multid (version 1.0.2)

sem_dadas: Predicting algebraic difference scores in structural equation model

Description

Predicting algebraic difference scores in structural equation model

Usage

sem_dadas(
  data,
  var1,
  var2,
  center = FALSE,
  scale = FALSE,
  predictor,
  covariates = NULL,
  estimator = "MLR",
  level = 0.95,
  sampling.weights = NULL,
  abs_coef_diff_test = 0
)

Value

descriptives

Means, standard deviations, and intercorrelations.

parameter_estimates

Parameter estimates from the structural equation model.

variance_test

Variances and covariances of component scores.

transformed_data

Data frame with variables used in SEM.

dadas

One sided dadas-test for positivity of abs(b_11-b_21)-abs(b_11+b_21).

results

Summary of key results.

Arguments

data

A data frame.

var1

Character string. Variable name of first component score of difference score (Y_1).

var2

Character string. Variable name of second component score of difference score (Y_2).

center

Logical. Should var1 and var2 be centered around their grand mean? (Default FALSE)

scale

Logical. Should var1 and var2 be scaled with their pooled sd? (Default FALSE)

predictor

Character string. Variable name of independent variable predicting difference score.

covariates

Character string or vector. Variable names of covariates (Default NULL).

estimator

Character string. Estimator used in SEM (Default "MLR").

level

Numeric. The confidence level required for the result output (Default .95)

sampling.weights

Character string. Name of sampling weights variable.

abs_coef_diff_test

Numeric. A value against which absolute difference between component score predictions is tested (Default 0).

References

Edwards, J. R. (1995). Alternatives to Difference Scores as Dependent Variables in the Study of Congruence in Organizational Research. Organizational Behavior and Human Decision Processes, 64(3), 307–324.

Examples

if (FALSE) {
set.seed(342356)
d <- data.frame(
  var1 = rnorm(50),
  var2 = rnorm(50),
  x = rnorm(50)
)
sem_dadas(
  data = d, var1 = "var1", var2 = "var2",
  predictor = "x", center = TRUE, scale = TRUE,
  abs_coef_diff_test = 0.20
)$results
}