Parent-child relationship variables are often measured using a two-part approach. For example, when assessing the warmth of the father-child relationship, a child is first asked if they have contact with their father; if so, the level of warmth they feel toward him is ascertained. In this setting, data on the warmth measure is missing for children without contact with their father, and such missing data can pose a significant methodological and substantive challenge when the variable is used as an outcome or antecedent variable in a model. In both cases, it is advantageous to use an analytic method that simultaneously models whether the child has contact with the father, and if they do, the degree to which the father-child relationship is characterized by warmth. This is particularly relevant when the two-part variable is measured over time, as contact status may change. We offer a pragmatic tutorial for using two-part variables in regression models, including a brief overview of growth modeling, an explanation of the techniques to handle two-part variables as predictors and outcomes in the context of growth modeling, examples with real data, and syntax in both R and Mplus for fitting all discussed models.
Keywords: father-child relationships; father–child contact; two-part growth models; two-part models; two-part predictors.