Table 1.
Overview of Analytic Approaches to use with Matched-Pairs Dyadic Distinguishable Data
| Analytic Approaches | Essential Measured Variables | Analysis | Example Research Questions |
|---|---|---|---|
|
Actor-Partner Interdependence Model (Kenny et al., 2006; Ledermann & Kenny, 2017; Van Dulmen, & Goncy, 2010) |
Y1a, Y1b X1a, X1b |
|
|
|
-Two-Intercept Model (Barnett et al., 1993; Wendorf, 2002) |
Y1a, Y1b X1 |
|
|
|
Difference Model (Newsom, 2002) |
Y1a, Y1b X1 |
|
|
|
Common Fate Model (Cook, 1998; Ledermann, & Kenny, 2011) |
Y1a, Y1b X1a, X1b |
|
|
|
Mutual Influence / Dyadic Feedback Model (Cook, 1998) |
Y1a, Y1b X1a, X1b |
|
|
|
Dyadic Growth Curve Model (Kurdek, 2003; Lyons & Sayer, 2005) |
Y1a, Y1b over at least four times |
|
|
|
Cluster / Latent Class / Mixture Models (Whiteman & Loken, 2006) |
Multiple Y variables for each dyad member |
|
|
Note. X = predictor or independent or exogenous variable. Y = outcome or dependent or endogenous variable. a = dyad member a. b = dyad member b. MLM = multilevel modeling framework. SEM = structural equation modeling framework. Basic models are presented, but most of these models can be extended to more complex versions – see noted citations for extensions.