From 2a5004e92fbc7f07bd641dd8610d3f6863308e88 Mon Sep 17 00:00:00 2001 From: rehughes07 <58590776+rehughes07@users.noreply.github.com> Date: Tue, 2 Nov 2021 18:08:31 +0000 Subject: [PATCH] Data Finagling and Analysis Created new .csv file for the analyses columns Qs [22, 23, 27] (in order to be less messy extracting columns). Separated out answers to Q20 in order to test whether there is a different in response. Sorted out ANOVA and MANOVA analyses - preliminary results indicate no significant difference on Q22 or Q27 in regards to social issues of importance. Significant diff on Q23 with economy, health and environment - explore means to see where. Could be interesting --- Connect Project R Markdown.Rmd | 79 ++++++++++++++++++++++++++++++- data/Q20_data.csv | 86 ++++++++++++++++++++++++++++++++++ 2 files changed, 163 insertions(+), 2 deletions(-) create mode 100644 data/Q20_data.csv diff --git a/Connect Project R Markdown.Rmd b/Connect Project R Markdown.Rmd index 2a704e0..1b7ab00 100644 --- a/Connect Project R Markdown.Rmd +++ b/Connect Project R Markdown.Rmd @@ -74,6 +74,7 @@ pie(Q26_freq) #very messy as a pie chart - split by type? Or is it important to see crossover Could potentially see crossover with crosstabs by type (since response is now binary variable T/F), maybe chi square; perhaps just descriptives + ```{r Q3 bar/pie} @@ -112,8 +113,9 @@ pie(test2$Frequency, labels = c("Worldviews", "Religion", "Theology", "Ethics", # JK note on Q3: consider here whether to use alternative forms of visualiation to reflect the overlaps when respondents picked multiple categories in responses - ``` +``` +xtabs(Frequency ~ Subject, test2) pie(Q3_freq) #also not optimal as pie...perhaps bar @@ -159,9 +161,82 @@ table(Q3_1factor) - Make Q20 a factor with 14 levels - Collapse 2 Q22 columns into one mean for analyses - Analyse 1 way anova Q20 (14 levels) by Q22; Q23[1-2]; Q27[1-7] + - 1 way within subjects?? Though not all participants ticked every box... Would it then be best to separate them out and do 14 separate analyses with bonferroni correction due to the multiple tests? - could then be 14 different t tests based on whether they ticked each one as important or not... Many analyses but that may be the most straightforward way to go. Factorial mixed ANOVA? 14 predictors, each with 2 levels (yes/no)?? + - 14 predictors, within subjects, 2 levels (yes/no). DV as responses to questions. Q22 would be a factorial between subjects (only 1 option on IVs) ANOVA. Qs 23, 27 would be factorial between subjects MANOVA -```{r Correlation 1} +```{r Analyses 1 - As Factor} +social_issues_data <- read.csv("./data/Q20_data.csv") +head(social_issues_data) + +# All 14 as factors, with 2 levels: 1=YES, 2=NO + +social_issues_data$brexit <- factor(social_issues_data$brexit, levels = c(1, 2), labels = c("Yes", "No")) +class(social_issues_data$brexit) + +#social_issues_data[ ,4:5] <- factor(social_issues_data[ ,4:5], levels = c(1, 2), labels = c("Yes", "No")) + #Did not work; made 2 columns "NA" so am going through to make factors individually + + ### OR ### + +#social_issues_data[ ,4:5] <- lapply(social_issues_data[ ,4:5], factor(social_issues_data[ ,4:5], levels = c(1, 2), labels = c("Yes", "No"))) + +social_issues_data$economy <- factor(social_issues_data$economy, levels = c(1, 2), labels = c("Yes", "No")) + +social_issues_data$immigration <- factor(social_issues_data$immigration, levels = c(1, 2), labels = c("Yes", "No")) + +social_issues_data$crime <- factor(social_issues_data$crime, levels = c(1, 2), labels = c("Yes", "No")) + +social_issues_data$health <- factor(social_issues_data$health, levels = c(1, 2), labels = c("Yes", "No")) + +social_issues_data$education <- factor(social_issues_data$education, levels = c(1, 2), labels = c("Yes", "No")) + +social_issues_data$housing <- factor(social_issues_data$housing, levels = c(1, 2), labels = c("Yes", "No")) + +social_issues_data$welfare <- factor(social_issues_data$welfare, levels = c(1, 2), labels = c("Yes", "No")) + +social_issues_data$defence <- factor(social_issues_data$defence, levels = c(1, 2), labels = c("Yes", "No")) + +social_issues_data$environment <- factor(social_issues_data$environment, levels = c(1, 2), labels = c("Yes", "No")) + +social_issues_data$tax <- factor(social_issues_data$tax, levels = c(1, 2), labels = c("Yes", "No")) + +social_issues_data$pensions <- factor(social_issues_data$pensions, levels = c(1, 2), labels = c("Yes", "No")) + +social_issues_data$family.life <- factor(social_issues_data$family.life, levels = c(1, 2), labels = c("Yes", "No")) + +social_issues_data$transport <- factor(social_issues_data$transport, levels = c(1, 2), labels = c("Yes", "No")) +``` + +``` {r Analyses 2 - ANOVA and MANOVA} +## Q22; Q23[1-2]; Q27[1-7] + #Q22_average + #Q23_1, Q23_2 + #Q27_1 - Q27_7 +#t.test to see if difference in one variable - Q22_average +hist(social_issues_data$Q22_average) +t.test(Q22_average~brexit, data = social_issues_data, paired = FALSE) + #no significant difference between scores on Q22, and whether they thought brexit was important + + +Q_22test <- aov(Q22_average ~ brexit + economy + immigration + crime + health + education + housing + welfare + defence + environment + tax + pensions + family.life + transport, data = social_issues_data) +summary(Q_22test) + #no significant different between scores on Q22 and their opinion on social issues + +Q_23test <- manova(cbind(Q23_1, Q23_2) ~ brexit + economy + immigration + crime + health + education + housing + welfare + defence + environment + tax + pensions + family.life + transport, data = social_issues_data) +summary(Q_23test) + #significant difference between scores on Q23 with economy, health, and environment + +econ <- aggregate(cbind(Q23_1, Q23_2) ~ economy, data = social_issues_data, FUN = mean) +health <- aggregate(cbind(Q23_1, Q23_2) ~ health, data = social_issues_data, FUN = mean) +env <- aggregate(cbind(Q23_1, Q23_2) ~ environment, data = social_issues_data, FUN = mean) + + +#SORT OUT MEANS FOR THIS -- interesting pattern viewed with means + +Q_27test <- manova(cbind(Q27_1, Q27_2, Q27_3, Q27_4, Q27_5, Q27_6, Q27_7) ~ brexit + economy + immigration + crime + health + education + housing + welfare + defence + environment + tax + pensions + family.life + transport, data = social_issues_data) +summary(Q_27test) + #No significant difference in responses to Q27 based on what they considered important ``` - RH: test for correlation between responses to religion questions: Q12-14, Q15-16 and Q21 and responses to Q22, Q23, Q27, [Q24, Q25, Q30] diff --git a/data/Q20_data.csv b/data/Q20_data.csv new file mode 100644 index 0000000..a0ba5cc --- /dev/null +++ b/data/Q20_data.csv @@ -0,0 +1,86 @@ +Q20,Q20_recode,brexit,economy,immigration,crime,health,education,housing,welfare,defence,environment,tax,pensions,family life,transport,Q22_1,Q22_2,Q22_average,Q23_1,Q23_2,Q27_1,Q27_2,Q27_3,Q27_4,Q27_5,Q27_6,Q27_7 +"health,education,environment","5,6,10",2,2,2,2,1,1,2,2,2,1,2,2,2,2,4,4,4,3,5,4,4,4,4,4,4,4 +"economy,immigration,crime,health,education,housing,welfare,defence,environment,family life,transport","2,3,4,5,6,7,8,9,10,13,14",2,1,1,1,1,1,1,1,1,1,2,2,1,1,4,3,3.5,2,5,4,5,5,3,4,4,4 +"crime,health,housing,welfare,environment","4,5,7,8,10",2,2,2,1,1,2,1,1,2,1,2,2,2,2,3,3,3,3,4,4,4,4,4,4,4,4 +"brexit,economy,immigration,crime,health,education,housing,welfare,defence,environment,tax,pensions,family life,transport","1,2,3,4,5,6,7,8,9,10,11,12,13,14",1,1,1,1,1,1,1,1,1,1,1,1,1,1,4,2,3,4,5,4,3,2,3,2,2,2 +"health,education,housing,welfare,environment,family life","5,6,7,8,10,13",2,2,2,2,1,1,1,1,2,1,2,2,1,2,4,2,3,1,4,5,4,2,5,4,3,3 +"brexit,economy,immigration,crime,health,education,housing,welfare,environment,tax,pensions,family life","1,2,3,4,5,6,7,8,10,11,12,13",1,1,1,1,1,1,1,1,2,1,1,1,1,2,4,3,3.5,3,3,4,4,2,3,2,3,2 +"crime,housing,environment","4,7,10",2,2,2,1,2,2,1,2,2,1,2,2,2,2,5,5,5,4,5,4,4,4,4,4,4,4 +,,2,2,2,2,2,2,2,2,2,2,2,2,2,2,5,5,5,5,1,5,5,5,5,5,5,5 +"economy,crime,health,welfare,environment","2,4,5,8,10",2,1,2,1,1,2,2,1,2,1,2,2,2,2,5,5,5,5,5,5,5,5,5,5,5,4 +"crime,health,education,housing,welfare,environment,family life","4,5,6,7,8,10,13",2,2,2,1,1,1,1,1,2,1,2,2,1,2,4,4,4,3,5,4,4,4,4,4,4,4 +"immigration,crime,health,education,housing,welfare,environment,family life","3,4,5,6,7,8,10,13",2,2,1,1,1,1,1,1,2,1,2,2,1,2,2,2,2,2,4,5,4,2,3,2,3,2 +"health,education,environment","5,6,10",2,2,2,2,1,1,2,2,2,1,2,2,2,2,2,2,2,4,4,3,4,2,4,4,4,3 +"immigration,crime,health,housing,welfare,family life","3,4,5,7,8,13",2,2,1,1,1,2,1,1,2,2,2,2,1,2,4,4,4,3,4,4,4,4,4,4,5,4 +"economy,immigration,health,education,housing,environment,pensions","2,3,5,6,7,10,12",2,1,1,2,1,1,1,2,2,1,2,1,2,2,3,3,3,2,4,3,3,3,4,4,4,5 +"brexit,economy,immigration,crime,health,education,housing,welfare,environment,family life","1,2,3,4,5,6,7,8,10,13",1,1,1,1,1,1,1,1,2,1,2,2,1,2,5,4,4.5,2,4,4,4,2,5,3,4,3 +"immigration,crime,health,education,welfare,family life","3,4,5,6,8,13",2,2,1,1,1,1,2,1,2,2,2,2,1,2,4,2,3,2,3,4,4,3,2,3,3,3 +"health,education,housing,welfare,environment,pensions,family life,transport","5,6,7,8,10,12,13,14",2,2,2,2,1,1,1,1,2,1,2,1,1,1,5,5,5,2,5,5,5,2,5,5,5,2 +"economy,education,environment","2,6,10",2,1,2,2,2,1,2,2,2,1,2,2,2,2,4,4,4,2,4,4,4,4,3,4,4,4 +"health,education,welfare,environment","5,6,8,10",2,2,2,2,1,1,2,1,2,1,2,2,2,2,4,2,3,2,3,2,2,2,1,2,3,1 +"brexit,economy,immigration,health,education,housing,welfare,environment","1,2,3,5,6,7,8,10",1,1,1,2,1,1,1,1,2,1,2,2,2,2,4,2,3,1,5,4,4,4,4,4,2,2 +"brexit,economy,immigration,crime,health,education,housing,welfare,defence,environment,tax,pensions,family life,transport","1,2,3,4,5,6,7,8,9,10,11,12,13,14",1,1,1,1,1,1,1,1,1,1,1,1,1,1,5,4,4.5,4,3,5,5,4,5,5,5,5 +"brexit,economy,immigration,crime,health,education,housing,welfare,defence,environment,tax,pensions,family life,transport","1,2,3,4,5,6,7,8,9,10,11,12,13,14",1,1,1,1,1,1,1,1,1,1,1,1,1,1,5,5,5,5,3,4,4,4,5,5,4,5 +"brexit,economy,immigration,crime,health,education,housing,welfare,defence,environment,tax,pensions,family life,transport","1,2,3,4,5,6,7,8,9,10,11,12,13,14",1,1,1,1,1,1,1,1,1,1,1,1,1,1,5,4,4.5,4,4,4,5,3,3,4,3,3 +"brexit,economy,immigration,crime,health,education,housing,welfare,defence,environment,tax,pensions,family life,transport","1,2,3,4,5,6,7,8,9,10,11,12,13,14",1,1,1,1,1,1,1,1,1,1,1,1,1,1,5,4,4.5,2,3,5,5,4,5,5,5,4 +"economy,immigration,health,education,environment","2,3,5,6,10",2,1,1,2,1,1,2,2,2,1,2,2,2,2,5,4,4.5,2,3,5,5,4,4,5,5,4 +"economy,health,education,housing,environment,family life","2,5,6,7,10,13",2,1,2,2,1,1,1,2,2,1,2,2,1,2,5,5,5,4,4,5,5,5,5,5,5,5 +"immigration,crime,health,education,housing,environment,family life","3,4,5,6,7,10,13",2,2,1,1,1,1,1,2,2,1,2,2,1,2,4,2,3,2,5,5,5,4,2,4,2,2 +"health,education,housing,welfare,environment,family life","5,6,7,8,10,13",2,2,2,2,1,1,1,1,2,1,2,2,1,2,4,4,4,2,4,4,4,2,4,4,4,3 +"crime,health,education,welfare,environment,family life","4,5,6,8,10,13",2,2,2,1,1,1,2,1,2,1,2,2,1,2,4,2,3,2,1,5,5,2,4,4,4,4 +"immigration,crime,health,education,welfare,environment,tax,transport","3,4,5,6,8,10,11,14",2,2,1,1,1,1,2,1,2,1,1,2,2,1,4,2,3,4,4,5,5,4,4,2,4,2 +"crime,health,education,housing,welfare,family life","4,5,6,7,8,13",2,2,2,1,1,1,1,1,2,2,2,2,1,2,4,4,4,2,2,4,4,4,1,2,2,2 +"brexit,economy,immigration,crime,health,education,housing,welfare,defence,environment,tax,pensions,family life,transport","1,2,3,4,5,6,7,8,9,10,11,12,13,14",1,1,1,1,1,1,1,1,1,1,1,1,1,1,4,3,3.5,3,4,4,5,3,4,5,5,3 +"economy,immigration,crime,health,education,housing,welfare,environment,family life","2,3,4,5,6,7,8,10,13",2,1,1,1,1,1,1,1,2,1,2,2,1,2,5,4,4.5,4,5,5,5,5,4,4,4,4 +"immigration,health,education,housing,welfare,environment","3,5,6,7,8,10",2,2,1,2,1,1,1,1,2,1,2,2,2,2,4,4,4,4,4,4,5,4,5,5,5,4 +"immigration,crime,health,education,housing,welfare,defence,environment,tax,pensions,family life,transport","3,4,5,6,7,8,9,10,11,12,13,14",2,2,1,1,1,1,1,1,1,1,1,1,1,1,4,4,4,2,3,4,4,3,5,4,4,4 +"immigration,education,family life","3,6,13",2,2,1,2,2,1,2,2,2,2,2,2,1,2,4,3,3.5,4,3,4,2,4,1,2,2,2 +"economy,immigration,health,education,environment,family life","2,3,5,6,10,13",2,1,1,2,1,1,2,2,2,1,2,2,1,2,4,3,3.5,4,5,5,5,2,4,4,5,2 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life","3,4,5,6,8,10,13",2,2,1,1,1,1,2,1,2,1,2,2,1,2,5,4,4.5,2,2,5,4,4,4,2,5,2 +"brexit,economy,immigration,crime,health,education,housing,welfare,defence,environment,tax,pensions,family life,transport","1,2,3,4,5,6,7,8,9,10,11,12,13,14",1,1,1,1,1,1,1,1,1,1,1,1,1,1,5,2,3.5,2,5,4,4,2,4,4,4,2 +"crime,health,education,environment,family life","4,5,6,10,13",2,2,2,1,1,1,2,2,2,1,2,2,1,2,4,4,4,4,3,4,4,4,4,4,4,3 +"immigration,crime,health,education,housing,welfare,environment,family life","3,4,5,6,7,8,10,13",2,2,1,1,1,1,1,1,2,1,2,2,1,2,4,2,3,2,4,2,4,2,4,2,4,2 +"health,education,housing,welfare,environment,tax","5,6,7,8,10,11",2,2,2,2,1,1,1,1,2,1,1,2,2,2,5,4,4.5,2,3,4,4,3,4,4,4,3 +housing,7,2,2,2,2,2,2,1,2,2,2,2,2,2,2,3,3,3,3,3,4,4,4,3,4,5,4 +"crime,education,family life","4,6,13",2,2,2,1,2,1,2,2,2,2,2,2,1,2,4,2,3,2,3,4,3,3,4,4,4,2 +"economy,crime,health,education,housing,welfare,environment,family life,transport","2,4,5,6,7,8,10,13,14",2,1,2,1,1,1,1,1,2,1,2,2,1,1,2,2,2,2,5,3,3,3,1,3,3,3 +"economy,immigration,health,education,welfare,defence,tax,pensions,family life","2,3,5,6,8,9,11,12,13",2,1,1,2,1,1,2,1,1,2,1,1,1,2,2,2,2,1,4,3,2,2,4,3,4,2 +"immigration,crime,health,education,housing,welfare,environment,tax,pensions,family life,transport","3,4,5,6,7,8,10,11,12,13,14",2,2,1,1,1,1,1,1,2,1,1,1,1,1,4,4,4,2,4,2,2,2,1,2,4,2 +"economy,crime,health,education,housing,welfare,environment,transport","2,4,5,6,7,8,10,14",2,1,2,1,1,1,1,1,2,1,2,2,2,1,3,3,3,2,4,4,4,2,2,4,4,2 +"brexit,economy,immigration,crime,health,education,housing,welfare,defence,environment,tax,pensions,family life,transport","1,2,3,4,5,6,7,8,9,10,11,12,13,14",1,1,1,1,1,1,1,1,1,1,1,1,1,1,5,4,4.5,4,3,4,4,4,3,4,4,4 +"health,education,welfare,environment","5,6,8,10",2,2,2,2,1,1,2,1,2,1,2,2,2,2,5,4,4.5,5,5,5,4,4,3,4,4,4 +,,2,2,2,2,2,2,2,2,2,2,2,2,2,2,4,4,4,4,2,5,4,4,4,4,4,4 +,,2,2,2,2,2,2,2,2,2,2,2,2,2,2,3,2,2.5,1,1,5,3,4,2,4,4,4 +"health,education,housing,environment","5,6,7,10",2,2,2,2,1,1,1,2,2,1,2,2,2,2,2,2,2,2,4,3,2,2,1,2,2,1 +"brexit,immigration,crime,health,education,housing,welfare,environment,family life","1,3,4,5,6,7,8,10,13",1,2,1,1,1,1,1,1,2,1,2,2,1,2,2,2,2,1,5,4,4,3,4,4,3,3 +,,2,2,2,2,2,2,2,2,2,2,2,2,2,2,4,3,3.5,3,4,3,4,3,2,3,3,4 +,,2,2,2,2,2,2,2,2,2,2,2,2,2,2,5,4,4.5,5,5,4,4,4,4,4,4,4 +"economy,health,education,environment,family life","2,5,6,10,13",2,1,2,2,1,1,2,2,2,1,2,2,1,2,4,4,4,5,5,5,5,5,4,4,5,5 +,,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,1,1.5,1,3,,,,,,, +"immigration,crime,health,environment","3,4,5,10",2,2,1,1,1,2,2,2,2,1,2,2,2,2,1,1,1,1,5,1,3,1,1,1,3,1 +,,2,2,2,2,2,2,2,2,2,2,2,2,2,2,4,3,3.5,3,3,,,,,,, \ No newline at end of file