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Learning and Evaluation/Evaluation reports/2013/Edit-a-thons

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Learning and Evaluation

This page is part of the Evaluation Report (beta). Important background information can be found on the Overview page.

This is the Program Evaluation page for edit-a-thons. It currently contains information based on data been collected in late 2013 and will be updated on a regular basis. For additional information about this first round of evaluation, please see the overview page.

This page reports data for 28 program leaders, about a total of 46 edit-a-thons, which includes 20 that were mined for additional data. Program leaders who responded about edit-a-thons were a mix of people associated with chapters (staff/volunteers) and individuals who work "solo" without chapters/affiliates in the movement. Some were associated with organizations, such as GLAMs or educational institutions.

Key lessons include:

  • Edit-a-thons are popular! They are the most commonly reported program by program leaders who responded to our survey - we were also able to easily pull additional data on 26 more to fulfill some areas where we were missing data. 28 program leaders reported 26 edit-a-thons, making them the highest reported program in this first round of data collection.
  • Edit-a-thons have four priority goals with a primary focus on increasing contributions, skill sets, recruitment and perceptions about Wikimedia projects.
  • Many program leaders aren't tracking participant usernames which makes it a challenge to track retention and contributions of participants. We hope through improved program design, tools, and sharing among program leaders of what works to track usernames, we can make this a standard for edit-a-thons.
  • Most program leaders are tracking budget and donated resources, but most aren't tracking staff and volunteer hours which are also two critical inputs for evaluation. We aim to work with program leaders to make tracking hours - staff and volunteer - easier.
  • Edit-a-thons rely more on donated resources than any other reported program with meeting space being the most commonly donated resource.
  • Participants average 3 pages of text each out of all of the reported events which averages out to almost 24,000 characters per event by all event attendees.
  • Edit-a-thons are productive for generating decent amounts of content regardless of size or cost, but the more participants, the more the content produced. This shows that edit-a-thons are successful at content production.
  • Budget size doesn't appear to have an effect on the amount of content produced; events with small budgets can be as equally productive as events with large budgets. We also learned that having staff support, doesn't necessarily suggest a more productive or impactful event.
  • Events with lots of new editors can be just as productive as events with lots of experienced editors
Can experimenting with program design help retain new editors after edit-a-thons end?
  • Out of 328 new editors who attended the reported edit-a-thons, three were retained 6-months after the event. Experimentation with edit-a-thon series, surveying, follow-up and more pro-active program design is worth trying to learn more about how these events could possibly retain new editors - or we might see a change in the primary goals selected by program leaders.
  • Qualitative research shows that having edit-a-thon series and more frequent events with follow up, and improved training, could potentially help retain new editors afterwards, which we learned through interviews with new editors who did not edit after they attended (and edited at!) an edit-a-thon. We hope to work with program leaders to experiment with these ideas, and support them in evaluation.
  • Early indicators show that experienced editors might edit more on average during the event than they do in an average day. They also appear to edit more on average after the event than they did before the event. We plan on researching this further to learn more about the impact of edit-a-thons on experienced editors.

Planning an edit-a-thon? Check out some process, tracking, and reporting tools in our portal and find some helpful tips and links on this resource page

Program basics and history

Participants at an edit-a-thon held at the Salvador Dali Foundation in 2012

Edit-a-thons are outreach events that bring together Wikipedians, and those interested in editing Wikipedia, to do just that: edit Wikipedia in a collaborative setting. These events, which last generally between 3–5 hours, provide a social environment for new and experienced editors to edit together, often about a specific subject matter. Many events take place in educational and cultural facilities, such as libraries, museums, and universities, when others might take place in offices buildings, homes, and public venues such as cafes and restaurants. Sometimes edit-a-thons are combined with training lessons, where experienced Wikipedians educate participants about the basic "how-to's" on editing, followed by an editing session. Other events might include backstage tours of cultural institutions that are hosting the event, followed by editing, or just a simple edit-a-thon where participants start editing upon arrival at the venue.

An early editathon for a wikiproject was proposed on the English Wikipedia in February 2004. Jimbo proposed a library-based Editing Weekend in mid-2004. Neither were pursued at the time.

In 2009, Australia's Powerhouse Museum hosted what is believed to be the first GLAM edit-a-thon. Wikipedians were given a tour of the museum, met with curators, were able to take photographs, and improved and created articles related to the Powerhouse and its collections. The Wikipedian who coordinated the Powerhouse event, Wittylama, eventually organized another event at the British Museum, which featured a tour of the museum and a contest focused around improving the article about the Hoxne Hoard. Called the "Hoxne Challenge," it has served as an inspiration for edit-a-thons in partnership with cultural institutions since. One of the first events to use the "editathon" name was the British Library Editathon in January 2011 (with the more recent emphasis on multiple topics), and the pace of such events increased over the following years.

Data report


Response rates and data quality/limitations

Edit-a-thons are popular! 28 program leaders reported 26 edit-a-thons. But program leaders need to track usernames of participants better. We had to pull additional data to supplement the lack of username reporting.
Edit-a-thons were the most frequently self-reported program type – 26 edit-a-thons were reported directly through the survey by program leaders. However, many program leaders did not track usernames of participants in order to track their contributions made before, during, and after the event. Aside from the 26 edit-a-thons that were self-reported, our team pulled data on 20 additional English Wikipedia edit-a-thons for which public records of participants were available on wiki.[1]

A total of 46 edit-a-thons completed between February 2012 and October 2013 were reviewed for this report. As with all the program report data reviewed in this report, report data were often partial and incomplete, please refer to the notes, if any, in the bottom left corner of each graph below.

Report on the submitted and mined data


Priority goals

According to program leaders, edit-a-thons have four priority goals.

We asked program leaders to select their priority goals for edit-a-thons. We provided 18 priority goals with an additional 19th option to report on "other" goals as well, and they could select as many or as little as they saw fit. 13 program leaders chose between seven and 18 priority goals.[2] Our team noted four stand out goals that appeared as priorities amongst the reporting program leaders (see table below):


Most program leaders were able to report budgets for their edit-a-thons. This is an important part of evaluating these events.
In order to learn more about the inputs that went into planning edit-a-thons, we asked program leaders to report on
  • The budget that went into planning and implementing the edit-a-thon
  • The hours that went into planning and implementing the edit-a-thon
  • Any donations that they might have received for the event: a venue, equipment, food, drink, giveaways, etc.
The majority of program leaders were able to report budget for edit-a-thons.
In the survey, budget was reported for 16 edit-a-thons. Whereas 7 of these budgets were reported as zero dollars, 9 of the budgets were reported as ranging from $10.00 US to $750.00 US (with an average budget of $359.99).[3][4]
While 62% of direct reports that came in through our survey included budget, this represents only 35% of all edit-a-thons reviewed in this report, because we didn't have budget numbers for the edit-a-thons we mined.
Most program leaders were unable to provide data for how many staff hours went into implementing edit-a-thons. But, little over half were able to submit data for volunteer hours.

Staff and volunteers put the following time into implementing edit-a-thons, according to respondents:

  • 39% of program reports included staff hours, which ranged from 0 to 200 hours with an average of 3 hours.[5]
  • 54% of program reports included volunteer hours, which ranged from 2 to 215 hours with an average of 15 hours.[6]
  • Total hours (staff and volunteer hours combined) ranged from 9 to 300 hours with an average of 15 hours.[7]
Donated resources
Edit-a-thons rely more on donated resources than any other reported program.

Program leaders reported use of donated resources for their edit-a-thons more than any of the other programs reviewed in the survey. In most cases, edit-a-thons were held using donated meeting space (85%) and materials or equipment (58%), while donations of food (46%) and prizes/giveaways (23%) were also noted for some events (see Graph 2).

Graph 2: Use of donated resources. This bar graph shows that the majority of edit-a-thons used donated resources. The most commonly donated resources were meeting space and materials or equipment. Edit-a-thons reported use of donated resources more than any other program examined in this report.


We also asked about two outputs in this section
  • How many hours did the edit-a-thon last?
  • How many people participated in the edit-a-thon?
Event length
The average edit-a-thon is five hours long.

Edit-a-thons lasted from 2 to 24 hours with an average of 5 hours (see Graph 1).[8]

Graph 1: Event length The pie chart shows that edit-a-thons varied in their number of event hours with the majority lasting between 3 to 5 hours. Nearly all events (91%) were less than 8 hours, only one lasted longer, it was 24 hours.
The average edit-a-thon has 16 participants.

Out of all edit-a-thons included in this report, the number of participants ranged from 2 to 74 with an average of 16 participants.[9]


Content production and quality improvement[10]
The majority of program leaders reported about content production. Reported events averaged almost 24,000 characters in total, with individual participants averaging almost 3 pages of characters added each.
Participants at the reported edit-a-thons averaged almost 3 pages of content each.

The majority of program leaders were able to provide us with the amount of new content that got added to Wikipedia's article namespace during the event.[11] Events produced an average 23,993 characters with the most productive event reporting 157,586 characters of text produced during the event.[12]

In order to make the metrics easier to understand, we converted "characters" into "printed pages," assuming that one printed page equals 1,500 characters. Printed pages of text produced during edit-a-thons ranged from .1 to 8.6 pages an hour with an average of 2.9[13] and from 0.1 to 3.8 pages of text per participant with an average of nearly three-quarters a page of text (0.7 pages) per participant. [14]

Reported events averaged three media uploads each. Half reported had no or unknown numbers of media uploads.

Regarding image uploads, the average was 3 uploads per event, with 11 edit-a-thons reporting with 0 or no known uploads. The largest reported number of uploads was 85.[15]

Content produced at events varies, and even smaller events—participant or budget wise—can produce lots of content. Events with more participants generally produce more content, but the cost of the event doesn't necessarily effect participation rate or content production rates.

We were able to analyze the dollars to content produced for only 5 edit-a-thons. This is because only those 5 reported both the budget and the amount of content added during the event. We looked at the budgets and characters added at those five events and were able to determine how much one "printed page" added costs. The average cost for one printed page of content for these five events was $17.15 US (see Graphs 3 and 4).[16]

During edit-a-thons, it takes little over one hour to produce one page of content.

We also wanted to know how many hours it took to produce one page of content based on the hours that were put into implementing an event. Reports of staff and volunteer hours input into edit-a-thons was available for 11 of the events reviewed. [17] Using those reports, we were able to take those hours and the amount of characters added during those 11 events and calculate that the average was 1.11 hours to produce one page of content. The smallest amount of time was 0.25 hours and 4.5 hours was the largest amount of time to produce a page worth of content[18] (see Graphs 5 and 6).

Hourly productivity at edit-a-thons is all over the place, regardless of size and length. Edit-a-thons with lots of new editors can also be just as productive as those with lots of experienced editors.

We also looked into how productive new editors at the events were, to see if the budget and hours put into implementing the edit-a-thons were supporting new editors to be productive at the event (see Graph 7).

Graph 7: Hourly productivity by participants This bubble graph shows how the number of participants and proportion of new account creators relate to the number of pages of text produced each edit-a-thon hour. As illustrated by bubble size and label, in which larger bubbles represent more pages of text each hour, there was a lot of variation in the number of pages of text produced per hour. The two largest bubbles demonstrate an interesting thing, that high proportions of new editors in an edit-a-thon group does not necessarily lead to less productivity, new user groups can be highly productive in terms of adding content to the article namespace.
The most commonly reported data about production at edit-a-thons was about article creation/improvement. This is followed by media and article quality, respectively.

The majority (72%) edit-a-thons reported included how many pages were created or improved on wiki during the event. In total, 620 pages were created or improved during the 46 edit-a-thons! It was also reported that 334 images or media were added, and 81 of those were added to project pages. 7 of the edit-a-thons reported producing Good Articles, totaling 51. Two of the edit-a-thons reported 4 Featured Articles coming out of the event (see Graph 8).

Graph 8: Edit-a-thons bar graph: increasing quality The bars on the graph illustrated the total number of pages created, photos added, photos used on other Wikimedia projects, good articles, and featured articles. The largest impacts in terms of quality include that a total of 620 pages were created or improved, 51 of which were rated as “good articles” and 4 that became "featured articles." During the edit-a-thons, there were also 81 photos incorporated into Wikipedia articles.
Recruitment and retention of new editors
The majority of the 46 edit-a-thons reported were able to pull data about retention. Out of 328 new editors who attended these edit-a-thons, after 6-months, only three were actively editing.

We also wanted to learn about the retention of active editors after they attended an edit-a-thon. In the survey, we asked program leaders to report the retention of their active editors 3 and 6 months after the end of the event. A retained active editor is considered a Wikipedia editor making an average of five or more edits a month. Out of the 46 edit-a-thons reported on, 37 of them (80%) had reached or passed the 3 months after their event end date. 29 of the 46 (63%) passed their 6 month mark, so we were able to gather retention data for those edit-a-thons only.

In total, the edit-a-thons reported attracted 328 new editors (36% of 906 total participants). The number of active editors (5+ edits/month) at 6-months follow-up time were reported for 27 of the edit-a-thons (59%), 15 of the events (33%) had not yet reached the point of 6-month follow-up.[19] Only 18 reports provided a separate count for number of active editors at 6-months follow-up for new editors (39%). Active editor retention rates for new users were most often 0% (83% of the reports).[20] For all edit-a-thons reported, the total number of new user participants was 328, of which, only 3 were active editors at 6 month follow-up, 1.4% of new users who had reached the 6-month follow-up window (see Graph 9).

Graph 9: Recruitment and retention The bars on this graph summarize the total number of edit-a-thon participants who created new user accounts as part of the event and the 6-month retention rates for those new users. For all edit-a-thons reported, the total number of new user participants was 328, of which, only 3 were active editors (i.e. 5+ edits per month) 6 months after the event.
Replication and shared learning
Promotional materials can help not only attract participants, but also help others replicate edit-a-thons successfully.
Edit-a-thon program leaders are pro-active at producing materials, blogs, online resources, and other information related to their event which can help others implement their own events.

Finally, we asked program leaders to share with us how replicable their edit-a-thons could be, and what types of shared learning resources were produced for and after the event. This allows us to learn if the reporting program leaders considered themselves experienced in implementing edit-a-thons, which would allow them to perhaps help others design and implement edit-a-thons of their own. We also are able to learn how program leaders and others (i.e. chapters, press, bloggers, etc) were covering the events, and if resources were available for others to use to produce their own events.

For the 46 edit-a-thons reported by program leaders, we learned that the majority (96%) are experienced at producing edit-a-thons and could help others conduct their own. The majority (62%) reported having blogs or other online information available for others to learn more about the event. A smaller amount of program leaders reported that they developed brochures and/or printed materials (27%) and guides or instructions on how to contribute to Wikipedia for event participants (23%) (see Graph 10).

Graph 10: replication and learning The biggest strengths that edit-a-thons demonstrated in terms of potential replication and shared learning were that 96% of the events were run by an experienced program leader who could help to guide others and 62% had blogs or online resources for others to learn from. Additionally, 27% reported developed brochures and printed materials and 23% reported they had guides and instructions to inform others how they could implement a similar program.

Summary, suggestions, and open questions


How does the program deliver against its own goals?


Being branded as the "Swiss Army knife" of the Wikimedia movement by some, edit-a-thons are expected to serve a number of different goals. We didn't have sufficient quantitative data on whether edit-a-thons "increased the positive perception of Wikipedia" and to which extend edit-a-thons "increased the editing skills of newcomers". For this reason, we focused our quantitative analysis on whether and to which extent edit-a-thons (a) increased the amount of content on Wikipedia and (b) retained newcomers who have been taught how to edit during this type of event.

First, we looked at the amount of new content created by edit-a-thon participants. For 30 edit-a-thons, we knew the total number of characters added during the event (either through self-reported or mined data). In order to make these numbers easier to grasp, we assumed that 1,500 characters account for one printed page. The median number of printed pages that participants produced during the afore-mentioned edit-a-thons on average per person was 0.7 (with a high of 3.8 and a low of 0.1).

Then, we looked into the retention rate for edit-a-thons. Out of 328 edit-a-thon participants who created new user accounts during the events, only 3 (1.4%) qualified as "active editors" half a year after the event.

What attracts people to edit-a-thons, and how can those editors be retained after the event?

In order to understand the low retention rate better, we took a sample of people who had created new user accounts during edit-a-thons and asked them via email why they stopped editing. Here are the two most common reasons:

  • lack of time ("A full-time job takes up much of my time" / "other priorities just got in the way" / "have not found the time" / "time is pretty restricted so it might well be that I would never have the time to actually contribute")
  • no obvious editing opportunities ("I just don't know where to start in Wikipedia" / " when using Wikipedia […] I didn't spot anything that I felt compelled to edit instantly.")

Answers like "I would hope to get back into editing in the future, although now I might need a refresher course", "The longer I do not do it, the more it goes to the back of my mind, and the harder it will be to remember how to do it again" and "I haven't edited mainly because I wouldn't be able to remember how to do it!" indicate that a series of events or follow-ups might serve the learning needs of the participants better than one-off events. Also, edit-a-thon organizers might want to look more in-depth into whether the training they offer is effective (one participant shared: "the training we got during the event […] wasn't straightforward").

Although we have some early indicators that edit-a-thons lead to an increase of editing frequency in the article namespace among long-term Wikipedians in the weeks after the event, this area needs further investigation. Given the fact that edit-a-thons have become very popular across language versions over the last two years, it also seems to be worthwhile to investigate on a more general level what draws Wikimedians to this event type. We think that edit-a-thons might have an impact that goes beyond the mere fact that they increase Wikipedia's article quality and quantity. With early indicators that Wikipedians edit the article namespace more after the event and the fact that edit-a-thons have become so popular, they might play a role in increasing long-term editors' motivation by strengthening the social ties of our community.

How does the cost of the program compare to its outcomes?


Five out of eight edit-a-thon organizers reported that they were running their events at zero costs for the event budget and without any staff costs. In three of these cases, implementers also reported the amount of characters that have been added to Wikipedia's article space: 15, 16 and 20 pages (@ 1,500 characters). This means that staff support and a budget are not necessarily preconditions for an increase in content on Wikipedia:

Budget Staff time @ salary/hour Total monetary cost Content added[21]
(in printed text pages)
Monetary cost per printed page
0 0 0 15 $0.00
0 0 0 16 $0.00
0 0 0 20 $0.00

Also, we had two self-reported data sets at our disposal for which edit-a-thon organizers reported an event budget that was not zero and the number of staff hours that were needed to plan and execute the event. The following table gives an overview of how much money was spent on these edit-a-thons, how much content got added to Wikipedia's article namespace and how much money was spent per printed page (at 1,500 characters per page):

Budget Staff time @ salary/hour Total monetary cost Content added[21]
(in printed text pages)
Monetary cost per printed page
$469 10 hrs @ $20[22] $669 20 $33.45
$300 35 hrs @ $20[22] $1,000 34 $29.41

How easily can the program be replicated?


Over the last couple of years, edit-a-thons have been documented widely through blog posts and chapters' quarterly/annual reports. And the general concept of having Wikipedians spend some editing time together at the same physical location is easy to replicate.[23] What seems to be missing though, is an in-depth analysis of what makes some edit-a-thons more successful than others. It would include numbers that indicate to which extend the specific goals of the events have been met (e.g. retention rate, number of articles improved and created, etc.). Ideally, it would also include qualitative data (e.g. a survey among participants who were new to editing measuring to which extend their editing skills improved, whether their attitude towards Wikipedia changed, and whether their expectations were met). Based on such an analysis, people around the world would be better able to build on the learning of others and edit-a-thon outcomes could improve over time.

Next steps

Next steps in brief
  • Increased tracking of detailed budgets and hour inputs by program leaders
  • Increased tracking of usernames and event dates by program leaders
  • Pre- and post- edit-a-thon surveys to understand more about what participants know going in, and leaving, the workshop to see if it's meeting priority goals about Wikimedia movement education and skill improvement.
  • More experimentation in edit-a-thon design including experimentation with edit-a-thon series, invitations, personal follow-up after the event, etc.
  • Exploring opportunities to determine the value of edit-a-thons through Return on Investment analysis
  • Investigate effect of edit-a-thons on editing habits and productivity of experienced editors before, during, and after edit-a-thons.
Next steps in detail

As with all of the programs reviewed in this report, it is key that efforts are made toward properly tracking and valuing programming inputs in terms of budgets and hours invested as well a tracking usernames and event dates for proper monitoring of user behaviors. Further investigation of expectations and efforts directed toward the other goal priorities (i.e., increasing skills for editing and increasing awareness of Wikimedia projects) and the development of strategies for measuring such potential impacts will be important next steps as well as making efforts toward valuations for future Return on Investment analysis.

External resources




Summative data table: Edit-a-thons (raw data)

Percent reporting Low High Mean Median Mode SD
Non-zero budgets 20% $10.00 $750.00 $332.97 $359.99 $239.98
Staff hours 39% 0.00 200.00 16.33 3.00 0.00 46.64
Volunteer hours 54% 2.00 215.00 28.36 15.00 15.00 43.85
Total hours 54% 9.00 300.00 40.12 15.00 15.00 67.90
Donated meeting space 85%[24] Not applicable (frequency of selection only)
Donated materials/equipment 58%[25] Not applicable (frequency of selection only)
Donated food 46%[26] Not applicable (frequency of selection only)
Donated prizes/give-aways 23%[27] Not applicable (frequency of selection only)
Participants 100% 2 74 20 16 9 2
Dollars to participants 35% $0.00 $39.47 $7.14 $3.50 $0.00 $10.40
Input hours to participants 54% 0.33 7.50 2.16 1.42 1.00 1.96
Bytes added 65% 991 157586 29862 23993 32576
Dollars to text pages (by byte count) 11% $7.08 $153.44 $41.96 $17.15 $62.66
Input hours to text pages (by byte count) 20% 0.25 4.50 1.52 1.11 1.38
Photos added 67% 0 85 11 3 0 22
Dollars to photos 24% Not appropriate metric calculation for program type
Input hours to photos 26% Not appropriate metric calculation for program type
Pages created or improved 72% 1.00 71.00 18.79 15.00 18.00 15.42
Dollars to pages created/improved 30% $0.00 $32.73 $6.55 $1.00 $0.00 $9.40
Input hours to pages created/improved 37% 0.42 15.00 2.40 1.40 none 3.40
Unique photos used 48% 0 17 4 2 0 5
Dollars to photos used (non-duplicated count) 22% Not appropriate metric calculation for program type
Input hours to photos used (non-duplicated count) 24% Not appropriate metric calculation for program type
Good article count 52% 0 30 2 1 0 7
Featured article count 48% 0 2 0 0 0 1
Quality image count 0% Not applicable (none reported)
Valued image count 0% Not applicable (none reported)
Featured picture count 0% Not applicable (none reported)
3 month retention[28] 89% 3% 100% 35% 35% 40% 18%
6 month retention[29] 93% 12% 73% 35% 34% 15%
Percent experienced program leader 96%[30] Not Applicable (frequency of selection only)
Percent developed brochures and printed materials 27%[31] Not Applicable (frequency of selection only)
Percent blogs or online sharing 62%[32] Not Applicable (frequency of selection only)
Percent with program guide or instructions 23%[33] Not Applicable (frequency of selection only)

Bubble graph data

Data for graph 3. Dollars to pages
Budget Number of participants Bubble size: number of printed pages
added to Wikipedia's article namespace
$300.00 20 34
$500.00 74
$0.00 13
$0.00 9
$0.00 26 28
$750.00 19 5
$0.00 36 20
$427.38 46 60
$30.00 15
$10.00 2
$0.00 31 16
$359.99 21 21
$0.00 30 29
$0.00 4 15
$469.40 40 20
$150.00 19
Data for graph 5. Hours to pages.
Participants Hours Bubble size: number of printed pages
added to Wikipedia's article namespace
Hours per Page
20 65 34 1.92
40 300 105 2.86
26 15 28 0.55
19 22 5 4.50
36 51 20 2.50
46 15 60 0.25
31 31 16 2.00
21 15 21 0.72
30 15 29 0.52
4 17 15 1.11
40 25 20 1.24
Data for graph 7. Hourly productivity by participants.
(Replaced missing with median 33% for graphing purposes)
Percent new accounts b1 participants Bubble size: number of printed pages
added to Wikipedia's article namespace per hour
80% 20 8
58% 40 4
32% 74
38% 13
74% 38
71% 17
0% 7
78% 9
80% 25
33% 26 4
38% 13 2
53% 19 2
33% 36 5
50% 4
0% 9 3
11% 46 9
20% 15
17% 12 2
18% 17 3
13% 16 1
50% 2
3% 31 4
33% 21 3
33% 30 4
33% 6 0
0% 2
50% 4
25% 4 2
73% 11 4
29% 35 2
18% 40 3
25% 24 3
29% 17 1

More data


Note: the Report ID is a randomly assigned ID variable in order to match the data across the inputs, outputs, and outcomes data tables.

Program inputs
Report ID Budget Staff hours Volunteer hours Donated space Donated equipment Donated food Donated prizes
56 $300.00 35 30 Yes Yes
11 200 100 Yes Yes
14 $500.00 0 215 Yes
25 $0.00 0 15 Yes Yes Yes
23 0 30 Yes Yes Yes Yes
33 0 14 Yes Yes
43 $0.00 0 16 Yes Yes Yes
50 0 40 Yes Yes Yes Yes
13 $0.00 15 Yes Yes Yes
15 $750.00 15 7 Yes Yes Yes
22 $0.00 0 51 Yes
17 7 4 Yes Yes
18 $427.38 15 Yes Yes
19 $30.00 15 Yes Yes Yes
26 $10.00 15 Yes Yes
31 $0.00 0 31 Yes
32 $359.99 15 Yes Yes
39 $0.00 15 Yes Yes Yes Yes
40 7 2 Yes Yes
46 7 4 Yes Yes
49 $0.00 0 17
54 $469.40 10 15 Yes Yes
55 $150.00 20 Yes Yes
59 7 2 Yes Yes Yes
65 6 6
Outputs: Participation and content production
Report ID Event length (hours) Number of participants Number of new user accounts Total bytes added Text pages (by bytes added) Dollars per page of bytes Hours per page of bytes
56 4 20 16 50798 33.87 $8.86 1.9
11 24 40 23 157586 105.06 2.9
14 18 74 24
25 3.5 13 5
23 7 38 28
28 3 17 12
33 6 7 0
43 6 9 7
50 7 25 20
13 7.75 26 41259 27.51 0.6
104 4.5 13 5 13560 9.04
15 3 19 10 7332 4.89 $153.44 4.5
22 4 36 12 30594 20.4
17 5 4 2
108 2 9 0 8802 5.87
18 7 46 5 90532 60.35 $7.08 0.3
19 5 15 3
109 4 12 2 13863 9.24
110 6 17 3 28551 19.03
111 4.5 16 2 5298 3.53
26 2 2 1
31 4 31 1 23251 15.5
32 7 21 31480 20.99 $17.15 0.7
39 7.75 30 43085 28.72 0.5
113 4 6 2 1692 1.13
40 5 2 0
46 5 4 2
49 10 4 1 22953 15.3 1.1
106 4 11 8 24734 16.49
123 8.5 35 10 29105 19.40
54 7.5 40 7 30240 20.16 $23.28 1.2
115 5.5 24 6 26707 17.80
119 3.5 17 5 5156 3.44
55 6 19 8
114 6.5 39 16 79334 52.89
122 4.5 11 3 9387 6.26
105 8 10 2 41040 27.36
121 2 10 4 4468 2.98
118 6 9 4 991 0.66
116 4 10 6 2486 1.66
59 5 9 4
117 5 15 1 20950 13.97
120 5 13 6 2046 1.36
63 7 37 29 48580 32.39
64 2 35 17
65 3.5 6 6
Outcomes: Quality improvement and active editor recruitment and retention
Report ID Number photos/media added Number unique photos used Number pages created or improved Number good articles Number featured articles 3 month active editor rate 6 month active editor rate 6 month retention (new contributors only) rate
56 0 0 20 55% 55%
11 46 7 71 0 0 18% 18%
14 20 30 2 n/a n/a n/a
25 0 7 0 0 31% n/a n/a
23 10 16 0 3% n/a n/a
28 16 2 n/a n/a
33 1 1 10 100% n/a n/a
43 7 7 9 n/a n/a n/a
50 84 17 18 n/a n/a n/a
13 1 1 34 35% 27%
104 18 12 14 0 0 46% 46% 0%
15 2 32% 21%
22 1 33 1 0 19% 19% 0%
108 0 0 44% 56%
18 0 36 26% 24%
19 6 15 1 0 40% 40%
109 3 3 6 0 0 42% 50% 0%
110 3 3 18 0 0 35% 41% 0%
111 0 4 0 0 25% 25% 0%
26 0 1 0 0 50% n/a n/a
31 24 1 0 58% 58% 0%
32 5 5 11 38% n/a n/a
39 2 2 27 n/a n/a n/a
113 0 0 3 0 0 n/a n/a n/a
40 n/a n/a n/a
46 n/a n/a n/a
49 3 3 7 0 0 n/a n/a n/a
106 21 18% 18% 0%
123 42 34% 34% 10%
54 0 40 20% 20%
115 0 0 18 0 0 46% 46% 0%
119 6% 12% 0%
55 7 3 18 1 0 n/a n/a n/a
114 13 13 43 1 0 38% 33% 0%
122 3 6 0 0 18% 45% 0%
105 1 1 11 0 0 40% 30% 0%
121 40% 40% 0%
118 0 2 0 0 33% 33% 0%
116 0 0 6 0 0 20% 20% 0%
117 0 0 14 0 0 60% 73% 100%
120 0 4 31% 38% 17%
63 27 0 0 14% n/a n/a
64 85 40% n/a n/a


  1. Based on the List of edit-a-thons available on the English Wikipedia.
  2. For edit-a-thon reports, 13 program leaders (87% of the 15 who provided direct reports) reported and the number of selected priority goals ranged from 7 to 18 with an average of 12 (Mean=12, Standard deviation=3).
  3. Averages reported refer to the median response.
  4. The mean=$332.97 and standard deviation was $240.
  5. Mean=16.3, Standard deviation= 47
  6. Mean=28.4, Standard deviation=44
  7. Mean=40.1, Standard deviation= 68
  8. Mean= 5.9, Standard deviation= 4
  9. Mean= 20, Standard deviation= 2
  10. Note: Although "content production" is a direct product of the program event itself and technically a program output rather than outcome most of the program leaders who participated in the logic modeling session felt this direct product was the target outcome for their programming. To honor this community perspective, we include it as an outcome along with quality improvement and retention of "active " editors
  11. We transformed the WikiMetrics metric "bytes added" to character count in order improve understandability. In most European languages, one byte equals one character.
  12. 65% of program leaders reported total number of bytes added during their Edit-a-thon event, which ranged from 991 to 157,586 bytes with an average of 23,992.5 bytes (Mean=29,862, Standard deviation=32,576).
  13. Mean = 3.0, Standard deviation = 2
  14. Mean = 0.9, Standard deviation = 1
  15. 67% of of reports also included the number of photos added which ranged from 0 to 85 with an average of 3. (Mean=11, Standard deviation=22)
  16. Calculating the Pages of Text Added (in bytes) to the Dollars invested, the cost per Pages of Text Added (in bytes) ranged from $7.08 to $153.44 with an average of $17.15 (Mean= $41.96, Standard deviation=$63)
  17. (24% of all reviewed, 42% of those directly reported)
  18. the cost per pages of text added (in bytes) ranged from to 0.25 to 4.5 hours with an average of 1.11 hours (Mean= 1.52, Standard deviation= 1)
  19. For the 15 edit-a-thons that had not yet reached the 6-month follow-up time, there were 173 existing, and 110 new editors not yet eligible for 6-month retention follow-up
  20. At 6-month follow-up retention rates ranged from 0% to 100% with an average of 0% (Mean= 7%, Standard deviation= 24%).
  21. a b To the article namespace.
  22. a b Estimated salary per hour.
  23. On the English Wikipedia, a well developed how-to document exists: Wikipedia:Edit-a-thon.
  24. Percentages out of 26 who provided direct report
  25. Percentages out of 26 who provided direct report
  26. Percentages out of 26 who provided direct report
  27. Percentages out of 26 who provided direct report
  28. Edit-a-thons retention listed refers to both new and existing users see program specific reporting for details on retention of new vs existing contributor, and the reporting percentage out of 37 who were ready to report 3 month retention
  29. Edit-a-thons retention listed refers to both new and existing users see program specific reporting for details on retention of new vs existing contributor, and the reporting percentage out of 29 who were ready to report 6 month retention
  30. Percentages out of 26 who provided direct report
  31. Percentages out of 26 who provided direct report
  32. Percentages out of 26 who provided direct report
  33. Percentages out of 26 who provided direct report