Estimating the Size of Hard-to-Count Populations
{'Code': '04050300', 'Directorate': {'Abbreviation': 'SBE', 'LongName': 'Direct For Social, Behav & Economic Scie'}, 'Division': {'Abbreviation': 'SES', 'LongName': 'Divn Of Social and Economic Sciences'}}
{'SignBlockName': 'jerry blackmon', 'PO_EMAI': None, 'PO_PHON': None}
Society has come to rely heavily on standard census and                    survey methods for determining the number of individuals who may                be classified as belonging to some definable subpopulation.                     However there are many subpopulations that cannot be enumerated                 by direct count, such as the number of women who have been raped,               the number of alcohol or drug abusers, the number of runaway                    children, or the number people who are particularly susceptible                 to certain diseases. The increasing frequency with which hard-to-               count situations are being encountered has greatly heightened                   interest in finding probabilistic methods for estimating the size               of such sets, and the present project offers an original and                    promising approach to the problem. The research team consists of                an anthropologist, a physicist, and a mathematician who have                    successfully collaborated for many years--most recently in                      developing a form of social network analysis in which relatively                small-scale personal acquaintanceship data are projected into                   minimum and maximum boundaries according to a mathematical model                of network size. Activities over the next two years will focus on               improving the theoretical basis for the model, testing and                      refining it through reanalysis of existing data, and performing                 further fieldwork aimed at exploring the effects of different                   techniques of data collection. The anticipated results should                   considerably increase our knowledge of social network phenomena,                and perhaps open the way for developing a family of models                      capable of predicting the size of different kinds of hard-to-                   count populations.
{'FirstName': 'H. Russell', 'LastName': 'Bernard', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'H. Russell Bernard', 'EmailAddress': 'asuruss@asu.edu', 'NSF_ID': '000171184', 'StartDate': '06/01/1988', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
{'Name': 'University of Florida', 'CityName': 'GAINESVILLE', 'ZipCode': '326111941', 'PhoneNumber': '3523923516', 'StreetAddress': '1523 UNION RD RM 207', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Florida', 'StateCode': 'FL', 'CONGRESSDISTRICT': '03', 'CONGRESS_DISTRICT_ORG': 'FL03', 'ORG_UEI_NUM': 'NNFQH1JAPEP3', 'ORG_LGL_BUS_NAME': 'UNIVERSITY OF FLORIDA', 'ORG_PRNT_UEI_NUM': None}
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[{'Code': '133300', 'Text': 'Methodology, Measuremt & Stats'}, {'Code': '139000', 'Text': 'Cultural Anthropology'}, {'Code': '912000', 'Text': 'CROSS-DIRECTORATE PROGRAMS'}]
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