Hi, everyone. I woke up with a bit of insomnia, so I thought that I would post something that had occurred to me in my sleep. (Yes, I know that it makes me an inveterate dork that I think about these things in my sleep. I don't care.) Recently, my colleague, Student Redux, has been exploring methodological problems in sampling and weighting, and I had an idea that I wanted to explore as a possible solution.  Now, granted, I'm not committed to this, and I'd love to have your comments as further exploration of the topic.

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Hi, everyone.  Last time, we discussed the mechanics of random sampling in SPSS. To summarize, we discussed how you randomize a list and then how you pick out some 500 records from the list at random. Today, we're going to talk a little bit more about what we did and why we did it that way.

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In the course of a long and fascinating post on the Public Policy Polling blog, Tom Jensen notes something that I found kind of neat:

Another thing to consider when looking at polls is the number of respondents- not just whether it's a large or small number, but whether it's a nice even number or not. If it's a nice even number a lot of the time that means a pollster is just getting a set number of interviews and then stopping. That may have the potential to undercount support for Obama, again because his folks are harder to find. If it's a random number, that's an indication that folks have their sample and they've decided they're going to call everyone in it 'x' number of times and try to get as many respondents as they can get.

Overall, it's an interesting read about the potential for undercounting Obama supporters, and a good "behind-the-scenes" look at what goes into a poll.  If you're not reading the PPP blog, you should be.

UPDATE: Another cool thing that PPP has been doing recently--allow readers of the blog to vote on where they should poll next.  There's one up now; you can vote for either Georgia or Wisconsin.  Check it out.

Pursuant to some correspondance with my colleagues Blue Leader and Dirty D, I'd like to give a brief follow-up to my post from Monday about the problems associated with stratified sampling.  BL and DD brought up a pair of topics related to my last post but not covered therein: demographic weighting in poll samples, and cluster sampling.  These are alternate methodologies that can be used in polling, and both deserve separate discussion.  Now that I've established some groundwork on the topic of stratified sampling, I think it should be easier to tackle these two topics.

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It's been a while since we've had an update in the Building a Poll series, and I'm excited to pick it up again. When we last left off, we were talking about using a list strategically. I'd like to continue on the subject of using lists, but we're going talk about something more mechanical aspect of using the list: building a random sample.

Granted, if you're a pollster, you're likely to have purchased a random sample from your data vendor, but it never hurts you to understand the mechanics of how this works.  Heck, maybe one of your clients stuck you with a membership list or some other targeted list and asked you to call a random sample of that list.  You can't very well send that list off to your data vendor and ask them to sample it for you!

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Politicians and the public have an historically uneasy relationship, governed by two competing paradigms: (1) politicians ask the public what it wants, or (2) politicians tell the public what it wants.

For those of us who grew up in democratic societies, it’s easy to forget that the weight of historical precedent falls squarely on the second paradigm.  Popular sovereignty isn’t a new invention – the Greeks and Romans toyed with it millennia ago – but history shows us far fewer democracies than dictatorships.

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