Statistical Tools in the Joint Analysis of Closed and Open-Ended Questions

Balbi, Simona; Triunfo, Nicole

doi:10.1007/978-3-642-21308-3_4

Cited by 2 publications

(2 citation statements)

References 4 publications

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“…Statistical institutions are focusing on variety of data sources [32]: traditional surveys, administrative sources [5,10], alternative sources [6] (sensors [25], mobile positioning data [1,2], and social networks [31] among others) which measure development in the society and people's opinions about this development [3]. Many of these data and concepts can be expressed as sharp (crisp) numbers or clear expressions of natural language.…”

Section: Fuzziness In the Real Worldmentioning

confidence: 99%

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Collecting and managing fuzzy data in statistical relational databases

Hudec¹,

Praženka²

2016

SJI

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Abstract. Statistical institutes are focusing on variety of data sources from traditional surveys to big-data. Many of these data and concepts can be expressed as crisp values. But many other data cannot be expressed by precise values. In order to collect, store and manage the fuzziness in data we have adapted the fuzzy meta model as an extension of traditional relational database. Furthermore, experts' knowledge often contains vagueness and subjectivity. If we store this knowledge in a fuzzy database we can build knowledge management systems capable to cope with fuzziness. Statistical institutes cooperate in the data exchange. We have briefly discussed a simple way of extending the SDMX standard to cope with the fuzzy data in a way that does not influence exchanging precise values. Our research was focused on examining promising ways for managing fuzziness of real world because statistical institutes have been starting to analyze variety of promising data sources where not all data are always precise.

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Section: Fuzziness In the Real Worldmentioning

confidence: 99%

“…Nowadays, statistical institutes are launching traditional surveys but they are also looking for a variety of promising alternative data sources [1][2][3]6,25,31]. Many data are vague and imprecise (fuzzy) either by their nature, e.g.…”

Section: Introductionmentioning

confidence: 99%

Collecting and managing fuzzy data in statistical relational databases

Hudec¹,

Praženka²

2016

SJI

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Farmers’ Perceptions of the Effectiveness of the Cocoa Disease and Pest Control Programme (CODAPEC) in Ghana and Its Effects on Poverty Reduction

Kumi

Daymond

2015

AJEA

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This work was carried out in collaboration between both authors. Author EK was involved in the study design, data collection and analysis. He managed the literature review as well as the writing up of the introduction and the first draft of the results of the manuscript. Author AJD was involved in the analysis, writing up of the discussion and the proof reading of the manuscript. Both authors read and approved the final manuscript.

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Statistical Tools in the Joint Analysis of Closed and Open-Ended Questions

Cited by 2 publications

References 4 publications

Collecting and managing fuzzy data in statistical relational databases

Collecting and managing fuzzy data in statistical relational databases

Farmers’ Perceptions of the Effectiveness of the Cocoa Disease and Pest Control Programme (CODAPEC) in Ghana and Its Effects on Poverty Reduction

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