Advances in multidimensional unfolding
Busing, F.M.T.A.
Citation
Busing, F. M. T. A. (2010, April 21). Advances in multidimensional unfolding. Retrieved from https://hdl.handle.net/1887/15279
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Fra nk B usi ng
Frank Busing (Amsterdam, 28.xii.1963) received his master's degree in Psychology in 1993 and is currently in the employ of the Section for Psychometrics and Research Methodology, Leiden University, both as researcher and teacher. He teaches statistics to first-year psychology students. His research topics are estimation and resampling procedures in multi- level analysis, multidimensional scaling and unfolding. In his capacity as researcher, he is responsible for the procedures proxscal and prefscal in ibm spss statistics.
Frank Busing
σ Ad nfo en im id ult n M s i nce va l U ldi na ng sio mu lt idi adv men un ances fol sional 2 d in in g Multidimensional unfolding is an analy- sis technique that creates configurations for two sets of objects based on the pair- wise preferences between elements of these two sets. The distances between the objects correspond as closely as possible with the given preferences between them, such that high preferences corre- spond to small distances and low prefer- ences to large distances.
For example, in Green and Rao, 42 respondents (21 MBA students and their spouses) rank ordered 15 breakfast items according to their preference. Unfolding now portrays both respondents (white dots) and items (red dots) as points in a configuration (see figure below), such that respondents are closest to their first ranked item and furthest from their last ranked item. Moving away in any direc- tion from a respondent's point thus decreases his/her preference for an item.
It may be said that multidimensional unfolding is a truly amazing technique, which can handle all kinds of distance- like data, uses a simple and transparent minimization method (implementation of PREFSCAL), and produces commonly understandable graphical results. Too bad it was not working from the beginning.
