From 0d7d2902502c81306b4242507f3d5f92cbc7a902 Mon Sep 17 00:00:00 2001
From: rehughes07 <58590776+rehughes07@users.noreply.github.com>
Date: Mon, 15 Nov 2021 19:37:30 +0000
Subject: [PATCH] More Analyses on Religious Affiliation

Completed analyses on both the informal and formal religious affiliation of schools data.

Results indicate there is no significant difference in the answers to questions 22, 23a-b, and 27a-g based upon whether participants indicated that their school had a formal affiliation (Yes/No), what that affiliation was (list of 7 possible), or an informal affiliation (Yes/No). Personal affiliation has not been analyzed yet. I'm trying to figure the best way to code - perhaps as a Yes/No, and then subset the Yeses like I did for formal religious affiliation (lines 270-289).
---
 Connect Project R Markdown.Rmd | 23 +++++++++++++++++++++++
 1 file changed, 23 insertions(+)

diff --git a/Connect Project R Markdown.Rmd b/Connect Project R Markdown.Rmd
index ecb4bff..7a89256 100644
--- a/Connect Project R Markdown.Rmd	
+++ b/Connect Project R Markdown.Rmd	
@@ -270,18 +270,41 @@ head(Q13_data)
   # then analyze based on specific one
 Q13_data$Q13_recode <- factor(Q13_data$Q13_recode, levels = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10), labels = c("Church of England", "Roman Catholic", "Methodist", "Other Christian", "Jewish", "Muslim", "Sikh", "Hindu", "Multi-Faith", "None of the above"))
 
+# Test with only included levels
+Q13_data$Q13_recode <- factor(Q13_data$Q13_recode, levels = c(1, 2, 4, 6), labels = c("Church of England", "Roman Catholic", "Other Christian", "Muslim"))
+# No change with this one...still nonsignificant difference
+
   # Q22
 hist(Q13_data$Q22_average)
 specific_affiliation_test <- aov(Q22_average ~ Q13_recode, data = Q13_data)
+typeof(Q13_data$Q13_recode)
 summary(specific_affiliation_test)
 
   # Q23
+specific_affiliation_test_Q23 <- manova(cbind(Q23_1, Q23_2) ~ Q13_recode, data = Q13_data)
+summary(specific_affiliation_test_Q23)
 
   # Q27
+specific_affiliation_test_Q27 <- manova(cbind(Q27_1, Q27_2, Q27_3, Q27_4, Q27_5, Q27_6, Q27_7) ~ Q13_recode, data = Q13_data)
+summary(specific_affiliation_test_Q27)
 
 ## Q15-16 with Q22, Q23, Q27
   # Q15 is binary; 1st test whether difference in answers based on whether the school has an informal religious character or not. Q16 provides further detail and can be explored
 
+religion_affiliation_data$Q15 <- factor(religion_affiliation_data$Q15, levels = c("No", "Yes"), labels = c("No", "Yes"))
+
+  ## Q22
+informal_affiliation_test_Q22 <- t.test(Q22_average ~ Q15, data = religion_affiliation_data, paired = FALSE)
+informal_affiliation_test_Q22
+
+  ## Q23
+informal_affiliation_test_Q23 <- manova(cbind(Q23_1, Q23_2) ~ Q15, data = religion_affiliation_data)
+summary(informal_affiliation_test_Q23)
+
+  ## Q27
+informal_affiliation_test_Q27 <- manova(cbind(Q27_1, Q27_2, Q27_3, Q27_4, Q27_5, Q27_6, Q27_7) ~ Q15, data = religion_affiliation_data)
+summary(informal_affiliation_test_Q27)
+
 
 ## Q21 with Q22, Q23, Q27
   # Q21 is personal religious affiliation. This may be more tricky as it is a free answer...but can code the type of religious affiliation and test that way? -- would be chi-square or some sort of non-para analysis due to the small number of respondents who answered this