Since its introduction in 2001, the Electronically Activated Recorder (EAR) method has become an established and broadly used tool for the naturalistic observation of daily social behavior in clinical, health, personality, and social science research. Previous treatments of the method have focused primarily on its measurement approach (relative to other ecological assessment methods), research design considerations (e.g., sampling schemes, privacy considerations), and the properties of its data

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... (i.e., reliability, validity, and added measurement value). However, the evolved procedures and practices related to arguably one of the most critical parts of EAR research-the coding process that converts the sampled raw ambient sounds into quantitative behavioral data for statistical analysis-so far have largely been communicated informally between EAR researchers. This article documents "best practices" for processing EAR data, which have been tested and refined in our research over the years. Our aim is to provide practical information on important topics such as the development of a coding system, the training and supervision of EAR coders, EAR data preparation and database optimization, the troubleshooting of common coding challenges, and coding considerations specific to diverse populations.