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dc.contributor.advisorFricker, Ronald D., Jr.
dc.contributor.authorEvans, William
dc.dateSep-13
dc.date.accessioned2013-11-20T23:36:05Z
dc.date.available2013-11-20T23:36:05Z
dc.date.issued2013-09
dc.identifier.urihttp://hdl.handle.net/10945/37623
dc.descriptionApproved for public release; distribution is unlimiteden_US
dc.description.abstractFor presentation of survey results, social science data, and other geospatial statistics requires careful attention in order to facilitate fast and accurate interpretation. Adding dimensionality can easily saturate the observer, leading to confusion instead of adding perspective. We produce over a dozen techniques to facilitate multivariate geospatial visualization, filter them with pilot groups, and then design a computer-based human experiment to evaluate their relative performance. In the experiment, the participants locate (with a mouse click) regions with extreme primary or secondary values and then later estimate numerically the values of these variables. We analyze these data with linear and logistic regression and general additive models to characterize the variance due to a learning effect, and then use general linear mixed-effects models to block out the variability due to individual participants and the independent and randomly-generated survey data used to generate the experiment plots. The effectiveness of a particular technique depends heavily on the goal of the presentation: a technique that provides relative perspective without distracting from the primary variable may not facilitate estimation that is as accurate as other techniques. Four scenarios are provided to qualify the presenters intent. Only one technique performed poorly in all four scenarios and only one technique was average in all four; all remaining varied from very good to very bad between scenarios.en_US
dc.publisherMonterey, California: Naval Postgraduate Schoolen_US
dc.rightsThis publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. As such, it is in the public domain, and under the provisions of Title 17, United States Code, Section 105, may not be copyrighted.en_US
dc.titleMultivariate visualization in social sciences and survey dataen_US
dc.typeThesisen_US
dc.contributor.secondreaderButtrey, Samuel E.
dc.contributor.departmentOperations Research
dc.subject.authorgeneral linear model (GLM)en_US
dc.subject.authorgeneral additive model (GAM)en_US
dc.subject.authorgeneral linear mixed-effects model (GLMM)en_US
dc.subject.authormultivariateen_US
dc.subject.authorvisualizationen_US
dc.subject.authorsurveyen_US
dc.subject.authorsocial scienceen_US
dc.subject.authorgeospatialen_US
dc.subject.authorexperimenten_US
dc.subject.authormultidimensionalen_US
dc.description.serviceLieutenant Commander, U.S. Navyen_US
etd.thesisdegree.nameMaster Of Science In Operations Researchen_US
etd.thesisdegree.levelMastersen_US
etd.thesisdegree.disciplineOperations Researchen_US
etd.thesisdegree.grantorNaval Postgraduate Schoolen_US


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