Rationale

Framing theory suggests that cues embedded in a media message can invite message consumers to interpret the message in a particular way. In this experiment, two randomly created groups viewed an ad for a new fast food sandwich, then were asked to estimate the number of calories in the sandwich. The ads were identical, with one exception. One group’s ad showed a slim, physically fit actor eating the sandwich. The other group’s ad showed a heavier actor eating the sandwich. Based on framing theory, one would expect ad viewers to notice the actor’s physique and consider it when guessing the number of calories in the sandwich. Specifically, one would expect viewers of the ad featuring the slim, physically fit actor to assume the sandwich was low in calories, having reasoned that physically fit people usually avoid high-calorie food. Meanwhile, one would expect viewers of the ad featuring the heavier actor to assume the sandwich was high in calories, having reasoned that fast food tends to be calorie dense.

Hypothesis

The analysis tested the hypothesis that the average calorie estimates for the two groups would differ significantly.

Variables & method

The continuous variable named "estimate" measured each participant's estimate of the number of calories in the sandwich shown in the ad. The categorical variable named "group" indicated whether each participant belonged to the group whose ad featured the slim actor or to the group whose ad featured the heavier actor. The analysis treated "estimate" as the dependent variable, treated "group" as the independent variable, and used an independent-samples t-test to evaluate the hypothesis.

Results

First, the analysis examined descriptive statistics for the group distributions and tested the distributions for normality.

# Installing required packages
if (!require("dplyr"))
  install.packages("dplyr")
if (!require("tidyverse"))
  install.packages("tidyverse")
library(dplyr)
library(ggplot2)
options(scipen = 999)

# Read the data
mydata <- read.csv("SandwichExperiment.csv") #Edit YOURFILENAME.csv

# Specify the DV and IV
mydata$DV <- mydata$estimate
mydata$IV <- mydata$group

# Graph the group distributions and averages
averages <- group_by(mydata, IV) %>%
  summarise(mean = mean(DV, na.rm = TRUE))
ggplot(mydata, aes(x = DV)) +
  geom_histogram() +
  facet_grid(IV ~ .) +
  geom_histogram(color = "black", fill = "dodgerblue") +
  geom_vline(data = averages, aes(xintercept = mean, ))

# Calculate and show the group counts, means, standard
# deviations, minimums, and maximums
group_by(mydata, IV) %>%
  summarise(
    count = n(),
    mean = mean(DV, na.rm = TRUE),
    sd = sd(DV, na.rm = TRUE),
    min = min(DV, na.rm = TRUE),
    max = max(DV, na.rm = TRUE)
  )
## # A tibble: 2 × 6
##   IV           count  mean    sd   min   max
##   <chr>        <int> <dbl> <dbl> <int> <int>
## 1 Muscular guy    40  212.  90.6    30   380
## 2 Rounder guy     40  292   92.4    40   450

Calorie estimates by the group that viewed the slim actor's ad averaged 212 calories, with a standard deviation of 91.0. Meanwhile, calorie estimates by the group that viewed the heavier actor's ad averaged 292 calories, with a standard deviation of 92.4. Group distributions showed some possible outliers, especially among participants who viewed the heavier actor's ad. But Shapiro-Wilks tests found no evidence of nonnormality in either distribution. These findings suggested a independent-samples t-test would be suitable for the analysis, as planned.

# If both groups are normally distributed, though, you may run a t-test.
options(scipen = 999)
t.test(mydata$DV ~ mydata$IV,
       var.equal = FALSE)
## 
##  Welch Two Sample t-test
## 
## data:  mydata$DV by mydata$IV
## t = -3.9223, df = 77.967, p-value = 0.0001875
## alternative hypothesis: true difference in means between group Muscular guy and group Rounder guy is not equal to 0
## 95 percent confidence interval:
##  -120.98249  -39.51751
## sample estimates:
## mean in group Muscular guy  mean in group Rounder guy 
##                     211.75                     292.00

The test found the difference between the group avererages to be statistically significant, t (77.97) = -3.92, p < .05. The results suggest that calorie estimates by the group that viewed the slim actor's ad averaged significantly lower than did calorie estimates by the group that viewed the heavier actor's ad.