Experiment 1
BLINDED FOR PEER REVIEW
May 26, 2021
Data Waves
As pre-registered, we sought to collect data to attain 200 analyzable responses for each between-subjects condition (i.e., 200 participants who passed all attention checks for each between-subjects condition, totalling 600 usable responses across the entire experiment).
On the first wave of data collection (N = 694), applying the pre-registered exclusion criteria led to adequate samples (i.e., Ns > 200) for two between-subjects datasets, but an inadequate sample (i.e., N = 195) for one between-subjects dataset (specifically, the Stranger and Sibling dataset). Therefore, we launched a small second wave (N = 10) to attain an adequate sample for the Stranger and Sibling dataset.
Data Cleaning
Before data were loaded into R (below), the following changes were made:
Raw variable names from Qualtrics were renamed to be more descriptive.
If there were any responses for the field “Bot_Catcher,” these cases were deleted. This field was designed to be an invisible question that only bots would answer (as human respondents would not see the field). However, 0 cases were detected.
Duplicate IP addresses were removed. There was only 1 instance of a duplicate IP address, but it came from two different Prolific IDs (and both responses were removed for failing attention checks).
All other identifying information was removed (e.g., IP addresses, longitude/latitude, etc.).
Loading Data/Packages
Before running this chunk, please load “E1_raw_data.csv” into the R environment.
# packages should be loaded in the following order to avoid function conflicts
library(psych) # for describing data
library(effsize) # for mean difference effect sizes
library(sjstats) # for eta-squared effect sizes
library(correlation) # for cleaner correlation test output
library(rmcorr) # for repeated-measures correlation tests
library(cocor) # for comparing dependent correlation coefficients
library(tidyverse) # for data manipulation and plottingData Separation/Recombining
Data were separated into three distinct data sets (for each between-subjects condition). Then, a between-subjects variable was created within each between-subjects dataset. Last, all three datasets were recombined.
# creates dataset that only has partipants who made judgments of agents who helped strangers and cousins
E1_STRnCUZ <- E1_raw_data %>%
filter(STRnCUZ_STR_m1 >= 0 | STRnCUZ_STR_m2 >= 0 | STRnCUZ_STR_m3 >= 0)
# creates dataset that only has partipants who made judgments of agents who helped strangers and siblings
E1_STRnSIB <- E1_raw_data %>%
filter(STRnSIB_STR_m1 >= 0 | STRnSIB_STR_m2 >= 0 | STRnSIB_STR_m3 >= 0)
# creates dataset that only has partipants who made judgments of agents who helped cousins and siblings
E1_CUZnSIB <- E1_raw_data %>%
filter(CUZnSIB_CUZ_m1 >= 0 | CUZnSIB_CUZ_m2 >= 0 | CUZnSIB_CUZ_m3 >= 0)
# create between-subjects condition variable
E1_STRnCUZ$BSs_cond <- rep("STR vs CUZ", nrow(E1_STRnCUZ))
E1_STRnSIB$BSs_cond <- rep("STR vs SIB", nrow(E1_STRnSIB))
E1_CUZnSIB$BSs_cond <- rep("CUZ vs SIB", nrow(E1_CUZnSIB))
# recombine between-subjects data
E1_all <- rbind(E1_STRnCUZ, E1_STRnSIB, E1_CUZnSIB)Implementing Attention Checks
Based on our pre-registered criteria, participants who failed any of 3 attention checks were to be excluded from all analyses. This led to a final analyzable N = 611 (an 87% retention rate).
The first attention check was disguised as an experimental scenario; in the scenario text, participants were instructed to respond with the left-most option on the scale for all three pre-outcome measures (obligation, likelihood, and descriptive normativity). Participants who responded above a 10 on any of the three 100-point scales were excluded.
The second and third attention checks explicitly instructed participants to respond in a particular way on two individual difference measures. The second attention check instructed participants to respond “strongly agree” on the MAC’s 100 point scale; participants who responded below 90 were excluded. The third attention check instructed participants to respond “strongly disagree” on the MFQ’s scale; participants who responded anything other than “strongly disagree” (a 1 on the 6-point scale) were excluded.
# Experimental task attention check variables
E1_all$AC_oblig <- rowSums(E1_all[, c("AC_oblig1", "AC_oblig2", "AC_oblig3")], na.rm = T)
E1_all$AC_likely <- rowSums(E1_all[, c("AC_likely1", "AC_likely2", "AC_likely3")], na.rm = T)
E1_all$AC_dnorm <- rowSums(E1_all[, c("AC_dnorm1", "AC_dnorm2", "AC_dnorm3")], na.rm = T) # if any are > 10, exclude
# Individual difference attention check variables
E1_all$MAC_AC <- E1_all$MAC_Jud_4 # if < 90, exclude
E1_all$MFQ_AC <- E1_all$MFQ_Jud_4 # if != 1, exclude
# Create dataset that filters out inattentive participants
E1_all_clean <- E1_all %>%
# excludes participants who were not paying attention during experimental task
filter(AC_oblig <= 10 & AC_likely <= 10 & AC_dnorm <= 10) %>%
# exclude participants who were not paying attention during MAC scale
filter(MAC_AC >= 90) %>%
# exclude participants who were not paying attention during MFQ scale
filter(MFQ_AC == 1)Creating Analysis Variables
# Main DVs
# create single column for each condition's variables that collapses across presentation order of DV
# e.g., STRnCUZ_STR_o1 = STR vs CUZ dataset, "No Choice" condition, STR obligation judgment, obligation judgment presented first
# to clarify, as noted in the Method section (and SOM), two other pre-outcome judgments were collected, equaling three different
# orders of presentation, each of which started with a different DV (these additional judgments are not included here)
E1_all_clean$NoChoice_STR_oblig <- rowSums(E1_all_clean[, c("STRnCUZ_STR_o1", "STRnCUZ_STR_o2", "STRnCUZ_STR_o3",
"STRnSIB_STR_o1", "STRnSIB_STR_o2", "STRnSIB_STR_o3")], na.rm = T)
E1_all_clean$NoChoice_STR_moral <- rowSums(E1_all_clean[, c("STRnCUZ_STR_m1", "STRnCUZ_STR_m2", "STRnCUZ_STR_m3",
"STRnSIB_STR_m1", "STRnSIB_STR_m2", "STRnSIB_STR_m3")], na.rm = T)
E1_all_clean$NoChoice_CUZ_oblig <- rowSums(E1_all_clean[, c("STRnCUZ_CUZ_o1", "STRnCUZ_CUZ_o2", "STRnCUZ_CUZ_o3",
"CUZnSIB_CUZ_o1", "CUZnSIB_CUZ_o2", "CUZnSIB_CUZ_o3")], na.rm = T)
E1_all_clean$NoChoice_CUZ_moral <- rowSums(E1_all_clean[, c("STRnCUZ_CUZ_m1", "STRnCUZ_CUZ_m2", "STRnCUZ_CUZ_m3",
"CUZnSIB_CUZ_m1", "CUZnSIB_CUZ_m2", "CUZnSIB_CUZ_m3")], na.rm = T)
E1_all_clean$NoChoice_SIB_oblig <- rowSums(E1_all_clean[, c("STRnSIB_SIB_o1", "STRnSIB_SIB_o2", "STRnSIB_SIB_o3",
"CUZnSIB_SIB_o1", "CUZnSIB_SIB_o2", "CUZnSIB_SIB_o3")], na.rm = T)
E1_all_clean$NoChoice_SIB_moral <- rowSums(E1_all_clean[, c("STRnSIB_SIB_m1", "STRnSIB_SIB_m2", "STRnSIB_SIB_m3",
"CUZnSIB_SIB_m1", "CUZnSIB_SIB_m2", "CUZnSIB_SIB_m3")], na.rm = T)
# e.g., STRoCUZ_STR_o11 = STR vs CUZ dataset, "Choice" condition, STR is eventually helped instead of CUZ,
# obligation judgments in general presented first (first 1), but obligation to STR presented before obligation to CUZ (second 1)
# to clarify, as noted in the Method section, two obligation judgments were collected in these conditions,
# one for each potential beneficiary (e.g., STR and CUZ)
E1_all_clean$STRoCUZ_STR_oblig <- rowSums(E1_all_clean[, c("STRoCUZ_STR_o11", "STRoCUZ_STR_o12", "STRoCUZ_STR_o31",
"STRoCUZ_STR_o21", "STRoCUZ_STR_o22", "STRoCUZ_STR_o32")], na.rm = T)
E1_all_clean$STRoCUZ_CUZ_oblig <- rowSums(E1_all_clean[, c("STRoCUZ_CUZ_o11", "STRoCUZ_CUZ_o12", "STRoCUZ_CUZ_o31",
"STRoCUZ_CUZ_o21", "STRoCUZ_CUZ_o22", "STRoCUZ_CUZ_o32")], na.rm = T)
E1_all_clean$STRoCUZ_STR_moral <- rowSums(E1_all_clean[, c("STRoCUZ_STR_m11", "STRoCUZ_STR_m12", "STRoCUZ_STR_m31",
"STRoCUZ_STR_m21", "STRoCUZ_STR_m22", "STRoCUZ_STR_m32")], na.rm = T)
E1_all_clean$CUZoSTR_STR_oblig <- rowSums(E1_all_clean[, c("CUZoSTR_STR_o11", "CUZoSTR_STR_o12", "CUZoSTR_STR_o31",
"CUZoSTR_STR_o21", "CUZoSTR_STR_o22", "CUZoSTR_STR_o32")], na.rm = T)
E1_all_clean$CUZoSTR_CUZ_oblig <- rowSums(E1_all_clean[, c("CUZoSTR_CUZ_o11", "CUZoSTR_CUZ_o12", "CUZoSTR_CUZ_o31",
"CUZoSTR_CUZ_o21", "CUZoSTR_CUZ_o22", "CUZoSTR_CUZ_o32")], na.rm = T)
E1_all_clean$CUZoSTR_CUZ_moral <- rowSums(E1_all_clean[, c("CUZoSTR_CUZ_m11", "CUZoSTR_CUZ_m12", "CUZoSTR_CUZ_m31",
"CUZoSTR_CUZ_m21", "CUZoSTR_CUZ_m22", "CUZoSTR_CUZ_m32")], na.rm = T)
E1_all_clean$Choice_STRnCUZ_STR_oblig <- (E1_all_clean$STRoCUZ_STR_oblig + E1_all_clean$CUZoSTR_STR_oblig)/2 # creates pre-registered obligation index for mean comparisons
E1_all_clean$Choice_STRnCUZ_STR_moral <- rowSums(E1_all_clean[, c(
"STRoCUZ_STR_m11", "STRoCUZ_STR_m12", "STRoCUZ_STR_m31",
"STRoCUZ_STR_m21", "STRoCUZ_STR_m22", "STRoCUZ_STR_m32")], na.rm = T) # should be a duplicate of "STRoCUZ_STR_moral"
E1_all_clean$Choice_STRnCUZ_CUZ_oblig <- (E1_all_clean$STRoCUZ_CUZ_oblig + E1_all_clean$CUZoSTR_CUZ_oblig)/2 # creates pre-registered obligation index for mean comparisons
E1_all_clean$Choice_STRnCUZ_CUZ_moral <- rowSums(E1_all_clean[, c(
"CUZoSTR_CUZ_m11", "CUZoSTR_CUZ_m12", "CUZoSTR_CUZ_m31",
"CUZoSTR_CUZ_m21", "CUZoSTR_CUZ_m22", "CUZoSTR_CUZ_m32")], na.rm = T) # should be a duplicate of "CUZoSTR_CUZ_moral"
E1_all_clean$STRoSIB_STR_oblig <- rowSums(E1_all_clean[, c("STRoSIB_STR_o11", "STRoSIB_STR_o12", "STRoSIB_STR_o31",
"STRoSIB_STR_o21", "STRoSIB_STR_o22", "STRoSIB_STR_o32")], na.rm = T)
E1_all_clean$STRoSIB_SIB_oblig <- rowSums(E1_all_clean[, c("STRoSIB_SIB_o11", "STRoSIB_SIB_o12", "STRoSIB_SIB_o31",
"STRoSIB_SIB_o21", "STRoSIB_SIB_o22", "STRoSIB_SIB_o32")], na.rm = T)
E1_all_clean$STRoSIB_STR_moral <- rowSums(E1_all_clean[, c("STRoSIB_STR_m11", "STRoSIB_STR_m12", "STRoSIB_STR_m31",
"STRoSIB_STR_m21", "STRoSIB_STR_m22", "STRoSIB_STR_m32")], na.rm = T)
E1_all_clean$SIBoSTR_STR_oblig <- rowSums(E1_all_clean[, c("SIBoSTR_STR_o11", "SIBoSTR_STR_o12", "SIBoSTR_STR_o31",
"SIBoSTR_STR_o21", "SIBoSTR_STR_o22", "SIBoSTR_STR_o32")], na.rm = T)
E1_all_clean$SIBoSTR_SIB_oblig <- rowSums(E1_all_clean[, c("SIBoSTR_SIB_o11", "SIBoSTR_SIB_o12", "SIBoSTR_SIB_o31",
"SIBoSTR_SIB_o21", "SIBoSTR_SIB_o22", "SIBoSTR_SIB_o32")], na.rm = T)
E1_all_clean$SIBoSTR_SIB_moral <- rowSums(E1_all_clean[, c("SIBoSTR_SIB_m11", "SIBoSTR_SIB_m12", "SIBoSTR_SIB_m31",
"SIBoSTR_SIB_m21", "SIBoSTR_SIB_m22", "SIBoSTR_SIB_m32")], na.rm = T)
E1_all_clean$Choice_STRnSIB_STR_oblig <- (E1_all_clean$STRoSIB_STR_oblig + E1_all_clean$SIBoSTR_STR_oblig)/2 # creates pre-registered obligation index for mean comparisons
E1_all_clean$Choice_STRnSIB_STR_moral <- rowSums(E1_all_clean[, c(
"STRoSIB_STR_m11", "STRoSIB_STR_m12", "STRoSIB_STR_m31",
"STRoSIB_STR_m21", "STRoSIB_STR_m22", "STRoSIB_STR_m32")], na.rm = T) # should be a duplicate of "STRoSIB_STR_moral"
E1_all_clean$Choice_STRnSIB_SIB_oblig <- (E1_all_clean$STRoSIB_SIB_oblig + E1_all_clean$SIBoSTR_SIB_oblig)/2 # creates pre-registered obligation index for mean comparisons
E1_all_clean$Choice_STRnSIB_SIB_moral <- rowSums(E1_all_clean[, c(
"SIBoSTR_SIB_m11", "SIBoSTR_SIB_m12", "SIBoSTR_SIB_m31",
"SIBoSTR_SIB_m21", "SIBoSTR_SIB_m22", "SIBoSTR_SIB_m32")], na.rm = T) # should be a duplicate of "SIBoSTR_SIB_moral"
E1_all_clean$CUZoSIB_CUZ_oblig <- rowSums(E1_all_clean[, c("CUZoSIB_CUZ_o11", "CUZoSIB_CUZ_o12", "CUZoSIB_CUZ_o31",
"CUZoSIB_CUZ_o21", "CUZoSIB_CUZ_o22", "CUZoSIB_CUZ_o32")], na.rm = T)
E1_all_clean$CUZoSIB_SIB_oblig <- rowSums(E1_all_clean[, c("CUZoSIB_SIB_o11", "CUZoSIB_SIB_o12", "CUZoSIB_SIB_o31",
"CUZoSIB_SIB_o21", "CUZoSIB_SIB_o22", "CUZoSIB_SIB_o32")], na.rm = T)
E1_all_clean$CUZoSIB_CUZ_moral <- rowSums(E1_all_clean[, c("CUZoSIB_CUZ_m11", "CUZoSIB_CUZ_m12", "CUZoSIB_CUZ_m31",
"CUZoSIB_CUZ_m21", "CUZoSIB_CUZ_m22", "CUZoSIB_CUZ_m32")], na.rm = T)
E1_all_clean$SIBoCUZ_CUZ_oblig <- rowSums(E1_all_clean[, c("SIBoCUZ_CUZ_o11", "SIBoCUZ_CUZ_o12", "SIBoCUZ_CUZ_o31",
"SIBoCUZ_CUZ_o21", "SIBoCUZ_CUZ_o22", "SIBoCUZ_CUZ_o32")], na.rm = T)
E1_all_clean$SIBoCUZ_SIB_oblig <- rowSums(E1_all_clean[, c("SIBoCUZ_SIB_o11", "SIBoCUZ_SIB_o12", "SIBoCUZ_SIB_o31",
"SIBoCUZ_SIB_o21", "SIBoCUZ_SIB_o22", "SIBoCUZ_SIB_o32")], na.rm = T)
E1_all_clean$SIBoCUZ_SIB_moral <- rowSums(E1_all_clean[, c("SIBoCUZ_SIB_m11", "SIBoCUZ_SIB_m12", "SIBoCUZ_SIB_m31",
"SIBoCUZ_SIB_m21", "SIBoCUZ_SIB_m22", "SIBoCUZ_SIB_m32")], na.rm = T)
E1_all_clean$Choice_CUZnSIB_CUZ_oblig <- (E1_all_clean$CUZoSIB_CUZ_oblig + E1_all_clean$SIBoCUZ_CUZ_oblig)/2 # creates pre-registered obligation index for mean comparisons
E1_all_clean$Choice_CUZnSIB_CUZ_moral <- rowSums(E1_all_clean[, c(
"CUZoSIB_CUZ_m11", "CUZoSIB_CUZ_m12", "CUZoSIB_CUZ_m31",
"CUZoSIB_CUZ_m21", "CUZoSIB_CUZ_m22", "CUZoSIB_CUZ_m32")], na.rm = T) # should be a duplicate of "CUZoSIB_CUZ_moral"
E1_all_clean$Choice_CUZnSIB_SIB_oblig <- (E1_all_clean$CUZoSIB_SIB_oblig + E1_all_clean$SIBoCUZ_SIB_oblig)/2 # creates pre-registered obligation index for mean comparisons
E1_all_clean$Choice_CUZnSIB_SIB_moral <- rowSums(E1_all_clean[, c(
"SIBoCUZ_SIB_m11", "SIBoCUZ_SIB_m12", "SIBoCUZ_SIB_m31",
"SIBoCUZ_SIB_m21", "SIBoCUZ_SIB_m22", "SIBoCUZ_SIB_m32")], na.rm = T) # should be a duplicate of "SIBoCUZ_SIB_moral"
# Difference Scores
# e.g., STR minus CUZ obligation within No Choice or Choice conditions (for diff score corrs and ind. diffs analyses)
# STR vs CUZ
E1_all_clean$NoChoice_STRminusCUZ_oblig <- E1_all_clean$NoChoice_STR_oblig - E1_all_clean$NoChoice_CUZ_oblig
E1_all_clean$NoChoice_STRminusCUZ_moral <- E1_all_clean$NoChoice_STR_moral - E1_all_clean$NoChoice_CUZ_moral
E1_all_clean$Choice_STRminusCUZ_oblig <- E1_all_clean$Choice_STRnCUZ_STR_oblig - E1_all_clean$Choice_STRnCUZ_CUZ_oblig
E1_all_clean$Choice_STRminusCUZ_moral <- E1_all_clean$Choice_STRnCUZ_STR_moral - E1_all_clean$Choice_STRnCUZ_CUZ_moral
# STR vs SIB
E1_all_clean$NoChoice_STRminusSIB_oblig <- E1_all_clean$NoChoice_STR_oblig - E1_all_clean$NoChoice_SIB_oblig
E1_all_clean$NoChoice_STRminusSIB_moral <- E1_all_clean$NoChoice_STR_moral - E1_all_clean$NoChoice_SIB_moral
E1_all_clean$Choice_STRminusSIB_oblig <- E1_all_clean$Choice_STRnSIB_STR_oblig - E1_all_clean$Choice_STRnSIB_SIB_oblig
E1_all_clean$Choice_STRminusSIB_moral <- E1_all_clean$Choice_STRnSIB_STR_moral - E1_all_clean$Choice_STRnSIB_SIB_moral
# CUZ vs SIB
E1_all_clean$NoChoice_CUZminusSIB_oblig <- E1_all_clean$NoChoice_CUZ_oblig - E1_all_clean$NoChoice_SIB_oblig
E1_all_clean$NoChoice_CUZminusSIB_moral <- E1_all_clean$NoChoice_CUZ_moral - E1_all_clean$NoChoice_SIB_moral
E1_all_clean$Choice_CUZminusSIB_oblig <- E1_all_clean$Choice_CUZnSIB_CUZ_oblig - E1_all_clean$Choice_CUZnSIB_SIB_oblig
E1_all_clean$Choice_CUZminusSIB_moral <- E1_all_clean$Choice_CUZnSIB_CUZ_moral - E1_all_clean$Choice_CUZnSIB_SIB_moral
# Individual Difference Measures (for ind. diffs analyses)
# MAC (Morality-as-Cooperation scale) composite
E1_all_clean$MAC_Fam_Combined <- ((E1_all_clean$MAC_Jud_1 + E1_all_clean$MAC_Jud_2 + E1_all_clean$MAC_Jud_3 +
E1_all_clean$MAC_Rel_1 + E1_all_clean$MAC_Rel_2 + E1_all_clean$MAC_Rel_3)/6)
E1_all_clean$MAC_Fam_Jud <- ((E1_all_clean$MAC_Jud_1 + E1_all_clean$MAC_Jud_2 + E1_all_clean$MAC_Jud_3)/3)
E1_all_clean$MAC_Fam_Rel <- ((E1_all_clean$MAC_Rel_1 + E1_all_clean$MAC_Rel_2 + E1_all_clean$MAC_Rel_3)/3)
# MFQ (Moral Foundations Theory scale) composite
E1_all_clean$MFQ_Loyalty_Combined <- ((E1_all_clean$MFQ_Jud_1 + E1_all_clean$MFQ_Jud_2 + E1_all_clean$MFQ_Jud_3 +
E1_all_clean$MFQ_Rel_1 + E1_all_clean$MFQ_Rel_2 + E1_all_clean$MFQ_Rel_3)/6)
E1_all_clean$MFQ_Loyalty_Jud <- ((E1_all_clean$MFQ_Jud_1 + E1_all_clean$MFQ_Jud_2 + E1_all_clean$MFQ_Jud_3)/3)
E1_all_clean$MFQ_Loyalty_Rel <- ((E1_all_clean$MFQ_Rel_1 + E1_all_clean$MFQ_Rel_2 + E1_all_clean$MFQ_Rel_3)/3)
# OUS (Oxford Utilitarianism Scale: Impartial Beneficence subscale) composite
E1_all_clean$OUS_IB <- ((E1_all_clean$OUS_IB1 + E1_all_clean$OUS_IB2 + E1_all_clean$OUS_IB3 +
E1_all_clean$OUS_IB4 + E1_all_clean$OUS_IB5)/5)Creating Analyzable Between-Subjects Datasets
# STR vs CUZ
E1_STRnCUZ_clean <- E1_all_clean %>%
filter(BSs_cond == 'STR vs CUZ') %>%
# select only variables that are relevant to STRnCUZ
select(
ResponseId, # selects variable
Age:Political_Overall, # selects demographic variables
MAC_Jud_1:MAC_Jud_3, MAC_Rel_1:MAC_Rel_3, MFQ_Jud_1:MFQ_Jud_3, MFQ_Rel_1:MFQ_Rel_3, OUS_IB1:OUS_IB5, # selects raw ind. diff variables (for reliabilty check)
MAC_Fam_Combined:OUS_IB, # selects composited ind. diff variables
BSs_cond, # selects variable for between-subjects condition
Dist_Scen:CloseODist_Scen, # selects within-subjects condition variable
NoChoice_STR_oblig:NoChoice_CUZ_moral, # selects NoChoice DVs for STRnCUZ dataset
Choice_STRnCUZ_STR_oblig:Choice_STRnCUZ_CUZ_moral, # selects Choice DVs for STRnCUZ dataset
NoChoice_STRminusCUZ_oblig:Choice_STRminusCUZ_moral # selects difference score variables for STRnCUZ dataset
)
# STR vs SIB
E1_STRnSIB_clean <- E1_all_clean %>%
filter(BSs_cond == 'STR vs SIB') %>%
# select only variables that are relevant to STRnCUZ
select(
ResponseId, # selects variable
Age:Political_Overall, # selects demographic variables
MAC_Jud_1:MAC_Jud_3, MAC_Rel_1:MAC_Rel_3, MFQ_Jud_1:MFQ_Jud_3, MFQ_Rel_1:MFQ_Rel_3, OUS_IB1:OUS_IB5, # selects raw ind. diff variables (for reliabilty check)
MAC_Fam_Combined:OUS_IB, # selects composited ind. diff variables
BSs_cond, # selects variable for between-subjects condition
Dist_Scen:CloseODist_Scen, # sselects within-subjects condition variable
NoChoice_STR_oblig:NoChoice_STR_moral, NoChoice_SIB_oblig:NoChoice_SIB_moral, # selects NoChoice DVs for STRnSIB dataset
Choice_STRnSIB_STR_oblig:Choice_STRnSIB_SIB_moral, # selects Choice DVs for STRnSIB dataset
NoChoice_STRminusSIB_oblig:Choice_STRminusSIB_moral # selects difference score variables for STRnSIB dataset
)
# CUZ vs SIB
E1_CUZnSIB_clean <- E1_all_clean %>%
filter(BSs_cond == 'CUZ vs SIB') %>%
# select only variables that are relevant to STRnCUZ
select(
ResponseId, # selects variable
Age:Political_Overall, # selects demographic variables
MAC_Jud_1:MAC_Jud_3, MAC_Rel_1:MAC_Rel_3, MFQ_Jud_1:MFQ_Jud_3, MFQ_Rel_1:MFQ_Rel_3, OUS_IB1:OUS_IB5, # selects raw ind. diff variables (for reliabilty check)
MAC_Fam_Combined:OUS_IB, # selects composited ind. diff variables
BSs_cond, # selects variable for between-subjects condition
Dist_Scen:CloseODist_Scen, # selects within-subjects condition variable
NoChoice_CUZ_oblig:NoChoice_SIB_moral, # selects NoChoice DVs for CUZnSIB dataset
Choice_CUZnSIB_CUZ_oblig:Choice_CUZnSIB_SIB_moral, # selects Choice DVs for CUZnSIB dataset
NoChoice_CUZminusSIB_oblig:Choice_CUZminusSIB_moral # selects difference score variables for CUZnSIB dataset
)Tidying Data
# Convert data from wide to long format for obligation and moral character
# STRnCUZ
E1_STRnCUZ_cond_long <- E1_STRnCUZ_clean %>%
pivot_longer(
cols = c(Dist_Scen, Close_Scen, DistOClose_Scen, CloseODist_Scen),
names_to = "WSs_cond",
values_to = "Condition"
)
E1_STRnCUZ_oblig_long <- E1_STRnCUZ_clean %>%
pivot_longer(
cols = c(NoChoice_STR_oblig, NoChoice_CUZ_oblig, Choice_STRnCUZ_STR_oblig, Choice_STRnCUZ_CUZ_oblig),
names_to = "WSs_cond",
values_to = "oblig"
)
E1_STRnCUZ_moral_long <- E1_STRnCUZ_clean %>%
pivot_longer(
cols = c(NoChoice_STR_moral, NoChoice_CUZ_moral, Choice_STRnCUZ_STR_moral, Choice_STRnCUZ_CUZ_moral),
names_to = "WSs_cond",
values_to = "moral"
)
# Combine long STRnCUZ datasets, select plotting variables, and create condition variable for each factor (Relation + Choice Context)
E1_STRnCUZ_long <- cbind(E1_STRnCUZ_cond_long, E1_STRnCUZ_oblig_long, E1_STRnCUZ_moral_long)
E1_STRnCUZ_long <- E1_STRnCUZ_long[, !duplicated(colnames(E1_STRnCUZ_long))] %>% # get rid of duplicate columns
select(ResponseId,
Age:OUS_IB,
BSs_cond,
WSs_cond,
Condition,
oblig, moral) %>%
mutate(Relation = case_when(
WSs_cond == "Dist_Scen" ~ "Distant",
WSs_cond == "Close_Scen" ~ "Close",
WSs_cond == "DistOClose_Scen" ~ "Distant",
WSs_cond == "CloseODist_Scen" ~ "Close")) %>%
mutate(`Choice Context` = case_when(
WSs_cond == "Dist_Scen" ~ "No Choice",
WSs_cond == "Close_Scen" ~ "No Choice",
WSs_cond == "DistOClose_Scen" ~ "Choice",
WSs_cond == "CloseODist_Scen" ~ "Choice"))
# Reorder/rename condition and participant factors
E1_STRnCUZ_long$Relation <- as.factor(E1_STRnCUZ_long$Relation)
E1_STRnCUZ_long$Relation <- ordered(E1_STRnCUZ_long$Relation, levels = c("Distant", "Close"))
E1_STRnCUZ_long$`Choice Context` <- as.factor(E1_STRnCUZ_long$`Choice Context`)
E1_STRnCUZ_long$`Choice Context` <- ordered(E1_STRnCUZ_long$`Choice Context`, levels = c("No Choice", "Choice"))
E1_STRnCUZ_long$ResponseId <- as.factor(E1_STRnCUZ_long$ResponseId)
# Convert data from wide to long format for obligation and moral character
# STRnSIB
E1_STRnSIB_cond_long <- E1_STRnSIB_clean %>%
pivot_longer(
cols = c(Dist_Scen, Close_Scen, DistOClose_Scen, CloseODist_Scen),
names_to = "WSs_cond",
values_to = "Condition"
)
E1_STRnSIB_oblig_long <- E1_STRnSIB_clean %>%
pivot_longer(
cols = c(NoChoice_STR_oblig, NoChoice_SIB_oblig, Choice_STRnSIB_STR_oblig, Choice_STRnSIB_SIB_oblig),
names_to = "WSs_cond",
values_to = "oblig"
)
E1_STRnSIB_moral_long <- E1_STRnSIB_clean %>%
pivot_longer(
cols = c(NoChoice_STR_moral, NoChoice_SIB_moral, Choice_STRnSIB_STR_moral, Choice_STRnSIB_SIB_moral),
names_to = "WSs_cond",
values_to = "moral"
)
# Combine long STRnSIB datasets, select plotting variables, and create condition variable for each factor (Relation + Choice Context)
E1_STRnSIB_long <- cbind(E1_STRnSIB_cond_long, E1_STRnSIB_oblig_long, E1_STRnSIB_moral_long)
E1_STRnSIB_long <- E1_STRnSIB_long[, !duplicated(colnames(E1_STRnSIB_long))] %>% # get rid of duplicate columns
select(ResponseId,
Age:OUS_IB,
BSs_cond,
WSs_cond,
Condition,
oblig, moral) %>%
mutate(Relation = case_when(
WSs_cond == "Dist_Scen" ~ "Distant",
WSs_cond == "Close_Scen" ~ "Close",
WSs_cond == "DistOClose_Scen" ~ "Distant",
WSs_cond == "CloseODist_Scen" ~ "Close")) %>%
mutate(`Choice Context` = case_when(
WSs_cond == "Dist_Scen" ~ "No Choice",
WSs_cond == "Close_Scen" ~ "No Choice",
WSs_cond == "DistOClose_Scen" ~ "Choice",
WSs_cond == "CloseODist_Scen" ~ "Choice"))
# Reorder/rename condition and participant factors
E1_STRnSIB_long$Relation <- as.factor(E1_STRnSIB_long$Relation)
E1_STRnSIB_long$Relation <- ordered(E1_STRnSIB_long$Relation, levels = c("Distant", "Close"))
E1_STRnSIB_long$`Choice Context` <- as.factor(E1_STRnSIB_long$`Choice Context`)
E1_STRnSIB_long$`Choice Context` <- ordered(E1_STRnSIB_long$`Choice Context`, levels = c("No Choice", "Choice"))
E1_STRnSIB_long$ResponseId <- as.factor(E1_STRnSIB_long$ResponseId)
# Convert data from wide to long format for obligation and moral character
# CUZnSIB
E1_CUZnSIB_cond_long <- E1_CUZnSIB_clean %>%
pivot_longer(
cols = c(Dist_Scen, Close_Scen, DistOClose_Scen, CloseODist_Scen),
names_to = "WSs_cond",
values_to = "Condition"
)
E1_CUZnSIB_oblig_long <- E1_CUZnSIB_clean %>%
pivot_longer(
cols = c(NoChoice_CUZ_oblig, NoChoice_SIB_oblig, Choice_CUZnSIB_CUZ_oblig, Choice_CUZnSIB_SIB_oblig),
names_to = "WSs_cond",
values_to = "oblig"
)
E1_CUZnSIB_moral_long <- E1_CUZnSIB_clean %>%
pivot_longer(
cols = c(NoChoice_CUZ_moral, NoChoice_SIB_moral, Choice_CUZnSIB_CUZ_moral, Choice_CUZnSIB_SIB_moral),
names_to = "WSs_cond",
values_to = "moral"
)
# Combine long CUZnSIB datasets, select plotting variables, and create condition variable for each factor (Relation + Choice Context)
E1_CUZnSIB_long <- cbind(E1_CUZnSIB_cond_long, E1_CUZnSIB_oblig_long, E1_CUZnSIB_moral_long)
E1_CUZnSIB_long <- E1_CUZnSIB_long[, !duplicated(colnames(E1_CUZnSIB_long))] %>% # get rid of duplicate columns
select(ResponseId,
Age:OUS_IB,
BSs_cond,
WSs_cond,
Condition,
oblig, moral) %>%
mutate(Relation = case_when(
WSs_cond == "Dist_Scen" ~ "Distant",
WSs_cond == "Close_Scen" ~ "Close",
WSs_cond == "DistOClose_Scen" ~ "Distant",
WSs_cond == "CloseODist_Scen" ~ "Close")) %>%
mutate(`Choice Context` = case_when(
WSs_cond == "Dist_Scen" ~ "No Choice",
WSs_cond == "Close_Scen" ~ "No Choice",
WSs_cond == "DistOClose_Scen" ~ "Choice",
WSs_cond == "CloseODist_Scen" ~ "Choice"))
# Reorder/rename condition and participant factors
E1_CUZnSIB_long$Relation <- as.factor(E1_CUZnSIB_long$Relation)
E1_CUZnSIB_long$Relation <- ordered(E1_CUZnSIB_long$Relation, levels = c("Distant", "Close"))
E1_CUZnSIB_long$`Choice Context` <- as.factor(E1_CUZnSIB_long$`Choice Context`)
E1_CUZnSIB_long$`Choice Context` <- ordered(E1_CUZnSIB_long$`Choice Context`, levels = c("No Choice", "Choice"))
E1_CUZnSIB_long$ResponseId <- as.factor(E1_CUZnSIB_long$ResponseId)
# Combine into one dataset for later exploratory analyses
E1_all_long <- rbind(E1_STRnSIB_long, E1_STRnCUZ_long, E1_CUZnSIB_long)
# Reorder All_long BSs_cond
E1_all_long$BSs_cond <- as.factor(E1_all_long$BSs_cond)
E1_all_long$BSs_cond <- ordered(E1_all_long$BSs_cond, levels = c("STR vs SIB", "STR vs CUZ", "CUZ vs SIB")) # order is due to likely differences between datasets (largest to smallest - see pre-registration's "exploratory analysis" section)Descriptive Statistics
Oblig
STRnSIB
describeBy(E1_STRnSIB_long$oblig, list(E1_STRnSIB_long$Relation, E1_STRnSIB_long$`Choice Context`), mat = T)STRnCUZ
describeBy(E1_STRnCUZ_long$oblig, list(E1_STRnCUZ_long$Relation, E1_STRnCUZ_long$`Choice Context`), mat = T)CUZnSIB
describeBy(E1_CUZnSIB_long$oblig, list(E1_CUZnSIB_long$Relation, E1_CUZnSIB_long$`Choice Context`), mat = T)Moral
STRnSIB
describeBy(E1_STRnSIB_long$moral, list(E1_STRnSIB_long$Relation, E1_STRnSIB_long$`Choice Context`), mat = T)STRnCUZ
describeBy(E1_STRnCUZ_long$moral, list(E1_STRnCUZ_long$Relation, E1_STRnCUZ_long$`Choice Context`), mat = T)CUZnSIB
describeBy(E1_CUZnSIB_long$moral, list(E1_CUZnSIB_long$Relation, E1_CUZnSIB_long$`Choice Context`), mat = T)Mean Difference Plots
# Set dodge for plotting crossed factors
dodge = position_dodge(width = 1) Oblig
STRnSIB
print(oblig_plot_STRnSIB <- ggplot(data = E1_STRnSIB_long, aes(x = `Choice Context`, y = oblig, fill = Relation)) +
geom_violin(aes(fill = Relation), position = dodge) +
geom_boxplot(width = 0.1, color = "black", outlier.shape = NA, position = dodge) +
scale_fill_manual(values = c("lightskyblue3", "indianred3")) +
stat_summary(fun = mean, geom = "point", shape = 18, color = "white", size = 3, position = dodge) +
theme(legend.position = "right") +
theme_classic() +
xlab("Choice Context") +
ylab("Obligation Strength") +
theme(axis.title.x = element_text(size = 14),
axis.title.y = element_text(size = 14),
axis.text.x = element_text(color = "black", size = 12),
axis.text.y = element_text(color = "black", size = 12),
legend.position = "right",
legend.title = element_text(color = "black", size = 14),
legend.text = element_text(color = "black", size = 12)))
STRnCUZ
print(oblig_plot_STRnCUZ <- ggplot(data = E1_STRnCUZ_long, aes(x = `Choice Context`, y = oblig, fill = Relation)) +
geom_violin(aes(fill = Relation), position = dodge) +
geom_boxplot(width = 0.1, color = "black", outlier.shape = NA, position = dodge) +
scale_fill_manual(values = c("lightskyblue3", "indianred3")) +
stat_summary(fun = mean, geom = "point", shape = 18, color = "white", size = 3, position = dodge) +
theme(legend.position = "right") +
theme_classic() +
xlab("Choice Context") +
ylab("Obligation Strength") +
theme(axis.title.x = element_text(size = 14),
axis.title.y = element_text(size = 14),
axis.text.x = element_text(color = "black", size = 12),
axis.text.y = element_text(color = "black", size = 12),
legend.position = "right",
legend.title = element_text(color = "black", size = 14),
legend.text = element_text(color = "black", size = 12)))
CUZnSIB
print(oblig_plot_CUZnSIB <- ggplot(data = E1_CUZnSIB_long, aes(x = `Choice Context`, y = oblig, fill = Relation)) +
geom_violin(aes(fill = Relation), position = dodge) +
geom_boxplot(width = 0.1, color = "black", outlier.shape = NA, position = dodge) +
scale_fill_manual(values = c("lightskyblue3", "indianred3")) +
stat_summary(fun = mean, geom = "point", shape = 18, color = "white", size = 3, position = dodge) +
theme(legend.position = "right") +
theme_classic() +
xlab("Choice Context") +
ylab("Obligation Strength") +
theme(axis.title.x = element_text(size = 14),
axis.title.y = element_text(size = 14),
axis.text.x = element_text(color = "black", size = 12),
axis.text.y = element_text(color = "black", size = 12),
legend.position = "right",
legend.title = element_text(color = "black", size = 14),
legend.text = element_text(color = "black", size = 12)))
Combined
print(oblig_plot_combined <- ggplot(data = E1_all_long, aes(x = `Choice Context`, y = oblig, fill = Relation)) +
geom_violin(aes(fill = Relation), position = dodge) +
geom_boxplot(width = 0.1, color = "black", outlier.shape = NA, position = dodge) +
scale_fill_manual(values = c("lightskyblue3", "indianred3")) +
stat_summary(fun = mean, geom = "point", shape = 18, color = "white", size = 3, position = dodge) +
theme(legend.position = "right") +
theme_classic() +
facet_wrap(~BSs_cond, nrow = 3) +
xlab("\nChoice Context") +
ylab("Obligation Strength\n") +
theme(axis.title.x = element_text(size = 18),
axis.title.y = element_text(size = 18),
axis.text.x = element_text(color = "black", size = 16),
axis.text.y = element_text(color = "black", size = 16),
strip.text.x = element_text(color = "black", size = 16),
legend.position = "right",
legend.title = element_text(color = "black", size = 18),
legend.text = element_text(color = "black", size = 16)))
ggsave("E1_oblig_plot.png")Saving 14 x 9 in imageMoral
STRnSIB
print(moral_plot_STRnSIB <- ggplot(data = E1_STRnSIB_long, aes(x = `Choice Context`, y = moral, fill = Relation)) +
geom_hline(yintercept = 50, linetype = "dashed", color = "black") +
geom_violin(aes(fill = Relation), position = dodge) +
geom_boxplot(width = 0.1, color = "black", outlier.shape = NA, position = dodge) +
scale_fill_manual(values = c("lightskyblue3", "indianred3")) +
stat_summary(fun = mean, geom = "point", shape = 18, color = "white", size = 3, position = dodge) +
theme(legend.position = "right") +
theme_classic() +
xlab("Choice Context") +
ylab("Moral Character") +
theme(axis.title.x = element_text(size = 14),
axis.title.y = element_text(size = 14),
axis.text.x = element_text(color = "black", size = 12),
axis.text.y = element_text(color = "black", size = 12),
legend.position = "right",
legend.title = element_text(color = "black", size = 14),
legend.text = element_text(color = "black", size = 12)))
STRnCUZ
print(moral_plot_STRnCUZ <- ggplot(data = E1_STRnCUZ_long, aes(x = `Choice Context`, y = moral, fill = Relation)) +
geom_hline(yintercept = 50, linetype = "dashed", color = "black") +
geom_violin(aes(fill = Relation), position = dodge) +
geom_boxplot(width = 0.1, color = "black", outlier.shape = NA, position = dodge) +
scale_fill_manual(values = c("lightskyblue3", "indianred3")) +
stat_summary(fun = mean, geom = "point", shape = 18, color = "white", size = 3, position = dodge) +
theme(legend.position = "right") +
theme_classic() +
xlab("Choice Context") +
ylab("Moral Character") +
theme(axis.title.x = element_text(size = 14),
axis.title.y = element_text(size = 14),
axis.text.x = element_text(color = "black", size = 12),
axis.text.y = element_text(color = "black", size = 12),
legend.position = "right",
legend.title = element_text(color = "black", size = 14),
legend.text = element_text(color = "black", size = 12)))
CUZnSIB
print(moral_plot_CUZnSIB <- ggplot(data = E1_CUZnSIB_long, aes(x = `Choice Context`, y = moral, fill = Relation)) +
geom_hline(yintercept = 50, linetype = "dashed", color = "black") +
geom_violin(aes(fill = Relation), position = dodge) +
geom_boxplot(width = 0.1, color = "black", outlier.shape = NA, position = dodge) +
scale_fill_manual(values = c("lightskyblue3", "indianred3")) +
stat_summary(fun = mean, geom = "point", shape = 18, color = "white", size = 3, position = dodge) +
theme(legend.position = "right") +
theme_classic() +
xlab("Choice Context") +
ylab("Moral Character") +
theme(axis.title.x = element_text(size = 14),
axis.title.y = element_text(size = 14),
axis.text.x = element_text(color = "black", size = 12),
axis.text.y = element_text(color = "black", size = 12),
legend.position = "right",
legend.title = element_text(color = "black", size = 14),
legend.text = element_text(color = "black", size = 12)))
Combined
print(moral_plot_combined <- ggplot(data = E1_all_long, aes(x = `Choice Context`, y = moral, fill = Relation)) +
geom_hline(yintercept = 50, linetype = "dashed", color = "black") +
geom_violin(aes(fill = Relation), position = dodge) +
geom_boxplot(width = 0.1, color = "black", outlier.shape = NA, position = dodge) +
scale_fill_manual(values = c("lightskyblue3", "indianred3")) +
stat_summary(fun = mean, geom = "point", shape = 18, color = "white", size = 3, position = dodge) +
theme(legend.position = "right") +
theme_classic() +
facet_wrap(~BSs_cond, nrow = 3) +
xlab("\nChoice Context") +
ylab("Moral Character\n") +
theme(axis.title.x = element_text(size = 18),
axis.title.y = element_text(size = 18),
axis.text.x = element_text(color = "black", size = 16),
axis.text.y = element_text(color = "black", size = 16),
strip.text.x = element_text(color = "black", size = 16),
legend.position = "right",
legend.title = element_text(color = "black", size = 18),
legend.text = element_text(color = "black", size = 16)))
ggsave("E1_moral_plot.png")Saving 14 x 9 in imageMean Difference Tests
See our pre-registration (https://osf.io/6q4kr/?view_only=8033c65c9dfc41bcbc528fe94409e4e8) for our predictions related to obligation judgments and moral character judgments.
Oblig
STRnSIB
No Choice
# returns t-test results
t.test(oblig ~ Relation,
data = E1_all_long %>% filter(BSs_cond == "STR vs SIB") %>% filter(`Choice Context` == "No Choice") %>% droplevels(),
paired = T)
Paired t-test
data: oblig by Relation
t = -6.1598, df = 202, p-value = 3.873e-09
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-22.86444 -11.77596
sample estimates:
mean of the differences
-17.3202 # returns dz effect size and 95% CIs
effsize::cohen.d(oblig ~ Relation | Subject(ResponseId),
data = E1_all_long %>% filter(BSs_cond == "STR vs SIB") %>% filter(`Choice Context` == "No Choice") %>% droplevels(),
paired = T,
within = F) # setting this to false ensures dz is calculated, using difference score
Cohen's d
d estimate: -0.4323351 (small)
95 percent confidence interval:
lower upper
-0.5766144 -0.2880559 # returns d-av effect size and 95% CIs
effsize::cohen.d(oblig ~ Relation | Subject(ResponseId),
data = E1_all_long %>% filter(BSs_cond == "STR vs SIB") %>% filter(`Choice Context` == "No Choice") %>% droplevels(),
paired = T,
within = T) # setting this to true ensures d-av is calculated, using raw scores
Cohen's d
d estimate: -0.5435963 (medium)
95 percent confidence interval:
lower upper
-0.7294547 -0.3577378 # returns correlation between variables
cor_test(data = E1_STRnSIB_clean, "NoChoice_STR_oblig", "NoChoice_SIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(201) | p
--------------------------------------------------------------------------------
NoChoice_STR_oblig | NoChoice_SIB_oblig | 0.21 | [0.07, 0.34] | 3.04 | 0.003**
Observations: 203Choice
# returns t-test results
t.test(oblig ~ Relation,
data = E1_all_long %>% filter(BSs_cond == "STR vs SIB") %>% filter(`Choice Context` == "Choice") %>% droplevels(),
paired = T)
Paired t-test
data: oblig by Relation
t = -11.476, df = 202, p-value < 2.2e-16
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-21.11008 -14.91947
sample estimates:
mean of the differences
-18.01478 # returns dz effect size and 95% CIs
effsize::cohen.d(oblig ~ Relation | Subject(ResponseId),
data = E1_all_long %>% filter(BSs_cond == "STR vs SIB") %>% filter(`Choice Context` == "Choice") %>% droplevels(),
paired = T,
within = F) # setting this to false ensures dz is calculated, using difference score
Cohen's d
d estimate: -0.8054442 (large)
95 percent confidence interval:
lower upper
-0.9642286 -0.6466598 # returns d-av effect size and 95% CIs
effsize::cohen.d(oblig ~ Relation | Subject(ResponseId),
data = E1_all_long %>% filter(BSs_cond == "STR vs SIB") %>% filter(`Choice Context` == "Choice") %>% droplevels(),
paired = T,
within = T) # setting this to true ensures d-av is calculated, using raw scores
Cohen's d
d estimate: -0.6748516 (medium)
95 percent confidence interval:
lower upper
-0.8029442 -0.5467589 # returns correlation between variables
cor_test(data = E1_STRnSIB_clean, "Choice_STRnSIB_STR_oblig", "Choice_STRnSIB_SIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(201) | p
----------------------------------------------------------------------------------------------
Choice_STRnSIB_STR_oblig | Choice_STRnSIB_SIB_oblig | 0.65 | [0.56, 0.72] | 12.09 | < .001***
Observations: 203STRnCUZ
No Choice
# returns t-test results
t.test(oblig ~ Relation,
data = E1_all_long %>% filter(BSs_cond == "STR vs CUZ") %>% filter(`Choice Context` == "No Choice") %>% droplevels(),
paired = T)
Paired t-test
data: oblig by Relation
t = -6.28, df = 202, p-value = 2.037e-09
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-23.97522 -12.51739
sample estimates:
mean of the differences
-18.24631 # returns dz effect size and 95% CIs
effsize::cohen.d(oblig ~ Relation | Subject(ResponseId),
data = E1_all_long %>% filter(BSs_cond == "STR vs CUZ") %>% filter(`Choice Context` == "No Choice") %>% droplevels(),
paired = T,
within = F) # setting this to false ensures dz is calculated, using difference score
Cohen's d
d estimate: -0.4407704 (small)
95 percent confidence interval:
lower upper
-0.5852924 -0.2962484 # returns d-av effect size and 95% CIs
effsize::cohen.d(oblig ~ Relation | Subject(ResponseId),
data = E1_all_long %>% filter(BSs_cond == "STR vs CUZ") %>% filter(`Choice Context` == "No Choice") %>% droplevels(),
paired = T,
within = T) # setting this to true ensures d-av is calculated, using raw scores
Cohen's d
d estimate: -0.5701483 (medium)
95 percent confidence interval:
lower upper
-0.7625822 -0.3777145 # returns correlation between variables
cor_test(data = E1_STRnCUZ_clean, "NoChoice_STR_oblig", "NoChoice_CUZ_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(201) | p
-------------------------------------------------------------------------------
NoChoice_STR_oblig | NoChoice_CUZ_oblig | 0.16 | [0.03, 0.29] | 2.35 | 0.020*
Observations: 203Choice
# returns t-test results
t.test(oblig ~ Relation,
data = E1_all_long %>% filter(BSs_cond == "STR vs CUZ") %>% filter(`Choice Context` == "Choice") %>% droplevels(),
paired = T)
Paired t-test
data: oblig by Relation
t = -10.245, df = 202, p-value < 2.2e-16
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-15.32575 -10.37869
sample estimates:
mean of the differences
-12.85222 # returns dz effect size and 95% CIs
effsize::cohen.d(oblig ~ Relation | Subject(ResponseId),
data = E1_all_long %>% filter(BSs_cond == "STR vs CUZ") %>% filter(`Choice Context` == "Choice") %>% droplevels(),
paired = T,
within = F) # setting this to false ensures dz is calculated, using difference score
Cohen's d
d estimate: -0.7190697 (medium)
95 percent confidence interval:
lower upper
-0.8738569 -0.5642825 # returns d-av effect size and 95% CIs
effsize::cohen.d(oblig ~ Relation | Subject(ResponseId),
data = E1_all_long %>% filter(BSs_cond == "STR vs CUZ") %>% filter(`Choice Context` == "Choice") %>% droplevels(),
paired = T,
within = T) # setting this to true ensures d-av is calculated, using raw scores
Cohen's d
d estimate: -0.491025 (small)
95 percent confidence interval:
lower upper
-0.590761 -0.391289 # returns correlation between variables
cor_test(data = E1_STRnCUZ_clean, "Choice_STRnCUZ_STR_oblig", "Choice_STRnCUZ_CUZ_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(201) | p
----------------------------------------------------------------------------------------------
Choice_STRnCUZ_STR_oblig | Choice_STRnCUZ_CUZ_oblig | 0.77 | [0.70, 0.82] | 16.94 | < .001***
Observations: 203CUZnSIB
No Choice
# returns t-test results
t.test(oblig ~ Relation,
data = E1_all_long %>% filter(BSs_cond == "CUZ vs SIB") %>% filter(`Choice Context` == "No Choice") %>% droplevels(),
paired = T)
Paired t-test
data: oblig by Relation
t = -2.4887, df = 204, p-value = 0.01362
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-11.688854 -1.355049
sample estimates:
mean of the differences
-6.521951 # returns dz effect size and 95% CIs
effsize::cohen.d(oblig ~ Relation | Subject(ResponseId),
data = E1_all_long %>% filter(BSs_cond == "CUZ vs SIB") %>% filter(`Choice Context` == "No Choice") %>% droplevels(),
paired = T,
within = F) # setting this to false ensures dz is calculated, using difference score
Cohen's d
d estimate: -0.1738211 (negligible)
95 percent confidence interval:
lower upper
-0.31215141 -0.03549085 # returns d-av effect size and 95% CIs
effsize::cohen.d(oblig ~ Relation | Subject(ResponseId),
data = E1_all_long %>% filter(BSs_cond == "CUZ vs SIB") %>% filter(`Choice Context` == "No Choice") %>% droplevels(),
paired = T,
within = T) # setting this to true ensures d-av is calculated, using raw scores
Cohen's d
d estimate: -0.1892702 (negligible)
95 percent confidence interval:
lower upper
-0.3401031 -0.0384373 # returns correlation between variables
cor_test(data = E1_CUZnSIB_clean, "NoChoice_CUZ_oblig", "NoChoice_SIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(203) | p
----------------------------------------------------------------------------------
NoChoice_CUZ_oblig | NoChoice_SIB_oblig | 0.41 | [0.29, 0.52] | 6.35 | < .001***
Observations: 205Choice
# returns t-test results
t.test(oblig ~ Relation,
data = E1_all_long %>% filter(BSs_cond == "CUZ vs SIB") %>% filter(`Choice Context` == "Choice") %>% droplevels(),
paired = T)
Paired t-test
data: oblig by Relation
t = -7.1341, df = 204, p-value = 1.666e-11
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-7.960208 -4.512963
sample estimates:
mean of the differences
-6.236585 # returns dz effect size and 95% CIs
effsize::cohen.d(oblig ~ Relation | Subject(ResponseId),
data = E1_all_long %>% filter(BSs_cond == "CUZ vs SIB") %>% filter(`Choice Context` == "Choice") %>% droplevels(),
paired = T,
within = F) # setting this to false ensures dz is calculated, using difference score
Cohen's d
d estimate: -0.4982647 (small)
95 percent confidence interval:
lower upper
-0.6438342 -0.3526952 # returns d-av effect size and 95% CIs
effsize::cohen.d(oblig ~ Relation | Subject(ResponseId),
data = E1_all_long %>% filter(BSs_cond == "CUZ vs SIB") %>% filter(`Choice Context` == "Choice") %>% droplevels(),
paired = T,
within = T) # setting this to true ensures d-av is calculated, using raw scores
Cohen's d
d estimate: -0.2211563 (small)
95 percent confidence interval:
lower upper
-0.2828367 -0.1594759 # returns correlation between variables
cor_test(data = E1_CUZnSIB_clean, "Choice_CUZnSIB_CUZ_oblig", "Choice_CUZnSIB_SIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(203) | p
----------------------------------------------------------------------------------------------
Choice_CUZnSIB_CUZ_oblig | Choice_CUZnSIB_SIB_oblig | 0.90 | [0.87, 0.92] | 29.68 | < .001***
Observations: 205Moral
ANOVAs
STRnSIB
# returns 2 x 2 within-subject ANOVA results
aov_moral_STRnSIB <- aov(moral ~ Relation*`Choice Context` + Error(ResponseId/(Relation*`Choice Context`)), data = E1_STRnSIB_long)
summary(aov_moral_STRnSIB)
Error: ResponseId
Df Sum Sq Mean Sq F value Pr(>F)
Residuals 202 101536 502.7
Error: ResponseId:Relation
Df Sum Sq Mean Sq F value Pr(>F)
Relation 1 2554 2553.7 12.22 0.00058 ***
Residuals 202 42199 208.9
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Error: ResponseId:`Choice Context`
Df Sum Sq Mean Sq F value Pr(>F)
`Choice Context` 1 70237 70237 319.7 <2e-16 ***
Residuals 202 44384 220
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Error: ResponseId:Relation:`Choice Context`
Df Sum Sq Mean Sq F value Pr(>F)
Relation:`Choice Context` 1 8121 8121 44.26 2.65e-10 ***
Residuals 202 37069 184
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1# returns eta-sq effect size
eta_sq(aov_moral_STRnSIB, partial = TRUE)'eta_sq' is deprecated.
Use 'effectsize::eta_squared()' instead.
See help("Deprecated")Group | Parameter | Eta2 (partial) | 90% CI
----------------------------------------------------------------------------------------------
ResponseId:Relation | Relation | 0.06 | [0.02, 0.12]
ResponseId:`Choice Context` | Choice Context | 0.61 | [0.55, 0.67]
ResponseId:Relation:`Choice Context` | Relation:Choice Context | 0.18 | [0.11, 0.26]STRnCUZ
# returns 2 x 2 within-subject ANOVA results
aov_moral_STRnCUZ <- aov(moral ~ Relation*`Choice Context` + Error(ResponseId/(Relation*`Choice Context`)), data = E1_STRnCUZ_long)
summary(aov_moral_STRnCUZ)
Error: ResponseId
Df Sum Sq Mean Sq F value Pr(>F)
Residuals 202 115423 571.4
Error: ResponseId:Relation
Df Sum Sq Mean Sq F value Pr(>F)
Relation 1 99 98.63 0.445 0.505
Residuals 202 44764 221.60
Error: ResponseId:`Choice Context`
Df Sum Sq Mean Sq F value Pr(>F)
`Choice Context` 1 51060 51060 239.6 <2e-16 ***
Residuals 202 43042 213
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Error: ResponseId:Relation:`Choice Context`
Df Sum Sq Mean Sq F value Pr(>F)
Relation:`Choice Context` 1 2473 2472.8 13.14 0.000365 ***
Residuals 202 38005 188.1
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1# returns eta-sq effect size
eta_sq(aov_moral_STRnCUZ, partial = TRUE)'eta_sq' is deprecated.
Use 'effectsize::eta_squared()' instead.
See help("Deprecated")Group | Parameter | Eta2 (partial) | 90% CI
----------------------------------------------------------------------------------------------
ResponseId:Relation | Relation | 2.20e-03 | [0.00, 0.03]
ResponseId:`Choice Context` | Choice Context | 0.54 | [0.47, 0.60]
ResponseId:Relation:`Choice Context` | Relation:Choice Context | 0.06 | [0.02, 0.12]CUZnSIB
# returns 2 x 2 within-subject ANOVA results
aov_moral_CUZnSIB <- aov(moral ~ Relation*`Choice Context` + Error(ResponseId/(Relation*`Choice Context`)), data = E1_CUZnSIB_long)
summary(aov_moral_CUZnSIB)
Error: ResponseId
Df Sum Sq Mean Sq F value Pr(>F)
Residuals 204 114949 563.5
Error: ResponseId:Relation
Df Sum Sq Mean Sq F value Pr(>F)
Relation 1 2585 2585 14.77 0.000162 ***
Residuals 204 35699 175
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Error: ResponseId:`Choice Context`
Df Sum Sq Mean Sq F value Pr(>F)
`Choice Context` 1 60167 60167 263.4 <2e-16 ***
Residuals 204 46603 228
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Error: ResponseId:Relation:`Choice Context`
Df Sum Sq Mean Sq F value Pr(>F)
Relation:`Choice Context` 1 3114 3114.2 21.49 6.35e-06 ***
Residuals 204 29564 144.9
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1# returns eta-sq effect size
eta_sq(aov_moral_CUZnSIB, partial = TRUE)'eta_sq' is deprecated.
Use 'effectsize::eta_squared()' instead.
See help("Deprecated")Group | Parameter | Eta2 (partial) | 90% CI
----------------------------------------------------------------------------------------------
ResponseId:Relation | Relation | 0.07 | [0.02, 0.13]
ResponseId:`Choice Context` | Choice Context | 0.56 | [0.49, 0.62]
ResponseId:Relation:`Choice Context` | Relation:Choice Context | 0.10 | [0.04, 0.16]t-tests
STRnSIB
No Choice
# returns t-test results
t.test(moral ~ Relation,
data = E1_all_long %>% filter(BSs_cond == "STR vs SIB") %>% filter(`Choice Context` == "No Choice") %>% droplevels(),
paired = T)
Paired t-test
data: moral by Relation
t = 2.6937, df = 202, p-value = 0.007661
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
0.7446009 4.8120494
sample estimates:
mean of the differences
2.778325 # returns dz effect size and 95% CIs
effsize::cohen.d(moral ~ Relation | Subject(ResponseId),
data = E1_all_long %>% filter(BSs_cond == "STR vs SIB") %>% filter(`Choice Context` == "No Choice") %>% droplevels(),
paired = T,
within = F) # setting this to false ensures dz is calculated, using difference score
Cohen's d
d estimate: 0.1890605 (negligible)
95 percent confidence interval:
lower upper
0.04985719 0.32826387 # returns d-av effect size and 95% CIs
effsize::cohen.d(moral ~ Relation | Subject(ResponseId),
data = E1_all_long %>% filter(BSs_cond == "STR vs SIB") %>% filter(`Choice Context` == "No Choice") %>% droplevels(),
paired = T,
within = T) # setting this to true ensures d-av is calculated, using raw scores
Cohen's d
d estimate: 0.185017 (negligible)
95 percent confidence interval:
lower upper
0.04884148 0.32119244 # returns correlation between variables
cor_test(data = E1_STRnSIB_clean, "NoChoice_STR_moral", "NoChoice_SIB_moral", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(201) | p
----------------------------------------------------------------------------------
NoChoice_STR_moral | NoChoice_SIB_moral | 0.52 | [0.41, 0.61] | 8.66 | < .001***
Observations: 203Choice
# returns t-test results
t.test(moral ~ Relation,
data = E1_all_long %>% filter(BSs_cond == "STR vs SIB") %>% filter(`Choice Context` == "Choice") %>% droplevels(),
paired = T)
Paired t-test
data: moral by Relation
t = -5.8971, df = 202, p-value = 1.535e-08
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-13.172713 -6.571129
sample estimates:
mean of the differences
-9.871921 # returns dz effect size and 95% CIs
effsize::cohen.d(moral ~ Relation | Subject(ResponseId),
data = E1_all_long %>% filter(BSs_cond == "STR vs SIB") %>% filter(`Choice Context` == "Choice") %>% droplevels(),
paired = T,
within = F) # setting this to false ensures dz is calculated, using difference score
Cohen's d
d estimate: -0.4138981 (small)
95 percent confidence interval:
lower upper
-0.5576617 -0.2701344 # returns d-av effect size and 95% CIs
effsize::cohen.d(moral ~ Relation | Subject(ResponseId),
data = E1_all_long %>% filter(BSs_cond == "STR vs SIB") %>% filter(`Choice Context` == "Choice") %>% droplevels(),
paired = T,
within = T) # setting this to true ensures d-av is calculated, using raw scores
Cohen's d
d estimate: -0.5410311 (medium)
95 percent confidence interval:
lower upper
-0.7341354 -0.3479268 # returns correlation between variables
cor_test(data = E1_STRnSIB_clean, "Choice_STRnSIB_STR_moral", "Choice_STRnSIB_SIB_moral", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(201) | p
-------------------------------------------------------------------------------------------
Choice_STRnSIB_STR_moral | Choice_STRnSIB_SIB_moral | 0.15 | [0.01, 0.28] | 2.09 | 0.038*
Observations: 203STRnCUZ
No Choice
# returns t-test results
t.test(moral ~ Relation,
data = E1_all_long %>% filter(BSs_cond == "STR vs CUZ") %>% filter(`Choice Context` == "No Choice") %>% droplevels(),
paired = T)
Paired t-test
data: moral by Relation
t = 2.771, df = 202, p-value = 0.00611
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
0.8055855 4.7806214
sample estimates:
mean of the differences
2.793103 # returns dz effect size and 95% CIs
effsize::cohen.d(moral ~ Relation | Subject(ResponseId),
data = E1_all_long %>% filter(BSs_cond == "STR vs CUZ") %>% filter(`Choice Context` == "No Choice") %>% droplevels(),
paired = T,
within = F) # setting this to false ensures dz is calculated, using difference score
Cohen's d
d estimate: 0.1944849 (negligible)
95 percent confidence interval:
lower upper
0.05521042 0.33375932 # returns d-av effect size and 95% CIs
effsize::cohen.d(moral ~ Relation | Subject(ResponseId),
data = E1_all_long %>% filter(BSs_cond == "STR vs CUZ") %>% filter(`Choice Context` == "No Choice") %>% droplevels(),
paired = T,
within = T) # setting this to true ensures d-av is calculated, using raw scores
Cohen's d
d estimate: 0.1761995 (negligible)
95 percent confidence interval:
lower upper
0.05022958 0.30216940 # returns correlation between variables
cor_test(data = E1_STRnCUZ_clean, "NoChoice_STR_moral", "NoChoice_CUZ_moral", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(201) | p
----------------------------------------------------------------------------------
NoChoice_STR_moral | NoChoice_CUZ_moral | 0.59 | [0.49, 0.67] | 10.35 | < .001***
Observations: 203Choice
# returns t-test results
t.test(moral ~ Relation,
data = E1_all_long %>% filter(BSs_cond == "STR vs CUZ") %>% filter(`Choice Context` == "Choice") %>% droplevels(),
paired = T)
Paired t-test
data: moral by Relation
t = -2.4091, df = 202, p-value = 0.01689
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-7.6142845 -0.7600997
sample estimates:
mean of the differences
-4.187192 # returns dz effect size and 95% CIs
effsize::cohen.d(moral ~ Relation | Subject(ResponseId),
data = E1_all_long %>% filter(BSs_cond == "STR vs CUZ") %>% filter(`Choice Context` == "Choice") %>% droplevels(),
paired = T,
within = F) # setting this to false ensures dz is calculated, using difference score
Cohen's d
d estimate: -0.1690857 (negligible)
95 percent confidence interval:
lower upper
-0.30804426 -0.03012719 # returns d-av effect size and 95% CIs
effsize::cohen.d(moral ~ Relation | Subject(ResponseId),
data = E1_all_long %>% filter(BSs_cond == "STR vs CUZ") %>% filter(`Choice Context` == "Choice") %>% droplevels(),
paired = T,
within = T) # setting this to true ensures d-av is calculated, using raw scores
Cohen's d
d estimate: -0.22502 (small)
95 percent confidence interval:
lower upper
-0.41094859 -0.03909133 # returns correlation between variables
cor_test(data = E1_STRnCUZ_clean, "Choice_STRnCUZ_STR_moral", "Choice_STRnCUZ_CUZ_moral", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(201) | p
-------------------------------------------------------------------------------------------
Choice_STRnCUZ_STR_moral | Choice_STRnCUZ_CUZ_moral | 0.11 | [-0.02, 0.25] | 1.63 | 0.104
Observations: 203CUZnSIB
No Choice
# returns t-test results
t.test(moral ~ Relation,
data = E1_all_long %>% filter(BSs_cond == "CUZ vs SIB") %>% filter(`Choice Context` == "No Choice") %>% droplevels(),
paired = T)
Paired t-test
data: moral by Relation
t = 0.34573, df = 204, p-value = 0.7299
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-1.628815 2.321498
sample estimates:
mean of the differences
0.3463415 # returns dz effect size and 95% CIs
effsize::cohen.d(moral ~ Relation | Subject(ResponseId),
data = E1_all_long %>% filter(BSs_cond == "CUZ vs SIB") %>% filter(`Choice Context` == "No Choice") %>% droplevels(),
paired = T,
within = F) # setting this to false ensures dz is calculated, using difference score
Cohen's d
d estimate: 0.02414673 (negligible)
95 percent confidence interval:
lower upper
-0.1131704 0.1614638 # returns d-av effect size and 95% CIs
effsize::cohen.d(moral ~ Relation | Subject(ResponseId),
data = E1_all_long %>% filter(BSs_cond == "CUZ vs SIB") %>% filter(`Choice Context` == "No Choice") %>% droplevels(),
paired = T,
within = T) # setting this to true ensures d-av is calculated, using raw scores
Cohen's d
d estimate: 0.02173942 (negligible)
95 percent confidence interval:
lower upper
-0.1018844 0.1453633 # returns correlation between variables
cor_test(data = E1_CUZnSIB_clean, "NoChoice_CUZ_moral", "NoChoice_SIB_moral", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(203) | p
----------------------------------------------------------------------------------
NoChoice_CUZ_moral | NoChoice_SIB_moral | 0.59 | [0.50, 0.68] | 10.54 | < .001***
Observations: 205Choice
# returns t-test results
t.test(moral ~ Relation,
data = E1_all_long %>% filter(BSs_cond == "CUZ vs SIB") %>% filter(`Choice Context` == "Choice") %>% droplevels(),
paired = T)
Paired t-test
data: moral by Relation
t = -5.1188, df = 204, p-value = 7.088e-07
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-10.317915 -4.579646
sample estimates:
mean of the differences
-7.44878 # returns dz effect size and 95% CIs
effsize::cohen.d(moral ~ Relation | Subject(ResponseId),
data = E1_all_long %>% filter(BSs_cond == "CUZ vs SIB") %>% filter(`Choice Context` == "Choice") %>% droplevels(),
paired = T,
within = F) # setting this to false ensures dz is calculated, using difference score
Cohen's d
d estimate: -0.3575111 (small)
95 percent confidence interval:
lower upper
-0.4991274 -0.2158948 # returns d-av effect size and 95% CIs
effsize::cohen.d(moral ~ Relation | Subject(ResponseId),
data = E1_all_long %>% filter(BSs_cond == "CUZ vs SIB") %>% filter(`Choice Context` == "Choice") %>% droplevels(),
paired = T,
within = T) # setting this to true ensures d-av is calculated, using raw scores
Cohen's d
d estimate: -0.4281153 (small)
95 percent confidence interval:
lower upper
-0.5998953 -0.2563353 # returns correlation between variables
cor_test(data = E1_CUZnSIB_clean, "Choice_CUZnSIB_CUZ_moral", "Choice_CUZnSIB_SIB_moral", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(203) | p
----------------------------------------------------------------------------------------------
Choice_CUZnSIB_CUZ_moral | Choice_CUZnSIB_SIB_moral | 0.28 | [0.15, 0.40] | 4.20 | < .001***
Observations: 205Moral Diff ~ Oblig Diff Plots
# Create difference score datasets for plotting of diff score correlations
# STRnCUZ
E1_diff_STRnCUZ_cond_long <- E1_STRnCUZ_clean %>%
pivot_longer(
cols = c(Dist_Scen, Close_Scen),
names_to = "WSs_cond",
values_to = "Condition"
)
E1_diff_STRnCUZ_oblig_long <- E1_STRnCUZ_clean %>%
pivot_longer(
cols = c(NoChoice_STRminusCUZ_oblig, Choice_STRminusCUZ_oblig),
names_to = "WSs_cond",
values_to = "oblig"
)
E1_diff_STRnCUZ_moral_long <- E1_STRnCUZ_clean %>%
pivot_longer(
cols = c(NoChoice_STRminusCUZ_moral, Choice_STRminusCUZ_moral),
names_to = "WSs_cond",
values_to = "moral"
)
# Combine long STRnCUZ datasets, select plotting variables, and create condition variable for `Choice Context` factor
E1_diff_STRnCUZ_long <- cbind(E1_diff_STRnCUZ_cond_long, E1_diff_STRnCUZ_oblig_long, E1_diff_STRnCUZ_moral_long)
E1_diff_STRnCUZ_long <- E1_diff_STRnCUZ_long[, !duplicated(colnames(E1_diff_STRnCUZ_long))] # get rid of duplicate columns
E1_diff_STRnCUZ_long <- E1_diff_STRnCUZ_long %>%
select(ResponseId,
Age:OUS_IB,
BSs_cond,
WSs_cond,
Condition,
oblig, moral) %>%
mutate(`Choice Context` = case_when(
WSs_cond == "Dist_Scen" ~ "No Choice",
WSs_cond == "Close_Scen" ~ "Choice"))
# Reorder/rename condition, and participant factors
E1_diff_STRnCUZ_long$`Choice Context` <- as.factor(E1_diff_STRnCUZ_long$`Choice Context`)
E1_diff_STRnCUZ_long$`Choice Context` <- ordered(E1_diff_STRnCUZ_long$`Choice Context`, levels = c("No Choice", "Choice"))
E1_diff_STRnCUZ_long$ResponseId <- as.factor(E1_diff_STRnCUZ_long$ResponseId)
# STRnSIB
E1_diff_STRnSIB_cond_long <- E1_STRnSIB_clean %>%
pivot_longer(
cols = c(Dist_Scen, Close_Scen),
names_to = "WSs_cond",
values_to = "Condition"
)
E1_diff_STRnSIB_oblig_long <- E1_STRnSIB_clean %>%
pivot_longer(
cols = c(NoChoice_STRminusSIB_oblig, Choice_STRminusSIB_oblig),
names_to = "WSs_cond",
values_to = "oblig"
)
E1_diff_STRnSIB_moral_long <- E1_STRnSIB_clean %>%
pivot_longer(
cols = c(NoChoice_STRminusSIB_moral, Choice_STRminusSIB_moral),
names_to = "WSs_cond",
values_to = "moral"
)
# Combine long STRnSIB datasets, select plotting variables, and create condition variable for `Choice Context` factor
E1_diff_STRnSIB_long <- cbind(E1_diff_STRnSIB_cond_long, E1_diff_STRnSIB_oblig_long, E1_diff_STRnSIB_moral_long)
E1_diff_STRnSIB_long <- E1_diff_STRnSIB_long[, !duplicated(colnames(E1_diff_STRnSIB_long))] # get rid of duplicate columns
E1_diff_STRnSIB_long <- E1_diff_STRnSIB_long %>%
select(ResponseId,
Age:OUS_IB,
BSs_cond,
WSs_cond,
Condition,
oblig, moral) %>%
mutate(`Choice Context` = case_when(
WSs_cond == "Dist_Scen" ~ "No Choice",
WSs_cond == "Close_Scen" ~ "Choice"))
# Reorder/rename condition, and participant factors
E1_diff_STRnSIB_long$`Choice Context` <- as.factor(E1_diff_STRnSIB_long$`Choice Context`)
E1_diff_STRnSIB_long$`Choice Context` <- ordered(E1_diff_STRnSIB_long$`Choice Context`, levels = c("No Choice", "Choice"))
E1_diff_STRnSIB_long$ResponseId <- as.factor(E1_diff_STRnSIB_long$ResponseId)
# CUZnSIB
E1_diff_CUZnSIB_cond_long <- E1_CUZnSIB_clean %>%
pivot_longer(
cols = c(Dist_Scen, Close_Scen),
names_to = "WSs_cond",
values_to = "Condition"
)
E1_diff_CUZnSIB_oblig_long <- E1_CUZnSIB_clean %>%
pivot_longer(
cols = c(NoChoice_CUZminusSIB_oblig, Choice_CUZminusSIB_oblig),
names_to = "WSs_cond",
values_to = "oblig"
)
E1_diff_CUZnSIB_moral_long <- E1_CUZnSIB_clean %>%
pivot_longer(
cols = c(NoChoice_CUZminusSIB_moral, Choice_CUZminusSIB_moral),
names_to = "WSs_cond",
values_to = "moral"
)
# Combine long CUZnSIB datasets, select plotting variables, and create condition variable for `Choice Context` factor
E1_diff_CUZnSIB_long <- cbind(E1_diff_CUZnSIB_cond_long, E1_diff_CUZnSIB_oblig_long, E1_diff_CUZnSIB_moral_long)
E1_diff_CUZnSIB_long <- E1_diff_CUZnSIB_long[, !duplicated(colnames(E1_diff_CUZnSIB_long))] # get rid of duplicate columns
E1_diff_CUZnSIB_long <- E1_diff_CUZnSIB_long %>%
select(ResponseId,
Age:OUS_IB,
BSs_cond,
WSs_cond,
Condition,
oblig, moral) %>%
mutate(`Choice Context` = case_when(
WSs_cond == "Dist_Scen" ~ "No Choice",
WSs_cond == "Close_Scen" ~ "Choice"))
# Reorder/rename condition, and participant factors
E1_diff_CUZnSIB_long$`Choice Context` <- as.factor(E1_diff_CUZnSIB_long$`Choice Context`)
E1_diff_CUZnSIB_long$`Choice Context` <- ordered(E1_diff_CUZnSIB_long$`Choice Context`, levels = c("No Choice", "Choice"))
E1_diff_CUZnSIB_long$ResponseId <- as.factor(E1_diff_CUZnSIB_long$ResponseId)
# Combine into one dataset for plotting
E1_diff_all_long <- rbind(E1_diff_STRnSIB_long, E1_diff_STRnCUZ_long, E1_diff_CUZnSIB_long)
# Reorder All_long BSs_cond
E1_diff_all_long$BSs_cond <- as.factor(E1_diff_all_long$BSs_cond)
E1_diff_all_long$BSs_cond <- ordered(E1_diff_all_long$BSs_cond, levels = c("STR vs SIB", "STR vs CUZ", "CUZ vs SIB"))STRnSIB
print(oblig_moral_diff_plot_STRnSIB <- ggplot(data = E1_diff_STRnSIB_long, aes(x = oblig, y = moral)) +
geom_jitter(color = "darkorchid1", alpha = 0.5) +
geom_smooth(method = 'lm', color = "darkorchid1") +
facet_wrap(BSs_cond~`Choice Context`) +
scale_x_continuous(limits = c(-101,101), breaks = c(-100,-50,0,50,100)) +
scale_y_continuous(limits = c(-101,101), breaks = c(-100,-50,0,50,100)) +
theme_classic() +
xlab("Obligation Strength Difference (Distant - Close)") +
ylab("Moral Character Difference (Distant - Close)") +
theme(axis.title.x = element_text(size = 14),
axis.title.y = element_text(size = 14),
axis.text.x = element_text(color = "black", size = 12),
axis.text.y = element_text(color = "black", size = 12)))
STRnCUZ
print(oblig_moral_diff_plot_STRnCUZ <- ggplot(data = E1_diff_STRnCUZ_long, aes(x = oblig, y = moral)) +
geom_jitter(color = "darkorchid1", alpha = 0.5) +
geom_smooth(method = 'lm', color = "darkorchid1") +
facet_wrap(BSs_cond~`Choice Context`) +
scale_x_continuous(limits = c(-101,101), breaks = c(-100,-50,0,50,100)) +
scale_y_continuous(limits = c(-101,101), breaks = c(-100,-50,0,50,100)) +
theme_classic() +
xlab("Obligation Strength Difference (Distant - Close)") +
ylab("Moral Character Difference (Distant - Close)") +
theme(axis.title.x = element_text(size = 14),
axis.title.y = element_text(size = 14),
axis.text.x = element_text(color = "black", size = 12),
axis.text.y = element_text(color = "black", size = 12)))
CUZnSIB
print(oblig_moral_diff_plot_CUZnSIB <- ggplot(data = E1_diff_CUZnSIB_long, aes(x = oblig, y = moral)) +
geom_jitter(color = "darkorchid1", alpha = 0.5) +
geom_smooth(method = 'lm', color = "darkorchid1") +
facet_wrap(BSs_cond~`Choice Context`) +
scale_x_continuous(limits = c(-101,101), breaks = c(-100,-50,0,50,100)) +
scale_y_continuous(limits = c(-101,101), breaks = c(-100,-50,0,50,100)) +
theme_classic() +
xlab("Obligation Strength Difference (Distant - Close)") +
ylab("Moral Character Difference (Distant - Close)") +
theme(axis.title.x = element_text(size = 14),
axis.title.y = element_text(size = 14),
axis.text.x = element_text(color = "black", size = 12),
axis.text.y = element_text(color = "black", size = 12)))
Combined
print(oblig_moral_diff_plot_combined <- ggplot(data = E1_diff_all_long, aes(x = oblig, y = moral)) +
geom_jitter(color = "darkorchid1", alpha = 0.5) +
geom_smooth(method = 'lm', color = "darkorchid1") +
facet_wrap(BSs_cond~`Choice Context`, nrow = 3) +
scale_x_continuous(limits = c(-101,101), breaks = c(-100,-50,0,50,100)) +
scale_y_continuous(limits = c(-101,101), breaks = c(-100,-50,0,50,100)) +
theme_classic() +
xlab("\nObligation Strength Difference (Distant - Close)") +
ylab("Moral Character Difference (Distant - Close)\n") +
theme(axis.title.x = element_text(size = 18),
axis.title.y = element_text(size = 18),
axis.text.x = element_text(color = "black", size = 16),
axis.text.y = element_text(color = "black", size = 16),
strip.text.x = element_text(color = "black", size = 16)))
ggsave("E1_moral~oblig_plot.png")Saving 14 x 9 in imageMoral Diff ~ Oblig Diff Tests
See our pre-registration (https://osf.io/6q4kr/?view_only=8033c65c9dfc41bcbc528fe94409e4e8) and manuscript for our predictions about the relationship between obligation differences and moral character differences.
STRnSIB
No Choice
# pearson's r
cor_test(E1_STRnSIB_clean, "NoChoice_STRminusSIB_oblig", "NoChoice_STRminusSIB_moral", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(201) | p
-----------------------------------------------------------------------------------------------
NoChoice_STRminusSIB_oblig | NoChoice_STRminusSIB_moral | 0.07 | [-0.07, 0.21] | 1.01 | 0.316
Observations: 203Choice
# pearson's r
cor_test(E1_STRnSIB_clean, "Choice_STRminusSIB_oblig", "Choice_STRminusSIB_moral", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(201) | p
--------------------------------------------------------------------------------------------
Choice_STRminusSIB_oblig | Choice_STRminusSIB_moral | 0.18 | [0.05, 0.31] | 2.65 | 0.009**
Observations: 203STRnCUZ
No Choice
# pearson's r
cor_test(E1_STRnCUZ_clean, "NoChoice_STRminusCUZ_oblig", "NoChoice_STRminusCUZ_moral", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(201) | p
-----------------------------------------------------------------------------------------------
NoChoice_STRminusCUZ_oblig | NoChoice_STRminusCUZ_moral | 0.03 | [-0.11, 0.16] | 0.38 | 0.702
Observations: 203Choice
# pearson's r
cor_test(E1_STRnCUZ_clean, "Choice_STRminusCUZ_oblig", "Choice_STRminusCUZ_moral", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(201) | p
----------------------------------------------------------------------------------------------
Choice_STRminusCUZ_oblig | Choice_STRminusCUZ_moral | 0.39 | [0.27, 0.50] | 6.00 | < .001***
Observations: 203CUZnSIB
No Choice
# pearson's r
cor_test(E1_CUZnSIB_clean, "NoChoice_CUZminusSIB_oblig", "NoChoice_CUZminusSIB_moral", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(203) | p
------------------------------------------------------------------------------------------------
NoChoice_CUZminusSIB_oblig | NoChoice_CUZminusSIB_moral | -0.03 | [-0.17, 0.10] | -0.48 | 0.635
Observations: 205Choice
# pearson's r
cor_test(E1_CUZnSIB_clean, "Choice_CUZminusSIB_oblig", "Choice_CUZminusSIB_moral", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(203) | p
--------------------------------------------------------------------------------------------
Choice_CUZminusSIB_oblig | Choice_CUZminusSIB_moral | 0.19 | [0.06, 0.32] | 2.78 | 0.006**
Observations: 205Moral ~ Oblig R-M Plots
STRnSIB
No Choice
rmcorr_STRnSIB_NoChoice <- rmcorr(participant = ResponseId,
measure1 = oblig,
measure2 = moral,
dataset = E1_all_long %>%
filter(BSs_cond == "STR vs SIB") %>%
filter(`Choice Context` == "No Choice"))print(rmcorr_plot_STRnSIB_NoChoice <- ggplot(data = E1_all_long %>%
filter(BSs_cond == "STR vs SIB") %>%
filter(`Choice Context` == "No Choice"),
aes(x = oblig, y = moral, group = ResponseId, color = ResponseId)) +
geom_point(aes(color = ResponseId)) +
geom_line(aes(y = rmcorr_STRnSIB_NoChoice$model$fitted.values), linetype = 1) +
scale_x_continuous(limits = c(-1,101), breaks = c(0,25,50,75,100)) +
scale_y_continuous(limits = c(-1,101), breaks = c(0,25,50,75,100)) +
theme_classic() +
xlab("Obligation Strength") +
ylab("Moral Character") +
theme(axis.title.x = element_text(size = 14),
axis.title.y = element_text(size = 14),
axis.text.x = element_text(color = "black", size = 12),
axis.text.y = element_text(color = "black", size = 12),
legend.position = "none"))
Choice
rmcorr_STRnSIB_Choice <- rmcorr(participant = ResponseId,
measure1 = oblig,
measure2 = moral,
dataset = E1_all_long %>%
filter(BSs_cond == "STR vs SIB") %>%
filter(`Choice Context` == "Choice"))print(rmcorr_plot_STRnSIB_Choice <- ggplot(data = E1_all_long %>%
filter(BSs_cond == "STR vs SIB") %>%
filter(`Choice Context` == "Choice"),
aes(x = oblig, y = moral, group = ResponseId, color = ResponseId)) +
geom_point(aes(color = ResponseId)) +
geom_line(aes(y = rmcorr_STRnSIB_Choice$model$fitted.values), linetype = 1) +
scale_x_continuous(limits = c(-1,101), breaks = c(0,25,50,75,100)) +
scale_y_continuous(limits = c(-1,101), breaks = c(0,25,50,75,100)) +
theme_classic() +
xlab("Obligation Strength") +
ylab("Moral Character") +
theme(axis.title.x = element_text(size = 14),
axis.title.y = element_text(size = 14),
axis.text.x = element_text(color = "black", size = 12),
axis.text.y = element_text(color = "black", size = 12),
legend.position = "none"))
STRnCUZ
No Choice
rmcorr_STRnCUZ_NoChoice <- rmcorr(participant = ResponseId,
measure1 = oblig,
measure2 = moral,
dataset = E1_all_long %>%
filter(BSs_cond == "STR vs CUZ") %>%
filter(`Choice Context` == "No Choice"))print(rmcorr_plot_STRnCUZ_NoChoice <- ggplot(data = E1_all_long %>%
filter(BSs_cond == "STR vs CUZ") %>%
filter(`Choice Context` == "No Choice"),
aes(x = oblig, y = moral, group = ResponseId, color = ResponseId)) +
geom_point(aes(color = ResponseId)) +
geom_line(aes(y = rmcorr_STRnCUZ_NoChoice$model$fitted.values), linetype = 1) +
scale_x_continuous(limits = c(-1,101), breaks = c(0,25,50,75,100)) +
scale_y_continuous(limits = c(-1,101), breaks = c(0,25,50,75,100)) +
theme_classic() +
xlab("Obligation Strength") +
ylab("Moral Character") +
theme(axis.title.x = element_text(size = 14),
axis.title.y = element_text(size = 14),
axis.text.x = element_text(color = "black", size = 12),
axis.text.y = element_text(color = "black", size = 12),
legend.position = "none"))
Choice
rmcorr_STRnCUZ_Choice <- rmcorr(participant = ResponseId,
measure1 = oblig,
measure2 = moral,
dataset = E1_all_long %>%
filter(BSs_cond == "STR vs CUZ") %>%
filter(`Choice Context` == "Choice"))print(rmcorr_plot_STRnCUZ_Choice <- ggplot(data = E1_all_long %>%
filter(BSs_cond == "STR vs CUZ") %>%
filter(`Choice Context` == "Choice"),
aes(x = oblig, y = moral, group = ResponseId, color = ResponseId)) +
geom_point(aes(color = ResponseId)) +
geom_line(aes(y = rmcorr_STRnCUZ_Choice$model$fitted.values), linetype = 1) +
scale_x_continuous(limits = c(-1,101), breaks = c(0,25,50,75,100)) +
scale_y_continuous(limits = c(-1,101), breaks = c(0,25,50,75,100)) +
theme_classic() +
xlab("Obligation Strength") +
ylab("Moral Character") +
theme(axis.title.x = element_text(size = 14),
axis.title.y = element_text(size = 14),
axis.text.x = element_text(color = "black", size = 12),
axis.text.y = element_text(color = "black", size = 12),
legend.position = "none"))
CUZnSIB
No Choice
rmcorr_CUZnSIB_NoChoice <- rmcorr(participant = ResponseId,
measure1 = oblig,
measure2 = moral,
dataset = E1_all_long %>%
filter(BSs_cond == "CUZ vs SIB") %>%
filter(`Choice Context` == "No Choice"))print(rmcorr_plot_CUZnSIB_NoChoice <- ggplot(data = E1_all_long %>%
filter(BSs_cond == "CUZ vs SIB") %>%
filter(`Choice Context` == "No Choice"),
aes(x = oblig, y = moral, group = ResponseId, color = ResponseId)) +
geom_point(aes(color = ResponseId)) +
geom_line(aes(y = rmcorr_CUZnSIB_NoChoice$model$fitted.values), linetype = 1) +
scale_x_continuous(limits = c(-1,101), breaks = c(0,25,50,75,100)) +
scale_y_continuous(limits = c(-1,101), breaks = c(0,25,50,75,100)) +
theme_classic() +
xlab("Obligation Strength") +
ylab("Moral Character") +
theme(axis.title.x = element_text(size = 14),
axis.title.y = element_text(size = 14),
axis.text.x = element_text(color = "black", size = 12),
axis.text.y = element_text(color = "black", size = 12),
legend.position = "none"))
Choice
rmcorr_CUZnSIB_Choice <- rmcorr(participant = ResponseId,
measure1 = oblig,
measure2 = moral,
dataset = E1_all_long %>%
filter(BSs_cond == "CUZ vs SIB") %>%
filter(`Choice Context` == "Choice"))print(rmcorr_plot_CUZnSIB_Choice <- ggplot(data = E1_all_long %>%
filter(BSs_cond == "CUZ vs SIB") %>%
filter(`Choice Context` == "Choice"),
aes(x = oblig, y = moral, group = ResponseId, color = ResponseId)) +
geom_point(aes(color = ResponseId)) +
geom_line(aes(y = rmcorr_CUZnSIB_Choice$model$fitted.values), linetype = 1) +
scale_x_continuous(limits = c(-1,101), breaks = c(0,25,50,75,100)) +
scale_y_continuous(limits = c(-1,101), breaks = c(0,25,50,75,100)) +
theme_classic() +
xlab("Obligation Strength") +
ylab("Moral Character") +
theme(axis.title.x = element_text(size = 14),
axis.title.y = element_text(size = 14),
axis.text.x = element_text(color = "black", size = 12),
axis.text.y = element_text(color = "black", size = 12),
legend.position = "none"))
Moral ~ Oblig R-M Tests
See our manuscript for our predictions about the within-individual relationship between obligation judgments and moral character judgments.
STRnSIB
No Choice
print(rmcorr_STRnSIB_NoChoice)
Repeated measures correlation
r
-0.0102866
degrees of freedom
202
p-value
0.8839031
95% confidence interval
-0.1481273 0.1279461 Choice
print(rmcorr_STRnSIB_Choice)
Repeated measures correlation
r
0.3726286
degrees of freedom
202
p-value
4.05481e-08
95% confidence interval
0.2472991 0.4856947 STRnCUZ
No Choice
print(rmcorr_STRnCUZ_NoChoice)
Repeated measures correlation
r
-0.05309295
degrees of freedom
202
p-value
0.4507343
95% confidence interval
-0.1897531 0.08558536 Choice
print(rmcorr_STRnCUZ_Choice)
Repeated measures correlation
r
0.4095764
degrees of freedom
202
p-value
1.18001e-09
95% confidence interval
0.2877982 0.5183203 CUZnSIB
No Choice
print(rmcorr_CUZnSIB_NoChoice)
Repeated measures correlation
r
-0.03701313
degrees of freedom
204
p-value
0.5973697
95% confidence interval
-0.1735021 0.1008709 Choice
print(rmcorr_CUZnSIB_Choice)
Repeated measures correlation
r
0.3122796
degrees of freedom
204
p-value
4.887046e-06
95% confidence interval
0.182748 0.431155 Ind. Diff Internal Reliabilty Tests
MAC Family Values
STRnSIB
# create dataset with only MAC Family Values variables
E1_STRnSIB_clean_MAConly <- E1_STRnSIB_clean %>% select(MAC_Jud_1:MAC_Rel_3)
psych::alpha(E1_STRnSIB_clean_MAConly)Number of categories should be increased in order to count frequencies.
Reliability analysis
Call: psych::alpha(x = E1_STRnSIB_clean_MAConly)
lower alpha upper 95% confidence boundaries
0.9 0.92 0.94
Reliability if an item is dropped:
Item statistics STRnCUZ
# create dataset with only MAC Family Values variables
E1_STRnCUZ_clean_MAConly <- E1_STRnCUZ_clean %>% select(MAC_Jud_1:MAC_Rel_3)
psych::alpha(E1_STRnCUZ_clean_MAConly)Number of categories should be increased in order to count frequencies.
Reliability analysis
Call: psych::alpha(x = E1_STRnCUZ_clean_MAConly)
lower alpha upper 95% confidence boundaries
0.9 0.92 0.93
Reliability if an item is dropped:
Item statistics CUZnSIB
# create dataset with only MAC Family Values variables
E1_CUZnSIB_clean_MAConly <- E1_CUZnSIB_clean %>% select(MAC_Jud_1:MAC_Rel_3)
psych::alpha(E1_CUZnSIB_clean_MAConly)Number of categories should be increased in order to count frequencies.
Reliability analysis
Call: psych::alpha(x = E1_CUZnSIB_clean_MAConly)
lower alpha upper 95% confidence boundaries
0.9 0.92 0.94
Reliability if an item is dropped:
Item statistics MFT Ingroup Loyalty
STRnSIB
# create dataset with only MFQ Family Values variables
E1_STRnSIB_clean_MFQonly <- E1_STRnSIB_clean %>% select(MFQ_Jud_1:MFQ_Rel_3)
psych::alpha(E1_STRnSIB_clean_MFQonly)
Reliability analysis
Call: psych::alpha(x = E1_STRnSIB_clean_MFQonly)
lower alpha upper 95% confidence boundaries
0.76 0.8 0.84
Reliability if an item is dropped:
Item statistics
Non missing response frequency for each item
1 2 3 4 5 6 miss
MFQ_Jud_1 0.23 0.18 0.18 0.19 0.16 0.06 0
MFQ_Jud_2 0.20 0.23 0.23 0.19 0.13 0.03 0
MFQ_Jud_3 0.12 0.13 0.26 0.33 0.09 0.05 0
MFQ_Rel_1 0.33 0.30 0.17 0.15 0.05 0.01 0
MFQ_Rel_2 0.08 0.15 0.19 0.29 0.20 0.09 0
MFQ_Rel_3 0.08 0.11 0.26 0.30 0.20 0.05 0STRnCUZ
# create dataset with only MFQ Family Values variables
E1_STRnCUZ_clean_MFQonly <- E1_STRnCUZ_clean %>% select(MFQ_Jud_1:MFQ_Rel_3)
psych::alpha(E1_STRnCUZ_clean_MFQonly)
Reliability analysis
Call: psych::alpha(x = E1_STRnCUZ_clean_MFQonly)
lower alpha upper 95% confidence boundaries
0.76 0.8 0.84
Reliability if an item is dropped:
Item statistics
Non missing response frequency for each item
1 2 3 4 5 6 miss
MFQ_Jud_1 0.18 0.17 0.17 0.24 0.12 0.12 0
MFQ_Jud_2 0.18 0.27 0.21 0.19 0.12 0.04 0
MFQ_Jud_3 0.07 0.21 0.31 0.24 0.15 0.03 0
MFQ_Rel_1 0.28 0.28 0.16 0.18 0.07 0.03 0
MFQ_Rel_2 0.06 0.15 0.19 0.28 0.23 0.09 0
MFQ_Rel_3 0.07 0.15 0.22 0.30 0.21 0.05 0CUZnSIB
# create dataset with only MFQ Family Values variables
E1_CUZnSIB_clean_MFQonly <- E1_CUZnSIB_clean %>% select(MFQ_Jud_1:MFQ_Rel_3)
psych::alpha(E1_CUZnSIB_clean_MFQonly)
Reliability analysis
Call: psych::alpha(x = E1_CUZnSIB_clean_MFQonly)
lower alpha upper 95% confidence boundaries
0.68 0.73 0.79
Reliability if an item is dropped:
Item statistics
Non missing response frequency for each item
1 2 3 4 5 6 miss
MFQ_Jud_1 0.17 0.20 0.18 0.23 0.15 0.08 0
MFQ_Jud_2 0.17 0.16 0.31 0.19 0.15 0.02 0
MFQ_Jud_3 0.09 0.17 0.30 0.28 0.13 0.03 0
MFQ_Rel_1 0.27 0.27 0.14 0.20 0.08 0.03 0
MFQ_Rel_2 0.05 0.18 0.21 0.27 0.19 0.09 0
MFQ_Rel_3 0.06 0.16 0.18 0.32 0.19 0.09 0OUS Impartial Beneficence
STRnSIB
# create dataset with only MFQ Family Values variables
E1_STRnSIB_clean_OUSonly <- E1_STRnSIB_clean %>% select(OUS_IB1:OUS_IB5)
psych::alpha(E1_STRnSIB_clean_OUSonly)
Reliability analysis
Call: psych::alpha(x = E1_STRnSIB_clean_OUSonly)
lower alpha upper 95% confidence boundaries
0.68 0.74 0.8
Reliability if an item is dropped:
Item statistics
Non missing response frequency for each item
1 2 3 4 5 6 7 miss
OUS_IB1 0.23 0.22 0.19 0.17 0.13 0.04 0.01 0
OUS_IB2 0.18 0.22 0.17 0.16 0.17 0.06 0.03 0
OUS_IB3 0.04 0.14 0.22 0.09 0.27 0.15 0.09 0
OUS_IB4 0.15 0.26 0.21 0.14 0.11 0.09 0.03 0
OUS_IB5 0.10 0.25 0.23 0.10 0.18 0.11 0.02 0STRnCUZ
# create dataset with only MFQ Family Values variables
E1_STRnCUZ_clean_OUSonly <- E1_STRnCUZ_clean %>% select(OUS_IB1:OUS_IB5)
psych::alpha(E1_STRnCUZ_clean_OUSonly)
Reliability analysis
Call: psych::alpha(x = E1_STRnCUZ_clean_OUSonly)
lower alpha upper 95% confidence boundaries
0.73 0.78 0.83
Reliability if an item is dropped:
Item statistics
Non missing response frequency for each item
1 2 3 4 5 6 7 miss
OUS_IB1 0.24 0.22 0.14 0.18 0.14 0.06 0.02 0
OUS_IB2 0.18 0.18 0.17 0.15 0.19 0.11 0.02 0
OUS_IB3 0.08 0.13 0.15 0.15 0.19 0.19 0.10 0
OUS_IB4 0.15 0.24 0.21 0.12 0.18 0.06 0.05 0
OUS_IB5 0.14 0.21 0.19 0.14 0.19 0.07 0.05 0CUZnSIB
# create dataset with only MFQ Family Values variables
E1_CUZnSIB_clean_OUSonly <- E1_CUZnSIB_clean %>% select(OUS_IB1:OUS_IB5)
psych::alpha(E1_CUZnSIB_clean_OUSonly)
Reliability analysis
Call: psych::alpha(x = E1_CUZnSIB_clean_OUSonly)
lower alpha upper 95% confidence boundaries
0.68 0.74 0.79
Reliability if an item is dropped:
Item statistics
Non missing response frequency for each item
1 2 3 4 5 6 7 miss
OUS_IB1 0.19 0.22 0.15 0.20 0.14 0.08 0.01 0
OUS_IB2 0.15 0.20 0.21 0.18 0.16 0.08 0.02 0
OUS_IB3 0.05 0.08 0.18 0.16 0.25 0.20 0.08 0
OUS_IB4 0.09 0.19 0.20 0.18 0.20 0.12 0.02 0
OUS_IB5 0.07 0.18 0.19 0.21 0.23 0.10 0.01 0Oblig ~ Ind. Diff Plots
MAC Family Values
print(oblig_mac_comp_plot <- ggplot(data = E1_all_long, aes(x = MAC_Fam_Combined, y = oblig, color = Relation)) +
geom_jitter(aes(color = Relation), alpha = 0.5) +
scale_color_manual(values = c("lightskyblue3", "indianred3")) +
geom_smooth(method = 'lm') +
facet_wrap(BSs_cond~`Choice Context`, ncol = 2) +
scale_x_continuous(limits = c(-1,101), breaks = c(0,25,50,75,100)) +
scale_y_continuous(limits = c(-1,101), breaks = c(0,25,50,75,100)) +
theme_classic() +
xlab("\nMAC Family Values Composite") +
ylab("Obligation Strength\n") +
theme(axis.title.x = element_text(size = 18),
axis.title.y = element_text(size = 18),
axis.text.x = element_text(color = "black", size = 16),
axis.text.y = element_text(color = "black", size = 16),
strip.text.x = element_text(color = "black", size = 16),
legend.position = "right",
legend.title = element_text(color = "black", size = 18),
legend.text = element_text(color = "black", size = 16)))
ggsave("E1_oblig~MAC_plot.png")Saving 14 x 9 in imageMFT Ingroup Loyalty
print(oblig_mft_comp_plot <- ggplot(data = E1_all_long, aes(x = MFQ_Loyalty_Combined, y = oblig, color = Relation)) +
geom_jitter(aes(color = Relation), alpha = 0.5) +
scale_color_manual(values = c("lightskyblue3", "indianred3")) +
geom_smooth(method = 'lm') +
facet_wrap(BSs_cond~`Choice Context`, ncol = 2) +
scale_x_continuous(limits = c(.5,6.5), breaks = c(1,2,3,4,5,6)) +
scale_y_continuous(limits = c(-1,101), breaks = c(0,25,50,75,100)) +
theme_classic() +
xlab("\nMFT Ingroup Loyalty Composite") +
ylab("Obligation Strength\n") +
theme(axis.title.x = element_text(size = 18),
axis.title.y = element_text(size = 18),
axis.text.x = element_text(color = "black", size = 16),
axis.text.y = element_text(color = "black", size = 16),
strip.text.x = element_text(color = "black", size = 16),
legend.position = "right",
legend.title = element_text(color = "black", size = 18),
legend.text = element_text(color = "black", size = 16)))
ggsave("E1_oblig~MFT_plot.png")Saving 14 x 9 in imageOUS Impartial Beneficence
print(oblig_ous_comp_plot <- ggplot(data = E1_all_long, aes(x = OUS_IB, y = oblig, color = Relation)) +
geom_jitter(aes(color = Relation), alpha = 0.5) +
scale_color_manual(values = c("lightskyblue3", "indianred3")) +
geom_smooth(method = 'lm') +
facet_wrap(BSs_cond~`Choice Context`, ncol = 2) +
scale_x_continuous(limits = c(.5,7.5), breaks = c(1,2,3,4,5,6,7)) +
scale_y_continuous(limits = c(-1,101), breaks = c(0,25,50,75,100)) +
theme_classic() +
xlab("\nOUS Impartial Beneficence Composite") +
ylab("Obligation Strength\n") +
theme(axis.title.x = element_text(size = 18),
axis.title.y = element_text(size = 18),
axis.text.x = element_text(color = "black", size = 16),
axis.text.y = element_text(color = "black", size = 16),
strip.text.x = element_text(color = "black", size = 16),
legend.position = "right",
legend.title = element_text(color = "black", size = 18),
legend.text = element_text(color = "black", size = 16)))
ggsave("E1_oblig~OUS_plot.png")Saving 14 x 9 in imageOblig ~ Ind. Diff Tests
See our pre-registration (https://osf.io/6q4kr/?view_only=8033c65c9dfc41bcbc528fe94409e4e8) and manuscript for our predictions about the relationship between individual differences and obligation judgments.
MAC Family Values
STRnSIB
No Choice
# distant pearson's r
cor_test(E1_STRnSIB_clean, "MAC_Fam_Combined", "NoChoice_STR_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(201) | p
------------------------------------------------------------------------------
MAC_Fam_Combined | NoChoice_STR_oblig | 0.23 | [0.09, 0.36] | 3.33 | 0.001**
Observations: 203# close pearson's r
cor_test(E1_STRnSIB_clean, "MAC_Fam_Combined", "NoChoice_SIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(201) | p
--------------------------------------------------------------------------------
MAC_Fam_Combined | NoChoice_SIB_oblig | 0.35 | [0.23, 0.47] | 5.34 | < .001***
Observations: 203# diff pearson's r
cor_test(E1_STRnSIB_clean, "MAC_Fam_Combined", "NoChoice_STRminusSIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(201) | p
--------------------------------------------------------------------------------------
MAC_Fam_Combined | NoChoice_STRminusSIB_oblig | -0.12 | [-0.25, 0.02] | -1.74 | 0.084
Observations: 203Choice
# distant pearson's r
cor_test(E1_STRnSIB_clean, "MAC_Fam_Combined", "Choice_STRnSIB_STR_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(201) | p
--------------------------------------------------------------------------------------
MAC_Fam_Combined | Choice_STRnSIB_STR_oblig | 0.25 | [0.12, 0.38] | 3.73 | < .001***
Observations: 203# distant pearson's r
cor_test(E1_STRnSIB_clean, "MAC_Fam_Combined", "Choice_STRnSIB_SIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(201) | p
--------------------------------------------------------------------------------------
MAC_Fam_Combined | Choice_STRnSIB_SIB_oblig | 0.43 | [0.31, 0.54] | 6.79 | < .001***
Observations: 203# diff pearson's r
cor_test(E1_STRnSIB_clean, "MAC_Fam_Combined", "Choice_STRminusSIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(201) | p
-----------------------------------------------------------------------------------------
MAC_Fam_Combined | Choice_STRminusSIB_oblig | -0.30 | [-0.42, -0.17] | -4.40 | < .001***
Observations: 203STRnCUZ
No Choice
# distant pearson's r
cor_test(E1_STRnCUZ_clean, "MAC_Fam_Combined", "NoChoice_STR_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(201) | p
-----------------------------------------------------------------------------
MAC_Fam_Combined | NoChoice_STR_oblig | 0.06 | [-0.07, 0.20] | 0.91 | 0.365
Observations: 203# close pearson's r
cor_test(E1_STRnCUZ_clean, "MAC_Fam_Combined", "NoChoice_CUZ_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(201) | p
------------------------------------------------------------------------------
MAC_Fam_Combined | NoChoice_CUZ_oblig | 0.22 | [0.08, 0.34] | 3.16 | 0.002**
Observations: 203# diff pearson's r
cor_test(E1_STRnCUZ_clean, "MAC_Fam_Combined", "NoChoice_STRminusCUZ_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(201) | p
--------------------------------------------------------------------------------------
MAC_Fam_Combined | NoChoice_STRminusCUZ_oblig | -0.14 | [-0.27, 0.00] | -1.95 | 0.053
Observations: 203Choice
# distant pearson's r
cor_test(E1_STRnCUZ_clean, "MAC_Fam_Combined", "Choice_STRnCUZ_STR_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(201) | p
------------------------------------------------------------------------------------
MAC_Fam_Combined | Choice_STRnCUZ_STR_oblig | 0.19 | [0.05, 0.32] | 2.68 | 0.008**
Observations: 203# distant pearson's r
cor_test(E1_STRnCUZ_clean, "MAC_Fam_Combined", "Choice_STRnCUZ_CUZ_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(201) | p
--------------------------------------------------------------------------------------
MAC_Fam_Combined | Choice_STRnCUZ_CUZ_oblig | 0.36 | [0.24, 0.48] | 5.50 | < .001***
Observations: 203# diff pearson's r
cor_test(E1_STRnCUZ_clean, "MAC_Fam_Combined", "Choice_STRminusCUZ_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(201) | p
-----------------------------------------------------------------------------------------
MAC_Fam_Combined | Choice_STRminusCUZ_oblig | -0.30 | [-0.42, -0.17] | -4.51 | < .001***
Observations: 203CUZnSIB
No Choice
# distant pearson's r
cor_test(E1_CUZnSIB_clean, "MAC_Fam_Combined", "NoChoice_CUZ_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(203) | p
--------------------------------------------------------------------------------
MAC_Fam_Combined | NoChoice_CUZ_oblig | 0.26 | [0.12, 0.38] | 3.78 | < .001***
Observations: 205# close pearson's r
cor_test(E1_CUZnSIB_clean, "MAC_Fam_Combined", "NoChoice_SIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(203) | p
--------------------------------------------------------------------------------
MAC_Fam_Combined | NoChoice_SIB_oblig | 0.48 | [0.36, 0.58] | 7.75 | < .001***
Observations: 205# diff pearson's r
cor_test(E1_CUZnSIB_clean, "MAC_Fam_Combined", "NoChoice_CUZminusSIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(203) | p
-----------------------------------------------------------------------------------------
MAC_Fam_Combined | NoChoice_CUZminusSIB_oblig | -0.21 | [-0.34, -0.08] | -3.07 | 0.002**
Observations: 205Choice
# distant pearson's r
cor_test(E1_CUZnSIB_clean, "MAC_Fam_Combined", "Choice_CUZnSIB_CUZ_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(203) | p
--------------------------------------------------------------------------------------
MAC_Fam_Combined | Choice_CUZnSIB_CUZ_oblig | 0.32 | [0.19, 0.44] | 4.86 | < .001***
Observations: 205# distant pearson's r
cor_test(E1_CUZnSIB_clean, "MAC_Fam_Combined", "Choice_CUZnSIB_SIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(203) | p
--------------------------------------------------------------------------------------
MAC_Fam_Combined | Choice_CUZnSIB_SIB_oblig | 0.40 | [0.28, 0.51] | 6.23 | < .001***
Observations: 205# diff pearson's r
cor_test(E1_CUZnSIB_clean, "MAC_Fam_Combined", "Choice_CUZminusSIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(203) | p
-----------------------------------------------------------------------------------------
MAC_Fam_Combined | Choice_CUZminusSIB_oblig | -0.23 | [-0.36, -0.10] | -3.41 | < .001***
Observations: 205MFT Ingroup Loyalty
STRnSIB
No Choice
# distant pearson's r
cor_test(E1_STRnSIB_clean, "MFQ_Loyalty_Combined", "NoChoice_STR_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(201) | p
------------------------------------------------------------------------------------
MFQ_Loyalty_Combined | NoChoice_STR_oblig | 0.29 | [0.16, 0.41] | 4.34 | < .001***
Observations: 203# close pearson's r
cor_test(E1_STRnSIB_clean, "MFQ_Loyalty_Combined", "NoChoice_SIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(201) | p
------------------------------------------------------------------------------------
MFQ_Loyalty_Combined | NoChoice_SIB_oblig | 0.32 | [0.19, 0.44] | 4.80 | < .001***
Observations: 203# diff pearson's r
cor_test(E1_STRnSIB_clean, "MFQ_Loyalty_Combined", "NoChoice_STRminusSIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(201) | p
------------------------------------------------------------------------------------------
MFQ_Loyalty_Combined | NoChoice_STRminusSIB_oblig | -0.05 | [-0.18, 0.09] | -0.67 | 0.505
Observations: 203Choice
# distant pearson's r
cor_test(E1_STRnSIB_clean, "MFQ_Loyalty_Combined", "Choice_STRnSIB_STR_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(201) | p
----------------------------------------------------------------------------------------
MFQ_Loyalty_Combined | Choice_STRnSIB_STR_oblig | 0.22 | [0.09, 0.35] | 3.26 | 0.001**
Observations: 203# distant pearson's r
cor_test(E1_STRnSIB_clean, "MFQ_Loyalty_Combined", "Choice_STRnSIB_SIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(201) | p
------------------------------------------------------------------------------------------
MFQ_Loyalty_Combined | Choice_STRnSIB_SIB_oblig | 0.36 | [0.24, 0.48] | 5.54 | < .001***
Observations: 203# diff pearson's r
cor_test(E1_STRnSIB_clean, "MFQ_Loyalty_Combined", "Choice_STRminusSIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(201) | p
---------------------------------------------------------------------------------------------
MFQ_Loyalty_Combined | Choice_STRminusSIB_oblig | -0.24 | [-0.37, -0.11] | -3.50 | < .001***
Observations: 203STRnCUZ
No Choice
# distant pearson's r
cor_test(E1_STRnCUZ_clean, "MFQ_Loyalty_Combined", "NoChoice_STR_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(201) | p
---------------------------------------------------------------------------------
MFQ_Loyalty_Combined | NoChoice_STR_oblig | 0.04 | [-0.10, 0.18] | 0.59 | 0.555
Observations: 203# close pearson's r
cor_test(E1_STRnCUZ_clean, "MFQ_Loyalty_Combined", "NoChoice_CUZ_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(201) | p
---------------------------------------------------------------------------------
MFQ_Loyalty_Combined | NoChoice_CUZ_oblig | 0.16 | [0.02, 0.29] | 2.24 | 0.026*
Observations: 203# diff pearson's r
cor_test(E1_STRnCUZ_clean, "MFQ_Loyalty_Combined", "NoChoice_STRminusCUZ_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(201) | p
------------------------------------------------------------------------------------------
MFQ_Loyalty_Combined | NoChoice_STRminusCUZ_oblig | -0.10 | [-0.24, 0.04] | -1.44 | 0.152
Observations: 203Choice
# distant pearson's r
cor_test(E1_STRnCUZ_clean, "MFQ_Loyalty_Combined", "Choice_STRnCUZ_STR_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(201) | p
----------------------------------------------------------------------------------------
MFQ_Loyalty_Combined | Choice_STRnCUZ_STR_oblig | 0.22 | [0.08, 0.35] | 3.19 | 0.002**
Observations: 203# distant pearson's r
cor_test(E1_STRnCUZ_clean, "MFQ_Loyalty_Combined", "Choice_STRnCUZ_CUZ_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(201) | p
------------------------------------------------------------------------------------------
MFQ_Loyalty_Combined | Choice_STRnCUZ_CUZ_oblig | 0.37 | [0.24, 0.48] | 5.60 | < .001***
Observations: 203# diff pearson's r
cor_test(E1_STRnCUZ_clean, "MFQ_Loyalty_Combined", "Choice_STRminusCUZ_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(201) | p
---------------------------------------------------------------------------------------------
MFQ_Loyalty_Combined | Choice_STRminusCUZ_oblig | -0.27 | [-0.39, -0.13] | -3.91 | < .001***
Observations: 203CUZnSIB
No Choice
# distant pearson's r
cor_test(E1_CUZnSIB_clean, "MFQ_Loyalty_Combined", "NoChoice_CUZ_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(203) | p
---------------------------------------------------------------------------------
MFQ_Loyalty_Combined | NoChoice_CUZ_oblig | 0.17 | [0.03, 0.30] | 2.47 | 0.014*
Observations: 205# close pearson's r
cor_test(E1_CUZnSIB_clean, "MFQ_Loyalty_Combined", "NoChoice_SIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(203) | p
------------------------------------------------------------------------------------
MFQ_Loyalty_Combined | NoChoice_SIB_oblig | 0.25 | [0.11, 0.37] | 3.64 | < .001***
Observations: 205# diff pearson's r
cor_test(E1_CUZnSIB_clean, "MFQ_Loyalty_Combined", "NoChoice_CUZminusSIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(203) | p
------------------------------------------------------------------------------------------
MFQ_Loyalty_Combined | NoChoice_CUZminusSIB_oblig | -0.07 | [-0.21, 0.06] | -1.07 | 0.288
Observations: 205Choice
# distant pearson's r
cor_test(E1_CUZnSIB_clean, "MFQ_Loyalty_Combined", "Choice_CUZnSIB_CUZ_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(203) | p
---------------------------------------------------------------------------------------
MFQ_Loyalty_Combined | Choice_CUZnSIB_CUZ_oblig | 0.16 | [0.02, 0.29] | 2.32 | 0.021*
Observations: 205# distant pearson's r
cor_test(E1_CUZnSIB_clean, "MFQ_Loyalty_Combined", "Choice_CUZnSIB_SIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(203) | p
------------------------------------------------------------------------------------------
MFQ_Loyalty_Combined | Choice_CUZnSIB_SIB_oblig | 0.24 | [0.10, 0.36] | 3.45 | < .001***
Observations: 205# diff pearson's r
cor_test(E1_CUZnSIB_clean, "MFQ_Loyalty_Combined", "Choice_CUZminusSIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(203) | p
-------------------------------------------------------------------------------------------
MFQ_Loyalty_Combined | Choice_CUZminusSIB_oblig | -0.20 | [-0.33, -0.06] | -2.88 | 0.004**
Observations: 205OUS Impartial Beneficence
STRnSIB
No Choice
# distant pearson's r
cor_test(E1_STRnSIB_clean, "OUS_IB", "NoChoice_STR_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(201) | p
-----------------------------------------------------------------------
OUS_IB | NoChoice_STR_oblig | 0.15 | [0.02, 0.29] | 2.22 | 0.027*
Observations: 203# close pearson's r
cor_test(E1_STRnSIB_clean, "OUS_IB", "NoChoice_SIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(201) | p
-----------------------------------------------------------------------
OUS_IB | NoChoice_SIB_oblig | 0.08 | [-0.05, 0.22] | 1.21 | 0.228
Observations: 203# diff pearson's r
cor_test(E1_STRnSIB_clean, "OUS_IB", "NoChoice_STRminusSIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(201) | p
-------------------------------------------------------------------------------
OUS_IB | NoChoice_STRminusSIB_oblig | 0.05 | [-0.09, 0.18] | 0.65 | 0.515
Observations: 203Choice
# distant pearson's r
cor_test(E1_STRnSIB_clean, "OUS_IB", "Choice_STRnSIB_STR_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(201) | p
--------------------------------------------------------------------------------
OUS_IB | Choice_STRnSIB_STR_oblig | 0.31 | [0.18, 0.43] | 4.57 | < .001***
Observations: 203# distant pearson's r
cor_test(E1_STRnSIB_clean, "OUS_IB", "Choice_STRnSIB_SIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(201) | p
------------------------------------------------------------------------------
OUS_IB | Choice_STRnSIB_SIB_oblig | 0.21 | [0.08, 0.34] | 3.08 | 0.002**
Observations: 203# diff pearson's r
cor_test(E1_STRnSIB_clean, "OUS_IB", "Choice_STRminusSIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(201) | p
-----------------------------------------------------------------------------
OUS_IB | Choice_STRminusSIB_oblig | 0.04 | [-0.10, 0.18] | 0.58 | 0.560
Observations: 203STRnCUZ
No Choice
# distant pearson's r
cor_test(E1_STRnCUZ_clean, "OUS_IB", "NoChoice_STR_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(201) | p
-----------------------------------------------------------------------
OUS_IB | NoChoice_STR_oblig | 0.17 | [0.03, 0.30] | 2.44 | 0.015*
Observations: 203# close pearson's r
cor_test(E1_STRnCUZ_clean, "OUS_IB", "NoChoice_CUZ_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(201) | p
--------------------------------------------------------------------------
OUS_IB | NoChoice_CUZ_oblig | 0.24 | [0.11, 0.37] | 3.52 | < .001***
Observations: 203# diff pearson's r
cor_test(E1_STRnCUZ_clean, "OUS_IB", "NoChoice_STRminusCUZ_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(201) | p
--------------------------------------------------------------------------------
OUS_IB | NoChoice_STRminusCUZ_oblig | -0.08 | [-0.22, 0.06] | -1.16 | 0.248
Observations: 203Choice
# distant pearson's r
cor_test(E1_STRnCUZ_clean, "OUS_IB", "Choice_STRnCUZ_STR_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(201) | p
--------------------------------------------------------------------------------
OUS_IB | Choice_STRnCUZ_STR_oblig | 0.42 | [0.30, 0.53] | 6.62 | < .001***
Observations: 203# distant pearson's r
cor_test(E1_STRnCUZ_clean, "OUS_IB", "Choice_STRnCUZ_CUZ_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(201) | p
--------------------------------------------------------------------------------
OUS_IB | Choice_STRnCUZ_CUZ_oblig | 0.46 | [0.34, 0.56] | 7.24 | < .001***
Observations: 203# diff pearson's r
cor_test(E1_STRnCUZ_clean, "OUS_IB", "Choice_STRminusCUZ_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(201) | p
------------------------------------------------------------------------------
OUS_IB | Choice_STRminusCUZ_oblig | -0.12 | [-0.26, 0.01] | -1.77 | 0.078
Observations: 203CUZnSIB
No Choice
# distant pearson's r
cor_test(E1_CUZnSIB_clean, "OUS_IB", "NoChoice_CUZ_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(203) | p
--------------------------------------------------------------------------
OUS_IB | NoChoice_CUZ_oblig | 0.23 | [0.10, 0.36] | 3.38 | < .001***
Observations: 205# close pearson's r
cor_test(E1_CUZnSIB_clean, "OUS_IB", "NoChoice_SIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(203) | p
--------------------------------------------------------------------------
OUS_IB | NoChoice_SIB_oblig | 0.38 | [0.25, 0.49] | 5.81 | < .001***
Observations: 205# diff pearson's r
cor_test(E1_CUZnSIB_clean, "OUS_IB", "NoChoice_CUZminusSIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(203) | p
----------------------------------------------------------------------------------
OUS_IB | NoChoice_CUZminusSIB_oblig | -0.14 | [-0.27, 0.00] | -2.02 | 0.044*
Observations: 205Choice
# distant pearson's r
cor_test(E1_CUZnSIB_clean, "OUS_IB", "Choice_CUZnSIB_CUZ_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(203) | p
--------------------------------------------------------------------------------
OUS_IB | Choice_CUZnSIB_CUZ_oblig | 0.36 | [0.24, 0.48] | 5.58 | < .001***
Observations: 205# distant pearson's r
cor_test(E1_CUZnSIB_clean, "OUS_IB", "Choice_CUZnSIB_SIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(203) | p
--------------------------------------------------------------------------------
OUS_IB | Choice_CUZnSIB_SIB_oblig | 0.34 | [0.22, 0.46] | 5.19 | < .001***
Observations: 205# diff pearson's r
cor_test(E1_CUZnSIB_clean, "OUS_IB", "Choice_CUZminusSIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(203) | p
----------------------------------------------------------------------------------
OUS_IB | Choice_CUZminusSIB_oblig | -9.06e-03 | [-0.15, 0.13] | -0.13 | 0.897
Observations: 205Oblig ~ MAC Family Values vs MFT Ingroup Loyalty Tests
These analyses were only conducted within the CUZnSIB dataset due to potential confounding of moral values’ effects on obligation judgments in other datasets.
No Choice
# correlation values are taken from the oblig ~ ind. diffs analyses
## r.jk = oblig ~ family values corr; r.jh = oblig ~ ingroup loyalty corr; r.kh = family values ~ ingroup loyalty corr
# distant
cocor.dep.groups.overlap(r.jk = .26, r.jh = .17, r.kh = .65, 205, alternative = "two.sided",
test = "steiger1980", alpha = 0.05, conf.level = 0.95, null.value = 0)
Results of a comparison of two overlapping correlations based on dependent groups
Comparison between r.jk = 0.26 and r.jh = 0.17
Difference: r.jk - r.jh = 0.09
Related correlation: r.kh = 0.65
Group size: n = 205
Null hypothesis: r.jk is equal to r.jh
Alternative hypothesis: r.jk is not equal to r.jh (two-sided)
Alpha: 0.05
steiger1980: Steiger's (1980) modification of Dunn and Clark's z (1969) using average correlations
z = 1.5735, p-value = 0.1156
Null hypothesis retained# close
cocor.dep.groups.overlap(r.jk = .48, r.jh = .25, r.kh = .65, 205, alternative = "two.sided",
test = "steiger1980", alpha = 0.05, conf.level = 0.95, null.value = 0)
Results of a comparison of two overlapping correlations based on dependent groups
Comparison between r.jk = 0.48 and r.jh = 0.25
Difference: r.jk - r.jh = 0.23
Related correlation: r.kh = 0.65
Group size: n = 205
Null hypothesis: r.jk is equal to r.jh
Alternative hypothesis: r.jk is not equal to r.jh (two-sided)
Alpha: 0.05
steiger1980: Steiger's (1980) modification of Dunn and Clark's z (1969) using average correlations
z = 4.2898, p-value = 0.0000
Null hypothesis rejected# difference
cocor.dep.groups.overlap(r.jk = -.21, r.jh = -.07, r.kh = .65, 205, alternative = "two.sided",
test = "steiger1980", alpha = 0.05, conf.level = 0.95, null.value = 0)
Results of a comparison of two overlapping correlations based on dependent groups
Comparison between r.jk = -0.21 and r.jh = -0.07
Difference: r.jk - r.jh = -0.14
Related correlation: r.kh = 0.65
Group size: n = 205
Null hypothesis: r.jk is equal to r.jh
Alternative hypothesis: r.jk is not equal to r.jh (two-sided)
Alpha: 0.05
steiger1980: Steiger's (1980) modification of Dunn and Clark's z (1969) using average correlations
z = -2.4104, p-value = 0.0159
Null hypothesis rejectedChoice
# correlation values are taken from the oblig ~ ind. diffs analyses
## r.jk = oblig ~ family values corr; r.jh = oblig ~ ingroup loyalty corr; r.kh = family values ~ ingroup loyalty corr
# distant
cocor.dep.groups.overlap(r.jk = .32, r.jh = .16, r.kh = .65, 205, alternative = "two.sided",
test = "steiger1980", alpha = 0.05, conf.level = 0.95, null.value = 0)
Results of a comparison of two overlapping correlations based on dependent groups
Comparison between r.jk = 0.32 and r.jh = 0.16
Difference: r.jk - r.jh = 0.16
Related correlation: r.kh = 0.65
Group size: n = 205
Null hypothesis: r.jk is equal to r.jh
Alternative hypothesis: r.jk is not equal to r.jh (two-sided)
Alpha: 0.05
steiger1980: Steiger's (1980) modification of Dunn and Clark's z (1969) using average correlations
z = 2.8229, p-value = 0.0048
Null hypothesis rejected# close
cocor.dep.groups.overlap(r.jk = .40, r.jh = .24, r.kh = .65, 205, alternative = "two.sided",
test = "steiger1980", alpha = 0.05, conf.level = 0.95, null.value = 0)
Results of a comparison of two overlapping correlations based on dependent groups
Comparison between r.jk = 0.4 and r.jh = 0.24
Difference: r.jk - r.jh = 0.16
Related correlation: r.kh = 0.65
Group size: n = 205
Null hypothesis: r.jk is equal to r.jh
Alternative hypothesis: r.jk is not equal to r.jh (two-sided)
Alpha: 0.05
steiger1980: Steiger's (1980) modification of Dunn and Clark's z (1969) using average correlations
z = 2.9080, p-value = 0.0036
Null hypothesis rejected# difference
cocor.dep.groups.overlap(r.jk = -.23, r.jh = -.20, r.kh = .65, 205, alternative = "two.sided",
test = "steiger1980", alpha = 0.05, conf.level = 0.95, null.value = 0)
Results of a comparison of two overlapping correlations based on dependent groups
Comparison between r.jk = -0.23 and r.jh = -0.2
Difference: r.jk - r.jh = -0.03
Related correlation: r.kh = 0.65
Group size: n = 205
Null hypothesis: r.jk is equal to r.jh
Alternative hypothesis: r.jk is not equal to r.jh (two-sided)
Alpha: 0.05
steiger1980: Steiger's (1980) modification of Dunn and Clark's z (1969) using average correlations
z = -0.5241, p-value = 0.6002
Null hypothesis 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B_SIB_oblig + E1_all_clean$SIBoCUZ_SIB_oblig)/2 # creates pre-registered obligation index for mean comparisons
E1_all_clean$Choice_CUZnSIB_SIB_moral  <- rowSums(E1_all_clean[, c(
                                                  "SIBoCUZ_SIB_m11", "SIBoCUZ_SIB_m12", "SIBoCUZ_SIB_m31", 
                                                  "SIBoCUZ_SIB_m21", "SIBoCUZ_SIB_m22", "SIBoCUZ_SIB_m32")], na.rm = T) # should be a duplicate of "SIBoCUZ_SIB_moral"


# Difference Scores
# e.g., STR minus CUZ obligation within No Choice or Choice conditions (for diff score corrs and ind. diffs analyses)

# STR vs CUZ
E1_all_clean$NoChoice_STRminusCUZ_oblig <- E1_all_clean$NoChoice_STR_oblig - E1_all_clean$NoChoice_CUZ_oblig
E1_all_clean$NoChoice_STRminusCUZ_moral <- E1_all_clean$NoChoice_STR_moral - E1_all_clean$NoChoice_CUZ_moral

E1_all_clean$Choice_STRminusCUZ_oblig <- E1_all_clean$Choice_STRnCUZ_STR_oblig - E1_all_clean$Choice_STRnCUZ_CUZ_oblig
E1_all_clean$Choice_STRminusCUZ_moral <- E1_all_clean$Choice_STRnCUZ_STR_moral - E1_all_clean$Choice_STRnCUZ_CUZ_moral

# STR vs SIB
E1_all_clean$NoChoice_STRminusSIB_oblig <- E1_all_clean$NoChoice_STR_oblig - E1_all_clean$NoChoice_SIB_oblig
E1_all_clean$NoChoice_STRminusSIB_moral <- E1_all_clean$NoChoice_STR_moral - E1_all_clean$NoChoice_SIB_moral

E1_all_clean$Choice_STRminusSIB_oblig <- E1_all_clean$Choice_STRnSIB_STR_oblig - E1_all_clean$Choice_STRnSIB_SIB_oblig
E1_all_clean$Choice_STRminusSIB_moral <- E1_all_clean$Choice_STRnSIB_STR_moral - E1_all_clean$Choice_STRnSIB_SIB_moral

# CUZ vs SIB
E1_all_clean$NoChoice_CUZminusSIB_oblig <- E1_all_clean$NoChoice_CUZ_oblig - E1_all_clean$NoChoice_SIB_oblig
E1_all_clean$NoChoice_CUZminusSIB_moral <- E1_all_clean$NoChoice_CUZ_moral - E1_all_clean$NoChoice_SIB_moral

E1_all_clean$Choice_CUZminusSIB_oblig <- E1_all_clean$Choice_CUZnSIB_CUZ_oblig - E1_all_clean$Choice_CUZnSIB_SIB_oblig
E1_all_clean$Choice_CUZminusSIB_moral <- E1_all_clean$Choice_CUZnSIB_CUZ_moral - E1_all_clean$Choice_CUZnSIB_SIB_moral


# Individual Difference Measures (for ind. diffs analyses)

# MAC (Morality-as-Cooperation scale) composite
E1_all_clean$MAC_Fam_Combined <- ((E1_all_clean$MAC_Jud_1 + E1_all_clean$MAC_Jud_2 + E1_all_clean$MAC_Jud_3 +
                                       E1_all_clean$MAC_Rel_1 + E1_all_clean$MAC_Rel_2 + E1_all_clean$MAC_Rel_3)/6)
E1_all_clean$MAC_Fam_Jud <- ((E1_all_clean$MAC_Jud_1 + E1_all_clean$MAC_Jud_2 + E1_all_clean$MAC_Jud_3)/3)
E1_all_clean$MAC_Fam_Rel <- ((E1_all_clean$MAC_Rel_1 + E1_all_clean$MAC_Rel_2 + E1_all_clean$MAC_Rel_3)/3)

# MFQ (Moral Foundations Theory scale) composite
E1_all_clean$MFQ_Loyalty_Combined <- ((E1_all_clean$MFQ_Jud_1 + E1_all_clean$MFQ_Jud_2 + E1_all_clean$MFQ_Jud_3 +
                                       E1_all_clean$MFQ_Rel_1 + E1_all_clean$MFQ_Rel_2 + E1_all_clean$MFQ_Rel_3)/6)
E1_all_clean$MFQ_Loyalty_Jud <- ((E1_all_clean$MFQ_Jud_1 + E1_all_clean$MFQ_Jud_2 + E1_all_clean$MFQ_Jud_3)/3)
E1_all_clean$MFQ_Loyalty_Rel <- ((E1_all_clean$MFQ_Rel_1 + E1_all_clean$MFQ_Rel_2 + E1_all_clean$MFQ_Rel_3)/3)

# OUS (Oxford Utilitarianism Scale: Impartial Beneficence subscale) composite
E1_all_clean$OUS_IB <- ((E1_all_clean$OUS_IB1 + E1_all_clean$OUS_IB2 + E1_all_clean$OUS_IB3 +
                             E1_all_clean$OUS_IB4 + E1_all_clean$OUS_IB5)/5)
```

## Creating Analyzable Between-Subjects Datasets
```{r}
# STR vs CUZ
E1_STRnCUZ_clean <- E1_all_clean %>%
  filter(BSs_cond == 'STR vs CUZ') %>%
  # select only variables that are relevant to STRnCUZ
  select(
    ResponseId, # selects variable
    Age:Political_Overall, # selects demographic variables
    MAC_Jud_1:MAC_Jud_3, MAC_Rel_1:MAC_Rel_3, MFQ_Jud_1:MFQ_Jud_3, MFQ_Rel_1:MFQ_Rel_3, OUS_IB1:OUS_IB5, # selects raw ind. diff variables (for reliabilty check)
    MAC_Fam_Combined:OUS_IB, # selects composited ind. diff variables
    BSs_cond, # selects variable for between-subjects condition
    Dist_Scen:CloseODist_Scen, # selects within-subjects condition variable
    NoChoice_STR_oblig:NoChoice_CUZ_moral, # selects NoChoice DVs for STRnCUZ dataset
    Choice_STRnCUZ_STR_oblig:Choice_STRnCUZ_CUZ_moral, # selects Choice DVs for STRnCUZ dataset
    NoChoice_STRminusCUZ_oblig:Choice_STRminusCUZ_moral # selects difference score variables for STRnCUZ dataset
    )

# STR vs SIB
E1_STRnSIB_clean <- E1_all_clean %>%
  filter(BSs_cond == 'STR vs SIB') %>%
  # select only variables that are relevant to STRnCUZ
  select(
    ResponseId, # selects variable
    Age:Political_Overall, # selects demographic variables
    MAC_Jud_1:MAC_Jud_3, MAC_Rel_1:MAC_Rel_3, MFQ_Jud_1:MFQ_Jud_3, MFQ_Rel_1:MFQ_Rel_3, OUS_IB1:OUS_IB5, # selects raw ind. diff variables (for reliabilty check)
    MAC_Fam_Combined:OUS_IB, # selects composited ind. diff variables
    BSs_cond, # selects variable for between-subjects condition
    Dist_Scen:CloseODist_Scen, # sselects within-subjects condition variable
    NoChoice_STR_oblig:NoChoice_STR_moral, NoChoice_SIB_oblig:NoChoice_SIB_moral, # selects NoChoice DVs for STRnSIB dataset
    Choice_STRnSIB_STR_oblig:Choice_STRnSIB_SIB_moral, # selects Choice DVs for STRnSIB dataset
    NoChoice_STRminusSIB_oblig:Choice_STRminusSIB_moral # selects difference score variables for STRnSIB dataset
    )

# CUZ vs SIB
E1_CUZnSIB_clean <- E1_all_clean %>%
  filter(BSs_cond == 'CUZ vs SIB') %>%
  # select only variables that are relevant to STRnCUZ
  select(
    ResponseId, # selects variable
    Age:Political_Overall, # selects demographic variables
    MAC_Jud_1:MAC_Jud_3, MAC_Rel_1:MAC_Rel_3, MFQ_Jud_1:MFQ_Jud_3, MFQ_Rel_1:MFQ_Rel_3, OUS_IB1:OUS_IB5, # selects raw ind. diff variables (for reliabilty check)
    MAC_Fam_Combined:OUS_IB, # selects composited ind. diff variables
    BSs_cond, # selects variable for between-subjects condition
    Dist_Scen:CloseODist_Scen, # selects within-subjects condition variable
    NoChoice_CUZ_oblig:NoChoice_SIB_moral, # selects NoChoice DVs for CUZnSIB dataset
    Choice_CUZnSIB_CUZ_oblig:Choice_CUZnSIB_SIB_moral, # selects Choice DVs for CUZnSIB dataset
    NoChoice_CUZminusSIB_oblig:Choice_CUZminusSIB_moral # selects difference score variables for CUZnSIB dataset
    )
```

## Tidying Data
```{r}
# Convert data from wide to long format for obligation and moral character
# STRnCUZ
E1_STRnCUZ_cond_long <- E1_STRnCUZ_clean %>%
  pivot_longer(
    cols = c(Dist_Scen, Close_Scen, DistOClose_Scen, CloseODist_Scen),
    names_to = "WSs_cond",
    values_to = "Condition"
  )

E1_STRnCUZ_oblig_long <- E1_STRnCUZ_clean %>%
  pivot_longer(
    cols = c(NoChoice_STR_oblig, NoChoice_CUZ_oblig, Choice_STRnCUZ_STR_oblig, Choice_STRnCUZ_CUZ_oblig),
    names_to = "WSs_cond",
    values_to = "oblig"
  )


E1_STRnCUZ_moral_long <- E1_STRnCUZ_clean %>%
  pivot_longer(
    cols = c(NoChoice_STR_moral, NoChoice_CUZ_moral, Choice_STRnCUZ_STR_moral, Choice_STRnCUZ_CUZ_moral),
    names_to = "WSs_cond",
    values_to = "moral"
  )

# Combine long STRnCUZ datasets, select plotting variables, and create condition variable for each factor (Relation + Choice Context)
E1_STRnCUZ_long <- cbind(E1_STRnCUZ_cond_long, E1_STRnCUZ_oblig_long, E1_STRnCUZ_moral_long)

E1_STRnCUZ_long <- E1_STRnCUZ_long[, !duplicated(colnames(E1_STRnCUZ_long))] %>% # get rid of duplicate columns
  select(ResponseId,
         Age:OUS_IB,
         BSs_cond,
         WSs_cond,
         Condition,
         oblig, moral) %>%
  mutate(Relation = case_when(
    WSs_cond == "Dist_Scen" ~ "Distant",
    WSs_cond == "Close_Scen" ~ "Close",
    WSs_cond == "DistOClose_Scen" ~ "Distant",
    WSs_cond == "CloseODist_Scen" ~ "Close")) %>%
  mutate(`Choice Context` = case_when(
    WSs_cond == "Dist_Scen" ~ "No Choice",
    WSs_cond == "Close_Scen" ~ "No Choice",
    WSs_cond == "DistOClose_Scen" ~ "Choice",
    WSs_cond == "CloseODist_Scen" ~ "Choice"))

# Reorder/rename condition and participant factors
E1_STRnCUZ_long$Relation <- as.factor(E1_STRnCUZ_long$Relation)
E1_STRnCUZ_long$Relation <- ordered(E1_STRnCUZ_long$Relation, levels = c("Distant", "Close"))
E1_STRnCUZ_long$`Choice Context` <- as.factor(E1_STRnCUZ_long$`Choice Context`)
E1_STRnCUZ_long$`Choice Context` <- ordered(E1_STRnCUZ_long$`Choice Context`, levels = c("No Choice", "Choice"))
E1_STRnCUZ_long$ResponseId <- as.factor(E1_STRnCUZ_long$ResponseId)


# Convert data from wide to long format for obligation and moral character
# STRnSIB
E1_STRnSIB_cond_long <- E1_STRnSIB_clean %>%
  pivot_longer(
    cols = c(Dist_Scen, Close_Scen, DistOClose_Scen, CloseODist_Scen),
    names_to = "WSs_cond",
    values_to = "Condition"
  )

E1_STRnSIB_oblig_long <- E1_STRnSIB_clean %>%
  pivot_longer(
    cols = c(NoChoice_STR_oblig, NoChoice_SIB_oblig, Choice_STRnSIB_STR_oblig, Choice_STRnSIB_SIB_oblig),
    names_to = "WSs_cond",
    values_to = "oblig"
  )


E1_STRnSIB_moral_long <- E1_STRnSIB_clean %>%
  pivot_longer(
    cols = c(NoChoice_STR_moral, NoChoice_SIB_moral, Choice_STRnSIB_STR_moral, Choice_STRnSIB_SIB_moral),
    names_to = "WSs_cond",
    values_to = "moral"
  )

# Combine long STRnSIB datasets, select plotting variables, and create condition variable for each factor (Relation + Choice Context)
E1_STRnSIB_long <- cbind(E1_STRnSIB_cond_long, E1_STRnSIB_oblig_long, E1_STRnSIB_moral_long)

E1_STRnSIB_long <- E1_STRnSIB_long[, !duplicated(colnames(E1_STRnSIB_long))] %>% # get rid of duplicate columns
  select(ResponseId,
         Age:OUS_IB,
         BSs_cond,
         WSs_cond,
         Condition,
         oblig, moral) %>%
  mutate(Relation = case_when(
    WSs_cond == "Dist_Scen" ~ "Distant",
    WSs_cond == "Close_Scen" ~ "Close",
    WSs_cond == "DistOClose_Scen" ~ "Distant",
    WSs_cond == "CloseODist_Scen" ~ "Close")) %>%
  mutate(`Choice Context` = case_when(
    WSs_cond == "Dist_Scen" ~ "No Choice",
    WSs_cond == "Close_Scen" ~ "No Choice",
    WSs_cond == "DistOClose_Scen" ~ "Choice",
    WSs_cond == "CloseODist_Scen" ~ "Choice"))

# Reorder/rename condition and participant factors
E1_STRnSIB_long$Relation <- as.factor(E1_STRnSIB_long$Relation)
E1_STRnSIB_long$Relation <- ordered(E1_STRnSIB_long$Relation, levels = c("Distant", "Close"))
E1_STRnSIB_long$`Choice Context` <- as.factor(E1_STRnSIB_long$`Choice Context`)
E1_STRnSIB_long$`Choice Context` <- ordered(E1_STRnSIB_long$`Choice Context`, levels = c("No Choice", "Choice"))
E1_STRnSIB_long$ResponseId <- as.factor(E1_STRnSIB_long$ResponseId)


# Convert data from wide to long format for obligation and moral character
# CUZnSIB
E1_CUZnSIB_cond_long <- E1_CUZnSIB_clean %>%
  pivot_longer(
    cols = c(Dist_Scen, Close_Scen, DistOClose_Scen, CloseODist_Scen),
    names_to = "WSs_cond",
    values_to = "Condition"
  )

E1_CUZnSIB_oblig_long <- E1_CUZnSIB_clean %>%
  pivot_longer(
    cols = c(NoChoice_CUZ_oblig, NoChoice_SIB_oblig, Choice_CUZnSIB_CUZ_oblig, Choice_CUZnSIB_SIB_oblig),
    names_to = "WSs_cond",
    values_to = "oblig"
  )


E1_CUZnSIB_moral_long <- E1_CUZnSIB_clean %>%
  pivot_longer(
    cols = c(NoChoice_CUZ_moral, NoChoice_SIB_moral, Choice_CUZnSIB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ames(E1_diff_STRnSIB_long))] # get rid of duplicate columns

E1_diff_STRnSIB_long <- E1_diff_STRnSIB_long %>%
  select(ResponseId,
         Age:OUS_IB,
         BSs_cond,
         WSs_cond,
         Condition,
         oblig, moral) %>%
  mutate(`Choice Context` = case_when(
    WSs_cond == "Dist_Scen" ~ "No Choice",
    WSs_cond == "Close_Scen" ~ "Choice"))

# Reorder/rename condition, and participant factors
E1_diff_STRnSIB_long$`Choice Context` <- as.factor(E1_diff_STRnSIB_long$`Choice Context`)
E1_diff_STRnSIB_long$`Choice Context` <- ordered(E1_diff_STRnSIB_long$`Choice Context`, levels = c("No Choice", "Choice"))
E1_diff_STRnSIB_long$ResponseId <- as.factor(E1_diff_STRnSIB_long$ResponseId)


# CUZnSIB
E1_diff_CUZnSIB_cond_long <- E1_CUZnSIB_clean %>%
  pivot_longer(
    cols = c(Dist_Scen, Close_Scen),
    names_to = "WSs_cond",
    values_to = "Condition"
  )

E1_diff_CUZnSIB_oblig_long <- E1_CUZnSIB_clean %>%
  pivot_longer(
    cols = c(NoChoice_CUZminusSIB_oblig, Choice_CUZminusSIB_oblig),
    names_to = "WSs_cond",
    values_to = "oblig"
  )

E1_diff_CUZnSIB_moral_long <- E1_CUZnSIB_clean %>%
  pivot_longer(
    cols = c(NoChoice_CUZminusSIB_moral, Choice_CUZminusSIB_moral),
    names_to = "WSs_cond",
    values_to = "moral"
  )

# Combine long CUZnSIB datasets, select plotting variables, and create condition variable for `Choice Context` factor
E1_diff_CUZnSIB_long <- cbind(E1_diff_CUZnSIB_cond_long, E1_diff_CUZnSIB_oblig_long, E1_diff_CUZnSIB_moral_long)
E1_diff_CUZnSIB_long <- E1_diff_CUZnSIB_long[, !duplicated(colnames(E1_diff_CUZnSIB_long))] # get rid of duplicate columns

E1_diff_CUZnSIB_long <- E1_diff_CUZnSIB_long %>%
  select(ResponseId,
         Age:OUS_IB,
         BSs_cond,
         WSs_cond,
         Condition,
         oblig, moral) %>%
  mutate(`Choice Context` = case_when(
    WSs_cond == "Dist_Scen" ~ "No Choice",
    WSs_cond == "Close_Scen" ~ "Choice"))

# Reorder/rename condition, and participant factors
E1_diff_CUZnSIB_long$`Choice Context` <- as.factor(E1_diff_CUZnSIB_long$`Choice Context`)
E1_diff_CUZnSIB_long$`Choice Context` <- ordered(E1_diff_CUZnSIB_long$`Choice Context`, levels = c("No Choice", "Choice"))
E1_diff_CUZnSIB_long$ResponseId <- as.factor(E1_diff_CUZnSIB_long$ResponseId)


# Combine into one dataset for plotting
E1_diff_all_long <- rbind(E1_diff_STRnSIB_long, E1_diff_STRnCUZ_long, E1_diff_CUZnSIB_long)
# Reorder All_long BSs_cond
E1_diff_all_long$BSs_cond <- as.factor(E1_diff_all_long$BSs_cond)
E1_diff_all_long$BSs_cond <- ordered(E1_diff_all_long$BSs_cond, levels = c("STR vs SIB", "STR vs CUZ", "CUZ vs SIB"))
```

## STRnSIB
```{r, fig.width = 14, fig.height = 9, out.width = "75%", out.height = "75%"}
print(oblig_moral_diff_plot_STRnSIB <- ggplot(data = E1_diff_STRnSIB_long, aes(x = oblig, y = moral)) +
        geom_jitter(color = "darkorchid1", alpha = 0.5) +
        geom_smooth(method = 'lm', color = "darkorchid1") +
        facet_wrap(BSs_cond~`Choice Context`) +
        scale_x_continuous(limits = c(-101,101), breaks = c(-100,-50,0,50,100)) +
        scale_y_continuous(limits = c(-101,101), breaks = c(-100,-50,0,50,100)) +
        theme_classic() +
        xlab("Obligation Strength Difference (Distant - Close)") +
        ylab("Moral Character Difference (Distant - Close)") +
        theme(axis.title.x = element_text(size = 14), 
              axis.title.y = element_text(size = 14),
              axis.text.x = element_text(color = "black", size = 12), 
              axis.text.y = element_text(color = "black", size = 12)))
```
## STRnCUZ
```{r, fig.width = 14, fig.height = 9, out.width = "75%", out.height = "75%"}
print(oblig_moral_diff_plot_STRnCUZ <- ggplot(data = E1_diff_STRnCUZ_long, aes(x = oblig, y = moral)) +
        geom_jitter(color = "darkorchid1", alpha = 0.5) +
        geom_smooth(method = 'lm', color = "darkorchid1") +
        facet_wrap(BSs_cond~`Choice Context`) +
        scale_x_continuous(limits = c(-101,101), breaks = c(-100,-50,0,50,100)) +
        scale_y_continuous(limits = c(-101,101), breaks = c(-100,-50,0,50,100)) +
        theme_classic() +
        xlab("Obligation Strength Difference (Distant - Close)") +
        ylab("Moral Character Difference (Distant - Close)") +
        theme(axis.title.x = element_text(size = 14), 
              axis.title.y = element_text(size = 14),
              axis.text.x = element_text(color = "black", size = 12), 
              axis.text.y = element_text(color = "black", size = 12)))
```
## CUZnSIB
```{r, fig.width = 14, fig.height = 9, out.width = "75%", out.height = "75%"}
print(oblig_moral_diff_plot_CUZnSIB <- ggplot(data = E1_diff_CUZnSIB_long, aes(x = oblig, y = moral)) +
        geom_jitter(color = "darkorchid1", alpha = 0.5) +
        geom_smooth(method = 'lm', color = "darkorchid1") +
        facet_wrap(BSs_cond~`Choice Context`) +
        scale_x_continuous(limits = c(-101,101), breaks = c(-100,-50,0,50,100)) +
        scale_y_continuous(limits = c(-101,101), breaks = c(-100,-50,0,50,100)) +
        theme_classic() +
        xlab("Obligation Strength Difference (Distant - Close)") +
        ylab("Moral Character Difference (Distant - Close)") +
        theme(axis.title.x = element_text(size = 14), 
              axis.title.y = element_text(size = 14),
              axis.text.x = element_text(color = "black", size = 12), 
              axis.text.y = element_text(color = "black", size = 12)))
```
## Combined
```{r, fig.width = 14, fig.height = 9, out.width = "75%", out.height = "75%"}
print(oblig_moral_diff_plot_combined <- ggplot(data = E1_diff_all_long, aes(x = oblig, y = moral)) +
        geom_jitter(color = "darkorchid1", alpha = 0.5) +
        geom_smooth(method = 'lm', color = "darkorchid1") +
        facet_wrap(BSs_cond~`Choice Context`, nrow = 3) +
        scale_x_continuous(limits = c(-101,101), breaks = c(-100,-50,0,50,100)) +
        scale_y_continuous(limits = c(-101,101), breaks = c(-100,-50,0,50,100)) +
        theme_classic() +
        xlab("\nObligation Strength Difference (Distant - Close)") +
        ylab("Moral Character Difference (Distant - Close)\n") +
        theme(axis.title.x = element_text(size = 18), 
              axis.title.y = element_text(size = 18),
              axis.text.x = element_text(color = "black", size = 16), 
              axis.text.y = element_text(color = "black", size = 16),
              strip.text.x = element_text(color = "black", size = 16)))

ggsave("E1_moral~oblig_plot.png")
```


# Moral Diff ~ Oblig Diff Tests {.tabset}

<br>

See our pre-registration (https://osf.io/6q4kr/?view_only=8033c65c9dfc41bcbc528fe94409e4e8) and manuscript for our predictions about the relationship between obligation differences and moral character differences.

<br>

## STRnSIB {.tabset}

### No Choice
```{r}
# pearson's r
cor_test(E1_STRnSIB_clean, "NoChoice_STRminusSIB_oblig", "NoChoice_STRminusSIB_moral", method = "Pearson")
```
### Choice
```{r}
# pearson's r
cor_test(E1_STRnSIB_clean, "Choice_STRminusSIB_oblig", "Choice_STRminusSIB_moral", method = "Pearson")
```

## STRnCUZ {.tabset}

### No Choice
```{r}
# pearson's r
cor_test(E1_STRnCUZ_clean, "NoChoice_STRminusCUZ_oblig", "NoChoice_STRminusCUZ_moral", method = "Pearson")
```
### Choice
```{r}
# pearson's r
cor_test(E1_STRnCUZ_clean, "Choice_STRminusCUZ_oblig", "Choice_STRminusCUZ_moral", me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 method = "Pearson")

# diff pearson's r
cor_test(E1_CUZnSIB_clean, "MAC_Fam_Combined", "NoChoice_CUZminusSIB_oblig", method = "Pearson")
```
#### Choice
```{r}
# distant pearson's r
cor_test(E1_CUZnSIB_clean, "MAC_Fam_Combined", "Choice_CUZnSIB_CUZ_oblig", method = "Pearson")

# distant pearson's r
cor_test(E1_CUZnSIB_clean, "MAC_Fam_Combined", "Choice_CUZnSIB_SIB_oblig", method = "Pearson")

# diff pearson's r
cor_test(E1_CUZnSIB_clean, "MAC_Fam_Combined", "Choice_CUZminusSIB_oblig", method = "Pearson")
```


## MFT Ingroup Loyalty {.tabset}

### STRnSIB {.tabset}

#### No Choice
```{r}
# distant pearson's r
cor_test(E1_STRnSIB_clean, "MFQ_Loyalty_Combined", "NoChoice_STR_oblig", method = "Pearson")

# close pearson's r
cor_test(E1_STRnSIB_clean, "MFQ_Loyalty_Combined", "NoChoice_SIB_oblig", method = "Pearson")

# diff pearson's r
cor_test(E1_STRnSIB_clean, "MFQ_Loyalty_Combined", "NoChoice_STRminusSIB_oblig", method = "Pearson")
```
#### Choice
```{r}
# distant pearson's r
cor_test(E1_STRnSIB_clean, "MFQ_Loyalty_Combined", "Choice_STRnSIB_STR_oblig", method = "Pearson")

# distant pearson's r
cor_test(E1_STRnSIB_clean, "MFQ_Loyalty_Combined", "Choice_STRnSIB_SIB_oblig", method = "Pearson")

# diff pearson's r
cor_test(E1_STRnSIB_clean, "MFQ_Loyalty_Combined", "Choice_STRminusSIB_oblig", method = "Pearson")
```

### STRnCUZ {.tabset}

#### No Choice
```{r}
# distant pearson's r
cor_test(E1_STRnCUZ_clean, "MFQ_Loyalty_Combined", "NoChoice_STR_oblig", method = "Pearson")

# close pearson's r
cor_test(E1_STRnCUZ_clean, "MFQ_Loyalty_Combined", "NoChoice_CUZ_oblig", method = "Pearson")

# diff pearson's r
cor_test(E1_STRnCUZ_clean, "MFQ_Loyalty_Combined", "NoChoice_STRminusCUZ_oblig", method = "Pearson")
```
#### Choice
```{r}
# distant pearson's r
cor_test(E1_STRnCUZ_clean, "MFQ_Loyalty_Combined", "Choice_STRnCUZ_STR_oblig", method = "Pearson")

# distant pearson's r
cor_test(E1_STRnCUZ_clean, "MFQ_Loyalty_Combined", "Choice_STRnCUZ_CUZ_oblig", method = "Pearson")

# diff pearson's r
cor_test(E1_STRnCUZ_clean, "MFQ_Loyalty_Combined", "Choice_STRminusCUZ_oblig", method = "Pearson")
```

### CUZnSIB {.tabset}

#### No Choice
```{r}
# distant pearson's r
cor_test(E1_CUZnSIB_clean, "MFQ_Loyalty_Combined", "NoChoice_CUZ_oblig", method = "Pearson")

# close pearson's r
cor_test(E1_CUZnSIB_clean, "MFQ_Loyalty_Combined", "NoChoice_SIB_oblig", method = "Pearson")

# diff pearson's r
cor_test(E1_CUZnSIB_clean, "MFQ_Loyalty_Combined", "NoChoice_CUZminusSIB_oblig", method = "Pearson")
```
#### Choice
```{r}
# distant pearson's r
cor_test(E1_CUZnSIB_clean, "MFQ_Loyalty_Combined", "Choice_CUZnSIB_CUZ_oblig", method = "Pearson")

# distant pearson's r
cor_test(E1_CUZnSIB_clean, "MFQ_Loyalty_Combined", "Choice_CUZnSIB_SIB_oblig", method = "Pearson")

# diff pearson's r
cor_test(E1_CUZnSIB_clean, "MFQ_Loyalty_Combined", "Choice_CUZminusSIB_oblig", method = "Pearson")
```


## OUS Impartial Beneficence {.tabset}

### STRnSIB {.tabset}

#### No Choice
```{r}
# distant pearson's r
cor_test(E1_STRnSIB_clean, "OUS_IB", "NoChoice_STR_oblig", method = "Pearson")

# close pearson's r
cor_test(E1_STRnSIB_clean, "OUS_IB", "NoChoice_SIB_oblig", method = "Pearson")

# diff pearson's r
cor_test(E1_STRnSIB_clean, "OUS_IB", "NoChoice_STRminusSIB_oblig", method = "Pearson")
```
#### Choice
```{r}
# distant pearson's r
cor_test(E1_STRnSIB_clean, "OUS_IB", "Choice_STRnSIB_STR_oblig", method = "Pearson")

# distant pearson's r
cor_test(E1_STRnSIB_clean, "OUS_IB", "Choice_STRnSIB_SIB_oblig", method = "Pearson")

# diff pearson's r
cor_test(E1_STRnSIB_clean, "OUS_IB", "Choice_STRminusSIB_oblig", method = "Pearson")
```

### STRnCUZ {.tabset}

#### No Choice
```{r}
# distant pearson's r
cor_test(E1_STRnCUZ_clean, "OUS_IB", "NoChoice_STR_oblig", method = "Pearson")

# close pearson's r
cor_test(E1_STRnCUZ_clean, "OUS_IB", "NoChoice_CUZ_oblig", method = "Pearson")

# diff pearson's r
cor_test(E1_STRnCUZ_clean, "OUS_IB", "NoChoice_STRminusCUZ_oblig", method = "Pearson")
```
#### Choice
```{r}
# distant pearson's r
cor_test(E1_STRnCUZ_clean, "OUS_IB", "Choice_STRnCUZ_STR_oblig", method = "Pearson")

# distant pearson's r
cor_test(E1_STRnCUZ_clean, "OUS_IB", "Choice_STRnCUZ_CUZ_oblig", method = "Pearson")

# diff pearson's r
cor_test(E1_STRnCUZ_clean, "OUS_IB", "Choice_STRminusCUZ_oblig", method = "Pearson")
```

### CUZnSIB {.tabset}

#### No Choice
```{r}
# distant pearson's r
cor_test(E1_CUZnSIB_clean, "OUS_IB", "NoChoice_CUZ_oblig", method = "Pearson")

# close pearson's r
cor_test(E1_CUZnSIB_clean, "OUS_IB", "NoChoice_SIB_oblig", method = "Pearson")

# diff pearson's r
cor_test(E1_CUZnSIB_clean, "OUS_IB", "NoChoice_CUZminusSIB_oblig", method = "Pearson")
```
#### Choice
```{r}
# distant pearson's r
cor_test(E1_CUZnSIB_clean, "OUS_IB", "Choice_CUZnSIB_CUZ_oblig", method = "Pearson")

# distant pearson's r
cor_test(E1_CUZnSIB_clean, "OUS_IB", "Choice_CUZnSIB_SIB_oblig", method = "Pearson")

# diff pearson's r
cor_test(E1_CUZnSIB_clean, "OUS_IB", "Choice_CUZminusSIB_oblig", method = "Pearson")
```


# Oblig ~ MAC Family Values vs MFT Ingroup Loyalty Tests {.tabset}

<br>

These analyses were only conducted within the CUZnSIB dataset due to potential confounding of moral values' effects on obligation judgments in other datasets.

<br>

## No Choice
```{r}
# correlation values are taken from the oblig ~ ind. diffs analyses
## r.jk = oblig ~ family values corr; r.jh = oblig ~ ingroup loyalty corr; r.kh = family values ~ ingroup loyalty corr

# distant
cocor.dep.groups.overlap(r.jk = .26, r.jh = .17, r.kh = .65, 205, alternative = "two.sided",
                         test = "steiger1980", alpha = 0.05, conf.level = 0.95, null.value = 0)

# close
cocor.dep.groups.overlap(r.jk = .48, r.jh = .25, r.kh = .65, 205, alternative = "two.sided",
                         test = "steiger1980", alpha = 0.05, conf.level = 0.95, null.value = 0)

# difference
cocor.dep.groups.overlap(r.jk = -.21, r.jh = -.07, r.kh = .65, 205, alternative = "two.sided",
                         test = "steiger1980", alpha = 0.05, conf.level = 0.95, null.value = 0)
```
## Choice
```{r}
# correlation values are taken from the oblig ~ ind. diffs analyses
## r.jk = oblig ~ family values corr; r.jh = oblig ~ ingroup loyalty corr; r.kh = family values ~ ingroup loyalty corr

# distant
cocor.dep.groups.overlap(r.jk = .32, r.jh = .16, r.kh = .65, 205, alternative = "two.sided",
                         test = "steiger1980", alpha = 0.05, conf.level = 0.95, null.value = 0)

# close
cocor.dep.groups.overlap(r.jk = .40, r.jh = .24, r.kh = .65, 205, alternative = "two.sided",
                         test = "steiger1980", alpha = 0.05, conf.level = 0.95, null.value = 0)

# difference
cocor.dep.groups.overlap(r.jk = -.23, r.jh = -.20, r.kh = .65, 205, alternative = "two.sided",
                         test = "steiger1980", alpha = 0.05, conf.level = 0.95, null.value = 0)
```

