Journal article
Improving Sampling Probability Definitions with Predictive Algorithms
Field methods, p1525822
15 Sep 2022
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Place-based initiatives often use resident surveys to inform and evaluate interventions. Sampling based on well-defined sampling frames is important but challenging for initiatives that target subpopulations. Databases that enumerate total population counts can produce overinclusive sampling frames, resulting in costly outreach to ineligible participants. Quantifying eligibility before sampling using machine learning algorithms can improve efficiency and reduce costs. We developed a model to improve sampling for the West Philly Promise Neighborhood's biennial population-representative survey of households with children within a geographic footprint. This study proposes a method to estimate probability of study eligibility by building a well-calibrated predictive model using existing administrative data sources. Six machine-learning models were evaluated; logistic regression provided the best balance of accuracy and understandable probabilities. This approach can be a blueprint for other population-based studies whose sampling frames cannot be well defined using traditional sources.
Metrics
Details
- Title
- Improving Sampling Probability Definitions with Predictive Algorithms
- Creators
- Matthew Jannetti - Drexel UniversityAmy Carroll-Scott - Drexel UniversityErikka Gilliam - Drexel UniversityIrene Headen - Drexel UniversityMaggie Beverly - Drexel UniversityFelice Le-Scherban - Drexel University
- Publication Details
- Field methods, p1525822
- Publisher
- Sage
- Number of pages
- 16
- Grant note
- West Philly Promise Neighborhood Initiative U215N160055 / U.S. Department of Education; US Department of Education
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Urban Health Collaborative; Community Health and Prevention; Drexel University
- Web of Science ID
- WOS:000854314300001
- Scopus ID
- 2-s2.0-85138289530
- Other Identifier
- 991020099801204721
UN Sustainable Development Goals (SDGs)
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InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Anthropology
- Social Sciences, Interdisciplinary