Data Integration Techniques

S. P. Mukherjee (), Bikas K. Sinha and Asis Kumar Chattopadhyay ()
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S. P. Mukherjee: University of Calcutta, Department of Statistics
Bikas K. Sinha: Indian Statistical Institute
Asis Kumar Chattopadhyay: University of Calcutta, Department of Statistics

Chapter Chapter 5 in Statistical Methods in Social Science Research, 2018, pp 53-60 from Springer

Abstract: Abstract We intend to discuss some standard and non-standard Data Integration Techniques with a hypothetical illustration from a study of environmental factors [to be called sources] and the extent of their toxic release in the surface areas of several locations in a closed region. The purpose is to rank the locations—by ’integrating’ the effects of toxic release from all the potential sources. From a practical point of view, it is highly unlikely that a single location can be the ’absolute best’ [i.e., the location possesses minimum toxic release for each of the toxicity sources] for any reasonable environmental study. Hence, there is a need for data integration to arrive at an overall ranking of the locations, keeping the differential toxic effects in mind. Naturally, rank 1 corresponds to the least ’compound’ effect of the toxicity sources.

Keywords: Multiple criteria decision making; AM-GM-HM summary measures; Source weights; Weighted averages; Ranks; Technique for order preference by similarity to ideal solution [TOPSIS] (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-13-2146-7_5

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DOI: 10.1007/978-981-13-2146-7_5

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