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More Analyses on Religious Affiliation

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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).
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rehughes07 2021-11-15 19:37:30 +00:00
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@ -270,18 +270,41 @@ head(Q13_data)
# then analyze based on specific one # 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")) 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 # Q22
hist(Q13_data$Q22_average) hist(Q13_data$Q22_average)
specific_affiliation_test <- aov(Q22_average ~ Q13_recode, data = Q13_data) specific_affiliation_test <- aov(Q22_average ~ Q13_recode, data = Q13_data)
typeof(Q13_data$Q13_recode)
summary(specific_affiliation_test) summary(specific_affiliation_test)
# Q23 # Q23
specific_affiliation_test_Q23 <- manova(cbind(Q23_1, Q23_2) ~ Q13_recode, data = Q13_data)
summary(specific_affiliation_test_Q23)
# Q27 # Q27
specific_affiliation_test_Q27 <- manova(cbind(Q27_1, Q27_2, Q27_3, Q27_4, Q27_5, Q27_6, Q27_7) ~ Q13_recode, data = Q13_data)
summary(specific_affiliation_test_Q27)
## Q15-16 with Q22, Q23, Q27 ## Q15-16 with Q22, Q23, Q27
# Q15 is binary; 1st test whether difference in answers based on whether the school has an informal religious character or not. Q16 provides further detail and can be explored # Q15 is binary; 1st test whether difference in answers based on whether the school has an informal religious character or not. Q16 provides further detail and can be explored
religion_affiliation_data$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 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 # 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