Dual Stage Moderated Mediation Models Overview

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Из курса от партнера University of Virginia

Understanding Your Data: Analytical Tools

26 оценок

University of Virginia

Understanding Your Data: Analytical Tools

26 оценок

Are you trying to understand data from your research? Learn how and when to conduct mediation, moderation, and conditional indirect effects analyses? Or, perhaps, how to theorize and test your theoretical models? If so, this is the course for you! We will walk you through the steps of conducting multilevel analyses using a real dataset and provide articles and templates designed to facilitate your learning. You'll leave with the tools you need to analyze and interpret the results of the datasets you collect as a researcher. By the end of this course, you will understand the differences between mediation and moderation and between moderated mediation and mediated moderation models (conditional indirect effects), and the importance of multilevel analysis. Most important, you will be able to run mediation, moderation, conditional indirect effect and multilevel models and interpret the results. This course is supported by the BRAD Lab at the Darden School of Business, which studies organizational behavior, marketing, business ethics, judgment and decision-making, behavioral operations, and entrepreneurship, among other areas. More: http://www.darden.virginia.edu/brad-lab/

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Conditional Indirect Effects

Now that you know more about mediation and moderation, let's take a look at conditional indirect effects models, which are a combination of mediation and moderation models. First we will get to the heart of the differences between moderated mediation and mediated moderation models.This will allow you to fully understand the relationships between independent variables and dependent variables. By the end of the module, you will be able to theorize about conditional indirect effect models on SPSS and to test which path of the mediation model is affected by the moderator. Then you'll dive into SPSS, run different models, and learn how to interpret the results.

Conditional Indirect Effects: Mediated Moderation Models8:17

Testing Mediated Moderation Models: Steps 1-25:48

Testing Mediated Moderation Models: Steps 3-56:50

Moderated Mediation Models Overview7:30

Testing First Stage Moderated Mediation Models8:55

Second Stage Moderated Mediation Models Overview5:11

Testing Second Stage Moderated Mediation Models9:57

Dual Stage Moderated Mediation Models Overview5:18

Testing Dual Stage Moderated Mediation Models11:27

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Cristiano Guarana
Post-Doctoral Researcher
Darden School of Business

In this session, we'll be covering dual-stage moderated mediation.

So the agenda for today is, again, pretty simple.

Let's think about how we theorize and then how we test for these dual-stage models.

I must tell you, since the beginning,

that we don't many dual stage models, in our journals.

This is also an opportunity to start looking at dual stage models,

but also our liability, if you will,

because models can get too complex and very difficult to explain.

So I would rather, if you would, err on the parsimonious side and

have stage 1 or stage 2 models.

But if you have a strong theoretical argument for

having dual stage models, go for it.

We can definitely learn from these models.

So, dual-stage models, we have two moderators basically.

We have a moderator that is in the first stage, and

a moderator that's in the second stage of this interact effect.

So, the first stage, do you remember, is in this first path.

So we are looking for the interactive term of the independent variable and

moderator one on the mediator.

And the second stage is the second path.

The path between the mediator and the dependent variable.

And we'll be looking at the interaction term between the mediator and

the second moderator on the dependent variable.

So you can find a few papers that describe these dual-stage models.

One, for example, is Liu, Liao & Loi in 2012.

You can go back to that paper and take a look on how they described these models.

The first step when conducting these dual-stage models

is to theorize about the indirect effect.

Again, the starting point for moderated mediation models is the indirect effect.

That's different from mediated moderation models

in which the first step is the moderation, okay?

So here we are looking at the indirect effect.

So theorize about the effects of independent variable,

only the dependent variable via a mediator.

And then in step 2, we theorize about the relationship

between our independent variable and

our mediator being a function of the moderator one.

Okay, so we are looking at the interaction term and

the effects of that interaction term on the mediator.

And for our third step and

this is different, we are adding another moderation.

We are looking at the indirect, or the effect,

if the effects of our mediator on the dependent variable

varies based on levels of our moderator two.

So in this case we are looking at an interaction term between mediator and

moderator two on the dependent variable.

Finally we theorize about this overall more complex model, if you will.

And we are theorizing that the effects of the independent variable

on the dependent variable via the mediator can change,

based on different levels of our first moderator or

our second moderator, I should say.

How do we test for that?

Pretty similar to what we've been doing so far.

We first do the random model, a mediation model.

And we adopt Hayes' PROCESS macro for that.

And at step 2, we run a traditional test of moderation and

we are looking at the relationship, the interaction term of the independent

variable and moderator on the mediator.

Step 3 is a similar moderation model but

now we are looking at the interaction term between the mediator and

our second moderator in that effect on our dependent variable.

And then we adopt process.

Now we are looking at model number 29.

It's important to chose the correct model again.

In one of our prior sessions, I mentioned that there are more than 70 models.

And it's critical to select the correct model,

otherwise you may have error terms or just bogus output or

outcomes that don't test for your theoretical model.

Finally you look at the bootstrapping analysis and the output of that.

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