Optimizing Count Responses in Surveys : A Machine-learning Approach
Count responses with grouping and right censoring have long been used in surveys to study a variety of behaviors, status, and attitudes. Yet grouping or right-censoring decisions of count responses still rely on arbitrary choices made by researchers. We develop a new method for evaluating grouping a...
| Veröffentlicht in: | Sociological methods & research. - 1977. - 49(2020), 3 vom: 27. Aug., Seite 637-671 |
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| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2020
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| Zugriff auf das übergeordnete Werk: | Sociological methods & research |
| Schlagworte: | Journal Article experimental design fisher information machine learning optimality poisson distribution right censoring search algorithm survey methodology zero inflation |
| Zusammenfassung: | Count responses with grouping and right censoring have long been used in surveys to study a variety of behaviors, status, and attitudes. Yet grouping or right-censoring decisions of count responses still rely on arbitrary choices made by researchers. We develop a new method for evaluating grouping and right-censoring decisions of count responses from a (semisupervised) machine-learning perspective. This article uses Poisson multinomial mixture models to conceptualize the data-generating process of count responses with grouping and right censoring and demonstrates the link between grouping-scheme choices and asymptotic distributions of the Poisson mixture. To search for the optimal grouping scheme maximizing objective functions of the Fisher information (matrix), an innovative three-step M algorithm is then proposed to process infinitely many grouping schemes based on Bayesian A-, D-, and E-optimalities. A new R package is developed to implement this algorithm and evaluate grouping schemes of count responses. Results show that an optimal grouping scheme not only leads to a more efficient sampling design but also outperforms a nonoptimal one even if the latter has more groups |
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| Beschreibung: | Date Revised 02.08.2021 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
| ISSN: | 0049-1241 |
| DOI: | 10.1177/0049124117747302 |