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Introduction to Inferential Statistics

Levente Littvay
littvayl@ceu.edu

Central European University

Levente Littvay researches survey and quantitative methodology, twin and family studies and the psychology of radicalism and populism.

He is an award-winning teacher of graduate courses in applied statistics with a topical emphasis in electoral politics, voting behaviour, political psychology and American politics.

He is one of the Academic Convenors of ECPR’s Methods School, and is Associate Editor of Twin Research and Human Genetics and head of the survey team at Team Populism.

 @littvay

Course Dates and Times

Monday 27 ꟷ Friday 31 July 2020
2 hours of live teaching per day
Courses will be either morning or afternoon to suit participants’ requirements

Prerequisite Knowledge

NONE. (REALLY!) If you can remember how to add, subtract, divide and multiply using both negative and positive numbers, you are ready to take this course. There may be an occasional square and square root that, in this class, we are happy to calculate with a calculator.


Short Outline

This course provides a highly interactive online teaching and learning environment, using state of the art online pedagogical tools. It is designed for a demanding audience (researchers, professional analysts, advanced students) and capped at a maximum of 16 participants so that the teaching team (the Instructor plus one highly qualified Teaching Assistant) can cater to the specific needs of each individual.

Purpose of the course

Open any top political or social science journal and half (if not more) of the articles are littered with tables, numbers, statistical findings. The purpose of this course is to demystify these and make you an informed consumer of quantitative research. 

In this course, you will learn the basics of statistical inference and the most commonly used statistical techniques in political science research for assessing the difference between groups or to see if there is an association between two variables.

The course provides the foundation for multiple regression and is a must for anyone wanting to take that course as long as you are not familiar or comfortable with the topics listed in this outline.

ECTS Credits

3 credits Engage fully with class activities
4 credits Complete a post-class assignment


Long Course Outline

Day 1

First we discuss basic distributions with a strong emphasis on the normal distribution (otherwise known as the bell curve) but considering others too. We cover the types of variables and how central tendency (mean, median, mode) and variability (range, variance and standard deviation) can be measured, as these are the essential foundational building blocks of statistical inference.

Day 2

We continue by understanding how standardized scores (such as IQ, SAT, GRE and, most importantly, z-scores) can get us one step closer to comparable statistical estimates used for inference. Then we turn back to the normal distribution to see how the concepts of probability, distributions and standardised scores come together, highlighting the power of both the normal distribution and what it can tell us.

Day 3

We explore why it is not even crucial to have normally distributed variables when doing statistical inference. With our newly gained knowledge we learn to leverage statistical properties of group means and learn how to formulate (and even test) simple hypotheses using statistical inference.

Day 4

We leverage the discoveries of an old brewery employee who also revolutionised the world of statistics by allowing us to move from simple hypothesis testing situations with too many assumptions (z test) to a realistic testing of hypotheses commonly found in the social sciences (t test and ANOVA). We explore both group comparisons, changes over time and comparisons to some gold standard value.

Day 5

We move to testing relationships between variables using correlations and we introduce bivariate regression which provides the foundation of multiple regression analysis. We also explore another class of statistics which make fewer assumptions about distributions, and we learn how to answer different types of research questions with this different type of analytical technique: the Chi-Square test.


How the course will work online

Pre-recorded lectures will become available to you immediately before and during the course week. These will feature the Instructor with his trusty electronic whiteboard ensuring understandable and natural pacing for the sometimes mathematical, though always simplified, course material. 

Watch these sessions right before the daily live discussion and Q&A where we will also do breakout group exercises and hands-on tutorials on the (software) application of the methods using web apps, spreadsheets or other simple to use, freely available software. I will also provide individual post-course exercises where everyone may ꟷ individually or in small teams ꟷ practice the course material. 

For the week (and beyond) we will be in touch with each other on Slack. Individual consultations will be available, as needed, either to clear up course material (with the Teaching Assistant) or discuss research applications (with the Instructor). We'll also do one social session where we may all ‘share’ a beverage.


Additional Information

Disclaimer

This course description may be subject to subsequent adaptations (e.g. taking into account new developments in the field, participant demands, group size, etc). Registered participants will be informed at the time of change.

By registering for this course, you confirm that you possess the knowledge required to follow it. The instructor will not teach these prerequisite items. If in doubt, please contact us before registering.