Member-only story
Path modeling (or) structural equation modeling (SEM), is a powerful statistical technique used to analyze complex relationships between variables. It is widely employed in various fields, including social sciences, psychology, economics, and biology. In this article, we will explore path modeling in detail and provide a Python code sample to demonstrate how to perform path modeling using the popular `statsmodels` library.
What is Path Modeling?
Path modeling is a technique that allows researchers to investigate causal relationships among variables. It provides a comprehensive framework for assessing both the direct and indirect effects of one or more independent variables on a dependent variable. In path modeling, we create a graphical representation of the relationships between variables, where each arrow represents a hypothesized causal relationship. These relationships are expressed as structural equations.
The primary components of a path model include:
1. Latent Variables: Unobservable variables that are inferred from a set of observable variables. Latent variables are often represented by ellipses in the graphical model.
2. Observed Variables: Measurable variables that are directly observed and used as input to the model.
3. Paths: Arrows connecting latent and observed variables, representing the hypothesized relationships between them.
