MEDIATION Flashcards
It is desirable that the _ variable be uncorrelated with both the predictor and the criterion (DV) to provide a clearly interpretable interaction term.
moderator
moderators and predictors are at the same level in regard to their role as _ variables antecedent or exogenous to certain criterion effects.
causal variables
mediator-predictor relation where the _ is causally antecedent to the mediator
predictor
moderator variables always function as _ variables, whereas mediating events shift roles from effects to
causes, depending on the focus of the analysis.
independent
variables
a) variations in levels of the independent variable significantly account for variations in the presumed mediator (Path a)
effect of X on M
b) variations in the mediator significantly account for variations in the dependent variable (Path b)
effect of M on Y
c) when Paths a and b are controlled, a previously significant relation between the independent and dependent variables is no longer significant, with the strongest demonstration of mediation occurring when Path c is zero.
A variable functions as a mediator
variations in levels of the independent variable significantly
account for variations in the presumed mediator
Path a
variations in the mediator significantly account for variations in
the dependent variable
Path b
when Paths a and b are controlled, a previously significant
relation between the independent and dependent variables is no
longer significant, with the strongest demonstration of
mediation occurring when path _ is 0
Path c
Questions like these suggest a chain of relations where an antecedent variable affects a mediating variable, which then affects an outcome variable.
MEDIATION/Mediating variables form the basis of many questions in psychology
• Will changing social norms about science improve children’s achievement in
science?
MEDIATION/Mediating variables form the basis of many questions in psychology
• If an intervention increases secure attachment among young children, do
behavioral problems decrease when the children enter school?
MEDIATION/Mediating variables form the basis of many questions in psychology
• Does physical abuse in early childhood lead to deviant processing of social
information that leads to aggressive behavior?
MEDIATION/Mediating variables form the basis of many questions in psychology
• Do expectations start a self-fulfilling prophecy that affects behavior?
MEDIATION/Mediating variables form the basis of many questions in psychology
• Can changes in cognitive attributions reduce depression?
MEDIATION/Mediating variables form the basis of many questions in psychology
• Does trauma affect brain stem activation in a way that inhibits memory?
MEDIATION/Mediating variables form the basis of many questions in psychology
• Does secondary rehearsal increase image formation, which increases word
recall?
MEDIATION/Mediating variables form the basis of many questions in psychology
implies a situation where the effect of the
independent variable on the dependent variable can best be
explained using a third mediator variable which is caused by
the independent variable and is itself a cause for the
dependent variable. This answers the question “why?”
Mediation
Mediation implies a situation where the effect of the
independent variable on the dependent variable can best be
explained using a third mediator variable
Mediation
which is caused by
the independent variable and is itself a cause for the
dependent variable.
third mediator variable
This answers the question “why?”
Mediation
X is causing the
mediator M, and M is in turn causing Y.
Mediation
The _ is called an intervening or process variable
mediator
The causal relationship between X and Y in this case is said to
be _.
indirect
the relationships between the independent, the
mediator and the dependent variables can be depicted in form
of a path _
path diagram/model
Involves a set of causal hypotheses where an initial variable may influence an outcome variable through a mediating variable.
mediation
Also referred to as a causal chain in which one variable [IV] (X) affects a second variable [M] that, in turn, affects an outcome variable [DV] (Y).
mediation
A variable may be considered a _ to the extent it carries the
influence of a given IV to a given DV
mediator
*The IV significantly affects the mediator
*The IV significantly affects the DV in the absence of a mediator.
* The mediator has a significant unique effect on the DV.
* The effect of the IV on the DV shrinks upon the addition of the M to the model.
mediation affects the DV in the absence of a
The IV significantly affects the _
mediator
The IV significantly affects the _ in the absence of a mediator.
DV
The _ has a significant unique effect on the DV.
mediator
The effect of the IV on the DV _ upon the addition of the M to the
model.
shrinks
Each arrow in a path diagram represents a causal relationship between two variables to which a _ or weight is assigned.
coefficient
are nothing but the standardized regression coefficients (betas) showing the
direction and magnitude of the effect of one variable on the other.
coefficient
Moderation and Mediation are used to explore the interrelationships
among _ variables
3
if you have only 2 variables, do a simple _ or _ regression
correlation or linear regression
Having _ variables means that one can examine their various
relationships in more complicated ways.
3
Mediation and moderation are tests of _, but structured so that
particular questions can be answered
tests of association
Moderation and Mediation will require a _, _, or _
theory, model or principle
Self-esteem (X) will affect academic success (Y) “because of” social support (M).
MEDIATION
Instead of using the terms independent and dependent variables, it would
make more sense in the context of path models to speak of _ and
_ variables.
exogenous and endogenous variables.
variables which in the context of the model have
no explicit causes. That is to say, they have no arrows pointing to them.
(IV)
Exogenous Variables
variables which in the context of the model are
causally affected by other variables. That is to say, they have arrows
pointing to them. (DV)
Endogenous Variables
From a regression standpoint, for every _ variable in the
regression model should be fitted.
endogenous variable
• Continuous Measurements
• Normality
• Independence
• Linearity
Assumptions on Mediation
All variables are assumed to be measured on a continuous scale
Continuous Measurements
All variables are assumed to follow a Normal distribution
Normality
The errors associated with one observation are not
correlated with the errors of any other observation
Independence
Relationships among the variables are assumed to be linear
Linearity
- Estimate the direct (path c) and indirect or mediation effects {path a x path b}
(thru a series of
regression analysis) - Statistical Inference
(test the significance
of the indirect effect just multiply a and b
Direct effect = Significance test (p-value)
Indirect effect = Bootstrap confidence interval
sample simple mediation analysis steps
• Direct effect = path C
• Indirect effect (or mediation effect) = (path a) x (path b) The effects can be estimated using two regression equations.
Indirect effect, just multiply a and b
Step 1 sample simple mediation analysis steps
• Direct effect = Significance test (p-value)
• Indirect effect = Bootstrap confidence interval
Step 2 sample simple mediation analysis steps
Direct effect = path _
C
Indirect effect (or mediation effect) = (path _) x (path _)
(path a) x (path b)
Direct effect = _ test (p-value)
Significance test
_ effect = Bootstrap confidence interval
Indirect effect
The effects can be estimated using two _ equations.
regression
the initial _ variable loses its significance when the mediator is included in the model
IV
- Confirm the significance of the relationship between the initial IV and DV.
(X → Y) - Confirm the significance of the relationship between the initial IV and the
mediator. (X → M) - Confirm the significance of relationship between the mediator and the DV in the presence of the IV. (M|X → Y)
- Confirm the insignificance (or the meaningful reduction in effect) of the relationship between the initial IV and the DV in the presence of the mediator
(X|M → Y)
Steps in Testing Mediation
significance of the relationship between the initial IV and DV
(X → Y)
significance of the relationship between the initial IV and the
mediator.
(X → M)
significance of relationship between the mediator and the DV in
the presence of the IV
(M|X → Y)
insignificance or the meaningful reduction in effect) of the
relationship between the initial IV and the DV in the presence of the mediator
(X|M → Y)
- Indirect effect
- Partial mediation
- Full mediation
3 Main Types of Mediation
predicts no direct effect from X to Y
Indirect effect Mediation
X has a direct effect on the mediator, and the mediator has a direct effect on Y.
Indirect effect Mediation
X is said to have an indirect effect on Y.
Indirect effect Mediation
This hypothesis can only be supported if the direct effect of X to Y is insignificant before testing for indirect effects
Indirect effect Mediation
predicts significant direct and indirect effects from X to Y
Partial mediation
the unmediated relationship is significant as well as the X to the
mediator and mediator to Y relationships
Partial mediation
predicts that the direct effect of X to Y will be significant
only if the mediator is absent
Full mediation
When the mediator is present, this direct effect becomes insignificant, while the indirect effect is significant.
Full mediation
if the X to the mediator and/or the mediator-to-Y relationships are insignificant, _ mediation is taking
no mediation (Full mediation)
_ and _ proposed a four step approach in which several regression analyses are conducted and significance of the coefficients is examined at each step.
Baron and Kenny (1986)
Step 1 Conduct a simple regression analysis with X predicting Y to test for path c alone
Step 2 Conduct a simple regression analysis with X predicting M to test for path a
Step 3 Conduct a simple regression analysis with M predicting Y to test the significance for path b alone
Step 4 Conduct a multiple regression analysis with X and M predicting Y
steps in testing for mediation
Step 1 Conduct a simple regression analysis with X predicting Y to test for path c alone
Y=B₀+B₁ X+e
Step2 Conduct a simple regression analysis with X predicting M to test for path a
M=B₀+B₁ X+e
Step 3 Conduct a simple regression analysis with M predicting Y to test the significance for path b alone
Y=B₀+B₁ M+e
Step 4 Conduct a multiple regression analysis with X and M predicting Y
Y=B₀+B₁ X+B₂M+e
could also be called a direct effect
c’
