The results of the microsurvey are at:

This was a survey of new account holders (not necessarily editors). The results were 67% male, 22% female, 11% prefer not to say. I think the survey was useful in that it let us know that the gender gap exists as early as the account sign-up funnel.


On Thu, Aug 28, 2014 at 1:25 PM, Andrew Gray <> wrote:
I believe we did a one-question gender microsurvey before (linked to
one of the new-user features?). I don't know whether the data was
useful or not, but I do remember the act of asking the question itself
got some pushback as being invasive/unwelcoming/weirdly
communicated/etc. (and I can certainly symapthise with this)

So as well as the value of the data, we should consider whether the
act/method of asking is going to have knock-on effects on what we're
trying to measure.


On 28 August 2014 20:55, Jonathan Morgan <> wrote:
> Stepping back...
> We all seem to agree that user-set gender preference is a problematic
> measure. We don't trust it. We can come up with plausible hypotheses for why
> someone would mis-report their gender. And we can be almost certain it's not
> a representative sample.
> Do we have any ideas for what a better measure would be? Seems to me that
> we're dealing with self-report data no matter what. But perhaps a more
> explicit elicitation would  be better? Folks have suggested a one-question
> gender microsurvey before. Of course that will come with its own sources of
> bias, and I don't quite see how we can control for them.
> Given that it would be useful to have some data on gendered editing patterns
> (whether we share it publicly or not), what are our options?
> - Jonathan
> On Thu, Aug 28, 2014 at 10:03 AM, Ryan Kaldari <>
> wrote:
>> And because I know someone is going to point this out... Actually,
>> restricting the data to only editors who have explicitly set their gender
>> would not completely control for changes in the rate of setting the
>> preference since that rate could change differently for men and women. It
>> would at least help to control for overall changes in the rate, for example,
>> due to the change in the interface that Steven mentioned.
>> Kaldari
>> On Aug 28, 2014, at 9:50 AM, Ryan Kaldari <> wrote:
>> We could restrict the query to only look at editors who had explicitly set
>> their gender preference. That would control for changes in the rate of
>> setting the preference. The data would then only be biased by users who had
>> explicitly set their gender to the incorrect gender, which I imagine would
>> be a very small percentage.
>> Also, I would like to point out that even our most fundamental metrics are
>> affected by similar biases and inconsistencies. For example, the rate of new
>> editors is polluted by long-time IP editors who suddenly decide to create an
>> account. If there is an increase in IP editors converting to registered
>> editors, it can mislead us into thinking that we are suddenly attracting a
>> lot of new editors. This is just one of many examples I'm sure you're
>> already familiar with.
>> To answer your question though, I think if we notice something interesting
>> in the data (especially a downward trend), we would start a discussion about
>> it (as we would with any interesting data) and hopefully inspire someone to
>> dig deeper. Right now though we are mostly in the dark. See, for example,
>> Phoebe's most recent email to the gendergap list lamenting the lack of
>> research and data.
>> Kaldari
>> On Thu, Aug 28, 2014 at 1:43 AM, Aaron Halfaker <>
>> wrote:
>>> I think the biggest problem is this:
>>> Let's say that we see the proportion of users who set their gender
>>> preference to female falling.  Is that because women are becoming less
>>> likely to set their gender preference or because the ratio is actually
>>> becoming more extreme?
>>> Let's say that we see a trend in the messy data.  What do we do about
>>> that?  Do we assume that it is a change in the actual ratio?  Do we assume
>>> that it is a change in the propensity of females to set their gender
>>> preference and there's nothing for us to do?  Or do we then decide that it
>>> is important for us to gather good data so that we can actually know what's
>>> going on?
>>> -Aaron
>>> On Thu, Aug 28, 2014 at 4:50 AM, Ryan Kaldari <>
>>> wrote:
>>>> On Tue, Aug 26, 2014 at 9:53 AM, Leila Zia <> wrote:
>>>>> 1. We look at the self-reported gender data and do some simple
>>>>> observations.
>>>>> Pros:
>>>>>    + we will have an updated view of the gender gap problem.
>>>>>    + we may spread seeds for further internal and/or external research
>>>>> about it.
>>>>> Cons:
>>>>>    - If simple observations are not communicated properly, they will
>>>>> result in misinformation, that can possibly do more harm than good.
>>>>>    - The results will be very limited given that we know the data is
>>>>> very limited and contains biases.
>>>> I would definitely like to avoid spreading misinformation, which is why
>>>> I proposed only looking at the percentage change per month rather than raw
>>>> numbers or raw percentages. The raw numbers are almost certainly off-base
>>>> and would be much more likely to be latched onto by the public and the
>>>> media. Percentage change per month is a less 'sexy' statistic, but might
>>>> give us better clues about what's actually going on with the gender gap over
>>>> time. It would also, for the first time, give us some window into how new
>>>> features or issues may be actively affecting the gender gap. But again, it
>>>> would only be a canary in a coal mine, not a tool to draw reliable
>>>> conclusions from. For that, we need more extensive tools and analysis.
>>>>> 2. We do extensive gender gap analysis internally.
>>>>> Proper gender gap analysis, in a way that can result in meaningful
>>>>> interventions (think products and features by us or the community) requires
>>>>> one person from R&D to work on it almost full time for a long period of time
>>>>> (at least six months, more probably a year). In this case, the question
>>>>> becomes: How should we prioritize this question? Just to give you some
>>>>> context: Which of the following areas should this one person from R&D work
>>>>> on?
>>>>>    * reducing gender gap
>>>>>    * increasing editor diversity in terms of nationality/language/...
>>>>>    * increasing the number of active editors independent of gender
>>>>>    * identifying areas Wikipedia is covered the least and finding
>>>>> editors who can contribute to those areas
>>>>>    * ...
>>>> I think it's very difficult to judge how to set those priorities without
>>>> having more data. We know that the active editors number is on a downward
>>>> trajectory. Is the nationality/language diversity increasing or decreasing?
>>>> Is the gender gap increasing or decreasing? In cases where things are
>>>> actively getting worse, we should set our priorities to address them sooner,
>>>> but without knowing those trajectories it's impossible to say.
>>>> Kaldari
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> Jonathan T. Morgan
> Learning Strategist
> Wikimedia Foundation
> User:Jmorgan (WMF)
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