For this exercise, please try to reproduce the results from Study 1 of the associated paper (Joel, Teper, & MacDonald, 2014). The PDF of the paper is included in the same folder as this Rmd file.

Methods summary:

In study 1, 150 introductory psychology students were randomly assigned to a “real” or a “hypothetical” condition. In the real condition, participants believed that they would have a real opportuniy to connect with potential romantic partners. In the hypothetical condition, participants simply imagined that they are on a date. All participants were required to select their favorite profile and answer whether they were willing to exchange contact information.


Target outcomes:

Below is the specific result you will attempt to reproduce (quoted directly from the results section of Study 1):

We next tested our primary hypothesis that participants would be more reluctant to reject the unattractive date when they believed the situation to be real rather than hypothetical. Only 10 of the 61 participants in the hypothetical condition chose to exchange contact information with the unattractive potential date (16%). In contrast, 26 of the 71 participants in the real condition chose to exchange contact information (37%). A chi-square test of independence indicated that participants were significantly less likely to reject the unattractive potential date in the real condition compared with the hypothetical condition, X^2(1, N = 132) = 6.77, p = .009.


Step 1: Load packages

library(tidyverse) # for data munging
library(knitr) # for kable table formating
library(haven) # import and export 'SPSS', 'Stata' and 'SAS' Files
library(readxl) # import excel files

# #optional packages:
# library(broom)
# library(labelled)# converts SPSS's labelled to R's factor 

Step 2: Load data

# Just Study 1
d <- read_sav('data/Empathy Gap Study 1 data.sav')

Step 3: Tidy data

d <- d |>
    select(condition, exchangeinfo) |> 
    mutate(condition = case_when(
        condition == 0 ~ "hypothetical",
        condition == 1 ~ "real"
    )) |> 
    mutate(exchangeinfo = case_when(
        exchangeinfo == 1 ~ TRUE,
        exchangeinfo == 2 ~ FALSE
    )) 

Step 4: Run analysis

Descriptive statistics

Only 10 of the 61 participants in the hypothetical condition chose to exchange contact information with the unattractive potential date (16%). In contrast, 26 of the 71 participants in the real condition chose to exchange contact information (37%).

# reproduce the above results here
d |> 
    group_by(condition) |> 
    summarise(
        n = n(),
        exchanged_n = sum(exchangeinfo),
        exchanged_pct = round(exchanged_n/n * 100, 0)
    )
## # A tibble: 2 × 4
##   condition        n exchanged_n exchanged_pct
##   <chr>        <int>       <int>         <dbl>
## 1 hypothetical    61          10            16
## 2 real            71          26            37

Inferential statistics

A chi-square test of independence indicated that participants were significantly less likely to reject the unattractive potential date in the real condition compared with the hypothetical condition, X^2(1, N = 132) = 6.77, p = .009.

Hint: if you are using the function chisq.test(), make sure to set the continuity correction to false (“correct = FALSE”) since sample size is greater than 20.

# reproduce the above results here
chisq.test(d$condition, d$exchangeinfo, correct = FALSE)
## 
##  Pearson's Chi-squared test
## 
## data:  d$condition and d$exchangeinfo
## X-squared = 6.7674, df = 1, p-value = 0.009284

Step 5: Reflection

Were you able to reproduce the results you attempted to reproduce? If not, what part(s) were you unable to reproduce?

Yes

How difficult was it to reproduce your results?

Fairly straightforward!

What aspects made it difficult? What aspects made it easy?

It took me a while to realize there were more columns to the dataset, View(d) shows just the first 50 by default. Other than that, it was straightforward, just had to find out what the 0/1/2’s meant for the condition and exchangeinfo columns, but I figured it out after doing some filtering and nrows.