Using geographically weighted regression to explore spatial variation in survey data Article Swipe
Nonresponse can undermine the quality of social survey data. Understanding who does/does not respond to surveys is important for those involved in the collection and analysis of these data. Levels of nonresponse are known to vary geographically. However, there has been little consideration of how the predictors of survey nonresponse might vary geographically within countries. This study examines the possibility of spatial variation in response behavior using regional interactions and geographically weighted regression. Our results suggest that there is geographical variation in response behavior. Relying on “one size fits all” global models in nonresponse modelling might, therefore, be insufficient.
Related Topics
Concepts
Variation (astronomy)
Geographically Weighted Regression
Regression analysis
Survey data collection
Regression
Geography
Econometrics
Spatial variability
Data collection
Data quality
Non-response bias
Geographic variation
Quality (philosophy)
Statistics
Demography
Mathematics
Population
Sociology
Philosophy
Economics
Physics
Operations management
Epistemology
Astrophysics
Metric (unit)
Metadata
- Type
- article
- Language
- en
- https://openaccess.city.ac.uk/id/eprint/14509/1/LahtinenBrunsdonButt_FINAL.pdf
- OA Status
- green
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W2494706110
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W2494706110Canonical identifier for this work in OpenAlex
- Title
-
Using geographically weighted regression to explore spatial variation in survey dataWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2016Year of publication
- Publication date
-
2016-02-08Full publication date if available
- Authors
-
Sarah Butt, Kaisa Lahtinen, Chris BrunsdonList of authors in order
- PDF URL
-
https://openaccess.city.ac.uk/id/eprint/14509/1/LahtinenBrunsdonButt_FINAL.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://openaccess.city.ac.uk/id/eprint/14509/1/LahtinenBrunsdonButt_FINAL.pdfDirect OA link when available
- Concepts
-
Variation (astronomy), Geographically Weighted Regression, Regression analysis, Survey data collection, Regression, Geography, Econometrics, Spatial variability, Data collection, Data quality, Non-response bias, Geographic variation, Quality (philosophy), Statistics, Demography, Mathematics, Population, Sociology, Philosophy, Economics, Physics, Operations management, Epistemology, Astrophysics, Metric (unit)Top concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
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20Other works algorithmically related by OpenAlex
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| abstract_inverted_index.important | 17 |
| abstract_inverted_index.modelling | 94 |
| abstract_inverted_index.undermine | 2 |
| abstract_inverted_index.variation | 62, 80 |
| abstract_inverted_index.collection | 23 |
| abstract_inverted_index.countries. | 54 |
| abstract_inverted_index.predictors | 46 |
| abstract_inverted_index.therefore, | 96 |
| abstract_inverted_index.Nonresponse | 0 |
| abstract_inverted_index.nonresponse | 31, 49, 93 |
| abstract_inverted_index.possibility | 59 |
| abstract_inverted_index.regression. | 72 |
| abstract_inverted_index.geographical | 79 |
| abstract_inverted_index.interactions | 68 |
| abstract_inverted_index.Understanding | 9 |
| abstract_inverted_index.consideration | 42 |
| abstract_inverted_index.insufficient. | 98 |
| abstract_inverted_index.geographically | 52, 70 |
| abstract_inverted_index.geographically. | 36 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.08267151 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |