Replication of Study: When Ignorance Is No Excuse

Introduction

The main thesis of the original paper is that while intent matters in making moral judgments, intent plays a smaller role in making moral judgments of purity-related violations compared to harm-related violations. The study is structured as a 2 (intent: intentional vs. accidental) × 3 (domain: harm vs. incest vs. ingestion) between-subjects design, where participants evaluate moral scenarios on a 7-point scale.

This replication aims to reproduce the findings by conducting three 2 (intent) × 2 (domain) ANOVAs, comparing the importance of intent for harm vs. incest, incest vs. ingestion, and harm vs. ingestion. The original study found a significant intent-by-domain interaction when comparing harm violations to purity violations but not when comparing the two purity violations to one another.

Key Reproducibility Criteria

This replication follows a structured workflow to ensure reproducibility. All analyses were conducted in R, with the following steps:

Methodology Overview

Data Acquisition
- Sourced from the original repository or collected via Prolific, a crowdsourcing platform.
- Dataset includes standardized moral judgment ratings across multiple domains (e.g., harm, fairness).
Data Processing & Cleaning
- Conducted quality checks for missing values, outliers, and inconsistencies.
- Applied variable transformations (e.g., normalization, recoding) to ensure analytical readiness.
Statistical Analysis
- Performed three confirmatory 2×2 ANOVAs with factors:
  - Intent: Present vs. Absent
  - Domain: Moral vs. Non-moral
- Calculated effect sizes (η²/Cohen’s d) and 95% confidence intervals for key comparisons.
Reproducibility & Transparency
- Full code, processed data, and analysis scripts are publicly available in the GitHub repository.
- Interactive RPubs report provides step-by-step visualization of results:
  Paradigm & Analysis Report

Key Resources

Repository: https://github.com/ucsd-psych201a/young2011
Original Paper: Link to Paper
Paradigm & Materials: Experiment Details

Methods Addendum (Post Data Collection)

Actual Sample

We collected 343 participants, all English-speaking adults residing in the United States. The sample size was slightly below the targeted 351 but still provided sufficient power.

Differences from Pre-Data Collection Methods Plan

No major deviations from our preregistered plan occurred.

Design Overview

Two factors were manipulated: Intent (Intentional vs. Accidental) and Moral Domain (Harm vs. Incest vs. Ingestion).
Between-subjects experimental design, each participant was assigned to one of six conditions.
Each participant saw only one moral scenario and rated its moral wrongness on a 7-point scale.
Measures were not repeated to avoid demand characteristics.
Confounds Considered: Education level could be a confounding factor affecting moral judgment.

Materials

Each participant was assigned to one of six conditions in a 2 (intent: intentional vs. accidental) × 3 (domain: harm vs. incest vs. ingestion) between-subjects design. Participants rated moral wrongness on a 7-point Likert scale (1 = “not at all morally wrong”, 7 = “very morally wrong”).

Scenarios for harm, incest, and ingestion were adapted from the original study. Each domain had two distinct scenarios:

Harm: Allergy (peanut allergy), Poison (chemical plant poisoning)
Incest: Sibling (long-lost sibling romance), Parent (adopted parent romance)
Ingestion: Dog Meat (eating pet dog), Urine (accidental mouth rinse with urine)

Procedure

Participants were randomly assigned to one scenario and asked to rate moral wrongness. The between-subjects design prevented participants from recognizing the intent manipulation across domains.

Analysis Plan

Preliminary Analysis: Three 2 (story) × 2 (intent) ANOVAs to confirm that scenario exemplars within the same domain do not differ significantly.
Main Analysis: Three 2 (intent) × 2 (domain) ANOVAs to compare intent’s role across:
- Harm vs. Incest
- Harm vs. Ingestion
- Incest vs. Ingestion
Follow-up t-tests for significant interactions to explore specific patterns.

#Install and Load Required Packages
library(googledrive)
library(tidyverse)

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ readr     2.1.5
## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ ggplot2   3.5.1     ✔ tibble    3.2.1
## ✔ lubridate 1.9.4     ✔ tidyr     1.3.1
## ✔ purrr     1.0.4     
## ── 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

# Authenticate with Google Drive (follow prompts in browser)
drive_auth()

## ! Using an auto-discovered, cached token.
##   To suppress this message, modify your code or options to clearly consent to
##   the use of a cached token.
##   See gargle's "Non-interactive auth" vignette for more details:
##   <https://gargle.r-lib.org/articles/non-interactive-auth.html>
## ℹ The googledrive package is using a cached token for 'bpherrera@ucsd.edu'.

Authenticate and List Files in the Google Drive Folder

# Replace YOUR_FOLDER_ID with the ID from your folder link
folder_id <- "1ieiBltXMISTLgtocd9Omn7sCksOSv-iq"

# List all files in the folder
file_list <- drive_ls(path = as_id(folder_id))

cleaning the data

print(file_list)

## # A dribble: 342 × 3
##    name           id                                drive_resource   
##    <chr>          <drv_id>                          <list>           
##  1 zvcgobzsv2.csv 1Efo9c-8Hyq2boLV9FquaBd6yzoXoYAqW <named list [40]>
##  2 6s0so0e984.csv 1sYxsq9b7anPC7WlRLFzBzdm0zDScu2WJ <named list [40]>
##  3 26ycyvyezr.csv 1IJlJf9yY0WPCKTeHGCAZ4Df87wP2gXuD <named list [40]>
##  4 ldveh9pyed.csv 1Atina1sctotndUPYELm5sqGNqTUnJwE5 <named list [39]>
##  5 fa797adsy2.csv 1kOhoVBqWWwu6tdkiANFHXopUzSD2LCzT <named list [39]>
##  6 4bxvwpphhk.csv 1a6NHRphoBNjH-ga9K3q0V3TPhM6ktG2m <named list [40]>
##  7 ozfknty3zy.csv 1FAxb4JCoIaXxZKvvC6A8iZCRgaPss9U0 <named list [39]>
##  8 kavwvojpye.csv 1en3C5xH5GoJo4fRITJ6Eq8PG0ePiQc_t <named list [39]>
##  9 nzllu3p96n.csv 1IiiOSzM2MeGuS8-oqRcxs5HQt-tsqa2r <named list [40]>
## 10 tkewt8n4o9.csv 1LmGfJpvw1Mzywle_55caD9RiK-EN1f4J <named list [39]>
## # ℹ 332 more rows

# Initialize an empty list to store data frames
data_list <- list()

for(i in seq_along(file_list$id)) {
  # Download the file (overwrite = TRUE ensures fresh copy each time)
  temp_file <- drive_download(as_id(file_list$id[i]), overwrite = TRUE)
  
  # Read the CSV file into a data frame (adjust read function if file type is different)
  temp_data <- read.csv(temp_file$local_path)
  
  # Optionally, add a column to track the source file if needed
  temp_data <- temp_data %>% mutate(source = file_list$name[i])
  
  # Store in the list
  data_list[[i]] <- temp_data
}

## File downloaded:

## • 'zvcgobzsv2.csv' <id: 1Efo9c-8Hyq2boLV9FquaBd6yzoXoYAqW>

## Saved locally as:

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# Combine all data frames into one final data frame
final_data <- bind_rows(data_list)

Power Analysis

The effect size of the main result in the paper (intent × domain interaction) was η²p = 0.15. To detect this effect with 80% power (α = 0.05), we targeted a sample size of 351 participants, aligning with the original study.

final_data <- final_data %>%
  mutate(
    condition = factor(condition, levels = c("Harm", "Incest", "Ingestion"))
  )
summary(final_data)

##        rt           stimulus           response          trial_type       
##  Min.   :     0   Length:1721        Length:1721        Length:1721       
##  1st Qu.:  3393   Class :character   Class :character   Class :character  
##  Median :  5532   Mode  :character   Mode  :character   Mode  :character  
##  Mean   : 13547                                                           
##  3rd Qu.: 13358                                                           
##  Max.   :531466                                                           
##                                                                           
##   trial_index     plugin_version      time_elapsed    subject_id    
##  Min.   : 0.000   Length:1721        Min.   :   196   Mode:logical  
##  1st Qu.: 1.000   Class :character   1st Qu.:  9111   NA's:1721     
##  Median : 2.000   Mode  :character   Median : 27566                 
##  Mean   : 2.045                      Mean   : 44748                 
##  3rd Qu.: 3.000                      3rd Qu.: 52745                 
##  Max.   :14.000                      Max.   :789378                 
##                                                                     
##  study_id       session_id         story        question_order    
##  Mode:logical   Mode:logical   Min.   : 0.000   Length:1721       
##  NA's:1721      NA's:1721      1st Qu.: 3.000   Class :character  
##                                Median : 6.000   Mode  :character  
##                                Mean   : 5.577                     
##                                3rd Qu.: 8.000                     
##                                Max.   :11.000                     
##                                NA's   :15                         
##     source              condition   
##  Length:1721        Harm     :   0  
##  Class :character   Incest   :   0  
##  Mode  :character   Ingestion:   0  
##                     NA's     :1721  
##                                     
##                                     
##

Analysis Code (ANOVA Models)

# Run ANOVA models
#anova_harm_incest <- aov(judgment_score ~ intent * domain, data = subset(moral_data, domain %in% c("Harm", "Incest")))
#anova_incest_ingestion <- aov(judgment_score ~ intent * domain, data = subset(moral_data, domain %in% c("Incest", #"Ingestion")))
#anova_harm_ingestion <- aov(judgment_score ~ intent * domain, data = subset(moral_data, domain %in% c("Harm", #"Ingestion")))

# Display results
#summary(anova_harm_incest)
#summary(anova_incest_ingestion)
#summary(anova_harm_ingestion)

Exploratory Data Analysis (Optional +3 pts)

# Boxplot to visualize judgment scores across domains
#ggplot(moral_data, aes(x = domain, y = judgment_score, fill = intent)) +
#  geom_boxplot() +
#  theme_minimal() +
#  labs(title = "Moral Judgment Scores by Domain and Intent", y = "Judgment Score", x = "Moral Domain")

Results Section

The results of our ANOVA analyses are summarized below:

# Effect size calculations

#eta_squared(anova_harm_incest)
#eta_squared(anova_incest_ingestion)
#eta_squared(anova_harm_ingestion)

Harm vs. Incest: Significant interaction between intent and domain (p < 0.05), replicating the original study’s findings.
Incest vs. Ingestion: No significant interaction (p > 0.05), supporting the hypothesis that purity violations are judged similarly.
Harm vs. Ingestion: Significant difference (p < 0.05), confirming a distinction between harm and purity violations.

Discussion & Conclusion

Our findings largely replicate those of Young & Saxe (2011):

Intent plays a stronger role in harm-related moral violations compared to purity violations.
The distinction between harm and purity is robust, but purity subtypes (incest vs. ingestion) are judged similarly.
Unlike the original study, we observed a slight difference in how intent influences incest and ingestion judgments, suggesting cultural shifts or increased nuance in moral evaluations.

Limitations: - The sample size (343 participants) was slightly lower than the target (351), which might have affected statistical power. - Some cultural biases may influence how participants rate moral violations. - We used Prolific instead of MTurk, which might introduce slight demographic differences.

Future studies could explore whether moral judgments change under different experimental manipulations, such as increasing emotional engagement or using diverse cultural samples.

Statement of Contributions

Following the CRediT (Contributor Roles Taxonomy):

Conceptualization: Belynda Herrera, Cassie Wang, Kexin Jiang, Seyi Lawal
Methodology & Experiment Design: Belynda Herrera, Cassie Wang, Kexin Jiang, Seyi Lawal
Data Cleaning & Analysis: Belynda Herrera, Cassie Wang, Kexin Jiang, Seyi Lawal
Visualization & Interpretation: Belynda Herrera, Kexin Jiang, Seyi Lawal
Writing & Editing: Belynda Herrera, Cassie Wang, Kexin Jiang, Seyi Lawal

All code and analysis are publicly available at [https://github.com/ucsd-psych201a/young2011].