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multid (version 1.0.2)

value_correlation: Testing and quantifying how much ipsatization (profile centering) influence associations between value and a correlate

Description

Testing and quantifying how much ipsatization (profile centering) influence associations between value and a correlate

Usage

value_correlation(
  data,
  rv,
  cf,
  correlate,
  scale_by_rv = FALSE,
  standardize_correlate = FALSE,
  estimator = "ML",
  level = 0.95,
  sampling.weights = NULL,
  sesoi = 0
)

Value

parameter_estimates

Parameter estimates from the structural equation model.

transformed_data

Data frame with variables used in SEM (after scaling is applied).

results

Summary of key results.

Arguments

data

A data frame.

rv

Character string or vector. Variable name(s) of the non-ipsatized value variable(s) (raw value score).

cf

Character string. Variable name of the common factor that is used for ipsatizing raw value scores.

correlate

Character string. Name of the variable to which associations with values are examined.

scale_by_rv

Logical. Is standard deviation of the raw non-ipsatized value score used for scaling the common factor as well? (Default FALSE)

standardize_correlate

Logical. Should the correlate be standardized? (Default FALSE)

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.

sesoi

Numeric. Smallest effect size of interest. Used for equivalence testing differences in ipsatized and non-ipsatized value associations (Default 0).

Examples

Run this code
if (FALSE) {
set.seed(342356)
d <- data.frame(
 rv1 = rnorm(50),
 rv2 = rnorm(50),
 rv3 = rnorm(50),
 rv4 = rnorm(50),
 x = rnorm(50)
)
d$cf<-rowMeans(d[,c("rv1","rv2","rv3","rv4")])
fit<-value_correlation(
 data = d, rv = c("rv1","rv2","rv3","rv4"), cf = "cf",
 correlate = "x",scale_by_rv = TRUE,
 standardize_correlate = TRUE,
 sesoi = 0.10
)
round(fit$variability_summary,3)
round(fit$association_summary,3)
}

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