Leveraging Methods in Partial Least Squares Structural Equation Modeling to Analyze Seattle Housing Data

Theory of Statistics I Final Project

Primary Citation

The objective of this project was to examine the methods implemented by the following paper within a new context (housing data).

Sharma, Pratyush & Sarstedt, Marko & Shmueli, Galit & Kim, Kevin & Thiele, Kai. (2018). PLS-Based Model Selection: The Role of Alternative Explanations in Information Systems Research. Journal of the Association for Information Systems. 10.17705/1jais.00538.

Summary

PLS SEM modeling of Seattle area housing data. Statistical selection of different pathway models.
final report: 625_725_FA22_Rose_Victoria_final_github.pdf

Environment

All packages needed are included in requirements.txt

Running + Replication

To regenerate figures included in the paper: figure_generation.ipynb
To revisit the parameter selection for model selection: data_generation.ipynb
To regenerate the raw data in the paper: model_generation.py
To regenerate the rendered heatmaps + latex for the tables: model_analysis.ipynb