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Patterns of Actor and Partner Effects. David A. Kenny. You need to know the Actor Partner Interdependence Model!. APIM. APIM Patterns: Couple Model. Model Equal actor and partner effects: a = p

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Patterns of Actor and Partner Effects

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Patterns of actor and partner effects
Patterns of Actor and Partner Effects

David A. Kenny



Apim patterns couple model
APIM Patterns: Couple Model

  • Model

    • Equal actor and partner effects: a = p

    • e.g., my depressive symptoms has the same effect on my quality of life as does my partner’s depressive symptoms on my quality of life

  • Average or sum as the predictor

    • Although measured individually, the predictor variable is a “dyadic” variable, not an individual one


Apim patterns contrast
APIM Patterns: Contrast

  • Model

    • Actor plus partner effects equals zero: a – p = 0

    • Klumb et al. (2006): time spent doing household labor on stress levels

      • The more household labor I do, the more stressed I feel.

      • The more household labor my partner does, the less stress I feel.

    • Difference score (actor X minus partner X) as the predictor


Apim patterns actor or partner only
APIM Patterns: Actor or Partner Only

  • Actor Only

    • Actor present but no partner effect

    • Fix the partner effect to zero.

  • Partner Only

    • Partner present but no partner effect

    • Fix the actor effect to zero.

    • Relatively rare.


Testing patterns
Testing Patterns

  • Multilevel Modeling

    • Sum and difference approach

  • Structural Equation Modeling

    • Setting coefficients equal

    • Use of phantom variables

  • General approach to patterns: k


Sum and difference approach
Sum and DifferenceApproach

  • Remove the actor and partner variables from the model.

  • Add to the model the Sum and the Difference score as predictors.

  • If Sum is present, but not the Difference, you have a couple model.

  • If Sum is not present, but the Difference is, you have a contrast model.


Acitelli example
Acitelli Example

  • Distinguishable

    • Husbands

      • Sum: 0.392, p < .001

      • Difference: 0.131, p = .088

    • Wives

      • Sum: 0.373, p < .001

      • Difference: 0.001, p = .986

  • Indistinguishable

    • Sum: 0.344, p < .001

    • Difference: 0.056, p = .052


Testing the couple model using sem
Testing the Couple Model Using SEM

  • Actor effect equal to the partner effect.

  • Can be done by setting paths equal.

  • Distinguishable dyads

    a1 = p12 and a2 = p21

  • Indistinguishable dyads

    a = p


Acitelli example1
Acitelli Example

  • Distinguishable

    • Husbands: 0.346

    • Wives: 0.347

    • Test: c2(2) = 4.491, p = .106

  • Indistinguishable

    • Effect: 0.344

    • Test: c2(1) = 3.803, p = .051


Testing the contrast model using sem
Testing the Contrast Model Using SEM

  • Actor effect equal to the partner effect times minus 1.

  • Can be done by using a phantom variable.

  • Phantom variable

    • No conceptual meaning

    • Forces a constraint

    • Latent variable

    • No disturbance


Patterns of actor and partner effects

Contrast Constraint Forced by Phantom Variables (P1 and P2)

  • Now the indirect effect from X2 to Y1, p12 equals (-1)a1

X1

a1

Y1

1

E1

-1

a2

P1

a1

P2

-1

X2

Y2

1

E2

a2


Acitelli example2
Acitelli Example

c2(2) = 69.791, p < .001


Conclusion
Conclusion

Using patterns can link the APIM to theory and simplify the model.

The k parameter is a general way to measure and test patterns

Readings

pp. 147-149, in Dyadic Data Analysis by Kenny, Kashy, and Cook

Kenny & Cook, (1999), Personal Relationships, 6, pp. 433-448.


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