Using robust design models, I am getting very similar standard errors for my beta values for parameters where the structures are an interaction of two categorical factors. Below is an excerpt from the MARK output for a model where S(sex*season). I just wanted to put out feelers and see if anyone has experienced anything similar, and/or if anyone sees this as problematic.
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LOGIT Link Function Parameters of { S(~SEX:Season)Gamma''(~SEX)Gamma'(~SEX)pi(~1)p(~session)c(~session) }
95% Confidence Interval
Parameter Beta Standard Error Lower Upper
------------------------- -------------- -------------- -------------- --------------
1:S:(Intercept) 0.2279715 36.828500 -71.955890 72.411833
2:S:SEXF:SeasonFall 0.5941570 36.829831 -71.592313 72.780627
3:S:SEXM:SeasonFall 0.9831622 36.830056 -71.203750 73.170074
4:S:SEXF:SeasonSpring -0.2447608 36.828685 -72.428984 71.939462
5:S:SEXM:SeasonSpring 0.3811150 36.828570 -71.802884 72.565114
6:S:SEXF:SeasonSummer -0.0984097 36.828839 -72.282935 72.086116
7:S:SEXM:SeasonSummer 0.5520083 36.828778 -71.632398 72.736415
8:S:SEXF:SeasonWinter -1.1145898 36.829233 -73.299887 71.070708
9:S:SEXM:SeasonWinter -0.8813907 36.828749 -73.065741 71.302959