Dear all,

I want to investigate which child in a sibling group takes over the care of a parent. The focus is on gender (of both the caregiving child and the siblings), but other characteristics of the children and siblings (e.g., education, employment status, own children, spatial proximity to the parent) will also be examined as influencing factors.

To do so, I use a multilevel design that first includes characteristics of the child (individual level), then characteristics of sliblings and the parent (contextual level), and finally cross-level interactions between child gender and sibling characteristics. Sibling characteristics are summary variables of the children in a family group, for example the number of full-time employed sons or the average age of all daughters in a family. The main interest in these cross-level interactions is to determine whether characteristics of male and female siblings affect a male child differently than a female child in the likelihood of assuming parental care.

However, the number of cross-level interactions is high due to a large number of variables. Therefore, I wondered if I could, in principle, compute two separate multi-level models for male children and for female children so that – as in ordinary regression models – the interaction terms are inherent. To test for significant differences between male and female children, I would compute the individual interaction terms in the overall multilevel model.

Does anyone know if that would be a correct approach? Please let me know if you need any additional information.

Thanks for any advice!