Estimation of dyadic characteristics of family networks using sample survey data Article Swipe
We consider the use of sample survey data to estimate dyadic characteristics of family networks, with an application to noncoresident parent–child dyads. We suppose that survey respondents report either from a parent or child perspective about a dyad, depending on their membership of the dyad. We construct separate estimators of common dyadic characteristics using data from both a parent and a child perspective and show how comparisons of these estimators can shed light on data quality issues including differential missingness and reporting error. In our application we find that a simple missingness model explains some striking patterns of discrepancies between the estimators and consider the use of poststratification and a related redefinition of count variables to adjust for these discrepancies. We also develop approaches to combining the separate estimators efficiently to estimate means and frequency distributions within subpopulations.
Related Topics
Concepts
Dyad
Estimator
Perspective (graphical)
Missing data
Sample (material)
Survey data collection
Statistics
Econometrics
Construct (python library)
Estimation
Differential (mechanical device)
Computer science
Psychology
Mathematics
Social psychology
Artificial intelligence
Economics
Aerospace engineering
Chromatography
Chemistry
Management
Engineering
Programming language
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://orcid.org/0000-0001-6417-7444>
- http://eprints.lse.ac.uk/102338/1/AOAS_final_paper.pdf
- OA Status
- green
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W3037225851
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3037225851Canonical identifier for this work in OpenAlex
- Title
-
Estimation of dyadic characteristics of family networks using sample survey dataWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-01-01Full publication date if available
- Authors
-
Chris Skinner, Fiona SteeleList of authors in order
- Landing page
-
https://orcid.org/0000-0001-6417-7444>Publisher landing page
- PDF URL
-
https://eprints.lse.ac.uk/102338/1/AOAS_final_paper.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://eprints.lse.ac.uk/102338/1/AOAS_final_paper.pdfDirect OA link when available
- Concepts
-
Dyad, Estimator, Perspective (graphical), Missing data, Sample (material), Survey data collection, Statistics, Econometrics, Construct (python library), Estimation, Differential (mechanical device), Computer science, Psychology, Mathematics, Social psychology, Artificial intelligence, Economics, Aerospace engineering, Chromatography, Chemistry, Management, Engineering, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
20Other works algorithmically related by OpenAlex
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| abstract_inverted_index.means | 132 |
| abstract_inverted_index.model | 92 |
| abstract_inverted_index.their | 40 |
| abstract_inverted_index.these | 68, 118 |
| abstract_inverted_index.using | 53 |
| abstract_inverted_index.adjust | 116 |
| abstract_inverted_index.common | 50 |
| abstract_inverted_index.dyadic | 10, 51 |
| abstract_inverted_index.dyads. | 21 |
| abstract_inverted_index.either | 28 |
| abstract_inverted_index.error. | 82 |
| abstract_inverted_index.family | 13 |
| abstract_inverted_index.issues | 76 |
| abstract_inverted_index.parent | 31, 58 |
| abstract_inverted_index.report | 27 |
| abstract_inverted_index.sample | 5 |
| abstract_inverted_index.simple | 90 |
| abstract_inverted_index.survey | 6, 25 |
| abstract_inverted_index.within | 136 |
| abstract_inverted_index.between | 99 |
| abstract_inverted_index.develop | 122 |
| abstract_inverted_index.quality | 75 |
| abstract_inverted_index.related | 110 |
| abstract_inverted_index.suppose | 23 |
| abstract_inverted_index.consider | 1, 103 |
| abstract_inverted_index.estimate | 9, 131 |
| abstract_inverted_index.explains | 93 |
| abstract_inverted_index.patterns | 96 |
| abstract_inverted_index.separate | 47, 127 |
| abstract_inverted_index.striking | 95 |
| abstract_inverted_index.combining | 125 |
| abstract_inverted_index.construct | 46 |
| abstract_inverted_index.depending | 38 |
| abstract_inverted_index.frequency | 134 |
| abstract_inverted_index.including | 77 |
| abstract_inverted_index.networks, | 14 |
| abstract_inverted_index.reporting | 81 |
| abstract_inverted_index.variables | 114 |
| abstract_inverted_index.approaches | 123 |
| abstract_inverted_index.estimators | 48, 69, 101, 128 |
| abstract_inverted_index.membership | 41 |
| abstract_inverted_index.application | 17, 85 |
| abstract_inverted_index.comparisons | 66 |
| abstract_inverted_index.efficiently | 129 |
| abstract_inverted_index.missingness | 79, 91 |
| abstract_inverted_index.perspective | 34, 62 |
| abstract_inverted_index.respondents | 26 |
| abstract_inverted_index.differential | 78 |
| abstract_inverted_index.redefinition | 111 |
| abstract_inverted_index.discrepancies | 98 |
| abstract_inverted_index.distributions | 135 |
| abstract_inverted_index.noncoresident | 19 |
| abstract_inverted_index.discrepancies. | 119 |
| abstract_inverted_index.parent–child | 20 |
| abstract_inverted_index.characteristics | 11, 52 |
| abstract_inverted_index.subpopulations. | 137 |
| abstract_inverted_index.poststratification | 107 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 2 |
| citation_normalized_percentile.value | 0.11589352 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |