# University of Connecticut

## Presentation on theme: "University of Connecticut"— Presentation transcript:

University of Connecticut
IARR – Crete Modeling Nonindependence: The Actor-Partner Interdependence Model 3/27/2017 David A. Kenny University of Connecticut IARR 2006

Overview Model Three Brief Examples Estimation
Multilevel Modeling (SPSS) Structural Equation Modeling (AMOS)

Between

Within

Mixed

Mixed Independent Variable
Definition X does not equal X’ for all pairs Or X + X’ equal the same value for every pair Allows for the estimation of partner effects

Dyads with a categorical within-dyads variables that makes a difference E. g., parent-child Indistinguishable Ordering of the two members is arbitrary E.g. roommates Whether dyads are distinguishable or not is matter of theoretical and statistical considerations.

Actor-Partner Interdependence Model
X Y partner partner actor X' Y'

Types of APIM Models actor only a > 0; p = 0 partner only
couple model a = p social comparison model a + p = 0

Example 1: Kraemer-Jacklin Study
Children in dyads are observed playing Variables X – Gender X’ – Partner Gender XX’ – Same vs. Opposite Gender Y – Share toys with partner

APIM Effects Actor: Do girls share more than boys?
Yes, but the effect is small. Partner: Do children share more when their partner is a girl? Yes and the effect is twice as large as the actor effect. Actor-Partner Interaction: Is there more sharing with same-gendered partners? Not much of a difference.

Example 2: Personality and Perceived of Control (Cook)
Siblings: one college student and one adolescent Variables Relative age (within dyads) Assertiveness (mixed) Cooperativeness (mixed) Perceived Control (outcome variable)

Example 2: Results Gender: no effects Relative age
older seen as more powerful Assertiveness positive actor effect negative partner effect Cooperativeness no actor effect positive partner effect

Example 3: Perception of Romantic Partners: Measures and Sample
Both partners form a perception A’s perception -- P(A) Each guesses how his or her partner’s view A’s guess of how B views the issue -- P(AB) Probability Sample (Acitelli) 248 married couples 90 dating couples

Perception of Romantic Partners: Path Model
bias P(A) P(AB) accuracy accuracy bias P(BA) P(B)

Perception of Romantic Partners: Conclusions
Few Gender Differences Few Effects for Married vs. Dating Accuracy and Bias for Each Measure Strength of Effects Varies by Measure

Perception of Romantic Partners: Results