Mixed-effects models are an analytic technique for modeling repeated measurement or nested data. This paper explains the logic of mixed-effects modeling and describes two examples of mixed-effects analyses using R. The intended audience of the paper is psychologists who specialize in cognitive development research. Therefore, the concepts and examples covered will focus primarily on repeated-measurement data resulting from studies in which participants respond to multiple items or trials. However, many of the concepts and examples we cover will likely be of use to readers outside this area of psychology. Finally, we discuss recommendations for dealing with practical challenges, suggest approaches for troubleshooting, and provide guidance on reporting results from mixed-effects models.