Analysis of networks with missing data with application to the National Longitudinal Study of Adolescent Health
It is common in the analysis of social network data to assume a census of the networked population of interest. Often the observations are subject to partial observation due to a known sampling or unknown missing data mechanism. However, most social network analysis ignores the problem of missing da...
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| Published in | Journal of the Royal Statistical Society Series C (Applied Statistics) Vol. 66; no. 3; pp. 501 - 519 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
England
John Wiley & Sons Ltd
01.04.2017
Oxford University Press |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0035-9254 1467-9876 1467-9876 |
| DOI | 10.1111/rssc.12184 |
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| Abstract | It is common in the analysis of social network data to assume a census of the networked population of interest. Often the observations are subject to partial observation due to a known sampling or unknown missing data mechanism. However, most social network analysis ignores the problem of missing data by including only actors with complete observations. We address the modelling of networks with missing data, developing previous ideas in missing data, network modelling and network sampling. We use several methods including the mean value parameterization to show the quantitative and substantive differences between naive and principled modelling approaches. We also develop goodness-of-fit techniques to understand model fit better. The ideas are motivated by an analysis of a friendship network from the National Longitudinal Study of Adolescent Health. |
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| AbstractList | It is common in the analysis of social network data to assume a census of the networked population of interest. Often the observations are subject to partial observation due to a known sampling or unknown missing data mechanism. However, most social network analysis ignores the problem of missing data by including only actors with complete observations. We address the modelling of networks with missing data, developing previous ideas in missing data, network modelling and network sampling. We use several methods including the mean value parameterization to show the quantitative and substantive differences between naive and principled modelling approaches. We also develop goodness-of-fit techniques to understand model fit better. The ideas are motivated by an analysis of a friendship network from the National Longitudinal Study of Adolescent Health. It is common in the analysis of social network data to assume a census of the networked population of interest. Often the observations are subject to partial observation due to a known sampling or unknown missing data mechanism. However, most social network analysis ignores the problem of missing data by including only actors with complete observations. In this paper we address the modeling of networks with missing data, developing previous ideas in missing data, network modeling, and network sampling. We use several methods including the mean value parameterization to show the quantitative and substantive differences between naive and principled modeling approaches. We also develop goodness-of-fit techniques to better understand model fit. The ideas are motivated by an analysis of a friendship network from the National Longitudinal Study of Adolescent Health.It is common in the analysis of social network data to assume a census of the networked population of interest. Often the observations are subject to partial observation due to a known sampling or unknown missing data mechanism. However, most social network analysis ignores the problem of missing data by including only actors with complete observations. In this paper we address the modeling of networks with missing data, developing previous ideas in missing data, network modeling, and network sampling. We use several methods including the mean value parameterization to show the quantitative and substantive differences between naive and principled modeling approaches. We also develop goodness-of-fit techniques to better understand model fit. The ideas are motivated by an analysis of a friendship network from the National Longitudinal Study of Adolescent Health. It is common in the analysis of social network data to assume a census of the networked population of interest. Often the observations are subject to partial observation due to a known sampling or unknown missing data mechanism. However, most social network analysis ignores the problem of missing data by including only actors with complete observations. In this paper we address the modeling of networks with missing data, developing previous ideas in missing data, network modeling, and network sampling. We use several methods including the mean value parameterization to show the quantitative and substantive differences between naive and principled modeling approaches. We also develop goodness-of-fit techniques to better understand model fit. The ideas are motivated by an analysis of a friendship network from the National Longitudinal Study of Adolescent Health. Summary It is common in the analysis of social network data to assume a census of the networked population of interest. Often the observations are subject to partial observation due to a known sampling or unknown missing data mechanism. However, most social network analysis ignores the problem of missing data by including only actors with complete observations. We address the modelling of networks with missing data, developing previous ideas in missing data, network modelling and network sampling. We use several methods including the mean value parameterization to show the quantitative and substantive differences between naive and principled modelling approaches. We also develop goodness‐of‐fit techniques to understand model fit better. The ideas are motivated by an analysis of a friendship network from the National Longitudinal Study of Adolescent Health. |
| Author | Handcock, Mark S. Gile, Krista J. |
| Author_xml | – sequence: 1 givenname: Krista J. surname: Gile fullname: Gile, Krista J. – sequence: 2 givenname: Mark S. surname: Handcock fullname: Handcock, Mark S. |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35095118$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1016/j.socnet.2013.07.003 10.1080/01621459.1981.10477598 10.1017/CBO9780511894701 10.1111/j.2517-6161.1992.tb01443.x 10.1086/386272 10.1093/biomet/63.3.581 10.1198/016214507000000446 10.1016/j.socnet.2008.10.003 10.1001/jama.1997.03550100049038 10.1080/01621459.1995.10476615 10.1016/j.socnet.2004.05.001 10.1080/01621459.1986.10478342 10.1002/9781119013563 10.1198/106186006X133069 10.1007/978-3-319-11257-2_12 10.1146/annurev.soc.27.1.415 10.1214/12-AOS1044 10.1214/08-AOAS221 10.1086/226141 |
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| Snippet | It is common in the analysis of social network data to assume a census of the networked population of interest. Often the observations are subject to partial... Summary It is common in the analysis of social network data to assume a census of the networked population of interest. Often the observations are subject to... |
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| SubjectTerms | Adolescents Censuses Dependent data Exponential random‐graph model Friendship Health Longitudinal studies Missing data Missingness not at random Modelling Networks Parametrization Sampling Social network analysis Social networks Studies |
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| Title | Analysis of networks with missing data with application to the National Longitudinal Study of Adolescent Health |
| URI | https://www.jstor.org/stable/44682588 https://onlinelibrary.wiley.com/doi/abs/10.1111%2Frssc.12184 https://www.ncbi.nlm.nih.gov/pubmed/35095118 https://www.proquest.com/docview/1872570114 https://www.proquest.com/docview/1884107806 https://www.proquest.com/docview/2624202786 https://pubmed.ncbi.nlm.nih.gov/PMC8797509 https://www.ncbi.nlm.nih.gov/pmc/articles/8797509 |
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