Mulitple regression using backward entry

Hi everyone,

can someone give me advice on sample size when conducting a linear regression using the backward method of entry (note the backward entry, as this has created this quandry)?

I've seen various guidelines about sample size in relation to the number of IVs being investigated (e.g., Tabachnick & Fidell) who suggest N >= 104 + m, where m = number of independent variables. If this is the case, do I calculate my sample size:

a) of 104 and the value of 'm' after running the multiple regression (strange I know), so that I can see the number of predictors appearing in the final model after all non-significant predictors have been discarded?

b) Or is the inverse correct, whereby I calculate 104 plus the value of 'm', where 'm' is taken to be the number of IVs inputted into the multiple regression to begin with?

I basically need to know since there are cases of missing data, and I'd much rather exclude them listwise, but the final N hovers around 110 (fine if sample size is calculated via method (a) above since the final model produced by the multiple regression has about 6 predictors), but obviously not fine when considering (b) as I have about 20 IVs inputted into the model at the start.

I've also noted guidance where sample size should be 20:1 (20 cases for every one IV) - again, problematic if calculated with (b), but not (a).

Hope that makes sense to someone out there. Here's hoping and praying for guidance.

Thanks all.

Jon