Multilevel modeling: Analyzing complex data when one level is not enough

When individuals are nested within groups or organizations, or when repeated measures are taken from the same individuals, data points are not independent and standard techniques such as linear regression models are not suitable. Multilevel modeling (aka linear mixed-effects regression) is an extension of linear regression models that uses additional variance and covariance terms to account for the local dependencies implied by nested data structures. How do they work? How to conduct multilevel analyses? How to intepret the results? I'll try to answer these questions with the help of R.

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