jackman.stanford.edu/blog
• 114th U.S. Senate
• ideal point estimates svg pdf csv 3/4/15
• scatterplot against 2012 Obama vote share svg pdf
• roll call object: RData
• 114th U.S. House
• ideal point estimates svg pdf csv 3/4/15
• scatterplot vs Obama vote share svg pdf
• roll call object: RData
• Bayesian Analysis for the Social Sciences Wiley; Amazon; errata as of 5/23/13

## Thursday May 31, 2012

Filed under: Australian Politics — jackman @ 5:26 pm

The Coalition is at 1.17 at Centrebet; the ALP is at 7.00 at sportsbet.

1/1.17 + 1/7 = .9976 < 1. But not by much.

Do ya best.

## Sunday May 27, 2012

Filed under: flight nerdery — jackman @ 3:30 pm

Janet got back this morning from visiting family in Brisbane. She went on Star Alliance miles, with the return we could find being via Singapore and Narita (not the most direct BNE-SFO routing).

Janet flew over 20,000 miles flown on this trip. That is the equivalent of 4 SFO-JFK roundtrips, or about 2 SFO-LHR roundtrips. And this is the West coast of the US to the East coast of Australia. NYC – Perth via DFW and BNE is 11,940 miles, which is an awful long way for a family visit.

Interesting coincidence that BNE is basically on the GC route between AKL and SIN, and that NRT is close to the SIN-SFO GC.

## Friday May 25, 2012

Filed under: politics,statistics — jackman @ 10:28 am

“Good luck with that…”

How many times do you see a post to a list like PolMeth where someone is looking for data on x, by state, by year? What would see if you looked at the helpful follow up posts? This x is over there, but only goes back annually to 2000, then in five year intervals to 1980. Some other variable lives somewhere else, but only for Census years, but does goes back to 1900. Or, the Census has x in a flat file ready for download, but only for 2010. Really quite a mess… Lots of scanning or key-punching, scraping and merging. And probably much wasted effort, when one thinks about people having to do this for themselves, across the profession..

Comparative politics has resources like the Penn World Table, or the Luxembourg Income Survey, etc. IR has COW, MAR, Polity, Freedom House. The World Bank. The IMF. The OECD. The UN.

Why is data about the American states so much harder to get? Or am I missing something?

## Tuesday May 22, 2012

Filed under: general — jackman @ 9:42 pm

“And now for something completely different…”

Click on the thumbnail for higher-res JPG.

## Saturday May 19, 2012

Filed under: ANES,statistics — jackman @ 5:51 pm

As one of the PIs of the 2012 ANES, I gained some exposure to the nitty-gritty of how area probability samples work in practice. We’re using an ABS-frame (the USPS Delivery Sequence File), which we will supplement with some field enumeration in Census tracts where the DSF is thought to be subject to a reasonable amount of under-coverage.

What I’ve learned thus far:

(1) Kish’s Survey Sampling remains something of a bible for practitioners.

(2) This book really is for practitioners, with large sections devoted to what actually occurs in the field, how to walk around a block, listing addresses, etc. Its odd to read this stuff. I mean the rubber does have to hit the road at some point. But so much of it seems a little, well, folksy and even ad hoc, unless I’m missing other parts of the book where the underlying rationales are more rigorously explicated. I guess it has to be that way, when you are trying to keep things simple for the non-statistician field workers.

(3) Take this, the case of how to augment a listing of dwellings when the field worker encounters dwellings not on the list (in our case this would be finding dwellings not on the DSF adjacent to a dwelling sampled from the DSF). From pp341-2 of Survey Sampling (JPGs below are clickable thumbnails), something of a “how-to” guide for the Half Open Interval procedure:

Take all unlisted dwellings if there are less than 5 of them? What is special about 5? If 5 or more, “write the office quickly” (presumably today, you’d call) and “wait for instructions”. And what, exactly, will those instructions be?

I’m sure there is some well-worked out basis for these recommendations somewhere, perhaps elsewhere in the book. At p56 Kish says that the “missed [but discovered] elements receive the same probability of selection as the pre-specified unique listings”.

Ok, but might you get too many unlisted dwelling this way? Interviewer workload becomes an issue then, which where I guess “no more than 5″ might come from.

But could you exploit whatever prior information about DSF under-coverage specific to the locality you’re working in? And at that point I guess you might be stratifying dwellings in a given geographic unit into listed and unlisted and heading towards a dual frame design etc.

Sub-sampling seems another idea: e.g., the design calls for r attempted interviews in a given locale. We sample r listed dwellings in the locale from, say, the DSF; field enumeration adds k to the frame around the listed r, we attempt interviews at r SWOR from the r+k? This keeps the IWR workload down to r attempted interviews and the selection probabilities are “known”.

The literature on snowball or “respondent-driven” sampling in social network land must have some relevant ideas here too. Hitting r listed dwellings and then looking around for unlisted dwellings seems a lot like what goes on with sampling on networks for “hidden” populations etc.

Finally – I have to note that this stuff really is probably 2nd order at best. We’re doing our best on the design for the in-person components of ANES 2012, I think. But there is this big scary monster out there, waiting for us in the Fall when we go into the field, and its name is non-response. As a source of bias this has to be 10x what we’re looking at from DSF under-coverage.

## Friday May 11, 2012

Filed under: politics — jackman @ 5:05 pm

The fight to save the American Community Survey crowds out the voices trying to keep NSF funding for political science…

## Wednesday May 9, 2012

Filed under: politics — jackman @ 11:45 pm

At 11.57pm tonight the House passed an amendment to HR 5236 cutting NSF funding to political science, 218-208. 5 Dems voted Aye. 27 Reps voted Nay.

## Sunday May 6, 2012

Filed under: Australian Politics — jackman @ 2:43 pm

This could well be the longest odds I’ve ever seen in Australian national-level political betting markets. 6.00 to 1.13 at Sportsbet at 7am this morning, Sydney time. I’ll ransack the historical data to verify that.

Others will likely follow as that 15% ROI offered by Centrebet (inter alia) for a Coalition win gets stomped on.