This class is envisioned as a class on Intermediate Statistical Data Analysis for Social Sciences. In it we will present concepts and methods that are widespread and important for a successful career as an academic. The class is geared towards application and puts an emphasis on “doing” and computation/simulation (as opposed to theory). The course is self-contained, but it helps tremendously if the students had some exposure to statistical thinking and methods prior in their studies (e.g., an introductory course in statistics and/or regression analysis).

Throughout the course we will make extensive use of the statistical programming language R for illustration, practicals, homework and midterm and final project. It is highly recommended to have prior working knowledge of R. If not, then students are expected to catch up within the first few units (the lecturer can provide intro videos).

Here is the course plan (numbers refer to topic units, not necessarily actual units):

The core units that we strive to cover definitely:

1. We start with an introduction to the R environment and make attempts on coding with R.
2. The next topic is Monte Carlo simulations and resampling, which covers exploration of probability distributions, how to simulate data from these distributions and why that is useful in data analysis.
3. Next we talk about statistical decision theory and inference, including the concepts of p-values and confidence intervals.
4. The next topic covers resampling/simulation-based approaches to statistical inference.
5. Next we talk about measuring association between two variables, including the concept of correlation.
6.  In this topic we lay the ground for one of the main methods of statistics, regression analysis.
7.  In this topic we discuss two-sample t-test and ANOVA with and without repeated measurement.
8. In these units we cover the general linear model as the unifying framework under which we can subsume everything from Pearson correlation to linear regression and ANOVA.

The next few units are optional and we select them based on the interest and requirements of the students:

• Optional: In this unit we will cover effects sizes, power and the role that the sample size plays for plannin
• Optional: In this unit we extend the general linear model to the class of mixed effects (aka hierarchical aka multilevel) models.
• Optional: Here we extend regression analysis to work for additional distributions. These models are called generalized linear models.
• Optional: This topic unit covers other regression topics including regression with data transformations, polynomial regression and robust regression.

This constitutes the last unit:

We wrap everything up and connect the dots of what we learned in a big picture view.

After the course participants will be familiar with a broad array of fundamental statistical procedures prevalent in business research and social sciences. They will be able to conduct statistical data analyses with these methods and know how to implement analyses in R.

At the end of the course participants will have a broad toolbox of statistical methods to choose from for typical research projects, or to build upon for more complicated data analyses. They will be able to comprehend the statistical analyses in quantitative studies and their results and critically evaluate them.