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200301 r ||| eng |
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|a Murphy, Joe J.
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| 245 |
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|a Interactive visualization to facilitate monitoring longitudinal survey data and paradata
|h Elektronische Ressource
|c Joe J. Murphy [and 5 others]
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| 260 |
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|a Research Triangle Park, NC
|b RTI Press
|c May 2019, 2019
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| 300 |
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|a 1 PDF file (15 pages)
|b illustrations
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|a Includes bibliographical references
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| 653 |
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|a Data Accuracy
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| 653 |
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|a Longitudinal Studies
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| 653 |
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|a United States
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| 653 |
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|a Data Visualization
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| 653 |
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|a Research Design
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| 653 |
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|a Health Surveys / methods
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| 710 |
2 |
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|a RTI International
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| 041 |
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7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b NCBI
|a National Center for Biotechnology Information
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| 490 |
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|a Occasional paper
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|u https://www.ncbi.nlm.nih.gov/books/NBK545492
|3 Volltext
|n NLM Bookshelf Books
|3 Volltext
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| 082 |
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|a 610
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| 520 |
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|a Surveys often require monitoring during data collection to ensure progress in meeting goals or to evaluate the interim results of an embedded experiment. Under complex designs, the amount of data available to monitor may be overwhelming and the production of reports and charts can be costly and time consuming. This is especially true in the case of longitudinal surveys, where data may originate from multiple waves. Other such complex scenarios include adaptive and responsive designs, which were developed to act on the results of such monitoring to implement prespecified options or alternatives in protocols. This paper discusses the development of an interactive web-based data visualization tool, the Adaptive Total Design (ATD) Dashboard, which we designed to provide a wide array of survey staff with the information needed to monitor data collection daily. The dashboard was built using the R programming language and Shiny framework and provides users with a wide range of functionality to quickly assess trends. We present the structure of the data used to populate the dashboard, its design, and the process for hosting it on the web. Furthermore, we provide guidance on graphic design, data taxonomy, and software decisions that can help guide others in the process of developing their own data collection monitoring systems. To illustrate the benefits of the dashboard, we present examples from the National Longitudinal Study of Adolescent to Adult Health (Add Health). We also discuss features of the dashboard to be developed for future waves of Add Health
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