Year: 2017
Type: article
Abstract: Objectives: 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. Method: We introduce an analytical paradigm based on the feature importance estimation and recursive feature elimination with decision tree ensembles and illustrate its utility using empirical data from the German Socio-Economic Panel (SOEP). Results: We identified a subset of 20 measures from the SOEP data set that maintain much of the ability of the original data set to predict life satisfaction and health across younger, middle, and older age groups. Discussion: Feature selection techniques permit researchers to choose measures that are maximally predictive of relevant outcomes, even when there are interactions or nonlinearities. These techniques facilitate decisions about which measures may be dropped from a study while maintaining efficiency of prediction across groups and reducing costs to the researcher and burden on the participants.
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: bronze
Funders: National Science Foundation, Deutsche Forschungsgemeinschaft, Bundesministerium für Bildung und Forschung, National Institutes of Health, Social Science Research Institute, Pennsylvania State University, National Institute on Aging, Eunice Kennedy Shriver National Institute of Child Health and Human Development
Awards: G2068763256, IGERT: Big Data Social Science - An Integrative Education and Research Program in Social Data Analytics, Toward a Unifying Model of Self-Regulation and its Development, Population Research Institute, G7864424134, G9000228955, Psychosocial Determinants and Biological Pathways to Healthy Aging (Pathways)
OpenAlex ID: W2586305624