A Random Effects Model is a statistical approach used in data analysis, particularly in the context of panel data or hierarchical data structures. It assumes that individual differences across subjects or groups are random and not fixed. This model helps account for variability that is not explained by the observed variables, allowing researchers to make inferences about the population as a whole. In this model, the effects of certain variables are treated as random variables, which means they can vary across different entities, such as individuals, schools, or countries. This approach is useful in fields like economics, psychology, and medicine, where researchers want to understand the impact of certain factors while considering the inherent differences among subjects.