In jeksterslabds/jeksterslabRdatarepo: Data Repository

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library(jeksterslabRdatarepo)
library(knitr)
library(lavaan)
library(semPlot)

Load Data

data(
  alienation,
  package = "jeksterslabRdatarepo"
)

Data Table

Variance-Covariance Matrix

kable(
  x = alienation
)

Model

Saturated Model

# model specification
model_cov <- "
  # covariances

  # col 1
  anomia67 ~~ anomia67
  anomia71 ~~ anomia67
  powerless67 ~~ anomia67
  powerless71 ~~ anomia67
  education ~~ anomia67
  sei ~~ anomia67

  # col 2
  anomia71 ~~ anomia71
  powerless67 ~~ anomia71
  powerless71 ~~ anomia71
  education ~~ anomia71
  sei ~~ anomia71

  # col 3
  powerless67 ~~ powerless67
  powerless71 ~~ powerless67
  education ~~ powerless67
  sei ~~ powerless67

  # col 4
  powerless71 ~~ powerless71
  education ~~ powerless71
  sei ~~ powerless71

  # col 5
  education ~~ education
  sei ~~ education

  # col 6
  sei ~~ sei

"
# model fitting
fit_cov <- sem(
  model_cov,
  sample.cov = alienation,
  sample.nobs = 932
)
# results
summary(fit_cov, fit.measures = TRUE, standardized = TRUE)
semPaths(fit_cov, what = "path", whatLabels = "est", style = "ram", layout = "circle")

Linear Regression Model

# model specification
model_regression <- "
  # regression
  powerless71 ~ powerless67 + education

  #-------------------------------------------------------
  # The syntax above is sufficient to specify this model.
  # The syntax below is added to be more explicit
  # about the parameters being estimated.
  #-------------------------------------------------------

  # variances of regressors
  powerless67 ~~ powerless67
  education ~~ education

  # covariance of regressors
  powerless67 ~~ education

  # residual variance
  powerless71 ~~ powerless71
"
# model fitting
fit_regression <- sem(
  model_regression,
  sample.cov = alienation,
  sample.nobs = 932
)
# results
summary(fit_regression, fit.measures = TRUE, standardized = TRUE)
semPaths(fit_regression, what = "path", whatLabels = "est", style = "ram")

Two Equation Path Model

# model specification
model_path <- "
  # regression
  anomia71 ~ anomia67 + powerless67
  powerless71 ~ anomia67 + powerless67

  #-------------------------------------------------------
  # The syntax above is sufficient to specify this model.
  # The syntax below is added to be more explicit
  # about the parameters being estimated.
  #-------------------------------------------------------

  # variances of regressors
  anomia67 ~~ anomia67
  powerless67 ~~ powerless67

  # covariance of regressors
  anomia67 ~~ powerless67

  # residual variances
  anomia71 ~~ anomia71
  powerless71 ~~ powerless71
"
# model fitting
fit_path <- sem(
  model_path,
  sample.cov = alienation,
  sample.nobs = 932
)
# results
summary(fit_path, fit.measures = TRUE, standardized = TRUE)
semPaths(object = fit_path, what = "path", whatLabels = "est", style = "ram")

Factor Model

One-Factor Model with Two Indicators

# model specification
model_cfa_1 <- "
  # measurement model
  alienation =~ 1 * anomia67 + powerless67

  #-------------------------------------------------------
  # The syntax above is sufficient to specify this model.
  # The syntax below is added to be more explicit
  # about the parameters being estimated.
  #-------------------------------------------------------

  # residual variances
  anomia67 ~~ anomia67
  powerless67 ~~ powerless67
  alienation ~~ alienation
"
# model fitting
fit_cfa_1 <- sem(
  model_cfa_1,
  sample.cov = alienation,
  sample.nobs = 932
)
# results
summary(fit_cfa_1, fit.measures = TRUE, standardized = TRUE)
semPaths(fit_cfa_1, what = "path", whatLabels = "est", style = "ram")

One-Factor Model with Four Indicators

# model specification
model_cfa_2 <- "
  # measurement model
  alienation =~ 1 * anomia67 + powerless67 + anomia71 + powerless71

  #-------------------------------------------------------
  # The syntax above is sufficient to specify this model.
  # The syntax below is added to be more explicit
  # about the parameters being estimated.
  #-------------------------------------------------------

  # residual variances
  anomia67 ~~ anomia67
  powerless67 ~~ powerless67
  anomia71 ~~ anomia71
  powerless71 ~~ powerless71
  alienation ~~ alienation
"
# model fitting
fit_cfa_2 <- sem(
  model_cfa_2,
  sample.cov = alienation,
  sample.nobs = 932
)
# results
summary(fit_cfa_2, fit.measures = TRUE, standardized = TRUE)
semPaths(fit_cfa_2, what = "path", whatLabels = "est", style = "ram")

Two-Factor Model - Parameterization 1

# model specification
model_cfa_3 <- "
  # measurement model
  alienation67 =~ 1 * anomia67 + powerless67
  alienation71 =~ 1 * anomia71 + powerless71

  #-------------------------------------------------------
  # The syntax above is sufficient to specify this model.
  # The syntax below is added to be more explicit
  # about the parameters being estimated.
  #-------------------------------------------------------

  # covariance of factors
  alienation67 ~~ alienation71

  # variances of factors
  alienation67 ~~ alienation67
  alienation71 ~~ alienation71

  # residual variances
  anomia67 ~~ anomia67
  powerless67 ~~ powerless67
  anomia71 ~~ anomia71
  powerless71 ~~ powerless71
"
# model fitting
fit_cfa_3 <- sem(
  model_cfa_3,
  sample.cov = alienation,
  sample.nobs = 932
)
# results
summary(fit_cfa_3, fit.measures = TRUE, standardized = TRUE)
semPaths(fit_cfa_3, what = "path", whatLabels = "est", style = "ram")

Two-Factor Model - Parameterization 2

# model specification
model_cfa_4 <- "
  # measurement model
  alienation67 =~ NA * anomia67 + powerless67
  alienation71 =~ NA * anomia71 + powerless71

  # variances of factors
  alienation67 ~~ 1 * alienation67
  alienation71 ~~ 1 * alienation71

  #-------------------------------------------------------
  # The syntax above is sufficient to specify this model.
  # The syntax below is added to be more explicit
  # about the parameters being estimated.
  #-------------------------------------------------------

  # covariance of factors
  alienation67 ~~ alienation71

  # measurement error variances
  anomia67 ~~ anomia67
  powerless67 ~~ powerless67
  anomia71 ~~ anomia71
  powerless71 ~~ powerless71
"
# model fitting
fit_cfa_4 <- sem(
  model_cfa_4,
  sample.cov = alienation,
  sample.nobs = 932
)
# results
summary(fit_cfa_4, fit.measures = TRUE, standardized = TRUE)
semPaths(fit_cfa_4, what = "path", whatLabels = "est", style = "ram")

Two-Factor Model - Correlated Measurement Errors

# model specification
model_cfa_5 <- "
  # measurement model
  alienation67 =~ 1 * anomia67 + powerless67
  alienation71 =~ 1 * anomia71 + powerless71

  # measurement error covariances
  anomia67 ~~ anomia71
  powerless67 ~~ powerless71

  #-------------------------------------------------------
  # The syntax above is sufficient to specify this model.
  # The syntax below is added to be more explicit
  # about the parameters being estimated.
  #-------------------------------------------------------

  # covariance of factors
  alienation67 ~~ alienation71

  # variances of factors
  alienation67 ~~ alienation67
  alienation71 ~~ alienation71

  # measurement error variances
  anomia67 ~~ anomia67
  powerless67 ~~ powerless67
  anomia71 ~~ anomia71
  powerless71 ~~ powerless71
"
# model fitting
fit_cfa_5 <- sem(
  model_cfa_5,
  sample.cov = alienation,
  sample.nobs = 932
)
# results
summary(fit_cfa_5, fit.measures = TRUE, standardized = TRUE)
semPaths(fit_cfa_5, what = "path", whatLabels = "est", style = "ram")

Complete Latent Variable Model

# model specification
model_cfa_6 <- "
  # measurement model
  alienation67 =~ 1 * anomia67 + powerless67
  alienation71 =~ 1 * anomia71 + powerless71
  ses =~ 1 * education + sei 

  # measurement error covariances
  anomia67 ~~ anomia71
  powerless67 ~~ powerless71

  # regression
  alienation71 ~ alienation67 + ses
  alienation67 ~ ses

  #-------------------------------------------------------
  # The syntax above is sufficient to specify this model.
  # The syntax below is added to be more explicit
  # about the parameters being estimated.
  #-------------------------------------------------------

  # variances of factors
  ses ~~ ses

  # residual variances of factors
  alienation67 ~~ alienation67
  alienation71 ~~ alienation71

  # measurement error variances
  anomia67 ~~ anomia67
  powerless67 ~~ powerless67
  anomia71 ~~ anomia71
  powerless71 ~~ powerless71
"
# model fitting
fit_cfa_6 <- sem(
  model_cfa_6,
  sample.cov = alienation,
  sample.nobs = 932
)
# results
summary(fit_cfa_6, fit.measures = TRUE, standardized = TRUE)
semPaths(fit_cfa_6, what = "path", whatLabels = "est", style = "ram")

Second-Order Factor Model

# model specification
model_cfa_7 <- "
  # measurement model
  alienation67 =~ 1 * anomia67 + powerless67
  alienation71 =~ 1 * anomia71 + powerless71
  alienation =~ a * alienation67 + a * alienation71

  # measurement error covariances
  anomia67 ~~ anomia71
  powerless67 ~~ powerless71

  #-------------------------------------------------------
  # The syntax above is sufficient to specify this model.
  # The syntax below is added to be more explicit
  # about the parameters being estimated.
  #-------------------------------------------------------

  # variance of second-order factor
  alienation ~~ alienation

  # residual variances of factors
  alienation67 ~~ alienation67
  alienation71 ~~ alienation71
"
# model fitting
fit_cfa_7 <- sem(
  model_cfa_7,
  sample.cov = alienation,
  sample.nobs = 932
)
# results
summary(fit_cfa_7, fit.measures = TRUE, standardized = TRUE)
semPaths(fit_cfa_7, what = "path", whatLabels = "est", style = "ram")

jeksterslabds/jeksterslabRdatarepo documentation built on Jan. 5, 2021, 3:27 a.m.