Methods in Action: Behavioral Tracking


It’s a safe bet that any academics wanting to study problem gambling are going to have a hard time getting their subjects to deal candidly with them. But the advent of online gambling creates the potential for an excellent natural experiment into a gambler’s behavior – if only you can get the data and then develop innovative methods to analyze the information.

In this, the first of monthly series we’re calling Methods in Action, Mark Griffiths reprises his SAGE Research Methods case study — “The Use of Behavioural Tracking Methodologies in the Study of Online Gambling” — to explain how he and his research partner harnessed the big data possibilities of online gambling to both assess behavior and see if ‘responsible gambling’ interventions really work. Addressing the nexus of data collection and technology, Griffiths also touches on a difficulty peculiar to his field – seeking data from gaming enterprises chary of sharing their data and presenting his analysis to peers suspicious of the gambling industry.

In the coming months we will present other hot topics drawn from SAGE Research Methods.

Mark Griffiths

Mark Griffiths

Griffiths, a professor of behavioral addiction at Nottingham Trent University, where he directs the International Gaming Research Unit, is a good choice to begin this series of posts, having won a number of awards for both his scholarship and his ability to talk about his scholarship. The British Association for the Advancement of Science in 2004 awarded him its Joseph Lister Prize for Social Sciences as one of the UK’s “outstanding scientific communicators” and the British Psychological Society gave him its Fellowship Award for “exceptional contributions to psychology.”

What led you to study this issue?

I’ve been working in the area of gambling for nearly 30 years and over the past 15 years I have been carrying out research into both online gambling and responsible gambling. The chance to carry out innovative research in both areas using a new methodology was highly appealing – especially as I have used so many other methods in my gambling research (including online and offline surveys, experiments in laboratories and ecologically valid settings, offline focus groups, online and offline case study interviews, participant and non-participation, secondary analysis of survey data, and analysis of various forms of online data such as those found in online forums and online diary blogs).

Over the last decade there has been a big push by gambling regulators for gambling operators to be more socially responsible towards its clientele and this has led to the use of many different responsible gambling (RG) tools and initiatives such as voluntary self-exclusion schemes (where gamblers can ban themselves from gambling), limit setting (where gamblers can choose how much time and/or money they want to lose while gambling), personalized feedback (where gamblers can get personal feedback and advice based on their actual gambling behavior) and pop-up messages (where gamblers receive a pop-up message during play that informs them how long they have been playing or how much money that have spent during the session).

However, very little is known about whether these RG tools and initiatives actually work, and most of the research that has been published relies on laboratory methods and self-reports – both of which have problems as reliable methods when it comes to evaluating whether RG tools work. Laboratory experiments typically contain very few participants and are carried out in non-ecologically valid settings, and self-reports are prone to many biases (including social desirability and recall biases). Additionally, the sample sizes are also relatively small (although bigger than experiments).

What did you want your final dataset to look like? What methods proved most useful?

The datasets to analyze player behavior are huge and can include hundreds of thousands of online gamblers. Given that my first empirical paper on gambling was a participant observational analysis of eight slot machine gamblers at one British amusement arcade, it is extraordinary to think that decades later I have access to datasets beyond anything I could have imagined back in the 1980s when I began my research career. The data analysis is carried with my research colleague Michael Auer – [the head of consulting services at London’s a2mlab] who has a specific expertise in data mining — and we use traditional statistical tests to analyze the data. However, the hardest part is always trying to work out which parameters to use in assessing whether the RG tool worked or not. The kind of data we have includes how much time and money that players are spending on the gambling website, and using that data we can assess to what extent the amount of time and money decreases as a result of using limit setting measures, or receiving personalized feedback or a pop-up message.

What problems did you encounter in getting there? And what innovative ways did you use to address that?

One of the biggest problems in doing this type of research in the gambling studies field is getting access to the data in the first place and the associated issue of whether academics should be working with the gambling industry in the first place. The bottom line is that we would never have been able to undertake this kind of innovative research with participant sizes of hundreds of thousands of real gamblers without working in co-operation with the gambling industry. (It should also be noted that the gambling companies in question did not fund the research but simply provided access to their databases and customers). In fact, we would go as far as to say the research would have been impossible without gambling industry co-operation. Data access provided by the gambling industry has to be one of the key ways forward if the field is to progress.

Is that fact that online gambling is, well, online make this research easier?

Unlike other consumptive and potentially addictive behaviors (smoking cigarettes, drinking alcohol, etc.), researchers can study real-time gambling (and other potentially addictive behaviors like video gaming and social networking) in a way that just cannot be done in other chemical and behavioral addictions (e.g., sex, exercise, work, etc.) because of online and/or card-based technologies (such as loyalty cards and player cards). There is no equivalent of this is the tobacco or alcohol industry, and is one of the reasons why researchers in the gambling field are beginning to liaise and/or collaborate with gambling operators. As researchers, we should always strive to improve our theories and models and it appears strange to neglect this purely objective information simply because it involves working together with the gambling industry. This is especially important given the recent research using data from gamblers on the bwin website showing that self-recollected information does not match with objective behavioral tracking data.

When are behavioral tracking methodologies the only reliable way of collecting data? Why is that?

The great thing about online behavioral tracking data collected from gamblers is that it is totally objective (as it provides a true record of what every gambler does click-by-click), is collected from real world gambling websites (so is ecologically valid), and has large sample sizes (typically tens of thousands of online gamblers). There of course some disadvantages, the main ones being that the sample is unrepresentative of all online gamblers (as the data only comes from gamblers at one website) and nothing is known about the person’s gambling activity at other websites (research has shown that online gamblers typically gamble at a number of different websites and not just one). Despite these limitations, the analysis of behavioral tracking data (so-called ‘big data’) is a reliable and cutting-edge way to assess and evaluate online gambling behavior and to assess whether RG tools actually work in real world gambling settings with real online gamblers in real time.

What advice would you offer anyone conducting similar research?

To get access to such data you will have to cultivate a trusting relationship with the data providers. It took me years to build up trust with the gambling industry because researchers who study problem gambling are often perceived by the gambling industry to be ‘anti-gambling,’ but in my case this wasn’t true. I am ‘pro-responsible gambling’ and gamble myself, so it would be hypocritical to be anti-gambling. My main aim in my gambling research is to protect players and minimize harm. Problem gambling will never be totally eliminated — but it can be minimized. If gambling companies share the same aim and philosophy of not wanting to make money from problem gamblers but to make money from non-problem gamblers, then I would be prepared to help and collaborate.

You also need to be thick-skinned. If you are analyzing any behavioral tracking data provided by the gambling industry, then you need to be prepared for others in the field criticizing you for working in collaboration with the industry. Although none of this research is funded by the industry, the fact that you are collaborating is enough for some people to accuse you of not being independent and/or being in the pockets of the gambling industry. Neither of these are true — but it won’t stop the criticism.


Further reading

Auer, M. & Griffiths, M.D. (2013). Behavioral tracking tools, regulation and corporate social responsibility in online gambling. Gaming Law Review and Economics, 17, 579-583.

Auer, M. & Griffiths, M.D. (2013). Voluntary limit setting and player choice in most intense online gamblers: An empirical study of gambling behavior. Journal of Gambling Studies, 29, 647-660.

Auer, M. & Griffiths, M.D. (2014). Personalised feedback in the promotion of responsible gambling: A brief overview. Responsible Gambling Review, 1, 27-36.

Auer, M. & Griffiths, M.D. (2014). An empirical investigation of theoretical loss and gambling intensity. Journal of Gambling Studies, 30, 879-887.

Auer, M. & Griffiths, M.D. (2015). Testing normative and self-appraisal feedback in an online slot-machine pop-up message in a real-world setting. Frontiers in Psychology, 6, 339. doi: 10.3389/fpsyg.2015.00339.

Auer, M. & Griffiths, M.D. (2015). Theoretical loss and gambling intensity (revisited): A response to Braverman et al (2013). Journal of Gambling Studies, 31, 921-931.

Auer, M. & Griffiths, M.D. (2015). The use of personalized behavioral feedback for problematic online gamblers: An empirical study. Frontiers in Psychology, 6, 1406. doi: 10.3389/fpsyg.2015.01406.

Auer, M., Littler, A. & Griffiths, M.D. (2015). Legal aspects of responsible gaming pre-commitment and personal feedback initiatives. Gaming Law Review and Economics, 6, 444-456.

Auer, M., Malischnig, D. & Griffiths, M.D. (2014). Is ‘pop-up’ messaging in online slot machine gambling effective? An empirical research note. Journal of Gambling Issues, 29, 1-10.

Auer, M., Schneeberger, A. & Griffiths, M.D. (2012). Theoretical loss and gambling intensity: A simulation study. Gaming Law Review and Economics, 16, 269-273.

Griffiths, M.D. & Auer, M. (2011). Approaches to understanding online versus offline gaming impacts. Casino and Gaming International, 7(3), 45-48.

Griffiths, M.D. & Auer, M. (2015). Research funding in gambling studies: Some further observations. International Gambling Studies, 15, 15-19.