Rationale

The eaboration Likleihood model suggests that enduring attitude change depends upon time spent processing information-rich, high-quality persuasive arguments. In this experiment the dependent variable, “Favor_1”, measures whether a research participant favored (1) or opposed (0) legalizing recreational marijuana, while the independent variable “Time”, measures how many seconds the participant spent looking at the information-rich, high-quality persuasive arguments in support of legalizing recreational marijuana.

Hypothesis

Based on ELM, I hypothesized that the odds of supporting marijuana legalization will increase significantly as time spent on reading supporting arguments increases.

Variables & Method

In this analysis the dependent variable is “Favor_1,” while the independent variable is “Time.” Because the dependent variable is categorical with two categories and the independent variable is continuous, logistic regression is a suitable statistical test of the hypothesis.

Results

The analysis found that odds of favoring legalization of recreational marijuana increased significantly ( z= 4.8, p< .05), supporting the hypothesis.

Code and output

# Installing required packages
if (!require("tidyverse"))
  install.packages("tidyverse")
## Loading required package: tidyverse
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.3     ✔ readr     2.1.4
## ✔ forcats   1.0.0     ✔ stringr   1.5.0
## ✔ ggplot2   3.4.3     ✔ tibble    3.2.1
## ✔ lubridate 1.9.3     ✔ tidyr     1.3.0
## ✔ purrr     1.0.2     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(ggplot2)

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

# Specify the DV and IV
mydata$DV <- mydata$Favor_1 #Edit YOURDVNAME
mydata$IV <- mydata$Time #Edit YOURIVNAME

# Look at the DV and IV
ggplot(mydata, aes(x = DV)) + geom_bar(color = "black", fill = "#1f78b4")

ggplot(mydata, aes(x = IV)) + geom_histogram(color = "black", fill = "#1f78b4")
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

summary(mydata$IV)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    30.0   153.8   230.0   230.8   300.0   450.0
# Logistic regression plot
ggplot(mydata, aes(x = IV,
                   y = DV))+
  geom_jitter(height = .03,
              alpha = .5) +
  geom_smooth(method = "glm",
              method.args = list(family = "binomial"),
              se = FALSE,
              color = "#1f78b4")
## `geom_smooth()` using formula = 'y ~ x'

# Run the logistic regression and view the summary results
options(scipen = 999)
log.ed <- glm(DV ~ IV, data = mydata, family = "binomial")
summary(log.ed)
## 
## Call:
## glm(formula = DV ~ IV, family = "binomial", data = mydata)
## 
## Coefficients:
##              Estimate Std. Error z value    Pr(>|z|)    
## (Intercept) -4.567169   0.920675  -4.961 0.000000702 ***
## IV           0.016209   0.003383   4.791 0.000001661 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 131.791  on 99  degrees of freedom
## Residual deviance:  94.123  on 98  degrees of freedom
## AIC: 98.123
## 
## Number of Fisher Scoring iterations: 5
p <- .50
Inflection_point <- (log(p/(1-p))-coef(log.ed)[1])/coef(log.ed)[2]
Inflection_point
## (Intercept) 
##    281.7672