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2015 | 16 | nr 4 The Measurement of Subjective Well-Being in Survey Research | 611--630
Tytuł artykułu

SAE Education Challenges to Academics and NSI

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The aim of the paper is to present some experiences in teaching Small Area Estimation (SAE). SAE education experiences and challenges are analysed from the academic side and from the NSI side. An attempt was undertaken to discuss SAE issues in a wider perspective of teaching statistics. In particular, the topics refer to Polish conditions, but they are presented against the background of selected international experiences and practices. Information comes from a special inquiry - a survey conducted among employees of statistical offices and academics from universities involved in SAE research. A further issue is inclusion of SAE in the EMOS project (European Master in Official Statistics). The survey is extended with information collected by monitoring of trainings and projects organized by the leading centres dealing with SAE. The results obtained are related to a similar survey within Eurostat project: ESSnet on Small Area Estimation, which was conducted in 2010. The study includes interest in learning and the need to implement SAE methodology, a range of subjects taught as well as a range of applications, forms of training, type of courses, software used and teaching methods. In particular, it intends to answer how strong the interest in small area estimation is, what the demand for practical and theoretical knowledge in the field is and what the recommendations for universities and statistical institutes are. (original abstract)
Rocznik
Tom
16
Strony
611--630
Opis fizyczny
Twórcy
  • Poznań University of Economics
Bibliografia
  • MAPLES, J. J., BELL, W. R., HUANG, E. T., (2009). Small Area Variance Modelling with Application to County Poverty Estimates from the American Community Survey Statistical Research Division, U.S. Census Bureau, Washington, DC.
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  • CHANCE, B., (2002). Components of Statistical Thinking and Implications for Instruction and Assessment. Journal of Statistics Education Volume 10, Number 3 (2002).
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  • DATTA, G. S., HALL, P., MANDAL, A., (2011). Model selection by testing for the presence of small-area effects, and applications to area-level data. Journal of the American Statistical Association, 106, 361-374.
  • FULLER, W. A., (2009). Sampling Statistics, Hoboken, New Jersey: John Wiley & Sons.
  • GHOSH, M., (2001). Model-Dependent Small Area Estimation - Theory and Practice, in: Lectures Notes on Estimation for Population Domains and Small Areas, eds. R. Lehtonen, K. Djerf, "Reviews" no. 5, Statistics Finland, University of Jyväskylä.
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  • HIDIROGLOU, M. A., SMITH, P., (2005). Benchmarking through calibration of weights for microdata. Working Papers and Studies, European Communities, Eurostat, Luxembourg.
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  • LEHTONEN, R., (2014). Experiences and challenges in teaching Small Area Estimation, presentation during International Conference on Small Area Estimation SAE Poznan 2014.
  • LONGFORD, N. T., (2005). Missing Data and Small-Area Estimation, Springer.
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  • PKA 2014. The report on the program assessment held on 17-18 May 2014 for the field of study: computer science and econometrics conducted within the area of social sciences (first- and second-degree) at the Faculty of Informatics and Electronic Economy, Poznan University of Economics, State Accreditation Commission), http://ue.poznan.pl/data/upload/articles/20141031/ce25bb287995611010/011-4-raport-pka-2014.pdf.
  • RAO, J. N. K., (2003). Small Area Estimation, John Wiley & Sons. Ltd.
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  • SÄRNDAL, C-E., (2007). The Calibration Approach in Survey Theory and Practice, Survey Methodology, Vol. 33, No. 2, 99-119.
  • SORVILLO, M. P., (2014). EMOS as a new tool for training professionals in official statistics: NSIs' point of view, Paper available on EMOS website: http: //www .crosportal .eu/sites/default/files//NTTS2013fullPaper_241%20Sor villo.pdf.
  • SZYMKOWIAK, M., (2010). ESSnet on Small Area Estimation. Report on the analysis of questionnaires used in WP 2, October 2010.
  • TORABI, M., SHOKOOHI, F., (2015). Non- parametric generalized linear mixed models in small area estimation. Canadian Journal of Statistics Volume 43, Issue 1, pages 82-96, March 2015.
  • WALLGREN, A., WALLGREN, B., (2007, 2014). Register-based Statistics. Statistical Methods for Administrative Data. John Wiley & Sons. Ltd.
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  • ZHANG, L.-C., (2012). Topics of statistical theory for register-based statistics and data integration. Statistica Neerlandica, Vol. 66, No. 1. p. 41-63.
Typ dokumentu
Bibliografia
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Identyfikator YADDA
bwmeta1.element.ekon-element-000171400567

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