The mediation package in R returns results in which:

- The Average Causal Mediated Effect (ACME) (the effect of the mediator alone) is positive and statistically significant
- Average Direct Effect (ADE) (the unmediated effect) and the Total Effect (ADE+ACME) is not statistically significant

Does this indicate that the IV positively influences my DV via my mediator, however there are other mediators through which the IV has a negative effect on the DV (and as a result, the total effect can't be differentiated from 0?).

Thank you for any help.

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#### Best Answer

This should not happen if you use only one mediator in the model. If there are other mediators for a negative hidden away, they should reflect in the direct effect.

But there are certain conditions under which it would happen. Most likely, the IV weakly relates or has no relation to the DV in these conditions.

`# first, some syntax for Sobel's test sobel <- function(mm, my) { a <- coef(mm)["x"] ase <- coef(summary(mm))["x", 2] b <- coef(my)["m"] bse <- coef(summary(my))["m", 2] abse <- sqrt(a ^ 2 * bse ^ 2 + b ^ 2 * ase ^ 2) # return ACME and z-test c(acme = a * b, z = a * b / abse, p = (1 - pnorm(a * b / abse)) * 2) } set.seed(899398) n <- 100 x <- rnorm(n) m <- rnorm(1) * x + rnorm(n) y <- rnorm(1) * m + rnorm(1) * x + rnorm(n) coef(summary(lm(y ~ x)))["x", ] # total effect not stat sig Estimate Std. Error t value Pr(>|t|) 0.2125229 0.1470346 1.4453944 0.1515374 coef(summary(mm <- lm(m ~ x)))["x", ] # IV affects MV Estimate Std. Error t value Pr(>|t|) 0.25667463 0.10464021 2.45292550 0.01593713 coef(summary(my <- lm(y ~ m + x))) # Only MV significant for DV Estimate Std. Error t value Pr(>|t|) (Intercept) -0.06147867 0.09632753 -0.6382253 5.248309e-01 m 1.08186228 0.09104230 11.8830723 1.241992e-20 x -0.06516366 0.09716138 -0.6706745 5.040217e-01 sobel(mm, my) # returns ACME, z and z-test of ACME acme.x z.x p.x 0.27768660 2.40227887 0.01629328 `

The conditions OP described are all present here. This is one example of how it could happen. In reality, the relationships are probably way more complicated but it definitely is possible. If you have a not statistically significant total effect and the mediated effect is statistically significant in a one mediator model, I'd doubt any mediation was going on.

The way I found this example was by using a search for the right seed:

`find <- FALSE tab <- 1:1e6 while (find == FALSE) { seed <- sample(tab, 1) set.seed(seed) n <- 100 x <- rnorm(n) m <- rnorm(1) * x + rnorm(n) y <- rnorm(1) * m + rnorm(1) * x + rnorm(n) te <- coef(summary(lm(y ~ x)))["x", 4] mm <- lm(m ~ x) dme <- coef(summary(my <- lm(y ~ m + x)))[c("m", "x"), 4] me <- dme[1]; de <- dme[2] ie <- sobel(mm, my)[3] if (te > .15 & de > .15 & ie < .04) { find <- TRUE } } `

It is possible to expand the search function to store all such conditions and study the patterns ACME, ADE and ATE that produce the OP's situation.

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