Using k to Estimate and Test Patterns in the APIM

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Using k to Estimate and Test Patterns in the APIM. David A. Kenny. You need to know the Actor Partner Interdependence Model and APIM patterns!. APIM. APIM Patterns. APIM Patterns. Couple Model Equal Actor and Partner Effects: a = p Contrast Model - PowerPoint PPT Presentation

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Using k to Estimate and Test Patterns in the APIM

David A. Kenny

February 17, 2013

You need to know the Actor Partner Interdependence Model and APIM patterns!

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APIM APIM Patterns

APIM Patterns• Couple Model

– Equal Actor and Partner Effects: a = p• Contrast Model

– Actor plus partner sums to zero: a – p = 0• Actor Only Model

– Partner effect is zero: p = 0• Partner Only Model

– Actor effect is zero: a = 0

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• Suggested by Kenny and Ledermann (2010) • k is the ratio of the partner effect to the actor

effect or p/a• k is named after Larry Kurdek, a pioneer in the

study of dyadic data• Special cases of k:

–k is 1, couple model–k equal to −1, contrast model–k equal to zero, actor-only model

The Parameter k

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-1 0 +1

Contrast Actor Only Couple a = -p p = 0 a = p

k5

-1 0 +1

Contrast Actor Only Couple a = -p p = 0 a = p

But k might equal 0.5.

k6

Phantom Variables• One way to estimate k is using a phantom

variable.• Phantom variable

– No conceptual meaning– Forces a constraint– Latent variable– No disturbance

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Standard APIM

X1

X2

Y1

Y2

E1

E2

1

1

a1

p21

p 12

a2

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Phantom Variables to Estimate k

• Now the indirect effect from X2 to Y1, p12 equals a1k1

• Thus, k1 = and k2 = and

X1

X2

Y1

Y2

E1

E2

1

1

a1

a2

P1

a1

k1

P2

a2

k2

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Estimates and Confidence Interval

• Use bootstrapping to obtain the asymmetric confidence interval (CI).

• Check to see if 1, -1, or 0 are in the CI of k.

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• Note that k is not defined when the actor effect is zero.

• Thus, k and its confidence interval should not be computed if the actor effect is small.

Caution in Computing the Parameter k

Distinguishability and k

• For distinguishable dyads, k may differ for the two members which might be theoretically interesting: e.g., wives couple model and husbands contrast model.

• Need to test to see if k varies across the distinguishing variable.

• Note that k may not vary, even if a and p vary by the distinguishing variable:

k = = 12

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ResultsDistinguishable

Wives: kW = 0.851 (0.223 to 2.038 )Husbands: kH = 0.616 (0.294 to 1.187)

Equal values of k kW = kH = 0.710 (0.489 to

0.989 )c2(1) = 0.320, p = .571

Indistinguishable: k = 0.719 (0.484 to 1.027)14

CI

Example SetupsAmos and Mplus (and soon laavan) setups can be downloaded at

davidakenny.net/papers/k_apim/k_apim.htm

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• When dyads are distinguishable, we previously took the two paths leading into Y to define k: k1X = and k2X =

• Alternatively k can be defined by the two paths coming from X:

k1X = and k2X = • For instance if one person is more “influential”

than the other, that person would have kX of 1 and the partner may have a kX of zero.

Defining k in Terms of X or kX

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X1

X2

Y1

Y2

E1

E2

1

1

a1

p21

p 12

a2

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X1

X2

Y1

Y2

E1

E2

1

1

a1

p21

p 12

a2

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• In some contexts the partner effect is larger than the actor effect, i.e., partner-only models.

• Note if a = 0, k = ∞! • In this case, it may make more sense to

define k as the ratio of the actor to the partner effect or kʹ =

Defining k in as Actor Effect Divided by Partner Effect

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ConclusionUsing k can simplify the model and link the model to theory.

ReadingKenny & Ledermann (2010), Journal of Family Psychology, 24, pp. 359-366.

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