Simple bar plots of Q22 and Q23 by requested variables (with the exception of a couple: 1 I couldn't see the options, and the other is a type-in response so I'm figuring how to sort it).
Q24, 25, and 26 will likely need a different type of plot as they are all categorical variables -- Stacked bar chart perhaps
The colors are gray and boring at the moment, but can/will be changed later to suit what you prefer.
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.
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).
Beginning to work on the analyses based on participants' answers to the religious affiliation questions, including cleaning of the data, removal of incomplete data sets (based on visual inspection), and creation of separate .csv files as I have done with previous analyses.
So far no significant differences between the answers to questions and formal religious affiliation of the school.
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
Figuring how to put frequencies of multiple response data into a clean format, then creating rough code for a simple pie chart (to be made pretty later).
Finished this for the 3 questions needing pie charts.
Cleaned datafile more for analysis and ease of viewing (changed text to numbers for Q25, Q26, Q3 in order to create frequency tables for pie charts and bar graphs).
Preliminary checking of pie chart and bar graph for frequencies
Notes on how to analyze responses in last section (ANOVA)
Created R Markdown with RH Tasks to begin to have consolidated file for graphs and plots code.
Duplicated .xlsx data file in order to clean and upload to R for analyses and graph creation - replaced Likert questions with numerical equivalents for later analysis (higher score indicates more agreement)
