Thank you, thank you. Your responses are most helpful

I think I've got you thoughts for dealing with replicate weights. This question is a followup on the last part of that post, which raises a concern about perwt that I still stuck on. Here is the exchange. My, hopefully, clarified questions follows:

CS: "Finally, I'm still thrown by how perwt weights each case so the n matches the population. This makes it so in regresssions even tiny relationships are significant at p < .001. I stil think I need to adjust perwt as I first described so the n matches the sample size. What am I not getting?"

JB: "For each observation, PERWT indicates how many persons in the US population the observation represents. As mentioned above, if you are pooling two IPUMS samples you'll need to adjust the values of PERWT somehow. Otherwise you'll calculate a population size of roughly twice the actual size. I may not have completely answered your question, if so feel free to follow up."

CS: Yes, I understand the need to approximately halve perwt since I'm using two 5-year ACS files combined. This is a separate issue, so let's assume I'm gonna do that as described in my orginal #1 and leave that aside. I think this other weighting problem stems from SPSS treating the weighted data as if the n weighted (population n) is the actual sample size when calculating standard errors. A simple illustration: if I had 100 cases, each weighted 10.0, I believe SPSS computes standard errors and significance levels as if the actual n is 1000 (100 x 10). Thus, since perwt, even ~halved, averages each case representing 10 people, I believe the effect is to greatly lower p values. So when I'm doing OLS regressions explaining occupation earnings score for people ages 25-64 of a small immigrant group with an n of 4500, it is treating it like 45,000 cases, and a number of variables that are not close to significant when I take the weights off, are highly significant with the weights on. So my thought is to save the weighting information in perwt, but reduce them to representing the sample size as the actual sample size, rather than representing the sample size as the population size. I can do this by mulitiplying perwt by sample n/population n. I may be wrong about this, but I don't see my error yet.

Many thanks!