000			01691cam a2200301 i 4500
001			94162
003			CY-NiPEI
005			20221125111832.0
007			ta
008			201127s2021 xxka b 001 0 eng d
020			_a9780198859987 _q(hbk.)
040			_aYDX _beng _cCY-NiPEI _dCY-NiPEI _eAACR2
041	0		_aeng
082	7		_223 _a519.53
245	0	0	_aMeasurement error in longitudinal data / _cedited by Alexandru Cernat, Joseph W. Sakshaug.
250			_a1st ed.
260			_a[Oxford] : _bOxford University Press, _c[2021].
300			_axii, 448 p. : _bill. ; _c24 cm.
504			_aIncludes bibliographical references and index.
520			_aLongitudinal data is essential for understanding how the world around us changes. Most theories in the social sciences and elsewhere have a focus on change, be it of individuals, of countries, of organisations, or of systems, and this is reflected in the myriad of longitudinal data that are being collected using large panel surveys. This type of data collection has been made easier in the age of Big Data and with the rise of social media. Yet our measurements of the world are often imperfect, and longitudinal data is vulnerable to measurement errors which can lead to flawed and misleading conclusions. This book tackles the important issue of how to investigate change in the context of imperfect data.
650		0	_aLongitudinal method _9166257
650		0	_aError analysis (Mathematics) _9131699
700	1		_4edt _aCernat, Alexandru _9166258
700	1		_4edt _aSakshaug, Joseph W. _9166259
710	2		_4pbl _9155536 _aOxford University Press
942			_2ddc _cBK _h519.53 MEA
970			_cAnastasia P.
999			_c94162 _d94162