Dealing with Missing Data
A Comparative Exploration of Approaches Using the Integrated City Sustainability Database
Bibliographic Data
| ID | 6403789 |
|---|---|
| Authors | Cali Curley (0000-0003-3244-2623, Indiana University–Purdue University Indianapolis, Indianapolis, IN, USA, corresponding author), Rachel M Krause (0000-0003-1490-1996, University of Kansas, Lawrence, KS, USA), Richard Feiock (0000-0001-5215-0391, Florida State University, Tallahassee, FL, USA), Christopher V Hawkins (0009-0005-8709-8892, University of Central Florida, Orlando, FL, USA) |
| Year | 2019 |
| Volume | 55 |
| Issue | 2 |
| Pages | 591-615 |
| Publication date | 2019-03-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Urban Affairs Review (JOURNAL) |
| Journal identifiers | ISSN: 1078-0874 • E-ISSN: 1552-8332 |
| Publisher | SAGE Publications Inc (PUBLISHER) |
| DOI | 10.1177/1078087417726394 |
| OpenAlex | W2749057300 |
| Language | EN |
| Citations received | 20 |
| References cited | 48 |
Studies of governments and local organizations using survey data have played a critical role in the development of urban studies and related disciplines. However, missing data pose a daunting challenge for this research. This article seeks to raise awareness about the treatment of missing data in urban studies research by comparing and evaluating three commonly used approaches to deal with missing data—listwise deletion, single imputation, and multiple imputation. Comparative analyses illustrate the relative performance of these approaches using the second-generation Integrated City Sustainability Database (ICSD). The results demonstrate the benefit of using an approach to missing data based on multiple imputation, using a theoretically informed and statistically supported set of predictor variables to develop a more complete sample that is free of issues raised by nonresponse in survey data. The results confirm the usefulness of the ICSD in the study of environmental and sustainability and other policy in U.S. cities. We conclude with a discussion of results and provide a set of recommendations for urban researcher scholars
Data collection · Data mining · Data science · Data set · Imputation (statistics · Machine learning · Missing data · Statistics · Survey data collection · Sustainability · Computer Science · Mathematics · Spatial and Panel Data Analysis · Survey Methodology and Nonresponse · Urban, Neighborhood, and Segregation Studies · Artificial Intelligence
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| Unique citing works | 20 |
|---|---|
| Citations per year | 2,86 |
| Citation span | 2019 - 2026 (8) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 20 |