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Comparing the Auditability of Optical Scan, Voter Verified Paper Audit Trail (VVPAT) and Video (VVVAT) Ballot Systems


Stephen N. Goggin and Michael D. Byrne

Department of Psychology

Rice University, MS-25

Houston, TX 77005-1892 USA

{goggin, byrne}


Juan E. Gilbert, Gregory Rogers, and Jerome McClendon

Department of Computer Science and Software Engineering

Auburn University

Auburn, AL 36849-5347 USA

{gilbert, rogergd, mccleje}



With many states beginning to require manual audits of election ballots, comparing the auditability of different types of ballot systems has become an important issue. Because the majority of counties in the United States are now using either Direct Recording Electronic (DRE) voting systems equipped with Voter Verified Paper Audit Trail (VVPAT) modules or optical scan ballot systems, we examined the usability of an audit or recount on these two systems, and compared it with the usability of a prototype Voter Verified Video Audit Trail (VVVAT) system. Error rates, time, satisfaction, and confidence in each recount were measured. For the VVPAT, Optical Scan, and Video systems, only 45.0%, 65.0% and 23.7% of participants provided the correct vote counts, respectively. VVPATs were slowest to audit. However, there were no meaningful differences in subjective satisfaction between the three methods. Furthermore, confidence in count accuracy was uncorrelated with objective accuracy. These results suggest that redundant or error-correcting count procedures are vital to ensure audit accuracy.




Since the Help America Vote Act (HAVA) of 2002, many jurisdictions in the United States have used federal funds intended to help modernize their voting systems by purchasing newer Direct Recording Electronic (DRE) voting machines. With security concerns mounting over purely electronic election results, 37 states have chosen to require physical copies of every ballot cast on an electronic system. The requirement for physical copies of ballots cast is usually met by a voting machine vendor’s implementation of a Voter Verified Paper Audit Trail (VVPAT) system.


VVPAT systems usually consist of a thermal printer attached to a DRE voting system with a spool of ballots enclosed within the machine. Each voter is to inspect his or her paper ballot to verify it matches the electronic record before casting the ballot. These paper records can also be used for a recount. While VVPAT implementations are common, 40.8% of voters in the 2006 election used some type of optical scan voting system (Election Data Services, 2006). These optical scan ballots could also be used in manual auditing procedures. New technologies are being developed as well, such as both Audio (VVAAT) and Video (VVVAT) audit systems.



Currently, 19 states require at least some ballots to be recounted in every election (Verified Voting Foundation, 2008). Of these states, 17 mandate recounts of VVPAT systems, while 2 only mandate recounts of summary results, not individual ballots. As the auditing of elections by manual recounts becomes mandated by more states, it is necessary to examine usability issues in conducting these recounts.


In addition, the draft revision of the federal Voluntary Voting System Guidelines (2007) contains recommendations regarding the manual audit capacity of ballots. Specifically, requirements 4.4.1-A.2 and A.3 in the document specify an Independent Voter-Verifiable Record (IVVR) must have the capacity for a software-independent, manual audit by election officials. While the VVSG requires this, it does not preclude the possibility of machine-assisted auditing, through optical scan and optical character recognition (OCR). In fact, both the original VVSG (2005) and the rewrite specifically demand that IVVR records must contain the ballot information in a machine-readable form.


While Goggin and Byrne (2007) and the Georgia Secretary of State’s Office (2006) have previously examined the auditability of VVPAT ballots, we know of no other research examining human performance with auditing or recounting election records. With states beginning to require auditing of all systems, it is important to examine the impact of different ballot systems in their ability to support a manual audit. While hand audits in studies such as Ansolabehere and Reeves (2004) have usually been considered the “gold standard” against which other vote counts are compared, the way in which election officials can manually audit different types of ballots should also be studied.


While VVPAT and VVVAT systems are both designed primarily for audit purposes, the actual implementation of VVPAT auditing has not been free from problems. For example, the Election Science Institute (ESI) examined all aspects of election administration in Cuyahoga County, Ohio during the May 2006 primary election. The ESI report found that 10% of VVPAT spools were unreadable or missing, while 19% of the spools indicated discrepancies with the reported counts (ESI, 2006). Alternatives like VVVAT systems are still currently under development.


Optical scan ballot systems, while also providing a paper record of a voter’s ballot, are not designed simply for audits; an optical scan ballot is the primary record of the voter’s intentions, which is then read by an optical scan machine. Because a voter interacts with an optical scan ballot by hand using a marking device, most commonly a pencil, this also places the additional burden of not just conducting a recount of computer-printed ballots, but interpreting the marks made by voters on the ballot. Unfortunately, the accuracy and time cost of conducting a manual audit of optical scan ballots after an election has never been systematically examined.


Naturally, the most important characteristic of an audit system should be accuracy, but that should not be the only consideration. The U.S. National Institute of Standards and Technology (Laskowski, et al., 2004) has recommended that voting systems be evaluated on the ISO criteria of effectiveness, efficiency, and satisfaction. While effectiveness can be equated to auditability in that it is a measure of accuracy, it is also important to include the other two metrics in the analysis. If an audit system is not efficient, it may pose unnecessary costs to counties and states that implement it. Furthermore, if auditors are not satisfied with the system they are using, they may lack confidence in the results and undesired and unnecessary strain may be placed on those conducting the audit.


In an important sense, our study represents a best-case audit scenario. All the ballots provided to participants were accurately completed and marked, and in ideal physical condition. While our study does differ from actual auditing in that real audits often use multiple counters for the same ballots to improve accuracy, we sought to establish a base rate of error in auditing that this redundancy guards against.





Twenty-eight adults participated in the study on a volunteer basis. One participant declined to provide their demographic information and complete the second part of the experiment. There were 11 male and 16 female participants (1 declined to report gender), with an average age of 73 years old (SD = 7.5). All participants were fluent English speakers, and all had normal or corrected to normal vision. Eight participants had previously worked as election officials; those that had worked in elections had worked in an average of 16. The sample was quite well-educated, with 4 participants completing some college, 5 with bachelor’s degrees, and 18 holding advanced educational degrees. While this sample is obviously not representative of the overall voting population, it is a reasonable representation of the poll worker population.



Three independent variables were manipulated in the current study, two between-subjects and one within. The first between-subjects factor was technology: participants counted either a spool of 120 VVPAT ballots, 120 optical scan ballots, or 120 video ballots. The second between-subjects variable was the rejection rate, or the number of invalid ballots in the VVPAT spools or the optical-scan ballots. Due to the nature of the Video ballots, no “rejected” ballots could be included in this condition. There were two levels of the rejection rate; high, in which 8 of 120 ballots (6.6%) were invalid, and low, where only 4 ballots (3.3%) were invalid. The within-subjects variable was the closeness of the counted races. In the close condition, the margin of victory was roughly 5% of the total vote, while in the lopsided condition, the margin of victory was roughly 30% of the total vote.


There were three dependent variables measured in the study, each corresponding to one of the three usability metrics: effectiveness, efficiency, and satisfaction. For effectiveness, error rates in the counted totals were used. These were calculated in multiple ways, which will be discussed within the results section. Next, for efficiency, simply the time participants took to count all 120 ballots for one of the races was used. Finally, for satisfaction, the common System Usability Scale (SUS), developed by Brooke (1996) was used. This common, 10-question, standardized subjective scale was used to assess participant’s reactions to the different audit systems; the scores range from 0-100, with a score of 100 representing an ideal technology in terms of usability. Additionally, participants were asked to rate their confidence in the accuracy of their counts on a 5-point Likert scale. To supplement the quantitative results, several open-ended questions were asked of participants about their confidence in the accuracy of their counts and for comments and suggestions regarding problems encountered with the audit system.



All ballots counted were cast based on a fictional, 27-race ballot, originally prepared by Everett, Byrne and Greene (2006). The ballot contained 21 political races and 6 propositions; only 2 of the 27 races were counted by participants. To make the ballots appear similar to those that might be cast in a real election, the ballot roll-off rate, or the rate of abstention as a function of ballot position, was made higher for those races further down the ballot based on the findings of Nichols and Strizek (1995). Specifically, the abstention rate for the upper race audited, the US House of Representatives contest, was set at 9% while for the lower race, County District Attorney, was set at 15%.


The VVPAT ballot spools, identical to those used by Goggin and Byrne (2007), met both the 2005 VVSG standards regarding VVPAT usability in section 7.9.6 (pp. 143-144) and the draft VVSG standards released in 2007. These VVPATs were prepared to appear as similar as possible to those stored in actual DRE machines manufactured by major voting machine vendors (See Figure 1). During an election, these VVPAT ballots are wound onto a secondary spool inside the DRE, after which they are removed and counted. A ballot bore a “rejected” notation at the bottom if it was invalidated by the voter during the verification process, as suggested by the 2005 VVSG in paragraph 7.9.2 (p. 137). Although not all counties use an audit procedure in which the VVPATs are manually separated, participants were allowed to separate the ballots using a scissors during the study to make them easier to count.


The optical-scan ballots were printed on legal-sized paper, and were identical to those first used by Everett, Byrne and Greene (2006) (See Figure 2). The ballots were completed prior to the study in pencil, as they would normally be filled out by voters. In order to match the “rejected” status of ballots for VVPAT’s, some ballots were intentionally over-voted to render them invalid.


Figure 1. Partial VVPAT ballot


Figure 2. Partial optical scan ballot



The video ballots were created using the Prime III system (Cross, et. al., 2007; McMillian, et. al., 2007). The Prime III system uses video surveillance to monitor the voting machines. The voter can review the video screen capture of their own voting process to verify accuracy. This produces a voter-verified video audit trail (VVVAT). During a recount or audit, the video and audio ballots are played back on a video player. The review screen was designed with a yellow background to contrast against the other video frames that contain a neutral background. The yellow background enables the auditor to easily find the ballot frames. In the lower right hand corner of the video ballot, the video player places a number that represents the ballots in sequence from 1 to N, where N is the total number of ballots on the video. Also notice that the video text on the video ballot alternates in color from black to blue. This color scheme was implemented to make the ballots easier to read. The video player is currently under development; therefore, the video player was simulated using Microsoft Powerpoint. An image of the ballot was captured from the video with its corresponding audio to produce a video ballot (See Figure 3). The audio read the ballot. The study participants would simply advance the images using Powerpoint to hear the ballot and conduct the audit. Each slide was a ballot with audio.


Figure 3. Video Ballot



Participants completed both a short demographic survey before beginning the counting procedure, and a longer, detailed questionnaire about the counting procedure after completing the counting tasks. Participants were given detailed written instructions for the counting procedure, including visual diagrams of important aspects of the ballot to examine. The instructions, although concise, provided a step-by-step procedure for counting the ballots.


For the VVPAT condition, the instructions were similar to those given by Goggin and Byrne (2007), instructing participants to first separate the ballots from the spool using scissors, discarding all “rejected”, and therefore invalid ballots. Next, participants were instructed to count one of the two selected races on the ballot using a provided tally sheet, on which participants could write the counted totals. After the count of one race was complete, participants were given a second tally sheet for the second race, and were asked to count the ballots again; because the ballots were already separated, this task was not present in the second race that was audited in the VVPAT condition.


For the optical-scan ballots, the instructions asked the participants to tally the marked votes on the stack of ballots. Because the ballots were carefully and clearly marked, there were no ambiguous or stray marks that could cause problems with interpretation and optical-scan readers. Some ballots, however, were over-voted in the specific races that were audited. Participants were instructed to treat these ballots as invalid – neither an under-vote nor a valid vote for either candidate.


For the video ballot condition, participants were instructed to tally the votes using the video player simulation tool, Powerpoint. They were given instructions on how to advance from ballot to ballot using the arrow keys and the space bar. They were also instructed to count only the indicated race and mark their totals on their tally sheet.





This is clearly the most important metric for auditing or recounting. Because there are two candidates per each race counted, there are several different calculations that could quantify error rates. We first calculated error on the level of each individual candidate, using signed differences to account for both over- and under-counts. As is apparent in Figure 4, the optical scan ballots tended to produce over-counts for each candidate while the video ballots tended to produce undercounts. The effect of technology was statistically reliable, F(2, 22) = 7.95, p = .003. Posthoc tests reveal the Video to be reliably different from the others, but no reliable difference was found between VVPAT and Optical Scan. (The Ryan-Einot-Gabriel-Welsch test was used for all posthocs.) We found no reliable effects of the rate of rejected ballots or the closeness of the race that was counted.


Taking the absolute values of the error measures above, that is, treating an undercount the same as an overcount, produces the data shown in Table 1. While the VVVAT produced the highest error rate, this difference, while suggestive, is not significant at conventional alpha levels, F(2, 22) = 2.60, p = .097.


Figure 4. Signed error rate by technology




Error Rate

95% Confidence Interval

Optical Scan


0% to 2.1%



0.2% to 2.6%



1.5% to 4.0%

Table 1. Absolute error rates as a percent of candidate’s votes by technology




Lopsided Race

Close Race

Optical Scan









Table 2. Percentage of perfectly-counted races by technology and race closeness.


We also calculated whether participants had correctly counted each race, which produced two dichotomous variables for each participant, one for the lopsided race counted by each participant and one for the close race. These results are summarized in Table 2. For the close race, logistic regression revealed that Optical Scan was reliably better than VVPAT (b = 1.56, w = 5.14, p = .02) and Video was reliably worse than VVPAT (b = -1.67, w = 4.09, p = .04). The differences in the lopsided race were not reliable.



One participant was excluded from the efficiency analysis due to extreme counting times on both races; we believe this participant did not accurately report not-fully-corrected low vision. Results for counting time are presented in Figure 5. Obviously, VVPATs suffered from an extremely slow first count; this is due to the need to physically separate the ballots from the spool in the first count. (This difference is reliable; interaction F(2, 24) = 45.20, p < .001.) However, simple main effects analysis showed a reliable effect of technology in both the first race, F(2, 25) = 33.59, p < .001, and the second race, F(2, 24) = 4.53, p = .02. In the first race posthocs revealed that VVPAT counting was slower than both other types, but in the second race VVPATs could only be discriminated from Video, with Optical Scan being indistinguishable from both other technologies.



Figure 5. Counting time by count order and technology


Satisfaction and Subjective Measures

The mean SUS score for Optical Scan was 67.2, for VVPAT was 70.3 and for Video was 82.5; however, there was enormous variability in satisfaction and so this difference was not statistically reliable, F(2, 21) = 2.08, p = .15. Mean confidence ratings for the three groups were 4.0, 4.6, and 4.3, which was also not a reliable difference, F(2, 21) = 0.85, p = .44. Interestingly, the ratings of confidence in the accuracy of their counts were not significantly correlated with any of the measures of effectiveness above; the largest absolute correlation was with the signed error rate for the second candidate in the lopsided race, r = .36, p = .07. While this is somewhat suggestive, one has to keep in mind that the average correlation across all measures was statistically indistinguishable from zero. People’s sense of their own accuracy is not related to objective accuracy.



Clearly, individuals auditing or counting ballots is an error-prone process. Overall, no technology fared particularly well in terms of producing perfect counts. Our results suggest that people count optical scan ballots somewhat more accurately than VVPAT paper tapes or video records. VVPATs also have the drawback of being slower to count than other ballot types. Interestingly, these performance differences did not manifest themselves in the subjective ratings. This dissociation between subjective and objective measures is similar to those found by Everett, et al. (2008) except in reverse; they found strong differences in preference associated with essentially no difference in performance. It seems clear that in the election domain preference and performance are not strongly linked, counter to many people’s intuitions. This also manifested itself in the fact that people’s subjective sense of confidence in the count is not a predictor of objective count accuracy.


Of course, the inaccuracies in individual counts should not be taken to mean that all audits are suspect (though it is not encouraging, either). Instead, they point to the need for election officials to make sure counts are double-checked. And, in fact, our procedure does differ from the actual procedure used by many election officials around the United States in that we did not use multiple auditors to check the counts for accuracy. However, we did pilot group counting procedures with all three technologies. Our experience with this strongly suggested that clear standardization of the group procedures, particularly how to reconcile disparities, is likely to have a far more substantial impact on both time and accuracy than is the underlying technology.


This raises a difficult research issue. Group counting methods range from having two individuals count and recount until they both agree to larger groups where every group member is supposed to agree on the count as every ballot passes through the process, and mostly likely many other variants we have never seen. Presumably all such methods have the goal of mitigating individual inaccuracy, but as far as we know no group counting procedure has been empirically validated. The question, then, for follow-up research is “Which group procedure to measure”? Furthermore, even if one selects a handful of group procedures to measure, the results will have limited generality, since any particular method only represents a small fraction of the methods actually in use today.


Our results suggest that whatever safeguards are in place need to be particularly well-employed if optical scan ballots are replaced by VVPATs or video systems because such systems can have substantially greater needs for error mitigation. In the best-case scenario for these two technologies a mere half the counts were actually correct, this seems like a great deal of error for any redundancy or other procedural solution to address.


Of course, these results apply only to the particular video system tested; our results do not imply that a video-based system cannot be the equal of paper-based systems, only that this one presently is not, at least in terms of effectiveness. There are hints in the data that the video system may be able to outperform the others on speed and satisfaction, thus if the video system could be equated on accuracy it might be an important advance. Perhaps changes in screen design or other user interface features of the video system can close the accuracy gap; clearly, more research will be necessary to produce better systems.


Regardless of the underlying technology, it is clear that individual counts are neither rapid nor especially accurate. This in and of itself is not particularly surprising. However, the extent of this phenomenon has not been well documented. Furthermore, the fact that reported confidence in a count does not predict the actual accuracy of the count suggests that checks need to be based only on objective counts and not reports from auditors about how well they thought the count went.



This research was supported by the National Science Foundation under grants #CNS-0524211 (the ACCURATE center) and #IIS-0738175. The views and conclusions contained herein are those of the authors and should not be interpreted as representing the official policies or endorsements, either expressed or implied, of the NSF, the U.S. Government, or any other organization.



Ansolabehere, S. & Reeves, A. (2004). Using Recounts to Measure the Accuracy of Vote Tabulations: Evidence from New Hampshire Elections 1946-2002. <>

Brooke, J. (1996) SUS: a “quick and dirty” usability scale. In P. W. Jordan, B. Thomas, B. A. Weerdmeester & A. L. McClelland (Eds.) Usability Evaluation in Industry. London: Taylor and Francis.

Cross, E.V., Rogers, G., McClendon, J., Mitchell, W., Rouse, K., Gupta, P., Williams, P., Mkpong-Ruffin, I., McMillian, Y., Neely, E., Lane, J., Blunt, H. & Gilbert, J.E. (2007) Prime III: One Machine, One Vote for Everyone. VoComp 2007, Portland, OR, July 16, 2007.

Election Assistance Commission. (2005, December 15). Voluntary Voting System Guidelines. <>

Election Assistance Commission. (2007, October 31). Draft of Voluntary Voting System Guidelines. <>

Election Data Services. (2006, February 6). 69 Million Voters will use Optical Scan Ballots in 2006; 66 Million Voters will use Electronic Equipment. Election Data Services Press Release. <>

Election Science Institute. (2006). DRE Analysis for May 2006 Primary Cuyahoga County, Ohio. San Fransisco, CA. <>

Everett, S. P., Byrne, M. D., & Greene, K. K. (2006). Measuring the usability of paper ballots: Efficiency, effectiveness, and satisfaction. Proceedings of the Human Factors and Ergonomics Society 50th Annual Meeting. Santa Monica, CA: Human Factors and Ergonomics Society.

Everett, S. P., Greene, K. K., Byrne, M. D., Wallach, D. S., Derr, K., Sandler, D., & Torous, T. (2008, in press). Electronic voting machines versus traditional methods: Improved preference, similar performance. To appear in Human Factors in Computing Systems: Proceedings of CHI 2008. New York: ACM.

Georgia Secretary of State, Elections Division. (2007). Voter Verified Paper Audit Trail: Pilot Project Report, SB500 2006 Georgia Accuracy in Elections Act. <>

Goggin, S. N. & Byrne, M. D. (2007). An Examination of the Auditability of Voter Verified Paper Audit Trail (VVPAT) Ballots. Proceedings of the 2007 USENIX/ACCURATE Electronic Voting Technology Workshop. Boston, MA.

Laskowski, S. J., Autry, M., Cugini, J., Killam, W., & Yen, J. (2004). Improving the usability and accessibility of voting systems and products. NIST Special Publication 500-256.

McMillian, Y., Williams, P., Cross, E.V., Mkpong-Ruffin, I., Nobles, K., Gupta, P. & Gilbert, J.E. (2007) Prime III: Where Usable Security & Electronic Voting Meet. Usable Security (USEC ’07), Lowlands, Scarborough, Trinidad/Tobago, February 15-16, 2007.

Nichols, S.M. & Strizek, G.A. (1995). Electronic Voting Machines and Ballot Roll-Off. American Politics Quarterly. 23(3), 300-318.

Verified Voting Foundation. (2008). Manual Audit Requirements. <>