Bad news: This means I will definitely have to (*gulp*) learn new software and maybe even upgrade my computer in order to handle next year's responses, assuming they increase at a similar rate.
Good news: Maybe tomorrow I can start writing up the worldwide report for 2021??
Please note, this is very optimistic, I may not manage it because I have a LOT of fatigue today. On the other hand, it might be a pretty effective way to keep me resting on the sofa. 🤔
I wish I could somehow crowdfund "Cassian learns a new software" - no amount of money will make my brain do the thing, you know?
On that note! Statisticians, can you help out?
I'm going to need some software that can handle a LOT of calculations on a fairly average computer, involving probably 50,000 survey responses. The most important factor is user-friendliness.
Money is a secondary consideration to user-friendliness, it doesn't have to be free - if I can't learn how to use it without going to uni then the Gender Census is kaput, y'know? So if it has to cost money to be solo-learnable (a real phrase) then I may have to crowdfund.
Another option might be... to limit the survey to 40,000 responses per year? 🤨 (Very much not what I want to do.)
@gendercensus It'll be fine when doing simple distributions for each individual question as 50,000 rows per sheet is manageable. But, when you want to create statistics across multiple questions to tease out potentially significant and actionable nuances (e.g., x% of age group y represent the majority of answer a in question 3), the spreadsheet format and interface will start to be a barrier, and increase the likelihood of errors.
I have experience working with large complex spreadsheets, and I find them very challenging to debug (I'm currently doing this for a spreadsheet with 96 sheets, 127,141 cells, 56,917 formulas, and 864 charts so I have experience here).
R allows analysis of statistically significant correlations across any number of questions with literally 1 - 5 lines of code. It's much easier to debug. I will also claim that, for statistics, you'll have many more options and much more confidence in the results using R.
@jqiriazi Thanks for the detailed explanation, much appreciated! :)
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