R/mdt_within_index.R
add_index.within_participant_mediation.Rd
Adds the confidence interval for the index of
within-participant mediation to a model fitted with
mdt_within
or mdt_within_wide
.
# S3 method for class 'within_participant_mediation'
add_index(mediation_model, times = 5000, level = 0.05, ...)
Indirect effect index for within-participant mediation uses \(a\) and \(b\) estimates and their standard error to compute the \(ab\) product distribution using Monte Carlo methods (see MacKinnon, Lockwood, & Williams, 2004).
MacKinnon, D. P., Lockwood, C. M., & Williams, J. (2004). Confidence Limits for the Indirect Effect: Distribution of the Product and Resampling Methods. Multivariate Behavioral Research, 39(1), 99-128. doi: 10.1207/s15327906mbr3901_4
## getting an indirect effect index
within_model <- mdt_within(data = dohle_siegrist,
IV = name,
DV = willingness,
M = hazardousness,
grouping = participant)
add_index(within_model)
#> Test of mediation (within-participant_mediation)
#> ==============================================
#>
#> Variables:
#>
#> - IV: name (difference: simple - complex)
#> - DV: willingness
#> - M: hazardousness
#>
#> Paths:
#>
#> ==== ============== ===== ======================
#> Path Point estimate SE APA
#> ==== ============== ===== ======================
#> a -0.800 0.258 t(21) = 3.10, p = .005
#> b -0.598 0.113 t(19) = 5.29, p < .001
#> c 0.564 0.193 t(21) = 2.92, p = .008
#> c' 0.085 0.158 t(19) = 0.54, p = .596
#> ==== ============== ===== ======================
#>
#> Indirect effect index:
#>
#> - type: Within-participant indirect effect
#> - point estimate: 0.479
#> - confidence interval:
#> - method: Monte Carlo (5000 iterations)
#> - level: 0.05
#> - CI: [0.165; 0.867]
#>
#> Fitted models:
#>
#> - 1 -> DV_diff
#> - 1 -> M_diff
#> - 1 + M_diff + M_mean -> DV_diff