To analyze your data, you need to become familiar with one (or multiple) statistical methods.

Below, I am collecting a few links for commonly used methods in thesis projects (without claiming this list is complete - consider it work in progress).

Method-focused ebooks

• Principles of Econometrics with R
• Causal Inference in Education
• A Guide on Data Analysis

Specific methods

Next to the general references above, these are some useful tutorials for specific methods.

Regression analysis

• https://www.statmethods.net/stats/regression.html
• http://r-statistics.co/Linear-Regression.html

Quasi-experimental design

• Goldfarb, A., Tucker, C., & Wang, Y. (2022). Conducting Research in Marketing with Quasi-Experiments. Journal of Marketing. https://doi.org/10.1177./00222429221082977
• Li, Kathleen T., & Christophe Van den Bulte (2022). Augmented Difference-in-Differences. Marketing Science. https://doi.org/10.1287/mksc.2022.1406.

Tutorials for difference-in-difference regressions

• http://www.princeton.edu/~otorres/DID101R.pdf
• https://rpubs.com/phle/r_tutorial_difference_in_differences

Tutorials for propensity score matching

• https://cran.r-project.org/web/packages/MatchIt/vignettes/MatchIt.html
• https://sejdemyr.github.io/r-tutorials/statistics/tutorial8.html