Patterns of actor and partner effects
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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


You need to know the actor partner interdependence model

You need to know the Actor Partner Interdependence Model!

APIM


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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