Moderation in the Actor-Partner Interdependence Model · Moderation in the Actor-Partner...
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Moderation in the Actor-Partner Interdependence Model
Randi Garcia, David A. Kenny and Thomas Ledermann
International Association for Relational Research
Chicago, 2012
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Typology of the types of APIMs with moderation
Finding patterns and estimating simpler submodels
Today’s Talk
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Types of Moderators
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Within-Dyads Moderator
Example: Gender
Two moderation effects
Between-Dyads Moderator
Example: Years Married
Indistinguishable: Two moderation effects
Distinguishable: Four moderation effects
Types of Moderators
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Mixed Moderator
Example: Feelings of closeness
There are actually two moderator variables
1. Member 1’s feelings of closeness
2. Member 2’s feelings of closeness
Indistinguishable: Four moderation effects
Distinguishable: Eight moderation effects!!
Types of Moderators
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Patterns of Moderation Effects
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Moderation Patterns
Individual tests of moderation have lower power than the tests for patterns
Trimming may be problematic since it may be unlikely that any one moderation effect is significant
Finding patterns among the moderation effects can help to reduce this complexity and aid in the theoretical understanding of the results
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Distinguishable dyads
Moderation Patterns – between-dyads moderator
Model
Moderation Effect
Actor-only Partner-only Couple Contrast
Member 1
Actor by Moderator
a 0 a a
Partner by Moderator
0 a a −a
Member 2
Actor by Moderator
b 0 b b
Partner by Moderator
0 b b −b
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The 500 Family Study (1998-2000: United States; Schneider & Waite, 2008)
Middle class, dual-career couples living in the United States (N= 290)
Distinguishing Variable Gender
Y = Happy with role responsibility X = Actor depressive symptoms X′ = Partner depressive symptoms
Between-Dyads Moderator M = Time living together
Example Data
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Saturated Model
Wife’s Depression
Husband’s Depression
Decades Together
Wife’s Depression by Decades
Together
Husband’s Depression by
Decades Together
Wife’s Happiness
Husband’s Happiness
EW
Eh
1
1
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Effect Coefficient SE p
Intercept Husband 3.736 0.167 <.001 Wife 4.082 0.137 <.001
Wife Main Effects Depression Actor -0.026 0.009 .006
Partner -0.036 0.009 <.001 Husband Main Effects Depression
Actor -0.032 0.008 <.001 Partner -0.018 0.008 .021
Saturated Model – DV: Happiness
Decades Together Main Effect Husband -2.611 7.211 .717
Wife -0.131 8.767 .988
Wife Depression by Decades Actor by Moderator -0.031 0.011 .005 Partner by Moderator -0.014 0.012 .244
Husband Depression by Decades
Actor by Moderator 0.024 0.010 .019 Partner by Moderator -0.023 0.009 .011
Three of the four interactions are statistically significant.
The pattern of moderation for wives suggests a couple moderation model while for husbands it suggests a contrast moderation model.
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Between Dyads Moderator – Distinguishable Dyads
Wife Couple and Husband Contrast model: For wives, the longer the couple has lived together the stronger the
effect of depression of the couple is on her happiness.
For husbands, the longer the two have lived together the greater the effect of his wife’s depression relative to his depression on his unhappiness.
Patterns
Effect Interacting with Decades Together Fit
Wife Actor Wife Partner Husband Actor Husband Partner
c2 df p SABIC
Saturated Model -0.031 -0.014 0.024 -0.023 --- --- --- ---
Both Couple -0.023 -0.023 -0.002 -0.002 10.95 2 .004 30.94
Both Contrast -0.011 0.011 0.023 -0.023 8.83 2 .012 28.82
Wife Couple Husband Contrast 0.023 0.023 0.021 -0.021 0.954 2 .621 20.94
Wife Contrast Husband Couple
-0.002 0.002 0.004 0.004 19.444 2 <.001 39.43
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Moderation of the Parameter k
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Kenny and Ledermann (2010)
k is the ratio of the partner effect to the actor effect
When k is 1 we have couple level model, k equal to −1 is contrast model, and k equal to zero is actor-only model
The parameter k, and its confidence interval, is estimated using phantom variables
Introduction to the Parameter k
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Define kM as the ratio of the effect of X′M to the effect of XM
For the wives in our example, we might expect kM to be equal to 1, indicating a couple moderation model
Test for moderation of k
The common k model: If we assume that k = kM
The moderator changes both the actor and partner effects, but the ratio of partner to actor effects does not change as the moderator changes – k is not moderated
Moderation of the Parameter k
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Estimating the Common k Model
Wife’s Depression
Husband’s Depression
Decades Together
Wife’s Depression by Decades
Together
Husband’s Depression by
Decades Together
Wife’s Happiness
Husband’s Happiness
EW
Eh
1
1
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Estimating the Common k Model
Wife’s Depression
Husband’s Depression
Decades Together
Wife’s Depression by Decades
Together
Husband’s Depression by
Decades Together
Wife’s Happiness
Husband’s Happiness
EW
Eh
1
1
kW = kMW
kH = kMH
P1
P2
P3
P4
kW
kH
kMW
kMH
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For wives this was a good fitting model (c2(1) = 1.50, p = .221), but for Husbands, the fit for the common k model was poor (c2(1) = 9.68, p = .002)
The value of the common k for wives was 1.05 [0.31, 3.34]
For men: k = .605 [.120, 1.677], kM = -1.002 [-5.234, -.209]
Thus, we conclude for wives, k is not moderated by Decades Together, but for husbands it is. The parameter k decreases for husbands the longer that the couple has been together.
Moderation of k
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-1
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-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
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15 17.5 20 22.5 25 27.5 30 32.5
k ac
ross
Ye
ars
Mar
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d
Years Married
Couple Model
Contrast Model
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APIM used frequently by dyadic researchers and moderation is included more and more–important to think carefully about these interactions
Finding patterns in the moderation effects can simplify APIM models with a large number of moderation terms
Help dyadic researchers arrive at a summary of their results that is more easily related to theory
Conclusion
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Thank You