One of the things that makes AAVSO citizen science projects unique is that the data analysis stage is also open to anyone. AAVSO members and observers aren't asked to simply to collect data for scientists to analyse, but they can do some analysis themselves. This includes steps such as identifying a project/problem/goal, collecting data, performing quality control procedures on it, analysing it and communicating results to others.
The 5-Star Analysis Tutorial (originally written by Aaron Price as part of the Citizen Sky Project, but re-written by Paul York in June 2014) is meant to introduce you to basic ways of data processing and inspection. It does not assume a background in astrophysics, statistics or anything similar. However, all good researchers need to understand what their data actually mean.
Grant Foster has written a new blog post titled "Through a Picket Fence". It describes the effect of aliasing when analyzing variable star data. It's written at an introductory level appropriate for those new to data analysis, but with brief references to the math more advanced people may find interesting. The post is at the URL below.
A number of papers have been written discussing visual magnitude estimates in comparison to photometry. Multiple studies have shown that visual magnitude estimates compare very favorably with instrumental photometry in terms of sensitivity and consistency. As with all data, care must be taken to understand what visual magnitude estimates represent, what their bandpass is, and when their use is appropriate.
What is VStar?