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008			050915s2001 caua b 001 0 eng
010			_a 2001001295
020			_a0761916725 : _c14.95
040			_aDLC _cDLC _dDLC
042			_apcc
050	0	0	_aQA276 _b.A55 2002
069			_a03407590
090			_aQA 276 .A55 2002
090			_aQA 276 .A55 2002
100	1		_aAllison, Paul David. _971682
245	1	0	_aMissing data / _cPaul D. Allison.
260			_aThousand Oaks, Calif. : _bSage Publications, _cc2002.
300			_avi, 93 p. : _bill. ; _c22 cm.
490	1		_aSage university papers. Quantitative applications in the social sciences ; _vno. 07-136
500			_a"A SAGE university paper"--Cover.
504			_aIncludes bibliographical references (p. 89-91) and index.
520		0	_aSooner or later anyone who does statistical analysis runs into problems with missing data in which information for some variables is missing for some cases. Why is this a problem? Because most statistical methods presume that every case has information on all the variables to be included in the analysis. Using numerous examples and practical tips, this book offers a nontechnical explanation of the standard methods for missing data (such as listwise or casewise deletion) as well as two newer (and, better) methods, maximum likelihood and multiple imputation. Anyone who has been relying on ad-hoc methods that are statistically inefficient or biased will find this book a welcome and accessible solution to their problems with handling missing data.
520	8	0	_a
520	8	0	_a
650		0	_aMathematical statistics. _971683
650		0	_aMissing observations (Statistics) _971684
830		0	_aSage university papers series. _pQuantitative applications in the social sciences ; _vno. 07-136. _971685
852			_9p14.95 _y09-14-2002
907			_a12760 _b08-06-10 _c08-06-10
942			_cBOOK _00
998			_aaudmc _b09-14-02 _cm _da _e- _feng _gcau _h0
935			_aPO10329-GEN3
945			_g0 _i631770 _j0 _laudmc _nCopy Type:01 - Books _o- _p54.94 _q- _r- _s- _t1 _u0 _v0 _w0 _x0 _yi10181477 _z08-06-10
999			_c12760 _d12760