Contents
Which is the best model for dyadic data analysis?
Standard mixed and marginal models are limited in that they typically do not account for the potential effects from a dyadic partner. In response to these limitations, David Kenny developed the Actor Partner Interdependence Model (APIM)3, the most widely used analytical model of dyadic data in the epidemiological literature.
When to use multilevel modeling in APIM?
In APIM there are two options to examine indistinguishable dyads when the outcome is continuous.7 The first option is multilevel modeling (MLM) in which the non-independence of the outcome errors is specified as a variance.
How many rows are in a dyad dataset?
There are two rows for each dyad, which represents each individual in the dyad. A pairwise dataset is needed in order to add both individual-specific actor and partner variables in the same model. IV. Continuous outcomes
Which is a distinguishing variable in a dyad?
APIM cannot be conducted without an understanding of distinguishability. Distinguishable dyads have a characteristic that differentiates the members within a dyad. This characteristic is the same across all dyads in the study sample. For example, among heterosexual couples, gender is a distinguishing variable.
What is the definition of a dyadic network?
Dyads are a type of social network in which there are two individuals that are linked. The analysis of dyadic data has its origins in psychology in the study of couples and romantic relationships but its methodology has recently emerged in the field of epidemiology.
What are the effects of a dyadic predictor?
In brief, for an individual-level predictor, there is an actor effect (the effect of an individual’s predictor on that individual’s outcome) and a partner effect (the effect of that same predictor but from the dyadic partner on the individual’s outcome).
Can a dyadic partner have an effect on an individual?
APIM posits that both an individual and his/her dyadic partner can have an effect on the outcome of interest, simultaneously, and these two effects explain the interdependence of outcome errors (Fig. 1).