On using a non-probability sample for the estimation of population parameters
We aim to find a way to effectively integrate a non-probability (voluntary) sample under the data framework, where the study variable is also observed in a probability sample of some statistical survey. The selection bias that arises from voluntary participation in the survey is corrected by estima...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Vilnius University Press
2023-11-01
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| Series: | Lietuvos Matematikos Rinkinys |
| Subjects: | |
| Online Access: | https://www.journals.vu.lt/LMR/article/view/33587 |
| _version_ | 1797194500013031424 |
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| author | Ieva Burakauskaitė Andrius Čiginas |
| author_facet | Ieva Burakauskaitė Andrius Čiginas |
| author_sort | Ieva Burakauskaitė |
| collection | DOAJ |
| description |
We aim to find a way to effectively integrate a non-probability (voluntary) sample under the data framework, where the study variable is also observed in a probability sample of some statistical survey. The selection bias that arises from voluntary participation in the survey is corrected by estimating the inclusion into the sample probabilities (propensity scores) for the units in the non-probability sample. The estimators for the propensity scores are constructed using a parametric logistic regression model. We consider two modeling scenarios: with an assumption that the willingness to participate in the voluntary survey does not depend on the survey variable itself and that such a variable does contribute to whether the individual responds or not. The maximum likelihood method is applied in both scenarios to estimate the propensity scores. The estimators of the population mean based on the estimated propensity scores are linearly combined with the unbiased estimator using the probability sample data. We compare the constructed estimators in the simulation study, where we estimate the population proportions using data from the Population and Housing Census surveys.
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| first_indexed | 2024-03-07T15:41:11Z |
| format | Article |
| id | doaj.art-f67ef73d18e0447b9e480d4a67df0174 |
| institution | Directory Open Access Journal |
| issn | 0132-2818 2335-898X |
| language | English |
| last_indexed | 2024-04-24T05:57:16Z |
| publishDate | 2023-11-01 |
| publisher | Vilnius University Press |
| record_format | Article |
| series | Lietuvos Matematikos Rinkinys |
| spelling | doaj.art-f67ef73d18e0447b9e480d4a67df01742024-04-23T09:00:37ZengVilnius University PressLietuvos Matematikos Rinkinys0132-28182335-898X2023-11-0164A10.15388/LMR.2003.33587On using a non-probability sample for the estimation of population parametersIeva Burakauskaitė0Andrius Čiginas1Vilnius UniversityVilnius University We aim to find a way to effectively integrate a non-probability (voluntary) sample under the data framework, where the study variable is also observed in a probability sample of some statistical survey. The selection bias that arises from voluntary participation in the survey is corrected by estimating the inclusion into the sample probabilities (propensity scores) for the units in the non-probability sample. The estimators for the propensity scores are constructed using a parametric logistic regression model. We consider two modeling scenarios: with an assumption that the willingness to participate in the voluntary survey does not depend on the survey variable itself and that such a variable does contribute to whether the individual responds or not. The maximum likelihood method is applied in both scenarios to estimate the propensity scores. The estimators of the population mean based on the estimated propensity scores are linearly combined with the unbiased estimator using the probability sample data. We compare the constructed estimators in the simulation study, where we estimate the population proportions using data from the Population and Housing Census surveys. https://www.journals.vu.lt/LMR/article/view/33587data integrationnot missing at randompropensity score adjustmentpopulation census |
| spellingShingle | Ieva Burakauskaitė Andrius Čiginas On using a non-probability sample for the estimation of population parameters Lietuvos Matematikos Rinkinys data integration not missing at random propensity score adjustment population census |
| title | On using a non-probability sample for the estimation of population parameters |
| title_full | On using a non-probability sample for the estimation of population parameters |
| title_fullStr | On using a non-probability sample for the estimation of population parameters |
| title_full_unstemmed | On using a non-probability sample for the estimation of population parameters |
| title_short | On using a non-probability sample for the estimation of population parameters |
| title_sort | on using a non probability sample for the estimation of population parameters |
| topic | data integration not missing at random propensity score adjustment population census |
| url | https://www.journals.vu.lt/LMR/article/view/33587 |
| work_keys_str_mv | AT ievaburakauskaite onusinganonprobabilitysamplefortheestimationofpopulationparameters AT andriusciginas onusinganonprobabilitysamplefortheestimationofpopulationparameters |