Extending and Implementing Multiple Imputation Technology
{'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}
Multiple imputation technology refers to a family of                       techniques for dealing with incomplete data. This innovative                    technology, to be perfected under the recommended accomplishment-               based renewal, will be especially useful in the context of data                 bases that have been constructed at great public expense and are                to be made available to many data analysts with their arsenals of               standard statistical software. Therefore, and in addition to its                importance for the academic research community, this innovative                 technology has implications for statistical endeavors undertaken                by government, such as census undercount adjustment and similar                 forms of compensation for deficient data (so-called "statistical                repair work"). Such work is needed more than ever before as data                collection resources shrink and missing data problems multiply                  owing to financial and other restrictions. Good quality analysis                is also at stake when, as is becoming increasingly true,                        incompleteness pervades intensively used scientific data from                   sources as far-ranging as social surveys and clinical medical                   protocols.
{'FirstName': 'Donald', 'LastName': 'Rubin', 'PI_MID_INIT': 'B', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Donald B Rubin', 'EmailAddress': 'donald.rubin@temple.edudisabled', 'NSF_ID': '000308024', 'StartDate': '07/01/1988', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
{'Name': 'Harvard University', 'CityName': 'CAMBRIDGE', 'ZipCode': '021385366', 'PhoneNumber': '6174955501', 'StreetAddress': '1033 MASSACHUSETTS AVE STE 3', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Massachusetts', 'StateCode': 'MA', 'CONGRESSDISTRICT': '05', 'CONGRESS_DISTRICT_ORG': 'MA05', 'ORG_UEI_NUM': 'LN53LCFJFL45', 'ORG_LGL_BUS_NAME': 'PRESIDENT AND FELLOWS OF HARVARD COLLEGE', 'ORG_PRNT_UEI_NUM': None}
{'Name': None, 'CityName': None, 'StateCode': None, 'ZipCode': None, 'StreetAddress': None, 'CountryCode': None, 'CountryName': None, 'StateName': None, 'CountryFlag': None, 'CONGRESSDISTRICT': None, 'CONGRESS_DISTRICT_PERF': '""'}
[{'Code': '126900', 'Text': 'STATISTICS'}, {'Code': '133300', 'Text': 'Methodology, Measuremt & Stats'}, {'Code': '371500', 'Text': None}]
{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=1988&All=true', 'xml': '8805433.xml'}