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Patterns in response to web surveys

December 19, 2017

In my experience response to web surveys tends to follow a very particular pattern when you send out an email invitation to a closed list.

You get the vast majority of responses within 24 hours of sending out the link to respondents, then a few more responses trickle in.  If you send a reminder you’ll see another flurry of responses which will typically be about half of the response you got in the first 24 hours.  Then another little trickle.  If you send a second reminder you’ll see a further flurry of responses which will typically be about half of the response to the first reminder.  Then it is time to stop annoying people with reminders!

The final level of response tends to be about twice the total you had after 24 hours.

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Check out my guest blog for SRN

December 13, 2017

I have written a guest blog about one of my new projects, and you can read it here on the Scottish Recovery Network website.

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Research with your eyes open: who can see your bad news stories?

December 4, 2017

As an independent researcher and an accredited member of the Market Research Society I am required to report on research in an accurate and neutral way.  Basically, I always set up an unbiased research project and give a ‘truthful’ account of the findings (insofar as ‘truth’ is ever achievable… muses on the philosophy of truth… anyway…)

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Blipfoto research being used as a case study!

November 17, 2017

You may be aware of the photo journaling website Blipfoto, where I have been ‘blipping’ on and off for nine years.  You may also be aware that the company went into liquidation and a group of volunteer Directors (including my lovely husband!) set up a crowdfunding campaign which resulted in the successful acquisition of the site as a Community Interest Company.

Well I have also played a role in this in a professional capacity, working with the Directors to help them make evidence-based decisions about how to take the Community Interest Company forward.  I planned and undertook a substantial research project consulting with the Blipfoto community and testing out a number of financial models and more than a thousand Blippers shared their opinions.  The data from this was used this to build a new membership package which (within the first month!) generated enough income to sustain Blipfoto for a year.  A great result!

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Use of decimals – TMI!

November 15, 2017

In a previous blog post I said “I think non-integer percentage points are almost always unnecessary” and I thought I’d write a bit more about that.

“An integer (from the Latin integer meaning “whole”) is a number that can be written without a fractional component. For example, 21, 4, 0, and −2048 are integers, while 9.75, 5 12, and √2 are not.”

Thanks Wikipedia.

What I’m saying is that when you’re writing about research I don’t really think it is particularly useful to be so detailed and specific as to include any decimal places after your percentages.  Obviously you have that information, but there is no need to actually do anything with it.  Just round it to a whole number and move on.  Lovely.

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How to clean your own Surveymonkey data

October 31, 2017

Second in an unexpected mini-series on the professional use of Surveymonkey, I’m going to get even more geeky… brace yourself…

Quantitative data is all about ‘facts’ and ‘hard stats’, but in order to present a set of objective data you sometimes need to use your subjective judgment to ensure that your dataset is as strong as possible.  But sometimes – by no fault of your own – your data isn’t strong because crap gets in there.  Respondents fill your survey in wrong, or give up half way through, or (shudder) lie, and all that crap makes your dataset weak.  So if you are analysing data from a survey, you want to make sure there is no crap in there cluttering your data up and skewing it unintentionally.

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It’s all about the base (aka don’t use Surveymonkey graphs)

October 9, 2017

In quantitative research we ask lots of people lots of questions and we look for patterns within the data.  To do this in a robust way, ideally we do all of our analysis and manipulation within a fixed framework of data so that when we are presenting findings, we are comparing like with like.

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