Foundational Social Psychology Experiments (And Why Analysts Should Know Them) – Part 3 of 5
Foundational Social Psychology Experiments
(And Why Analysts Should Know Them) – Part 3 of 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.
- 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 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.
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!)
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?