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

False Consensus

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.

Homogeneity of the Outgroup

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.

The Hawthorne Effect

In 1955, Henry Landsberger analyzed several studies conducted between 1924 and 1932 at the Hawthorne Works factory. These studies were examining the factors related to worker productivity, including whether the level of light within a building changed the productivity of workers. They found that, while the level of light changing appeared to be related to increased productivity, it was actually the fact that something changed that mattered. (For example, they saw an increase in productivity even in low light conditions, which should make work more difficult…) 

However, this study has been the source of much criticism, and was referred to by Dr. Richard Nisbett as a “glorified anecdote.” Alternative explanations include that Orne’s “Demand Characteristics” were in fact at work (that the changes were due to the workers knowing they were a part of the experiment), or the fact that the changes were always made on a Sunday, and Mondays normally show increased productivity, due to employee’s having a day off. (Levitt & List, 2011.)

Why this matters for analysts: “Demand Characteristics” could mean that your data is subject to influence, if people know they are being observed. For example, in user testing, participants are very aware they are being studied, and may act differently. Your digital analytics data however, 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.) The Sunday vs. Monday explanation reminds us to consider other explanations or variables that may be at play, and be aware of when we are not fully in control of all the variables influencing our data, or our A/B test. However, the Hawthorne studies are also a good example where interpretations of the data may vary! There may be multiple explanations for what you’re seeing in the data, so it’s important to vet your findings with others. 

Conclusion

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!

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

The Bystander Effect (or “Diffusion of Responsibility”)

In 1964 in New York City, a woman name Kitty Genovese was murdered. A newspaper report at the time claimed that 38 people had witnessed the attack (which lasted an hour) yet no one called the police. (Later reports suggested this was an exaggeration – that there had been fewer witnesses, and that some had, in fact, called the police.)

However, this event fascinated psychologists, and triggered several experiments. Darley & Latane (1968) manufactured a medical emergency, where one participant was allegedly having an epileptic seizure, and measured how long it took for participants to help. They found that the more participants, the longer it took to respond to the emergency.

This became known as the “Bystander Effect”, which proposes that the more bystanders that are present, the less likely it is that an individual will step in and help. (Based on this research, CPR training started instructing participants to tell a specific individual, “You! Go call 911” – because if they generally tell a group to call 911, there’s a good chance no one will do it.)

Why this matters for analysts: Think about how you present your analyses and recommendations. If you offer them to a large group, without specific responsibility to any individual to act upon them, you decrease the likelihood of any action being taken at all. So when you make a recommendation, be specific. Who should be taking action on this? If your recommendation is a generic “we should do X”, it’s far less likely to happen.

Selective Attention

Before you read the next part, watch this video and follow the instructions. Go ahead – I’ll wait here.

In 1999, Simons and Chabris conducted an experiment in awareness at Harvard University. Participants were asked to watch a video of basketball players, where one team was wearing white shirts, and the other team was wearing black shirts. In the video, the white team and black team respectively were passing the ball to each other. Participants were asked to count the number of passes between players of the white team. During the video, a man dressed as a gorilla walked into the middle of the court, faced the camera and thumps his chest, then leaves (spending a total of 9 seconds on the screen.) Amazingly? Half of the participants missed the gorilla entirely! Since then, this has been termed “the Invisible Gorilla” experiment. 

Why this matters for analysts: As you are analyzing data, there can be huge, gaping issues that you may not even notice. When we focus on a particular task (for example, counting passes by the white-shirt players only, or analyzing one subset of our customers) we may overlook something significant. Take time before you finalize or present your analysis to think of what other possible explanations or variables there could be (what could you be missing?) or invite a colleague to poke holes in your work.

Stay tuned

More to come!

What are your thoughts? Do these pivotal social psychology experiments help to explain some of the challenges you face with analyzing and presenting data?

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.

• 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

Primacy and Recency Effects

The serial position effect (so named by Ebbinghaus in 1913) finds that we are most likely to recall the first and last items in a list, and least likely to recall those in the middle. For example, let’s say you are asked to recall apple, orange, banana, watermelon and pear. The serial position effect suggests that individuals are more likely to remember apple (the first item; primacy effect) and pear (the final item; recency effect) and less likely to remember orange, banana and watermelon.

The explanation cited is that the first item/s in a list are the most likely to have made it to long-term memory, and benefit from being repeated multiple times. (For example, we may think to ourselves, “Okay, remember apple. Now, apple and orange. Now, apple, orange and banana.”) The primacy effect is reduced when items are presented in quick succession (probably because we don’t have time to do that rehearsal!) and is more prominent when items are presented more slowly. Longer lists tend to see a decrease in the primacy effect (Murdock, 1962.)

The recency effect, that we’re more likely to remember the last items, is explained because the most recent item/s are recalled, since they are still contained within our short-term memory (remember, 7 +/- 2!) However, the items in the middle of the list benefit from neither long, nor short, term memory, and therefore are forgotten.

This doesn’t just affect your recall of random lists of items. When participants are given a list of attributes of a person, their order appears to matter. For example, Asch (1964) found participants told “Steve is smart, diligent, critical, impulsive, and jealous” had a positive evaluation of Steve, whereas participants told “Steve is jealous, impulsive, critical, diligent, and smart” had a negative evaluation of Steve. Even though the adjectives are the exact same – only the order is different!

Why this matters for analysts: When you present information, your audience is unlikely to remember everything you tell them. So choose wisely. What do you lead with? What do you end with? And what do you prioritize lower, and save for the middle?

These findings may also affect the amount of information you provide at one time, and the cadence with which you do so. If you want more retained, you may wish to present smaller amounts of data more slowly, rather than rapid-firing with constant information. For example, rather than presenting twelve different “optimisation opportunities” at once, focusing on one may increase the likelihood that action is taken.

This is also an excellent argument against a 50-slide PowerPoint presentation – while you may have mentioned something in it, if it was 22 slides ago, the chance of your audience remembering are slim.

The Halo Effect

Psychologists have found that our positive impressions in one area (for example, looks) can “bleed over” to our perceptions in another, unrelated area (for example, intelligence.) This has been termed the “halo effect.”

In 1977, Nisbet and Wilson conducted an experiment with university students. The two students watched a video of the same lecturer deliver the same material, but one group saw a warm and friendly “version” of the lecturer, while the other saw the lecturer present in a cold and distant way. The group who saw the friendly version rated the lecturer as more attractive and likeable.

There are plenty of other examples of this. For example, “physically attractive” students have been found to receive higher grades and/or test scores than “unattractive” students at a variety of ages, including elementary school (Salvia, Algozzine, & Sheare, 1977; Zahr, 1985), high school (Felson, 1980) and college (Singer, 1964.) Thorndike (1920) found similar effects within the military, where a perception of a subordinate’s intelligence tended to lead to a perception of other positive characteristics such as loyalty or bravery.

Why this matters for analysts: The appearance of your reports/dashboards/analyses, the way you present to a group, your presentation style, even your appearance may affect how others judge your credibility and intelligence.

The Halo Effect can also influence the data you are analysing! It is common with surveys (especially in the case of lengthy surveys) that happy customers will simply respond “10/10” for everything, and unhappy customers will rate “1/10” for everything – even if parts of the experience differed from their overall perception. For example, if a customer had a poor shipping experience, they may extend that negative feeling about the interaction with the brand to all aspects of the interaction – even if only the last part was bad! (And note here: There’s a definite interplay between the Halo Effect and the Recency Effect!)

Stay tuned

More to come soon!

What are your thoughts? Do these pivotal social psychology experiments help to explain some of the challenges you face with analyzing and presenting data?