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Linda Kaye

Using online behaviour in psychological enquiry

22 September 2017 | by Linda Kaye

As we know, psychology is primarily the study of human thought and behaviour, but how often do we actually take measures of behaviour within research?

Of course, the use of self-reports to gain average reports of attitudes and behaviours is commonplace within psychology, and are strongly embedded within psychological practice, however, they may be a misnomer and may not be accurately reflecting the behaviour we assume we are measuring.

One issue with self-reports is that they are often obtained out of the context of the relevant behaviour they are intending to measure, and this is problematic for a number of reasons.

Firstly, it is known that contextual cues play a role in the way in which we recall things such as consumption behaviours (Monk & Heim, 2013, 2014; Monk, Heim, Qureshi & Price, 2015). Self-reports of behaviours which are taken away from these rich contexts therefore may lack meaningful and salient cues on recall.

Secondly, it is becoming increasingly apparent that participants’ reports of their behaviours are often largely inaccurate. Indeed, this has been found to be the case when asking Smartphone users to recall the amount of times they check their Smartphone on a daily basis.

Specifically, it has been found that users grossly underestimate this behaviour when compared to records of their actual checking behaviour garnered through their digital traces (Andrews, Ellis, Shaw & Piwek, 2015).

Clearly this presents some issues and raises concerns over the validity of so-called measures of behaviour which may be collated through self-reports. It is particularly pertinent to consider this in the context of the current replication crisis, in which obtaining more rigorous, valid and objective measures of behaviour may provide a more stable basis of enquiry.

So, what is the solution to these issues?

In a digital society, it seems logical to capitalise on alternative forms of data to overcome the reliance on self-reported behaviour. Namely, the wide availability of online data, which may take the form of social media posts, online consumer behaviour, or digital traces through technology itself (e.g., wearable technology or Smartphones) can provide a wealth of research opportunities from which to obtain objective behavioural data.

Indeed, our previous research on emojis garnered emoji behaviour through users’ Facebook profiles from which to correspond to psychometric measures (Wall, Kaye & Malone, 2016). Other research on visual online self-presentation has used Facebook users’ photo uploads as a basis for data analysis to corroborate with personality traits (Eftekhar, Fullwood, & Morris, 2014).

There are many constructs and areas of enquiry which may lend themselves to using online behavioural data.

For example, group membership and identity can be garnered through metrics such as users’ online group affiliation and online community activities. Interpersonal relations can be obtained across a range of online contexts, including interactions on social media posts or online discussion boards. Even using Instagram photos has been suggested to be revealing of users’ depressive states (Reece & Danforth, 2017).

Although such data may not always be appropriate for all research issues, the discipline certainly warrants further development of alternative approaches to data gathering. The advancement of cyberpsychology as a sub-discipline may aid this endeavour to some extent, but more creative uses of digital data to integrate into research protocols more generally is greatly needed. Not only is this important for our research efficacy, but also may enrich the research methodology training within university-level education, to help our students develop as digital citizens, prepared with 21st Century skills.

In a digital world which comprises a multitude of digital environments, it seems reasonable to capitalise on these as research environments to optimise our potential to observe actual behaviour occurring in meaningful contexts. 



  • Andrews, S., Ellis, D.A., Shaw, H.. & Piwek, L. (2015). Beyond self-report: Tools to compare estimated and real-world smartphone use. PLOS ONE, 10, e0139004.
  • Eftekhar, A., Fullwood, C., & Morris, N. (2014). Capturing personality from Facebook photos and photo-related activities: How much exposure do you need? Computers in Human Behavior, 37, 162–170.
  • Monk, R. L., & Heim, D. (2013). Environmental context effects on alcohol-related outcome expectancies, efficacy and norms: A field study. Psychology of Addictive Behaviors, 27, 814-818.
  • Monk, R.L., & Heim, D. (2014). A real-time examination of context effects on alcohol cognitions Alcoholism: Clinical and Experimental Research, 38, 2452-2459.
  • Monk, R.L., Heim, D., Qureshi, A., & Price, A. (2015). “I have no clue what I drunk last night” Using Smartphone technology to compare in-vivo and retrospective self-reports of alcohol consumption. PLoS ONE. e0126209
  • Reece, A. G. & Danforth, C. M. (2017). Instagram photos reveal predictive markers of depression. EPJ Data Science, 6, 15. doi: 10.1140/epjds/s13688-017-0110-z
  • Wall, H. J, Kaye, L. K., & Malone, S. A. (2016). An exploration of psychological factors on emoticon usage and implications for judgement accuracy. Computers in Human Behavior, 62, 70-78. doi: 10.1016/j.chb.2016.03.040


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