The full random-effects model (FREM) is a method for studying how different factors affect outcomes in mixed-effects models. It looks at how these factors vary and interact with each other. FREM can handle tricky situations where traditional methods struggle, like when factors are related and hard to separate. By using different mathematical relationships, FREM can estimate these effects accurately. Comparisons show that FREM is slightly better at estimating variability between individuals. It also allows for easy estimation of how each factor affects outcomes on its own. This makes it useful for analyzing data with different sets of factors or explaining how factors influence outcomes independently.