IAT ANALYSIS
2024-12-10
#LIBRARIES
library(foreign)
library(ggplot2)
library(RColorBrewer)
library(tidyr)
library(scales)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(readxl)
library(emmeans)## Warning: package 'emmeans' was built under R version 4.4.1## Welcome to emmeans.
## Caution: You lose important information if you filter this package's results.
## See '? untidy'data = read_excel("Current_Dataset_.xlsx")PLOT BLOCKS
means <- data.frame(
Variable = c("Mean_Men_Women", "Mean_Women_Men", "Mean_Carry_support", "Mean_Mix_Compatible", "Mean_Mix_Imcompatible"),
Mean_Value = colMeans(data[c("Mean_Men_Women", "Mean_Women_Men", "Mean_Carry_support", "Mean_Mix_Compatible", "Mean_Mix_Imcompatible")], na.rm = TRUE)
)
means$Variable <- factor(means$Variable,
levels = c("Mean_Men_Women", "Mean_Women_Men", "Mean_Carry_support", "Mean_Mix_Compatible", "Mean_Mix_Imcompatible"))
ggplot(data = means, aes(x = Variable, y = Mean_Value, fill = Variable)) +
geom_bar(stat = "identity", show.legend = FALSE) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
text = element_text(size = 14)
) +
labs(
title = "Mean IAT Times by Variable",
x = "Variable",
y = "Mean Time (ms)"
) +
scale_fill_brewer(palette = "Set2")
Analysis on separate Blocks
for every champions in the experiment, participants were asked to express their familiarity
familiarity_columns <- grep("^F_", colnames(data), value = TRUE)
response_time_columns <- grep("^Mean_", colnames(data), value = TRUE)
familiarity_data <- data %>% select(all_of(familiarity_columns))
response_time_data <- data %>% select(all_of(response_time_columns))
correlation_matrix <- cor(familiarity_data, response_time_data, use = "complete.obs")
print("Correlation Matrix: Familiarity vs. Response Times")## [1] "Correlation Matrix: Familiarity vs. Response Times"print(correlation_matrix)## Mean_Men_Women Mean_Women_Men Mean_Mix_Compatible
## F_Lux 0.0460780090 -0.197285845 0.1288248
## F_Senna 0.0459365640 0.061375920 -0.1833324
## F_Leona 0.2570934417 0.017892421 0.1528739
## F_Thresh 0.4542622140 0.230594644 0.3031184
## F_Pyke 0.3063266907 0.360143030 -0.1257230
## F_BlitzCrank 0.3328314387 0.007589123 0.1248623
## F_MissFortune 0.3527723872 0.118568053 0.2656500
## F_Jinx 0.0007897103 -0.148869489 0.1388304
## F_Kaisa -0.0697645588 -0.072177261 -0.3050481
## F_Jhin 0.1238841382 0.138252657 -0.3006590
## F_Lucian 0.4132046971 0.112237696 0.3198403
## F_Varus 0.3208019417 -0.041687369 0.1529651
## Mean_Mix_Imcompatible Mean_Carry_support
## F_Lux 0.14587833 -0.14394197
## F_Senna 0.01931727 -0.45284644
## F_Leona 0.26636606 -0.15508862
## F_Thresh 0.52372887 0.16391399
## F_Pyke 0.22125730 -0.34045657
## F_BlitzCrank 0.42580227 -0.05702051
## F_MissFortune 0.29700682 0.01716114
## F_Jinx -0.11141389 -0.23385053
## F_Kaisa -0.11401275 -0.56235755
## F_Jhin 0.05645247 -0.52654055
## F_Lucian 0.40363659 0.06568603
## F_Varus 0.39677271 -0.09067578Positive Correlations—>Some characters show moderate positive correlations with response times.This indicates that greater familiarity with these characters might be associated with slower response times in those trials.
LUX, THRESH, LUCIAN = Mean_Men_Woman
THRESH = Mean_Mix_ Incompatible
Negative Correlations—>Some characters exhibit negative correlations with certain response times. This suggests higher familiarity with these characters could be linked to faster responses in those trials.
SENNA, KAISA, JHIN = Mean_Carry_support
Neutral Correlations—>Many characters have low or near-zero correlations with specific response times, indicating no strong relationship
for (familiarity_var in familiarity_columns) {
for (resp_var in response_time_columns) {
test <- cor.test(data[[familiarity_var]], data[[resp_var]], method = "spearman", use = "complete.obs")
print(paste("Spearman Correlation: Familiarity (", familiarity_var, ") vs", resp_var))
print(test)
}
}## Warning in cor.test.default(data[[familiarity_var]], data[[resp_var]], method =
## "spearman", : Cannot compute exact p-value with ties## [1] "Spearman Correlation: Familiarity ( F_Lux ) vs Mean_Men_Women"
##
## Spearman's rank correlation rho
##
## data: data[[familiarity_var]] and data[[resp_var]]
## S = 736.77, p-value = 0.7109
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.09709195## Warning in cor.test.default(data[[familiarity_var]], data[[resp_var]], method =
## "spearman", : Cannot compute exact p-value with ties## [1] "Spearman Correlation: Familiarity ( F_Lux ) vs Mean_Women_Men"
##
## Spearman's rank correlation rho
##
## data: data[[familiarity_var]] and data[[resp_var]]
## S = 950.69, p-value = 0.5267
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## -0.1650563## Warning in cor.test.default(data[[familiarity_var]], data[[resp_var]], method =
## "spearman", : Cannot compute exact p-value with ties## [1] "Spearman Correlation: Familiarity ( F_Lux ) vs Mean_Mix_Compatible"
##
## Spearman's rank correlation rho
##
## data: data[[familiarity_var]] and data[[resp_var]]
## S = 823.92, p-value = 0.9705
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## -0.009709195## Warning in cor.test.default(data[[familiarity_var]], data[[resp_var]], method =
## "spearman", : Cannot compute exact p-value with ties## [1] "Spearman Correlation: Familiarity ( F_Lux ) vs Mean_Mix_Imcompatible"
##
## Spearman's rank correlation rho
##
## data: data[[familiarity_var]] and data[[resp_var]]
## S = 689.24, p-value = 0.5516
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.1553471## Warning in cor.test.default(data[[familiarity_var]], data[[resp_var]], method =
## "spearman", : Cannot compute exact p-value with ties## [1] "Spearman Correlation: Familiarity ( F_Lux ) vs Mean_Carry_support"
##
## Spearman's rank correlation rho
##
## data: data[[familiarity_var]] and data[[resp_var]]
## S = 1069.5, p-value = 0.2248
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## -0.3106943## Warning in cor.test.default(data[[familiarity_var]], data[[resp_var]], method =
## "spearman", : Cannot compute exact p-value with ties## [1] "Spearman Correlation: Familiarity ( F_Senna ) vs Mean_Men_Women"
##
## Spearman's rank correlation rho
##
## data: data[[familiarity_var]] and data[[resp_var]]
## S = 786.78, p-value = 0.8915
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.03581063## Warning in cor.test.default(data[[familiarity_var]], data[[resp_var]], method =
## "spearman", : Cannot compute exact p-value with ties## [1] "Spearman Correlation: Familiarity ( F_Senna ) vs Mean_Women_Men"
##
## Spearman's rank correlation rho
##
## data: data[[familiarity_var]] and data[[resp_var]]
## S = 828.36, p-value = 0.954
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## -0.01515065## Warning in cor.test.default(data[[familiarity_var]], data[[resp_var]], method =
## "spearman", : Cannot compute exact p-value with ties## [1] "Spearman Correlation: Familiarity ( F_Senna ) vs Mean_Mix_Compatible"
##
## Spearman's rank correlation rho
##
## data: data[[familiarity_var]] and data[[resp_var]]
## S = 782.28, p-value = 0.8749
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.04131996## Warning in cor.test.default(data[[familiarity_var]], data[[resp_var]], method =
## "spearman", : Cannot compute exact p-value with ties## [1] "Spearman Correlation: Familiarity ( F_Senna ) vs Mean_Mix_Imcompatible"
##
## Spearman's rank correlation rho
##
## data: data[[familiarity_var]] and data[[resp_var]]
## S = 810.38, p-value = 0.9791
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.00688666## Warning in cor.test.default(data[[familiarity_var]], data[[resp_var]], method =
## "spearman", : Cannot compute exact p-value with ties## [1] "Spearman Correlation: Familiarity ( F_Senna ) vs Mean_Carry_support"
##
## Spearman's rank correlation rho
##
## data: data[[familiarity_var]] and data[[resp_var]]
## S = 1097, p-value = 0.1759
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## -0.344333## Warning in cor.test.default(data[[familiarity_var]], data[[resp_var]], method =
## "spearman", : Cannot compute exact p-value with ties## [1] "Spearman Correlation: Familiarity ( F_Leona ) vs Mean_Men_Women"
##
## Spearman's rank correlation rho
##
## data: data[[familiarity_var]] and data[[resp_var]]
## S = 607.35, p-value = 0.3219
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.2557033## Warning in cor.test.default(data[[familiarity_var]], data[[resp_var]], method =
## "spearman", : Cannot compute exact p-value with ties## [1] "Spearman Correlation: Familiarity ( F_Leona ) vs Mean_Women_Men"
##
## Spearman's rank correlation rho
##
## data: data[[familiarity_var]] and data[[resp_var]]
## S = 784.95, p-value = 0.8847
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.03805109## Warning in cor.test.default(data[[familiarity_var]], data[[resp_var]], method =
## "spearman", : Cannot compute exact p-value with ties## [1] "Spearman Correlation: Familiarity ( F_Leona ) vs Mean_Mix_Compatible"
##
## Spearman's rank correlation rho
##
## data: data[[familiarity_var]] and data[[resp_var]]
## S = 694.29, p-value = 0.5677
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.1491603## Warning in cor.test.default(data[[familiarity_var]], data[[resp_var]], method =
## "spearman", : Cannot compute exact p-value with ties## [1] "Spearman Correlation: Familiarity ( F_Leona ) vs Mean_Mix_Imcompatible"
##
## Spearman's rank correlation rho
##
## data: data[[familiarity_var]] and data[[resp_var]]
## S = 618.52, p-value = 0.3494
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.2420049## Warning in cor.test.default(data[[familiarity_var]], data[[resp_var]], method =
## "spearman", : Cannot compute exact p-value with ties## [1] "Spearman Correlation: Familiarity ( F_Leona ) vs Mean_Carry_support"
##
## Spearman's rank correlation rho
##
## data: data[[familiarity_var]] and data[[resp_var]]
## S = 978.7, p-value = 0.4429
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## -0.1993877## Warning in cor.test.default(data[[familiarity_var]], data[[resp_var]], method =
## "spearman", : Cannot compute exact p-value with ties## [1] "Spearman Correlation: Familiarity ( F_Thresh ) vs Mean_Men_Women"
##
## Spearman's rank correlation rho
##
## data: data[[familiarity_var]] and data[[resp_var]]
## S = 381.53, p-value = 0.02779
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.5324396## Warning in cor.test.default(data[[familiarity_var]], data[[resp_var]], method =
## "spearman", : Cannot compute exact p-value with ties## [1] "Spearman Correlation: Familiarity ( F_Thresh ) vs Mean_Women_Men"
##
## Spearman's rank correlation rho
##
## data: data[[familiarity_var]] and data[[resp_var]]
## S = 520.89, p-value = 0.1538
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.3616571## Warning in cor.test.default(data[[familiarity_var]], data[[resp_var]], method =
## "spearman", : Cannot compute exact p-value with ties## [1] "Spearman Correlation: Familiarity ( F_Thresh ) vs Mean_Mix_Compatible"
##
## Spearman's rank correlation rho
##
## data: data[[familiarity_var]] and data[[resp_var]]
## S = 472.87, p-value = 0.09283
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.4204981## Warning in cor.test.default(data[[familiarity_var]], data[[resp_var]], method =
## "spearman", : Cannot compute exact p-value with ties## [1] "Spearman Correlation: Familiarity ( F_Thresh ) vs Mean_Mix_Imcompatible"
##
## Spearman's rank correlation rho
##
## data: data[[familiarity_var]] and data[[resp_var]]
## S = 414.32, p-value = 0.04472
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.4922555## Warning in cor.test.default(data[[familiarity_var]], data[[resp_var]], method =
## "spearman", : Cannot compute exact p-value with ties## [1] "Spearman Correlation: Familiarity ( F_Thresh ) vs Mean_Carry_support"
##
## Spearman's rank correlation rho
##
## data: data[[familiarity_var]] and data[[resp_var]]
## S = 618.09, p-value = 0.3483
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.2425399## Warning in cor.test.default(data[[familiarity_var]], data[[resp_var]], method =
## "spearman", : Cannot compute exact p-value with ties## [1] "Spearman Correlation: Familiarity ( F_Pyke ) vs Mean_Men_Women"
##
## Spearman's rank correlation rho
##
## data: data[[familiarity_var]] and data[[resp_var]]
## S = 580.76, p-value = 0.2618
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.28828## Warning in cor.test.default(data[[familiarity_var]], data[[resp_var]], method =
## "spearman", : Cannot compute exact p-value with ties## [1] "Spearman Correlation: Familiarity ( F_Pyke ) vs Mean_Women_Men"
##
## Spearman's rank correlation rho
##
## data: data[[familiarity_var]] and data[[resp_var]]
## S = 611.26, p-value = 0.3314
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.2509103## Warning in cor.test.default(data[[familiarity_var]], data[[resp_var]], method =
## "spearman", : Cannot compute exact p-value with ties## [1] "Spearman Correlation: Familiarity ( F_Pyke ) vs Mean_Mix_Compatible"
##
## Spearman's rank correlation rho
##
## data: data[[familiarity_var]] and data[[resp_var]]
## S = 720.16, p-value = 0.6535
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.1174474## Warning in cor.test.default(data[[familiarity_var]], data[[resp_var]], method =
## "spearman", : Cannot compute exact p-value with ties## [1] "Spearman Correlation: Familiarity ( F_Pyke ) vs Mean_Mix_Imcompatible"
##
## Spearman's rank correlation rho
##
## data: data[[familiarity_var]] and data[[resp_var]]
## S = 633.04, p-value = 0.387
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.2242177## Warning in cor.test.default(data[[familiarity_var]], data[[resp_var]], method =
## "spearman", : Cannot compute exact p-value with ties## [1] "Spearman Correlation: Familiarity ( F_Pyke ) vs Mean_Carry_support"
##
## Spearman's rank correlation rho
##
## data: data[[familiarity_var]] and data[[resp_var]]
## S = 885.7, p-value = 0.7445
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## -0.08541628## Warning in cor.test.default(data[[familiarity_var]], data[[resp_var]], method =
## "spearman", : Cannot compute exact p-value with ties## [1] "Spearman Correlation: Familiarity ( F_BlitzCrank ) vs Mean_Men_Women"
##
## Spearman's rank correlation rho
##
## data: data[[familiarity_var]] and data[[resp_var]]
## S = 459.55, p-value = 0.07956
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.4368301## Warning in cor.test.default(data[[familiarity_var]], data[[resp_var]], method =
## "spearman", : Cannot compute exact p-value with ties## [1] "Spearman Correlation: Familiarity ( F_BlitzCrank ) vs Mean_Women_Men"
##
## Spearman's rank correlation rho
##
## data: data[[familiarity_var]] and data[[resp_var]]
## S = 662.66, p-value = 0.4701
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.1879193## Warning in cor.test.default(data[[familiarity_var]], data[[resp_var]], method =
## "spearman", : Cannot compute exact p-value with ties## [1] "Spearman Correlation: Familiarity ( F_BlitzCrank ) vs Mean_Mix_Compatible"
##
## Spearman's rank correlation rho
##
## data: data[[familiarity_var]] and data[[resp_var]]
## S = 707.05, p-value = 0.6094
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.1335216## Warning in cor.test.default(data[[familiarity_var]], data[[resp_var]], method =
## "spearman", : Cannot compute exact p-value with ties## [1] "Spearman Correlation: Familiarity ( F_BlitzCrank ) vs Mean_Mix_Imcompatible"
##
## Spearman's rank correlation rho
##
## data: data[[familiarity_var]] and data[[resp_var]]
## S = 462.24, p-value = 0.08212
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.4335332## Warning in cor.test.default(data[[familiarity_var]], data[[resp_var]], method =
## "spearman", : Cannot compute exact p-value with ties## [1] "Spearman Correlation: Familiarity ( F_BlitzCrank ) vs Mean_Carry_support"
##
## Spearman's rank correlation rho
##
## data: data[[familiarity_var]] and data[[resp_var]]
## S = 868.46, p-value = 0.8064
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## -0.0642882## Warning in cor.test.default(data[[familiarity_var]], data[[resp_var]], method =
## "spearman", : Cannot compute exact p-value with ties## [1] "Spearman Correlation: Familiarity ( F_MissFortune ) vs Mean_Men_Women"
##
## Spearman's rank correlation rho
##
## data: data[[familiarity_var]] and data[[resp_var]]
## S = 575.52, p-value = 0.2509
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.2947009## Warning in cor.test.default(data[[familiarity_var]], data[[resp_var]], method =
## "spearman", : Cannot compute exact p-value with ties## [1] "Spearman Correlation: Familiarity ( F_MissFortune ) vs Mean_Women_Men"
##
## Spearman's rank correlation rho
##
## data: data[[familiarity_var]] and data[[resp_var]]
## S = 786.67, p-value = 0.8911
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.03593913## Warning in cor.test.default(data[[familiarity_var]], data[[resp_var]], method =
## "spearman", : Cannot compute exact p-value with ties## [1] "Spearman Correlation: Familiarity ( F_MissFortune ) vs Mean_Mix_Compatible"
##
## Spearman's rank correlation rho
##
## data: data[[familiarity_var]] and data[[resp_var]]
## S = 671.71, p-value = 0.4972
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.1768205## Warning in cor.test.default(data[[familiarity_var]], data[[resp_var]], method =
## "spearman", : Cannot compute exact p-value with ties## [1] "Spearman Correlation: Familiarity ( F_MissFortune ) vs Mean_Mix_Imcompatible"
##
## Spearman's rank correlation rho
##
## data: data[[familiarity_var]] and data[[resp_var]]
## S = 576.7, p-value = 0.2533
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.2932633## Warning in cor.test.default(data[[familiarity_var]], data[[resp_var]], method =
## "spearman", : Cannot compute exact p-value with ties## [1] "Spearman Correlation: Familiarity ( F_MissFortune ) vs Mean_Carry_support"
##
## Spearman's rank correlation rho
##
## data: data[[familiarity_var]] and data[[resp_var]]
## S = 906.33, p-value = 0.6723
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## -0.1106925## Warning in cor.test.default(data[[familiarity_var]], data[[resp_var]], method =
## "spearman", : Cannot compute exact p-value with ties## [1] "Spearman Correlation: Familiarity ( F_Jinx ) vs Mean_Men_Women"
##
## Spearman's rank correlation rho
##
## data: data[[familiarity_var]] and data[[resp_var]]
## S = 816, p-value = 1
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0## Warning in cor.test.default(data[[familiarity_var]], data[[resp_var]], method =
## "spearman", : Cannot compute exact p-value with ties## [1] "Spearman Correlation: Familiarity ( F_Jinx ) vs Mean_Women_Men"
##
## Spearman's rank correlation rho
##
## data: data[[familiarity_var]] and data[[resp_var]]
## S = 944.51, p-value = 0.5461
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## -0.1574852## Warning in cor.test.default(data[[familiarity_var]], data[[resp_var]], method =
## "spearman", : Cannot compute exact p-value with ties## [1] "Spearman Correlation: Familiarity ( F_Jinx ) vs Mean_Mix_Compatible"
##
## Spearman's rank correlation rho
##
## data: data[[familiarity_var]] and data[[resp_var]]
## S = 816, p-value = 1
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0## Warning in cor.test.default(data[[familiarity_var]], data[[resp_var]], method =
## "spearman", : Cannot compute exact p-value with ties## [1] "Spearman Correlation: Familiarity ( F_Jinx ) vs Mean_Mix_Imcompatible"
##
## Spearman's rank correlation rho
##
## data: data[[familiarity_var]] and data[[resp_var]]
## S = 893.1, p-value = 0.7183
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## -0.09449112## Warning in cor.test.default(data[[familiarity_var]], data[[resp_var]], method =
## "spearman", : Cannot compute exact p-value with ties## [1] "Spearman Correlation: Familiarity ( F_Jinx ) vs Mean_Carry_support"
##
## Spearman's rank correlation rho
##
## data: data[[familiarity_var]] and data[[resp_var]]
## S = 1150.1, p-value = 0.1026
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## -0.4094615## Warning in cor.test.default(data[[familiarity_var]], data[[resp_var]], method =
## "spearman", : Cannot compute exact p-value with ties## [1] "Spearman Correlation: Familiarity ( F_Kaisa ) vs Mean_Men_Women"
##
## Spearman's rank correlation rho
##
## data: data[[familiarity_var]] and data[[resp_var]]
## S = 886.44, p-value = 0.7418
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## -0.08632674## Warning in cor.test.default(data[[familiarity_var]], data[[resp_var]], method =
## "spearman", : Cannot compute exact p-value with ties## [1] "Spearman Correlation: Familiarity ( F_Kaisa ) vs Mean_Women_Men"
##
## Spearman's rank correlation rho
##
## data: data[[familiarity_var]] and data[[resp_var]]
## S = 952.19, p-value = 0.522
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## -0.1668984## Warning in cor.test.default(data[[familiarity_var]], data[[resp_var]], method =
## "spearman", : Cannot compute exact p-value with ties## [1] "Spearman Correlation: Familiarity ( F_Kaisa ) vs Mean_Mix_Compatible"
##
## Spearman's rank correlation rho
##
## data: data[[familiarity_var]] and data[[resp_var]]
## S = 820.7, p-value = 0.9825
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## -0.005755116## Warning in cor.test.default(data[[familiarity_var]], data[[resp_var]], method =
## "spearman", : Cannot compute exact p-value with ties## [1] "Spearman Correlation: Familiarity ( F_Kaisa ) vs Mean_Mix_Imcompatible"
##
## Spearman's rank correlation rho
##
## data: data[[familiarity_var]] and data[[resp_var]]
## S = 905.23, p-value = 0.6761
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## -0.1093472## Warning in cor.test.default(data[[familiarity_var]], data[[resp_var]], method =
## "spearman", : Cannot compute exact p-value with ties## [1] "Spearman Correlation: Familiarity ( F_Kaisa ) vs Mean_Carry_support"
##
## Spearman's rank correlation rho
##
## data: data[[familiarity_var]] and data[[resp_var]]
## S = 1125.9, p-value = 0.1326
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## -0.3798376## Warning in cor.test.default(data[[familiarity_var]], data[[resp_var]], method =
## "spearman", : Cannot compute exact p-value with ties## [1] "Spearman Correlation: Familiarity ( F_Jhin ) vs Mean_Men_Women"
##
## Spearman's rank correlation rho
##
## data: data[[familiarity_var]] and data[[resp_var]]
## S = 764.43, p-value = 0.8096
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.06319961## Warning in cor.test.default(data[[familiarity_var]], data[[resp_var]], method =
## "spearman", : Cannot compute exact p-value with ties## [1] "Spearman Correlation: Familiarity ( F_Jhin ) vs Mean_Women_Men"
##
## Spearman's rank correlation rho
##
## data: data[[familiarity_var]] and data[[resp_var]]
## S = 824.2, p-value = 0.9694
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## -0.01005448## Warning in cor.test.default(data[[familiarity_var]], data[[resp_var]], method =
## "spearman", : Cannot compute exact p-value with ties## [1] "Spearman Correlation: Familiarity ( F_Jhin ) vs Mean_Mix_Compatible"
##
## Spearman's rank correlation rho
##
## data: data[[familiarity_var]] and data[[resp_var]]
## S = 975.4, p-value = 0.4524
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## -0.1953442## Warning in cor.test.default(data[[familiarity_var]], data[[resp_var]], method =
## "spearman", : Cannot compute exact p-value with ties## [1] "Spearman Correlation: Familiarity ( F_Jhin ) vs Mean_Mix_Imcompatible"
##
## Spearman's rank correlation rho
##
## data: data[[familiarity_var]] and data[[resp_var]]
## S = 779.67, p-value = 0.8653
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.044527## Warning in cor.test.default(data[[familiarity_var]], data[[resp_var]], method =
## "spearman", : Cannot compute exact p-value with ties## [1] "Spearman Correlation: Familiarity ( F_Jhin ) vs Mean_Carry_support"
##
## Spearman's rank correlation rho
##
## data: data[[familiarity_var]] and data[[resp_var]]
## S = 1171.1, p-value = 0.08081
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## -0.4352155## Warning in cor.test.default(data[[familiarity_var]], data[[resp_var]], method =
## "spearman", : Cannot compute exact p-value with ties## [1] "Spearman Correlation: Familiarity ( F_Lucian ) vs Mean_Men_Women"
##
## Spearman's rank correlation rho
##
## data: data[[familiarity_var]] and data[[resp_var]]
## S = 511.42, p-value = 0.14
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.373257## Warning in cor.test.default(data[[familiarity_var]], data[[resp_var]], method =
## "spearman", : Cannot compute exact p-value with ties## [1] "Spearman Correlation: Familiarity ( F_Lucian ) vs Mean_Women_Men"
##
## Spearman's rank correlation rho
##
## data: data[[familiarity_var]] and data[[resp_var]]
## S = 762.05, p-value = 0.801
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.06611194## Warning in cor.test.default(data[[familiarity_var]], data[[resp_var]], method =
## "spearman", : Cannot compute exact p-value with ties## [1] "Spearman Correlation: Familiarity ( F_Lucian ) vs Mean_Mix_Compatible"
##
## Spearman's rank correlation rho
##
## data: data[[familiarity_var]] and data[[resp_var]]
## S = 583.35, p-value = 0.2673
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.2851077## Warning in cor.test.default(data[[familiarity_var]], data[[resp_var]], method =
## "spearman", : Cannot compute exact p-value with ties## [1] "Spearman Correlation: Familiarity ( F_Lucian ) vs Mean_Mix_Imcompatible"
##
## Spearman's rank correlation rho
##
## data: data[[familiarity_var]] and data[[resp_var]]
## S = 495.69, p-value = 0.1191
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.3925396## Warning in cor.test.default(data[[familiarity_var]], data[[resp_var]], method =
## "spearman", : Cannot compute exact p-value with ties## [1] "Spearman Correlation: Familiarity ( F_Lucian ) vs Mean_Carry_support"
##
## Spearman's rank correlation rho
##
## data: data[[familiarity_var]] and data[[resp_var]]
## S = 859.83, p-value = 0.8378
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## -0.05371595## Warning in cor.test.default(data[[familiarity_var]], data[[resp_var]], method =
## "spearman", : Cannot compute exact p-value with ties## [1] "Spearman Correlation: Familiarity ( F_Varus ) vs Mean_Men_Women"
##
## Spearman's rank correlation rho
##
## data: data[[familiarity_var]] and data[[resp_var]]
## S = 541.98, p-value = 0.1876
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.3358092## Warning in cor.test.default(data[[familiarity_var]], data[[resp_var]], method =
## "spearman", : Cannot compute exact p-value with ties## [1] "Spearman Correlation: Familiarity ( F_Varus ) vs Mean_Women_Men"
##
## Spearman's rank correlation rho
##
## data: data[[familiarity_var]] and data[[resp_var]]
## S = 816, p-value = 1
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0## Warning in cor.test.default(data[[familiarity_var]], data[[resp_var]], method =
## "spearman", : Cannot compute exact p-value with ties## [1] "Spearman Correlation: Familiarity ( F_Varus ) vs Mean_Mix_Compatible"
##
## Spearman's rank correlation rho
##
## data: data[[familiarity_var]] and data[[resp_var]]
## S = 638.56, p-value = 0.4018
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.2174502## Warning in cor.test.default(data[[familiarity_var]], data[[resp_var]], method =
## "spearman", : Cannot compute exact p-value with ties## [1] "Spearman Correlation: Familiarity ( F_Varus ) vs Mean_Mix_Imcompatible"
##
## Spearman's rank correlation rho
##
## data: data[[familiarity_var]] and data[[resp_var]]
## S = 490.32, p-value = 0.1125
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.3991175## Warning in cor.test.default(data[[familiarity_var]], data[[resp_var]], method =
## "spearman", : Cannot compute exact p-value with ties## [1] "Spearman Correlation: Familiarity ( F_Varus ) vs Mean_Carry_support"
##
## Spearman's rank correlation rho
##
## data: data[[familiarity_var]] and data[[resp_var]]
## S = 914.83, p-value = 0.6433
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## -0.1211115# Gender and response times (ANOVA)
for (resp_var in response_time_columns) {
test <- aov(data[[resp_var]] ~ as.factor(data$Gender), data = data)
print(paste("ANOVA: Gender vs", resp_var))
print(summary(test))
}## [1] "ANOVA: Gender vs Mean_Men_Women"
## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(data$Gender) 2 40594 20297 0.86 0.444
## Residuals 14 330409 23601
## [1] "ANOVA: Gender vs Mean_Women_Men"
## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(data$Gender) 2 51648 25824 1.379 0.284
## Residuals 14 262232 18731
## [1] "ANOVA: Gender vs Mean_Mix_Compatible"
## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(data$Gender) 2 213610 106805 3.489 0.059 .
## Residuals 14 428542 30610
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "ANOVA: Gender vs Mean_Mix_Imcompatible"
## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(data$Gender) 2 122499 61250 1.607 0.235
## Residuals 14 533726 38123
## [1] "ANOVA: Gender vs Mean_Carry_support"
## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(data$Gender) 2 72280 36140 1.859 0.192
## Residuals 14 272169 19441The p-value is slightly above 0.05 but close to being significant.
This result suggests a potential trend where gender might influence
Mean_Mix_Compatible response times.
# Race and response times (ANOVA)
for (resp_var in response_time_columns) {
test <- aov(data[[resp_var]] ~ as.factor(data$Race), data = data)
print(paste("ANOVA: Race vs", resp_var))
print(summary(test))
}## [1] "ANOVA: Race vs Mean_Men_Women"
## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(data$Race) 3 195467 65156 4.825 0.018 *
## Residuals 13 175536 13503
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "ANOVA: Race vs Mean_Women_Men"
## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(data$Race) 3 103289 34430 2.125 0.146
## Residuals 13 210591 16199
## [1] "ANOVA: Race vs Mean_Mix_Compatible"
## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(data$Race) 3 35196 11732 0.251 0.859
## Residuals 13 606955 46689
## [1] "ANOVA: Race vs Mean_Mix_Imcompatible"
## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(data$Race) 3 77380 25793 0.579 0.639
## Residuals 13 578845 44527
## [1] "ANOVA: Race vs Mean_Carry_support"
## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(data$Race) 3 6557 2186 0.084 0.968
## Residuals 13 337892 25992The p-value is less than 0.05, indicating a statistically significant
difference in Mean_Men_Women response times across race
groups. NOT ENOUGH VARIATION
# Employment and response times (ANOVA)
for (resp_var in response_time_columns) {
test <- aov(data[[resp_var]] ~ as.factor(data$Employment), data = data)
print(paste("ANOVA: Employment vs", resp_var))
print(summary(test))
}## [1] "ANOVA: Employment vs Mean_Men_Women"
## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(data$Employment) 2 23773 11887 0.479 0.629
## Residuals 14 347230 24802
## [1] "ANOVA: Employment vs Mean_Women_Men"
## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(data$Employment) 2 3594 1797 0.081 0.923
## Residuals 14 310286 22163
## [1] "ANOVA: Employment vs Mean_Mix_Compatible"
## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(data$Employment) 2 117821 58911 1.573 0.242
## Residuals 14 524330 37452
## [1] "ANOVA: Employment vs Mean_Mix_Imcompatible"
## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(data$Employment) 2 125756 62878 1.659 0.226
## Residuals 14 530470 37891
## [1] "ANOVA: Employment vs Mean_Carry_support"
## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(data$Employment) 2 122064 61032 3.842 0.0468 *
## Residuals 14 222384 15885
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1The p-value is slightly below 0.05, indicating a statistically
significant difference in Mean_Carry_support response times
across employment groups. This result suggests that
Employment influences Mean_Carry_support
# Marital status and response times (ANOVA)
for (resp_var in response_time_columns) {
test <- aov(data[[resp_var]] ~ as.factor(data$Marital_status), data = data)
print(paste("ANOVA: Marital Status vs", resp_var))
print(summary(test))
}## [1] "ANOVA: Marital Status vs Mean_Men_Women"
## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(data$Marital_status) 1 2040 2040 0.083 0.777
## Residuals 15 368963 24598
## [1] "ANOVA: Marital Status vs Mean_Women_Men"
## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(data$Marital_status) 1 4181 4181 0.203 0.659
## Residuals 15 309698 20647
## [1] "ANOVA: Marital Status vs Mean_Mix_Compatible"
## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(data$Marital_status) 1 28295 28295 0.691 0.419
## Residuals 15 613856 40924
## [1] "ANOVA: Marital Status vs Mean_Mix_Imcompatible"
## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(data$Marital_status) 1 8132 8132 0.188 0.671
## Residuals 15 648094 43206
## [1] "ANOVA: Marital Status vs Mean_Carry_support"
## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(data$Marital_status) 1 76073 76073 4.252 0.057 .
## Residuals 15 268375 17892
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1The p-value is slightly above 0.05 but close enough to suggest a
potential trend in the data where marital status might influence
Mean_Carry_support response times.
# Political Orientation and response times (Spearman Correlation)
for (resp_var in response_time_columns) {
test <- cor.test(data$Pol_Ori, data[[resp_var]], method = "spearman")
print(paste("Spearman Correlation: Political Orientation vs", resp_var))
print(test)
}## Warning in cor.test.default(data$Pol_Ori, data[[resp_var]], method =
## "spearman"): Cannot compute exact p-value with ties## [1] "Spearman Correlation: Political Orientation vs Mean_Men_Women"
##
## Spearman's rank correlation rho
##
## data: data$Pol_Ori and data[[resp_var]]
## S = 712.81, p-value = 0.6286
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.1264568## Warning in cor.test.default(data$Pol_Ori, data[[resp_var]], method =
## "spearman"): Cannot compute exact p-value with ties## [1] "Spearman Correlation: Political Orientation vs Mean_Women_Men"
##
## Spearman's rank correlation rho
##
## data: data$Pol_Ori and data[[resp_var]]
## S = 525.39, p-value = 0.1606
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.3561436## Warning in cor.test.default(data$Pol_Ori, data[[resp_var]], method =
## "spearman"): Cannot compute exact p-value with ties## [1] "Spearman Correlation: Political Orientation vs Mean_Mix_Compatible"
##
## Spearman's rank correlation rho
##
## data: data$Pol_Ori and data[[resp_var]]
## S = 664.38, p-value = 0.4752
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.185814## Warning in cor.test.default(data$Pol_Ori, data[[resp_var]], method =
## "spearman"): Cannot compute exact p-value with ties## [1] "Spearman Correlation: Political Orientation vs Mean_Mix_Imcompatible"
##
## Spearman's rank correlation rho
##
## data: data$Pol_Ori and data[[resp_var]]
## S = 596.99, p-value = 0.2976
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.268398## Warning in cor.test.default(data$Pol_Ori, data[[resp_var]], method =
## "spearman"): Cannot compute exact p-value with ties## [1] "Spearman Correlation: Political Orientation vs Mean_Carry_support"
##
## Spearman's rank correlation rho
##
## data: data$Pol_Ori and data[[resp_var]]
## S = 607.52, p-value = 0.3223
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.2554943Political Orientation does not show a significant correlation
# Education and response times (ANOVA)
for (resp_var in response_time_columns) {
test <- aov(data[[resp_var]] ~ as.factor(data$Education), data = data)
print(paste("ANOVA: Education vs", resp_var))
print(summary(test))
}## [1] "ANOVA: Education vs Mean_Men_Women"
## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(data$Education) 3 47508 15836 0.636 0.605
## Residuals 13 323495 24884
## [1] "ANOVA: Education vs Mean_Women_Men"
## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(data$Education) 3 26619 8873 0.402 0.754
## Residuals 13 287261 22097
## [1] "ANOVA: Education vs Mean_Mix_Compatible"
## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(data$Education) 3 100132 33377 0.801 0.515
## Residuals 13 542020 41694
## [1] "ANOVA: Education vs Mean_Mix_Imcompatible"
## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(data$Education) 3 149542 49847 1.279 0.323
## Residuals 13 506683 38976
## [1] "ANOVA: Education vs Mean_Carry_support"
## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(data$Education) 3 40900 13633 0.584 0.636
## Residuals 13 303549 23350Education does not significantly influence response times
# Religion and response times (ANOVA)
for (resp_var in response_time_columns) {
test <- aov(data[[resp_var]] ~ as.factor(data$Religion), data = data)
print(paste("ANOVA: Religion vs", resp_var))
print(summary(test))
}## [1] "ANOVA: Religion vs Mean_Men_Women"
## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(data$Religion) 2 104363 52182 2.74 0.099 .
## Residuals 14 266640 19046
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "ANOVA: Religion vs Mean_Women_Men"
## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(data$Religion) 2 158166 79083 7.11 0.0074 **
## Residuals 14 155714 11122
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "ANOVA: Religion vs Mean_Mix_Compatible"
## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(data$Religion) 2 75403 37702 0.931 0.417
## Residuals 14 566748 40482
## [1] "ANOVA: Religion vs Mean_Mix_Imcompatible"
## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(data$Religion) 2 24096 12048 0.267 0.77
## Residuals 14 632129 45152
## [1] "ANOVA: Religion vs Mean_Carry_support"
## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(data$Religion) 2 37051 18525 0.844 0.451
## Residuals 14 307398 21957Religion has no significant effect.
data$Ide_Religious <- as.numeric(data$Ide_Religious)
# Ideology of Religiousness and response times (Spearman Correlation)
for (resp_var in response_time_columns) {
test <- cor.test(data$Ide_Religious, data[[resp_var]], method = "spearman")
print(paste("Spearman Correlation: Ideology of Religiousness vs", resp_var))
print(test)
}## Warning in cor.test.default(data$Ide_Religious, data[[resp_var]], method =
## "spearman"): Cannot compute exact p-value with ties## [1] "Spearman Correlation: Ideology of Religiousness vs Mean_Men_Women"
##
## Spearman's rank correlation rho
##
## data: data$Ide_Religious and data[[resp_var]]
## S = 749.19, p-value = 0.7548
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.08187222## Warning in cor.test.default(data$Ide_Religious, data[[resp_var]], method =
## "spearman"): Cannot compute exact p-value with ties## [1] "Spearman Correlation: Ideology of Religiousness vs Mean_Women_Men"
##
## Spearman's rank correlation rho
##
## data: data$Ide_Religious and data[[resp_var]]
## S = 643.71, p-value = 0.4159
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.2111441## Warning in cor.test.default(data$Ide_Religious, data[[resp_var]], method =
## "spearman"): Cannot compute exact p-value with ties## [1] "Spearman Correlation: Ideology of Religiousness vs Mean_Mix_Compatible"
##
## Spearman's rank correlation rho
##
## data: data$Ide_Religious and data[[resp_var]]
## S = 615.58, p-value = 0.342
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.2456167## Warning in cor.test.default(data$Ide_Religious, data[[resp_var]], method =
## "spearman"): Cannot compute exact p-value with ties## [1] "Spearman Correlation: Ideology of Religiousness vs Mean_Mix_Imcompatible"
##
## Spearman's rank correlation rho
##
## data: data$Ide_Religious and data[[resp_var]]
## S = 840.61, p-value = 0.9085
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## -0.03016345## Warning in cor.test.default(data$Ide_Religious, data[[resp_var]], method =
## "spearman"): Cannot compute exact p-value with ties## [1] "Spearman Correlation: Ideology of Religiousness vs Mean_Carry_support"
##
## Spearman's rank correlation rho
##
## data: data$Ide_Religious and data[[resp_var]]
## S = 745.68, p-value = 0.7422
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.08618128There is no significant correlation between Ide_Religious (Ideology of Religiousness) and any of the response time variables
# Ensure 'RolePreference' is numeric
data$RolePreference <- as.numeric(data$Role_Preference)
# Role Preference and response times (ANOVA)
for (resp_var in response_time_columns) {
test <- aov(data[[resp_var]] ~ as.factor(data$Role_Preference), data = data)
print(paste("ANOVA: Role Preference vs", resp_var))
print(summary(test))
}## [1] "ANOVA: Role Preference vs Mean_Men_Women"
## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(data$Role_Preference) 4 130796 32699 1.634 0.229
## Residuals 12 240207 20017
## [1] "ANOVA: Role Preference vs Mean_Women_Men"
## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(data$Role_Preference) 4 100876 25219 1.421 0.286
## Residuals 12 213004 17750
## [1] "ANOVA: Role Preference vs Mean_Mix_Compatible"
## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(data$Role_Preference) 4 154166 38542 0.948 0.47
## Residuals 12 487985 40665
## [1] "ANOVA: Role Preference vs Mean_Mix_Imcompatible"
## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(data$Role_Preference) 4 169850 42463 1.048 0.423
## Residuals 12 486375 40531
## [1] "ANOVA: Role Preference vs Mean_Carry_support"
## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(data$Role_Preference) 4 71075 17769 0.78 0.559
## Residuals 12 273374 22781No significant effects of Role Preference on response times were found in this dataset.
# Ensure 'Familiarity_Champ' is numeric
data$Familiarity_Champ <- as.numeric(data$Familiarity_Champ)
# Familiarity with Champions and Response Times (Spearman Correlation)
if ("Familiarity_Champ" %in% colnames(data)) {
for (resp_var in response_time_columns) {
test <- cor.test(data$Familiarity_Champ, data[[resp_var]], method = "spearman", use = "complete.obs")
print(paste("Spearman Correlation: Familiarity with Champions vs", resp_var))
print(test)
}
} ## Warning in cor.test.default(data$Familiarity_Champ, data[[resp_var]], method =
## "spearman", : Cannot compute exact p-value with ties## [1] "Spearman Correlation: Familiarity with Champions vs Mean_Men_Women"
##
## Spearman's rank correlation rho
##
## data: data$Familiarity_Champ and data[[resp_var]]
## S = 792.29, p-value = 0.9118
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.02905924## Warning in cor.test.default(data$Familiarity_Champ, data[[resp_var]], method =
## "spearman", : Cannot compute exact p-value with ties## [1] "Spearman Correlation: Familiarity with Champions vs Mean_Women_Men"
##
## Spearman's rank correlation rho
##
## data: data$Familiarity_Champ and data[[resp_var]]
## S = 915.37, p-value = 0.6415
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## -0.121772## Warning in cor.test.default(data$Familiarity_Champ, data[[resp_var]], method =
## "spearman", : Cannot compute exact p-value with ties## [1] "Spearman Correlation: Familiarity with Champions vs Mean_Mix_Compatible"
##
## Spearman's rank correlation rho
##
## data: data$Familiarity_Champ and data[[resp_var]]
## S = 981.99, p-value = 0.4336
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## -0.2034147## Warning in cor.test.default(data$Familiarity_Champ, data[[resp_var]], method =
## "spearman", : Cannot compute exact p-value with ties## [1] "Spearman Correlation: Familiarity with Champions vs Mean_Mix_Imcompatible"
##
## Spearman's rank correlation rho
##
## data: data$Familiarity_Champ and data[[resp_var]]
## S = 1120.9, p-value = 0.1396
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## -0.3736188## Warning in cor.test.default(data$Familiarity_Champ, data[[resp_var]], method =
## "spearman", : Cannot compute exact p-value with ties## [1] "Spearman Correlation: Familiarity with Champions vs Mean_Carry_support"
##
## Spearman's rank correlation rho
##
## data: data$Familiarity_Champ and data[[resp_var]]
## S = 1013.6, p-value = 0.349
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## -0.2421603There is no statistically significant correlation between Familiarity with Champions and any of the response time variables (Mean_)
# Highest Rank and Response Times (ANOVA)
if ("Highest_Rank" %in% colnames(data)) {
for (resp_var in response_time_columns) {
test <- aov(data[[resp_var]] ~ as.factor(data$Highest_Rank), data = data)
print(paste("ANOVA: Highest Rank vs", resp_var))
print(summary(test))
}
}## [1] "ANOVA: Highest Rank vs Mean_Men_Women"
## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(data$Highest_Rank) 4 132326 33081 1.663 0.223
## Residuals 12 238677 19890
## [1] "ANOVA: Highest Rank vs Mean_Women_Men"
## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(data$Highest_Rank) 4 158104 39526 3.045 0.0601 .
## Residuals 12 155776 12981
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "ANOVA: Highest Rank vs Mean_Mix_Compatible"
## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(data$Highest_Rank) 4 314194 78549 2.874 0.0698 .
## Residuals 12 327957 27330
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "ANOVA: Highest Rank vs Mean_Mix_Imcompatible"
## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(data$Highest_Rank) 4 248247 62062 1.825 0.189
## Residuals 12 407979 33998
## [1] "ANOVA: Highest Rank vs Mean_Carry_support"
## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(data$Highest_Rank) 4 178435 44609 3.224 0.0515 .
## Residuals 12 166014 13834
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1The p-value is slightly above 0.05, suggesting a potential trend
where rank might influence Mean_Women_Men response
times.
The p-value is slightly above 0.05, indicating a potential trend
where rank might influence Mean_Mix_Compatible response
times.
The p-value is just above 0.05, suggesting a potential trend where
rank might influence Mean_Carry_support response times.
ANALYSIS WITH THE IAT COLUMN
# Daily Play and Response Times (Spearman Correlation) ### IAT ANALYSIS
if ("Daily_Play" %in% colnames(data)) {
for (resp_var in response_time_columns) {
test <- cor.test(data$Daily_Play, data[[resp_var]], method = "spearman", use = "complete.obs")
print(paste("Spearman Correlation: Daily Play vs", resp_var))
print(test)
}
}## Warning in cor.test.default(data$Daily_Play, data[[resp_var]], method =
## "spearman", : Cannot compute exact p-value with ties## [1] "Spearman Correlation: Daily Play vs Mean_Men_Women"
##
## Spearman's rank correlation rho
##
## data: data$Daily_Play and data[[resp_var]]
## S = 571.93, p-value = 0.2435
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.2991077## Warning in cor.test.default(data$Daily_Play, data[[resp_var]], method =
## "spearman", : Cannot compute exact p-value with ties## [1] "Spearman Correlation: Daily Play vs Mean_Women_Men"
##
## Spearman's rank correlation rho
##
## data: data$Daily_Play and data[[resp_var]]
## S = 420.4, p-value = 0.04857
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.4848038## Warning in cor.test.default(data$Daily_Play, data[[resp_var]], method =
## "spearman", : Cannot compute exact p-value with ties## [1] "Spearman Correlation: Daily Play vs Mean_Mix_Compatible"
##
## Spearman's rank correlation rho
##
## data: data$Daily_Play and data[[resp_var]]
## S = 808.88, p-value = 0.9735
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.008723976## Warning in cor.test.default(data$Daily_Play, data[[resp_var]], method =
## "spearman", : Cannot compute exact p-value with ties## [1] "Spearman Correlation: Daily Play vs Mean_Mix_Imcompatible"
##
## Spearman's rank correlation rho
##
## data: data$Daily_Play and data[[resp_var]]
## S = 828.2, p-value = 0.9546
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## -0.01495539## Warning in cor.test.default(data$Daily_Play, data[[resp_var]], method =
## "spearman", : Cannot compute exact p-value with ties## [1] "Spearman Correlation: Daily Play vs Mean_Carry_support"
##
## Spearman's rank correlation rho
##
## data: data$Daily_Play and data[[resp_var]]
## S = 787.52, p-value = 0.8942
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.0348959- The correlation is moderately positive (
rho = 0.4848), suggesting that higherDaily_Playis associated with higherMean_Women_Men. The p-value is slightly below 0.05, indicating a statistically significant relationship.
# Plot
plot_dailyplay <- ggplot(data, aes(x = Daily_Play, y = Mean_Women_Men)) +
geom_point(alpha = 0.6, color = "orange", size = 3) +
geom_smooth(method = "lm", color = "chocolate3", se = TRUE, linewidth = 1) +
ylab("Daily Play") +
xlab("Response Time (ms) (Mean Women Men)") +
ggtitle("Daily Play vs. Mean_Women_Men") +
theme_classic() +
theme(
text = element_text(size = 14),
plot.title = element_text(size = 12, hjust = 0.5)
)
# Print the plot
print(plot_dailyplay)## `geom_smooth()` using formula = 'y ~ x'
# Familiarity with Champions and IAT_Effect (Spearman Correlation) ##IAT ANALYSIS
familiarity_columns <- grep("^F_", colnames(data), value = TRUE)
for (familiarity_var in familiarity_columns) {
test <- cor.test(data[[familiarity_var]], data$IAT_Effect, method = "spearman", use = "complete.obs")
print(paste("Spearman Correlation: Familiarity (", familiarity_var, ") vs IAT_Effect"))
print(test)
}## Warning in cor.test.default(data[[familiarity_var]], data$IAT_Effect, method =
## "spearman", : Cannot compute exact p-value with ties## [1] "Spearman Correlation: Familiarity ( F_Lux ) vs IAT_Effect"
##
## Spearman's rank correlation rho
##
## data: data[[familiarity_var]] and data$IAT_Effect
## S = 760.54, p-value = 0.7955
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.06796437## Warning in cor.test.default(data[[familiarity_var]], data$IAT_Effect, method =
## "spearman", : Cannot compute exact p-value with ties## [1] "Spearman Correlation: Familiarity ( F_Senna ) vs IAT_Effect"
##
## Spearman's rank correlation rho
##
## data: data[[familiarity_var]] and data$IAT_Effect
## S = 697.99, p-value = 0.5797
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.1446199## Warning in cor.test.default(data[[familiarity_var]], data$IAT_Effect, method =
## "spearman", : Cannot compute exact p-value with ties## [1] "Spearman Correlation: Familiarity ( F_Leona ) vs IAT_Effect"
##
## Spearman's rank correlation rho
##
## data: data[[familiarity_var]] and data$IAT_Effect
## S = 720.37, p-value = 0.6542
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.1171974## Warning in cor.test.default(data[[familiarity_var]], data$IAT_Effect, method =
## "spearman", : Cannot compute exact p-value with ties## [1] "Spearman Correlation: Familiarity ( F_Thresh ) vs IAT_Effect"
##
## Spearman's rank correlation rho
##
## data: data[[familiarity_var]] and data$IAT_Effect
## S = 688.35, p-value = 0.5488
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.156431## Warning in cor.test.default(data[[familiarity_var]], data$IAT_Effect, method =
## "spearman", : Cannot compute exact p-value with ties## [1] "Spearman Correlation: Familiarity ( F_Pyke ) vs IAT_Effect"
##
## Spearman's rank correlation rho
##
## data: data[[familiarity_var]] and data$IAT_Effect
## S = 515.42, p-value = 0.1457
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.3683577## Warning in cor.test.default(data[[familiarity_var]], data$IAT_Effect, method =
## "spearman", : Cannot compute exact p-value with ties## [1] "Spearman Correlation: Familiarity ( F_BlitzCrank ) vs IAT_Effect"
##
## Spearman's rank correlation rho
##
## data: data[[familiarity_var]] and data$IAT_Effect
## S = 491.83, p-value = 0.1143
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.3972681## Warning in cor.test.default(data[[familiarity_var]], data$IAT_Effect, method =
## "spearman", : Cannot compute exact p-value with ties## [1] "Spearman Correlation: Familiarity ( F_MissFortune ) vs IAT_Effect"
##
## Spearman's rank correlation rho
##
## data: data[[familiarity_var]] and data$IAT_Effect
## S = 791.37, p-value = 0.9084
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.03018887## Warning in cor.test.default(data[[familiarity_var]], data$IAT_Effect, method =
## "spearman", : Cannot compute exact p-value with ties## [1] "Spearman Correlation: Familiarity ( F_Jinx ) vs IAT_Effect"
##
## Spearman's rank correlation rho
##
## data: data[[familiarity_var]] and data$IAT_Effect
## S = 1021.6, p-value = 0.3292
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## -0.2519763## Warning in cor.test.default(data[[familiarity_var]], data$IAT_Effect, method =
## "spearman", : Cannot compute exact p-value with ties## [1] "Spearman Correlation: Familiarity ( F_Kaisa ) vs IAT_Effect"
##
## Spearman's rank correlation rho
##
## data: data[[familiarity_var]] and data$IAT_Effect
## S = 745.56, p-value = 0.7418
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.08632674## Warning in cor.test.default(data[[familiarity_var]], data$IAT_Effect, method =
## "spearman", : Cannot compute exact p-value with ties## [1] "Spearman Correlation: Familiarity ( F_Jhin ) vs IAT_Effect"
##
## Spearman's rank correlation rho
##
## data: data[[familiarity_var]] and data$IAT_Effect
## S = 527.67, p-value = 0.1641
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.3533433## Warning in cor.test.default(data[[familiarity_var]], data$IAT_Effect, method =
## "spearman", : Cannot compute exact p-value with ties## [1] "Spearman Correlation: Familiarity ( F_Lucian ) vs IAT_Effect"
##
## Spearman's rank correlation rho
##
## data: data[[familiarity_var]] and data$IAT_Effect
## S = 695.74, p-value = 0.5724
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.1473745## Warning in cor.test.default(data[[familiarity_var]], data$IAT_Effect, method =
## "spearman", : Cannot compute exact p-value with ties## [1] "Spearman Correlation: Familiarity ( F_Varus ) vs IAT_Effect"
##
## Spearman's rank correlation rho
##
## data: data[[familiarity_var]] and data$IAT_Effect
## S = 692.47, p-value = 0.5619
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.1513894Familiarity with Champions does not significantly correlate with the IAT_Effect in this dataset, as all p-values are greater than 0.05.
MODELS
filtered_data <- data[data$Gender != 5, ] ##filtered the 5 out
# Gender and IAT Effect (ANOVA)
modelg<-aov(filtered_data$IAT_Effect ~ as.factor(filtered_data$Gender), data = filtered_data)
summary(modelg)## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(filtered_data$Gender) 1 275735 275735 28.79 9.95e-05 ***
## Residuals 14 134065 9576
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1There is a significant effect of gender on the IAT effect
pairwise_results <- pairwise.t.test(
filtered_data$IAT_Effect,
as.factor(filtered_data$Gender),
p.adjust.method = "bonferroni"
)
print(pairwise_results)##
## Pairwise comparisons using t tests with pooled SD
##
## data: filtered_data$IAT_Effect and as.factor(filtered_data$Gender)
##
## 1
## 2 1e-04
##
## P value adjustment method: bonferroni# Race and IAT Effect (ANOVA)
modelr<-aov(data$IAT_Effect ~ as.factor(data$Race), data = data)
summary(modelr)## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(data$Race) 3 8806 2935 0.095 0.962
## Residuals 13 402204 30939# Employment and IAT Effect (ANOVA)
modele<-aov(data$IAT_Effect ~ as.factor(data$Employment), data = data)
summary(modele)## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(data$Employment) 2 10103 5051 0.176 0.84
## Residuals 14 400907 28636# Marital status and IAT Effect (ANOVA)
modelm<-aov(data$IAT_Effect ~ as.factor(data$Marital_status), data = data)
summary(modelm)## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(data$Marital_status) 1 6089 6089 0.226 0.642
## Residuals 15 404920 26995# Political Orientation and IAT Effect (Spearman Correlation)
cor.test(data$Pol_Ori, data$IAT_Effect, method = "spearman")## Warning in cor.test.default(data$Pol_Ori, data$IAT_Effect, method =
## "spearman"): Cannot compute exact p-value with ties##
## Spearman's rank correlation rho
##
## data: data$Pol_Ori and data$IAT_Effect
## S = 628.58, p-value = 0.3752
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.2296868# Education and IAT Effect (ANOVA)
modeled<-aov(data$IAT_Effect ~ as.factor(data$Education), data = data)
summary(modeled)## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(data$Education) 3 241167 80389 6.153 0.00779 **
## Residuals 13 169843 13065
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(modeled, ~ Education)## Education emmean SE df lower.CL upper.CL
## 4 -0.107 80.8 13 -175 174.5
## 6 -130.732 80.8 13 -305 43.9
## 7 208.359 36.1 13 130 286.4
## 8 193.593 66.0 13 51 336.2
##
## Confidence level used: 0.95# Religion and IAT Effect (ANOVA)
modelre<-aov(data$IAT_Effect ~ as.factor(data$Religion), data = data)
summary(modelre)## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(data$Religion) 2 65959 32980 1.338 0.294
## Residuals 14 345050 24646# Ideology of Religiousness and IAT Effect (Spearman Correlation)
cor.test(data$Ide_Religious, data$IAT_Effect, method = "spearman")## Warning in cor.test.default(data$Ide_Religious, data$IAT_Effect, method =
## "spearman"): Cannot compute exact p-value with ties##
## Spearman's rank correlation rho
##
## data: data$Ide_Religious and data$IAT_Effect
## S = 925, p-value = 0.6093
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## -0.133581# Role Preference and IAT Effect (ANOVA)
modelpr<-aov(data$IAT_Effect ~ as.factor(data$Role_Preference), data = data)
summary(modelpr)## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(data$Role_Preference) 4 145478 36369 1.644 0.227
## Residuals 12 265532 22128# Familiarity with Champions and IAT Effect (Spearman Correlation)
cor.test(data$Familiarity_Champ, data$IAT_Effect, method = "spearman", use = "complete.obs")## Warning in cor.test.default(data$Familiarity_Champ, data$IAT_Effect, method =
## "spearman", : Cannot compute exact p-value with ties##
## Spearman's rank correlation rho
##
## data: data$Familiarity_Champ and data$IAT_Effect
## S = 876.97, p-value = 0.7756
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## -0.07472376# Highest Rank and IAT Effect (ANOVA)
modelhr<-aov(data$IAT_Effect ~ as.factor(data$Highest_Rank), data = data)
summary(modelhr)## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(data$Highest_Rank) 4 119779 29945 1.234 0.348
## Residuals 12 291231 24269# Daily Play and IAT Effect (Spearman Correlation)
cor.test(data$Daily_Play, data$IAT_Effect, method = "spearman", use = "complete.obs")## Warning in cor.test.default(data$Daily_Play, data$IAT_Effect, method =
## "spearman", : Cannot compute exact p-value with ties##
## Spearman's rank correlation rho
##
## data: data$Daily_Play and data$IAT_Effect
## S = 751.93, p-value = 0.7645
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.07851578Plots significant results
filtered_data$Gender <- factor(filtered_data$Gender, levels = c(1, 2), labels = c("Male", "Female"))# Gender and Mix Compatible Response Times
ggplot(filtered_data, aes(x = as.factor(Gender), y = Mean_Mix_Compatible, fill = as.factor(Gender))) +
geom_boxplot() +
scale_fill_brewer(palette = "Set2") +
labs(
title = "Gender and Mean Mix Compatible Response Times",
x = "Gender",
y = "Mean Mix Compatible Response Time (ms)"
) +
theme_minimal() +
theme(text = element_text(size = 14))
ggplot(filtered_data, aes(x = Gender, y = Mean_Mix_Compatible, fill = Gender)) +
geom_boxplot() +
scale_fill_brewer(palette = "Set2") +
labs(
title = "Participant Gender and Mean_Mix_Compatible",
x = "Participant Gender",
y = "Mean Mix Compatible Response Time (ms)"
) +
theme_minimal() +
theme(text = element_text(size = 14))
# Gender and Mix Compatible Response Times
ggplot(data, aes(x = as.factor(Gender), y = Mean_Mix_Imcompatible, fill = as.factor(Gender))) +
geom_boxplot() +
scale_fill_brewer(palette = "Set2") +
labs(
title = "Gender and Mean Mix Imcompatible Response Times",
x = "Gender",
y = "Mean Mix Compatible Response Time (ms)"
) +
theme_minimal() +
theme(text = element_text(size = 14))
ggplot(filtered_data, aes(x = Gender, y = Mean_Mix_Imcompatible, fill = Gender)) +
geom_boxplot() +
scale_fill_brewer(palette = "Set2") +
labs(
title = "Participant Gender and Mean_Mix_Imcompatible",
x = "Participant Gender",
y = "Mean Mix Imcompatible Response Time (ms)"
) +
theme_minimal() +
theme(text = element_text(size = 14))
data$Education <- factor(data$Education,
levels = c(1, 2, 3, 4, 5, 6, 7, 8, 9),
labels = c(
"Less than Primary",
"Primary",
"Some secondary",
"Secondary",
"Vocational or Similar",
"University but No degree",
"Bachelors Degree",
"Professional degree",
"Prefer not to say"
))ggplot(data, aes(x = Education, y = Mean_Mix_Compatible, fill = Education)) +
geom_boxplot() +
scale_fill_brewer(palette = "Set3") +
labs(
title = "Education Level and Mean Mix Compatible Response Times",
x = "Education Level",
y = "Mean Mix Compatible Response Time (ms)"
) +
theme_minimal() +
theme(
text = element_text(size = 10),
axis.text.x = element_text(size = 8, angle = 45, hjust = 1),
axis.text.y = element_text(size = 8),
plot.margin = margin(t = 40, r = 30, b = 30, l = 50)
)
ggplot(data, aes(x = Education, y = Mean_Mix_Imcompatible, fill = Education)) +
geom_boxplot() +
scale_fill_brewer(palette = "Set3") +
labs(
title = "Education Level and Mean Mix Imcompatible Response Times",
x = "Education Level",
y = "Mean Mix Imcompatible Response Time (ms)"
) +
theme_minimal() +
theme(
text = element_text(size = 10),
axis.text.x = element_text(size = 8, angle = 45, hjust = 1),
axis.text.y = element_text(size = 8),
plot.margin = margin(t = 40, r = 30, b = 30, l = 50)
)
filtered_data <- data[data$Gender %in% c(1, 2), ]
ggplot(filtered_data, aes(x = Education, y = Mean_Mix_Imcompatible, fill = Education)) +
geom_boxplot() +
scale_fill_brewer(palette = "Set3") +
labs(
title = "Education Level and Mean Mix Imcompatible Response Times by Gender",
x = "Education Level",
y = "Mean Mix Imcompatible Response Time (ms)"
) +
facet_wrap(~ Gender, labeller = labeller(Gender = c(`1` = "Male", `2` = "Female"))) +
theme_minimal() +
theme(
text = element_text(size = 10),
axis.text.x = element_text(size = 8, angle = 45, hjust = 1),
axis.text.y = element_text(size = 8),
plot.margin = margin(t = 40, r = 30, b = 30, l = 50)
)
ggplot(filtered_data, aes(x = Education, y = Mean_Mix_Compatible, fill = Education)) +
geom_boxplot() +
scale_fill_brewer(palette = "Set3") +
labs(
title = "Education Level and Mean Mix Compatible Response Times by Gender",
x = "Education Level",
y = "Mean Mix Imcompatible Response Time (ms)"
) +
facet_wrap(~ Gender, labeller = labeller(Gender = c(`1` = "Male", `2` = "Female"))) +
theme_minimal() +
theme(
text = element_text(size = 10),
axis.text.x = element_text(size = 8, angle = 45, hjust = 1),
axis.text.y = element_text(size = 8),
plot.margin = margin(t = 40, r = 30, b = 30, l = 50)
)
ggplot(filtered_data, aes(x = as.factor(Daily_Play), y = Mean_Mix_Imcompatible, fill = as.factor(Daily_Play))) +
geom_boxplot() +
scale_fill_brewer(palette = "Set2", name = "Daily Play (Hours)") +
labs(
title = "Daily Play and Mean Mix Imcompatible Response Times",
x = "Daily Play (Hours)",
y = "Mean Mix Imcompatible Response Time (ms)"
) +
theme_minimal() +
theme(
text = element_text(size = 10),
axis.text.x = element_text(size = 8, angle = 45, hjust = 1),
axis.text.y = element_text(size = 8),
plot.margin = margin(t = 40, r = 30, b = 30, l = 50)
)
ggplot(filtered_data, aes(x = as.factor(Daily_Play), y = Mean_Mix_Compatible, fill = as.factor(Daily_Play))) +
geom_boxplot() +
scale_fill_brewer(palette = "Set2", name = "Daily Play (Hours)") +
labs(
title = "Daily Play and Mean Mix Compatible Response Times",
x = "Daily Play (Hours)",
y = "Mean Mix Imcompatible Response Time (ms)"
) +
theme_minimal() +
theme(
text = element_text(size = 10),
axis.text.x = element_text(size = 8, angle = 45, hjust = 1),
axis.text.y = element_text(size = 8),
plot.margin = margin(t = 40, r = 30, b = 30, l = 50)
)
ggplot(filtered_data, aes(x = as.factor(Daily_Play), y = Mean_Mix_Imcompatible, fill = as.factor(Daily_Play))) +
geom_boxplot() +
scale_fill_brewer(palette = "Set2", name = "Daily Play in Hour") +
labs(
title = "Daily Play and Mean Mix Imcompatible Response Times by Gender",
x = "Daily Play (Hours)",
y = "Mean Mix Imcompatible Response Time (ms)"
) +
facet_wrap(~ Gender, labeller = labeller(Gender = c(`1` = "Male", `2` = "Female"))) +
theme_minimal() +
theme(
text = element_text(size = 10),
axis.text.x = element_text(size = 8, angle = 45, hjust = 1),
axis.text.y = element_text(size = 8),
plot.margin = margin(t = 40, r = 30, b = 30, l = 50)
)
ggplot(filtered_data, aes(x = as.factor(Daily_Play), y = Mean_Mix_Compatible, fill = as.factor(Daily_Play))) +
geom_boxplot() +
scale_fill_brewer(palette = "Set2", name = "Daily Play in Hour") +
labs(
title = "Daily Play and Mean Mix Compatible Response Times by Gender",
x = "Daily Play (Hours)",
y = "Mean Mix Imcompatible Response Time (ms)"
) +
facet_wrap(~ Gender, labeller = labeller(Gender = c(`1` = "Male", `2` = "Female"))) +
theme_minimal() +
theme(
text = element_text(size = 10),
axis.text.x = element_text(size = 8, angle = 45, hjust = 1),
axis.text.y = element_text(size = 8),
plot.margin = margin(t = 40, r = 30, b = 30, l = 50)
)
filtered_data$Employment <- factor(filtered_data$Employment,
levels = c(1, 2, 5),
labels = c("Working full-time", "Working part-time", "Student"))ggplot(filtered_data, aes(x = Employment, y = Mean_Mix_Imcompatible, fill = Employment)) +
geom_boxplot() +
scale_fill_brewer(palette = "Set1", name = "Employment") +
labs(
title = "Employment and Mean Mix Imcompatible Response Times",
x = "Employment",
y = "Mean Mix Imcompatible Response Time (ms)"
) +
theme_minimal() +
theme(
text = element_text(size = 10),
axis.text.x = element_text(size = 8, angle = 45, hjust = 1),
axis.text.y = element_text(size = 8),
plot.margin = margin(t = 40, r = 30, b = 30, l = 50)
)
ggplot(filtered_data, aes(x = Employment, y = Mean_Mix_Compatible, fill = Employment)) +
geom_boxplot() +
scale_fill_brewer(palette = "Set1", name = "Employment") +
labs(
title = "Employment and Mean Mix Compatible Response Times",
x = "Employment",
y = "Mean Mix Imcompatible Response Time (ms)"
) +
theme_minimal() +
theme(
text = element_text(size = 10),
axis.text.x = element_text(size = 8, angle = 45, hjust = 1),
axis.text.y = element_text(size = 8),
plot.margin = margin(t = 40, r = 30, b = 30, l = 50)
)
ggplot(filtered_data, aes(x = Employment, y = Mean_Mix_Imcompatible, fill = Employment)) +
geom_boxplot() +
scale_fill_brewer(palette = "Set1", name = "Employment") +
labs(
title = "Employment and Mean Mix Imcompatible Response Times by Gender",
x = "Employment",
y = "Mean Mix Imcompatible Response Time (ms)"
) +
facet_wrap(~ Gender, labeller = labeller(Gender = c(`1` = "Male", `2` = "Female"))) +
theme_minimal() +
theme(
text = element_text(size = 10),
axis.text.x = element_text(size = 8, angle = 45, hjust = 1),
axis.text.y = element_text(size = 8),
plot.margin = margin(t = 40, r = 30, b = 30, l = 50)
)
ggplot(filtered_data, aes(x = Employment, y = Mean_Mix_Compatible, fill = Employment)) +
geom_boxplot() +
scale_fill_brewer(palette = "Set1", name = "Employment") +
labs(
title = "Employment and Mean Mix Compatible Response Times by Gender",
x = "Employment",
y = "Mean Mix Imcompatible Response Time (ms)"
) +
facet_wrap(~ Gender, labeller = labeller(Gender = c(`1` = "Male", `2` = "Female"))) +
theme_minimal() +
theme(
text = element_text(size = 10),
axis.text.x = element_text(size = 8, angle = 45, hjust = 1),
axis.text.y = element_text(size = 8),
plot.margin = margin(t = 40, r = 30, b = 30, l = 50)
)
Error analysis
iat_vars <- c("Mean_Men_Women", "Mean_Women_Men", "Mean_Mix_Compatible", "Mean_Mix_Imcompatible", "Mean_Carry_support")
er_vars <- c("ER_Men_Women", "ER_Women_Men", "ER_Compatible", "ER_Imcompatible", "ER_Carry_support")
cor_results <- data.frame()
for (i in seq_along(iat_vars)) {
cor_test <- cor.test(data[[iat_vars[i]]], data[[er_vars[i]]], method = "pearson", use = "complete.obs")
cor_results <- rbind(cor_results, data.frame(
IAT_Variable = iat_vars[i],
ER_Variable = er_vars[i],
Correlation = cor_test$estimate,
P_Value = cor_test$p.value
))
}
print(cor_results)## IAT_Variable ER_Variable Correlation P_Value
## cor Mean_Men_Women ER_Men_Women -0.3284374 0.19806020
## cor1 Mean_Women_Men ER_Women_Men -0.4704333 0.05668667
## cor2 Mean_Mix_Compatible ER_Compatible 0.2968986 0.24717787
## cor3 Mean_Mix_Imcompatible ER_Imcompatible -0.4272521 0.08716206
## cor4 Mean_Carry_support ER_Carry_support -0.1037928 0.69179117for (i in seq_along(iat_vars)) {
plot <- ggplot(data, aes_string(x = iat_vars[i], y = er_vars[i])) +
geom_point(alpha = 0.6) +
geom_smooth(method = "lm", color = "blue", se = TRUE) +
labs(
title = paste("Relationship Between", iat_vars[i], "and", er_vars[i]),
x = iat_vars[i],
y = er_vars[i]
) +
theme_minimal() +
theme(text = element_text(size = 14))
print(plot) # Ensure the ggplot object is printed
}## Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
## ℹ Please use tidy evaluation idioms with `aes()`.
## ℹ See also `vignette("ggplot2-in-packages")` for more information.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
# Regression Analysis
lm_results <- list()
for (i in seq_along(iat_vars)) {
model <- lm(as.formula(paste(er_vars[i], "~", iat_vars[i])), data = data)
summary_model <- summary(model)
lm_results[[iat_vars[i]]] <- summary_model
print(paste("Regression Results for", iat_vars[i], "and", er_vars[i]))
print(summary_model)
}## [1] "Regression Results for Mean_Men_Women and ER_Men_Women"
##
## Call:
## lm(formula = as.formula(paste(er_vars[i], "~", iat_vars[i])),
## data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.609 -3.048 -2.175 1.336 19.279
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 13.12029 6.85432 1.914 0.0749 .
## Mean_Men_Women -0.01381 0.01026 -1.347 0.1981
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.247 on 15 degrees of freedom
## Multiple R-squared: 0.1079, Adjusted R-squared: 0.0484
## F-statistic: 1.814 on 1 and 15 DF, p-value: 0.1981
##
## [1] "Regression Results for Mean_Women_Men and ER_Women_Men"
##
## Call:
## lm(formula = as.formula(paste(er_vars[i], "~", iat_vars[i])),
## data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.7080 -4.6404 -2.4353 -0.0155 13.6049
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 19.92161 7.33893 2.715 0.0160 *
## Mean_Women_Men -0.02312 0.01120 -2.065 0.0567 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.274 on 15 degrees of freedom
## Multiple R-squared: 0.2213, Adjusted R-squared: 0.1694
## F-statistic: 4.263 on 1 and 15 DF, p-value: 0.05669
##
## [1] "Regression Results for Mean_Mix_Compatible and ER_Compatible"
##
## Call:
## lm(formula = as.formula(paste(er_vars[i], "~", iat_vars[i])),
## data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.8855 -4.2172 -2.3026 0.7931 19.8227
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.821140 7.227519 -0.252 0.804
## Mean_Mix_Compatible 0.010955 0.009097 1.204 0.247
##
## Residual standard error: 7.29 on 15 degrees of freedom
## Multiple R-squared: 0.08815, Adjusted R-squared: 0.02736
## F-statistic: 1.45 on 1 and 15 DF, p-value: 0.2472
##
## [1] "Regression Results for Mean_Mix_Imcompatible and ER_Imcompatible"
##
## Call:
## lm(formula = as.formula(paste(er_vars[i], "~", iat_vars[i])),
## data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -11.731 -4.479 -1.659 1.997 14.079
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 30.18723 10.05039 3.004 0.00891 **
## Mean_Mix_Imcompatible -0.01972 0.01078 -1.830 0.08716 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.73 on 15 degrees of freedom
## Multiple R-squared: 0.1825, Adjusted R-squared: 0.128
## F-statistic: 3.35 on 1 and 15 DF, p-value: 0.08716
##
## [1] "Regression Results for Mean_Carry_support and ER_Carry_support"
##
## Call:
## lm(formula = as.formula(paste(er_vars[i], "~", iat_vars[i])),
## data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.2338 -2.9160 -2.3146 0.4553 23.3394
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.484817 9.565489 0.992 0.337
## Mean_Carry_support -0.005055 0.012507 -0.404 0.692
##
## Residual standard error: 7.34 on 15 degrees of freedom
## Multiple R-squared: 0.01077, Adjusted R-squared: -0.05518
## F-statistic: 0.1634 on 1 and 15 DF, p-value: 0.6918Mix Incompatible Trials showed the strongest relationship with a tendency that faster responses might lead to more errors.
# Plot
plot_mix_incompatible <- ggplot(data, aes(x = Mean_Mix_Imcompatible, y = ER_Imcompatible)) +
geom_point(alpha = 0.6, color = "orange", size = 3) +
geom_smooth(method = "lm", color = "chocolate3", se = TRUE, linewidth = 1) +
ylab("Error Count (ER_Imcompatible)") +
xlab("Response Time (ms) (Mean_Mix_Imcompatible)") +
ggtitle("Relationship Between Response Time and Error Count (Mix Incompatible)") +
theme_classic() +
theme(
text = element_text(size = 14),
plot.title = element_text(size = 12, hjust = 0.5)
)
# Print the plot
print(plot_mix_incompatible)## `geom_smooth()` using formula = 'y ~ x'
Perception Stereotype Analysis
data$IAT <- as.numeric(as.character(data$IAT_Effect))
data$Asc_carry <- as.numeric(as.character(data$Asc_carry))
cor.test(data$IAT_Effect, data$Asc_carry, method = "pearson", use = "complete.obs")##
## Pearson's product-moment correlation
##
## data: data$IAT_Effect and data$Asc_carry
## t = 0.023431, df = 15, p-value = 0.9816
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.4759791 0.4852834
## sample estimates:
## cor
## 0.006049688cor.test(data$IAT_Effect, data$Asc_support, method = "pearson", use = "complete.obs")##
## Pearson's product-moment correlation
##
## data: data$IAT_Effect and data$Asc_support
## t = -0.62611, df = 15, p-value = 0.5407
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.5946230 0.3477283
## sample estimates:
## cor
## -0.1595892data <- data %>%
mutate(Att_Carry = recode(
Att_Carry,
"strongly like" = 1,
"like" = 2,
"neither like nor dislike" = 3,
"dislike" = 4,
"strongly dislike" = 5
))## Warning: There was 1 warning in `mutate()`.
## ℹ In argument: `Att_Carry = recode(...)`.
## Caused by warning in `recode.numeric()`:
## ! NAs introduced by coercioncor.test(data$IAT_Effect, data$Att_Carry, method = "pearson", use = "complete.obs")##
## Pearson's product-moment correlation
##
## data: data$IAT_Effect and data$Att_Carry
## t = 0.22514, df = 15, p-value = 0.8249
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.4347389 0.5240593
## sample estimates:
## cor
## 0.05803194data <- data %>%
mutate(Att_support = recode(
Att_support,
"strongly like" = 1,
"like" = 2,
"neither like nor dislike" = 3,
"dislike" = 4,
"strongly dislike" = 5
))## Warning: There was 1 warning in `mutate()`.
## ℹ In argument: `Att_support = recode(...)`.
## Caused by warning in `recode.numeric()`:
## ! NAs introduced by coercioncor.test(data$IAT, data$Att_support, method = "pearson", use = "complete.obs")##
## Pearson's product-moment correlation
##
## data: data$IAT and data$Att_support
## t = 0.34494, df = 15, p-value = 0.7349
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.4093890 0.5460725
## sample estimates:
## cor
## 0.08871157data <- data %>%
mutate(Gender_Carry_1 = recode(
Gender_Carry_1,
"strongle agree" = 1,
"agree" = 2,
"neither nor" = 3,
"disagree" = 4,
"strongly disagree" = 5
))## Warning: There was 1 warning in `mutate()`.
## ℹ In argument: `Gender_Carry_1 = recode(...)`.
## Caused by warning in `recode.numeric()`:
## ! NAs introduced by coercioncor.test(data$IAT, data$Gender_Carry_1, method = "pearson", use = "complete.obs")##
## Pearson's product-moment correlation
##
## data: data$IAT and data$Gender_Carry_1
## t = 0.041299, df = 15, p-value = 0.9676
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.4724031 0.4888023
## sample estimates:
## cor
## 0.01066267data <- data %>%
mutate(Gender_Carry_2 = recode(
Gender_Carry_2,
"strongle agree" = 1,
"agree" = 2,
"neither nor" = 3,
"disagree" = 4,
"strongly disagree" = 5
))## Warning: There was 1 warning in `mutate()`.
## ℹ In argument: `Gender_Carry_2 = recode(...)`.
## Caused by warning in `recode.numeric()`:
## ! NAs introduced by coercioncor.test(data$IAT, data$Gender_Carry_2, method = "pearson", use = "complete.obs")##
## Pearson's product-moment correlation
##
## data: data$IAT and data$Gender_Carry_2
## t = 0.091487, df = 15, p-value = 0.9283
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.4622765 0.4986007
## sample estimates:
## cor
## 0.02361533data <- data %>%
mutate(Gender_role_1 = recode(
Gender_role_1,
"strongle agree" = 1,
"agree" = 2,
"neither nor" = 3,
"disagree" = 4,
"strongly disagree" = 5
))## Warning: There was 1 warning in `mutate()`.
## ℹ In argument: `Gender_role_1 = recode(...)`.
## Caused by warning in `recode.numeric()`:
## ! NAs introduced by coercioncor.test(data$IAT, data$Gender_role_1, method = "pearson", use = "complete.obs")##
## Pearson's product-moment correlation
##
## data: data$IAT and data$Gender_role_1
## t = 1.1208, df = 15, p-value = 0.28
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2339238 0.6692086
## sample estimates:
## cor
## 0.2779749data$Gender_role_2 <- recode(
data$Gender_role_2,
"strongle agree" = 5,
"agree" = 4,
"neither nor" = 3,
"disagree" = 2,
"strongly disagree" = 1
)## Warning in recode.numeric(data$Gender_role_2, `strongle agree` = 5, agree = 4,
## : NAs introduced by coercioncor.test(data$IAT, data$Gender_role_2, method = "pearson", use = "complete.obs")##
## Pearson's product-moment correlation
##
## data: data$IAT and data$Gender_role_2
## t = 0.8667, df = 15, p-value = 0.3998
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2930214 0.6326226
## sample estimates:
## cor
## 0.2183798data$Women_carry <- recode(
data$Women_carry,
"strongle agree" = 5,
"agree" = 4,
"neither nor" = 3,
"disagree" = 2,
"strongly disagree" = 1
)## Warning in recode.numeric(data$Women_carry, `strongle agree` = 5, agree = 4, :
## NAs introduced by coercioncor.test(data$IAT, data$Women_carry, method = "pearson", use = "complete.obs")##
## Pearson's product-moment correlation
##
## data: data$IAT and data$Women_carry
## t = -1.0794, df = 15, p-value = 0.2974
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.6635006 0.2436053
## sample estimates:
## cor
## -0.2684745data$Men_support <- recode(
data$Men_support,
"strongle agree" = 5,
"agree" = 4,
"neither nor" = 3,
"disagree" = 2,
"strongly disagree" = 1
)## Warning in recode.numeric(data$Men_support, `strongle agree` = 5, agree = 4, :
## NAs introduced by coercioncor.test(data$IAT, data$Men_support, method = "pearson", use = "complete.obs")##
## Pearson's product-moment correlation
##
## data: data$IAT and data$Men_support
## t = 0.40298, df = 15, p-value = 0.6926
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.3968965 0.5564562
## sample estimates:
## cor
## 0.1034908data <- data %>%
rowwise() %>%
mutate(Average_Collective = mean(
c(Gender_Carry_1, Gender_Carry_2, Gender_role_1, Gender_role_2, Women_carry, Men_support),
na.rm = TRUE
))cor.test(data$IAT, data$Average_Collective, method = "pearson", use = "complete.obs")##
## Pearson's product-moment correlation
##
## data: data$IAT and data$Average_Collective
## t = 0.4879, df = 15, p-value = 0.6327
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.3783879 0.5713117
## sample estimates:
## cor
## 0.1249886