Foundational Social Psychology Experiments (And Why Analysts Should Know Them) – Part 5 of 5
Foundational Social Psychology Experiments
(And Why Analysts Should Know Them) – Part 5
Digital Analytics is a relatively new field, and as such, we can learn a lot from other disciplines. This post continues exploring classic studies from social psychology, and what we analysts can learn from them.
Jump to an individual topic:
- The Magic Number 7 (or, 7 +/- 2)
- When The Facts Don’t Matter
- Confirmation Bias
- Conformity to the Norm
- Primacy and Recency Effects
- The Halo Effect
- The Bystander Effect (or “Diffusion of Responsibility”)
- Selection Attention
- False Consensus
- Homogeneity of the Outgroup
- The Hawthorne Effect
Experiments have revealed that we tend to believe in a false consensus: that others would respond similarly to the way that we would. For example, Ross, Greene & House (1977) provided participants with a scenario, with two different possible ways of responding. Participants were asked to explain which option they would choose, and guess what other people would choose. Regardless of which option they actually chose, participants believed that other people would choose the same one.
Why this matters for analysts: As you are analyzing data, you are looking at the behaviour of real people. It’s easy to make assumptions about how they will react, or why they did what they did, based on what you would do. But our analysis will be far more valuable if we can be aware of those assumptions, and actively seek to understand why our actual customers did these things – without relying on assumptions.
There is a related effect here: the Homogeneity of the Outgroup. (Quattrone & Jones, 1980.) In short, we tend to view those who are different to us (the “outgroup”) as all being very similar, while those who are like us (the “ingroup”) are more diverse. For example, all women are chatty, but some men are talkative, some are quiet, some are stoic, some are more emotional, some are cautious, others are more risky… etc.
Why this matters for analysts: Similar to the False Consensus Effect, where we may analyse user behaviour assuming everyone thinks as we do, the Homogeneity of the Outgroup suggests that we may oversimplify the behaviour of customers who are different to us, and fail to fully appreciate the nuance of varied behaviour. This may seriously bias our analyses! For example, if we are a large global company, an analysis of customers in another region may be seriously flawed if we are assuming customers in the region are “all the same.” To overcome this tendency, we might consider leveraging local teams or local analysts to conduct or vet such analyses.
In 1955, Henry Landsberger analyzed several studies conducted between 1924 and 1932 at the Hawthorne Works factory. These studies originally intended to discover whether the level of light within a building changed the productivity of workers. However, the studies found no effect of the level of light – but they did reveal that the output of the workers changed because they were being observed. This came to be known as the “Hawthorne effect” – that our behaviour will change purely because we are being observed, with no actual manipulation of variables.
Why this matters for analysts: The Hawthorne Effect can significantly impact your findings, depending on the type of data you are using. Qualitative methods like surveys, research groups or user testing may be more heavily impacted; data capture via a web analytics tool may be less impacted. (While people may technically know their website activity is being tracked, it may not be “top of mind” enough during the browsing experience to trigger this effect.) That’s not to say there aren’t huge benefits of using qualitative data – just that you need to be mindful of the effects of observation.
What are your thoughts? Do these pivotal social psychology experiments help to explain some of the challenges you face with analyzing and presenting data? Are there any interesting studies you have heard of, that hold important lessons for analysts? Please share them in the comments!