 0 (0.0) 0.109
## 2 34 (97.1) 1 (2.9)
## 3 11 (84.6) 2 (15.4)
## 4 0 (NaN) 0 (NaN)
## 5 1 (100.0) 0 (0.0)
## 6 197 (98.0) 4 (2.0)
## 7 26 (96.3) 1 (3.7)
## Mean (SD) 6.3 (1.3) 5.9 (1.2) 0.433
## Mean (SD) 5.0 (2.9) 5.0 (2.8) 0.987
## 1 31 (93.9) 2 (6.1) 0.357
## 2 63 (100.0) 0 (0.0)
## 3 130 (97.0) 4 (3.0)
## 4 64 (97.0) 2 (3.0)
## 1 227 (97.0) 7 (3.0) 0.971
## 2 11 (100.0) 0 (0.0)
## 3 34 (97.1) 1 (2.9)
## 4 1 (100.0) 0 (0.0)
## 5 15 (100.0) 0 (0.0)
## 6 2 (100.0) 0 (0.0)
## Mean (SD) 5.0 (2.0) 3.2 (2.3) 0.012
## 1 72 (96.0) 3 (4.0) 0.642
## 2 64 (97.0) 2 (3.0)
## 3 154 (98.1) 3 (1.9)
## 1 33 (91.7) 3 (8.3) 0.071
## 2 238 (97.9) 5 (2.1)
## 3 19 (100.0) 0 (0.0)
## Mean (SD) 1.8 (1.1) 2.4 (1.5) 0.195
## Mean (SD) 1.3 (0.8) 1.6 (1.4) 0.272
## Mean (SD) 39.0 (8.1) 33.9 (14.5) 0.086
## Mean (SD) 11.2 (4.5) 11.9 (6.0) 0.680
explanatory2 = c("Age", "Edu", "Gender_5cat", "Gender_3cat", "Income", "Geo", "SO", "Politic", "Parent", "Vote", "Important_Middle", "Important_High", "Sex_Pos", "Sex_Neg")
dependent2 = "Race_cat"
csek12 %>% missing_compare(dependent2, explanatory2)
## Warning: There was 1 warning in `dplyr::summarise()`.
## ℹ In argument: `chisq.test(Gender_5cat, Race_cat)$p.value`.
## Caused by warning in `chisq.test()`:
## ! Chi-squared approximation may be incorrect
## Warning: There was 1 warning in `dplyr::summarise()`.
## ℹ In argument: `chisq.test(Gender_3cat, Race_cat)$p.value`.
## Caused by warning in `chisq.test()`:
## ! Chi-squared approximation may be incorrect
## Warning: There was 1 warning in `dplyr::summarise()`.
## ℹ In argument: `chisq.test(Geo, Race_cat)$p.value`.
## Caused by warning in `chisq.test()`:
## ! Chi-squared approximation may be incorrect
## Warning: There was 1 warning in `dplyr::summarise()`.
## ℹ In argument: `chisq.test(SO, Race_cat)$p.value`.
## Caused by warning in `chisq.test()`:
## ! Chi-squared approximation may be incorrect
## Warning: There was 1 warning in `dplyr::summarise()`.
## ℹ In argument: `chisq.test(Parent, Race_cat)$p.value`.
## Caused by warning in `chisq.test()`:
## ! Chi-squared approximation may be incorrect
## Warning: There was 1 warning in `dplyr::summarise()`.
## ℹ In argument: `chisq.test(Vote, Race_cat)$p.value`.
## Caused by warning in `chisq.test()`:
## ! Chi-squared approximation may be incorrect
## Missing data analysis: Race_cat
## What is your age?
## What is the highest level of education you have completed?
## Gender_5cat
##
##
##
##
## Gender_3cat
##
##
## What is your household income before taxes?
## Geo
##
##
##
## SO
##
##
##
##
##
## How would you describe your political affiliations?
## Parent
##
##
## Vote
##
##
## How important do you think it is to have sex education in middle school?
## How important do you think it is to have sex education in high school?
## Sex positivity scale composite score. Range 8-48. Higher scores = more sex positivity.
## Sex negtivity scale composite score. Range 8-48. Higher scores = more sex negativity.
## Not missing Missing p
## Mean (SD) 45.2 (15.7) 50.0 (14.1) 0.667
## Mean (SD) 6.2 (1.3) 6.0 (1.4) 0.798
## 1 144 (99.3) 1 (0.7) 0.001
## 2 0 (NaN) 0 (NaN)
## 3 134 (100.0) 0 (0.0)
## 4 3 (100.0) 0 (0.0)
## 5 7 (87.5) 1 (12.5)
## 1 144 (99.3) 1 (0.7) <0.001
## 2 137 (100.0) 0 (0.0)
## 3 7 (87.5) 1 (12.5)
## Mean (SD) 5.0 (2.9) 2.0 (1.4) 0.139
## 1 33 (100.0) 0 (0.0) 0.487
## 2 63 (100.0) 0 (0.0)
## 3 132 (98.5) 2 (1.5)
## 4 66 (100.0) 0 (0.0)
## 1 233 (99.6) 1 (0.4) 0.713
## 2 11 (100.0) 0 (0.0)
## 3 34 (97.1) 1 (2.9)
## 4 1 (100.0) 0 (0.0)
## 5 15 (100.0) 0 (0.0)
## 6 2 (100.0) 0 (0.0)
## Mean (SD) 5.0 (2.0) 4.5 (3.5) 0.724
## 1 75 (100.0) 0 (0.0) 0.405
## 2 66 (100.0) 0 (0.0)
## 3 155 (98.7) 2 (1.3)
## 1 36 (100.0) 0 (0.0) 0.796
## 2 241 (99.2) 2 (0.8)
## 3 19 (100.0) 0 (0.0)
## Mean (SD) 1.9 (1.1) 1.0 (0.0) 0.286
## Mean (SD) 1.3 (0.8) 1.0 (0.0) 0.612
## Mean (SD) 38.9 (8.3) 32.5 (13.4) 0.279
## Mean (SD) 11.2 (4.6) 14.0 (1.4) 0.386
explanatory3 = c("Age", "Gender_5cat", "Gender_3cat", "Race_cat", "Edu", "Income", "SO", "Politic", "Parent", "Vote", "Important_Middle", "Important_High", "Sex_Pos", "Sex_Neg")
dependent3 = "Geo"
csek12 %>% missing_compare(dependent3, explanatory3)
## Warning: There was 1 warning in `dplyr::summarise()`.
## ℹ In argument: `chisq.test(Gender_5cat, Geo)$p.value`.
## Caused by warning in `chisq.test()`:
## ! Chi-squared approximation may be incorrect
## Warning: There was 1 warning in `dplyr::summarise()`.
## ℹ In argument: `chisq.test(Gender_3cat, Geo)$p.value`.
## Caused by warning in `chisq.test()`:
## ! Chi-squared approximation may be incorrect
## Warning: There was 1 warning in `dplyr::summarise()`.
## ℹ In argument: `chisq.test(Race_cat, Geo)$p.value`.
## Caused by warning in `chisq.test()`:
## ! Chi-squared approximation may be incorrect
## Warning: There was 1 warning in `dplyr::summarise()`.
## ℹ In argument: `chisq.test(SO, Geo)$p.value`.
## Caused by warning in `chisq.test()`:
## ! Chi-squared approximation may be incorrect
## Warning: There was 1 warning in `dplyr::summarise()`.
## ℹ In argument: `chisq.test(Parent, Geo)$p.value`.
## Caused by warning in `chisq.test()`:
## ! Chi-squared approximation may be incorrect
## Warning: There was 1 warning in `dplyr::summarise()`.
## ℹ In argument: `chisq.test(Vote, Geo)$p.value`.
## Caused by warning in `chisq.test()`:
## ! Chi-squared approximation may be incorrect
## Missing data analysis: Geo
## What is your age?
## Gender_5cat
##
##
##
##
## Gender_3cat
##
##
## Race_cat
##
##
##
##
##
##
## What is the highest level of education you have completed?
## What is your household income before taxes?
## SO
##
##
##
##
##
## How would you describe your political affiliations?
## Parent
##
##
## Vote
##
##
## How important do you think it is to have sex education in middle school?
## How important do you think it is to have sex education in high school?
## Sex positivity scale composite score. Range 8-48. Higher scores = more sex positivity.
## Sex negtivity scale composite score. Range 8-48. Higher scores = more sex negativity.
## Not missing Missing p
## Mean (SD) 45.2 (15.7) 48.0 (21.2) 0.802
## 1 145 (100.0) 0 (0.0) 0.504
## 2 0 (NaN) 0 (NaN)
## 3 132 (98.5) 2 (1.5)
## 4 3 (100.0) 0 (0.0)
## 5 8 (100.0) 0 (0.0)
## 1 145 (100.0) 0 (0.0) 0.325
## 2 135 (98.5) 2 (1.5)
## 3 8 (100.0) 0 (0.0)
## 1 19 (100.0) 0 (0.0) 0.702
## 2 34 (97.1) 1 (2.9)
## 3 13 (100.0) 0 (0.0)
## 4 0 (NaN) 0 (NaN)
## 5 1 (100.0) 0 (0.0)
## 6 200 (99.5) 1 (0.5)
## 7 27 (100.0) 0 (0.0)
## Mean (SD) 6.2 (1.3) 7.0 (0.0) 0.422
## Mean (SD) 5.0 (2.9) 5.0 (4.2) 0.993
## 1 232 (99.1) 2 (0.9) 0.990
## 2 11 (100.0) 0 (0.0)
## 3 35 (100.0) 0 (0.0)
## 4 1 (100.0) 0 (0.0)
## 5 15 (100.0) 0 (0.0)
## 6 2 (100.0) 0 (0.0)
## Mean (SD) 5.0 (2.0) 4.5 (2.1) 0.724
## 1 74 (98.7) 1 (1.3) 0.624
## 2 66 (100.0) 0 (0.0)
## 3 156 (99.4) 1 (0.6)
## 1 35 (97.2) 1 (2.8) 0.250
## 2 242 (99.6) 1 (0.4)
## 3 19 (100.0) 0 (0.0)
## Mean (SD) 1.9 (1.1) 1.5 (0.7) 0.656
## Mean (SD) 1.3 (0.8) 1.0 (0.0) 0.612
## Mean (SD) 38.9 (8.4) 41.0 (1.4) 0.718
## Mean (SD) 11.2 (4.6) 11.0 (4.2) 0.946
mcar_data <- csek12[,c(5:13, 15:36, 38:53, 55:67, 69, 78:80, 82:86)]
#amount of missingness so small that there are issues even running this test
naniar::mcar_test(mcar_data)
## Warning in norm::prelim.norm(data): NAs introduced by coercion to integer range
## Error in `dplyr::mutate()`:
## ℹ In argument: `d2 = purrr::pmap_dbl(...)`.
## ℹ In group 1: `miss_pattern = 1`.
## Caused by error in `purrr::pmap_dbl()`:
## ℹ In index: 1.
## Caused by error in `solve.default()`:
## ! system is computationally singular: reciprocal condition number = 5.46712e-20
misty::na.test(mcar_data)
## Warning: Function run into numerical problems, i.e., results are not
## trustworthy.
## Error in solve.default(grand.cov[which(rownames(grand.cov) %in% keep), : system is computationally singular: reciprocal condition number = 5.46712e-20
#cfa
library(lavaan)
## Warning: package 'lavaan' was built under R version 4.3.3
## This is lavaan 0.6-18
## lavaan is FREE software! Please report any bugs.
##
## Attaching package: 'lavaan'
## The following object is masked from 'package:psych':
##
## cor2cov
library(faoutlier)
## Warning: package 'faoutlier' was built under R version 4.3.2
## Loading required package: sem
## Warning: package 'sem' was built under R version 4.3.2
##
## Attaching package: 'sem'
## The following objects are masked from 'package:lavaan':
##
## cfa, sem
## The following object is masked from 'package:misty':
##
## std.coef
## Loading required package: mvtnorm
## Warning: package 'mvtnorm' was built under R version 4.3.3
## Loading required package: parallel
#one factor model
csek12one <- '
CSE =~ Q2_1 + Q2_2 + Q2_3 + Q2_4 + Q2_5 + Q2_6 + Q2_7 + Q2_8 + Q2_9 + Q3_1 + Q3_2 + Q3_3 + Q3_4 + Q3_5 + Q3_6 + Q3_7 + Q3_8 + Q3_9 + Q3_10 + Q4_1 + Q4_2 + Q4_3 + Q4_4 + Q4_5 + Q4_6 + Q4_7 + Q4_8 + Q4_9 + Q4_10 + Q5_1 + Q5_2 + Q5_4 + Q5_5 + Q5_6 + Q5_7 + Q5_8 + Q5_9 + Q5_10 + Q5_11'
csek12one.fit <- lavaan::cfa(csek12one, data = csek12, std.lv = TRUE, missing = "FIML", estimator = "MLR", meanstructure = TRUE)
dynamic::cfaOne(csek12one.fit, plot = TRUE)
## Warning: Returning more (or less) than 1 row per `summarise()` group was deprecated in
## dplyr 1.1.0.
## ℹ Please use `reframe()` instead.
## ℹ When switching from `summarise()` to `reframe()`, remember that `reframe()`
## always returns an ungrouped data frame and adjust accordingly.
## ℹ The deprecated feature was likely used in the dynamic package.
## Please report the issue at <https://github.com/melissagwolf/dynamic/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Your DFI cutoffs:
## SRMR RMSEA CFI
## Level 1: 95/5 .017 .025 .991
## Level 1: 90/10 -- -- --
## Level 2: 95/5 .018 .036 .982
## Level 2: 90/10 -- -- --
## Level 3: 95/5 .019 .047 .97
## Level 3: 90/10 -- -- --
##
## Empirical fit indices:
## Chi-Square df p-value SRMR RMSEA CFI
## 2612.185 702 0 0.033 0.096 0.885
##
## The distributions for each level are in the Plots tab
## [[1]]
##
## [[2]]
##
## [[3]]
summary(csek12one.fit, fit.measures = TRUE, standardized = TRUE)
## lavaan 0.6-18 ended normally after 147 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 117
##
## Number of observations 298
## Number of missing patterns 2
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 2612.185 1508.418
## Degrees of freedom 702 702
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.732
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 17308.096 9485.700
## Degrees of freedom 741 741
## P-value 0.000 0.000
## Scaling correction factor 1.825
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.885 0.908
## Tucker-Lewis Index (TLI) 0.878 0.903
##
## Robust Comparative Fit Index (CFI) 0.914
## Robust Tucker-Lewis Index (TLI) 0.909
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -9408.315 -9408.315
## Scaling correction factor 2.300
## for the MLR correction
## Loglikelihood unrestricted model (H1) NA NA
## Scaling correction factor 1.813
## for the MLR correction
##
## Akaike (AIC) 19050.631 19050.631
## Bayesian (BIC) 19483.191 19483.191
## Sample-size adjusted Bayesian (SABIC) 19112.141 19112.141
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.096 0.062
## 90 Percent confidence interval - lower 0.092 0.059
## 90 Percent confidence interval - upper 0.099 0.065
## P-value H_0: RMSEA <= 0.050 0.000 0.000
## P-value H_0: RMSEA >= 0.080 1.000 0.000
##
## Robust RMSEA 0.081
## 90 Percent confidence interval - lower 0.075
## 90 Percent confidence interval - upper 0.087
## P-value H_0: Robust RMSEA <= 0.050 0.000
## P-value H_0: Robust RMSEA >= 0.080 0.639
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.033 0.033
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## CSE =~
## Q2_1 0.894 0.064 13.872 0.000 0.894 0.853
## Q2_2 0.807 0.070 11.484 0.000 0.807 0.729
## Q2_3 0.826 0.068 12.085 0.000 0.826 0.875
## Q2_4 0.896 0.067 13.400 0.000 0.896 0.843
## Q2_5 0.914 0.069 13.294 0.000 0.914 0.856
## Q2_6 0.871 0.073 11.952 0.000 0.871 0.826
## Q2_7 0.813 0.076 10.696 0.000 0.813 0.854
## Q2_8 0.805 0.072 11.104 0.000 0.805 0.831
## Q2_9 0.701 0.075 9.393 0.000 0.701 0.784
## Q3_1 0.929 0.067 13.953 0.000 0.929 0.906
## Q3_2 0.919 0.068 13.453 0.000 0.919 0.913
## Q3_3 0.815 0.078 10.503 0.000 0.815 0.885
## Q3_4 0.959 0.068 14.116 0.000 0.959 0.927
## Q3_5 0.864 0.077 11.295 0.000 0.864 0.889
## Q3_6 0.813 0.077 10.614 0.000 0.813 0.843
## Q3_7 0.971 0.065 14.903 0.000 0.971 0.823
## Q3_8 0.959 0.068 14.134 0.000 0.959 0.718
## Q3_9 0.996 0.057 17.617 0.000 0.996 0.691
## Q3_10 0.867 0.069 12.593 0.000 0.867 0.819
## Q4_1 0.923 0.071 13.056 0.000 0.923 0.929
## Q4_2 0.942 0.067 14.022 0.000 0.942 0.894
## Q4_3 0.923 0.069 13.344 0.000 0.923 0.949
## Q4_4 0.922 0.074 12.464 0.000 0.922 0.925
## Q4_5 0.849 0.075 11.277 0.000 0.849 0.875
## Q4_6 0.947 0.070 13.547 0.000 0.947 0.923
## Q4_7 1.009 0.062 16.177 0.000 1.009 0.866
## Q4_8 0.849 0.081 10.451 0.000 0.849 0.870
## Q4_9 0.626 0.079 7.926 0.000 0.626 0.577
## Q4_10 0.907 0.070 12.997 0.000 0.907 0.904
## Q5_1 0.904 0.067 13.451 0.000 0.904 0.876
## Q5_2 0.919 0.070 13.048 0.000 0.919 0.864
## Q5_4 0.866 0.075 11.514 0.000 0.866 0.922
## Q5_5 0.804 0.075 10.657 0.000 0.804 0.825
## Q5_6 0.789 0.075 10.482 0.000 0.789 0.874
## Q5_7 0.945 0.073 13.002 0.000 0.945 0.913
## Q5_8 0.707 0.083 8.483 0.000 0.707 0.669
## Q5_9 0.782 0.075 10.404 0.000 0.782 0.891
## Q5_10 0.735 0.079 9.314 0.000 0.735 0.766
## Q5_11 0.904 0.073 12.410 0.000 0.904 0.919
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Q2_1 4.275 0.061 70.405 0.000 4.275 4.079
## .Q2_2 4.208 0.064 65.628 0.000 4.208 3.802
## .Q2_3 4.440 0.055 81.208 0.000 4.440 4.704
## .Q2_4 4.282 0.062 69.578 0.000 4.282 4.031
## .Q2_5 4.289 0.062 69.393 0.000 4.289 4.020
## .Q2_6 4.369 0.061 71.522 0.000 4.369 4.143
## .Q2_7 4.456 0.055 80.830 0.000 4.456 4.682
## .Q2_8 4.349 0.056 77.492 0.000 4.349 4.489
## .Q2_9 4.453 0.052 86.045 0.000 4.453 4.984
## .Q3_1 4.369 0.059 73.547 0.000 4.369 4.260
## .Q3_2 4.403 0.058 75.537 0.000 4.403 4.376
## .Q3_3 4.483 0.053 84.091 0.000 4.483 4.871
## .Q3_4 4.386 0.060 73.221 0.000 4.386 4.242
## .Q3_5 4.460 0.056 79.138 0.000 4.460 4.584
## .Q3_6 4.490 0.056 80.300 0.000 4.490 4.658
## .Q3_7 4.228 0.068 61.890 0.000 4.228 3.585
## .Q3_8 4.074 0.077 52.615 0.000 4.074 3.048
## .Q3_9 3.815 0.083 45.705 0.000 3.815 2.648
## .Q3_10 4.248 0.061 69.312 0.000 4.248 4.015
## .Q4_1 4.419 0.058 76.735 0.000 4.419 4.445
## .Q4_2 4.322 0.061 70.787 0.000 4.322 4.101
## .Q4_3 4.463 0.056 79.187 0.000 4.463 4.587
## .Q4_4 4.463 0.058 77.292 0.000 4.463 4.477
## .Q4_5 4.497 0.056 80.009 0.000 4.497 4.635
## .Q4_6 4.406 0.059 74.112 0.000 4.406 4.293
## .Q4_7 4.228 0.067 62.650 0.000 4.228 3.629
## .Q4_8 4.540 0.057 80.283 0.000 4.540 4.651
## .Q4_9 4.205 0.063 66.927 0.000 4.205 3.877
## .Q4_10 4.406 0.058 75.823 0.000 4.406 4.392
## .Q5_1 4.322 0.060 72.333 0.000 4.322 4.190
## .Q5_2 4.369 0.062 70.883 0.000 4.369 4.106
## .Q5_4 4.503 0.054 82.831 0.000 4.503 4.798
## .Q5_5 4.419 0.056 78.347 0.000 4.419 4.539
## .Q5_6 4.510 0.052 86.312 0.000 4.510 5.000
## .Q5_7 4.423 0.060 73.826 0.000 4.423 4.277
## .Q5_8 4.362 0.061 71.255 0.000 4.362 4.128
## .Q5_9 4.557 0.051 89.635 0.000 4.557 5.192
## .Q5_10 4.379 0.056 78.816 0.000 4.379 4.566
## .Q5_11 4.477 0.057 78.561 0.000 4.477 4.551
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Q2_1 0.300 0.046 6.571 0.000 0.300 0.273
## .Q2_2 0.575 0.078 7.390 0.000 0.575 0.469
## .Q2_3 0.209 0.037 5.684 0.000 0.209 0.235
## .Q2_4 0.326 0.051 6.440 0.000 0.326 0.289
## .Q2_5 0.304 0.041 7.347 0.000 0.304 0.267
## .Q2_6 0.354 0.079 4.488 0.000 0.354 0.318
## .Q2_7 0.245 0.041 5.975 0.000 0.245 0.271
## .Q2_8 0.291 0.040 7.250 0.000 0.291 0.310
## .Q2_9 0.307 0.039 7.801 0.000 0.307 0.385
## .Q3_1 0.189 0.056 3.391 0.001 0.189 0.180
## .Q3_2 0.168 0.032 5.245 0.000 0.168 0.166
## .Q3_3 0.183 0.050 3.639 0.000 0.183 0.216
## .Q3_4 0.150 0.030 4.968 0.000 0.150 0.140
## .Q3_5 0.199 0.043 4.607 0.000 0.199 0.210
## .Q3_6 0.268 0.046 5.824 0.000 0.268 0.289
## .Q3_7 0.448 0.078 5.735 0.000 0.448 0.322
## .Q3_8 0.866 0.122 7.105 0.000 0.866 0.485
## .Q3_9 1.084 0.118 9.180 0.000 1.084 0.522
## .Q3_10 0.368 0.069 5.374 0.000 0.368 0.329
## .Q4_1 0.136 0.034 4.002 0.000 0.136 0.137
## .Q4_2 0.224 0.041 5.405 0.000 0.224 0.201
## .Q4_3 0.095 0.016 5.925 0.000 0.095 0.100
## .Q4_4 0.143 0.025 5.664 0.000 0.143 0.144
## .Q4_5 0.220 0.034 6.435 0.000 0.220 0.234
## .Q4_6 0.156 0.025 6.291 0.000 0.156 0.148
## .Q4_7 0.339 0.057 5.960 0.000 0.339 0.250
## .Q4_8 0.232 0.035 6.573 0.000 0.232 0.243
## .Q4_9 0.784 0.100 7.809 0.000 0.784 0.667
## .Q4_10 0.183 0.039 4.660 0.000 0.183 0.182
## .Q5_1 0.247 0.040 6.159 0.000 0.247 0.232
## .Q5_2 0.288 0.056 5.136 0.000 0.288 0.254
## .Q5_4 0.131 0.024 5.586 0.000 0.131 0.149
## .Q5_5 0.302 0.058 5.220 0.000 0.302 0.319
## .Q5_6 0.192 0.031 6.095 0.000 0.192 0.236
## .Q5_7 0.177 0.028 6.444 0.000 0.177 0.166
## .Q5_8 0.617 0.105 5.865 0.000 0.617 0.552
## .Q5_9 0.159 0.020 7.798 0.000 0.159 0.207
## .Q5_10 0.380 0.054 7.074 0.000 0.380 0.413
## .Q5_11 0.151 0.035 4.335 0.000 0.151 0.156
## CSE 1.000 1.000 1.000
#does not meet thresholds as indicated by dynamic fit indices
#To improve fit, covariance set 1 added to model:
csek12one.mod1 <- '
CSE =~ Q2_1 + Q2_2 + Q2_3 + Q2_4 + Q2_5 + Q2_6 + Q2_7 + Q2_8 + Q2_9 + Q3_1 + Q3_2 + Q3_3 + Q3_4 + Q3_5 + Q3_6 + Q3_7 + Q3_8 + Q3_9 + Q3_10 + Q4_1 + Q4_2 + Q4_3 + Q4_4 + Q4_5 + Q4_6 + Q4_7 + Q4_8 + Q4_9 + Q4_10 + Q5_1 + Q5_2 + Q5_4 + Q5_5 + Q5_6 + Q5_7 + Q5_8 + Q5_9 + Q5_10 + Q5_11
#covariances
Q2_3 ~~ Q2_4
Q2_3 ~~ Q2_5
Q2_3 ~~ Q5_5
Q2_3 ~~ Q5_6
Q2_4 ~~ Q2_5
Q2_4 ~~ Q5_5
Q2_4 ~~ Q5_6
Q2_5 ~~ Q5_5
Q2_5 ~~ Q5_6
Q5_5 ~~ Q5_6
'
csek12one.mod1.fit <- lavaan::cfa(csek12one.mod1, data = csek12, std.lv = TRUE, missing = "FIML", estimator = "MLR")
summary(csek12one.mod1.fit, fit.measures = TRUE, standardized = TRUE)
## lavaan 0.6-18 ended normally after 141 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 127
##
## Number of observations 298
## Number of missing patterns 2
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 2504.201 1450.294
## Degrees of freedom 692 692
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.727
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 17308.096 9485.700
## Degrees of freedom 741 741
## P-value 0.000 0.000
## Scaling correction factor 1.825
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.891 0.913
## Tucker-Lewis Index (TLI) 0.883 0.907
##
## Robust Comparative Fit Index (CFI) 0.919
## Robust Tucker-Lewis Index (TLI) 0.914
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -9354.323 -9354.323
## Scaling correction factor 2.283
## for the MLR correction
## Loglikelihood unrestricted model (H1) NA NA
## Scaling correction factor 1.813
## for the MLR correction
##
## Akaike (AIC) 18962.646 18962.646
## Bayesian (BIC) 19432.177 19432.177
## Sample-size adjusted Bayesian (SABIC) 19029.414 19029.414
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.094 0.061
## 90 Percent confidence interval - lower 0.090 0.057
## 90 Percent confidence interval - upper 0.098 0.064
## P-value H_0: RMSEA <= 0.050 0.000 0.000
## P-value H_0: RMSEA >= 0.080 1.000 0.000
##
## Robust RMSEA 0.079
## 90 Percent confidence interval - lower 0.073
## 90 Percent confidence interval - upper 0.085
## P-value H_0: Robust RMSEA <= 0.050 0.000
## P-value H_0: Robust RMSEA >= 0.080 0.406
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.033 0.033
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## CSE =~
## Q2_1 0.892 0.065 13.825 0.000 0.892 0.851
## Q2_2 0.806 0.070 11.466 0.000 0.806 0.728
## Q2_3 0.823 0.068 12.017 0.000 0.823 0.872
## Q2_4 0.891 0.067 13.258 0.000 0.891 0.839
## Q2_5 0.910 0.069 13.166 0.000 0.910 0.853
## Q2_6 0.870 0.073 11.903 0.000 0.870 0.825
## Q2_7 0.813 0.076 10.700 0.000 0.813 0.854
## Q2_8 0.804 0.073 11.079 0.000 0.804 0.830
## Q2_9 0.700 0.075 9.372 0.000 0.700 0.783
## Q3_1 0.929 0.066 13.977 0.000 0.929 0.906
## Q3_2 0.919 0.068 13.459 0.000 0.919 0.913
## Q3_3 0.815 0.078 10.498 0.000 0.815 0.885
## Q3_4 0.959 0.068 14.132 0.000 0.959 0.928
## Q3_5 0.865 0.077 11.297 0.000 0.865 0.889
## Q3_6 0.814 0.077 10.636 0.000 0.814 0.845
## Q3_7 0.970 0.065 14.887 0.000 0.970 0.823
## Q3_8 0.958 0.068 14.100 0.000 0.958 0.717
## Q3_9 0.994 0.057 17.525 0.000 0.994 0.690
## Q3_10 0.867 0.069 12.596 0.000 0.867 0.819
## Q4_1 0.924 0.071 13.075 0.000 0.924 0.929
## Q4_2 0.942 0.067 14.024 0.000 0.942 0.894
## Q4_3 0.923 0.069 13.363 0.000 0.923 0.949
## Q4_4 0.922 0.074 12.455 0.000 0.922 0.925
## Q4_5 0.848 0.075 11.257 0.000 0.848 0.875
## Q4_6 0.947 0.070 13.537 0.000 0.947 0.923
## Q4_7 1.009 0.062 16.165 0.000 1.009 0.866
## Q4_8 0.850 0.081 10.461 0.000 0.850 0.871
## Q4_9 0.626 0.079 7.927 0.000 0.626 0.577
## Q4_10 0.908 0.070 13.016 0.000 0.908 0.905
## Q5_1 0.904 0.067 13.448 0.000 0.904 0.876
## Q5_2 0.920 0.070 13.068 0.000 0.920 0.864
## Q5_4 0.866 0.075 11.532 0.000 0.866 0.923
## Q5_5 0.802 0.076 10.581 0.000 0.802 0.823
## Q5_6 0.791 0.075 10.523 0.000 0.791 0.876
## Q5_7 0.945 0.073 13.007 0.000 0.945 0.914
## Q5_8 0.708 0.083 8.478 0.000 0.708 0.669
## Q5_9 0.782 0.075 10.402 0.000 0.782 0.891
## Q5_10 0.735 0.079 9.313 0.000 0.735 0.766
## Q5_11 0.904 0.073 12.408 0.000 0.904 0.919
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Q2_3 ~~
## .Q2_4 0.069 0.035 1.980 0.048 0.069 0.258
## .Q2_5 0.030 0.023 1.315 0.188 0.030 0.117
## .Q5_5 0.012 0.021 0.550 0.582 0.012 0.045
## .Q5_6 0.004 0.015 0.282 0.778 0.004 0.022
## .Q2_4 ~~
## .Q2_5 0.138 0.033 4.206 0.000 0.138 0.428
## .Q5_5 0.016 0.023 0.713 0.476 0.016 0.051
## .Q5_6 -0.033 0.015 -2.131 0.033 -0.033 -0.130
## .Q2_5 ~~
## .Q5_5 0.062 0.029 2.131 0.033 0.062 0.202
## .Q5_6 -0.055 0.017 -3.181 0.001 -0.055 -0.226
## .Q5_5 ~~
## .Q5_6 0.001 0.019 0.066 0.947 0.001 0.005
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Q2_1 4.275 0.061 70.405 0.000 4.275 4.079
## .Q2_2 4.208 0.064 65.628 0.000 4.208 3.802
## .Q2_3 4.440 0.055 81.208 0.000 4.440 4.704
## .Q2_4 4.282 0.062 69.578 0.000 4.282 4.031
## .Q2_5 4.289 0.062 69.393 0.000 4.289 4.020
## .Q2_6 4.369 0.061 71.522 0.000 4.369 4.143
## .Q2_7 4.456 0.055 80.829 0.000 4.456 4.682
## .Q2_8 4.349 0.056 77.492 0.000 4.349 4.489
## .Q2_9 4.453 0.052 86.045 0.000 4.453 4.985
## .Q3_1 4.369 0.059 73.547 0.000 4.369 4.261
## .Q3_2 4.403 0.058 75.537 0.000 4.403 4.376
## .Q3_3 4.483 0.053 84.091 0.000 4.483 4.871
## .Q3_4 4.386 0.060 73.222 0.000 4.386 4.242
## .Q3_5 4.460 0.056 79.138 0.000 4.460 4.584
## .Q3_6 4.490 0.056 80.303 0.000 4.490 4.658
## .Q3_7 4.228 0.068 61.889 0.000 4.228 3.585
## .Q3_8 4.074 0.077 52.615 0.000 4.074 3.048
## .Q3_9 3.815 0.083 45.705 0.000 3.815 2.648
## .Q3_10 4.248 0.061 69.311 0.000 4.248 4.015
## .Q4_1 4.419 0.058 76.735 0.000 4.419 4.445
## .Q4_2 4.322 0.061 70.787 0.000 4.322 4.101
## .Q4_3 4.463 0.056 79.187 0.000 4.463 4.587
## .Q4_4 4.463 0.058 77.292 0.000 4.463 4.478
## .Q4_5 4.497 0.056 80.009 0.000 4.497 4.635
## .Q4_6 4.406 0.059 74.112 0.000 4.406 4.293
## .Q4_7 4.228 0.067 62.650 0.000 4.228 3.629
## .Q4_8 4.540 0.057 80.283 0.000 4.540 4.651
## .Q4_9 4.205 0.063 66.927 0.000 4.205 3.877
## .Q4_10 4.406 0.058 75.823 0.000 4.406 4.392
## .Q5_1 4.322 0.060 72.333 0.000 4.322 4.190
## .Q5_2 4.369 0.062 70.883 0.000 4.369 4.106
## .Q5_4 4.503 0.054 82.830 0.000 4.503 4.798
## .Q5_5 4.419 0.056 78.347 0.000 4.419 4.539
## .Q5_6 4.510 0.052 86.312 0.000 4.510 5.000
## .Q5_7 4.423 0.060 73.826 0.000 4.423 4.277
## .Q5_8 4.362 0.061 71.254 0.000 4.362 4.128
## .Q5_9 4.557 0.051 89.635 0.000 4.557 5.193
## .Q5_10 4.379 0.056 78.816 0.000 4.379 4.566
## .Q5_11 4.477 0.057 78.561 0.000 4.477 4.551
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Q2_1 0.303 0.046 6.591 0.000 0.303 0.276
## .Q2_2 0.575 0.078 7.379 0.000 0.575 0.470
## .Q2_3 0.213 0.038 5.617 0.000 0.213 0.239
## .Q2_4 0.335 0.052 6.434 0.000 0.335 0.296
## .Q2_5 0.310 0.042 7.402 0.000 0.310 0.272
## .Q2_6 0.356 0.080 4.467 0.000 0.356 0.320
## .Q2_7 0.245 0.041 5.990 0.000 0.245 0.270
## .Q2_8 0.293 0.040 7.259 0.000 0.293 0.312
## .Q2_9 0.308 0.039 7.821 0.000 0.308 0.386
## .Q3_1 0.188 0.056 3.381 0.001 0.188 0.179
## .Q3_2 0.168 0.032 5.251 0.000 0.168 0.166
## .Q3_3 0.183 0.051 3.629 0.000 0.183 0.216
## .Q3_4 0.149 0.030 4.959 0.000 0.149 0.139
## .Q3_5 0.199 0.043 4.580 0.000 0.199 0.210
## .Q3_6 0.266 0.046 5.831 0.000 0.266 0.287
## .Q3_7 0.450 0.078 5.759 0.000 0.450 0.323
## .Q3_8 0.868 0.122 7.091 0.000 0.868 0.486
## .Q3_9 1.089 0.119 9.176 0.000 1.089 0.524
## .Q3_10 0.369 0.068 5.384 0.000 0.369 0.329
## .Q4_1 0.135 0.034 3.979 0.000 0.135 0.136
## .Q4_2 0.223 0.041 5.405 0.000 0.223 0.201
## .Q4_3 0.094 0.016 5.858 0.000 0.094 0.099
## .Q4_4 0.143 0.025 5.614 0.000 0.143 0.144
## .Q4_5 0.221 0.035 6.409 0.000 0.221 0.235
## .Q4_6 0.156 0.025 6.304 0.000 0.156 0.148
## .Q4_7 0.339 0.057 5.954 0.000 0.339 0.250
## .Q4_8 0.230 0.035 6.524 0.000 0.230 0.241
## .Q4_9 0.784 0.100 7.815 0.000 0.784 0.667
## .Q4_10 0.182 0.039 4.640 0.000 0.182 0.181
## .Q5_1 0.247 0.040 6.158 0.000 0.247 0.232
## .Q5_2 0.286 0.056 5.132 0.000 0.286 0.253
## .Q5_4 0.130 0.024 5.504 0.000 0.130 0.148
## .Q5_5 0.306 0.058 5.242 0.000 0.306 0.322
## .Q5_6 0.189 0.031 6.055 0.000 0.189 0.232
## .Q5_7 0.176 0.028 6.388 0.000 0.176 0.165
## .Q5_8 0.616 0.105 5.858 0.000 0.616 0.552
## .Q5_9 0.159 0.021 7.736 0.000 0.159 0.206
## .Q5_10 0.380 0.054 7.083 0.000 0.380 0.413
## .Q5_11 0.151 0.035 4.342 0.000 0.151 0.156
## CSE 1.000 1.000 1.000
anova(csek12one.fit, csek12one.mod1.fit) #sig reduction in model chi square
##
## Scaled Chi-Squared Difference Test (method = "satorra.bentler.2001")
##
## lavaan->lavTestLRT():
## lavaan NOTE: The "Chisq" column contains standard test statistics, not the
## robust test that should be reported per model. A robust difference test is
## a function of two standard (not robust) statistics.
## Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
## csek12one.mod1.fit 692 18963 19432 2504.2
## csek12one.fit 702 19051 19483 2612.2 51.881 10 1.201e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
dynamic::cfaOne(csek12one.mod1.fit, plot = TRUE) #still does not meet dynamic fit indices
## Your DFI cutoffs:
## SRMR RMSEA CFI
## Level 1: 95/5 .017 NONE .993
## Level 1: 90/10 -- .024 --
## Level 2: 95/5 .018 .035 .983
## Level 2: 90/10 -- -- --
## Level 3: 95/5 .018 .045 .973
## Level 3: 90/10 -- -- --
##
## Empirical fit indices:
## Chi-Square df p-value SRMR RMSEA CFI
## 2504.201 692 0 0.033 0.094 0.891
##
## The distributions for each level are in the Plots tab
## [[1]]
##
## [[2]]
##
## [[3]]
#To further improve fit, covariance set 2 added to model:
csek12one.mod2 <- '
CSE =~ Q2_1 + Q2_2 + Q2_3 + Q2_4 + Q2_5 + Q2_6 + Q2_7 + Q2_8 + Q2_9 + Q3_1 + Q3_2 + Q3_3 + Q3_4 + Q3_5 + Q3_6 + Q3_7 + Q3_8 + Q3_9 + Q3_10 + Q4_1 + Q4_2 + Q4_3 + Q4_4 + Q4_5 + Q4_6 + Q4_7 + Q4_8 + Q4_9 + Q4_10 + Q5_1 + Q5_2 + Q5_4 + Q5_5 + Q5_6 + Q5_7 + Q5_8 + Q5_9 + Q5_10 + Q5_11
#covariances
Q2_3 ~~ Q2_4
Q2_3 ~~ Q2_5
Q2_3 ~~ Q5_5
Q2_3 ~~ Q5_6
Q2_4 ~~ Q2_5
Q2_4 ~~ Q5_5
Q2_4 ~~ Q5_6
Q2_5 ~~ Q5_5
Q2_5 ~~ Q5_6
Q5_5 ~~ Q5_6
Q4_4 ~~ Q4_5
Q4_4 ~~ Q4_6
Q4_5 ~~ Q4_6
'
csek12one.mod2.fit <- lavaan::cfa(csek12one.mod2, data = csek12, std.lv = TRUE, missing = "FIML", estimator = "MLR")
summary(csek12one.mod2.fit, fit.measures = TRUE, standardized = TRUE)
## lavaan 0.6-18 ended normally after 153 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 130
##
## Number of observations 298
## Number of missing patterns 2
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 2437.184 1417.282
## Degrees of freedom 689 689
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.720
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 17308.096 9485.700
## Degrees of freedom 741 741
## P-value 0.000 0.000
## Scaling correction factor 1.825
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.894 0.917
## Tucker-Lewis Index (TLI) 0.887 0.910
##
## Robust Comparative Fit Index (CFI) 0.923
## Robust Tucker-Lewis Index (TLI) 0.917
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -9320.815 -9320.815
## Scaling correction factor 2.308
## for the MLR correction
## Loglikelihood unrestricted model (H1) NA NA
## Scaling correction factor 1.813
## for the MLR correction
##
## Akaike (AIC) 18901.629 18901.629
## Bayesian (BIC) 19382.251 19382.251
## Sample-size adjusted Bayesian (SABIC) 18969.974 18969.974
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.092 0.060
## 90 Percent confidence interval - lower 0.088 0.056
## 90 Percent confidence interval - upper 0.096 0.063
## P-value H_0: RMSEA <= 0.050 0.000 0.000
## P-value H_0: RMSEA >= 0.080 1.000 0.000
##
## Robust RMSEA 0.077
## 90 Percent confidence interval - lower 0.072
## 90 Percent confidence interval - upper 0.083
## P-value H_0: Robust RMSEA <= 0.050 0.000
## P-value H_0: Robust RMSEA >= 0.080 0.240
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.033 0.033
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## CSE =~
## Q2_1 0.893 0.064 13.872 0.000 0.893 0.852
## Q2_2 0.809 0.070 11.524 0.000 0.809 0.731
## Q2_3 0.823 0.069 12.017 0.000 0.823 0.872
## Q2_4 0.891 0.067 13.220 0.000 0.891 0.838
## Q2_5 0.908 0.069 13.118 0.000 0.908 0.851
## Q2_6 0.868 0.073 11.884 0.000 0.868 0.824
## Q2_7 0.812 0.076 10.701 0.000 0.812 0.854
## Q2_8 0.804 0.073 11.095 0.000 0.804 0.830
## Q2_9 0.701 0.075 9.402 0.000 0.701 0.785
## Q3_1 0.931 0.066 14.035 0.000 0.931 0.908
## Q3_2 0.919 0.068 13.467 0.000 0.919 0.913
## Q3_3 0.816 0.078 10.515 0.000 0.816 0.886
## Q3_4 0.961 0.068 14.174 0.000 0.961 0.929
## Q3_5 0.865 0.076 11.317 0.000 0.865 0.889
## Q3_6 0.816 0.076 10.681 0.000 0.816 0.846
## Q3_7 0.969 0.065 14.859 0.000 0.969 0.822
## Q3_8 0.959 0.068 14.103 0.000 0.959 0.717
## Q3_9 0.996 0.057 17.585 0.000 0.996 0.691
## Q3_10 0.868 0.069 12.634 0.000 0.868 0.820
## Q4_1 0.924 0.071 13.093 0.000 0.924 0.930
## Q4_2 0.942 0.067 14.035 0.000 0.942 0.894
## Q4_3 0.924 0.069 13.375 0.000 0.924 0.949
## Q4_4 0.919 0.074 12.387 0.000 0.919 0.921
## Q4_5 0.842 0.076 11.148 0.000 0.842 0.868
## Q4_6 0.943 0.070 13.435 0.000 0.943 0.919
## Q4_7 1.009 0.062 16.159 0.000 1.009 0.866
## Q4_8 0.849 0.081 10.437 0.000 0.849 0.870
## Q4_9 0.627 0.079 7.925 0.000 0.627 0.578
## Q4_10 0.908 0.070 13.049 0.000 0.908 0.906
## Q5_1 0.904 0.067 13.454 0.000 0.904 0.876
## Q5_2 0.920 0.070 13.071 0.000 0.920 0.864
## Q5_4 0.866 0.075 11.529 0.000 0.866 0.923
## Q5_5 0.800 0.076 10.555 0.000 0.800 0.822
## Q5_6 0.792 0.075 10.528 0.000 0.792 0.878
## Q5_7 0.944 0.073 12.971 0.000 0.944 0.913
## Q5_8 0.709 0.083 8.506 0.000 0.709 0.671
## Q5_9 0.782 0.075 10.406 0.000 0.782 0.891
## Q5_10 0.736 0.079 9.342 0.000 0.736 0.767
## Q5_11 0.904 0.073 12.422 0.000 0.904 0.919
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Q2_3 ~~
## .Q2_4 0.069 0.035 1.997 0.046 0.069 0.258
## .Q2_5 0.032 0.023 1.368 0.171 0.032 0.122
## .Q5_5 0.013 0.021 0.602 0.547 0.013 0.049
## .Q5_6 0.003 0.015 0.219 0.827 0.003 0.017
## .Q2_4 ~~
## .Q2_5 0.140 0.033 4.238 0.000 0.140 0.432
## .Q5_5 0.018 0.023 0.775 0.438 0.018 0.056
## .Q5_6 -0.033 0.015 -2.215 0.027 -0.033 -0.133
## .Q2_5 ~~
## .Q5_5 0.065 0.029 2.213 0.027 0.065 0.209
## .Q5_6 -0.054 0.017 -3.136 0.002 -0.054 -0.223
## .Q5_5 ~~
## .Q5_6 0.002 0.020 0.083 0.934 0.002 0.007
## .Q4_4 ~~
## .Q4_5 0.060 0.022 2.755 0.006 0.060 0.323
## .Q4_6 0.040 0.017 2.311 0.021 0.040 0.253
## .Q4_5 ~~
## .Q4_6 0.064 0.021 3.121 0.002 0.064 0.331
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Q2_1 4.275 0.061 70.405 0.000 4.275 4.078
## .Q2_2 4.208 0.064 65.628 0.000 4.208 3.802
## .Q2_3 4.440 0.055 81.208 0.000 4.440 4.704
## .Q2_4 4.282 0.062 69.578 0.000 4.282 4.031
## .Q2_5 4.289 0.062 69.393 0.000 4.289 4.020
## .Q2_6 4.369 0.061 71.522 0.000 4.369 4.143
## .Q2_7 4.456 0.055 80.830 0.000 4.456 4.682
## .Q2_8 4.349 0.056 77.492 0.000 4.349 4.489
## .Q2_9 4.453 0.052 86.045 0.000 4.453 4.984
## .Q3_1 4.369 0.059 73.547 0.000 4.369 4.260
## .Q3_2 4.403 0.058 75.538 0.000 4.403 4.376
## .Q3_3 4.483 0.053 84.091 0.000 4.483 4.871
## .Q3_4 4.386 0.060 73.222 0.000 4.386 4.242
## .Q3_5 4.460 0.056 79.138 0.000 4.460 4.584
## .Q3_6 4.490 0.056 80.320 0.000 4.490 4.658
## .Q3_7 4.228 0.068 61.889 0.000 4.228 3.585
## .Q3_8 4.074 0.077 52.615 0.000 4.074 3.048
## .Q3_9 3.815 0.083 45.705 0.000 3.815 2.648
## .Q3_10 4.248 0.061 69.311 0.000 4.248 4.015
## .Q4_1 4.419 0.058 76.735 0.000 4.419 4.445
## .Q4_2 4.322 0.061 70.787 0.000 4.322 4.101
## .Q4_3 4.463 0.056 79.187 0.000 4.463 4.587
## .Q4_4 4.463 0.058 77.293 0.000 4.463 4.477
## .Q4_5 4.497 0.056 80.009 0.000 4.497 4.635
## .Q4_6 4.406 0.059 74.112 0.000 4.406 4.293
## .Q4_7 4.228 0.067 62.650 0.000 4.228 3.629
## .Q4_8 4.540 0.057 80.283 0.000 4.540 4.651
## .Q4_9 4.205 0.063 66.927 0.000 4.205 3.877
## .Q4_10 4.406 0.058 75.823 0.000 4.406 4.392
## .Q5_1 4.322 0.060 72.333 0.000 4.322 4.190
## .Q5_2 4.369 0.062 70.883 0.000 4.369 4.106
## .Q5_4 4.503 0.054 82.831 0.000 4.503 4.798
## .Q5_5 4.419 0.056 78.348 0.000 4.419 4.539
## .Q5_6 4.510 0.052 86.312 0.000 4.510 5.000
## .Q5_7 4.423 0.060 73.826 0.000 4.423 4.277
## .Q5_8 4.362 0.061 71.255 0.000 4.362 4.128
## .Q5_9 4.557 0.051 89.636 0.000 4.557 5.192
## .Q5_10 4.379 0.056 78.816 0.000 4.379 4.566
## .Q5_11 4.477 0.057 78.561 0.000 4.477 4.551
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Q2_1 0.300 0.046 6.543 0.000 0.300 0.273
## .Q2_2 0.571 0.078 7.368 0.000 0.571 0.466
## .Q2_3 0.213 0.038 5.654 0.000 0.213 0.239
## .Q2_4 0.335 0.052 6.455 0.000 0.335 0.297
## .Q2_5 0.313 0.042 7.438 0.000 0.313 0.275
## .Q2_6 0.358 0.080 4.488 0.000 0.358 0.322
## .Q2_7 0.246 0.041 6.004 0.000 0.246 0.271
## .Q2_8 0.292 0.040 7.220 0.000 0.292 0.311
## .Q2_9 0.306 0.039 7.831 0.000 0.306 0.384
## .Q3_1 0.185 0.055 3.354 0.001 0.185 0.176
## .Q3_2 0.168 0.032 5.302 0.000 0.168 0.166
## .Q3_3 0.182 0.050 3.603 0.000 0.182 0.215
## .Q3_4 0.146 0.029 5.001 0.000 0.146 0.136
## .Q3_5 0.198 0.043 4.581 0.000 0.198 0.209
## .Q3_6 0.263 0.045 5.862 0.000 0.263 0.284
## .Q3_7 0.451 0.078 5.770 0.000 0.451 0.325
## .Q3_8 0.868 0.123 7.074 0.000 0.868 0.486
## .Q3_9 1.084 0.119 9.131 0.000 1.084 0.522
## .Q3_10 0.366 0.068 5.361 0.000 0.366 0.327
## .Q4_1 0.134 0.034 3.978 0.000 0.134 0.136
## .Q4_2 0.224 0.041 5.409 0.000 0.224 0.202
## .Q4_3 0.093 0.016 5.907 0.000 0.093 0.099
## .Q4_4 0.150 0.026 5.664 0.000 0.150 0.151
## .Q4_5 0.232 0.036 6.436 0.000 0.232 0.246
## .Q4_6 0.164 0.025 6.486 0.000 0.164 0.155
## .Q4_7 0.340 0.057 5.968 0.000 0.340 0.251
## .Q4_8 0.232 0.035 6.572 0.000 0.232 0.243
## .Q4_9 0.784 0.100 7.812 0.000 0.784 0.666
## .Q4_10 0.181 0.039 4.652 0.000 0.181 0.180
## .Q5_1 0.248 0.040 6.173 0.000 0.248 0.233
## .Q5_2 0.286 0.055 5.170 0.000 0.286 0.253
## .Q5_4 0.131 0.024 5.512 0.000 0.131 0.149
## .Q5_5 0.308 0.058 5.298 0.000 0.308 0.325
## .Q5_6 0.187 0.031 6.065 0.000 0.187 0.230
## .Q5_7 0.179 0.028 6.416 0.000 0.179 0.167
## .Q5_8 0.615 0.105 5.851 0.000 0.615 0.550
## .Q5_9 0.159 0.020 7.782 0.000 0.159 0.206
## .Q5_10 0.379 0.053 7.100 0.000 0.379 0.412
## .Q5_11 0.150 0.035 4.332 0.000 0.150 0.155
## CSE 1.000 1.000 1.000
anova(csek12one.mod1.fit, csek12one.mod2.fit) #sig reduction in model chi
##
## Scaled Chi-Squared Difference Test (method = "satorra.bentler.2001")
##
## lavaan->lavTestLRT():
## lavaan NOTE: The "Chisq" column contains standard test statistics, not the
## robust test that should be reported per model. A robust difference test is
## a function of two standard (not robust) statistics.
## Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
## csek12one.mod2.fit 689 18902 19382 2437.2
## csek12one.mod1.fit 692 18963 19432 2504.2 20.006 3 0.0001692 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
dynamic::cfaOne(csek12one.mod2.fit, plot = TRUE) #still does not meet dynamic fit indices
## Your DFI cutoffs:
## SRMR RMSEA CFI
## Level 1: 95/5 .017 .023 .993
## Level 1: 90/10 -- -- --
## Level 2: 95/5 .017 .033 .985
## Level 2: 90/10 -- -- --
## Level 3: 95/5 .018 .042 .977
## Level 3: 90/10 -- -- --
##
## Empirical fit indices:
## Chi-Square df p-value SRMR RMSEA CFI
## 2437.184 689 0 0.033 0.092 0.894
##
## The distributions for each level are in the Plots tab
## [[1]]
##
## [[2]]
##
## [[3]]
#To further improve fit, covariance set 3 added to model:
csek12one.mod3 <- '
CSE =~ Q2_1 + Q2_2 + Q2_3 + Q2_4 + Q2_5 + Q2_6 + Q2_7 + Q2_8 + Q2_9 + Q3_1 + Q3_2 + Q3_3 + Q3_4 + Q3_5 + Q3_6 + Q3_7 + Q3_8 + Q3_9 + Q3_10 + Q4_1 + Q4_2 + Q4_3 + Q4_4 + Q4_5 + Q4_6 + Q4_7 + Q4_8 + Q4_9 + Q4_10 + Q5_1 + Q5_2 + Q5_4 + Q5_5 + Q5_6 + Q5_7 + Q5_8 + Q5_9 + Q5_10 + Q5_11
#covariances
Q2_3 ~~ Q2_4
Q2_3 ~~ Q2_5
Q2_3 ~~ Q5_5
Q2_3 ~~ Q5_6
Q2_4 ~~ Q2_5
Q2_4 ~~ Q5_5
Q2_4 ~~ Q5_6
Q2_5 ~~ Q5_5
Q2_5 ~~ Q5_6
Q5_5 ~~ Q5_6
Q4_4 ~~ Q4_5
Q4_4 ~~ Q4_6
Q4_5 ~~ Q4_6
Q4_7 ~~ Q4_8
'
csek12one.mod3.fit <- lavaan::cfa(csek12one.mod3, data = csek12, std.lv = TRUE, missing = "FIML", estimator = "MLR")
summary(csek12one.mod3.fit, fit.measures = TRUE, standardized = TRUE)
## lavaan 0.6-18 ended normally after 154 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 131
##
## Number of observations 298
## Number of missing patterns 2
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 2435.540 1416.891
## Degrees of freedom 688 688
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.719
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 17308.096 9485.700
## Degrees of freedom 741 741
## P-value 0.000 0.000
## Scaling correction factor 1.825
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.895 0.917
## Tucker-Lewis Index (TLI) 0.886 0.910
##
## Robust Comparative Fit Index (CFI) 0.923
## Robust Tucker-Lewis Index (TLI) 0.917
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -9319.993 -9319.993
## Scaling correction factor 2.307
## for the MLR correction
## Loglikelihood unrestricted model (H1) NA NA
## Scaling correction factor 1.813
## for the MLR correction
##
## Akaike (AIC) 18901.986 18901.986
## Bayesian (BIC) 19386.305 19386.305
## Sample-size adjusted Bayesian (SABIC) 18970.856 18970.856
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.092 0.060
## 90 Percent confidence interval - lower 0.088 0.056
## 90 Percent confidence interval - upper 0.096 0.063
## P-value H_0: RMSEA <= 0.050 0.000 0.000
## P-value H_0: RMSEA >= 0.080 1.000 0.000
##
## Robust RMSEA 0.077
## 90 Percent confidence interval - lower 0.072
## 90 Percent confidence interval - upper 0.083
## P-value H_0: Robust RMSEA <= 0.050 0.000
## P-value H_0: Robust RMSEA >= 0.080 0.245
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.033 0.033
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## CSE =~
## Q2_1 0.894 0.064 13.878 0.000 0.894 0.852
## Q2_2 0.809 0.070 11.533 0.000 0.809 0.731
## Q2_3 0.823 0.069 12.013 0.000 0.823 0.872
## Q2_4 0.891 0.067 13.221 0.000 0.891 0.838
## Q2_5 0.908 0.069 13.117 0.000 0.908 0.851
## Q2_6 0.868 0.073 11.886 0.000 0.868 0.824
## Q2_7 0.812 0.076 10.699 0.000 0.812 0.854
## Q2_8 0.804 0.073 11.096 0.000 0.804 0.830
## Q2_9 0.701 0.075 9.407 0.000 0.701 0.785
## Q3_1 0.931 0.066 14.038 0.000 0.931 0.908
## Q3_2 0.919 0.068 13.473 0.000 0.919 0.913
## Q3_3 0.816 0.078 10.517 0.000 0.816 0.886
## Q3_4 0.961 0.068 14.178 0.000 0.961 0.929
## Q3_5 0.865 0.076 11.316 0.000 0.865 0.889
## Q3_6 0.816 0.076 10.678 0.000 0.816 0.846
## Q3_7 0.969 0.065 14.857 0.000 0.969 0.822
## Q3_8 0.958 0.068 14.104 0.000 0.958 0.717
## Q3_9 0.996 0.057 17.585 0.000 0.996 0.691
## Q3_10 0.868 0.069 12.640 0.000 0.868 0.821
## Q4_1 0.924 0.071 13.094 0.000 0.924 0.930
## Q4_2 0.942 0.067 14.038 0.000 0.942 0.894
## Q4_3 0.924 0.069 13.378 0.000 0.924 0.949
## Q4_4 0.918 0.074 12.384 0.000 0.918 0.921
## Q4_5 0.842 0.076 11.146 0.000 0.842 0.868
## Q4_6 0.943 0.070 13.434 0.000 0.943 0.919
## Q4_7 1.008 0.062 16.132 0.000 1.008 0.865
## Q4_8 0.849 0.081 10.432 0.000 0.849 0.869
## Q4_9 0.627 0.079 7.928 0.000 0.627 0.578
## Q4_10 0.908 0.070 13.051 0.000 0.908 0.906
## Q5_1 0.903 0.067 13.449 0.000 0.903 0.876
## Q5_2 0.920 0.070 13.074 0.000 0.920 0.865
## Q5_4 0.866 0.075 11.530 0.000 0.866 0.923
## Q5_5 0.800 0.076 10.555 0.000 0.800 0.822
## Q5_6 0.791 0.075 10.526 0.000 0.791 0.877
## Q5_7 0.944 0.073 12.971 0.000 0.944 0.913
## Q5_8 0.709 0.083 8.506 0.000 0.709 0.671
## Q5_9 0.782 0.075 10.405 0.000 0.782 0.891
## Q5_10 0.736 0.079 9.345 0.000 0.736 0.767
## Q5_11 0.904 0.073 12.424 0.000 0.904 0.919
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Q2_3 ~~
## .Q2_4 0.069 0.035 1.999 0.046 0.069 0.258
## .Q2_5 0.032 0.023 1.373 0.170 0.032 0.122
## .Q5_5 0.013 0.021 0.603 0.546 0.013 0.049
## .Q5_6 0.004 0.015 0.230 0.818 0.004 0.018
## .Q2_4 ~~
## .Q2_5 0.140 0.033 4.237 0.000 0.140 0.432
## .Q5_5 0.018 0.023 0.774 0.439 0.018 0.056
## .Q5_6 -0.033 0.015 -2.202 0.028 -0.033 -0.133
## .Q2_5 ~~
## .Q5_5 0.065 0.029 2.210 0.027 0.065 0.209
## .Q5_6 -0.054 0.017 -3.134 0.002 -0.054 -0.222
## .Q5_5 ~~
## .Q5_6 0.002 0.019 0.088 0.929 0.002 0.007
## .Q4_4 ~~
## .Q4_5 0.060 0.022 2.759 0.006 0.060 0.323
## .Q4_6 0.040 0.017 2.314 0.021 0.040 0.253
## .Q4_5 ~~
## .Q4_6 0.065 0.021 3.127 0.002 0.065 0.331
## .Q4_7 ~~
## .Q4_8 0.021 0.025 0.853 0.394 0.021 0.076
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Q2_1 4.275 0.061 70.405 0.000 4.275 4.078
## .Q2_2 4.208 0.064 65.628 0.000 4.208 3.802
## .Q2_3 4.440 0.055 81.208 0.000 4.440 4.704
## .Q2_4 4.282 0.062 69.578 0.000 4.282 4.031
## .Q2_5 4.289 0.062 69.393 0.000 4.289 4.020
## .Q2_6 4.369 0.061 71.522 0.000 4.369 4.143
## .Q2_7 4.456 0.055 80.830 0.000 4.456 4.682
## .Q2_8 4.349 0.056 77.492 0.000 4.349 4.489
## .Q2_9 4.453 0.052 86.045 0.000 4.453 4.984
## .Q3_1 4.369 0.059 73.547 0.000 4.369 4.260
## .Q3_2 4.403 0.058 75.537 0.000 4.403 4.376
## .Q3_3 4.483 0.053 84.091 0.000 4.483 4.871
## .Q3_4 4.386 0.060 73.221 0.000 4.386 4.242
## .Q3_5 4.460 0.056 79.138 0.000 4.460 4.584
## .Q3_6 4.490 0.056 80.321 0.000 4.490 4.658
## .Q3_7 4.228 0.068 61.889 0.000 4.228 3.585
## .Q3_8 4.074 0.077 52.615 0.000 4.074 3.048
## .Q3_9 3.815 0.083 45.705 0.000 3.815 2.648
## .Q3_10 4.248 0.061 69.311 0.000 4.248 4.015
## .Q4_1 4.419 0.058 76.735 0.000 4.419 4.445
## .Q4_2 4.322 0.061 70.787 0.000 4.322 4.101
## .Q4_3 4.463 0.056 79.187 0.000 4.463 4.587
## .Q4_4 4.463 0.058 77.292 0.000 4.463 4.477
## .Q4_5 4.497 0.056 80.009 0.000 4.497 4.635
## .Q4_6 4.406 0.059 74.112 0.000 4.406 4.293
## .Q4_7 4.228 0.067 62.650 0.000 4.228 3.629
## .Q4_8 4.540 0.057 80.283 0.000 4.540 4.651
## .Q4_9 4.205 0.063 66.927 0.000 4.205 3.877
## .Q4_10 4.406 0.058 75.823 0.000 4.406 4.392
## .Q5_1 4.322 0.060 72.333 0.000 4.322 4.190
## .Q5_2 4.369 0.062 70.883 0.000 4.369 4.106
## .Q5_4 4.503 0.054 82.831 0.000 4.503 4.798
## .Q5_5 4.419 0.056 78.347 0.000 4.419 4.539
## .Q5_6 4.510 0.052 86.312 0.000 4.510 5.000
## .Q5_7 4.423 0.060 73.826 0.000 4.423 4.277
## .Q5_8 4.362 0.061 71.254 0.000 4.362 4.128
## .Q5_9 4.557 0.051 89.635 0.000 4.557 5.192
## .Q5_10 4.379 0.056 78.816 0.000 4.379 4.566
## .Q5_11 4.477 0.057 78.561 0.000 4.477 4.551
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Q2_1 0.300 0.046 6.552 0.000 0.300 0.273
## .Q2_2 0.571 0.078 7.363 0.000 0.571 0.466
## .Q2_3 0.213 0.038 5.661 0.000 0.213 0.239
## .Q2_4 0.335 0.052 6.455 0.000 0.335 0.297
## .Q2_5 0.313 0.042 7.435 0.000 0.313 0.275
## .Q2_6 0.358 0.080 4.489 0.000 0.358 0.322
## .Q2_7 0.246 0.041 6.002 0.000 0.246 0.271
## .Q2_8 0.291 0.040 7.217 0.000 0.291 0.310
## .Q2_9 0.306 0.039 7.828 0.000 0.306 0.384
## .Q3_1 0.185 0.055 3.353 0.001 0.185 0.176
## .Q3_2 0.168 0.032 5.307 0.000 0.168 0.166
## .Q3_3 0.182 0.050 3.599 0.000 0.182 0.214
## .Q3_4 0.146 0.029 5.000 0.000 0.146 0.136
## .Q3_5 0.198 0.043 4.580 0.000 0.198 0.209
## .Q3_6 0.264 0.045 5.864 0.000 0.264 0.284
## .Q3_7 0.452 0.078 5.770 0.000 0.452 0.325
## .Q3_8 0.868 0.123 7.074 0.000 0.868 0.486
## .Q3_9 1.085 0.119 9.130 0.000 1.085 0.522
## .Q3_10 0.366 0.068 5.356 0.000 0.366 0.327
## .Q4_1 0.134 0.034 3.977 0.000 0.134 0.136
## .Q4_2 0.224 0.041 5.408 0.000 0.224 0.201
## .Q4_3 0.093 0.016 5.912 0.000 0.093 0.099
## .Q4_4 0.150 0.027 5.660 0.000 0.150 0.151
## .Q4_5 0.232 0.036 6.432 0.000 0.232 0.247
## .Q4_6 0.164 0.025 6.496 0.000 0.164 0.155
## .Q4_7 0.342 0.057 6.000 0.000 0.342 0.252
## .Q4_8 0.233 0.036 6.523 0.000 0.233 0.244
## .Q4_9 0.783 0.100 7.810 0.000 0.783 0.666
## .Q4_10 0.181 0.039 4.652 0.000 0.181 0.180
## .Q5_1 0.248 0.040 6.175 0.000 0.248 0.233
## .Q5_2 0.286 0.055 5.174 0.000 0.286 0.253
## .Q5_4 0.131 0.024 5.514 0.000 0.131 0.149
## .Q5_5 0.308 0.058 5.291 0.000 0.308 0.325
## .Q5_6 0.187 0.031 6.052 0.000 0.187 0.230
## .Q5_7 0.179 0.028 6.406 0.000 0.179 0.167
## .Q5_8 0.615 0.105 5.851 0.000 0.615 0.550
## .Q5_9 0.159 0.020 7.785 0.000 0.159 0.206
## .Q5_10 0.379 0.053 7.093 0.000 0.379 0.412
## .Q5_11 0.150 0.035 4.329 0.000 0.150 0.155
## CSE 1.000 1.000 1.000
anova(csek12one.mod2.fit, csek12one.mod3.fit) #non-significant reduction in model chi sq
##
## Scaled Chi-Squared Difference Test (method = "satorra.bentler.2001")
##
## lavaan->lavTestLRT():
## lavaan NOTE: The "Chisq" column contains standard test statistics, not the
## robust test that should be reported per model. A robust difference test is
## a function of two standard (not robust) statistics.
## Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
## csek12one.mod3.fit 688 18902 19386 2435.5
## csek12one.mod2.fit 689 18902 19382 2437.2 0.75022 1 0.3864
dynamic::cfaOne(csek12one.mod3.fit, plot = TRUE) #still does not meet dynamic fit indices
## Your DFI cutoffs:
## SRMR RMSEA CFI
## Level 1: 95/5 NONE NONE NONE
## Level 1: 90/10 .017 .021 .994
## Level 2: 95/5 .017 .031 .987
## Level 2: 90/10 -- -- --
## Level 3: 95/5 .018 .04 .978
## Level 3: 90/10 -- -- --
##
## Empirical fit indices:
## Chi-Square df p-value SRMR RMSEA CFI
## 2435.54 688 0 0.033 0.092 0.895
##
## The distributions for each level are in the Plots tab
## [[1]]
##
## [[2]]
##
## [[3]]
#To improve model fit, covariance set 4 added to model:
csek12one.mod4 <- '
CSE =~ Q2_1 + Q2_2 + Q2_3 + Q2_4 + Q2_5 + Q2_6 + Q2_7 + Q2_8 + Q2_9 + Q3_1 + Q3_2 + Q3_3 + Q3_4 + Q3_5 + Q3_6 + Q3_7 + Q3_8 + Q3_9 + Q3_10 + Q4_1 + Q4_2 + Q4_3 + Q4_4 + Q4_5 + Q4_6 + Q4_7 + Q4_8 + Q4_9 + Q4_10 + Q5_1 + Q5_2 + Q5_4 + Q5_5 + Q5_6 + Q5_7 + Q5_8 + Q5_9 + Q5_10 + Q5_11
#covariances
Q2_3 ~~ Q2_4
Q2_3 ~~ Q2_5
Q2_3 ~~ Q5_5
Q2_3 ~~ Q5_6
Q2_4 ~~ Q2_5
Q2_4 ~~ Q5_5
Q2_4 ~~ Q5_6
Q2_5 ~~ Q5_5
Q2_5 ~~ Q5_6
Q5_5 ~~ Q5_6
Q4_4 ~~ Q4_5
Q4_4 ~~ Q4_6
Q4_5 ~~ Q4_6
Q4_7 ~~ Q4_8
Q2_8 ~~ Q2_9
'
csek12one.mod4.fit <- lavaan::cfa(csek12one.mod4, data = csek12, std.lv = TRUE, missing = "FIML", estimator = "MLR")
anova(csek12one.fit, csek12one.mod4.fit) #sig reduction in model chi from one factor w/ no covariances
##
## Scaled Chi-Squared Difference Test (method = "satorra.bentler.2001")
##
## lavaan->lavTestLRT():
## lavaan NOTE: The "Chisq" column contains standard test statistics, not the
## robust test that should be reported per model. A robust difference test is
## a function of two standard (not robust) statistics.
## Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
## csek12one.mod4.fit 687 18833 19321 2364.0
## csek12one.fit 702 19051 19483 2612.2 105.88 15 9.932e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(csek12one.mod1.fit, csek12one.mod4.fit) #sig reduction in model chi from one factor + set 1
##
## Scaled Chi-Squared Difference Test (method = "satorra.bentler.2001")
##
## lavaan->lavTestLRT():
## lavaan NOTE: The "Chisq" column contains standard test statistics, not the
## robust test that should be reported per model. A robust difference test is
## a function of two standard (not robust) statistics.
## Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
## csek12one.mod4.fit 687 18833 19321 2364.0
## csek12one.mod1.fit 692 18963 19432 2504.2 48.865 5 2.365e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(csek12one.mod2.fit, csek12one.mod4.fit) #sig reduction in model chi from one factor + set 1 & 2
##
## Scaled Chi-Squared Difference Test (method = "satorra.bentler.2001")
##
## lavaan->lavTestLRT():
## lavaan NOTE: The "Chisq" column contains standard test statistics, not the
## robust test that should be reported per model. A robust difference test is
## a function of two standard (not robust) statistics.
## Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
## csek12one.mod4.fit 687 18833 19321 2364.0
## csek12one.mod2.fit 689 18902 19382 2437.2 34.081 2 3.975e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(csek12one.mod3.fit, csek12one.mod4.fit) #sig reduction in model chi from one factor + set 1, 2, and 3
##
## Scaled Chi-Squared Difference Test (method = "satorra.bentler.2001")
##
## lavaan->lavTestLRT():
## lavaan NOTE: The "Chisq" column contains standard test statistics, not the
## robust test that should be reported per model. A robust difference test is
## a function of two standard (not robust) statistics.
## Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
## csek12one.mod4.fit 687 18833 19321 2364.0
## csek12one.mod3.fit 688 18902 19386 2435.5 34.023 1 5.447e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
dynamic::cfaOne(csek12one.mod4.fit, plot = TRUE)
## Your DFI cutoffs:
## SRMR RMSEA CFI
## Level 1: 95/5 NONE NONE NONE
## Level 1: 90/10 .017 .021 .994
## Level 2: 95/5 .017 .029 .989
## Level 2: 90/10 -- -- --
## Level 3: 95/5 .017 .039 .98
## Level 3: 90/10 -- -- --
##
## Empirical fit indices:
## Chi-Square df p-value SRMR RMSEA CFI
## 2364.038 687 0 0.032 0.091 0.899
##
## The distributions for each level are in the Plots tab
## [[1]]
##
## [[2]]
##
## [[3]]
summary(csek12one.mod4.fit, fit.measures = TRUE, standardized = TRUE, rsquare = TRUE) #doesn't meet fit criteria from dynamic fit indices
## lavaan 0.6-18 ended normally after 148 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 132
##
## Number of observations 298
## Number of missing patterns 2
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 2364.038 1375.739
## Degrees of freedom 687 687
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.718
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 17308.096 9485.700
## Degrees of freedom 741 741
## P-value 0.000 0.000
## Scaling correction factor 1.825
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.899 0.921
## Tucker-Lewis Index (TLI) 0.891 0.915
##
## Robust Comparative Fit Index (CFI) 0.927
## Robust Tucker-Lewis Index (TLI) 0.922
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -9284.241 -9284.241
## Scaling correction factor 2.305
## for the MLR correction
## Loglikelihood unrestricted model (H1) NA NA
## Scaling correction factor 1.813
## for the MLR correction
##
## Akaike (AIC) 18832.483 18832.483
## Bayesian (BIC) 19320.499 19320.499
## Sample-size adjusted Bayesian (SABIC) 18901.879 18901.879
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.091 0.058
## 90 Percent confidence interval - lower 0.087 0.055
## 90 Percent confidence interval - upper 0.095 0.061
## P-value H_0: RMSEA <= 0.050 0.000 0.000
## P-value H_0: RMSEA >= 0.080 1.000 0.000
##
## Robust RMSEA 0.075
## 90 Percent confidence interval - lower 0.069
## 90 Percent confidence interval - upper 0.081
## P-value H_0: Robust RMSEA <= 0.050 0.000
## P-value H_0: Robust RMSEA >= 0.080 0.099
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.032 0.032
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## CSE =~
## Q2_1 0.893 0.064 13.860 0.000 0.893 0.852
## Q2_2 0.808 0.070 11.508 0.000 0.808 0.730
## Q2_3 0.823 0.069 12.010 0.000 0.823 0.872
## Q2_4 0.890 0.067 13.199 0.000 0.890 0.838
## Q2_5 0.908 0.069 13.100 0.000 0.908 0.851
## Q2_6 0.869 0.073 11.881 0.000 0.869 0.824
## Q2_7 0.812 0.076 10.692 0.000 0.812 0.853
## Q2_8 0.802 0.073 11.026 0.000 0.802 0.828
## Q2_9 0.698 0.075 9.343 0.000 0.698 0.781
## Q3_1 0.931 0.066 14.041 0.000 0.931 0.908
## Q3_2 0.919 0.068 13.473 0.000 0.919 0.914
## Q3_3 0.815 0.078 10.508 0.000 0.815 0.886
## Q3_4 0.961 0.068 14.170 0.000 0.961 0.929
## Q3_5 0.865 0.076 11.324 0.000 0.865 0.890
## Q3_6 0.816 0.076 10.678 0.000 0.816 0.846
## Q3_7 0.970 0.065 14.870 0.000 0.970 0.822
## Q3_8 0.959 0.068 14.114 0.000 0.959 0.718
## Q3_9 0.997 0.057 17.580 0.000 0.997 0.692
## Q3_10 0.867 0.069 12.603 0.000 0.867 0.820
## Q4_1 0.925 0.071 13.093 0.000 0.925 0.930
## Q4_2 0.942 0.067 14.027 0.000 0.942 0.894
## Q4_3 0.924 0.069 13.385 0.000 0.924 0.950
## Q4_4 0.919 0.074 12.388 0.000 0.919 0.922
## Q4_5 0.842 0.076 11.140 0.000 0.842 0.868
## Q4_6 0.944 0.070 13.438 0.000 0.944 0.919
## Q4_7 1.009 0.063 16.134 0.000 1.009 0.866
## Q4_8 0.849 0.081 10.435 0.000 0.849 0.870
## Q4_9 0.625 0.079 7.906 0.000 0.625 0.577
## Q4_10 0.908 0.070 13.039 0.000 0.908 0.905
## Q5_1 0.904 0.067 13.446 0.000 0.904 0.876
## Q5_2 0.920 0.070 13.078 0.000 0.920 0.865
## Q5_4 0.866 0.075 11.536 0.000 0.866 0.923
## Q5_5 0.800 0.076 10.535 0.000 0.800 0.821
## Q5_6 0.792 0.075 10.531 0.000 0.792 0.878
## Q5_7 0.944 0.073 12.965 0.000 0.944 0.913
## Q5_8 0.708 0.083 8.480 0.000 0.708 0.670
## Q5_9 0.782 0.075 10.406 0.000 0.782 0.891
## Q5_10 0.734 0.079 9.300 0.000 0.734 0.765
## Q5_11 0.904 0.073 12.418 0.000 0.904 0.919
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Q2_3 ~~
## .Q2_4 0.070 0.035 2.012 0.044 0.070 0.260
## .Q2_5 0.032 0.023 1.387 0.165 0.032 0.123
## .Q5_5 0.013 0.021 0.634 0.526 0.013 0.052
## .Q5_6 0.003 0.015 0.226 0.822 0.003 0.017
## .Q2_4 ~~
## .Q2_5 0.140 0.033 4.247 0.000 0.140 0.433
## .Q5_5 0.019 0.023 0.812 0.417 0.019 0.059
## .Q5_6 -0.033 0.015 -2.195 0.028 -0.033 -0.132
## .Q2_5 ~~
## .Q5_5 0.066 0.029 2.231 0.026 0.066 0.211
## .Q5_6 -0.054 0.017 -3.129 0.002 -0.054 -0.222
## .Q5_5 ~~
## .Q5_6 0.002 0.019 0.106 0.916 0.002 0.009
## .Q4_4 ~~
## .Q4_5 0.060 0.022 2.752 0.006 0.060 0.322
## .Q4_6 0.039 0.017 2.271 0.023 0.039 0.249
## .Q4_5 ~~
## .Q4_6 0.064 0.021 3.106 0.002 0.064 0.330
## .Q4_7 ~~
## .Q4_8 0.021 0.025 0.820 0.412 0.021 0.073
## .Q2_8 ~~
## .Q2_9 0.141 0.032 4.384 0.000 0.141 0.466
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Q2_1 4.275 0.061 70.405 0.000 4.275 4.078
## .Q2_2 4.208 0.064 65.628 0.000 4.208 3.802
## .Q2_3 4.440 0.055 81.208 0.000 4.440 4.704
## .Q2_4 4.282 0.062 69.578 0.000 4.282 4.031
## .Q2_5 4.289 0.062 69.393 0.000 4.289 4.020
## .Q2_6 4.369 0.061 71.522 0.000 4.369 4.143
## .Q2_7 4.456 0.055 80.830 0.000 4.456 4.682
## .Q2_8 4.349 0.056 77.492 0.000 4.349 4.489
## .Q2_9 4.453 0.052 86.045 0.000 4.453 4.984
## .Q3_1 4.369 0.059 73.547 0.000 4.369 4.260
## .Q3_2 4.403 0.058 75.537 0.000 4.403 4.376
## .Q3_3 4.483 0.053 84.091 0.000 4.483 4.871
## .Q3_4 4.386 0.060 73.222 0.000 4.386 4.242
## .Q3_5 4.460 0.056 79.139 0.000 4.460 4.584
## .Q3_6 4.490 0.056 80.326 0.000 4.490 4.659
## .Q3_7 4.228 0.068 61.890 0.000 4.228 3.585
## .Q3_8 4.074 0.077 52.615 0.000 4.074 3.048
## .Q3_9 3.815 0.083 45.705 0.000 3.815 2.648
## .Q3_10 4.248 0.061 69.311 0.000 4.248 4.015
## .Q4_1 4.419 0.058 76.735 0.000 4.419 4.445
## .Q4_2 4.322 0.061 70.787 0.000 4.322 4.101
## .Q4_3 4.463 0.056 79.187 0.000 4.463 4.587
## .Q4_4 4.463 0.058 77.293 0.000 4.463 4.477
## .Q4_5 4.497 0.056 80.009 0.000 4.497 4.635
## .Q4_6 4.406 0.059 74.112 0.000 4.406 4.293
## .Q4_7 4.228 0.067 62.650 0.000 4.228 3.629
## .Q4_8 4.540 0.057 80.283 0.000 4.540 4.651
## .Q4_9 4.205 0.063 66.927 0.000 4.205 3.877
## .Q4_10 4.406 0.058 75.823 0.000 4.406 4.392
## .Q5_1 4.322 0.060 72.333 0.000 4.322 4.190
## .Q5_2 4.369 0.062 70.883 0.000 4.369 4.106
## .Q5_4 4.503 0.054 82.831 0.000 4.503 4.798
## .Q5_5 4.419 0.056 78.348 0.000 4.419 4.539
## .Q5_6 4.510 0.052 86.312 0.000 4.510 5.000
## .Q5_7 4.423 0.060 73.826 0.000 4.423 4.277
## .Q5_8 4.362 0.061 71.255 0.000 4.362 4.128
## .Q5_9 4.557 0.051 89.636 0.000 4.557 5.192
## .Q5_10 4.379 0.056 78.816 0.000 4.379 4.566
## .Q5_11 4.477 0.057 78.561 0.000 4.477 4.551
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Q2_1 0.300 0.046 6.555 0.000 0.300 0.273
## .Q2_2 0.572 0.078 7.372 0.000 0.572 0.467
## .Q2_3 0.213 0.038 5.664 0.000 0.213 0.240
## .Q2_4 0.336 0.052 6.446 0.000 0.336 0.298
## .Q2_5 0.314 0.042 7.453 0.000 0.314 0.276
## .Q2_6 0.358 0.080 4.488 0.000 0.358 0.322
## .Q2_7 0.246 0.041 5.997 0.000 0.246 0.272
## .Q2_8 0.296 0.041 7.261 0.000 0.296 0.315
## .Q2_9 0.311 0.039 7.878 0.000 0.311 0.389
## .Q3_1 0.185 0.055 3.347 0.001 0.185 0.175
## .Q3_2 0.167 0.032 5.287 0.000 0.167 0.165
## .Q3_3 0.182 0.051 3.597 0.000 0.182 0.215
## .Q3_4 0.146 0.029 4.986 0.000 0.146 0.136
## .Q3_5 0.197 0.043 4.567 0.000 0.197 0.209
## .Q3_6 0.263 0.045 5.871 0.000 0.263 0.284
## .Q3_7 0.450 0.078 5.764 0.000 0.450 0.324
## .Q3_8 0.866 0.123 7.062 0.000 0.866 0.485
## .Q3_9 1.083 0.119 9.123 0.000 1.083 0.522
## .Q3_10 0.367 0.068 5.367 0.000 0.367 0.328
## .Q4_1 0.134 0.034 3.962 0.000 0.134 0.135
## .Q4_2 0.224 0.042 5.394 0.000 0.224 0.201
## .Q4_3 0.093 0.016 5.878 0.000 0.093 0.098
## .Q4_4 0.149 0.026 5.659 0.000 0.149 0.150
## .Q4_5 0.232 0.036 6.416 0.000 0.232 0.247
## .Q4_6 0.163 0.025 6.490 0.000 0.163 0.155
## .Q4_7 0.340 0.057 6.006 0.000 0.340 0.251
## .Q4_8 0.232 0.036 6.524 0.000 0.232 0.244
## .Q4_9 0.785 0.100 7.831 0.000 0.785 0.667
## .Q4_10 0.181 0.039 4.643 0.000 0.181 0.180
## .Q5_1 0.247 0.040 6.177 0.000 0.247 0.233
## .Q5_2 0.286 0.055 5.155 0.000 0.286 0.252
## .Q5_4 0.130 0.024 5.494 0.000 0.130 0.148
## .Q5_5 0.309 0.058 5.288 0.000 0.309 0.326
## .Q5_6 0.187 0.031 6.040 0.000 0.187 0.230
## .Q5_7 0.179 0.028 6.378 0.000 0.179 0.167
## .Q5_8 0.616 0.105 5.845 0.000 0.616 0.552
## .Q5_9 0.159 0.020 7.770 0.000 0.159 0.206
## .Q5_10 0.382 0.054 7.107 0.000 0.382 0.415
## .Q5_11 0.151 0.035 4.338 0.000 0.151 0.156
## CSE 1.000 1.000 1.000
##
## R-Square:
## Estimate
## Q2_1 0.727
## Q2_2 0.533
## Q2_3 0.760
## Q2_4 0.702
## Q2_5 0.724
## Q2_6 0.678
## Q2_7 0.728
## Q2_8 0.685
## Q2_9 0.611
## Q3_1 0.825
## Q3_2 0.835
## Q3_3 0.785
## Q3_4 0.864
## Q3_5 0.791
## Q3_6 0.716
## Q3_7 0.676
## Q3_8 0.515
## Q3_9 0.478
## Q3_10 0.672
## Q4_1 0.865
## Q4_2 0.799
## Q4_3 0.902
## Q4_4 0.850
## Q4_5 0.753
## Q4_6 0.845
## Q4_7 0.749
## Q4_8 0.756
## Q4_9 0.333
## Q4_10 0.820
## Q5_1 0.767
## Q5_2 0.748
## Q5_4 0.852
## Q5_5 0.674
## Q5_6 0.770
## Q5_7 0.833
## Q5_8 0.448
## Q5_9 0.794
## Q5_10 0.585
## Q5_11 0.844
#reliability
library(semTools)
## Warning: package 'semTools' was built under R version 4.3.2
##
## ###############################################################################
## This is semTools 0.5-6
## All users of R (or SEM) are invited to submit functions or ideas for functions.
## ###############################################################################
##
## Attaching package: 'semTools'
## The following object is masked from 'package:misty':
##
## kurtosis
## The following objects are masked from 'package:psych':
##
## reliability, skew
reliability(csek12one.mod4.fit) #alpha = .99, omega = .99
## CSE
## alpha 0.9894747
## omega 0.9886085
## omega2 0.9886085
## omega3 0.9865499
## avevar 0.7112986
csek12two <- '
CSE1 =~ Q2_2 + Q2_3 + Q2_4 + Q2_5 + Q2_6 + Q2_7 + Q2_8 + Q2_9 + Q3_2 + Q3_3 + Q3_4 + Q3_5 + Q3_6 + Q3_7 + Q3_8 + Q3_9 + Q3_10 + Q4_2 + Q4_3 + Q4_4 + Q4_5 + Q4_6 + Q4_7 + Q4_8 + Q4_9 + Q4_10 + Q5_2 + Q5_4 + Q5_5 + Q5_6 + Q5_7 + Q5_8 + Q5_9 + Q5_10 + Q5_11
CSE2 =~ Q2_1 + Q3_1 + Q4_1 + Q5_1'
csek12two.fit <- lavaan::cfa(csek12two, data = csek12, std.lv = TRUE, missing = "FIML", estimator = "MLR")
summary(csek12two.fit, fit.measures = TRUE, standardized = TRUE)
## lavaan 0.6-18 ended normally after 143 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 118
##
## Number of observations 298
## Number of missing patterns 2
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 2610.981 1508.295
## Degrees of freedom 701 701
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.731
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 17308.096 9485.700
## Degrees of freedom 741 741
## P-value 0.000 0.000
## Scaling correction factor 1.825
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.885 0.908
## Tucker-Lewis Index (TLI) 0.878 0.902
##
## Robust Comparative Fit Index (CFI) 0.914
## Robust Tucker-Lewis Index (TLI) 0.909
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -9407.713 -9407.713
## Scaling correction factor 2.299
## for the MLR correction
## Loglikelihood unrestricted model (H1) NA NA
## Scaling correction factor 1.813
## for the MLR correction
##
## Akaike (AIC) 19051.426 19051.426
## Bayesian (BIC) 19487.683 19487.683
## Sample-size adjusted Bayesian (SABIC) 19113.462 19113.462
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.096 0.062
## 90 Percent confidence interval - lower 0.092 0.059
## 90 Percent confidence interval - upper 0.100 0.065
## P-value H_0: RMSEA <= 0.050 0.000 0.000
## P-value H_0: RMSEA >= 0.080 1.000 0.000
##
## Robust RMSEA 0.081
## 90 Percent confidence interval - lower 0.076
## 90 Percent confidence interval - upper 0.087
## P-value H_0: Robust RMSEA <= 0.050 0.000
## P-value H_0: Robust RMSEA >= 0.080 0.647
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.033 0.033
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## CSE1 =~
## Q2_2 0.806 0.070 11.485 0.000 0.806 0.729
## Q2_3 0.826 0.068 12.085 0.000 0.826 0.875
## Q2_4 0.896 0.067 13.401 0.000 0.896 0.843
## Q2_5 0.913 0.069 13.288 0.000 0.913 0.856
## Q2_6 0.871 0.073 11.950 0.000 0.871 0.826
## Q2_7 0.813 0.076 10.696 0.000 0.813 0.854
## Q2_8 0.805 0.072 11.103 0.000 0.805 0.831
## Q2_9 0.701 0.075 9.391 0.000 0.701 0.784
## Q3_2 0.918 0.068 13.446 0.000 0.918 0.913
## Q3_3 0.815 0.078 10.502 0.000 0.815 0.885
## Q3_4 0.959 0.068 14.111 0.000 0.959 0.927
## Q3_5 0.864 0.077 11.292 0.000 0.864 0.889
## Q3_6 0.813 0.077 10.614 0.000 0.813 0.843
## Q3_7 0.971 0.065 14.905 0.000 0.971 0.823
## Q3_8 0.959 0.068 14.119 0.000 0.959 0.717
## Q3_9 0.996 0.057 17.596 0.000 0.996 0.691
## Q3_10 0.867 0.069 12.592 0.000 0.867 0.819
## Q4_2 0.942 0.067 14.025 0.000 0.942 0.894
## Q4_3 0.923 0.069 13.344 0.000 0.923 0.949
## Q4_4 0.923 0.074 12.466 0.000 0.923 0.925
## Q4_5 0.850 0.075 11.284 0.000 0.850 0.876
## Q4_6 0.948 0.070 13.548 0.000 0.948 0.923
## Q4_7 1.009 0.062 16.175 0.000 1.009 0.866
## Q4_8 0.850 0.081 10.455 0.000 0.850 0.870
## Q4_9 0.626 0.079 7.930 0.000 0.626 0.578
## Q4_10 0.907 0.070 12.997 0.000 0.907 0.904
## Q5_2 0.919 0.070 13.046 0.000 0.919 0.864
## Q5_4 0.866 0.075 11.515 0.000 0.866 0.923
## Q5_5 0.804 0.075 10.655 0.000 0.804 0.826
## Q5_6 0.789 0.075 10.481 0.000 0.789 0.874
## Q5_7 0.945 0.073 13.005 0.000 0.945 0.914
## Q5_8 0.708 0.083 8.490 0.000 0.708 0.670
## Q5_9 0.782 0.075 10.405 0.000 0.782 0.891
## Q5_10 0.735 0.079 9.315 0.000 0.735 0.766
## Q5_11 0.904 0.073 12.408 0.000 0.904 0.919
## CSE2 =~
## Q2_1 0.896 0.065 13.875 0.000 0.896 0.855
## Q3_1 0.931 0.067 13.993 0.000 0.931 0.908
## Q4_1 0.926 0.071 13.098 0.000 0.926 0.932
## Q5_1 0.906 0.067 13.458 0.000 0.906 0.879
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## CSE1 ~~
## CSE2 0.996 0.005 210.669 0.000 0.996 0.996
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Q2_2 4.208 0.064 65.628 0.000 4.208 3.802
## .Q2_3 4.440 0.055 81.208 0.000 4.440 4.704
## .Q2_4 4.282 0.062 69.578 0.000 4.282 4.031
## .Q2_5 4.289 0.062 69.393 0.000 4.289 4.020
## .Q2_6 4.369 0.061 71.522 0.000 4.369 4.143
## .Q2_7 4.456 0.055 80.830 0.000 4.456 4.682
## .Q2_8 4.349 0.056 77.492 0.000 4.349 4.489
## .Q2_9 4.453 0.052 86.045 0.000 4.453 4.984
## .Q3_2 4.403 0.058 75.537 0.000 4.403 4.376
## .Q3_3 4.483 0.053 84.091 0.000 4.483 4.871
## .Q3_4 4.386 0.060 73.221 0.000 4.386 4.242
## .Q3_5 4.460 0.056 79.138 0.000 4.460 4.584
## .Q3_6 4.490 0.056 80.298 0.000 4.490 4.658
## .Q3_7 4.228 0.068 61.889 0.000 4.228 3.585
## .Q3_8 4.074 0.077 52.615 0.000 4.074 3.048
## .Q3_9 3.815 0.083 45.705 0.000 3.815 2.648
## .Q3_10 4.248 0.061 69.311 0.000 4.248 4.015
## .Q4_2 4.322 0.061 70.787 0.000 4.322 4.101
## .Q4_3 4.463 0.056 79.187 0.000 4.463 4.587
## .Q4_4 4.463 0.058 77.292 0.000 4.463 4.477
## .Q4_5 4.497 0.056 80.009 0.000 4.497 4.635
## .Q4_6 4.406 0.059 74.112 0.000 4.406 4.293
## .Q4_7 4.228 0.067 62.650 0.000 4.228 3.629
## .Q4_8 4.540 0.057 80.283 0.000 4.540 4.651
## .Q4_9 4.205 0.063 66.927 0.000 4.205 3.877
## .Q4_10 4.406 0.058 75.823 0.000 4.406 4.392
## .Q5_2 4.369 0.062 70.883 0.000 4.369 4.106
## .Q5_4 4.503 0.054 82.831 0.000 4.503 4.798
## .Q5_5 4.419 0.056 78.347 0.000 4.419 4.539
## .Q5_6 4.510 0.052 86.312 0.000 4.510 5.000
## .Q5_7 4.423 0.060 73.826 0.000 4.423 4.277
## .Q5_8 4.362 0.061 71.255 0.000 4.362 4.128
## .Q5_9 4.557 0.051 89.635 0.000 4.557 5.192
## .Q5_10 4.379 0.056 78.816 0.000 4.379 4.566
## .Q5_11 4.477 0.057 78.561 0.000 4.477 4.551
## .Q2_1 4.275 0.061 70.405 0.000 4.275 4.078
## .Q3_1 4.369 0.059 73.547 0.000 4.369 4.260
## .Q4_1 4.419 0.058 76.735 0.000 4.419 4.445
## .Q5_1 4.322 0.060 72.333 0.000 4.322 4.190
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Q2_2 0.575 0.078 7.388 0.000 0.575 0.469
## .Q2_3 0.209 0.037 5.689 0.000 0.209 0.235
## .Q2_4 0.326 0.051 6.447 0.000 0.326 0.289
## .Q2_5 0.304 0.041 7.347 0.000 0.304 0.267
## .Q2_6 0.354 0.079 4.490 0.000 0.354 0.318
## .Q2_7 0.245 0.041 5.980 0.000 0.245 0.270
## .Q2_8 0.291 0.040 7.243 0.000 0.291 0.310
## .Q2_9 0.307 0.039 7.796 0.000 0.307 0.385
## .Q3_2 0.169 0.032 5.261 0.000 0.169 0.167
## .Q3_3 0.183 0.050 3.634 0.000 0.183 0.216
## .Q3_4 0.150 0.030 4.981 0.000 0.150 0.140
## .Q3_5 0.199 0.043 4.602 0.000 0.199 0.210
## .Q3_6 0.268 0.046 5.812 0.000 0.268 0.289
## .Q3_7 0.449 0.078 5.734 0.000 0.449 0.323
## .Q3_8 0.867 0.122 7.110 0.000 0.867 0.485
## .Q3_9 1.086 0.118 9.187 0.000 1.086 0.523
## .Q3_10 0.368 0.069 5.374 0.000 0.368 0.329
## .Q4_2 0.223 0.041 5.410 0.000 0.223 0.201
## .Q4_3 0.095 0.016 5.894 0.000 0.095 0.100
## .Q4_4 0.143 0.025 5.661 0.000 0.143 0.143
## .Q4_5 0.220 0.034 6.452 0.000 0.220 0.233
## .Q4_6 0.155 0.025 6.272 0.000 0.155 0.148
## .Q4_7 0.339 0.057 5.955 0.000 0.339 0.250
## .Q4_8 0.231 0.035 6.568 0.000 0.231 0.243
## .Q4_9 0.784 0.101 7.798 0.000 0.784 0.666
## .Q4_10 0.183 0.040 4.640 0.000 0.183 0.182
## .Q5_2 0.288 0.056 5.129 0.000 0.288 0.254
## .Q5_4 0.131 0.023 5.583 0.000 0.131 0.149
## .Q5_5 0.302 0.058 5.213 0.000 0.302 0.318
## .Q5_6 0.192 0.031 6.086 0.000 0.192 0.236
## .Q5_7 0.177 0.027 6.440 0.000 0.177 0.165
## .Q5_8 0.616 0.105 5.861 0.000 0.616 0.551
## .Q5_9 0.159 0.020 7.788 0.000 0.159 0.206
## .Q5_10 0.380 0.054 7.059 0.000 0.380 0.413
## .Q5_11 0.151 0.035 4.308 0.000 0.151 0.156
## .Q2_1 0.296 0.047 6.332 0.000 0.296 0.270
## .Q3_1 0.184 0.058 3.198 0.001 0.184 0.175
## .Q4_1 0.130 0.032 4.026 0.000 0.130 0.132
## .Q5_1 0.242 0.039 6.204 0.000 0.242 0.228
## CSE1 1.000 1.000 1.000
## CSE2 1.000 1.000 1.000
lavTestLRT(csek12two.fit, csek12one.fit) #not significantly different, use more parsimonious model
##
## Scaled Chi-Squared Difference Test (method = "satorra.bentler.2001")
##
## lavaan->lavTestLRT():
## lavaan NOTE: The "Chisq" column contains standard test statistics, not the
## robust test that should be reported per model. A robust difference test is
## a function of two standard (not robust) statistics.
## Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
## csek12two.fit 701 19051 19488 2611.0
## csek12one.fit 702 19051 19483 2612.2 0.54934 1 0.4586
dynamic::cfaHB(csek12two.fit, plot = TRUE) #does not meet dynamic fit indices; there are no cut offs that can correctly reject a misspecified model 90 or 95% of the time. No indices can distinguish between well and ill fitting models.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
## Your DFI cutoffs:
## SRMR RMSEA CFI Magnitude
## Level 1: 95/5 NONE NONE NONE .402
## Level 1: 90/10 NONE NONE NONE
##
## Empirical fit indices:
## Chi-Square df p-value SRMR RMSEA CFI
## 2610.981 701 0 0.033 0.096 0.885
##
## The distributions for each level are in the Plots tab
## [[1]]
csek12six <- '
LAge =~ Q2_1 + Q3_3 + Q4_2 + Q5_2
Info =~ Q2_2 + Q3_4 + Q4_3 + Q5_4
Outcomes =~ Q2_3 + Q2_4 + Q2_5 + Q3_5 + Q3_6 + Q3_7 + Q4_4 + Q4_5 + Q4_6 + Q5_5 + Q5_6
Topic =~ Q2_6 + Q2_7 + Q3_8 + Q3_9 + Q4_7 + Q4_8 + Q5_7 + Q5_8 + Q5_9
LParent =~ Q2_8 + Q2_9 + Q3_10 + Q4_9 + Q5_10
EdSuit =~ Q3_1 + Q3_2 + Q4_1 + Q4_10 + Q5_1 + Q5_11
'
csek12six.fit <- lavaan::cfa(csek12six, data = csek12, std.lv = TRUE, missing = "FIML", estimator = "MLR") #covariance matrix is not positive definite; extremely highly related latent variables -> singularity
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.
lavInspect(csek12six.fit, "cov.lv") #covariance over 1
## LAge Info Outcms Topic LParnt EdSuit
## LAge 1.000
## Info 0.992 1.000
## Outcomes 0.988 0.974 1.000
## Topic 0.983 0.976 1.004 1.000
## LParent 0.926 0.891 0.899 0.901 1.000
## EdSuit 0.989 0.974 0.978 0.973 0.904 1.000
summary(csek12six.fit, fit.measures = TRUE, standardized = TRUE)
## lavaan 0.6-18 ended normally after 215 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 132
##
## Number of observations 298
## Number of missing patterns 2
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 2263.526 1317.926
## Degrees of freedom 687 687
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.717
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 17308.096 9485.700
## Degrees of freedom 741 741
## P-value 0.000 0.000
## Scaling correction factor 1.825
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.905 0.928
## Tucker-Lewis Index (TLI) 0.897 0.922
##
## Robust Comparative Fit Index (CFI) 0.934
## Robust Tucker-Lewis Index (TLI) 0.929
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -9233.985 -9233.985
## Scaling correction factor 2.310
## for the MLR correction
## Loglikelihood unrestricted model (H1) NA NA
## Scaling correction factor 1.813
## for the MLR correction
##
## Akaike (AIC) 18731.971 18731.971
## Bayesian (BIC) 19219.987 19219.987
## Sample-size adjusted Bayesian (SABIC) 18801.367 18801.367
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.088 0.056
## 90 Percent confidence interval - lower 0.084 0.052
## 90 Percent confidence interval - upper 0.092 0.059
## P-value H_0: RMSEA <= 0.050 0.000 0.005
## P-value H_0: RMSEA >= 0.080 0.999 0.000
##
## Robust RMSEA 0.072
## 90 Percent confidence interval - lower 0.066
## 90 Percent confidence interval - upper 0.078
## P-value H_0: Robust RMSEA <= 0.050 0.000
## P-value H_0: Robust RMSEA >= 0.080 0.014
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.031 0.031
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## LAge =~
## Q2_1 0.896 0.064 13.897 0.000 0.896 0.855
## Q3_3 0.820 0.077 10.588 0.000 0.820 0.891
## Q4_2 0.942 0.067 14.000 0.000 0.942 0.893
## Q5_2 0.921 0.071 13.027 0.000 0.921 0.866
## Info =~
## Q2_2 0.821 0.070 11.800 0.000 0.821 0.742
## Q3_4 0.972 0.067 14.584 0.000 0.972 0.940
## Q4_3 0.937 0.069 13.641 0.000 0.937 0.963
## Q5_4 0.877 0.075 11.754 0.000 0.877 0.934
## Outcomes =~
## Q2_3 0.829 0.068 12.194 0.000 0.829 0.878
## Q2_4 0.897 0.067 13.475 0.000 0.897 0.844
## Q2_5 0.920 0.068 13.543 0.000 0.920 0.862
## Q3_5 0.863 0.077 11.182 0.000 0.863 0.887
## Q3_6 0.812 0.077 10.528 0.000 0.812 0.842
## Q3_7 0.980 0.065 15.118 0.000 0.980 0.831
## Q4_4 0.925 0.074 12.497 0.000 0.925 0.928
## Q4_5 0.856 0.075 11.425 0.000 0.856 0.883
## Q4_6 0.952 0.070 13.643 0.000 0.952 0.928
## Q5_5 0.809 0.075 10.732 0.000 0.809 0.831
## Q5_6 0.794 0.075 10.581 0.000 0.794 0.880
## Topic =~
## Q2_6 0.879 0.072 12.177 0.000 0.879 0.833
## Q2_7 0.817 0.076 10.793 0.000 0.817 0.858
## Q3_8 0.968 0.067 14.466 0.000 0.968 0.724
## Q3_9 1.000 0.056 17.928 0.000 1.000 0.694
## Q4_7 1.014 0.062 16.403 0.000 1.014 0.871
## Q4_8 0.859 0.081 10.639 0.000 0.859 0.880
## Q5_7 0.946 0.073 13.039 0.000 0.946 0.915
## Q5_8 0.703 0.085 8.304 0.000 0.703 0.665
## Q5_9 0.784 0.075 10.417 0.000 0.784 0.893
## LParent =~
## Q2_8 0.871 0.066 13.233 0.000 0.871 0.899
## Q2_9 0.780 0.069 11.317 0.000 0.780 0.873
## Q3_10 0.900 0.066 13.725 0.000 0.900 0.851
## Q4_9 0.698 0.076 9.222 0.000 0.698 0.643
## Q5_10 0.823 0.069 11.881 0.000 0.823 0.858
## EdSuit =~
## Q3_1 0.936 0.067 14.021 0.000 0.936 0.913
## Q3_2 0.926 0.068 13.543 0.000 0.926 0.921
## Q4_1 0.935 0.071 13.099 0.000 0.935 0.940
## Q4_10 0.920 0.070 13.055 0.000 0.920 0.917
## Q5_1 0.909 0.068 13.353 0.000 0.909 0.881
## Q5_11 0.917 0.073 12.600 0.000 0.917 0.933
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## LAge ~~
## Info 0.992 0.009 106.897 0.000 0.992 0.992
## Outcomes 0.988 0.008 119.331 0.000 0.988 0.988
## Topic 0.983 0.010 101.649 0.000 0.983 0.983
## LParent 0.926 0.026 35.894 0.000 0.926 0.926
## EdSuit 0.989 0.010 98.999 0.000 0.989 0.989
## Info ~~
## Outcomes 0.974 0.009 104.935 0.000 0.974 0.974
## Topic 0.976 0.009 108.410 0.000 0.976 0.976
## LParent 0.891 0.031 29.176 0.000 0.891 0.891
## EdSuit 0.974 0.012 79.972 0.000 0.974 0.974
## Outcomes ~~
## Topic 1.004 0.004 243.025 0.000 1.004 1.004
## LParent 0.899 0.028 31.953 0.000 0.899 0.899
## EdSuit 0.978 0.009 107.392 0.000 0.978 0.978
## Topic ~~
## LParent 0.901 0.028 32.702 0.000 0.901 0.901
## EdSuit 0.973 0.011 87.692 0.000 0.973 0.973
## LParent ~~
## EdSuit 0.904 0.027 33.491 0.000 0.904 0.904
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Q2_1 4.275 0.061 70.405 0.000 4.275 4.078
## .Q3_3 4.483 0.053 84.091 0.000 4.483 4.871
## .Q4_2 4.322 0.061 70.787 0.000 4.322 4.101
## .Q5_2 4.369 0.062 70.883 0.000 4.369 4.106
## .Q2_2 4.208 0.064 65.628 0.000 4.208 3.802
## .Q3_4 4.386 0.060 73.221 0.000 4.386 4.242
## .Q4_3 4.463 0.056 79.187 0.000 4.463 4.587
## .Q5_4 4.503 0.054 82.830 0.000 4.503 4.798
## .Q2_3 4.440 0.055 81.208 0.000 4.440 4.704
## .Q2_4 4.282 0.062 69.578 0.000 4.282 4.031
## .Q2_5 4.289 0.062 69.393 0.000 4.289 4.020
## .Q3_5 4.460 0.056 79.138 0.000 4.460 4.584
## .Q3_6 4.489 0.056 80.291 0.000 4.489 4.657
## .Q3_7 4.228 0.068 61.890 0.000 4.228 3.585
## .Q4_4 4.463 0.058 77.292 0.000 4.463 4.477
## .Q4_5 4.497 0.056 80.009 0.000 4.497 4.635
## .Q4_6 4.406 0.059 74.112 0.000 4.406 4.293
## .Q5_5 4.419 0.056 78.347 0.000 4.419 4.539
## .Q5_6 4.510 0.052 86.312 0.000 4.510 5.000
## .Q2_6 4.369 0.061 71.522 0.000 4.369 4.143
## .Q2_7 4.456 0.055 80.829 0.000 4.456 4.682
## .Q3_8 4.074 0.077 52.615 0.000 4.074 3.048
## .Q3_9 3.815 0.083 45.705 0.000 3.815 2.648
## .Q4_7 4.228 0.067 62.650 0.000 4.228 3.629
## .Q4_8 4.540 0.057 80.283 0.000 4.540 4.651
## .Q5_7 4.423 0.060 73.826 0.000 4.423 4.277
## .Q5_8 4.362 0.061 71.255 0.000 4.362 4.128
## .Q5_9 4.557 0.051 89.635 0.000 4.557 5.192
## .Q2_8 4.349 0.056 77.492 0.000 4.349 4.489
## .Q2_9 4.453 0.052 86.045 0.000 4.453 4.984
## .Q3_10 4.248 0.061 69.311 0.000 4.248 4.015
## .Q4_9 4.205 0.063 66.927 0.000 4.205 3.877
## .Q5_10 4.379 0.056 78.816 0.000 4.379 4.566
## .Q3_1 4.369 0.059 73.547 0.000 4.369 4.260
## .Q3_2 4.403 0.058 75.537 0.000 4.403 4.376
## .Q4_1 4.419 0.058 76.735 0.000 4.419 4.445
## .Q4_10 4.406 0.058 75.822 0.000 4.406 4.392
## .Q5_1 4.322 0.060 72.333 0.000 4.322 4.190
## .Q5_11 4.477 0.057 78.561 0.000 4.477 4.551
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Q2_1 0.296 0.045 6.600 0.000 0.296 0.270
## .Q3_3 0.175 0.050 3.492 0.000 0.175 0.207
## .Q4_2 0.224 0.043 5.258 0.000 0.224 0.202
## .Q5_2 0.284 0.055 5.139 0.000 0.284 0.251
## .Q2_2 0.551 0.077 7.167 0.000 0.551 0.450
## .Q3_4 0.125 0.027 4.588 0.000 0.125 0.117
## .Q4_3 0.069 0.017 4.063 0.000 0.069 0.073
## .Q5_4 0.113 0.022 5.005 0.000 0.113 0.128
## .Q2_3 0.204 0.037 5.536 0.000 0.204 0.229
## .Q2_4 0.324 0.051 6.351 0.000 0.324 0.287
## .Q2_5 0.292 0.041 7.185 0.000 0.292 0.256
## .Q3_5 0.202 0.044 4.564 0.000 0.202 0.213
## .Q3_6 0.270 0.049 5.478 0.000 0.270 0.291
## .Q3_7 0.431 0.078 5.555 0.000 0.431 0.310
## .Q4_4 0.139 0.024 5.732 0.000 0.139 0.140
## .Q4_5 0.208 0.034 6.189 0.000 0.208 0.221
## .Q4_6 0.146 0.027 5.523 0.000 0.146 0.139
## .Q5_5 0.294 0.057 5.133 0.000 0.294 0.310
## .Q5_6 0.183 0.031 5.829 0.000 0.183 0.225
## .Q2_6 0.340 0.075 4.504 0.000 0.340 0.305
## .Q2_7 0.239 0.040 5.956 0.000 0.239 0.264
## .Q3_8 0.850 0.119 7.154 0.000 0.850 0.476
## .Q3_9 1.077 0.115 9.343 0.000 1.077 0.519
## .Q4_7 0.328 0.055 5.963 0.000 0.328 0.242
## .Q4_8 0.215 0.034 6.351 0.000 0.215 0.225
## .Q5_7 0.175 0.026 6.639 0.000 0.175 0.164
## .Q5_8 0.623 0.106 5.861 0.000 0.623 0.558
## .Q5_9 0.156 0.022 7.219 0.000 0.156 0.202
## .Q2_8 0.180 0.027 6.661 0.000 0.180 0.191
## .Q2_9 0.189 0.029 6.626 0.000 0.189 0.237
## .Q3_10 0.309 0.066 4.685 0.000 0.309 0.276
## .Q4_9 0.689 0.104 6.612 0.000 0.689 0.586
## .Q5_10 0.242 0.038 6.428 0.000 0.242 0.263
## .Q3_1 0.175 0.058 3.022 0.003 0.175 0.166
## .Q3_2 0.155 0.032 4.883 0.000 0.155 0.153
## .Q4_1 0.115 0.027 4.330 0.000 0.115 0.116
## .Q4_10 0.160 0.036 4.432 0.000 0.160 0.159
## .Q5_1 0.238 0.037 6.401 0.000 0.238 0.224
## .Q5_11 0.126 0.026 4.862 0.000 0.126 0.130
## LAge 1.000 1.000 1.000
## Info 1.000 1.000 1.000
## Outcomes 1.000 1.000 1.000
## Topic 1.000 1.000 1.000
## LParent 1.000 1.000 1.000
## EdSuit 1.000 1.000 1.000
dynamic::cfaHB(csek12six.fit, plot = TRUE)
## Error in dynamic::cfaHB(csek12six.fit, plot = TRUE): dynamic Error: One of your loadings or correlations has an absolute value of 1 or above (an impossible value). Please use standardized loadings. If all of your loadings are under 1, try looking for a missing decimal somewhere in your model statement.