Experiment 2
BLINDED FOR PEER REVIEW
May 26, 2021
Data Waves
As pre-registered, we sought to collect data to attain 330 analyzable responses for each between-subjects condition (i.e., 330 participants who passed the attention check for each between-subjects condition, totalling 660 usable responses across the entire experiment).
On the first wave of data collection (N = 739), applying the pre-registered exclusion criterion led to adequate samples (i.e., Ns > 330) for all between-subjects datasets. Therefore, we did not launch a second wave of data collection.
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 were only 4 instances of a duplicate IP address, leading to an N = 735.
All other identifying information was removed (e.g., IP addresses, longitude/latitude, etc.).
Loading Data/Packages
Before running this chunk, please load “E2_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
Attaching package: 㤼㸱effsize㤼㸲
The following object is masked from 㤼㸱package:psych㤼㸲:
cohen.dlibrary(sjstats) # for eta-squared effect sizes
Attaching package: 㤼㸱sjstats㤼㸲
The following object is masked from 㤼㸱package:psych㤼㸲:
philibrary(correlation) # for cleaner correlation test output
library(rmcorr) # for repeated-measures correlation tests
library(tidyverse) # for data manipulation and plottingRegistered S3 methods overwritten by 'dbplyr':
method from
print.tbl_lazy
print.tbl_sql
-- Attaching packages --------------------------------------- tidyverse 1.3.0 --
v ggplot2 3.3.2 v dplyr 1.0.0
v tibble 3.0.1 v stringr 1.4.0
v tidyr 1.1.0 v forcats 0.5.0
v purrr 0.3.4
-- Conflicts ------------------------------------------ tidyverse_conflicts() --
x ggplot2::%+%() masks psych::%+%()
x ggplot2::alpha() masks psych::alpha()
x dplyr::filter() masks stats::filter()
x dplyr::lag() masks stats::lag()Data Separation/Recombining
Data were separated into two distinct data sets (for each between-subjects condition). Then, a between-subjects variable was created within each between-subjects dataset. Last, both datasets were recombined.
# creates dataset that only has participants who made judgments of agents who helped stranger-like family members
E2_SL <- E2_raw_data %>%
filter(SL_CnS_C_m1 >= 0 | SL_CnS_C_m2 >= 0)
# creates dataset that only has participants who made judgments of agents who friend-like family members
E2_FL <- E2_raw_data %>%
filter(FL_CnS_C_m1 >= 0 | FL_CnS_C_m2 >= 0)
# create between-subjects condition variable
E2_SL$BSs_cond <- rep("Stranger-Like", nrow(E2_SL))
E2_FL$BSs_cond <- rep("Friend-Like", nrow(E2_FL))
# recombine between-subjects data
E2_all <- rbind(E2_SL, E2_FL)Implementing Attention Checks
Based on our pre-registered criterion, participants who failed a pre-manipulation attention check were to be excluded from all analyses. The 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 seven pre-outcome measures.
Participants who responded on average above a 10 on the pre-outcome 100-points scales were excluded. (We chose to use an average because we realized that a small group of participants answered the left-most option on the scale for six of the seven pre-outcome measures, but for the seventh pre-outcome measure, they answered with a number slightly above 10. Through testing how this could have happened, we noticed that participants using a mouse-scroll could have answered the seventh pre-outcome measure correctly, but their mouse-scroll could have dislodged their last answer if they did not click off of the slider first.) This led to a final analyzable N = 699 (a 95% retention rate).
# Create an attention check average variable
E2_all$AC_AVG <- ((E2_all$AC_oblig + E2_all$AC_relate + E2_all$AC_close + E2_all$AC_priorhelp + E2_all$AC_futurehelp + E2_all$AC_priorinteract + E2_all$AC_futureinteract)/7)
# Create dataset that filters out inattentive participants
E2_all_clean <- E2_all %>%
# excludes participants who were not paying attention
filter(AC_AVG < 10)Creating Analysis Variables
# Main DVs
# create single column for each condition's variables that collapses across presentation order of DVs
# e.g., SL_CnS_C_o1 = "Stranger-Like" family members dataset, "No Choice" condition, CUZ obligation judgment, obligation judgment presented first
# to clarify, as noted in the Method section (and SOM), six other pre-outcome judgments were collected, counterbalanced so that obligation judgments were either first or last (1 = obligation first, 2 = obligation last)
E2_all_clean$NoChoice_CUZ_oblig <- rowSums(E2_all_clean[, c("SL_CnS_C_o1", "SL_CnS_C_o2",
"FL_CnS_C_o1", "FL_CnS_C_o2")],
na.rm = T)
E2_all_clean$NoChoice_CUZ_relate <- rowSums(E2_all_clean[, c("SL_CnS_C_r1", "SL_CnS_C_r2",
"FL_CnS_C_r1", "FL_CnS_C_r2")],
na.rm = T)
E2_all_clean$NoChoice_CUZ_close <- rowSums(E2_all_clean[, c("SL_CnS_C_c1", "SL_CnS_C_c2",
"FL_CnS_C_c1", "FL_CnS_C_c2")],
na.rm = T)
E2_all_clean$NoChoice_CUZ_priorhelp <- rowSums(E2_all_clean[, c("SL_CnS_C_ph1", "SL_CnS_C_ph2",
"FL_CnS_C_ph1", "FL_CnS_C_ph2")],
na.rm = T)
E2_all_clean$NoChoice_CUZ_futurehelp <- rowSums(E2_all_clean[, c("SL_CnS_C_fh1", "SL_CnS_C_fh2",
"FL_CnS_C_fh1", "FL_CnS_C_fh2")],
na.rm = T)
E2_all_clean$NoChoice_CUZ_priorinteract <- rowSums(E2_all_clean[, c("SL_CnS_C_pi1", "SL_CnS_C_pi2",
"FL_CnS_C_pi1", "FL_CnS_C_pi2")],
na.rm = T)
E2_all_clean$NoChoice_CUZ_futureinteract <- rowSums(E2_all_clean[, c("SL_CnS_C_fi1", "SL_CnS_C_fi2",
"FL_CnS_C_fi1", "FL_CnS_C_fi2")],
na.rm = T)
E2_all_clean$NoChoice_CUZ_moral <- rowSums(E2_all_clean[, c("SL_CnS_C_m1", "SL_CnS_C_m2",
"FL_CnS_C_m1", "FL_CnS_C_m2")],
na.rm = T)
E2_all_clean$NoChoice_SIB_oblig <- rowSums(E2_all_clean[, c("SL_CnS_S_o1", "SL_CnS_S_o2",
"FL_CnS_S_o1", "FL_CnS_S_o2")],
na.rm = T)
E2_all_clean$NoChoice_SIB_relate <- rowSums(E2_all_clean[, c("SL_CnS_S_r1", "SL_CnS_S_r2",
"FL_CnS_S_r1", "FL_CnS_S_r2")],
na.rm = T)
E2_all_clean$NoChoice_SIB_close <- rowSums(E2_all_clean[, c("SL_CnS_S_c1", "SL_CnS_S_c2",
"FL_CnS_S_c1", "FL_CnS_S_c2")],
na.rm = T)
E2_all_clean$NoChoice_SIB_priorhelp <- rowSums(E2_all_clean[, c("SL_CnS_S_ph1", "SL_CnS_S_ph2",
"FL_CnS_S_ph1", "FL_CnS_S_ph2")],
na.rm = T)
E2_all_clean$NoChoice_SIB_futurehelp <- rowSums(E2_all_clean[, c("SL_CnS_S_fh1", "SL_CnS_S_fh2",
"FL_CnS_S_fh1", "FL_CnS_S_fh2")],
na.rm = T)
E2_all_clean$NoChoice_SIB_priorinteract <- rowSums(E2_all_clean[, c("SL_CnS_S_pi1", "SL_CnS_S_pi2",
"FL_CnS_S_pi1", "FL_CnS_S_pi2")],
na.rm = T)
E2_all_clean$NoChoice_SIB_futureinteract <- rowSums(E2_all_clean[, c("SL_CnS_S_fi1", "SL_CnS_S_fi2",
"FL_CnS_S_fi1", "FL_CnS_S_fi2")],
na.rm = T)
E2_all_clean$NoChoice_SIB_moral <- rowSums(E2_all_clean[, c("SL_CnS_S_m1", "SL_CnS_S_m2",
"FL_CnS_S_m1", "FL_CnS_S_m2")],
na.rm = T)
# e.g., SL_CnS_CoS_C_o11 = "Stranger-Like" family members dataset, "Choice" condition, CUZ obligation judgment, CUZ measures first, obligation judgment presented first
# to clarify, as noted in the Method section, two obligation (and other pre-outcome) judgments were collected in these conditions -- one for each potential beneficiary (e.g., CUZ and SIB), and they get averaged together later on in this same code chunk
E2_all_clean$CUZoSIB_CUZ_oblig <- rowSums(E2_all_clean[, c("SL_CnS_CoS_C_o11", "SL_CnS_CoS_C_o12",
"SL_CnS_CoS_C_o21", "SL_CnS_CoS_C_o22",
"FL_CnS_CoS_C_o11", "FL_CnS_CoS_C_o12",
"FL_CnS_CoS_C_o21", "FL_CnS_CoS_C_o22")],
na.rm = T)
E2_all_clean$CUZoSIB_CUZ_relate <- rowSums(E2_all_clean[, c("SL_CnS_CoS_C_r11", "SL_CnS_CoS_C_r12",
"SL_CnS_CoS_C_r21", "SL_CnS_CoS_C_r22",
"FL_CnS_CoS_C_r11", "FL_CnS_CoS_C_r12",
"FL_CnS_CoS_C_r21", "FL_CnS_CoS_C_r22")],
na.rm = T)
E2_all_clean$CUZoSIB_CUZ_close <- rowSums(E2_all_clean[, c("SL_CnS_CoS_C_c11", "SL_CnS_CoS_C_c12",
"SL_CnS_CoS_C_c21", "SL_CnS_CoS_C_c22",
"FL_CnS_CoS_C_c11", "FL_CnS_CoS_C_c12",
"FL_CnS_CoS_C_c21", "FL_CnS_CoS_C_c22")],
na.rm = T)
E2_all_clean$CUZoSIB_CUZ_priorhelp <- rowSums(E2_all_clean[, c("SL_CnS_CoS_C_ph11", "SL_CnS_CoS_C_ph12",
"SL_CnS_CoS_C_ph21", "SL_CnS_CoS_C_ph22",
"FL_CnS_CoS_C_ph11", "FL_CnS_CoS_C_ph12",
"FL_CnS_CoS_C_ph21", "FL_CnS_CoS_C_ph22")],
na.rm = T)
E2_all_clean$CUZoSIB_CUZ_futurehelp <- rowSums(E2_all_clean[, c("SL_CnS_CoS_C_fh11", "SL_CnS_CoS_C_fh12",
"SL_CnS_CoS_C_fh21", "SL_CnS_CoS_C_fh22",
"FL_CnS_CoS_C_fh11", "FL_CnS_CoS_C_fh12",
"FL_CnS_CoS_C_fh21", "FL_CnS_CoS_C_fh22")],
na.rm = T)
E2_all_clean$CUZoSIB_CUZ_priorinteract <- rowSums(E2_all_clean[, c("SL_CnS_CoS_C_pi11", "SL_CnS_CoS_C_pi12",
"SL_CnS_CoS_C_pi21", "SL_CnS_CoS_C_pi22",
"FL_CnS_CoS_C_pi11", "FL_CnS_CoS_C_pi12",
"FL_CnS_CoS_C_pi21", "FL_CnS_CoS_C_pi22")],
na.rm = T)
E2_all_clean$CUZoSIB_CUZ_futureinteract <- rowSums(E2_all_clean[, c("SL_CnS_CoS_C_fi11", "SL_CnS_CoS_C_fi12",
"SL_CnS_CoS_C_fi21", "SL_CnS_CoS_C_fi22",
"FL_CnS_CoS_C_fi11", "FL_CnS_CoS_C_fi12",
"FL_CnS_CoS_C_fi21", "FL_CnS_CoS_C_fi22")],
na.rm = T)
E2_all_clean$CUZoSIB_SIB_oblig <- rowSums(E2_all_clean[, c("SL_CnS_CoS_S_o11", "SL_CnS_CoS_S_o12",
"SL_CnS_CoS_S_o21", "SL_CnS_CoS_S_o22",
"FL_CnS_CoS_S_o11", "FL_CnS_CoS_S_o12",
"FL_CnS_CoS_S_o21", "FL_CnS_CoS_S_o22")],
na.rm = T)
E2_all_clean$CUZoSIB_SIB_relate <- rowSums(E2_all_clean[, c("SL_CnS_CoS_S_r11", "SL_CnS_CoS_S_r12",
"SL_CnS_CoS_S_r21", "SL_CnS_CoS_S_r22",
"FL_CnS_CoS_S_r11", "FL_CnS_CoS_S_r12",
"FL_CnS_CoS_S_r21", "FL_CnS_CoS_S_r22")],
na.rm = T)
E2_all_clean$CUZoSIB_SIB_close <- rowSums(E2_all_clean[, c("SL_CnS_CoS_S_c11", "SL_CnS_CoS_S_c12",
"SL_CnS_CoS_S_c21", "SL_CnS_CoS_S_c22",
"FL_CnS_CoS_S_c11", "FL_CnS_CoS_S_c12",
"FL_CnS_CoS_S_c21", "FL_CnS_CoS_S_c22")],
na.rm = T)
E2_all_clean$CUZoSIB_SIB_priorhelp <- rowSums(E2_all_clean[, c("SL_CnS_CoS_S_ph11", "SL_CnS_CoS_S_ph12",
"SL_CnS_CoS_S_ph21", "SL_CnS_CoS_S_ph22",
"FL_CnS_CoS_S_ph11", "FL_CnS_CoS_S_ph12",
"FL_CnS_CoS_S_ph21", "FL_CnS_CoS_S_ph22")],
na.rm = T)
E2_all_clean$CUZoSIB_SIB_futurehelp <- rowSums(E2_all_clean[, c("SL_CnS_CoS_S_fh11", "SL_CnS_CoS_S_fh12",
"SL_CnS_CoS_S_fh21", "SL_CnS_CoS_S_fh22",
"FL_CnS_CoS_S_fh11", "FL_CnS_CoS_S_fh12",
"FL_CnS_CoS_S_fh21", "FL_CnS_CoS_S_fh22")],
na.rm = T)
E2_all_clean$CUZoSIB_SIB_priorinteract <- rowSums(E2_all_clean[, c("SL_CnS_CoS_S_pi11", "SL_CnS_CoS_S_pi12",
"SL_CnS_CoS_S_pi21", "SL_CnS_CoS_S_pi22",
"FL_CnS_CoS_S_pi11", "FL_CnS_CoS_S_pi12",
"FL_CnS_CoS_S_pi21", "FL_CnS_CoS_S_pi22")],
na.rm = T)
E2_all_clean$CUZoSIB_SIB_futureinteract <- rowSums(E2_all_clean[, c("SL_CnS_CoS_S_fi11", "SL_CnS_CoS_S_fi12",
"SL_CnS_CoS_S_fi21", "SL_CnS_CoS_S_fi22",
"FL_CnS_CoS_S_fi11", "FL_CnS_CoS_S_fi12",
"FL_CnS_CoS_S_fi21", "FL_CnS_CoS_S_fi22")],
na.rm = T)
E2_all_clean$CUZoSIB_CUZ_moral <- rowSums(E2_all_clean[, c("SL_CnS_CoS_C_m11", "SL_CnS_CoS_C_m12",
"SL_CnS_CoS_C_m21", "SL_CnS_CoS_C_m22",
"FL_CnS_CoS_C_m11", "FL_CnS_CoS_C_m12",
"FL_CnS_CoS_C_m21", "FL_CnS_CoS_C_m22")],
na.rm = T)
E2_all_clean$SIBoCUZ_CUZ_oblig <- rowSums(E2_all_clean[, c("SL_CnS_SoC_C_o11", "SL_CnS_SoC_C_o12",
"SL_CnS_SoC_C_o21", "SL_CnS_SoC_C_o22",
"FL_CnS_SoC_C_o11", "FL_CnS_SoC_C_o12",
"FL_CnS_SoC_C_o21", "FL_CnS_SoC_C_o22")],
na.rm = T)
E2_all_clean$SIBoCUZ_CUZ_relate <- rowSums(E2_all_clean[, c("SL_CnS_SoC_C_r11", "SL_CnS_SoC_C_r12",
"SL_CnS_SoC_C_r21", "SL_CnS_SoC_C_r22",
"FL_CnS_SoC_C_r11", "FL_CnS_SoC_C_r12",
"FL_CnS_SoC_C_r21", "FL_CnS_SoC_C_r22")],
na.rm = T)
E2_all_clean$SIBoCUZ_CUZ_close <- rowSums(E2_all_clean[, c("SL_CnS_SoC_C_c11", "SL_CnS_SoC_C_c12",
"SL_CnS_SoC_C_c21", "SL_CnS_SoC_C_c22",
"FL_CnS_SoC_C_c11", "FL_CnS_SoC_C_c12",
"FL_CnS_SoC_C_c21", "FL_CnS_SoC_C_c22")],
na.rm = T)
E2_all_clean$SIBoCUZ_CUZ_priorhelp <- rowSums(E2_all_clean[, c("SL_CnS_SoC_C_ph11", "SL_CnS_SoC_C_ph12",
"SL_CnS_SoC_C_ph21", "SL_CnS_SoC_C_ph22",
"FL_CnS_SoC_C_ph11", "FL_CnS_SoC_C_ph12",
"FL_CnS_SoC_C_ph21", "FL_CnS_SoC_C_ph22")],
na.rm = T)
E2_all_clean$SIBoCUZ_CUZ_futurehelp <- rowSums(E2_all_clean[, c("SL_CnS_SoC_C_fh11", "SL_CnS_SoC_C_fh12",
"SL_CnS_SoC_C_fh21", "SL_CnS_SoC_C_fh22",
"FL_CnS_SoC_C_fh11", "FL_CnS_SoC_C_fh12",
"FL_CnS_SoC_C_fh21", "FL_CnS_SoC_C_fh22")],
na.rm = T)
E2_all_clean$SIBoCUZ_CUZ_priorinteract <- rowSums(E2_all_clean[, c("SL_CnS_SoC_C_pi11", "SL_CnS_SoC_C_pi12",
"SL_CnS_SoC_C_pi21", "SL_CnS_SoC_C_pi22",
"FL_CnS_SoC_C_pi11", "FL_CnS_SoC_C_pi12",
"FL_CnS_SoC_C_pi21", "FL_CnS_SoC_C_pi22")],
na.rm = T)
E2_all_clean$SIBoCUZ_CUZ_futureinteract <- rowSums(E2_all_clean[, c("SL_CnS_SoC_C_fi11", "SL_CnS_SoC_C_fi12",
"SL_CnS_SoC_C_fi21", "SL_CnS_SoC_C_fi22",
"FL_CnS_SoC_C_fi11", "FL_CnS_SoC_C_fi12",
"FL_CnS_SoC_C_fi21", "FL_CnS_SoC_C_fi22")],
na.rm = T)
E2_all_clean$SIBoCUZ_SIB_oblig <- rowSums(E2_all_clean[, c("SL_CnS_SoC_S_o11", "SL_CnS_SoC_S_o12",
"SL_CnS_SoC_S_o21", "SL_CnS_SoC_S_o22",
"FL_CnS_SoC_S_o11", "FL_CnS_SoC_S_o12",
"FL_CnS_SoC_S_o21", "FL_CnS_SoC_S_o22")],
na.rm = T)
E2_all_clean$SIBoCUZ_SIB_relate <- rowSums(E2_all_clean[, c("SL_CnS_SoC_S_r11", "SL_CnS_SoC_S_r12",
"SL_CnS_SoC_S_r21", "SL_CnS_SoC_S_r22",
"FL_CnS_SoC_S_r11", "FL_CnS_SoC_S_r12",
"FL_CnS_SoC_S_r21", "FL_CnS_SoC_S_r22")],
na.rm = T)
E2_all_clean$SIBoCUZ_SIB_close <- rowSums(E2_all_clean[, c("SL_CnS_SoC_S_c11", "SL_CnS_SoC_S_c12",
"SL_CnS_SoC_S_c21", "SL_CnS_SoC_S_c22",
"FL_CnS_SoC_S_c11", "FL_CnS_SoC_S_c12",
"FL_CnS_SoC_S_c21", "FL_CnS_SoC_S_c22")],
na.rm = T)
E2_all_clean$SIBoCUZ_SIB_priorhelp <- rowSums(E2_all_clean[, c("SL_CnS_SoC_S_ph11", "SL_CnS_SoC_S_ph12",
"SL_CnS_SoC_S_ph21", "SL_CnS_SoC_S_ph22",
"FL_CnS_SoC_S_ph11", "FL_CnS_SoC_S_ph12",
"FL_CnS_SoC_S_ph21", "FL_CnS_SoC_S_ph22")],
na.rm = T)
E2_all_clean$SIBoCUZ_SIB_futurehelp <- rowSums(E2_all_clean[, c("SL_CnS_SoC_S_fh11", "SL_CnS_SoC_S_fh12",
"SL_CnS_SoC_S_fh21", "SL_CnS_SoC_S_fh22",
"FL_CnS_SoC_S_fh11", "FL_CnS_SoC_S_fh12",
"FL_CnS_SoC_S_fh21", "FL_CnS_SoC_S_fh22")],
na.rm = T)
E2_all_clean$SIBoCUZ_SIB_priorinteract <- rowSums(E2_all_clean[, c("SL_CnS_SoC_S_pi11", "SL_CnS_SoC_S_pi12",
"SL_CnS_SoC_S_pi21", "SL_CnS_SoC_S_pi22",
"FL_CnS_SoC_S_pi11", "FL_CnS_SoC_S_pi12",
"FL_CnS_SoC_S_pi21", "FL_CnS_SoC_S_pi22")],
na.rm = T)
E2_all_clean$SIBoCUZ_SIB_futureinteract <- rowSums(E2_all_clean[, c("SL_CnS_SoC_S_fi11", "SL_CnS_SoC_S_fi12",
"SL_CnS_SoC_S_fi21", "SL_CnS_SoC_S_fi22",
"FL_CnS_SoC_S_fi11", "FL_CnS_SoC_S_fi12",
"FL_CnS_SoC_S_fi21", "FL_CnS_SoC_S_fi22")],
na.rm = T)
E2_all_clean$SIBoCUZ_SIB_moral <- rowSums(E2_all_clean[, c("SL_CnS_SoC_S_m11", "SL_CnS_SoC_S_m12",
"SL_CnS_SoC_S_m21", "SL_CnS_SoC_S_m22",
"FL_CnS_SoC_S_m11", "FL_CnS_SoC_S_m12",
"FL_CnS_SoC_S_m21", "FL_CnS_SoC_S_m22")],
na.rm = T)
E2_all_clean$Choice_CUZ_oblig <- (E2_all_clean$CUZoSIB_CUZ_oblig +
E2_all_clean$SIBoCUZ_CUZ_oblig)/2 # creates pre-reg'd index
E2_all_clean$Choice_CUZ_relate <- (E2_all_clean$CUZoSIB_CUZ_relate +
E2_all_clean$SIBoCUZ_CUZ_relate)/2 # creates pre-reg'd index
E2_all_clean$Choice_CUZ_close <- (E2_all_clean$CUZoSIB_CUZ_close +
E2_all_clean$SIBoCUZ_CUZ_close)/2 # creates pre-reg'd index
E2_all_clean$Choice_CUZ_priorhelp <- (E2_all_clean$CUZoSIB_CUZ_priorhelp +
E2_all_clean$SIBoCUZ_CUZ_priorhelp)/2 # creates pre-reg'd index
E2_all_clean$Choice_CUZ_futurehelp <- (E2_all_clean$CUZoSIB_CUZ_futurehelp +
E2_all_clean$SIBoCUZ_CUZ_futurehelp)/2 # creates pre-reg'd index
E2_all_clean$Choice_CUZ_priorinteract <- (E2_all_clean$CUZoSIB_CUZ_priorinteract +
E2_all_clean$SIBoCUZ_CUZ_priorinteract)/2 # creates pre-reg'd index
E2_all_clean$Choice_CUZ_futureinteract <- (E2_all_clean$CUZoSIB_CUZ_futureinteract +
E2_all_clean$SIBoCUZ_CUZ_futureinteract)/2 # creates pre-reg'd index
E2_all_clean$Choice_CUZ_moral <- E2_all_clean$CUZoSIB_CUZ_moral # single judgment (post-outcome)
E2_all_clean$Choice_SIB_oblig <- (E2_all_clean$CUZoSIB_SIB_oblig +
E2_all_clean$SIBoCUZ_SIB_oblig)/2 # creates pre-reg'd index
E2_all_clean$Choice_SIB_relate <- (E2_all_clean$CUZoSIB_SIB_relate +
E2_all_clean$SIBoCUZ_SIB_relate)/2 # creates pre-reg'd index
E2_all_clean$Choice_SIB_close <- (E2_all_clean$CUZoSIB_SIB_close +
E2_all_clean$SIBoCUZ_SIB_close)/2 # creates pre-reg'd index
E2_all_clean$Choice_SIB_priorhelp <- (E2_all_clean$CUZoSIB_SIB_priorhelp +
E2_all_clean$SIBoCUZ_SIB_priorhelp)/2 # creates pre-reg'd index
E2_all_clean$Choice_SIB_futurehelp <- (E2_all_clean$CUZoSIB_SIB_futurehelp +
E2_all_clean$SIBoCUZ_SIB_futurehelp)/2 # creates pre-reg'd index
E2_all_clean$Choice_SIB_priorinteract <- (E2_all_clean$CUZoSIB_SIB_priorinteract +
E2_all_clean$SIBoCUZ_SIB_priorinteract)/2 # creates pre-reg'd index
E2_all_clean$Choice_SIB_futureinteract <- (E2_all_clean$CUZoSIB_SIB_futureinteract +
E2_all_clean$SIBoCUZ_SIB_futureinteract)/2 # creates pre-reg'd index
E2_all_clean$Choice_SIB_moral <- E2_all_clean$SIBoCUZ_SIB_moral # single judgment (post-outcome)
# Difference Scores
# CUZminusSIB obligation within No Choice or Choice conditions (for diff score corrs and ind. diffs analyses)
E2_all_clean$NoChoice_CUZminusSIB_oblig <- E2_all_clean$NoChoice_CUZ_oblig - E2_all_clean$NoChoice_SIB_oblig
E2_all_clean$NoChoice_CUZminusSIB_relate <- E2_all_clean$NoChoice_CUZ_relate - E2_all_clean$NoChoice_SIB_relate
E2_all_clean$NoChoice_CUZminusSIB_close <- E2_all_clean$NoChoice_CUZ_close - E2_all_clean$NoChoice_SIB_close
E2_all_clean$NoChoice_CUZminusSIB_priorhelp <- E2_all_clean$NoChoice_CUZ_priorhelp - E2_all_clean$NoChoice_SIB_priorhelp
E2_all_clean$NoChoice_CUZminusSIB_futurehelp <- E2_all_clean$NoChoice_CUZ_futurehelp - E2_all_clean$NoChoice_SIB_futurehelp
E2_all_clean$NoChoice_CUZminusSIB_priorinteract <- E2_all_clean$NoChoice_CUZ_priorinteract - E2_all_clean$NoChoice_SIB_priorinteract
E2_all_clean$NoChoice_CUZminusSIB_futureinteract <- E2_all_clean$NoChoice_CUZ_futureinteract - E2_all_clean$NoChoice_SIB_futureinteract
E2_all_clean$NoChoice_CUZminusSIB_moral <- E2_all_clean$NoChoice_CUZ_moral - E2_all_clean$NoChoice_SIB_moral
E2_all_clean$Choice_CUZminusSIB_oblig <- E2_all_clean$Choice_CUZ_oblig - E2_all_clean$Choice_SIB_oblig
E2_all_clean$Choice_CUZminusSIB_relate <- E2_all_clean$Choice_CUZ_relate - E2_all_clean$Choice_SIB_relate
E2_all_clean$Choice_CUZminusSIB_close <- E2_all_clean$Choice_CUZ_close - E2_all_clean$Choice_SIB_close
E2_all_clean$Choice_CUZminusSIB_priorhelp <- E2_all_clean$Choice_CUZ_priorhelp - E2_all_clean$Choice_SIB_priorhelp
E2_all_clean$Choice_CUZminusSIB_futurehelp <- E2_all_clean$Choice_CUZ_futurehelp - E2_all_clean$Choice_SIB_futurehelp
E2_all_clean$Choice_CUZminusSIB_priorinteract <- E2_all_clean$Choice_CUZ_priorinteract - E2_all_clean$Choice_SIB_priorinteract
E2_all_clean$Choice_CUZminusSIB_futureinteract <- E2_all_clean$Choice_CUZ_futureinteract - E2_all_clean$Choice_SIB_futureinteract
E2_all_clean$Choice_CUZminusSIB_moral <- E2_all_clean$Choice_CUZ_moral - E2_all_clean$Choice_SIB_moral
# Individual Difference Measures (for ind. diffs analyses)
# MAC (Morality-as-Cooperation scale) composites
# first need to reverse score property judgment subscale per Curry et al. 2019
E2_all_clean$MAC_Jud_19_r <- ((102 - (E2_all_clean$MAC_Jud_19 +1)) - 1)
E2_all_clean$MAC_Jud_20_r <- ((102 - (E2_all_clean$MAC_Jud_20 +1)) - 1)
E2_all_clean$MAC_Jud_21_r <- ((102 - (E2_all_clean$MAC_Jud_21 +1)) - 1)
E2_all_clean$MAC_Fam_Combined <- ((E2_all_clean$MAC_Jud_1 + E2_all_clean$MAC_Jud_2 + E2_all_clean$MAC_Jud_3 +
E2_all_clean$MAC_Rel_1 + E2_all_clean$MAC_Rel_2 + E2_all_clean$MAC_Rel_3)/6)
E2_all_clean$MAC_Fam_Jud <- ((E2_all_clean$MAC_Jud_1 + E2_all_clean$MAC_Jud_2 + E2_all_clean$MAC_Jud_3)/3)
E2_all_clean$MAC_Fam_Rel <- ((E2_all_clean$MAC_Rel_1 + E2_all_clean$MAC_Rel_2 + E2_all_clean$MAC_Rel_3)/3)
E2_all_clean$MAC_Group_Combined <- ((E2_all_clean$MAC_Jud_4 + E2_all_clean$MAC_Jud_5 + E2_all_clean$MAC_Jud_6 +
E2_all_clean$MAC_Rel_4 + E2_all_clean$MAC_Rel_5 + E2_all_clean$MAC_Rel_6)/6)
E2_all_clean$MAC_Group_Jud <- ((E2_all_clean$MAC_Jud_4 + E2_all_clean$MAC_Jud_5 + E2_all_clean$MAC_Jud_6)/3)
E2_all_clean$MAC_Group_Rel <- ((E2_all_clean$MAC_Rel_4 + E2_all_clean$MAC_Rel_5 + E2_all_clean$MAC_Rel_6)/3)
E2_all_clean$MAC_Rec_Combined <- ((E2_all_clean$MAC_Jud_7 + E2_all_clean$MAC_Jud_8 + E2_all_clean$MAC_Jud_9 +
E2_all_clean$MAC_Rel_7 + E2_all_clean$MAC_Rel_8 + E2_all_clean$MAC_Rel_9)/6)
E2_all_clean$MAC_Rec_Jud <- ((E2_all_clean$MAC_Jud_7 + E2_all_clean$MAC_Jud_8 + E2_all_clean$MAC_Jud_9)/3)
E2_all_clean$MAC_Rec_Rel <- ((E2_all_clean$MAC_Rel_7 + E2_all_clean$MAC_Rel_8 + E2_all_clean$MAC_Rel_9)/3)
E2_all_clean$MAC_Hero_Combined <- ((E2_all_clean$MAC_Jud_10 + E2_all_clean$MAC_Jud_11 + E2_all_clean$MAC_Jud_12 +
E2_all_clean$MAC_Rel_10 + E2_all_clean$MAC_Rel_11 + E2_all_clean$MAC_Rel_12)/6)
E2_all_clean$MAC_Hero_Jud <- ((E2_all_clean$MAC_Jud_10 + E2_all_clean$MAC_Jud_11 + E2_all_clean$MAC_Jud_12)/3)
E2_all_clean$MAC_Hero_Rel <- ((E2_all_clean$MAC_Rel_10 + E2_all_clean$MAC_Rel_11 + E2_all_clean$MAC_Rel_12)/3)
E2_all_clean$MAC_Def_Combined <- ((E2_all_clean$MAC_Jud_13 + E2_all_clean$MAC_Jud_14 + E2_all_clean$MAC_Jud_15 +
E2_all_clean$MAC_Rel_13 + E2_all_clean$MAC_Rel_14 + E2_all_clean$MAC_Rel_15)/6)
E2_all_clean$MAC_Def_Jud <- ((E2_all_clean$MAC_Jud_13 + E2_all_clean$MAC_Jud_14 + E2_all_clean$MAC_Jud_15)/3)
E2_all_clean$MAC_Def_Rel <- ((E2_all_clean$MAC_Rel_13 + E2_all_clean$MAC_Rel_14 + E2_all_clean$MAC_Rel_15)/3)
E2_all_clean$MAC_Fair_Combined <- ((E2_all_clean$MAC_Jud_16 + E2_all_clean$MAC_Jud_17 + E2_all_clean$MAC_Jud_18 +
E2_all_clean$MAC_Rel_16 + E2_all_clean$MAC_Rel_17 + E2_all_clean$MAC_Rel_18)/6)
E2_all_clean$MAC_Fair_Jud <- ((E2_all_clean$MAC_Jud_16 + E2_all_clean$MAC_Jud_17 + E2_all_clean$MAC_Jud_18)/3)
E2_all_clean$MAC_Fair_Rel <- ((E2_all_clean$MAC_Rel_16 + E2_all_clean$MAC_Rel_17 + E2_all_clean$MAC_Rel_18)/3)
E2_all_clean$MAC_Prop_Combined <- ((E2_all_clean$MAC_Jud_19_r + E2_all_clean$MAC_Jud_20_r + E2_all_clean$MAC_Jud_21_r +
E2_all_clean$MAC_Rel_19 + E2_all_clean$MAC_Rel_20 + E2_all_clean$MAC_Rel_21)/6)
E2_all_clean$MAC_Prop_Jud <- ((E2_all_clean$MAC_Jud_19_r + E2_all_clean$MAC_Jud_20_r + E2_all_clean$MAC_Jud_21_r)/3)
E2_all_clean$MAC_Prop_Rel <- ((E2_all_clean$MAC_Rel_19 + E2_all_clean$MAC_Rel_20 + E2_all_clean$MAC_Rel_21)/3)
# MFQ (Moral Foundations Theory scale) composites
E2_all_clean$MFQ_Harm_Combined <- ((E2_all_clean$MFQ_Jud_1 + E2_all_clean$MFQ_Jud_2 + E2_all_clean$MFQ_Jud_3 +
E2_all_clean$MFQ_Rel_1 + E2_all_clean$MFQ_Rel_2 + E2_all_clean$MFQ_Rel_3)/6)
E2_all_clean$MFQ_Harm_Jud <- ((E2_all_clean$MFQ_Jud_1 + E2_all_clean$MFQ_Jud_2 + E2_all_clean$MFQ_Jud_3)/3)
E2_all_clean$MFQ_Harm_Rel <- ((E2_all_clean$MFQ_Rel_1 + E2_all_clean$MFQ_Rel_2 + E2_all_clean$MFQ_Rel_3)/3)
E2_all_clean$MFQ_Fairness_Combined <- ((E2_all_clean$MFQ_Jud_4 + E2_all_clean$MFQ_Jud_5 + E2_all_clean$MFQ_Jud_6 +
E2_all_clean$MFQ_Rel_4 + E2_all_clean$MFQ_Rel_5 + E2_all_clean$MFQ_Rel_6)/6)
E2_all_clean$MFQ_Fairness_Jud <- ((E2_all_clean$MFQ_Jud_4 + E2_all_clean$MFQ_Jud_5 + E2_all_clean$MFQ_Jud_6)/3)
E2_all_clean$MFQ_Fairness_Rel <- ((E2_all_clean$MFQ_Rel_4 + E2_all_clean$MFQ_Rel_5 + E2_all_clean$MFQ_Rel_6)/3)
E2_all_clean$MFQ_Loyalty_Combined <- ((E2_all_clean$MFQ_Jud_7 + E2_all_clean$MFQ_Jud_8 + E2_all_clean$MFQ_Jud_9 +
E2_all_clean$MFQ_Rel_7 + E2_all_clean$MFQ_Rel_8 + E2_all_clean$MFQ_Rel_9)/6)
E2_all_clean$MFQ_Loyalty_Jud <- ((E2_all_clean$MFQ_Jud_7 + E2_all_clean$MFQ_Jud_8 + E2_all_clean$MFQ_Jud_9)/3)
E2_all_clean$MFQ_Loyalty_Rel <- ((E2_all_clean$MFQ_Rel_7 + E2_all_clean$MFQ_Rel_8 + E2_all_clean$MFQ_Rel_9)/3)
E2_all_clean$MFQ_Authority_Combined <- ((E2_all_clean$MFQ_Jud_10 + E2_all_clean$MFQ_Jud_11 + E2_all_clean$MFQ_Jud_12 +
E2_all_clean$MFQ_Rel_10 + E2_all_clean$MFQ_Rel_11 + E2_all_clean$MFQ_Rel_12)/6)
E2_all_clean$MFQ_Authority_Jud <- ((E2_all_clean$MFQ_Jud_10 + E2_all_clean$MFQ_Jud_11 + E2_all_clean$MFQ_Jud_12)/3)
E2_all_clean$MFQ_Authority_Rel <- ((E2_all_clean$MFQ_Rel_10 + E2_all_clean$MFQ_Rel_11 + E2_all_clean$MFQ_Rel_12)/3)
E2_all_clean$MFQ_Purity_Combined <- ((E2_all_clean$MFQ_Jud_13 + E2_all_clean$MFQ_Jud_14 + E2_all_clean$MFQ_Jud_15 +
E2_all_clean$MFQ_Rel_13 + E2_all_clean$MFQ_Rel_14 + E2_all_clean$MFQ_Rel_15)/6)
E2_all_clean$MFQ_Purity_Jud <- ((E2_all_clean$MFQ_Jud_13 + E2_all_clean$MFQ_Jud_14 + E2_all_clean$MFQ_Jud_15)/3)
E2_all_clean$MFQ_Purity_Rel <- ((E2_all_clean$MFQ_Rel_13 + E2_all_clean$MFQ_Rel_14 + E2_all_clean$MFQ_Rel_15)/3)
# OUS (Oxford Utilitarianism Scale) composites
E2_all_clean$OUS_IB <- ((E2_all_clean$OUS_IB1 + E2_all_clean$OUS_IB2 + E2_all_clean$OUS_IB3 +
E2_all_clean$OUS_IB4 + E2_all_clean$OUS_IB5)/5)
E2_all_clean$OUS_IH <- ((E2_all_clean$OUS_IH1 + E2_all_clean$OUS_IH2 + E2_all_clean$OUS_IH3 +
E2_all_clean$OUS_IH4)/4)Creating Analyzable Between-Subjects Datasets
# Stranger-Like family members
E2_SL_clean <- E2_all_clean %>%
filter(BSs_cond == 'Stranger-Like') %>%
# select only variables that are relevant to Stranger-Like data
select(
ResponseId, # selects variable
Age:Urban_Rural, # selects demographic variables
MAC_Jud_1:MAC_Jud_18, MAC_Jud_19_r:MAC_Jud_21_r, MAC_Rel_1:MAC_Rel_21,
MFQ_Jud_1:MFQ_Jud_15, MFQ_Rel_1:MFQ_Rel_15,
OUS_IB1:OUS_IB5, OUS_IH1:OUS_IH4, # selects raw ind. diff variables (for reliabilty check)
MAC_Fam_Combined:OUS_IH, # selects composited ind. diff variables
BSs_cond, # selects variable for between-subjects condition
SL_Dist_Scen:SL_CloseODist_Scen, # selects scenario-to-condition variables for SL data
NoChoice_CUZ_oblig:NoChoice_SIB_moral, # selects NoChoice DVs for SL data
Choice_CUZ_oblig:Choice_SIB_moral, # selects Choice DVs for SL data
NoChoice_CUZminusSIB_oblig:Choice_CUZminusSIB_moral # selects difference score variables for SL data
)
# Friend-like family members
E2_FL_clean <- E2_all_clean %>%
filter(BSs_cond == 'Friend-Like') %>%
# select only variables that are relevant to "Friend-Like" data
select(
ResponseId, # selects variable
Age:Urban_Rural, # selects demographic variables
MAC_Jud_1:MAC_Jud_18, MAC_Jud_19_r:MAC_Jud_21_r, MAC_Rel_1:MAC_Rel_21,
MFQ_Jud_1:MFQ_Jud_15, MFQ_Rel_1:MFQ_Rel_15,
OUS_IB1:OUS_IB5, OUS_IH1:OUS_IH4, # selects raw ind. diff variables (for reliabilty check)
MAC_Fam_Combined:OUS_IH, # selects composited ind. diff variables
BSs_cond, # selects variable for between-subjects condition
FL_Dist_Scen:FL_CloseODist_Scen, # selects scenario-to-condition variables for FL data
NoChoice_CUZ_oblig:NoChoice_SIB_moral, # selects NoChoice DVs for FL data
Choice_CUZ_oblig:Choice_SIB_moral, # selects Choice DVs for FL data
NoChoice_CUZminusSIB_oblig:Choice_CUZminusSIB_moral # selects difference score variables for FL data
)Tidying Data
# Convert data from wide to long format
# Stranger-Like
E2_SL_cond_long <- E2_SL_clean %>%
pivot_longer(
cols = c(SL_Dist_Scen, SL_Close_Scen, SL_DistOClose_Scen, SL_CloseODist_Scen),
names_to = "WSs_cond",
values_to = "Condition"
)
E2_SL_oblig_long <- E2_SL_clean %>%
pivot_longer(
cols = c(NoChoice_CUZ_oblig, NoChoice_SIB_oblig, Choice_CUZ_oblig, Choice_SIB_oblig),
names_to = "WSs_cond",
values_to = "oblig"
)
E2_SL_relate_long <- E2_SL_clean %>%
pivot_longer(
cols = c(NoChoice_CUZ_relate, NoChoice_SIB_relate, Choice_CUZ_relate, Choice_SIB_relate),
names_to = "WSs_cond",
values_to = "relate"
)
E2_SL_close_long <- E2_SL_clean %>%
pivot_longer(
cols = c(NoChoice_CUZ_close, NoChoice_SIB_close, Choice_CUZ_close, Choice_SIB_close),
names_to = "WSs_cond",
values_to = "close"
)
E2_SL_priorhelp_long <- E2_SL_clean %>%
pivot_longer(
cols = c(NoChoice_CUZ_priorhelp, NoChoice_SIB_priorhelp, Choice_CUZ_priorhelp, Choice_SIB_priorhelp),
names_to = "WSs_cond",
values_to = "priorhelp"
)
E2_SL_futurehelp_long <- E2_SL_clean %>%
pivot_longer(
cols = c(NoChoice_CUZ_futurehelp, NoChoice_SIB_futurehelp, Choice_CUZ_futurehelp, Choice_SIB_futurehelp),
names_to = "WSs_cond",
values_to = "futurehelp"
)
E2_SL_priorinteract_long <- E2_SL_clean %>%
pivot_longer(
cols = c(NoChoice_CUZ_priorinteract, NoChoice_SIB_priorinteract, Choice_CUZ_priorinteract, Choice_SIB_priorinteract),
names_to = "WSs_cond",
values_to = "priorinteract"
)
E2_SL_futureinteract_long <- E2_SL_clean %>%
pivot_longer(
cols = c(NoChoice_CUZ_futureinteract, NoChoice_SIB_futureinteract, Choice_CUZ_futureinteract, Choice_SIB_futureinteract),
names_to = "WSs_cond",
values_to = "futureinteract"
)
E2_SL_moral_long <- E2_SL_clean %>%
pivot_longer(
cols = c(NoChoice_CUZ_moral, NoChoice_SIB_moral, Choice_CUZ_moral, Choice_SIB_moral),
names_to = "WSs_cond",
values_to = "moral"
)
# Combine long SL datasets, select plotting variables, and create condition variable for each factor (Relation + Choice Context)
E2_SL_long <- cbind(E2_SL_cond_long,
E2_SL_oblig_long, E2_SL_relate_long, E2_SL_close_long,
E2_SL_priorhelp_long, E2_SL_futurehelp_long,
E2_SL_priorinteract_long, E2_SL_futureinteract_long,
E2_SL_moral_long)
E2_SL_long <- E2_SL_long[, !duplicated(colnames(E2_SL_long))] %>% # get rid of duplicate columns
select(ResponseId,
Age:OUS_IH,
BSs_cond,
WSs_cond,
Condition,
oblig, relate, close,
priorhelp, futurehelp,
priorinteract, futureinteract,
moral) %>%
mutate(Relation = case_when(
WSs_cond == "SL_Dist_Scen" ~ "Distant",
WSs_cond == "SL_Close_Scen" ~ "Close",
WSs_cond == "SL_DistOClose_Scen" ~ "Distant",
WSs_cond == "SL_CloseODist_Scen" ~ "Close")) %>%
mutate(`Choice Context` = case_when(
WSs_cond == "SL_Dist_Scen" ~ "No Choice",
WSs_cond == "SL_Close_Scen" ~ "No Choice",
WSs_cond == "SL_DistOClose_Scen" ~ "Choice",
WSs_cond == "SL_CloseODist_Scen" ~ "Choice"))
# Reorder/rename condition and participant factors
E2_SL_long$Relation <- as.factor(E2_SL_long$Relation)
E2_SL_long$Relation <- ordered(E2_SL_long$Relation, levels = c("Distant", "Close"))
E2_SL_long$`Choice Context` <- as.factor(E2_SL_long$`Choice Context`)
E2_SL_long$`Choice Context` <- ordered(E2_SL_long$`Choice Context`, levels = c("No Choice", "Choice"))
E2_SL_long$ResponseId <- as.factor(E2_SL_long$ResponseId)
# Friend-Like
E2_FL_cond_long <- E2_FL_clean %>%
pivot_longer(
cols = c(FL_Dist_Scen, FL_Close_Scen, FL_DistOClose_Scen, FL_CloseODist_Scen),
names_to = "WSs_cond",
values_to = "Condition"
)
E2_FL_oblig_long <- E2_FL_clean %>%
pivot_longer(
cols = c(NoChoice_CUZ_oblig, NoChoice_SIB_oblig, Choice_CUZ_oblig, Choice_SIB_oblig),
names_to = "WSs_cond",
values_to = "oblig"
)
E2_FL_relate_long <- E2_FL_clean %>%
pivot_longer(
cols = c(NoChoice_CUZ_relate, NoChoice_SIB_relate, Choice_CUZ_relate, Choice_SIB_relate),
names_to = "WSs_cond",
values_to = "relate"
)
E2_FL_close_long <- E2_FL_clean %>%
pivot_longer(
cols = c(NoChoice_CUZ_close, NoChoice_SIB_close, Choice_CUZ_close, Choice_SIB_close),
names_to = "WSs_cond",
values_to = "close"
)
E2_FL_priorhelp_long <- E2_FL_clean %>%
pivot_longer(
cols = c(NoChoice_CUZ_priorhelp, NoChoice_SIB_priorhelp, Choice_CUZ_priorhelp, Choice_SIB_priorhelp),
names_to = "WSs_cond",
values_to = "priorhelp"
)
E2_FL_futurehelp_long <- E2_FL_clean %>%
pivot_longer(
cols = c(NoChoice_CUZ_futurehelp, NoChoice_SIB_futurehelp, Choice_CUZ_futurehelp, Choice_SIB_futurehelp),
names_to = "WSs_cond",
values_to = "futurehelp"
)
E2_FL_priorinteract_long <- E2_FL_clean %>%
pivot_longer(
cols = c(NoChoice_CUZ_priorinteract, NoChoice_SIB_priorinteract, Choice_CUZ_priorinteract, Choice_SIB_priorinteract),
names_to = "WSs_cond",
values_to = "priorinteract"
)
E2_FL_futureinteract_long <- E2_FL_clean %>%
pivot_longer(
cols = c(NoChoice_CUZ_futureinteract, NoChoice_SIB_futureinteract, Choice_CUZ_futureinteract, Choice_SIB_futureinteract),
names_to = "WSs_cond",
values_to = "futureinteract"
)
E2_FL_moral_long <- E2_FL_clean %>%
pivot_longer(
cols = c(NoChoice_CUZ_moral, NoChoice_SIB_moral, Choice_CUZ_moral, Choice_SIB_moral),
names_to = "WSs_cond",
values_to = "moral"
)
# Combine long SL datasets, select plotting variables, and create condition variable for each factor (Relation + Choice Context)
E2_FL_long <- cbind(E2_FL_cond_long,
E2_FL_oblig_long, E2_FL_relate_long, E2_FL_close_long,
E2_FL_priorhelp_long, E2_FL_futurehelp_long,
E2_FL_priorinteract_long, E2_FL_futureinteract_long,
E2_FL_moral_long)
E2_FL_long <- E2_FL_long[, !duplicated(colnames(E2_FL_long))] %>% # get rid of duplicate columns
select(ResponseId,
Age:OUS_IH,
BSs_cond,
WSs_cond,
Condition,
oblig, relate, close,
priorhelp, futurehelp,
priorinteract, futureinteract,
moral) %>%
mutate(Relation = case_when(
WSs_cond == "FL_Dist_Scen" ~ "Distant",
WSs_cond == "FL_Close_Scen" ~ "Close",
WSs_cond == "FL_DistOClose_Scen" ~ "Distant",
WSs_cond == "FL_CloseODist_Scen" ~ "Close")) %>%
mutate(`Choice Context` = case_when(
WSs_cond == "FL_Dist_Scen" ~ "No Choice",
WSs_cond == "FL_Close_Scen" ~ "No Choice",
WSs_cond == "FL_DistOClose_Scen" ~ "Choice",
WSs_cond == "FL_CloseODist_Scen" ~ "Choice"))
# Reorder/rename condition and participant factors
E2_FL_long$Relation <- as.factor(E2_FL_long$Relation)
E2_FL_long$Relation <- ordered(E2_FL_long$Relation, levels = c("Distant", "Close"))
E2_FL_long$`Choice Context` <- as.factor(E2_FL_long$`Choice Context`)
E2_FL_long$`Choice Context` <- ordered(E2_FL_long$`Choice Context`, levels = c("No Choice", "Choice"))
E2_FL_long$ResponseId <- as.factor(E2_FL_long$ResponseId)
# Combine into one dataset for later analyses
E2_all_long <- rbind(E2_SL_long, E2_FL_long)
# Reorder all_long BSs_cond
E2_all_long$BSs_cond <- as.factor(E2_all_long$BSs_cond)
E2_all_long$BSs_cond <- ordered(E2_all_long$BSs_cond, levels = c("Stranger-Like", "Friend-Like"))Descriptive Statistics
Oblig
Stranger-Like
describeBy(E2_SL_long$oblig, list(E2_SL_long$Relation, E2_SL_long$`Choice Context`), mat = T)Friend-Like
describeBy(E2_FL_long$oblig, list(E2_FL_long$Relation, E2_FL_long$`Choice Context`), mat = T)Relate
Stranger-Like
describeBy(E2_SL_long$relate, list(E2_SL_long$Relation, E2_SL_long$`Choice Context`), mat = T)Friend-Like
describeBy(E2_FL_long$relate, list(E2_FL_long$Relation, E2_FL_long$`Choice Context`), mat = T)Close
Stranger-Like
describeBy(E2_SL_long$close, list(E2_SL_long$Relation, E2_SL_long$`Choice Context`), mat = T)Friend-Like
describeBy(E2_FL_long$close, list(E2_FL_long$Relation, E2_FL_long$`Choice Context`), mat = T)Prior Help
Stranger-Like
describeBy(E2_SL_long$priorhelp, list(E2_SL_long$Relation, E2_SL_long$`Choice Context`), mat = T)Friend-Like
describeBy(E2_FL_long$priorhelp, list(E2_FL_long$Relation, E2_FL_long$`Choice Context`), mat = T)Future Help
Stranger-Like
describeBy(E2_SL_long$futurehelp, list(E2_SL_long$Relation, E2_SL_long$`Choice Context`), mat = T)Friend-Like
describeBy(E2_FL_long$futurehelp, list(E2_FL_long$Relation, E2_FL_long$`Choice Context`), mat = T)Prior Interax
Stranger-Like
describeBy(E2_SL_long$priorinteract, list(E2_SL_long$Relation, E2_SL_long$`Choice Context`), mat = T)Friend-Like
describeBy(E2_FL_long$priorinteract, list(E2_FL_long$Relation, E2_FL_long$`Choice Context`), mat = T)Future Interax
Stranger-Like
describeBy(E2_SL_long$futureinteract, list(E2_SL_long$Relation, E2_SL_long$`Choice Context`), mat = T)Friend-Like
describeBy(E2_FL_long$futureinteract, list(E2_FL_long$Relation, E2_FL_long$`Choice Context`), mat = T)Moral
Stranger-Like
describeBy(E2_SL_long$moral, list(E2_SL_long$Relation, E2_SL_long$`Choice Context`), mat = T)Friend-Like
describeBy(E2_FL_long$moral, list(E2_FL_long$Relation, E2_FL_long$`Choice Context`), mat = T)Mean Difference Plots
# Set dodge for plotting crossed factors
dodge = position_dodge(width = 1) Oblig
Stranger-Like
print(oblig_plot_SL <- ggplot(data = E2_SL_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)))
Friend-Like
print(oblig_plot_FL <- ggplot(data = E2_FL_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 = E2_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 = 2) +
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("E2_oblig_plot.png")Saving 14 x 9 in imageRelate
Stranger-Like
print(relate_plot_SL <- ggplot(data = E2_SL_long, aes(x = `Choice Context`, y = relate, 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("Perceived Relatedness") +
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)))
Friend-Like
print(relate_plot_FL <- ggplot(data = E2_FL_long, aes(x = `Choice Context`, y = relate, 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("Perceived Relatedness") +
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(relate_plot_combined <- ggplot(data = E2_all_long, aes(x = `Choice Context`, y = relate, 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 = 2.5, position = dodge) +
theme(legend.position = "right") +
theme_classic() +
facet_wrap(~BSs_cond, nrow = 2) +
xlab("Choice Context") +
ylab("Perceived Relatedness") +
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("E2_relate_plot.png")Saving 14 x 9 in imageClose
Stranger-Like
print(close_plot_SL <- ggplot(data = E2_SL_long, aes(x = `Choice Context`, y = close, 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("Perceived Closeness") +
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)))
Friend-Like
print(close_plot_FL <- ggplot(data = E2_FL_long, aes(x = `Choice Context`, y = close, 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("Perceived Closeness") +
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(close_plot_combined <- ggplot(data = E2_all_long, aes(x = `Choice Context`, y = close, 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 = 2.5, position = dodge) +
theme(legend.position = "right") +
theme_classic() +
facet_wrap(~BSs_cond, nrow = 2) +
xlab("Choice Context") +
ylab("Perceived Closeness") +
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("E2_close_plot.png")Saving 14 x 9 in imagePrior Help
Stranger-Like
print(priorhelp_plot_SL <- ggplot(data = E2_SL_long, aes(x = `Choice Context`, y = priorhelp, 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("Perceived Frequency of Prior Help") +
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)))
Friend-Like
print(priorhelp_plot_FL <- ggplot(data = E2_FL_long, aes(x = `Choice Context`, y = priorhelp, 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("Perceived Frequency of Prior Help") +
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(priorhelp_plot_combined <- ggplot(data = E2_all_long, aes(x = `Choice Context`, y = priorhelp, 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 = 2.5, position = dodge) +
theme(legend.position = "right") +
theme_classic() +
facet_wrap(~BSs_cond, nrow = 2) +
xlab("Choice Context") +
ylab("Perceived Frequency of Prior Help") +
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("E2_priorhelp_plot.png")Saving 14 x 9 in imageFuture Help
Stranger-Like
print(futurehelp_plot_SL <- ggplot(data = E2_SL_long, aes(x = `Choice Context`, y = futurehelp, 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("Perceived Frequency of Future Help") +
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)))
Friend-Like
print(futurehelp_plot_FL <- ggplot(data = E2_FL_long, aes(x = `Choice Context`, y = futurehelp, 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("Perceived Frequency of Future Help") +
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(futurehelp_plot_combined <- ggplot(data = E2_all_long, aes(x = `Choice Context`, y = futurehelp, 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 = 2.5, position = dodge) +
theme(legend.position = "right") +
theme_classic() +
facet_wrap(~BSs_cond, nrow = 2) +
xlab("Choice Context") +
ylab("Perceived Frequency of Future Help") +
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("E2_futurehelp_plot.png")Saving 14 x 9 in imagePrior Interax
Stranger-Like
print(priorinteract_plot_SL <- ggplot(data = E2_SL_long, aes(x = `Choice Context`, y = priorinteract, 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("Perceived Frequency of Prior Interactions") +
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)))
Friend-Like
print(priorinteract_plot_FL <- ggplot(data = E2_FL_long, aes(x = `Choice Context`, y = priorinteract, 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("Perceived Frequency of Prior Interactions") +
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(priorinteract_plot_combined <- ggplot(data = E2_all_long, aes(x = `Choice Context`, y = priorinteract, 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 = 2.5, position = dodge) +
theme(legend.position = "right") +
theme_classic() +
facet_wrap(~BSs_cond, nrow = 2) +
xlab("Choice Context") +
ylab("Perceived Frequency of Prior Interactions") +
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("E2_priorinteract_plot.png")Saving 14 x 9 in imageFuture Interax
Stranger-Like
print(futureinteract_plot_SL <- ggplot(data = E2_SL_long, aes(x = `Choice Context`, y = futureinteract, 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("Perceived Frequency of Future Interactions") +
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)))
Friend-Like
print(futureinteract_plot_FL <- ggplot(data = E2_FL_long, aes(x = `Choice Context`, y = futureinteract, 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("Perceived Frequency of Future Interactions") +
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(futureinteract_plot_combined <- ggplot(data = E2_all_long, aes(x = `Choice Context`, y = futureinteract, 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 = 2.5, position = dodge) +
theme(legend.position = "right") +
theme_classic() +
facet_wrap(~BSs_cond, nrow = 2) +
xlab("Choice Context") +
ylab("Perceived Frequency of Future Interactions") +
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("E2_futureinteract_plot.png")Saving 14 x 9 in imageMoral
Stranger-Like
print(moral_plot_SL <- ggplot(data = E2_SL_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)))
Friend-Like
print(moral_plot_FL <- ggplot(data = E2_FL_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 = E2_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 = 2) +
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("E2_moral_plot.png")Saving 14 x 9 in imageMean Difference Tests
See our pre-registration (INSERT LINK HERE) for our predictions related to obligation judgments and moral character judgments.
Oblig
Stranger-Like
No Choice
# returns t-test results
t.test(oblig ~ Relation,
data = E2_all_long %>%
filter(BSs_cond == "Stranger-Like") %>%
filter(`Choice Context` == "No Choice") %>%
droplevels(),
paired = T)
Paired t-test
data: oblig by Relation
t = -4.0679, df = 353, p-value = 5.855e-05
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-12.152705 -4.231476
sample estimates:
mean of the differences
-8.19209 # returns dz effect size and 95% CIs
effsize::cohen.d(oblig ~ Relation | Subject(ResponseId),
data = E2_all_long %>%
filter(BSs_cond == "Stranger-Like") %>%
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: -0.2162072 (small)
95 percent confidence interval:
lower upper
-0.3217695 -0.1106450 # returns d-av effect size and 95% CIs
effsize::cohen.d(oblig ~ Relation | Subject(ResponseId),
data = E2_all_long %>%
filter(BSs_cond == "Stranger-Like") %>%
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: -0.2385614 (small)
95 percent confidence interval:
lower upper
-0.3553268 -0.1217959 # returns correlation between variables
cor_test(data = E2_SL_clean, "NoChoice_CUZ_oblig", "NoChoice_SIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
----------------------------------------------------------------------------------
NoChoice_CUZ_oblig | NoChoice_SIB_oblig | 0.39 | [0.30, 0.48] | 7.98 | < .001***
Observations: 354# returns histogram of differences score variable
print(hist(E2_SL_clean$NoChoice_CUZminusSIB_oblig, breaks = 100))$breaks
[1] -100 -98 -96 -94 -92 -90 -88 -86 -84 -82 -80 -78 -76 -74 -72 -70 -68 -66 -64
[20] -62 -60 -58 -56 -54 -52 -50 -48 -46 -44 -42 -40 -38 -36 -34 -32 -30 -28 -26
[39] -24 -22 -20 -18 -16 -14 -12 -10 -8 -6 -4 -2 0 2 4 6 8 10 12
[58] 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50
[77] 52 54 56 58 60 62 64 66 68 70 72 74 76 78 80 82 84 86 88
[96] 90 92 94 96 98 100
$counts
[1] 8 0 1 2 1 1 2 2 1 1 0 2 1 0 3 2 1 1 1 2 3 3 3 7 6 5 1 1 4 7 0 1
[33] 4 6 7 6 1 5 5 7 8 5 9 12 5 4 9 3 12 49 5 6 6 8 6 3 5 5 1 6 7 12 2 5
[65] 3 2 1 4 0 2 1 3 2 2 6 2 4 1 0 1 1 3 2 1 0 0 0 1 0 0 0 2 0 1 0 0
[97] 1 0 0 1
$density
[1] 0.011299435 0.000000000 0.001412429 0.002824859 0.001412429 0.001412429 0.002824859 0.002824859
[9] 0.001412429 0.001412429 0.000000000 0.002824859 0.001412429 0.000000000 0.004237288 0.002824859
[17] 0.001412429 0.001412429 0.001412429 0.002824859 0.004237288 0.004237288 0.004237288 0.009887006
[25] 0.008474576 0.007062147 0.001412429 0.001412429 0.005649718 0.009887006 0.000000000 0.001412429
[33] 0.005649718 0.008474576 0.009887006 0.008474576 0.001412429 0.007062147 0.007062147 0.009887006
[41] 0.011299435 0.007062147 0.012711864 0.016949153 0.007062147 0.005649718 0.012711864 0.004237288
[49] 0.016949153 0.069209040 0.007062147 0.008474576 0.008474576 0.011299435 0.008474576 0.004237288
[57] 0.007062147 0.007062147 0.001412429 0.008474576 0.009887006 0.016949153 0.002824859 0.007062147
[65] 0.004237288 0.002824859 0.001412429 0.005649718 0.000000000 0.002824859 0.001412429 0.004237288
[73] 0.002824859 0.002824859 0.008474576 0.002824859 0.005649718 0.001412429 0.000000000 0.001412429
[81] 0.001412429 0.004237288 0.002824859 0.001412429 0.000000000 0.000000000 0.000000000 0.001412429
[89] 0.000000000 0.000000000 0.000000000 0.002824859 0.000000000 0.001412429 0.000000000 0.000000000
[97] 0.001412429 0.000000000 0.000000000 0.001412429
$mids
[1] -99 -97 -95 -93 -91 -89 -87 -85 -83 -81 -79 -77 -75 -73 -71 -69 -67 -65 -63 -61 -59 -57 -55 -53
[25] -51 -49 -47 -45 -43 -41 -39 -37 -35 -33 -31 -29 -27 -25 -23 -21 -19 -17 -15 -13 -11 -9 -7 -5
[49] -3 -1 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43
[73] 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75 77 79 81 83 85 87 89 91
[97] 93 95 97 99
$xname
[1] "E2_SL_clean$NoChoice_CUZminusSIB_oblig"
$equidist
[1] TRUE
attr(,"class")
[1] "histogram"
Choice
# returns t-test results
t.test(oblig ~ Relation,
data = E2_all_long %>%
filter(BSs_cond == "Stranger-Like") %>%
filter(`Choice Context` == "Choice") %>%
droplevels(),
paired = T)
Paired t-test
data: oblig by Relation
t = -12.341, df = 353, p-value < 2.2e-16
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-9.233971 -6.695407
sample estimates:
mean of the differences
-7.964689 # returns dz effect size and 95% CIs
effsize::cohen.d(oblig ~ Relation | Subject(ResponseId),
data = E2_all_long %>%
filter(BSs_cond == "Stranger-Like") %>%
filter(`Choice Context` == "Choice") %>%
droplevels(),
paired = T,
within = F) # setting this to false ensures dz is calculated, using difference score
Cohen's d
d estimate: -0.6559167 (medium)
95 percent confidence interval:
lower upper
-0.7709438 -0.5408897 # returns d-av effect size and 95% CIs
effsize::cohen.d(oblig ~ Relation | Subject(ResponseId),
data = E2_all_long %>%
filter(BSs_cond == "Stranger-Like") %>%
filter(`Choice Context` == "Choice") %>%
droplevels(),
paired = T,
within = T) # setting this to true ensures d-av is calculated, using raw scores
Cohen's d
d estimate: -0.2703635 (small)
95 percent confidence interval:
lower upper
-0.3141546 -0.2265724 # returns correlation between variables
cor_test(data = E2_SL_clean, "Choice_CUZ_oblig", "Choice_SIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
------------------------------------------------------------------------------
Choice_CUZ_oblig | Choice_SIB_oblig | 0.92 | [0.90, 0.93] | 42.56 | < .001***
Observations: 354# returns histogram of differences score variable
print(hist(E2_SL_clean$Choice_CUZminusSIB_oblig, breaks = 100))$breaks
[1] -65 -64 -63 -62 -61 -60 -59 -58 -57 -56 -55 -54 -53 -52 -51 -50 -49 -48 -47 -46 -45 -44 -43 -42
[25] -41 -40 -39 -38 -37 -36 -35 -34 -33 -32 -31 -30 -29 -28 -27 -26 -25 -24 -23 -22 -21 -20 -19 -18
[49] -17 -16 -15 -14 -13 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6
[73] 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
$counts
[1] 1 0 0 0 0 0 0 0 1 2 0 0 0 0 0 0 0 0 0 1 1 0 0 2 1 2 2 1 2 3 1 1 0
[34] 1 4 0 1 3 0 1 9 1 2 4 3 8 4 2 2 6 5 8 9 13 7 5 14 11 14 9 9 9 17 21 74 29
[67] 10 4 4 0 1 0 1 0 1 2 1 2 0 0 0 1 0 0 0 0 0 1
$density
[1] 0.002824859 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
[9] 0.002824859 0.005649718 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
[17] 0.000000000 0.000000000 0.000000000 0.002824859 0.002824859 0.000000000 0.000000000 0.005649718
[25] 0.002824859 0.005649718 0.005649718 0.002824859 0.005649718 0.008474576 0.002824859 0.002824859
[33] 0.000000000 0.002824859 0.011299435 0.000000000 0.002824859 0.008474576 0.000000000 0.002824859
[41] 0.025423729 0.002824859 0.005649718 0.011299435 0.008474576 0.022598870 0.011299435 0.005649718
[49] 0.005649718 0.016949153 0.014124294 0.022598870 0.025423729 0.036723164 0.019774011 0.014124294
[57] 0.039548023 0.031073446 0.039548023 0.025423729 0.025423729 0.025423729 0.048022599 0.059322034
[65] 0.209039548 0.081920904 0.028248588 0.011299435 0.011299435 0.000000000 0.002824859 0.000000000
[73] 0.002824859 0.000000000 0.002824859 0.005649718 0.002824859 0.005649718 0.000000000 0.000000000
[81] 0.000000000 0.002824859 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.002824859
$mids
[1] -64.5 -63.5 -62.5 -61.5 -60.5 -59.5 -58.5 -57.5 -56.5 -55.5 -54.5 -53.5 -52.5 -51.5 -50.5 -49.5
[17] -48.5 -47.5 -46.5 -45.5 -44.5 -43.5 -42.5 -41.5 -40.5 -39.5 -38.5 -37.5 -36.5 -35.5 -34.5 -33.5
[33] -32.5 -31.5 -30.5 -29.5 -28.5 -27.5 -26.5 -25.5 -24.5 -23.5 -22.5 -21.5 -20.5 -19.5 -18.5 -17.5
[49] -16.5 -15.5 -14.5 -13.5 -12.5 -11.5 -10.5 -9.5 -8.5 -7.5 -6.5 -5.5 -4.5 -3.5 -2.5 -1.5
[65] -0.5 0.5 1.5 2.5 3.5 4.5 5.5 6.5 7.5 8.5 9.5 10.5 11.5 12.5 13.5 14.5
[81] 15.5 16.5 17.5 18.5 19.5 20.5 21.5 22.5
$xname
[1] "E2_SL_clean$Choice_CUZminusSIB_oblig"
$equidist
[1] TRUE
attr(,"class")
[1] "histogram"
Friend-Like
No Choice
# returns t-test results
t.test(oblig ~ Relation,
data = E2_all_long %>%
filter(BSs_cond == "Friend-Like") %>%
filter(`Choice Context` == "No Choice") %>%
droplevels(),
paired = T)
Paired t-test
data: oblig by Relation
t = -2.8839, df = 344, p-value = 0.004175
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-7.825057 -1.479290
sample estimates:
mean of the differences
-4.652174 # returns dz effect size and 95% CIs
effsize::cohen.d(oblig ~ Relation | Subject(ResponseId),
data = E2_all_long %>%
filter(BSs_cond == "Friend-Like") %>%
filter(`Choice Context` == "No Choice") %>%
droplevels(),
paired = T,
within = F) # setting this to false ensures dz is calculated, using difference score
Cohen's d
d estimate: -0.1552641 (negligible)
95 percent confidence interval:
lower upper
-0.26160612 -0.04892206 # returns d-av effect size and 95% CIs
effsize::cohen.d(oblig ~ Relation | Subject(ResponseId),
data = E2_all_long %>%
filter(BSs_cond == "Friend-Like") %>%
filter(`Choice Context` == "No Choice") %>%
droplevels(),
paired = T,
within = T) # setting this to true ensures d-av is calculated, using raw scores
Cohen's d
d estimate: -0.1512148 (negligible)
95 percent confidence interval:
lower upper
-0.25475162 -0.04767791 # returns correlation between variables
cor_test(data = E2_FL_clean, "NoChoice_CUZ_oblig", "NoChoice_SIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
----------------------------------------------------------------------------------
NoChoice_CUZ_oblig | NoChoice_SIB_oblig | 0.53 | [0.44, 0.60] | 11.45 | < .001***
Observations: 345# returns histogram of differences score variable
print(hist(E2_FL_clean$NoChoice_CUZminusSIB_oblig, breaks = 100))$breaks
[1] -100 -98 -96 -94 -92 -90 -88 -86 -84 -82 -80 -78 -76 -74 -72 -70 -68 -66 -64
[20] -62 -60 -58 -56 -54 -52 -50 -48 -46 -44 -42 -40 -38 -36 -34 -32 -30 -28 -26
[39] -24 -22 -20 -18 -16 -14 -12 -10 -8 -6 -4 -2 0 2 4 6 8 10 12
[58] 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50
[77] 52 54 56 58 60 62 64 66 68 70 72 74 76 78 80 82 84 86 88
[96] 90 92 94 96 98 100
$counts
[1] 5 1 1 0 0 0 1 0 0 1 0 1 2 0 1 0 2 0 0 1 0 2 1 3 8 5 5 1 2 0 1 2
[33] 1 6 1 5 1 6 6 6 9 6 4 5 10 6 6 8 17 73 13 7 6 7 4 9 8 3 6 7 1 6 6 2
[65] 8 3 2 3 1 3 2 2 1 2 3 3 2 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[97] 0 0 0 2
$density
[1] 0.007246377 0.001449275 0.001449275 0.000000000 0.000000000 0.000000000 0.001449275 0.000000000
[9] 0.000000000 0.001449275 0.000000000 0.001449275 0.002898551 0.000000000 0.001449275 0.000000000
[17] 0.002898551 0.000000000 0.000000000 0.001449275 0.000000000 0.002898551 0.001449275 0.004347826
[25] 0.011594203 0.007246377 0.007246377 0.001449275 0.002898551 0.000000000 0.001449275 0.002898551
[33] 0.001449275 0.008695652 0.001449275 0.007246377 0.001449275 0.008695652 0.008695652 0.008695652
[41] 0.013043478 0.008695652 0.005797101 0.007246377 0.014492754 0.008695652 0.008695652 0.011594203
[49] 0.024637681 0.105797101 0.018840580 0.010144928 0.008695652 0.010144928 0.005797101 0.013043478
[57] 0.011594203 0.004347826 0.008695652 0.010144928 0.001449275 0.008695652 0.008695652 0.002898551
[65] 0.011594203 0.004347826 0.002898551 0.004347826 0.001449275 0.004347826 0.002898551 0.002898551
[73] 0.001449275 0.002898551 0.004347826 0.004347826 0.002898551 0.000000000 0.000000000 0.000000000
[81] 0.000000000 0.001449275 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
[89] 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
[97] 0.000000000 0.000000000 0.000000000 0.002898551
$mids
[1] -99 -97 -95 -93 -91 -89 -87 -85 -83 -81 -79 -77 -75 -73 -71 -69 -67 -65 -63 -61 -59 -57 -55 -53
[25] -51 -49 -47 -45 -43 -41 -39 -37 -35 -33 -31 -29 -27 -25 -23 -21 -19 -17 -15 -13 -11 -9 -7 -5
[49] -3 -1 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43
[73] 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75 77 79 81 83 85 87 89 91
[97] 93 95 97 99
$xname
[1] "E2_FL_clean$NoChoice_CUZminusSIB_oblig"
$equidist
[1] TRUE
attr(,"class")
[1] "histogram"
Choice
# returns t-test results
t.test(oblig ~ Relation,
data = E2_all_long %>%
filter(BSs_cond == "Friend-Like") %>%
filter(`Choice Context` == "Choice") %>%
droplevels(),
paired = T)
Paired t-test
data: oblig by Relation
t = -10.577, df = 344, p-value < 2.2e-16
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-6.127427 -4.205907
sample estimates:
mean of the differences
-5.166667 # returns dz effect size and 95% CIs
effsize::cohen.d(oblig ~ Relation | Subject(ResponseId),
data = E2_all_long %>%
filter(BSs_cond == "Friend-Like") %>%
filter(`Choice Context` == "Choice") %>%
droplevels(),
paired = T,
within = F) # setting this to false ensures dz is calculated, using difference score
Cohen's d
d estimate: -0.569462 (medium)
95 percent confidence interval:
lower upper
-0.6834169 -0.4555071 # returns d-av effect size and 95% CIs
effsize::cohen.d(oblig ~ Relation | Subject(ResponseId),
data = E2_all_long %>%
filter(BSs_cond == "Friend-Like") %>%
filter(`Choice Context` == "Choice") %>%
droplevels(),
paired = T,
within = T) # setting this to true ensures d-av is calculated, using raw scores
Cohen's d
d estimate: -0.187572 (negligible)
95 percent confidence interval:
lower upper
-0.2226951 -0.1524488 # returns correlation between variables
cor_test(data = E2_FL_clean, "Choice_CUZ_oblig", "Choice_SIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
------------------------------------------------------------------------------
Choice_CUZ_oblig | Choice_SIB_oblig | 0.95 | [0.93, 0.96] | 53.91 | < .001***
Observations: 345# returns histogram of differences score variable
print(hist(E2_FL_clean$Choice_CUZminusSIB_oblig, breaks = 100))$breaks
[1] -58 -57 -56 -55 -54 -53 -52 -51 -50 -49 -48 -47 -46 -45 -44 -43 -42 -41 -40 -39 -38 -37 -36 -35
[25] -34 -33 -32 -31 -30 -29 -28 -27 -26 -25 -24 -23 -22 -21 -20 -19 -18 -17 -16 -15 -14 -13 -12 -11
[49] -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13
[73] 14 15 16 17 18
$counts
[1] 1 0 0 0 0 0 0 0 1 0 0 0 0 1 1 0 0 0 0 1 1 0 0 0 1 0 1 2 1 0 1 0 1
[34] 3 5 2 0 2 2 0 4 2 7 7 5 4 11 7 7 5 14 12 12 5 10 22 28 98 40 5 7 0 2 0 0 0
[67] 0 1 0 0 2 0 0 0 0 1
$density
[1] 0.002898551 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
[9] 0.002898551 0.000000000 0.000000000 0.000000000 0.000000000 0.002898551 0.002898551 0.000000000
[17] 0.000000000 0.000000000 0.000000000 0.002898551 0.002898551 0.000000000 0.000000000 0.000000000
[25] 0.002898551 0.000000000 0.002898551 0.005797101 0.002898551 0.000000000 0.002898551 0.000000000
[33] 0.002898551 0.008695652 0.014492754 0.005797101 0.000000000 0.005797101 0.005797101 0.000000000
[41] 0.011594203 0.005797101 0.020289855 0.020289855 0.014492754 0.011594203 0.031884058 0.020289855
[49] 0.020289855 0.014492754 0.040579710 0.034782609 0.034782609 0.014492754 0.028985507 0.063768116
[57] 0.081159420 0.284057971 0.115942029 0.014492754 0.020289855 0.000000000 0.005797101 0.000000000
[65] 0.000000000 0.000000000 0.000000000 0.002898551 0.000000000 0.000000000 0.005797101 0.000000000
[73] 0.000000000 0.000000000 0.000000000 0.002898551
$mids
[1] -57.5 -56.5 -55.5 -54.5 -53.5 -52.5 -51.5 -50.5 -49.5 -48.5 -47.5 -46.5 -45.5 -44.5 -43.5 -42.5
[17] -41.5 -40.5 -39.5 -38.5 -37.5 -36.5 -35.5 -34.5 -33.5 -32.5 -31.5 -30.5 -29.5 -28.5 -27.5 -26.5
[33] -25.5 -24.5 -23.5 -22.5 -21.5 -20.5 -19.5 -18.5 -17.5 -16.5 -15.5 -14.5 -13.5 -12.5 -11.5 -10.5
[49] -9.5 -8.5 -7.5 -6.5 -5.5 -4.5 -3.5 -2.5 -1.5 -0.5 0.5 1.5 2.5 3.5 4.5 5.5
[65] 6.5 7.5 8.5 9.5 10.5 11.5 12.5 13.5 14.5 15.5 16.5 17.5
$xname
[1] "E2_FL_clean$Choice_CUZminusSIB_oblig"
$equidist
[1] TRUE
attr(,"class")
[1] "histogram"
Relate
Stranger-Like
No Choice
# returns t-test results
t.test(relate ~ Relation,
data = E2_all_long %>%
filter(BSs_cond == "Stranger-Like") %>%
filter(`Choice Context` == "No Choice") %>%
droplevels(),
paired = T)
Paired t-test
data: relate by Relation
t = -34.659, df = 353, p-value < 2.2e-16
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-47.56245 -42.45450
sample estimates:
mean of the differences
-45.00847 # returns dz effect size and 95% CIs
effsize::cohen.d(relate ~ Relation | Subject(ResponseId),
data = E2_all_long %>%
filter(BSs_cond == "Stranger-Like") %>%
filter(`Choice Context` == "No Choice") %>%
droplevels(),
paired = T,
within = F) # setting this to false ensures dz is calculated, using difference score
Cohen's d
d estimate: -1.842109 (large)
95 percent confidence interval:
lower upper
-2.013468 -1.670750 # returns d-av effect size and 95% CIs
effsize::cohen.d(relate ~ Relation | Subject(ResponseId),
data = E2_all_long %>%
filter(BSs_cond == "Stranger-Like") %>%
filter(`Choice Context` == "No Choice") %>%
droplevels(),
paired = T,
within = T) # setting this to true ensures d-av is calculated, using raw scores
Cohen's d
d estimate: -1.999068 (large)
95 percent confidence interval:
lower upper
-2.195146 -1.802990 # returns correlation between variables
cor_test(data = E2_SL_clean, "NoChoice_CUZ_relate", "NoChoice_SIB_relate", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
------------------------------------------------------------------------------------
NoChoice_CUZ_relate | NoChoice_SIB_relate | 0.41 | [0.32, 0.49] | 8.46 | < .001***
Observations: 354# returns histogram of differences score variable
print(hist(E2_SL_clean$NoChoice_CUZminusSIB_relate, breaks = 100))$breaks
[1] -94 -93 -92 -91 -90 -89 -88 -87 -86 -85 -84 -83 -82 -81 -80 -79 -78 -77 -76 -75 -74 -73 -72 -71
[25] -70 -69 -68 -67 -66 -65 -64 -63 -62 -61 -60 -59 -58 -57 -56 -55 -54 -53 -52 -51 -50 -49 -48 -47
[49] -46 -45 -44 -43 -42 -41 -40 -39 -38 -37 -36 -35 -34 -33 -32 -31 -30 -29 -28 -27 -26 -25 -24 -23
[73] -22 -21 -20 -19 -18 -17 -16 -15 -14 -13 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1
[97] 2 3 4 5 6 7 8 9 10
$counts
[1] 2 1 1 1 0 1 1 1 3 0 2 5 5 2 7 5 3 12 11 9 9 5 7 3 0 2 1 3 0 3 2 0
[33] 1 1 0 3 6 3 1 0 2 1 8 16 9 5 1 4 3 2 2 2 1 4 9 6 2 6 3 2 5 3 5 9
[65] 9 10 11 10 12 11 5 6 2 3 1 3 2 3 2 1 5 1 1 2 2 0 1 2 1 1 1 1 2 4 0 2
[97] 2 0 0 0 0 2 0 1
$density
[1] 0.005649718 0.002824859 0.002824859 0.002824859 0.000000000 0.002824859 0.002824859 0.002824859
[9] 0.008474576 0.000000000 0.005649718 0.014124294 0.014124294 0.005649718 0.019774011 0.014124294
[17] 0.008474576 0.033898305 0.031073446 0.025423729 0.025423729 0.014124294 0.019774011 0.008474576
[25] 0.000000000 0.005649718 0.002824859 0.008474576 0.000000000 0.008474576 0.005649718 0.000000000
[33] 0.002824859 0.002824859 0.000000000 0.008474576 0.016949153 0.008474576 0.002824859 0.000000000
[41] 0.005649718 0.002824859 0.022598870 0.045197740 0.025423729 0.014124294 0.002824859 0.011299435
[49] 0.008474576 0.005649718 0.005649718 0.005649718 0.002824859 0.011299435 0.025423729 0.016949153
[57] 0.005649718 0.016949153 0.008474576 0.005649718 0.014124294 0.008474576 0.014124294 0.025423729
[65] 0.025423729 0.028248588 0.031073446 0.028248588 0.033898305 0.031073446 0.014124294 0.016949153
[73] 0.005649718 0.008474576 0.002824859 0.008474576 0.005649718 0.008474576 0.005649718 0.002824859
[81] 0.014124294 0.002824859 0.002824859 0.005649718 0.005649718 0.000000000 0.002824859 0.005649718
[89] 0.002824859 0.002824859 0.002824859 0.002824859 0.005649718 0.011299435 0.000000000 0.005649718
[97] 0.005649718 0.000000000 0.000000000 0.000000000 0.000000000 0.005649718 0.000000000 0.002824859
$mids
[1] -93.5 -92.5 -91.5 -90.5 -89.5 -88.5 -87.5 -86.5 -85.5 -84.5 -83.5 -82.5 -81.5 -80.5 -79.5 -78.5
[17] -77.5 -76.5 -75.5 -74.5 -73.5 -72.5 -71.5 -70.5 -69.5 -68.5 -67.5 -66.5 -65.5 -64.5 -63.5 -62.5
[33] -61.5 -60.5 -59.5 -58.5 -57.5 -56.5 -55.5 -54.5 -53.5 -52.5 -51.5 -50.5 -49.5 -48.5 -47.5 -46.5
[49] -45.5 -44.5 -43.5 -42.5 -41.5 -40.5 -39.5 -38.5 -37.5 -36.5 -35.5 -34.5 -33.5 -32.5 -31.5 -30.5
[65] -29.5 -28.5 -27.5 -26.5 -25.5 -24.5 -23.5 -22.5 -21.5 -20.5 -19.5 -18.5 -17.5 -16.5 -15.5 -14.5
[81] -13.5 -12.5 -11.5 -10.5 -9.5 -8.5 -7.5 -6.5 -5.5 -4.5 -3.5 -2.5 -1.5 -0.5 0.5 1.5
[97] 2.5 3.5 4.5 5.5 6.5 7.5 8.5 9.5
$xname
[1] "E2_SL_clean$NoChoice_CUZminusSIB_relate"
$equidist
[1] TRUE
attr(,"class")
[1] "histogram"
Choice
# returns t-test results
t.test(relate ~ Relation,
data = E2_all_long %>%
filter(BSs_cond == "Stranger-Like") %>%
filter(`Choice Context` == "Choice") %>%
droplevels(),
paired = T)
Paired t-test
data: relate by Relation
t = -35.606, df = 353, p-value < 2.2e-16
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-46.43032 -41.56968
sample estimates:
mean of the differences
-44 # returns dz effect size and 95% CIs
effsize::cohen.d(relate ~ Relation | Subject(ResponseId),
data = E2_all_long %>%
filter(BSs_cond == "Stranger-Like") %>%
filter(`Choice Context` == "Choice") %>%
droplevels(),
paired = T,
within = F) # setting this to false ensures dz is calculated, using difference score
Cohen's d
d estimate: -1.892461 (large)
95 percent confidence interval:
lower upper
-2.066782 -1.718140 # returns d-av effect size and 95% CIs
effsize::cohen.d(relate ~ Relation | Subject(ResponseId),
data = E2_all_long %>%
filter(BSs_cond == "Stranger-Like") %>%
filter(`Choice Context` == "Choice") %>%
droplevels(),
paired = T,
within = T) # setting this to true ensures d-av is calculated, using raw scores
Cohen's d
d estimate: -1.980539 (large)
95 percent confidence interval:
lower upper
-2.168465 -1.792613 # returns correlation between variables
cor_test(data = E2_SL_clean, "Choice_CUZ_relate", "Choice_SIB_relate", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
--------------------------------------------------------------------------------
Choice_CUZ_relate | Choice_SIB_relate | 0.45 | [0.37, 0.53] | 9.52 | < .001***
Observations: 354# returns histogram of differences score variable
print(hist(E2_SL_clean$Choice_CUZminusSIB_relate, breaks = 100))$breaks
[1] -95 -94 -93 -92 -91 -90 -89 -88 -87 -86 -85 -84 -83 -82 -81 -80 -79 -78 -77 -76 -75 -74 -73 -72
[25] -71 -70 -69 -68 -67 -66 -65 -64 -63 -62 -61 -60 -59 -58 -57 -56 -55 -54 -53 -52 -51 -50 -49 -48
[49] -47 -46 -45 -44 -43 -42 -41 -40 -39 -38 -37 -36 -35 -34 -33 -32 -31 -30 -29 -28 -27 -26 -25 -24
[73] -23 -22 -21 -20 -19 -18 -17 -16 -15 -14 -13 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0
[97] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
[121] 25 26 27 28 29 30
$counts
[1] 1 0 0 0 3 0 0 1 1 2 0 1 1 2 3 6 5 5 3 17 9 6 6 4 4 0 3 3 0 2 1 4
[33] 5 1 1 3 0 2 4 1 4 2 3 8 23 4 4 2 1 5 3 1 2 2 6 5 4 5 4 4 7 4 5 8
[65] 4 10 12 6 9 10 10 13 2 8 1 3 4 4 3 4 2 1 1 2 1 1 2 3 0 2 0 1 0 1 2 1
[97] 1 0 0 1 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1
$density
[1] 0.002824859 0.000000000 0.000000000 0.000000000 0.008474576 0.000000000 0.000000000 0.002824859
[9] 0.002824859 0.005649718 0.000000000 0.002824859 0.002824859 0.005649718 0.008474576 0.016949153
[17] 0.014124294 0.014124294 0.008474576 0.048022599 0.025423729 0.016949153 0.016949153 0.011299435
[25] 0.011299435 0.000000000 0.008474576 0.008474576 0.000000000 0.005649718 0.002824859 0.011299435
[33] 0.014124294 0.002824859 0.002824859 0.008474576 0.000000000 0.005649718 0.011299435 0.002824859
[41] 0.011299435 0.005649718 0.008474576 0.022598870 0.064971751 0.011299435 0.011299435 0.005649718
[49] 0.002824859 0.014124294 0.008474576 0.002824859 0.005649718 0.005649718 0.016949153 0.014124294
[57] 0.011299435 0.014124294 0.011299435 0.011299435 0.019774011 0.011299435 0.014124294 0.022598870
[65] 0.011299435 0.028248588 0.033898305 0.016949153 0.025423729 0.028248588 0.028248588 0.036723164
[73] 0.005649718 0.022598870 0.002824859 0.008474576 0.011299435 0.011299435 0.008474576 0.011299435
[81] 0.005649718 0.002824859 0.002824859 0.005649718 0.002824859 0.002824859 0.005649718 0.008474576
[89] 0.000000000 0.005649718 0.000000000 0.002824859 0.000000000 0.002824859 0.005649718 0.002824859
[97] 0.002824859 0.000000000 0.000000000 0.002824859 0.000000000 0.000000000 0.000000000 0.000000000
[105] 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.002824859 0.002824859 0.000000000
[113] 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
[121] 0.000000000 0.000000000 0.000000000 0.000000000 0.002824859
$mids
[1] -94.5 -93.5 -92.5 -91.5 -90.5 -89.5 -88.5 -87.5 -86.5 -85.5 -84.5 -83.5 -82.5 -81.5 -80.5 -79.5
[17] -78.5 -77.5 -76.5 -75.5 -74.5 -73.5 -72.5 -71.5 -70.5 -69.5 -68.5 -67.5 -66.5 -65.5 -64.5 -63.5
[33] -62.5 -61.5 -60.5 -59.5 -58.5 -57.5 -56.5 -55.5 -54.5 -53.5 -52.5 -51.5 -50.5 -49.5 -48.5 -47.5
[49] -46.5 -45.5 -44.5 -43.5 -42.5 -41.5 -40.5 -39.5 -38.5 -37.5 -36.5 -35.5 -34.5 -33.5 -32.5 -31.5
[65] -30.5 -29.5 -28.5 -27.5 -26.5 -25.5 -24.5 -23.5 -22.5 -21.5 -20.5 -19.5 -18.5 -17.5 -16.5 -15.5
[81] -14.5 -13.5 -12.5 -11.5 -10.5 -9.5 -8.5 -7.5 -6.5 -5.5 -4.5 -3.5 -2.5 -1.5 -0.5 0.5
[97] 1.5 2.5 3.5 4.5 5.5 6.5 7.5 8.5 9.5 10.5 11.5 12.5 13.5 14.5 15.5 16.5
[113] 17.5 18.5 19.5 20.5 21.5 22.5 23.5 24.5 25.5 26.5 27.5 28.5 29.5
$xname
[1] "E2_SL_clean$Choice_CUZminusSIB_relate"
$equidist
[1] TRUE
attr(,"class")
[1] "histogram"
Friend-Like
No Choice
# returns t-test results
t.test(relate ~ Relation,
data = E2_all_long %>%
filter(BSs_cond == "Friend-Like") %>%
filter(`Choice Context` == "No Choice") %>%
droplevels(),
paired = T)
Paired t-test
data: relate by Relation
t = -33.579, df = 344, p-value < 2.2e-16
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-46.58346 -41.42813
sample estimates:
mean of the differences
-44.0058 # returns dz effect size and 95% CIs
effsize::cohen.d(relate ~ Relation | Subject(ResponseId),
data = E2_all_long %>%
filter(BSs_cond == "Friend-Like") %>%
filter(`Choice Context` == "No Choice") %>%
droplevels(),
paired = T,
within = F) # setting this to false ensures dz is calculated, using difference score
Cohen's d
d estimate: -1.807811 (large)
95 percent confidence interval:
lower upper
-1.979372 -1.636250 # returns d-av effect size and 95% CIs
effsize::cohen.d(relate ~ Relation | Subject(ResponseId),
data = E2_all_long %>%
filter(BSs_cond == "Friend-Like") %>%
filter(`Choice Context` == "No Choice") %>%
droplevels(),
paired = T,
within = T) # setting this to true ensures d-av is calculated, using raw scores
Cohen's d
d estimate: -1.919301 (large)
95 percent confidence interval:
lower upper
-2.108489 -1.730112 # returns correlation between variables
cor_test(data = E2_FL_clean, "NoChoice_CUZ_relate", "NoChoice_SIB_relate", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
------------------------------------------------------------------------------------
NoChoice_CUZ_relate | NoChoice_SIB_relate | 0.44 | [0.35, 0.52] | 8.98 | < .001***
Observations: 345# returns histogram of differences score variable
print(hist(E2_FL_clean$NoChoice_CUZminusSIB_relate, breaks = 100))$breaks
[1] -91 -90 -89 -88 -87 -86 -85 -84 -83 -82 -81 -80 -79 -78 -77 -76 -75 -74 -73 -72 -71 -70 -69 -68
[25] -67 -66 -65 -64 -63 -62 -61 -60 -59 -58 -57 -56 -55 -54 -53 -52 -51 -50 -49 -48 -47 -46 -45 -44
[49] -43 -42 -41 -40 -39 -38 -37 -36 -35 -34 -33 -32 -31 -30 -29 -28 -27 -26 -25 -24 -23 -22 -21 -20
[73] -19 -18 -17 -16 -15 -14 -13 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4
[97] 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
[121] 29 30
$counts
[1] 1 1 0 1 3 1 2 4 4 2 1 2 7 4 12 11 10 11 5 7 5 1 1 1 3 4 1 0 0 0 1 0
[33] 2 2 1 0 2 2 2 8 13 15 1 5 2 6 0 1 1 1 3 4 1 3 2 4 5 1 11 6 6 8 3 10
[65] 9 18 11 11 6 2 3 6 1 2 6 2 2 3 2 3 3 2 1 3 0 1 2 2 0 1 4 0 0 0 0 0
[97] 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
$density
[1] 0.002898551 0.002898551 0.000000000 0.002898551 0.008695652 0.002898551 0.005797101 0.011594203
[9] 0.011594203 0.005797101 0.002898551 0.005797101 0.020289855 0.011594203 0.034782609 0.031884058
[17] 0.028985507 0.031884058 0.014492754 0.020289855 0.014492754 0.002898551 0.002898551 0.002898551
[25] 0.008695652 0.011594203 0.002898551 0.000000000 0.000000000 0.000000000 0.002898551 0.000000000
[33] 0.005797101 0.005797101 0.002898551 0.000000000 0.005797101 0.005797101 0.005797101 0.023188406
[41] 0.037681159 0.043478261 0.002898551 0.014492754 0.005797101 0.017391304 0.000000000 0.002898551
[49] 0.002898551 0.002898551 0.008695652 0.011594203 0.002898551 0.008695652 0.005797101 0.011594203
[57] 0.014492754 0.002898551 0.031884058 0.017391304 0.017391304 0.023188406 0.008695652 0.028985507
[65] 0.026086957 0.052173913 0.031884058 0.031884058 0.017391304 0.005797101 0.008695652 0.017391304
[73] 0.002898551 0.005797101 0.017391304 0.005797101 0.005797101 0.008695652 0.005797101 0.008695652
[81] 0.008695652 0.005797101 0.002898551 0.008695652 0.000000000 0.002898551 0.005797101 0.005797101
[89] 0.000000000 0.002898551 0.011594203 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
[97] 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.002898551
[105] 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
[113] 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
[121] 0.002898551
$mids
[1] -90.5 -89.5 -88.5 -87.5 -86.5 -85.5 -84.5 -83.5 -82.5 -81.5 -80.5 -79.5 -78.5 -77.5 -76.5 -75.5
[17] -74.5 -73.5 -72.5 -71.5 -70.5 -69.5 -68.5 -67.5 -66.5 -65.5 -64.5 -63.5 -62.5 -61.5 -60.5 -59.5
[33] -58.5 -57.5 -56.5 -55.5 -54.5 -53.5 -52.5 -51.5 -50.5 -49.5 -48.5 -47.5 -46.5 -45.5 -44.5 -43.5
[49] -42.5 -41.5 -40.5 -39.5 -38.5 -37.5 -36.5 -35.5 -34.5 -33.5 -32.5 -31.5 -30.5 -29.5 -28.5 -27.5
[65] -26.5 -25.5 -24.5 -23.5 -22.5 -21.5 -20.5 -19.5 -18.5 -17.5 -16.5 -15.5 -14.5 -13.5 -12.5 -11.5
[81] -10.5 -9.5 -8.5 -7.5 -6.5 -5.5 -4.5 -3.5 -2.5 -1.5 -0.5 0.5 1.5 2.5 3.5 4.5
[97] 5.5 6.5 7.5 8.5 9.5 10.5 11.5 12.5 13.5 14.5 15.5 16.5 17.5 18.5 19.5 20.5
[113] 21.5 22.5 23.5 24.5 25.5 26.5 27.5 28.5 29.5
$xname
[1] "E2_FL_clean$NoChoice_CUZminusSIB_relate"
$equidist
[1] TRUE
attr(,"class")
[1] "histogram"
Choice
# returns t-test results
t.test(relate ~ Relation,
data = E2_all_long %>%
filter(BSs_cond == "Friend-Like") %>%
filter(`Choice Context` == "Choice") %>%
droplevels(),
paired = T)
Paired t-test
data: relate by Relation
t = -33.766, df = 344, p-value < 2.2e-16
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-44.46029 -39.56580
sample estimates:
mean of the differences
-42.01304 # returns dz effect size and 95% CIs
effsize::cohen.d(relate ~ Relation | Subject(ResponseId),
data = E2_all_long %>%
filter(BSs_cond == "Friend-Like") %>%
filter(`Choice Context` == "Choice") %>%
droplevels(),
paired = T,
within = F) # setting this to false ensures dz is calculated, using difference score
Cohen's d
d estimate: -1.817923 (large)
95 percent confidence interval:
lower upper
-1.990080 -1.645766 # returns d-av effect size and 95% CIs
effsize::cohen.d(relate ~ Relation | Subject(ResponseId),
data = E2_all_long %>%
filter(BSs_cond == "Friend-Like") %>%
filter(`Choice Context` == "Choice") %>%
droplevels(),
paired = T,
within = T) # setting this to true ensures d-av is calculated, using raw scores
Cohen's d
d estimate: -1.86697 (large)
95 percent confidence interval:
lower upper
-2.046759 -1.687182 # returns correlation between variables
cor_test(data = E2_FL_clean, "Choice_CUZ_relate", "Choice_SIB_relate", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
--------------------------------------------------------------------------------
Choice_CUZ_relate | Choice_SIB_relate | 0.47 | [0.39, 0.55] | 9.93 | < .001***
Observations: 345# returns histogram of differences score variable
print(hist(E2_FL_clean$Choice_CUZminusSIB_relate, breaks = 100))$breaks
[1] -92 -91 -90 -89 -88 -87 -86 -85 -84 -83 -82 -81 -80 -79 -78 -77 -76 -75 -74 -73 -72 -71 -70 -69
[25] -68 -67 -66 -65 -64 -63 -62 -61 -60 -59 -58 -57 -56 -55 -54 -53 -52 -51 -50 -49 -48 -47 -46 -45
[49] -44 -43 -42 -41 -40 -39 -38 -37 -36 -35 -34 -33 -32 -31 -30 -29 -28 -27 -26 -25 -24 -23 -22 -21
[73] -20 -19 -18 -17 -16 -15 -14 -13 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3
[97] 4 5 6 7 8 9 10 11 12 13 14 15 16
$counts
[1] 1 1 0 0 0 1 0 0 1 4 4 3 1 5 6 10 13 9 9 6 1 0 1 0 2 0 2 0 0 0 3 3
[33] 2 1 1 0 0 1 0 2 6 22 16 4 3 3 4 3 4 3 0 3 3 10 4 5 3 6 4 3 8 2 6 5
[65] 9 9 14 15 10 5 6 2 5 3 3 4 7 2 5 2 1 1 3 2 0 0 2 2 0 0 0 4 0 2 0 1
[97] 0 0 0 0 1 1 0 0 0 0 0 1
$density
[1] 0.002898551 0.002898551 0.000000000 0.000000000 0.000000000 0.002898551 0.000000000 0.000000000
[9] 0.002898551 0.011594203 0.011594203 0.008695652 0.002898551 0.014492754 0.017391304 0.028985507
[17] 0.037681159 0.026086957 0.026086957 0.017391304 0.002898551 0.000000000 0.002898551 0.000000000
[25] 0.005797101 0.000000000 0.005797101 0.000000000 0.000000000 0.000000000 0.008695652 0.008695652
[33] 0.005797101 0.002898551 0.002898551 0.000000000 0.000000000 0.002898551 0.000000000 0.005797101
[41] 0.017391304 0.063768116 0.046376812 0.011594203 0.008695652 0.008695652 0.011594203 0.008695652
[49] 0.011594203 0.008695652 0.000000000 0.008695652 0.008695652 0.028985507 0.011594203 0.014492754
[57] 0.008695652 0.017391304 0.011594203 0.008695652 0.023188406 0.005797101 0.017391304 0.014492754
[65] 0.026086957 0.026086957 0.040579710 0.043478261 0.028985507 0.014492754 0.017391304 0.005797101
[73] 0.014492754 0.008695652 0.008695652 0.011594203 0.020289855 0.005797101 0.014492754 0.005797101
[81] 0.002898551 0.002898551 0.008695652 0.005797101 0.000000000 0.000000000 0.005797101 0.005797101
[89] 0.000000000 0.000000000 0.000000000 0.011594203 0.000000000 0.005797101 0.000000000 0.002898551
[97] 0.000000000 0.000000000 0.000000000 0.000000000 0.002898551 0.002898551 0.000000000 0.000000000
[105] 0.000000000 0.000000000 0.000000000 0.002898551
$mids
[1] -91.5 -90.5 -89.5 -88.5 -87.5 -86.5 -85.5 -84.5 -83.5 -82.5 -81.5 -80.5 -79.5 -78.5 -77.5 -76.5
[17] -75.5 -74.5 -73.5 -72.5 -71.5 -70.5 -69.5 -68.5 -67.5 -66.5 -65.5 -64.5 -63.5 -62.5 -61.5 -60.5
[33] -59.5 -58.5 -57.5 -56.5 -55.5 -54.5 -53.5 -52.5 -51.5 -50.5 -49.5 -48.5 -47.5 -46.5 -45.5 -44.5
[49] -43.5 -42.5 -41.5 -40.5 -39.5 -38.5 -37.5 -36.5 -35.5 -34.5 -33.5 -32.5 -31.5 -30.5 -29.5 -28.5
[65] -27.5 -26.5 -25.5 -24.5 -23.5 -22.5 -21.5 -20.5 -19.5 -18.5 -17.5 -16.5 -15.5 -14.5 -13.5 -12.5
[81] -11.5 -10.5 -9.5 -8.5 -7.5 -6.5 -5.5 -4.5 -3.5 -2.5 -1.5 -0.5 0.5 1.5 2.5 3.5
[97] 4.5 5.5 6.5 7.5 8.5 9.5 10.5 11.5 12.5 13.5 14.5 15.5
$xname
[1] "E2_FL_clean$Choice_CUZminusSIB_relate"
$equidist
[1] TRUE
attr(,"class")
[1] "histogram"
Close
Stranger-Like
No Choice
# returns t-test results
t.test(close ~ Relation,
data = E2_all_long %>%
filter(BSs_cond == "Stranger-Like") %>%
filter(`Choice Context` == "No Choice") %>%
droplevels(),
paired = T)
Paired t-test
data: close by Relation
t = -3.8599, df = 353, p-value = 0.0001349
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-5.816367 -1.889847
sample estimates:
mean of the differences
-3.853107 # returns dz effect size and 95% CIs
effsize::cohen.d(close ~ Relation | Subject(ResponseId),
data = E2_all_long %>%
filter(BSs_cond == "Stranger-Like") %>%
filter(`Choice Context` == "No Choice") %>%
droplevels(),
paired = T,
within = F) # setting this to false ensures dz is calculated, using difference score
Cohen's d
d estimate: -0.2051499 (small)
95 percent confidence interval:
lower upper
-0.31059191 -0.09970788 # returns d-av effect size and 95% CIs
effsize::cohen.d(close ~ Relation | Subject(ResponseId),
data = E2_all_long %>%
filter(BSs_cond == "Stranger-Like") %>%
filter(`Choice Context` == "No Choice") %>%
droplevels(),
paired = T,
within = T) # setting this to true ensures d-av is calculated, using raw scores
Cohen's d
d estimate: -0.1754968 (negligible)
95 percent confidence interval:
lower upper
-0.2654483 -0.0855454 # returns correlation between variables
cor_test(data = E2_SL_clean, "NoChoice_CUZ_close", "NoChoice_SIB_close", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
----------------------------------------------------------------------------------
NoChoice_CUZ_close | NoChoice_SIB_close | 0.63 | [0.57, 0.69] | 15.39 | < .001***
Observations: 354# returns histogram of differences score variable
print(hist(E2_SL_clean$NoChoice_CUZminusSIB_close, breaks = 100))$breaks
[1] -90 -88 -86 -84 -82 -80 -78 -76 -74 -72 -70 -68 -66 -64 -62 -60 -58 -56 -54 -52 -50 -48 -46 -44
[25] -42 -40 -38 -36 -34 -32 -30 -28 -26 -24 -22 -20 -18 -16 -14 -12 -10 -8 -6 -4 -2 0 2 4
[49] 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52
[73] 54 56 58 60 62 64
$counts
[1] 1 0 0 0 0 0 2 0 0 2 0 0 1 1 1 0 1 0 2 1 1 3 2 2 1 3 1 4 1 2 1 1 3
[34] 6 3 7 4 8 12 5 12 15 15 25 75 25 18 15 15 6 10 9 7 5 2 2 2 1 4 2 2 1 1 1 0 0
[67] 0 0 0 1 0 0 0 0 0 0 1
$density
[1] 0.001412429 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.002824859 0.000000000
[9] 0.000000000 0.002824859 0.000000000 0.000000000 0.001412429 0.001412429 0.001412429 0.000000000
[17] 0.001412429 0.000000000 0.002824859 0.001412429 0.001412429 0.004237288 0.002824859 0.002824859
[25] 0.001412429 0.004237288 0.001412429 0.005649718 0.001412429 0.002824859 0.001412429 0.001412429
[33] 0.004237288 0.008474576 0.004237288 0.009887006 0.005649718 0.011299435 0.016949153 0.007062147
[41] 0.016949153 0.021186441 0.021186441 0.035310734 0.105932203 0.035310734 0.025423729 0.021186441
[49] 0.021186441 0.008474576 0.014124294 0.012711864 0.009887006 0.007062147 0.002824859 0.002824859
[57] 0.002824859 0.001412429 0.005649718 0.002824859 0.002824859 0.001412429 0.001412429 0.001412429
[65] 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.001412429 0.000000000 0.000000000
[73] 0.000000000 0.000000000 0.000000000 0.000000000 0.001412429
$mids
[1] -89 -87 -85 -83 -81 -79 -77 -75 -73 -71 -69 -67 -65 -63 -61 -59 -57 -55 -53 -51 -49 -47 -45 -43
[25] -41 -39 -37 -35 -33 -31 -29 -27 -25 -23 -21 -19 -17 -15 -13 -11 -9 -7 -5 -3 -1 1 3 5
[49] 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53
[73] 55 57 59 61 63
$xname
[1] "E2_SL_clean$NoChoice_CUZminusSIB_close"
$equidist
[1] TRUE
attr(,"class")
[1] "histogram"
Choice
# returns t-test results
t.test(close ~ Relation,
data = E2_all_long %>%
filter(BSs_cond == "Stranger-Like") %>%
filter(`Choice Context` == "Choice") %>%
droplevels(),
paired = T)
Paired t-test
data: close by Relation
t = -9.9187, df = 353, p-value < 2.2e-16
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-7.550201 -5.051494
sample estimates:
mean of the differences
-6.300847 # returns dz effect size and 95% CIs
effsize::cohen.d(close ~ Relation | Subject(ResponseId),
data = E2_all_long %>%
filter(BSs_cond == "Stranger-Like") %>%
filter(`Choice Context` == "Choice") %>%
droplevels(),
paired = T,
within = F) # setting this to false ensures dz is calculated, using difference score
Cohen's d
d estimate: -0.5271712 (medium)
95 percent confidence interval:
lower upper
-0.6385352 -0.4158072 # returns d-av effect size and 95% CIs
effsize::cohen.d(close ~ Relation | Subject(ResponseId),
data = E2_all_long %>%
filter(BSs_cond == "Stranger-Like") %>%
filter(`Choice Context` == "Choice") %>%
droplevels(),
paired = T,
within = T) # setting this to true ensures d-av is calculated, using raw scores
Cohen's d
d estimate: -0.2651506 (small)
95 percent confidence interval:
lower upper
-0.3185498 -0.2117514 # returns correlation between variables
cor_test(data = E2_SL_clean, "Choice_CUZ_close", "Choice_SIB_close", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
------------------------------------------------------------------------------
Choice_CUZ_close | Choice_SIB_close | 0.87 | [0.85, 0.90] | 33.67 | < .001***
Observations: 354# returns histogram of differences score variable
print(hist(E2_SL_clean$Choice_CUZminusSIB_close, breaks = 100))$breaks
[1] -83 -82 -81 -80 -79 -78 -77 -76 -75 -74 -73 -72 -71 -70 -69 -68 -67 -66 -65 -64 -63 -62 -61 -60
[25] -59 -58 -57 -56 -55 -54 -53 -52 -51 -50 -49 -48 -47 -46 -45 -44 -43 -42 -41 -40 -39 -38 -37 -36
[49] -35 -34 -33 -32 -31 -30 -29 -28 -27 -26 -25 -24 -23 -22 -21 -20 -19 -18 -17 -16 -15 -14 -13 -12
[73] -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12
[97] 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32
$counts
[1] 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
[33] 0 0 0 1 0 0 0 0 2 0 3 0 5 3 1 0 2 1 0 2 0 4 0 1 3 3 1 3 0 4 4 1
[65] 2 1 0 3 4 9 4 4 10 5 9 8 12 8 13 15 26 28 92 29 5 6 2 3 1 1 1 1 0 0 0 0
[97] 1 0 1 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1
$density
[1] 0.002824859 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
[9] 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
[17] 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
[25] 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.002824859
[33] 0.000000000 0.000000000 0.000000000 0.002824859 0.000000000 0.000000000 0.000000000 0.000000000
[41] 0.005649718 0.000000000 0.008474576 0.000000000 0.014124294 0.008474576 0.002824859 0.000000000
[49] 0.005649718 0.002824859 0.000000000 0.005649718 0.000000000 0.011299435 0.000000000 0.002824859
[57] 0.008474576 0.008474576 0.002824859 0.008474576 0.000000000 0.011299435 0.011299435 0.002824859
[65] 0.005649718 0.002824859 0.000000000 0.008474576 0.011299435 0.025423729 0.011299435 0.011299435
[73] 0.028248588 0.014124294 0.025423729 0.022598870 0.033898305 0.022598870 0.036723164 0.042372881
[81] 0.073446328 0.079096045 0.259887006 0.081920904 0.014124294 0.016949153 0.005649718 0.008474576
[89] 0.002824859 0.002824859 0.002824859 0.002824859 0.000000000 0.000000000 0.000000000 0.000000000
[97] 0.002824859 0.000000000 0.002824859 0.002824859 0.002824859 0.000000000 0.000000000 0.000000000
[105] 0.000000000 0.002824859 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
[113] 0.000000000 0.000000000 0.002824859
$mids
[1] -82.5 -81.5 -80.5 -79.5 -78.5 -77.5 -76.5 -75.5 -74.5 -73.5 -72.5 -71.5 -70.5 -69.5 -68.5 -67.5
[17] -66.5 -65.5 -64.5 -63.5 -62.5 -61.5 -60.5 -59.5 -58.5 -57.5 -56.5 -55.5 -54.5 -53.5 -52.5 -51.5
[33] -50.5 -49.5 -48.5 -47.5 -46.5 -45.5 -44.5 -43.5 -42.5 -41.5 -40.5 -39.5 -38.5 -37.5 -36.5 -35.5
[49] -34.5 -33.5 -32.5 -31.5 -30.5 -29.5 -28.5 -27.5 -26.5 -25.5 -24.5 -23.5 -22.5 -21.5 -20.5 -19.5
[65] -18.5 -17.5 -16.5 -15.5 -14.5 -13.5 -12.5 -11.5 -10.5 -9.5 -8.5 -7.5 -6.5 -5.5 -4.5 -3.5
[81] -2.5 -1.5 -0.5 0.5 1.5 2.5 3.5 4.5 5.5 6.5 7.5 8.5 9.5 10.5 11.5 12.5
[97] 13.5 14.5 15.5 16.5 17.5 18.5 19.5 20.5 21.5 22.5 23.5 24.5 25.5 26.5 27.5 28.5
[113] 29.5 30.5 31.5
$xname
[1] "E2_SL_clean$Choice_CUZminusSIB_close"
$equidist
[1] TRUE
attr(,"class")
[1] "histogram"
Friend-Like
No Choice
# returns t-test results
t.test(close ~ Relation,
data = E2_all_long %>%
filter(BSs_cond == "Friend-Like") %>%
filter(`Choice Context` == "No Choice") %>%
droplevels(),
paired = T)
Paired t-test
data: close by Relation
t = -2.8987, df = 344, p-value = 0.003987
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-5.0209996 -0.9616091
sample estimates:
mean of the differences
-2.991304 # returns dz effect size and 95% CIs
effsize::cohen.d(close ~ Relation | Subject(ResponseId),
data = E2_all_long %>%
filter(BSs_cond == "Friend-Like") %>%
filter(`Choice Context` == "No Choice") %>%
droplevels(),
paired = T,
within = F) # setting this to false ensures dz is calculated, using difference score
Cohen's d
d estimate: -0.1560626 (negligible)
95 percent confidence interval:
lower upper
-0.26241121 -0.04971409 # returns d-av effect size and 95% CIs
effsize::cohen.d(close ~ Relation | Subject(ResponseId),
data = E2_all_long %>%
filter(BSs_cond == "Friend-Like") %>%
filter(`Choice Context` == "No Choice") %>%
droplevels(),
paired = T,
within = T) # setting this to true ensures d-av is calculated, using raw scores
Cohen's d
d estimate: -0.1661081 (negligible)
95 percent confidence interval:
lower upper
-0.27939261 -0.05282365 # returns correlation between variables
cor_test(data = E2_FL_clean, "NoChoice_CUZ_close", "NoChoice_SIB_close", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
----------------------------------------------------------------------------------
NoChoice_CUZ_close | NoChoice_SIB_close | 0.43 | [0.34, 0.52] | 8.91 | < .001***
Observations: 345# returns histogram of differences score variable
print(hist(E2_FL_clean$NoChoice_CUZminusSIB_close, breaks = 100))$breaks
[1] -80 -78 -76 -74 -72 -70 -68 -66 -64 -62 -60 -58 -56 -54 -52 -50 -48 -46 -44 -42 -40 -38 -36 -34
[25] -32 -30 -28 -26 -24 -22 -20 -18 -16 -14 -12 -10 -8 -6 -4 -2 0 2 4 6 8 10 12 14
[49] 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58 60 62
[73] 64 66 68 70 72 74 76 78
$counts
[1] 1 0 0 1 0 0 0 0 1 0 0 0 1 0 4 2 1 2 0 1 0 2 1 2 3 6 6 3 6 9 8 8 10
[34] 14 9 9 14 12 14 87 8 20 12 6 9 6 10 4 2 5 3 2 4 3 0 1 1 1 0 2 1 0 1 2 0 1
[67] 0 1 1 0 0 0 0 0 0 1 0 0 1
$density
[1] 0.001449275 0.000000000 0.000000000 0.001449275 0.000000000 0.000000000 0.000000000 0.000000000
[9] 0.001449275 0.000000000 0.000000000 0.000000000 0.001449275 0.000000000 0.005797101 0.002898551
[17] 0.001449275 0.002898551 0.000000000 0.001449275 0.000000000 0.002898551 0.001449275 0.002898551
[25] 0.004347826 0.008695652 0.008695652 0.004347826 0.008695652 0.013043478 0.011594203 0.011594203
[33] 0.014492754 0.020289855 0.013043478 0.013043478 0.020289855 0.017391304 0.020289855 0.126086957
[41] 0.011594203 0.028985507 0.017391304 0.008695652 0.013043478 0.008695652 0.014492754 0.005797101
[49] 0.002898551 0.007246377 0.004347826 0.002898551 0.005797101 0.004347826 0.000000000 0.001449275
[57] 0.001449275 0.001449275 0.000000000 0.002898551 0.001449275 0.000000000 0.001449275 0.002898551
[65] 0.000000000 0.001449275 0.000000000 0.001449275 0.001449275 0.000000000 0.000000000 0.000000000
[73] 0.000000000 0.000000000 0.000000000 0.001449275 0.000000000 0.000000000 0.001449275
$mids
[1] -79 -77 -75 -73 -71 -69 -67 -65 -63 -61 -59 -57 -55 -53 -51 -49 -47 -45 -43 -41 -39 -37 -35 -33
[25] -31 -29 -27 -25 -23 -21 -19 -17 -15 -13 -11 -9 -7 -5 -3 -1 1 3 5 7 9 11 13 15
[49] 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63
[73] 65 67 69 71 73 75 77
$xname
[1] "E2_FL_clean$NoChoice_CUZminusSIB_close"
$equidist
[1] TRUE
attr(,"class")
[1] "histogram"
Choice
# returns t-test results
t.test(close ~ Relation,
data = E2_all_long %>%
filter(BSs_cond == "Friend-Like") %>%
filter(`Choice Context` == "Choice") %>%
droplevels(),
paired = T)
Paired t-test
data: close by Relation
t = -9.7685, df = 344, p-value < 2.2e-16
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-6.572598 -4.369431
sample estimates:
mean of the differences
-5.471014 # returns dz effect size and 95% CIs
effsize::cohen.d(close ~ Relation | Subject(ResponseId),
data = E2_all_long %>%
filter(BSs_cond == "Friend-Like") %>%
filter(`Choice Context` == "Choice") %>%
droplevels(),
paired = T,
within = F) # setting this to false ensures dz is calculated, using difference score
Cohen's d
d estimate: -0.5259201 (medium)
95 percent confidence interval:
lower upper
-0.6386998 -0.4131404 # returns d-av effect size and 95% CIs
effsize::cohen.d(close ~ Relation | Subject(ResponseId),
data = E2_all_long %>%
filter(BSs_cond == "Friend-Like") %>%
filter(`Choice Context` == "Choice") %>%
droplevels(),
paired = T,
within = T) # setting this to true ensures d-av is calculated, using raw scores
Cohen's d
d estimate: -0.329588 (small)
95 percent confidence interval:
lower upper
-0.3976085 -0.2615675 # returns correlation between variables
cor_test(data = E2_FL_clean, "Choice_CUZ_close", "Choice_SIB_close", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
------------------------------------------------------------------------------
Choice_CUZ_close | Choice_SIB_close | 0.80 | [0.76, 0.84] | 25.01 | < .001***
Observations: 345# returns histogram of differences score variable
print(hist(E2_FL_clean$Choice_CUZminusSIB_close, breaks = 100))$breaks
[1] -75 -74 -73 -72 -71 -70 -69 -68 -67 -66 -65 -64 -63 -62 -61 -60 -59 -58 -57 -56 -55 -54 -53 -52
[25] -51 -50 -49 -48 -47 -46 -45 -44 -43 -42 -41 -40 -39 -38 -37 -36 -35 -34 -33 -32 -31 -30 -29 -28
[49] -27 -26 -25 -24 -23 -22 -21 -20 -19 -18 -17 -16 -15 -14 -13 -12 -11 -10 -9 -8 -7 -6 -5 -4
[73] -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
$counts
[1] 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0
[34] 0 1 1 0 1 0 0 0 0 1 0 1 0 1 1 1 0 2 2 0 3 0 4 5 9 2 7 3 5 8 6 6 3
[67] 7 13 11 18 16 13 13 25 99 23 12 5 2 0 2 1 1 0 3 1 1 0 0 0 0 0 1
$density
[1] 0.002898551 0.000000000 0.000000000 0.000000000 0.000000000 0.002898551 0.002898551 0.000000000
[9] 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
[17] 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
[25] 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.002898551 0.002898551 0.000000000
[33] 0.000000000 0.000000000 0.002898551 0.002898551 0.000000000 0.002898551 0.000000000 0.000000000
[41] 0.000000000 0.000000000 0.002898551 0.000000000 0.002898551 0.000000000 0.002898551 0.002898551
[49] 0.002898551 0.000000000 0.005797101 0.005797101 0.000000000 0.008695652 0.000000000 0.011594203
[57] 0.014492754 0.026086957 0.005797101 0.020289855 0.008695652 0.014492754 0.023188406 0.017391304
[65] 0.017391304 0.008695652 0.020289855 0.037681159 0.031884058 0.052173913 0.046376812 0.037681159
[73] 0.037681159 0.072463768 0.286956522 0.066666667 0.034782609 0.014492754 0.005797101 0.000000000
[81] 0.005797101 0.002898551 0.002898551 0.000000000 0.008695652 0.002898551 0.002898551 0.000000000
[89] 0.000000000 0.000000000 0.000000000 0.000000000 0.002898551
$mids
[1] -74.5 -73.5 -72.5 -71.5 -70.5 -69.5 -68.5 -67.5 -66.5 -65.5 -64.5 -63.5 -62.5 -61.5 -60.5 -59.5
[17] -58.5 -57.5 -56.5 -55.5 -54.5 -53.5 -52.5 -51.5 -50.5 -49.5 -48.5 -47.5 -46.5 -45.5 -44.5 -43.5
[33] -42.5 -41.5 -40.5 -39.5 -38.5 -37.5 -36.5 -35.5 -34.5 -33.5 -32.5 -31.5 -30.5 -29.5 -28.5 -27.5
[49] -26.5 -25.5 -24.5 -23.5 -22.5 -21.5 -20.5 -19.5 -18.5 -17.5 -16.5 -15.5 -14.5 -13.5 -12.5 -11.5
[65] -10.5 -9.5 -8.5 -7.5 -6.5 -5.5 -4.5 -3.5 -2.5 -1.5 -0.5 0.5 1.5 2.5 3.5 4.5
[81] 5.5 6.5 7.5 8.5 9.5 10.5 11.5 12.5 13.5 14.5 15.5 16.5 17.5
$xname
[1] "E2_FL_clean$Choice_CUZminusSIB_close"
$equidist
[1] TRUE
attr(,"class")
[1] "histogram"
Prior Help
Stranger-Like
No Choice
# returns t-test results
t.test(priorhelp ~ Relation,
data = E2_all_long %>%
filter(BSs_cond == "Stranger-Like") %>%
filter(`Choice Context` == "No Choice") %>%
droplevels(),
paired = T)
Paired t-test
data: priorhelp by Relation
t = -6.2413, df = 353, p-value = 1.244e-09
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-8.763683 -4.564000
sample estimates:
mean of the differences
-6.663842 # returns dz effect size and 95% CIs
effsize::cohen.d(priorhelp ~ Relation | Subject(ResponseId),
data = E2_all_long %>%
filter(BSs_cond == "Stranger-Like") %>%
filter(`Choice Context` == "No Choice") %>%
droplevels(),
paired = T,
within = F) # setting this to false ensures dz is calculated, using difference score
Cohen's d
d estimate: -0.3317235 (small)
95 percent confidence interval:
lower upper
-0.4389055 -0.2245415 # returns d-av effect size and 95% CIs
effsize::cohen.d(priorhelp ~ Relation | Subject(ResponseId),
data = E2_all_long %>%
filter(BSs_cond == "Stranger-Like") %>%
filter(`Choice Context` == "No Choice") %>%
droplevels(),
paired = T,
within = T) # setting this to true ensures d-av is calculated, using raw scores
Cohen's d
d estimate: -0.2868032 (small)
95 percent confidence interval:
lower upper
-0.3788590 -0.1947473 # returns correlation between variables
cor_test(data = E2_SL_clean, "NoChoice_CUZ_priorhelp", "NoChoice_SIB_priorhelp", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
------------------------------------------------------------------------------------------
NoChoice_CUZ_priorhelp | NoChoice_SIB_priorhelp | 0.63 | [0.56, 0.69] | 15.07 | < .001***
Observations: 354# returns histogram of differences score variable
print(hist(E2_SL_clean$NoChoice_CUZminusSIB_priorhelp, breaks = 100))$breaks
[1] -88 -86 -84 -82 -80 -78 -76 -74 -72 -70 -68 -66 -64 -62 -60 -58 -56 -54 -52 -50 -48 -46 -44 -42
[25] -40 -38 -36 -34 -32 -30 -28 -26 -24 -22 -20 -18 -16 -14 -12 -10 -8 -6 -4 -2 0 2 4 6
[49] 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54
[73] 56 58 60 62 64 66 68
$counts
[1] 1 0 0 0 1 0 0 1 0 0 0 1 0 1 1 1 1 4 2 1 5 6 1 3 2 4 6 1 2 2 1 4 8
[34] 6 5 10 9 12 11 13 13 31 29 53 15 13 9 13 9 7 5 6 3 4 4 2 0 2 1 0 2 2 1 0 0 0
[67] 1 0 0 0 1 1 0 0 0 0 0 1
$density
[1] 0.001412429 0.000000000 0.000000000 0.000000000 0.001412429 0.000000000 0.000000000 0.001412429
[9] 0.000000000 0.000000000 0.000000000 0.001412429 0.000000000 0.001412429 0.001412429 0.001412429
[17] 0.001412429 0.005649718 0.002824859 0.001412429 0.007062147 0.008474576 0.001412429 0.004237288
[25] 0.002824859 0.005649718 0.008474576 0.001412429 0.002824859 0.002824859 0.001412429 0.005649718
[33] 0.011299435 0.008474576 0.007062147 0.014124294 0.012711864 0.016949153 0.015536723 0.018361582
[41] 0.018361582 0.043785311 0.040960452 0.074858757 0.021186441 0.018361582 0.012711864 0.018361582
[49] 0.012711864 0.009887006 0.007062147 0.008474576 0.004237288 0.005649718 0.005649718 0.002824859
[57] 0.000000000 0.002824859 0.001412429 0.000000000 0.002824859 0.002824859 0.001412429 0.000000000
[65] 0.000000000 0.000000000 0.001412429 0.000000000 0.000000000 0.000000000 0.001412429 0.001412429
[73] 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.001412429
$mids
[1] -87 -85 -83 -81 -79 -77 -75 -73 -71 -69 -67 -65 -63 -61 -59 -57 -55 -53 -51 -49 -47 -45 -43 -41
[25] -39 -37 -35 -33 -31 -29 -27 -25 -23 -21 -19 -17 -15 -13 -11 -9 -7 -5 -3 -1 1 3 5 7
[49] 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55
[73] 57 59 61 63 65 67
$xname
[1] "E2_SL_clean$NoChoice_CUZminusSIB_priorhelp"
$equidist
[1] TRUE
attr(,"class")
[1] "histogram"
Choice
# returns t-test results
t.test(priorhelp ~ Relation,
data = E2_all_long %>%
filter(BSs_cond == "Stranger-Like") %>%
filter(`Choice Context` == "Choice") %>%
droplevels(),
paired = T)
Paired t-test
data: priorhelp by Relation
t = -13.075, df = 353, p-value < 2.2e-16
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-11.045985 -8.157405
sample estimates:
mean of the differences
-9.601695 # returns dz effect size and 95% CIs
effsize::cohen.d(priorhelp ~ Relation | Subject(ResponseId),
data = E2_all_long %>%
filter(BSs_cond == "Stranger-Like") %>%
filter(`Choice Context` == "Choice") %>%
droplevels(),
paired = T,
within = F) # setting this to false ensures dz is calculated, using difference score
Cohen's d
d estimate: -0.6949149 (medium)
95 percent confidence interval:
lower upper
-0.8111820 -0.5786478 # returns d-av effect size and 95% CIs
effsize::cohen.d(priorhelp ~ Relation | Subject(ResponseId),
data = E2_all_long %>%
filter(BSs_cond == "Stranger-Like") %>%
filter(`Choice Context` == "Choice") %>%
droplevels(),
paired = T,
within = T) # setting this to true ensures d-av is calculated, using raw scores
Cohen's d
d estimate: -0.3909815 (small)
95 percent confidence interval:
lower upper
-0.4518944 -0.3300685 # returns correlation between variables
cor_test(data = E2_SL_clean, "Choice_CUZ_priorhelp", "Choice_SIB_priorhelp", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
--------------------------------------------------------------------------------------
Choice_CUZ_priorhelp | Choice_SIB_priorhelp | 0.84 | [0.81, 0.87] | 29.25 | < .001***
Observations: 354# returns histogram of differences score variable
print(hist(E2_SL_clean$Choice_CUZminusSIB_priorhelp, breaks = 100))$breaks
[1] -74 -73 -72 -71 -70 -69 -68 -67 -66 -65 -64 -63 -62 -61 -60 -59 -58 -57 -56 -55 -54 -53 -52 -51
[25] -50 -49 -48 -47 -46 -45 -44 -43 -42 -41 -40 -39 -38 -37 -36 -35 -34 -33 -32 -31 -30 -29 -28 -27
[49] -26 -25 -24 -23 -22 -21 -20 -19 -18 -17 -16 -15 -14 -13 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3
[73] -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
[97] 22 23 24 25 26
$counts
[1] 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 2 1 2 1 0 0 1 0 1 0 2
[33] 2 1 2 1 1 2 1 2 1 2 2 2 3 2 2 2 2 4 3 2 3 3 2 4 2 9 2 8 15 5 16 7
[65] 8 11 13 13 8 17 10 17 20 60 24 9 4 1 1 2 2 0 0 1 0 0 0 0 0 0 1 0 0 0 1 0
[97] 0 0 1 1
$density
[1] 0.002824859 0.000000000 0.002824859 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
[9] 0.000000000 0.000000000 0.000000000 0.000000000 0.002824859 0.000000000 0.000000000 0.002824859
[17] 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.005649718 0.002824859 0.005649718
[25] 0.002824859 0.000000000 0.000000000 0.002824859 0.000000000 0.002824859 0.000000000 0.005649718
[33] 0.005649718 0.002824859 0.005649718 0.002824859 0.002824859 0.005649718 0.002824859 0.005649718
[41] 0.002824859 0.005649718 0.005649718 0.005649718 0.008474576 0.005649718 0.005649718 0.005649718
[49] 0.005649718 0.011299435 0.008474576 0.005649718 0.008474576 0.008474576 0.005649718 0.011299435
[57] 0.005649718 0.025423729 0.005649718 0.022598870 0.042372881 0.014124294 0.045197740 0.019774011
[65] 0.022598870 0.031073446 0.036723164 0.036723164 0.022598870 0.048022599 0.028248588 0.048022599
[73] 0.056497175 0.169491525 0.067796610 0.025423729 0.011299435 0.002824859 0.002824859 0.005649718
[81] 0.005649718 0.000000000 0.000000000 0.002824859 0.000000000 0.000000000 0.000000000 0.000000000
[89] 0.000000000 0.000000000 0.002824859 0.000000000 0.000000000 0.000000000 0.002824859 0.000000000
[97] 0.000000000 0.000000000 0.002824859 0.002824859
$mids
[1] -73.5 -72.5 -71.5 -70.5 -69.5 -68.5 -67.5 -66.5 -65.5 -64.5 -63.5 -62.5 -61.5 -60.5 -59.5 -58.5
[17] -57.5 -56.5 -55.5 -54.5 -53.5 -52.5 -51.5 -50.5 -49.5 -48.5 -47.5 -46.5 -45.5 -44.5 -43.5 -42.5
[33] -41.5 -40.5 -39.5 -38.5 -37.5 -36.5 -35.5 -34.5 -33.5 -32.5 -31.5 -30.5 -29.5 -28.5 -27.5 -26.5
[49] -25.5 -24.5 -23.5 -22.5 -21.5 -20.5 -19.5 -18.5 -17.5 -16.5 -15.5 -14.5 -13.5 -12.5 -11.5 -10.5
[65] -9.5 -8.5 -7.5 -6.5 -5.5 -4.5 -3.5 -2.5 -1.5 -0.5 0.5 1.5 2.5 3.5 4.5 5.5
[81] 6.5 7.5 8.5 9.5 10.5 11.5 12.5 13.5 14.5 15.5 16.5 17.5 18.5 19.5 20.5 21.5
[97] 22.5 23.5 24.5 25.5
$xname
[1] "E2_SL_clean$Choice_CUZminusSIB_priorhelp"
$equidist
[1] TRUE
attr(,"class")
[1] "histogram"
Friend-Like
No Choice
# returns t-test results
t.test(priorhelp ~ Relation,
data = E2_all_long %>%
filter(BSs_cond == "Friend-Like") %>%
filter(`Choice Context` == "No Choice") %>%
droplevels(),
paired = T)
Paired t-test
data: priorhelp by Relation
t = -3.8733, df = 344, p-value = 0.0001285
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-5.720921 -1.867484
sample estimates:
mean of the differences
-3.794203 # returns dz effect size and 95% CIs
effsize::cohen.d(priorhelp ~ Relation | Subject(ResponseId),
data = E2_all_long %>%
filter(BSs_cond == "Friend-Like") %>%
filter(`Choice Context` == "No Choice") %>%
droplevels(),
paired = T,
within = F) # setting this to false ensures dz is calculated, using difference score
Cohen's d
d estimate: -0.2085314 (small)
95 percent confidence interval:
lower upper
-0.3153813 -0.1016815 # returns d-av effect size and 95% CIs
effsize::cohen.d(priorhelp ~ Relation | Subject(ResponseId),
data = E2_all_long %>%
filter(BSs_cond == "Friend-Like") %>%
filter(`Choice Context` == "No Choice") %>%
droplevels(),
paired = T,
within = T) # setting this to true ensures d-av is calculated, using raw scores
Cohen's d
d estimate: -0.2119308 (small)
95 percent confidence interval:
lower upper
-0.3205604 -0.1033011 # returns correlation between variables
cor_test(data = E2_FL_clean, "NoChoice_CUZ_priorhelp", "NoChoice_SIB_priorhelp", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
------------------------------------------------------------------------------------------
NoChoice_CUZ_priorhelp | NoChoice_SIB_priorhelp | 0.48 | [0.40, 0.56] | 10.23 | < .001***
Observations: 345# returns histogram of differences score variable
print(hist(E2_FL_clean$NoChoice_CUZminusSIB_priorhelp, breaks = 100))$breaks
[1] -88 -86 -84 -82 -80 -78 -76 -74 -72 -70 -68 -66 -64 -62 -60 -58 -56 -54 -52 -50 -48 -46 -44 -42
[25] -40 -38 -36 -34 -32 -30 -28 -26 -24 -22 -20 -18 -16 -14 -12 -10 -8 -6 -4 -2 0 2 4 6
[49] 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54
[73] 56 58 60 62 64 66 68 70 72 74 76
$counts
[1] 1 0 0 0 0 0 0 0 0 0 0 1 0 0 2 1 2 0 2 2 0 1 1 0 1 1 7 4 1 1 2 7 7
[34] 5 7 8 5 13 9 14 16 14 13 95 17 17 12 6 10 5 4 6 2 3 4 3 1 1 1 0 0 0 1 3 0 0
[67] 0 1 0 1 0 0 3 0 0 0 0 0 0 0 0 1
$density
[1] 0.001449275 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
[9] 0.000000000 0.000000000 0.000000000 0.001449275 0.000000000 0.000000000 0.002898551 0.001449275
[17] 0.002898551 0.000000000 0.002898551 0.002898551 0.000000000 0.001449275 0.001449275 0.000000000
[25] 0.001449275 0.001449275 0.010144928 0.005797101 0.001449275 0.001449275 0.002898551 0.010144928
[33] 0.010144928 0.007246377 0.010144928 0.011594203 0.007246377 0.018840580 0.013043478 0.020289855
[41] 0.023188406 0.020289855 0.018840580 0.137681159 0.024637681 0.024637681 0.017391304 0.008695652
[49] 0.014492754 0.007246377 0.005797101 0.008695652 0.002898551 0.004347826 0.005797101 0.004347826
[57] 0.001449275 0.001449275 0.001449275 0.000000000 0.000000000 0.000000000 0.001449275 0.004347826
[65] 0.000000000 0.000000000 0.000000000 0.001449275 0.000000000 0.001449275 0.000000000 0.000000000
[73] 0.004347826 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
[81] 0.000000000 0.001449275
$mids
[1] -87 -85 -83 -81 -79 -77 -75 -73 -71 -69 -67 -65 -63 -61 -59 -57 -55 -53 -51 -49 -47 -45 -43 -41
[25] -39 -37 -35 -33 -31 -29 -27 -25 -23 -21 -19 -17 -15 -13 -11 -9 -7 -5 -3 -1 1 3 5 7
[49] 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55
[73] 57 59 61 63 65 67 69 71 73 75
$xname
[1] "E2_FL_clean$NoChoice_CUZminusSIB_priorhelp"
$equidist
[1] TRUE
attr(,"class")
[1] "histogram"
Choice
# returns t-test results
t.test(priorhelp ~ Relation,
data = E2_all_long %>%
filter(BSs_cond == "Friend-Like") %>%
filter(`Choice Context` == "Choice") %>%
droplevels(),
paired = T)
Paired t-test
data: priorhelp by Relation
t = -9.0624, df = 344, p-value < 2.2e-16
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-6.314485 -4.062326
sample estimates:
mean of the differences
-5.188406 # returns dz effect size and 95% CIs
effsize::cohen.d(priorhelp ~ Relation | Subject(ResponseId),
data = E2_all_long %>%
filter(BSs_cond == "Friend-Like") %>%
filter(`Choice Context` == "Choice") %>%
droplevels(),
paired = T,
within = F) # setting this to false ensures dz is calculated, using difference score
Cohen's d
d estimate: -0.4879037 (small)
95 percent confidence interval:
lower upper
-0.5997247 -0.3760828 # returns d-av effect size and 95% CIs
effsize::cohen.d(priorhelp ~ Relation | Subject(ResponseId),
data = E2_all_long %>%
filter(BSs_cond == "Friend-Like") %>%
filter(`Choice Context` == "Choice") %>%
droplevels(),
paired = T,
within = T) # setting this to true ensures d-av is calculated, using raw scores
Cohen's d
d estimate: -0.3099237 (small)
95 percent confidence interval:
lower upper
-0.3786638 -0.2411837 # returns correlation between variables
cor_test(data = E2_FL_clean, "Choice_CUZ_priorhelp", "Choice_SIB_priorhelp", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
--------------------------------------------------------------------------------------
Choice_CUZ_priorhelp | Choice_SIB_priorhelp | 0.80 | [0.76, 0.83] | 24.54 | < .001***
Observations: 345# returns histogram of differences score variable
print(hist(E2_FL_clean$Choice_CUZminusSIB_priorhelp, breaks = 100))$breaks
[1] -85 -84 -83 -82 -81 -80 -79 -78 -77 -76 -75 -74 -73 -72 -71 -70 -69 -68 -67 -66 -65 -64 -63 -62
[25] -61 -60 -59 -58 -57 -56 -55 -54 -53 -52 -51 -50 -49 -48 -47 -46 -45 -44 -43 -42 -41 -40 -39 -38
[49] -37 -36 -35 -34 -33 -32 -31 -30 -29 -28 -27 -26 -25 -24 -23 -22 -21 -20 -19 -18 -17 -16 -15 -14
[73] -13 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10
[97] 11 12 13 14 15 16 17
$counts
[1] 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[25] 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 2 1 0
[49] 0 0 0 1 0 0 0 1 2 0 0 4 1 4 1 4 5 1 4 2 9 1 5 4
[73] 4 7 9 3 7 9 7 10 10 13 16 27 105 31 11 6 3 1 1 3 0 0 1 1
[97] 1 1 0 1 0 1
$density
[1] 0.002898551 0.000000000 0.000000000 0.002898551 0.000000000 0.000000000 0.000000000 0.000000000
[9] 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
[17] 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
[25] 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
[33] 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.002898551 0.000000000 0.000000000
[41] 0.000000000 0.002898551 0.000000000 0.000000000 0.000000000 0.005797101 0.002898551 0.000000000
[49] 0.000000000 0.000000000 0.000000000 0.002898551 0.000000000 0.000000000 0.000000000 0.002898551
[57] 0.005797101 0.000000000 0.000000000 0.011594203 0.002898551 0.011594203 0.002898551 0.011594203
[65] 0.014492754 0.002898551 0.011594203 0.005797101 0.026086957 0.002898551 0.014492754 0.011594203
[73] 0.011594203 0.020289855 0.026086957 0.008695652 0.020289855 0.026086957 0.020289855 0.028985507
[81] 0.028985507 0.037681159 0.046376812 0.078260870 0.304347826 0.089855072 0.031884058 0.017391304
[89] 0.008695652 0.002898551 0.002898551 0.008695652 0.000000000 0.000000000 0.002898551 0.002898551
[97] 0.002898551 0.002898551 0.000000000 0.002898551 0.000000000 0.002898551
$mids
[1] -84.5 -83.5 -82.5 -81.5 -80.5 -79.5 -78.5 -77.5 -76.5 -75.5 -74.5 -73.5 -72.5 -71.5 -70.5 -69.5
[17] -68.5 -67.5 -66.5 -65.5 -64.5 -63.5 -62.5 -61.5 -60.5 -59.5 -58.5 -57.5 -56.5 -55.5 -54.5 -53.5
[33] -52.5 -51.5 -50.5 -49.5 -48.5 -47.5 -46.5 -45.5 -44.5 -43.5 -42.5 -41.5 -40.5 -39.5 -38.5 -37.5
[49] -36.5 -35.5 -34.5 -33.5 -32.5 -31.5 -30.5 -29.5 -28.5 -27.5 -26.5 -25.5 -24.5 -23.5 -22.5 -21.5
[65] -20.5 -19.5 -18.5 -17.5 -16.5 -15.5 -14.5 -13.5 -12.5 -11.5 -10.5 -9.5 -8.5 -7.5 -6.5 -5.5
[81] -4.5 -3.5 -2.5 -1.5 -0.5 0.5 1.5 2.5 3.5 4.5 5.5 6.5 7.5 8.5 9.5 10.5
[97] 11.5 12.5 13.5 14.5 15.5 16.5
$xname
[1] "E2_FL_clean$Choice_CUZminusSIB_priorhelp"
$equidist
[1] TRUE
attr(,"class")
[1] "histogram"
Future Help
Stranger-Like
No Choice
# returns t-test results
t.test(futurehelp ~ Relation,
data = E2_all_long %>%
filter(BSs_cond == "Stranger-Like") %>%
filter(`Choice Context` == "No Choice") %>%
droplevels(),
paired = T)
Paired t-test
data: futurehelp by Relation
t = -3.8295, df = 353, p-value = 0.0001519
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-7.234338 -2.324984
sample estimates:
mean of the differences
-4.779661 # returns dz effect size and 95% CIs
effsize::cohen.d(futurehelp ~ Relation | Subject(ResponseId),
data = E2_all_long %>%
filter(BSs_cond == "Stranger-Like") %>%
filter(`Choice Context` == "No Choice") %>%
droplevels(),
paired = T,
within = F) # setting this to false ensures dz is calculated, using difference score
Cohen's d
d estimate: -0.2035358 (small)
95 percent confidence interval:
lower upper
-0.30896074 -0.09811078 # returns d-av effect size and 95% CIs
effsize::cohen.d(futurehelp ~ Relation | Subject(ResponseId),
data = E2_all_long %>%
filter(BSs_cond == "Stranger-Like") %>%
filter(`Choice Context` == "No Choice") %>%
droplevels(),
paired = T,
within = T) # setting this to true ensures d-av is calculated, using raw scores
Cohen's d
d estimate: -0.1789109 (negligible)
95 percent confidence interval:
lower upper
-0.27136706 -0.08645483 # returns correlation between variables
cor_test(data = E2_SL_clean, "NoChoice_CUZ_futurehelp", "NoChoice_SIB_futurehelp", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
--------------------------------------------------------------------------------------------
NoChoice_CUZ_futurehelp | NoChoice_SIB_futurehelp | 0.61 | [0.54, 0.67] | 14.58 | < .001***
Observations: 354# returns histogram of differences score variable
print(hist(E2_SL_clean$NoChoice_CUZminusSIB_futurehelp, breaks = 100))$breaks
[1] -80 -78 -76 -74 -72 -70 -68 -66 -64 -62 -60 -58 -56 -54 -52 -50 -48 -46 -44 -42 -40 -38 -36 -34
[25] -32 -30 -28 -26 -24 -22 -20 -18 -16 -14 -12 -10 -8 -6 -4 -2 0 2 4 6 8 10 12 14
[49] 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58 60 62
[73] 64 66 68 70 72 74 76 78 80
$counts
[1] 2 1 0 0 0 1 0 0 0 2 1 1 2 3 4 4 2 2 3 4 2 4 2 2 4 5 2 9 7 7 6 9 7
[34] 8 5 8 12 20 29 33 31 21 9 6 14 7 8 7 2 5 3 6 1 2 4 1 1 1 0 0 0 1 2 1 1 2
[67] 0 0 0 0 0 0 0 0 0 1 1 0 1 2
$density
[1] 0.002824859 0.001412429 0.000000000 0.000000000 0.000000000 0.001412429 0.000000000 0.000000000
[9] 0.000000000 0.002824859 0.001412429 0.001412429 0.002824859 0.004237288 0.005649718 0.005649718
[17] 0.002824859 0.002824859 0.004237288 0.005649718 0.002824859 0.005649718 0.002824859 0.002824859
[25] 0.005649718 0.007062147 0.002824859 0.012711864 0.009887006 0.009887006 0.008474576 0.012711864
[33] 0.009887006 0.011299435 0.007062147 0.011299435 0.016949153 0.028248588 0.040960452 0.046610169
[41] 0.043785311 0.029661017 0.012711864 0.008474576 0.019774011 0.009887006 0.011299435 0.009887006
[49] 0.002824859 0.007062147 0.004237288 0.008474576 0.001412429 0.002824859 0.005649718 0.001412429
[57] 0.001412429 0.001412429 0.000000000 0.000000000 0.000000000 0.001412429 0.002824859 0.001412429
[65] 0.001412429 0.002824859 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
[73] 0.000000000 0.000000000 0.000000000 0.001412429 0.001412429 0.000000000 0.001412429 0.002824859
$mids
[1] -79 -77 -75 -73 -71 -69 -67 -65 -63 -61 -59 -57 -55 -53 -51 -49 -47 -45 -43 -41 -39 -37 -35 -33
[25] -31 -29 -27 -25 -23 -21 -19 -17 -15 -13 -11 -9 -7 -5 -3 -1 1 3 5 7 9 11 13 15
[49] 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63
[73] 65 67 69 71 73 75 77 79
$xname
[1] "E2_SL_clean$NoChoice_CUZminusSIB_futurehelp"
$equidist
[1] TRUE
attr(,"class")
[1] "histogram"
Choice
# returns t-test results
t.test(futurehelp ~ Relation,
data = E2_all_long %>%
filter(BSs_cond == "Stranger-Like") %>%
filter(`Choice Context` == "Choice") %>%
droplevels(),
paired = T)
Paired t-test
data: futurehelp by Relation
t = -12.09, df = 353, p-value < 2.2e-16
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-10.007543 -7.207147
sample estimates:
mean of the differences
-8.607345 # returns dz effect size and 95% CIs
effsize::cohen.d(futurehelp ~ Relation | Subject(ResponseId),
data = E2_all_long %>%
filter(BSs_cond == "Stranger-Like") %>%
filter(`Choice Context` == "Choice") %>%
droplevels(),
paired = T,
within = F) # setting this to false ensures dz is calculated, using difference score
Cohen's d
d estimate: -0.6425661 (medium)
95 percent confidence interval:
lower upper
-0.7571821 -0.5279500 # returns d-av effect size and 95% CIs
effsize::cohen.d(futurehelp ~ Relation | Subject(ResponseId),
data = E2_all_long %>%
filter(BSs_cond == "Stranger-Like") %>%
filter(`Choice Context` == "Choice") %>%
droplevels(),
paired = T,
within = T) # setting this to true ensures d-av is calculated, using raw scores
Cohen's d
d estimate: -0.3432908 (small)
95 percent confidence interval:
lower upper
-0.4006586 -0.2859229 # returns correlation between variables
cor_test(data = E2_SL_clean, "Choice_CUZ_futurehelp", "Choice_SIB_futurehelp", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
----------------------------------------------------------------------------------------
Choice_CUZ_futurehelp | Choice_SIB_futurehelp | 0.86 | [0.83, 0.88] | 31.24 | < .001***
Observations: 354# returns histogram of differences score variable
print(hist(E2_SL_clean$Choice_CUZminusSIB_futurehelp, breaks = 100))$breaks
[1] -74 -73 -72 -71 -70 -69 -68 -67 -66 -65 -64 -63 -62 -61 -60 -59 -58 -57 -56 -55 -54 -53 -52 -51
[25] -50 -49 -48 -47 -46 -45 -44 -43 -42 -41 -40 -39 -38 -37 -36 -35 -34 -33 -32 -31 -30 -29 -28 -27
[49] -26 -25 -24 -23 -22 -21 -20 -19 -18 -17 -16 -15 -14 -13 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3
[73] -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
[97] 22 23 24 25 26 27 28 29 30 31 32
$counts
[1] 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 2 0 0 1 1 2 1 0 1 0 0 0 0
[33] 0 5 2 1 0 0 2 0 3 1 1 0 3 2 2 4 5 4 2 2 4 4 1 1 4 3 3 6 5 4 16 10
[65] 12 9 17 13 15 15 12 19 25 49 29 6 9 2 1 2 1 0 0 0 2 0 0 1 0 1 0 0 0 0 0 0
[97] 0 1 0 0 0 0 0 1 0 1
$density
[1] 0.002824859 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
[9] 0.000000000 0.000000000 0.000000000 0.000000000 0.002824859 0.000000000 0.000000000 0.002824859
[17] 0.000000000 0.000000000 0.000000000 0.005649718 0.000000000 0.000000000 0.002824859 0.002824859
[25] 0.005649718 0.002824859 0.000000000 0.002824859 0.000000000 0.000000000 0.000000000 0.000000000
[33] 0.000000000 0.014124294 0.005649718 0.002824859 0.000000000 0.000000000 0.005649718 0.000000000
[41] 0.008474576 0.002824859 0.002824859 0.000000000 0.008474576 0.005649718 0.005649718 0.011299435
[49] 0.014124294 0.011299435 0.005649718 0.005649718 0.011299435 0.011299435 0.002824859 0.002824859
[57] 0.011299435 0.008474576 0.008474576 0.016949153 0.014124294 0.011299435 0.045197740 0.028248588
[65] 0.033898305 0.025423729 0.048022599 0.036723164 0.042372881 0.042372881 0.033898305 0.053672316
[73] 0.070621469 0.138418079 0.081920904 0.016949153 0.025423729 0.005649718 0.002824859 0.005649718
[81] 0.002824859 0.000000000 0.000000000 0.000000000 0.005649718 0.000000000 0.000000000 0.002824859
[89] 0.000000000 0.002824859 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
[97] 0.000000000 0.002824859 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.002824859
[105] 0.000000000 0.002824859
$mids
[1] -73.5 -72.5 -71.5 -70.5 -69.5 -68.5 -67.5 -66.5 -65.5 -64.5 -63.5 -62.5 -61.5 -60.5 -59.5 -58.5
[17] -57.5 -56.5 -55.5 -54.5 -53.5 -52.5 -51.5 -50.5 -49.5 -48.5 -47.5 -46.5 -45.5 -44.5 -43.5 -42.5
[33] -41.5 -40.5 -39.5 -38.5 -37.5 -36.5 -35.5 -34.5 -33.5 -32.5 -31.5 -30.5 -29.5 -28.5 -27.5 -26.5
[49] -25.5 -24.5 -23.5 -22.5 -21.5 -20.5 -19.5 -18.5 -17.5 -16.5 -15.5 -14.5 -13.5 -12.5 -11.5 -10.5
[65] -9.5 -8.5 -7.5 -6.5 -5.5 -4.5 -3.5 -2.5 -1.5 -0.5 0.5 1.5 2.5 3.5 4.5 5.5
[81] 6.5 7.5 8.5 9.5 10.5 11.5 12.5 13.5 14.5 15.5 16.5 17.5 18.5 19.5 20.5 21.5
[97] 22.5 23.5 24.5 25.5 26.5 27.5 28.5 29.5 30.5 31.5
$xname
[1] "E2_SL_clean$Choice_CUZminusSIB_futurehelp"
$equidist
[1] TRUE
attr(,"class")
[1] "histogram"
Friend-Like
No Choice
# returns t-test results
t.test(futurehelp ~ Relation,
data = E2_all_long %>%
filter(BSs_cond == "Friend-Like") %>%
filter(`Choice Context` == "No Choice") %>%
droplevels(),
paired = T)
Paired t-test
data: futurehelp by Relation
t = -3.1602, df = 344, p-value = 0.001717
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-4.829576 -1.124047
sample estimates:
mean of the differences
-2.976812 # returns dz effect size and 95% CIs
effsize::cohen.d(futurehelp ~ Relation | Subject(ResponseId),
data = E2_all_long %>%
filter(BSs_cond == "Friend-Like") %>%
filter(`Choice Context` == "No Choice") %>%
droplevels(),
paired = T,
within = F) # setting this to false ensures dz is calculated, using difference score
Cohen's d
d estimate: -0.1701376 (negligible)
95 percent confidence interval:
lower upper
-0.27660668 -0.06366849 # returns d-av effect size and 95% CIs
effsize::cohen.d(futurehelp ~ Relation | Subject(ResponseId),
data = E2_all_long %>%
filter(BSs_cond == "Friend-Like") %>%
filter(`Choice Context` == "No Choice") %>%
droplevels(),
paired = T,
within = T) # setting this to true ensures d-av is calculated, using raw scores
Cohen's d
d estimate: -0.167496 (negligible)
95 percent confidence interval:
lower upper
-0.272289 -0.062703 # returns correlation between variables
cor_test(data = E2_FL_clean, "NoChoice_CUZ_futurehelp", "NoChoice_SIB_futurehelp", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
--------------------------------------------------------------------------------------------
NoChoice_CUZ_futurehelp | NoChoice_SIB_futurehelp | 0.52 | [0.43, 0.59] | 11.14 | < .001***
Observations: 345# returns histogram of differences score variable
print(hist(E2_FL_clean$NoChoice_CUZminusSIB_futurehelp, breaks = 100))$breaks
[1] -94 -92 -90 -88 -86 -84 -82 -80 -78 -76 -74 -72 -70 -68 -66 -64 -62 -60 -58 -56 -54 -52 -50 -48
[25] -46 -44 -42 -40 -38 -36 -34 -32 -30 -28 -26 -24 -22 -20 -18 -16 -14 -12 -10 -8 -6 -4 -2 0
[49] 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48
[73] 50 52 54 56 58 60
$counts
[1] 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 2 0 1 4 2 0 0 1 1 1 2 3 2 1
[34] 2 3 9 4 4 4 11 15 10 12 16 15 20 99 18 10 11 7 11 8 0 5 6 5 2 0 0 1 3 0 1 1 0
[67] 2 0 0 1 1 0 0 1 0 3 1
$density
[1] 0.001449275 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
[9] 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
[17] 0.000000000 0.001449275 0.001449275 0.002898551 0.000000000 0.001449275 0.005797101 0.002898551
[25] 0.000000000 0.000000000 0.001449275 0.001449275 0.001449275 0.002898551 0.004347826 0.002898551
[33] 0.001449275 0.002898551 0.004347826 0.013043478 0.005797101 0.005797101 0.005797101 0.015942029
[41] 0.021739130 0.014492754 0.017391304 0.023188406 0.021739130 0.028985507 0.143478261 0.026086957
[49] 0.014492754 0.015942029 0.010144928 0.015942029 0.011594203 0.000000000 0.007246377 0.008695652
[57] 0.007246377 0.002898551 0.000000000 0.000000000 0.001449275 0.004347826 0.000000000 0.001449275
[65] 0.001449275 0.000000000 0.002898551 0.000000000 0.000000000 0.001449275 0.001449275 0.000000000
[73] 0.000000000 0.001449275 0.000000000 0.004347826 0.001449275
$mids
[1] -93 -91 -89 -87 -85 -83 -81 -79 -77 -75 -73 -71 -69 -67 -65 -63 -61 -59 -57 -55 -53 -51 -49 -47
[25] -45 -43 -41 -39 -37 -35 -33 -31 -29 -27 -25 -23 -21 -19 -17 -15 -13 -11 -9 -7 -5 -3 -1 1
[49] 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49
[73] 51 53 55 57 59
$xname
[1] "E2_FL_clean$NoChoice_CUZminusSIB_futurehelp"
$equidist
[1] TRUE
attr(,"class")
[1] "histogram"
Choice
# returns t-test results
t.test(futurehelp ~ Relation,
data = E2_all_long %>%
filter(BSs_cond == "Friend-Like") %>%
filter(`Choice Context` == "Choice") %>%
droplevels(),
paired = T)
Paired t-test
data: futurehelp by Relation
t = -9.3125, df = 344, p-value < 2.2e-16
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-6.147323 -4.003401
sample estimates:
mean of the differences
-5.075362 # returns dz effect size and 95% CIs
effsize::cohen.d(futurehelp ~ Relation | Subject(ResponseId),
data = E2_all_long %>%
filter(BSs_cond == "Friend-Like") %>%
filter(`Choice Context` == "Choice") %>%
droplevels(),
paired = T,
within = F) # setting this to false ensures dz is calculated, using difference score
Cohen's d
d estimate: -0.5013687 (medium)
95 percent confidence interval:
lower upper
-0.6135219 -0.3892155 # returns d-av effect size and 95% CIs
effsize::cohen.d(futurehelp ~ Relation | Subject(ResponseId),
data = E2_all_long %>%
filter(BSs_cond == "Friend-Like") %>%
filter(`Choice Context` == "Choice") %>%
droplevels(),
paired = T,
within = T) # setting this to true ensures d-av is calculated, using raw scores
Cohen's d
d estimate: -0.3114277 (small)
95 percent confidence interval:
lower upper
-0.3786613 -0.2441941 # returns correlation between variables
cor_test(data = E2_FL_clean, "Choice_CUZ_futurehelp", "Choice_SIB_futurehelp", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
----------------------------------------------------------------------------------------
Choice_CUZ_futurehelp | Choice_SIB_futurehelp | 0.81 | [0.77, 0.84] | 25.32 | < .001***
Observations: 345# returns histogram of differences score variable
print(hist(E2_FL_clean$Choice_CUZminusSIB_futurehelp, breaks = 100))$breaks
[1] -78 -77 -76 -75 -74 -73 -72 -71 -70 -69 -68 -67 -66 -65 -64 -63 -62 -61 -60 -59 -58 -57 -56 -55
[25] -54 -53 -52 -51 -50 -49 -48 -47 -46 -45 -44 -43 -42 -41 -40 -39 -38 -37 -36 -35 -34 -33 -32 -31
[49] -30 -29 -28 -27 -26 -25 -24 -23 -22 -21 -20 -19 -18 -17 -16 -15 -14 -13 -12 -11 -10 -9 -8 -7
[73] -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12
$counts
[1] 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[25] 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 2
[49] 2 1 0 3 4 3 3 1 3 2 4 1 6 4 3 2 4 3 6 7 3 11 9 5
[73] 12 9 18 14 24 113 27 9 7 5 3 1 1 3 0 0 1 1
$density
[1] 0.005797101 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
[9] 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
[17] 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
[25] 0.002898551 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
[33] 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.002898551
[41] 0.000000000 0.000000000 0.002898551 0.000000000 0.000000000 0.000000000 0.000000000 0.005797101
[49] 0.005797101 0.002898551 0.000000000 0.008695652 0.011594203 0.008695652 0.008695652 0.002898551
[57] 0.008695652 0.005797101 0.011594203 0.002898551 0.017391304 0.011594203 0.008695652 0.005797101
[65] 0.011594203 0.008695652 0.017391304 0.020289855 0.008695652 0.031884058 0.026086957 0.014492754
[73] 0.034782609 0.026086957 0.052173913 0.040579710 0.069565217 0.327536232 0.078260870 0.026086957
[81] 0.020289855 0.014492754 0.008695652 0.002898551 0.002898551 0.008695652 0.000000000 0.000000000
[89] 0.002898551 0.002898551
$mids
[1] -77.5 -76.5 -75.5 -74.5 -73.5 -72.5 -71.5 -70.5 -69.5 -68.5 -67.5 -66.5 -65.5 -64.5 -63.5 -62.5
[17] -61.5 -60.5 -59.5 -58.5 -57.5 -56.5 -55.5 -54.5 -53.5 -52.5 -51.5 -50.5 -49.5 -48.5 -47.5 -46.5
[33] -45.5 -44.5 -43.5 -42.5 -41.5 -40.5 -39.5 -38.5 -37.5 -36.5 -35.5 -34.5 -33.5 -32.5 -31.5 -30.5
[49] -29.5 -28.5 -27.5 -26.5 -25.5 -24.5 -23.5 -22.5 -21.5 -20.5 -19.5 -18.5 -17.5 -16.5 -15.5 -14.5
[65] -13.5 -12.5 -11.5 -10.5 -9.5 -8.5 -7.5 -6.5 -5.5 -4.5 -3.5 -2.5 -1.5 -0.5 0.5 1.5
[81] 2.5 3.5 4.5 5.5 6.5 7.5 8.5 9.5 10.5 11.5
$xname
[1] "E2_FL_clean$Choice_CUZminusSIB_futurehelp"
$equidist
[1] TRUE
attr(,"class")
[1] "histogram"
Prior Interax
Stranger-Like
No Choice
# returns t-test results
t.test(priorinteract ~ Relation,
data = E2_all_long %>%
filter(BSs_cond == "Stranger-Like") %>%
filter(`Choice Context` == "No Choice") %>%
droplevels(),
paired = T)
Paired t-test
data: priorinteract by Relation
t = -11.696, df = 353, p-value < 2.2e-16
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-18.90171 -13.45987
sample estimates:
mean of the differences
-16.18079 # returns dz effect size and 95% CIs
effsize::cohen.d(priorinteract ~ Relation | Subject(ResponseId),
data = E2_all_long %>%
filter(BSs_cond == "Stranger-Like") %>%
filter(`Choice Context` == "No Choice") %>%
droplevels(),
paired = T,
within = F) # setting this to false ensures dz is calculated, using difference score
Cohen's d
d estimate: -0.6216164 (medium)
95 percent confidence interval:
lower upper
-0.7356017 -0.5076311 # returns d-av effect size and 95% CIs
effsize::cohen.d(priorinteract ~ Relation | Subject(ResponseId),
data = E2_all_long %>%
filter(BSs_cond == "Stranger-Like") %>%
filter(`Choice Context` == "No Choice") %>%
droplevels(),
paired = T,
within = T) # setting this to true ensures d-av is calculated, using raw scores
Cohen's d
d estimate: -0.5954452 (medium)
95 percent confidence interval:
lower upper
-0.7039004 -0.4869900 # returns correlation between variables
cor_test(data = E2_SL_clean, "NoChoice_CUZ_priorinteract", "NoChoice_SIB_priorinteract", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
--------------------------------------------------------------------------------------------------
NoChoice_CUZ_priorinteract | NoChoice_SIB_priorinteract | 0.54 | [0.46, 0.61] | 12.08 | < .001***
Observations: 354# returns histogram of differences score variable
print(hist(E2_SL_clean$NoChoice_CUZminusSIB_priorinteract, breaks = 100))$breaks
[1] -100 -98 -96 -94 -92 -90 -88 -86 -84 -82 -80 -78 -76 -74 -72 -70 -68 -66 -64
[20] -62 -60 -58 -56 -54 -52 -50 -48 -46 -44 -42 -40 -38 -36 -34 -32 -30 -28 -26
[39] -24 -22 -20 -18 -16 -14 -12 -10 -8 -6 -4 -2 0 2 4 6 8 10 12
[58] 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50
[77] 52 54 56
$counts
[1] 1 0 0 0 0 0 3 2 2 0 1 2 3 2 1 1 2 2 2 4 1 5 1 3 4 3 5 4 5 4 8 3 9
[34] 7 6 6 4 8 7 8 7 10 14 7 7 16 22 12 17 30 13 16 12 6 6 4 2 2 3 5 1 0 2 2 1 2
[67] 0 1 0 1 0 0 2 1 0 0 0 1
$density
[1] 0.001412429 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.004237288 0.002824859
[9] 0.002824859 0.000000000 0.001412429 0.002824859 0.004237288 0.002824859 0.001412429 0.001412429
[17] 0.002824859 0.002824859 0.002824859 0.005649718 0.001412429 0.007062147 0.001412429 0.004237288
[25] 0.005649718 0.004237288 0.007062147 0.005649718 0.007062147 0.005649718 0.011299435 0.004237288
[33] 0.012711864 0.009887006 0.008474576 0.008474576 0.005649718 0.011299435 0.009887006 0.011299435
[41] 0.009887006 0.014124294 0.019774011 0.009887006 0.009887006 0.022598870 0.031073446 0.016949153
[49] 0.024011299 0.042372881 0.018361582 0.022598870 0.016949153 0.008474576 0.008474576 0.005649718
[57] 0.002824859 0.002824859 0.004237288 0.007062147 0.001412429 0.000000000 0.002824859 0.002824859
[65] 0.001412429 0.002824859 0.000000000 0.001412429 0.000000000 0.001412429 0.000000000 0.000000000
[73] 0.002824859 0.001412429 0.000000000 0.000000000 0.000000000 0.001412429
$mids
[1] -99 -97 -95 -93 -91 -89 -87 -85 -83 -81 -79 -77 -75 -73 -71 -69 -67 -65 -63 -61 -59 -57 -55 -53
[25] -51 -49 -47 -45 -43 -41 -39 -37 -35 -33 -31 -29 -27 -25 -23 -21 -19 -17 -15 -13 -11 -9 -7 -5
[49] -3 -1 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43
[73] 45 47 49 51 53 55
$xname
[1] "E2_SL_clean$NoChoice_CUZminusSIB_priorinteract"
$equidist
[1] TRUE
attr(,"class")
[1] "histogram"
Choice
# returns t-test results
t.test(priorinteract ~ Relation,
data = E2_all_long %>%
filter(BSs_cond == "Stranger-Like") %>%
filter(`Choice Context` == "Choice") %>%
droplevels(),
paired = T)
Paired t-test
data: priorinteract by Relation
t = -17.344, df = 353, p-value < 2.2e-16
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-21.68756 -17.27006
sample estimates:
mean of the differences
-19.47881 # returns dz effect size and 95% CIs
effsize::cohen.d(priorinteract ~ Relation | Subject(ResponseId),
data = E2_all_long %>%
filter(BSs_cond == "Stranger-Like") %>%
filter(`Choice Context` == "Choice") %>%
droplevels(),
paired = T,
within = F) # setting this to false ensures dz is calculated, using difference score
Cohen's d
d estimate: -0.9218361 (large)
95 percent confidence interval:
lower upper
-1.046397 -0.797275 # returns d-av effect size and 95% CIs
effsize::cohen.d(priorinteract ~ Relation | Subject(ResponseId),
data = E2_all_long %>%
filter(BSs_cond == "Stranger-Like") %>%
filter(`Choice Context` == "Choice") %>%
droplevels(),
paired = T,
within = T) # setting this to true ensures d-av is calculated, using raw scores
Cohen's d
d estimate: -0.7099864 (medium)
95 percent confidence interval:
lower upper
-0.7999148 -0.6200579 # returns correlation between variables
cor_test(data = E2_SL_clean, "Choice_CUZ_priorinteract", "Choice_SIB_priorinteract", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
----------------------------------------------------------------------------------------------
Choice_CUZ_priorinteract | Choice_SIB_priorinteract | 0.70 | [0.65, 0.75] | 18.57 | < .001***
Observations: 354# returns histogram of differences score variable
print(hist(E2_SL_clean$Choice_CUZminusSIB_priorinteract, breaks = 100))$breaks
[1] -96 -94 -92 -90 -88 -86 -84 -82 -80 -78 -76 -74 -72 -70 -68 -66 -64 -62 -60 -58 -56 -54 -52 -50
[25] -48 -46 -44 -42 -40 -38 -36 -34 -32 -30 -28 -26 -24 -22 -20 -18 -16 -14 -12 -10 -8 -6 -4 -2
[49] 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46
[73] 48
$counts
[1] 1 0 0 0 0 0 1 1 0 1 2 0 1 3 2 1 1 5 1 4 6 5 7 4 2 4 5 6 7 7 7 4 10
[34] 9 9 12 12 8 9 7 7 18 12 14 21 16 19 53 13 5 4 1 2 1 1 0 0 1 0 0 0 0 1 0 0 0
[67] 0 0 0 0 0 1
$density
[1] 0.001412429 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.001412429 0.001412429
[9] 0.000000000 0.001412429 0.002824859 0.000000000 0.001412429 0.004237288 0.002824859 0.001412429
[17] 0.001412429 0.007062147 0.001412429 0.005649718 0.008474576 0.007062147 0.009887006 0.005649718
[25] 0.002824859 0.005649718 0.007062147 0.008474576 0.009887006 0.009887006 0.009887006 0.005649718
[33] 0.014124294 0.012711864 0.012711864 0.016949153 0.016949153 0.011299435 0.012711864 0.009887006
[41] 0.009887006 0.025423729 0.016949153 0.019774011 0.029661017 0.022598870 0.026836158 0.074858757
[49] 0.018361582 0.007062147 0.005649718 0.001412429 0.002824859 0.001412429 0.001412429 0.000000000
[57] 0.000000000 0.001412429 0.000000000 0.000000000 0.000000000 0.000000000 0.001412429 0.000000000
[65] 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.001412429
$mids
[1] -95 -93 -91 -89 -87 -85 -83 -81 -79 -77 -75 -73 -71 -69 -67 -65 -63 -61 -59 -57 -55 -53 -51 -49
[25] -47 -45 -43 -41 -39 -37 -35 -33 -31 -29 -27 -25 -23 -21 -19 -17 -15 -13 -11 -9 -7 -5 -3 -1
[49] 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47
$xname
[1] "E2_SL_clean$Choice_CUZminusSIB_priorinteract"
$equidist
[1] TRUE
attr(,"class")
[1] "histogram"
Friend-Like
No Choice
# returns t-test results
t.test(priorinteract ~ Relation,
data = E2_all_long %>%
filter(BSs_cond == "Friend-Like") %>%
filter(`Choice Context` == "No Choice") %>%
droplevels(),
paired = T)
Paired t-test
data: priorinteract by Relation
t = -4.4011, df = 344, p-value = 1.439e-05
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-5.812775 -2.222008
sample estimates:
mean of the differences
-4.017391 # returns dz effect size and 95% CIs
effsize::cohen.d(priorinteract ~ Relation | Subject(ResponseId),
data = E2_all_long %>%
filter(BSs_cond == "Friend-Like") %>%
filter(`Choice Context` == "No Choice") %>%
droplevels(),
paired = T,
within = F) # setting this to false ensures dz is calculated, using difference score
Cohen's d
d estimate: -0.2369497 (small)
95 percent confidence interval:
lower upper
-0.3441300 -0.1297694 # returns d-av effect size and 95% CIs
effsize::cohen.d(priorinteract ~ Relation | Subject(ResponseId),
data = E2_all_long %>%
filter(BSs_cond == "Friend-Like") %>%
filter(`Choice Context` == "No Choice") %>%
droplevels(),
paired = T,
within = T) # setting this to true ensures d-av is calculated, using raw scores
Cohen's d
d estimate: -0.2408293 (small)
95 percent confidence interval:
lower upper
-0.3498136 -0.1318450 # returns correlation between variables
cor_test(data = E2_FL_clean, "NoChoice_CUZ_priorinteract", "NoChoice_SIB_priorinteract", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
--------------------------------------------------------------------------------------------------
NoChoice_CUZ_priorinteract | NoChoice_SIB_priorinteract | 0.48 | [0.40, 0.56] | 10.23 | < .001***
Observations: 345# returns histogram of differences score variable
print(hist(E2_FL_clean$NoChoice_CUZminusSIB_priorinteract, breaks = 100))$breaks
[1] -76 -74 -72 -70 -68 -66 -64 -62 -60 -58 -56 -54 -52 -50 -48 -46 -44 -42 -40 -38 -36 -34 -32 -30
[25] -28 -26 -24 -22 -20 -18 -16 -14 -12 -10 -8 -6 -4 -2 0 2 4 6 8 10 12 14 16 18
[49] 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58 60 62 64 66
[73] 68 70 72
$counts
[1] 2 0 0 0 0 0 0 0 1 1 1 1 3 1 1 1 1 1 1 2 2 4 4 3
[25] 2 3 4 4 2 4 10 13 15 11 12 14 19 119 21 7 7 8 7 7 5 3 1 2
[49] 5 1 0 0 0 2 0 0 0 1 0 1 0 1 0 0 0 2 0 1 0 0 0 0
[73] 0 1
$density
[1] 0.002898551 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
[9] 0.001449275 0.001449275 0.001449275 0.001449275 0.004347826 0.001449275 0.001449275 0.001449275
[17] 0.001449275 0.001449275 0.001449275 0.002898551 0.002898551 0.005797101 0.005797101 0.004347826
[25] 0.002898551 0.004347826 0.005797101 0.005797101 0.002898551 0.005797101 0.014492754 0.018840580
[33] 0.021739130 0.015942029 0.017391304 0.020289855 0.027536232 0.172463768 0.030434783 0.010144928
[41] 0.010144928 0.011594203 0.010144928 0.010144928 0.007246377 0.004347826 0.001449275 0.002898551
[49] 0.007246377 0.001449275 0.000000000 0.000000000 0.000000000 0.002898551 0.000000000 0.000000000
[57] 0.000000000 0.001449275 0.000000000 0.001449275 0.000000000 0.001449275 0.000000000 0.000000000
[65] 0.000000000 0.002898551 0.000000000 0.001449275 0.000000000 0.000000000 0.000000000 0.000000000
[73] 0.000000000 0.001449275
$mids
[1] -75 -73 -71 -69 -67 -65 -63 -61 -59 -57 -55 -53 -51 -49 -47 -45 -43 -41 -39 -37 -35 -33 -31 -29
[25] -27 -25 -23 -21 -19 -17 -15 -13 -11 -9 -7 -5 -3 -1 1 3 5 7 9 11 13 15 17 19
[49] 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67
[73] 69 71
$xname
[1] "E2_FL_clean$NoChoice_CUZminusSIB_priorinteract"
$equidist
[1] TRUE
attr(,"class")
[1] "histogram"
Choice
# returns t-test results
t.test(priorinteract ~ Relation,
data = E2_all_long %>%
filter(BSs_cond == "Friend-Like") %>%
filter(`Choice Context` == "Choice") %>%
droplevels(),
paired = T)
Paired t-test
data: priorinteract by Relation
t = -9.6968, df = 344, p-value < 2.2e-16
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-7.237946 -4.796837
sample estimates:
mean of the differences
-6.017391 # returns dz effect size and 95% CIs
effsize::cohen.d(priorinteract ~ Relation | Subject(ResponseId),
data = E2_all_long %>%
filter(BSs_cond == "Friend-Like") %>%
filter(`Choice Context` == "Choice") %>%
droplevels(),
paired = T,
within = F) # setting this to false ensures dz is calculated, using difference score
Cohen's d
d estimate: -0.5220599 (medium)
95 percent confidence interval:
lower upper
-0.6347394 -0.4093805 # returns d-av effect size and 95% CIs
effsize::cohen.d(priorinteract ~ Relation | Subject(ResponseId),
data = E2_all_long %>%
filter(BSs_cond == "Friend-Like") %>%
filter(`Choice Context` == "Choice") %>%
droplevels(),
paired = T,
within = T) # setting this to true ensures d-av is calculated, using raw scores
Cohen's d
d estimate: -0.3853272 (small)
95 percent confidence interval:
lower upper
-0.4661926 -0.3044618 # returns correlation between variables
cor_test(data = E2_FL_clean, "Choice_CUZ_priorinteract", "Choice_SIB_priorinteract", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
----------------------------------------------------------------------------------------------
Choice_CUZ_priorinteract | Choice_SIB_priorinteract | 0.73 | [0.67, 0.77] | 19.64 | < .001***
Observations: 345# returns histogram of differences score variable
print(hist(E2_FL_clean$Choice_CUZminusSIB_priorinteract, breaks = 100))$breaks
[1] -81 -80 -79 -78 -77 -76 -75 -74 -73 -72 -71 -70 -69 -68 -67 -66 -65 -64 -63 -62 -61 -60 -59 -58
[25] -57 -56 -55 -54 -53 -52 -51 -50 -49 -48 -47 -46 -45 -44 -43 -42 -41 -40 -39 -38 -37 -36 -35 -34
[49] -33 -32 -31 -30 -29 -28 -27 -26 -25 -24 -23 -22 -21 -20 -19 -18 -17 -16 -15 -14 -13 -12 -11 -10
[73] -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
[97] 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38
[121] 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56
$counts
[1] 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0
[25] 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 2 0 1 1 3 1 0 0 1
[49] 0 1 1 0 0 2 2 1 4 4 2 1 2 3 2 2 4 6 5 10 9 4 3 3
[73] 9 8 14 16 12 12 11 21 118 18 9 3 0 2 1 1 2 0 1 0 1 0 1 0
[97] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[121] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
$density
[1] 0.002898551 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
[9] 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.002898551 0.000000000
[17] 0.000000000 0.000000000 0.000000000 0.002898551 0.000000000 0.000000000 0.000000000 0.000000000
[25] 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
[33] 0.000000000 0.000000000 0.002898551 0.000000000 0.000000000 0.000000000 0.000000000 0.005797101
[41] 0.000000000 0.002898551 0.002898551 0.008695652 0.002898551 0.000000000 0.000000000 0.002898551
[49] 0.000000000 0.002898551 0.002898551 0.000000000 0.000000000 0.005797101 0.005797101 0.002898551
[57] 0.011594203 0.011594203 0.005797101 0.002898551 0.005797101 0.008695652 0.005797101 0.005797101
[65] 0.011594203 0.017391304 0.014492754 0.028985507 0.026086957 0.011594203 0.008695652 0.008695652
[73] 0.026086957 0.023188406 0.040579710 0.046376812 0.034782609 0.034782609 0.031884058 0.060869565
[81] 0.342028986 0.052173913 0.026086957 0.008695652 0.000000000 0.005797101 0.002898551 0.002898551
[89] 0.005797101 0.000000000 0.002898551 0.000000000 0.002898551 0.000000000 0.002898551 0.000000000
[97] 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
[105] 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
[113] 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
[121] 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
[129] 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
[137] 0.002898551
$mids
[1] -80.5 -79.5 -78.5 -77.5 -76.5 -75.5 -74.5 -73.5 -72.5 -71.5 -70.5 -69.5 -68.5 -67.5 -66.5 -65.5
[17] -64.5 -63.5 -62.5 -61.5 -60.5 -59.5 -58.5 -57.5 -56.5 -55.5 -54.5 -53.5 -52.5 -51.5 -50.5 -49.5
[33] -48.5 -47.5 -46.5 -45.5 -44.5 -43.5 -42.5 -41.5 -40.5 -39.5 -38.5 -37.5 -36.5 -35.5 -34.5 -33.5
[49] -32.5 -31.5 -30.5 -29.5 -28.5 -27.5 -26.5 -25.5 -24.5 -23.5 -22.5 -21.5 -20.5 -19.5 -18.5 -17.5
[65] -16.5 -15.5 -14.5 -13.5 -12.5 -11.5 -10.5 -9.5 -8.5 -7.5 -6.5 -5.5 -4.5 -3.5 -2.5 -1.5
[81] -0.5 0.5 1.5 2.5 3.5 4.5 5.5 6.5 7.5 8.5 9.5 10.5 11.5 12.5 13.5 14.5
[97] 15.5 16.5 17.5 18.5 19.5 20.5 21.5 22.5 23.5 24.5 25.5 26.5 27.5 28.5 29.5 30.5
[113] 31.5 32.5 33.5 34.5 35.5 36.5 37.5 38.5 39.5 40.5 41.5 42.5 43.5 44.5 45.5 46.5
[129] 47.5 48.5 49.5 50.5 51.5 52.5 53.5 54.5 55.5
$xname
[1] "E2_FL_clean$Choice_CUZminusSIB_priorinteract"
$equidist
[1] TRUE
attr(,"class")
[1] "histogram"
Future Interax
Stranger-Like
No Choice
# returns t-test results
t.test(futureinteract ~ Relation,
data = E2_all_long %>%
filter(BSs_cond == "Stranger-Like") %>%
filter(`Choice Context` == "No Choice") %>%
droplevels(),
paired = T)
Paired t-test
data: futureinteract by Relation
t = -5.5451, df = 353, p-value = 5.766e-08
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-10.408796 -4.958435
sample estimates:
mean of the differences
-7.683616 # returns dz effect size and 95% CIs
effsize::cohen.d(futureinteract ~ Relation | Subject(ResponseId),
data = E2_all_long %>%
filter(BSs_cond == "Stranger-Like") %>%
filter(`Choice Context` == "No Choice") %>%
droplevels(),
paired = T,
within = F) # setting this to false ensures dz is calculated, using difference score
Cohen's d
d estimate: -0.2947192 (small)
95 percent confidence interval:
lower upper
-0.4013108 -0.1881275 # returns d-av effect size and 95% CIs
effsize::cohen.d(futureinteract ~ Relation | Subject(ResponseId),
data = E2_all_long %>%
filter(BSs_cond == "Stranger-Like") %>%
filter(`Choice Context` == "No Choice") %>%
droplevels(),
paired = T,
within = T) # setting this to true ensures d-av is calculated, using raw scores
Cohen's d
d estimate: -0.2886645 (small)
95 percent confidence interval:
lower upper
-0.392978 -0.184351 # returns correlation between variables
cor_test(data = E2_SL_clean, "NoChoice_CUZ_futureinteract", "NoChoice_SIB_futureinteract", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
----------------------------------------------------------------------------------------------------
NoChoice_CUZ_futureinteract | NoChoice_SIB_futureinteract | 0.52 | [0.44, 0.59] | 11.43 | < .001***
Observations: 354# returns histogram of differences score variable
print(hist(E2_SL_clean$NoChoice_CUZminusSIB_futureinteract, breaks = 100))$breaks
[1] -94 -92 -90 -88 -86 -84 -82 -80 -78 -76 -74 -72 -70 -68 -66 -64 -62 -60 -58 -56 -54 -52 -50 -48
[25] -46 -44 -42 -40 -38 -36 -34 -32 -30 -28 -26 -24 -22 -20 -18 -16 -14 -12 -10 -8 -6 -4 -2 0
[49] 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48
[73] 50 52 54 56 58 60 62 64 66 68 70 72 74 76 78 80 82
$counts
[1] 1 0 0 1 0 1 0 1 1 0 2 1 0 1 1 3 1 0 0 0 0 5 5 3 3 1 4 7 1 8 3 4 6
[34] 8 6 6 9 9 7 10 17 10 16 14 19 21 25 17 18 5 9 6 4 6 5 7 1 3 4 2 2 2 1 2 1 1
[67] 1 0 2 2 2 0 0 1 1 1 2 0 1 0 1 0 0 0 1 0 0 2
$density
[1] 0.001412429 0.000000000 0.000000000 0.001412429 0.000000000 0.001412429 0.000000000 0.001412429
[9] 0.001412429 0.000000000 0.002824859 0.001412429 0.000000000 0.001412429 0.001412429 0.004237288
[17] 0.001412429 0.000000000 0.000000000 0.000000000 0.000000000 0.007062147 0.007062147 0.004237288
[25] 0.004237288 0.001412429 0.005649718 0.009887006 0.001412429 0.011299435 0.004237288 0.005649718
[33] 0.008474576 0.011299435 0.008474576 0.008474576 0.012711864 0.012711864 0.009887006 0.014124294
[41] 0.024011299 0.014124294 0.022598870 0.019774011 0.026836158 0.029661017 0.035310734 0.024011299
[49] 0.025423729 0.007062147 0.012711864 0.008474576 0.005649718 0.008474576 0.007062147 0.009887006
[57] 0.001412429 0.004237288 0.005649718 0.002824859 0.002824859 0.002824859 0.001412429 0.002824859
[65] 0.001412429 0.001412429 0.001412429 0.000000000 0.002824859 0.002824859 0.002824859 0.000000000
[73] 0.000000000 0.001412429 0.001412429 0.001412429 0.002824859 0.000000000 0.001412429 0.000000000
[81] 0.001412429 0.000000000 0.000000000 0.000000000 0.001412429 0.000000000 0.000000000 0.002824859
$mids
[1] -93 -91 -89 -87 -85 -83 -81 -79 -77 -75 -73 -71 -69 -67 -65 -63 -61 -59 -57 -55 -53 -51 -49 -47
[25] -45 -43 -41 -39 -37 -35 -33 -31 -29 -27 -25 -23 -21 -19 -17 -15 -13 -11 -9 -7 -5 -3 -1 1
[49] 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49
[73] 51 53 55 57 59 61 63 65 67 69 71 73 75 77 79 81
$xname
[1] "E2_SL_clean$NoChoice_CUZminusSIB_futureinteract"
$equidist
[1] TRUE
attr(,"class")
[1] "histogram"
Choice
# returns t-test results
t.test(futureinteract ~ Relation,
data = E2_all_long %>%
filter(BSs_cond == "Stranger-Like") %>%
filter(`Choice Context` == "Choice") %>%
droplevels(),
paired = T)
Paired t-test
data: futureinteract by Relation
t = -13.382, df = 353, p-value < 2.2e-16
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-11.140761 -8.285793
sample estimates:
mean of the differences
-9.713277 # returns dz effect size and 95% CIs
effsize::cohen.d(futureinteract ~ Relation | Subject(ResponseId),
data = E2_all_long %>%
filter(BSs_cond == "Stranger-Like") %>%
filter(`Choice Context` == "Choice") %>%
droplevels(),
paired = T,
within = F) # setting this to false ensures dz is calculated, using difference score
Cohen's d
d estimate: -0.7112667 (medium)
95 percent confidence interval:
lower upper
-0.8280709 -0.5944625 # returns d-av effect size and 95% CIs
effsize::cohen.d(futureinteract ~ Relation | Subject(ResponseId),
data = E2_all_long %>%
filter(BSs_cond == "Stranger-Like") %>%
filter(`Choice Context` == "Choice") %>%
droplevels(),
paired = T,
within = T) # setting this to true ensures d-av is calculated, using raw scores
Cohen's d
d estimate: -0.3950859 (small)
95 percent confidence interval:
lower upper
-0.4552682 -0.3349035 # returns correlation between variables
cor_test(data = E2_SL_clean, "Choice_CUZ_futureinteract", "Choice_SIB_futureinteract", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
------------------------------------------------------------------------------------------------
Choice_CUZ_futureinteract | Choice_SIB_futureinteract | 0.85 | [0.81, 0.87] | 29.74 | < .001***
Observations: 354# returns histogram of differences score variable
print(hist(E2_SL_clean$Choice_CUZminusSIB_futureinteract, breaks = 100))$breaks
[1] -67 -66 -65 -64 -63 -62 -61 -60 -59 -58 -57 -56 -55 -54 -53 -52 -51 -50 -49 -48 -47 -46 -45 -44
[25] -43 -42 -41 -40 -39 -38 -37 -36 -35 -34 -33 -32 -31 -30 -29 -28 -27 -26 -25 -24 -23 -22 -21 -20
[49] -19 -18 -17 -16 -15 -14 -13 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4
[73] 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
[97] 29 30 31 32 33
$counts
[1] 1 0 0 0 0 0 1 0 0 1 0 0 1 0 1 1 1 2 0 2 2 1 0 0 0 1 1 0 1 2 1 2
[33] 4 2 1 1 3 2 1 2 5 1 3 4 8 6 2 2 2 6 2 4 7 10 6 12 11 9 15 13 18 12 12 12
[65] 19 22 45 21 8 7 1 3 0 0 1 0 1 1 0 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 1 0
[97] 0 0 0 1
$density
[1] 0.002824859 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.002824859 0.000000000
[9] 0.000000000 0.002824859 0.000000000 0.000000000 0.002824859 0.000000000 0.002824859 0.002824859
[17] 0.002824859 0.005649718 0.000000000 0.005649718 0.005649718 0.002824859 0.000000000 0.000000000
[25] 0.000000000 0.002824859 0.002824859 0.000000000 0.002824859 0.005649718 0.002824859 0.005649718
[33] 0.011299435 0.005649718 0.002824859 0.002824859 0.008474576 0.005649718 0.002824859 0.005649718
[41] 0.014124294 0.002824859 0.008474576 0.011299435 0.022598870 0.016949153 0.005649718 0.005649718
[49] 0.005649718 0.016949153 0.005649718 0.011299435 0.019774011 0.028248588 0.016949153 0.033898305
[57] 0.031073446 0.025423729 0.042372881 0.036723164 0.050847458 0.033898305 0.033898305 0.033898305
[65] 0.053672316 0.062146893 0.127118644 0.059322034 0.022598870 0.019774011 0.002824859 0.008474576
[73] 0.000000000 0.000000000 0.002824859 0.000000000 0.002824859 0.002824859 0.000000000 0.000000000
[81] 0.002824859 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
[89] 0.000000000 0.002824859 0.000000000 0.002824859 0.000000000 0.000000000 0.002824859 0.000000000
[97] 0.000000000 0.000000000 0.000000000 0.002824859
$mids
[1] -66.5 -65.5 -64.5 -63.5 -62.5 -61.5 -60.5 -59.5 -58.5 -57.5 -56.5 -55.5 -54.5 -53.5 -52.5 -51.5
[17] -50.5 -49.5 -48.5 -47.5 -46.5 -45.5 -44.5 -43.5 -42.5 -41.5 -40.5 -39.5 -38.5 -37.5 -36.5 -35.5
[33] -34.5 -33.5 -32.5 -31.5 -30.5 -29.5 -28.5 -27.5 -26.5 -25.5 -24.5 -23.5 -22.5 -21.5 -20.5 -19.5
[49] -18.5 -17.5 -16.5 -15.5 -14.5 -13.5 -12.5 -11.5 -10.5 -9.5 -8.5 -7.5 -6.5 -5.5 -4.5 -3.5
[65] -2.5 -1.5 -0.5 0.5 1.5 2.5 3.5 4.5 5.5 6.5 7.5 8.5 9.5 10.5 11.5 12.5
[81] 13.5 14.5 15.5 16.5 17.5 18.5 19.5 20.5 21.5 22.5 23.5 24.5 25.5 26.5 27.5 28.5
[97] 29.5 30.5 31.5 32.5
$xname
[1] "E2_SL_clean$Choice_CUZminusSIB_futureinteract"
$equidist
[1] TRUE
attr(,"class")
[1] "histogram"
Friend-Like
No Choice
# returns t-test results
t.test(futureinteract ~ Relation,
data = E2_all_long %>%
filter(BSs_cond == "Friend-Like") %>%
filter(`Choice Context` == "No Choice") %>%
droplevels(),
paired = T)
Paired t-test
data: futureinteract by Relation
t = -3.4364, df = 344, p-value = 0.0006617
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-4.767245 -1.296523
sample estimates:
mean of the differences
-3.031884 # returns dz effect size and 95% CIs
effsize::cohen.d(futureinteract ~ Relation | Subject(ResponseId),
data = E2_all_long %>%
filter(BSs_cond == "Friend-Like") %>%
filter(`Choice Context` == "No Choice") %>%
droplevels(),
paired = T,
within = F) # setting this to false ensures dz is calculated, using difference score
Cohen's d
d estimate: -0.1850086 (negligible)
95 percent confidence interval:
lower upper
-0.29161617 -0.07840102 # returns d-av effect size and 95% CIs
effsize::cohen.d(futureinteract ~ Relation | Subject(ResponseId),
data = E2_all_long %>%
filter(BSs_cond == "Friend-Like") %>%
filter(`Choice Context` == "No Choice") %>%
droplevels(),
paired = T,
within = T) # setting this to true ensures d-av is calculated, using raw scores
Cohen's d
d estimate: -0.1806067 (negligible)
95 percent confidence interval:
lower upper
-0.28463664 -0.07657682 # returns correlation between variables
cor_test(data = E2_FL_clean, "NoChoice_CUZ_futureinteract", "NoChoice_SIB_futureinteract", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
----------------------------------------------------------------------------------------------------
NoChoice_CUZ_futureinteract | NoChoice_SIB_futureinteract | 0.52 | [0.44, 0.60] | 11.38 | < .001***
Observations: 345# returns histogram of differences score variable
print(hist(E2_FL_clean$NoChoice_CUZminusSIB_futureinteract, breaks = 100))$breaks
[1] -84 -82 -80 -78 -76 -74 -72 -70 -68 -66 -64 -62 -60 -58 -56 -54 -52 -50 -48 -46 -44 -42 -40 -38
[25] -36 -34 -32 -30 -28 -26 -24 -22 -20 -18 -16 -14 -12 -10 -8 -6 -4 -2 0 2 4 6 8 10
[49] 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58
[73] 60 62
$counts
[1] 1 0 0 1 0 0 0 0 0 0 1 0 0 0 1 1 1 2 0 1 0 0 4 0
[25] 1 2 2 1 4 2 4 6 6 9 15 3 7 19 12 20 24 110 11 9 11 8 9 4
[49] 9 1 4 2 1 1 1 1 2 3 0 0 1 1 2 0 1 0 0 0 0 0 2 0
[73] 1
$density
[1] 0.001449275 0.000000000 0.000000000 0.001449275 0.000000000 0.000000000 0.000000000 0.000000000
[9] 0.000000000 0.000000000 0.001449275 0.000000000 0.000000000 0.000000000 0.001449275 0.001449275
[17] 0.001449275 0.002898551 0.000000000 0.001449275 0.000000000 0.000000000 0.005797101 0.000000000
[25] 0.001449275 0.002898551 0.002898551 0.001449275 0.005797101 0.002898551 0.005797101 0.008695652
[33] 0.008695652 0.013043478 0.021739130 0.004347826 0.010144928 0.027536232 0.017391304 0.028985507
[41] 0.034782609 0.159420290 0.015942029 0.013043478 0.015942029 0.011594203 0.013043478 0.005797101
[49] 0.013043478 0.001449275 0.005797101 0.002898551 0.001449275 0.001449275 0.001449275 0.001449275
[57] 0.002898551 0.004347826 0.000000000 0.000000000 0.001449275 0.001449275 0.002898551 0.000000000
[65] 0.001449275 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.002898551 0.000000000
[73] 0.001449275
$mids
[1] -83 -81 -79 -77 -75 -73 -71 -69 -67 -65 -63 -61 -59 -57 -55 -53 -51 -49 -47 -45 -43 -41 -39 -37
[25] -35 -33 -31 -29 -27 -25 -23 -21 -19 -17 -15 -13 -11 -9 -7 -5 -3 -1 1 3 5 7 9 11
[49] 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59
[73] 61
$xname
[1] "E2_FL_clean$NoChoice_CUZminusSIB_futureinteract"
$equidist
[1] TRUE
attr(,"class")
[1] "histogram"
Choice
# returns t-test results
t.test(futureinteract ~ Relation,
data = E2_all_long %>%
filter(BSs_cond == "Friend-Like") %>%
filter(`Choice Context` == "Choice") %>%
droplevels(),
paired = T)
Paired t-test
data: futureinteract by Relation
t = -9.1648, df = 344, p-value < 2.2e-16
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-5.905830 -3.818808
sample estimates:
mean of the differences
-4.862319 # returns dz effect size and 95% CIs
effsize::cohen.d(futureinteract ~ Relation | Subject(ResponseId),
data = E2_all_long %>%
filter(BSs_cond == "Friend-Like") %>%
filter(`Choice Context` == "Choice") %>%
droplevels(),
paired = T,
within = F) # setting this to false ensures dz is calculated, using difference score
Cohen's d
d estimate: -0.4934186 (small)
95 percent confidence interval:
lower upper
-0.6053747 -0.3814626 # returns d-av effect size and 95% CIs
effsize::cohen.d(futureinteract ~ Relation | Subject(ResponseId),
data = E2_all_long %>%
filter(BSs_cond == "Friend-Like") %>%
filter(`Choice Context` == "Choice") %>%
droplevels(),
paired = T,
within = T) # setting this to true ensures d-av is calculated, using raw scores
Cohen's d
d estimate: -0.3139896 (small)
95 percent confidence interval:
lower upper
-0.3828948 -0.2450845 # returns correlation between variables
cor_test(data = E2_FL_clean, "Choice_CUZ_futureinteract", "Choice_SIB_futureinteract", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
------------------------------------------------------------------------------------------------
Choice_CUZ_futureinteract | Choice_SIB_futureinteract | 0.80 | [0.76, 0.83] | 24.48 | < .001***
Observations: 345# returns histogram of differences score variable
print(hist(E2_FL_clean$Choice_CUZminusSIB_futureinteract, breaks = 100))$breaks
[1] -73 -72 -71 -70 -69 -68 -67 -66 -65 -64 -63 -62 -61 -60 -59 -58 -57 -56 -55 -54 -53 -52 -51 -50
[25] -49 -48 -47 -46 -45 -44 -43 -42 -41 -40 -39 -38 -37 -36 -35 -34 -33 -32 -31 -30 -29 -28 -27 -26
[49] -25 -24 -23 -22 -21 -20 -19 -18 -17 -16 -15 -14 -13 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2
[73] -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22
[97] 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44
$counts
[1] 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
[25] 0 0 0 0 0 0 0 0 0 0 0 1 1 1 2 0 0 0 0 1 0 1 0 3
[49] 1 1 2 4 1 3 3 3 3 1 4 7 8 5 3 8 12 12 13 12 10 13 15 25
[73] 123 18 6 3 3 3 1 1 2 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0
[97] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
$density
[1] 0.005797101 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
[9] 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
[17] 0.000000000 0.000000000 0.000000000 0.000000000 0.002898551 0.000000000 0.000000000 0.000000000
[25] 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
[33] 0.000000000 0.000000000 0.000000000 0.002898551 0.002898551 0.002898551 0.005797101 0.000000000
[41] 0.000000000 0.000000000 0.000000000 0.002898551 0.000000000 0.002898551 0.000000000 0.008695652
[49] 0.002898551 0.002898551 0.005797101 0.011594203 0.002898551 0.008695652 0.008695652 0.008695652
[57] 0.008695652 0.002898551 0.011594203 0.020289855 0.023188406 0.014492754 0.008695652 0.023188406
[65] 0.034782609 0.034782609 0.037681159 0.034782609 0.028985507 0.037681159 0.043478261 0.072463768
[73] 0.356521739 0.052173913 0.017391304 0.008695652 0.008695652 0.008695652 0.002898551 0.002898551
[81] 0.005797101 0.000000000 0.000000000 0.000000000 0.000000000 0.002898551 0.002898551 0.000000000
[89] 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
[97] 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
[105] 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
[113] 0.000000000 0.000000000 0.000000000 0.000000000 0.002898551
$mids
[1] -72.5 -71.5 -70.5 -69.5 -68.5 -67.5 -66.5 -65.5 -64.5 -63.5 -62.5 -61.5 -60.5 -59.5 -58.5 -57.5
[17] -56.5 -55.5 -54.5 -53.5 -52.5 -51.5 -50.5 -49.5 -48.5 -47.5 -46.5 -45.5 -44.5 -43.5 -42.5 -41.5
[33] -40.5 -39.5 -38.5 -37.5 -36.5 -35.5 -34.5 -33.5 -32.5 -31.5 -30.5 -29.5 -28.5 -27.5 -26.5 -25.5
[49] -24.5 -23.5 -22.5 -21.5 -20.5 -19.5 -18.5 -17.5 -16.5 -15.5 -14.5 -13.5 -12.5 -11.5 -10.5 -9.5
[65] -8.5 -7.5 -6.5 -5.5 -4.5 -3.5 -2.5 -1.5 -0.5 0.5 1.5 2.5 3.5 4.5 5.5 6.5
[81] 7.5 8.5 9.5 10.5 11.5 12.5 13.5 14.5 15.5 16.5 17.5 18.5 19.5 20.5 21.5 22.5
[97] 23.5 24.5 25.5 26.5 27.5 28.5 29.5 30.5 31.5 32.5 33.5 34.5 35.5 36.5 37.5 38.5
[113] 39.5 40.5 41.5 42.5 43.5
$xname
[1] "E2_FL_clean$Choice_CUZminusSIB_futureinteract"
$equidist
[1] TRUE
attr(,"class")
[1] "histogram"
Moral
ANOVAs
Stranger-Like
# returns 2 x 2 within-subject ANOVA results
aov_moral_SL <- aov(moral ~ Relation*`Choice Context` + Error(ResponseId/(Relation*`Choice Context`)),
data = E2_all_long %>%
filter(BSs_cond == "Stranger-Like"))
summary(aov_moral_SL)
Error: ResponseId
Df Sum Sq Mean Sq F value Pr(>F)
Residuals 353 240678 681.8
Error: ResponseId:Relation
Df Sum Sq Mean Sq F value Pr(>F)
Relation 1 10075 10075 59.36 1.34e-13 ***
Residuals 353 59909 170
---
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 136782 136782 469.8 <2e-16 ***
Residuals 353 102785 291
---
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 11260 11260 64.77 1.3e-14 ***
Residuals 353 61365 174
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1# returns eta-sq effect size
effectsize::eta_squared(aov_moral_SL, partial = TRUE)Group | Parameter | Eta2 (partial) | 90% CI
----------------------------------------------------------------------------------------------
ResponseId:Relation | Relation | 0.14 | [0.09, 0.20]
ResponseId:`Choice Context` | Choice Context | 0.57 | [0.52, 0.62]
ResponseId:Relation:`Choice Context` | Relation:Choice Context | 0.16 | [0.10, 0.21]Friend-Like
# returns 2 x 2 within-subject ANOVA results
aov_moral_FL <- aov(moral ~ Relation*`Choice Context` + Error(ResponseId/(Relation*`Choice Context`)),
data = E2_all_long %>%
filter(BSs_cond == "Friend-Like"))
summary(aov_moral_FL)
Error: ResponseId
Df Sum Sq Mean Sq F value Pr(>F)
Residuals 344 236889 688.6
Error: ResponseId:Relation
Df Sum Sq Mean Sq F value Pr(>F)
Relation 1 2827 2826.5 15.26 0.000113 ***
Residuals 344 63709 185.2
---
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 147539 147539 412.6 <2e-16 ***
Residuals 344 123000 358
---
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 3744 3744 17.74 3.23e-05 ***
Residuals 344 72585 211
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1# returns eta-sq effect size
effectsize::eta_squared(aov_moral_FL, partial = TRUE)Group | Parameter | Eta2 (partial) | 90% CI
----------------------------------------------------------------------------------------------
ResponseId:Relation | Relation | 0.04 | [0.01, 0.08]
ResponseId:`Choice Context` | Choice Context | 0.55 | [0.49, 0.59]
ResponseId:Relation:`Choice Context` | Relation:Choice Context | 0.05 | [0.02, 0.09]t-tests
Stranger-Like
No Choice
# returns t-test results
t.test(moral ~ Relation,
data = E2_all_long %>%
filter(BSs_cond == "Stranger-Like") %>%
filter(`Choice Context` == "No Choice") %>%
droplevels(),
paired = T)
Paired t-test
data: moral by Relation
t = 0.45528, df = 353, p-value = 0.6492
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-1.012816 1.622985
sample estimates:
mean of the differences
0.3050847 # returns dz effect size and 95% CIs
effsize::cohen.d(moral ~ Relation | Subject(ResponseId),
data = E2_all_long %>%
filter(BSs_cond == "Stranger-Like") %>%
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: 0.0241978 (negligible)
95 percent confidence interval:
lower upper
-0.08016727 0.12856288 # returns d-av effect size and 95% CIs
effsize::cohen.d(moral ~ Relation | Subject(ResponseId),
data = E2_all_long %>%
filter(BSs_cond == "Stranger-Like") %>%
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: 0.01849127 (negligible)
95 percent confidence interval:
lower upper
-0.06125669 0.09823923 # returns correlation between variables
cor_test(data = E2_SL_clean, "NoChoice_CUZ_moral", "NoChoice_SIB_moral", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
----------------------------------------------------------------------------------
NoChoice_CUZ_moral | NoChoice_SIB_moral | 0.71 | [0.65, 0.76] | 18.81 | < .001***
Observations: 354# returns histogram of differences score variable
print(hist(E2_SL_clean$NoChoice_CUZminusSIB_moral, breaks = 100))$breaks
[1] -75 -74 -73 -72 -71 -70 -69 -68 -67 -66 -65 -64 -63 -62 -61 -60 -59 -58 -57 -56 -55 -54 -53 -52
[25] -51 -50 -49 -48 -47 -46 -45 -44 -43 -42 -41 -40 -39 -38 -37 -36 -35 -34 -33 -32 -31 -30 -29 -28
[49] -27 -26 -25 -24 -23 -22 -21 -20 -19 -18 -17 -16 -15 -14 -13 -12 -11 -10 -9 -8 -7 -6 -5 -4
[73] -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
[97] 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44
[121] 45 46 47 48 49 50 51 52
$counts
[1] 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0
[33] 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 1 0 1 3 1 3 1 0 3 1 3 3 2 5 6 4 6
[65] 5 8 6 9 6 3 7 13 7 15 90 21 14 10 9 2 7 7 5 4 6 6 5 2 2 3 1 1 2 3 2 3
[97] 3 2 3 1 0 1 5 2 1 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1
$density
[1] 0.002824859 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
[9] 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
[17] 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
[25] 0.000000000 0.002824859 0.002824859 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
[33] 0.000000000 0.000000000 0.002824859 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
[41] 0.000000000 0.000000000 0.002824859 0.000000000 0.000000000 0.000000000 0.000000000 0.002824859
[49] 0.000000000 0.002824859 0.008474576 0.002824859 0.008474576 0.002824859 0.000000000 0.008474576
[57] 0.002824859 0.008474576 0.008474576 0.005649718 0.014124294 0.016949153 0.011299435 0.016949153
[65] 0.014124294 0.022598870 0.016949153 0.025423729 0.016949153 0.008474576 0.019774011 0.036723164
[73] 0.019774011 0.042372881 0.254237288 0.059322034 0.039548023 0.028248588 0.025423729 0.005649718
[81] 0.019774011 0.019774011 0.014124294 0.011299435 0.016949153 0.016949153 0.014124294 0.005649718
[89] 0.005649718 0.008474576 0.002824859 0.002824859 0.005649718 0.008474576 0.005649718 0.008474576
[97] 0.008474576 0.005649718 0.008474576 0.002824859 0.000000000 0.002824859 0.014124294 0.005649718
[105] 0.002824859 0.000000000 0.002824859 0.000000000 0.002824859 0.000000000 0.000000000 0.000000000
[113] 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.002824859 0.000000000
[121] 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.002824859
$mids
[1] -74.5 -73.5 -72.5 -71.5 -70.5 -69.5 -68.5 -67.5 -66.5 -65.5 -64.5 -63.5 -62.5 -61.5 -60.5 -59.5
[17] -58.5 -57.5 -56.5 -55.5 -54.5 -53.5 -52.5 -51.5 -50.5 -49.5 -48.5 -47.5 -46.5 -45.5 -44.5 -43.5
[33] -42.5 -41.5 -40.5 -39.5 -38.5 -37.5 -36.5 -35.5 -34.5 -33.5 -32.5 -31.5 -30.5 -29.5 -28.5 -27.5
[49] -26.5 -25.5 -24.5 -23.5 -22.5 -21.5 -20.5 -19.5 -18.5 -17.5 -16.5 -15.5 -14.5 -13.5 -12.5 -11.5
[65] -10.5 -9.5 -8.5 -7.5 -6.5 -5.5 -4.5 -3.5 -2.5 -1.5 -0.5 0.5 1.5 2.5 3.5 4.5
[81] 5.5 6.5 7.5 8.5 9.5 10.5 11.5 12.5 13.5 14.5 15.5 16.5 17.5 18.5 19.5 20.5
[97] 21.5 22.5 23.5 24.5 25.5 26.5 27.5 28.5 29.5 30.5 31.5 32.5 33.5 34.5 35.5 36.5
[113] 37.5 38.5 39.5 40.5 41.5 42.5 43.5 44.5 45.5 46.5 47.5 48.5 49.5 50.5 51.5
$xname
[1] "E2_SL_clean$NoChoice_CUZminusSIB_moral"
$equidist
[1] TRUE
attr(,"class")
[1] "histogram"
Choice
# returns t-test results
t.test(moral ~ Relation,
data = E2_all_long %>%
filter(BSs_cond == "Stranger-Like") %>%
filter(`Choice Context` == "Choice") %>%
droplevels(),
paired = T)
Paired t-test
data: moral by Relation
t = -8.9849, df = 353, p-value < 2.2e-16
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-13.376804 -8.572349
sample estimates:
mean of the differences
-10.97458 # returns dz effect size and 95% CIs
effsize::cohen.d(moral ~ Relation | Subject(ResponseId),
data = E2_all_long %>%
filter(BSs_cond == "Stranger-Like") %>%
filter(`Choice Context` == "Choice") %>%
droplevels(),
paired = T,
within = F) # setting this to false ensures dz is calculated, using difference score
Cohen's d
d estimate: -0.4775421 (small)
95 percent confidence interval:
lower upper
-0.5876805 -0.3674037 # returns d-av effect size and 95% CIs
effsize::cohen.d(moral ~ Relation | Subject(ResponseId),
data = E2_all_long %>%
filter(BSs_cond == "Stranger-Like") %>%
filter(`Choice Context` == "Choice") %>%
droplevels(),
paired = T,
within = T) # setting this to true ensures d-av is calculated, using raw scores
Cohen's d
d estimate: -0.5583514 (medium)
95 percent confidence interval:
lower upper
-0.6895242 -0.4271787 # returns correlation between variables
cor_test(data = E2_SL_clean, "Choice_CUZ_moral", "Choice_SIB_moral", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
------------------------------------------------------------------------------
Choice_CUZ_moral | Choice_SIB_moral | 0.32 | [0.22, 0.41] | 6.26 | < .001***
Observations: 354# returns histogram of differences score variable
print(hist(E2_SL_clean$Choice_CUZminusSIB_moral, breaks = 100))$breaks
[1] -94 -92 -90 -88 -86 -84 -82 -80 -78 -76 -74 -72 -70 -68 -66 -64 -62 -60 -58 -56 -54 -52 -50 -48
[25] -46 -44 -42 -40 -38 -36 -34 -32 -30 -28 -26 -24 -22 -20 -18 -16 -14 -12 -10 -8 -6 -4 -2 0
[49] 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48
[73] 50 52 54 56 58 60
$counts
[1] 1 0 0 1 0 1 0 0 0 1 0 0 0 1 1 1 2 1 1 0 1 9 9 2 1 2 2 6 7 4 7 7 4
[34] 13 12 7 9 11 10 11 10 9 9 9 12 12 55 24 7 4 6 11 9 5 2 2 1 4 0 2 1 3 1 0 1 3
[67] 0 0 1 0 1 1 2 0 0 0 2
$density
[1] 0.001412429 0.000000000 0.000000000 0.001412429 0.000000000 0.001412429 0.000000000 0.000000000
[9] 0.000000000 0.001412429 0.000000000 0.000000000 0.000000000 0.001412429 0.001412429 0.001412429
[17] 0.002824859 0.001412429 0.001412429 0.000000000 0.001412429 0.012711864 0.012711864 0.002824859
[25] 0.001412429 0.002824859 0.002824859 0.008474576 0.009887006 0.005649718 0.009887006 0.009887006
[33] 0.005649718 0.018361582 0.016949153 0.009887006 0.012711864 0.015536723 0.014124294 0.015536723
[41] 0.014124294 0.012711864 0.012711864 0.012711864 0.016949153 0.016949153 0.077683616 0.033898305
[49] 0.009887006 0.005649718 0.008474576 0.015536723 0.012711864 0.007062147 0.002824859 0.002824859
[57] 0.001412429 0.005649718 0.000000000 0.002824859 0.001412429 0.004237288 0.001412429 0.000000000
[65] 0.001412429 0.004237288 0.000000000 0.000000000 0.001412429 0.000000000 0.001412429 0.001412429
[73] 0.002824859 0.000000000 0.000000000 0.000000000 0.002824859
$mids
[1] -93 -91 -89 -87 -85 -83 -81 -79 -77 -75 -73 -71 -69 -67 -65 -63 -61 -59 -57 -55 -53 -51 -49 -47
[25] -45 -43 -41 -39 -37 -35 -33 -31 -29 -27 -25 -23 -21 -19 -17 -15 -13 -11 -9 -7 -5 -3 -1 1
[49] 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49
[73] 51 53 55 57 59
$xname
[1] "E2_SL_clean$Choice_CUZminusSIB_moral"
$equidist
[1] TRUE
attr(,"class")
[1] "histogram"
Friend-Like
No Choice
# returns t-test results
t.test(moral ~ Relation,
data = E2_all_long %>%
filter(BSs_cond == "Friend-Like") %>%
filter(`Choice Context` == "No Choice") %>%
droplevels(),
paired = T)
Paired t-test
data: moral by Relation
t = 0.64188, df = 344, p-value = 0.5214
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.8915215 1.7552896
sample estimates:
mean of the differences
0.4318841 # returns dz effect size and 95% CIs
effsize::cohen.d(moral ~ Relation | Subject(ResponseId),
data = E2_all_long %>%
filter(BSs_cond == "Friend-Like") %>%
filter(`Choice Context` == "No Choice") %>%
droplevels(),
paired = T,
within = F) # setting this to false ensures dz is calculated, using difference score
Cohen's d
d estimate: 0.03455759 (negligible)
95 percent confidence interval:
lower upper
-0.07118084 0.14029602 # returns d-av effect size and 95% CIs
effsize::cohen.d(moral ~ Relation | Subject(ResponseId),
data = E2_all_long %>%
filter(BSs_cond == "Friend-Like") %>%
filter(`Choice Context` == "No Choice") %>%
droplevels(),
paired = T,
within = T) # setting this to true ensures d-av is calculated, using raw scores
Cohen's d
d estimate: 0.02713705 (negligible)
95 percent confidence interval:
lower upper
-0.0558867 0.1101608 # returns correlation between variables
cor_test(data = E2_FL_clean, "NoChoice_CUZ_moral", "NoChoice_SIB_moral", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
----------------------------------------------------------------------------------
NoChoice_CUZ_moral | NoChoice_SIB_moral | 0.69 | [0.63, 0.74] | 17.74 | < .001***
Observations: 345# returns histogram of differences score variable
print(hist(E2_FL_clean$NoChoice_CUZminusSIB_moral, breaks = 100))$breaks
[1] -50 -49 -48 -47 -46 -45 -44 -43 -42 -41 -40 -39 -38 -37 -36 -35 -34 -33 -32 -31 -30 -29 -28 -27
[25] -26 -25 -24 -23 -22 -21 -20 -19 -18 -17 -16 -15 -14 -13 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3
[49] -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
[73] 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45
[97] 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66
$counts
[1] 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 2 1 0 0 1 1
[25] 1 1 2 4 1 1 0 1 1 2 6 2 3 6 3 11 3 2 5 11 2 10 13 9
[49] 8 103 15 13 9 8 9 6 8 3 1 6 5 3 3 5 2 1 1 4 0 6 1 2
[73] 1 0 0 3 1 0 2 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 1
[97] 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
$density
[1] 0.002898551 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
[9] 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.002898551
[17] 0.002898551 0.002898551 0.005797101 0.002898551 0.000000000 0.000000000 0.002898551 0.002898551
[25] 0.002898551 0.002898551 0.005797101 0.011594203 0.002898551 0.002898551 0.000000000 0.002898551
[33] 0.002898551 0.005797101 0.017391304 0.005797101 0.008695652 0.017391304 0.008695652 0.031884058
[41] 0.008695652 0.005797101 0.014492754 0.031884058 0.005797101 0.028985507 0.037681159 0.026086957
[49] 0.023188406 0.298550725 0.043478261 0.037681159 0.026086957 0.023188406 0.026086957 0.017391304
[57] 0.023188406 0.008695652 0.002898551 0.017391304 0.014492754 0.008695652 0.008695652 0.014492754
[65] 0.005797101 0.002898551 0.002898551 0.011594203 0.000000000 0.017391304 0.002898551 0.005797101
[73] 0.002898551 0.000000000 0.000000000 0.008695652 0.002898551 0.000000000 0.005797101 0.000000000
[81] 0.000000000 0.002898551 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
[89] 0.000000000 0.000000000 0.002898551 0.000000000 0.000000000 0.000000000 0.002898551 0.002898551
[97] 0.000000000 0.000000000 0.002898551 0.002898551 0.000000000 0.000000000 0.000000000 0.000000000
[105] 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
[113] 0.000000000 0.000000000 0.000000000 0.002898551
$mids
[1] -49.5 -48.5 -47.5 -46.5 -45.5 -44.5 -43.5 -42.5 -41.5 -40.5 -39.5 -38.5 -37.5 -36.5 -35.5 -34.5
[17] -33.5 -32.5 -31.5 -30.5 -29.5 -28.5 -27.5 -26.5 -25.5 -24.5 -23.5 -22.5 -21.5 -20.5 -19.5 -18.5
[33] -17.5 -16.5 -15.5 -14.5 -13.5 -12.5 -11.5 -10.5 -9.5 -8.5 -7.5 -6.5 -5.5 -4.5 -3.5 -2.5
[49] -1.5 -0.5 0.5 1.5 2.5 3.5 4.5 5.5 6.5 7.5 8.5 9.5 10.5 11.5 12.5 13.5
[65] 14.5 15.5 16.5 17.5 18.5 19.5 20.5 21.5 22.5 23.5 24.5 25.5 26.5 27.5 28.5 29.5
[81] 30.5 31.5 32.5 33.5 34.5 35.5 36.5 37.5 38.5 39.5 40.5 41.5 42.5 43.5 44.5 45.5
[97] 46.5 47.5 48.5 49.5 50.5 51.5 52.5 53.5 54.5 55.5 56.5 57.5 58.5 59.5 60.5 61.5
[113] 62.5 63.5 64.5 65.5
$xname
[1] "E2_FL_clean$NoChoice_CUZminusSIB_moral"
$equidist
[1] TRUE
attr(,"class")
[1] "histogram"
Choice
# returns t-test results
t.test(moral ~ Relation,
data = E2_all_long %>%
filter(BSs_cond == "Friend-Like") %>%
filter(`Choice Context` == "Choice") %>%
droplevels(),
paired = T)
Paired t-test
data: moral by Relation
t = -4.5336, df = 344, p-value = 8.017e-06
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-8.827515 -3.485529
sample estimates:
mean of the differences
-6.156522 # returns dz effect size and 95% CIs
effsize::cohen.d(moral ~ Relation | Subject(ResponseId),
data = E2_all_long %>%
filter(BSs_cond == "Friend-Like") %>%
filter(`Choice Context` == "Choice") %>%
droplevels(),
paired = T,
within = F) # setting this to false ensures dz is calculated, using difference score
Cohen's d
d estimate: -0.2440798 (small)
95 percent confidence interval:
lower upper
-0.3513495 -0.1368101 # returns d-av effect size and 95% CIs
effsize::cohen.d(moral ~ Relation | Subject(ResponseId),
data = E2_all_long %>%
filter(BSs_cond == "Friend-Like") %>%
filter(`Choice Context` == "Choice") %>%
droplevels(),
paired = T,
within = T) # setting this to true ensures d-av is calculated, using raw scores
Cohen's d
d estimate: -0.2843231 (small)
95 percent confidence interval:
lower upper
-0.4099225 -0.1587236 # returns correlation between variables
cor_test(data = E2_FL_clean, "Choice_CUZ_moral", "Choice_SIB_moral", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
------------------------------------------------------------------------------
Choice_CUZ_moral | Choice_SIB_moral | 0.32 | [0.22, 0.41] | 6.29 | < .001***
Observations: 345# returns histogram of differences score variable
print(hist(E2_FL_clean$Choice_CUZminusSIB_moral, breaks = 100))$breaks
[1] -88 -86 -84 -82 -80 -78 -76 -74 -72 -70 -68 -66 -64 -62 -60 -58 -56 -54 -52 -50 -48 -46 -44 -42
[25] -40 -38 -36 -34 -32 -30 -28 -26 -24 -22 -20 -18 -16 -14 -12 -10 -8 -6 -4 -2 0 2 4 6
[49] 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54
[73] 56 58 60 62 64 66 68 70 72 74 76 78 80 82 84 86 88 90 92 94 96 98 100
$counts
[1] 3 0 1 1 0 2 0 1 0 1 2 0 3 1 3 0 2 0 3 5 1 2 1 2 3 3 3 4 4 2 7 2 7
[34] 6 5 9 5 7 8 12 9 9 23 69 30 7 4 6 5 6 7 7 2 3 3 6 6 3 3 1 2 2 0 1 1 2
[67] 1 0 2 2 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1
$density
[1] 0.004347826 0.000000000 0.001449275 0.001449275 0.000000000 0.002898551 0.000000000 0.001449275
[9] 0.000000000 0.001449275 0.002898551 0.000000000 0.004347826 0.001449275 0.004347826 0.000000000
[17] 0.002898551 0.000000000 0.004347826 0.007246377 0.001449275 0.002898551 0.001449275 0.002898551
[25] 0.004347826 0.004347826 0.004347826 0.005797101 0.005797101 0.002898551 0.010144928 0.002898551
[33] 0.010144928 0.008695652 0.007246377 0.013043478 0.007246377 0.010144928 0.011594203 0.017391304
[41] 0.013043478 0.013043478 0.033333333 0.100000000 0.043478261 0.010144928 0.005797101 0.008695652
[49] 0.007246377 0.008695652 0.010144928 0.010144928 0.002898551 0.004347826 0.004347826 0.008695652
[57] 0.008695652 0.004347826 0.004347826 0.001449275 0.002898551 0.002898551 0.000000000 0.001449275
[65] 0.001449275 0.002898551 0.001449275 0.000000000 0.002898551 0.002898551 0.000000000 0.000000000
[73] 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
[81] 0.000000000 0.001449275 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
[89] 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.001449275
$mids
[1] -87 -85 -83 -81 -79 -77 -75 -73 -71 -69 -67 -65 -63 -61 -59 -57 -55 -53 -51 -49 -47 -45 -43 -41
[25] -39 -37 -35 -33 -31 -29 -27 -25 -23 -21 -19 -17 -15 -13 -11 -9 -7 -5 -3 -1 1 3 5 7
[49] 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55
[73] 57 59 61 63 65 67 69 71 73 75 77 79 81 83 85 87 89 91 93 95 97 99
$xname
[1] "E2_FL_clean$Choice_CUZminusSIB_moral"
$equidist
[1] TRUE
attr(,"class")
[1] "histogram"
Moral Diff ~ Oblig Diff Plots
# Create difference score datasets for plotting of diff score correlations
# Stranger-Like
E2_diff_SL_cond_long <- E2_SL_clean %>%
pivot_longer(
cols = c(SL_Dist_Scen, SL_Close_Scen),
names_to = "WSs_cond",
values_to = "Condition"
)
E2_diff_SL_oblig_long <- E2_SL_clean %>%
pivot_longer(
cols = c(NoChoice_CUZminusSIB_oblig, Choice_CUZminusSIB_oblig),
names_to = "WSs_cond",
values_to = "oblig"
)
E2_diff_SL_relate_long <- E2_SL_clean %>%
pivot_longer(
cols = c(NoChoice_CUZminusSIB_relate, Choice_CUZminusSIB_relate),
names_to = "WSs_cond",
values_to = "relate"
)
E2_diff_SL_close_long <- E2_SL_clean %>%
pivot_longer(
cols = c(NoChoice_CUZminusSIB_close, Choice_CUZminusSIB_close),
names_to = "WSs_cond",
values_to = "close"
)
E2_diff_SL_priorhelp_long <- E2_SL_clean %>%
pivot_longer(
cols = c(NoChoice_CUZminusSIB_priorhelp, Choice_CUZminusSIB_priorhelp),
names_to = "WSs_cond",
values_to = "priorhelp"
)
E2_diff_SL_futurehelp_long <- E2_SL_clean %>%
pivot_longer(
cols = c(NoChoice_CUZminusSIB_futurehelp, Choice_CUZminusSIB_futurehelp),
names_to = "WSs_cond",
values_to = "futurehelp"
)
E2_diff_SL_priorinteract_long <- E2_SL_clean %>%
pivot_longer(
cols = c(NoChoice_CUZminusSIB_priorinteract, Choice_CUZminusSIB_priorinteract),
names_to = "WSs_cond",
values_to = "priorinteract"
)
E2_diff_SL_futureinteract_long <- E2_SL_clean %>%
pivot_longer(
cols = c(NoChoice_CUZminusSIB_futureinteract, Choice_CUZminusSIB_futureinteract),
names_to = "WSs_cond",
values_to = "futureinteract"
)
E2_diff_SL_moral_long <- E2_SL_clean %>%
pivot_longer(
cols = c(NoChoice_CUZminusSIB_moral, Choice_CUZminusSIB_moral),
names_to = "WSs_cond",
values_to = "moral"
)
# Combine long SL datasets, select plotting variables, and create condition variable for `Choice Context`
E2_diff_SL_long <- cbind(E2_diff_SL_cond_long,
E2_diff_SL_oblig_long,
E2_diff_SL_relate_long, E2_diff_SL_close_long, E2_diff_SL_priorhelp_long, E2_diff_SL_futurehelp_long,
E2_diff_SL_priorinteract_long, E2_diff_SL_futureinteract_long,
E2_diff_SL_moral_long)
E2_diff_SL_long <- E2_diff_SL_long[, !duplicated(colnames(E2_diff_SL_long))] # get rid of duplicate columns
E2_diff_SL_long <- E2_diff_SL_long %>%
select(ResponseId,
Age:OUS_IH,
BSs_cond,
WSs_cond,
Condition,
oblig,
relate, close,
priorhelp, futurehelp,
priorinteract, futureinteract,
moral) %>%
mutate(`Choice Context` = case_when(
WSs_cond == "SL_Dist_Scen" ~ "No Choice",
WSs_cond == "SL_Close_Scen" ~ "Choice"))
# Reorder/rename condition, and participant factors
E2_diff_SL_long$`Choice Context` <- as.factor(E2_diff_SL_long$`Choice Context`)
E2_diff_SL_long$`Choice Context` <- ordered(E2_diff_SL_long$`Choice Context`, levels = c("No Choice", "Choice"))
E2_diff_SL_long$ResponseId <- as.factor(E2_diff_SL_long$ResponseId)
# Friend-Like
E2_diff_FL_cond_long <- E2_FL_clean %>%
pivot_longer(
cols = c(FL_Dist_Scen, FL_Close_Scen),
names_to = "WSs_cond",
values_to = "Condition"
)
E2_diff_FL_oblig_long <- E2_FL_clean %>%
pivot_longer(
cols = c(NoChoice_CUZminusSIB_oblig, Choice_CUZminusSIB_oblig),
names_to = "WSs_cond",
values_to = "oblig"
)
E2_diff_FL_relate_long <- E2_FL_clean %>%
pivot_longer(
cols = c(NoChoice_CUZminusSIB_relate, Choice_CUZminusSIB_relate),
names_to = "WSs_cond",
values_to = "relate"
)
E2_diff_FL_close_long <- E2_FL_clean %>%
pivot_longer(
cols = c(NoChoice_CUZminusSIB_close, Choice_CUZminusSIB_close),
names_to = "WSs_cond",
values_to = "close"
)
E2_diff_FL_priorhelp_long <- E2_FL_clean %>%
pivot_longer(
cols = c(NoChoice_CUZminusSIB_priorhelp, Choice_CUZminusSIB_priorhelp),
names_to = "WSs_cond",
values_to = "priorhelp"
)
E2_diff_FL_futurehelp_long <- E2_FL_clean %>%
pivot_longer(
cols = c(NoChoice_CUZminusSIB_futurehelp, Choice_CUZminusSIB_futurehelp),
names_to = "WSs_cond",
values_to = "futurehelp"
)
E2_diff_FL_priorinteract_long <- E2_FL_clean %>%
pivot_longer(
cols = c(NoChoice_CUZminusSIB_priorinteract, Choice_CUZminusSIB_priorinteract),
names_to = "WSs_cond",
values_to = "priorinteract"
)
E2_diff_FL_futureinteract_long <- E2_FL_clean %>%
pivot_longer(
cols = c(NoChoice_CUZminusSIB_futureinteract, Choice_CUZminusSIB_futureinteract),
names_to = "WSs_cond",
values_to = "futureinteract"
)
E2_diff_FL_moral_long <- E2_FL_clean %>%
pivot_longer(
cols = c(NoChoice_CUZminusSIB_moral, Choice_CUZminusSIB_moral),
names_to = "WSs_cond",
values_to = "moral"
)
# Combine long SL datasets, select plotting variables, and create condition variable for `Choice Context`
E2_diff_FL_long <- cbind(E2_diff_FL_cond_long,
E2_diff_FL_oblig_long,
E2_diff_FL_relate_long, E2_diff_FL_close_long, E2_diff_FL_priorhelp_long, E2_diff_FL_futurehelp_long,
E2_diff_FL_priorinteract_long, E2_diff_FL_futureinteract_long,
E2_diff_FL_moral_long)
E2_diff_FL_long <- E2_diff_FL_long[, !duplicated(colnames(E2_diff_FL_long))] # get rid of duplicate columns
E2_diff_FL_long <- E2_diff_FL_long %>%
select(ResponseId,
Age:OUS_IH,
BSs_cond,
WSs_cond,
Condition,
oblig,
relate, close,
priorhelp, futurehelp,
priorinteract, futureinteract,
moral) %>%
mutate(`Choice Context` = case_when(
WSs_cond == "FL_Dist_Scen" ~ "No Choice",
WSs_cond == "FL_Close_Scen" ~ "Choice"))
# Reorder/rename condition, and participant factors
E2_diff_FL_long$`Choice Context` <- as.factor(E2_diff_FL_long$`Choice Context`)
E2_diff_FL_long$`Choice Context` <- ordered(E2_diff_FL_long$`Choice Context`, levels = c("No Choice", "Choice"))
E2_diff_FL_long$ResponseId <- as.factor(E2_diff_FL_long$ResponseId)
# Combine into one dataset for plotting
E2_diff_all_long <- rbind(E2_diff_SL_long, E2_diff_FL_long)
# Reorder All_long BSs_cond
E2_diff_all_long$BSs_cond <- as.factor(E2_diff_all_long$BSs_cond)
E2_diff_all_long$BSs_cond <- ordered(E2_diff_all_long$BSs_cond, levels = c("Stranger-Like", "Friend-Like"))Stranger-Like
print(oblig_moral_diff_plot_SL <- ggplot(data = E2_diff_SL_long,
aes(x = oblig, y = moral)) +
geom_jitter(color = "darkorchid1", alpha = 0.5) +
geom_smooth(method = 'lm', color = "darkorchid1") +
facet_wrap(~`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)))
Friend-Like
print(oblig_moral_diff_plot_FL <- ggplot(data = E2_diff_FL_long,
aes(x = oblig, y = moral)) +
geom_jitter(color = "darkorchid1", alpha = 0.5) +
geom_smooth(method = 'lm', color = "darkorchid1") +
facet_wrap(~`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 = E2_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 = 2) +
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("E2_moral~oblig_plot.png")Saving 14 x 9 in imageMoral Diff ~ Oblig Diff Tests
See our pre-registration (INSERT LINK) and manuscript for our predictions about the relationship between obligation differences and moral character differences.
Stranger-Like
No Choice
# pearson's r
cor_test(E2_SL_clean, "NoChoice_CUZminusSIB_oblig", "NoChoice_CUZminusSIB_moral", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
------------------------------------------------------------------------------------------------
NoChoice_CUZminusSIB_oblig | NoChoice_CUZminusSIB_moral | -0.02 | [-0.13, 0.08] | -0.42 | 0.674
Observations: 354Choice
# pearson's r
cor_test(E2_SL_clean, "Choice_CUZminusSIB_oblig", "Choice_CUZminusSIB_moral", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
----------------------------------------------------------------------------------------------
Choice_CUZminusSIB_oblig | Choice_CUZminusSIB_moral | 0.27 | [0.17, 0.36] | 5.20 | < .001***
Observations: 354Friend-Like
No Choice
# pearson's r
cor_test(E2_FL_clean, "NoChoice_CUZminusSIB_oblig", "NoChoice_CUZminusSIB_moral", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
-----------------------------------------------------------------------------------------------
NoChoice_CUZminusSIB_oblig | NoChoice_CUZminusSIB_moral | 0.06 | [-0.05, 0.16] | 1.07 | 0.284
Observations: 345Choice
# pearson's r
cor_test(E2_FL_clean, "Choice_CUZminusSIB_oblig", "Choice_CUZminusSIB_moral", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
--------------------------------------------------------------------------------------------
Choice_CUZminusSIB_oblig | Choice_CUZminusSIB_moral | 0.14 | [0.04, 0.24] | 2.67 | 0.008**
Observations: 345Moral ~ Oblig R-M Plots
Stranger-Like
No Choice
rmcorr_SL_NoChoice <- rmcorr(participant = ResponseId,
measure1 = oblig,
measure2 = moral,
dataset = E2_all_long %>%
filter(BSs_cond == "Stranger-Like") %>%
filter(`Choice Context` == "No Choice"))print(rmcorr_plot_SL_NoChoice <- ggplot(data = E2_all_long %>%
filter(BSs_cond == "Stranger-Like") %>%
filter(`Choice Context` == "No Choice"),
aes(x = oblig, y = moral, group = ResponseId, color = ResponseId)) +
geom_point(aes(color = ResponseId)) +
geom_line(aes(y = rmcorr_SL_NoChoice$model$fitted.values), linetype = 1) +
scale_x_continuous(limits = c(-5,105), breaks = c(0,25,50,75,100)) +
scale_y_continuous(limits = c(-5,105), breaks = c(0,25,50,75,100)) +
theme_classic() +
xlab("Obligation Strength") +
ylab("Moral Character") +
theme(axis.title.x = element_text(size = 14),
axis.title.y = element_text(size = 14),
axis.text.x = element_text(color = "black", size = 12),
axis.text.y = element_text(color = "black", size = 12),
legend.position = "none"))
Choice
rmcorr_SL_Choice <- rmcorr(participant = ResponseId,
measure1 = oblig,
measure2 = moral,
dataset = E2_all_long %>%
filter(BSs_cond == "Stranger-Like") %>%
filter(`Choice Context` == "Choice"))print(rmcorr_plot_SL_Choice <- ggplot(data = E2_all_long %>%
filter(BSs_cond == "Stranger-Like") %>%
filter(`Choice Context` == "Choice"),
aes(x = oblig, y = moral, group = ResponseId, color = ResponseId)) +
geom_point(aes(color = ResponseId)) +
geom_line(aes(y = rmcorr_SL_Choice$model$fitted.values), linetype = 1) +
scale_x_continuous(limits = c(-5,105), breaks = c(0,25,50,75,100)) +
scale_y_continuous(limits = c(-5,105), breaks = c(0,25,50,75,100)) +
theme_classic() +
xlab("Obligation Strength") +
ylab("Moral Character") +
theme(axis.title.x = element_text(size = 14),
axis.title.y = element_text(size = 14),
axis.text.x = element_text(color = "black", size = 12),
axis.text.y = element_text(color = "black", size = 12),
legend.position = "none"))
Friend-Like
No Choice
rmcorr_FL_NoChoice <- rmcorr(participant = ResponseId,
measure1 = oblig,
measure2 = moral,
dataset = E2_all_long %>%
filter(BSs_cond == "Friend-Like") %>%
filter(`Choice Context` == "No Choice"))print(rmcorr_plot_FL_NoChoice <- ggplot(data = E2_all_long %>%
filter(BSs_cond == "Friend-Like") %>%
filter(`Choice Context` == "No Choice"),
aes(x = oblig, y = moral, group = ResponseId, color = ResponseId)) +
geom_point(aes(color = ResponseId)) +
geom_line(aes(y = rmcorr_FL_NoChoice$model$fitted.values), linetype = 1) +
scale_x_continuous(limits = c(-5,105), breaks = c(0,25,50,75,100)) +
scale_y_continuous(limits = c(-5,105), breaks = c(0,25,50,75,100)) +
theme_classic() +
xlab("Obligation Strength") +
ylab("Moral Character") +
theme(axis.title.x = element_text(size = 14),
axis.title.y = element_text(size = 14),
axis.text.x = element_text(color = "black", size = 12),
axis.text.y = element_text(color = "black", size = 12),
legend.position = "none"))
Choice
rmcorr_FL_Choice <- rmcorr(participant = ResponseId,
measure1 = oblig,
measure2 = moral,
dataset = E2_all_long %>%
filter(BSs_cond == "Friend-Like") %>%
filter(`Choice Context` == "Choice"))print(rmcorr_plot_FL_Choice <- ggplot(data = E2_all_long %>%
filter(BSs_cond == "Friend-Like") %>%
filter(`Choice Context` == "Choice"),
aes(x = oblig, y = moral, group = ResponseId, color = ResponseId)) +
geom_point(aes(color = ResponseId)) +
geom_line(aes(y = rmcorr_FL_Choice$model$fitted.values), linetype = 1) +
scale_x_continuous(limits = c(-5,105), breaks = c(0,25,50,75,100)) +
scale_y_continuous(limits = c(-5,105), 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 pre-registration (INSERT LINK) for our predictions about the within-individual relationship between obligation judgments and moral character judgments.
Stranger-Like
No Choice
print(rmcorr_SL_NoChoice)
Repeated measures correlation
r
-0.02706391
degrees of freedom
353
p-value
0.6113006
95% confidence interval
-0.1310765 0.07753801 Choice
print(rmcorr_SL_Choice)
Repeated measures correlation
r
0.4382157
degrees of freedom
353
p-value
4.325398e-18
95% confidence interval
0.3498351 0.5188646 Friend-Like
No Choice
print(rmcorr_FL_NoChoice)
Repeated measures correlation
r
0.05185481
degrees of freedom
344
p-value
0.3361943
95% confidence interval
-0.05418352 0.1567366 Choice
print(rmcorr_FL_Choice)
Repeated measures correlation
r
0.2379516
degrees of freedom
344
p-value
7.656461e-06
95% confidence interval
0.1356228 0.3352573 Ind. Diff Internal Reliability Tests
MAC
Family
Stranger-Like
# create dataset with only MAC Family Values variables
E2_SL_clean_MAC_Fam_only <- E2_SL_clean %>% select(MAC_Jud_1:MAC_Jud_3, MAC_Rel_1:MAC_Rel_3)
psych::alpha(E2_SL_clean_MAC_Fam_only)Number of categories should be increased in order to count frequencies.
Reliability analysis
Call: psych::alpha(x = E2_SL_clean_MAC_Fam_only)
lower alpha upper 95% confidence boundaries
0.88 0.9 0.92
Reliability if an item is dropped:
Item statistics Friend-Like
# create dataset with only MAC Family Values variables
E2_FL_clean_MAC_Fam_only <- E2_FL_clean %>% select(MAC_Jud_1:MAC_Jud_3, MAC_Rel_1:MAC_Rel_3)
psych::alpha(E2_FL_clean_MAC_Fam_only)Number of categories should be increased in order to count frequencies.
Reliability analysis
Call: psych::alpha(x = E2_FL_clean_MAC_Fam_only)
lower alpha upper 95% confidence boundaries
0.87 0.89 0.91
Reliability if an item is dropped:
Item statistics Group
Stranger-Like
# create dataset with only MAC Groupily Values variables
E2_SL_clean_MAC_Group_only <- E2_SL_clean %>% select(MAC_Jud_4:MAC_Jud_6, MAC_Rel_4:MAC_Rel_6)
psych::alpha(E2_SL_clean_MAC_Group_only)Number of categories should be increased in order to count frequencies.
Reliability analysis
Call: psych::alpha(x = E2_SL_clean_MAC_Group_only)
lower alpha upper 95% confidence boundaries
0.85 0.87 0.89
Reliability if an item is dropped:
Item statistics Friend-Like
# create dataset with only MAC Groupily Values variables
E2_FL_clean_MAC_Group_only <- E2_FL_clean %>% select(MAC_Jud_4:MAC_Jud_6, MAC_Rel_4:MAC_Rel_6)
psych::alpha(E2_FL_clean_MAC_Group_only)Number of categories should be increased in order to count frequencies.
Reliability analysis
Call: psych::alpha(x = E2_FL_clean_MAC_Group_only)
lower alpha upper 95% confidence boundaries
0.82 0.85 0.87
Reliability if an item is dropped:
Item statistics Reciprocity
Stranger-Like
# create dataset with only MAC Recily Values variables
E2_SL_clean_MAC_Rec_only <- E2_SL_clean %>% select(MAC_Jud_7:MAC_Jud_9, MAC_Rel_7:MAC_Rel_9)
psych::alpha(E2_SL_clean_MAC_Rec_only)Number of categories should be increased in order to count frequencies.
Reliability analysis
Call: psych::alpha(x = E2_SL_clean_MAC_Rec_only)
lower alpha upper 95% confidence boundaries
0.79 0.82 0.85
Reliability if an item is dropped:
Item statistics Friend-Like
# create dataset with only MAC Recily Values variables
E2_FL_clean_MAC_Rec_only <- E2_FL_clean %>% select(MAC_Jud_7:MAC_Jud_9, MAC_Rel_7:MAC_Rel_9)
psych::alpha(E2_FL_clean_MAC_Rec_only)Number of categories should be increased in order to count frequencies.
Reliability analysis
Call: psych::alpha(x = E2_FL_clean_MAC_Rec_only)
lower alpha upper 95% confidence boundaries
0.8 0.83 0.85
Reliability if an item is dropped:
Item statistics Heroism
Stranger-Like
# create dataset with only MAC Heroily Values variables
E2_SL_clean_MAC_Hero_only <- E2_SL_clean %>% select(MAC_Jud_10:MAC_Jud_12, MAC_Rel_10:MAC_Rel_12)
psych::alpha(E2_SL_clean_MAC_Hero_only)Number of categories should be increased in order to count frequencies.
Reliability analysis
Call: psych::alpha(x = E2_SL_clean_MAC_Hero_only)
lower alpha upper 95% confidence boundaries
0.83 0.85 0.88
Reliability if an item is dropped:
Item statistics Friend-Like
# create dataset with only MAC Heroily Values variables
E2_FL_clean_MAC_Hero_only <- E2_FL_clean %>% select(MAC_Jud_10:MAC_Jud_12, MAC_Rel_10:MAC_Rel_12)
psych::alpha(E2_FL_clean_MAC_Hero_only)Number of categories should be increased in order to count frequencies.
Reliability analysis
Call: psych::alpha(x = E2_FL_clean_MAC_Hero_only)
lower alpha upper 95% confidence boundaries
0.8 0.83 0.86
Reliability if an item is dropped:
Item statistics Fairness
Stranger-Like
# create dataset with only MAC Fairily Values variables
E2_SL_clean_MAC_Fair_only <- E2_SL_clean %>% select(MAC_Jud_16:MAC_Jud_18, MAC_Rel_16:MAC_Rel_18)
psych::alpha(E2_SL_clean_MAC_Fair_only)Number of categories should be increased in order to count frequencies.
Reliability analysis
Call: psych::alpha(x = E2_SL_clean_MAC_Fair_only)
lower alpha upper 95% confidence boundaries
0.66 0.7 0.75
Reliability if an item is dropped:
Item statistics Friend-Like
# create dataset with only MAC Fairily Values variables
E2_FL_clean_MAC_Fair_only <- E2_FL_clean %>% select(MAC_Jud_16:MAC_Jud_18, MAC_Rel_16:MAC_Rel_18)
psych::alpha(E2_FL_clean_MAC_Fair_only)Number of categories should be increased in order to count frequencies.
Reliability analysis
Call: psych::alpha(x = E2_FL_clean_MAC_Fair_only)
lower alpha upper 95% confidence boundaries
0.69 0.74 0.78
Reliability if an item is dropped:
Item statistics Property
Stranger-Like
# create dataset with only MAC Propily Values variables
E2_SL_clean_MAC_Prop_only <- E2_SL_clean %>% select(MAC_Jud_19_r:MAC_Jud_21_r, MAC_Rel_19:MAC_Rel_21)
psych::alpha(E2_SL_clean_MAC_Prop_only)Number of categories should be increased in order to count frequencies.
Reliability analysis
Call: psych::alpha(x = E2_SL_clean_MAC_Prop_only)
lower alpha upper 95% confidence boundaries
0.59 0.64 0.7
Reliability if an item is dropped:
Item statistics Friend-Like
# create dataset with only MAC Propily Values variables
E2_FL_clean_MAC_Prop_only <- E2_FL_clean %>% select(MAC_Jud_19_r:MAC_Jud_21_r, MAC_Rel_19:MAC_Rel_21)
psych::alpha(E2_FL_clean_MAC_Prop_only)Number of categories should be increased in order to count frequencies.
Reliability analysis
Call: psych::alpha(x = E2_FL_clean_MAC_Prop_only)
lower alpha upper 95% confidence boundaries
0.64 0.69 0.74
Reliability if an item is dropped:
Item statistics MFT
Harm
Stranger-Like
E2_SL_clean_MFQ_Harm_only <- E2_SL_clean %>% select(MFQ_Jud_1:MFQ_Jud_3, MFQ_Rel_1:MFQ_Rel_3)
psych::alpha(E2_SL_clean_MFQ_Harm_only)
Reliability analysis
Call: psych::alpha(x = E2_SL_clean_MFQ_Harm_only)
lower alpha upper 95% confidence boundaries
0.7 0.74 0.78
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 0.02 0.01 0.05 0.22 0.30 0.40 0
MFQ_Jud_2 0.02 0.03 0.07 0.13 0.22 0.53 0
MFQ_Jud_3 0.13 0.16 0.18 0.13 0.19 0.21 0
MFQ_Rel_1 0.02 0.06 0.12 0.22 0.34 0.24 0
MFQ_Rel_2 0.02 0.03 0.07 0.22 0.33 0.33 0
MFQ_Rel_3 0.02 0.01 0.04 0.14 0.30 0.48 0Friend-Like
E2_FL_clean_MFQ_Harm_only <- E2_FL_clean %>% select(MFQ_Jud_1:MFQ_Jud_3, MFQ_Rel_1:MFQ_Rel_3)
psych::alpha(E2_FL_clean_MFQ_Harm_only)
Reliability analysis
Call: psych::alpha(x = E2_FL_clean_MFQ_Harm_only)
lower alpha upper 95% confidence boundaries
0.73 0.77 0.81
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 0.01 0.02 0.07 0.20 0.33 0.37 0
MFQ_Jud_2 0.04 0.06 0.06 0.10 0.21 0.52 0
MFQ_Jud_3 0.14 0.14 0.18 0.16 0.21 0.17 0
MFQ_Rel_1 0.04 0.05 0.08 0.23 0.33 0.26 0
MFQ_Rel_2 0.04 0.05 0.07 0.21 0.34 0.30 0
MFQ_Rel_3 0.02 0.02 0.03 0.17 0.32 0.43 0Fairness
Stranger-Like
E2_SL_clean_MFQ_Fair_only <- E2_SL_clean %>% select(MFQ_Jud_4:MFQ_Jud_6, MFQ_Rel_4:MFQ_Rel_6)
psych::alpha(E2_SL_clean_MFQ_Fair_only)
Reliability analysis
Call: psych::alpha(x = E2_SL_clean_MFQ_Fair_only)
lower alpha upper 95% confidence boundaries
0.65 0.7 0.75
Reliability if an item is dropped:
Item statistics
Non missing response frequency for each item
1 2 3 4 5 6 miss
MFQ_Jud_4 0.01 0.01 0.05 0.13 0.28 0.53 0
MFQ_Jud_5 0.02 0.04 0.10 0.22 0.33 0.29 0
MFQ_Jud_6 0.25 0.16 0.14 0.18 0.12 0.16 0
MFQ_Rel_4 0.01 0.04 0.07 0.25 0.32 0.31 0
MFQ_Rel_5 0.00 0.02 0.11 0.21 0.33 0.32 0
MFQ_Rel_6 0.01 0.01 0.06 0.13 0.32 0.47 0Friend-Like
E2_FL_clean_MFQ_Fair_only <- E2_FL_clean %>% select(MFQ_Jud_4:MFQ_Jud_6, MFQ_Rel_4:MFQ_Rel_6)
psych::alpha(E2_FL_clean_MFQ_Fair_only)
Reliability analysis
Call: psych::alpha(x = E2_FL_clean_MFQ_Fair_only)
lower alpha upper 95% confidence boundaries
0.72 0.76 0.8
Reliability if an item is dropped:
Item statistics
Non missing response frequency for each item
1 2 3 4 5 6 miss
MFQ_Jud_4 0.02 0.04 0.04 0.15 0.27 0.48 0
MFQ_Jud_5 0.03 0.04 0.09 0.24 0.32 0.28 0
MFQ_Jud_6 0.22 0.17 0.17 0.16 0.14 0.13 0
MFQ_Rel_4 0.03 0.05 0.10 0.17 0.37 0.28 0
MFQ_Rel_5 0.03 0.03 0.09 0.21 0.35 0.29 0
MFQ_Rel_6 0.02 0.02 0.05 0.12 0.30 0.48 0Loyalty
Stranger-Like
E2_SL_clean_MFQ_Loyalty_only <- E2_SL_clean %>% select(MFQ_Jud_7:MFQ_Jud_9, MFQ_Rel_7:MFQ_Rel_9)
psych::alpha(E2_SL_clean_MFQ_Loyalty_only)
Reliability analysis
Call: psych::alpha(x = E2_SL_clean_MFQ_Loyalty_only)
lower alpha upper 95% confidence boundaries
0.76 0.79 0.82
Reliability if an item is dropped:
Item statistics
Non missing response frequency for each item
1 2 3 4 5 6 miss
MFQ_Jud_7 0.16 0.17 0.17 0.22 0.19 0.11 0
MFQ_Jud_8 0.17 0.19 0.22 0.22 0.14 0.06 0
MFQ_Jud_9 0.13 0.18 0.31 0.25 0.10 0.03 0
MFQ_Rel_7 0.22 0.23 0.21 0.20 0.09 0.05 0
MFQ_Rel_8 0.10 0.19 0.15 0.29 0.19 0.07 0
MFQ_Rel_9 0.10 0.19 0.18 0.30 0.16 0.06 0Friend-Like
E2_FL_clean_MFQ_Loyalty_only <- E2_FL_clean %>% select(MFQ_Jud_7:MFQ_Jud_9, MFQ_Rel_7:MFQ_Rel_9)
psych::alpha(E2_FL_clean_MFQ_Loyalty_only)
Reliability analysis
Call: psych::alpha(x = E2_FL_clean_MFQ_Loyalty_only)
lower alpha upper 95% confidence boundaries
0.7 0.74 0.78
Reliability if an item is dropped:
Item statistics
Non missing response frequency for each item
1 2 3 4 5 6 miss
MFQ_Jud_7 0.13 0.14 0.17 0.24 0.19 0.12 0
MFQ_Jud_8 0.14 0.17 0.22 0.24 0.17 0.06 0
MFQ_Jud_9 0.10 0.20 0.29 0.25 0.12 0.04 0
MFQ_Rel_7 0.24 0.27 0.16 0.19 0.10 0.05 0
MFQ_Rel_8 0.09 0.14 0.23 0.26 0.19 0.10 0
MFQ_Rel_9 0.10 0.18 0.19 0.25 0.18 0.10 0Purity
Stranger-Like
E2_SL_clean_MFQ_Purity_only <- E2_SL_clean %>% select(MFQ_Jud_13:MFQ_Jud_15, MFQ_Rel_13:MFQ_Rel_15)
psych::alpha(E2_SL_clean_MFQ_Purity_only)
Reliability analysis
Call: psych::alpha(x = E2_SL_clean_MFQ_Purity_only)
lower alpha upper 95% confidence boundaries
0.88 0.9 0.91
Reliability if an item is dropped:
Item statistics
Non missing response frequency for each item
1 2 3 4 5 6 miss
MFQ_Jud_13 0.17 0.16 0.14 0.21 0.16 0.16 0
MFQ_Jud_14 0.19 0.18 0.14 0.22 0.12 0.14 0
MFQ_Jud_15 0.26 0.13 0.12 0.20 0.16 0.14 0
MFQ_Rel_13 0.18 0.16 0.15 0.20 0.17 0.13 0
MFQ_Rel_14 0.20 0.18 0.19 0.19 0.14 0.09 0
MFQ_Rel_15 0.41 0.09 0.09 0.14 0.10 0.17 0Friend-Like
E2_FL_clean_MFQ_Purity_only <- E2_FL_clean %>% select(MFQ_Jud_13:MFQ_Jud_15, MFQ_Rel_13:MFQ_Rel_15)
psych::alpha(E2_FL_clean_MFQ_Purity_only)
Reliability analysis
Call: psych::alpha(x = E2_FL_clean_MFQ_Purity_only)
lower alpha upper 95% confidence boundaries
0.83 0.85 0.88
Reliability if an item is dropped:
Item statistics
Non missing response frequency for each item
1 2 3 4 5 6 miss
MFQ_Jud_13 0.13 0.18 0.18 0.21 0.15 0.15 0
MFQ_Jud_14 0.15 0.19 0.15 0.23 0.15 0.13 0
MFQ_Jud_15 0.28 0.13 0.12 0.21 0.13 0.13 0
MFQ_Rel_13 0.12 0.24 0.17 0.18 0.17 0.12 0
MFQ_Rel_14 0.19 0.26 0.19 0.15 0.13 0.08 0
MFQ_Rel_15 0.43 0.11 0.08 0.12 0.10 0.17 0OUS
Impartial Beneficence
Stranger-Like
E2_SL_clean_OUS_IB_only <- E2_SL_clean %>% select(OUS_IB1:OUS_IB5)
psych::alpha(E2_SL_clean_OUS_IB_only)
Reliability analysis
Call: psych::alpha(x = E2_SL_clean_OUS_IB_only)
lower alpha upper 95% confidence boundaries
0.68 0.72 0.77
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 0.21 0.19 0.16 0.19 0.16 0.06 0.03 0
OUS_IB2 0.16 0.23 0.15 0.19 0.17 0.06 0.03 0
OUS_IB3 0.06 0.11 0.16 0.16 0.22 0.17 0.12 0
OUS_IB4 0.10 0.20 0.17 0.14 0.21 0.11 0.06 0
OUS_IB5 0.16 0.21 0.17 0.15 0.19 0.09 0.03 0Friend-Like
E2_FL_clean_OUS_IB_only <- E2_FL_clean %>% select(OUS_IB1:OUS_IB5)
psych::alpha(E2_FL_clean_OUS_IB_only)
Reliability analysis
Call: psych::alpha(x = E2_FL_clean_OUS_IB_only)
lower alpha upper 95% confidence boundaries
0.74 0.78 0.82
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 0.18 0.19 0.21 0.15 0.04 0.03 0
OUS_IB2 0.18 0.18 0.16 0.22 0.19 0.07 0.02 0
OUS_IB3 0.07 0.12 0.17 0.13 0.21 0.19 0.11 0
OUS_IB4 0.10 0.15 0.21 0.15 0.22 0.13 0.04 0
OUS_IB5 0.16 0.19 0.19 0.19 0.17 0.06 0.04 0Instrumental Harm
Stranger-Like
E2_SL_clean_OUS_IH_only <- E2_SL_clean %>% select(OUS_IH1:OUS_IH4)
psych::alpha(E2_SL_clean_OUS_IH_only)
Reliability analysis
Call: psych::alpha(x = E2_SL_clean_OUS_IH_only)
lower alpha upper 95% confidence boundaries
0.72 0.76 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_IH1 0.15 0.22 0.18 0.24 0.14 0.05 0.02 0
OUS_IH2 0.30 0.26 0.16 0.14 0.11 0.01 0.02 0
OUS_IH3 0.19 0.19 0.12 0.20 0.16 0.10 0.04 0
OUS_IH4 0.14 0.17 0.15 0.28 0.20 0.05 0.01 0Friend-Like
E2_FL_clean_OUS_IH_only <- E2_FL_clean %>% select(OUS_IH1:OUS_IH4)
psych::alpha(E2_FL_clean_OUS_IH_only)
Reliability analysis
Call: psych::alpha(x = E2_FL_clean_OUS_IH_only)
lower alpha upper 95% confidence boundaries
0.75 0.79 0.82
Reliability if an item is dropped:
Item statistics
Non missing response frequency for each item
1 2 3 4 5 6 7 miss
OUS_IH1 0.19 0.18 0.15 0.26 0.18 0.02 0.03 0
OUS_IH2 0.29 0.22 0.15 0.17 0.13 0.04 0.01 0
OUS_IH3 0.20 0.15 0.11 0.22 0.17 0.09 0.05 0
OUS_IH4 0.16 0.15 0.11 0.27 0.23 0.05 0.03 0Oblig ~ Ind. Diff Plots
MAC
Family
print(oblig_mac_Fam_plot <- ggplot(data = E2_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 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("E2_oblig~MAC_plot.png")Saving 14 x 9 in imageGroup
print(oblig_mac_Group_plot <- ggplot(data = E2_all_long,
aes(x = MAC_Group_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 Group 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)))
Reciprocity
print(oblig_mac_Rec_plot <- ggplot(data = E2_all_long,
aes(x = MAC_Rec_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 Reciprocity 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)))
Heroism
print(oblig_mac_Hero_plot <- ggplot(data = E2_all_long,
aes(x = MAC_Hero_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 Heroism 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)))
Authority
print(oblig_mac_Auth_plot <- ggplot(data = E2_all_long,
aes(x = MAC_Def_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 Authority 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)))
Fairness
print(oblig_mac_Fair_plot <- ggplot(data = E2_all_long,
aes(x = MAC_Fair_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 Fairness 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)))
Property
print(oblig_mac_Prop_plot <- ggplot(data = E2_all_long,
aes(x = MAC_Prop_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 Property 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)))
MFT
Harm
print(oblig_mft_Harm_plot <- ggplot(data = E2_all_long,
aes(x = MFQ_Harm_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 Harm 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)))
Fairness
print(oblig_mft_Fairness_plot <- ggplot(data = E2_all_long,
aes(x = MFQ_Fairness_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 Fairness 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)))
Loyalty
print(oblig_mft_Loyalty_plot <- ggplot(data = E2_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 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("E2_oblig~MFT_plot.png")Saving 14 x 9 in imageAuthority
print(oblig_mft_Authority_plot <- ggplot(data = E2_all_long,
aes(x = MFQ_Authority_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 Authority 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)))
Purity
print(oblig_mft_Purity_plot <- ggplot(data = E2_all_long,
aes(x = MFQ_Purity_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 Purity 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)))
OUS
Impartial Beneficence
print(oblig_ous_ib_plot <- ggplot(data = E2_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("E2_oblig~OUS_plot.png")Saving 14 x 9 in imageInstrumental Harm
print(oblig_ous_ih_plot <- ggplot(data = E2_all_long,
aes(x = OUS_IH, 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 Instrumental Harm 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)))
Oblig ~ Ind. Diff Tests
See our pre-registration (INSERT LINK) and manuscript for our predictions about the relationship between individual differences and obligation judgments.
MAC
Family
Stranger-Like
No Choice
# distant pearson's r
cor_test(E2_SL_clean, "MAC_Fam_Combined", "NoChoice_CUZ_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
--------------------------------------------------------------------------------
MAC_Fam_Combined | NoChoice_CUZ_oblig | 0.31 | [0.21, 0.40] | 6.03 | < .001***
Observations: 354# close pearson's r
cor_test(E2_SL_clean, "MAC_Fam_Combined", "NoChoice_SIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
--------------------------------------------------------------------------------
MAC_Fam_Combined | NoChoice_SIB_oblig | 0.33 | [0.23, 0.42] | 6.48 | < .001***
Observations: 354# diff pearson's r
cor_test(E2_SL_clean, "MAC_Fam_Combined", "NoChoice_CUZminusSIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
--------------------------------------------------------------------------------------
MAC_Fam_Combined | NoChoice_CUZminusSIB_oblig | -0.03 | [-0.13, 0.08] | -0.47 | 0.638
Observations: 354Choice
# distant pearson's r
cor_test(E2_SL_clean, "MAC_Fam_Combined", "Choice_CUZ_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
------------------------------------------------------------------------------
MAC_Fam_Combined | Choice_CUZ_oblig | 0.37 | [0.28, 0.46] | 7.54 | < .001***
Observations: 354# close pearson's r
cor_test(E2_SL_clean, "MAC_Fam_Combined", "Choice_SIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
------------------------------------------------------------------------------
MAC_Fam_Combined | Choice_SIB_oblig | 0.43 | [0.34, 0.51] | 8.82 | < .001***
Observations: 354# diff pearson's r
cor_test(E2_SL_clean, "MAC_Fam_Combined", "Choice_CUZminusSIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
-----------------------------------------------------------------------------------------
MAC_Fam_Combined | Choice_CUZminusSIB_oblig | -0.20 | [-0.30, -0.09] | -3.77 | < .001***
Observations: 354Friend-Like
No Choice
# distant pearson's r
cor_test(E2_FL_clean, "MAC_Fam_Combined", "NoChoice_CUZ_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
--------------------------------------------------------------------------------
MAC_Fam_Combined | NoChoice_CUZ_oblig | 0.25 | [0.15, 0.35] | 4.85 | < .001***
Observations: 345# close pearson's r
cor_test(E2_FL_clean, "MAC_Fam_Combined", "NoChoice_SIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
--------------------------------------------------------------------------------
MAC_Fam_Combined | NoChoice_SIB_oblig | 0.32 | [0.23, 0.42] | 6.36 | < .001***
Observations: 345# diff pearson's r
cor_test(E2_FL_clean, "MAC_Fam_Combined", "NoChoice_CUZminusSIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
--------------------------------------------------------------------------------------
MAC_Fam_Combined | NoChoice_CUZminusSIB_oblig | -0.06 | [-0.16, 0.05] | -1.02 | 0.307
Observations: 345Choice
# distant pearson's r
cor_test(E2_FL_clean, "MAC_Fam_Combined", "Choice_CUZ_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
------------------------------------------------------------------------------
MAC_Fam_Combined | Choice_CUZ_oblig | 0.29 | [0.19, 0.38] | 5.64 | < .001***
Observations: 345# close pearson's r
cor_test(E2_FL_clean, "MAC_Fam_Combined", "Choice_SIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
------------------------------------------------------------------------------
MAC_Fam_Combined | Choice_SIB_oblig | 0.34 | [0.25, 0.43] | 6.78 | < .001***
Observations: 345# diff pearson's r
cor_test(E2_FL_clean, "MAC_Fam_Combined", "Choice_CUZminusSIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
---------------------------------------------------------------------------------------
MAC_Fam_Combined | Choice_CUZminusSIB_oblig | -0.17 | [-0.27, -0.06] | -3.11 | 0.002**
Observations: 345Group
Stranger-Like
No Choice
# distant pearson's r
cor_test(E2_SL_clean, "MAC_Group_Combined", "NoChoice_CUZ_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
----------------------------------------------------------------------------------
MAC_Group_Combined | NoChoice_CUZ_oblig | 0.22 | [0.12, 0.32] | 4.25 | < .001***
Observations: 354# close pearson's r
cor_test(E2_SL_clean, "MAC_Group_Combined", "NoChoice_SIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
----------------------------------------------------------------------------------
MAC_Group_Combined | NoChoice_SIB_oblig | 0.25 | [0.15, 0.34] | 4.77 | < .001***
Observations: 354# diff pearson's r
cor_test(E2_SL_clean, "MAC_Group_Combined", "NoChoice_CUZminusSIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
----------------------------------------------------------------------------------------
MAC_Group_Combined | NoChoice_CUZminusSIB_oblig | -0.03 | [-0.13, 0.08] | -0.52 | 0.602
Observations: 354Choice
# distant pearson's r
cor_test(E2_SL_clean, "MAC_Group_Combined", "Choice_CUZ_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
--------------------------------------------------------------------------------
MAC_Group_Combined | Choice_CUZ_oblig | 0.28 | [0.18, 0.37] | 5.45 | < .001***
Observations: 354# close pearson's r
cor_test(E2_SL_clean, "MAC_Group_Combined", "Choice_SIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
--------------------------------------------------------------------------------
MAC_Group_Combined | Choice_SIB_oblig | 0.26 | [0.16, 0.36] | 5.13 | < .001***
Observations: 354# diff pearson's r
cor_test(E2_SL_clean, "MAC_Group_Combined", "Choice_CUZminusSIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
--------------------------------------------------------------------------------------
MAC_Group_Combined | Choice_CUZminusSIB_oblig | -0.01 | [-0.12, 0.09] | -0.23 | 0.821
Observations: 354Friend-Like
No Choice
# distant pearson's r
cor_test(E2_FL_clean, "MAC_Group_Combined", "NoChoice_CUZ_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
----------------------------------------------------------------------------------
MAC_Group_Combined | NoChoice_CUZ_oblig | 0.32 | [0.22, 0.41] | 6.24 | < .001***
Observations: 345# close pearson's r
cor_test(E2_FL_clean, "MAC_Group_Combined", "NoChoice_SIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
----------------------------------------------------------------------------------
MAC_Group_Combined | NoChoice_SIB_oblig | 0.26 | [0.16, 0.36] | 4.98 | < .001***
Observations: 345# diff pearson's r
cor_test(E2_FL_clean, "MAC_Group_Combined", "NoChoice_CUZminusSIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
---------------------------------------------------------------------------------------
MAC_Group_Combined | NoChoice_CUZminusSIB_oblig | 0.08 | [-0.03, 0.18] | 1.48 | 0.140
Observations: 345Choice
# distant pearson's r
cor_test(E2_FL_clean, "MAC_Group_Combined", "Choice_CUZ_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
--------------------------------------------------------------------------------
MAC_Group_Combined | Choice_CUZ_oblig | 0.34 | [0.24, 0.43] | 6.68 | < .001***
Observations: 345# close pearson's r
cor_test(E2_FL_clean, "MAC_Group_Combined", "Choice_SIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
--------------------------------------------------------------------------------
MAC_Group_Combined | Choice_SIB_oblig | 0.34 | [0.24, 0.43] | 6.69 | < .001***
Observations: 345# diff pearson's r
cor_test(E2_FL_clean, "MAC_Group_Combined", "Choice_CUZminusSIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
------------------------------------------------------------------------------------------
MAC_Group_Combined | Choice_CUZminusSIB_oblig | -8.27e-03 | [-0.11, 0.10] | -0.15 | 0.878
Observations: 345Reciprocity
Stranger-Like
No Choice
# distant pearson's r
cor_test(E2_SL_clean, "MAC_Rec_Combined", "NoChoice_CUZ_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
--------------------------------------------------------------------------------
MAC_Rec_Combined | NoChoice_CUZ_oblig | 0.21 | [0.10, 0.30] | 3.94 | < .001***
Observations: 354# close pearson's r
cor_test(E2_SL_clean, "MAC_Rec_Combined", "NoChoice_SIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
--------------------------------------------------------------------------------
MAC_Rec_Combined | NoChoice_SIB_oblig | 0.30 | [0.20, 0.39] | 5.92 | < .001***
Observations: 354# diff pearson's r
cor_test(E2_SL_clean, "MAC_Rec_Combined", "NoChoice_CUZminusSIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
--------------------------------------------------------------------------------------
MAC_Rec_Combined | NoChoice_CUZminusSIB_oblig | -0.09 | [-0.19, 0.01] | -1.73 | 0.085
Observations: 354Choice
# distant pearson's r
cor_test(E2_SL_clean, "MAC_Rec_Combined", "Choice_CUZ_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
------------------------------------------------------------------------------
MAC_Rec_Combined | Choice_CUZ_oblig | 0.30 | [0.21, 0.40] | 5.98 | < .001***
Observations: 354# close pearson's r
cor_test(E2_SL_clean, "MAC_Rec_Combined", "Choice_SIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
------------------------------------------------------------------------------
MAC_Rec_Combined | Choice_SIB_oblig | 0.34 | [0.25, 0.43] | 6.85 | < .001***
Observations: 354# diff pearson's r
cor_test(E2_SL_clean, "MAC_Rec_Combined", "Choice_CUZminusSIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
---------------------------------------------------------------------------------------
MAC_Rec_Combined | Choice_CUZminusSIB_oblig | -0.15 | [-0.25, -0.05] | -2.87 | 0.004**
Observations: 354Friend-Like
No Choice
# distant pearson's r
cor_test(E2_FL_clean, "MAC_Rec_Combined", "NoChoice_CUZ_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
--------------------------------------------------------------------------------
MAC_Rec_Combined | NoChoice_CUZ_oblig | 0.25 | [0.15, 0.34] | 4.73 | < .001***
Observations: 345# close pearson's r
cor_test(E2_FL_clean, "MAC_Rec_Combined", "NoChoice_SIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
--------------------------------------------------------------------------------
MAC_Rec_Combined | NoChoice_SIB_oblig | 0.29 | [0.19, 0.38] | 5.51 | < .001***
Observations: 345# diff pearson's r
cor_test(E2_FL_clean, "MAC_Rec_Combined", "NoChoice_CUZminusSIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
--------------------------------------------------------------------------------------
MAC_Rec_Combined | NoChoice_CUZminusSIB_oblig | -0.02 | [-0.13, 0.08] | -0.40 | 0.690
Observations: 345Choice
# distant pearson's r
cor_test(E2_FL_clean, "MAC_Rec_Combined", "Choice_CUZ_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
------------------------------------------------------------------------------
MAC_Rec_Combined | Choice_CUZ_oblig | 0.35 | [0.26, 0.44] | 7.01 | < .001***
Observations: 345# close pearson's r
cor_test(E2_FL_clean, "MAC_Rec_Combined", "Choice_SIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
------------------------------------------------------------------------------
MAC_Rec_Combined | Choice_SIB_oblig | 0.37 | [0.27, 0.46] | 7.31 | < .001***
Observations: 345# diff pearson's r
cor_test(E2_FL_clean, "MAC_Rec_Combined", "Choice_CUZminusSIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
------------------------------------------------------------------------------------
MAC_Rec_Combined | Choice_CUZminusSIB_oblig | -0.05 | [-0.15, 0.06] | -0.85 | 0.398
Observations: 345Heroism
Stranger-Like
No Choice
# distant pearson's r
cor_test(E2_SL_clean, "MAC_Hero_Combined", "NoChoice_CUZ_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
-------------------------------------------------------------------------------
MAC_Hero_Combined | NoChoice_CUZ_oblig | 0.17 | [0.07, 0.27] | 3.28 | 0.001**
Observations: 354# close pearson's r
cor_test(E2_SL_clean, "MAC_Hero_Combined", "NoChoice_SIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
---------------------------------------------------------------------------------
MAC_Hero_Combined | NoChoice_SIB_oblig | 0.22 | [0.12, 0.32] | 4.22 | < .001***
Observations: 354# diff pearson's r
cor_test(E2_SL_clean, "MAC_Hero_Combined", "NoChoice_CUZminusSIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
---------------------------------------------------------------------------------------
MAC_Hero_Combined | NoChoice_CUZminusSIB_oblig | -0.05 | [-0.15, 0.06] | -0.88 | 0.381
Observations: 354Choice
# distant pearson's r
cor_test(E2_SL_clean, "MAC_Hero_Combined", "Choice_CUZ_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
-------------------------------------------------------------------------------
MAC_Hero_Combined | Choice_CUZ_oblig | 0.21 | [0.10, 0.30] | 3.95 | < .001***
Observations: 354# close pearson's r
cor_test(E2_SL_clean, "MAC_Hero_Combined", "Choice_SIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
-------------------------------------------------------------------------------
MAC_Hero_Combined | Choice_SIB_oblig | 0.24 | [0.13, 0.33] | 4.54 | < .001***
Observations: 354# diff pearson's r
cor_test(E2_SL_clean, "MAC_Hero_Combined", "Choice_CUZminusSIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
---------------------------------------------------------------------------------------
MAC_Hero_Combined | Choice_CUZminusSIB_oblig | -0.11 | [-0.21, 0.00] | -2.06 | 0.040*
Observations: 354Friend-Like
No Choice
# distant pearson's r
cor_test(E2_FL_clean, "MAC_Hero_Combined", "NoChoice_CUZ_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
---------------------------------------------------------------------------------
MAC_Hero_Combined | NoChoice_CUZ_oblig | 0.21 | [0.10, 0.31] | 3.93 | < .001***
Observations: 345# close pearson's r
cor_test(E2_FL_clean, "MAC_Hero_Combined", "NoChoice_SIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
---------------------------------------------------------------------------------
MAC_Hero_Combined | NoChoice_SIB_oblig | 0.21 | [0.11, 0.31] | 4.06 | < .001***
Observations: 345# diff pearson's r
cor_test(E2_FL_clean, "MAC_Hero_Combined", "NoChoice_CUZminusSIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
------------------------------------------------------------------------------------------
MAC_Hero_Combined | NoChoice_CUZminusSIB_oblig | 6.60e-03 | [-0.10, 0.11] | 0.12 | 0.903
Observations: 345Choice
# distant pearson's r
cor_test(E2_FL_clean, "MAC_Hero_Combined", "Choice_CUZ_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
-------------------------------------------------------------------------------
MAC_Hero_Combined | Choice_CUZ_oblig | 0.23 | [0.13, 0.33] | 4.35 | < .001***
Observations: 345# close pearson's r
cor_test(E2_FL_clean, "MAC_Hero_Combined", "Choice_SIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
-------------------------------------------------------------------------------
MAC_Hero_Combined | Choice_SIB_oblig | 0.24 | [0.14, 0.33] | 4.53 | < .001***
Observations: 345# diff pearson's r
cor_test(E2_FL_clean, "MAC_Hero_Combined", "Choice_CUZminusSIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
-------------------------------------------------------------------------------------
MAC_Hero_Combined | Choice_CUZminusSIB_oblig | -0.03 | [-0.14, 0.08] | -0.57 | 0.572
Observations: 345Fairness
Stranger-Like
No Choice
# distant pearson's r
cor_test(E2_SL_clean, "MAC_Fair_Combined", "NoChoice_CUZ_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
------------------------------------------------------------------------------
MAC_Fair_Combined | NoChoice_CUZ_oblig | 0.10 | [0.00, 0.21] | 1.97 | 0.050*
Observations: 354# close pearson's r
cor_test(E2_SL_clean, "MAC_Fair_Combined", "NoChoice_SIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
---------------------------------------------------------------------------------
MAC_Fair_Combined | NoChoice_SIB_oblig | 0.18 | [0.08, 0.28] | 3.52 | < .001***
Observations: 354# diff pearson's r
cor_test(E2_SL_clean, "MAC_Fair_Combined", "NoChoice_CUZminusSIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
---------------------------------------------------------------------------------------
MAC_Fair_Combined | NoChoice_CUZminusSIB_oblig | -0.08 | [-0.18, 0.03] | -1.42 | 0.156
Observations: 354Choice
# distant pearson's r
cor_test(E2_SL_clean, "MAC_Fair_Combined", "Choice_CUZ_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
-------------------------------------------------------------------------------
MAC_Fair_Combined | Choice_CUZ_oblig | 0.21 | [0.11, 0.31] | 4.02 | < .001***
Observations: 354# close pearson's r
cor_test(E2_SL_clean, "MAC_Fair_Combined", "Choice_SIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
-------------------------------------------------------------------------------
MAC_Fair_Combined | Choice_SIB_oblig | 0.20 | [0.10, 0.30] | 3.84 | < .001***
Observations: 354# diff pearson's r
cor_test(E2_SL_clean, "MAC_Fair_Combined", "Choice_CUZminusSIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
-------------------------------------------------------------------------------------
MAC_Fair_Combined | Choice_CUZminusSIB_oblig | -0.02 | [-0.12, 0.09] | -0.29 | 0.770
Observations: 354Friend-Like
No Choice
# distant pearson's r
cor_test(E2_FL_clean, "MAC_Fair_Combined", "NoChoice_CUZ_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
---------------------------------------------------------------------------------
MAC_Fair_Combined | NoChoice_CUZ_oblig | 0.18 | [0.08, 0.28] | 3.42 | < .001***
Observations: 345# close pearson's r
cor_test(E2_FL_clean, "MAC_Fair_Combined", "NoChoice_SIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
---------------------------------------------------------------------------------
MAC_Fair_Combined | NoChoice_SIB_oblig | 0.19 | [0.09, 0.29] | 3.60 | < .001***
Observations: 345# diff pearson's r
cor_test(E2_FL_clean, "MAC_Fair_Combined", "NoChoice_CUZminusSIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
------------------------------------------------------------------------------------------
MAC_Fair_Combined | NoChoice_CUZminusSIB_oblig | 2.92e-03 | [-0.10, 0.11] | 0.05 | 0.957
Observations: 345Choice
# distant pearson's r
cor_test(E2_FL_clean, "MAC_Fair_Combined", "Choice_CUZ_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
-------------------------------------------------------------------------------
MAC_Fair_Combined | Choice_CUZ_oblig | 0.25 | [0.15, 0.35] | 4.87 | < .001***
Observations: 345# close pearson's r
cor_test(E2_FL_clean, "MAC_Fair_Combined", "Choice_SIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
-------------------------------------------------------------------------------
MAC_Fair_Combined | Choice_SIB_oblig | 0.27 | [0.17, 0.36] | 5.17 | < .001***
Observations: 345# diff pearson's r
cor_test(E2_FL_clean, "MAC_Fair_Combined", "Choice_CUZminusSIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
-------------------------------------------------------------------------------------
MAC_Fair_Combined | Choice_CUZminusSIB_oblig | -0.05 | [-0.15, 0.06] | -0.90 | 0.368
Observations: 345Property
Stranger-Like
No Choice
# distant pearson's r
cor_test(E2_SL_clean, "MAC_Prop_Combined", "NoChoice_CUZ_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
-------------------------------------------------------------------------------
MAC_Prop_Combined | NoChoice_CUZ_oblig | 0.17 | [0.07, 0.27] | 3.25 | 0.001**
Observations: 354# close pearson's r
cor_test(E2_SL_clean, "MAC_Prop_Combined", "NoChoice_SIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
------------------------------------------------------------------------------
MAC_Prop_Combined | NoChoice_SIB_oblig | 0.12 | [0.01, 0.22] | 2.25 | 0.025*
Observations: 354# diff pearson's r
cor_test(E2_SL_clean, "MAC_Prop_Combined", "NoChoice_CUZminusSIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
--------------------------------------------------------------------------------------
MAC_Prop_Combined | NoChoice_CUZminusSIB_oblig | 0.04 | [-0.06, 0.15] | 0.82 | 0.411
Observations: 354Choice
# distant pearson's r
cor_test(E2_SL_clean, "MAC_Prop_Combined", "Choice_CUZ_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
-----------------------------------------------------------------------------
MAC_Prop_Combined | Choice_CUZ_oblig | 0.14 | [0.04, 0.24] | 2.70 | 0.007**
Observations: 354# close pearson's r
cor_test(E2_SL_clean, "MAC_Prop_Combined", "Choice_SIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
----------------------------------------------------------------------------
MAC_Prop_Combined | Choice_SIB_oblig | 0.12 | [0.02, 0.23] | 2.36 | 0.019*
Observations: 354# diff pearson's r
cor_test(E2_SL_clean, "MAC_Prop_Combined", "Choice_CUZminusSIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
------------------------------------------------------------------------------------
MAC_Prop_Combined | Choice_CUZminusSIB_oblig | 0.02 | [-0.09, 0.12] | 0.35 | 0.727
Observations: 354Friend-Like
No Choice
# distant pearson's r
cor_test(E2_FL_clean, "MAC_Prop_Combined", "NoChoice_CUZ_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
------------------------------------------------------------------------------
MAC_Prop_Combined | NoChoice_CUZ_oblig | 0.06 | [-0.05, 0.16] | 1.05 | 0.293
Observations: 345# close pearson's r
cor_test(E2_FL_clean, "MAC_Prop_Combined", "NoChoice_SIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
------------------------------------------------------------------------------
MAC_Prop_Combined | NoChoice_SIB_oblig | 0.09 | [-0.01, 0.20] | 1.74 | 0.083
Observations: 345# diff pearson's r
cor_test(E2_FL_clean, "MAC_Prop_Combined", "NoChoice_CUZminusSIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
---------------------------------------------------------------------------------------
MAC_Prop_Combined | NoChoice_CUZminusSIB_oblig | -0.03 | [-0.14, 0.07] | -0.61 | 0.544
Observations: 345Choice
# distant pearson's r
cor_test(E2_FL_clean, "MAC_Prop_Combined", "Choice_CUZ_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
----------------------------------------------------------------------------
MAC_Prop_Combined | Choice_CUZ_oblig | 0.12 | [0.01, 0.22] | 2.22 | 0.027*
Observations: 345# close pearson's r
cor_test(E2_FL_clean, "MAC_Prop_Combined", "Choice_SIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
----------------------------------------------------------------------------
MAC_Prop_Combined | Choice_SIB_oblig | 0.13 | [0.02, 0.23] | 2.37 | 0.018*
Observations: 345# diff pearson's r
cor_test(E2_FL_clean, "MAC_Prop_Combined", "Choice_CUZminusSIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
-------------------------------------------------------------------------------------
MAC_Prop_Combined | Choice_CUZminusSIB_oblig | -0.03 | [-0.13, 0.08] | -0.50 | 0.619
Observations: 345MFT
Harm
Stranger-Like
No Choice
# distant pearson's r
cor_test(E2_SL_clean, "MFQ_Harm_Combined", "NoChoice_CUZ_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
-------------------------------------------------------------------------------
MFQ_Harm_Combined | NoChoice_CUZ_oblig | 0.14 | [0.04, 0.24] | 2.71 | 0.007**
Observations: 354# close pearson's r
cor_test(E2_SL_clean, "MFQ_Harm_Combined", "NoChoice_SIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
---------------------------------------------------------------------------------
MFQ_Harm_Combined | NoChoice_SIB_oblig | 0.21 | [0.11, 0.31] | 4.00 | < .001***
Observations: 354# diff pearson's r
cor_test(E2_SL_clean, "MFQ_Harm_Combined", "NoChoice_CUZminusSIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
---------------------------------------------------------------------------------------
MFQ_Harm_Combined | NoChoice_CUZminusSIB_oblig | -0.06 | [-0.17, 0.04] | -1.19 | 0.235
Observations: 354Choice
# distant pearson's r
cor_test(E2_SL_clean, "MFQ_Harm_Combined", "Choice_CUZ_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
-------------------------------------------------------------------------------
MFQ_Harm_Combined | Choice_CUZ_oblig | 0.18 | [0.07, 0.28] | 3.36 | < .001***
Observations: 354# close pearson's r
cor_test(E2_SL_clean, "MFQ_Harm_Combined", "Choice_SIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
-------------------------------------------------------------------------------
MFQ_Harm_Combined | Choice_SIB_oblig | 0.18 | [0.08, 0.28] | 3.46 | < .001***
Observations: 354# diff pearson's r
cor_test(E2_SL_clean, "MFQ_Harm_Combined", "Choice_CUZminusSIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
-------------------------------------------------------------------------------------
MFQ_Harm_Combined | Choice_CUZminusSIB_oblig | -0.04 | [-0.15, 0.06] | -0.83 | 0.406
Observations: 354Friend-Like
No Choice
# distant pearson's r
cor_test(E2_FL_clean, "MFQ_Harm_Combined", "NoChoice_CUZ_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
------------------------------------------------------------------------------
MFQ_Harm_Combined | NoChoice_CUZ_oblig | 0.12 | [0.01, 0.22] | 2.19 | 0.029*
Observations: 345# close pearson's r
cor_test(E2_FL_clean, "MFQ_Harm_Combined", "NoChoice_SIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
------------------------------------------------------------------------------
MFQ_Harm_Combined | NoChoice_SIB_oblig | 0.14 | [0.03, 0.24] | 2.56 | 0.011*
Observations: 345# diff pearson's r
cor_test(E2_FL_clean, "MFQ_Harm_Combined", "NoChoice_CUZminusSIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
---------------------------------------------------------------------------------------
MFQ_Harm_Combined | NoChoice_CUZminusSIB_oblig | -0.01 | [-0.12, 0.09] | -0.22 | 0.826
Observations: 345Choice
# distant pearson's r
cor_test(E2_FL_clean, "MFQ_Harm_Combined", "Choice_CUZ_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
-----------------------------------------------------------------------------
MFQ_Harm_Combined | Choice_CUZ_oblig | 0.16 | [0.05, 0.26] | 2.95 | 0.003**
Observations: 345# close pearson's r
cor_test(E2_FL_clean, "MFQ_Harm_Combined", "Choice_SIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
-----------------------------------------------------------------------------
MFQ_Harm_Combined | Choice_SIB_oblig | 0.17 | [0.07, 0.28] | 3.29 | 0.001**
Observations: 345# diff pearson's r
cor_test(E2_FL_clean, "MFQ_Harm_Combined", "Choice_CUZminusSIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
-------------------------------------------------------------------------------------
MFQ_Harm_Combined | Choice_CUZminusSIB_oblig | -0.06 | [-0.16, 0.05] | -1.03 | 0.303
Observations: 345Fairness
Stranger-Like
No Choice
# distant pearson's r
cor_test(E2_SL_clean, "MFQ_Fairness_Combined", "NoChoice_CUZ_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
----------------------------------------------------------------------------------
MFQ_Fairness_Combined | NoChoice_CUZ_oblig | 0.09 | [-0.01, 0.19] | 1.71 | 0.088
Observations: 354# close pearson's r
cor_test(E2_SL_clean, "MFQ_Fairness_Combined", "NoChoice_SIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
-----------------------------------------------------------------------------------
MFQ_Fairness_Combined | NoChoice_SIB_oblig | 0.15 | [0.05, 0.25] | 2.94 | 0.003**
Observations: 354# diff pearson's r
cor_test(E2_SL_clean, "MFQ_Fairness_Combined", "NoChoice_CUZminusSIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
-------------------------------------------------------------------------------------------
MFQ_Fairness_Combined | NoChoice_CUZminusSIB_oblig | -0.06 | [-0.16, 0.04] | -1.14 | 0.255
Observations: 354Choice
# distant pearson's r
cor_test(E2_SL_clean, "MFQ_Fairness_Combined", "Choice_CUZ_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
--------------------------------------------------------------------------------
MFQ_Fairness_Combined | Choice_CUZ_oblig | 0.10 | [ 0.00, 0.20] | 1.89 | 0.060
Observations: 354# close pearson's r
cor_test(E2_SL_clean, "MFQ_Fairness_Combined", "Choice_SIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
--------------------------------------------------------------------------------
MFQ_Fairness_Combined | Choice_SIB_oblig | 0.11 | [0.01, 0.21] | 2.11 | 0.036*
Observations: 354# diff pearson's r
cor_test(E2_SL_clean, "MFQ_Fairness_Combined", "Choice_CUZminusSIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
-----------------------------------------------------------------------------------------
MFQ_Fairness_Combined | Choice_CUZminusSIB_oblig | -0.05 | [-0.15, 0.06] | -0.87 | 0.387
Observations: 354Friend-Like
No Choice
# distant pearson's r
cor_test(E2_FL_clean, "MFQ_Fairness_Combined", "NoChoice_CUZ_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
----------------------------------------------------------------------------------
MFQ_Fairness_Combined | NoChoice_CUZ_oblig | 0.11 | [0.00, 0.21] | 1.99 | 0.047*
Observations: 345# close pearson's r
cor_test(E2_FL_clean, "MFQ_Fairness_Combined", "NoChoice_SIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
-----------------------------------------------------------------------------------
MFQ_Fairness_Combined | NoChoice_SIB_oblig | 0.14 | [0.04, 0.24] | 2.63 | 0.009**
Observations: 345# diff pearson's r
cor_test(E2_FL_clean, "MFQ_Fairness_Combined", "NoChoice_CUZminusSIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
-------------------------------------------------------------------------------------------
MFQ_Fairness_Combined | NoChoice_CUZminusSIB_oblig | -0.03 | [-0.13, 0.08] | -0.49 | 0.623
Observations: 345Choice
# distant pearson's r
cor_test(E2_FL_clean, "MFQ_Fairness_Combined", "Choice_CUZ_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
---------------------------------------------------------------------------------
MFQ_Fairness_Combined | Choice_CUZ_oblig | 0.18 | [0.07, 0.28] | 3.31 | 0.001**
Observations: 345# close pearson's r
cor_test(E2_FL_clean, "MFQ_Fairness_Combined", "Choice_SIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
---------------------------------------------------------------------------------
MFQ_Fairness_Combined | Choice_SIB_oblig | 0.17 | [0.07, 0.27] | 3.21 | 0.001**
Observations: 345# diff pearson's r
cor_test(E2_FL_clean, "MFQ_Fairness_Combined", "Choice_CUZminusSIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
----------------------------------------------------------------------------------------
MFQ_Fairness_Combined | Choice_CUZminusSIB_oblig | 0.01 | [-0.09, 0.12] | 0.23 | 0.818
Observations: 345Loyalty
Stranger-Like
No Choice
# distant pearson's r
cor_test(E2_SL_clean, "MFQ_Loyalty_Combined", "NoChoice_CUZ_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
------------------------------------------------------------------------------------
MFQ_Loyalty_Combined | NoChoice_CUZ_oblig | 0.21 | [0.10, 0.30] | 3.95 | < .001***
Observations: 354# close pearson's r
cor_test(E2_SL_clean, "MFQ_Loyalty_Combined", "NoChoice_SIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
------------------------------------------------------------------------------------
MFQ_Loyalty_Combined | NoChoice_SIB_oblig | 0.18 | [0.08, 0.28] | 3.50 | < .001***
Observations: 354# diff pearson's r
cor_test(E2_SL_clean, "MFQ_Loyalty_Combined", "NoChoice_CUZminusSIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
-----------------------------------------------------------------------------------------
MFQ_Loyalty_Combined | NoChoice_CUZminusSIB_oblig | 0.02 | [-0.09, 0.12] | 0.31 | 0.756
Observations: 354Choice
# distant pearson's r
cor_test(E2_SL_clean, "MFQ_Loyalty_Combined", "Choice_CUZ_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
----------------------------------------------------------------------------------
MFQ_Loyalty_Combined | Choice_CUZ_oblig | 0.27 | [0.17, 0.36] | 5.23 | < .001***
Observations: 354# close pearson's r
cor_test(E2_SL_clean, "MFQ_Loyalty_Combined", "Choice_SIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
----------------------------------------------------------------------------------
MFQ_Loyalty_Combined | Choice_SIB_oblig | 0.28 | [0.18, 0.37] | 5.46 | < .001***
Observations: 354# diff pearson's r
cor_test(E2_SL_clean, "MFQ_Loyalty_Combined", "Choice_CUZminusSIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
----------------------------------------------------------------------------------------
MFQ_Loyalty_Combined | Choice_CUZminusSIB_oblig | -0.07 | [-0.18, 0.03] | -1.40 | 0.162
Observations: 354Friend-Like
No Choice
# distant pearson's r
cor_test(E2_FL_clean, "MFQ_Loyalty_Combined", "NoChoice_CUZ_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
---------------------------------------------------------------------------------
MFQ_Loyalty_Combined | NoChoice_CUZ_oblig | 0.14 | [0.03, 0.24] | 2.54 | 0.012*
Observations: 345# close pearson's r
cor_test(E2_FL_clean, "MFQ_Loyalty_Combined", "NoChoice_SIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
------------------------------------------------------------------------------------
MFQ_Loyalty_Combined | NoChoice_SIB_oblig | 0.19 | [0.08, 0.29] | 3.50 | < .001***
Observations: 345# diff pearson's r
cor_test(E2_FL_clean, "MFQ_Loyalty_Combined", "NoChoice_CUZminusSIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
------------------------------------------------------------------------------------------
MFQ_Loyalty_Combined | NoChoice_CUZminusSIB_oblig | -0.04 | [-0.15, 0.06] | -0.76 | 0.447
Observations: 345Choice
# distant pearson's r
cor_test(E2_FL_clean, "MFQ_Loyalty_Combined", "Choice_CUZ_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
--------------------------------------------------------------------------------
MFQ_Loyalty_Combined | Choice_CUZ_oblig | 0.15 | [0.05, 0.25] | 2.83 | 0.005**
Observations: 345# close pearson's r
cor_test(E2_FL_clean, "MFQ_Loyalty_Combined", "Choice_SIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
--------------------------------------------------------------------------------
MFQ_Loyalty_Combined | Choice_SIB_oblig | 0.17 | [0.06, 0.27] | 3.17 | 0.002**
Observations: 345# diff pearson's r
cor_test(E2_FL_clean, "MFQ_Loyalty_Combined", "Choice_CUZminusSIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
----------------------------------------------------------------------------------------
MFQ_Loyalty_Combined | Choice_CUZminusSIB_oblig | -0.06 | [-0.16, 0.05] | -1.06 | 0.288
Observations: 345Purity
Stranger-Like
No Choice
# distant pearson's r
cor_test(E2_SL_clean, "MFQ_Purity_Combined", "NoChoice_CUZ_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
--------------------------------------------------------------------------------
MFQ_Purity_Combined | NoChoice_CUZ_oblig | 0.11 | [0.00, 0.21] | 2.06 | 0.040*
Observations: 354# close pearson's r
cor_test(E2_SL_clean, "MFQ_Purity_Combined", "NoChoice_SIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
---------------------------------------------------------------------------------
MFQ_Purity_Combined | NoChoice_SIB_oblig | 0.15 | [0.04, 0.25] | 2.76 | 0.006**
Observations: 354# diff pearson's r
cor_test(E2_SL_clean, "MFQ_Purity_Combined", "NoChoice_CUZminusSIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
-----------------------------------------------------------------------------------------
MFQ_Purity_Combined | NoChoice_CUZminusSIB_oblig | -0.04 | [-0.14, 0.07] | -0.67 | 0.505
Observations: 354Choice
# distant pearson's r
cor_test(E2_SL_clean, "MFQ_Purity_Combined", "Choice_CUZ_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
------------------------------------------------------------------------------
MFQ_Purity_Combined | Choice_CUZ_oblig | 0.12 | [0.01, 0.22] | 2.25 | 0.025*
Observations: 354# close pearson's r
cor_test(E2_SL_clean, "MFQ_Purity_Combined", "Choice_SIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
------------------------------------------------------------------------------
MFQ_Purity_Combined | Choice_SIB_oblig | 0.13 | [0.03, 0.23] | 2.46 | 0.014*
Observations: 354# diff pearson's r
cor_test(E2_SL_clean, "MFQ_Purity_Combined", "Choice_CUZminusSIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
---------------------------------------------------------------------------------------
MFQ_Purity_Combined | Choice_CUZminusSIB_oblig | -0.05 | [-0.15, 0.06] | -0.91 | 0.364
Observations: 354Friend-Like
No Choice
# distant pearson's r
cor_test(E2_FL_clean, "MFQ_Purity_Combined", "NoChoice_CUZ_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
--------------------------------------------------------------------------------
MFQ_Purity_Combined | NoChoice_CUZ_oblig | 0.02 | [-0.08, 0.13] | 0.44 | 0.657
Observations: 345# close pearson's r
cor_test(E2_FL_clean, "MFQ_Purity_Combined", "NoChoice_SIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
--------------------------------------------------------------------------------
MFQ_Purity_Combined | NoChoice_SIB_oblig | 0.10 | [-0.01, 0.20] | 1.77 | 0.077
Observations: 345# diff pearson's r
cor_test(E2_FL_clean, "MFQ_Purity_Combined", "NoChoice_CUZminusSIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
-----------------------------------------------------------------------------------------
MFQ_Purity_Combined | NoChoice_CUZminusSIB_oblig | -0.07 | [-0.17, 0.04] | -1.28 | 0.200
Observations: 345Choice
# distant pearson's r
cor_test(E2_FL_clean, "MFQ_Purity_Combined", "Choice_CUZ_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
-----------------------------------------------------------------------------------
MFQ_Purity_Combined | Choice_CUZ_oblig | -9.45e-03 | [-0.11, 0.10] | -0.17 | 0.861
Observations: 345# close pearson's r
cor_test(E2_FL_clean, "MFQ_Purity_Combined", "Choice_SIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
-----------------------------------------------------------------------------------
MFQ_Purity_Combined | Choice_SIB_oblig | -4.09e-03 | [-0.11, 0.10] | -0.08 | 0.940
Observations: 345# diff pearson's r
cor_test(E2_FL_clean, "MFQ_Purity_Combined", "Choice_CUZminusSIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
---------------------------------------------------------------------------------------
MFQ_Purity_Combined | Choice_CUZminusSIB_oblig | -0.02 | [-0.12, 0.09] | -0.30 | 0.765
Observations: 345OUS
Impartial Beneficence
Stranger-Like
No Choice
# distant pearson's r
cor_test(E2_SL_clean, "OUS_IB", "NoChoice_CUZ_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
-----------------------------------------------------------------------
OUS_IB | NoChoice_CUZ_oblig | 0.13 | [0.02, 0.23] | 2.42 | 0.016*
Observations: 354# close pearson's r
cor_test(E2_SL_clean, "OUS_IB", "NoChoice_SIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
-----------------------------------------------------------------------
OUS_IB | NoChoice_SIB_oblig | 0.13 | [0.03, 0.23] | 2.46 | 0.014*
Observations: 354# diff pearson's r
cor_test(E2_SL_clean, "OUS_IB", "NoChoice_CUZminusSIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
------------------------------------------------------------------------------------
OUS_IB | NoChoice_CUZminusSIB_oblig | -4.55e-03 | [-0.11, 0.10] | -0.09 | 0.932
Observations: 354Choice
# distant pearson's r
cor_test(E2_SL_clean, "OUS_IB", "Choice_CUZ_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
------------------------------------------------------------------------
OUS_IB | Choice_CUZ_oblig | 0.19 | [0.09, 0.29] | 3.59 | < .001***
Observations: 354# close pearson's r
cor_test(E2_SL_clean, "OUS_IB", "Choice_SIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
----------------------------------------------------------------------
OUS_IB | Choice_SIB_oblig | 0.16 | [0.05, 0.26] | 2.95 | 0.003**
Observations: 354# diff pearson's r
cor_test(E2_SL_clean, "OUS_IB", "Choice_CUZminusSIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
-----------------------------------------------------------------------------
OUS_IB | Choice_CUZminusSIB_oblig | 0.05 | [-0.06, 0.15] | 0.88 | 0.381
Observations: 354Friend-Like
No Choice
# distant pearson's r
cor_test(E2_FL_clean, "OUS_IB", "NoChoice_CUZ_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
-----------------------------------------------------------------------
OUS_IB | NoChoice_CUZ_oblig | 0.12 | [0.01, 0.22] | 2.18 | 0.030*
Observations: 345# close pearson's r
cor_test(E2_FL_clean, "OUS_IB", "NoChoice_SIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
-----------------------------------------------------------------------
OUS_IB | NoChoice_SIB_oblig | 0.06 | [-0.04, 0.17] | 1.17 | 0.243
Observations: 345# diff pearson's r
cor_test(E2_FL_clean, "OUS_IB", "NoChoice_CUZminusSIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
-------------------------------------------------------------------------------
OUS_IB | NoChoice_CUZminusSIB_oblig | 0.06 | [-0.04, 0.17] | 1.13 | 0.258
Observations: 345Choice
# distant pearson's r
cor_test(E2_FL_clean, "OUS_IB", "Choice_CUZ_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
---------------------------------------------------------------------
OUS_IB | Choice_CUZ_oblig | 0.12 | [0.01, 0.22] | 2.15 | 0.032*
Observations: 345# close pearson's r
cor_test(E2_FL_clean, "OUS_IB", "Choice_SIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
---------------------------------------------------------------------
OUS_IB | Choice_SIB_oblig | 0.11 | [0.00, 0.21] | 2.00 | 0.046*
Observations: 345# diff pearson's r
cor_test(E2_FL_clean, "OUS_IB", "Choice_CUZminusSIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
-----------------------------------------------------------------------------
OUS_IB | Choice_CUZminusSIB_oblig | 0.02 | [-0.08, 0.13] | 0.41 | 0.683
Observations: 345Instrumental Harm
Stranger-Like
No Choice
# distant pearson's r
cor_test(E2_SL_clean, "OUS_IH", "NoChoice_CUZ_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
------------------------------------------------------------------------
OUS_IH | NoChoice_CUZ_oblig | -0.01 | [-0.12, 0.09] | -0.21 | 0.831
Observations: 354# close pearson's r
cor_test(E2_SL_clean, "OUS_IH", "NoChoice_SIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
------------------------------------------------------------------------
OUS_IH | NoChoice_SIB_oblig | -0.06 | [-0.16, 0.05] | -1.04 | 0.300
Observations: 354# diff pearson's r
cor_test(E2_SL_clean, "OUS_IH", "NoChoice_CUZminusSIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
-------------------------------------------------------------------------------
OUS_IH | NoChoice_CUZminusSIB_oblig | 0.04 | [-0.06, 0.14] | 0.76 | 0.449
Observations: 354Choice
# distant pearson's r
cor_test(E2_SL_clean, "OUS_IH", "Choice_CUZ_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
---------------------------------------------------------------------
OUS_IH | Choice_CUZ_oblig | 0.02 | [-0.09, 0.12] | 0.34 | 0.736
Observations: 354# close pearson's r
cor_test(E2_SL_clean, "OUS_IH", "Choice_SIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
-------------------------------------------------------------------------
OUS_IH | Choice_SIB_oblig | 1.76e-03 | [-0.10, 0.11] | 0.03 | 0.974
Observations: 354# diff pearson's r
cor_test(E2_SL_clean, "OUS_IH", "Choice_CUZminusSIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
-----------------------------------------------------------------------------
OUS_IH | Choice_CUZminusSIB_oblig | 0.04 | [-0.07, 0.14] | 0.70 | 0.487
Observations: 354Friend-Like
No Choice
# distant pearson's r
cor_test(E2_FL_clean, "OUS_IH", "NoChoice_CUZ_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
-----------------------------------------------------------------------
OUS_IH | NoChoice_CUZ_oblig | 0.06 | [-0.04, 0.17] | 1.16 | 0.245
Observations: 345# close pearson's r
cor_test(E2_FL_clean, "OUS_IH", "NoChoice_SIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
---------------------------------------------------------------------------
OUS_IH | NoChoice_SIB_oblig | 2.63e-03 | [-0.10, 0.11] | 0.05 | 0.961
Observations: 345# diff pearson's r
cor_test(E2_FL_clean, "OUS_IH", "NoChoice_CUZminusSIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
-------------------------------------------------------------------------------
OUS_IH | NoChoice_CUZminusSIB_oblig | 0.06 | [-0.04, 0.17] | 1.18 | 0.238
Observations: 345Choice
# distant pearson's r
cor_test(E2_FL_clean, "OUS_IH", "Choice_CUZ_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
---------------------------------------------------------------------
OUS_IH | Choice_CUZ_oblig | 0.02 | [-0.08, 0.13] | 0.43 | 0.667
Observations: 345# close pearson's r
cor_test(E2_FL_clean, "OUS_IH", "Choice_SIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
-------------------------------------------------------------------------
OUS_IH | Choice_SIB_oblig | 8.16e-03 | [-0.10, 0.11] | 0.15 | 0.880
Observations: 345# diff pearson's r
cor_test(E2_FL_clean, "OUS_IH", "Choice_CUZminusSIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
-----------------------------------------------------------------------------
OUS_IH | Choice_CUZminusSIB_oblig | 0.05 | [-0.06, 0.15] | 0.84 | 0.400
Observations: 345Oblig ~ MAC Family Values vs MFT Ingroup Loyalty Tests
Stranger-Like
No Choice
# correlation values are taken from the above 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 = .31, r.jh = .21, r.kh = .62, 354, 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 = 0.31 and r.jh = 0.21
Difference: r.jk - r.jh = 0.1
Related correlation: r.kh = 0.62
Group size: n = 354
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 = 2.2437, p-value = 0.0249
Null hypothesis rejected# close
cocor.dep.groups.overlap(r.jk = .33, r.jh = .18, r.kh = .62, 354, 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 = 0.33 and r.jh = 0.18
Difference: r.jk - r.jh = 0.15
Related correlation: r.kh = 0.62
Group size: n = 354
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 = 3.3647, p-value = 0.0008
Null hypothesis rejected# difference
cocor.dep.groups.overlap(r.jk = -.03, r.jh = .02, r.kh = .62, 354, 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 = -0.03 and r.jh = 0.02
Difference: r.jk - r.jh = -0.05
Related correlation: r.kh = 0.62
Group size: n = 354
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 = -1.0748, p-value = 0.2825
Null hypothesis retainedChoice
# 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 = .37, r.jh = .27, r.kh = .62, 354, 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 = 0.37 and r.jh = 0.27
Difference: r.jk - r.jh = 0.1
Related correlation: r.kh = 0.62
Group size: n = 354
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 = 2.2964, p-value = 0.0217
Null hypothesis rejected# close
cocor.dep.groups.overlap(r.jk = .43, r.jh = .28, r.kh = .62, 354, 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 = 0.43 and r.jh = 0.28
Difference: r.jk - r.jh = 0.15
Related correlation: r.kh = 0.62
Group size: n = 354
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 = 3.5083, p-value = 0.0005
Null hypothesis rejected# difference
cocor.dep.groups.overlap(r.jk = -.20, r.jh = -.07, r.kh = .62, 354, 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 = -0.2 and r.jh = -0.07
Difference: r.jk - r.jh = -0.13
Related correlation: r.kh = 0.62
Group size: n = 354
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 = -2.8289, p-value = 0.0047
Null hypothesis rejectedFriend-Like
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 = .25, r.jh = .14, r.kh = .64, 345, 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 = 0.25 and r.jh = 0.14
Difference: r.jk - r.jh = 0.11
Related correlation: r.kh = 0.64
Group size: n = 345
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 = 2.4560, p-value = 0.0140
Null hypothesis rejected# close
cocor.dep.groups.overlap(r.jk = .33, r.jh = .19, r.kh = .64, 345, 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 = 0.33 and r.jh = 0.19
Difference: r.jk - r.jh = 0.14
Related correlation: r.kh = 0.64
Group size: n = 345
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 = 3.1879, p-value = 0.0014
Null hypothesis rejected# difference
cocor.dep.groups.overlap(r.jk = -.06, r.jh = -.04, r.kh = .64, 345, 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 = -0.06 and r.jh = -0.04
Difference: r.jk - r.jh = -0.02
Related correlation: r.kh = 0.64
Group size: n = 345
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 = -0.4365, p-value = 0.6624
Null hypothesis retainedChoice
# 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 = .29, r.jh = .15, r.kh = .64, 345, 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 = 0.29 and r.jh = 0.15
Difference: r.jk - r.jh = 0.14
Related correlation: r.kh = 0.64
Group size: n = 345
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 = 3.1488, p-value = 0.0016
Null hypothesis rejected# close
cocor.dep.groups.overlap(r.jk = .34, r.jh = .17, r.kh = .64, 345, 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 = 0.34 and r.jh = 0.17
Difference: r.jk - r.jh = 0.17
Related correlation: r.kh = 0.64
Group size: n = 345
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 = 3.8687, p-value = 0.0001
Null hypothesis rejected# difference
cocor.dep.groups.overlap(r.jk = -.17, r.jh = -.06, r.kh = .64, 345, 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 = -0.17 and r.jh = -0.06
Difference: r.jk - r.jh = -0.11
Related correlation: r.kh = 0.64
Group size: n = 345
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 = -2.4189, p-value = 0.0156
Null hypothesis rejectedOblig Diff ~ Other Pre-Outcome Diff Tests
Relate
Stranger-Like
No Choice
# diff pearson's r
cor_test(E2_SL_clean, "NoChoice_CUZminusSIB_relate", "NoChoice_CUZminusSIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
------------------------------------------------------------------------------------------------
NoChoice_CUZminusSIB_relate | NoChoice_CUZminusSIB_oblig | 0.10 | [ 0.00, 0.21] | 1.95 | 0.052
Observations: 354Choice
# diff pearson's r
cor_test(E2_SL_clean, "Choice_CUZminusSIB_relate", "Choice_CUZminusSIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
--------------------------------------------------------------------------------------------
Choice_CUZminusSIB_relate | Choice_CUZminusSIB_oblig | 0.10 | [ 0.00, 0.21] | 1.95 | 0.052
Observations: 354Friend-Like
No Choice
# diff pearson's r
cor_test(E2_FL_clean, "NoChoice_CUZminusSIB_relate", "NoChoice_CUZminusSIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
------------------------------------------------------------------------------------------------
NoChoice_CUZminusSIB_relate | NoChoice_CUZminusSIB_oblig | 0.02 | [-0.09, 0.12] | 0.32 | 0.747
Observations: 345Choice
# diff pearson's r
cor_test(E2_FL_clean, "Choice_CUZminusSIB_relate", "Choice_CUZminusSIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
--------------------------------------------------------------------------------------------
Choice_CUZminusSIB_relate | Choice_CUZminusSIB_oblig | 0.13 | [0.02, 0.23] | 2.42 | 0.016*
Observations: 345Close
Stranger-Like
No Choice
# diff pearson's r
cor_test(E2_SL_clean, "NoChoice_CUZminusSIB_close", "NoChoice_CUZminusSIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
--------------------------------------------------------------------------------------------------
NoChoice_CUZminusSIB_close | NoChoice_CUZminusSIB_oblig | 0.22 | [0.12, 0.32] | 4.21 | < .001***
Observations: 354Choice
# diff pearson's r
cor_test(E2_SL_clean, "Choice_CUZminusSIB_close", "Choice_CUZminusSIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
----------------------------------------------------------------------------------------------
Choice_CUZminusSIB_close | Choice_CUZminusSIB_oblig | 0.37 | [0.28, 0.46] | 7.48 | < .001***
Observations: 354Friend-Like
No Choice
# diff pearson's r
cor_test(E2_FL_clean, "NoChoice_CUZminusSIB_close", "NoChoice_CUZminusSIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
--------------------------------------------------------------------------------------------------
NoChoice_CUZminusSIB_close | NoChoice_CUZminusSIB_oblig | 0.25 | [0.15, 0.35] | 4.79 | < .001***
Observations: 345Choice
# diff pearson's r
cor_test(E2_FL_clean, "Choice_CUZminusSIB_close", "Choice_CUZminusSIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
----------------------------------------------------------------------------------------------
Choice_CUZminusSIB_close | Choice_CUZminusSIB_oblig | 0.60 | [0.53, 0.66] | 13.92 | < .001***
Observations: 345Prior Help
Stranger-Like
No Choice
# diff pearson's r
cor_test(E2_SL_clean, "NoChoice_CUZminusSIB_priorhelp", "NoChoice_CUZminusSIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
------------------------------------------------------------------------------------------------------
NoChoice_CUZminusSIB_priorhelp | NoChoice_CUZminusSIB_oblig | 0.21 | [0.10, 0.30] | 3.96 | < .001***
Observations: 354Choice
# diff pearson's r
cor_test(E2_SL_clean, "Choice_CUZminusSIB_priorhelp", "Choice_CUZminusSIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
--------------------------------------------------------------------------------------------------
Choice_CUZminusSIB_priorhelp | Choice_CUZminusSIB_oblig | 0.42 | [0.33, 0.50] | 8.72 | < .001***
Observations: 354Friend-Like
No Choice
# diff pearson's r
cor_test(E2_FL_clean, "NoChoice_CUZminusSIB_priorhelp", "NoChoice_CUZminusSIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
------------------------------------------------------------------------------------------------------
NoChoice_CUZminusSIB_priorhelp | NoChoice_CUZminusSIB_oblig | 0.26 | [0.16, 0.36] | 5.00 | < .001***
Observations: 345Choice
# diff pearson's r
cor_test(E2_FL_clean, "Choice_CUZminusSIB_priorhelp", "Choice_CUZminusSIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
--------------------------------------------------------------------------------------------------
Choice_CUZminusSIB_priorhelp | Choice_CUZminusSIB_oblig | 0.56 | [0.49, 0.63] | 12.67 | < .001***
Observations: 345Future Help
Stranger-Like
No Choice
# diff pearson's r
cor_test(E2_SL_clean, "NoChoice_CUZminusSIB_futurehelp", "NoChoice_CUZminusSIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
-------------------------------------------------------------------------------------------------------
NoChoice_CUZminusSIB_futurehelp | NoChoice_CUZminusSIB_oblig | 0.36 | [0.27, 0.45] | 7.32 | < .001***
Observations: 354Choice
# diff pearson's r
cor_test(E2_SL_clean, "Choice_CUZminusSIB_futurehelp", "Choice_CUZminusSIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
---------------------------------------------------------------------------------------------------
Choice_CUZminusSIB_futurehelp | Choice_CUZminusSIB_oblig | 0.51 | [0.43, 0.58] | 11.14 | < .001***
Observations: 354Friend-Like
No Choice
# diff pearson's r
cor_test(E2_FL_clean, "NoChoice_CUZminusSIB_futurehelp", "NoChoice_CUZminusSIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
-------------------------------------------------------------------------------------------------------
NoChoice_CUZminusSIB_futurehelp | NoChoice_CUZminusSIB_oblig | 0.35 | [0.25, 0.44] | 6.84 | < .001***
Observations: 345Choice
# diff pearson's r
cor_test(E2_FL_clean, "Choice_CUZminusSIB_futurehelp", "Choice_CUZminusSIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
---------------------------------------------------------------------------------------------------
Choice_CUZminusSIB_futurehelp | Choice_CUZminusSIB_oblig | 0.60 | [0.53, 0.66] | 13.86 | < .001***
Observations: 345Prior Interax
Stranger-Like
No Choice
# diff pearson's r
cor_test(E2_SL_clean, "NoChoice_CUZminusSIB_priorinteract", "NoChoice_CUZminusSIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
--------------------------------------------------------------------------------------------------------
NoChoice_CUZminusSIB_priorinteract | NoChoice_CUZminusSIB_oblig | 0.16 | [0.05, 0.26] | 2.96 | 0.003**
Observations: 354Choice
# diff pearson's r
cor_test(E2_SL_clean, "Choice_CUZminusSIB_priorinteract", "Choice_CUZminusSIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
------------------------------------------------------------------------------------------------------
Choice_CUZminusSIB_priorinteract | Choice_CUZminusSIB_oblig | 0.34 | [0.24, 0.43] | 6.75 | < .001***
Observations: 354Friend-Like
No Choice
# diff pearson's r
cor_test(E2_FL_clean, "NoChoice_CUZminusSIB_priorinteract", "NoChoice_CUZminusSIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
----------------------------------------------------------------------------------------------------------
NoChoice_CUZminusSIB_priorinteract | NoChoice_CUZminusSIB_oblig | 0.26 | [0.16, 0.35] | 4.92 | < .001***
Observations: 345Choice
# diff pearson's r
cor_test(E2_FL_clean, "Choice_CUZminusSIB_priorinteract", "Choice_CUZminusSIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
------------------------------------------------------------------------------------------------------
Choice_CUZminusSIB_priorinteract | Choice_CUZminusSIB_oblig | 0.45 | [0.36, 0.53] | 9.27 | < .001***
Observations: 345Future Interax
Stranger-Like
No Choice
# diff pearson's r
cor_test(E2_SL_clean, "NoChoice_CUZminusSIB_futureinteract", "NoChoice_CUZminusSIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
-----------------------------------------------------------------------------------------------------------
NoChoice_CUZminusSIB_futureinteract | NoChoice_CUZminusSIB_oblig | 0.19 | [0.09, 0.29] | 3.59 | < .001***
Observations: 354Choice
# diff pearson's r
cor_test(E2_SL_clean, "Choice_CUZminusSIB_futureinteract", "Choice_CUZminusSIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(352) | p
-------------------------------------------------------------------------------------------------------
Choice_CUZminusSIB_futureinteract | Choice_CUZminusSIB_oblig | 0.45 | [0.37, 0.53] | 9.52 | < .001***
Observations: 354Friend-Like
No Choice
# diff pearson's r
cor_test(E2_FL_clean, "NoChoice_CUZminusSIB_futureinteract", "NoChoice_CUZminusSIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
-----------------------------------------------------------------------------------------------------------
NoChoice_CUZminusSIB_futureinteract | NoChoice_CUZminusSIB_oblig | 0.19 | [0.09, 0.29] | 3.65 | < .001***
Observations: 345Choice
# diff pearson's r
cor_test(E2_FL_clean, "Choice_CUZminusSIB_futureinteract", "Choice_CUZminusSIB_oblig", method = "Pearson")Parameter1 | Parameter2 | r | 95% CI | t(343) | p
-------------------------------------------------------------------------------------------------------
Choice_CUZminusSIB_futureinteract | Choice_CUZminusSIB_oblig | 0.54 | [0.46, 0.61] | 11.78 | < .001***
Observations: 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GxfY2xlYW4kU0lCb0NVWl9TSUJfZnV0dXJlaW50ZXJhY3QpLzIgIyBjcmVhdGVzIHByZS1yZWcnZCBpbmRleA0KRTJfYWxsX2NsZWFuJENob2ljZV9TSUJfbW9yYWwgIDwtIEUyX2FsbF9jbGVhbiRTSUJvQ1VaX1NJQl9tb3JhbCAjIHNpbmdsZSBqdWRnbWVudCAocG9zdC1vdXRjb21lKQ0KDQoNCiMgRGlmZmVyZW5jZSBTY29yZXMNCiMgQ1VabWludXNTSUIgb2JsaWdhdGlvbiB3aXRoaW4gTm8gQ2hvaWNlIG9yIENob2ljZSBjb25kaXRpb25zIChmb3IgZGlmZiBzY29yZSBjb3JycyBhbmQgaW5kLiBkaWZmcyBhbmFseXNlcykNCkUyX2FsbF9jbGVhbiROb0Nob2ljZV9DVVptaW51c1NJQl9vYmxpZyA8LSBFMl9hbGxfY2xlYW4kTm9DaG9pY2VfQ1VaX29ibGlnIC0gRTJfYWxsX2NsZWFuJE5vQ2hvaWNlX1NJQl9vYmxpZw0KRTJfYWxsX2NsZWFuJE5vQ2hvaWNlX0NVWm1pbnVzU0lCX3JlbGF0ZSA8LSBFMl9hbGxfY2xlYW4kTm9DaG9pY2VfQ1VaX3JlbGF0ZSAtIEUyX2FsbF9jbGVhbiROb0Nob2ljZV9TSUJfcmVsYXRlDQpFMl9hbGxfY2xlYW4kTm9DaG9pY2VfQ1VabWludXNTSUJfY2xvc2UgPC0gRTJfYWxsX2NsZWFuJE5vQ2hvaWNlX0NVWl9jbG9zZSAtIEUyX2FsbF9jbGVhbiROb0Nob2ljZV9TSUJfY2xvc2UNCkUyX2FsbF9jbGVhbiROb0Nob2ljZV9DVVptaW51c1NJQl9wcmlvcmhlbHAgPC0gRTJfYWxsX2NsZWFuJE5vQ2hvaWNlX0NVWl9wcmlvcmhlbHAgLSBFMl9hbGxfY2xlYW4kTm9DaG9pY2VfU0lCX3ByaW9yaGVscA0KRTJfYWxsX2NsZWFuJE5vQ2hvaWNlX0NVWm1pbnVzU0lCX2Z1dHVyZWhlbHAgPC0gRTJfYWxsX2NsZWFuJE5vQ2hvaWNlX0NVWl9mdXR1cmVoZWxwIC0gRTJfYWxsX2NsZWFuJE5vQ2hvaWNlX1NJQl9mdXR1cmVoZWxwDQpFMl9hbGxfY2xlYW4kTm9DaG9pY2VfQ1VabWludXNTSUJfcHJpb3JpbnRlcmFjdCA8LSBFMl9hbGxfY2xlYW4kTm9DaG9pY2VfQ1VaX3ByaW9yaW50ZXJhY3QgLSBFMl9hbGxfY2xlYW4kTm9DaG9pY2VfU0lCX3ByaW9yaW50ZXJhY3QNCkUyX2FsbF9jbGVhbiROb0Nob2ljZV9DVVptaW51c1NJQl9mdXR1cmVpbnRlcmFjdCA8LSBFMl9hbGxfY2xlYW4kTm9DaG9pY2VfQ1VaX2Z1dHVyZWludGVyYWN0IC0gRTJfYWxsX2NsZWFuJE5vQ2hvaWNlX1NJQl9mdXR1cmVpbnRlcmFjdA0KRTJfYWxsX2NsZWFuJE5vQ2hvaWNlX0NVWm1pbnVzU0lCX21vcmFsIDwtIEUyX2FsbF9jbGVhbiROb0Nob2ljZV9DVVpfbW9yYWwgLSBFMl9hbGxfY2xlYW4kTm9DaG9pY2VfU0lCX21vcmFsDQoNCkUyX2FsbF9jbGVhbiRDaG9pY2VfQ1VabWludXNTSUJfb2JsaWcgPC0gRTJfYWxsX2NsZWFuJENob2ljZV9DVVpfb2JsaWcgLSBFMl9hbGxfY2xlYW4kQ2hvaWNlX1NJQl9vYmxpZw0KRTJfYWxsX2NsZWFuJENob2ljZV9DVVptaW51c1NJQl9yZWxhdGUgPC0gRTJfYWxsX2NsZWFuJENob2ljZV9DVVpfcmVsYXRlIC0gRTJfYWxsX2NsZWFuJENob2ljZV9TSUJfcmVsYXRlDQpFMl9hbGxfY2xlYW4kQ2hvaWNlX0NVWm1pbnVzU0lCX2Nsb3NlIDwtIEUyX2FsbF9jbGVhbiRDaG9pY2VfQ1VaX2Nsb3NlIC0gRTJfYWxsX2NsZWFuJENob2ljZV9TSUJfY2xvc2UNCkUyX2FsbF9jbGVhbiRDaG9pY2VfQ1VabWludXNTSUJfcHJpb3JoZWxwIDwtIEUyX2FsbF9jbGVhbiRDaG9pY2VfQ1VaX3ByaW9yaGVscCAtIEUyX2FsbF9jbGVhbiRDaG9pY2VfU0lCX3ByaW9yaGVscA0KRTJfYWxsX2NsZWFuJENob2ljZV9DVVptaW51c1NJQl9mdXR1cmVoZWxwIDwtIEUyX2FsbF9jbGVhbiRDaG9pY2VfQ1VaX2Z1dHVyZWhlbHAgLSBFMl9hbGxfY2xlYW4kQ2hvaWNlX1NJQl9mdXR1cmVoZWxwDQpFMl9hbGxfY2xlYW4kQ2hvaWNlX0NVWm1pbnVzU0lCX3ByaW9yaW50ZXJhY3QgPC0gRTJfYWxsX2NsZWFuJENob2ljZV9DVVpfcHJpb3JpbnRlcmFjdCAtIEUyX2FsbF9jbGVhbiRDaG9pY2VfU0lCX3ByaW9yaW50ZXJhY3QNCkUyX2FsbF9jbGVhbiRDaG9pY2VfQ1VabWludXNTSUJfZnV0dXJlaW50ZXJhY3QgPC0gRTJfYWxsX2NsZWFuJENob2ljZV9DVVpfZnV0dXJlaW50ZXJhY3QgLSBFMl9hbGxfY2xlYW4kQ2hvaWNlX1NJQl9mdXR1cmVpbnRlcmFjdA0KRTJfYWxsX2NsZWFuJENob2ljZV9DVVptaW51c1NJQl9tb3JhbCA8LSBFMl9hbGxfY2xlYW4kQ2hvaWNlX0NVWl9tb3JhbCAtIEUyX2FsbF9jbGVhbiRDaG9pY2VfU0lCX21vcmFsDQoNCg0KIyBJbmRpdmlkdWFsIERpZmZlcmVuY2UgTWVhc3VyZXMgKGZvciBpbmQuIGRpZmZzIGFuYWx5c2VzKQ0KDQojIE1BQyAoTW9yYWxpdHktYXMtQ29vcGVyYXRpb24gc2NhbGUpIGNvbXBvc2l0ZXMNCiMgZmlyc3QgbmVlZCB0byByZXZlcnNlIHNjb3JlIHByb3BlcnR5IGp1ZGdtZW50IHN1YnNjYWxlIHBlciBDdXJyeSBldCBhbC4gMjAxOQ0KRTJfYWxsX2NsZWFuJE1BQ19KdWRfMTlfciA8LSAoKDEwMiAtIChFMl9hbGxfY2xlYW4kTUFDX0p1ZF8xOSArMSkpIC0gMSkgDQpFMl9hbGxfY2xlYW4kTUFDX0p1ZF8yMF9yIDwtICgoMTAyIC0gKEUyX2FsbF9jbGVhbiRNQUNfSnVkXzIwICsxKSkgLSAxKQ0KRTJfYWxsX2NsZWFuJE1BQ19KdWRfMjFfciA8LSAoKDEwMiAtIChFMl9hbGxfY2xlYW4kTUFDX0p1ZF8yMSArMSkpIC0gMSkNCg0KRTJfYWxsX2NsZWFuJE1BQ19GYW1fQ29tYmluZWQgPC0gKChFMl9hbGxfY2xlYW4kTUFDX0p1ZF8xICsgRTJfYWxsX2NsZWFuJE1BQ19KdWRfMiArIEUyX2FsbF9jbGVhbiRNQUNfSnVkXzMgKw0KICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgRTJfYWxsX2NsZWFuJE1BQ19SZWxfMSArIEUyX2FsbF9jbGVhbiRNQUNfUmVsXzIgKyBFMl9hbGxfY2xlYW4kTUFDX1JlbF8zKS82KQ0KRTJfYWxsX2NsZWFuJE1BQ19GYW1fSnVkIDwtICgoRTJfYWxsX2NsZWFuJE1BQ19KdWRfMSArIEUyX2FsbF9jbGVhbiRNQUNfSnVkXzIgKyBFMl9hbGxfY2xlYW4kTUFDX0p1ZF8zKS8zKQ0KRTJfYWxsX2NsZWFuJE1BQ19GYW1fUmVsIDwtICgoRTJfYWxsX2NsZWFuJE1BQ19SZWxfMSArIEUyX2FsbF9jbGVhbiRNQUNfUmVsXzIgKyBFMl9hbGxfY2xlYW4kTUFDX1JlbF8zKS8zKQ0KDQpFMl9hbGxfY2xlYW4kTUFDX0dyb3VwX0NvbWJpbmVkIDwtICgoRTJfYWxsX2NsZWFuJE1BQ19KdWRfNCArIEUyX2FsbF9jbGVhbiRNQUNfSnVkXzUgKyBFMl9hbGxfY2xlYW4kTUFDX0p1ZF82ICsNCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIEUyX2FsbF9jbGVhbiRNQUNfUmVsXzQgKyBFMl9hbGxfY2xlYW4kTUFDX1JlbF81ICsgRTJfYWxsX2NsZWFuJE1BQ19SZWxfNikvNikNCkUyX2FsbF9jbGVhbiRNQUNfR3JvdXBfSnVkIDwtICgoRTJfYWxsX2NsZWFuJE1BQ19KdWRfNCArIEUyX2FsbF9jbGVhbiRNQUNfSnVkXzUgKyBFMl9hbGxfY2xlYW4kTUFDX0p1ZF82KS8zKQ0KRTJfYWxsX2NsZWFuJE1BQ19Hcm91cF9SZWwgPC0gKChFMl9hbGxfY2xlYW4kTUFDX1JlbF80ICsgRTJfYWxsX2NsZWFuJE1BQ19SZWxfNSArIEUyX2FsbF9jbGVhbiRNQUNfUmVsXzYpLzMpDQoNCkUyX2FsbF9jbGVhbiRNQUNfUmVjX0NvbWJpbmVkIDwtICgoRTJfYWxsX2NsZWFuJE1BQ19KdWRfNyArIEUyX2FsbF9jbGVhbiRNQUNfSnVkXzggKyBFMl9hbGxfY2xlYW4kTUFDX0p1ZF85ICsNCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIEUyX2FsbF9jbGVhbiRNQUNfUmVsXzcgKyBFMl9hbGxfY2xlYW4kTUFDX1JlbF84ICsgRTJfYWxsX2NsZWFuJE1BQ19SZWxfOSkvNikNCkUyX2FsbF9jbGVhbiRNQUNfUmVjX0p1ZCA8LSAoKEUyX2FsbF9jbGVhbiRNQUNfSnVkXzcgKyBFMl9hbGxfY2xlYW4kTUFDX0p1ZF84ICsgRTJfYWxsX2NsZWFuJE1BQ19KdWRfOSkvMykNCkUyX2FsbF9jbGVhbiRNQUNfUmVjX1JlbCA8LSAoKEUyX2FsbF9jbGVhbiRNQUNfUmVsXzcgKyBFMl9hbGxfY2xlYW4kTUFDX1JlbF84ICsgRTJfYWxsX2NsZWFuJE1BQ19SZWxfOSkvMykNCg0KRTJfYWxsX2NsZWFuJE1BQ19IZXJvX0NvbWJpbmVkIDwtICgoRTJfYWxsX2NsZWFuJE1BQ19KdWRfMTAgKyBFMl9hbGxfY2xlYW4kTUFDX0p1ZF8xMSArIEUyX2FsbF9jbGVhbiRNQUNfSnVkXzEyICsNCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIEUyX2FsbF9jbGVhbiRNQUNfUmVsXzEwICsgRTJfYWxsX2NsZWFuJE1BQ19SZWxfMTEgKyBFMl9hbGxfY2xlYW4kTUFDX1JlbF8xMikvNikNCkUyX2FsbF9jbGVhbiRNQUNfSGVyb19KdWQgPC0gKChFMl9hbGxfY2xlYW4kTUFDX0p1ZF8xMCArIEUyX2FsbF9jbGVhbiRNQUNfSnVkXzExICsgRTJfYWxsX2NsZWFuJE1BQ19KdWRfMTIpLzMpDQpFMl9hbGxfY2xlYW4kTUFDX0hlcm9fUmVsIDwtICgoRTJfYWxsX2NsZWFuJE1BQ19SZWxfMTAgKyBFMl9hbGxfY2xlYW4kTUFDX1JlbF8xMSArIEUyX2FsbF9jbGVhbiRNQUNfUmVsXzEyKS8zKQ0KDQpFMl9hbGxfY2xlYW4kTUFDX0RlZl9Db21iaW5lZCA8LSAoKEUyX2FsbF9jbGVhbiRNQUNfSnVkXzEzICsgRTJfYWxsX2NsZWFuJE1BQ19KdWRfMTQgKyBFMl9hbGxfY2xlYW4kTUFDX0p1ZF8xNSArDQogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICBFMl9hbGxfY2xlYW4kTUFDX1JlbF8xMyArIEUyX2FsbF9jbGVhbiRNQUNfUmVsXzE0ICsgRTJfYWxsX2NsZWFuJE1BQ19SZWxfMTUpLzYpDQpFMl9hbGxfY2xlYW4kTUFDX0RlZl9KdWQgPC0gKChFMl9hbGxfY2xlYW4kTUFDX0p1ZF8xMyArIEUyX2FsbF9jbGVhbiRNQUNfSnVkXzE0ICsgRTJfYWxsX2NsZWFuJE1BQ19KdWRfMTUpLzMpDQpFMl9hbGxfY2xlYW4kTUFDX0RlZl9SZWwgPC0gKChFMl9hbGxfY2xlYW4kTUFDX1JlbF8xMyArIEUyX2FsbF9jbGVhbiRNQUNfUmVsXzE0ICsgRTJfYWxsX2NsZWFuJE1BQ19SZWxfMTUpLzMpDQoNCkUyX2FsbF9jbGVhbiRNQUNfRmFpcl9Db21iaW5lZCA8LSAoKEUyX2FsbF9jbGVhbiRNQUNfSnVkXzE2ICsgRTJfYWxsX2NsZWFuJE1BQ19KdWRfMTcgKyBFMl9hbGxfY2xlYW4kTUFDX0p1ZF8xOCArDQogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICBFMl9hbGxfY2xlYW4kTUFDX1JlbF8xNiArIEUyX2FsbF9jbGVhbiRNQUNfUmVsXzE3ICsgRTJfYWxsX2NsZWFuJE1BQ19SZWxfMTgpLzYpDQpFMl9hbGxfY2xlYW4kTUFDX0ZhaXJfSnVkIDwtICgoRTJfYWxsX2NsZWFuJE1BQ19KdWRfMTYgKyBFMl9hbGxfY2xlYW4kTUFDX0p1ZF8xNyArIEUyX2FsbF9jbGVhbiRNQUNfSnVkXzE4KS8zKQ0KRTJfYWxsX2NsZWFuJE1BQ19GYWlyX1JlbCA8LSAoKEUyX2FsbF9jbGVhbiRNQUNfUmVsXzE2ICsgRTJfYWxsX2NsZWFuJE1BQ19SZWxfMTcgKyBFMl9hbGxfY2xlYW4kTUFDX1JlbF8xOCkvMykNCg0KRTJfYWxsX2NsZWFuJE1BQ19Qcm9wX0NvbWJpbmVkIDwtICgoRTJfYWxsX2NsZWFuJE1BQ19KdWRfMTlfciArIEUyX2FsbF9jbGVhbiRNQUNfSnVkXzIwX3IgKyBFMl9hbGxfY2xlYW4kTUFDX0p1ZF8yMV9yICsNCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIEUyX2FsbF9jbGVhbiRNQUNfUmVsXzE5ICsgRTJfYWxsX2NsZWFuJE1BQ19SZWxfMjAgKyBFMl9hbGxfY2xlYW4kTUFDX1JlbF8yMSkvNikNCkUyX2FsbF9jbGVhbiRNQUNfUHJvcF9KdWQgPC0gKChFMl9hbGxfY2xlYW4kTUFDX0p1ZF8xOV9yICsgRTJfYWxsX2NsZWFuJE1BQ19KdWRfMjBfciArIEUyX2FsbF9jbGVhbiRNQUNfSnVkXzIxX3IpLzMpDQpFMl9hbGxfY2xlYW4kTUFDX1Byb3BfUmVsIDwtICgoRTJfYWxsX2NsZWFuJE1BQ19SZWxfMTkgKyBFMl9hbGxfY2xlYW4kTUFDX1JlbF8yMCArIEUyX2FsbF9jbGVhbiRNQUNfUmVsXzIxKS8zKQ0KDQoNCiMgTUZRIChNb3JhbCBGb3VuZGF0aW9ucyBUaGVvcnkgc2NhbGUpIGNvbXBvc2l0ZXMNCkUyX2FsbF9jbGVhbiRNRlFfSGFybV9Db21iaW5lZCA8LSAoKEUyX2FsbF9jbGVhbiRNRlFfSnVkXzEgKyBFMl9hbGxfY2xlYW4kTUZRX0p1ZF8yICsgRTJfYWxsX2NsZWFuJE1GUV9KdWRfMyArDQogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICBFMl9hbGxfY2xlYW4kTUZRX1JlbF8xICsgRTJfYWxsX2NsZWFuJE1GUV9SZWxfMiArIEUyX2FsbF9jbGVhbiRNRlFfUmVsXzMpLzYpDQpFMl9hbGxfY2xlYW4kTUZRX0hhcm1fSnVkIDwtICgoRTJfYWxsX2NsZWFuJE1GUV9KdWRfMSArIEUyX2FsbF9jbGVhbiRNRlFfSnVkXzIgKyBFMl9hbGxfY2xlYW4kTUZRX0p1ZF8zKS8zKQ0KRTJfYWxsX2NsZWFuJE1GUV9IYXJtX1JlbCA8LSAoKEUyX2FsbF9jbGVhbiRNRlFfUmVsXzEgKyBFMl9hbGxfY2xlYW4kTUZRX1JlbF8yICsgRTJfYWxsX2NsZWFuJE1GUV9SZWxfMykvMykNCg0KRTJfYWxsX2NsZWFuJE1GUV9GYWlybmVzc19Db21iaW5lZCA8LSAoKEUyX2FsbF9jbGVhbiRNRlFfSnVkXzQgKyBFMl9hbGxfY2xlYW4kTUZRX0p1ZF81ICsgRTJfYWxsX2NsZWFuJE1GUV9KdWRfNiArDQogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICBFMl9hbGxfY2xlYW4kTUZRX1JlbF80ICsgRTJfYWxsX2NsZWFuJE1GUV9SZWxfNSArIEUyX2FsbF9jbGVhbiRNRlFfUmVsXzYpLzYpDQpFMl9hbGxfY2xlYW4kTUZRX0ZhaXJuZXNzX0p1ZCA8LSAoKEUyX2FsbF9jbGVhbiRNRlFfSnVkXzQgKyBFMl9hbGxfY2xlYW4kTUZRX0p1ZF81ICsgRTJfYWxsX2NsZWFuJE1GUV9KdWRfNikvMykNCkUyX2FsbF9jbGVhbiRNRlFfRmFpcm5lc3NfUmVsIDwtICgoRTJfYWxsX2NsZWFuJE1GUV9SZWxfNCArIEUyX2FsbF9jbGVhbiRNRlFfUmVsXzUgKyBFMl9hbGxfY2xlYW4kTUZRX1JlbF82KS8zKQ0KDQpFMl9hbGxfY2xlYW4kTUZRX0xveWFsdHlfQ29tYmluZWQgPC0gKChFMl9hbGxfY2xlYW4kTUZRX0p1ZF83ICsgRTJfYWxsX2NsZWFuJE1GUV9KdWRfOCArIEUyX2FsbF9jbGVhbiRNRlFfSnVkXzkgKw0KICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgRTJfYWxsX2NsZWFuJE1GUV9SZWxfNyArIEUyX2FsbF9jbGVhbiRNRlFfUmVsXzggKyBFMl9hbGxfY2xlYW4kTUZRX1JlbF85KS82KQ0KRTJfYWxsX2NsZWFuJE1GUV9Mb3lhbHR5X0p1ZCA8LSAoKEUyX2FsbF9jbGVhbiRNRlFfSnVkXzcgKyBFMl9hbGxfY2xlYW4kTUZRX0p1ZF84ICsgRTJfYWxsX2NsZWFuJE1GUV9KdWRfOSkvMykNCkUyX2FsbF9jbGVhbiRNRlFfTG95YWx0eV9SZWwgPC0gKChFMl9hbGxfY2xlYW4kTUZRX1JlbF83ICsgRTJfYWxsX2NsZWFuJE1GUV9SZWxfOCArIEUyX2FsbF9jbGVhbiRNRlFfUmVsXzkpLzMpDQoNCkUyX2FsbF9jbGVhbiRNRlFfQXV0aG9yaXR5X0NvbWJpbmVkIDwtICgoRTJfYWxsX2NsZWFuJE1GUV9KdWRfMTAgKyBFMl9hbGxfY2xlYW4kTUZRX0p1ZF8xMSArIEUyX2FsbF9jbGVhbiRNRlFfSnVkXzEyICsNCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIEUyX2FsbF9jbGVhbiRNRlFfUmVsXzEwICsgRTJfYWxsX2NsZWFuJE1GUV9SZWxfMTEgKyBFMl9hbGxfY2xlYW4kTUZRX1JlbF8xMikvNikNCkUyX2FsbF9jbGVhbiRNRlFfQXV0aG9yaXR5X0p1ZCA8LSAoKEUyX2FsbF9jbGVhb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