The researchers developed new ways to measure how well mixed-effects models explain variation in data. They created measures for the total model, fixed effects, and random effects. By calculating unexplained variations based on individual effects, they captured unique differences between data points. These measures can be applied to different types of models, even when the data has varying levels of spread.