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Voting Machines and Electoral Results in Florida:

The Statistical Evidence

http://ustogether.org/ , November 21004
www.globalresearch.ca 12 November 2004

The URL of this article is: http://globalresearch.ca/articles/DOP411A.html


Editor's note

we bring to the attention of our readers, the incisive analysis of Kathy Dopp, who carefully analyzed the Florida election results, the day after. Also included are further statistical analyses of Elizabeth Liddle and Josh Mitteldorf.


PART I

Surprising Pattern of Florida's Election Results

by Kathy Dopp

Wednesday November 3, 2004

Look at the Percent Change columns.
Notice how the percents vary much more widely in the Op-Scan counties versus the Touchscreen counties.

Explanation, Sources, and Graphical Plots are Below the Chart
Voting Machine Type by County 2004

New! Cross-party voting in Florida seems to depend on the local voting technology

While the heavily scrutinized touch-screen voting machines seemed to produce results in which the registered Democrat/Republican ratios largely matched the Kerry/Bush vote, in Florida's counties using optically scanned paper ballots the results seem to contain anomalies. Mathematicians are interested in investigating the November 2004 election because if exit polls from various states use the same scientific methodology, then the likelihood of election results being significantly different than exit polls results in half a dozen swing states is very very low. By the 2006 election, we need by county exit polls to do a better analysis.

Note: This is a scientific study. Small op-scan counties must be excluded for valid analysis. This relationship with voting machines is statistically significant. No conclusions as to the causes of the pattern can be drawn at this time. I am putting my ideas for a complete study out to statisticians and programmers to be able to fully analyze 2004 election results beginning with Florida, so that we can develop and test the efficacy of a system to put in place by 2006 to pinpoint counties or even precincts which warrant recounts.


Please Subscribe to our mail list for updates and to learn how you can help this project. With your help, we can put measures in place by 2006 to find patterns allowing us to pinpoint possible election rigging/hacking/innocent-errors by the day after the election, so that candidates will immediately know if precincts or counties need to be recounted prior to conceding.


 
E-Touch Voting         

(%Regist)*(TotalVotes)

 

 

(Actual-Exp)/(Exp)

 

COUNTY vendor REGISTERED VOTERS ACTUAL RESULTS EXPECTED_VOTES PERCENT CHANGE
%REP %DEM TOT_REG REP DEM TOT_VOTES REP DEM REP DEM
Broward ES&S 26.8% 50.5% 1,058,069 236,794 441,733 686,715 184,152 346,565 28.6% 27.5%
Charlotte ES&S 44.9% 31.9% 113,808 44,402 34,227 79,730 35,806 25,435 24.0% 34.6%
Collier ES&S 53.1% 24.4% 168,673 82,493 43,277 126,916 67,388 30,912 22.4% 40.0%
Hillsborough Sequoia  35.1% 41.7% 621,201 241,630 210,892 455,970 159,843 190,023 51.2% 11.0%
Indian River Sequoia  51.3% 30.0% 81,643 36,744 23,850 61,087 31,325 18,343 17.3% 30.0%
Lake ES&S 47.4% 34.3% 161,269 73,971 47,963 123,269 58,388 42,237 26.7% 13.6%
Lee ES&S 47.5% 29.7% 304,937 114,153 76,874 193,326 91,895 57,513 24.2% 33.7%
Martin ES&S 52.5% 27.5% 98,857 41,303 30,149 72,334 37,953 19,905 8.8% 51.5%
Miami-Dade ES&S 34.8% 42.8% 1,058,801 326,362 383,032 713,022 248,045 305,486 31.6% 25.4%
Nassau ES&S 49.1% 36.8% 41,353 23,726 8,543 32,656 16,031 12,017 48.0% -28.9%
Palm Beach Sequoia  32.0% 45.1% 729,575 174,233 275,030 452,061 144,679 204,000 20.4% 34.8%
Pasco ES&S 40.1% 37.3% 265,974 103,195 84,729 190,861 76,531 71,237 34.8% 18.9%
Pinellas Sequoia  39.2% 37.8% 590,989 222,630 222,103 448,875 175,947 169,789 26.5% 30.8%
Sarasota ES&S 47.9% 31.2% 240,592 104,446 88,225 195,183 93,552 60,833 11.6% 45.0%
Sumter ES&S 43.5% 40.8% 40,523 19,794 11,583 31,835 13,851 13,004 42.9% -10.9%
5,576,264 1,845,876 1,982,210 3,863,840 1,435,385 1,567,297
 

 

         
Op-Scan Precinct       (%Regist)*(TotalVotes)  

(Actual-Exp)/(Exp)

 

COUNTY vendor REGISTERED VOTERS ACTUAL RESULTS EXPECTED_VOTES PERCENT CHANGE
%REP %DEM TOT_REG REP DEM TOT_VOTES REP DEM REP DEM
Alachua Diebold 27.8% 50.5% 142,358 47,615 62,348 111,022 30,887 56,111 54.2% 11.1%
Baker Sequoia  24.3% 69.3% 12,887 7,738 2,180 9,955 2,415 6,895 220.4% -68.4%
Bay ES&S 44.2% 39.2% 101,315 53,305 21,034 74,890 33,079 29,351 61.1% -28.3%
Bradford ES&S 28.3% 61.4% 14,721 7,553 3,244 10,851 3,072 6,663 145.8% -51.3%
Brevard Diebold 44.8% 36.5% 338,195 152,838 110,153 265,075 118,772 96,860 28.7% 13.7%
Calhoun Diebold 11.9% 82.4% 8,350 3,780 2,116 5,961 709 4,911 433.2% -56.9%
Citrus Diebold 41.5% 38.9% 90,780 39,496 29,271 69,457 28,809 27,039 37.1% 8.3%
Clay ES&S 56.5% 25.6% 106,464 61,813 18,887 81,144 45,877 20,794 34.7% -9.2%
Columbia Diebold 31.3% 56.5% 34,282 16,753 8,029 24,984 7,825 14,119 114.1% -43.1%
DeSoto Diebold 25.4% 59.3% 14,901 5,510 3,910 9,493 2,413 5,630 128.4% -30.6%
Dixie Diebold 15.0% 77.5% 9,676 4,433 1,959 6,440 968 4,988 358.1% -60.7%
Duval Diebold 36.9% 46.2% 515,202 218,476 157,624 378,330 139,605 174,965 56.5% -9.9%
Escambia ES&S 43.8% 40.7% 189,833 93,311 48,207 142,895 62,602 58,149 49.1% -17.1%
Flagler Diebold 40.7% 38.1% 47,068 19,624 18,563 38,455 15,669 14,657 25.2% 26.6%
Franklin ES&S 15.9% 77.3% 7,620 3,472 2,400 5,930 943 4,586 268.1% -47.7%
Gadsden ES&S 11.2% 82.9% 26,884 6,236 14,610 20,948 2,347 17,361 165.7% -15.8%
Gilchrist Diebold 30.4% 58.6% 9,035 4,930 2,015 7,007 2,133 4,106 131.2% -50.9%
Glades Diebold 24.8% 64.8% 5,963 1,983 1,434 3,434 852 2,227 132.8% -35.6%
Gulf ES&S 26.6% 67.1% 9,627 4,797 2,398 7,259 1,928 4,874 148.8% -50.8%
Hamilton ES&S 14.9% 78.9% 7,645 2,786 2,252 5,065 755 3,994 268.9% -43.6%
Hardee Diebold 26.7% 63.8% 10,399 5,047 2,147 7,245 1,936 4,619 160.7% -53.5%
Hendry ES&S 30.8% 56.5% 17,144 5,756 3,960 9,774 3,010 5,523 91.3% -28.3%
Hernando Diebold 41.3% 38.8% 109,656 40,137 35,006 75,832 31,303 29,428 28.2% 19.0%
Highlands ES&S 44.5% 39.8% 60,176 20,475 12,986 33,687 14,976 13,401 36.7% -3.1%
Holmes ES&S 21.3% 72.7% 10,982 6,410 1,810 8,298 1,771 6,036 261.9% -70.0%
Jackson ES&S 22.0% 71.5% 27,138 12,092 7,529 19,750 4,339 14,127 178.7% -46.7%
Jefferson Diebold 20.7% 72.3% 9,300 3,298 4,134 7,477 1,551 5,408 112.7% -23.6%
Lafayette ES&S 13.2% 82.8% 4,309 2,460 845 3,325 440 2,755 459.3% -69.3%
Leon Diebold 26.6% 57.1% 171,182 47,902 79,591 128,316 34,165 73,214 40.2% 8.7%
Levy Diebold 27.6% 59.7% 22,617 10,408 6,073 16,649 4,594 9,940 126.5% -38.9%
Liberty ES&S 7.9% 88.3% 4,075 1,927 1,070 3,021 237 2,667 712.3% -59.9%
Madison Diebold 14.9% 79.5% 11,371 4,195 4,048 8,306 1,238 6,605 238.8% -38.7%
Manatee Diebold 44.3% 33.0% 191,635 81,237 61,193 143,469 63,489 47,394 28.0% 29.1%
Marion ES&S 43.2% 39.7% 184,257 81,235 57,225 139,581 60,279 55,427 34.8% 3.2%
Monroe Diebold 38.7% 36.1% 51,377 19,457 19,646 39,517 15,286 14,278 27.3% 37.6%
Okaloosa Diebold 57.2% 24.7% 127,455 69,320 19,276 89,288 51,059 22,085 35.8% -12.7%
Okeechobee Diebold 29.7% 58.5% 18,627 6,975 5,150 12,184 3,622 7,124 92.6% -27.7%
Orange ES&S 35.1% 40.2% 531,774 191,389 192,030 385,547 135,299 154,938 41.5% 23.9%
Osceola Diebold 32.8% 40.2% 129,487 32,812 30,295 63,440 20,804 25,508 57.7% 18.8%
Polk Diebold 39.0% 42.6% 295,742 123,457 85,923 210,642 82,059 89,651 50.4% -4.2%
Putnam Diebold 28.1% 57.7% 45,344 18,303 12,407 30,960 8,690 17,878 110.6% -30.6%
Santa Rosa ES&S 55.9% 28.1% 96,359 51,952 14,635 67,175 37,543 18,880 38.4% -22.5%
Seminole Diebold 44.6% 32.3% 241,230 107,913 76,802 185,762 82,869 60,037 30.2% 27.9%
St.Johns Diebold 53.3% 28.3% 109,635 58,802 26,215 85,699 45,678 24,272 28.7% 8.0%
St.Lucie Diebold 36.6% 41.4% 137,951 38,919 43,367 82,798 30,272 34,288 28.6% 26.5%
Suwannee ES&S 26.8% 63.6% 21,930 11,145 4,513 15,785 4,236 10,035 163.1% -55.0%
Taylor Diebold 18.9% 75.6% 11,481 5,466 3,049 8,580 1,622 6,486 237.1% -53.0%
Union ES&S 18.3% 75.5% 7,063 3,396 1,251 4,675 855 3,529 297.4% -64.5%
Volusia Diebold 35.9% 40.8% 309,930 100,209 106,853 208,410 74,891 85,000 33.8% 25.7%
Wakulla Diebold 24.2% 66.9% 15,396 6,777 4,896 11,763 2,850 7,864 137.8% -37.7%
Walton Diebold 50.1% 36.8% 32,777 17,526 6,205 23,939 11,987 8,802 46.2% -29.5%
Washington Diebold 25.4% 67.0% 14,421 7,367 2,911 10,363 2,634 6,947 179.6% -58.1%
4,725,026 1,950,213 1,445,675 3,419,852 1,337,242 1,432,425

Note: Election Results were taken on Nov 3, when the Florida vote was 98.6% in and the Voter Registration Numbers are from 10-04.

Explanation of What these numbers are, and how they were calculated:

PERCENT CHANGE for DEM, for example, = (Actual DEM Vote - Expected DEM Vote) / (Expected DEM Vote)

This is a simple percent change measure taught in highschool mathematics.

EXPECTED_VOTES REP = the percentage of registered REP * the total number of voters who voted in each county on Tuesday.

EXPECTED votes would normally vary from the ACTUAL votes due to increased voter turnout by one party, Independents voting REP or DEM or other factors. What seems very odd in these numbers is that the increase in ACTUAL votes from EXPECTED votes has a striking pattern of being so much higher for REPs than that for DEMs in counties using optical scan voting machines, even when smaller counties are excluded from the analysis.

http://enight.dos.state.fl.us/ and http://election.dos.state.fl.us/voterreg/index.shtml for registered voters by county and election results by county
http://vevo.verifiedvoting.org/verifier/ for voting machine type by county


Statistical Analysis and Visual Charts of the Data

Graphical Plots of FL 2004 Data
Simple pictures of counties by voting machine type - Op Scan- Precinct Counties and Touchscreen Counties
Statistical Significance FL 2004 & Graph
Pearson's Correlations FL 2004
Interesting but Not Rigorous because the data was plotted using counties with smaller population.

Criticisms of Our Work & Our Responses

An analyses of our data http://synapse.princeton.edu/~sam/royle_florida.html which neglected to remove smaller counties from the study before doing the analyses and so is not a valid critique of our analyses but is interesting. Here is another critique of our analyses by Poli Scientists and explanations of why these Cornell interpretations aren't suported by the data by Elizbeth Liddle and Marc Sapir and Kathy Dopp .

Other Election Results by County:

Florida Presidential 2000
Pennsylvania Presidential 2004


An open source election and vote-counting system with voter verifiable paper ballot and two independently-programmed, always-reconciled ballot counting system that needs your support.

Voters nationally voted along party lines by about 90% and Florida exit polls favored Kerry. Interesting manipulations have been done to the exit polls after the election to change their results. Further study is needed of other numerical by county measures for Florida and other states' election results and races. This site was mentioned in a http://www.house.gov/judiciary_democrats/gaoinvestvote2004ltr11504.pdf letter from three congressmen to the GAO urging an investigation.

And an interesting look at this data from Florida.

Truthout and Thom Hartman of CommonDreams is covering us. http://www.truthout.org/docs_04/110804Z.shtml


PART 2

2004 Presidential Florida By County By Voting Machine Type Election Analysis

by Elizabeth Liddle

This analysis is derived from the above tables presented by Kathy Dopp  this Florida Election Data. Graph

Op-scan machines tended to be used in counties with small numbers of registered voters, while very largest counties tended to used E-touch, so that the entire two groups of counties (E-touch and Op-scan users in Florida) cannot be validly compared, as county-size itself might account for the data. However, for the 26 mid-sized counties with between 80,000 and 500,000 registered voters, the type of machine used was not significantly related to the number of registered voters in the county. Eight of these counties used E-touch machines, and 18 used Op-scan machines. There was no significant difference between these two groups of counties in either their numbers of registered voters or their proportion of registered Republicans to registered Democrats. Neither covariate was a significant predictor of change. However, "machine used" was very significant (p<.01), with Op scan favoring repubs.

An analysis of variance (ANOVA) conducted on the percent change for each party ([Actual vote minus expected vote]/expected vote) in each county, with "machine type" as a predictive factor, indicated that machine type was a significant predictor of percent change in voting. Counties using E-touch machines showed significantly positive percent changes in vote for both Republican and Democrat candidates, with greater mean percent changes for the Democrat. However counties using Op-scan machines showed significant positive percent change only for the Republican candidate, the mean change for the Democrat being insignificantly greater than zero.

Caveats: The number of counties is small, and the groups unequal in size; this means that the probability of the results occurring by chance may be somewhat greater than quoted. It is also possible that a county's choice of machine or voting pattern may be influenced by a third factor that also influenced voter behaviour. The magnitude of the apparent effect of voting machine type on voter behaviour nonetheless would seem to warrant investigation.


PART 3

Cross-party voting in Florida seems to depend on the local voting technology

by Josh Mitteldorf

The day after the election, Kathy Dopp noticed a pattern in Florida’s voting that seemed to relate to the type of voting machine used in each county (see data above). Nationwide, exit polls showed that 90% of party-registered voters tend to vote for the party to which they are registered. In Florida ’04, counties that used electronic touch-screen voting showed a shift from this expectation toward Kerry; but among counties that used opti-scan paper ballots, there was a shift toward Bush.

One suggested explanation for this pattern was that it was small counties that haven’t yet made the switch to electronic technologies, and in these areas "Dixiecrats" tend to register Democrat for local elections, but vote Republican in national elections. To test this hypothesis, Elizabeth Liddle refined Dopp’s study, eliminating the smallest counties, all of which used the opti-scan technology, and also the largest counties, which tended to use the touch-screen machines. There remained 26 mid-size counties, 8 of which use touch-screen and 18 use opti-scan. Within this group, there is no significant relationship between county size and voting technology.

A significant, unexplained relationship remained between voting technology and party shift. In the graph, opti-scan counties are represented by blue markers and touch-screen counties by red markers. County size is plotted horizontally, and party shift is the vertical axis. Upward displacement represents unexpected votes for Bush, and downward displacement is unexpected votes for Kerry. It is easy to see that the opti-scan counties shifted toward Bush, while touch-screen counties show a smaller shift toward Kerry.

 

 

On this map, just the 26 mid-size counties are shown in color. Red means opti-scan and blue means touch-screen. The opti-scan counties tended to be more in the north of the state. Still, the effect can be observed in mixed areas like the Southeast. On the right of the map, near the bottom, Red St Lucie County is sandwiched between Martin and Indian River Counties, both Blue. St Lucie County shifted to Bush, while Martin and Indian River shifted to Kerry.

Technical description of the analysis

The registration percentage was defined as R/(R+D), such that independent and third-party registrants were not part of the measure. Similarly, voting percentage was defined as Bush/(Bush+Kerry). Party shift was defined as the difference between Republican voting percentage and Republican registration percentage.

In touch-screen counties, mean party shift was -2.9% in the D direction, with a standard deviation of 3.5% (z=2.3, n=8). In opti-scan counties, mean party shift was +6.0% in the R direction, with a standard deviation of 4.9% (z=5.2, n=18).

Analysis of Variance was performed on party shift as a dependent variable, with county size and voting machine type as the two independent variables. There was no significant relationship between county size and party shift (p=0.6), but the relationship between voting technology and party shift was highly significant (p=0.00026).

Corresponding analysis for results of the 2000 election showed a similar pattern, though less pronounced.

Touch-screen: mean party shift was -6.1% stdev=3.8%, (z=4.5, n=18)

Opti-scan: mean party shift was +4.0% stdev=7.4%, (z=2.3, n=18)

ANOVA: significant relationship between ’04 voting technology and ’00 party shift (p=0.0023) but not county size and party shift (p=0.4).

Note that the same counties were analyzed with the same division, based on '04 voting technology, even though that technology was not in place in most of these counties in 2000.

 


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