As said in the title, I know almost nothing about statistics.
My hypothesis for my dissertation is that UK Members of Parliament with a larger margin of victory will do less work than those with a lower margin of victory -- because they will inevitably reelected to have less incentive to work hard. To determine this, I have collected data for all MPs on the frequency they carried out the following activities in parliament using various APIs and web scraping: sponsoring an amendment, sponsoring a private members bill, sponsoring an early day motions, voting in a division, membership of a select committee, writing a question to a minister, speaking a word in parliament.
I have then converted these variables into the amount of hours it would've taken to carry out that activity -- which was determined in discussion with parliamentary staff. For example, signing an early day motion should take around 4 minutes, so I divided the frequency of this activity by 15. Once all the variables have been converted into hours, I aggregate them to create a "Hours spent on legislative activity" variable.
I've read "Statistics for Dummies" to try and make some more sense of what I should do. I then decided to take a similar approach to Bowler (2010), who's done similar research but only looking at whether there is a correlation between the number of private members bills introduced and margin of victory -- using a Poisson model -- see this album for a table and graph. To do so he took the average margin of victory, then defined marginal constituencies as one standard deviation below the average, and safe seats one above the average. Here are my preliminary results (need to work out how to plot it and break it down by party):
So it seems to prove my hypothesis -- the average hours spent on legislative activity in marginals is more than normal and safe seats. I am pretty happy with this methodology, however I really want the methodology to be as close to "flawless" as possible so I can get higher marks. There are two other UK-based pieces of research, both from the same author but only measure one parliamentary activity:
- Kellermann (2013): https://imgur.com/a/VAjuQvV (methodology)
"Posterior predictive intervals for the mean number of EDMs
introduced as a function of party and vote margin"
- this seems years above my level and I can't find any ELI5 explanations, but is a "Bayesian hierarchical negative binomial hurdle model" something I should look into doing -- is this even applicable to my dataset? Even if not it would be great if you guys could point me in the right direction so I can gain an understanding so I can explain why I shouldn't choose this kind of model. If it's too hard to explain, I have a few days I can spend learning so any resources you can point me to that I'd understand would be much appreciated!
Kellermann (2015) -- "Relative Change in [Written] Question Frequency as a Function of Electoral Margin", uses "a negative binomial regression model with MP-level random effects. The random effects allow for systematic individual-speciﬁc differences in the frequency with which MPs ask questions, while the use of the negative binomial distribution allows for within-MP overdispersion in the number of questions asked by members from session to session."
- This seems slightly less complicated than the one above, -- not sure why he chose a simpler approach in later research. Another comment suggested I should look into adding "random effects" so maybe this one is more appropriate?
If neither of these models are appropriate, are there any other changes I should make or models that I should consider? Thanks all!