uscountvotes on Wisconsin Public Radio tomorrow

[bananas]

A couple of interesting programs on wpr tomorrow.
(Wisconsin Public Radio)
Listen live online at wpr.org
The shows are also archived there.


THE IDEAS NETWORK PROGRAM NOTES FOR THURSDAY, JUNE 2, 2005

...

3:00 PM Ben Merens - 06/02K
After three, Ben Merens talks with a researcher who says a large
discrepancy between exit polls and vote counts in the 2004 election
has still not been accounted for. Guest: Ron Baiman, researcher,
Institute of Government and Public Affairs, University of Illinois at
Chicago. Volunteer with U.S. Count Votes’ National Election Data
Archive Project. www.uscountvotes.org

4:00 PM Ben Merens - 06/02L


5:00 PM Ben Merens - 06/02M
Ben Merens’ guest, after five, says Alan Greenspan has had an
extraordinary... and largely negative.. impact on the economy over
his tenure as Federal Reserve chair.Guest: Ravi Batra, Professor of
Economics, Southern Methodist University. Author, “Greenspan's
Fraud: How Two Decades of His Policies Have Undermined the Global
Economy”

...

[Wilms]

1. .

http://wpr.org/merens

To join Ben's program live, call toll-free 1-800-486-8655 or 227-2050 if you're in the Milwaukee area.

[glitch]

10. For those that missed the show, like me, they provide realplayer archives

I love shows that do that:
http://wpr.org/webcasting/ideas_audioarchives.cfm?Code=bme

[Febble]

2. New post from Bruce O'Dell, VP of USCV, here:

http://www.democraticunderground.com/discuss/duboard.php?az=show_mesg&forum=203&topic_id=372464&mesg_id=374259&page=

might be of interest. I've copied it below:

USCV's Working Paper - and this analysis of WPE - is fatally flawed

I'm Bruce O'Dell - the Vice President and co-founder of US Count Votes.

With all due respect, I believe Ron's interpretation of Mitofsky's findings is fundamentally mistaken, and so is the USCV Working Paper, first published May 12.

After unsuccessfully working within US Count Votes to revise or retract the Working Paper that a minority of the USCV membership recently published, I see no alternative but to publicly challenge the report’s methodology and conclusions.

The key argument of the USCV Working Paper is that Edison/Mitofsky’s exit poll data cannot be explained without either (1) highly improbable patterns of exit poll participation between Kerry and Bush supporters that vary significantly depending on the partisanship of the precinct in a way that is impossible to explain, or (2) vote fraud. Since they rule out the first explanation, the authors of the Working Paper believe they have made the case that widespread vote fraud must have actually occurred.

However, a closer look at the data they cite in their report reveals that Kerry and Bush supporter exit poll response rates actually did not vary significantly by precinct partisanship. Systematic exit poll bias cannot be ruled out as an explanation of the 2004 Presidential exit poll discrepancy – nor can widespread vote count corruption. The case for fraud is still unproven, and I believe will never be able to be proven through exit poll analysis alone.

The fact that I chose not to endorse the USCV Working Paper should be a clear indication that I do not support its central thesis, and in fact believe that the simulation data they cite refutes the Working Paper’s conclusions.

I am not a statistician, but as a computer systems architect, I create mathematical models to simulate the performance of large-scale computer systems, and mathematical simulation of the cost and efficiency of business processes is a significant part of my consulting practice. My own election simulation results are cited on pp. 9 -10 and in Appendix G of the May 12th Working Paper; as the creator of the only USCV simulation which accurately reproduces aggregate Mean WPE, Median WPE and participation rate data from the E/M January report, I feel an obligation to ensure that my work is correctly interpreted.

I can show that several of the USCV election simulation programs are flawed, and that when the Liddle Bias Index is applied to the “USCV O’Dell simulation” data cited in the Working Paper, it produces results consistent with those recently reported by Warren Mitofsky for the E/M data as a whole.

I respect Ron's opinion, but his insistence on using aggregate WPE as a tool to interpret poll response bias (or vote fraud) is mistaken. His analysis of the Liddle Bias Index is also off-target. Liddle's Bias Index is an inherently superior metric to WPE, and analyses based on aggregate WPE are highly misleading.

I've written a paper that addressed this issues in detail, that can be found at www.digitalagility.com/data/ODell_Response_to_USCV_Work... .

If anyone can show me where I'm wrong, I'll be the first to admit it.

I'm disappointed that I was not able to resolve our disagreement within USCV, but I simply cannot allow a fundamental misinterpretation of my data - the USCV O'Dell simulator they cite in their paper - to continue to go unchallenged.

In addition responding to this posting, please feel free to contact me at my email address at USCV, [e-mail] - or at my corporate email address at [e-mail] if you have any questions.

[Febble]

3. And I should declare an interest

in drawing people's attention to Bruce's post, as I share his view.

But given the skepticism which which my paper, and my views have been, rightly, regarded on DU (skepticism is good), I think it is important for people here to be aware that my view is not just that of an isolated USCV renegade, but is largely shared by a man who has worked tirelessly to advance the core mission of US Counts Votes, and whose integrity is simply not in doubt.

I agree completely with Bruce that neither vote-count corruption nor differential response bias can be ruled out by the evidence we now have.

All we can say is that variance in bias (whether in count or poll) was enormous - in both directions, and across the whole spectrum of precincts.

[pauldp]

4. OK... and when are we going to see the raw data? n/t

[Febble]

5. I have no idea.

"Scrubbed" data was made available for the ESI study in Ohio, so it seems it is possible.

[pauldp]

6. I have an idea...

It has nothing to do with how the Liddle Bias Index is applied to the “USCV O’Dell simulation” or WPE or scatter plots - I'm not a statistician. BUT maybe all of you statisticians could agree on one thing - We need the raw exit poll data. How about we get as many of you together as we can - 100 or 1000, or 10,000 and hold a press conference - jeez even a petition would be nice, and demand that - whoever it is - Mitofsky or the Media or better yet both of them, release the raw data.

What do you think ?

[Febble]

7. I'm the wrong person to ask

because I work in a psychology department where there are incredibly strict ethical guidelines on participant confidentiality. We have to go through all kinds of hoops to preserve confidentiality and to ensure that no participant is traceable from the data. AAPOR has its own ethical guidelines, and these will (and should) prevent any public release of truly "raw" data.

"Scrubbed" data on Ohio was prepared and released. I imagine other releases are possible, given funds (the preparation time would be extensive). But the exit polls were not designed to audit the election - and are a very poor tool with which to do it. Even money spent getting "scrubbed" data sets may not give the answers you want.

The election itself ought to be auditable. That's why I am totally with the USCV core mission to secure fair and auditable elections - and to create an election data archive where precinct-level data can be stored and analysed and anomalies detected.

[pauldp]

8. Would scrubbed data give you any anwers that you want? n/t

[Febble]

9. Probably

What is required, as Ron Baiman has frequently pointed out, is a multiple regression analysis, where some measure of the discrepancy (traditionally WPE, but better, I suggest, something like my function which is the log of the ratio between the sampling rates for each candidate) is regressed on factors likely to be associated with sampling bias. If this accounts for all the bias, then fraud is not ruled out, but can probably ruled out as a major causal factor in the discrepancy.

If it doesn't then it leaves some question.

One problem is that we are short of good fraud hypotheses - there are plenty of factors we know might affect sampling bias, and we know in which precincts these factors were present (from the E-M report) - what we don't know is whether they added up to a sufficient explanation for the total bias, because no statistical details were given in the report.

But by definition, we don't know where fraud occurred. If we could find a "fingerprint" to look for, that would help, and that is where these simulations (Bruce's; mine, I would argue) help. Where I don't personally find the Baiman Dopp analysis helpful is that it doesn't have an a priori fraud hypothesis - it seems rather to look at a rather meagre set of data points (the ones in the E-M report) and infer a fraudulent pattern from them. Which was sort of fair enough, when it was all we had. But we know have more, and it doesn't really support their inference.

Bruce's simulation is terrific. It doesn't prove fraud, nor does it prove that the explanation for the discrepancy is differential response rates. But it does suggest the kinds of pattern we might want to look for as a finger print of fraud, given what we know about the way that such a fingerprint may be hidden. And it does look as though it is hidden, if it is there at all.

Whether the fingerprint is really there or not, we may never know. Just because the finger-print is missing doesn't mean the crime didn't happen. It just means that fingerprints aren't going to be very useful for the prosecution case.

But it may also be worth saying that the better the non-fraud variables account for the data, the less chance there is of finding wiggle room for major fraud in the exit polls.

[OnTheOtherHand]

11. it depends on how you "scrub"

The idea behind the Ohio analysis was to introduce enough noise to make it impossible to match precincts exactly (and, therefore, match people exactly), but not so much that it would substantially interfere with statistical analysis.

Some very smart and (as far as I can tell) honest people worked on that project, but I haven't looked closely at their work (the writeups weren't available last time I checked), so I don't know.