Sandwich Advertisement Framing Analysis

Rationale

Framing theory suggests that the way information is presented can influence how people interpret and respond to a message. Certain characteristics of a message can draw attention to particular ideas or associations and influence how an audience perceives the subject being presented.

In advertising, visual characteristics can act as framing cues that influence how viewers perceive a product. In this experiment, the physique of the actor eating the sandwich may serve as such a cue. A muscular or “buff” actor may be associated with fitness or health, while an actor with a “dad bod” may create different associations. Therefore, the actor’s physique may influence viewers’ perceptions of the calorie content of the sandwich being advertised.

Hypothesis

On average, viewers who see the advertisement featuring the buff actor will provide different estimates of the sandwich’s calorie content than viewers who see the advertisement featuring the dad bod actor.

Variables & method

The independent variable in this analysis was Actor, a categorical variable identifying which version of the advertisement each participant viewed. Participants viewed either an advertisement featuring a “Buff” actor or an advertisement featuring a “Dad bod” actor.

The dependent variable was Calories, a continuous variable representing each participant’s estimate of the number of calories contained in the sandwich.

The experiment included 100 participants who were randomly divided into two groups of 50. Each group viewed one version of the advertisement and then provided an estimate of the sandwich’s calorie content.

An independent-samples t-test was used to determine whether the average calorie estimate differed significantly between participants who viewed the buff actor and participants who viewed the dad bod actor.

Results & discussion

First, descriptive statistics were calculated for the calorie estimates in each group.

Calorie Estimates by Actor Type

Actor	N	Mean	SD
Buff	50	584.26	128.03
Dad bod	50	656.28	141.25

The distribution of calorie estimates for the two groups can also be compared visually.

An independent-samples t-test was conducted to determine whether the mean calorie estimate differed between the two actor conditions.

## 
##  Welch Two Sample t-test
## 
## data:  Calories by Actor
## t = -2.6713, df = 97.069, p-value = 0.008862
## alternative hypothesis: true difference in means between group Buff and group Dad bod is not equal to 0
## 95 percent confidence interval:
##  -125.52936  -18.51064
## sample estimates:
##    mean in group Buff mean in group Dad bod 
##                584.26                656.28

Participants who viewed the advertisement featuring the buff actor estimated that the sandwich contained an average of approximately 584 calories, while participants who viewed the advertisement featuring the dad bod actor estimated an average of approximately 656 calories.

The independent-samples t-test found a statistically significant difference between the two groups, t(97.07) = -2.67, p = .009. Participants who viewed the dad bod actor estimated that the sandwich contained more calories, on average, than participants who viewed the buff actor.

Because the p-value was less than .05, the difference between the groups was considered statistically significant. Therefore, the findings supported the hypothesis that viewers would provide different estimates of the sandwich’s calorie content depending on the physique of the actor shown in the advertisement.

These findings suggest that the physical appearance of an actor in an advertisement may act as a framing cue that influences viewers’ perceptions of the advertised product.

Code

The following R code was used to conduct the analysis.

library(ggplot2)
library(dplyr)
library(knitr)

mydata <- read.csv("SandwichAd.csv")

# Descriptive statistics
group_stats <- mydata %>%
  group_by(Actor) %>%
  summarise(
    N = n(),
    Mean = mean(Calories),
    SD = sd(Calories)
  )

kable(group_stats, digits = 2)

# Figure
ggplot(mydata, aes(x = Actor, y = Calories)) +
  geom_boxplot() +
  labs(
    title = "Estimated Calories by Actor Type",
    x = "Actor Type",
    y = "Estimated Calories"
  ) +
  theme_minimal()

# Independent-samples t-test
test_results <- t.test(
  Calories ~ Actor,
  data = mydata,
  var.equal = FALSE
)

test_results