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APIM with Between- and Within Dyads Outcomes David A. Kenny December 11, 2014.

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Presentation on theme: "APIM with Between- and Within Dyads Outcomes David A. Kenny December 11, 2014."— Presentation transcript:

1 APIM with Between- and Within Dyads Outcomes David A. Kenny December 11, 2014

2 You Need to Know Between- and within-dyads variables (definitions) Actor-Partner Interdependence Model 2

3 Between-Dyads Outcome

4 Definition 1.The scores of the two members are the same or were slightly different and so were averaged to produce one score. 2.Outcome measured on only one member (e.g., outcome is measured only for the person who has the disease).

5 Analysis Strategy Dyad as unit (Do not use multilevel modeling!) Use multiple regression or logistic regression if the outcome is dichotomous.

6 Mixed Predictors: Distinguishable Dyads Persons 1 and 2 Enter X 1 and X 2 as predictors: Y = aX 1 + bX 2 If the outcome is measured from person 1 (2), then a would be an actor (partner) effect and b would be partner (actor) effect.

7 Complications Test if a = b –Estimate Y = c(X 1 + X 2 ) + d(X 1 - X 2 ) –If d is significant, then reject the null that a = b. –If d not significant, one can simply estimate Y = e(X 1 + X 2 ) Add interactions: discrepancy or product

8 Mixed Predictors: Indistinguishable Dyads Do not enter X 1 and X 2 as individual predictors (indistinguishability). Y = c(X 1 + X 2 ) Can be interpreted and presented as Y = cX 1 + cX 2 Can also include interactions

9 Special Case Outcome might be some non-additive composite of the couple: e.g., agreement or the absolute value of Y 1 – Y 2. This would be between-dyads outcome. Control for main effects of Y 1 and Y 2.

10 Continuous Within-Dyads Outcome

11 Definition The sum of the two scores for all dyads the same.

12 Analysis Strategy Dyad as Unit (Do not use multilevel modeling!) Compute Y 1 – Y 2 as the outcome. Difference score of Xs as predictors. Do not fit an intercept unless members are distinguishable in which case the intercept represents the effect of the distinguishing variable.


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