000			01885nam a2200337 i 4500
003			CSPC
005			20250902163739.0
008			241009s2021 caua b 001 0 eng d
020			_a9781526424730
040			_cCSPC _aCSPC _beng _erda
050	0	4	_aHA29 _b.M4384 2021
082		4	_a519.53 _bM459s _223
100	1		_aMcbee, Matthew, _eauthor.
245	1	0	_aStatistical approaches to casual analysis / _cMatthew Mcbee.
264		1	_aLos Angeles : _bSAGE, _c2021.
300			_axi, 234 pages : _billustrations ; _c24 cm.
336			_atext _2rdacontent
337			_aunmediated _2rdamedia
338			_avolume _2rdacarrier
490	0		_aThe Sage quantitative research kit
504			_aIncludes bibliographical references and index.
505	0		_aIntroduction -- Conditioning -- Directed acyclic graphs -- Rubin's causal model and the propensity score -- Propensity score analysis -- Instrumental variable analysis -- Regression discontinuity design -- Conclusion.
520			_a"A practical, up-to-date, step-by-step and accessible introduction to causal inference in quantitative research. Featuring worked example datasets throughout, it clearly outlines the steps involve in carrying out various types of statistical causal analysis. Matthew McBee evaluates the issue of causal inference in quantitative research, while providing guidance on how to apply these analyses to data, discussing key concepts such as: directed acyclic graphs (DAGs), Rubin’s Causal Model (RCM), Propensity Score Analysis, and Regression Discontinuity Design."
650		0	_aCausation.
650		0	_aCausation _xStatistical methods.
650		0	_aCasual inference.
650		0	_aStatistical methods.
650		0	_aStatistics.
942			_2ddc _n0 _e23 _cBK _h519.53 _iM459s _kGRD _m2021
999			_c28360 _d28360