W1L1: Introduction

POLS 642 Intermediate Analysis of Political Data

Dr. Ches Thurber

Diving right in…

  • in groups, check out this APSR article; read only the abstract and intro

  • what’s the research question? what’s the hypothesis?

  • what are the IV and DV? how are they measured?

  • what are the research methods?

  • what challenges is the author trying to overcome by using these methods?

Challenges to inference

Inference in the process of trying to use facts we know learn facts we don’t know

Bailey describes two fundamental challenges to inference:

  • the problem of randomness: how do we know if patterns in our sample are true in the broader population

  • the problem of causality: when can we be confident that a relationship between two variables is due to one causing the other?

This course focuses on both, maybe more on the former?

Some important points

  • The challenges of randomness and causality are not unique to quantitative research

  • They are not the only things we should be thinking about in assessing “good” research

  • What makes for “good” research?

My View on “Good Research”

“The best advice I received in the discipline was that it is better to take B+ swings at A+ questions than A+ swings at B+ questions.”

  • Erica Chenoweth, Harvard Kennedy School

My View on Quantitative Methods

  • it can help spot trends and puzzles

  • it can put a specific case in context

  • it can assess the generalizability of a pattern

  • it can adjudicate between competing explanations

  • it opens up specific toolkits (surveys, experiments)

  • it’s a tool in service of a bigger goal

Dr. Kuehl’s View

  • “our focus will be on causal inference”

  • “underlying everything we are doing is the concept of causality”

My View on R

  • it’s a tool to help make working with data easier (ha!)

  • the replicability advantage

  • the ability to manipulate data

  • graphical representation

  • FREE!

What We Will Learn in this Course

  • Review of basic stats and regression (641)
  • Deeper dive into regression with focus on assumptions, violations, remedies
  • Introduction to thinking about Causal Inference
  • Additional methods, if time permits

Looking Ahead

  • Today: Intro and Syllabus

  • Thursday: Data Viz, Tidyverse, and GGPLOT

    • try to do DataCamp by Thursday

  • Friday: Data Camp Due

  • Next Week: Describing Single Variables

An Unsolicited Note on Software

  • Automatic backup!!!!
  • Bibliographic management
  • Statistical Analysis
  • Editor/Document Producer

Some Software Options

  • Backup: OneDrive, DropBox, Google Drive, iCloud

  • Citations: Zotero, Mendeley, EndNote

  • Statistical Analysis: R with RStudio IDE

  • Editor: MS Word, Quarto in R Studio, LaTex (?)

The Syllabus

I am going to turn to the actual syllabus document now…