000			01724naa a2200181uu 4500
001			7356
003			OSt
005			20190211154243.0
008			020927s2005 xx ||||gr |0|| 0 eng d
245	1	0	_aAnalysing incomplete political science data : _ban alternative algorithm for multiple imputation
260			_c2001
520	3		_aWe propose a remedy for the discrepancy between the way political scientists analyze data with missing values and the recommendations of the statistics community. Methodologists and statisticians agree that "multiple imputation" is a superior approach to the problem of missing data scattered through one`s explanatory and dependent variables than the methods currently used in applied data analysis. The discrepance occurs because the computational algorithms used to aply the best multiple imputation models have been slow, difficult to implement, impossible to run with existing commercial statistical packages, and have demanded considerable expertise. We adapt an algorithm and use it to implement a general-purpose, multiple imputation model for missing data. This algorithm is considerably faster and easier to use than the leading method recommended in the statistics literature. We also quantify the risks of current missing data practices, ilustrate how to use the new procedure, and evaluate this alternative through simulated data as well as actual empirical examples. Finally, we offer easy-to-use software that implements all methods discussed
773	0	8	_tAmerican Political Science Review _g95, 1, p. 49-70 _d, 2001 _w
942			_cS
998			_a20020927 _bCassio _cCassio
998			_a20060515 _b1502^b _cQuiteria
999			_aConvertido do Formato PHL _bPHL2MARC21 1.1 _c7509 _d7509
700			_a
041			_aeng