Electronic Resource
Three Methods for Occupation Coding Based on Statistical Learning
| العنوان: | Three Methods for Occupation Coding Based on Statistical Learning |
|---|---|
| المؤلفون: | Gweon, Hyukjun, Schonlau, Matthias, Kaczmirek, Lars, Blohm, Michael, Steiner, Stefan |
| المصدر: | Journal of Official Statistics; 33; 1; 101-122 |
| بيانات النشر: | DEU 2019-02-28T09:53:50Z 2019-02-28T09:53:50Z 2017 |
| نوع الوثيقة: | Electronic Resource |
| مستخلص: | Occupation coding, an important task in official statistics, refers to coding a respondent's text answer into one of many hundreds of occupation codes. To date, occupation coding is still at least partially conducted manually, at great expense. We propose three methods for automatic coding: combining separate models for the detailed occupation codes and for aggregate occupation codes, a hybrid method that combines a duplicate-based approach with a statistical learning algorithm, and a modified nearest neighbor approach. Using data from the German General Social Survey (ALLBUS), we show that the proposed methods improve on both the coding accuracy of the underlying statistical learning algorithm and the coding accuracy of duplicates where duplicates exist. Further, we find defining duplicates based on ngram variables (a concept from text mining) is preferable to one based on exact string matches. |
| مصطلحات الفهرس: | Sozialwissenschaften, Soziologie, Social sciences, sociology, anthropology, Automated coding; Machine learning; ISCO-88, Erhebungstechniken und Analysetechniken der Sozialwissenschaften, Methods and Techniques of Data Collection and Data Analysis, Statistical Methods, Computer Methods, official statistics, ALLBUS, occupation, algorithm, method, coding, Codierung, Beruf, Algorithmus, amtliche Statistik, Methode, journal article, Zeitschriftenartikel |
| URL: | |
| الاتاحة: | Open access content. Open access content Creative Commons - Attribution-Noncommercial-No Derivative Works 4.0 Creative Commons - Namensnennung, Nicht kommerz., Keine Bearbeitung 4.0 |
| Other Numbers: | DEGES oai:gesis.izsoz.de:document/61576 2001-7367 1256789892 |
| المصدر المساهم: | LEIBNIZ INST FOR THE SOCIAL SCIS GESIS From OAIster®, provided by the OCLC Cooperative. |
| رقم الانضمام: | edsoai.on1256789892 |
| قاعدة البيانات: | OAIster |
| الوصف غير متاح. |