Loading libraries

library(ggplot2)
library(ggpubr)
library(tidyverse)
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ tibble  3.1.8      ✔ dplyr   1.0.10
## ✔ tidyr   1.2.1      ✔ stringr 1.4.1 
## ✔ readr   2.1.3      ✔ forcats 0.5.2 
## ✔ purrr   0.3.5      
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
library(effectsize)
library(tidyr)
library(rstatix)
## 
## Attaching package: 'rstatix'
## 
## The following objects are masked from 'package:effectsize':
## 
##     cohens_d, eta_squared
## 
## The following object is masked from 'package:stats':
## 
##     filter
library(report)

Reading in data

PS4_DATA <- read.csv("ps4data.csv")

Changing gender into factor

PS4_DATA$id <- as.factor(PS4_DATA$id)
levels(PS4_DATA$id) <- list('Female' = "1", 'Male' = "2")

Descriptives for search eye mean by gender

mean(PS4_DATA$search_eyex_speed[PS4_DATA$id=="Male"])
## [1] 1.218702
sd(PS4_DATA$search_eyex_speed[PS4_DATA$id=="Male"])
## [1] 0.2848641
mean(PS4_DATA$search_eyex_speed[PS4_DATA$id=="Female"])
## [1] 1.36408
sd(PS4_DATA$search_eyex_speed[PS4_DATA$id=="Female"])
## [1] 0.4422632

Descriptives for search eye mean by eye movement while walking (low/high)

mean(PS4_DATA$search_eyex_speed[PS4_DATA$walk_eyex_factor=="1"])
## [1] 1.107506
sd(PS4_DATA$search_eyex_speed[PS4_DATA$walk_eyex_factor=="1"])
## [1] 0.2494364
mean(PS4_DATA$search_eyex_speed[PS4_DATA$walk_eyex_factor=="2"])
## [1] 1.47911
sd(PS4_DATA$search_eyex_speed[PS4_DATA$walk_eyex_factor=="2"])
## [1] 0.3919304

Changing eye movement while walking into factor

PS4_DATA$walk_eyex_factor <- as.factor(PS4_DATA$walk_eyex_factor)
levels(PS4_DATA$walk_eyex_factor) <- list('LowSpeed' = "1", 'HighSpeed' = "2")
ggplot(PS4_DATA, aes(fill=id, y=search_eyex_speed, x=walk_eyex_factor)) + 
    geom_bar(position="dodge", stat="identity")+
  xlab("Eye Movement Speed Walking")+
  ylab("Eye Movement Speed Searching")

Cell means table

cellDesc <- with(PS4_DATA, aggregate(x=list(Mean=search_eyex_speed, SD=search_eyex_speed),
                                     by=list(F1=id, F2=walk_eyex_factor),
                                     FUN=mean_sd))
View(cellDesc)
## Warning in format.data.frame(x0): corrupt data frame: columns will be truncated
## or padded with NAs

Factorial ANOVA: seeing if outcome of interest (search eye mean) is dependent on gender and walking eye movement

anova <- aov(search_eyex_speed~id*walk_eyex_factor,data=PS4_DATA)
summary(anova)
##                     Df Sum Sq Mean Sq F value   Pr(>F)    
## id                   1  0.312  0.3116   3.153   0.0813 .  
## walk_eyex_factor     1  1.961  1.9615  19.844 4.17e-05 ***
## id:walk_eyex_factor  1  0.432  0.4320   4.371   0.0412 *  
## Residuals           55  5.436  0.0988                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
report(anova)
## Warning: Could not find Sum-of-Squares for the (Intercept) in the ANOVA table.
## The ANOVA (formula: search_eyex_speed ~ id * walk_eyex_factor) suggests that:
## 
##   - The main effect of id is statistically not significant and small (F(1, 55) =
## 3.15, p = 0.081; Eta2 (partial) = 0.05, 95% CI [0.00, 1.00])
##   - The main effect of walk_eyex_factor is statistically significant and large
## (F(1, 55) = 19.84, p < .001; Eta2 (partial) = 0.27, 95% CI [0.11, 1.00])
##   - The interaction between id and walk_eyex_factor is statistically significant
## and medium (F(1, 55) = 4.37, p = 0.041; Eta2 (partial) = 0.07, 95% CI
## [1.62e-03, 1.00])
## 
## Effect sizes were labelled following Field's (2013) recommendations.

Simple Effects - males

simple_effects_male <- PS4_DATA %>% filter(id=="Male") %>% 
  aov(search_eyex_speed~walk_eyex_factor,data=.) 

summary(simple_effects_male)
##                  Df Sum Sq Mean Sq F value Pr(>F)  
## walk_eyex_factor  1  0.288 0.28802   3.905 0.0581 .
## Residuals        28  2.065 0.07376                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
report(simple_effects_male)
## Warning: Could not find Sum-of-Squares for the (Intercept) in the ANOVA table.
## The ANOVA (formula: search_eyex_speed ~ walk_eyex_factor) suggests that:
## 
##   - The main effect of walk_eyex_factor is statistically not significant and
## medium (F(1, 28) = 3.90, p = 0.058; Eta2 = 0.12, 95% CI [0.00, 1.00])
## 
## Effect sizes were labelled following Field's (2013) recommendations.

Simple effects - Females

simple_effects_female <- PS4_DATA %>% filter(id=="Female") %>% 
  aov(search_eyex_speed~walk_eyex_factor,data=.) 

summary(simple_effects_female)
##                  Df Sum Sq Mean Sq F value   Pr(>F)    
## walk_eyex_factor  1  2.105  2.1055   16.86 0.000334 ***
## Residuals        27  3.371  0.1249                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
report(simple_effects_female)
## Warning: Could not find Sum-of-Squares for the (Intercept) in the ANOVA table.
## The ANOVA (formula: search_eyex_speed ~ walk_eyex_factor) suggests that:
## 
##   - The main effect of walk_eyex_factor is statistically significant and large
## (F(1, 27) = 16.86, p < .001; Eta2 = 0.38, 95% CI [0.15, 1.00])
## 
## Effect sizes were labelled following Field's (2013) recommendations.