Heywood Cases on the Latent Variable Erin Buchanan Professor - - PowerPoint PPT Presentation

▶

heywood cases on the latent variable

Heywood Cases on the Latent Variable Erin Buchanan Professor - - PowerPoint PPT Presentation

Feb 01, 2024 541 likes •775 views

DataCamp Structural Equation Modeling with lavaan in R STRUCTURAL EQUATION MODELING WITH LAVAAN IN R Heywood Cases on the Latent Variable Erin Buchanan Professor DataCamp Structural Equation Modeling with lavaan in R Heywood Cases

slide-1

SLIDE 1

DataCamp Structural Equation Modeling with lavaan in R

Heywood Cases on the Latent Variable

STRUCTURAL EQUATION MODELING WITH LAVAAN IN R

Erin Buchanan

Professor

slide-2

SLIDE 2

DataCamp Structural Equation Modeling with lavaan in R

Heywood Cases

Correlations that are out of bounds Negative variances

slide-3

SLIDE 3

DataCamp Structural Equation Modeling with lavaan in R

A Latent Variable Example

epi.model <- 'extraversion =~ V3 + V7 + V11 + V15 neuroticism =~ V1 + V5 + V9 + V13 lying =~ V4 + V8 + V12 + V16' epi.fit <- cfa(model = epi.model, data = epi) Warning message: In lav_object_post_check(object) : lavaan WARNING: covariance matrix of latent variables is not positive definite; use inspect(fit,"cov.lv") to investigate.

slide-4

SLIDE 4

DataCamp Structural Equation Modeling with lavaan in R

How to Fix the Error

Warning message: In lav_object_post_check(object) : lavaan WARNING: covariance matrix of latent variables is not positive definite; use inspect(fit,"cov.lv") to investigate. summary(epi.fit, standardized = TRUE, fit.measures = TRUE) Covariances: Estimate Std.Err z-value P(>|z|) Std.lv Std.all extraversion ~~ neuroticism -0.011 0.002 -6.822 0.000 -0.894 -0.894 lying -0.012 0.002 -6.801 0.000 -0.777 -0.777 neuroticism ~~ lying 0.012 0.002 7.023 0.000 0.982 0.982

slide-5

SLIDE 5

DataCamp Structural Equation Modeling with lavaan in R

How to Fix the Error

#original model epi.model <- 'extraversion =~ V3 + V7 + V11 + V15 neuroticism =~ V1 + V5 + V9 + V13 lying =~ V4 + V8 + V12 + V16' #respecify the model epi.model2 <- 'extraversion =~ V3 + V7 + V11 + V15 neuroticism_lie =~ V1 + V5 + V9 + V13 + V4 + V8 + V12 + V16' epi.fit2 <- cfa(model = epi.model2, data = epi) summary(epi.fit2, standardized = T, fit.measures = T) Covariances: Estimate Std.Err z-value P(>|z|) Std.lv Std.all extraversion ~~ neuroticism_li -0.011 0.002 -6.939 0.000 -0.843 -0.843

slide-6

SLIDE 6

DataCamp Structural Equation Modeling with lavaan in R

Let's practice!

STRUCTURAL EQUATION MODELING WITH LAVAAN IN R

slide-7

SLIDE 7

DataCamp Structural Equation Modeling with lavaan in R

Heywood Cases on the Manifest Variables

STRUCTURAL EQUATION MODELING WITH LAVAAN IN R

Erin Buchanan

Professor

slide-8

SLIDE 8

DataCamp Structural Equation Modeling with lavaan in R

Why Negative Variances?

Reasons a negative variance might occur: The model might be misspecified or under identified Smaller sample size or sampling fluctuations Manifest variables have different scales Data is skewed or otherwise non-normal

slide-9

SLIDE 9

DataCamp Structural Equation Modeling with lavaan in R

Negative Variance Example

negative.model <- 'latent1 =~ V1 + V2 + V3 latent2 =~ V4 + V5 + V6' negative.fit <- cfa(negative.model, data = negative_data) Warning message: In lavaan::lavaan(model = negative.model, data = negative_data, : lavaan WARNING: model has NOT converged!

slide-10

SLIDE 10

DataCamp Structural Equation Modeling with lavaan in R

Negative Variance Example (2)

summary(negative.fit, standardized = TRUE, fit.measures = TRUE, rsquare = TRUE) ** WARNING ** lavaan (0.5-23.1097) did NOT converge after 10000 iterations ** WARNING ** Estimates below are most likely unreliable Variances: Estimate Std.Err z-value P(>|z|) Std.lv Std.all .V1 9.990 NA 9.990 0.999 .V2 -3949.334 NA -3949.334 -211.745 .V3 20.620 NA 20.620 1.000 .V4 14.999 NA 14.999 1.000 .V5 11885.910 NA 11885.910 615.668 .V6 11.185 NA 11.185 1.000 latent1 0.006 NA 1.000 1.000 latent2 -0.001 NA NaN NaN

slide-11

SLIDE 11

DataCamp Structural Equation Modeling with lavaan in R

Negative Variance Example (3)

R-Square: Estimate V1 0.001 V2 NA V3 0.000 V4 -0.000 V5 -614.668 V6 -0.000 var(negative_data$V2) [1] 18.83833

slide-12

SLIDE 12

DataCamp Structural Equation Modeling with lavaan in R

Negative Variance Example (4)

negative.model <- 'latent1 =~ V1 + V2 + V3 latent2 =~ V4 + V5 + V6 V2 ~~ 18.83833*V2' negative.fit <- cfa(negative.model, data = negative_data) summary(negative.fit, standardized = TRUE, fit.measures = TRUE, rsquare = TRUE) Variances: Estimate Std.Err z-value P(>|z|) Std.lv Std.all .V2 18.838 18.838 0.962 .V1 7.655 1.145 6.687 0.000 7.655 0.772 .V3 16.100 1.580 10.189 0.000 16.100 0.848 .V4 13.866 1.017 13.638 0.000 13.866 0.876 .V5 8.851 5.472 1.617 0.106 8.851 0.400 .V6 12.336 0.599 20.596 0.000 12.336 0.956 latent1 2.261 1.128 2.004 0.045 1.000 1.000 latent2 1.956 0.875 2.236 0.025 1.000 1.000

slide-13

SLIDE 13

DataCamp Structural Equation Modeling with lavaan in R

Negative Variance Example (5)

R-Square: Estimate V2 0.038 V1 0.228 V3 0.152 V4 0.124 V5 0.600 V6 0.044

slide-14

SLIDE 14

DataCamp Structural Equation Modeling with lavaan in R

Let's practice!

STRUCTURAL EQUATION MODELING WITH LAVAAN IN R

slide-15

SLIDE 15

DataCamp Structural Equation Modeling with lavaan in R

Create Diagrams with semPaths()

STRUCTURAL EQUATION MODELING WITH LAVAAN IN R

Erin Buchanan

Professor

slide-16

SLIDE 16

DataCamp Structural Equation Modeling with lavaan in R

semPlot and semPaths()

#load libraries library(lavaan) library(semPlot) #run your model twofactor.model <- 'text =~ x4 + x5 + x6 speed =~ x7 + x8 + x9' twofactor.fit <- cfa(model = twofactor.model, data = HolzingerSwineford1939) #basic diagram semPaths(object = twofactor.fit)

slide-17

SLIDE 17

DataCamp Structural Equation Modeling with lavaan in R

semPaths() Default Output

slide-18

SLIDE 18

DataCamp Structural Equation Modeling with lavaan in R

Editing the Picture

semPaths(object = twofactor.fit, whatLabels = "std", edge.label.cex = 1) #whatLabels can also be "par"

slide-19

SLIDE 19

DataCamp Structural Equation Modeling with lavaan in R

Picture Layout

semPaths(object = twofactor.fit, whatLabels = "std", edge.label.cex = 1, layout = "circle") #layout options are tree, circle, spring, tree2, circle2

slide-20

SLIDE 20

DataCamp Structural Equation Modeling with lavaan in R

Picture Rotation

semPaths(object = twofactor.fit, whatLabels = "std", edge.label.cex = 1, layout = "tree", rotation = 2) #layout options are tree, circle, spring, tree2, circle2 #rotation options are 1, 2, 3, 4 for tree layouts

slide-21

SLIDE 21

DataCamp Structural Equation Modeling with lavaan in R

Color Visualization

semPaths(object = twofactor.fit, whatLabels = "std", edge.label.cex = 1, layout = "tree", rotation = 2, what = "std", edge.color = "purple") #what options include par and std

slide-22

SLIDE 22

DataCamp Structural Equation Modeling with lavaan in R

Let's practice!

STRUCTURAL EQUATION MODELING WITH LAVAAN IN R