HC Assessment R Code
Claude Haneum Lee
12/17/2020
Data Export
Residence Info
StarRez <- read_csv("Data 12-2-20/StarRez Data.csv",
col_types = cols(Location = col_factor(levels = c("Shoemaker Hall",
"Mitchell Hall",
"Sherburne Hall",
"Stearns Hall",
"Case Hall",
"Hill Hall",
"Stateview North",
"Stateview South",
"Lawrence Hall")),
`Tech ID` = col_character()))CA Data
Base Data
CA <- read_csv("Data 12-2-20/HC Survey - CAs.csv",
col_names = FALSE, col_types = cols(X18 = col_character()),
skip = 3)
CA <- CA %>%
select(-c(1:6, 8:17))
names(CA) <- c("Finished", "StudentID", "LastName", "FirstName", "Hall1", "Floor",
"Sched", "SchedOther", "SchedNT", "SchedNTOther",
"Comm", "CommOther", "CommNT", "CommNTOther", "Meet", "MeetOther", "MeetNT", "MeetNTOther",
"Feedback", "FeedbackOther", "FeedbackReason",
"SC1", "SC2", "SC3", "SC4", "SC5", "CM1", "CM2", "CM3", "CM4", "CM5", "VC1", "VC2", "VC3", "VC4", "VC5")
#Check Student ID entry error
for (i in 1:nrow(CA)) {
if ((CA$StudentID[i] %in% StarRez$'Tech ID') == FALSE) {print(CA[i,c(2:6)])}
}
#Remove Unfinished Response
o <- vector()
for (k in 1:nrow(CA)) {
if (CA$Finished[k] == FALSE) {o <- append(o, k, length(o))}
}
CA <- CA[-o, ]
#Check Duplicate Entry
l <- as.data.frame(table(CA$StudentID))
for (j in 1:nrow(l)) {
if (l[j,2] != 1) {print(l[j,])}
}
#Fluency Score Convert
for (i in (ncol(CA)-14):ncol(CA)) {
a <- vector()
for (j in 1:nrow(CA)) {
if (is.na(CA[j, i])) {a[j] <- 0}
else if (CA[j, i] == "Know how to use this feature") {a[j] <- 1}
else if (CA[j, i] == "Use this feature frequently") {a[j] <- 2}
else if (CA[j, i] == "Know how to use this feature,Use this feature frequently") {a[j] <- 2}
}
CA[i] <- a
}
#Change "Other ..." Entry
for (i in 1:ncol(CA)) {
for (j in 1:nrow(CA)) {
if (startsWith(toString(CA[j, i]), "Other") == TRUE) {
CA[j, i] <- "Other"
}
}
}
#Join StarRez Data
CA2 <- inner_join(CA, StarRez, by = c("StudentID" = "Tech ID")) %>%
select(StudentID, Location, Floor, Sched:VC5) %>%
mutate(SCkn = (SC1 %in% c(1,2)) + (SC2 %in% c(1,2)) + (SC3 %in% c(1,2)) + (SC4 %in% c(1,2)) + (SC5 %in% c(1,2)),
SCus = (SC1 == 1) + (SC2 == 1) + (SC3 == 1) + (SC4 == 1) + (SC5 == 1),
CMkn = (CM1 %in% c(1,2)) + (CM2 %in% c(1,2)) + (CM3 %in% c(1,2)) + (CM4 %in% c(1,2)) + (CM5 %in% c(1,2)),
CMus = (CM1 == 1) + (CM2 == 1) + (CM3 == 1) + (CM4 == 1) + (CM5 == 1),
VCkn = (VC1 %in% c(1,2)) + (VC2 %in% c(1,2)) + (VC3 %in% c(1,2)) + (VC4 %in% c(1,2)) + (VC5 %in% c(1,2)),
VCus = (VC1 == 1) + (VC2 == 1) + (VC3 == 1) + (VC4 == 1) + (VC5 == 1))Clean Response
#Group/Rename Halls
Hall <- vector()
for (i in 1:nrow(CA2)) {
if (CA2$Location[i]== "Shoemaker Hall") {Hall[i] <- "Shoemaker"}
else if (CA2$Location[i]== "Mitchell Hall") {Hall[i] <- "Mitchell"}
else if (CA2$Location[i]== "Sherburne Hall") {Hall[i] <- "Sherburne"}
else if (CA2$Location[i]== "Stearns Hall") {Hall[i] <- "Stearns"}
else if (CA2$Location[i]%in% c("Case Hall", "Hill Hall")) {Hall[i] <- "Case-Hill"}
else if (CA2$Location[i]%in% c("Stateview North", "Stateview South")) {Hall[i] <- "Stateview"}
}
CA2 <- as.data.frame(cbind(Hall, CA2))
# Sched
for (i in 1:nrow(CA2)) {
if (CA2$Sched[i] == "Hand-written schedule") {CA2$Sched[i] <- "Hand-Written"}
else if (startsWith(CA2$Sched[i], "Online")) {CA2$Sched[i] <- "Online App"}
}
# SchedNT
for (i in 1:nrow(CA2)) {
if (is.na(CA2$SchedNT[i]) == FALSE) {
if (startsWith(CA2$SchedNT[i],"Hand-written schedule")) {CA2$SchedNT[i] <- "Easy to keep up"}
else if (startsWith(CA2$SchedNT[i],"I am not")) {CA2$SchedNT[i] <- "Not fluent in such technology"}
else if (startsWith(CA2$SchedNT[i],"It is")) {CA2$SchedNT[i] <- "Privacy"}
}
}
# Comm
for (i in 1:nrow(CA2)) {
if (startsWith(CA2$Comm[i], "Phone")) {CA2$Comm[i] <- "Phone/PhoneApp"}
}
# CommNT
for (i in 1:nrow(CA2)) {
if (is.na(CA2$CommNT[i]) == FALSE) {
if (startsWith(CA2$CommNT[i],"It is")) {CA2$CommNT[i] <- "Easy to get ahold"}
else if (startsWith(CA2$CommNT[i],"I prefer")) {CA2$CommNT[i] <- "In-person Connections"}
else if (startsWith(CA2$CommNT[i],"I am")) {CA2$CommNT[i] <- "Not fluent in other methods"}
}
}
# Meet
for (i in 1:nrow(CA2)) {
if (startsWith(CA2$Meet[i], "Video")) {CA2$Meet[i] <- "Online App"}
else if (startsWith(CA2$Meet[i], "Phone")) {CA2$Meet[i] <- "Phone/PhoneApp"}
else if (startsWith(CA2$Meet[i], "I will")) {CA2$Meet[i] <- "Still in-person"}
}
# MeetNT
for (i in 1:nrow(CA2)) {
if (is.na(CA2$MeetNT[i]) == FALSE) {
if (startsWith(CA2$MeetNT[i],"Possible")) {CA2$MeetNT[i] <- "In-person Connections"}
else if (startsWith(CA2$MeetNT[i],"Easier")) {CA2$MeetNT[i] <- "Easy to facilitate"}
else if (startsWith(CA2$MeetNT[i],"I am")) {CA2$MeetNT[i] <- "Not fluent in other methods"}
}
}Multiple Selection Data
# Feedback
CAFeedback <- vector()
for (i in 1:nrow(CA2)) {
CAFeedback<- append(CAFeedback, unlist(strsplit(CA2$Feedback[i], ",")), length(CAFeedback))
}Student Data
Base Data
Student <- read_csv("Data 12-2-20/HC Survey - Students.csv",
col_names = FALSE, col_types = cols(X18 = col_character()),
skip = 5)
Student <- Student %>%
select(-c(1:6, 8:17))
names(Student) <- c("Finished", "StudentID", "LastName", "FirstName", "Hall1", "Referral",
"SchedCA", "SchedCAApp", "SchedCAAppOther", "Sched", "SchedOther", "SchedNT", "SchedNTOther",
"CommCA", "CommCAOther", "CommCAApp", "CommCAAppOther", "Comm", "CommOther", "CommNT", "CommNTOther",
"MeetCA", "MeetCAOther", "MeetCAApp", "MeetCAAppOther", "Meet", "MeetOther", "MeetNT", "MeetNTOther",
"Feedback", "FeedbackOther", "FeedbackReason",
"SC1", "SC2", "SC3", "SC4", "SC5", "CM1", "CM2", "CM3", "CM4", "CM5", "VC1", "VC2", "VC3", "VC4", "VC5")
#Check Student ID entry error
for (i in 1:nrow(Student)) {
if ((Student$StudentID[i] %in% StarRez$'Tech ID') == FALSE) {print(Student[i,c(2:6)])}
}
#Check CA entry in Student Survey and remove
c <- vector()
for (i in 1:nrow(Student)) {
if ((Student$StudentID[i] %in% CA$'StudentID')) {
c <- append(c, i, length(c))}
}
Student <- Student[-c,]
#Remove Unfinished Response
o <- vector()
for (k in 1:nrow(Student)) {
if (Student$Finished[k] == FALSE) {o <- append(o, k, length(o))}
}
Student <- Student[-o, ]
#Check Duplicate Entry
l <- as.data.frame(table(Student$StudentID))
for (j in 1:nrow(l)) {
if (l[j,2] != 1) {print(l[j,])}
}
#Fluency Score Convert
for (i in (ncol(Student)-14):ncol(Student)) {
a <- vector()
for (j in 1:nrow(Student)) {
if (is.na(Student[j, i])) {a[j] <- 0}
else if (Student[j, i] == "Know how to use this feature") {a[j] <- 1}
else if (Student[j, i] == "Use this feature frequently") {a[j] <- 2}
else if (Student[j, i] == "Know how to use this feature,Use this feature frequently") {a[j] <- 2}
}
Student[i] <- a
}
#Change "Other ..." Entry
for (i in 1:ncol(Student)) {
for (j in 1:nrow(Student)) {
if (startsWith(toString(Student[j, i]), "Other") == TRUE) {
Student[j, i] <- "Other"
}
}
}
#Join StarRez Data
Student2 <- inner_join(Student, StarRez, by = c("StudentID" = "Tech ID")) %>%
select(StudentID, Location, Referral:VC5) %>%
mutate(SCkn = (SC1 %in% c(1,2)) + (SC2 %in% c(1,2)) + (SC3 %in% c(1,2)) + (SC4 %in% c(1,2)) + (SC5 %in% c(1,2)),
SCus = (SC1 == 1) + (SC2 == 1) + (SC3 == 1) + (SC4 == 1) + (SC5 == 1),
CMkn = (CM1 %in% c(1,2)) + (CM2 %in% c(1,2)) + (CM3 %in% c(1,2)) + (CM4 %in% c(1,2)) + (CM5 %in% c(1,2)),
CMus = (CM1 == 1) + (CM2 == 1) + (CM3 == 1) + (CM4 == 1) + (CM5 == 1),
VCkn = (VC1 %in% c(1,2)) + (VC2 %in% c(1,2)) + (VC3 %in% c(1,2)) + (VC4 %in% c(1,2)) + (VC5 %in% c(1,2)),
VCus = (VC1 == 1) + (VC2 == 1) + (VC3 == 1) + (VC4 == 1) + (VC5 == 1))Clean Response
#Group/Rename Halls
Hall <- vector()
for (i in 1:nrow(Student2)) {
if (Student2$Location[i]== "Shoemaker Hall") {Hall[i] <- "Shoemaker"}
else if (Student2$Location[i]== "Mitchell Hall") {Hall[i] <- "Mitchell"}
else if (Student2$Location[i]== "Sherburne Hall") {Hall[i] <- "Sherburne"}
else if (Student2$Location[i]== "Stearns Hall") {Hall[i] <- "Stearns"}
else if (Student2$Location[i]%in% c("Case Hall", "Hill Hall")) {Hall[i] <- "Case-Hill"}
else if (Student2$Location[i]%in% c("Stateview North", "Stateview South")) {Hall[i] <- "Stateview"}
}
Student2 <- as.data.frame(cbind(Hall, Student2))
# Sched
for (i in 1:nrow(Student2)) {
if (Student2$Sched[i] == "Hand-written schedule") {Student2$Sched[i] <- "Hand-Written"}
else if (startsWith(Student2$Sched[i], "Online")) {Student2$Sched[i] <- "Online App"}
}
# SchedNT
for (i in 1:nrow(Student2)) {
if (is.na(Student2$SchedNT[i]) == FALSE) {
if (startsWith(Student2$SchedNT[i],"Hand-written schedule")) {Student2$SchedNT[i] <- "Easy to keep up"}
else if (startsWith(Student2$SchedNT[i],"I am not")) {Student2$SchedNT[i] <- "Not fluent in such technology"}
else if (startsWith(Student2$SchedNT[i],"It is")) {Student2$SchedNT[i] <- "Privacy"}
}
}
# Comm
for (i in 1:nrow(Student2)) {
if (startsWith(Student2$Comm[i], "Phone")) {Student2$Comm[i] <- "Phone/PhoneApp"}
}
# CommNT
for (i in 1:nrow(Student2)) {
if (is.na(Student2$CommNT[i]) == FALSE) {
if (startsWith(Student2$CommNT[i],"It is")) {Student2$CommNT[i] <- "Easy to get ahold"}
else if (startsWith(Student2$CommNT[i],"I prefer")) {Student2$CommNT[i] <- "In-person Connections"}
else if (startsWith(Student2$CommNT[i],"I am")) {Student2$CommNT[i] <- "Not fluent in other methods"}
}
}
# MeetCA
for (i in 1:nrow(Student2)) {
if (startsWith(Student2$MeetCA[i], "Video")) {Student2$MeetCA[i] <- "Online App"}
else if (startsWith(Student2$MeetCA[i], "Phone")) {Student2$MeetCA[i] <- "Phone/PhoneApp"}
else if (startsWith(Student2$MeetCA[i], "In-")) {Student2$MeetCA[i] <- "In-Person"}
}
# Meet
for (i in 1:nrow(Student2)) {
if (startsWith(Student2$Meet[i], "Video")) {Student2$Meet[i] <- "Online App"}
else if (startsWith(Student2$Meet[i], "Phone")) {Student2$Meet[i] <- "Phone/PhoneApp"}
else if (startsWith(Student2$Meet[i], "I will")) {Student2$Meet[i] <- "Still in-person"}
}
# MeetNT
for (i in 1:nrow(Student2)) {
if (is.na(Student2$MeetNT[i]) == FALSE) {
if (startsWith(Student2$MeetNT[i],"Possible")) {Student2$MeetNT[i] <- "In-person Connections"}
else if (startsWith(Student2$MeetNT[i],"Easier")) {Student2$MeetNT[i] <- "Easy to be part of"}
else if (startsWith(Student2$MeetNT[i],"I am")) {Student2$MeetNT[i] <- "Not fluent in other methods"}
}
}Multiple Selection Data
# SchedCAApp
StudentSchedCAApp <- vector()
for (i in 1:nrow(Student2)) {
if (is.na(Student2$SchedCAApp[i]) == FALSE) {
StudentSchedCAApp <- append(StudentSchedCAApp, unlist(strsplit(Student2$SchedCAApp[i], ",")), length(StudentSchedCAApp))
}
}
for (i in 1:length(StudentSchedCAApp)) {
if (startsWith(StudentSchedCAApp[i], "Other") == TRUE) {StudentSchedCAApp[i] <- "Other"}
else if (startsWith(StudentSchedCAApp[i], "Outlook") == TRUE) {StudentSchedCAApp[i] <- "Outlook"}
}
# CommCAApp
StudentCommCAApp <- vector()
for (i in 1:nrow(Student2)) {
if (is.na(Student2$CommCAApp[i]) == FALSE) {
StudentCommCAApp <- append(StudentCommCAApp, unlist(strsplit(Student2$CommCAApp[i], ",")), length(StudentCommCAApp))
}
}
for (i in 1:length(StudentCommCAApp)) {
if (startsWith(StudentCommCAApp[i], "Other") == TRUE) {StudentCommCAApp[i] <- "Other"}
else if (startsWith(StudentCommCAApp[i], "Phone") == TRUE) {StudentCommCAApp[i] <- "Phone"}
else if (startsWith(StudentCommCAApp[i], "Outlook") == TRUE) {StudentCommCAApp[i] <- "Outlook"}
}
# MeetCAApp
StudentMeetCAApp <- vector()
for (i in 1:nrow(Student2)) {
if (is.na(Student2$MeetCAApp[i]) == FALSE) {
StudentMeetCAApp <- append(StudentMeetCAApp, unlist(strsplit(Student2$MeetCAApp[i], ",")), length(StudentMeetCAApp))
}
}
for (i in 1:length(StudentMeetCAApp)) {
if (startsWith(StudentMeetCAApp[i], "Other") == TRUE) {StudentMeetCAApp[i] <- "Other"}
else if (startsWith(StudentMeetCAApp[i], "Phone") == TRUE) {StudentMeetCAApp[i] <- "Phone"}
else if (startsWith(StudentMeetCAApp[i], "Outlook") == TRUE) {StudentMeetCAApp[i] <- "Outlook"}
}
# Feedback
StudentFeedback <- vector()
for (i in 1:nrow(Student2)) {
StudentFeedback<- append(StudentFeedback, unlist(strsplit(Student2$Feedback[i], ",")), length(StudentFeedback))
}Student Data Student ID List
Join Demographic Data
library(readxl)
library(eeptools)## Warning: package 'eeptools' was built under R version 4.0.3Dem <- read_excel("Data 12-2-20/HC Demographic Data.xlsx",
col_types = c("text", "text", "date", "text", "text", "text"))
Dem$Class <- factor(Dem$Class,levels = c("FR", "SO", "JR", "SR"))
Dem <- mutate(Dem, Age = floor(age_calc(as.Date(Dem$DOB), unit = "years")))
Student2 <- inner_join(Student2, Dem, by = c("StudentID" = "StudentID"))Use of Technology
Completion by Hall
library(tidyverse)## -- Attaching packages ------------------------------------------------------ tidyverse 1.3.0 --## v tibble 3.0.3 v purrr 0.3.4
## v tidyr 1.1.2 v forcats 0.5.0## -- Conflicts --------------------------------------------------------- tidyverse_conflicts() --
## x gridExtra::combine() masks dplyr::combine()
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()# CA
p <- ggplot(CA2, aes(x = Location)) +
geom_bar() +
theme(axis.text.x = element_text(angle = 15),
axis.title.x = element_blank())
k <- tableGrob(unname(as.data.frame(table(CA2$Location))), rows = NULL)
grid.arrange(p, k, ncol=2, widths = c(3, 1), top = textGrob("CA Completion Count", gp = gpar(fontsize = 30)))
# Student
m <- as.data.frame(cbind(round(table(Student2$Location)/table(StarRez$Location) * 100, 2), table(Student2$Location), round(table(StarRez$Location)*0.2)))
names(m) <- c("Prop", "Count", "Goal")
m <- m %>%
arrange(desc(Prop))
k <- tableGrob(m)
grid.arrange(k, top = textGrob("Student Completion", gp = gpar(fontsize = 30)))
Scheduling
Scheduling Method Preference
# a. CA Preference
ggplot(CA2, aes(x = Sched)) +
geom_bar() +
geom_text(stat='count', aes(label=..count..), vjust=1, size = 8, color = "blue") +
labs(title = "CA Scheduling Preference") +
theme(plot.title = element_text(hjust = 0.5, size = 20),
axis.title.x = element_blank(),
axis.text.x = element_text(size = 15),
axis.text.y = element_text(size = 12))
# b. Student Experience from CA
ggplot(Student2, aes(x = SchedCA)) +
geom_bar() +
geom_text(stat='count', aes(label=..count..), vjust=1, size = 8, color = "blue") +
labs(title = "Has your CA use any online app to schedule an event with you?") +
theme(plot.title = element_text(hjust = 0.5, size = 17),
axis.title.x = element_blank(),
axis.text.x = element_text(size = 15),
axis.text.y = element_text(size = 12))
# c. Student Preference
ggplot(Student2, aes(x = Sched)) +
geom_bar() +
geom_text(stat='count', aes(label=..count..), vjust=1, size = 8, color = "blue") +
labs(title = "Student Scheduling Preference") +
theme(plot.title = element_text(hjust = 0.5, size = 20),
axis.title.x = element_blank(),
axis.text.x = element_text(size = 15),
axis.text.y = element_text(size = 12))
Online Scheduling App
ggplot(as.data.frame(StudentSchedCAApp), aes(x = StudentSchedCAApp)) +
geom_bar() +
geom_text(stat='count', aes(label=..count..), vjust=1, size = 8, color = "blue") +
labs(title = "What online app did your CA use to schedule an event with you?") +
theme(plot.title = element_text(hjust = 0.5, size = 17),
axis.title.x = element_blank(),
axis.text.x = element_text(size = 15),
axis.text.y = element_text(size = 12))
Hand-Written Scheduling Reason
# a. CA Reason
ggplot(drop_na(CA2, SchedNT), aes(x = SchedNT)) +
geom_bar() +
geom_text(stat='count', aes(label=..count..), vjust=1, size = 8, color = "blue") +
labs(title = "CA Why Hand-Written Schedule?") +
theme(plot.title = element_text(hjust = 0.5, size = 20),
axis.title.x = element_blank(),
axis.text.x = element_text(size = 14),
axis.text.y = element_text(size = 12))
# b. Student Reason
ggplot(drop_na(Student2, SchedNT), aes(x = SchedNT)) +
geom_bar() +
geom_text(stat='count', aes(label=..count..), vjust=1, size = 8, color = "blue") +
labs(title = "Student Why Hand-Written Schedule?") +
theme(plot.title = element_text(hjust = 0.5, size = 20),
axis.title.x = element_blank(),
axis.text.x = element_text(size = 14),
axis.text.y = element_text(size = 12))
Communication
Communication Method Preference
# a. CA Preference
ggplot(CA2, aes(x = Comm)) +
geom_bar() +
geom_text(stat='count', aes(label=..count..), vjust=1, size = 8, color = "blue") +
labs(title = "CA Communication Preference") +
theme(plot.title = element_text(hjust = 0.5, size = 20),
axis.title.x = element_blank(),
axis.text.x = element_text(size = 15),
axis.text.y = element_text(size = 12))
# b. Student Experience from CA
ggplot(Student2, aes(x = CommCA)) +
geom_bar() +
geom_text(stat='count', aes(label=..count..), vjust=1, size = 8, color = "blue") +
labs(title = "What method does your CA primarily use to contact you?") +
theme(plot.title = element_text(hjust = 0.5, size = 17),
axis.title.x = element_blank(),
axis.text.x = element_text(size = 15),
axis.text.y = element_text(size = 12))
# c. Student Preference
ggplot(Student2, aes(x = Comm)) +
geom_bar() +
geom_text(stat='count', aes(label=..count..), vjust=1, size = 8, color = "blue") +
labs(title = "Student Communication Preference") +
theme(plot.title = element_text(hjust = 0.5, size = 20),
axis.title.x = element_blank(),
axis.text.x = element_text(size = 15),
axis.text.y = element_text(size = 12))
Online Communication App
ggplot(as.data.frame(StudentCommCAApp), aes(x = StudentCommCAApp)) +
geom_bar() +
geom_text(stat='count', aes(label=..count..), vjust=1, size = 8, color = "blue") +
labs(title = "What online app did your CA use to communicate with you?") +
theme(plot.title = element_text(hjust = 0.5, size = 17),
axis.title.x = element_blank(),
axis.text.x = element_text(size = 15),
axis.text.y = element_text(size = 12))
In-Person Communication Reason
# a. CA Reason
ggplot(drop_na(CA2, CommNT), aes(x = CommNT)) +
geom_bar() +
geom_text(stat='count', aes(label=..count..), vjust=1, size = 8, color = "blue") +
labs(title = "CA Why in-person communication?") +
theme(plot.title = element_text(hjust = 0.5, size = 20),
axis.title.x = element_blank(),
axis.text.x = element_text(size = 15),
axis.text.y = element_text(size = 12))
# b. Student Reason
ggplot(drop_na(Student2, CommNT), aes(x = CommNT)) +
geom_bar() +
geom_text(stat='count', aes(label=..count..), vjust=1, size = 8, color = "blue") +
labs(title = "Student Why in-person communication?") +
theme(plot.title = element_text(hjust = 0.5, size = 20),
axis.title.x = element_blank(),
axis.text.x = element_text(size = 15),
axis.text.y = element_text(size = 12))
Meeting
Meeting Method Preference
# a. CA Preference
ggplot(CA2, aes(x = Meet)) +
geom_bar() +
geom_text(stat='count', aes(label=..count..), vjust=1, size = 8, color = "blue") +
labs(title = "CA Meeting Preference") +
theme(plot.title = element_text(hjust = 0.5, size = 20),
axis.title.x = element_blank(),
axis.text.x = element_text(size = 15),
axis.text.y = element_text(size = 12))
# b. Student Experience from CA
ggplot(Student2, aes(x = MeetCA)) +
geom_bar() +
geom_text(stat='count', aes(label=..count..), vjust=1, size = 8, color = "blue") +
labs(title = "Which method did your CA used in meeting with you?") +
theme(plot.title = element_text(hjust = 0.5, size = 18),
axis.title.x = element_blank(),
axis.text.x = element_text(size = 15),
axis.text.y = element_text(size = 12))
# c. Student Preference
ggplot(Student2, aes(x = Meet)) +
geom_bar() +
geom_text(stat='count', aes(label=..count..), vjust=1, size = 8, color = "blue") +
labs(title = "Student Meeting Preference") +
theme(plot.title = element_text(hjust = 0.5, size = 20),
axis.title.x = element_blank(),
axis.text.x = element_text(size = 15),
axis.text.y = element_text(size = 12))
Online Meeting App
ggplot(as.data.frame(StudentMeetCAApp), aes(x = StudentMeetCAApp)) +
geom_bar() +
geom_text(stat='count', aes(label=..count..), vjust=1, size = 8, color = "blue") +
labs(title = "What online app did your CA use to facilitate meetings with you?") +
theme(plot.title = element_text(hjust = 0.5, size = 17),
axis.title.x = element_blank(),
axis.text.x = element_text(size = 14),
axis.text.y = element_text(size = 12))
In-Person Reason
# a. CA Reason
ggplot(drop_na(CA2, MeetNT), aes(x = MeetNT)) +
geom_bar() +
geom_text(stat='count', aes(label=..count..), vjust=1, size = 8, color = "blue") +
labs(title = "CA Why in-person meeting?") +
theme(plot.title = element_text(hjust = 0.5, size = 20),
axis.title.x = element_blank(),
axis.text.x = element_text(size = 15),
axis.text.y = element_text(size = 12))
# b. Student Reason
ggplot(drop_na(Student2, MeetNT), aes(x = MeetNT)) +
geom_bar() +
geom_text(stat='count', aes(label=..count..), vjust=1, size = 8, color = "blue") +
labs(title = "Student Why in-person meeting?") +
theme(plot.title = element_text(hjust = 0.5, size = 20),
axis.title.x = element_blank(),
axis.text.x = element_text(size = 15),
axis.text.y = element_text(size = 12))
Technology Area Feedback
# a. CA Feedback
ggplot(as.data.frame(CAFeedback), aes(x = CAFeedback)) +
geom_bar() +
geom_text(stat='count', aes(label=..count..), vjust=1, size = 8, color = "blue") +
labs(title = "CA Which areas in Res Life need more use of technology?") +
theme(plot.title = element_text(hjust = 0.5, size = 17),
axis.title.x = element_blank(),
axis.text.x = element_text(angle = 15, size = 15),
axis.text.y = element_text(size = 12))
# b. Student Reason
ggplot(as.data.frame(StudentFeedback), aes(x = StudentFeedback)) +
geom_bar() +
geom_text(stat='count', aes(label=..count..), vjust=1, size = 8, color = "blue") +
labs(title = "Student CA Which areas in Res Life need more use of technology?") +
theme(plot.title = element_text(hjust = 0.5, size = 17),
axis.title.x = element_blank(),
axis.text.x = element_text(angle = 15, size = 15),
axis.text.y = element_text(size = 12))
Technology Fluency & Usage
Fluency vs Usage
#Scheduling
t.test(Student2$SCkn, Student2$SCus, paired = TRUE, alternative = "greater")##
## Paired t-test
##
## data: Student2$SCkn and Student2$SCus
## t = 11.737, df = 100, p-value < 2.2e-16
## alternative hypothesis: true difference in means is greater than 0
## 95 percent confidence interval:
## 1.802106 Inf
## sample estimates:
## mean of the differences
## 2.09901#Communication
t.test(Student2$CMkn, Student2$CMus, paired = TRUE, alternative = "greater")##
## Paired t-test
##
## data: Student2$CMkn and Student2$CMus
## t = 13.975, df = 100, p-value < 2.2e-16
## alternative hypothesis: true difference in means is greater than 0
## 95 percent confidence interval:
## 2.268441 Inf
## sample estimates:
## mean of the differences
## 2.574257#Meeting
t.test(Student2$VCkn, Student2$VCus, paired = TRUE, alternative = "greater")##
## Paired t-test
##
## data: Student2$VCkn and Student2$VCus
## t = 9.0852, df = 100, p-value = 5.002e-15
## alternative hypothesis: true difference in means is greater than 0
## 95 percent confidence interval:
## 1.359402 Inf
## sample estimates:
## mean of the differences
## 1.663366Three Scores
Fluency
mean(Student2$SCkn)## [1] 3.594059mean(Student2$CMkn)## [1] 3.752475mean(Student2$VCkn)## [1] 3.059406#Scheduling vs Communication
t.test(Student2$SCkn, Student2$CMkn, paired = TRUE)##
## Paired t-test
##
## data: Student2$SCkn and Student2$CMkn
## t = -1.1387, df = 100, p-value = 0.2575
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.4344162 0.1175845
## sample estimates:
## mean of the differences
## -0.1584158#Scheduling vs Meeting
t.test(Student2$SCkn, Student2$VCkn, paired = TRUE)##
## Paired t-test
##
## data: Student2$SCkn and Student2$VCkn
## t = 3.9071, df = 100, p-value = 0.00017
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.2631637 0.8061432
## sample estimates:
## mean of the differences
## 0.5346535#Communication vs Meeting
t.test(Student2$CMkn, Student2$VCkn, paired = TRUE)##
## Paired t-test
##
## data: Student2$CMkn and Student2$VCkn
## t = 4.578, df = 100, p-value = 1.353e-05
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.3927133 0.9934253
## sample estimates:
## mean of the differences
## 0.6930693Usage
mean(Student2$SCus)## [1] 1.49505mean(Student2$CMus)## [1] 1.178218mean(Student2$VCus)## [1] 1.39604#Scheduling vs Communication
t.test(Student2$SCus, Student2$CMus, paired = TRUE)##
## Paired t-test
##
## data: Student2$SCus and Student2$CMus
## t = 2.2869, df = 100, p-value = 0.02431
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.04196608 0.59169729
## sample estimates:
## mean of the differences
## 0.3168317#Scheduling vs Meeting
t.test(Student2$SCus, Student2$VCus, paired = TRUE)##
## Paired t-test
##
## data: Student2$SCus and Student2$VCus
## t = 0.60443, df = 100, p-value = 0.5469
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.2259781 0.4239979
## sample estimates:
## mean of the differences
## 0.0990099#Communication vs Meeting
t.test(Student2$CMus, Student2$VCus, paired = TRUE)##
## Paired t-test
##
## data: Student2$CMus and Student2$VCus
## t = -1.4213, df = 100, p-value = 0.1583
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.52186781 0.08622425
## sample estimates:
## mean of the differences
## -0.2178218CA vs Students
Fluency
#Scheduling
SCkn <- data.frame(score <- c(CA2$SCkn, Student2$SCkn),
group <- c(rep("CA", nrow(CA2)), rep("Student", nrow(Student2))))
ggplot(SCkn, aes(x = group, y = score, color = group)) +
geom_point(position = "jitter", show.legend = FALSE) +
geom_hline(yintercept = mean(CA2$SCkn), color = "pink") +
geom_hline(yintercept = mean(Student2$SCkn), color = "skyblue") +
labs(title = "Scheduling Technology Fluency") +
theme(plot.title = element_text(hjust = 0.5, size = 20),
axis.title.x = element_blank(),
axis.text.x = element_text(size = 15),
axis.text.y = element_text(size = 12))
t.test(score ~ group, data = SCkn, alternative = "greater")##
## Welch Two Sample t-test
##
## data: score by group
## t = 0.22414, df = 49.527, p-value = 0.4118
## alternative hypothesis: true difference in means is greater than 0
## 95 percent confidence interval:
## -0.5400525 Inf
## sample estimates:
## mean in group CA mean in group Student
## 3.677419 3.594059mean(CA2$SCkn) - mean(Student2$SCkn)## [1] 0.08335995#Communication
CMkn <- data.frame(score <- c(CA2$CMkn, Student2$CMkn),
group <- c(rep("CA", nrow(CA2)), rep("Student", nrow(Student2))))
ggplot(CMkn, aes(x = group, y = score, color = group)) +
geom_point(position = "jitter", show.legend = FALSE) +
geom_hline(yintercept = mean(CA2$CMkn), color = "pink") +
geom_hline(yintercept = mean(Student2$CMkn), color = "skyblue") +
labs(title = "Communication Technology Fluency") +
theme(plot.title = element_text(hjust = 0.5, size = 20),
axis.title.x = element_blank(),
axis.text.x = element_text(size = 15),
axis.text.y = element_text(size = 12))
t.test(score ~ group, data = CMkn, alternative = "greater")##
## Welch Two Sample t-test
##
## data: score by group
## t = 0.6139, df = 61.416, p-value = 0.2708
## alternative hypothesis: true difference in means is greater than 0
## 95 percent confidence interval:
## -0.3148485 Inf
## sample estimates:
## mean in group CA mean in group Student
## 3.935484 3.752475mean(CA2$CMkn) - mean(Student2$CMkn)## [1] 0.1830086#Meeting
VCkn <- data.frame(score <- c(CA2$VCkn, Student2$VCkn),
group <- c(rep("CA", nrow(CA2)), rep("Student", nrow(Student2))))
ggplot(VCkn, aes(x = group, y = score, color = group)) +
geom_point(position = "jitter", show.legend = FALSE) +
geom_hline(yintercept = mean(CA2$VCkn), color = "pink") +
geom_hline(yintercept = mean(Student2$VCkn), color = "skyblue") +
labs(title = "Meeting Technology Fluency") +
theme(plot.title = element_text(hjust = 0.5, size = 20),
axis.title.x = element_blank(),
axis.text.x = element_text(size = 15),
axis.text.y = element_text(size = 12))
t.test(score ~ group, data = VCkn, alternative = "greater")##
## Welch Two Sample t-test
##
## data: score by group
## t = 1.4779, df = 67.571, p-value = 0.07205
## alternative hypothesis: true difference in means is greater than 0
## 95 percent confidence interval:
## -0.06696099 Inf
## sample estimates:
## mean in group CA mean in group Student
## 3.580645 3.059406mean(CA2$VCkn) - mean(Student2$VCkn)## [1] 0.5212392Usage
#Scheduling
SCus <- data.frame(score <- c(CA2$SCus, Student2$SCus),
group <- c(rep("CA", nrow(CA2)), rep("Student", nrow(Student2))))
ggplot(SCus, aes(x = group, y = score, color = group)) +
geom_point(position = "jitter", show.legend = FALSE) +
geom_hline(yintercept = mean(CA2$SCus), color = "#F8766D") +
geom_hline(yintercept = mean(Student2$SCus), color = "#00BFC4") +
labs(title = "Scheduling Technology Usage") +
theme(plot.title = element_text(hjust = 0.5, size = 20),
axis.title.x = element_blank(),
axis.text.x = element_text(size = 15),
axis.text.y = element_text(size = 12))
t.test(score ~ group, data = SCus, alternative = "less")##
## Welch Two Sample t-test
##
## data: score by group
## t = -0.5968, df = 55.848, p-value = 0.2765
## alternative hypothesis: true difference in means is less than 0
## 95 percent confidence interval:
## -Inf 0.3690453
## sample estimates:
## mean in group CA mean in group Student
## 1.290323 1.495050mean(CA2$SCus) - mean(Student2$SCus)## [1] -0.2047269#Communication
CMus <- data.frame(score <- c(CA2$CMus, Student2$CMus),
group <- c(rep("CA", nrow(CA2)), rep("Student", nrow(Student2))))
ggplot(CMus, aes(x = group, y = score, color = group)) +
geom_point(position = "jitter", show.legend = FALSE) +
geom_hline(yintercept = mean(CA2$CMus), color = "#F8766D") +
geom_hline(yintercept = mean(Student2$CMus), color = "#00BFC4") +
labs(title = "Communication Technology Usage") +
theme(plot.title = element_text(hjust = 0.5, size = 20),
axis.title.x = element_blank(),
axis.text.x = element_text(size = 15),
axis.text.y = element_text(size = 12))
t.test(score ~ group, data = CMus, alternative = "greater")##
## Welch Two Sample t-test
##
## data: score by group
## t = 0.49093, df = 45.326, p-value = 0.3129
## alternative hypothesis: true difference in means is greater than 0
## 95 percent confidence interval:
## -0.4274944 Inf
## sample estimates:
## mean in group CA mean in group Student
## 1.354839 1.178218mean(CA2$CMus) - mean(Student2$CMus)## [1] 0.1766209#Meeting
VCus <- data.frame(score <- c(CA2$VCus, Student2$VCus),
group <- c(rep("CA", nrow(CA2)), rep("Student", nrow(Student2))))
ggplot(VCus, aes(x = group, y = score, color = group)) +
geom_point(position = "jitter", show.legend = FALSE) +
geom_hline(yintercept = mean(CA2$VCus), color = "#F8766D") +
geom_hline(yintercept = mean(Student2$VCus), color = "#00BFC4") +
labs(title = "Meeting Technology Usage") +
theme(plot.title = element_text(hjust = 0.5, size = 20),
axis.title.x = element_blank(),
axis.text.x = element_text(size = 15),
axis.text.y = element_text(size = 12))
t.test(score ~ group, data = VCus, alternative = "less")##
## Welch Two Sample t-test
##
## data: score by group
## t = -0.6104, df = 59.112, p-value = 0.272
## alternative hypothesis: true difference in means is less than 0
## 95 percent confidence interval:
## -Inf 0.3518512
## sample estimates:
## mean in group CA mean in group Student
## 1.193548 1.396040mean(CA2$VCus) - mean(Student2$VCus)## [1] -0.2024912Hall/Community
Fluency
#Scheduling
SCkn <- data.frame(score = Student2$SCkn,
group = as.factor(Student2$Hall))
ggplot(SCkn, aes(x = group, y = score, color = group)) +
geom_point(position = "jitter", show.legend = FALSE) +
geom_hline(yintercept = mean(filter(Student2, Hall == "Shoemaker")$SCkn), color = "#F8766D") +
geom_hline(yintercept = mean(filter(Student2, Hall == "Mitchell")$SCkn), color = "#B79F00") +
geom_hline(yintercept = mean(filter(Student2, Hall == "Sherburne")$SCkn), color = "#00BA38") +
geom_hline(yintercept = mean(filter(Student2, Hall == "Stearns")$SCkn), color = "#00BFC4") +
geom_hline(yintercept = mean(filter(Student2, Hall == "Case-Hill")$SCkn), color = "#619CFF") +
geom_hline(yintercept = mean(filter(Student2, Hall == "Stateview")$SCkn), color = "#F564E3") +
labs(title = "Scheduling Technology Fluency") +
theme(plot.title = element_text(hjust = 0.5, size = 20),
axis.title.x = element_blank(),
axis.text.x = element_text(size = 15),
axis.text.y = element_text(size = 12))
meanSCkn <- data.frame(Mean = c(mean(filter(Student2, Hall == "Shoemaker")$SCkn),
mean(filter(Student2, Hall == "Mitchell")$SCkn),
mean(filter(Student2, Hall == "Sherburne")$SCkn),
mean(filter(Student2, Hall == "Stearns")$SCkn),
mean(filter(Student2, Hall == "Case-Hill")$SCkn),
mean(filter(Student2, Hall == "Stateview")$SCkn)),
Hall = c("Shoemaker", "Mitchell", "Sherburne", "Stearns", "Case-Hill", "Stateview")) %>%
arrange(Mean)
meanSCkn## Mean Hall
## 1 3.166667 Mitchell
## 2 3.652174 Case-Hill
## 3 3.666667 Shoemaker
## 4 3.666667 Sherburne
## 5 3.760000 Stearns
## 6 4.200000 Stateviewkruskal_test(score ~ group, data = SCkn, distribution = approximate())##
## Approximative Kruskal-Wallis Test
##
## data: score by
## group (Case-Hill, Mitchell, Sherburne, Shoemaker, Stateview, Stearns)
## chi-squared = 2.8201, p-value = 0.731#Communication
CMkn <- data.frame(score <- Student2$CMkn,
group <- as.factor(Student2$Hall))
ggplot(CMkn, aes(x = group, y = score, color = group)) +
geom_point(position = "jitter", show.legend = FALSE) +
geom_hline(yintercept = mean(filter(Student2, Hall == "Shoemaker")$CMkn), color = "#F8766D") +
geom_hline(yintercept = mean(filter(Student2, Hall == "Mitchell")$CMkn), color = "#B79F00") +
geom_hline(yintercept = mean(filter(Student2, Hall == "Sherburne")$CMkn), color = "#00BA38") +
geom_hline(yintercept = mean(filter(Student2, Hall == "Stearns")$CMkn), color = "#00BFC4") +
geom_hline(yintercept = mean(filter(Student2, Hall == "Case-Hill")$CMkn), color = "#619CFF") +
geom_hline(yintercept = mean(filter(Student2, Hall == "Stateview")$CMkn), color = "#F564E3") +
labs(title = "Communication Technology Fluency") +
theme(plot.title = element_text(hjust = 0.5, size = 20),
axis.title.x = element_blank(),
axis.text.x = element_text(size = 15),
axis.text.y = element_text(size = 12))
meanCMkn <- data.frame(Mean = c(mean(filter(Student2, Hall == "Shoemaker")$CMkn),
mean(filter(Student2, Hall == "Mitchell")$CMkn),
mean(filter(Student2, Hall == "Sherburne")$CMkn),
mean(filter(Student2, Hall == "Stearns")$CMkn),
mean(filter(Student2, Hall == "Case-Hill")$CMkn),
mean(filter(Student2, Hall == "Stateview")$CMkn)),
Hall = c("Shoemaker", "Mitchell", "Sherburne", "Stearns", "Case-Hill", "Stateview")) %>%
arrange(Mean)
meanCMkn## Mean Hall
## 1 3.416667 Mitchell
## 2 3.600000 Stateview
## 3 3.800000 Shoemaker
## 4 3.840000 Stearns
## 5 3.913043 Case-Hill
## 6 4.000000 Sherburnekruskal_test(score ~ group, data = CMkn, distribution = approximate())##
## Approximative Kruskal-Wallis Test
##
## data: score by
## group (Case-Hill, Mitchell, Sherburne, Shoemaker, Stateview, Stearns)
## chi-squared = 2.8235, p-value = 0.7354#Meeting
VCkn <- data.frame(score <- Student2$VCkn,
group <- as.factor(Student2$Hall))
ggplot(VCkn, aes(x = group, y = score, color = group)) +
geom_point(position = "jitter", show.legend = FALSE) +
geom_hline(yintercept = mean(filter(Student2, Hall == "Shoemaker")$VCkn), color = "#F8766D") +
geom_hline(yintercept = mean(filter(Student2, Hall == "Mitchell")$VCkn), color = "#B79F00") +
geom_hline(yintercept = mean(filter(Student2, Hall == "Sherburne")$VCkn), color = "#00BA38") +
geom_hline(yintercept = mean(filter(Student2, Hall == "Stearns")$VCkn), color = "#00BFC4") +
geom_hline(yintercept = mean(filter(Student2, Hall == "Case-Hill")$VCkn), color = "#619CFF") +
geom_hline(yintercept = mean(filter(Student2, Hall == "Stateview")$VCkn), color = "#F564E3") +
labs(title = "Meeting Technology Fluency") +
theme(plot.title = element_text(hjust = 0.5, size = 20),
axis.title.x = element_blank(),
axis.text.x = element_text(size = 15),
axis.text.y = element_text(size = 12))
meanVCkn <- data.frame(Mean = c(mean(filter(Student2, Hall == "Shoemaker")$VCkn),
mean(filter(Student2, Hall == "Mitchell")$VCkn),
mean(filter(Student2, Hall == "Sherburne")$VCkn),
mean(filter(Student2, Hall == "Stearns")$VCkn),
mean(filter(Student2, Hall == "Case-Hill")$VCkn),
mean(filter(Student2, Hall == "Stateview")$VCkn)),
Hall = c("Shoemaker", "Mitchell", "Sherburne", "Stearns", "Case-Hill", "Stateview")) %>%
arrange(Mean)
meanVCkn## Mean Hall
## 1 2.458333 Mitchell
## 2 2.956522 Case-Hill
## 3 3.200000 Stearns
## 4 3.266667 Shoemaker
## 5 3.600000 Stateview
## 6 3.888889 Sherburnekruskal_test(score ~ group, data = VCkn, distribution = approximate())##
## Approximative Kruskal-Wallis Test
##
## data: score by
## group (Case-Hill, Mitchell, Sherburne, Shoemaker, Stateview, Stearns)
## chi-squared = 4.3467, p-value = 0.5155Usage
#Scheduling
SCus <- data.frame(score = Student2$SCus,
group = as.factor(Student2$Hall))
ggplot(SCus, aes(x = group, y = score, color = group)) +
geom_point(position = "jitter", show.legend = FALSE) +
geom_hline(yintercept = mean(filter(Student2, Hall == "Shoemaker")$SCus), color = "#F8766D") +
geom_hline(yintercept = mean(filter(Student2, Hall == "Mitchell")$SCus), color = "#B79F00") +
geom_hline(yintercept = mean(filter(Student2, Hall == "Sherburne")$SCus), color = "#00BA38") +
geom_hline(yintercept = mean(filter(Student2, Hall == "Stearns")$SCus), color = "#00BFC4") +
geom_hline(yintercept = mean(filter(Student2, Hall == "Case-Hill")$SCus), color = "#619CFF") +
geom_hline(yintercept = mean(filter(Student2, Hall == "Stateview")$SCus), color = "#F564E3") +
labs(title = "Scheduling Technology Usage") +
theme(plot.title = element_text(hjust = 0.5, size = 20),
axis.title.x = element_blank(),
axis.text.x = element_text(size = 15),
axis.text.y = element_text(size = 12))
meanSCus <- data.frame(Mean = c(mean(filter(Student2, Hall == "Shoemaker")$SCus),
mean(filter(Student2, Hall == "Mitchell")$SCus),
mean(filter(Student2, Hall == "Sherburne")$SCus),
mean(filter(Student2, Hall == "Stearns")$SCus),
mean(filter(Student2, Hall == "Case-Hill")$SCus),
mean(filter(Student2, Hall == "Stateview")$SCus)),
Hall = c("Shoemaker", "Mitchell", "Sherburne", "Stearns", "Case-Hill", "Stateview")) %>%
arrange(Mean)
meanSCus## Mean Hall
## 1 1.043478 Case-Hill
## 2 1.066667 Shoemaker
## 3 1.222222 Sherburne
## 4 1.500000 Mitchell
## 5 1.600000 Stateview
## 6 2.240000 Stearnskruskal_test(score ~ group, data = SCus, distribution = approximate())##
## Approximative Kruskal-Wallis Test
##
## data: score by
## group (Case-Hill, Mitchell, Sherburne, Shoemaker, Stateview, Stearns)
## chi-squared = 5.9111, p-value = 0.3139#Communication
CMus <- data.frame(score <- Student2$CMus,
group <- as.factor(Student2$Hall))
ggplot(CMus, aes(x = group, y = score, color = group)) +
geom_point(position = "jitter", show.legend = FALSE) +
geom_hline(yintercept = mean(filter(Student2, Hall == "Shoemaker")$CMus), color = "#F8766D") +
geom_hline(yintercept = mean(filter(Student2, Hall == "Mitchell")$CMus), color = "#B79F00") +
geom_hline(yintercept = mean(filter(Student2, Hall == "Sherburne")$CMus), color = "#00BA38") +
geom_hline(yintercept = mean(filter(Student2, Hall == "Stearns")$CMus), color = "#00BFC4") +
geom_hline(yintercept = mean(filter(Student2, Hall == "Case-Hill")$CMus), color = "#619CFF") +
geom_hline(yintercept = mean(filter(Student2, Hall == "Stateview")$CMus), color = "#F564E3") +
labs(title = "Communication Technology Usage") +
theme(plot.title = element_text(hjust = 0.5, size = 20),
axis.title.x = element_blank(),
axis.text.x = element_text(size = 15),
axis.text.y = element_text(size = 12))
meanCMus <- data.frame(Mean = c(mean(filter(Student2, Hall == "Shoemaker")$CMus),
mean(filter(Student2, Hall == "Mitchell")$CMus),
mean(filter(Student2, Hall == "Sherburne")$CMus),
mean(filter(Student2, Hall == "Stearns")$CMus),
mean(filter(Student2, Hall == "Case-Hill")$CMus),
mean(filter(Student2, Hall == "Stateview")$CMus)),
Hall = c("Shoemaker", "Mitchell", "Sherburne", "Stearns", "Case-Hill", "Stateview")) %>%
arrange(Mean)
meanCMus## Mean Hall
## 1 0.8260870 Case-Hill
## 2 0.9333333 Shoemaker
## 3 1.1111111 Sherburne
## 4 1.3750000 Mitchell
## 5 1.4000000 Stateview
## 6 1.4400000 Stearnskruskal_test(score ~ group, data = CMus, distribution = approximate())##
## Approximative Kruskal-Wallis Test
##
## data: score by
## group (Case-Hill, Mitchell, Sherburne, Shoemaker, Stateview, Stearns)
## chi-squared = 1.8292, p-value = 0.8857#Meeting
VCus <- data.frame(score <- Student2$VCus,
group <- as.factor(Student2$Hall))
ggplot(VCus, aes(x = group, y = score, color = group)) +
geom_point(position = "jitter", show.legend = FALSE) +
geom_hline(yintercept = mean(filter(Student2, Hall == "Shoemaker")$VCus), color = "#F8766D") +
geom_hline(yintercept = mean(filter(Student2, Hall == "Mitchell")$VCus), color = "#B79F00") +
geom_hline(yintercept = mean(filter(Student2, Hall == "Sherburne")$VCus), color = "#00BA38") +
geom_hline(yintercept = mean(filter(Student2, Hall == "Stearns")$VCus), color = "#00BFC4") +
geom_hline(yintercept = mean(filter(Student2, Hall == "Case-Hill")$VCus), color = "#619CFF") +
geom_hline(yintercept = mean(filter(Student2, Hall == "Stateview")$VCus), color = "#F564E3") +
labs(title = "Meeting Technology Usage") +
theme(plot.title = element_text(hjust = 0.5, size = 20),
axis.title.x = element_blank(),
axis.text.x = element_text(size = 15),
axis.text.y = element_text(size = 12))
meanVCus <- data.frame(Mean = c(mean(filter(Student2, Hall == "Shoemaker")$VCus),
mean(filter(Student2, Hall == "Mitchell")$VCus),
mean(filter(Student2, Hall == "Sherburne")$VCus),
mean(filter(Student2, Hall == "Stearns")$VCus),
mean(filter(Student2, Hall == "Case-Hill")$VCus),
mean(filter(Student2, Hall == "Stateview")$VCus)),
Hall = c("Shoemaker", "Mitchell", "Sherburne", "Stearns", "Case-Hill", "Stateview")) %>%
arrange(Mean)
meanVCus## Mean Hall
## 1 1.000000 Shoemaker
## 2 1.208333 Mitchell
## 3 1.304348 Case-Hill
## 4 1.333333 Sherburne
## 5 1.720000 Stearns
## 6 2.400000 Stateviewkruskal_test(score ~ group, data = VCus, distribution = approximate())##
## Approximative Kruskal-Wallis Test
##
## data: score by
## group (Case-Hill, Mitchell, Sherburne, Shoemaker, Stateview, Stearns)
## chi-squared = 2.5871, p-value = 0.7787Gender
table(Student2$Gender)##
## Female Male Unknown
## 70 30 1#Since there is only one unknown, let's only focus on Male and Female
#Remove Unknown
Student3 <- filter(Student2, Gender != "Unknown")Fluency
#Scheduling
SCkn <- data.frame(score = Student3$SCkn,
gender = as.factor(Student3$Gender))
ggplot(SCkn, aes(x = gender, y = score, color = gender)) +
geom_point(position = "jitter", show.legend = FALSE) +
geom_hline(yintercept = mean(filter(Student2, Gender == "Female")$SCkn), color = "#F8766D") +
geom_hline(yintercept = mean(filter(Student2, Gender == "Male")$SCkn), color = "#00BFC4") +
labs(title = "Scheduling Technology Fluency") +
theme(plot.title = element_text(hjust = 0.5, size = 20),
axis.title.x = element_blank(),
axis.text.x = element_text(size = 15),
axis.text.y = element_text(size = 12))
t.test(score ~ gender, data = SCkn)##
## Welch Two Sample t-test
##
## data: score by gender
## t = -0.24541, df = 57.782, p-value = 0.807
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.8721102 0.6816340
## sample estimates:
## mean in group Female mean in group Male
## 3.571429 3.666667mean(filter(Student2, Gender == "Female")$SCkn) - mean(filter(Student2, Gender == "Male")$SCkn)## [1] -0.0952381#Communication
CMkn <- data.frame(score = Student3$CMkn,
gender = as.factor(Student3$Gender))
ggplot(CMkn, aes(x = gender, y = score, color = gender)) +
geom_point(position = "jitter", show.legend = FALSE) +
geom_hline(yintercept = mean(filter(Student2, Gender == "Female")$CMkn), color = "#F8766D") +
geom_hline(yintercept = mean(filter(Student2, Gender == "Male")$CMkn), color = "#00BFC4") +
labs(title = "Communication Technology Fluency") +
theme(plot.title = element_text(hjust = 0.5, size = 20),
axis.title.x = element_blank(),
axis.text.x = element_text(size = 15),
axis.text.y = element_text(size = 12))
t.test(score ~ gender, data = CMkn)##
## Welch Two Sample t-test
##
## data: score by gender
## t = 0.99445, df = 42.998, p-value = 0.3256
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.4307584 1.2688536
## sample estimates:
## mean in group Female mean in group Male
## 3.885714 3.466667mean(filter(Student2, Gender == "Female")$CMkn) - mean(filter(Student2, Gender == "Male")$CMkn)## [1] 0.4190476#Meeting
VCkn <- data.frame(score = Student3$VCkn,
gender = as.factor(Student3$Gender))
ggplot(VCkn, aes(x = gender, y = score, color = gender)) +
geom_point(position = "jitter", show.legend = FALSE) +
geom_hline(yintercept = mean(filter(Student2, Gender == "Female")$VCkn), color = "#F8766D") +
geom_hline(yintercept = mean(filter(Student2, Gender == "Male")$VCkn), color = "#00BFC4") +
labs(title = "Meeting Technology Fluency") +
theme(plot.title = element_text(hjust = 0.5, size = 20),
axis.title.x = element_blank(),
axis.text.x = element_text(size = 15),
axis.text.y = element_text(size = 12))
t.test(score ~ gender, data = VCkn)##
## Welch Two Sample t-test
##
## data: score by gender
## t = 0.47248, df = 53.004, p-value = 0.6385
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.7262934 1.1739125
## sample estimates:
## mean in group Female mean in group Male
## 3.157143 2.933333mean(filter(Student2, Gender == "Female")$VCkn) - mean(filter(Student2, Gender == "Male")$VCkn)## [1] 0.2238095Usage
#Scheduling
SCus <- data.frame(score = Student3$SCus,
gender = as.factor(Student3$Gender))
ggplot(SCus, aes(x = gender, y = score, color = gender)) +
geom_point(position = "jitter", show.legend = FALSE) +
geom_hline(yintercept = mean(filter(Student2, Gender == "Female")$SCus), color = "#F8766D") +
geom_hline(yintercept = mean(filter(Student2, Gender == "Male")$SCus), color = "#00BFC4") +
labs(title = "Scheduling Technology Usage") +
theme(plot.title = element_text(hjust = 0.5, size = 20),
axis.title.x = element_blank(),
axis.text.x = element_text(size = 15),
axis.text.y = element_text(size = 12))
t.test(score ~ gender, data = SCus)##
## Welch Two Sample t-test
##
## data: score by gender
## t = 0.047721, df = 56.298, p-value = 0.9621
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.7804377 0.8185329
## sample estimates:
## mean in group Female mean in group Male
## 1.485714 1.466667mean(filter(Student2, Gender == "Female")$SCus) - mean(filter(Student2, Gender == "Male")$SCus)## [1] 0.01904762#Communication
CMus <- data.frame(score = Student3$CMus,
gender = as.factor(Student3$Gender))
ggplot(CMus, aes(x = gender, y = score, color = gender)) +
geom_point(position = "jitter", show.legend = FALSE) +
geom_hline(yintercept = mean(filter(Student2, Gender == "Female")$CMus), color = "#F8766D") +
geom_hline(yintercept = mean(filter(Student2, Gender == "Male")$CMus), color = "#00BFC4") +
labs(title = "Communication Technology Usage") +
theme(plot.title = element_text(hjust = 0.5, size = 20),
axis.title.x = element_blank(),
axis.text.x = element_text(size = 15),
axis.text.y = element_text(size = 12))
t.test(score ~ gender, data = CMus)##
## Welch Two Sample t-test
##
## data: score by gender
## t = 0.71506, df = 51.592, p-value = 0.4778
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.4646036 0.9788893
## sample estimates:
## mean in group Female mean in group Male
## 1.257143 1.000000mean(filter(Student2, Gender == "Female")$CMus) - mean(filter(Student2, Gender == "Male")$CMus)## [1] 0.2571429#Meeting
VCus <- data.frame(score = Student3$VCus,
gender = as.factor(Student3$Gender))
ggplot(VCus, aes(x = gender, y = score, color = gender)) +
geom_point(position = "jitter", show.legend = FALSE) +
geom_hline(yintercept = mean(filter(Student2, Gender == "Female")$VCus), color = "#F8766D") +
geom_hline(yintercept = mean(filter(Student2, Gender == "Male")$VCus), color = "#00BFC4") +
labs(title = "Meeting Technology Usage") +
theme(plot.title = element_text(hjust = 0.5, size = 20),
axis.title.x = element_blank(),
axis.text.x = element_text(size = 15),
axis.text.y = element_text(size = 12))
t.test(score ~ gender, data = VCus)##
## Welch Two Sample t-test
##
## data: score by gender
## t = -0.079172, df = 50.889, p-value = 0.9372
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.8786234 0.8119567
## sample estimates:
## mean in group Female mean in group Male
## 1.400000 1.433333mean(filter(Student2, Gender == "Female")$VCus) - mean(filter(Student2, Gender == "Male")$VCus)## [1] -0.03333333Age
Fluency
mean(Student2$Age)## [1] 20.0495#Scheduling
cor.test(Student2$SCkn, Student2$Age)##
## Pearson's product-moment correlation
##
## data: Student2$SCkn and Student2$Age
## t = 0.25336, df = 99, p-value = 0.8005
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1708335 0.2198014
## sample estimates:
## cor
## 0.02545569ggplot(Student2, aes(x = Age, y = SCkn, color = Age)) +
geom_point(position = "jitter", show.legend = FALSE) +
labs(title = "Scheduling Technology Fluency") +
theme(plot.title = element_text(hjust = 0.5, size = 20),
axis.text.x = element_text(size = 15),
axis.text.y = element_text(size = 12))
#Communication
cor.test(Student2$CMkn, Student2$Age)##
## Pearson's product-moment correlation
##
## data: Student2$CMkn and Student2$Age
## t = 0.5032, df = 99, p-value = 0.6159
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1463753 0.2435440
## sample estimates:
## cor
## 0.05050889ggplot(Student2, aes(x = Age, y = CMkn, color = Age)) +
geom_point(position = "jitter", show.legend = FALSE) +
labs(title = "Communication Technology Fluency") +
theme(plot.title = element_text(hjust = 0.5, size = 20),
axis.text.x = element_text(size = 15),
axis.text.y = element_text(size = 12))
#Meeting
cor.test(Student2$VCkn, Student2$Age)##
## Pearson's product-moment correlation
##
## data: Student2$VCkn and Student2$Age
## t = 0.49295, df = 99, p-value = 0.6231
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1473824 0.2425755
## sample estimates:
## cor
## 0.04948217ggplot(Student2, aes(x = Age, y = VCkn, color = Age)) +
geom_point(position = "jitter", show.legend = FALSE) +
labs(title = "Meeting Technology Fluency") +
theme(plot.title = element_text(hjust = 0.5, size = 20),
axis.text.x = element_text(size = 15),
axis.text.y = element_text(size = 12))
Usage
#Scheduling
cor.test(Student2$SCus, Student2$Age)##
## Pearson's product-moment correlation
##
## data: Student2$SCus and Student2$Age
## t = -0.22276, df = 99, p-value = 0.8242
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2168730 0.1738171
## sample estimates:
## cor
## -0.02238248ggplot(Student2, aes(x = Age, y = SCus, color = Age)) +
geom_point(position = "jitter", show.legend = FALSE) +
labs(title = "Scheduling Technology Usage") +
theme(plot.title = element_text(hjust = 0.5, size = 20),
axis.text.x = element_text(size = 15),
axis.text.y = element_text(size = 12))
#Communication
cor.test(Student2$CMus, Student2$Age)##
## Pearson's product-moment correlation
##
## data: Student2$CMus and Student2$Age
## t = -0.84082, df = 99, p-value = 0.4025
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2751171 0.1130950
## sample estimates:
## cor
## -0.08420549ggplot(Student2, aes(x = Age, y = CMus, color = Age)) +
geom_point(position = "jitter", show.legend = FALSE) +
labs(title = "Communication Technology Usage") +
theme(plot.title = element_text(hjust = 0.5, size = 20),
axis.text.x = element_text(size = 15),
axis.text.y = element_text(size = 12))
#Meeting
cor.test(Student2$VCus, Student2$Age)##
## Pearson's product-moment correlation
##
## data: Student2$VCus and Student2$Age
## t = 0.87328, df = 99, p-value = 0.3846
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1098850 0.2781188
## sample estimates:
## cor
## 0.08743197ggplot(Student2, aes(x = Age, y = VCus, color = Age)) +
geom_point(position = "jitter", show.legend = FALSE) +
labs(title = "Meeting Technology Usage") +
theme(plot.title = element_text(hjust = 0.5, size = 20),
axis.text.x = element_text(size = 15),
axis.text.y = element_text(size = 12))
Class (Year in College)
table(Student2$Class)##
## FR SO JR SR
## 56 19 16 10Fluency
#Scheduling
SCkn <- data.frame(score = Student2$SCkn,
group = Student2$Class)
ggplot(SCkn, aes(x = group, y = score, color = group)) +
geom_point(position = "jitter", show.legend = FALSE) +
geom_hline(yintercept = mean(filter(Student2, Class == "FR")$SCkn), color = "#F8766D") +
geom_hline(yintercept = mean(filter(Student2, Class == "SO")$SCkn), color = "#00BFC4") +
geom_hline(yintercept = mean(filter(Student2, Class == "JR")$SCkn), color = "#7CAE00") +
geom_hline(yintercept = mean(filter(Student2, Class == "SR")$SCkn), color = "#C77CFF") +
labs(title = "Scheduling Technology Fluency") +
theme(plot.title = element_text(hjust = 0.5, size = 20),
axis.title.x = element_blank(),
axis.text.x = element_text(size = 15),
axis.text.y = element_text(size = 12))
meanSCkn <- data.frame(Mean = c(mean(filter(Student2, Class == "FR")$SCkn),
mean(filter(Student2, Class == "SO")$SCkn),
mean(filter(Student2, Class == "FR")$SCkn),
mean(filter(Student2, Class == "SR")$SCkn)),
Class = c("FR", "SO", "JR", "SR")) %>%
arrange(Mean)
meanSCkn## Mean Class
## 1 2.900000 SR
## 2 3.473684 SO
## 3 3.732143 FR
## 4 3.732143 JRkruskal_test(score ~ group, data = SCkn, distribution = approximate())##
## Approximative Kruskal-Wallis Test
##
## data: score by group (FR, SO, JR, SR)
## chi-squared = 2.2671, p-value = 0.5281#Communication
CMkn <- data.frame(score = Student2$CMkn,
group = Student2$Class)
ggplot(CMkn, aes(x = group, y = score, color = group)) +
geom_point(position = "jitter", show.legend = FALSE) +
geom_hline(yintercept = mean(filter(Student2, Class == "FR")$CMkn), color = "#F8766D") +
geom_hline(yintercept = mean(filter(Student2, Class == "SO")$CMkn), color = "#00BFC4") +
geom_hline(yintercept = mean(filter(Student2, Class == "JR")$CMkn), color = "#7CAE00") +
geom_hline(yintercept = mean(filter(Student2, Class == "SR")$CMkn), color = "#C77CFF") +
labs(title = "Communication Technology Fluency") +
theme(plot.title = element_text(hjust = 0.5, size = 20),
axis.title.x = element_blank(),
axis.text.x = element_text(size = 15),
axis.text.y = element_text(size = 12))
meanCMkn <- data.frame(Mean = c(mean(filter(Student2, Class == "FR")$CMkn),
mean(filter(Student2, Class == "SO")$CMkn),
mean(filter(Student2, Class == "FR")$CMkn),
mean(filter(Student2, Class == "SR")$CMkn)),
Class = c("FR", "SO", "JR", "SR")) %>%
arrange(Mean)
meanCMkn## Mean Class
## 1 3.263158 SO
## 2 3.892857 FR
## 3 3.892857 JR
## 4 4.000000 SRkruskal_test(score ~ group, data = CMkn, distribution = approximate())##
## Approximative Kruskal-Wallis Test
##
## data: score by group (FR, SO, JR, SR)
## chi-squared = 2.5993, p-value = 0.4625#Meeting
VCkn <- data.frame(score = Student2$VCkn,
group = Student2$Class)
ggplot(VCkn, aes(x = group, y = score, color = group)) +
geom_point(position = "jitter", show.legend = FALSE) +
geom_hline(yintercept = mean(filter(Student2, Class == "FR")$VCkn), color = "#F8766D") +
geom_hline(yintercept = mean(filter(Student2, Class == "SO")$VCkn), color = "#00BFC4") +
geom_hline(yintercept = mean(filter(Student2, Class == "JR")$VCkn), color = "#7CAE00") +
geom_hline(yintercept = mean(filter(Student2, Class == "SR")$VCkn), color = "#C77CFF") +
labs(title = "Meeting Technology Fluency") +
theme(plot.title = element_text(hjust = 0.5, size = 20),
axis.title.x = element_blank(),
axis.text.x = element_text(size = 15),
axis.text.y = element_text(size = 12))
meanVCkn <- data.frame(Mean = c(mean(filter(Student2, Class == "FR")$VCkn),
mean(filter(Student2, Class == "SO")$VCkn),
mean(filter(Student2, Class == "FR")$VCkn),
mean(filter(Student2, Class == "SR")$VCkn)),
Class = c("FR", "SO", "JR", "SR")) %>%
arrange(Mean)
meanVCkn## Mean Class
## 1 2.400000 SR
## 2 2.631579 SO
## 3 3.267857 FR
## 4 3.267857 JRkruskal_test(score ~ group, data = VCkn, distribution = approximate())##
## Approximative Kruskal-Wallis Test
##
## data: score by group (FR, SO, JR, SR)
## chi-squared = 2.1118, p-value = 0.5622Usage
#Scheduling
SCus <- data.frame(score = Student2$SCus,
group = Student2$Class)
ggplot(SCus, aes(x = group, y = score, color = group)) +
geom_point(position = "jitter", show.legend = FALSE) +
geom_hline(yintercept = mean(filter(Student2, Class == "FR")$SCus), color = "#F8766D") +
geom_hline(yintercept = mean(filter(Student2, Class == "SO")$SCus), color = "#00BFC4") +
geom_hline(yintercept = mean(filter(Student2, Class == "JR")$SCus), color = "#7CAE00") +
geom_hline(yintercept = mean(filter(Student2, Class == "SR")$SCus), color = "#C77CFF") +
labs(title = "Scheduling Technology Usage") +
theme(plot.title = element_text(hjust = 0.5, size = 20),
axis.title.x = element_blank(),
axis.text.x = element_text(size = 15),
axis.text.y = element_text(size = 12))
meanSCus <- data.frame(Mean = c(mean(filter(Student2, Class == "FR")$SCus),
mean(filter(Student2, Class == "SO")$SCus),
mean(filter(Student2, Class == "FR")$SCus),
mean(filter(Student2, Class == "SR")$SCus)),
Class = c("FR", "SO", "JR", "SR")) %>%
arrange(Mean)
meanSCus## Mean Class
## 1 0.9473684 SO
## 2 1.4000000 SR
## 3 1.5714286 FR
## 4 1.5714286 JRkruskal_test(score ~ group, data = SCus, distribution = approximate())##
## Approximative Kruskal-Wallis Test
##
## data: score by group (FR, SO, JR, SR)
## chi-squared = 2.0063, p-value = 0.5741#Communication
CMus <- data.frame(score = Student2$CMus,
group = Student2$Class)
ggplot(CMus, aes(x = group, y = score, color = group)) +
geom_point(position = "jitter", show.legend = FALSE) +
geom_hline(yintercept = mean(filter(Student2, Class == "FR")$CMus), color = "#F8766D") +
geom_hline(yintercept = mean(filter(Student2, Class == "SO")$CMus), color = "#00BFC4") +
geom_hline(yintercept = mean(filter(Student2, Class == "JR")$CMus), color = "#7CAE00") +
geom_hline(yintercept = mean(filter(Student2, Class == "SR")$CMus), color = "#C77CFF") +
labs(title = "Communication Technology Usage") +
theme(plot.title = element_text(hjust = 0.5, size = 20),
axis.title.x = element_blank(),
axis.text.x = element_text(size = 15),
axis.text.y = element_text(size = 12))
meanCMus <- data.frame(Mean = c(mean(filter(Student2, Class == "FR")$CMus),
mean(filter(Student2, Class == "SO")$CMus),
mean(filter(Student2, Class == "FR")$CMus),
mean(filter(Student2, Class == "SR")$CMus)),
Class = c("FR", "SO", "JR", "SR")) %>%
arrange(Mean)
meanCMus## Mean Class
## 1 0.7000000 SR
## 2 0.9473684 SO
## 3 1.2857143 FR
## 4 1.2857143 JRkruskal_test(score ~ group, data = CMus, distribution = approximate())##
## Approximative Kruskal-Wallis Test
##
## data: score by group (FR, SO, JR, SR)
## chi-squared = 1.8166, p-value = 0.6203#Meeting
VCus <- data.frame(score = Student2$VCus,
group = Student2$Class)
ggplot(VCus, aes(x = group, y = score, color = group)) +
geom_point(position = "jitter", show.legend = FALSE) +
geom_hline(yintercept = mean(filter(Student2, Class == "FR")$VCus), color = "#F8766D") +
geom_hline(yintercept = mean(filter(Student2, Class == "SO")$VCus), color = "#00BFC4") +
geom_hline(yintercept = mean(filter(Student2, Class == "JR")$VCus), color = "#7CAE00") +
geom_hline(yintercept = mean(filter(Student2, Class == "SR")$VCus), color = "#C77CFF") +
labs(title = "Meeting Technology Usage") +
theme(plot.title = element_text(hjust = 0.5, size = 20),
axis.title.x = element_blank(),
axis.text.x = element_text(size = 15),
axis.text.y = element_text(size = 12))
meanVCus <- data.frame(Mean = c(mean(filter(Student2, Class == "FR")$VCus),
mean(filter(Student2, Class == "SO")$VCus),
mean(filter(Student2, Class == "FR")$VCus),
mean(filter(Student2, Class == "SR")$VCus)),
Class = c("FR", "SO", "JR", "SR")) %>%
arrange(Mean)
meanVCus## Mean Class
## 1 1.000000 SR
## 2 1.315789 SO
## 3 1.517857 FR
## 4 1.517857 JRkruskal_test(score ~ group, data = VCus, distribution = approximate())##
## Approximative Kruskal-Wallis Test
##
## data: score by group (FR, SO, JR, SR)
## chi-squared = 0.38322, p-value = 0.9449College/School
table(Student2$`College/School`)##
## College of Liberal Arts College of Science and Engineering
## 24 34
## Herberger Business School School of Education
## 11 7
## School of Health and Human Services School of Public Affairs
## 14 7
## University College Unknown
## 1 3#Since there are only three unknown and one University College, let's remove them for simplicity.
Student3 <- filter(Student2, !(`College/School` %in% c("University College", "Unknown")))
#Recode school names into abbr.
for (i in 1:length(Student3$`College/School`)) {
if (Student3$`College/School`[i] == "College of Liberal Arts") {Student3$`College/School`[i] <- "CLA"}
else if (Student3$`College/School`[i] == "College of Science and Engineering") {Student3$`College/School`[i] <- "COSE"}
else if (Student3$`College/School`[i] == "Herberger Business School") {Student3$`College/School`[i] <- "HBS"}
else if (Student3$`College/School`[i] == "School of Education") {Student3$`College/School`[i] <- "SOE"}
else if (Student3$`College/School`[i] == "School of Health and Human Services") {Student3$`College/School`[i] <- "SHHS"}
else if (Student3$`College/School`[i] == "School of Public Affairs") {Student3$`College/School`[i] <- "SOPA"}
}Fluency
#Scheduling
SCkn <- data.frame(score = Student3$SCkn,
group = as.factor(Student3$`College/School`))
ggplot(SCkn, aes(x = group, y = score, color = group)) +
geom_point(position = "jitter", show.legend = FALSE) +
geom_hline(yintercept = mean(filter(Student3, `College/School` == "CLA")$SCkn), color = "#F8766D") +
geom_hline(yintercept = mean(filter(Student3, `College/School` == "COSE")$SCkn), color = "#B79F00") +
geom_hline(yintercept = mean(filter(Student3, `College/School` == "HBS")$SCkn), color = "#00BA38") +
geom_hline(yintercept = mean(filter(Student3, `College/School` == "SOE")$SCkn), color = "#00BFC4") +
geom_hline(yintercept = mean(filter(Student3, `College/School` == "SHHS")$SCkn), color = "#619CFF") +
geom_hline(yintercept = mean(filter(Student3, `College/School` == "SOPA")$SCkn), color = "#F564E3") +
labs(title = "Scheduling Technology Fluency") +
theme(plot.title = element_text(hjust = 0.5, size = 20),
axis.title.x = element_blank(),
axis.text.x = element_text(size = 15),
axis.text.y = element_text(size = 12))
meanSCkn <- data.frame(Mean = c(mean(filter(Student3, `College/School` == "CLA")$SCkn),
mean(filter(Student3, `College/School` == "COSE")$SCkn),
mean(filter(Student3, `College/School` == "HBS")$SCkn),
mean(filter(Student3, `College/School` == "SOE")$SCkn),
mean(filter(Student3, `College/School` == "SHHS")$SCkn),
mean(filter(Student3, `College/School` == "SOPA")$SCkn)),
School = c("CLA", "COSE",
"HBS", "SOE",
"SHHS", "SOPA")) %>%
arrange(Mean)
meanSCkn## Mean School
## 1 3.142857 SHHS
## 2 3.352941 COSE
## 3 3.428571 SOPA
## 4 3.750000 CLA
## 5 4.428571 SOE
## 6 4.454545 HBSkruskal_test(score ~ group, data = SCkn, distribution = approximate())##
## Approximative Kruskal-Wallis Test
##
## data: score by group (CLA, COSE, HBS, SHHS, SOE, SOPA)
## chi-squared = 7.1637, p-value = 0.2005#Communication
CMkn <- data.frame(score = Student3$CMkn,
group = as.factor(Student3$`College/School`))
ggplot(CMkn, aes(x = group, y = score, color = group)) +
geom_point(position = "jitter", show.legend = FALSE) +
geom_hline(yintercept = mean(filter(Student3, `College/School` == "CLA")$CMkn), color = "#F8766D") +
geom_hline(yintercept = mean(filter(Student3, `College/School` == "COSE")$CMkn), color = "#B79F00") +
geom_hline(yintercept = mean(filter(Student3, `College/School` == "HBS")$CMkn), color = "#00BA38") +
geom_hline(yintercept = mean(filter(Student3, `College/School` == "SOE")$CMkn), color = "#00BFC4") +
geom_hline(yintercept = mean(filter(Student3, `College/School` == "SHHS")$CMkn), color = "#619CFF") +
geom_hline(yintercept = mean(filter(Student3, `College/School` == "SOPA")$CMkn), color = "#F564E3") +
labs(title = "Communication Technology Fluency") +
theme(plot.title = element_text(hjust = 0.5, size = 20),
axis.title.x = element_blank(),
axis.text.x = element_text(size = 15),
axis.text.y = element_text(size = 12))
meanCMkn <- data.frame(Mean = c(mean(filter(Student3, `College/School` == "CLA")$CMkn),
mean(filter(Student3, `College/School` == "COSE")$CMkn),
mean(filter(Student3, `College/School` == "HBS")$CMkn),
mean(filter(Student3, `College/School` == "SOE")$CMkn),
mean(filter(Student3, `College/School` == "SHHS")$CMkn),
mean(filter(Student3, `College/School` == "SOPA")$CMkn)),
School = c("CLA", "COSE",
"HBS", "SOE",
"SHHS", "SOPA")) %>%
arrange(Mean)
meanCMkn## Mean School
## 1 3.071429 SHHS
## 2 3.558824 COSE
## 3 3.571429 SOPA
## 4 3.958333 CLA
## 5 4.285714 SOE
## 6 4.545455 HBSkruskal_test(score ~ group, data = CMkn, distribution = approximate())##
## Approximative Kruskal-Wallis Test
##
## data: score by group (CLA, COSE, HBS, SHHS, SOE, SOPA)
## chi-squared = 5.6996, p-value = 0.3449#Meeting
VCkn <- data.frame(score = Student3$VCkn,
group = as.factor(Student3$`College/School`))
ggplot(VCkn, aes(x = group, y = score, color = group)) +
geom_point(position = "jitter", show.legend = FALSE) +
geom_hline(yintercept = mean(filter(Student3, `College/School` == "CLA")$VCkn), color = "#F8766D") +
geom_hline(yintercept = mean(filter(Student3, `College/School` == "COSE")$VCkn), color = "#B79F00") +
geom_hline(yintercept = mean(filter(Student3, `College/School` == "HBS")$VCkn), color = "#00BA38") +
geom_hline(yintercept = mean(filter(Student3, `College/School` == "SOE")$VCkn), color = "#00BFC4") +
geom_hline(yintercept = mean(filter(Student3, `College/School` == "SHHS")$VCkn), color = "#619CFF") +
geom_hline(yintercept = mean(filter(Student3, `College/School` == "SOPA")$VCkn), color = "#F564E3") +
labs(title = "Meeting Technology Fluency") +
theme(plot.title = element_text(hjust = 0.5, size = 20),
axis.title.x = element_blank(),
axis.text.x = element_text(size = 15),
axis.text.y = element_text(size = 12))
meanVCkn <- data.frame(Mean = c(mean(filter(Student3, `College/School` == "CLA")$VCkn),
mean(filter(Student3, `College/School` == "COSE")$VCkn),
mean(filter(Student3, `College/School` == "HBS")$VCkn),
mean(filter(Student3, `College/School` == "SOE")$VCkn),
mean(filter(Student3, `College/School` == "SHHS")$VCkn),
mean(filter(Student3, `College/School` == "SOPA")$VCkn)),
School = c("CLA", "COSE",
"HBS", "SOE",
"SHHS", "SOPA")) %>%
arrange(Mean)
meanVCkn## Mean School
## 1 2.071429 SHHS
## 2 2.714286 SOPA
## 3 3.000000 COSE
## 4 3.458333 CLA
## 5 3.727273 HBS
## 6 4.285714 SOEkruskal_test(score ~ group, data = VCkn, distribution = approximate())##
## Approximative Kruskal-Wallis Test
##
## data: score by group (CLA, COSE, HBS, SHHS, SOE, SOPA)
## chi-squared = 7.4559, p-value = 0.1854Usage
#Scheduling
SCus <- data.frame(score = Student3$SCus,
group = as.factor(Student3$`College/School`))
ggplot(SCus, aes(x = group, y = score, color = group)) +
geom_point(position = "jitter", show.legend = FALSE) +
geom_hline(yintercept = mean(filter(Student3, `College/School` == "CLA")$SCus), color = "#F8766D") +
geom_hline(yintercept = mean(filter(Student3, `College/School` == "COSE")$SCus), color = "#B79F00") +
geom_hline(yintercept = mean(filter(Student3, `College/School` == "HBS")$SCus), color = "#00BA38") +
geom_hline(yintercept = mean(filter(Student3, `College/School` == "SOE")$SCus), color = "#00BFC4") +
geom_hline(yintercept = mean(filter(Student3, `College/School` == "SHHS")$SCus), color = "#619CFF") +
geom_hline(yintercept = mean(filter(Student3, `College/School` == "SOPA")$SCus), color = "#F564E3") +
labs(title = "Scheduling Technology Usage") +
theme(plot.title = element_text(hjust = 0.5, size = 20),
axis.title.x = element_blank(),
axis.text.x = element_text(size = 15),
axis.text.y = element_text(size = 12))
meanSCus <- data.frame(Mean = c(mean(filter(Student3, `College/School` == "CLA")$SCus),
mean(filter(Student3, `College/School` == "COSE")$SCus),
mean(filter(Student3, `College/School` == "HBS")$SCus),
mean(filter(Student3, `College/School` == "SOE")$SCus),
mean(filter(Student3, `College/School` == "SHHS")$SCus),
mean(filter(Student3, `College/School` == "SOPA")$SCus)),
School = c("CLA", "COSE",
"HBS", "SOE",
"SHHS", "SOPA")) %>%
arrange(Mean)
meanSCus## Mean School
## 1 1.142857 SOPA
## 2 1.250000 CLA
## 3 1.500000 COSE
## 4 1.545455 HBS
## 5 1.571429 SHHS
## 6 1.857143 SOEkruskal_test(score ~ group, data = SCus, distribution = approximate())##
## Approximative Kruskal-Wallis Test
##
## data: score by group (CLA, COSE, HBS, SHHS, SOE, SOPA)
## chi-squared = 1.2693, p-value = 0.9441#Communication
CMus <- data.frame(score = Student3$CMus,
group = as.factor(Student3$`College/School`))
ggplot(CMus, aes(x = group, y = score, color = group)) +
geom_point(position = "jitter", show.legend = FALSE) +
geom_hline(yintercept = mean(filter(Student3, `College/School` == "CLA")$CMus), color = "#F8766D") +
geom_hline(yintercept = mean(filter(Student3, `College/School` == "COSE")$CMus), color = "#B79F00") +
geom_hline(yintercept = mean(filter(Student3, `College/School` == "HBS")$CMus), color = "#00BA38") +
geom_hline(yintercept = mean(filter(Student3, `College/School` == "SOE")$CMus), color = "#00BFC4") +
geom_hline(yintercept = mean(filter(Student3, `College/School` == "SHHS")$CMus), color = "#619CFF") +
geom_hline(yintercept = mean(filter(Student3, `College/School` == "SOPA")$CMus), color = "#F564E3") +
labs(title = "Communication Technology Usage") +
theme(plot.title = element_text(hjust = 0.5, size = 20),
axis.title.x = element_blank(),
axis.text.x = element_text(size = 15),
axis.text.y = element_text(size = 12))
meanCMus <- data.frame(Mean = c(mean(filter(Student3, `College/School` == "CLA")$CMus),
mean(filter(Student3, `College/School` == "COSE")$CMus),
mean(filter(Student3, `College/School` == "HBS")$CMus),
mean(filter(Student3, `College/School` == "SOE")$CMus),
mean(filter(Student3, `College/School` == "SHHS")$CMus),
mean(filter(Student3, `College/School` == "SOPA")$CMus)),
School = c("CLA", "COSE",
"HBS", "SOE",
"SHHS", "SOPA")) %>%
arrange(Mean)
meanCMus## Mean School
## 1 0.2857143 SOE
## 2 0.7272727 HBS
## 3 0.8571429 SOPA
## 4 1.2916667 CLA
## 5 1.3529412 COSE
## 6 1.5000000 SHHSkruskal_test(score ~ group, data = CMus, distribution = approximate())##
## Approximative Kruskal-Wallis Test
##
## data: score by group (CLA, COSE, HBS, SHHS, SOE, SOPA)
## chi-squared = 3.9449, p-value = 0.5635#Meeting
VCus <- data.frame(score = Student3$VCus,
group = as.factor(Student3$`College/School`))
ggplot(VCus, aes(x = group, y = score, color = group)) +
geom_point(position = "jitter", show.legend = FALSE) +
geom_hline(yintercept = mean(filter(Student3, `College/School` == "CLA")$VCus), color = "#F8766D") +
geom_hline(yintercept = mean(filter(Student3, `College/School` == "COSE")$VCus), color = "#B79F00") +
geom_hline(yintercept = mean(filter(Student3, `College/School` == "HBS")$VCus), color = "#00BA38") +
geom_hline(yintercept = mean(filter(Student3, `College/School` == "SOE")$VCus), color = "#00BFC4") +
geom_hline(yintercept = mean(filter(Student3, `College/School` == "SHHS")$VCus), color = "#619CFF") +
geom_hline(yintercept = mean(filter(Student3, `College/School` == "SOPA")$VCus), color = "#F564E3") +
labs(title = "Meeting Technology Usage") +
theme(plot.title = element_text(hjust = 0.5, size = 20),
axis.title.x = element_blank(),
axis.text.x = element_text(size = 15),
axis.text.y = element_text(size = 12))
meanVCus <- data.frame(Mean = c(mean(filter(Student3, `College/School` == "CLA")$VCus),
mean(filter(Student3, `College/School` == "COSE")$VCus),
mean(filter(Student3, `College/School` == "HBS")$VCus),
mean(filter(Student3, `College/School` == "SOE")$VCus),
mean(filter(Student3, `College/School` == "SHHS")$VCus),
mean(filter(Student3, `College/School` == "SOPA")$VCus)),
School = c("CLA", "COSE",
"HBS", "SOE",
"SHHS", "SOPA")) %>%
arrange(Mean)
meanVCus## Mean School
## 1 0.4545455 HBS
## 2 1.0000000 SOPA
## 3 1.3823529 COSE
## 4 1.4285714 SHHS
## 5 1.7500000 CLA
## 6 2.4285714 SOEkruskal_test(score ~ group, data = VCus, distribution = approximate())##
## Approximative Kruskal-Wallis Test
##
## data: score by group (CLA, COSE, HBS, SHHS, SOE, SOPA)
## chi-squared = 5.203, p-value = 0.4077Overall Scores
Student3 <- Student2 %>%
mutate(Fluency = SCkn + CMkn + VCkn,
Usage = SCus + CMus + VCus)Simple OLS
#Fluency
full <- lm(Fluency ~ Gender + Age + `College/School` + Class + Location, data = Student3)
summary(full)##
## Call:
## lm(formula = Fluency ~ Gender + Age + `College/School` + Class +
## Location, data = Student3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -11.337 -3.056 1.091 3.232 8.418
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 9.1269 3.8877 2.348
## GenderMale 1.1650 1.4495 0.804
## GenderUnknown -3.1615 5.5911 -0.565
## Age 0.1427 0.1917 0.744
## `College/School`College of Science and Engineering -1.0961 1.5133 -0.724
## `College/School`Herberger Business School 0.9449 1.9511 0.484
## `College/School`School of Education 1.1597 2.2747 0.510
## `College/School`School of Health and Human Services -3.2149 1.9186 -1.676
## `College/School`School of Public Affairs -0.3650 2.3965 -0.152
## `College/School`University College -7.6840 5.5845 -1.376
## `College/School`Unknown -4.3025 3.2610 -1.319
## ClassSO -2.0417 1.5285 -1.336
## ClassJR -0.3425 1.9383 -0.177
## ClassSR -3.8453 2.1906 -1.755
## LocationMitchell Hall -1.6094 1.7382 -0.926
## LocationSherburne Hall 0.7715 2.2542 0.342
## LocationStearns Hall 0.7381 1.9074 0.387
## LocationCase Hall -3.5354 2.8527 -1.239
## LocationHill Hall 1.0265 1.9071 0.538
## LocationStateview North 2.6087 3.6398 0.717
## LocationStateview South -4.0985 4.3724 -0.937
## Pr(>|t|)
## (Intercept) 0.0214 *
## GenderMale 0.4240
## GenderUnknown 0.5733
## Age 0.4589
## `College/School`College of Science and Engineering 0.4710
## `College/School`Herberger Business School 0.6295
## `College/School`School of Education 0.6116
## `College/School`School of Health and Human Services 0.0977 .
## `College/School`School of Public Affairs 0.8793
## `College/School`University College 0.1727
## `College/School`Unknown 0.1908
## ClassSO 0.1854
## ClassJR 0.8602
## ClassSR 0.0830 .
## LocationMitchell Hall 0.3573
## LocationSherburne Hall 0.7331
## LocationStearns Hall 0.6998
## LocationCase Hall 0.2189
## LocationHill Hall 0.5919
## LocationStateview North 0.4756
## LocationStateview South 0.3514
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.13 on 80 degrees of freedom
## Multiple R-squared: 0.1911, Adjusted R-squared: -0.01109
## F-statistic: 0.9452 on 20 and 80 DF, p-value: 0.5345GenderAge <- lm(Fluency ~ Gender + Age, data = Student3)
summary(GenderAge)##
## Call:
## lm(formula = Fluency ~ Gender + Age, data = Student3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.545 -4.545 2.534 4.455 5.125
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.05002 3.19121 2.836 0.00556 **
## GenderMale -0.59070 1.12651 -0.524 0.60122
## GenderUnknown -4.62328 5.18382 -0.892 0.37467
## Age 0.07866 0.15747 0.500 0.61852
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.147 on 97 degrees of freedom
## Multiple R-squared: 0.01249, Adjusted R-squared: -0.01805
## F-statistic: 0.4091 on 3 and 97 DF, p-value: 0.7468SchoolClass <- lm(Fluency ~ `College/School` + Class, data = Student3)
summary(SchoolClass)##
## Call:
## lm(formula = Fluency ~ `College/School` + Class, data = Student3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.284 -3.678 1.716 3.798 7.828
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 11.67806 1.17264 9.959
## `College/School`College of Science and Engineering -1.15342 1.36998 -0.842
## `College/School`Herberger Business School 1.44346 1.88822 0.764
## `College/School`School of Education 1.60539 2.19495 0.731
## `College/School`School of Health and Human Services -3.07868 1.73949 -1.770
## `College/School`School of Public Affairs -1.47641 2.18543 -0.676
## `College/School`University College -5.62347 5.30987 -1.059
## `College/School`Unknown -3.34473 3.15926 -1.059
## ClassSO -1.42739 1.38296 -1.032
## ClassJR -0.05459 1.49604 -0.036
## ClassSR -1.92959 1.79241 -1.077
## Pr(>|t|)
## (Intercept) 3.48e-16 ***
## `College/School`College of Science and Engineering 0.4021
## `College/School`Herberger Business School 0.4466
## `College/School`School of Education 0.4664
## `College/School`School of Health and Human Services 0.0801 .
## `College/School`School of Public Affairs 0.5010
## `College/School`University College 0.2924
## `College/School`Unknown 0.2926
## ClassSO 0.3048
## ClassJR 0.9710
## ClassSR 0.2846
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.081 on 90 degrees of freedom
## Multiple R-squared: 0.1071, Adjusted R-squared: 0.007919
## F-statistic: 1.08 on 10 and 90 DF, p-value: 0.3861LocationClass <- lm(Fluency ~ Location + Class, data = Student3)
summary(LocationClass)##
## Call:
## lm(formula = Fluency ~ Location + Class, data = Student3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.054 -4.020 1.935 3.605 6.271
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11.3954 1.3881 8.210 1.51e-12 ***
## LocationMitchell Hall -1.8231 1.6946 -1.076 0.285
## LocationSherburne Hall 0.4011 2.1857 0.183 0.855
## LocationStearns Hall 0.6591 1.8223 0.362 0.718
## LocationCase Hall -2.2045 2.3683 -0.931 0.354
## LocationHill Hall 0.6701 1.8550 0.361 0.719
## LocationStateview North 4.3311 3.3804 1.281 0.203
## LocationStateview South -3.0517 4.1739 -0.731 0.467
## ClassSO -2.1679 1.4296 -1.516 0.133
## ClassJR -0.8436 1.8296 -0.461 0.646
## ClassSR -2.3755 1.9311 -1.230 0.222
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.124 on 90 degrees of freedom
## Multiple R-squared: 0.09204, Adjusted R-squared: -0.008845
## F-statistic: 0.9123 on 10 and 90 DF, p-value: 0.5256