kidwellj/re_connect_survey
1
0
Fork 0
mirror of https://github.com/kidwellj/re_connect_survey.git synced 2025-07-06 20:34:10 +00:00
Code Issues Projects Releases Packages Wiki Activity

Descriptives and Visualization beginning

Browse source 
I finished the analyses for each section, and have moved onto beginning a file for visualizing all the data. The new "visualization data" csv file has only the columns that were asked for in lines 134-158. I'll continue to work on those plots this week.
This commit is contained in:
rehughes07 2021-11-24 13:12:50 +00:00
parent 0d7d290250
commit 6a8fc8f590
4 changed files with 198 additions and 1 deletions
Download patch file Download diff file Expand all files Collapse all files

33
Connect Project R Markdown.Rmd
View file

@ -143,8 +143,10 @@ table(Q3_1factor)
- Q19 (respondent ethnic self-desc)
```{r Plots}
# Q22
library(ggplot2)
# Q22
testplot <-
# Q23
# Q24
@ -305,7 +307,36 @@ summary(informal_affiliation_test_Q23)
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)
```
```{r Analyses based on personal religious affiliation}
## 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
personal_religious_affiliation_data <- read.csv("./data/Personal religious affiliation data.csv")
head(personal_religious_affiliation_data)
personal_religious_affiliation_data$Q21_binaryrecode <- factor(personal_religious_affiliation_data$Q21_binaryrecode, levels = c(1, 2), labels = c("none", "answered"))
## Q22
personal_religious_affiliation_test_Q22 <- t.test(Q22_avg ~ Q21_binaryrecode, data = personal_religious_affiliation_data, paired = FALSE)
personal_religious_affiliation_test_Q22
## Q23
personal_religious_affiliation_test_Q23 <- manova(cbind(Q23_1, Q23_2) ~ Q21_binaryrecode, data = personal_religious_affiliation_data)
summary(personal_religious_affiliation_test_Q23)
## Q27
personal_religious_affiliation_test_Q27 <- manova(cbind(Q27_1, Q27_2, Q27_3, Q27_4, Q27_5, Q27_6, Q27_7) ~ Q21_binaryrecode, data = personal_religious_affiliation_data)
summary(personal_religious_affiliation_test_Q27)
# Significant difference between answers to Q27 and whether participants indicated a personal religious affiliation -- with the small sample size it may be easier to visualize the differences here based on freeform answer
head(personal_religious_affiliation_data)
personal_religious_affiliation_means <- aggregate(cbind(Q27_1, Q27_2, Q27_3, Q27_4, Q27_5, Q27_6, Q27_7) ~ Q21_binaryrecode, data = personal_religious_affiliation_data, FUN = mean)
personal_religious_affiliation_means
## In viewing the means, it is likely the significant difference viewed in the above MANOVA is within Q27_7, with those who indicated having a personal religious affiliation reporting lower scores (M = 2.94) than those who did not answer or indicated they had no religious affiliation (M = 3.83). This makes sense as a higher score indicates they disagree that they know about "how they put their beliefs about the climate/biodiversity crisis into practice"
# Also a slight difference in Q27_3 with those indicating having a personal religious affiliation reporting slightly lower scores (M = 3.24) than those who did not answer to indicated they had no religious affiliation (M = 3.76)
```