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As diary, panel, and experience sampling methods become easier to implement, studies of development and aging are adopting more and more intensive study designs. However, if too many measures are included in such designs, interruptions for measurement may constitute a significant burden for participants. We propose the use of feature selection-a data-driven machine learning process-in study design and selection of measures that show the most predictive power in pilot data.

Feature Selection Methods for Optimal Design of Studies for Developmental Inquiry