Matches in DBpedia 2016-04 for { ?s ?p "Best-Worst Scaling (BWS) techniques involve Choice modelling (or Discrete Choice Experiment - \"DCE\") and were invented by Jordan Louviere in 1987 while on the faculty at the University of Alberta. In general with BWS, survey respondents are shown a subset of items from a master list and are asked to indicate the best and worst items (or most and least important, or most and least appealing, etc.). The task is repeated a number of times, varying the particular subset of items in a systematic way, typically according to a statistical design. Analysis is typically conducted, as with DCEs more generally, assuming that respondents makes choices according to a random utility model (RUM). RUMs assume that an estimate of how much a respondent prefers item A over item B is provided by how often item A is chosen over item B in repeated choices. Thus, choice frequencies estimate the utilities on the relevant latent scale. BWS essentially aims to provide more choice information at the lower end of this scale without having to ask additional questions that are specific to lower ranked items."@en }
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- Best-Worst_Scaling abstract "Best-Worst Scaling (BWS) techniques involve Choice modelling (or Discrete Choice Experiment - \"DCE\") and were invented by Jordan Louviere in 1987 while on the faculty at the University of Alberta. In general with BWS, survey respondents are shown a subset of items from a master list and are asked to indicate the best and worst items (or most and least important, or most and least appealing, etc.). The task is repeated a number of times, varying the particular subset of items in a systematic way, typically according to a statistical design. Analysis is typically conducted, as with DCEs more generally, assuming that respondents makes choices according to a random utility model (RUM). RUMs assume that an estimate of how much a respondent prefers item A over item B is provided by how often item A is chosen over item B in repeated choices. Thus, choice frequencies estimate the utilities on the relevant latent scale. BWS essentially aims to provide more choice information at the lower end of this scale without having to ask additional questions that are specific to lower ranked items.".