ddsc_sem {multid}

Deconstructing difference score correlation with structural equation modeling

Description

Deconstructs a bivariate association between x and a difference score y1-y2 with SEM. A difference score correlation is indicative that slopes for y1 as function of x and y2 as function of x are non-parallel. Deconstructing the bivariate association to these slopes allows for understanding the pattern and magnitude of this non-parallelism.

Usage

ddsc_sem(
  data,
  x,
  y1,
  y2,
  center_yvars = FALSE,
  covariates = NULL,
  estimator = "ML",
  level = 0.95,
  sampling.weights = NULL,
  q_sesoi = 0,
  min_cross_over_point_location = 0,
  boot_ci = FALSE,
  boot_n = 5000,
  boot_ci_type = "perc"
)

Arguments

data

A data frame.

x

Character string. Variable name of independent variable.

y1

Character string. Variable name of first component score of difference score.

y2

Character string. Variable name of second component score of difference score.

center_yvars

Logical. Should y1 and y2 be centered around their grand mean? (Default FALSE)

covariates

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

estimator

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

level

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

sampling.weights

Character string. Name of sampling weights variable.

q_sesoi

Numeric. The smallest effect size of interest for Cohen's q estimates (Default 0; See Lakens et al. 2018).

min_cross_over_point_location

Numeric. Z-score for the minimal slope cross-over point of interest (Default 0).

boot_ci

Logical. Calculate confidence intervals based on bootstrap (Default FALSE).

boot_n

Numeric. How many bootstrap redraws (Default 5000).

boot_ci_type

If bootstrapping was used, the type of interval required. The value should be one of "norm", "basic", "perc" (default), or "bca.simple".

Value

descriptives

Means, standard deviations, and intercorrelations.

parameter_estimates

Parameter estimates from the structural equation model.

variance_test

Variances and covariances of component scores.

data

Data frame with original and scaled variables used in SEM.

results

Summary of key results.

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.

Lakens, D., Scheel, A. M., & Isager, P. M. (2018). Equivalence Testing for Psychological Research: A Tutorial. Advances in Methods and Practices in Psychological Science, 1(2), 259–269. https://doi.org/10.1177/2515245918770963

Examples

## Not run: 
set.seed(342356)
d <- data.frame(
  y1 = rnorm(50),
  y2 = rnorm(50),
  x = rnorm(50)
)
ddsc_sem(
  data = d, y1 = "y1", y2 = "y2",
  x = "x",
  q_sesoi = 0.20,
  min_cross_over_point_location = 1
)$results

## End(Not run)