Analysis on perceived certainty
The aim of this analysis is to perform a 2 (moral condition: moral vs. immoral) x 2 (decision speed: quick vs. slow) repeated-measures ANOVA to compare group means for decision certainty evaluations of Justin and Nate.
Items accounting for decision certainty evaluation (Question 5-8):
- Would you say [name] was quite certain in his decision, or did [name] have hesitations about his decision? (1 = completely certain, 7 = considerable hesitations)
- How close do you think [name] was to choosing the alternate course of action? (1 = very close to, 7 = not close at all)
- How conflicted do you think [name] felt in making the decision? (1 = very conflicted, 7 = not at all conflicted)
- Based on the information provided, do you think [name] had many reservations about the decision? (1 = none at all, 7 = a whole lot)
{r} ### Step 1: Calculate Average Scores for Justin and Nate
# Since Questions 5 & 8 are reverse scored, we need to reverse code those items before we proceed with further analysis Reverse coding is performed on a 1-7 scale to align with the scoring direction. # High score = higher certainty on decision
#1a: Subset relevant columns
certain_dat <- cleaned_data[, c(1:2, 7:10, 17:20, 23)]
#1b: Reverse code Questions 5 and 8 for both Justin and Nate certain_dat\(Justin_Q5R <- 8 - certain_dat\)Justin_Q5 # Reverse code for Justin’s Q5 certain_dat\(Justin_Q8R <- 8 - certain_dat\)Justin_Q8 # Reverse code for Justin’s Q8 certain_dat\(Nate_Q5R <- 8 - certain_dat\)Nate_Q5 # Reverse code for Nate’s Q5 certain_dat\(Nate_Q8R <- 8 - certain_dat\)Nate_Q8 # Reverse code for Nate’s Q8
#1c: Calculate combined certainty scores certain_eval_combined <- certain_dat %>% mutate( quick_justin_score = rowMeans(select(., Justin_Q5R, Justin_Q6, Justin_Q7, Justin_Q8R), na.rm = TRUE), slow_nate_score = rowMeans(select(., Nate_Q5R, Nate_Q6, Nate_Q7, Nate_Q8R), na.rm = TRUE) )
Step 2: Summarize Average Scores by Condition
# Group data by condition for comparison of Justin’s decision certainty evaluation and Nate’s decision certainty evaluation across condition
certain_eval_combined_stats <- certain_eval_combined %>% group_by(condition) %>% # Group data by the ‘condition’ column summarize( mean_justin = mean(quick_justin_score), # Mean of Justin’s character evaluation mean_nate = mean(slow_nate_score) # Mean of Nate’s character evaluation )
Step 3: Reshape Data for ANOVA
certain_eval_combined_long <- certain_eval_combined %>% pivot_longer( cols = c(quick_justin_score, slow_nate_score), # Select the new columns names_to = “speed”, # Create a column for speed values_to = “score” # Create a column for scores ) %>% mutate( speed = ifelse(speed == “quick_justin_score”, “Justin”, “Nate”) # Relabel speed column ) %>% select(Participant_ID, condition, speed, score) # Keep only essential columns
Step 4: Run Repeated-Measures ANOVA
anova_certain_combined <- aov(score ~ condition * speed + Error(Participant_ID/speed), data = certain_eval_combined_long)
print(certain_eval_combined_stats) #Descriptive Stats summary(anova_certain_combined)
Visualization
{r} ggplot(certain_eval_combined_long, aes(x = speed, y = score, fill = condition)) + geom_bar( stat = “summary”, fun = “mean”, position = position_dodge(width = 0.6), # Columns closer together width = 0.5 # Narrower columns ) + labs( title = “Decision Certainty Evaluations (Combined Data)”, x = “Decision Speed (Justin / Nate)”, # Updated x-axis label y = “Mean Score (Likert Scale)”, # Updated y-axis label fill = “Moral Condition” # Legend title for conditions ) + theme_minimal() + theme( text = element_text(size = 12), plot.title = element_text(hjust = 0.5, face = “bold”, size = 14), # Centered title axis.text.x = element_text(size = 10), # Adjust x-axis labels axis.title.y = element_text(size = 12, margin = margin(r = 15)), # Add spacing to y-axis title axis.title.x = element_text(size = 12, margin = margin(t = 10)) # Add spacing to x-axis title ) + scale_fill_manual( values = c(“moral” = “#4F81BD”, “immoral” = “#9DC3E6”), # Two shades for moral/immoral labels = c(“moral” = “Moral”, “immoral” = “Immoral”) # Custom legend labels ) + scale_y_continuous( limits = c(0, 7), # Y-axis limits for Likert scale breaks = 1:7, # Label y-axis by 1’s expand = c(0, 0) # No extra space above/below )
Post Hoc & Effect size calculation
Post-hoc and effect size analyses were conducted using a mixed-effects model to account for participant variability and potential unbalanced data. The model included the same fixed effects structure (condition, speed, and their interaction) as the repeated measures ANOVA.
Effect Size Calculation on both Moral Character Evaluation & Decision Certainty Evaluation tests
Moral Character Evaluation
{r} # Combined Data # Refit the repeated measures model anova_combined_lmer <- lmer( score ~ condition * speed + (1 | Participant_ID), data = character_eval_combined_long )
