Model the WAIS-III IQ Scale Erin Buchanan Professor DataCamp - - PowerPoint PPT Presentation

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Mar 30, 2023 518 likes •751 views

DataCamp Structural Equation Modeling with lavaan in R STRUCTURAL EQUATION MODELING WITH LAVAAN IN R Model the WAIS-III IQ Scale Erin Buchanan Professor DataCamp Structural Equation Modeling with lavaan in R DataCamp Structural Equation

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DataCamp Structural Equation Modeling with lavaan in R

Model the WAIS-III IQ Scale

STRUCTURAL EQUATION MODELING WITH LAVAAN IN R

Erin Buchanan

Professor

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DataCamp Structural Equation Modeling with lavaan in R

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DataCamp Structural Equation Modeling with lavaan in R

WAIS-III Model

The WAIS-III has four latent variables measured by 12 manifest variables. The model also includes a second layer of latent variables: Verbal IQ predicts verbal comprehension and working memory. Perceptual IQ predicts perceptual organization and processing speed. These factors are likely highly correlated.

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DataCamp Structural Equation Modeling with lavaan in R

How to Get Started

Build the first level of latents. Make sure the four factors of the WAIS-III run properly. Check for Heywood cases and bad fit. Add the second level of latents.

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DataCamp Structural Equation Modeling with lavaan in R

Let's practice!

STRUCTURAL EQUATION MODELING WITH LAVAAN IN R

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DataCamp Structural Equation Modeling with lavaan in R

Update the WAIS-III Model

STRUCTURAL EQUATION MODELING WITH LAVAAN IN R

Erin Buchanan

Professor

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DataCamp Structural Equation Modeling with lavaan in R

Three-Factor WAIS-III

semPaths(object = wais.fit, layout = "tree", rotation = 1, whatLabels = "std", edge.label.cex = 1, what = "std", edge.color = "black")

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DataCamp Structural Equation Modeling with lavaan in R

Factor Loadings

summary(wais.fit, standardized = TRUE, fit.measures = TRUE) Latent Variables: Estimate Std.Err z-value P(>|z|) Std.lv Std.all verbalcomp =~ vocab 1.000 6.281 0.879 simil 0.296 0.031 9.483 0.000 1.861 0.581 inform 0.449 0.043 10.481 0.000 2.822 0.644 compreh 0.315 0.035 8.999 0.000 1.981 0.552 workingmemory =~ arith 1.000 2.528 0.844 digspan 0.881 0.152 5.786 0.000 2.227 0.565 lnseq 0.205 0.107 1.920 0.055 0.518 0.129 perceptorg =~ piccomp 1.000 1.517 0.650 block 3.739 0.390 9.583 0.000 5.672 0.735 matrixreason 0.832 0.117 7.099 0.000 1.262 0.493 digsym 1.603 0.507 3.160 0.002 2.431 0.207 symbolsearch 1.880 0.204 9.236 0.000 2.852 0.690

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DataCamp Structural Equation Modeling with lavaan in R

Variances

Variances: Estimate Std.Err z-value P(>|z|) Std.lv Std.all .vocab 11.577 2.651 4.367 0.000 11.577 0.227 .simil 6.787 0.620 10.950 0.000 6.787 0.662 .inform 11.218 1.085 10.342 0.000 11.218 0.585 .compreh 8.962 0.803 11.155 0.000 8.962 0.696 .arith 2.571 1.014 2.535 0.011 2.571 0.287 .digspan 10.590 1.161 9.121 0.000 10.590 0.681 .lnseq 15.807 1.297 12.183 0.000 15.807 0.983 .piccomp 3.138 0.317 9.913 0.000 3.138 0.577 .block 27.343 3.226 8.476 0.000 27.343 0.459 .matrixreason 4.960 0.441 11.243 0.000 4.960 0.757 .digsym 132.291 10.925 12.109 0.000 132.291 0.957 .symbolsearch 8.936 0.957 9.333 0.000 8.936 0.524 verbalcomp 39.455 4.754 8.299 0.000 1.000 1.000 workingmemory 6.388 1.215 5.259 0.000 1.000 1.000 perceptorg 2.301 0.408 5.646 0.000 1.000 1.000 var(IQdata$digsym) [1] 138.665

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DataCamp Structural Equation Modeling with lavaan in R

Fit Indices

User model versus baseline model: Comparative Fit Index (CFI) 0.793 Tucker-Lewis Index (TLI) 0.733 __ __ __ Root Mean Square Error of Approximation: RMSEA 0.115 90 Percent Confidence Interval 0.101 0.129 P-value RMSEA <= 0.05 0.000 Standardized Root Mean Square Residual: SRMR 0.076

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DataCamp Structural Equation Modeling with lavaan in R

Modification Indices

modificationindices(wais.fit, sort = TRUE) lhs op rhs mi epc sepc.lv sepc.all sepc.nox 66 simil ~~ inform 35.879 -3.757 -3.757 -0.268 -0.268 56 vocab ~~ inform 28.377 9.783 9.783 0.313 0.313 48 perceptorg =~ vocab 21.865 -2.077 -3.151 -0.441 -0.441 115 block ~~ matrixreason 16.209 -3.622 -3.622 -0.183 -0.183 96 arith ~~ block 15.061 3.679 3.679 0.159 0.159 117 block ~~ symbolsearch 13.144 5.725 5.725 0.180 0.180 47 workingmemory =~ symbolsearch 12.272 -0.467 -1.181 -0.286 -0.286 81 inform ~~ block 12.269 4.358 4.358 0.129 0.129 64 vocab ~~ digsym 11.578 -11.261 -11.261 -0.134 -0.134 40 workingmemory =~ simil 11.383 0.278 0.703 0.220 0.220 72 simil ~~ block 10.605 -3.084 -3.084 -0.125 -0.125 45 workingmemory =~ matrixreason 9.685 0.267 0.675 0.264 0.264

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DataCamp Structural Equation Modeling with lavaan in R

Let's practice!

STRUCTURAL EQUATION MODELING WITH LAVAAN IN R

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DataCamp Structural Equation Modeling with lavaan in R

A Hierarchical Model of IQ

STRUCTURAL EQUATION MODELING WITH LAVAAN IN R

Erin Buchanan

Professor

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DataCamp Structural Equation Modeling with lavaan in R

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DataCamp Structural Equation Modeling with lavaan in R

Model Specification

#updated model with correlated error from exercise wais.model2 <- 'verbalcomp =~ vocab + simil + inform + compreh workingmemory =~ arith + digspan + lnseq perceptorg =~ piccomp + block + matrixreason + digsym + symbolsearch simil ~~ inform' #updated model with hierarchy added wais.model3 <- 'verbalcomp =~ vocab + simil + inform + compreh workingmemory =~ arith + digspan + lnseq perceptorg =~ piccomp + block + matrixreason + digsym + symbolsearch simil ~~ inform general =~ verbalcomp + workingmemory + perceptorg' #regular model wais.fit2 <- cfa(model = wais.model2, data = IQdata) summary(wais.fit2) #hierarchical model wais.fit3 <- cfa(model = wais.model3, data = IQdata) summary(wais.fit3)

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DataCamp Structural Equation Modeling with lavaan in R

No Change in Model Fit

#regular model fitmeasures(wais.fit2, c("cfi", "tli")) cfi tli 0.833 0.780 #hierarchical model fitmeasures(wais.fit3, c("cfi", "tli")) cfi tli 0.833 0.780

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DataCamp Structural Equation Modeling with lavaan in R

Why Use Hierarchical Models

summary(wais.fit2, standardized = TRUE, fit.measures = TRUE) Covariances: Estimate Std.Err z-value P(>|z|) Std.lv Std.all verbalcomp ~~ workingmemory 6.278 1.181 5.315 0.000 0.416 0.416 perceptorg 5.654 0.859 6.583 0.000 0.634 0.634 workingmemory ~~ perceptorg 2.237 0.363 6.172 0.000 0.576 0.576 summary(wais.fit3, standardized = TRUE, fit.measures = TRUE) Latent Variables: Estimate Std.Err z-value P(>|z|) Std.lv Std.all general =~ verbalcomp 1.000 0.676 0.676 workingmemory 0.396 0.060 6.635 0.000 0.615 0.615 perceptorg 0.356 0.062 5.713 0.000 0.937 0.937

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DataCamp Structural Equation Modeling with lavaan in R

Let's practice!

STRUCTURAL EQUATION MODELING WITH LAVAAN IN R

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DataCamp Structural Equation Modeling with lavaan in R

Course Wrap Up

STRUCTURAL EQUATION MODELING WITH LAVAAN IN R

Erin Buchanan

Professor

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DataCamp Structural Equation Modeling with lavaan in R

What You've Learned

Model Syntax

=~ to define latent variables ~~ to define covariance and

correlation

~ to define direct prediction

Model Types One-Factor Models Multifactor Models Hierarchical Models Data Visualization

semPlot library

Rotations, layout, and font sizes Enhanced coloring

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DataCamp Structural Equation Modeling with lavaan in R

Congratulations!

STRUCTURAL EQUATION MODELING WITH LAVAAN IN R