Statistical Methods for Multidimensional Scaling and Non-   Linear MIMIC Models in Political Science
{'Code': '04050200', 'Directorate': {'Abbreviation': 'SBE', 'LongName': 'Direct For Social, Behav & Economic Scie'}, 'Division': {'Abbreviation': 'SES', 'LongName': 'Divn Of Social and Economic Sciences'}}
{'SignBlockName': 'Frank P. Scioli  Jr.', 'PO_EMAI': None, 'PO_PHON': None}
Dimensional thinking is a mainstay of political discussion.                     Journalists identify political candidates as on the left or on the              right of the ideological spectrum.  Voters describe politicians as              close to them or far away from them.  Politicians take sides, defend            positions, move towards some groups, and move away from others.  As             political events unfold, politicians, voters, and issues are often              described by using these kinds of geometric and spatial terms.                                                                                                  Not surprisingly, psychologists have found that most people, not just           sophisticated political analysts, often conceptualize and describe the          social and political world in spatial and geometric terms.  This                provides a key to understanding voting behavior, public opinion, and            many other forms of political behavior, but to be truly useful, we must         have a better understanding of what people mean when they say that one          politician is close to another or that they feel closer to one issue            position than another.                                                                                                                                          This project aims to develop a better understanding of how people think         about politics by developing statistical methods for uncovering the             dimensional structure of people's choices, opinions, and judgments.             For example, the project will develop statistical methods and computer          programs for relating peoples' rankings of political candidates to              their underlying opinions and beliefs about the characteristics of              these candidates.  Preliminary research has shown that peoples' choices         can be explained partly by a remarkably small number of underlying              dimensions.  The methods to be developed in this research will enable a         clearer interpretation of why this small number of dimensions explains          so much.                                                                                                                                                        These methods should make it possible to get a clearer understanding of         the major concerns which motivate voters and other people who make              political choices.  In addition, since similar methods have been widely         applied to understanding economic choices, it seems likely that the             methods developed in this project will be useful in areas outside               political science.
{'FirstName': 'Henry', 'LastName': 'Brady', 'PI_MID_INIT': 'E', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Henry E Brady', 'EmailAddress': 'hbrady@berkeley.edu', 'NSF_ID': '000230106', 'StartDate': '02/15/1989', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
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{'Code': '1371', 'Text': 'Political Science'}
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