Similarity Ratings Analyses
Libraries and Data Files
#load libraries
library(dplyr)
library(ggplot2)
library(rstatix)
## Warning: package 'rstatix' was built under R version 4.5.2
library(ez)
library(sciplot)
## Warning: package 'sciplot' was built under R version 4.5.2
library(gplots)
## Warning: package 'gplots' was built under R version 4.5.2
library(car)
library(afex)
## Warning: package 'afex' was built under R version 4.5.2
library(emmeans)
## Warning: package 'emmeans' was built under R version 4.5.2
#load data
data<-read.csv("AllData_SimilarityRatings.csv", header = T)
Data Pre-processing
#rename useful variables
data$sbj<-data$participant
data$resp<-data$slider_exp.response
data$block<-data$blocks.thisN +1
data <- mutate_if(data, is.character, as.factor)
data$sbj<-as.factor(data$sbj)
str(data)
## 'data.frame': 9648 obs. of 38 variables:
## $ trl : int 20 12 17 14 21 3 10 13 6 23 ...
## $ cnd : Factor w/ 2 levels "between","within": 2 2 2 2 1 2 2 2 2 1 ...
## $ dim : Factor w/ 2 levels "irrel","rel": 2 1 2 2 2 1 1 2 1 2 ...
## $ size1 : num 3.4 2.6 2.2 2.6 2.2 2.2 2.6 2.2 3 3 ...
## $ hue1 : num 0.1 0.3 0.1 -0.3 -0.1 0.1 -0.1 -0.3 -0.1 -0.1 ...
## $ size2 : num 3.4 3 2.2 2.6 2.2 2.6 3 2.2 3.4 3 ...
## $ hue2 : num 0.3 0.3 0.3 -0.1 0.1 0.1 -0.1 -0.1 -0.1 0.1 ...
## $ practice_trials.thisRepN : logi NA NA NA NA NA NA ...
## $ practice_trials.thisTrialN: logi NA NA NA NA NA NA ...
## $ practice_trials.thisN : logi NA NA NA NA NA NA ...
## $ practice_trials.thisIndex : logi NA NA NA NA NA NA ...
## $ blocks.thisRepN : int 0 0 0 0 0 0 0 0 0 0 ...
## $ blocks.thisTrialN : int 0 0 0 0 0 0 0 0 0 0 ...
## $ blocks.thisN : int 0 0 0 0 0 0 0 0 0 0 ...
## $ blocks.thisIndex : int 0 0 0 0 0 0 0 0 0 0 ...
## $ trials.thisRepN : int 0 0 0 0 0 0 0 0 0 0 ...
## $ trials.thisTrialN : int 0 1 2 3 4 5 6 7 8 9 ...
## $ trials.thisN : int 0 1 2 3 4 5 6 7 8 9 ...
## $ trials.thisIndex : int 19 11 16 13 20 2 9 12 5 22 ...
## $ break_loop.thisRepN : logi NA NA NA NA NA NA ...
## $ break_loop.thisTrialN : logi NA NA NA NA NA NA ...
## $ break_loop.thisN : logi NA NA NA NA NA NA ...
## $ break_loop.thisIndex : logi NA NA NA NA NA NA ...
## $ thisRow.t : num 68.9 73.1 75.9 79.7 82.8 ...
## $ notes : logi NA NA NA NA NA NA ...
## $ slider_exp.response : int 5 7 5 4 3 4 3 2 7 5 ...
## $ slider_exp.rt : num 3.89 2.6 3.45 2.87 4.87 ...
## $ participant : int 17202 17202 17202 17202 17202 17202 17202 17202 17202 17202 ...
## $ session : Factor w/ 2 levels "Post","Pre": 1 1 1 1 1 1 1 1 1 1 ...
## $ group : Factor w/ 4 levels "1100","1600",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ date : Factor w/ 134 levels "2025-02-25_11h40.18.454",..: 4 4 4 4 4 4 4 4 4 4 ...
## $ expName : Factor w/ 1 level "SimilarityRatings": 1 1 1 1 1 1 1 1 1 1 ...
## $ psychopyVersion : Factor w/ 1 level "2023.2.3": 1 1 1 1 1 1 1 1 1 1 ...
## $ frameRate : num 60 60 60 60 60 ...
## $ expStart : Factor w/ 134 levels "2025-02-25 11h48.02.736634 +0200",..: 4 4 4 4 4 4 4 4 4 4 ...
## $ sbj : Factor w/ 67 levels "341","1003","1206",..: 10 10 10 10 10 10 10 10 10 10 ...
## $ resp : int 5 7 5 4 3 4 3 2 7 5 ...
## $ block : num 1 1 1 1 1 1 1 1 1 1 ...
#re-order the levels of the session factor
data$session <- factor(data$session, levels = c("Pre", "Post"))
#exclude one participant (20335) for not following instructions
data<-droplevels(data[data$sbj!="20335",])
str(data)
## 'data.frame': 9504 obs. of 38 variables:
## $ trl : int 20 12 17 14 21 3 10 13 6 23 ...
## $ cnd : Factor w/ 2 levels "between","within": 2 2 2 2 1 2 2 2 2 1 ...
## $ dim : Factor w/ 2 levels "irrel","rel": 2 1 2 2 2 1 1 2 1 2 ...
## $ size1 : num 3.4 2.6 2.2 2.6 2.2 2.2 2.6 2.2 3 3 ...
## $ hue1 : num 0.1 0.3 0.1 -0.3 -0.1 0.1 -0.1 -0.3 -0.1 -0.1 ...
## $ size2 : num 3.4 3 2.2 2.6 2.2 2.6 3 2.2 3.4 3 ...
## $ hue2 : num 0.3 0.3 0.3 -0.1 0.1 0.1 -0.1 -0.1 -0.1 0.1 ...
## $ practice_trials.thisRepN : logi NA NA NA NA NA NA ...
## $ practice_trials.thisTrialN: logi NA NA NA NA NA NA ...
## $ practice_trials.thisN : logi NA NA NA NA NA NA ...
## $ practice_trials.thisIndex : logi NA NA NA NA NA NA ...
## $ blocks.thisRepN : int 0 0 0 0 0 0 0 0 0 0 ...
## $ blocks.thisTrialN : int 0 0 0 0 0 0 0 0 0 0 ...
## $ blocks.thisN : int 0 0 0 0 0 0 0 0 0 0 ...
## $ blocks.thisIndex : int 0 0 0 0 0 0 0 0 0 0 ...
## $ trials.thisRepN : int 0 0 0 0 0 0 0 0 0 0 ...
## $ trials.thisTrialN : int 0 1 2 3 4 5 6 7 8 9 ...
## $ trials.thisN : int 0 1 2 3 4 5 6 7 8 9 ...
## $ trials.thisIndex : int 19 11 16 13 20 2 9 12 5 22 ...
## $ break_loop.thisRepN : logi NA NA NA NA NA NA ...
## $ break_loop.thisTrialN : logi NA NA NA NA NA NA ...
## $ break_loop.thisN : logi NA NA NA NA NA NA ...
## $ break_loop.thisIndex : logi NA NA NA NA NA NA ...
## $ thisRow.t : num 68.9 73.1 75.9 79.7 82.8 ...
## $ notes : logi NA NA NA NA NA NA ...
## $ slider_exp.response : int 5 7 5 4 3 4 3 2 7 5 ...
## $ slider_exp.rt : num 3.89 2.6 3.45 2.87 4.87 ...
## $ participant : int 17202 17202 17202 17202 17202 17202 17202 17202 17202 17202 ...
## $ session : Factor w/ 2 levels "Pre","Post": 2 2 2 2 2 2 2 2 2 2 ...
## $ group : Factor w/ 4 levels "1100","1600",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ date : Factor w/ 132 levels "2025-02-25_11h40.18.454",..: 4 4 4 4 4 4 4 4 4 4 ...
## $ expName : Factor w/ 1 level "SimilarityRatings": 1 1 1 1 1 1 1 1 1 1 ...
## $ psychopyVersion : Factor w/ 1 level "2023.2.3": 1 1 1 1 1 1 1 1 1 1 ...
## $ frameRate : num 60 60 60 60 60 ...
## $ expStart : Factor w/ 132 levels "2025-02-25 11h48.02.736634 +0200",..: 4 4 4 4 4 4 4 4 4 4 ...
## $ sbj : Factor w/ 66 levels "341","1003","1206",..: 10 10 10 10 10 10 10 10 10 10 ...
## $ resp : int 5 7 5 4 3 4 3 2 7 5 ...
## $ block : num 1 1 1 1 1 1 1 1 1 1 ...
#exclude five participants for not meeting the learning criterion:
data <- droplevels( data[!(data$sbj %in% c("77623", "77337", "48888", "43423", "63018")), ])
str(data)
## 'data.frame': 8784 obs. of 38 variables:
## $ trl : int 20 12 17 14 21 3 10 13 6 23 ...
## $ cnd : Factor w/ 2 levels "between","within": 2 2 2 2 1 2 2 2 2 1 ...
## $ dim : Factor w/ 2 levels "irrel","rel": 2 1 2 2 2 1 1 2 1 2 ...
## $ size1 : num 3.4 2.6 2.2 2.6 2.2 2.2 2.6 2.2 3 3 ...
## $ hue1 : num 0.1 0.3 0.1 -0.3 -0.1 0.1 -0.1 -0.3 -0.1 -0.1 ...
## $ size2 : num 3.4 3 2.2 2.6 2.2 2.6 3 2.2 3.4 3 ...
## $ hue2 : num 0.3 0.3 0.3 -0.1 0.1 0.1 -0.1 -0.1 -0.1 0.1 ...
## $ practice_trials.thisRepN : logi NA NA NA NA NA NA ...
## $ practice_trials.thisTrialN: logi NA NA NA NA NA NA ...
## $ practice_trials.thisN : logi NA NA NA NA NA NA ...
## $ practice_trials.thisIndex : logi NA NA NA NA NA NA ...
## $ blocks.thisRepN : int 0 0 0 0 0 0 0 0 0 0 ...
## $ blocks.thisTrialN : int 0 0 0 0 0 0 0 0 0 0 ...
## $ blocks.thisN : int 0 0 0 0 0 0 0 0 0 0 ...
## $ blocks.thisIndex : int 0 0 0 0 0 0 0 0 0 0 ...
## $ trials.thisRepN : int 0 0 0 0 0 0 0 0 0 0 ...
## $ trials.thisTrialN : int 0 1 2 3 4 5 6 7 8 9 ...
## $ trials.thisN : int 0 1 2 3 4 5 6 7 8 9 ...
## $ trials.thisIndex : int 19 11 16 13 20 2 9 12 5 22 ...
## $ break_loop.thisRepN : logi NA NA NA NA NA NA ...
## $ break_loop.thisTrialN : logi NA NA NA NA NA NA ...
## $ break_loop.thisN : logi NA NA NA NA NA NA ...
## $ break_loop.thisIndex : logi NA NA NA NA NA NA ...
## $ thisRow.t : num 68.9 73.1 75.9 79.7 82.8 ...
## $ notes : logi NA NA NA NA NA NA ...
## $ slider_exp.response : int 5 7 5 4 3 4 3 2 7 5 ...
## $ slider_exp.rt : num 3.89 2.6 3.45 2.87 4.87 ...
## $ participant : int 17202 17202 17202 17202 17202 17202 17202 17202 17202 17202 ...
## $ session : Factor w/ 2 levels "Pre","Post": 2 2 2 2 2 2 2 2 2 2 ...
## $ group : Factor w/ 4 levels "1100","1600",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ date : Factor w/ 122 levels "2025-02-25_13h08.44.108",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ expName : Factor w/ 1 level "SimilarityRatings": 1 1 1 1 1 1 1 1 1 1 ...
## $ psychopyVersion : Factor w/ 1 level "2023.2.3": 1 1 1 1 1 1 1 1 1 1 ...
## $ frameRate : num 60 60 60 60 60 ...
## $ expStart : Factor w/ 122 levels "2025-02-25 13h09.40.421447 +0200",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ sbj : Factor w/ 61 levels "341","1003","1206",..: 10 10 10 10 10 10 10 10 10 10 ...
## $ resp : int 5 7 5 4 3 4 3 2 7 5 ...
## $ block : num 1 1 1 1 1 1 1 1 1 1 ...
Comparison of Responses from “Exclusion-Instructions”
Participants
#check if we can keep data from 5 first sbjs (instructions: we will delete your data)
data$excl_instr<-"no"
#For the first 5 participants, we need to register that the instructions were different.
#These 5 participants are: "17202" "40242" "57393" "89443" "94372"
data[data$sbj=="17202" | data$sbj=="40242" | data$sbj=="57393" | data$sbj=="89443" | data$sbj=="94372",]$excl_instr<-"yes"
data$excl_instr<-as.factor(data$excl_instr)
str(data)
## 'data.frame': 8784 obs. of 39 variables:
## $ trl : int 20 12 17 14 21 3 10 13 6 23 ...
## $ cnd : Factor w/ 2 levels "between","within": 2 2 2 2 1 2 2 2 2 1 ...
## $ dim : Factor w/ 2 levels "irrel","rel": 2 1 2 2 2 1 1 2 1 2 ...
## $ size1 : num 3.4 2.6 2.2 2.6 2.2 2.2 2.6 2.2 3 3 ...
## $ hue1 : num 0.1 0.3 0.1 -0.3 -0.1 0.1 -0.1 -0.3 -0.1 -0.1 ...
## $ size2 : num 3.4 3 2.2 2.6 2.2 2.6 3 2.2 3.4 3 ...
## $ hue2 : num 0.3 0.3 0.3 -0.1 0.1 0.1 -0.1 -0.1 -0.1 0.1 ...
## $ practice_trials.thisRepN : logi NA NA NA NA NA NA ...
## $ practice_trials.thisTrialN: logi NA NA NA NA NA NA ...
## $ practice_trials.thisN : logi NA NA NA NA NA NA ...
## $ practice_trials.thisIndex : logi NA NA NA NA NA NA ...
## $ blocks.thisRepN : int 0 0 0 0 0 0 0 0 0 0 ...
## $ blocks.thisTrialN : int 0 0 0 0 0 0 0 0 0 0 ...
## $ blocks.thisN : int 0 0 0 0 0 0 0 0 0 0 ...
## $ blocks.thisIndex : int 0 0 0 0 0 0 0 0 0 0 ...
## $ trials.thisRepN : int 0 0 0 0 0 0 0 0 0 0 ...
## $ trials.thisTrialN : int 0 1 2 3 4 5 6 7 8 9 ...
## $ trials.thisN : int 0 1 2 3 4 5 6 7 8 9 ...
## $ trials.thisIndex : int 19 11 16 13 20 2 9 12 5 22 ...
## $ break_loop.thisRepN : logi NA NA NA NA NA NA ...
## $ break_loop.thisTrialN : logi NA NA NA NA NA NA ...
## $ break_loop.thisN : logi NA NA NA NA NA NA ...
## $ break_loop.thisIndex : logi NA NA NA NA NA NA ...
## $ thisRow.t : num 68.9 73.1 75.9 79.7 82.8 ...
## $ notes : logi NA NA NA NA NA NA ...
## $ slider_exp.response : int 5 7 5 4 3 4 3 2 7 5 ...
## $ slider_exp.rt : num 3.89 2.6 3.45 2.87 4.87 ...
## $ participant : int 17202 17202 17202 17202 17202 17202 17202 17202 17202 17202 ...
## $ session : Factor w/ 2 levels "Pre","Post": 2 2 2 2 2 2 2 2 2 2 ...
## $ group : Factor w/ 4 levels "1100","1600",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ date : Factor w/ 122 levels "2025-02-25_13h08.44.108",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ expName : Factor w/ 1 level "SimilarityRatings": 1 1 1 1 1 1 1 1 1 1 ...
## $ psychopyVersion : Factor w/ 1 level "2023.2.3": 1 1 1 1 1 1 1 1 1 1 ...
## $ frameRate : num 60 60 60 60 60 ...
## $ expStart : Factor w/ 122 levels "2025-02-25 13h09.40.421447 +0200",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ sbj : Factor w/ 61 levels "341","1003","1206",..: 10 10 10 10 10 10 10 10 10 10 ...
## $ resp : int 5 7 5 4 3 4 3 2 7 5 ...
## $ block : num 1 1 1 1 1 1 1 1 1 1 ...
## $ excl_instr : Factor w/ 2 levels "no","yes": 2 2 2 2 2 2 2 2 2 2 ...
#test if there's a difference in response distribution among participants (excl_instr= "yes" vs. "no")
table_sim<-table(data$resp, data$excl_instr)
chi_sim<-chisq.test(table_sim)
## Warning in chisq.test(table_sim): Chi-squared approximation may be incorrect
print(chi_sim)
##
## Pearson's Chi-squared test
##
## data: table_sim
## X-squared = 18.417, df = 8, p-value = 0.01831
#function to calculate the percentage of expected counts >=5)
check_expected_frequencies <- function(chisq_test_result) {
expected <- chisq_test_result$expected
total_cells <- length(expected)
cells_over_5 <- sum(expected >= 5)
percent_over_5 <- (cells_over_5 / total_cells) * 100
return(percent_over_5)
}
check_expected_frequencies(chi_sim)
## [1] 94.44444
# it should be >80%
#plot distribution
plot_sim <- data %>%
group_by(resp, excl_instr) %>%
summarise(n = n()) %>%
group_by(excl_instr) %>%
mutate(freq = n / sum(n))
## `summarise()` has grouped output by 'resp'. You can override using the
## `.groups` argument.
ggplot(plot_sim, aes(x = as.factor(resp), y = freq, fill = excl_instr)) +
geom_bar(stat = "identity", position = "dodge") +
labs(title = "Response Distribution - Pre Session", x = "Response (1???9)", y = "Relative Frequency", fill = "Exclude\nInstructions") +
theme_minimal()
#Calculate standardized score for the resp variable.
data <- data %>%
group_by(sbj) %>%
mutate(z_resp = (resp - mean(resp, na.rm = TRUE)) / sd(resp, na.rm = TRUE))
Similarity Ratings Plot
# 600 ms
###########
#Between-category differences
################################
data_btw600<-droplevels(data[data$cnd=="between" & data$group=="600",])
data_btw_av600<-aggregate(data_btw600$z_resp, list(data_btw600$sbj, data_btw600$session), mean)
colnames(data_btw_av600)<-c("sbj", "session", "z_resp")
#Within-category differences, Relevant Dimension
#################################################
data_w_r600<-droplevels(data[data$cnd=="within" & data$dim=="rel" & data$group=="600",])
data_w_r_av600<-aggregate(data_w_r600$z_resp, list(data_w_r600$sbj, data_w_r600$session), mean)
colnames(data_w_r_av600)<-c("sbj", "session", "z_resp")
#Within-category differences, Irrelevant Dimension
#################################################
data_w_i600<-droplevels(data[data$cnd=="within" & data$dim=="irrel" & data$group=="600",])
data_w_i_av600<-aggregate(data_w_i600$z_resp, list(data_w_i600$sbj, data_w_i600$session), mean)
colnames(data_w_i_av600)<-c("sbj", "session", "z_resp")
# 1100 ms
###########
#Between-category differences
################################
data_btw1100<-droplevels(data[data$cnd=="between" & data$group=="1100",])
data_btw_av1100<-aggregate(data_btw1100$z_resp, list(data_btw1100$sbj, data_btw1100$session), mean)
colnames(data_btw_av1100)<-c("sbj", "session", "z_resp")
#Within-category differences, Relevant Dimension
#################################################
data_w_r1100<-droplevels(data[data$cnd=="within" & data$dim=="rel" & data$group=="1100",])
data_w_r_av1100<-aggregate(data_w_r1100$z_resp, list(data_w_r1100$sbj, data_w_r1100$session), mean)
colnames(data_w_r_av1100)<-c("sbj", "session", "z_resp")
#Within-category differences, Irrelevant Dimension
#################################################
data_w_i1100<-droplevels(data[data$cnd=="within" & data$dim=="irrel" & data$group=="1100",])
data_w_i_av1100<-aggregate(data_w_i1100$z_resp, list(data_w_i1100$sbj, data_w_i1100$session), mean)
colnames(data_w_i_av1100)<-c("sbj", "session", "z_resp")
# 1600 ms
###########
#Between-category differences
################################
data_btw1600<-droplevels(data[data$cnd=="between" & data$group=="1600",])
data_btw_av1600<-aggregate(data_btw1600$z_resp, list(data_btw1600$sbj, data_btw1600$session), mean)
colnames(data_btw_av1600)<-c("sbj", "session", "z_resp")
#Within-category differences, Relevant Dimension
#################################################
data_w_r1600<-droplevels(data[data$cnd=="within" & data$dim=="rel" & data$group=="1600",])
data_w_r_av1600<-aggregate(data_w_r1600$z_resp, list(data_w_r1600$sbj, data_w_r1600$session), mean)
colnames(data_w_r_av1600)<-c("sbj", "session", "z_resp")
#Within-category differences, Irrelevant Dimension
#################################################
data_w_i1600<-droplevels(data[data$cnd=="within" & data$dim=="irrel" & data$group=="1600",])
data_w_i_av1600<-aggregate(data_w_i1600$z_resp, list(data_w_i1600$sbj, data_w_i1600$session), mean)
colnames(data_w_i_av1600)<-c("sbj", "session", "z_resp")
# RD
###########
#Between-category differences
################################
data_btwRD<-droplevels(data[data$cnd=="between" & data$group=="RD",])
data_btw_avRD<-aggregate(data_btwRD$z_resp, list(data_btwRD$sbj, data_btwRD$session), mean)
colnames(data_btw_avRD)<-c("sbj", "session", "z_resp")
#Within-category differences, Relevant Dimension
#################################################
data_w_rRD<-droplevels(data[data$cnd=="within" & data$dim=="rel" & data$group=="RD",])
data_w_r_avRD<-aggregate(data_w_rRD$z_resp, list(data_w_rRD$sbj, data_w_rRD$session), mean)
colnames(data_w_r_avRD)<-c("sbj", "session", "z_resp")
#Within-category differences, Irrelevant Dimension
#################################################
data_w_iRD<-droplevels(data[data$cnd=="within" & data$dim=="irrel" & data$group=="RD",])
data_w_i_avRD<-aggregate(data_w_iRD$z_resp, list(data_w_iRD$sbj, data_w_iRD$session), mean)
colnames(data_w_i_avRD)<-c("sbj", "session", "z_resp")
##########################################################
# Similarity Ratings, by Group, Session, and Pair Type
# Violin plots + within-subject trajectories (ggplot2)
##########################################################
#create new factor, condition (pair type) with three levels
data$condition<-as.factor(ifelse(data$cnd=="between", "between", ifelse(data$dim=="rel","within_rel","within_irrel" )))
ylb <- "Standardized Similarity Ratings"
xlb <- "Session"
group_levels <- c("600", "1100", "1600", "RD")
group_labels <- c("Group: 600 ms", "Group: 1100 ms", "Group: 1600 ms", "Group: Response Defined")
cond_levels <- c("between", "within_rel", "within_irrel")
cond_labels <- c("Between Category Pairs\nRelevant Dimension",
"Within Category Pairs\nRelevant Dimension",
"Within Category Pairs\nIrrelevant Dimension")
# Participant means per cell (sbj × group × condition × session)
plot_df <- data %>%
mutate(
group = factor(group, levels = group_levels, labels = group_labels),
condition = factor(condition, levels = cond_levels, labels = cond_labels)
) %>%
group_by(sbj, group, condition, session) %>%
summarise(z_resp = mean(z_resp, na.rm = TRUE), .groups = "drop") %>%
filter(!is.na(z_resp))
p_sim <- ggplot(plot_df, aes(x = session, y = z_resp)) +
geom_violin(aes(fill = session),
trim = FALSE, width = 0.8, color = NA, alpha = 0.55) +
geom_line(aes(group = sbj), color = "grey40", linewidth = 0.30, alpha = 0.70) +
geom_point(aes(group = sbj), color = "grey40", size = 0.80, alpha = 0.70) +
coord_cartesian(ylim = c(-1.5, 1.5)) +
facet_grid(group ~ condition) +
scale_fill_manual(values = c("grey25", "grey75")) +
labs(
title = "Similarity Ratings by Group, Session, and Pair Type",
x = xlb, y = ylb
) +
theme_classic(base_size = 11) +
theme(
legend.position = "none",
strip.background = element_blank(),
strip.text = element_text(face = "bold"), # facet titles bold
axis.title.x = element_text(face = "bold"), # axis titles bold
axis.title.y = element_text(face = "bold"),
axis.text.x = element_text(face = "bold"), # Pre/Post bold
plot.title = element_text(face = "bold", hjust = 0.5)
)
print(p_sim)
#graph for paper 600 x 750
Inferential Statistics - ANOVA
#create new factor, condition (pair type) with three levels
data$condition<-as.factor(ifelse(data$cnd=="between", "between", ifelse(data$dim=="rel","within_rel","within_irrel" )))
ezANOVA(data=data, dv=z_resp,wid=sbj, within=.(session,condition), between=group, type=3)
## Warning: Data is unbalanced (unequal N per group). Make sure you specified a
## well-considered value for the type argument to ezANOVA().
## Warning: Collapsing data to cell means. *IF* the requested effects are a subset
## of the full design, you must use the "within_full" argument, else results may
## be inaccurate.
## $ANOVA
## Effect DFn DFd F p p<.05 ges
## 2 group 3 57 2.9180733 4.179359e-02 * 0.009906707
## 3 session 1 57 13.1225571 6.220974e-04 * 0.048932436
## 5 condition 2 114 42.3330979 1.780369e-14 * 0.288107750
## 4 group:session 3 57 0.8059441 4.957776e-01 0.009390649
## 6 group:condition 6 114 2.3418151 3.602935e-02 * 0.062936624
## 7 session:condition 2 114 10.0766925 9.338633e-05 * 0.028583838
## 8 group:session:condition 6 114 0.8783103 5.132696e-01 0.007635503
##
## $`Mauchly's Test for Sphericity`
## Effect W p p<.05
## 5 condition 0.1464522 4.360026e-24 *
## 6 group:condition 0.1464522 4.360026e-24 *
## 7 session:condition 0.4552354 2.695317e-10 *
## 8 group:session:condition 0.4552354 2.695317e-10 *
##
## $`Sphericity Corrections`
## Effect GGe p[GG] p[GG]<.05 HFe
## 5 condition 0.5395059 6.936719e-09 * 0.5416811
## 6 group:condition 0.5395059 7.736925e-02 0.5416811
## 7 session:condition 0.6473478 9.223859e-04 * 0.6560627
## 8 group:session:condition 0.6473478 4.786980e-01 0.6560627
## p[HF] p[HF]<.05
## 5 6.525744e-09 *
## 6 7.708394e-02
## 7 8.713289e-04 *
## 8 4.797683e-01
#report GG corrections
#2*0.5387983
#104*0.5387983
#2*0.6918409
#104*0.6918409
#6*0.5387983
#104*0.5387983
#post-hoc comparisons
afex_options(type = 3)
m <- aov_ez(id = "sbj",dv ="z_resp", data = data, within = c("session", "condition"), between= "group", type = 3, factorize = FALSE)
## Warning: More than one observation per design cell, aggregating data using `fun_aggregate = mean`.
## To turn off this warning, pass `fun_aggregate = mean` explicitly.
## Contrasts set to contr.sum for the following variables: group
anova(m)
## Anova Table (Type 3 tests)
##
## Response: z_resp
## num Df den Df MSE F ges Pr(>F)
## group 3.0000 57.000 0.11685 2.9181 0.009907 0.0417936 *
## session 1.0000 57.000 0.40085 13.1226 0.048932 0.0006221 ***
## group:session 3.0000 57.000 0.40085 0.8059 0.009391 0.4957776
## condition 1.0790 61.504 0.90583 42.3331 0.288108 6.937e-09 ***
## group:condition 3.2370 61.504 0.90583 2.3418 0.062937 0.0773693 .
## session:condition 1.2947 73.798 0.23059 10.0767 0.028584 0.0009224 ***
## group:session:condition 3.8841 73.798 0.23059 0.8783 0.007636 0.4786980
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
emm_sc <- emmeans(m, ~ session | condition)
########################################################
# Post–Pre within each condition
########################################################
post_pre_by_condition <- contrast(
emm_sc,
method = list("Post-Pre" = c(-1, 1)), # Pre, Post -> Post-Pre
adjust = "holm"
)
summary(post_pre_by_condition, infer = TRUE)
## condition = between:
## contrast estimate SE df lower.CL upper.CL t.ratio p.value
## Post-Pre -0.4098 0.0931 57 -0.596 -0.223 -4.402 <.0001
##
## condition = within_irrel:
## contrast estimate SE df lower.CL upper.CL t.ratio p.value
## Post-Pre 0.0115 0.0798 57 -0.148 0.171 0.144 0.8858
##
## condition = within_rel:
## contrast estimate SE df lower.CL upper.CL t.ratio p.value
## Post-Pre -0.3212 0.0890 57 -0.499 -0.143 -3.611 0.0006
##
## Results are averaged over the levels of: group
## Confidence level used: 0.95
########################################################
# Compare Post–Pre changes across conditions
########################################################
# EMMs for condition × session
emm_cs <- emmeans(m, ~ condition * session) # averaged over group
chg_pairs <- contrast(emm_cs, method = "pairwise", by = NULL, adjust = "holm")
summary(chg_pairs, infer = TRUE)
## contrast estimate SE df lower.CL upper.CL
## between Pre - within_irrel Pre 0.8770 0.0947 57 0.58688 1.1672
## between Pre - within_rel Pre -0.0422 0.0354 57 -0.15077 0.0664
## between Pre - between Post 0.4098 0.0931 57 0.12460 0.6951
## between Pre - within_irrel Post 0.8655 0.1270 57 0.47509 1.2559
## between Pre - within_rel Post 0.2791 0.0900 57 0.00323 0.5549
## within_irrel Pre - within_rel Pre -0.9192 0.0967 57 -1.21558 -0.6228
## within_irrel Pre - between Post -0.4672 0.1250 57 -0.85013 -0.0842
## within_irrel Pre - within_irrel Post -0.0115 0.0798 57 -0.25605 0.2330
## within_irrel Pre - within_rel Post -0.5980 0.1260 57 -0.98319 -0.2127
## within_rel Pre - between Post 0.4520 0.0915 57 0.17164 0.7324
## within_rel Pre - within_irrel Post 0.9077 0.1230 57 0.53039 1.2850
## within_rel Pre - within_rel Post 0.3212 0.0890 57 0.04871 0.5938
## between Post - within_irrel Post 0.4557 0.1420 57 0.01995 0.8914
## between Post - within_rel Post -0.1308 0.0361 57 -0.24137 -0.0202
## within_irrel Post - within_rel Post -0.5864 0.1470 57 -1.03776 -0.1351
## t.ratio p.value
## 9.261 <.0001
## -1.191 0.4774
## 4.402 0.0004
## 6.792 <.0001
## 3.100 0.0090
## -9.503 <.0001
## -3.738 0.0030
## -0.144 0.8858
## -4.756 0.0001
## 4.939 0.0001
## 7.371 <.0001
## 3.611 0.0037
## 3.204 0.0089
## -3.624 0.0037
## -3.981 0.0016
##
## Results are averaged over the levels of: group
## Confidence level used: 0.95
## Conf-level adjustment: bonferroni method for 15 estimates
## P value adjustment: holm method for 15 tests
Session Information
sessionInfo()
## R version 4.5.1 (2025-06-13 ucrt)
## Platform: x86_64-w64-mingw32/x64
## Running under: Windows 11 x64 (build 26200)
##
## Matrix products: default
## LAPACK version 3.12.1
##
## locale:
## [1] LC_COLLATE=English_United States.utf8
## [2] LC_CTYPE=English_United States.utf8
## [3] LC_MONETARY=English_United States.utf8
## [4] LC_NUMERIC=C
## [5] LC_TIME=English_United States.utf8
##
## time zone: Europe/Athens
## tzcode source: internal
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] emmeans_2.0.0 afex_1.5-0 lme4_1.1-37 Matrix_1.7-3 car_3.1-3
## [6] carData_3.0-5 gplots_3.2.0 sciplot_1.2-0 ez_4.4-0 rstatix_0.7.3
## [11] ggplot2_4.0.0 dplyr_1.1.4
##
## loaded via a namespace (and not attached):
## [1] gtable_0.3.6 xfun_0.53 bslib_0.9.0
## [4] caTools_1.18.3 lattice_0.22-7 numDeriv_2016.8-1.1
## [7] vctrs_0.6.5 tools_4.5.1 Rdpack_2.6.4
## [10] bitops_1.0-9 generics_0.1.4 sandwich_3.1-1
## [13] parallel_4.5.1 tibble_3.3.0 pkgconfig_2.0.3
## [16] KernSmooth_2.23-26 RColorBrewer_1.1-3 S7_0.2.0
## [19] lifecycle_1.0.4 compiler_4.5.1 farver_2.1.2
## [22] stringr_1.5.2 codetools_0.2-20 lmerTest_3.1-3
## [25] htmltools_0.5.8.1 sass_0.4.10 yaml_2.3.10
## [28] Formula_1.2-5 pillar_1.11.1 nloptr_2.2.1
## [31] jquerylib_0.1.4 tidyr_1.3.1 MASS_7.3-65
## [34] cachem_1.1.0 reformulas_0.4.1 multcomp_1.4-29
## [37] boot_1.3-31 abind_1.4-8 nlme_3.1-168
## [40] gtools_3.9.5 tidyselect_1.2.1 digest_0.6.37
## [43] mvtnorm_1.3-3 stringi_1.8.7 reshape2_1.4.4
## [46] purrr_1.1.0 labeling_0.4.3 splines_4.5.1
## [49] fastmap_1.2.0 grid_4.5.1 cli_3.6.5
## [52] magrittr_2.0.4 survival_3.8-3 TH.data_1.1-5
## [55] broom_1.0.10 withr_3.0.2 scales_1.4.0
## [58] backports_1.5.0 estimability_1.5.1 rmarkdown_2.30
## [61] zoo_1.8-14 evaluate_1.0.5 knitr_1.50
## [64] rbibutils_2.3 mgcv_1.9-3 rlang_1.1.6
## [67] Rcpp_1.1.0 xtable_1.8-4 glue_1.8.0
## [70] rstudioapi_0.17.1 minqa_1.2.8 jsonlite_2.0.0
## [73] R6_2.6.1 plyr_1.8.9