Interdependence Theory and the Actor–Partner Interdependence Model

Abstract

This work describes an application of the actor-partner interdependence model (APIM) that allows researchers to test hypotheses in terms of interdependence theory (IT). The authors' goal is to move beyond the obvious similarities of these two frameworks by providing a detailed conceptual integration. This analysis demonstrates that aspects of APIM analysis reveal a useful perspective on interdependence not explicitly articulated by IT. They also expand on ideas presented by Kenny and Ledermann by exploring the relationship between their ratio parameter k and IT, and introducing two additional ratios (h and c) also suggested by IT. A complete worked example of APIM analysis from the perspective of IT, along with a SAS MACRO that produces confidence intervals for k, h, and c, is provided.

Perceptions of partners’ problematic alcohol use affect relationship outcomes beyond partner self-reported drinking: Alcohol use in committed romantic relationships.

“…As mentioned previously, the interaction approach (Kenny et al, 2006; Wickham & Knee, in press) was used to evaluate whether the two-way interaction of interest was different for males and females. The three-way interaction among gender, PPP, and the drinking composite was significant for both relationship outcomes.…”

“…In these analyses, gender must be added in order to obtain gender effects. When all main effects and all interactions are included, these two approaches are mathematically equivalent (Wickham & Knee, in press). The interaction approach was used in the current analyses because it directly tests whether the effect of interest (i.e., partner drinking × actor perception of partner drinking as problematic) is different for males and females, via the three-way interaction.…”

How to Use the Actor-Partner Interdependence Model (APIM) To Estimate Different Dyadic Patterns in MPLUS: A Step-by-Step Tutorial

“…For example, Ledermann, Macho, and Kenny (2011) have expanded the APIM presented by Kenny and Ledermann (2010) to enable the assessment of mediating manifest variables and guidelines have been developed by West, Popp, and Kenny (2008) for investigating different types of moderation effects in dyadic data, while others have investigated moderation with SEM (Kivlighan, Marmarosh, & Hilsenroth, 2014;Maleck & Papp, 2015;Papp, Kouros, & Cummings, 2010, e.g.m,). Wickham and Knee (2012) have also suggested the calculation of an interaction term between X 1 and X 2 (i.e., moderation) and have added two other ratios (h = a/x1x2; c = p/x1x2) in order to improve the methodological-substantive synergy between the APIM and interdependence theory. Marsh, Wen, and Hau (2004) have investigated different strategies for testing interaction terms in SEM; results of simulation studies indicate that the unconstrained approach performs just as well as the more traditional and technically demanding constrained approach (Marsh, Wen, Nagengast, & Hau, 2012).…”

“…In light of these new advances, future research could also use latent rather than manifest variables because interactions with latent variables have the advantage of controlling for measurement error in the interaction term in order to increase the statistical power associated with the interaction effect. Moreover, the different ratios that are proposed by Wickham and Knee (2012) could be computed with more advanced mathematical constraints (i.e., MODEL CONSTRAINT function in MPLUS) and a new parameter may be computed for bootstrapped 95% CIs. On a final note, a study of Sagan and Kowalska-Musial (2010) has recently used a Bayesian approach in their APIM analyses of dyadic data.…”

Dyadic instruction for middle school students: Liking promotes learning

“…Combination ratios provide a measure of the effect size of the influence paths (Wickham & Knee, 2012). Combination ratios indicate the relative strength of the beta weights of two paths, typically a within-individual influence path and a between-individual influence path (e.g., β c / β g ).…”