Moderation Flashcards
in jamovi the moderation term is operationalized as…
> as an interaction term.
> IV*ModV
the ModV effects the ___ of the basic relationship
The ModV impacts the strength of the IV-DV relationship
If we use a dichotomous demographic categorical ModV we can compare…
If we use a dichotomous categorical ModV variable such as sex (male or female) we can see if the strength of the relationship (social support to depression) is different between females and males.
e.g. is B1 statistically different from B2
The effect size is important:
• It tells us if the statistically significant difference between two groups is meaningful! • As the sample size increases the statistical power to find associations or differences within the data increases.
addative model?
Two variables predict the DV in an additive fashion.
Note: additive models = adding one variables ability to predict the DV to another’s
ability to predict the DV (IV-MedV-DV or B1-B2- B3 or multiple predictors i.e. regression model).
- Y = B1 (IV) + B2 (ModV) + B3
(IV*ModV)
Moderations has 3 terms: 1 __ and 2 ___?
DV= 3 terms: 2x main effects
(IV and ModV) and
interaction term (IVModV) +
e
• By adding the interaction term we go from a simple additive model (i.e. a multiple regression) to a moderation model.
the old school method for moderation?
• The “old school” method would be looking at two separate regressions: > IV-DV (females) > IV-DV (males)
*one regression for each of the two subgroups of the population, as identified by our dichotomous categorical variable (i.e. male and female)
How would we interperet this? How do we compare the strength of these two unstandardised regression coefficients (B) in a statistically valid way? Comparing these two values using a test of correlations is awkward and quite frankly not a good method.
Instead, of conducting two regressions and comparing the B’s we use a regression- based moderation module in Jamovi!
Constraints on Regression-Based Moderation
(A) The IV must be continuous
(*a condition for any
regression-based analysis)
(B) The ModV may be
continuous or categorical
(C) The IV*ModV (could be
con*con or con*cat)
*the types of variables in the moderation model is important because it determines the types of analysis conducted and the interpretations made.
A regression-based moderation cannot be done in ANOVA, duh, but a moderation can be done in ANOVA- IF…
If you have two IV-IV (if both categorical) If you had a categorical IV and a continuous ModV you need to convert the ModV into a categorical variable using a median split. Is not an ideal method, its clumsy, awkward and not very illuminating.
Dummy Coding of Categorical Moderator
• If the IV is dichotomous (2 groups) they are arbitrarily coded into 0 and 1. • You will always have one less dummy variables than the total number of categories. • You will need to designate one category as the “comparison” group o Comparison groups are coded as 0 • The “absence” code has to be zero
Why? • Because the b3 is multiplying the IV by the ModV, so one of them needs to be 0 (for mathematical reasons). • Must be coded into 0 and 1 for a regression-based moderation only in an ANOVA moderation can the IV be coded into 1 and 2.
*Jamovi calculates the interaction term (IV*ModV) for computations but it does not show you what they are (in red).
Notice: SS x ModV E.g. 7 x 0 will equal 0 and not be inserted into the dataset. E.g. 17 x 1 will equal 17
Are we “loosing data” when we dummy code? • The short answer, no. The whole point of dummy coding is that we are comparing one group of all 0’s to another group of numerical values. • It’s too compilated to explain how it works. Just trust Paul that dummy coding is necessary.
how do you interpret the regression output of a interaction term?
we cannot interpret a categorical moderation effect from the regression output alone. You HAVE to graph it.
To graph a moderation we need to
You will need the: - Means and SD’s for IV and ModV - B (unstandardised regression coefficient) for IV, ModV and IV*ModV - Constant
*you can calculate the 6 mean cells (high, average,low IV for both groups male and female) by hand or plug the values into modgrpah.
constraints on graphing a moderation?
Rules/Constraints:
a. IV goes on the x-axis
b. DV goes on the y-axis
c. There needs to be two lines
for the moderation (one for
each group-male or female)
The two lines depict the slopes (strength and direction) of the relationship between IV-DV by group (ModV).
Simple Slopes for categorical moderation variable?
Jamovi does not adjust it’s computations to fit a categorical variable. Thus, it produces three lines (high, average and low SS) and not a line for female and male.
modgraph is used in categorical moderations to…
*to calculate cell means for
the graphical display of
moderation analysis.
Step 1: Calculate Means, SD, B and constant’s using a linear regression.
Step 2: Plug in values into Modgraph
Step 3: click view figure
Jamovi can not handle categorical moderation calculations! To get around this we need to get our interaction term (IV*ModV) by transformation tool in Jamovi. With the transformation interaction term we can now calculate a linear regression to obtain our B-values. Means and SD’s of IV obtained from descriptives
Interpreation for a categorical moderation?
There are two types:
(A) Focus on the slope of the
line
(B) Focus on the difference in
distance between lines at
different levels of the IVMulti-Categorical Moderators
*when categorical moderators
have more than two levels.
The most common example, is ethnicity-
Terms for the moderation equation:
(A) IV is continuous-rumination
(B) DV is continuous-
depression
(C) ModV- dummy codes for
ethnicity (x3)
(D) Interaction terms-dummy
code x IV (x3)
*will have 3 lines
Continuous Moderation
*all three variables are
continuous
Equation:
Y = b1(IV) + b2 (ModV) + b3 (IV*ModV) + e’
*equation for moderation is
the same for continuous and
categorical.
A simple slopes analysis is ___ for continuous moderation…
simple slope analysis is jamovi is good for continuous moderation.
An interpretation of a moderation requires us to talk about….
Interpretation needs to cover: • The slopes of all three lines • The main effect and interaction *all in one sentence
the positive association between stress and depression was stronger and statistically significant for low levels of social support and average levels of social support and not for high levels of social support. This suggests that high levels of social support acts as a buffer which supresses the effect of stress on depression.
A continuous moderator could be a…
(A) Buffer:
a. Supresses or weakens the
IV-DV relationship
b. Highest (ModV) +1SD is
flatter
c. Higher levels of ModV leads
to lower levels of a negative
DV
d. Makes a bad thing better
(B) Exacerbator:
a. Amplifies IV-DV relationship
b. Highest (ModV) -1SD is
steeper
c. Higher levels of ModV leads
to higher levels of a
negative DV
d. Makes a bad thing worse
(C) Enhancer
a. higher levels of ModV is a
stepper slope
b. higher levels of ModV
fosters higher levels of the
positive DV
c. makes a positive thing more
positive
(D) Dampener
a. higher levels of the ModV is
a flatter slope
b. higher levels of the ModV
leads to lower levels of a
positive DV
c. makes a positive thing less
positive
Other more complicated moderations possible:
Other more complicated moderations possible: • Latent variable • Longitudinal • Multiple • Curvilinear (Z and Z2) • Moderation in multilevel modelling
Things to avoid doing in a moderation:
Things to avoid doing in a moderation: • Categorical IV and ModV- need to do an ANOVA • Confusion about what types of variables are proper ModV’s (stable, relatively unchanging variables) • Unfamiliarity with simple slope analysis • Inexperience with making interpretations of moderation results • An inclination to use causal language in the interpretation.
Interpretating moderation patterns:
• Some researchers focus on the levels of the means • Some researchers focus on the slopes of the obtained coefficient lines (simple slopes analysis)
