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Regression: Moderation A moderator must not be the causal result of the…
Regression: Moderation
A moderator must
not
be the causal result of the predictor
we need to 'centre the continuous variables'
Open EXCEL
make a copy as we want to have an orginal copy of the raw data
create new columns in the copy excel file
title column 1 as the predictor c (just an example, this is a centred/continuous variable)
enter '=' into the cell below this title
select the first cell from the predictor variable
then type
' - AVERAGE'
and select ALL of the cells in that predictor variable column
the completed equation should be
'= (cell) - AVERAGE'
when this computed, drag the corner of the cell to create the averages for the rest of the data
title column 1 as the moderator c (just an example, this is a centred/continuous variable)
enter '=' into the cell below this title
select the first cell from the moderators
then type
' - AVERAGE'
and select ALL of the cells in that coumn with moderator variables (i.e. age)
the completed equation should be
'= (cell) - AVERAGE'
when this computed, drag the corner of the cell to create the averages for the rest of the data
NOW SAVE & return to JASP
look at the assumptions for the centred variable by going to 'Regression' and clicking 'linear regression'
ASSUMPTIONS
1. test for residuals/outliers
go to
'statistics'
look for the header
'residuals'
and
select
:
a) casewise disgnostics for outliers
b) Keep the standard residuals as 3
JASP will create a table called 'casewise diagnostic'
IF there were any residuals, there would be a 'case number' of the residual
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2. are residuals /outliers normally distributed?
go to
'plots'
dropbar
select 'residual vs. histogram'
ensure 'standardised residual' is still selected
review it, does it look normal?
NO
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YES: Gaussian distribution (bell curve)
ASSUMPTIONS P.2
3. test for homoscedasticity (& look for linearity)
under
'residual plots',
select 'residual vs. predicted'
a spotted graph should appear
the dots
should
be randomly distributed (look like a potato), not a 'funnelled' triangular pattern
if they are randomly distributed, this suggests there is linearity
IF it is not linear, there will be an inverted U shape etc
4. test for multicolinearity
go to 'regression and click on
'correlation'
only
move the predictor (IV's) to the 'variables'
Under the header 'sample correlation coefficient, select
'pearson's r
'
under 'additional ... select
'display pairwise'
deselect
'report significance
'
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go into
'regression'
click on
'linear regression'
place the outcome variable into
'deviation variable'
mental note: 'dependent variable section' = outcome variable
'covariates' = predictor variable
place the centred variables that you did on excel (with the c beside them) in the
'covariates'
column
this will compute a 'coefficients' table
go to 'statistics'
select
'confidence intervals'
as it indicates significiance and size of the relationship
review data
look at the 'model summary' table
what does the r2 / adjusted r 2suggest
look at the ANOVA table, what does the p vaue suggest
look at the interaction
(this will tell us if we have a moderation)
click on the
'model'
dropbar
from the 'components' header, select the predictor and moderator variables
move them over to the
'model terms'
this will add the interaction to the
'coefficients'
table
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breaking down interactions IF they are significant
Go back to Excel
create 2 columns titled .... high and ....low
(depends on the moderator)
(moderator title) low
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(moderator title) high
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we need to identify the low and high groups in the data
Computing single slopes
you may rename the linear regression to 'full model as 2 more will be made and it will be clearer
go to
'regression'
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go back to
'regression'
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Go to the top bar and click on
'visual Modeling'
IF this feature is not in the top bar, click the + sign (show modules menu)
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Example research question: is the blood alcohol level affecting people's driving skills
Moderator: Participants age
Predictor variable(IV): Blood alcohol level
Outcome variable (DV): Trajectory deviation
the moderator does not cause trajectory deviation
1.LOW
2.HIGH
Visualising the simple slopes