2017
DOI: 10.1016/j.neucom.2016.11.087
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Supporting theoretically-grounded model building in the social sciences through interactive visualisation

Abstract: This is the accepted version of the paper.This version of the publication may differ from the final published version. Permanent repository link AbstractThe primary purpose for which statistical models are employed in the social sciences is to understand and explain phenomena occurring in the world around us. In order to be scientifically valid and actionable, the construction of such models need to be strongly informed by theory. To accomplish this, there is a need for methodologies that can enable scientist… Show more

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Cited by 9 publications
(5 citation statements)
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“…The grounded construction process operates with three goals (Turkay et al, 2017). First, we seek to construct an outcome variable that provides an accurate measure of prison response to COVID-19 by reducing the impact of missing data.…”
Section: Variables and Operationalizationmentioning
confidence: 99%
See 1 more Smart Citation
“…The grounded construction process operates with three goals (Turkay et al, 2017). First, we seek to construct an outcome variable that provides an accurate measure of prison response to COVID-19 by reducing the impact of missing data.…”
Section: Variables and Operationalizationmentioning
confidence: 99%
“…The exclusion of questions with a "Yes" response rate is also designed to hedge against missing data or potential measurement error. We further decrease the potential impact of measurement error by excluding questions with a "NA" response greater than 30% (Turkay et al, 2017).…”
Section: Variables and Operationalizationmentioning
confidence: 99%
“…The research team utilizes a grounded process for the construction of the outcome variable in this analysis. The grounded construction process operates with three goals (Turkay et al, 2017). First, we aim to develop an outcome variable that provides an accurate measure of COVID-19 response by reducing the impact of missing data.…”
Section: Outcome Variablementioning
confidence: 99%
“…This idiom makes MDS suitable for visualising changes in correlations [GF00] and other network topologies [LS08, LSSdN08] over time. In a static setting, MDS has been used as a layout to communicate similarities between data dimensions as represented through their correlations [YPH*, TSL*17]. However, due to the stochastic nature of the computation, and its susceptibility to errors in data and over‐sensitivity to inherent structures [BGM12], MDS needs to be incorporated with care.…”
Section: Related Workmentioning
confidence: 99%