Upon meeting new people, many individuals tend to make quick judgments of another person—even without much information to go on.
For instance, at a social gathering, someone might immediately think that the guy with cool glasses, whom they’ve never met, is likeable based solely on his appearance. As it turns out, he is easy-going and has a lot of friends.
Remarkably, people are surprisingly accurate when making these first impressions—referred to as snap judgments—simply based on visual cues.
Based on the seminal work of Ambady and Rosenthal, this video demonstrates the experimental techniques used to make snap judgments of instructors’ personalities in comparison to actual evaluations of their teaching effectiveness. We will also explore how such inferences can be applied to other professions that rely on analyzing characteristics.
In this study, participants are asked to watch short, muted video compilations of novel college instructors teaching a variety of subjects and must judge certain attributes. Other trained coders count more specific nonverbal behaviors, as well as rate their physical appearance.
These assessments are ultimately compared to actual teaching evaluations to examine the accuracy of first impressions based on visual traits and distinct, objective actions.
Participants first provide molar ratings—broad trait judgments—based on 15 teaching-related dimensions, such as whether they seem enthusiastic, likeable, and confident. The Likert scale ranges from 1 (not at all) to 9 (very).
In addition, research assistants watch the same clips and tally molecular behaviors—actions that are momentary and discrete—like smiling or nodding. They are also asked to report on the teacher’s symmetry and body posture.
Lastly, based on a single photo taken from the videos, the assistants are asked to rate the physical appeal of each instructor on a 5-point Likert scale, where 1 means "not at all" and 5 equals "very", to account for effects of attractiveness.
To examine the predictive utility of these snap judgments, each instructor’s end-of-semester teaching evaluations are compiled for nonbiased quantitative comparisons.
Using these forms, the dependent variable is teaching effectiveness, based on averaging two items where students rated the instructors’ performances and overall quality of the courses.
Ultimately, participants’ assessments of molar nonverbal behaviors—given 30 s of film from one day of instruction—are expected to be highly correlated with students’ evaluations of their instructors, which are based on a much longer span—a semester’s worth of interaction.
These findings suggest that very little time is needed to make an accurate first impression, which is known as thin slicing—the ability to quickly infer another person’s character from a very short exposure.
Prior to the experiment, conduct a power analysis to recruit a sufficient number of participants. Additionally, use previously filmed footage of ten college instructors to generate three separate, 10-s clips from each to end up with a total of 30 videos.
For every one, capture a frame to save as their photo for subsequent observations. To complete preparation, compile the end of semester student evaluations for each of the 10 instructors shown, from the actual courses that correspond to the footage.
To begin, escort each participant into the testing room and explain that they will watch videos and assess molar nonverbal behavior—in this case personality traits.
As they view each set of randomized clips, have them judge every instructor’s nonverbal behavior—15 teaching-related adjectives—on a 9-point Likert scale.
Next, to measure molecular nonverbal behavior, ask two trained coders to watch the same segments and tally the number of times each instructor makes one of 12 distinct behaviors, along with details about their symmetry and body posture.
Lastly, to account for the effects of attractiveness, have each coder view the saved images and judge the physical appearances of each instructor on a 5-point Likert scale.
To conclude the experiment, fully debrief participants regarding the actual purpose and procedures of the study.
To compile the data, make sure that the two evaluation responses have been converted into percentages and averaged for each instructor.
Then, create separate graphs to compare the mean values of molar and molecular categories against teaching effectiveness. Plot the correlations for each nonverbal behavior measured.
First, notice that 10 of the 15 molar ratings of nonverbal behavior were significantly and positively correlated, including the overall composite average—the global variable.
However, molecular behaviors were less predictive. Only fidgeting negatively correlated with teaching effectiveness. Moreover, the relationships remained even after controlling for instructor attractiveness.
In the end, students were able to formulate reliable impressions of instructors’ teaching effectiveness using only 30 s of nonverbal video footage.
Now that you are familiar with how to design a study to evaluate snap judgments in an educational setting, let’s look at how this research extends to other professions that rely on quick inferences to understand other people’s character.
During a poker game, many players rely on snap judgments to size up their competition. Those who make quick inferences about their opponents’ playing style—solely based on a limited amount of visual cues—can win the pot.
However, maintaining accuracy when thin-slicing others depends largely on knowing which factors are important. For example, researchers have shown that divorce can be predicted above chance levels by viewing a very short video of a couple interacting.
In this case, the expected behaviors of complaining or anger did not predict divorce, but rather, defensiveness and withdrawing did. Thus, it may be that implicitly or explicitly learning to attune to the right signals is crucial to developing this expertise.
You’ve just watched JoVE’s video on how to evaluate the accuracy of snap judgments. Now you should have a good understanding of how to design, conduct, and analyze an experiment to study how only a short time is needed to make predictive inferences, as well as how this skill can be useful in other professions.
Thanks for watching!