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New Approaches to Data Collection

We can’t keep collecting data the old way, and we don’t have to. Here are seven presentations showing news ways to collect market research information.

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Does Running Damage Your Heart? Another example of the problems of using observational data to infer cause and effect.

Back in 2012, there were several research papers that suggested that running for more than about 20 miles a week was either not giving any benefit, or, worse still, it could be damaging hearts and making death more likely. The key sources were a piece of research by Dr Duck-chul Lee with 50,000 patients (presented at the American College of Sports Medicine (ACSM) 59th Annual Meeting 2012, see The Not-So-Long Run: Mortality Benefit of Running Less Than 20 Miles per Week) and heart findings from Dr James O’Keefe that looked at issues such as fibrosis, calcified arteries, and arrhythmias. This was picked up by a joyful media, with stories about how running was bad for you, and that anything other than a small amount of exercise was either useless or damaging. However, as is often the case with observational data (as opposed to control and test experiments), there were several problems with the conclusions. The key problem with Lee’s study was identified in 2013 by Dr Thomas Weber. In the sample of 50,000 people there were some long distance runners, for example, marathon runners. Lee wanted to assess these people against the non-runners and the occasional runners. However, he needed […]

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Researchers should be aware of the problems with observational data

Posted by Ray Poynter, 18 May, 2018 The world is shifting from asking questions to utilising observational data (mostly for very good reasons) and this is creating a new set of problems that researchers need to recognise and address. What is observational data? Observational data refers to information gathered without the subject of the research (for example an individual customer, patient, employee, etc.) having to be explicitly involved in recording what they are doing. For example, collecting data without people having to respond to a questionnaire, without having to take part in a depth interview, and without having to maintain a research diary. Most big data is observational data, for example, the transaction records from a bank, people’s viewing habits on a video streaming service, or posts in social media. But, observational data can also be small data (based on just a few people). For example, participant ethnographic methods, used to to study people in their everyday lives, collects observational data, that is clearly not ‘big data’. Observational data can be based on census or it can be based on sample. For example, a few years ago a leading mobile phone company was able to sell very detailed data about […]

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Blockchain and Market Research – A force for disruption soon?

Post by Ray Poynter, 3 March 2018 Have you noticed that there seems to a lot of people who are shouting about how important blockchain (and or cryptocurrencies like Bitcoin) are going to be for market research? In November 2017 a Quirks article claimed “blockchain is turning marketing research on its head” and at IIeX Europe Rolfe Swinton and Snorri Gudmundsson presented “10 Reasons Why Blockchain is a Big Deal for MR – And What You Can do About it”. Although I am a fan of technology, I am a sceptical fan, and all the buzz about blockchain stimulated both my interest and my scepticism, so I set about finding out more about blockchain and issues such as cryptocurrencies (like Bitcoin) and smart contracts (such as those created using Solidity and Ethereum). Whilst I can’t be 100% sure, my gut feel is that blockchain will not disrupt market research within the next five years (and maybe never), and here is my line of thinking. Of course, I could be wrong 🙂 What is blockchain? Blockchain is the technology that underpins Bitcoin. Indeed, in 2008 blockchain was Bitcoin, and Bitcoin was blockchain – but now there are other blockchains, some with […]

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Why should I use international standards?

The post below is a guest post from Will Poynter, lead engineer at CLOSER Discovery, based in the UK. Space is big. Really big. You just won’t believe how vastly, hugely, mind-bogglingly big it is. – Douglas Adams And now data is big too. Lots of you will have heard, or read, that in the past two years we have created more data than all previous years combined. And there appears to be no indication or reason to think that this rate of growth is going to slow down anytime soon. In a previous post, I shared my thoughts on the big data issue, and why, in order to correctly utilize the new quantity of data, we need new tools and infrastructure. But currently, there is a huge impediment in the way of achieving these new tools and infrastructure. Most data is not held using international standards. Let me jump back a little. I am an alumnus of the University of Manchester, where an impressive number of buildings, roads and parks are named after Joseph Whitworth. Whitworth was a pivotal figure, during the 19th century, for his creation of a set of standards covering screws, nuts, bolts and tools. This may […]

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Open data, what does it mean and why do we need it?

The post below is a guest post from Will Poynter, lead engineer at CLOSER Discovery, based in the UK. There is a common misconception that open data means making data public. This is one, very narrow, way of opening up data. What is open data? I prefer to refer to opening up data, an action, rather than open data, a noun. This is because open data suggests an absolute state, while openness is relative to the environment and user. I.e. data is not either open or not, it exists along a spectrum depending who you are, what you would like to do with the data and where we are in the timeline of the data. Before we get too abstract let me set out an example. Let’s use a teacher’s notebook. This notebook contains everything for our teacher, including comments on pupils, marking, ideas for lesson plans and personal notes. Currently our teacher keeps this notebook to himself and shares it with no one; definitely not open data. Now suppose our teacher would like to begin opening up the data inside his notebook so that he can share ideas for lesson plans with all the teachers in the school. Even though […]

Discovery: a new tool for accessing longitudinal cohort studies

Are you interested in longitudinal data? For example, The Hertfordshire Cohort Study (following 3000 men and women since they were born in the 1930s), or the British Cohort Study (17,000 people born in a single week in 1970). If so, you will be aware of the problems such as: knowing what data is available, what questions were asked, and where is the data stored. Discovery is a new tool (still in a relatively early stage of development) from CLOSER, that makes eight longitudinal studies more accessible. Please try it Where are the variables I am interested in? Perhaps the best way to understand the usefulness and power of Discovery is an example based on finding variables of interest. The image below shows the home page of Discovery, and it shows that the eight studies include over 55,000 variables [note, if you click on the images they should enlarge]. With so many variables, nobody wants to scroll through them all to find items that are useful to their project or query. By clicking on Variables we can filter the list by Study, Life Stage and Topic, as in the image below. In this example, let’s assume we are interested in alcohol consumption. […]