1 Purpose
In this R Markdown file we analyze the response frequency. This software is free for private, non-commercial use only. Please contact the authors for commercial usage.
2 Summary
This code analyzes the response frequency, including differences in responses by block, component, task sequence, attribute order, object location, and ten respondent characteristics (5 demographic and 5 SES).
3 Load the libraries
setwd("C:/Users/maksat/Box Sync/USF PC/EuroQol/Kaisen task/paper1/Wave 1/markdown")
# clear memory
rm(list = ls())
# use libraries
library(gtsummary) # for tbl_summary command
library(reshape2) # for command mint and dcast
library(dplyr) # for command rename
library(huxtable) # for command as_hux_table
library(knitr) # for command kable
library(kableExtra)4 Data Notation
Below, we present data notations and definitions of new variables created.
4.1 Subject and task index
subject, i = 1 to N; task, t = 1 to T;
T1 is the number of coma comparison tasks and paired comparison tasks including warmup
T2 is the number of kaizen tasks including warmup
4.2 Demographic variables
‘age_range’ is an indicator variable for respondent age range
1: 18 and 34 years of age
2: 35 and 54 years of age
3: 55+
‘female’
0: male/other
1: female
‘hispanic’ is an indicator variable for hispanic respondents
1: hispanic
0: not hispanic
‘race’ will be categorized as following:
1 White alone
2 Black or African American alone
3 Other
‘region’ is an indicator variable for the US regions
1 Northeast region of the US
2 Midwest region of the US
3 South region of the US
4 West region of the US
4.3 Socioeconomic status (SES) variables
‘parent’ is an indicator variable for type of parenting
1 the respondent is a parent
0 the respondent is not a parent
‘marital’ is a categorical variable for mariatal status
1 married
2 never married
3 other
‘edu’ is an indicator variable
1: high school or less
2: associate/some college
3: bachelors or more
‘income’ is a categorical variable for respondent’s income
1 Less than 49,999 USD
2 50,000 USD to 99,999 USD
3 100,000 USD or more
‘community’ is a categorical variable for where respondents
reside
1 urban
2 suburban/don’t know/not sure
3 rural
‘comment_cat’ is a categorical variable for survey comments
1 informative
2 non-informative positive
3 non-informative negative
4 non-informative neutral/none/nothing/no
5 missing
5 Specify the settings
# specify the setting
N = 314 ## number of survey record
Z = 230 ## number of variables per record
Z1 = 131 ## the number of respondent-specific variables
Z2 = 28 ## the number of task-specific variables
Z3 = 71 ## the number of temporal variables (e.g., page times)
T1 = 17 ## number of paired comparisons per respondent
T2 = 11 ## number of kaizen tasks per respondent
spec = c(N, Z, Z1, Z2, Z3, T1, T2) ## summary of setting specification6 Specify the functions
# function to analyze choices by component sequence and block
choice_vs_compseq_by_question_and_block <- function(variable1, variable2, data) {
ifelse(deparse(substitute(data)) == "pc2_it",
component_data <- data[data$task == variable1 & data$subjectindex == variable2,] %>%
select(c(actual_choice, pc_cnum2)) %>%
mutate(actual_choice = case_when(actual_choice == "0"~"0",
actual_choice == "1"~"1")) %>%
tbl_summary(by = "pc_cnum2") %>% add_p(everything() ~ "chisq.test") %>%
as_hux_table(),
component_data <- data[data$task == variable1 & data$subjectindex == variable2,] %>%
select(c(actual_choice, pc_cnum2)) %>%
tbl_summary(by = "pc_cnum2") %>% add_p(everything() ~ "chisq.test") %>%
as_hux_table())
assign(paste(variable1, "_", "block", variable2, sep = ""), component_data,
envir = .GlobalEnv)
print(paste(variable1, "_", "block", variable2, sep = ""))
print(component_data)
kable(component_data, align = "c", caption = paste("**", variable1, " and Task Sequence ", variable2, "**", sep = ""), row.names = FALSE, escape = FALSE, centering = T) %>% kable_styling()
}
# function to analyze choices by task sequence and block
choice_vs_taskseq_by_question_and_block <- function(variable1, variable2, data) {
ifelse(deparse(substitute(data)) == "pc2_it",
component_data <- data[data$task == variable1 & data$subjectindex == variable2,] %>%
select(c(actual_choice, tnum2)) %>%
mutate(actual_choice = case_when(actual_choice == "0"~"0",
actual_choice == "1"~"1")) %>%
tbl_summary(by = "tnum2") %>% add_p(everything() ~ "chisq.test") %>%
as_hux_table(),
component_data <- data[data$task == variable1 & data$subjectindex == variable2,] %>%
select(c(actual_choice, tnum2)) %>%
tbl_summary(by = "tnum2") %>% add_p(everything() ~ "chisq.test") %>%
as_hux_table())
assign(paste(variable1, "_", "taskseq", variable2, sep = ""), component_data,
envir = .GlobalEnv)
print(paste(variable1, "_", "taskseq", variable2, sep = ""))
print(t(component_data))
#kable(component_data, align = "c", caption = paste("**", variable1, " and Task Sequence ",
#variable2, "**", sep = ""), row.names = FALSE, escape = FALSE, centering = T)
}
# function to perform t-test by question and block
ttest_by_question_and_block <- function(variable1, variable2, data) {
t_test = t.test(pc2_it$final_choice[pc2_it$task == variable1 &
pc2_it$subjectindex == variable2], mu = 0.5)
print(paste(variable1, "_", "block", variable2, sep = ""))
print(t_test$p.value)
}
# function to analyze straight-lining
straight_lining = function(variable, data) {
component_data = data %>% select(subjectindex, quota, female, age_range, race, hispanic,
pc_cnum2, region, parent, marital, edu, income, community, variable) %>%
mutate_if(is.numeric, as.character) %>%
tbl_summary(by = variable) %>% add_p(everything() ~ "chisq.test") %>%
as_hux_table()
assign(paste(variable, sep = ""), component_data, envir = .GlobalEnv)
print(variable)
print(component_data)
}
# function to generate alternatives in kaizen tasks
generate_A <- function(a_i, alt1, alt2) {
if (a_i %in% 0:4) {
substr1 <- substr(alt1, 1, a_i)
substr2 <- substr(alt2, a_i+1, a_i+1)
substr3 <- substr(alt1, a_i+2, 5)
return(paste(substr1, substr2, substr3, sep = ""))
} else {
return("-1")
}
}
# function to generate alternatives in kaizen tasks
generate_A2 <- function(a_i, b_i, alt1, alt2) {
if ((a_i %in% 0:4) & (b_i %in% 0:4)) {
substr(alt1,a_i+1,a_i+1) <- substr(alt2, a_i+1, a_i+1)
substr(alt1,b_i+1,b_i+1) <- substr(alt2, b_i+1, b_i+1)
return(alt1)
} else {
return("-1")
}
}7 Load the Data
## load the respondent data
resp_i = read.csv("resp_i_231010.csv")
resp_i <- resp_i[resp_i$incomplete==0,] ## Only completes
# load the paired comparison data
pc_it = read.csv("pc_it_231010.csv")
pc_it <- pc_it[pc_it$incomplete==0,] ## Only completes
# load the kaizen task data
kz_it = read.csv("kz_it_231010.csv")
kz_it <- kz_it[kz_it$incomplete==0,] ## Only completes8 Recode the Data
In this section we recode the data to create the variables defined above
8.1 Quota and demographic variables
# create 'quota'
resp_i$quota = apply(subset(resp_i, select = c(quota01:quota18)), 1,
function(x) ifelse(x[1]!="",as.integer(which(x == "yes")),""))
# create 'age_range'
resp_i$age_range = as.numeric(cut(resp_i$G02Q02, breaks = c(18, 34, 54,
max(resp_i$G02Q02, na.rm = T)), labels = c(1, 2, 3), include.lowest = T))
# create 'female'
resp_i$female = as.numeric(ifelse(resp_i$G02Q03 == "female", 1, 0))
# create 'race'
resp_i$race = apply((subset(resp_i, select = c(G02Q05.AO00.:G02Q05.AO14.))
== "Yes")*1, 1, function(x) ifelse(sum(x) == 1 & x[1] == 1, 1,
ifelse(sum(x) == 1 & x[2] == 1, 2, 3)))
# create 'hispanic'
resp_i$hispanic = ifelse(substr(resp_i$G02Q04,1,1) == "y", 1, 0)
# create a list consisting of four US regions
states = subset(resp_i, select = c(id, G02Q01))
regions = list(
northeast = c("Connecticut", "Maine", "Massachusetts", "New Hampshire",
"Rhode Island", "Vermont", "New York", "Pennsylvania", "New Jersey"),
midwest = c("Indiana", "Illinois", "Michigan", "Ohio", "Wisconsin", "Iowa",
"Kansas", "Minnesota", "Missouri", "Nebraska", "North Dakota",
"South Dakota"),
south = c("Delaware", "Washington, DC", "Florida", "Georgia", "Maryland",
"North Carolina", "South Carolina", "Virginia", "West Virginia",
"Alabama", "Kentucky", "Mississippi", "Tennessee", "Arkansas",
"Louisiana", "Oklahoma", "Texas"),
west = c("Arizona", "Colorado", "Idaho", "New Mexico", "Montana", "Utah",
"Nevada", "Wyoming", "Alaska", "California", "Hawaii", "Oregon",
"Washington"))
# create a 0/1 variable or each state indicating which region they belong
for(region in names(regions)) {
resp_i[region] = as.integer(as.logical(sapply(resp_i$G02Q01,
function(x) any(trimws(x) %in% regions[[region]]))))}
# convert 0/1 to a categorical variable
# (1: Northeast, 2: Midwest, 3: South, 4: West)
resp_i$region = apply(resp_i[,c("northeast", "midwest", "south", "west")], 1,
function(x) ifelse(x[1] == 1, 1, ifelse(x[2] == 1, 2, ifelse(x[3] == 1, 3,
ifelse(x[4] == 1, 4, "")))))8.2 Socio-economic status (SES) variables
# create 'parent'
resp_i$parent = apply((subset(resp_i, select = c(G11Q02.SQ001.:G11Q02.SQ007.))
== "Yes")*1, 1, function(x) ifelse(any(1 %in% x[1:4]), 1, 0))
# create 'marital'
resp_i$marital = ifelse(resp_i$G11Q06 == "now married" |
resp_i$G11Q06 == "married", 1, ifelse(resp_i$G11Q06 == "never married", 2, 3))
# create 'edu'
resp_i$edu = ifelse(resp_i$G11Q07 == "less than 1st grade" |
resp_i$G11Q07 == "1st, 2nd, 3rd, or 4th grade" | resp_i$G11Q07 ==
"5th or 6th grade" | resp_i$G11Q07 == "7th and 8th grade" |
resp_i$G11Q07 == "9th grade" |
resp_i$G11Q07 == "10th grade" | resp_i$G11Q07 == "11th grade" |
resp_i$G11Q07 == "12th grade no diploma" |
resp_i$G11Q07 == "high school graduate - high school diploma or equivalent", 1,
ifelse(resp_i$G11Q07 == "some college but no degree" | resp_i$G11Q07 ==
"associate degree in college - occupation/vocation program" |
resp_i$G11Q07 == "associate degree in college - academic program", 2, 3))
# create 'income'
resp_i$income = ifelse(resp_i$G11Q08 == "under $10,000" |
resp_i$G11Q08 == "$10,000 to under $20,000" |
resp_i$G11Q08 == "$20,000 to under $30,000" | resp_i$G11Q08 ==
"$30,000 to under $40,000" | resp_i$G11Q08 == "$40,000 to under $50,000", 1,
ifelse(resp_i$G11Q08 == "$50,000 to under $75,000" |
resp_i$G11Q08 == "$75,000 to under $100,000", 2, 3))
# create 'community'
resp_i$community = ifelse(resp_i$G11Q05 == "urban", 1,
ifelse(resp_i$G11Q05 == "rural", 3, 2))
# merge task data with subjectindex quota and 5 demographic and 5 SES variables
resp_char = c("id", "subjectindex", "quota", "pc_cnum2", "female", "age_range", "race",
"hispanic", "region", "parent", "marital", "edu", "income", "community")
pc_it <- merge(pc_it, subset(resp_i, select = resp_char), by = "id", all.x = T)
kz_it <- merge(kz_it, subset(resp_i, select = resp_char), by = "id", all.x = T)
# Subset the tasks by component, excluding warmups
cc2_it <- pc_it[ pc_it$tnum2 >1 & pc_it$tnum2 <= 6,]
pc2_it <- pc_it[ pc_it$tnum2 >7,]
kz2_it <- kz_it[ kz_it$tnum2 >1,]9 Descriptive Analyses
In this section we perform chi-square test of independence to analyze differences in responses by block, component, task sequence, attribute order, object location, and ten respondent characteristics.
9.1 Component sequence
We first look at the association between component sequence and the warm-up responses for paired comparisons.
# assess whether warmup associated with component sequence in pc
chisq.test(data.frame(unclass(table(pc_it$final_choice[pc_it$task == "G07Q02"],
pc_it$pc_cnum2[pc_it$task == "G07Q02"]))))##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: data.frame(unclass(table(pc_it$final_choice[pc_it$task == "G07Q02"], pc_it$pc_cnum2[pc_it$task == "G07Q02"])))
## X-squared = 0.18228, df = 1, p-value = 0.6694We now look at the association between component sequence and the warm-up responses for kaizen tasks. The first column on the table below represents the final choice (e.g., 0|1|2|3).
# assess whether warmup associated with component sequence in kz
wu_kz = kz_it[kz_it$task == "G09Q02",]
a = wu_kz %>% select(final_choice, pc_cnum2) %>% tbl_summary(by = "pc_cnum2") %>%
add_p(everything() ~ "chisq.test")
kable(a, align = "c", caption = "**Association between Component Sequence and the Warm-up Responses for Kaizen Tasks**", row.names = FALSE, escape = FALSE, centering = T)| Characteristic | 2, N = 150 | 3, N = 164 | p-value |
|---|---|---|---|
| final_choice | NA | NA | 0.5 |
| 0|1|2|3 | 2 (1.3%) | 2 (1.2%) | NA |
| 0|1|2|4 | 2 (1.3%) | 7 (4.3%) | NA |
| 0|1|3|2 | 1 (0.7%) | 2 (1.2%) | NA |
| 0|1|3|4 | 2 (1.3%) | 2 (1.2%) | NA |
| 0|1|4|2 | 2 (1.3%) | 0 (0%) | NA |
| 0|1|4|3 | 2 (1.3%) | 1 (0.6%) | NA |
| 0|2|1|3 | 2 (1.3%) | 2 (1.2%) | NA |
| 0|2|1|4 | 0 (0%) | 1 (0.6%) | NA |
| 0|2|3|1 | 2 (1.3%) | 6 (3.7%) | NA |
| 0|2|3|4 | 4 (2.7%) | 5 (3.0%) | NA |
| 0|2|4|1 | 2 (1.3%) | 4 (2.4%) | NA |
| 0|2|4|3 | 1 (0.7%) | 3 (1.8%) | NA |
| 0|3|1|2 | 0 (0%) | 1 (0.6%) | NA |
| 0|3|1|4 | 1 (0.7%) | 1 (0.6%) | NA |
| 0|3|2|1 | 2 (1.3%) | 0 (0%) | NA |
| 0|3|2|4 | 1 (0.7%) | 1 (0.6%) | NA |
| 0|3|4|1 | 6 (4.0%) | 1 (0.6%) | NA |
| 0|3|4|2 | 0 (0%) | 1 (0.6%) | NA |
| 0|4|1|2 | 2 (1.3%) | 0 (0%) | NA |
| 0|4|1|3 | 2 (1.3%) | 1 (0.6%) | NA |
| 0|4|2|1 | 2 (1.3%) | 2 (1.2%) | NA |
| 0|4|2|3 | 2 (1.3%) | 5 (3.0%) | NA |
| 0|4|3|1 | 0 (0%) | 3 (1.8%) | NA |
| 0|4|3|2 | 1 (0.7%) | 2 (1.2%) | NA |
| 1|0|3|2 | 1 (0.7%) | 1 (0.6%) | NA |
| 1|0|3|4 | 1 (0.7%) | 2 (1.2%) | NA |
| 1|0|4|2 | 1 (0.7%) | 4 (2.4%) | NA |
| 1|0|4|3 | 0 (0%) | 2 (1.2%) | NA |
| 1|2|0|3 | 2 (1.3%) | 2 (1.2%) | NA |
| 1|2|0|4 | 1 (0.7%) | 1 (0.6%) | NA |
| 1|2|3|0 | 2 (1.3%) | 1 (0.6%) | NA |
| 1|2|3|4 | 4 (2.7%) | 5 (3.0%) | NA |
| 1|2|4|0 | 3 (2.0%) | 0 (0%) | NA |
| 1|2|4|3 | 0 (0%) | 2 (1.2%) | NA |
| 1|3|0|2 | 0 (0%) | 1 (0.6%) | NA |
| 1|3|0|4 | 2 (1.3%) | 0 (0%) | NA |
| 1|3|2|0 | 0 (0%) | 1 (0.6%) | NA |
| 1|3|2|4 | 1 (0.7%) | 0 (0%) | NA |
| 1|3|4|0 | 1 (0.7%) | 0 (0%) | NA |
| 1|3|4|2 | 1 (0.7%) | 3 (1.8%) | NA |
| 1|4|0|2 | 0 (0%) | 2 (1.2%) | NA |
| 1|4|2|3 | 2 (1.3%) | 1 (0.6%) | NA |
| 1|4|3|2 | 1 (0.7%) | 3 (1.8%) | NA |
| 2|0|1|3 | 2 (1.3%) | 2 (1.2%) | NA |
| 2|0|1|4 | 3 (2.0%) | 2 (1.2%) | NA |
| 2|0|3|1 | 1 (0.7%) | 0 (0%) | NA |
| 2|0|3|4 | 1 (0.7%) | 2 (1.2%) | NA |
| 2|0|4|1 | 0 (0%) | 2 (1.2%) | NA |
| 2|0|4|3 | 2 (1.3%) | 2 (1.2%) | NA |
| 2|1|0|3 | 1 (0.7%) | 0 (0%) | NA |
| 2|1|0|4 | 0 (0%) | 1 (0.6%) | NA |
| 2|1|3|0 | 1 (0.7%) | 2 (1.2%) | NA |
| 2|1|3|4 | 1 (0.7%) | 1 (0.6%) | NA |
| 2|1|4|0 | 1 (0.7%) | 0 (0%) | NA |
| 2|1|4|3 | 4 (2.7%) | 3 (1.8%) | NA |
| 2|3|0|4 | 2 (1.3%) | 1 (0.6%) | NA |
| 2|3|1|0 | 2 (1.3%) | 1 (0.6%) | NA |
| 2|3|1|4 | 1 (0.7%) | 2 (1.2%) | NA |
| 2|3|4|0 | 0 (0%) | 1 (0.6%) | NA |
| 2|3|4|1 | 1 (0.7%) | 1 (0.6%) | NA |
| 2|4|0|1 | 2 (1.3%) | 1 (0.6%) | NA |
| 2|4|0|3 | 1 (0.7%) | 2 (1.2%) | NA |
| 2|4|1|0 | 0 (0%) | 1 (0.6%) | NA |
| 2|4|3|0 | 0 (0%) | 1 (0.6%) | NA |
| 2|4|3|1 | 2 (1.3%) | 1 (0.6%) | NA |
| 3|0|1|2 | 2 (1.3%) | 0 (0%) | NA |
| 3|0|1|4 | 2 (1.3%) | 1 (0.6%) | NA |
| 3|0|2|1 | 1 (0.7%) | 0 (0%) | NA |
| 3|0|2|4 | 2 (1.3%) | 4 (2.4%) | NA |
| 3|0|4|1 | 2 (1.3%) | 0 (0%) | NA |
| 3|0|4|2 | 2 (1.3%) | 2 (1.2%) | NA |
| 3|1|0|2 | 2 (1.3%) | 0 (0%) | NA |
| 3|1|0|4 | 2 (1.3%) | 1 (0.6%) | NA |
| 3|1|2|0 | 1 (0.7%) | 2 (1.2%) | NA |
| 3|1|2|4 | 1 (0.7%) | 0 (0%) | NA |
| 3|1|4|2 | 1 (0.7%) | 1 (0.6%) | NA |
| 3|2|0|1 | 2 (1.3%) | 0 (0%) | NA |
| 3|2|0|4 | 0 (0%) | 1 (0.6%) | NA |
| 3|2|1|0 | 0 (0%) | 1 (0.6%) | NA |
| 3|2|1|4 | 4 (2.7%) | 2 (1.2%) | NA |
| 3|2|4|0 | 2 (1.3%) | 0 (0%) | NA |
| 3|2|4|1 | 2 (1.3%) | 0 (0%) | NA |
| 3|4|0|1 | 0 (0%) | 1 (0.6%) | NA |
| 3|4|0|2 | 0 (0%) | 1 (0.6%) | NA |
| 3|4|1|0 | 1 (0.7%) | 0 (0%) | NA |
| 3|4|1|2 | 0 (0%) | 1 (0.6%) | NA |
| 3|4|2|0 | 0 (0%) | 3 (1.8%) | NA |
| 3|4|2|1 | 2 (1.3%) | 3 (1.8%) | NA |
| 4|0|1|2 | 0 (0%) | 3 (1.8%) | NA |
| 4|0|1|3 | 1 (0.7%) | 1 (0.6%) | NA |
| 4|0|2|1 | 1 (0.7%) | 4 (2.4%) | NA |
| 4|0|2|3 | 2 (1.3%) | 0 (0%) | NA |
| 4|0|3|1 | 0 (0%) | 2 (1.2%) | NA |
| 4|0|3|2 | 1 (0.7%) | 1 (0.6%) | NA |
| 4|1|0|2 | 1 (0.7%) | 0 (0%) | NA |
| 4|1|0|3 | 3 (2.0%) | 0 (0%) | NA |
| 4|1|2|3 | 1 (0.7%) | 3 (1.8%) | NA |
| 4|1|3|0 | 2 (1.3%) | 0 (0%) | NA |
| 4|1|3|2 | 1 (0.7%) | 0 (0%) | NA |
| 4|2|0|1 | 1 (0.7%) | 0 (0%) | NA |
| 4|2|0|3 | 2 (1.3%) | 2 (1.2%) | NA |
| 4|2|1|0 | 2 (1.3%) | 2 (1.2%) | NA |
| 4|2|1|3 | 1 (0.7%) | 2 (1.2%) | NA |
| 4|2|3|0 | 1 (0.7%) | 0 (0%) | NA |
| 4|2|3|1 | 1 (0.7%) | 1 (0.6%) | NA |
| 4|3|0|1 | 2 (1.3%) | 0 (0%) | NA |
| 4|3|0|2 | 1 (0.7%) | 0 (0%) | NA |
| 4|3|1|0 | 2 (1.3%) | 1 (0.6%) | NA |
| 4|3|1|2 | 1 (0.7%) | 1 (0.6%) | NA |
| 4|3|2|0 | 0 (0%) | 2 (1.2%) | NA |
| 4|3|2|1 | 0 (0%) | 2 (1.2%) | NA |
9.2 Block
Here we look at the association between blocks and the warm-up responses for paired comparisons.
# assess whether warmup associated with block in pc
chisq.test(data.frame(unclass(table(pc_it$final_choice[pc_it$task == "G07Q02"],
pc_it$subjectindex[pc_it$task == "G07Q02"]))))##
## Pearson's Chi-squared test
##
## data: data.frame(unclass(table(pc_it$final_choice[pc_it$task == "G07Q02"], pc_it$subjectindex[pc_it$task == "G07Q02"])))
## X-squared = 4.9825, df = 3, p-value = 0.1731Here we look at the association between blocks and the warm-up responses for kaizen tasks.
# assess whether warmup associated with block in kz
a = wu_kz %>% select(final_choice, subjectindex) %>% tbl_summary(by = "subjectindex") %>%
add_p(everything() ~ "chisq.test")
kable(a, align = "c", caption = "**Association between Blocks and the Warm-up Responses for Kaizen Tasks**", row.names = FALSE, escape = FALSE, centering = T)| Characteristic | 1, N = 82 | 2, N = 81 | 3, N = 77 | 4, N = 74 | p-value |
|---|---|---|---|---|---|
| final_choice | NA | NA | NA | NA | 0.5 |
| 0|1|2|3 | 1 (1.2%) | 2 (2.5%) | 1 (1.3%) | 0 (0%) | NA |
| 0|1|2|4 | 2 (2.4%) | 1 (1.2%) | 3 (3.9%) | 3 (4.1%) | NA |
| 0|1|3|2 | 0 (0%) | 2 (2.5%) | 0 (0%) | 1 (1.4%) | NA |
| 0|1|3|4 | 1 (1.2%) | 2 (2.5%) | 0 (0%) | 1 (1.4%) | NA |
| 0|1|4|2 | 0 (0%) | 2 (2.5%) | 0 (0%) | 0 (0%) | NA |
| 0|1|4|3 | 1 (1.2%) | 1 (1.2%) | 0 (0%) | 1 (1.4%) | NA |
| 0|2|1|3 | 3 (3.7%) | 0 (0%) | 0 (0%) | 1 (1.4%) | NA |
| 0|2|1|4 | 0 (0%) | 1 (1.2%) | 0 (0%) | 0 (0%) | NA |
| 0|2|3|1 | 3 (3.7%) | 2 (2.5%) | 1 (1.3%) | 2 (2.7%) | NA |
| 0|2|3|4 | 3 (3.7%) | 2 (2.5%) | 2 (2.6%) | 2 (2.7%) | NA |
| 0|2|4|1 | 1 (1.2%) | 2 (2.5%) | 2 (2.6%) | 1 (1.4%) | NA |
| 0|2|4|3 | 1 (1.2%) | 2 (2.5%) | 1 (1.3%) | 0 (0%) | NA |
| 0|3|1|2 | 0 (0%) | 0 (0%) | 1 (1.3%) | 0 (0%) | NA |
| 0|3|1|4 | 0 (0%) | 0 (0%) | 1 (1.3%) | 1 (1.4%) | NA |
| 0|3|2|1 | 1 (1.2%) | 1 (1.2%) | 0 (0%) | 0 (0%) | NA |
| 0|3|2|4 | 0 (0%) | 0 (0%) | 2 (2.6%) | 0 (0%) | NA |
| 0|3|4|1 | 1 (1.2%) | 2 (2.5%) | 0 (0%) | 4 (5.4%) | NA |
| 0|3|4|2 | 1 (1.2%) | 0 (0%) | 0 (0%) | 0 (0%) | NA |
| 0|4|1|2 | 0 (0%) | 1 (1.2%) | 0 (0%) | 1 (1.4%) | NA |
| 0|4|1|3 | 1 (1.2%) | 1 (1.2%) | 0 (0%) | 1 (1.4%) | NA |
| 0|4|2|1 | 0 (0%) | 0 (0%) | 2 (2.6%) | 2 (2.7%) | NA |
| 0|4|2|3 | 2 (2.4%) | 0 (0%) | 2 (2.6%) | 3 (4.1%) | NA |
| 0|4|3|1 | 2 (2.4%) | 0 (0%) | 1 (1.3%) | 0 (0%) | NA |
| 0|4|3|2 | 0 (0%) | 1 (1.2%) | 1 (1.3%) | 1 (1.4%) | NA |
| 1|0|3|2 | 0 (0%) | 2 (2.5%) | 0 (0%) | 0 (0%) | NA |
| 1|0|3|4 | 2 (2.4%) | 1 (1.2%) | 0 (0%) | 0 (0%) | NA |
| 1|0|4|2 | 2 (2.4%) | 1 (1.2%) | 1 (1.3%) | 1 (1.4%) | NA |
| 1|0|4|3 | 0 (0%) | 0 (0%) | 2 (2.6%) | 0 (0%) | NA |
| 1|2|0|3 | 1 (1.2%) | 2 (2.5%) | 0 (0%) | 1 (1.4%) | NA |
| 1|2|0|4 | 2 (2.4%) | 0 (0%) | 0 (0%) | 0 (0%) | NA |
| 1|2|3|0 | 0 (0%) | 2 (2.5%) | 1 (1.3%) | 0 (0%) | NA |
| 1|2|3|4 | 3 (3.7%) | 2 (2.5%) | 3 (3.9%) | 1 (1.4%) | NA |
| 1|2|4|0 | 1 (1.2%) | 0 (0%) | 1 (1.3%) | 1 (1.4%) | NA |
| 1|2|4|3 | 0 (0%) | 0 (0%) | 1 (1.3%) | 1 (1.4%) | NA |
| 1|3|0|2 | 0 (0%) | 0 (0%) | 0 (0%) | 1 (1.4%) | NA |
| 1|3|0|4 | 1 (1.2%) | 0 (0%) | 1 (1.3%) | 0 (0%) | NA |
| 1|3|2|0 | 0 (0%) | 1 (1.2%) | 0 (0%) | 0 (0%) | NA |
| 1|3|2|4 | 1 (1.2%) | 0 (0%) | 0 (0%) | 0 (0%) | NA |
| 1|3|4|0 | 0 (0%) | 0 (0%) | 1 (1.3%) | 0 (0%) | NA |
| 1|3|4|2 | 3 (3.7%) | 0 (0%) | 1 (1.3%) | 0 (0%) | NA |
| 1|4|0|2 | 1 (1.2%) | 0 (0%) | 1 (1.3%) | 0 (0%) | NA |
| 1|4|2|3 | 0 (0%) | 1 (1.2%) | 1 (1.3%) | 1 (1.4%) | NA |
| 1|4|3|2 | 2 (2.4%) | 1 (1.2%) | 0 (0%) | 1 (1.4%) | NA |
| 2|0|1|3 | 1 (1.2%) | 1 (1.2%) | 1 (1.3%) | 1 (1.4%) | NA |
| 2|0|1|4 | 1 (1.2%) | 2 (2.5%) | 1 (1.3%) | 1 (1.4%) | NA |
| 2|0|3|1 | 1 (1.2%) | 0 (0%) | 0 (0%) | 0 (0%) | NA |
| 2|0|3|4 | 0 (0%) | 0 (0%) | 1 (1.3%) | 2 (2.7%) | NA |
| 2|0|4|1 | 2 (2.4%) | 0 (0%) | 0 (0%) | 0 (0%) | NA |
| 2|0|4|3 | 1 (1.2%) | 0 (0%) | 1 (1.3%) | 2 (2.7%) | NA |
| 2|1|0|3 | 1 (1.2%) | 0 (0%) | 0 (0%) | 0 (0%) | NA |
| 2|1|0|4 | 0 (0%) | 1 (1.2%) | 0 (0%) | 0 (0%) | NA |
| 2|1|3|0 | 1 (1.2%) | 1 (1.2%) | 1 (1.3%) | 0 (0%) | NA |
| 2|1|3|4 | 0 (0%) | 0 (0%) | 0 (0%) | 2 (2.7%) | NA |
| 2|1|4|0 | 0 (0%) | 0 (0%) | 1 (1.3%) | 0 (0%) | NA |
| 2|1|4|3 | 2 (2.4%) | 0 (0%) | 1 (1.3%) | 4 (5.4%) | NA |
| 2|3|0|4 | 2 (2.4%) | 0 (0%) | 0 (0%) | 1 (1.4%) | NA |
| 2|3|1|0 | 1 (1.2%) | 0 (0%) | 0 (0%) | 2 (2.7%) | NA |
| 2|3|1|4 | 0 (0%) | 2 (2.5%) | 0 (0%) | 1 (1.4%) | NA |
| 2|3|4|0 | 0 (0%) | 1 (1.2%) | 0 (0%) | 0 (0%) | NA |
| 2|3|4|1 | 0 (0%) | 0 (0%) | 1 (1.3%) | 1 (1.4%) | NA |
| 2|4|0|1 | 1 (1.2%) | 0 (0%) | 0 (0%) | 2 (2.7%) | NA |
| 2|4|0|3 | 0 (0%) | 2 (2.5%) | 1 (1.3%) | 0 (0%) | NA |
| 2|4|1|0 | 0 (0%) | 0 (0%) | 1 (1.3%) | 0 (0%) | NA |
| 2|4|3|0 | 1 (1.2%) | 0 (0%) | 0 (0%) | 0 (0%) | NA |
| 2|4|3|1 | 1 (1.2%) | 1 (1.2%) | 0 (0%) | 1 (1.4%) | NA |
| 3|0|1|2 | 0 (0%) | 0 (0%) | 0 (0%) | 2 (2.7%) | NA |
| 3|0|1|4 | 0 (0%) | 1 (1.2%) | 1 (1.3%) | 1 (1.4%) | NA |
| 3|0|2|1 | 0 (0%) | 0 (0%) | 0 (0%) | 1 (1.4%) | NA |
| 3|0|2|4 | 4 (4.9%) | 1 (1.2%) | 1 (1.3%) | 0 (0%) | NA |
| 3|0|4|1 | 0 (0%) | 1 (1.2%) | 1 (1.3%) | 0 (0%) | NA |
| 3|0|4|2 | 0 (0%) | 2 (2.5%) | 2 (2.6%) | 0 (0%) | NA |
| 3|1|0|2 | 0 (0%) | 1 (1.2%) | 1 (1.3%) | 0 (0%) | NA |
| 3|1|0|4 | 0 (0%) | 1 (1.2%) | 1 (1.3%) | 1 (1.4%) | NA |
| 3|1|2|0 | 0 (0%) | 1 (1.2%) | 1 (1.3%) | 1 (1.4%) | NA |
| 3|1|2|4 | 0 (0%) | 1 (1.2%) | 0 (0%) | 0 (0%) | NA |
| 3|1|4|2 | 1 (1.2%) | 1 (1.2%) | 0 (0%) | 0 (0%) | NA |
| 3|2|0|1 | 1 (1.2%) | 0 (0%) | 1 (1.3%) | 0 (0%) | NA |
| 3|2|0|4 | 0 (0%) | 0 (0%) | 0 (0%) | 1 (1.4%) | NA |
| 3|2|1|0 | 0 (0%) | 0 (0%) | 0 (0%) | 1 (1.4%) | NA |
| 3|2|1|4 | 3 (3.7%) | 1 (1.2%) | 1 (1.3%) | 1 (1.4%) | NA |
| 3|2|4|0 | 0 (0%) | 2 (2.5%) | 0 (0%) | 0 (0%) | NA |
| 3|2|4|1 | 0 (0%) | 2 (2.5%) | 0 (0%) | 0 (0%) | NA |
| 3|4|0|1 | 0 (0%) | 0 (0%) | 0 (0%) | 1 (1.4%) | NA |
| 3|4|0|2 | 1 (1.2%) | 0 (0%) | 0 (0%) | 0 (0%) | NA |
| 3|4|1|0 | 0 (0%) | 0 (0%) | 1 (1.3%) | 0 (0%) | NA |
| 3|4|1|2 | 0 (0%) | 0 (0%) | 1 (1.3%) | 0 (0%) | NA |
| 3|4|2|0 | 1 (1.2%) | 1 (1.2%) | 1 (1.3%) | 0 (0%) | NA |
| 3|4|2|1 | 2 (2.4%) | 1 (1.2%) | 2 (2.6%) | 0 (0%) | NA |
| 4|0|1|2 | 0 (0%) | 0 (0%) | 3 (3.9%) | 0 (0%) | NA |
| 4|0|1|3 | 0 (0%) | 0 (0%) | 1 (1.3%) | 1 (1.4%) | NA |
| 4|0|2|1 | 1 (1.2%) | 1 (1.2%) | 3 (3.9%) | 0 (0%) | NA |
| 4|0|2|3 | 0 (0%) | 1 (1.2%) | 1 (1.3%) | 0 (0%) | NA |
| 4|0|3|1 | 1 (1.2%) | 0 (0%) | 0 (0%) | 1 (1.4%) | NA |
| 4|0|3|2 | 0 (0%) | 0 (0%) | 1 (1.3%) | 1 (1.4%) | NA |
| 4|1|0|2 | 0 (0%) | 1 (1.2%) | 0 (0%) | 0 (0%) | NA |
| 4|1|0|3 | 2 (2.4%) | 0 (0%) | 0 (0%) | 1 (1.4%) | NA |
| 4|1|2|3 | 1 (1.2%) | 2 (2.5%) | 0 (0%) | 1 (1.4%) | NA |
| 4|1|3|0 | 0 (0%) | 0 (0%) | 0 (0%) | 2 (2.7%) | NA |
| 4|1|3|2 | 0 (0%) | 0 (0%) | 1 (1.3%) | 0 (0%) | NA |
| 4|2|0|1 | 0 (0%) | 1 (1.2%) | 0 (0%) | 0 (0%) | NA |
| 4|2|0|3 | 0 (0%) | 1 (1.2%) | 1 (1.3%) | 2 (2.7%) | NA |
| 4|2|1|0 | 0 (0%) | 0 (0%) | 3 (3.9%) | 1 (1.4%) | NA |
| 4|2|1|3 | 1 (1.2%) | 2 (2.5%) | 0 (0%) | 0 (0%) | NA |
| 4|2|3|0 | 0 (0%) | 0 (0%) | 1 (1.3%) | 0 (0%) | NA |
| 4|2|3|1 | 1 (1.2%) | 1 (1.2%) | 0 (0%) | 0 (0%) | NA |
| 4|3|0|1 | 1 (1.2%) | 1 (1.2%) | 0 (0%) | 0 (0%) | NA |
| 4|3|0|2 | 0 (0%) | 0 (0%) | 1 (1.3%) | 0 (0%) | NA |
| 4|3|1|0 | 1 (1.2%) | 1 (1.2%) | 1 (1.3%) | 0 (0%) | NA |
| 4|3|1|2 | 1 (1.2%) | 0 (0%) | 0 (0%) | 1 (1.4%) | NA |
| 4|3|2|0 | 0 (0%) | 2 (2.5%) | 0 (0%) | 0 (0%) | NA |
| 4|3|2|1 | 0 (0%) | 1 (1.2%) | 1 (1.3%) | 0 (0%) | NA |
Next, we look at the association between each of the 40 paired comparison questions (i.e., actual_choice) with each component sequence and each block
# assess whether all responses by 40 pc associated with component sequence
# define tasks to loop over
tasks_pc = c("G08Q01","G08Q02","G08Q03","G08Q04","G08Q05","G08Q06","G08Q07",
"G08Q08","G08Q09","G08Q10")
tasks_kz = c("G10Q01","G10Q02","G10Q03","G10Q04","G10Q05","G10Q06","G10Q07",
"G10Q08","G10Q09","G10Q10")
block = c(1:4)
# assess 'actual_choice' vs component sequence by question and block for pc
results = lapply(tasks_pc, function(variable1) {
lapply(block, function(variable2) {
choice_vs_compseq_by_question_and_block(variable1, variable2, pc2_it)
})})## [1] "G08Q01_block1"
## Characteristic 2, N = 32 3, N = 50 p-value
## ----------------------------------------------------
## actual_choice >0.9
## 0 12 (38%) 19 (38%)
## 1 20 (63%) 31 (62%)
## ----------------------------------------------------
## n (%)
## Pearson's Chi-squared test
##
## Column names: label, stat_1, stat_2, p.value
## [1] "G08Q01_block2"
## Characteristic 2, N = 34 3, N = 47 p-value
## ----------------------------------------------------
## actual_choice >0.9
## 0 23 (68%) 32 (68%)
## 1 11 (32%) 15 (32%)
## ----------------------------------------------------
## n (%)
## Pearson's Chi-squared test
##
## Column names: label, stat_1, stat_2, p.value
## [1] "G08Q01_block3"
## Characteristic 2, N = 40 3, N = 37 p-value
## ----------------------------------------------------
## actual_choice 0.2
## 0 28 (70%) 20 (54%)
## 1 12 (30%) 17 (46%)
## ----------------------------------------------------
## n (%)
## Pearson's Chi-squared test
##
## Column names: label, stat_1, stat_2, p.value
## [1] "G08Q01_block4"
## Characteristic 2, N = 44 3, N = 30 p-value
## ----------------------------------------------------
## actual_choice 0.8
## 0 27 (61%) 20 (67%)
## 1 17 (39%) 10 (33%)
## ----------------------------------------------------
## n (%)
## Pearson's Chi-squared test
##
## Column names: label, stat_1, stat_2, p.value
## [1] "G08Q02_block1"
## Characteristic 2, N = 32 3, N = 50 p-value
## ----------------------------------------------------
## actual_choice 0.4
## 0 14 (44%) 28 (56%)
## 1 18 (56%) 22 (44%)
## ----------------------------------------------------
## n (%)
## Pearson's Chi-squared test
##
## Column names: label, stat_1, stat_2, p.value
## [1] "G08Q02_block2"
## Characteristic 2, N = 34 3, N = 47 p-value
## ----------------------------------------------------
## actual_choice 0.6
## 0 24 (71%) 29 (62%)
## 1 10 (29%) 18 (38%)
## ----------------------------------------------------
## n (%)
## Pearson's Chi-squared test
##
## Column names: label, stat_1, stat_2, p.value
## [1] "G08Q02_block3"
## Characteristic 2, N = 40 3, N = 37 p-value
## ----------------------------------------------------
## actual_choice 0.8
## 0 28 (70%) 28 (76%)
## 1 12 (30%) 9 (24%)
## ----------------------------------------------------
## n (%)
## Pearson's Chi-squared test
##
## Column names: label, stat_1, stat_2, p.value
## [1] "G08Q02_block4"
## Characteristic 2, N = 44 3, N = 30 p-value
## ----------------------------------------------------
## actual_choice 0.6
## 0 18 (41%) 15 (50%)
## 1 26 (59%) 15 (50%)
## ----------------------------------------------------
## n (%)
## Pearson's Chi-squared test
##
## Column names: label, stat_1, stat_2, p.value
## [1] "G08Q03_block1"
## Characteristic 2, N = 32 3, N = 50 p-value
## ----------------------------------------------------
## actual_choice 0.4
## 0 16 (50%) 31 (62%)
## 1 16 (50%) 19 (38%)
## ----------------------------------------------------
## n (%)
## Pearson's Chi-squared test
##
## Column names: label, stat_1, stat_2, p.value
## [1] "G08Q03_block2"
## Characteristic 2, N = 34 3, N = 47 p-value
## ----------------------------------------------------
## actual_choice 0.2
## 0 30 (88%) 35 (74%)
## 1 4 (12%) 12 (26%)
## ----------------------------------------------------
## n (%)
## Pearson's Chi-squared test
##
## Column names: label, stat_1, stat_2, p.value
## [1] "G08Q03_block3"
## Characteristic 2, N = 40 3, N = 37 p-value
## ----------------------------------------------------
## actual_choice 0.9
## 0 32 (80%) 31 (84%)
## 1 8 (20%) 6 (16%)
## ----------------------------------------------------
## n (%)
## Pearson's Chi-squared test
##
## Column names: label, stat_1, stat_2, p.value
## [1] "G08Q03_block4"
## Characteristic 2, N = 44 3, N = 30 p-value
## ----------------------------------------------------
## actual_choice 0.7
## 0 35 (80%) 22 (73%)
## 1 9 (20%) 8 (27%)
## ----------------------------------------------------
## n (%)
## Pearson's Chi-squared test
##
## Column names: label, stat_1, stat_2, p.value
## [1] "G08Q04_block1"
## Characteristic 2, N = 32 3, N = 50 p-value
## ----------------------------------------------------
## actual_choice 0.5
## 0 26 (81%) 36 (72%)
## 1 6 (19%) 14 (28%)
## ----------------------------------------------------
## n (%)
## Pearson's Chi-squared test
##
## Column names: label, stat_1, stat_2, p.value
## [1] "G08Q04_block2"
## Characteristic 2, N = 34 3, N = 47 p-value
## ----------------------------------------------------
## actual_choice >0.9
## 0 18 (53%) 24 (51%)
## 1 16 (47%) 23 (49%)
## ----------------------------------------------------
## n (%)
## Pearson's Chi-squared test
##
## Column names: label, stat_1, stat_2, p.value
## [1] "G08Q04_block3"
## Characteristic 2, N = 40 3, N = 37 p-value
## ----------------------------------------------------
## actual_choice >0.9
## 0 20 (50%) 19 (51%)
## 1 20 (50%) 18 (49%)
## ----------------------------------------------------
## n (%)
## Pearson's Chi-squared test
##
## Column names: label, stat_1, stat_2, p.value
## [1] "G08Q04_block4"
## Characteristic 2, N = 44 3, N = 30 p-value
## ----------------------------------------------------
## actual_choice 0.5
## 0 19 (43%) 10 (33%)
## 1 25 (57%) 20 (67%)
## ----------------------------------------------------
## n (%)
## Pearson's Chi-squared test
##
## Column names: label, stat_1, stat_2, p.value
## [1] "G08Q05_block1"
## Characteristic 2, N = 32 3, N = 50 p-value
## ----------------------------------------------------
## actual_choice >0.9
## 0 17 (53%) 26 (52%)
## 1 15 (47%) 24 (48%)
## ----------------------------------------------------
## n (%)
## Pearson's Chi-squared test
##
## Column names: label, stat_1, stat_2, p.value
## [1] "G08Q05_block2"
## Characteristic 2, N = 34 3, N = 47 p-value
## ----------------------------------------------------
## actual_choice 0.7
## 0 24 (71%) 30 (64%)
## 1 10 (29%) 17 (36%)
## ----------------------------------------------------
## n (%)
## Pearson's Chi-squared test
##
## Column names: label, stat_1, stat_2, p.value
## [1] "G08Q05_block3"
## Characteristic 2, N = 40 3, N = 37 p-value
## ----------------------------------------------------
## actual_choice 0.9
## 0 32 (80%) 28 (76%)
## 1 8 (20%) 9 (24%)
## ----------------------------------------------------
## n (%)
## Pearson's Chi-squared test
##
## Column names: label, stat_1, stat_2, p.value
## [1] "G08Q05_block4"
## Characteristic 2, N = 44 3, N = 30 p-value
## ----------------------------------------------------
## actual_choice 0.9
## 0 20 (45%) 15 (50%)
## 1 24 (55%) 15 (50%)
## ----------------------------------------------------
## n (%)
## Pearson's Chi-squared test
##
## Column names: label, stat_1, stat_2, p.value
## [1] "G08Q06_block1"
## Characteristic 2, N = 32 3, N = 50 p-value
## ----------------------------------------------------
## actual_choice 0.5
## 0 18 (56%) 33 (66%)
## 1 14 (44%) 17 (34%)
## ----------------------------------------------------
## n (%)
## Pearson's Chi-squared test
##
## Column names: label, stat_1, stat_2, p.value
## [1] "G08Q06_block2"
## Characteristic 2, N = 34 3, N = 47 p-value
## ----------------------------------------------------
## actual_choice 0.5
## 0 13 (38%) 23 (49%)
## 1 21 (62%) 24 (51%)
## ----------------------------------------------------
## n (%)
## Pearson's Chi-squared test
##
## Column names: label, stat_1, stat_2, p.value
## [1] "G08Q06_block3"
## Characteristic 2, N = 40 3, N = 37 p-value
## ----------------------------------------------------
## actual_choice 0.14
## 0 31 (78%) 22 (59%)
## 1 9 (23%) 15 (41%)
## ----------------------------------------------------
## n (%)
## Pearson's Chi-squared test
##
## Column names: label, stat_1, stat_2, p.value
## [1] "G08Q06_block4"
## Characteristic 2, N = 44 3, N = 30 p-value
## ----------------------------------------------------
## actual_choice 0.3
## 0 34 (77%) 19 (63%)
## 1 10 (23%) 11 (37%)
## ----------------------------------------------------
## n (%)
## Pearson's Chi-squared test
##
## Column names: label, stat_1, stat_2, p.value
## [1] "G08Q07_block1"
## Characteristic 2, N = 32 3, N = 50 p-value
## ----------------------------------------------------
## actual_choice >0.9
## 0 15 (47%) 24 (48%)
## 1 17 (53%) 26 (52%)
## ----------------------------------------------------
## n (%)
## Pearson's Chi-squared test
##
## Column names: label, stat_1, stat_2, p.value
## [1] "G08Q07_block2"
## Characteristic 2, N = 34 3, N = 47 p-value
## ----------------------------------------------------
## actual_choice >0.9
## 0 20 (59%) 28 (60%)
## 1 14 (41%) 19 (40%)
## ----------------------------------------------------
## n (%)
## Pearson's Chi-squared test
##
## Column names: label, stat_1, stat_2, p.value
## [1] "G08Q07_block3"
## Characteristic 2, N = 40 3, N = 37 p-value
## ----------------------------------------------------
## actual_choice 0.5
## 0 30 (75%) 24 (65%)
## 1 10 (25%) 13 (35%)
## ----------------------------------------------------
## n (%)
## Pearson's Chi-squared test
##
## Column names: label, stat_1, stat_2, p.value
## [1] "G08Q07_block4"
## Characteristic 2, N = 44 3, N = 30 p-value
## ----------------------------------------------------
## actual_choice 0.3
## 0 29 (66%) 15 (50%)
## 1 15 (34%) 15 (50%)
## ----------------------------------------------------
## n (%)
## Pearson's Chi-squared test
##
## Column names: label, stat_1, stat_2, p.value
## [1] "G08Q08_block1"
## Characteristic 2, N = 32 3, N = 50 p-value
## ----------------------------------------------------
## actual_choice 0.8
## 0 15 (47%) 26 (52%)
## 1 17 (53%) 24 (48%)
## ----------------------------------------------------
## n (%)
## Pearson's Chi-squared test
##
## Column names: label, stat_1, stat_2, p.value
## [1] "G08Q08_block2"
## Characteristic 2, N = 34 3, N = 47 p-value
## ----------------------------------------------------
## actual_choice 0.7
## 0 17 (50%) 27 (57%)
## 1 17 (50%) 20 (43%)
## ----------------------------------------------------
## n (%)
## Pearson's Chi-squared test
##
## Column names: label, stat_1, stat_2, p.value
## [1] "G08Q08_block3"
## Characteristic 2, N = 40 3, N = 37 p-value
## ----------------------------------------------------
## actual_choice >0.9
## 0 22 (55%) 21 (57%)
## 1 18 (45%) 16 (43%)
## ----------------------------------------------------
## n (%)
## Pearson's Chi-squared test
##
## Column names: label, stat_1, stat_2, p.value
## [1] "G08Q08_block4"
## Characteristic 2, N = 44 3, N = 30 p-value
## ----------------------------------------------------
## actual_choice 0.020
## 0 16 (36%) 20 (67%)
## 1 28 (64%) 10 (33%)
## ----------------------------------------------------
## n (%)
## Pearson's Chi-squared test
##
## Column names: label, stat_1, stat_2, p.value
## [1] "G08Q09_block1"
## Characteristic 2, N = 32 3, N = 50 p-value
## ----------------------------------------------------
## actual_choice >0.9
## 0 22 (69%) 34 (68%)
## 1 10 (31%) 16 (32%)
## ----------------------------------------------------
## n (%)
## Pearson's Chi-squared test
##
## Column names: label, stat_1, stat_2, p.value
## [1] "G08Q09_block2"
## Characteristic 2, N = 34 3, N = 47 p-value
## ----------------------------------------------------
## actual_choice 0.3
## 0 22 (65%) 24 (51%)
## 1 12 (35%) 23 (49%)
## ----------------------------------------------------
## n (%)
## Pearson's Chi-squared test
##
## Column names: label, stat_1, stat_2, p.value
## [1] "G08Q09_block3"
## Characteristic 2, N = 40 3, N = 37 p-value
## ----------------------------------------------------
## actual_choice >0.9
## 0 11 (28%) 9 (24%)
## 1 29 (73%) 28 (76%)
## ----------------------------------------------------
## n (%)
## Pearson's Chi-squared test
##
## Column names: label, stat_1, stat_2, p.value
## [1] "G08Q09_block4"
## Characteristic 2, N = 44 3, N = 30 p-value
## ----------------------------------------------------
## actual_choice 0.5
## 0 28 (64%) 22 (73%)
## 1 16 (36%) 8 (27%)
## ----------------------------------------------------
## n (%)
## Pearson's Chi-squared test
##
## Column names: label, stat_1, stat_2, p.value
## [1] "G08Q10_block1"
## Characteristic 2, N = 32 3, N = 50 p-value
## ----------------------------------------------------
## actual_choice >0.9
## 0 24 (75%) 38 (76%)
## 1 8 (25%) 12 (24%)
## ----------------------------------------------------
## n (%)
## Pearson's Chi-squared test
##
## Column names: label, stat_1, stat_2, p.value
## [1] "G08Q10_block2"
## Characteristic 2, N = 34 3, N = 47 p-value
## ----------------------------------------------------
## actual_choice 0.10
## 0 7 (21%) 19 (40%)
## 1 27 (79%) 28 (60%)
## ----------------------------------------------------
## n (%)
## Pearson's Chi-squared test
##
## Column names: label, stat_1, stat_2, p.value
## [1] "G08Q10_block3"
## Characteristic 2, N = 40 3, N = 37 p-value
## ----------------------------------------------------
## actual_choice 0.8
## 0 16 (40%) 13 (35%)
## 1 24 (60%) 24 (65%)
## ----------------------------------------------------
## n (%)
## Pearson's Chi-squared test
##
## Column names: label, stat_1, stat_2, p.value
## [1] "G08Q10_block4"
## Characteristic 2, N = 44 3, N = 30 p-value
## ----------------------------------------------------
## actual_choice 0.4
## 0 11 (25%) 11 (37%)
## 1 33 (75%) 19 (63%)
## ----------------------------------------------------
## n (%)
## Pearson's Chi-squared test
##
## Column names: label, stat_1, stat_2, p.valueNext, we look at the association between each of the 40 kaizen task questions (i.e., actual_choice) with each component sequence and each block
# assess 'actual_choice' vs component sequence by question and block for kz
results = lapply(tasks_kz, function(variable1) {
lapply(block, function(variable2) {
choice_vs_compseq_by_question_and_block(variable1, variable2, kz2_it)
})})## [1] "G10Q01_block1"
## Characteristic 2, N = 32 3, N = 50 p-value
## ----------------------------------------------------
## actual_choice 0.15
## 1|2|3|4 1 (3.1%) 0 (0%)
## 1|3|2|4 2 (6.3%) 2 (4.0%)
## 1|4|2|3 2 (6.3%) 0 (0%)
## 1|4|3|2 1 (3.1%) 0 (0%)
## 2|1|3|4 1 (3.1%) 1 (2.0%)
## 2|1|4|3 0 (0%) 1 (2.0%)
## 2|3|1|4 0 (0%) 1 (2.0%)
## 2|3|4|1 2 (6.3%) 1 (2.0%)
## 2|4|1|3 2 (6.3%) 4 (8.0%)
## 2|4|3|1 2 (6.3%) 0 (0%)
## 3|1|2|4 3 (9.4%) 1 (2.0%)
## 3|1|4|2 2 (6.3%) 3 (6.0%)
## 3|2|1|4 3 (9.4%) 12 (24%)
## 3|2|4|1 2 (6.3%) 5 (10%)
## 3|4|1|2 0 (0%) 1 (2.0%)
## 3|4|2|1 4 (13%) 6 (12%)
## 4|1|2|3 3 (9.4%) 0 (0%)
## 4|2|1|3 0 (0%) 2 (4.0%)
## 4|2|3|1 0 (0%) 1 (2.0%)
## 4|3|1|2 0 (0%) 3 (6.0%)
## 4|3|2|1 2 (6.3%) 6 (12%)
## ----------------------------------------------------
## n (%)
## Pearson's Chi-squared test
##
## Column names: label, stat_1, stat_2, p.value
## [1] "G10Q01_block2"
## Characteristic 2, N = 34 3, N = 47 p-value
## ----------------------------------------------------
## actual_choice 0.4
## 1|2|3|4 2 (5.9%) 2 (4.3%)
## 1|2|4|3 1 (2.9%) 0 (0%)
## 1|3|2|4 1 (2.9%) 4 (8.5%)
## 1|4|2|3 1 (2.9%) 3 (6.4%)
## 2|1|3|4 0 (0%) 4 (8.5%)
## 2|1|4|3 2 (5.9%) 1 (2.1%)
## 2|3|1|4 0 (0%) 1 (2.1%)
## 2|4|1|3 1 (2.9%) 1 (2.1%)
## 2|4|3|1 2 (5.9%) 1 (2.1%)
## 3|1|2|4 5 (15%) 3 (6.4%)
## 3|1|4|2 1 (2.9%) 3 (6.4%)
## 3|2|1|4 4 (12%) 5 (11%)
## 3|2|4|1 3 (8.8%) 5 (11%)
## 3|4|1|2 1 (2.9%) 2 (4.3%)
## 3|4|2|1 3 (8.8%) 3 (6.4%)
## 4|1|2|3 1 (2.9%) 2 (4.3%)
## 4|1|3|2 0 (0%) 3 (6.4%)
## 4|2|1|3 3 (8.8%) 1 (2.1%)
## 4|3|1|2 0 (0%) 3 (6.4%)
## 4|3|2|1 3 (8.8%) 0 (0%)
## ----------------------------------------------------
## n (%)
## Pearson's Chi-squared test
##
## Column names: label, stat_1, stat_2, p.value
## [1] "G10Q01_block3"
## Characteristic 2, N = 40 3, N = 37 p-value
## ----------------------------------------------------
## actual_choice 0.2
## 1|2|4|3 0 (0%) 1 (2.7%)
## 1|3|2|4 3 (7.5%) 1 (2.7%)
## 1|3|4|2 0 (0%) 1 (2.7%)
## 1|4|3|2 1 (2.5%) 0 (0%)
## 2|1|4|3 0 (0%) 1 (2.7%)
## 2|3|1|4 1 (2.5%) 2 (5.4%)
## 2|3|4|1 2 (5.0%) 0 (0%)
## 2|4|1|3 0 (0%) 2 (5.4%)
## 2|4|3|1 1 (2.5%) 1 (2.7%)
## 3|1|2|4 2 (5.0%) 4 (11%)
## 3|1|4|2 4 (10%) 6 (16%)
## 3|2|1|4 2 (5.0%) 4 (11%)
## 3|2|4|1 2 (5.0%) 2 (5.4%)
## 3|4|1|2 12 (30%) 5 (14%)
## 3|4|2|1 5 (13%) 0 (0%)
## 4|1|3|2 0 (0%) 3 (8.1%)
## 4|2|3|1 1 (2.5%) 1 (2.7%)
## 4|3|1|2 3 (7.5%) 1 (2.7%)
## 4|3|2|1 1 (2.5%) 2 (5.4%)
## ----------------------------------------------------
## n (%)
## Pearson's Chi-squared test
##
## Column names: label, stat_1, stat_2, p.value
## [1] "G10Q01_block4"
## Characteristic 2, N = 44 3, N = 30 p-value
## ----------------------------------------------------
## actual_choice 0.11
## 1|2|3|4 2 (4.5%) 2 (6.7%)
## 1|2|4|3 0 (0%) 1 (3.3%)
## 1|4|2|3 0 (0%) 2 (6.7%)
## 1|4|3|2 1 (2.3%) 0 (0%)
## 2|1|3|4 0 (0%) 3 (10%)
## 2|1|4|3 2 (4.5%) 1 (3.3%)
## 2|3|1|4 1 (2.3%) 0 (0%)
## 2|4|1|3 1 (2.3%) 0 (0%)
## 2|4|3|1 1 (2.3%) 0 (0%)
## 3|1|2|4 3 (6.8%) 1 (3.3%)
## 3|1|4|2 0 (0%) 3 (10%)
## 3|2|1|4 1 (2.3%) 4 (13%)
## 3|2|4|1 2 (4.5%) 0 (0%)
## 3|4|1|2 10 (23%) 5 (17%)
## 3|4|2|1 8 (18%) 7 (23%)
## 4|1|2|3 1 (2.3%) 0 (0%)
## 4|1|3|2 1 (2.3%) 0 (0%)
## 4|2|1|3 1 (2.3%) 0 (0%)
## 4|2|3|1 2 (4.5%) 0 (0%)
## 4|3|1|2 3 (6.8%) 0 (0%)
## 4|3|2|1 4 (9.1%) 1 (3.3%)
## ----------------------------------------------------
## n (%)
## Pearson's Chi-squared test
##
## Column names: label, stat_1, stat_2, p.value
## [1] "G10Q02_block1"
## Characteristic 2, N = 32 3, N = 50 p-value
## ----------------------------------------------------
## actual_choice 0.4
## 1|2|3|4 1 (3.1%) 1 (2.0%)
## 1|3|2|4 0 (0%) 1 (2.0%)
## 1|3|4|2 0 (0%) 2 (4.0%)
## 1|4|2|3 2 (6.3%) 2 (4.0%)
## 1|4|3|2 2 (6.3%) 1 (2.0%)
## 2|1|3|4 1 (3.1%) 2 (4.0%)
## 2|4|1|3 0 (0%) 2 (4.0%)
## 2|4|3|1 1 (3.1%) 1 (2.0%)
## 3|1|2|4 5 (16%) 4 (8.0%)
## 3|1|4|2 1 (3.1%) 1 (2.0%)
## 3|2|1|4 2 (6.3%) 6 (12%)
## 3|2|4|1 3 (9.4%) 4 (8.0%)
## 3|4|1|2 0 (0%) 6 (12%)
## 3|4|2|1 7 (22%) 5 (10%)
## 4|1|2|3 2 (6.3%) 3 (6.0%)
## 4|1|3|2 3 (9.4%) 1 (2.0%)
## 4|2|1|3 0 (0%) 1 (2.0%)
## 4|2|3|1 1 (3.1%) 0 (0%)
## 4|3|1|2 1 (3.1%) 3 (6.0%)
## 4|3|2|1 0 (0%) 4 (8.0%)
## ----------------------------------------------------
## n (%)
## Pearson's Chi-squared test
##
## Column names: label, stat_1, stat_2, p.value
## [1] "G10Q02_block2"
## Characteristic 2, N = 34 3, N = 47 p-value
## ----------------------------------------------------
## actual_choice 0.2
## 1|2|3|4 1 (2.9%) 0 (0%)
## 1|2|4|3 0 (0%) 1 (2.1%)
## 1|3|2|4 0 (0%) 3 (6.4%)
## 1|3|4|2 0 (0%) 2 (4.3%)
## 1|4|3|2 0 (0%) 1 (2.1%)
## 2|1|3|4 0 (0%) 2 (4.3%)
## 2|1|4|3 1 (2.9%) 0 (0%)
## 2|3|4|1 1 (2.9%) 0 (0%)
## 2|4|1|3 1 (2.9%) 2 (4.3%)
## 2|4|3|1 3 (8.8%) 0 (0%)
## 3|1|2|4 4 (12%) 7 (15%)
## 3|1|4|2 3 (8.8%) 8 (17%)
## 3|2|1|4 9 (26%) 7 (15%)
## 3|2|4|1 3 (8.8%) 2 (4.3%)
## 3|4|1|2 2 (5.9%) 5 (11%)
## 3|4|2|1 4 (12%) 5 (11%)
## 4|1|2|3 1 (2.9%) 0 (0%)
## 4|3|1|2 0 (0%) 2 (4.3%)
## 4|3|2|1 1 (2.9%) 0 (0%)
## ----------------------------------------------------
## n (%)
## Pearson's Chi-squared test
##
## Column names: label, stat_1, stat_2, p.value
## [1] "G10Q02_block3"
## Characteristic 2, N = 40 3, N = 37 p-value
## ----------------------------------------------------
## actual_choice 0.3
## 1|2|4|3 0 (0%) 1 (2.7%)
## 1|3|2|4 3 (7.5%) 1 (2.7%)
## 1|3|4|2 0 (0%) 1 (2.7%)
## 2|1|3|4 1 (2.5%) 1 (2.7%)
## 2|1|4|3 0 (0%) 1 (2.7%)
## 2|3|1|4 1 (2.5%) 0 (0%)
## 2|3|4|1 4 (10%) 2 (5.4%)
## 2|4|1|3 0 (0%) 1 (2.7%)
## 2|4|3|1 1 (2.5%) 1 (2.7%)
## 3|1|2|4 2 (5.0%) 6 (16%)
## 3|1|4|2 0 (0%) 2 (5.4%)
## 3|2|1|4 7 (18%) 5 (14%)
## 3|2|4|1 9 (23%) 3 (8.1%)
## 3|4|1|2 1 (2.5%) 2 (5.4%)
## 3|4|2|1 6 (15%) 3 (8.1%)
## 4|1|2|3 1 (2.5%) 0 (0%)
## 4|2|1|3 1 (2.5%) 2 (5.4%)
## 4|2|3|1 0 (0%) 2 (5.4%)
## 4|3|1|2 1 (2.5%) 3 (8.1%)
## 4|3|2|1 2 (5.0%) 0 (0%)
## ----------------------------------------------------
## n (%)
## Pearson's Chi-squared test
##
## Column names: label, stat_1, stat_2, p.value
## [1] "G10Q02_block4"
## Characteristic 2, N = 44 3, N = 30 p-value
## ----------------------------------------------------
## actual_choice 0.5
## 1|2|4|3 0 (0%) 1 (3.3%)
## 1|3|2|4 2 (4.5%) 0 (0%)
## 1|3|4|2 2 (4.5%) 3 (10%)
## 1|4|2|3 2 (4.5%) 1 (3.3%)
## 1|4|3|2 2 (4.5%) 1 (3.3%)
## 2|1|4|3 0 (0%) 2 (6.7%)
## 2|3|1|4 2 (4.5%) 0 (0%)
## 3|1|2|4 7 (16%) 3 (10%)
## 3|1|4|2 4 (9.1%) 3 (10%)
## 3|2|1|4 2 (4.5%) 5 (17%)
## 3|2|4|1 1 (2.3%) 2 (6.7%)
## 3|4|1|2 6 (14%) 3 (10%)
## 3|4|2|1 3 (6.8%) 0 (0%)
## 4|1|2|3 2 (4.5%) 2 (6.7%)
## 4|1|3|2 3 (6.8%) 1 (3.3%)
## 4|2|1|3 0 (0%) 1 (3.3%)
## 4|2|3|1 1 (2.3%) 0 (0%)
## 4|3|1|2 4 (9.1%) 1 (3.3%)
## 4|3|2|1 1 (2.3%) 1 (3.3%)
## ----------------------------------------------------
## n (%)
## Pearson's Chi-squared test
##
## Column names: label, stat_1, stat_2, p.value
## [1] "G10Q03_block1"
## Characteristic 2, N = 32 3, N = 50 p-value
## ----------------------------------------------------
## actual_choice 0.6
## 0|2|3|4 1 (3.1%) 0 (0%)
## 0|2|4|3 2 (6.3%) 0 (0%)
## 0|3|2|4 1 (3.1%) 1 (2.0%)
## 0|3|4|2 1 (3.1%) 2 (4.0%)
## 0|4|2|3 1 (3.1%) 2 (4.0%)
## 2|0|3|4 2 (6.3%) 1 (2.0%)
## 2|0|4|3 2 (6.3%) 1 (2.0%)
## 2|3|0|4 0 (0%) 1 (2.0%)
## 2|4|0|3 1 (3.1%) 1 (2.0%)
## 3|0|2|4 3 (9.4%) 9 (18%)
## 3|0|4|2 3 (9.4%) 5 (10%)
## 3|2|0|4 4 (13%) 4 (8.0%)
## 3|2|4|0 0 (0%) 3 (6.0%)
## 3|4|0|2 6 (19%) 4 (8.0%)
## 3|4|2|0 2 (6.3%) 7 (14%)
## 4|0|2|3 0 (0%) 1 (2.0%)
## 4|0|3|2 0 (0%) 1 (2.0%)
## 4|2|0|3 0 (0%) 1 (2.0%)
## 4|3|0|2 1 (3.1%) 4 (8.0%)
## 4|3|2|0 2 (6.3%) 2 (4.0%)
## ----------------------------------------------------
## n (%)
## Pearson's Chi-squared test
##
## Column names: label, stat_1, stat_2, p.value
## [1] "G10Q03_block2"
## Characteristic 2, N = 34 3, N = 47 p-value
## ----------------------------------------------------
## actual_choice 0.5
## 0|2|3|4 0 (0%) 1 (2.1%)
## 0|3|2|4 0 (0%) 2 (4.3%)
## 0|3|4|2 0 (0%) 1 (2.1%)
## 0|4|3|2 0 (0%) 1 (2.1%)
## 2|0|3|4 0 (0%) 6 (13%)
## 2|0|4|3 1 (2.9%) 1 (2.1%)
## 2|3|0|4 1 (2.9%) 2 (4.3%)
## 2|3|4|0 1 (2.9%) 0 (0%)
## 2|4|0|3 1 (2.9%) 0 (0%)
## 2|4|3|0 0 (0%) 1 (2.1%)
## 3|0|2|4 3 (8.8%) 7 (15%)
## 3|0|4|2 4 (12%) 4 (8.5%)
## 3|2|0|4 10 (29%) 7 (15%)
## 3|2|4|0 3 (8.8%) 5 (11%)
## 3|4|0|2 3 (8.8%) 2 (4.3%)
## 3|4|2|0 4 (12%) 4 (8.5%)
## 4|0|2|3 1 (2.9%) 0 (0%)
## 4|0|3|2 0 (0%) 1 (2.1%)
## 4|2|3|0 1 (2.9%) 0 (0%)
## 4|3|0|2 1 (2.9%) 2 (4.3%)
## ----------------------------------------------------
## n (%)
## Pearson's Chi-squared test
##
## Column names: label, stat_1, stat_2, p.value
## [1] "G10Q03_block3"
## Characteristic 2, N = 40 3, N = 37 p-value
## ----------------------------------------------------
## actual_choice 0.5
## 0|3|2|4 1 (2.5%) 1 (2.7%)
## 0|3|4|2 0 (0%) 2 (5.4%)
## 0|4|2|3 0 (0%) 1 (2.7%)
## 2|0|3|4 0 (0%) 1 (2.7%)
## 2|3|0|4 1 (2.5%) 1 (2.7%)
## 2|3|4|0 0 (0%) 2 (5.4%)
## 2|4|3|0 1 (2.5%) 2 (5.4%)
## 3|0|2|4 10 (25%) 7 (19%)
## 3|0|4|2 7 (18%) 4 (11%)
## 3|2|0|4 4 (10%) 6 (16%)
## 3|2|4|0 2 (5.0%) 1 (2.7%)
## 3|4|0|2 10 (25%) 4 (11%)
## 3|4|2|0 3 (7.5%) 3 (8.1%)
## 4|0|2|3 0 (0%) 1 (2.7%)
## 4|0|3|2 1 (2.5%) 0 (0%)
## 4|3|0|2 0 (0%) 1 (2.7%)
## ----------------------------------------------------
## n (%)
## Pearson's Chi-squared test
##
## Column names: label, stat_1, stat_2, p.value
## [1] "G10Q03_block4"
## Characteristic 2, N = 44 3, N = 30 p-value
## ----------------------------------------------------
## actual_choice 0.6
## 0|2|3|4 1 (2.3%) 1 (3.3%)
## 0|3|2|4 0 (0%) 1 (3.3%)
## 2|0|3|4 0 (0%) 1 (3.3%)
## 2|0|4|3 0 (0%) 1 (3.3%)
## 2|3|4|0 1 (2.3%) 1 (3.3%)
## 2|4|3|0 1 (2.3%) 0 (0%)
## 3|0|2|4 13 (30%) 8 (27%)
## 3|0|4|2 3 (6.8%) 4 (13%)
## 3|2|0|4 5 (11%) 4 (13%)
## 3|2|4|0 1 (2.3%) 2 (6.7%)
## 3|4|0|2 8 (18%) 2 (6.7%)
## 3|4|2|0 4 (9.1%) 4 (13%)
## 4|0|2|3 1 (2.3%) 0 (0%)
## 4|0|3|2 0 (0%) 1 (3.3%)
## 4|2|0|3 1 (2.3%) 0 (0%)
## 4|2|3|0 2 (4.5%) 0 (0%)
## 4|3|0|2 1 (2.3%) 0 (0%)
## 4|3|2|0 2 (4.5%) 0 (0%)
## ----------------------------------------------------
## n (%)
## Pearson's Chi-squared test
##
## Column names: label, stat_1, stat_2, p.value
## [1] "G10Q04_block1"
## Characteristic 2, N = 32 3, N = 50 p-value
## ----------------------------------------------------
## actual_choice 0.5
## 0|2|3|4 0 (0%) 1 (2.0%)
## 0|2|4|3 2 (6.3%) 0 (0%)
## 0|3|2|4 2 (6.3%) 1 (2.0%)
## 0|3|4|2 0 (0%) 3 (6.0%)
## 0|4|2|3 1 (3.1%) 0 (0%)
## 2|0|3|4 2 (6.3%) 2 (4.0%)
## 2|3|0|4 0 (0%) 3 (6.0%)
## 3|0|2|4 7 (22%) 12 (24%)
## 3|0|4|2 8 (25%) 6 (12%)
## 3|2|0|4 1 (3.1%) 1 (2.0%)
## 3|2|4|0 1 (3.1%) 2 (4.0%)
## 3|4|0|2 3 (9.4%) 8 (16%)
## 3|4|2|0 4 (13%) 6 (12%)
## 4|0|2|3 0 (0%) 1 (2.0%)
## 4|0|3|2 0 (0%) 1 (2.0%)
## 4|2|0|3 1 (3.1%) 0 (0%)
## 4|2|3|0 0 (0%) 1 (2.0%)
## 4|3|0|2 0 (0%) 1 (2.0%)
## 4|3|2|0 0 (0%) 1 (2.0%)
## ----------------------------------------------------
## n (%)
## Pearson's Chi-squared test
##
## Column names: label, stat_1, stat_2, p.value
## [1] "G10Q04_block2"
## Characteristic 2, N = 34 3, N = 47 p-value
## ----------------------------------------------------
## actual_choice 0.4
## 0|2|3|4 1 (2.9%) 1 (2.1%)
## 0|2|4|3 0 (0%) 2 (4.3%)
## 0|3|2|4 0 (0%) 3 (6.4%)
## 0|3|4|2 2 (5.9%) 1 (2.1%)
## 0|4|2|3 0 (0%) 2 (4.3%)
## 0|4|3|2 1 (2.9%) 1 (2.1%)
## 2|0|3|4 1 (2.9%) 2 (4.3%)
## 2|0|4|3 2 (5.9%) 5 (11%)
## 2|3|0|4 1 (2.9%) 2 (4.3%)
## 2|3|4|0 0 (0%) 1 (2.1%)
## 2|4|3|0 0 (0%) 1 (2.1%)
## 3|0|2|4 3 (8.8%) 2 (4.3%)
## 3|0|4|2 6 (18%) 7 (15%)
## 3|2|0|4 3 (8.8%) 0 (0%)
## 3|2|4|0 0 (0%) 3 (6.4%)
## 3|4|0|2 7 (21%) 9 (19%)
## 3|4|2|0 2 (5.9%) 3 (6.4%)
## 4|0|2|3 1 (2.9%) 1 (2.1%)
## 4|0|3|2 1 (2.9%) 0 (0%)
## 4|2|0|3 1 (2.9%) 0 (0%)
## 4|3|0|2 2 (5.9%) 0 (0%)
## 4|3|2|0 0 (0%) 1 (2.1%)
## ----------------------------------------------------
## n (%)
## Pearson's Chi-squared test
##
## Column names: label, stat_1, stat_2, p.value
## [1] "G10Q04_block3"
## Characteristic 2, N = 40 3, N = 37 p-value
## ----------------------------------------------------
## actual_choice 0.2
## 0|2|3|4 0 (0%) 2 (5.4%)
## 0|2|4|3 0 (0%) 2 (5.4%)
## 0|3|2|4 3 (7.5%) 2 (5.4%)
## 0|3|4|2 2 (5.0%) 0 (0%)
## 0|4|2|3 1 (2.5%) 1 (2.7%)
## 0|4|3|2 1 (2.5%) 1 (2.7%)
## 2|0|3|4 1 (2.5%) 0 (0%)
## 2|0|4|3 0 (0%) 1 (2.7%)
## 2|3|0|4 2 (5.0%) 0 (0%)
## 2|3|4|0 0 (0%) 2 (5.4%)
## 2|4|3|0 0 (0%) 1 (2.7%)
## 3|0|2|4 3 (7.5%) 3 (8.1%)
## 3|0|4|2 3 (7.5%) 0 (0%)
## 3|2|0|4 2 (5.0%) 4 (11%)
## 3|2|4|0 3 (7.5%) 3 (8.1%)
## 3|4|0|2 3 (7.5%) 4 (11%)
## 3|4|2|0 2 (5.0%) 3 (8.1%)
## 4|0|2|3 0 (0%) 3 (8.1%)
## 4|0|3|2 4 (10%) 1 (2.7%)
## 4|2|3|0 1 (2.5%) 2 (5.4%)
## 4|3|0|2 5 (13%) 2 (5.4%)
## 4|3|2|0 4 (10%) 0 (0%)
## ----------------------------------------------------
## n (%)
## Pearson's Chi-squared test
##
## Column names: label, stat_1, stat_2, p.value
## [1] "G10Q04_block4"
## Characteristic 2, N = 44 3, N = 30 p-value
## ----------------------------------------------------
## actual_choice 0.10
## 0|2|3|4 1 (2.3%) 0 (0%)
## 0|3|2|4 0 (0%) 2 (6.7%)
## 0|3|4|2 2 (4.5%) 0 (0%)
## 2|0|3|4 1 (2.3%) 1 (3.3%)
## 2|0|4|3 0 (0%) 2 (6.7%)
## 2|3|4|0 1 (2.3%) 0 (0%)
## 2|4|0|3 1 (2.3%) 4 (13%)
## 2|4|3|0 2 (4.5%) 2 (6.7%)
## 3|0|2|4 4 (9.1%) 2 (6.7%)
## 3|0|4|2 7 (16%) 4 (13%)
## 3|2|0|4 2 (4.5%) 2 (6.7%)
## 3|2|4|0 1 (2.3%) 1 (3.3%)
## 3|4|0|2 2 (4.5%) 1 (3.3%)
## 3|4|2|0 5 (11%) 4 (13%)
## 4|0|3|2 2 (4.5%) 0 (0%)
## 4|2|0|3 0 (0%) 3 (10%)
## 4|2|3|0 7 (16%) 0 (0%)
## 4|3|0|2 2 (4.5%) 2 (6.7%)
## 4|3|2|0 4 (9.1%) 0 (0%)
## ----------------------------------------------------
## n (%)
## Pearson's Chi-squared test
##
## Column names: label, stat_1, stat_2, p.value
## [1] "G10Q05_block1"
## Characteristic 2, N = 32 3, N = 50 p-value
## ----------------------------------------------------
## actual_choice 0.066
## 0|1|3|4 3 (9.4%) 2 (4.0%)
## 0|1|4|3 0 (0%) 5 (10%)
## 0|3|1|4 0 (0%) 1 (2.0%)
## 0|3|4|1 0 (0%) 1 (2.0%)
## 0|4|1|3 0 (0%) 1 (2.0%)
## 0|4|3|1 1 (3.1%) 0 (0%)
## 1|0|3|4 2 (6.3%) 0 (0%)
## 1|0|4|3 2 (6.3%) 0 (0%)
## 1|3|0|4 0 (0%) 1 (2.0%)
## 1|4|0|3 1 (3.1%) 0 (0%)
## 1|4|3|0 0 (0%) 1 (2.0%)
## 3|0|1|4 4 (13%) 4 (8.0%)
## 3|0|4|1 2 (6.3%) 0 (0%)
## 3|1|0|4 4 (13%) 0 (0%)
## 3|1|4|0 1 (3.1%) 2 (4.0%)
## 3|4|0|1 3 (9.4%) 10 (20%)
## 3|4|1|0 3 (9.4%) 4 (8.0%)
## 4|0|1|3 0 (0%) 1 (2.0%)
## 4|0|3|1 1 (3.1%) 4 (8.0%)
## 4|1|0|3 1 (3.1%) 2 (4.0%)
## 4|3|0|1 4 (13%) 8 (16%)
## 4|3|1|0 0 (0%) 3 (6.0%)
## ----------------------------------------------------
## n (%)
## Pearson's Chi-squared test
##
## Column names: label, stat_1, stat_2, p.value
## [1] "G10Q05_block2"
## Characteristic 2, N = 34 3, N = 47 p-value
## ----------------------------------------------------
## actual_choice 0.12
## 0|1|3|4 2 (5.9%) 1 (2.1%)
## 0|1|4|3 2 (5.9%) 0 (0%)
## 0|3|1|4 0 (0%) 7 (15%)
## 0|3|4|1 0 (0%) 1 (2.1%)
## 1|0|3|4 0 (0%) 3 (6.4%)
## 1|0|4|3 1 (2.9%) 0 (0%)
## 1|3|0|4 0 (0%) 2 (4.3%)
## 1|4|0|3 1 (2.9%) 0 (0%)
## 3|0|1|4 9 (26%) 10 (21%)
## 3|0|4|1 4 (12%) 8 (17%)
## 3|1|0|4 1 (2.9%) 1 (2.1%)
## 3|1|4|0 1 (2.9%) 1 (2.1%)
## 3|4|0|1 9 (26%) 6 (13%)
## 3|4|1|0 2 (5.9%) 6 (13%)
## 4|0|3|1 0 (0%) 1 (2.1%)
## 4|1|3|0 1 (2.9%) 0 (0%)
## 4|3|0|1 1 (2.9%) 0 (0%)
## ----------------------------------------------------
## n (%)
## Pearson's Chi-squared test
##
## Column names: label, stat_1, stat_2, p.value
## [1] "G10Q05_block3"
## Characteristic 2, N = 40 3, N = 37 p-value
## ----------------------------------------------------
## actual_choice 0.2
## 0|1|3|4 0 (0%) 1 (2.7%)
## 0|1|4|3 1 (2.5%) 1 (2.7%)
## 0|3|1|4 0 (0%) 1 (2.7%)
## 0|3|4|1 1 (2.5%) 0 (0%)
## 0|4|1|3 1 (2.5%) 3 (8.1%)
## 0|4|3|1 2 (5.0%) 1 (2.7%)
## 1|0|3|4 1 (2.5%) 0 (0%)
## 1|3|0|4 0 (0%) 1 (2.7%)
## 3|0|1|4 5 (13%) 6 (16%)
## 3|0|4|1 10 (25%) 2 (5.4%)
## 3|1|0|4 8 (20%) 9 (24%)
## 3|1|4|0 1 (2.5%) 2 (5.4%)
## 3|4|0|1 6 (15%) 1 (2.7%)
## 3|4|1|0 2 (5.0%) 4 (11%)
## 4|0|1|3 1 (2.5%) 0 (0%)
## 4|0|3|1 0 (0%) 4 (11%)
## 4|3|0|1 1 (2.5%) 1 (2.7%)
## ----------------------------------------------------
## n (%)
## Pearson's Chi-squared test
##
## Column names: label, stat_1, stat_2, p.value
## [1] "G10Q05_block4"
## Characteristic 2, N = 44 3, N = 30 p-value
## ----------------------------------------------------
## actual_choice 0.2
## 0|1|4|3 0 (0%) 1 (3.3%)
## 0|3|1|4 0 (0%) 2 (6.7%)
## 0|3|4|1 1 (2.3%) 1 (3.3%)
## 0|4|3|1 3 (6.8%) 1 (3.3%)
## 1|0|4|3 0 (0%) 1 (3.3%)
## 1|3|0|4 2 (4.5%) 0 (0%)
## 1|4|0|3 3 (6.8%) 2 (6.7%)
## 1|4|3|0 0 (0%) 1 (3.3%)
## 3|0|1|4 3 (6.8%) 4 (13%)
## 3|0|4|1 1 (2.3%) 1 (3.3%)
## 3|1|0|4 4 (9.1%) 0 (0%)
## 3|1|4|0 3 (6.8%) 1 (3.3%)
## 3|4|0|1 3 (6.8%) 5 (17%)
## 3|4|1|0 4 (9.1%) 4 (13%)
## 4|0|3|1 0 (0%) 1 (3.3%)
## 4|1|0|3 1 (2.3%) 2 (6.7%)
## 4|1|3|0 7 (16%) 2 (6.7%)
## 4|3|0|1 5 (11%) 1 (3.3%)
## 4|3|1|0 4 (9.1%) 0 (0%)
## ----------------------------------------------------
## n (%)
## Pearson's Chi-squared test
##
## Column names: label, stat_1, stat_2, p.value
## [1] "G10Q06_block1"
## Characteristic 2, N = 32 3, N = 50 p-value
## ----------------------------------------------------
## actual_choice 0.2
## 0|1|3|4 0 (0%) 2 (4.0%)
## 0|3|1|4 2 (6.3%) 1 (2.0%)
## 0|3|4|1 0 (0%) 1 (2.0%)
## 0|4|1|3 5 (16%) 0 (0%)
## 0|4|3|1 0 (0%) 2 (4.0%)
## 1|0|3|4 1 (3.1%) 0 (0%)
## 1|3|4|0 1 (3.1%) 0 (0%)
## 1|4|0|3 0 (0%) 1 (2.0%)
## 3|0|1|4 4 (13%) 4 (8.0%)
## 3|0|4|1 5 (16%) 7 (14%)
## 3|1|0|4 3 (9.4%) 2 (4.0%)
## 3|1|4|0 4 (13%) 6 (12%)
## 3|4|0|1 4 (13%) 9 (18%)
## 3|4|1|0 2 (6.3%) 5 (10%)
## 4|0|1|3 0 (0%) 1 (2.0%)
## 4|0|3|1 0 (0%) 2 (4.0%)
## 4|1|3|0 0 (0%) 1 (2.0%)
## 4|3|0|1 1 (3.1%) 1 (2.0%)
## 4|3|1|0 0 (0%) 5 (10%)
## ----------------------------------------------------
## n (%)
## Pearson's Chi-squared test
##
## Column names: label, stat_1, stat_2, p.value
## [1] "G10Q06_block2"
## Characteristic 2, N = 34 3, N = 47 p-value
## ----------------------------------------------------
## actual_choice 0.3
## 0|1|3|4 0 (0%) 1 (2.1%)
## 0|1|4|3 2 (5.9%) 0 (0%)
## 0|3|1|4 1 (2.9%) 2 (4.3%)
## 0|3|4|1 1 (2.9%) 4 (8.5%)
## 0|4|1|3 1 (2.9%) 1 (2.1%)
## 0|4|3|1 0 (0%) 1 (2.1%)
## 1|0|3|4 0 (0%) 4 (8.5%)
## 1|0|4|3 0 (0%) 1 (2.1%)
## 1|3|0|4 0 (0%) 2 (4.3%)
## 1|3|4|0 2 (5.9%) 1 (2.1%)
## 1|4|3|0 1 (2.9%) 0 (0%)
## 3|0|1|4 4 (12%) 2 (4.3%)
## 3|0|4|1 4 (12%) 4 (8.5%)
## 3|1|0|4 3 (8.8%) 2 (4.3%)
## 3|4|0|1 4 (12%) 5 (11%)
## 3|4|1|0 3 (8.8%) 3 (6.4%)
## 4|0|1|3 4 (12%) 1 (2.1%)
## 4|0|3|1 2 (5.9%) 4 (8.5%)
## 4|1|0|3 0 (0%) 1 (2.1%)
## 4|3|0|1 2 (5.9%) 4 (8.5%)
## 4|3|1|0 0 (0%) 4 (8.5%)
## ----------------------------------------------------
## n (%)
## Pearson's Chi-squared test
##
## Column names: label, stat_1, stat_2, p.value
## [1] "G10Q06_block3"
## Characteristic 2, N = 40 3, N = 37 p-value
## ----------------------------------------------------
## actual_choice 0.4
## 0|1|3|4 0 (0%) 1 (2.7%)
## 0|1|4|3 1 (2.5%) 0 (0%)
## 0|3|1|4 2 (5.0%) 0 (0%)
## 0|4|1|3 0 (0%) 1 (2.7%)
## 0|4|3|1 1 (2.5%) 0 (0%)
## 1|0|3|4 2 (5.0%) 2 (5.4%)
## 1|3|0|4 0 (0%) 2 (5.4%)
## 1|4|0|3 0 (0%) 1 (2.7%)
## 3|0|1|4 4 (10%) 7 (19%)
## 3|0|4|1 5 (13%) 1 (2.7%)
## 3|1|0|4 4 (10%) 4 (11%)
## 3|1|4|0 1 (2.5%) 2 (5.4%)
## 3|4|0|1 5 (13%) 4 (11%)
## 3|4|1|0 12 (30%) 6 (16%)
## 4|1|0|3 1 (2.5%) 1 (2.7%)
## 4|3|0|1 1 (2.5%) 2 (5.4%)
## 4|3|1|0 1 (2.5%) 3 (8.1%)
## ----------------------------------------------------
## n (%)
## Pearson's Chi-squared test
##
## Column names: label, stat_1, stat_2, p.value
## [1] "G10Q06_block4"
## Characteristic 2, N = 44 3, N = 30 p-value
## ----------------------------------------------------
## actual_choice 0.3
## 0|1|3|4 2 (4.5%) 1 (3.3%)
## 0|1|4|3 2 (4.5%) 1 (3.3%)
## 0|3|1|4 1 (2.3%) 2 (6.7%)
## 0|4|1|3 2 (4.5%) 0 (0%)
## 0|4|3|1 1 (2.3%) 0 (0%)
## 1|0|3|4 0 (0%) 1 (3.3%)
## 1|0|4|3 1 (2.3%) 1 (3.3%)
## 1|3|0|4 1 (2.3%) 0 (0%)
## 1|3|4|0 3 (6.8%) 0 (0%)
## 1|4|0|3 2 (4.5%) 0 (0%)
## 3|0|1|4 14 (32%) 10 (33%)
## 3|0|4|1 6 (14%) 2 (6.7%)
## 3|1|0|4 0 (0%) 3 (10%)
## 3|1|4|0 0 (0%) 2 (6.7%)
## 3|4|0|1 2 (4.5%) 3 (10%)
## 3|4|1|0 4 (9.1%) 2 (6.7%)
## 4|0|1|3 0 (0%) 1 (3.3%)
## 4|0|3|1 0 (0%) 1 (3.3%)
## 4|1|0|3 1 (2.3%) 0 (0%)
## 4|3|0|1 1 (2.3%) 0 (0%)
## 4|3|1|0 1 (2.3%) 0 (0%)
## ----------------------------------------------------
## n (%)
## Pearson's Chi-squared test
##
## Column names: label, stat_1, stat_2, p.value
## [1] "G10Q07_block1"
## Characteristic 2, N = 32 3, N = 50 p-value
## ----------------------------------------------------
## actual_choice 0.4
## 0|1|2|4 2 (6.3%) 2 (4.0%)
## 0|1|4|2 3 (9.4%) 1 (2.0%)
## 0|2|1|4 4 (13%) 4 (8.0%)
## 0|2|4|1 0 (0%) 4 (8.0%)
## 0|4|1|2 2 (6.3%) 2 (4.0%)
## 0|4|2|1 0 (0%) 1 (2.0%)
## 1|0|2|4 2 (6.3%) 0 (0%)
## 1|2|0|4 3 (9.4%) 3 (6.0%)
## 1|2|4|0 1 (3.1%) 4 (8.0%)
## 1|4|0|2 1 (3.1%) 2 (4.0%)
## 2|0|1|4 3 (9.4%) 1 (2.0%)
## 2|0|4|1 2 (6.3%) 2 (4.0%)
## 2|1|0|4 2 (6.3%) 11 (22%)
## 2|1|4|0 1 (3.1%) 1 (2.0%)
## 2|4|0|1 1 (3.1%) 1 (2.0%)
## 2|4|1|0 0 (0%) 1 (2.0%)
## 4|0|1|2 0 (0%) 1 (2.0%)
## 4|0|2|1 1 (3.1%) 3 (6.0%)
## 4|1|2|0 1 (3.1%) 1 (2.0%)
## 4|2|0|1 0 (0%) 3 (6.0%)
## 4|2|1|0 3 (9.4%) 2 (4.0%)
## ----------------------------------------------------
## n (%)
## Pearson's Chi-squared test
##
## Column names: label, stat_1, stat_2, p.value
## [1] "G10Q07_block2"
## Characteristic 2, N = 34 3, N = 47 p-value
## ----------------------------------------------------
## actual_choice 0.3
## 0|1|2|4 5 (15%) 6 (13%)
## 0|1|4|2 5 (15%) 4 (8.5%)
## 0|2|1|4 3 (8.8%) 3 (6.4%)
## 0|2|4|1 3 (8.8%) 3 (6.4%)
## 0|4|1|2 1 (2.9%) 3 (6.4%)
## 0|4|2|1 0 (0%) 2 (4.3%)
## 1|0|2|4 0 (0%) 3 (6.4%)
## 1|0|4|2 1 (2.9%) 4 (8.5%)
## 1|2|0|4 1 (2.9%) 1 (2.1%)
## 1|2|4|0 0 (0%) 1 (2.1%)
## 1|4|0|2 1 (2.9%) 0 (0%)
## 1|4|2|0 0 (0%) 1 (2.1%)
## 2|0|1|4 3 (8.8%) 0 (0%)
## 2|0|4|1 1 (2.9%) 4 (8.5%)
## 2|1|0|4 0 (0%) 3 (6.4%)
## 2|1|4|0 0 (0%) 2 (4.3%)
## 2|4|1|0 1 (2.9%) 0 (0%)
## 4|0|1|2 2 (5.9%) 2 (4.3%)
## 4|0|2|1 4 (12%) 1 (2.1%)
## 4|1|2|0 0 (0%) 1 (2.1%)
## 4|2|0|1 1 (2.9%) 2 (4.3%)
## 4|2|1|0 2 (5.9%) 1 (2.1%)
## ----------------------------------------------------
## n (%)
## Pearson's Chi-squared test
##
## Column names: label, stat_1, stat_2, p.value
## [1] "G10Q07_block3"
## Characteristic 2, N = 40 3, N = 37 p-value
## ----------------------------------------------------
## actual_choice 0.3
## 0|1|2|4 2 (5.0%) 0 (0%)
## 0|1|4|2 3 (7.5%) 1 (2.7%)
## 0|2|1|4 3 (7.5%) 4 (11%)
## 0|2|4|1 1 (2.5%) 2 (5.4%)
## 0|4|1|2 1 (2.5%) 1 (2.7%)
## 0|4|2|1 3 (7.5%) 0 (0%)
## 1|0|2|4 1 (2.5%) 2 (5.4%)
## 1|0|4|2 1 (2.5%) 0 (0%)
## 1|2|0|4 0 (0%) 2 (5.4%)
## 1|2|4|0 1 (2.5%) 0 (0%)
## 1|4|2|0 0 (0%) 1 (2.7%)
## 2|0|1|4 1 (2.5%) 3 (8.1%)
## 2|0|4|1 1 (2.5%) 0 (0%)
## 2|1|0|4 2 (5.0%) 2 (5.4%)
## 2|1|4|0 0 (0%) 3 (8.1%)
## 2|4|0|1 0 (0%) 1 (2.7%)
## 4|0|1|2 5 (13%) 2 (5.4%)
## 4|0|2|1 2 (5.0%) 4 (11%)
## 4|1|0|2 2 (5.0%) 3 (8.1%)
## 4|1|2|0 2 (5.0%) 0 (0%)
## 4|2|0|1 5 (13%) 1 (2.7%)
## 4|2|1|0 4 (10%) 5 (14%)
## ----------------------------------------------------
## n (%)
## Pearson's Chi-squared test
##
## Column names: label, stat_1, stat_2, p.value
## [1] "G10Q07_block4"
## Characteristic 2, N = 44 3, N = 30 p-value
## ----------------------------------------------------
## actual_choice 0.5
## 0|1|2|4 0 (0%) 2 (6.7%)
## 0|1|4|2 0 (0%) 2 (6.7%)
## 0|2|1|4 4 (9.1%) 3 (10%)
## 0|4|2|1 5 (11%) 1 (3.3%)
## 1|2|0|4 1 (2.3%) 1 (3.3%)
## 1|2|4|0 2 (4.5%) 0 (0%)
## 1|4|0|2 2 (4.5%) 0 (0%)
## 2|0|1|4 6 (14%) 6 (20%)
## 2|0|4|1 3 (6.8%) 0 (0%)
## 2|1|0|4 3 (6.8%) 2 (6.7%)
## 2|1|4|0 3 (6.8%) 2 (6.7%)
## 2|4|0|1 1 (2.3%) 1 (3.3%)
## 2|4|1|0 2 (4.5%) 0 (0%)
## 4|0|1|2 3 (6.8%) 2 (6.7%)
## 4|0|2|1 1 (2.3%) 1 (3.3%)
## 4|1|0|2 2 (4.5%) 1 (3.3%)
## 4|1|2|0 0 (0%) 2 (6.7%)
## 4|2|0|1 3 (6.8%) 1 (3.3%)
## 4|2|1|0 3 (6.8%) 3 (10%)
## ----------------------------------------------------
## n (%)
## Pearson's Chi-squared test
##
## Column names: label, stat_1, stat_2, p.value
## [1] "G10Q08_block1"
## Characteristic 2, N = 32 3, N = 50 p-value
## ----------------------------------------------------
## actual_choice 0.7
## 0|1|2|4 3 (9.4%) 2 (4.0%)
## 0|1|4|2 1 (3.1%) 2 (4.0%)
## 0|2|1|4 2 (6.3%) 5 (10%)
## 0|2|4|1 1 (3.1%) 0 (0%)
## 0|4|1|2 2 (6.3%) 3 (6.0%)
## 0|4|2|1 1 (3.1%) 3 (6.0%)
## 1|0|2|4 2 (6.3%) 1 (2.0%)
## 1|0|4|2 2 (6.3%) 1 (2.0%)
## 1|2|0|4 3 (9.4%) 4 (8.0%)
## 1|2|4|0 3 (9.4%) 3 (6.0%)
## 1|4|2|0 1 (3.1%) 0 (0%)
## 2|0|1|4 0 (0%) 1 (2.0%)
## 2|0|4|1 1 (3.1%) 0 (0%)
## 2|1|0|4 1 (3.1%) 1 (2.0%)
## 2|1|4|0 2 (6.3%) 3 (6.0%)
## 4|0|1|2 0 (0%) 5 (10%)
## 4|0|2|1 2 (6.3%) 6 (12%)
## 4|1|0|2 1 (3.1%) 3 (6.0%)
## 4|1|2|0 1 (3.1%) 4 (8.0%)
## 4|2|0|1 3 (9.4%) 2 (4.0%)
## 4|2|1|0 0 (0%) 1 (2.0%)
## ----------------------------------------------------
## n (%)
## Pearson's Chi-squared test
##
## Column names: label, stat_1, stat_2, p.value
## [1] "G10Q08_block2"
## Characteristic 2, N = 34 3, N = 47 p-value
## ----------------------------------------------------
## actual_choice 0.4
## 0|1|2|4 1 (2.9%) 4 (8.5%)
## 0|1|4|2 5 (15%) 5 (11%)
## 0|2|1|4 2 (5.9%) 5 (11%)
## 0|2|4|1 1 (2.9%) 3 (6.4%)
## 0|4|2|1 1 (2.9%) 3 (6.4%)
## 1|0|4|2 2 (5.9%) 2 (4.3%)
## 1|2|0|4 2 (5.9%) 1 (2.1%)
## 1|2|4|0 0 (0%) 2 (4.3%)
## 1|4|0|2 1 (2.9%) 0 (0%)
## 1|4|2|0 2 (5.9%) 2 (4.3%)
## 2|0|1|4 2 (5.9%) 1 (2.1%)
## 2|1|0|4 0 (0%) 2 (4.3%)
## 2|1|4|0 2 (5.9%) 1 (2.1%)
## 2|4|0|1 2 (5.9%) 2 (4.3%)
## 2|4|1|0 0 (0%) 2 (4.3%)
## 4|0|1|2 1 (2.9%) 1 (2.1%)
## 4|0|2|1 4 (12%) 1 (2.1%)
## 4|1|0|2 0 (0%) 2 (4.3%)
## 4|1|2|0 0 (0%) 5 (11%)
## 4|2|0|1 3 (8.8%) 1 (2.1%)
## 4|2|1|0 3 (8.8%) 2 (4.3%)
## ----------------------------------------------------
## n (%)
## Pearson's Chi-squared test
##
## Column names: label, stat_1, stat_2, p.value
## [1] "G10Q08_block3"
## Characteristic 2, N = 40 3, N = 37 p-value
## ----------------------------------------------------
## actual_choice 0.4
## 0|1|2|4 1 (2.5%) 1 (2.7%)
## 0|1|4|2 2 (5.0%) 1 (2.7%)
## 0|2|1|4 2 (5.0%) 2 (5.4%)
## 0|2|4|1 4 (10%) 2 (5.4%)
## 0|4|1|2 1 (2.5%) 2 (5.4%)
## 0|4|2|1 4 (10%) 1 (2.7%)
## 1|0|2|4 0 (0%) 2 (5.4%)
## 1|0|4|2 1 (2.5%) 2 (5.4%)
## 1|2|0|4 2 (5.0%) 0 (0%)
## 1|2|4|0 0 (0%) 1 (2.7%)
## 2|0|1|4 2 (5.0%) 2 (5.4%)
## 2|0|4|1 1 (2.5%) 1 (2.7%)
## 2|1|0|4 0 (0%) 2 (5.4%)
## 2|1|4|0 0 (0%) 3 (8.1%)
## 4|0|1|2 2 (5.0%) 2 (5.4%)
## 4|0|2|1 8 (20%) 1 (2.7%)
## 4|1|0|2 1 (2.5%) 2 (5.4%)
## 4|1|2|0 1 (2.5%) 2 (5.4%)
## 4|2|0|1 6 (15%) 4 (11%)
## 4|2|1|0 2 (5.0%) 4 (11%)
## ----------------------------------------------------
## n (%)
## Pearson's Chi-squared test
##
## Column names: label, stat_1, stat_2, p.value
## [1] "G10Q08_block4"
## Characteristic 2, N = 44 3, N = 30 p-value
## ----------------------------------------------------
## actual_choice 0.11
## 0|1|2|4 1 (2.3%) 5 (17%)
## 0|2|1|4 5 (11%) 4 (13%)
## 0|2|4|1 0 (0%) 1 (3.3%)
## 0|4|2|1 3 (6.8%) 1 (3.3%)
## 1|0|2|4 4 (9.1%) 1 (3.3%)
## 1|0|4|2 0 (0%) 1 (3.3%)
## 1|2|0|4 2 (4.5%) 0 (0%)
## 1|2|4|0 1 (2.3%) 1 (3.3%)
## 1|4|2|0 1 (2.3%) 0 (0%)
## 2|0|1|4 8 (18%) 1 (3.3%)
## 2|0|4|1 3 (6.8%) 0 (0%)
## 2|1|0|4 2 (4.5%) 6 (20%)
## 2|1|4|0 0 (0%) 1 (3.3%)
## 2|4|0|1 1 (2.3%) 2 (6.7%)
## 2|4|1|0 1 (2.3%) 0 (0%)
## 4|0|1|2 1 (2.3%) 0 (0%)
## 4|0|2|1 2 (4.5%) 2 (6.7%)
## 4|1|0|2 2 (4.5%) 0 (0%)
## 4|1|2|0 2 (4.5%) 3 (10%)
## 4|2|0|1 3 (6.8%) 1 (3.3%)
## 4|2|1|0 2 (4.5%) 0 (0%)
## ----------------------------------------------------
## n (%)
## Pearson's Chi-squared test
##
## Column names: label, stat_1, stat_2, p.value
## [1] "G10Q09_block1"
## Characteristic 2, N = 32 3, N = 50 p-value
## ----------------------------------------------------
## actual_choice 0.7
## 0|1|2|3 1 (3.1%) 0 (0%)
## 0|2|1|3 4 (13%) 5 (10%)
## 0|2|3|1 2 (6.3%) 3 (6.0%)
## 0|3|1|2 0 (0%) 1 (2.0%)
## 1|0|2|3 1 (3.1%) 0 (0%)
## 1|0|3|2 0 (0%) 2 (4.0%)
## 1|2|0|3 2 (6.3%) 0 (0%)
## 1|3|0|2 0 (0%) 2 (4.0%)
## 2|0|1|3 2 (6.3%) 2 (4.0%)
## 2|0|3|1 1 (3.1%) 2 (4.0%)
## 2|1|0|3 1 (3.1%) 3 (6.0%)
## 2|1|3|0 0 (0%) 1 (2.0%)
## 3|0|1|2 3 (9.4%) 6 (12%)
## 3|0|2|1 5 (16%) 13 (26%)
## 3|1|0|2 1 (3.1%) 1 (2.0%)
## 3|1|2|0 4 (13%) 2 (4.0%)
## 3|2|0|1 4 (13%) 5 (10%)
## 3|2|1|0 1 (3.1%) 2 (4.0%)
## ----------------------------------------------------
## n (%)
## Pearson's Chi-squared test
##
## Column names: label, stat_1, stat_2, p.value
## [1] "G10Q09_block2"
## Characteristic 2, N = 34 3, N = 47 p-value
## ----------------------------------------------------
## actual_choice 0.2
## 0|1|2|3 0 (0%) 1 (2.1%)
## 0|1|3|2 0 (0%) 1 (2.1%)
## 0|2|3|1 1 (2.9%) 0 (0%)
## 0|3|1|2 1 (2.9%) 0 (0%)
## 0|3|2|1 0 (0%) 1 (2.1%)
## 1|0|2|3 1 (2.9%) 0 (0%)
## 1|0|3|2 0 (0%) 4 (8.5%)
## 1|2|0|3 0 (0%) 1 (2.1%)
## 1|2|3|0 2 (5.9%) 2 (4.3%)
## 2|0|1|3 1 (2.9%) 1 (2.1%)
## 2|1|0|3 0 (0%) 3 (6.4%)
## 2|1|3|0 4 (12%) 9 (19%)
## 2|3|0|1 1 (2.9%) 2 (4.3%)
## 2|3|1|0 0 (0%) 1 (2.1%)
## 3|0|1|2 2 (5.9%) 5 (11%)
## 3|0|2|1 10 (29%) 7 (15%)
## 3|1|0|2 1 (2.9%) 0 (0%)
## 3|1|2|0 1 (2.9%) 2 (4.3%)
## 3|2|0|1 9 (26%) 4 (8.5%)
## 3|2|1|0 0 (0%) 3 (6.4%)
## ----------------------------------------------------
## n (%)
## Pearson's Chi-squared test
##
## Column names: label, stat_1, stat_2, p.value
## [1] "G10Q09_block3"
## Characteristic 2, N = 40 3, N = 37 p-value
## ----------------------------------------------------
## actual_choice 0.2
## 0|1|3|2 1 (2.5%) 1 (2.7%)
## 0|2|1|3 0 (0%) 2 (5.4%)
## 0|2|3|1 2 (5.0%) 5 (14%)
## 0|3|1|2 3 (7.5%) 1 (2.7%)
## 0|3|2|1 2 (5.0%) 1 (2.7%)
## 1|0|3|2 0 (0%) 2 (5.4%)
## 1|2|3|0 1 (2.5%) 0 (0%)
## 1|3|0|2 0 (0%) 1 (2.7%)
## 1|3|2|0 1 (2.5%) 0 (0%)
## 2|0|1|3 2 (5.0%) 1 (2.7%)
## 2|0|3|1 1 (2.5%) 3 (8.1%)
## 2|1|0|3 0 (0%) 2 (5.4%)
## 2|1|3|0 0 (0%) 2 (5.4%)
## 2|3|0|1 2 (5.0%) 1 (2.7%)
## 2|3|1|0 0 (0%) 1 (2.7%)
## 3|0|1|2 10 (25%) 3 (8.1%)
## 3|0|2|1 7 (18%) 3 (8.1%)
## 3|1|0|2 2 (5.0%) 4 (11%)
## 3|1|2|0 1 (2.5%) 2 (5.4%)
## 3|2|0|1 3 (7.5%) 2 (5.4%)
## 3|2|1|0 2 (5.0%) 0 (0%)
## ----------------------------------------------------
## n (%)
## Pearson's Chi-squared test
##
## Column names: label, stat_1, stat_2, p.value
## [1] "G10Q09_block4"
## Characteristic 2, N = 44 3, N = 30 p-value
## ----------------------------------------------------
## actual_choice 0.4
## 0|1|2|3 1 (2.3%) 0 (0%)
## 0|1|3|2 2 (4.5%) 1 (3.3%)
## 0|2|1|3 1 (2.3%) 1 (3.3%)
## 0|2|3|1 1 (2.3%) 0 (0%)
## 0|3|1|2 0 (0%) 2 (6.7%)
## 0|3|2|1 0 (0%) 1 (3.3%)
## 1|0|2|3 4 (9.1%) 1 (3.3%)
## 1|0|3|2 1 (2.3%) 0 (0%)
## 1|2|0|3 3 (6.8%) 4 (13%)
## 1|2|3|0 3 (6.8%) 0 (0%)
## 1|3|0|2 3 (6.8%) 0 (0%)
## 2|1|0|3 0 (0%) 1 (3.3%)
## 2|1|3|0 0 (0%) 1 (3.3%)
## 3|0|1|2 12 (27%) 7 (23%)
## 3|0|2|1 2 (4.5%) 4 (13%)
## 3|1|0|2 4 (9.1%) 1 (3.3%)
## 3|1|2|0 2 (4.5%) 1 (3.3%)
## 3|2|0|1 2 (4.5%) 3 (10%)
## 3|2|1|0 3 (6.8%) 2 (6.7%)
## ----------------------------------------------------
## n (%)
## Pearson's Chi-squared test
##
## Column names: label, stat_1, stat_2, p.value
## [1] "G10Q10_block1"
## Characteristic 2, N = 32 3, N = 50 p-value
## ----------------------------------------------------
## actual_choice 0.6
## 0|1|2|3 0 (0%) 2 (4.0%)
## 0|2|1|3 1 (3.1%) 0 (0%)
## 0|2|3|1 0 (0%) 1 (2.0%)
## 0|3|1|2 0 (0%) 1 (2.0%)
## 0|3|2|1 1 (3.1%) 2 (4.0%)
## 1|0|2|3 1 (3.1%) 0 (0%)
## 1|0|3|2 2 (6.3%) 0 (0%)
## 1|3|0|2 1 (3.1%) 1 (2.0%)
## 1|3|2|0 1 (3.1%) 0 (0%)
## 2|0|1|3 1 (3.1%) 1 (2.0%)
## 2|1|3|0 0 (0%) 1 (2.0%)
## 2|3|0|1 0 (0%) 2 (4.0%)
## 2|3|1|0 1 (3.1%) 1 (2.0%)
## 3|0|1|2 2 (6.3%) 5 (10%)
## 3|0|2|1 4 (13%) 12 (24%)
## 3|1|0|2 2 (6.3%) 1 (2.0%)
## 3|1|2|0 5 (16%) 6 (12%)
## 3|2|0|1 5 (16%) 6 (12%)
## 3|2|1|0 5 (16%) 8 (16%)
## ----------------------------------------------------
## n (%)
## Pearson's Chi-squared test
##
## Column names: label, stat_1, stat_2, p.value
## [1] "G10Q10_block2"
## Characteristic 2, N = 34 3, N = 47 p-value
## ----------------------------------------------------
## actual_choice 0.10
## 0|1|2|3 2 (5.9%) 2 (4.3%)
## 0|1|3|2 2 (5.9%) 6 (13%)
## 0|2|1|3 1 (2.9%) 0 (0%)
## 0|2|3|1 1 (2.9%) 0 (0%)
## 0|3|1|2 0 (0%) 1 (2.1%)
## 0|3|2|1 0 (0%) 1 (2.1%)
## 1|0|2|3 2 (5.9%) 1 (2.1%)
## 1|0|3|2 1 (2.9%) 4 (8.5%)
## 1|2|3|0 1 (2.9%) 3 (6.4%)
## 1|3|2|0 0 (0%) 1 (2.1%)
## 2|0|1|3 1 (2.9%) 0 (0%)
## 2|1|0|3 0 (0%) 1 (2.1%)
## 2|1|3|0 1 (2.9%) 0 (0%)
## 2|3|0|1 0 (0%) 2 (4.3%)
## 2|3|1|0 0 (0%) 1 (2.1%)
## 3|0|1|2 6 (18%) 5 (11%)
## 3|0|2|1 8 (24%) 1 (2.1%)
## 3|1|0|2 1 (2.9%) 3 (6.4%)
## 3|1|2|0 1 (2.9%) 4 (8.5%)
## 3|2|0|1 4 (12%) 2 (4.3%)
## 3|2|1|0 2 (5.9%) 9 (19%)
## ----------------------------------------------------
## n (%)
## Pearson's Chi-squared test
##
## Column names: label, stat_1, stat_2, p.value
## [1] "G10Q10_block3"
## Characteristic 2, N = 40 3, N = 37 p-value
## ----------------------------------------------------
## actual_choice 0.3
## 0|1|2|3 0 (0%) 1 (2.7%)
## 0|1|3|2 0 (0%) 1 (2.7%)
## 0|2|3|1 0 (0%) 2 (5.4%)
## 0|3|1|2 1 (2.5%) 0 (0%)
## 0|3|2|1 2 (5.0%) 0 (0%)
## 1|0|2|3 0 (0%) 1 (2.7%)
## 1|0|3|2 1 (2.5%) 2 (5.4%)
## 1|2|0|3 2 (5.0%) 1 (2.7%)
## 1|2|3|0 3 (7.5%) 4 (11%)
## 1|3|0|2 1 (2.5%) 1 (2.7%)
## 2|0|3|1 1 (2.5%) 2 (5.4%)
## 2|1|3|0 1 (2.5%) 4 (11%)
## 2|3|0|1 2 (5.0%) 0 (0%)
## 2|3|1|0 4 (10%) 1 (2.7%)
## 3|0|1|2 4 (10%) 1 (2.7%)
## 3|0|2|1 6 (15%) 6 (16%)
## 3|1|0|2 6 (15%) 2 (5.4%)
## 3|1|2|0 1 (2.5%) 4 (11%)
## 3|2|0|1 4 (10%) 2 (5.4%)
## 3|2|1|0 1 (2.5%) 2 (5.4%)
## ----------------------------------------------------
## n (%)
## Pearson's Chi-squared test
##
## Column names: label, stat_1, stat_2, p.value
## [1] "G10Q10_block4"
## Characteristic 2, N = 44 3, N = 30 p-value
## ----------------------------------------------------
## actual_choice 0.3
## 0|1|2|3 0 (0%) 1 (3.3%)
## 0|1|3|2 1 (2.3%) 0 (0%)
## 0|2|1|3 2 (4.5%) 3 (10%)
## 0|2|3|1 2 (4.5%) 0 (0%)
## 0|3|1|2 1 (2.3%) 1 (3.3%)
## 0|3|2|1 2 (4.5%) 2 (6.7%)
## 1|0|2|3 0 (0%) 3 (10%)
## 1|2|0|3 4 (9.1%) 0 (0%)
## 1|2|3|0 1 (2.3%) 0 (0%)
## 2|0|1|3 2 (4.5%) 3 (10%)
## 2|0|3|1 2 (4.5%) 1 (3.3%)
## 2|1|0|3 1 (2.3%) 1 (3.3%)
## 2|1|3|0 1 (2.3%) 0 (0%)
## 2|3|0|1 2 (4.5%) 0 (0%)
## 3|0|1|2 3 (6.8%) 2 (6.7%)
## 3|0|2|1 7 (16%) 7 (23%)
## 3|1|0|2 2 (4.5%) 1 (3.3%)
## 3|1|2|0 2 (4.5%) 0 (0%)
## 3|2|0|1 4 (9.1%) 5 (17%)
## 3|2|1|0 5 (11%) 0 (0%)
## ----------------------------------------------------
## n (%)
## Pearson's Chi-squared test
##
## Column names: label, stat_1, stat_2, p.valueNext, we look at the association between each of the 40 paired comparison questions (i.e., actual_choice) with each task sequence and each block
# assess 'actual_choice' vs task sequence by question and block for pc
results = lapply(tasks_pc, function(variable1) {
lapply(block, function(variable2) {
choice_vs_taskseq_by_question_and_block(variable1, variable2, pc2_it)
})})## [1] "G08Q01_taskseq1"
## Characteri ¦ actual_cho 0 1 ¦ n (%) Pearson's
## stic ¦ ice ¦ Chi-square
## ¦ ¦ d test
## 8, N = 9 ¦ 3 (33%) 6 (67%) ¦
## 9, N = 11 ¦ 2 (18%) 9 (82%) ¦
## 10, N = 4 ¦ 3 (75%) 1 (25%) ¦
## 11, N = 10 ¦ 3 (30%) 7 (70%) ¦
## 12, N = 6 ¦ 3 (50%) 3 (50%) ¦
## 13, N = 8 ¦ 2 (25%) 6 (75%) ¦
## 14, N = 5 ¦ 0 (0%) 5 (100%) ¦
## 15, N = 9 ¦ 6 (67%) 3 (33%) ¦
## 16, N = 8 ¦ 7 (88%) 1 (13%) ¦
## 17, N = 12 ¦ 2 (17%) 10 (83%) ¦
## p-value ¦ 0.008 ¦
##
## Column names: , 1, 2, 3, .2, .1
## [1] "G08Q01_taskseq2"
## Characteri ¦ actual_cho 0 1 ¦ n (%) Pearson's
## stic ¦ ice ¦ Chi-square
## ¦ ¦ d test
## 8, N = 7 ¦ 3 (43%) 4 (57%) ¦
## 9, N = 7 ¦ 5 (71%) 2 (29%) ¦
## 10, N = 10 ¦ 5 (50%) 5 (50%) ¦
## 11, N = 5 ¦ 4 (80%) 1 (20%) ¦
## 12, N = 11 ¦ 5 (45%) 6 (55%) ¦
## 13, N = 7 ¦ 4 (57%) 3 (43%) ¦
## 14, N = 9 ¦ 6 (67%) 3 (33%) ¦
## 15, N = 10 ¦ 8 (80%) 2 (20%) ¦
## 16, N = 7 ¦ 7 (100%) 0 (0%) ¦
## 17, N = 8 ¦ 8 (100%) 0 (0%) ¦
## p-value ¦ 0.10 ¦
##
## Column names: , 1, 2, 3, .2, .1
## [1] "G08Q01_taskseq3"
## Characteri ¦ actual_cho 0 1 ¦ n (%) Pearson's
## stic ¦ ice ¦ Chi-square
## ¦ ¦ d test
## 8, N = 5 ¦ 3 (60%) 2 (40%) ¦
## 9, N = 4 ¦ 3 (75%) 1 (25%) ¦
## 10, N = 7 ¦ 4 (57%) 3 (43%) ¦
## 11, N = 12 ¦ 8 (67%) 4 (33%) ¦
## 12, N = 6 ¦ 4 (67%) 2 (33%) ¦
## 13, N = 6 ¦ 3 (50%) 3 (50%) ¦
## 14, N = 10 ¦ 6 (60%) 4 (40%) ¦
## 15, N = 8 ¦ 6 (75%) 2 (25%) ¦
## 16, N = 5 ¦ 4 (80%) 1 (20%) ¦
## 17, N = 14 ¦ 7 (50%) 7 (50%) ¦
## p-value ¦ >0.9 ¦
##
## Column names: , 1, 2, 3, .2, .1
## [1] "G08Q01_taskseq4"
## Characteri ¦ actual_cho 0 1 ¦ n (%) Pearson's
## stic ¦ ice ¦ Chi-square
## ¦ ¦ d test
## 8, N = 7 ¦ 5 (71%) 2 (29%) ¦
## 9, N = 7 ¦ 5 (71%) 2 (29%) ¦
## 10, N = 2 ¦ 1 (50%) 1 (50%) ¦
## 11, N = 10 ¦ 7 (70%) 3 (30%) ¦
## 12, N = 8 ¦ 2 (25%) 6 (75%) ¦
## 13, N = 6 ¦ 5 (83%) 1 (17%) ¦
## 14, N = 10 ¦ 6 (60%) 4 (40%) ¦
## 15, N = 6 ¦ 4 (67%) 2 (33%) ¦
## 16, N = 5 ¦ 3 (60%) 2 (40%) ¦
## 17, N = 13 ¦ 9 (69%) 4 (31%) ¦
## p-value ¦ 0.6 ¦
##
## Column names: , 1, 2, 3, .2, .1
## [1] "G08Q02_taskseq1"
## Characteri ¦ actual_cho 0 1 ¦ n (%) Pearson's
## stic ¦ ice ¦ Chi-square
## ¦ ¦ d test
## 8, N = 17 ¦ 9 (53%) 8 (47%) ¦
## 9, N = 7 ¦ 4 (57%) 3 (43%) ¦
## 10, N = 10 ¦ 7 (70%) 3 (30%) ¦
## 11, N = 8 ¦ 6 (75%) 2 (25%) ¦
## 12, N = 7 ¦ 5 (71%) 2 (29%) ¦
## 13, N = 3 ¦ 1 (33%) 2 (67%) ¦
## 14, N = 6 ¦ 1 (17%) 5 (83%) ¦
## 15, N = 3 ¦ 0 (0%) 3 (100%) ¦
## 16, N = 13 ¦ 5 (38%) 8 (62%) ¦
## 17, N = 8 ¦ 4 (50%) 4 (50%) ¦
## p-value ¦ 0.2 ¦
##
## Column names: , 1, 2, 3, .2, .1
## [1] "G08Q02_taskseq2"
## Characteri ¦ actual_cho 0 1 ¦ n (%) Pearson's
## stic ¦ ice ¦ Chi-square
## ¦ ¦ d test
## 8, N = 6 ¦ 4 (67%) 2 (33%) ¦
## 9, N = 10 ¦ 5 (50%) 5 (50%) ¦
## 10, N = 7 ¦ 5 (71%) 2 (29%) ¦
## 11, N = 9 ¦ 7 (78%) 2 (22%) ¦
## 12, N = 8 ¦ 6 (75%) 2 (25%) ¦
## 13, N = 9 ¦ 6 (67%) 3 (33%) ¦
## 14, N = 9 ¦ 3 (33%) 6 (67%) ¦
## 15, N = 4 ¦ 2 (50%) 2 (50%) ¦
## 16, N = 10 ¦ 9 (90%) 1 (10%) ¦
## 17, N = 9 ¦ 6 (67%) 3 (33%) ¦
## p-value ¦ 0.4 ¦
##
## Column names: , 1, 2, 3, .2, .1
## [1] "G08Q02_taskseq3"
## Characteri ¦ actual_cho 0 1 ¦ n (%) Pearson's
## stic ¦ ice ¦ Chi-square
## ¦ ¦ d test
## 8, N = 4 ¦ 4 (100%) 0 (0%) ¦
## 9, N = 10 ¦ 9 (90%) 1 (10%) ¦
## 10, N = 8 ¦ 8 (100%) 0 (0%) ¦
## 11, N = 13 ¦ 8 (62%) 5 (38%) ¦
## 12, N = 8 ¦ 6 (75%) 2 (25%) ¦
## 13, N = 9 ¦ 3 (33%) 6 (67%) ¦
## 14, N = 6 ¦ 4 (67%) 2 (33%) ¦
## 15, N = 8 ¦ 6 (75%) 2 (25%) ¦
## 16, N = 6 ¦ 4 (67%) 2 (33%) ¦
## 17, N = 5 ¦ 4 (80%) 1 (20%) ¦
## p-value ¦ 0.11 ¦
##
## Column names: , 1, 2, 3, .2, .1
## [1] "G08Q02_taskseq4"
## Characteri ¦ actual_cho 0 1 ¦ n (%) Pearson's
## stic ¦ ice ¦ Chi-square
## ¦ ¦ d test
## 8, N = 8 ¦ 2 (25%) 6 (75%) ¦
## 9, N = 5 ¦ 2 (40%) 3 (60%) ¦
## 10, N = 9 ¦ 4 (44%) 5 (56%) ¦
## 11, N = 8 ¦ 3 (38%) 5 (63%) ¦
## 12, N = 10 ¦ 5 (50%) 5 (50%) ¦
## 13, N = 5 ¦ 4 (80%) 1 (20%) ¦
## 14, N = 3 ¦ 0 (0%) 3 (100%) ¦
## 15, N = 4 ¦ 3 (75%) 1 (25%) ¦
## 16, N = 15 ¦ 7 (47%) 8 (53%) ¦
## 17, N = 7 ¦ 3 (43%) 4 (57%) ¦
## p-value ¦ 0.5 ¦
##
## Column names: , 1, 2, 3, .2, .1
## [1] "G08Q03_taskseq1"
## Characteri ¦ actual_cho 0 1 ¦ n (%) Pearson's
## stic ¦ ice ¦ Chi-square
## ¦ ¦ d test
## 8, N = 5 ¦ 4 (80%) 1 (20%) ¦
## 9, N = 11 ¦ 5 (45%) 6 (55%) ¦
## 10, N = 12 ¦ 8 (67%) 4 (33%) ¦
## 11, N = 9 ¦ 5 (56%) 4 (44%) ¦
## 12, N = 7 ¦ 1 (14%) 6 (86%) ¦
## 13, N = 10 ¦ 6 (60%) 4 (40%) ¦
## 14, N = 5 ¦ 4 (80%) 1 (20%) ¦
## 15, N = 4 ¦ 2 (50%) 2 (50%) ¦
## 16, N = 11 ¦ 7 (64%) 4 (36%) ¦
## 17, N = 8 ¦ 5 (63%) 3 (38%) ¦
## p-value ¦ 0.5 ¦
##
## Column names: , 1, 2, 3, .2, .1
## [1] "G08Q03_taskseq2"
## Characteri ¦ actual_cho 0 1 ¦ n (%) Pearson's
## stic ¦ ice ¦ Chi-square
## ¦ ¦ d test
## 8, N = 8 ¦ 7 (88%) 1 (13%) ¦
## 9, N = 6 ¦ 4 (67%) 2 (33%) ¦
## 10, N = 14 ¦ 10 (71%) 4 (29%) ¦
## 11, N = 13 ¦ 12 (92%) 1 (7.7%) ¦
## 12, N = 4 ¦ 3 (75%) 1 (25%) ¦
## 13, N = 12 ¦ 10 (83%) 2 (17%) ¦
## 14, N = 8 ¦ 6 (75%) 2 (25%) ¦
## 15, N = 3 ¦ 2 (67%) 1 (33%) ¦
## 16, N = 6 ¦ 6 (100%) 0 (0%) ¦
## 17, N = 7 ¦ 5 (71%) 2 (29%) ¦
## p-value ¦ 0.8 ¦
##
## Column names: , 1, 2, 3, .2, .1
## [1] "G08Q03_taskseq3"
## Characteri ¦ actual_cho 0 1 ¦ n (%) Pearson's
## stic ¦ ice ¦ Chi-square
## ¦ ¦ d test
## 8, N = 13 ¦ 12 (92%) 1 (7.7%) ¦
## 9, N = 9 ¦ 6 (67%) 3 (33%) ¦
## 10, N = 4 ¦ 4 (100%) 0 (0%) ¦
## 11, N = 3 ¦ 3 (100%) 0 (0%) ¦
## 12, N = 10 ¦ 8 (80%) 2 (20%) ¦
## 13, N = 7 ¦ 5 (71%) 2 (29%) ¦
## 14, N = 7 ¦ 4 (57%) 3 (43%) ¦
## 15, N = 14 ¦ 11 (79%) 3 (21%) ¦
## 16, N = 7 ¦ 7 (100%) 0 (0%) ¦
## 17, N = 3 ¦ 3 (100%) 0 (0%) ¦
## p-value ¦ 0.4 ¦
##
## Column names: , 1, 2, 3, .2, .1
## [1] "G08Q03_taskseq4"
## Characteri ¦ actual_cho 0 1 ¦ n (%) Pearson's
## stic ¦ ice ¦ Chi-square
## ¦ ¦ d test
## 8, N = 8 ¦ 7 (88%) 1 (13%) ¦
## 9, N = 5 ¦ 4 (80%) 1 (20%) ¦
## 10, N = 8 ¦ 6 (75%) 2 (25%) ¦
## 11, N = 3 ¦ 2 (67%) 1 (33%) ¦
## 12, N = 7 ¦ 5 (71%) 2 (29%) ¦
## 13, N = 13 ¦ 11 (85%) 2 (15%) ¦
## 14, N = 11 ¦ 8 (73%) 3 (27%) ¦
## 15, N = 6 ¦ 4 (67%) 2 (33%) ¦
## 16, N = 5 ¦ 3 (60%) 2 (40%) ¦
## 17, N = 8 ¦ 7 (88%) 1 (13%) ¦
## p-value ¦ >0.9 ¦
##
## Column names: , 1, 2, 3, .2, .1
## [1] "G08Q04_taskseq1"
## Characteri ¦ actual_cho 0 1 ¦ n (%) Pearson's
## stic ¦ ice ¦ Chi-square
## ¦ ¦ d test
## 8, N = 10 ¦ 6 (60%) 4 (40%) ¦
## 9, N = 11 ¦ 10 (91%) 1 (9.1%) ¦
## 10, N = 4 ¦ 3 (75%) 1 (25%) ¦
## 11, N = 7 ¦ 5 (71%) 2 (29%) ¦
## 12, N = 14 ¦ 11 (79%) 3 (21%) ¦
## 13, N = 7 ¦ 3 (43%) 4 (57%) ¦
## 14, N = 6 ¦ 5 (83%) 1 (17%) ¦
## 15, N = 6 ¦ 4 (67%) 2 (33%) ¦
## 16, N = 5 ¦ 4 (80%) 1 (20%) ¦
## 17, N = 12 ¦ 11 (92%) 1 (8.3%) ¦
## p-value ¦ 0.4 ¦
##
## Column names: , 1, 2, 3, .2, .1
## [1] "G08Q04_taskseq2"
## Characteri ¦ actual_cho 0 1 ¦ n (%) Pearson's
## stic ¦ ice ¦ Chi-square
## ¦ ¦ d test
## 8, N = 14 ¦ 6 (43%) 8 (57%) ¦
## 9, N = 5 ¦ 4 (80%) 1 (20%) ¦
## 10, N = 5 ¦ 1 (20%) 4 (80%) ¦
## 11, N = 9 ¦ 4 (44%) 5 (56%) ¦
## 12, N = 10 ¦ 5 (50%) 5 (50%) ¦
## 13, N = 8 ¦ 4 (50%) 4 (50%) ¦
## 14, N = 6 ¦ 5 (83%) 1 (17%) ¦
## 15, N = 9 ¦ 5 (56%) 4 (44%) ¦
## 16, N = 8 ¦ 4 (50%) 4 (50%) ¦
## 17, N = 7 ¦ 4 (57%) 3 (43%) ¦
## p-value ¦ 0.7 ¦
##
## Column names: , 1, 2, 3, .2, .1
## [1] "G08Q04_taskseq3"
## Characteri ¦ actual_cho 0 1 ¦ n (%) Pearson's
## stic ¦ ice ¦ Chi-square
## ¦ ¦ d test
## 8, N = 5 ¦ 3 (60%) 2 (40%) ¦
## 9, N = 5 ¦ 4 (80%) 1 (20%) ¦
## 10, N = 9 ¦ 5 (56%) 4 (44%) ¦
## 11, N = 8 ¦ 5 (63%) 3 (38%) ¦
## 12, N = 7 ¦ 4 (57%) 3 (43%) ¦
## 13, N = 8 ¦ 5 (63%) 3 (38%) ¦
## 14, N = 11 ¦ 7 (64%) 4 (36%) ¦
## 15, N = 5 ¦ 0 (0%) 5 (100%) ¦
## 16, N = 10 ¦ 2 (20%) 8 (80%) ¦
## 17, N = 9 ¦ 4 (44%) 5 (56%) ¦
## p-value ¦ 0.2 ¦
##
## Column names: , 1, 2, 3, .2, .1
## [1] "G08Q04_taskseq4"
## Characteri ¦ actual_cho 0 1 ¦ n (%) Pearson's
## stic ¦ ice ¦ Chi-square
## ¦ ¦ d test
## 8, N = 6 ¦ 4 (67%) 2 (33%) ¦
## 9, N = 9 ¦ 3 (33%) 6 (67%) ¦
## 10, N = 8 ¦ 4 (50%) 4 (50%) ¦
## 11, N = 6 ¦ 3 (50%) 3 (50%) ¦
## 12, N = 8 ¦ 2 (25%) 6 (75%) ¦
## 13, N = 10 ¦ 5 (50%) 5 (50%) ¦
## 14, N = 9 ¦ 3 (33%) 6 (67%) ¦
## 15, N = 5 ¦ 1 (20%) 4 (80%) ¦
## 16, N = 9 ¦ 4 (44%) 5 (56%) ¦
## 17, N = 4 ¦ 0 (0%) 4 (100%) ¦
## p-value ¦ 0.6 ¦
##
## Column names: , 1, 2, 3, .2, .1
## [1] "G08Q05_taskseq1"
## Characteri ¦ actual_cho 0 1 ¦ n (%) Pearson's
## stic ¦ ice ¦ Chi-square
## ¦ ¦ d test
## 8, N = 6 ¦ 3 (50%) 3 (50%) ¦
## 9, N = 7 ¦ 3 (43%) 4 (57%) ¦
## 10, N = 10 ¦ 5 (50%) 5 (50%) ¦
## 11, N = 6 ¦ 5 (83%) 1 (17%) ¦
## 12, N = 8 ¦ 3 (38%) 5 (63%) ¦
## 13, N = 8 ¦ 5 (63%) 3 (38%) ¦
## 14, N = 11 ¦ 6 (55%) 5 (45%) ¦
## 15, N = 10 ¦ 5 (50%) 5 (50%) ¦
## 16, N = 9 ¦ 4 (44%) 5 (56%) ¦
## 17, N = 7 ¦ 4 (57%) 3 (43%) ¦
## p-value ¦ >0.9 ¦
##
## Column names: , 1, 2, 3, .2, .1
## [1] "G08Q05_taskseq2"
## Characteri ¦ actual_cho 0 1 ¦ n (%) Pearson's
## stic ¦ ice ¦ Chi-square
## ¦ ¦ d test
## 8, N = 12 ¦ 7 (58%) 5 (42%) ¦
## 9, N = 8 ¦ 5 (63%) 3 (38%) ¦
## 10, N = 11 ¦ 7 (64%) 4 (36%) ¦
## 11, N = 7 ¦ 7 (100%) 0 (0%) ¦
## 12, N = 9 ¦ 7 (78%) 2 (22%) ¦
## 13, N = 4 ¦ 3 (75%) 1 (25%) ¦
## 14, N = 8 ¦ 6 (75%) 2 (25%) ¦
## 15, N = 8 ¦ 4 (50%) 4 (50%) ¦
## 16, N = 7 ¦ 5 (71%) 2 (29%) ¦
## 17, N = 7 ¦ 3 (43%) 4 (57%) ¦
## p-value ¦ 0.6 ¦
##
## Column names: , 1, 2, 3, .2, .1
## [1] "G08Q05_taskseq3"
## Characteri ¦ actual_cho 0 1 ¦ n (%) Pearson's
## stic ¦ ice ¦ Chi-square
## ¦ ¦ d test
## 8, N = 12 ¦ 8 (67%) 4 (33%) ¦
## 9, N = 8 ¦ 6 (75%) 2 (25%) ¦
## 10, N = 9 ¦ 8 (89%) 1 (11%) ¦
## 11, N = 6 ¦ 5 (83%) 1 (17%) ¦
## 12, N = 8 ¦ 6 (75%) 2 (25%) ¦
## 13, N = 1 ¦ 1 (100%) 0 (0%) ¦
## 14, N = 10 ¦ 7 (70%) 3 (30%) ¦
## 15, N = 5 ¦ 5 (100%) 0 (0%) ¦
## 16, N = 10 ¦ 6 (60%) 4 (40%) ¦
## 17, N = 8 ¦ 8 (100%) 0 (0%) ¦
## p-value ¦ 0.5 ¦
##
## Column names: , 1, 2, 3, .2, .1
## [1] "G08Q05_taskseq4"
## Characteri ¦ actual_cho 0 1 ¦ n (%) Pearson's
## stic ¦ ice ¦ Chi-square
## ¦ ¦ d test
## 8, N = 8 ¦ 4 (50%) 4 (50%) ¦
## 9, N = 7 ¦ 4 (57%) 3 (43%) ¦
## 10, N = 7 ¦ 3 (43%) 4 (57%) ¦
## 11, N = 7 ¦ 5 (71%) 2 (29%) ¦
## 12, N = 11 ¦ 3 (27%) 8 (73%) ¦
## 13, N = 6 ¦ 5 (83%) 1 (17%) ¦
## 14, N = 6 ¦ 2 (33%) 4 (67%) ¦
## 15, N = 10 ¦ 4 (40%) 6 (60%) ¦
## 16, N = 9 ¦ 3 (33%) 6 (67%) ¦
## 17, N = 3 ¦ 2 (67%) 1 (33%) ¦
## p-value ¦ 0.5 ¦
##
## Column names: , 1, 2, 3, .2, .1
## [1] "G08Q06_taskseq1"
## Characteri ¦ actual_cho 0 1 ¦ n (%) Pearson's
## stic ¦ ice ¦ Chi-square
## ¦ ¦ d test
## 8, N = 5 ¦ 1 (20%) 4 (80%) ¦
## 9, N = 6 ¦ 4 (67%) 2 (33%) ¦
## 10, N = 15 ¦ 9 (60%) 6 (40%) ¦
## 11, N = 7 ¦ 3 (43%) 4 (57%) ¦
## 12, N = 8 ¦ 7 (88%) 1 (13%) ¦
## 13, N = 11 ¦ 6 (55%) 5 (45%) ¦
## 14, N = 8 ¦ 5 (63%) 3 (38%) ¦
## 15, N = 7 ¦ 7 (100%) 0 (0%) ¦
## 16, N = 4 ¦ 3 (75%) 1 (25%) ¦
## 17, N = 11 ¦ 6 (55%) 5 (45%) ¦
## p-value ¦ 0.2 ¦
##
## Column names: , 1, 2, 3, .2, .1
## [1] "G08Q06_taskseq2"
## Characteri ¦ actual_cho 0 1 ¦ n (%) Pearson's
## stic ¦ ice ¦ Chi-square
## ¦ ¦ d test
## 8, N = 5 ¦ 3 (60%) 2 (40%) ¦
## 9, N = 6 ¦ 5 (83%) 1 (17%) ¦
## 10, N = 7 ¦ 3 (43%) 4 (57%) ¦
## 11, N = 11 ¦ 5 (45%) 6 (55%) ¦
## 12, N = 11 ¦ 4 (36%) 7 (64%) ¦
## 13, N = 8 ¦ 2 (25%) 6 (75%) ¦
## 14, N = 4 ¦ 1 (25%) 3 (75%) ¦
## 15, N = 13 ¦ 5 (38%) 8 (62%) ¦
## 16, N = 7 ¦ 3 (43%) 4 (57%) ¦
## 17, N = 9 ¦ 5 (56%) 4 (44%) ¦
## p-value ¦ 0.6 ¦
##
## Column names: , 1, 2, 3, .2, .1
## [1] "G08Q06_taskseq3"
## Characteri ¦ actual_cho 0 1 ¦ n (%) Pearson's
## stic ¦ ice ¦ Chi-square
## ¦ ¦ d test
## 8, N = 8 ¦ 2 (25%) 6 (75%) ¦
## 9, N = 9 ¦ 6 (67%) 3 (33%) ¦
## 10, N = 7 ¦ 7 (100%) 0 (0%) ¦
## 11, N = 10 ¦ 8 (80%) 2 (20%) ¦
## 12, N = 7 ¦ 6 (86%) 1 (14%) ¦
## 13, N = 5 ¦ 4 (80%) 1 (20%) ¦
## 14, N = 8 ¦ 7 (88%) 1 (13%) ¦
## 15, N = 7 ¦ 2 (29%) 5 (71%) ¦
## 16, N = 9 ¦ 6 (67%) 3 (33%) ¦
## 17, N = 7 ¦ 5 (71%) 2 (29%) ¦
## p-value ¦ 0.027 ¦
##
## Column names: , 1, 2, 3, .2, .1
## [1] "G08Q06_taskseq4"
## Characteri ¦ actual_cho 0 1 ¦ n (%) Pearson's
## stic ¦ ice ¦ Chi-square
## ¦ ¦ d test
## 8, N = 5 ¦ 3 (60%) 2 (40%) ¦
## 9, N = 8 ¦ 7 (88%) 1 (13%) ¦
## 10, N = 14 ¦ 10 (71%) 4 (29%) ¦
## 11, N = 5 ¦ 4 (80%) 1 (20%) ¦
## 12, N = 8 ¦ 7 (88%) 1 (13%) ¦
## 13, N = 7 ¦ 3 (43%) 4 (57%) ¦
## 14, N = 8 ¦ 6 (75%) 2 (25%) ¦
## 15, N = 4 ¦ 2 (50%) 2 (50%) ¦
## 16, N = 6 ¦ 5 (83%) 1 (17%) ¦
## 17, N = 9 ¦ 6 (67%) 3 (33%) ¦
## p-value ¦ 0.7 ¦
##
## Column names: , 1, 2, 3, .2, .1
## [1] "G08Q07_taskseq1"
## Characteri ¦ actual_cho 0 1 ¦ n (%) Pearson's
## stic ¦ ice ¦ Chi-square
## ¦ ¦ d test
## 8, N = 5 ¦ 2 (40%) 3 (60%) ¦
## 9, N = 9 ¦ 7 (78%) 2 (22%) ¦
## 10, N = 10 ¦ 4 (40%) 6 (60%) ¦
## 11, N = 12 ¦ 5 (42%) 7 (58%) ¦
## 12, N = 3 ¦ 1 (33%) 2 (67%) ¦
## 13, N = 11 ¦ 5 (45%) 6 (55%) ¦
## 14, N = 7 ¦ 2 (29%) 5 (71%) ¦
## 15, N = 10 ¦ 8 (80%) 2 (20%) ¦
## 16, N = 10 ¦ 3 (30%) 7 (70%) ¦
## 17, N = 5 ¦ 2 (40%) 3 (60%) ¦
## p-value ¦ 0.3 ¦
##
## Column names: , 1, 2, 3, .2, .1
## [1] "G08Q07_taskseq2"
## Characteri ¦ actual_cho 0 1 ¦ n (%) Pearson's
## stic ¦ ice ¦ Chi-square
## ¦ ¦ d test
## 8, N = 8 ¦ 3 (38%) 5 (63%) ¦
## 9, N = 8 ¦ 3 (38%) 5 (63%) ¦
## 10, N = 4 ¦ 4 (100%) 0 (0%) ¦
## 11, N = 9 ¦ 6 (67%) 3 (33%) ¦
## 12, N = 5 ¦ 2 (40%) 3 (60%) ¦
## 13, N = 4 ¦ 2 (50%) 2 (50%) ¦
## 14, N = 10 ¦ 8 (80%) 2 (20%) ¦
## 15, N = 7 ¦ 5 (71%) 2 (29%) ¦
## 16, N = 16 ¦ 6 (38%) 10 (63%) ¦
## 17, N = 10 ¦ 9 (90%) 1 (10%) ¦
## p-value ¦ 0.062 ¦
##
## Column names: , 1, 2, 3, .2, .1
## [1] "G08Q07_taskseq3"
## Characteri ¦ actual_cho 0 1 ¦ n (%) Pearson's
## stic ¦ ice ¦ Chi-square
## ¦ ¦ d test
## 8, N = 9 ¦ 5 (56%) 4 (44%) ¦
## 9, N = 7 ¦ 3 (43%) 4 (57%) ¦
## 10, N = 10 ¦ 9 (90%) 1 (10%) ¦
## 11, N = 2 ¦ 2 (100%) 0 (0%) ¦
## 12, N = 9 ¦ 5 (56%) 4 (44%) ¦
## 13, N = 10 ¦ 7 (70%) 3 (30%) ¦
## 14, N = 10 ¦ 7 (70%) 3 (30%) ¦
## 15, N = 6 ¦ 5 (83%) 1 (17%) ¦
## 16, N = 5 ¦ 5 (100%) 0 (0%) ¦
## 17, N = 9 ¦ 6 (67%) 3 (33%) ¦
## p-value ¦ 0.4 ¦
##
## Column names: , 1, 2, 3, .2, .1
## [1] "G08Q07_taskseq4"
## Characteri ¦ actual_cho 0 1 ¦ n (%) Pearson's
## stic ¦ ice ¦ Chi-square
## ¦ ¦ d test
## 8, N = 6 ¦ 3 (50%) 3 (50%) ¦
## 9, N = 9 ¦ 5 (56%) 4 (44%) ¦
## 10, N = 8 ¦ 3 (38%) 5 (63%) ¦
## 11, N = 7 ¦ 6 (86%) 1 (14%) ¦
## 12, N = 8 ¦ 5 (63%) 3 (38%) ¦
## 13, N = 8 ¦ 4 (50%) 4 (50%) ¦
## 14, N = 5 ¦ 3 (60%) 2 (40%) ¦
## 15, N = 13 ¦ 10 (77%) 3 (23%) ¦
## 16, N = 3 ¦ 1 (33%) 2 (67%) ¦
## 17, N = 7 ¦ 4 (57%) 3 (43%) ¦
## p-value ¦ 0.7 ¦
##
## Column names: , 1, 2, 3, .2, .1
## [1] "G08Q08_taskseq1"
## Characteri ¦ actual_cho 0 1 ¦ n (%) Pearson's
## stic ¦ ice ¦ Chi-square
## ¦ ¦ d test
## 8, N = 5 ¦ 2 (40%) 3 (60%) ¦
## 9, N = 7 ¦ 5 (71%) 2 (29%) ¦
## 10, N = 4 ¦ 3 (75%) 1 (25%) ¦
## 11, N = 9 ¦ 3 (33%) 6 (67%) ¦
## 12, N = 10 ¦ 4 (40%) 6 (60%) ¦
## 13, N = 5 ¦ 4 (80%) 1 (20%) ¦
## 14, N = 15 ¦ 7 (47%) 8 (53%) ¦
## 15, N = 13 ¦ 5 (38%) 8 (62%) ¦
## 16, N = 8 ¦ 4 (50%) 4 (50%) ¦
## 17, N = 6 ¦ 4 (67%) 2 (33%) ¦
## p-value ¦ 0.6 ¦
##
## Column names: , 1, 2, 3, .2, .1
## [1] "G08Q08_taskseq2"
## Characteri ¦ actual_cho 0 1 ¦ n (%) Pearson's
## stic ¦ ice ¦ Chi-square
## ¦ ¦ d test
## 8, N = 8 ¦ 4 (50%) 4 (50%) ¦
## 9, N = 9 ¦ 4 (44%) 5 (56%) ¦
## 10, N = 10 ¦ 7 (70%) 3 (30%) ¦
## 11, N = 5 ¦ 3 (60%) 2 (40%) ¦
## 12, N = 6 ¦ 4 (67%) 2 (33%) ¦
## 13, N = 13 ¦ 7 (54%) 6 (46%) ¦
## 14, N = 7 ¦ 3 (43%) 4 (57%) ¦
## 15, N = 7 ¦ 4 (57%) 3 (43%) ¦
## 16, N = 7 ¦ 2 (29%) 5 (71%) ¦
## 17, N = 9 ¦ 6 (67%) 3 (33%) ¦
## p-value ¦ 0.9 ¦
##
## Column names: , 1, 2, 3, .2, .1
## [1] "G08Q08_taskseq3"
## Characteri ¦ actual_cho 0 1 ¦ n (%) Pearson's
## stic ¦ ice ¦ Chi-square
## ¦ ¦ d test
## 8, N = 5 ¦ 1 (20%) 4 (80%) ¦
## 9, N = 8 ¦ 7 (88%) 1 (13%) ¦
## 10, N = 5 ¦ 2 (40%) 3 (60%) ¦
## 11, N = 8 ¦ 6 (75%) 2 (25%) ¦
## 12, N = 14 ¦ 6 (43%) 8 (57%) ¦
## 13, N = 6 ¦ 4 (67%) 2 (33%) ¦
## 14, N = 7 ¦ 4 (57%) 3 (43%) ¦
## 15, N = 5 ¦ 2 (40%) 3 (60%) ¦
## 16, N = 9 ¦ 4 (44%) 5 (56%) ¦
## 17, N = 10 ¦ 7 (70%) 3 (30%) ¦
## p-value ¦ 0.3 ¦
##
## Column names: , 1, 2, 3, .2, .1
## [1] "G08Q08_taskseq4"
## Characteri ¦ actual_cho 0 1 ¦ n (%) Pearson's
## stic ¦ ice ¦ Chi-square
## ¦ ¦ d test
## 8, N = 10 ¦ 4 (40%) 6 (60%) ¦
## 9, N = 9 ¦ 3 (33%) 6 (67%) ¦
## 10, N = 5 ¦ 5 (100%) 0 (0%) ¦
## 11, N = 12 ¦ 4 (33%) 8 (67%) ¦
## 12, N = 5 ¦ 1 (20%) 4 (80%) ¦
## 13, N = 4 ¦ 3 (75%) 1 (25%) ¦
## 14, N = 6 ¦ 3 (50%) 3 (50%) ¦
## 15, N = 7 ¦ 4 (57%) 3 (43%) ¦
## 16, N = 8 ¦ 4 (50%) 4 (50%) ¦
## 17, N = 8 ¦ 5 (63%) 3 (38%) ¦
## p-value ¦ 0.3 ¦
##
## Column names: , 1, 2, 3, .2, .1
## [1] "G08Q09_taskseq1"
## Characteri ¦ actual_cho 0 1 ¦ n (%) Pearson's
## stic ¦ ice ¦ Chi-square
## ¦ ¦ d test
## 8, N = 9 ¦ 8 (89%) 1 (11%) ¦
## 9, N = 5 ¦ 4 (80%) 1 (20%) ¦
## 10, N = 7 ¦ 6 (86%) 1 (14%) ¦
## 11, N = 6 ¦ 2 (33%) 4 (67%) ¦
## 12, N = 8 ¦ 6 (75%) 2 (25%) ¦
## 13, N = 7 ¦ 5 (71%) 2 (29%) ¦
## 14, N = 11 ¦ 6 (55%) 5 (45%) ¦
## 15, N = 14 ¦ 6 (43%) 8 (57%) ¦
## 16, N = 8 ¦ 8 (100%) 0 (0%) ¦
## 17, N = 7 ¦ 5 (71%) 2 (29%) ¦
## p-value ¦ 0.077 ¦
##
## Column names: , 1, 2, 3, .2, .1
## [1] "G08Q09_taskseq2"
## Characteri ¦ actual_cho 0 1 ¦ n (%) Pearson's
## stic ¦ ice ¦ Chi-square
## ¦ ¦ d test
## 8, N = 2 ¦ 0 (0%) 2 (100%) ¦
## 9, N = 13 ¦ 8 (62%) 5 (38%) ¦
## 10, N = 9 ¦ 5 (56%) 4 (44%) ¦
## 11, N = 5 ¦ 2 (40%) 3 (60%) ¦
## 12, N = 9 ¦ 6 (67%) 3 (33%) ¦
## 13, N = 9 ¦ 6 (67%) 3 (33%) ¦
## 14, N = 11 ¦ 6 (55%) 5 (45%) ¦
## 15, N = 10 ¦ 5 (50%) 5 (50%) ¦
## 16, N = 8 ¦ 5 (63%) 3 (38%) ¦
## 17, N = 5 ¦ 3 (60%) 2 (40%) ¦
## p-value ¦ 0.9 ¦
##
## Column names: , 1, 2, 3, .2, .1
## [1] "G08Q09_taskseq3"
## Characteri ¦ actual_cho 0 1 ¦ n (%) Pearson's
## stic ¦ ice ¦ Chi-square
## ¦ ¦ d test
## 8, N = 6 ¦ 1 (17%) 5 (83%) ¦
## 9, N = 6 ¦ 0 (0%) 6 (100%) ¦
## 10, N = 10 ¦ 2 (20%) 8 (80%) ¦
## 11, N = 9 ¦ 3 (33%) 6 (67%) ¦
## 12, N = 2 ¦ 1 (50%) 1 (50%) ¦
## 13, N = 13 ¦ 1 (7.7%) 12 (92%) ¦
## 14, N = 5 ¦ 3 (60%) 2 (40%) ¦
## 15, N = 6 ¦ 2 (33%) 4 (67%) ¦
## 16, N = 11 ¦ 2 (18%) 9 (82%) ¦
## 17, N = 9 ¦ 5 (56%) 4 (44%) ¦
## p-value ¦ 0.15 ¦
##
## Column names: , 1, 2, 3, .2, .1
## [1] "G08Q09_taskseq4"
## Characteri ¦ actual_cho 0 1 ¦ n (%) Pearson's
## stic ¦ ice ¦ Chi-square
## ¦ ¦ d test
## 8, N = 6 ¦ 4 (67%) 2 (33%) ¦
## 9, N = 9 ¦ 8 (89%) 1 (11%) ¦
## 10, N = 9 ¦ 7 (78%) 2 (22%) ¦
## 11, N = 8 ¦ 5 (63%) 3 (38%) ¦
## 12, N = 6 ¦ 4 (67%) 2 (33%) ¦
## 13, N = 5 ¦ 5 (100%) 0 (0%) ¦
## 14, N = 5 ¦ 2 (40%) 3 (60%) ¦
## 15, N = 11 ¦ 5 (45%) 6 (55%) ¦
## 16, N = 9 ¦ 7 (78%) 2 (22%) ¦
## 17, N = 6 ¦ 3 (50%) 3 (50%) ¦
## p-value ¦ 0.3 ¦
##
## Column names: , 1, 2, 3, .2, .1
## [1] "G08Q10_taskseq1"
## Characteri ¦ actual_cho 0 1 ¦ n (%) Pearson's
## stic ¦ ice ¦ Chi-square
## ¦ ¦ d test
## 8, N = 11 ¦ 6 (55%) 5 (45%) ¦
## 9, N = 8 ¦ 7 (88%) 1 (13%) ¦
## 10, N = 6 ¦ 4 (67%) 2 (33%) ¦
## 11, N = 8 ¦ 7 (88%) 1 (13%) ¦
## 12, N = 11 ¦ 7 (64%) 4 (36%) ¦
## 13, N = 12 ¦ 10 (83%) 2 (17%) ¦
## 14, N = 8 ¦ 5 (63%) 3 (38%) ¦
## 15, N = 6 ¦ 4 (67%) 2 (33%) ¦
## 16, N = 6 ¦ 6 (100%) 0 (0%) ¦
## 17, N = 6 ¦ 6 (100%) 0 (0%) ¦
## p-value ¦ 0.3 ¦
##
## Column names: , 1, 2, 3, .2, .1
## [1] "G08Q10_taskseq2"
## Characteri ¦ actual_cho 0 1 ¦ n (%) Pearson's
## stic ¦ ice ¦ Chi-square
## ¦ ¦ d test
## 8, N = 11 ¦ 2 (18%) 9 (82%) ¦
## 9, N = 9 ¦ 3 (33%) 6 (67%) ¦
## 10, N = 4 ¦ 2 (50%) 2 (50%) ¦
## 11, N = 8 ¦ 2 (25%) 6 (75%) ¦
## 12, N = 8 ¦ 3 (38%) 5 (63%) ¦
## 13, N = 7 ¦ 1 (14%) 6 (86%) ¦
## 14, N = 9 ¦ 0 (0%) 9 (100%) ¦
## 15, N = 10 ¦ 5 (50%) 5 (50%) ¦
## 16, N = 5 ¦ 2 (40%) 3 (60%) ¦
## 17, N = 10 ¦ 6 (60%) 4 (40%) ¦
## p-value ¦ 0.2 ¦
##
## Column names: , 1, 2, 3, .2, .1
## [1] "G08Q10_taskseq3"
## Characteri ¦ actual_cho 0 1 ¦ n (%) Pearson's
## stic ¦ ice ¦ Chi-square
## ¦ ¦ d test
## 8, N = 10 ¦ 4 (40%) 6 (60%) ¦
## 9, N = 11 ¦ 7 (64%) 4 (36%) ¦
## 10, N = 8 ¦ 4 (50%) 4 (50%) ¦
## 11, N = 6 ¦ 2 (33%) 4 (67%) ¦
## 12, N = 6 ¦ 2 (33%) 4 (67%) ¦
## 13, N = 12 ¦ 6 (50%) 6 (50%) ¦
## 14, N = 3 ¦ 1 (33%) 2 (67%) ¦
## 15, N = 13 ¦ 2 (15%) 11 (85%) ¦
## 16, N = 5 ¦ 0 (0%) 5 (100%) ¦
## 17, N = 3 ¦ 1 (33%) 2 (67%) ¦
## p-value ¦ 0.3 ¦
##
## Column names: , 1, 2, 3, .2, .1
## [1] "G08Q10_taskseq4"
## Characteri ¦ actual_cho 0 1 ¦ n (%) Pearson's
## stic ¦ ice ¦ Chi-square
## ¦ ¦ d test
## 8, N = 10 ¦ 4 (40%) 6 (60%) ¦
## 9, N = 6 ¦ 1 (17%) 5 (83%) ¦
## 10, N = 4 ¦ 1 (25%) 3 (75%) ¦
## 11, N = 8 ¦ 2 (25%) 6 (75%) ¦
## 12, N = 3 ¦ 2 (67%) 1 (33%) ¦
## 13, N = 10 ¦ 1 (10%) 9 (90%) ¦
## 14, N = 11 ¦ 4 (36%) 7 (64%) ¦
## 15, N = 8 ¦ 3 (38%) 5 (63%) ¦
## 16, N = 5 ¦ 1 (20%) 4 (80%) ¦
## 17, N = 9 ¦ 3 (33%) 6 (67%) ¦
## p-value ¦ 0.8 ¦
##
## Column names: , 1, 2, 3, .2, .19.3 Horizontal bias in paired comparisons
We will assess horizontal bias in the paired comparisons (e.g., always left) and run chi-square tests on whether these behaviors are associated with experimental or respondent characteristics.
# assess final choice overall (t-test P=0.5)
t.test(pc2_it$final_choice, mu = 0.5)##
## One Sample t-test
##
## data: pc2_it$final_choice
## t = -0.74947, df = 3139, p-value = 0.4536
## alternative hypothesis: true mean is not equal to 0.5
## 95 percent confidence interval:
## 0.4758156 0.5108086
## sample estimates:
## mean of x
## 0.4933121# assess final choice by question and block (t-test P=0.5)
results = lapply(tasks_pc, function(variable1) {
lapply(block, function(variable2) {
ttest_by_question_and_block(variable1, variable2, pc2_it)
})})## [1] "G08Q01_block1"
## [1] 0.5109305
## [1] "G08Q01_block2"
## [1] 0.9123437
## [1] "G08Q01_block3"
## [1] 0.9101486
## [1] "G08Q01_block4"
## [1] 0.4892333
## [1] "G08Q02_block1"
## [1] 0.3802483
## [1] "G08Q02_block2"
## [1] 0.09578925
## [1] "G08Q02_block3"
## [1] 0.08743033
## [1] "G08Q02_block4"
## [1] 0.3558883
## [1] "G08Q03_block1"
## [1] 1
## [1] "G08Q03_block2"
## [1] 0.4401167
## [1] "G08Q03_block3"
## [1] 0.73486
## [1] "G08Q03_block4"
## [1] 0.4892333
## [1] "G08Q04_block1"
## [1] 0.5109305
## [1] "G08Q04_block2"
## [1] 0.3203257
## [1] "G08Q04_block3"
## [1] 0.3081481
## [1] "G08Q04_block4"
## [1] 0.2476976
## [1] "G08Q05_block1"
## [1] 0.1227811
## [1] "G08Q05_block2"
## [1] 0.4401167
## [1] "G08Q05_block3"
## [1] 0.9101486
## [1] "G08Q05_block4"
## [1] 0.8179584
## [1] "G08Q06_block1"
## [1] 0.2720804
## [1] "G08Q06_block2"
## [1] 0.7411361
## [1] "G08Q06_block3"
## [1] 0.08743033
## [1] "G08Q06_block4"
## [1] 0.8179584
## [1] "G08Q07_block1"
## [1] 0.5109305
## [1] "G08Q07_block2"
## [1] 0.4401167
## [1] "G08Q07_block3"
## [1] 0.1395061
## [1] "G08Q07_block4"
## [1] 0.4892333
## [1] "G08Q08_block1"
## [1] 0.3802483
## [1] "G08Q08_block2"
## [1] 0.7411361
## [1] "G08Q08_block3"
## [1] 0.9101486
## [1] "G08Q08_block4"
## [1] 1
## [1] "G08Q09_block1"
## [1] 0.6614352
## [1] "G08Q09_block2"
## [1] 0.3203257
## [1] "G08Q09_block3"
## [1] 0.5721904
## [1] "G08Q09_block4"
## [1] 0.06244233
## [1] "G08Q10_block1"
## [1] 0.5109305
## [1] "G08Q10_block2"
## [1] 0.9123437
## [1] "G08Q10_block3"
## [1] 0.5721904
## [1] "G08Q10_block4"
## [1] 0.6450883# create 'always_left_pc', 'always_right_pc', 'straigth_line_pc'
uniq_ids = resp_i$id
resp_i$always_left_pc = 0
resp_i$always_right_pc = 0
resp_i$straight_line_pc = 0
for (i in 1:length(uniq_ids)) {
a = sum(pc2_it$final_choice[pc2_it == uniq_ids[i]])
if (a == 0) {resp_i$always_left_pc[i] = 1}
if (a == 10) {resp_i$always_right_pc[i] = 1}
if (a == 0 | a == 10) {resp_i$straight_line_pc[i] = 1}
}
# create 'always_left_cc', 'always_right_cc', 'stragth_line_cc'
resp_i$always_left_cc = 0
resp_i$always_right_cc = 0
resp_i$straight_line_cc = 0
for (i in 1:length(uniq_ids)) {
a = sum(cc2_it$final_choice[cc2_it == uniq_ids[i]])
if (a == 0) {resp_i$always_left_cc[i] = 1}
if (a == 10) {resp_i$always_right_cc[i] = 1}
if (a == 0 | a == 10) {resp_i$straight_line_cc[i] = 1}
}
# define variables to loop over
straight = c("always_left_pc", "always_right_pc", "straight_line_pc",
"always_left_cc", "always_right_cc", "straight_line_cc")
# assess three straight lining variables overall and by cc and pc
results = lapply(straight, function(variable){
straight_lining(variable, resp_i)
})## [1] "always_left_pc"
## Characteristic 0, N = 307 1, N = 7 p-value
## ----------------------------------------------------
## subjectindex 0.7
## 1 81 (26%) 1 (14%)
## 2 78 (25%) 3 (43%)
## 3 76 (25%) 1 (14%)
## 4 72 (23%) 2 (29%)
## quota 0.5
## 1 6 (2.0%) 0 (0%)
## 10 7 (2.3%) 1 (14%)
## 11 6 (2.0%) 0 (0%)
## 12 7 (2.3%) 0 (0%)
## 13 6 (2.0%) 0 (0%)
## 14 6 (2.0%) 0 (0%)
## 15 6 (2.0%) 1 (14%)
## 16 43 (14%) 1 (14%)
## 17 48 (16%) 0 (0%)
## 18 23 (7.5%) 1 (14%)
## 2 5 (1.6%) 0 (0%)
## 3 6 (2.0%) 0 (0%)
## 4 7 (2.3%) 0 (0%)
## 5 6 (2.0%) 1 (14%)
## 6 5 (1.6%) 0 (0%)
## 7 46 (15%) 1 (14%)
## 8 44 (14%) 1 (14%)
## 9 30 (9.8%) 0 (0%)
## female >0.9
## 0 152 (50%) 4 (57%)
## 1 155 (50%) 3 (43%)
## age_range 0.9
## 1 115 (37%) 3 (43%)
## 2 115 (37%) 2 (29%)
## 3 77 (25%) 2 (29%)
## race 0.048
## 1 231 (75%) 3 (43%)
## 2 37 (12%) 3 (43%)
## 3 39 (13%) 1 (14%)
## hispanic >0.9
## 0 270 (88%) 6 (86%)
## 1 37 (12%) 1 (14%)
## pc_cnum2 0.4
## 2 145 (47%) 5 (71%)
## 3 162 (53%) 2 (29%)
## region 0.9
## 1 65 (21%) 1 (14%)
## 2 68 (22%) 1 (14%)
## 3 114 (37%) 3 (43%)
## 4 60 (20%) 2 (29%)
## parent 0.6
## 0 143 (47%) 2 (29%)
## 1 164 (53%) 5 (71%)
## marital >0.9
## 1 135 (44%) 3 (43%)
## 2 131 (43%) 3 (43%)
## 3 41 (13%) 1 (14%)
## edu 0.3
## 1 139 (45%) 3 (43%)
## 2 63 (21%) 3 (43%)
## 3 105 (34%) 1 (14%)
## income 0.3
## 1 125 (41%) 4 (57%)
## 2 100 (33%) 3 (43%)
## 3 82 (27%) 0 (0%)
## community 0.2
## 1 85 (28%) 4 (57%)
## 2 165 (54%) 3 (43%)
## 3 57 (19%) 0 (0%)
## ----------------------------------------------------
## n (%)
## Pearson's Chi-squared test
##
## Column names: label, stat_1, stat_2, p.value
## [1] "always_right_pc"
## Characteristic 0, N = 311 1, N = 3 p-value
## ----------------------------------------------------
## subjectindex 0.3
## 1 81 (26%) 1 (33%)
## 2 79 (25%) 2 (67%)
## 3 77 (25%) 0 (0%)
## 4 74 (24%) 0 (0%)
## quota 0.2
## 1 6 (1.9%) 0 (0%)
## 10 8 (2.6%) 0 (0%)
## 11 5 (1.6%) 1 (33%)
## 12 7 (2.3%) 0 (0%)
## 13 6 (1.9%) 0 (0%)
## 14 6 (1.9%) 0 (0%)
## 15 7 (2.3%) 0 (0%)
## 16 44 (14%) 0 (0%)
## 17 48 (15%) 0 (0%)
## 18 23 (7.4%) 1 (33%)
## 2 5 (1.6%) 0 (0%)
## 3 6 (1.9%) 0 (0%)
## 4 7 (2.3%) 0 (0%)
## 5 7 (2.3%) 0 (0%)
## 6 5 (1.6%) 0 (0%)
## 7 46 (15%) 1 (33%)
## 8 45 (14%) 0 (0%)
## 9 30 (9.6%) 0 (0%)
## female >0.9
## 0 154 (50%) 2 (67%)
## 1 157 (50%) 1 (33%)
## age_range >0.9
## 1 117 (38%) 1 (33%)
## 2 116 (37%) 1 (33%)
## 3 78 (25%) 1 (33%)
## race 0.5
## 1 232 (75%) 2 (67%)
## 2 40 (13%) 0 (0%)
## 3 39 (13%) 1 (33%)
## hispanic 0.8
## 0 274 (88%) 2 (67%)
## 1 37 (12%) 1 (33%)
## pc_cnum2 0.3
## 2 150 (48%) 0 (0%)
## 3 161 (52%) 3 (100%)
## region 0.006
## 1 66 (21%) 0 (0%)
## 2 69 (22%) 0 (0%)
## 3 117 (38%) 0 (0%)
## 4 59 (19%) 3 (100%)
## parent >0.9
## 0 144 (46%) 1 (33%)
## 1 167 (54%) 2 (67%)
## marital 0.7
## 1 136 (44%) 2 (67%)
## 2 133 (43%) 1 (33%)
## 3 42 (14%) 0 (0%)
## edu 0.2
## 1 139 (45%) 3 (100%)
## 2 66 (21%) 0 (0%)
## 3 106 (34%) 0 (0%)
## income >0.9
## 1 128 (41%) 1 (33%)
## 2 102 (33%) 1 (33%)
## 3 81 (26%) 1 (33%)
## community 0.7
## 1 88 (28%) 1 (33%)
## 2 166 (53%) 2 (67%)
## 3 57 (18%) 0 (0%)
## ----------------------------------------------------
## n (%)
## Pearson's Chi-squared test
##
## Column names: label, stat_1, stat_2, p.value
## [1] "straight_line_pc"
## Characteristic 0, N = 304 1, N = 10 p-value
## -----------------------------------------------------
## subjectindex 0.3
## 1 80 (26%) 2 (20%)
## 2 76 (25%) 5 (50%)
## 3 76 (25%) 1 (10%)
## 4 72 (24%) 2 (20%)
## quota 0.4
## 1 6 (2.0%) 0 (0%)
## 10 7 (2.3%) 1 (10%)
## 11 5 (1.6%) 1 (10%)
## 12 7 (2.3%) 0 (0%)
## 13 6 (2.0%) 0 (0%)
## 14 6 (2.0%) 0 (0%)
## 15 6 (2.0%) 1 (10%)
## 16 43 (14%) 1 (10%)
## 17 48 (16%) 0 (0%)
## 18 22 (7.2%) 2 (20%)
## 2 5 (1.6%) 0 (0%)
## 3 6 (2.0%) 0 (0%)
## 4 7 (2.3%) 0 (0%)
## 5 6 (2.0%) 1 (10%)
## 6 5 (1.6%) 0 (0%)
## 7 45 (15%) 2 (20%)
## 8 44 (14%) 1 (10%)
## 9 30 (9.9%) 0 (0%)
## female 0.7
## 0 150 (49%) 6 (60%)
## 1 154 (51%) 4 (40%)
## age_range 0.9
## 1 114 (38%) 4 (40%)
## 2 114 (38%) 3 (30%)
## 3 76 (25%) 3 (30%)
## race 0.2
## 1 229 (75%) 5 (50%)
## 2 37 (12%) 3 (30%)
## 3 38 (13%) 2 (20%)
## hispanic 0.8
## 0 268 (88%) 8 (80%)
## 1 36 (12%) 2 (20%)
## pc_cnum2 >0.9
## 2 145 (48%) 5 (50%)
## 3 159 (52%) 5 (50%)
## region 0.10
## 1 65 (21%) 1 (10%)
## 2 68 (22%) 1 (10%)
## 3 114 (38%) 3 (30%)
## 4 57 (19%) 5 (50%)
## parent 0.5
## 0 142 (47%) 3 (30%)
## 1 162 (53%) 7 (70%)
## marital >0.9
## 1 133 (44%) 5 (50%)
## 2 130 (43%) 4 (40%)
## 3 41 (13%) 1 (10%)
## edu 0.3
## 1 136 (45%) 6 (60%)
## 2 63 (21%) 3 (30%)
## 3 105 (35%) 1 (10%)
## income 0.5
## 1 124 (41%) 5 (50%)
## 2 99 (33%) 4 (40%)
## 3 81 (27%) 1 (10%)
## community 0.2
## 1 84 (28%) 5 (50%)
## 2 163 (54%) 5 (50%)
## 3 57 (19%) 0 (0%)
## -----------------------------------------------------
## n (%)
## Pearson's Chi-squared test
##
## Column names: label, stat_1, stat_2, p.value
## [1] "always_left_cc"
## Characteristic 0, N = 208 1, N = 106 p-value
## ------------------------------------------------------
## subjectindex 0.4
## 1 52 (25%) 30 (28%)
## 2 59 (28%) 22 (21%)
## 3 47 (23%) 30 (28%)
## 4 50 (24%) 24 (23%)
## quota 0.15
## 1 4 (1.9%) 2 (1.9%)
## 10 8 (3.8%) 0 (0%)
## 11 5 (2.4%) 1 (0.9%)
## 12 3 (1.4%) 4 (3.8%)
## 13 5 (2.4%) 1 (0.9%)
## 14 5 (2.4%) 1 (0.9%)
## 15 4 (1.9%) 3 (2.8%)
## 16 35 (17%) 9 (8.5%)
## 17 27 (13%) 21 (20%)
## 18 12 (5.8%) 12 (11%)
## 2 3 (1.4%) 2 (1.9%)
## 3 3 (1.4%) 3 (2.8%)
## 4 7 (3.4%) 0 (0%)
## 5 4 (1.9%) 3 (2.8%)
## 6 3 (1.4%) 2 (1.9%)
## 7 33 (16%) 14 (13%)
## 8 30 (14%) 15 (14%)
## 9 17 (8.2%) 13 (12%)
## female >0.9
## 0 104 (50%) 52 (49%)
## 1 104 (50%) 54 (51%)
## age_range 0.001
## 1 92 (44%) 26 (25%)
## 2 74 (36%) 43 (41%)
## 3 42 (20%) 37 (35%)
## race 0.2
## 1 148 (71%) 86 (81%)
## 2 30 (14%) 10 (9.4%)
## 3 30 (14%) 10 (9.4%)
## hispanic >0.9
## 0 182 (88%) 94 (89%)
## 1 26 (13%) 12 (11%)
## pc_cnum2 0.7
## 2 97 (47%) 53 (50%)
## 3 111 (53%) 53 (50%)
## region 0.3
## 1 50 (24%) 16 (15%)
## 2 45 (22%) 24 (23%)
## 3 72 (35%) 45 (42%)
## 4 41 (20%) 21 (20%)
## parent 0.043
## 0 105 (50%) 40 (38%)
## 1 103 (50%) 66 (62%)
## marital 0.7
## 1 88 (42%) 50 (47%)
## 2 92 (44%) 42 (40%)
## 3 28 (13%) 14 (13%)
## edu 0.9
## 1 96 (46%) 46 (43%)
## 2 42 (20%) 24 (23%)
## 3 70 (34%) 36 (34%)
## income >0.9
## 1 86 (41%) 43 (41%)
## 2 67 (32%) 36 (34%)
## 3 55 (26%) 27 (25%)
## community 0.8
## 1 61 (29%) 28 (26%)
## 2 111 (53%) 57 (54%)
## 3 36 (17%) 21 (20%)
## ------------------------------------------------------
## n (%)
## Pearson's Chi-squared test
##
## Column names: label, stat_1, stat_2, p.value
## [1] "always_right_cc"
## Characteristic 0, N = 314 p-value
## -----------------------------------------
## subjectindex
## 1 82 (26%)
## 2 81 (26%)
## 3 77 (25%)
## 4 74 (24%)
## quota
## 1 6 (1.9%)
## 10 8 (2.5%)
## 11 6 (1.9%)
## 12 7 (2.2%)
## 13 6 (1.9%)
## 14 6 (1.9%)
## 15 7 (2.2%)
## 16 44 (14%)
## 17 48 (15%)
## 18 24 (7.6%)
## 2 5 (1.6%)
## 3 6 (1.9%)
## 4 7 (2.2%)
## 5 7 (2.2%)
## 6 5 (1.6%)
## 7 47 (15%)
## 8 45 (14%)
## 9 30 (9.6%)
## female
## 0 156 (50%)
## 1 158 (50%)
## age_range
## 1 118 (38%)
## 2 117 (37%)
## 3 79 (25%)
## race
## 1 234 (75%)
## 2 40 (13%)
## 3 40 (13%)
## hispanic
## 0 276 (88%)
## 1 38 (12%)
## pc_cnum2
## 2 150 (48%)
## 3 164 (52%)
## region
## 1 66 (21%)
## 2 69 (22%)
## 3 117 (37%)
## 4 62 (20%)
## parent
## 0 145 (46%)
## 1 169 (54%)
## marital
## 1 138 (44%)
## 2 134 (43%)
## 3 42 (13%)
## edu
## 1 142 (45%)
## 2 66 (21%)
## 3 106 (34%)
## income
## 1 129 (41%)
## 2 103 (33%)
## 3 82 (26%)
## community
## 1 89 (28%)
## 2 168 (54%)
## 3 57 (18%)
## -----------------------------------------
## n (%)
##
## Column names: label, stat_1, p.value
## [1] "straight_line_cc"
## Characteristic 0, N = 208 1, N = 106 p-value
## ------------------------------------------------------
## subjectindex 0.4
## 1 52 (25%) 30 (28%)
## 2 59 (28%) 22 (21%)
## 3 47 (23%) 30 (28%)
## 4 50 (24%) 24 (23%)
## quota 0.15
## 1 4 (1.9%) 2 (1.9%)
## 10 8 (3.8%) 0 (0%)
## 11 5 (2.4%) 1 (0.9%)
## 12 3 (1.4%) 4 (3.8%)
## 13 5 (2.4%) 1 (0.9%)
## 14 5 (2.4%) 1 (0.9%)
## 15 4 (1.9%) 3 (2.8%)
## 16 35 (17%) 9 (8.5%)
## 17 27 (13%) 21 (20%)
## 18 12 (5.8%) 12 (11%)
## 2 3 (1.4%) 2 (1.9%)
## 3 3 (1.4%) 3 (2.8%)
## 4 7 (3.4%) 0 (0%)
## 5 4 (1.9%) 3 (2.8%)
## 6 3 (1.4%) 2 (1.9%)
## 7 33 (16%) 14 (13%)
## 8 30 (14%) 15 (14%)
## 9 17 (8.2%) 13 (12%)
## female >0.9
## 0 104 (50%) 52 (49%)
## 1 104 (50%) 54 (51%)
## age_range 0.001
## 1 92 (44%) 26 (25%)
## 2 74 (36%) 43 (41%)
## 3 42 (20%) 37 (35%)
## race 0.2
## 1 148 (71%) 86 (81%)
## 2 30 (14%) 10 (9.4%)
## 3 30 (14%) 10 (9.4%)
## hispanic >0.9
## 0 182 (88%) 94 (89%)
## 1 26 (13%) 12 (11%)
## pc_cnum2 0.7
## 2 97 (47%) 53 (50%)
## 3 111 (53%) 53 (50%)
## region 0.3
## 1 50 (24%) 16 (15%)
## 2 45 (22%) 24 (23%)
## 3 72 (35%) 45 (42%)
## 4 41 (20%) 21 (20%)
## parent 0.043
## 0 105 (50%) 40 (38%)
## 1 103 (50%) 66 (62%)
## marital 0.7
## 1 88 (42%) 50 (47%)
## 2 92 (44%) 42 (40%)
## 3 28 (13%) 14 (13%)
## edu 0.9
## 1 96 (46%) 46 (43%)
## 2 42 (20%) 24 (23%)
## 3 70 (34%) 36 (34%)
## income >0.9
## 1 86 (41%) 43 (41%)
## 2 67 (32%) 36 (34%)
## 3 55 (26%) 27 (25%)
## community 0.8
## 1 61 (29%) 28 (26%)
## 2 111 (53%) 57 (54%)
## 3 36 (17%) 21 (20%)
## ------------------------------------------------------
## n (%)
## Pearson's Chi-squared test
##
## Column names: label, stat_1, stat_2, p.value9.4 Vertical bias in kaizen tasks
We will assess vertical bias in the kaizen tasks and run chi-square tests on whether these behaviors are associated with experimental or respondent characteristics.
# create vertical selection variable
kz2_it$vertical = ifelse(kz2_it$final_choice == "1|2|3|4" |
kz2_it$final_choice == "0|2|3|4" |
kz2_it$final_choice == "0|1|3|4" |
kz2_it$final_choice == "0|1|2|4" |
kz2_it$final_choice == "0|1|2|3", 1, 0)
table(kz2_it$vertical)##
## 0 1
## 2937 203# assess whether vertical selection is associated with block, ASO, pc_cnum2, tnum2
kz2_it %>% select(subjectindex, tnum2, pc_cnum2, vertical) %>%
mutate_if(is.numeric, as.character) %>%
tbl_summary(by = "vertical") %>% add_p(everything() ~ "chisq.test") %>%
as_hux_table()Characteristic | 0, N = 2,937 | 1, N = 203 | p-value |
|---|---|---|---|
| subjectindex | <0.001 | ||
| 1 | 787 (27%) | 33 (16%) | |
| 2 | 766 (26%) | 44 (22%) | |
| 3 | 708 (24%) | 62 (31%) | |
| 4 | 676 (23%) | 64 (32%) | |
| tnum2 | 0.2 | ||
| 10 | 298 (10%) | 16 (7.9%) | |
| 11 | 295 (10%) | 19 (9.4%) | |
| 2 | 299 (10%) | 15 (7.4%) | |
| 3 | 291 (9.9%) | 23 (11%) | |
| 4 | 295 (10%) | 19 (9.4%) | |
| 5 | 294 (10%) | 20 (9.9%) | |
| 6 | 281 (9.6%) | 33 (16%) | |
| 7 | 292 (9.9%) | 22 (11%) | |
| 8 | 299 (10%) | 15 (7.4%) | |
| 9 | 293 (10.0%) | 21 (10%) | |
| pc_cnum2 | <0.001 | ||
| 2 | 1,428 (49%) | 72 (35%) | |
| 3 | 1,509 (51%) | 131 (65%) | |
| n (%) | |||
| Pearson's Chi-squared test | |||
# create 'vertical_bias'
resp_i$vertical_bias = 0
for (i in 1:length(uniq_ids)) {
a = sum(kz2_it$vertical[kz2_it == uniq_ids[i]])
if (a == 10) {resp_i$vertical_bias[i] = 1}
}
table(resp_i$vertical_bias)##
## 0 1
## 313 1# assess whether vertical bias is associated with respondent and \\
# experimental characteristics
resp_i %>% select(subjectindex, quota, female, age_range, race, hispanic,
pc_cnum2, region, parent, marital, edu, income, community, vertical_bias) %>%
mutate_if(is.numeric, as.character) %>%
tbl_summary(by = "vertical_bias") %>% add_p(everything() ~ "chisq.test") %>%
as_hux_table()Characteristic | 0, N = 313 | 1, N = 1 | p-value |
|---|---|---|---|
| subjectindex | 0.4 | ||
| 1 | 82 (26%) | 0 (0%) | |
| 2 | 81 (26%) | 0 (0%) | |
| 3 | 77 (25%) | 0 (0%) | |
| 4 | 73 (23%) | 1 (100%) | |
| quota | 0.8 | ||
| 1 | 6 (1.9%) | 0 (0%) | |
| 10 | 8 (2.6%) | 0 (0%) | |
| 11 | 6 (1.9%) | 0 (0%) | |
| 12 | 7 (2.2%) | 0 (0%) | |
| 13 | 6 (1.9%) | 0 (0%) | |
| 14 | 6 (1.9%) | 0 (0%) | |
| 15 | 7 (2.2%) | 0 (0%) | |
| 16 | 44 (14%) | 0 (0%) | |
| 17 | 48 (15%) | 0 (0%) | |
| 18 | 23 (7.3%) | 1 (100%) | |
| 2 | 5 (1.6%) | 0 (0%) | |
| 3 | 6 (1.9%) | 0 (0%) | |
| 4 | 7 (2.2%) | 0 (0%) | |
| 5 | 7 (2.2%) | 0 (0%) | |
| 6 | 5 (1.6%) | 0 (0%) | |
| 7 | 47 (15%) | 0 (0%) | |
| 8 | 45 (14%) | 0 (0%) | |
| 9 | 30 (9.6%) | 0 (0%) | |
| female | >0.9 | ||
| 0 | 155 (50%) | 1 (100%) | |
| 1 | 158 (50%) | 0 (0%) | |
| age_range | 0.2 | ||
| 1 | 118 (38%) | 0 (0%) | |
| 2 | 117 (37%) | 0 (0%) | |
| 3 | 78 (25%) | 1 (100%) | |
| race | 0.8 | ||
| 1 | 233 (74%) | 1 (100%) | |
| 2 | 40 (13%) | 0 (0%) | |
| 3 | 40 (13%) | 0 (0%) | |
| hispanic | >0.9 | ||
| 0 | 275 (88%) | 1 (100%) | |
| 1 | 38 (12%) | 0 (0%) | |
| pc_cnum2 | >0.9 | ||
| 2 | 150 (48%) | 0 (0%) | |
| 3 | 163 (52%) | 1 (100%) | |
| region | 0.6 | ||
| 1 | 66 (21%) | 0 (0%) | |
| 2 | 69 (22%) | 0 (0%) | |
| 3 | 116 (37%) | 1 (100%) | |
| 4 | 62 (20%) | 0 (0%) | |
| parent | >0.9 | ||
| 0 | 144 (46%) | 1 (100%) | |
| 1 | 169 (54%) | 0 (0%) | |
| marital | 0.5 | ||
| 1 | 137 (44%) | 1 (100%) | |
| 2 | 134 (43%) | 0 (0%) | |
| 3 | 42 (13%) | 0 (0%) | |
| edu | 0.4 | ||
| 1 | 142 (45%) | 0 (0%) | |
| 2 | 66 (21%) | 0 (0%) | |
| 3 | 105 (34%) | 1 (100%) | |
| income | 0.2 | ||
| 1 | 129 (41%) | 0 (0%) | |
| 2 | 103 (33%) | 0 (0%) | |
| 3 | 81 (26%) | 1 (100%) | |
| community | 0.6 | ||
| 1 | 89 (28%) | 0 (0%) | |
| 2 | 167 (53%) | 1 (100%) | |
| 3 | 57 (18%) | 0 (0%) | |
| n (%) | |||
| Pearson's Chi-squared test | |||
10 Likelihood analysis
# coma comparisons
cc2_it$A1<-ifelse(cc2_it$actual_choice==1, cc2_it$alt2,cc2_it$alt1)
cc2_it$A2<-ifelse(cc2_it$actual_choice==1, cc2_it$alt1,cc2_it$alt2)
cc3_it<- subset(cc2_it, select=c("id","subjectindex", "task", "tnum2", "A1", "A2"))
cc3_ita<-melt(cc3_it, id.vars = c("id","subjectindex", "task", "tnum2"))
cc3_ita$set <- 0
# paired comparisons
pc2_it$A1<-ifelse(pc2_it$actual_choice==1, pc2_it$alt2,pc2_it$alt1)
pc2_it$A2<-ifelse(pc2_it$actual_choice==1, pc2_it$alt1,pc2_it$alt2)
pc3_it<- subset(pc2_it, select=c("id","subjectindex", "task","tnum2", "A1", "A2"))
pc3_ita<-melt(pc3_it, id.vars = c("id", "subjectindex", "task","tnum2"))
pc3_ita$set <- 0
# kaizen tasks
kz3_it<- subset(kz2_it, select=c("id","subjectindex", "task", "tnum2", "actual_choice", "alt1", "alt2"))
kz3_it$a1<-as.numeric(substr(kz3_it$actual_choice,1,1))
kz3_it$a2<-as.numeric(substr(kz3_it$actual_choice,3,3))
kz3_it$a3<-as.numeric(substr(kz3_it$actual_choice,5,5))
kz3_it$a4<-as.numeric(substr(kz3_it$actual_choice,7,7))
# Set 1 of kaizen tasks
set1 <- subset(kz3_it, select=c("id","subjectindex", "task","tnum2"))
# Apply the function to generate A1, A2, A3, and A4 columns
set1$A1 <- mapply(generate_A, kz3_it$a1, kz3_it$alt1, kz3_it$alt2)
set1$A2 <- mapply(generate_A, kz3_it$a2, kz3_it$alt1, kz3_it$alt2)
set1$A3 <- mapply(generate_A, kz3_it$a3, kz3_it$alt1, kz3_it$alt2)
set1$A4 <- mapply(generate_A, kz3_it$a4, kz3_it$alt1, kz3_it$alt2)
set1_ita<-melt(set1, id.vars = c("id", "subjectindex", "task","tnum2"))
set1_ita$set <- 1
# Set 2 of kaizen tasks
set2 <- subset(kz3_it, select=c("id","subjectindex", "task","tnum2"))
# Apply the function to generate A1, A2, A3, A4, A5, A6 column
set2$A1 <- mapply(generate_A2, kz3_it$a1, kz3_it$a2, as.character(kz3_it$alt1), as.character(kz3_it$alt2))
set2$A2 <- mapply(generate_A2, kz3_it$a1, kz3_it$a3, as.character(kz3_it$alt1), as.character(kz3_it$alt2))
set2$A3 <- mapply(generate_A2, kz3_it$a1, kz3_it$a4, as.character(kz3_it$alt1), as.character(kz3_it$alt2))
set2$A4 <- mapply(generate_A2, kz3_it$a2, kz3_it$a3, as.character(kz3_it$alt1), as.character(kz3_it$alt2))
set2$A5 <- mapply(generate_A2, kz3_it$a2, kz3_it$a4, as.character(kz3_it$alt1), as.character(kz3_it$alt2))
set2$A6 <- mapply(generate_A2, kz3_it$a3, kz3_it$a4, as.character(kz3_it$alt1), as.character(kz3_it$alt2))
set2_ita<-melt(set2, id.vars = c("id","subjectindex", "task", "tnum2"))
set2_ita$set <- 2
# Set 3 of kaizen tasks
set3 <- subset(kz3_it, select=c("id","subjectindex", "task","tnum2"))
# Apply the function to generate A1, A2, A3, and A4 columns
set3$A1 <- mapply(generate_A, kz3_it$a4, kz3_it$alt2, kz3_it$alt1)
set3$A2 <- mapply(generate_A, kz3_it$a3, kz3_it$alt2, kz3_it$alt1)
set3$A3 <- mapply(generate_A, kz3_it$a2, kz3_it$alt2, kz3_it$alt1)
set3$A4 <- mapply(generate_A, kz3_it$a1, kz3_it$alt2, kz3_it$alt1)
set3_ita<-melt(set3, id.vars = c("id","subjectindex", "task","tnum2"))
set3_ita$set <- 3
# combine sets
kz3_ita <- rbind(set1_ita,set2_ita,set3_ita)
# combine task
task_ita <- rbind(cc3_ita, pc3_ita, kz3_ita)
task_it <- dcast(task_ita, id+set+subjectindex+task+tnum2~variable)
# compute probability of A1 by subjectindex, task, and set
mu_f <- task_it %>% group_by(subjectindex, task, set, A1) %>% summarize(f=n())
mu_N <- task_it %>% group_by(subjectindex, task, set) %>% summarize(N=n())
mu_fN <- merge(mu_f, mu_N, by = c("subjectindex", "task", "set"))
mu_fN$P<- mu_fN$f/ mu_fN$N
mu_fN <- subset(mu_fN, select =c("subjectindex", "task", "set", "A1", "P") )
# compute the log-likelihood of each respondent
task_it <- merge(task_it, mu_fN, by = c("subjectindex", "task", "set", "A1"))
task_it$lnP <- log(task_it$P)
ll_i <- task_it[,c("id","lnP")] %>% group_by(id) %>% summarize(ll=sum(lnP))
names(ll_i)[2] = "all_ll"
cc_ll_i <- task_it[task_it$tnum2<=6 & task_it$set==0,c("id","lnP")] %>%
group_by(id) %>% summarize(ll=sum(lnP))
names(cc_ll_i)[2] = "cc_ll"
pc_ll_i <- task_it[task_it$tnum2>6 & task_it$set==0,c("id","lnP")] %>%
group_by(id) %>% summarize(ll=sum(lnP))
names(pc_ll_i)[2] = "pc_ll"
kz_ll_i <- task_it[task_it$set>0,c("id","lnP")] %>% group_by(id) %>% summarize(ll=sum(lnP))
names(kz_ll_i)[2] = "kz_ll"
# merge data
ll_i <- merge(ll_i, cc_ll_i, by = c("id"))
ll_i <- merge(ll_i, pc_ll_i, by = c("id"))
ll_i <- merge(ll_i, kz_ll_i, by = c("id"))
# plot histograms
hist(ll_i$all_ll, xlab = "Log-likelihood", main = "All three components")
hist(ll_i$cc_ll, xlab = "Log-likelihood", main = "Coma comparisons")
hist(ll_i$pc_ll, xlab = "Log-likelihood", main = "Paired comparisons")
hist(ll_i$kz_ll, xlab = "Log-likelihood", main = "Kaizen tasks")