A procedure for imposing a dichotomous incidence variable on the weighting of items in a dual scaling analysis of successive-categories (rating) data

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dc.contributor.advisor Lawrence, Daniel
dc.contributor.advisor Voelkel, Joseph
dc.contributor.advisor LaLonde, Steven
dc.contributor.author Wyse, Adam
dc.date.accessioned 2012-11-05T13:29:28Z
dc.date.available 2012-11-05T13:29:28Z
dc.date.issued 2005-08
dc.identifier.uri http://hdl.handle.net/1850/15446
dc.description.abstract Demographic information collected on surveys is often of certain interest to those who use these surveys to measure customer satisfaction or do market research. This thesis presents a method for imposing a dichotomous incidence variable, possibly a demographic variable, on the weighting of items in a set of successive-categories (rating) data using dual scaling. The idea is to augment the matrix of rating data with the "criterion variable" containing the dichotomous information so that this item determines one of the initial solutions of the dual scaling analysis. In conjunction with the augmentation of the criterion item, the original data are "centered" between two numbers that represent the two criterion groups. The resulting modified data matrix is then subjected to a dual scaling analysis. The procedure is discussed with practical guidelines for its use and interpretation of results. Examples of application involve both fabricated and actual data. en_US
dc.language.iso en_US en_US
dc.subject Applied statistics en_US
dc.subject Criterion variable en_US
dc.subject Dual scaling analysis en_US
dc.subject Engineering en_US
dc.subject Quality en_US
dc.subject Thesis en_US
dc.subject.lcc QA278.65 .W97 2005
dc.subject.lcsh Discriminant analysis en_US
dc.subject.lcsh Multivariate analysis en_US
dc.subject.lcsh Incidence functions en_US
dc.subject.lcsh Correspondence analysis (Statistics) en_US
dc.subject.lcsh Demographic surveys--Statistics en_US
dc.title A procedure for imposing a dichotomous incidence variable on the weighting of items in a dual scaling analysis of successive-categories (rating) data en_US
dc.type Thesis en_US
dc.description.college Kate Gleason College of Engineering en_US
dc.description.department Center for Quality and Applied Statistics en_US

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