Search Results

1 - 10 of 245 items :

  • "data quality" x
Clear All

:// (accessed August 2017). NISRA. 2016a. Population Estimates and Projections Data Quality Document. Belfast, UK: NISRA. Available at: (accessed August 2017). NISRA. 2016b. Methodology Paper - Mid-Year Population Estimates for Northern Ireland. Belfast, UK: NISRA. Available at: (accessed August 2017). Office for National Statistics (ONS). 2011. Improved Immigration

References [1] GKÚ 2010. Cenník produktov. Bratislava: GKÚ, 2010, [cit. 4/2013][online] Dostupné na internete: [2] [3] INSPIRE Thematic Working Group ELEVATION, 2011. Data Specification on Elevation - Draft Guidelines [online]. 2013, version 3.0rc3 [4] ISO/TS 19138:2006, Geographic information -- Data quality measures. [5] ISO/TS 19157:2012 Geographic information - Data quality(DRAFT) [6] Smernica Európskeho parlamentu a rady 2007/2/ES zo 14.marca 2007, ktorou sa zriaďuje Infraštruktúra pre priestorové informácie v

: Specification for single sampling plans indexed by acceptance quality limit (AQL) for lot-by-lot inspection for a single quality characteristic and a single AQL. Jakobsson, A. Giversen, J., 2009. Guidelines for Implementing the ISO 19100 Geographic Information Quality Standards in National Mapping and Cadastral Agencies. (view at 28 feb. 2015). Joos, G., 2006. Data quality standards. XXIII FIG Congress, Munich, Germany, october 8-13, 2006. https://www.Figurenet/pub/fig2006/papers/ws02/ws02_03_joos

Waal, T. 2013. “Selective Editing: A Quest for Efficiency and Data Quality.” Journal of Official Statistics 29(4): 473–488. Doi: . Fellegi, I.P. and D. Holt. 1976. “A Systematic Approach to Automatic Edit and Imputation.” Journal of the American Statistical Association 71: 17–35. Doi: . Groves, R.M. and L. Lyberg. 2010. “Total Survey Error: Past, Present, and Future.” Public Opinion Quarterly 74(5): 849–879. Doi: . Groves, R.M., W.D. Mosher


The paper presents the concept of a method ensuring quality of aeronautical data. European Union (among others UE 73/2010) as well as international (among others ICAO Annex 15) regulations introduce a number of requirements regarding the quality and safety of aeronautical data. Those directives set up a complementary regulations system. However with their objective and scope they determine mainly the specifications and requirements that are to be implemented and compatible. Mentioned regulations also refer to selected international standards (e.g. ISO 19157), focused on quality and safety of geographic data and information. Nevertheless within the scope of considered regulations and norms no algorithms and methods of ensuring required quality in the process of aeronautical data collection and processing were determined. Taking into account the identified needs, authors proposed the application of statistical method for process quality management – six-sigma.

Model of Work Motivation and Volition.” Academy of Management Review 29: 479-499. DOI: Kennedy, J., and P. Phipps. 1995 “Respondent Motivation, Response Burden, and Data Quality in the Survey of Employer-provided training.” Annual Meeting of the American Association for Public Opinion Research, May 1995, Ft. Lauderdale, FL. Available at: (accessed May 2014). Kruglanski, A.W. 1975. “The Endogenous-Exogenous Partition in Attribution Theory.” Psychological Review 82: 387-406. DOI: http

Household Surveys: Tools for Actively Controlling Survey Errors and Costs.” Journal of the Royal Statistical Society. Series A: Statistics in Society 169(3): 439–457. Doi: . Jans, M., R. Sirkis, and D. Morgan. 2013. “Managing Data Quality Indicators with Paradata based Statistical Quality Control Tools: the Keys to Survey Performance.” In Improving Surveys with Paradata. Analytic Uses of Process Information , edited by Frauke Kreuter, 191–229. Hoboken, New Jersey: John Wiley & Sons. Juran, J.M. and F.M. Gryna. 1988

1 History of ETER and its political importance for research on higher education Studies, analyzes and policy investigations about the positioning and the characterization of education and research systems need data to be performed. Whenever we need data, we need a method for the management of data, and in the Big Data era, a crucial role is played by data quality. Therefore, higher education policies and indicators development need data quality techniques to increase the value of data and improve the exploitation of the available data. The availability of data

Design Method (4th edition). Hoboken, NJ: John Wiley. Draisma, S. and W. Dijkstra. 2004. “Response Latency and (Para)linguistic Expression as Indicators of Response Error.” In Methods for Testing and Evaluating Survey Questionnaires , edited by S. Presser, J.M. Rothgeb, M.P. Couper, J.T. Lessler, E. Martin, J. Martin, and E. Singer, 131–148. New York: Springer-Verlag. Doi: . Dykema, J., J.M. Lepkowski, and S. Blixt. 1997. “The Effect of Interviewer and Respondent Behavior on Data Quality: Analysis of Interaction Coding in a

– Part 1: Quality model. International Organization for Standardization, Geneva. ISO/IEC 25012:2008. ISO/IEC 25012. Software engineering – Software Product Quality Requirements and Evaluation (SQuaRE) – Data quality model. International Organization for Standardization, Geneva. ISO/IEC FDIS 25010:2011. ISO/IEC FDIS 25010. Systems and software engineering – Systems and Software QualityRequirements and Evaluation (SQuaRE) – System and software quality models. International Organization for Standardization, Geneva. ISO/IEC 25024:2015. ISO/IEC 25024. Systems and software