Mediation via regression analysis - b path non-signifcant - how do I interpret? I'm doing mediation analysis using hierarchical regression, using Baron and Kenny's 4 steps. I have one IV (mental health stereotype activation), one mediator (rejection expectation), and one DV (comfort with disclosure).
Path c (IV -> DV) is significant, 
Path a (IV -> M) is significant, and 
Path b (M -> DV) is significant
In the second step, when I add the M to the IV, Path c' (IV -> DV) is significant, but the link between M and DV is non-significant.
Does this mean that M is not a mediator? Does it also mean that IV -> DV is significant even when M is controlled for?
A second related question, I have a variable that I would like to control for (well-being). Can I do mediation analysis using hierarchical regression, whilst controlling for another variable? To put this in context, I hypothesis that stereotypes cause discomfort with disclosure partly because people expect rejection. However, I believe this will be affected by how unwell one is (this factor does correlate with IV, M and DV). Is this a moderator?
I'm a novice in statistics, so this is all quite new to me and many of the tutorials I'm reading have significant outcomes.
 A: If you go back to Baron and Kenny's 1986 article, you will see that they outline the following conditions for mediation. 


*

*The IV is related to the DV (seems to be met in your case)

*The IV is related to the mediator (M). 

*M is related to the DV, controlling for the IV. 

*The relation between the IV and the DV is appreciably weakened (may or may not be reduced to non-significance, but the effect is not as strong when M is in the equation). 


If any one of those 4 conditions does not hold, according to Baron and Kenny, you don't have mediation. 
Regarding your second question, you can include a covariate like well-being. It just needs to be in every equation required of the mediation analysis. 
However, you should note that you seem to have asked about including a covariate (i.e., controlling for well-being), which is separate from testing the hypothesis that you have presented - that the relations between your variables are influenced by well-being. That sounds a lot like a moderation model to me. 
If that is where you want to take this analysis then you may want to look into moderated-mediation models. The Wikipedia page may be a good place to start. 
A: As Matt said, you do not have mediation because the third requirement of the causal steps approach is not met.
I would encourage you to explore alternative options to the causal steps approach. It's quite possible you're getting a Type II error (since the M -> DV relationship was significant before it was entered into the mediation model). You should look into a bootstrapping method; this will increase your power. The attached reference below may be helpful to you.
Hayes, A. F. (2009). Beyond Baron and Kenny: Statistical mediation analysis in the new millennium. Communication Monographs, 76(4), 408-420.
