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I've finished the coding for means of interest, knowledge, and employablity of Theology and Religious Studies by the other subjects. I've installed the required packages for making a graph of the Likert responses (categorical to go along with the already created means ones), but have not been able to figure how it works yet. I'll have another look at it later and work with it more.
771 lines
30 KiB
Plaintext
771 lines
30 KiB
Plaintext
---
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title: "RMarkdown Admissions_Survey2021"
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output: html_document
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---
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```{r setup, include=FALSE}
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knitr::opts_chunk$set(echo = TRUE)
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library(ggplot2)
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```
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# Upload Data
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```{r Upload Data}
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TSR_data <- read.csv("./data/TSR data complete.csv")
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subject_data <- read.csv("./data/Subject data.csv")
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```
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# Basic summary visualisations (RH):
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- Q2 (respondent age)
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```{r respondent age}
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TSR_data$Age <- factor(TSR_data$Age, levels = c(1, 2, 3, 4, 5, 6, 7, 8), labels = c("15 or under", "16", "17", "18", "19", "20", "21 or over", "Prefer not to say"))
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age_pie <- pie(table(TSR_data$Age))
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```
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- Q3 (year of study)
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```{r year of study}
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TSR_data$MOST.RECENT.year.of.study <- factor(TSR_data$MOST.RECENT.year.of.study, levels = c(1, 2, 3, 4, 5, 6, 7, 8, 9), labels = c("Year 11/S4/Year 12(NI)", "Year 12/S5/Year 13(NI)", "Year 13/S6/Year 14(NI)", "I am currently on a gap year", "I am currently on an undergraduate/HE college course", "I am in full-time employment", "I am unemployed", "Other", "Prefer not to say"))
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Year_study_pie <- pie(table(TSR_data$MOST.RECENT.year.of.study))
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```
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- Q16 (gender identity)
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```{r gender identity}
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TSR_data$Gender <- factor(TSR_data$Gender, levels = c(1, 2, 3, 4), labels = c("Male", "Female", "I identify my gender in another way", "Prefer not to say"))
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gender_pie <- pie(table(TSR_data$Gender))
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```
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- Q17 (ethnic self-id)
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```{r ethnic self-id}
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TSR_data$Ethnicity <- factor(TSR_data$Ethnicity, levels = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17 ,18, 19), labels = c("Arab", "Asian/Asian British - Indian", "Asian/Asian British - Pakistani", "Asian/Asian British - Bangladeshi", "Asian/Asian British - Chinese", "Asian/Asian British - Any other Asian background", "Black/Black British - African", "Black/Black British - Caribbean", "Black/Black British - Any other Black background", "Mixed/Multiple Ethnic Groups - White and Black Caribbean", "Mixed/Multiple Ethnic Groups - White and Black African", "Mixed/Multiple Ethnic Groups - White and Black Asian", "Mixed/Multiple Ethnic Groups - Any other Mixed/Multiple Ethnic background", "White - English/Welsh/Scottish/Northern Irish/British", "White - Irish", "White - Gypsy or Irish Traveller", "White - Any other White background", "Other Ethnic group, please describe", "Prefer not to say"))
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Ethnicity_bar <- ggplot(TSR_data, aes(Ethnicity)) + geom_bar() + coord_flip()
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Ethnicity_bar
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```
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- Q18 (religion)
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```{r religion}
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TSR_data$Religious.Affliation <- factor(TSR_data$Religious.Affliation, levels = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19), labels = c("Agnostic", "Atheist", "Baha'i", "Buddhist", "Christian", "Confucian", "Jain", "Jewish", "Hindu", "Indigenous Traditional Religious", "Muslim", "Pagan", "Shinto", "Sikh", "Spiritual but not religious", "Zoroastrian", "No religion", "Prefer not to say", "Other"))
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Religious_affiliation_bar <- ggplot(TSR_data, aes(Religious.Affliation)) + geom_bar() + coord_flip()
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Religious_affiliation_bar
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```
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# Visualisations of LIKERT responses (RH):
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- For questions Q6 (subject interest) / Q5 (subject knowledge) / Q7 employability prospects:
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- visualisation as summaries for all subjects LIKERT data as stacked bar chart (colours for bar segments from cool to warm)
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```{r Visualization by Subject}
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### Each Subject is a different column so will need to figure out how to code the columns together into one graph
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# Higher score indicates less agreement...need to reverse score
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## 1=5, 2=4, 3=3, 4=2, 5=1, 6=0 --- Not done yet. See what they look like without reverse scoring
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## Files have been reverse scored - Higher score now indicates more agreement
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#Q5 Subject Knowledge/Understanding
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subject_data$Subject <- factor(subject_data$Subject, levels = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), labels = c("Philosophy", "Sociology", "Psychology", "History", "Ethics", "Theology", "Religious Studies", "Politics", "English", "Math", "Computer Science", "Business"))
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understanding_mean <- aggregate(Understanding ~ Subject, data = subject_data, mean)
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understanding_bar <- ggplot(understanding_mean, aes(x = Subject, y = Understanding)) + stat_summary(fun = "mean", geom = "bar") + coord_flip()
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understanding_bar
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#Q6 Subject Interest
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interest_mean <- aggregate(Interest ~ Subject, data = subject_data, mean)
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interest_bar <- ggplot(interest_mean, aes(x = Subject, y = Interest)) + stat_summary(fun = "mean", geom = "bar") + coord_flip()
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interest_bar
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#Q7 Employability Prospects
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employability_mean <- aggregate(Employability ~ Subject, data = subject_data, mean)
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employability_bar <- ggplot(employability_mean, aes(x = Subject, y = Employability)) + stat_summary(fun = "mean", geom = "bar") + coord_flip()
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employability_bar
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### Categorical Graph ###
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install.packages("devtools")
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install.packages("usethis")
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library(devtools)
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require(likert)
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#devtools::install_github('jbryer/likert')
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#likert_test <- likert(subject_data)
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Understanding_data <- TSR_data[, 6:17]
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likert_test_understand <- likert(Understanding_data)
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Interest_data <- TSR_data[, 18:29]
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Employability_data <- TSR_data[, 30:41]
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```
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- separate visualisation of summary data as pie chart only for 4 key subjects: Philosophy, Ethics, Theology, Religious Studies, but with data represented as aggregated "Positive" / "Negative" responses
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```{r Visualization for 4 Key Subjects}
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## Subset by "Positive" / "Negative"
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keysubjects_data <- subject_data[subject_data$Subject == "Philosophy" | subject_data$Subject == "Ethics" | subject_data$Subject == "Theology" | subject_data$Subject == "Religious Studies", ]
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recode_interest <- ifelse(3 <= keysubjects_data$Interest & keysubjects_data$Interest >=5, "Positive", "Negative")
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keysubjects_data <- cbind(keysubjects_data, recode_interest)
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keysubjects_data$recode_interest <- factor(keysubjects_data$recode_interest)
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table(keysubjects_data$recode_interest, keysubjects_data$Subject)
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```
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- subsetted visualisations of responses with separate subsetting by response to Q8-9, Q18, Q17, Q16
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- For question Q8 + Q9 (for religious people)
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- visualisation summary of responses
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- show subsetted visualisations of responses by response to, Q18, Q17, Q16, Q13, Q14
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- For responses to Q10-12 (what subjects are involved in...):
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- represent answer counts as descending bar chart for each Q
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- subset answers by Q6 (positive / negative) and Q5 (positive / negative)
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# Correlation testing:
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- For Q6 (subject interest) / Q5 (subject knowledge) / Q7 employability prospects, test for nature / strength of correlation with responses to:
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- Q8-9 responses
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- Q18 responses
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- Q17
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```{r Q6 Correlations - Subject Interest}
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#Q8-9 (8 - Theology as subject for religious people; 9 - Religion as study for religious people)
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# This would be suitable for correlation
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#Q17 (Ethnicity)
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# This would be categorical, so ANOVA
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#Q18 (Religion)
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# This would also be categorical, so ANOVA
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```
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```{r Q5 Correlations - Subject Knowledge}
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#Q8-9 (8 - Theology as subject for religious people; 9 - Religion as study for religious people)
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# This would be suitable for correlation
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#Q17 (Ethnicity)
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# This would be categorical, so ANOVA
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#Q18 (Religion)
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# This would also be categorical, so ANOVA
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```
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```{r Q7 Correlations - Employability}
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#Q8-9 (8 - Theology as subject for religious people; 9 - Religion as study for religious people)
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# This would be suitable for correlation
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#Q17 (Ethnicity)
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# This would be categorical, so ANOVA
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#Q18 (Religion)
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# This would also be categorical, so ANOVA
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```
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```{r testing fun}
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#testcor_data <- subject_data[subject_data$Subject == "Psychology" | subject_data$Subject == "Theology", ]
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#t.test(Interest ~ Subject, data = testcor_data)
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# testsubset <- TSR_data[TSR_data$Interested.in.Studying.Psychology < 3 & TSR_data$Interested.in.Studying.Psychology != 0, ]
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# as.numeric(testsubset$Interested.in.studying.Theology)
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# mean(testsubset$Interested.in.studying.Theology, na.rm = TRUE)
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#
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# mean(as.numeric(testsubset$Interested.in.studying.Theology), na.rm = TRUE)
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#
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# as.numeric(testsubset$Interested.in.studying.Religious.Studies)
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# mean(testsubset$Interested.in.studying.Religious.Studies, na.rm = TRUE)
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#
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# testsubset2 <- TSR_data[TSR_data$Interested.in.Studying.Psychology > 3, ]
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# mean(as.numeric(testsubset2$Interested.in.studying.Theology), na.rm = TRUE)
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# mean(as.numeric(testsubset2$Interested.in.studying.Religious.Studies), na.rm = TRUE)
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```
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## Mean Interest in Theology and Religious Studies by High/Low Subject Interest
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```{r Philosophy}
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### Philosophy ###
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Philos_subset_Low <- TSR_data[TSR_data$InterestinstudyingPhilosophy < 3 & TSR_data$InterestinstudyingPhilosophy != 0, ]
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Philos_subset_High <- TSR_data[TSR_data$InterestinstudyingPhilosophy > 3, ]
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## Theology Interest
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#Low interest in Philosophy
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mean(as.numeric(Philos_subset_Low$InterestedinstudyingTheology), na.rm = TRUE)
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#High interest in Philosophy
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mean(as.numeric(Philos_subset_Low$InterestedinstudyingTheology), na.rm = TRUE)
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## Religious Studies Interest
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#Low interest in Philosophy
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mean(as.numeric(Philos_subset_Low$InterestedinstudyingReligiousStudies), na.rm = TRUE)
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#High interest in Philosophy
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mean(as.numeric(Philos_subset_High$InterestedinstudyingReligiousStudies), na.rm = TRUE)
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```
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```{r Sociology}
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### Sociology ###
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Soc_subset_Low <- TSR_data[TSR_data$InterestinstudyingSociaology < 3 & TSR_data$InterestinstudyingSociaology != 0, ]
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Soc_subset_High <- TSR_data[TSR_data$InterestinstudyingSociaology > 3, ]
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## Theology Interest
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#Low interest in Sociology
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lSocTheo <- mean(as.numeric(Soc_subset_Low$InterestedinstudyingTheology), na.rm = TRUE)
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#High interest in Sociology
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hSocTheo <- mean(as.numeric(Soc_subset_Low$InterestedinstudyingTheology), na.rm = TRUE)
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## Religious Studies Interest
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#Low interest in Sociology
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mean(as.numeric(Soc_subset_Low$InterestedinstudyingReligiousStudies), na.rm = TRUE)
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#High interest in Sociology
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mean(as.numeric(Soc_subset_High$InterestedinstudyingReligiousStudies), na.rm = TRUE)
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```
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```{r Psychology}
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### Psychology ###
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Psych_subset_Low <- TSR_data[TSR_data$InterestedinStudyingPsychology < 3 & TSR_data$InterestedinStudyingPsychology != 0, ]
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Psych_subset_High <- TSR_data[TSR_data$InterestedinStudyingPsychology > 3, ]
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## Theology Interest
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#Low interest in Psychology
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mean(as.numeric(Psych_subset_Low$InterestedinstudyingTheology), na.rm = TRUE)
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#High interest in Psychology
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mean(as.numeric(Psych_subset_Low$InterestedinstudyingTheology), na.rm = TRUE)
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## Religious Studies Interest
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#Low interest in Psychology
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mean(as.numeric(Psych_subset_Low$InterestedinstudyingReligiousStudies), na.rm = TRUE)
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#High interest in Psychology
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mean(as.numeric(Psych_subset_High$InterestedinstudyingReligiousStudies), na.rm = TRUE)
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```
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```{r History}
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### History ###
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Hist_subset_Low <- TSR_data[TSR_data$InterestedinstudyingHistory < 3 & TSR_data$InterestedinstudyingHistory != 0, ]
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Hist_subset_High <- TSR_data[TSR_data$InterestedinstudyingHistory > 3, ]
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## Theology Interest
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#Low interest in History
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mean(as.numeric(Hist_subset_Low$InterestedinstudyingTheology), na.rm = TRUE)
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#High interest in History
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mean(as.numeric(Hist_subset_Low$InterestedinstudyingTheology), na.rm = TRUE)
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## Religious Studies Interest
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#Low interest in History
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mean(as.numeric(Hist_subset_Low$InterestedinstudyingReligiousStudies), na.rm = TRUE)
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#High interest in History
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mean(as.numeric(Hist_subset_High$InterestedinstudyingReligiousStudies), na.rm = TRUE)
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```
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```{r Ethics}
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### Ethics ###
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Ethics_subset_Low <- TSR_data[TSR_data$InterestedinstudyingEthics < 3 & TSR_data$InterestedinstudyingEthics != 0, ]
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Ethics_subset_High <- TSR_data[TSR_data$InterestedinstudyingEthics > 3, ]
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## Theology Interest
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#Low interest in Ethics
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mean(as.numeric(Ethics_subset_Low$InterestedinstudyingTheology), na.rm = TRUE)
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#High interest in Ethics
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mean(as.numeric(Ethics_subset_Low$InterestedinstudyingTheology), na.rm = TRUE)
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## Religious Studies Interest
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#Low interest in Ethics
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mean(as.numeric(Ethics_subset_Low$InterestedinstudyingReligiousStudies), na.rm = TRUE)
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#High interest in Ethics
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mean(as.numeric(Ethics_subset_High$InterestedinstudyingReligiousStudies), na.rm = TRUE)
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```
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```{r Politics}
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### Politics ###
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Polit_subset_Low <- TSR_data[TSR_data$InterestedinstudyingPolitics < 3 & TSR_data$InterestedinstudyingPolitics != 0, ]
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Polit_subset_High <- TSR_data[TSR_data$InterestedinstudyingPolitics > 3, ]
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## Theology Interest
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#Low interest in Politics
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mean(as.numeric(Polit_subset_Low$InterestedinstudyingTheology), na.rm = TRUE)
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#High interest in Politics
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mean(as.numeric(Polit_subset_Low$InterestedinstudyingTheology), na.rm = TRUE)
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## Religious Studies Interest
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#Low interest in Politics
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mean(as.numeric(Polit_subset_Low$InterestedinstudyingReligiousStudies), na.rm = TRUE)
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#High interest in Politics
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mean(as.numeric(Polit_subset_High$InterestedinstudyingReligiousStudies), na.rm = TRUE)
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```
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```{r English}
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### English ###
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Eng_subset_Low <- TSR_data[TSR_data$InterestedinstudyingEnglish < 3 & TSR_data$InterestedinstudyingEnglish != 0, ]
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Eng_subset_High <- TSR_data[TSR_data$InterestedinstudyingEnglish > 3, ]
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## Theology Interest
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#Low interest in English
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mean(as.numeric(Eng_subset_Low$InterestedinstudyingTheology), na.rm = TRUE)
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#High interest in English
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mean(as.numeric(Eng_subset_Low$InterestedinstudyingTheology), na.rm = TRUE)
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## Religious Studies Interest
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#Low interest in English
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mean(as.numeric(Eng_subset_Low$InterestedinstudyingReligiousStudies), na.rm = TRUE)
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#High interest in English
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mean(as.numeric(Eng_subset_High$InterestedinstudyingReligiousStudies), na.rm = TRUE)
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```
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```{r Math}
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### Math ###
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Math_subset_Low <- TSR_data[TSR_data$InterestedinstudyingMath < 3 & TSR_data$InterestedinstudyingMathy != 0, ]
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Math_subset_High <- TSR_data[TSR_data$InterestedinstudyingMath > 3, ]
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## Theology Interest
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#Low interest in Math
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mean(as.numeric(Math_subset_Low$InterestedinstudyingTheology), na.rm = TRUE)
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#High interest in Math
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mean(as.numeric(Math_subset_Low$InterestedinstudyingTheology), na.rm = TRUE)
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## Religious Studies Interest
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#Low interest in Math
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mean(as.numeric(Math_subset_Low$InterestedinstudyingReligiousStudies), na.rm = TRUE)
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#High interest in Math
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mean(as.numeric(Math_subset_High$InterestedinstudyingReligiousStudies), na.rm = TRUE)
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```
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```{r Computer Science}
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### Computer Science ###
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CompSci_subset_Low <- TSR_data[TSR_data$InterestedinstudyingComputerScience < 3 & TSR_data$InterestedinstudyingComputerScience != 0, ]
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CompSci_subset_High <- TSR_data[TSR_data$InterestedinstudyingComputerScience > 3, ]
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## Theology Interest
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#Low interest in Computer Science
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mean(as.numeric(CompSci_subset_Low$InterestedinstudyingTheology), na.rm = TRUE)
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#High interest in Computer Science
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mean(as.numeric(CompSci_subset_Low$InterestedinstudyingTheology), na.rm = TRUE)
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## Religious Studies Interest
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#Low interest in Computer Science
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mean(as.numeric(CompSci_subset_Low$InterestedinstudyingReligiousStudies), na.rm = TRUE)
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#High interest in Computer Science
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mean(as.numeric(CompSci_subset_High$InterestedinstudyingReligiousStudies), na.rm = TRUE)
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```
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```{r Business}
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### Business ###
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Busi_subset_Low <- TSR_data[TSR_data$InterestedinstudyingBusiness < 3 & TSR_data$InterestedinstudyingBusiness != 0, ]
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Busi_subset_High <- TSR_data[TSR_data$InterestedinstudyingBusiness > 3, ]
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## Theology Interest
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#Low interest in Business
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mean(as.numeric(Busi_subset_Low$InterestedinstudyingTheology), na.rm = TRUE)
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#High interest in Business
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mean(as.numeric(Busi_subset_Low$InterestedinstudyingTheology), na.rm = TRUE)
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## Religious Studies Interest
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#Low interest in Business
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mean(as.numeric(Busi_subset_Low$InterestedinstudyingReligiousStudies), na.rm = TRUE)
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#High interest in Business
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mean(as.numeric(Busi_subset_High$InterestedinstudyingReligiousStudies), na.rm = TRUE)
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```
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## Mean Knowledge in Theology and Religious Studies by High/Low Subject Knowledge
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```{r Philosophy}
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### Philosophy ###
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Philos_subset_Low <- TSR_data[TSR_data$GoodunderstandingofPhilosophy < 3 & TSR_data$GoodunderstandingofPhilosophy != 0, ]
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Philos_subset_High <- TSR_data[TSR_data$GoodunderstandingofPhilosophy > 3, ]
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## Theology Knowledge
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#Low knowledge in Philosophy
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mean(as.numeric(Philos_subset_Low$GoodunderstandingofTheology), na.rm = TRUE)
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#High knowledge in Philosophy
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mean(as.numeric(Philos_subset_Low$GoodunderstandingofTheology), na.rm = TRUE)
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## Religious Studies Knowledge
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#Low knowledge in Philosophy
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mean(as.numeric(Philos_subset_Low$GoodunderstandingofReligiousStudies), na.rm = TRUE)
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#High knowledge in Philosophy
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mean(as.numeric(Philos_subset_High$GoodunderstandingofReligiousStudies), na.rm = TRUE)
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```
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```{r Sociology}
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### Sociology ###
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Soc_subset_Low <- TSR_data[TSR_data$GoodunderstandingofSociology < 3 & TSR_data$GoodunderstandingofSociology != 0, ]
|
|
|
|
Soc_subset_High <- TSR_data[TSR_data$GoodunderstandingofSociology > 3, ]
|
|
|
|
## Theology knowledge
|
|
#Low knowledge in Sociology
|
|
mean(as.numeric(Soc_subset_Low$GoodunderstandingofTheology), na.rm = TRUE)
|
|
#High knowledge in Sociology
|
|
mean(as.numeric(Soc_subset_Low$GoodunderstandingofTheology), na.rm = TRUE)
|
|
|
|
## Religious Studies knowledge
|
|
#Low knowledge in Sociology
|
|
mean(as.numeric(Soc_subset_Low$GoodunderstandingofReligiousStudies), na.rm = TRUE)
|
|
#High knowledge in Sociology
|
|
mean(as.numeric(Soc_subset_High$GoodunderstandingofReligiousStudies), na.rm = TRUE)
|
|
```
|
|
|
|
```{r Psychology}
|
|
### Psychology ###
|
|
Psych_subset_Low <- TSR_data[TSR_data$GoodunderstandingofPsychology < 3 & TSR_data$GoodunderstandingofPsychology != 0, ]
|
|
|
|
Psych_subset_High <- TSR_data[TSR_data$GoodunderstandingofPsychology > 3, ]
|
|
|
|
## Theology knowledge
|
|
#Low knowledge in Psychology
|
|
mean(as.numeric(Psych_subset_Low$GoodunderstandingofTheology), na.rm = TRUE)
|
|
#High knowledge in Psychology
|
|
mean(as.numeric(Psych_subset_Low$GoodunderstandingofTheology), na.rm = TRUE)
|
|
|
|
## Religious Studies knowledge
|
|
#Low knowledge in Psychology
|
|
mean(as.numeric(Psych_subset_Low$GoodunderstandingofReligiousStudies), na.rm = TRUE)
|
|
#High knowledge in Psychology
|
|
mean(as.numeric(Psych_subset_High$GoodunderstandingofReligiousStudies), na.rm = TRUE)
|
|
```
|
|
|
|
```{r History}
|
|
### History ###
|
|
Hist_subset_Low <- TSR_data[TSR_data$GoodunderstandingofHistory < 3 & TSR_data$GoodunderstandingofHistory != 0, ]
|
|
|
|
Hist_subset_High <- TSR_data[TSR_data$GoodunderstandingofHistory > 3, ]
|
|
|
|
## Theology knowledge
|
|
#Low knowledge in History
|
|
mean(as.numeric(Hist_subset_Low$GoodunderstandingofTheology), na.rm = TRUE)
|
|
#High knowledge in History
|
|
mean(as.numeric(Hist_subset_Low$GoodunderstandingofTheology), na.rm = TRUE)
|
|
|
|
## Religious Studies knowledge
|
|
#Low knowledge in History
|
|
mean(as.numeric(Hist_subset_Low$GoodunderstandingofReligiousStudies), na.rm = TRUE)
|
|
#High knowledge in History
|
|
mean(as.numeric(Hist_subset_High$GoodunderstandingofReligiousStudies), na.rm = TRUE)
|
|
```
|
|
|
|
```{r Ethics}
|
|
### Ethics ###
|
|
Ethics_subset_Low <- TSR_data[TSR_data$GoodunderstandingofEthics < 3 & TSR_data$GoodunderstandingofEthics != 0, ]
|
|
|
|
Ethics_subset_High <- TSR_data[TSR_data$GoodunderstandingofEthics > 3, ]
|
|
|
|
## Theology knowledge
|
|
#Low knowledge in Ethics
|
|
mean(as.numeric(Ethics_subset_Low$GoodunderstandingofTheology), na.rm = TRUE)
|
|
#High knowledge in Ethics
|
|
mean(as.numeric(Ethics_subset_Low$GoodunderstandingofTheology), na.rm = TRUE)
|
|
|
|
## Religious Studies knowledge
|
|
#Low knowledge in Ethics
|
|
mean(as.numeric(Ethics_subset_Low$GoodunderstandingofReligiousStudies), na.rm = TRUE)
|
|
#High knowledge in Ethics
|
|
mean(as.numeric(Ethics_subset_High$GoodunderstandingofReligiousStudies), na.rm = TRUE)
|
|
```
|
|
|
|
```{r Politics}
|
|
### Politics ###
|
|
Polit_subset_Low <- TSR_data[TSR_data$GoodunderstandingofPolitics < 3 & TSR_data$GoodunderstandingofPolitics != 0, ]
|
|
|
|
Polit_subset_High <- TSR_data[TSR_data$GoodunderstandingofPolitics > 3, ]
|
|
|
|
## Theology knowledge
|
|
#Low knowledge in Politics
|
|
mean(as.numeric(Polit_subset_Low$GoodunderstandingofTheology), na.rm = TRUE)
|
|
#High knowledge in Politics
|
|
mean(as.numeric(Polit_subset_Low$GoodunderstandingofTheology), na.rm = TRUE)
|
|
|
|
## Religious Studies knowledge
|
|
#Low knowledge in Politics
|
|
mean(as.numeric(Polit_subset_Low$GoodunderstandingofReligiousStudies), na.rm = TRUE)
|
|
#High knowledge in Politics
|
|
mean(as.numeric(Polit_subset_High$GoodunderstandingofReligiousStudies), na.rm = TRUE)
|
|
```
|
|
|
|
```{r English}
|
|
### English ###
|
|
Eng_subset_Low <- TSR_data[TSR_data$GoodunderstandingofEnglish < 3 & TSR_data$GoodunderstandingofEnglish != 0, ]
|
|
|
|
Eng_subset_High <- TSR_data[TSR_data$GoodunderstandingofEnglish > 3, ]
|
|
|
|
## Theology knowledge
|
|
#Low knowledge in English
|
|
mean(as.numeric(Eng_subset_Low$GoodunderstandingofTheology), na.rm = TRUE)
|
|
#High knowledge in English
|
|
mean(as.numeric(Eng_subset_Low$GoodunderstandingofTheology), na.rm = TRUE)
|
|
|
|
## Religious Studies knowledge
|
|
#Low knowledge in English
|
|
mean(as.numeric(Eng_subset_Low$GoodunderstandingofReligiousStudies), na.rm = TRUE)
|
|
#High knowledge in English
|
|
mean(as.numeric(Eng_subset_High$GoodunderstandingofReligiousStudies), na.rm = TRUE)
|
|
```
|
|
|
|
```{r Math}
|
|
### Math ###
|
|
Math_subset_Low <- TSR_data[TSR_data$GoodunderstandingofMath < 3 & TSR_data$GoodunderstandingofMath != 0, ]
|
|
|
|
Math_subset_High <- TSR_data[TSR_data$GoodunderstandingofMath > 3, ]
|
|
|
|
## Theology knowledge
|
|
#Low knowledge in Math
|
|
mean(as.numeric(Math_subset_Low$GoodunderstandingofTheology), na.rm = TRUE)
|
|
#High knowledge in Math
|
|
mean(as.numeric(Math_subset_Low$GoodunderstandingofTheology), na.rm = TRUE)
|
|
|
|
## Religious Studies knowledge
|
|
#Low knowledge in Math
|
|
mean(as.numeric(Math_subset_Low$GoodunderstandingofReligiousStudies), na.rm = TRUE)
|
|
#High knowledge in Math
|
|
mean(as.numeric(Math_subset_High$GoodunderstandingofReligiousStudies), na.rm = TRUE)
|
|
```
|
|
|
|
```{r Computer Science}
|
|
### Computer Science ###
|
|
CompSci_subset_Low <- TSR_data[TSR_data$GoodunderstandingofComputerScience < 3 & TSR_data$GoodunderstandingofComputerScience != 0, ]
|
|
|
|
CompSci_subset_High <- TSR_data[TSR_data$GoodunderstandingofComputerScience > 3, ]
|
|
|
|
## Theology knowledge
|
|
#Low knowledge in Computer Science
|
|
mean(as.numeric(CompSci_subset_Low$GoodunderstandingofTheology), na.rm = TRUE)
|
|
#High knowledge in Computer Science
|
|
mean(as.numeric(CompSci_subset_Low$GoodunderstandingofTheology), na.rm = TRUE)
|
|
|
|
## Religious Studies knowledge
|
|
#Low knowledge in Computer Science
|
|
mean(as.numeric(CompSci_subset_Low$GoodunderstandingofReligiousStudies), na.rm = TRUE)
|
|
#High knowledge in Computer Science
|
|
mean(as.numeric(CompSci_subset_High$GoodunderstandingofReligiousStudies), na.rm = TRUE)
|
|
```
|
|
|
|
```{r Business}
|
|
### Business ###
|
|
Busi_subset_Low <- TSR_data[TSR_data$GoodunderstandingofBusiness < 3 & TSR_data$GoodunderstandingofBusiness != 0, ]
|
|
|
|
Busi_subset_High <- TSR_data[TSR_data$GoodunderstandingofBusiness > 3, ]
|
|
|
|
## Theology knowledge
|
|
#Low knowledge in Business
|
|
mean(as.numeric(Busi_subset_Low$GoodunderstandingofTheology), na.rm = TRUE)
|
|
#High knowledge in Business
|
|
mean(as.numeric(Busi_subset_Low$GoodunderstandingofTheology), na.rm = TRUE)
|
|
|
|
## Religious Studies knowledge
|
|
#Low knowledge in Business
|
|
mean(as.numeric(Busi_subset_Low$GoodunderstandingofReligiousStudies), na.rm = TRUE)
|
|
#High knowledge in Business
|
|
mean(as.numeric(Busi_subset_High$GoodunderstandingofReligiousStudies), na.rm = TRUE)
|
|
|
|
```
|
|
|
|
|
|
## Mean Employability in Theology and Religious Studies by High/Low Subject Employability
|
|
|
|
```{r Philosophy}
|
|
### Philosophy ###
|
|
Philos_subset_Low <- TSR_data[TSR_data$Philosophyemployability < 3 & TSR_data$Philosophyemployability != 0, ]
|
|
|
|
Philos_subset_High <- TSR_data[TSR_data$Philosophyemployability > 3, ]
|
|
|
|
## Theology employability
|
|
#Low employability in Philosophy
|
|
mean(as.numeric(Philos_subset_Low$Theologyemployability), na.rm = TRUE)
|
|
#High employability in Philosophy
|
|
mean(as.numeric(Philos_subset_Low$Theologyemployability), na.rm = TRUE)
|
|
|
|
## Religious Studies employability
|
|
#Low employability in Philosophy
|
|
mean(as.numeric(Philos_subset_Low$ReligiousStudiesemployability), na.rm = TRUE)
|
|
#High employability in Philosophy
|
|
mean(as.numeric(Philos_subset_High$ReligiousStudiesemployability), na.rm = TRUE)
|
|
```
|
|
|
|
```{r Sociology}
|
|
### Sociology ###
|
|
Soc_subset_Low <- TSR_data[TSR_data$Sociologyemployability < 3 & TSR_data$Sociologyemployability != 0, ]
|
|
|
|
Soc_subset_High <- TSR_data[TSR_data$Sociologyemployability > 3, ]
|
|
|
|
## Theology employability
|
|
#Low employability in Sociology
|
|
mean(as.numeric(Soc_subset_Low$Theologyemployability), na.rm = TRUE)
|
|
#High employability in Sociology
|
|
mean(as.numeric(Soc_subset_Low$Theologyemployability), na.rm = TRUE)
|
|
|
|
## Religious Studies employability
|
|
#Low employability in Sociology
|
|
mean(as.numeric(Soc_subset_Low$ReligiousStudiesemployability), na.rm = TRUE)
|
|
#High employability in Sociology
|
|
mean(as.numeric(Soc_subset_High$ReligiousStudiesemployability), na.rm = TRUE)
|
|
```
|
|
|
|
```{r Psychology}
|
|
### Psychology ###
|
|
Psych_subset_Low <- TSR_data[TSR_data$PsychologyEmployability < 3 & TSR_data$PsychologyEmployability != 0, ]
|
|
|
|
Psych_subset_High <- TSR_data[TSR_data$PsychologyEmployability > 3, ]
|
|
|
|
## Theology employability
|
|
#Low employability in Psychology
|
|
mean(as.numeric(Psych_subset_Low$Theologyemployability), na.rm = TRUE)
|
|
#High employability in Psychology
|
|
mean(as.numeric(Psych_subset_Low$Theologyemployability), na.rm = TRUE)
|
|
|
|
## Religious Studies employability
|
|
#Low employability in Psychology
|
|
mean(as.numeric(Psych_subset_Low$ReligiousStudiesemployability), na.rm = TRUE)
|
|
#High employability in Psychology
|
|
mean(as.numeric(Psych_subset_High$ReligiousStudiesemployability), na.rm = TRUE)
|
|
```
|
|
|
|
```{r History}
|
|
### History ###
|
|
Hist_subset_Low <- TSR_data[TSR_data$Historyemployability < 3 & TSR_data$Historyemployability != 0, ]
|
|
|
|
Hist_subset_High <- TSR_data[TSR_data$Historyemployability > 3, ]
|
|
|
|
## Theology employability
|
|
#Low employability in History
|
|
mean(as.numeric(Hist_subset_Low$Theologyemployability), na.rm = TRUE)
|
|
#High employability in History
|
|
mean(as.numeric(Hist_subset_Low$Theologyemployability), na.rm = TRUE)
|
|
|
|
## Religious Studies employability
|
|
#Low employability in History
|
|
mean(as.numeric(Hist_subset_Low$ReligiousStudiesemployability), na.rm = TRUE)
|
|
#High employability in History
|
|
mean(as.numeric(Hist_subset_High$ReligiousStudiesemployability), na.rm = TRUE)
|
|
```
|
|
|
|
```{r Ethics}
|
|
### Ethics ###
|
|
Ethics_subset_Low <- TSR_data[TSR_data$Ethicsemployability < 3 & TSR_data$Ethicsemployability != 0, ]
|
|
|
|
Ethics_subset_High <- TSR_data[TSR_data$Ethicsemployability > 3, ]
|
|
|
|
## Theology employability
|
|
#Low employability in Ethics
|
|
mean(as.numeric(Ethics_subset_Low$Theologyemployability), na.rm = TRUE)
|
|
#High employability in Ethics
|
|
mean(as.numeric(Ethics_subset_Low$Theologyemployability), na.rm = TRUE)
|
|
|
|
## Religious Studies employability
|
|
#Low employability in Ethics
|
|
mean(as.numeric(Ethics_subset_Low$ReligiousStudiesemployability), na.rm = TRUE)
|
|
#High employability in Ethics
|
|
mean(as.numeric(Ethics_subset_High$ReligiousStudiesemployability), na.rm = TRUE)
|
|
```
|
|
|
|
```{r Politics}
|
|
### Politics ###
|
|
Polit_subset_Low <- TSR_data[TSR_data$Politicsemployability < 3 & TSR_data$Politicsemployability != 0, ]
|
|
|
|
Polit_subset_High <- TSR_data[TSR_data$Politicsemployability > 3, ]
|
|
|
|
## Theology employability
|
|
#Low employability in Politics
|
|
mean(as.numeric(Polit_subset_Low$Theologyemployability), na.rm = TRUE)
|
|
#High employability in Politics
|
|
mean(as.numeric(Polit_subset_Low$Theologyemployability), na.rm = TRUE)
|
|
|
|
## Religious Studies employability
|
|
#Low employability in Politics
|
|
mean(as.numeric(Polit_subset_Low$ReligiousStudiesemployability), na.rm = TRUE)
|
|
#High employability in Politics
|
|
mean(as.numeric(Polit_subset_High$ReligiousStudiesemployability), na.rm = TRUE)
|
|
```
|
|
|
|
```{r English}
|
|
### English ###
|
|
Eng_subset_Low <- TSR_data[TSR_data$Englishemployability < 3 & TSR_data$Englishemployability != 0, ]
|
|
|
|
Eng_subset_High <- TSR_data[TSR_data$Englishemployability > 3, ]
|
|
|
|
## Theology employability
|
|
#Low employability in English
|
|
mean(as.numeric(Eng_subset_Low$Theologyemployability), na.rm = TRUE)
|
|
#High employability in English
|
|
mean(as.numeric(Eng_subset_Low$Theologyemployability), na.rm = TRUE)
|
|
|
|
## Religious Studies employability
|
|
#Low employability in English
|
|
mean(as.numeric(Eng_subset_Low$ReligiousStudiesemployability), na.rm = TRUE)
|
|
#High employability in English
|
|
mean(as.numeric(Eng_subset_High$ReligiousStudiesemployability), na.rm = TRUE)
|
|
```
|
|
|
|
```{r Math}
|
|
### Math ###
|
|
Math_subset_Low <- TSR_data[TSR_data$Mathemployability < 3 & TSR_data$Mathemployability != 0, ]
|
|
|
|
Math_subset_High <- TSR_data[TSR_data$Mathemployability > 3, ]
|
|
|
|
## Theology employability
|
|
#Low employability in Math
|
|
mean(as.numeric(Math_subset_Low$Theologyemployability), na.rm = TRUE)
|
|
#High employability in Math
|
|
mean(as.numeric(Math_subset_Low$Theologyemployability), na.rm = TRUE)
|
|
|
|
## Religious Studies employability
|
|
#Low employability in Math
|
|
mean(as.numeric(Math_subset_Low$ReligiousStudiesemployability), na.rm = TRUE)
|
|
#High employability in Math
|
|
mean(as.numeric(Math_subset_High$ReligiousStudiesemployability), na.rm = TRUE)
|
|
```
|
|
|
|
```{r Computer Science}
|
|
### Computer Science ###
|
|
CompSci_subset_Low <- TSR_data[TSR_data$ComputerScienceemployability < 3 & TSR_data$ComputerScienceemployability != 0, ]
|
|
|
|
CompSci_subset_High <- TSR_data[TSR_data$ComputerScienceemployability > 3, ]
|
|
|
|
## Theology employability
|
|
#Low employability in Computer Science
|
|
mean(as.numeric(CompSci_subset_Low$Theologyemployability), na.rm = TRUE)
|
|
#High employability in Computer Science
|
|
mean(as.numeric(CompSci_subset_Low$Theologyemployability), na.rm = TRUE)
|
|
|
|
## Religious Studies employability
|
|
#Low employability in Computer Science
|
|
mean(as.numeric(CompSci_subset_Low$ReligiousStudiesemployability), na.rm = TRUE)
|
|
#High employability in Computer Science
|
|
mean(as.numeric(CompSci_subset_High$ReligiousStudiesemployability), na.rm = TRUE)
|
|
```
|
|
|
|
```{r Business}
|
|
### Business ###
|
|
Busi_subset_Low <- TSR_data[TSR_data$Businessemployability < 3 & TSR_data$Businessemployability != 0, ]
|
|
|
|
Busi_subset_High <- TSR_data[TSR_data$Businessemployability > 3, ]
|
|
|
|
## Theology employability
|
|
#Low employability in Business
|
|
mean(as.numeric(Busi_subset_Low$Theologyemployability), na.rm = TRUE)
|
|
#High employability in Business
|
|
mean(as.numeric(Busi_subset_Low$Theologyemployability), na.rm = TRUE)
|
|
|
|
## Religious Studies employability
|
|
#Low employability in Business
|
|
mean(as.numeric(Busi_subset_Low$ReligiousStudiesemployability), na.rm = TRUE)
|
|
#High employability in Business
|
|
mean(as.numeric(Busi_subset_High$ReligiousStudiesemployability), na.rm = TRUE)
|
|
|
|
```
|