Mastering Archival Data Methods for IS Research | From Data Sources to Causal Inference скачать в хорошем качестве

Mastering Archival Data Methods for IS Research | From Data Sources to Causal Inference

Everything you need to know about using archival and secondary data for Information Systems research — from finding the right databases to publishing in top journals like MISQ and ISR. In this video, I walk through the complete research pipeline: the major data sources (Compustat, WRDS, SEC EDGAR, digital platform data), how to clean and merge datasets, choosing the right econometric method (DiD, IV, RDD, fixed effects), and the robustness checks reviewers expect to see. I also cover common pitfalls like endogeneity, temporal mismatch, and measurement validity — with concrete examples of what to do instead. Whether you're a PhD student starting your first archival project or looking to level up your empirical toolkit, this guide covers the full journey from raw data to publication-quality causal inference. Topics covered: Why archival data has become the dominant approach in IS research Key data sources: Compustat, CRSP, SEC EDGAR, platform/digital trace data Data cleaning and panel construction Choosing your econometric method Merging multiple data sources with common identifiers Robustness checks that satisfy reviewers Essential technical skills (Stata, R, Python) An 8-month learning roadmap