I am compiling a data of club formation in which a person (id, the basic unit of observation) can belong to more than 1 club, while a majority of the sampled population do not belong to any clubs. Also, in a given year, more than 1 club were formed, so a person may share overlapping membership in multiple clubs with other persons; hence, the data has a cross membership structure (but not exactly a nested structure unless all clubs formed in a given year do not share overlapping membership). Finally, the data I have have observations spanning across 25 years.
To conceptualize the above description, my data structure looks somewhat like this:
there are many id(s) like id_5 who does not belong to any clubs in a given year.
The outcome of interest is to study a person's degree of involvement in social activities (on an ordinal scale).
One way to code the data is to have all individuals (id(s)) nested within year(s) and then have their club affiliation crossed-classified. This should look like the format suggested in this statalist thread; however, the data I have also contains many time-varying individual attributes which I would like to use as regressors so the recommended Stata
xtmixed syntax may not suit my case.
Alternatively, I am also thinking about modeling the outcome variable for each club (formed in a given year) separately as one panel so as to make those time-varying individual attributes "constant" in that year. Suppose the data records j clubs formed among n persons between the 25-year time span in the data, then I should have j panels and a total of j * n rows. But I am not sure if this makes any statistical sense.
Have anyone here dealt with data of this property before and could share insights on how to code and estimate this kind of data using Stata and/or R's
Much thanks to Erik for commenting on this. My earlier communication with Stata statistician suggests that one should use "wide format" to code each club as separate columns and then use each "id-year" observation's club membership to fill in each cell entry below those separate club columns as shown below. (also recommended using
as compared to the hierarchical format included in Erik's reply.
The wide format coding essentially treats each club affiliation as a separate level (to borrow a jargon from hierarchical model), it needs fewer rows (for each id, should it belongs to more than 1 club in a given year) but can get unwieldy as the number of higher level units (e.g., clubs) increases. Also, it's unclear how the outcome variable, y, should be coded. Some recent papers (e.g., Mo and Wellman (2014) follow Simmel (1955) and Breiger (1974) by treating such cross membership as meta level and use the share of one's involvement in a group as cell entry (for example, if a person joins three clubs in a given year, then its cell entry in the three club columns are all 0.333), but it's also unclear if researchers should take other club members' (say, j's and k's) club involvement into account when calculating person i's club involvement as well as what these cell entries add up to within an id or in the cross-section.
Erik's method makes more intuitive sense but I suspect more entries associated with a particular set of ids (because they are more connected, having more friends, etc.) in a given year will create unbalanced panel. My other proposed solution will be to treat each club formation from the full sample (all possible dyads) or apply cluster errors by club id, but I am not sure if this will violate any distributional assumptions.