Pfadmodellierung

Pfadmodellierung, also known as path modeling, is a statistical technique used to test and estimate causal relationships between a set of observed and latent variables. It is a broad term that encompasses various specific statistical methods, including regression analysis, factor analysis, and structural equation modeling (SEM). The core idea behind path modeling is to represent hypothesized causal relationships as a diagram, often called a path diagram, where variables are depicted as circles or boxes and causal influences are represented by arrows.

In path modeling, observed variables are those that are directly measured, while latent variables are unobserved constructs that are inferred from observed variables. The relationships between these variables can be direct or indirect. Direct effects are those that are not mediated by other variables, while indirect effects occur when the influence of one variable on another is transmitted through one or more intervening variables. The strength and statistical significance of these relationships are estimated using statistical software.

Path modeling is widely used in various fields, including psychology, sociology, education, marketing, and health sciences, to test theoretical models and understand complex phenomena. Researchers use it to explore how different factors influence an outcome of interest, to identify mediating and moderating effects, and to evaluate the overall fit of a theoretical model to the data. The technique allows for the examination of complex networks of relationships that might not be easily captured by simpler statistical methods.