Structural Equation Modeling (SEM) implementation example with Python Semopy
SEM is a statistical technique used to model complex and simultaneous relationships among variables. It is often used in social science research to study causal relationships among variables, such as the effect of government influence on firm performance. SEM allows researchers to test theoretical models of how different variables are related to one another, and can help identify which variables are most important in predicting an outcome of interest.
The data set being analyzed includes measurements of various variables related to government influence, circular economy, firm performance, and other factors. The script uses pandas to read in the data from an Excel file and preprocess it, including renaming columns to a standardized format. The script then imports the semopy package for structural equation modeling and graphviz for visualizing the resulting models. Semopy package is used for SEM analysis. This package provides tools for specifying and estimating SEM models, as well as for visualizing the resulting models. The remainder of the script includes code for estimating and visualizing SEM models using the data and the semopy.semplot function and graphviz.
Overall, this script is a comprehensive analysis pipeline for a specific data set, including data loading, preprocessing, and modeling using SEM techniques. It demonstrates the use of multiple Python packages and libraries for data analysis and visualization, and is likely useful for researchers or analysts interested in SEM application in Python.