Mixed models question Let's say that i have data with  5000 participants(rows) and their scores on some sports, their age, weather on each event, location etc. Is it appropriate to use linear mixed models(lmer in R) if i want to see how factors like age, weather, location affects the intra-individual variability for each participant on sports ? 
 A: Mixed models are used to account for the correlations in an outcome variable in grouped or clustered data. Like in your case, scores coming from the same participant will be correlated. However, there may also be other sources of correlations, e.g., scores from the same location could also be correlated. Mixed models do allow for such complex correlation structures by including a random effect for each level of groups/clusters.
A couple of examples:


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*lmer(scores ~ age + weather + (1 | id), data = <your_data>) fits a linear mixed models with normal error terms postulating the scores of each participant are associated with their age and the weather conditions, and the measurements from the same participant are correlated.

*lmer(scores ~ age + weather + (1 | id) + (1 | location), data = <your_data>) further postulates that scores from the same location are more correlated than measurements from different locations.
It may be also required to include random slopes to account for correlations that decay over time.
