An Approach to Integrating a Non-Probability Sample in the Population Census
Population censuses are increasingly using administrative information and sampling as alternatives to collecting detailed data from individuals. Non-probability samples can also be an additional, relatively inexpensive data source, although they require special treatment. In this paper, we consider...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/11/8/1782 |
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| author | Ieva Burakauskaitė Andrius Čiginas |
| author_facet | Ieva Burakauskaitė Andrius Čiginas |
| author_sort | Ieva Burakauskaitė |
| collection | DOAJ |
| description | Population censuses are increasingly using administrative information and sampling as alternatives to collecting detailed data from individuals. Non-probability samples can also be an additional, relatively inexpensive data source, although they require special treatment. In this paper, we consider methods for integrating a non-representative volunteer sample into a population census survey, where the complementary probability sample is drawn from the rest of the population. We investigate two approaches to correcting non-probability sample selection bias: adjustment using propensity scores, which models participation in the voluntary sample, and doubly robust estimation, which has the property of persisting possible misspecification of the latter model. We combine the estimators of population parameters that correct the selection bias with the estimators based on a representative union of both samples. Our analysis shows that the availability of detailed auxiliary information simplifies the applied estimation procedures, which are efficient in the Lithuanian census survey. Our findings also reveal the biased nature of the non-probability sample. For instance, when estimating the proportions of professed religions, smaller religious communities exhibit a higher participation rate than other groups. The combination of estimators corrects such selection bias. Our methodology for combining the voluntary and probability samples can be applied to other sample surveys. |
| first_indexed | 2024-03-11T04:46:36Z |
| format | Article |
| id | doaj.art-283f0e2e12b54d33b48a21244c056170 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-11T04:46:36Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-283f0e2e12b54d33b48a21244c0561702023-11-17T20:16:20ZengMDPI AGMathematics2227-73902023-04-01118178210.3390/math11081782An Approach to Integrating a Non-Probability Sample in the Population CensusIeva Burakauskaitė0Andrius Čiginas1Institute of Data Science and Digital Technologies, Vilnius University, Akademijos Str. 4, LT-08412 Vilnius, LithuaniaInstitute of Data Science and Digital Technologies, Vilnius University, Akademijos Str. 4, LT-08412 Vilnius, LithuaniaPopulation censuses are increasingly using administrative information and sampling as alternatives to collecting detailed data from individuals. Non-probability samples can also be an additional, relatively inexpensive data source, although they require special treatment. In this paper, we consider methods for integrating a non-representative volunteer sample into a population census survey, where the complementary probability sample is drawn from the rest of the population. We investigate two approaches to correcting non-probability sample selection bias: adjustment using propensity scores, which models participation in the voluntary sample, and doubly robust estimation, which has the property of persisting possible misspecification of the latter model. We combine the estimators of population parameters that correct the selection bias with the estimators based on a representative union of both samples. Our analysis shows that the availability of detailed auxiliary information simplifies the applied estimation procedures, which are efficient in the Lithuanian census survey. Our findings also reveal the biased nature of the non-probability sample. For instance, when estimating the proportions of professed religions, smaller religious communities exhibit a higher participation rate than other groups. The combination of estimators corrects such selection bias. Our methodology for combining the voluntary and probability samples can be applied to other sample surveys.https://www.mdpi.com/2227-7390/11/8/1782population censusauxiliary informationmissing at randompropensity score adjustmentinverse probability weightingsemiparametric estimation |
| spellingShingle | Ieva Burakauskaitė Andrius Čiginas An Approach to Integrating a Non-Probability Sample in the Population Census Mathematics population census auxiliary information missing at random propensity score adjustment inverse probability weighting semiparametric estimation |
| title | An Approach to Integrating a Non-Probability Sample in the Population Census |
| title_full | An Approach to Integrating a Non-Probability Sample in the Population Census |
| title_fullStr | An Approach to Integrating a Non-Probability Sample in the Population Census |
| title_full_unstemmed | An Approach to Integrating a Non-Probability Sample in the Population Census |
| title_short | An Approach to Integrating a Non-Probability Sample in the Population Census |
| title_sort | approach to integrating a non probability sample in the population census |
| topic | population census auxiliary information missing at random propensity score adjustment inverse probability weighting semiparametric estimation |
| url | https://www.mdpi.com/2227-7390/11/8/1782 |
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