Reporting Findings For Cross-Tabbed Variables: Is There A Rule of Thumb on "Minimum Number of Cases"

As I was doing some cross-tabs recently to generate county- and PUMA-level findings with respect to “persons-per-household”, in which I (for purposes of tracking households) cross-tabbed households categorized by three broad age groups (young adults householder, prime working-age householder, and senior householder), by 10 income categories (<$10K, $10K-14K, $15K-$24K, $25K-$29K , . . . ., $150K-$199K, >$200K), by tenure (Own versus Rent), and select number of household variables of interest (such as “not over-crowded living situation”, “over-crowded”, and “severe over-crowding”). (I did the same cross-tabbing to generate findings on the number of persons in households exhibiting the above cross-tabbed traits). Since I was slicing and dicing the cross-tabbed variables in such as granular fashion, I decided to report findings in situations where I had at a minimum 25 cases.

Question: Is there a “rule-of-thumb” as to minimum number of cases whose findings should be reported? I realize if I utilized the replicate weights (which would allow me to also report m.o.e.) I could report findings even for situations where less than 25 cases share the same cross-tabbed variables of interest to me – but, since I use Excel I prefer to not do this. So, I’ve used the “minimum” number of cases as a short-hand approach to focusing only on findings that I might have some level of comfort in. I came up with “25” as the cut-off purely by random.

In general there is no real “rule-of-thumb” regarding a minimum number of cases required for valid estimates. Generally speaking, the more observations that meet a given set of conditions, the more precise the estimate will be. That is, with more observations a given estimate will have a smaller margin of error. Your approach sounds just as reasonable as any, however you may want to look into calculating some measure of a margin of error for your estimates. You can estimate standard errors or standard deviations of your estimates in Excel by using the built-in functions. If you are applying sampling weights, you can follow this documentation on how to calculate a weighted standard deviation in excel.