Mediation Analysis

It currently only support simple mediation analysis using the path analysis approach with the lavaan package. I am trying to implement multilevel mediation in lavaan. In the future, I will try supporting moderated mediation (through lavaan or mediation) and mediation with latent variable (through lavaan).

mediation_summary(
  data,
  response_variable,
  mediator,
  predictor_variable,
  control_variable = NULL,
  group = NULL,
  standardize = TRUE,
  digits = 3,
  quite = FALSE,
  streamline = FALSE,
  return_result = FALSE
)

Arguments

data

data.frame

response_variable

response variable. Support dplyr::select() syntax.

mediator

mediator. Support dplyr::select() syntax.

predictor_variable

predictor variable. Support dplyr::select() syntax.

control_variable

control variables / covariate. Support dplyr::select() syntax.

group

nesting variable for multilevel mediation. Not confident about the implementation method.

standardize

standardized coefficients. Default is TRUE

digits

number of digits to round to

quite

suppress printing output

streamline

print streamlined output

return_result

If it is set to TRUE, it will return the lavaan object

Value

an object from lavaan

Examples

mediation_summary(
  data = lmerTest::carrots,
  response_variable = Preference,
  mediator = Sweetness,
  predictor_variable = Crisp
)
#> Warning: The following columns are coerced into numeric: Consumer, Frequency, Gender, Age, Homesize, Work, Income, Product
#> Model Summary
#> Model Type = Mediation Analysis (fitted using lavaan)
#> 
#> Effect Summary
#> ────────────────────────────────────────────────────────────────
#>   Effect Type  Est.Std     SE       z          p          95% CI
#> ────────────────────────────────────────────────────────────────
#>        direct    0.197  0.029   6.880  0.000 ***  [0.141, 0.253]
#>      indirect    0.219  0.017  12.656  0.000 ***  [0.185, 0.253]
#>         total    0.416  0.023  18.444  0.000 ***  [0.372, 0.460]
#> ────────────────────────────────────────────────────────────────
#> *** p < 0.001, ** p < 0.01, * p < 0.05, . p < 0.1
#> 
#> Regression Summary
#> ────────────────────────────────────────────────────────────────────────────────────
#>     Response  Operator    Predict  Est.Std     SE       z          p          95% CI
#> ────────────────────────────────────────────────────────────────────────────────────
#>    Sweetness         ~      Crisp    0.558  0.018  30.893  0.000 ***  [0.523, 0.593]
#>   Preference         ~  Sweetness    0.392  0.028  14.177  0.000 ***  [0.338, 0.446]
#>   Preference         ~      Crisp    0.197  0.029   6.880  0.000 ***  [0.141, 0.253]
#> ────────────────────────────────────────────────────────────────────────────────────
#> *** p < 0.001, ** p < 0.01, * p < 0.05, . p < 0.1