[method ramble #1] To start.

So it's been a while.
There hasn't simply been an issue I seriously though that I must write something about.
Or more honestly, I have grown increasingly lazier.

Instead of sporadically feeling guilty about a defunct blog, I decide to spend the space on something slightly, like really slightly, more useful.: my method.

I am not a methodologist. I like quant methods. I believe in it. But I simply wasn't trained as a methodologist and don't plan to be in the near future. But at the same time, it's been my obsession to rigorously `spend' the outcomes that methodologists `produced' because doing so seems to be leading to a better science (as well as looking cooler).

I've got a lot to catch up on that front. Methodological advancement political science as a social science field has made is more than astonishing particularly in the past 3-4 years--during which I depleted the usefulness of my outdated method skills.

A few things that I need to really LEARN pretty soon:

1. difference-in-difference
I would've used it for my dissertation if I knew it existed. I was too lazy to know that. I think I get the math, but need to get the hang of it if I want to use it to expand my speculative attacks project.

2. regression discontinuity
Again, I get the math. But need to learn the language.

3. text scraping
There are a few folks who have already well established ways in which researchers scrap data from various sources. I need to have an `original dataset' at some point and this seems to be the closest thing to tap into for now.

4. matching and other causality stuff
I mean, it's a sure thing.

These are long-term goals though. Each task wouldn't really take much time, but I am grappling with a lot of stuff and with the snowcolypse in the DC area, I have been taken hostage at home with kids for the whole first month of the year.

So mostly what I'll be posting here would be day-to-day issues and most likely frustrations I have related to methods (BROADLY DEFINED).