About Andrew Jeavons

With over 25 years in the market research industry, Andrew is a frequent writer and speaker for various publications and events around the world. He has a background in psychology, statistics and software development. Andrew is President of Survey Analytics.

Meet the Data Triplets: Data, Metadata and Paradata

tripletsThere are three sorts of data, and very often you need all three to understand and use the data you collect from your survey.

Here are the three sorts:

1. The Data.

The data is the data, that is the actually numbers, codes or open ended text that the respondent enters into the survey. There should probably be a better way of describing this than “the data.”, maybe raw data is a better term to use ?

2. The Metadata

Metadata describes the raw data.

For instance the raw data for question 2 may be a “1” or a “2”. The metadata would say that the value “1” means male and the value “2” means female.  Or the metadata may say that the values for question 3 could be a range of 1-100, or 32-78.

Metadata gives meaning to the raw data, and so it is vital to the analysis process of the raw data that the metadata is present.  Otherwise the raw data is just a collection of numbers with no meaning.

One of the problems with metadata is keeping it connected to the right raw data. The wrong metadata with raw data can be a disaster.

Over the years data formats have got more complex, and one of the big reasons is to keep the metadata with the data. More recent data exchange formats/protocols such as JSON (Javascript Object Notation for the technically minded) have capabilities for attaching metadata to the data, which is a very good thing.

Raw data with no metadata is just a load of junk.

3. The Paradata

Paradata is the least well known of the data triplets. In the past decade or so it has become much more important for the survey research world.

Paradata is data which describes something about the way the raw data was collected.

It is data about data.

The most commonly used form of paradata used at the moment is data about questionnaire and question timings. That is, the time a respondent takes to complete a question or questionnaire.

This type of data is now one of the cornerstones of quality measurements for web surveys.

Obviously there can be many different sorts of paradata. For open ended text questions the length of text entered by the respondent can be measure, as well as the “level of vocabulary” contained in the text.

One metric used for web surveys is that of “speeders,” that is, the number of people who complete the survey extremely quickly. The paradata for time take to complete the questionnaire is used here.

Paradata can also be useful in revealing hidden biases; for instance, using paradata in the gamificaton of surveys is a rising trend. The time taken to do something in a gamified survey as well the action can have a great deal of meaning. Some researchers claim that hidden racism, some times unknown to the subject themselves, can be revealed by measuring someone’s reaction time to specific questions.

In a future post we will delve more into exactly how paradata can be used for quality control of web surveys.

If you want to get ahead…

I was in a sales meeting a couple of days ago. We were telling a prospect about our new smartphone interviewing app, SurveySwipe. We were talking about apps and how they are used. There were several “old guys” i.e. over 40 and a couple of younger people (under 40) in the meeting.  For the record I am an old guy – way old. The discussion got on to who uses apps and what they are used for. My point was that apps are the new communication medium, because email is dying fast in the under 40 age group.

The older guys didn’t seem to agree, so I asked the younger guy how many apps he had on his smartphone – his answer was “50 – I think”. This clearly shocked the other older guys, they had smartphones but they used them like phones, not as computers to run apps. Smartphones are not phones. Smartphones are personal computing systems which are now the direct channel to respondents and consumers. And email isn’t going to cut it anymore.
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SurveySwipe Helps Kick Off MarketMix 2011

One of the most exciting marketing conference in the Pacific Northwest kicks off today: Market Mix 2011. This event brings together some of the most well-known and dynamic marketing practitioners in the country. The range of subjects covered is impressive, from mobile marketing strategies, partnership building and leveraging social media, to career development skills for marketing professionals and cost-effective lead generation approaches.

Along with this innovative programs comes another exciting announcement. SurveySwipe, the new smartphone data collection platform from SurveyAnalytics, is available to all of the attendees to MarketMix 2011. The SurveySwipe is available on the Android and iOS (including iPhone and iPad) platforms, with versions for Blackberry and Windows 7 coming soon. SurveySwipe provides a flexible smartphone survey system with a range of advanced features for realtime data collection. A related product, SurveyPocket allows data collection when users are out of range of an internet connection.

Using the SurveySwipe application, MarketMix 2011 attendees can provide realtime feedback and comments during their time at the conference. Instead of surveys carried out long after the conference has closed, the attendees can voice there their opinions during sessions, break; in fact anytime they like!

Vivek Bhaskaran, CEO of SurveyAnalytics, said “ We’re really pleased to be teaming up with MarketMix 2011. We know it is one of the most prestigious conferences in the Northwest for marketing professionals. SurveySwipe is the ideal tool for this sort of event; it means the organizers can get feedback on how the conference is going in realtime.”

 

Let’s Save Smartphone Surveys: 10-7-140

Surveys have a great new platform: smartphones! We have access (we can get to people pretty much anytime) and identification (we can be pretty sure who is taking the survey).  It’s all for us to screw up. Is the survey industry going to do what it always does? As Betty Adamou pointed out in her paper at the Newmr (www.newmr.org) conference last year, survey research takes a communication medium and beats it to death. Telephone? Web? All victims of over use and poorly designed, boring,  and long surveys.

There has been a “140 characters per question text limit” meme floating around recently started by Annie Pettit from Conversition Strategies. This made me think, “How can we apply this idea to smartphone surveys?” The 140 characters for the question text makes sense, but what about the rest? So here are my proposals for NOT destroying the smartphone platform:
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Will Smartphones Save the Survey ?

At Survey Analytics, we’ve been a bit smart phone focused recently, due primarily to our recent State of the Union dial test study. As I was testing the SurveySwipe software on which our study was based, it started me thinking about the whole idea of surveys on smartphones.

One thing that is clear is that the smartphone screen has some limitations. Obviously it is not a laptop or desktop monitor sized screen! You have a screen, but it is small. You can’t do anything really complex on the screen with a question in a survey without running out of room pretty quickly.

But this smartphone limitation is a tremendous opportunity for the market research industry. It may mean that the smartphone saves the survey as a way of collecting data in MR.
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Joining Survey Analytics

I just wanted to let you know that I have joined Survey Analytics (surveyanalytics.com) as Executive Vice President as of today.

I’m very excited to be joining such a dynamic company which is one of the market leaders in accessible in online survey and analytical tools. Survey Analytics clients include McGraw Hill, Experian, Roku, Career Builder, Texaco, Microsoft, Motorola, Qwest and many other leading companies.

Survey Analytics is fast becoming a leader in the consumer intelligence and “DIY” market research field, with innovative companies such as BadgeFarm.com and SurveySwipe.com, a new smart phone interviewing system.

Vivek Bhaskaran, CEO and founder of Survey Analytics commented:

“We are delighted that Andrew is joining us at this critical new phase of our growth. His experience and insights into the business domain are already proving of incredible value”.

You contact me on andrew.jeavons@surveyanalytics.com .

The Twitter Fire Hose

Have you ever heard of the Twitter “fire hose” or the “sprinkler” ? If you have, you have probably been doing software development work on connecting to Twitter. The “fire hose” and the “sprinkler” are just two of the terms Twitter use to describe the type of connections you can make to Twitter with an application. As the name implies the Firehose is everything anyone tweets anywhere, and very few companies need or can cope with the volume of data that is produced. Most applications filter or search the stream for hashtags or keywords or users. Absorbing the full force of the global twitter population is not for the meek. According to ComScore there were 25 billion tweets in 2010. That’s a lot of anything.
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Seems statistics in MR don’t matter…..

I was reading Ray Poynters excellent blog article here:
about the Likert scale. Ray stated:
“If a researcher makes the wild and unjustified decision (IMHO) to treat Likert numbers as an interval scale…”
I took this as him saying he agreed it was wrong to treat Likert as an interval scale, he did only say that it is “wild and unjustified” to treat them as interval. This is a decision that is made constantly in MR. I will go further and say it is plain wrong and bad statistical practice to treat Likert scales as interval data. It is an ordinal scale. It should be treated as such. You can’t take the mean and standard deviation of ordinal scales. They don’t have them. They have a mode (sometimes two modes), they have a median, they don’t have a mean.
I got these radical ideas some years ago. After my degree in Psychology and post graduate work I worked in a medical research facility called the “Institute of Neurology” (ION) in London UK. I worked in the “Computing and Medical Statistics Unit” in a basement in Guildford Street complete with IBM card punch machines, cockroaches and CDC UT2000 remote RJE terminal for the University of London Computer Center. I think the drum printer on the UT2000 gave me hearing damage, it sounded like a machine gun.
I worked for a medical statistician called Liz. I was what I called a “data monkey”, I ran programs, wrote them, cleaned data, killed cockroaches and generally helped out. Liz had worked with Sir Richard Peto, who is currently Professor of Medical Statistics and Epidemiology at the University of Oxford. Liz was passionate about her profession. I recall several studies we worked on together. One was a drug trial for a drug to cure Multiple Sclerosis, another was a long term epidemiological study of Multiple Sclerosis, another was a drug trial of a chemotherapy drug to be used against a particularly evil form of brain cancer called Gliomas. Then there were studies about stroke models in rats, muscular dystrophy and Tourettes syndrome.
We had a strict procedure for all data. First all interval data variables were plotted and looked at. Then these variables were tested for normality of distribution, if they were not normal appropriate transformations were applied to correct any anomalies and then they were re-tested for normality. If they still didn’t pass the normality test they were only analysed with non-parametric techniques. Other data variables were plotted too, and the histograms looked at carefully for anomalies. I can recall Liz spending quite some time researching if you could use a T-test on percentages. She concluded you could not. She decided percentages belonged to the Cauchy distribution, which has no mean or higher moments. Thus a T-test would be statistically invalid.
I asked Liz about this procedure of treating data and the rigour she applied. I came from psychology, we were a little more lax in our approach. She said we had to remember that the results we obtained mattered. They could be life and death decisions. A type I error on a drug trial could lead to more people dying because they were given a drug that didn’t really work. A type II error on the epidemiological work may miss an important antecedent to Multiple Sclerosis, a crippling disease.
We were working on data for the drug trial for treating Gliomas, a form of brain cancer. Gliomas remain a deadly form of cancer, with only a 50% survival rate within one year of diagnosis. We were using something called Survival Analysis to test the effect of a chemotherapy drug. Liz said we had to wait for more events to be sure of the results. An event was someone dying. I happened to look at the columns in the data which contained the ages of the subjects. 18, 19, 21 – these were people only a little younger than I was at the time. We had to wait for them to die to be sure the conclusions that were made about the drug (Vincristine) were correct. The results really mattered.
It seems to me the question about Likert scales is not so much about if you can treat them as an interval scale rather it is this: do the results matter ? Do you care that the results are correct ? Do the results matter enough to do the work properly ? If the results do matter, do it properly.
From what I can see very often it seems market researchers think the results don’t matter……

The Problem with Neuromarketing….

There are several problems really. The first is the name. It should really be called applied cognitive neuroscience (ACN), because that is what it is. Hopefully this would counter all the specious arguments about it being scientific. The New Scientist (http://www.prnewswire.com/news-releases/neurofocus-and-new-scientist-magazine-apply-neuromarketing-to-select-cover-design-100053049.html) test raised some comments about the science of ACN. I have to disclose in what seems like a previous life I studied cognitive neuroscience (we called it neuropsychology back then). Trust me, it is a science, it has been around a *long time*, many decades, and it also uses statistics correctly. This latter fact is a novelty for a lot of MR, I know. At least ACN tries.

There is also the privacy “discussion”. ACN is about as invasive as looking at someone who is blushing and deducing they may be embarrassed. ACN measures physiological correlates of mental states or processes. It happens to do them via electrical signals measures from the brain. We do this all the while with body language, speech tones and so on. Just because there is a lot of equipment in ACN and latin words doesn’t make it any different.

The biggest problem ACN has is sample size. N = 19, as in the New Scientist test, isn’t much. It is barely enough for a single quota cell. Making big decisions based on tiny samples mostly ends in tears. The sample size issue relates to the technology of ACN. The electrodes on the scalp can take time to set up and this limits sample sizes. However several companies have ways round this with either limited electrode placement (not so good – one electrode gets you nothing except muscle noise) and less “invasive” caps that hold the electrodes on the scalp without glue. The latter holds the most promise so far as I can see. Sample size is a solvable problem, scalability may take time, but compared to the rest of the technology used in ACN it is not the most complex problem. Several companies are building normative databases which will be hugely useful.

The problems with ACN are solvable, the potential is huge…

CrowdSolving – Beyond CrowdSourcing?

I’m not very convinced of the “wisdom of crowds.” There are numerous examples of how “the wisdom of crowds” is in fact the “idiocy of the mob.” Look at some political movements or some of the more extreme religions, for instance: a good few of these make no sense, but they have a lot of people who believe them. In Vanatu, an island in the Pacific, there is a cargo cult called the John Frum Cult that thinks building replicas of USA air force bases from World War II will bring the USA and all their goods back to the island. A lot of people believe this.

There is a lot of research from social psychology showing that groups polarize decisions in contrast to individuals. A group will make a more extreme decision (cautious or risky) than an individual. There is also the fact that estimations of physical sizes and weights will tend to show a normal distribution, with the most common estimate, the mode, being the correct one. Here there is wisdom in crowds, or more likely the wisdom of the normal distribution, the central limit theorem and statistics in general. Distributions are wonderful things.

One of the advantages of a large scale survey is that you are able to leverage a lot of people’s experience and knowledge. Recently, a company called “Netflix” in the USA utilized the web and their subscriber base to solve an interesting problem. While it is not the usual meaning of the term the “wisdom of crowds,” it is an example of how a crowd can solve a problem. Netflix (www.netflix.com) rents DVDs to their subscribers. They send the rentals via mail and their users maintain a list of which DVD’s they want. Netflix also tries to predict which DVDs people might like to watch based on the DVDs they have already rented. Amazon does a similar thing in making product recommendations to purchasers. Netflix wanted to improve their predictive algorithm by 10%, which is quite a large improvement. They could have tried to hire all sorts of geniuses, but they instead chose a very unique way to solve the problem. They set up a web site (www.netflixprize.com), posted a huge data set of movie DVDs, data about those movies, and subscriber choices. They then offered $1,000,000 to anyone who could improve their algorithm by 10%. There were two conditions: a deadline (September of 2009) and an agreement that anyone who submitted a solution had to document that solution publicly. Many companies allowed their employees to set up teams and compete, some individuals competed, and teams merged and re-formed over time. In the end there was a winning team: Bellkors Pragmatic Chaos.

In this case the wisdom was not “crowd think,” whatever that is. Instead, Netflix leveraged the web and all the people surfing it to source people who wanted to solve this problem. For Netflix, the $1,000,000 was cheap. They could never have afforded to hire all the people who took part in the contest. They got access to world-class computing facilities, superior minds, and  they received some great publicity as well.

The winning algorithm was a technique called a “Restricted Boltzmann Machine.” It proved that numbers and math matter. It wasn’t the crowd that solved the problem, but the crowd was the mechanism that made the solution possible. I’m inclined to think that this is the real wisdom of the crowd. People can come up with all sorts of strange beliefs; the ability to get people to address your problem is the wisdom of the crowd. It’s another example of how the web has changed the world in a radical way. Twenty years ago, it simply would not have been possible for Netflix to find a solution to their problem so gracefully. I hear there is going to be another Netflix contest. It’s nice that it was the math that was wise in the end….