Understanding Random Effects and Fixed Effects in Statistical Analysis | by Akif Mustafa | Medium

Statistical analysis like multilevel modelling, panel data analysis, and linear mixed models are widely used in various fields like social science, economics, epidemiology, biostatistics, business analytics, psychology, and many more. In these fields, the terms fixed effect and random effect are frequently encountered, and as we delve deeper into the technical differences and equations, it can become increasingly confusing. Well, in this article, we will gain a clear understanding of these terms and their practical applications through simplified explanations and illustrative examples. I am assuming that you, as a reader, have a basic understanding of statistical analysis, including key concepts such as dependent variables, independent variables, and regression. Fixed effect Fixed effects play a fundamental role in statistical analysis, providing a way to account for specific variables or factors that remain constant across observations. These effects allow us to capture the individual

Statistical analysis like multilevel modelling, panel data analysis, and linear mixed models are widely used in various fields like social science, economics, epidemiology, biostatistics, business analytics, psychology, and many more. In these fields, the terms fixed effect and random effect are frequently encountered, and as we delve deeper into the technical differences and equations, it can become increasingly confusing. Well, in this article, we will gain a clear understanding of these terms and their practical applications through simplified explanations and illustrative examples. I am as