000			02217cam a22003618i 4500
001			22101392
005			20220725150351.0
008			210625s2022 nyu b 001 0 eng
010			_a 2021031108
020			_a9781462549030 _q(cloth)
040			_aDLC _beng _erda _cIIMV
042			_apcc
050	0	0	_aHA31.3 _b.H39 2022
082	0	0	_a001.4/22 _223 _bHAY
100	1		_aHayes, Andrew F., _eauthor. _932230
245	1	0	_aIntroduction to mediation, moderation, and conditional process analysis : _ba regression-based approach / _cAndrew F. Hayes.
250			_aThird edition.
263			_a2112
264		1	_aNew York, NY : _bThe Guilford Press, _c[2022]
300			_apages cm.
336			_atext _btxt _2rdacontent
337			_aunmediated _bn _2rdamedia
338			_avolume _bnc _2rdacarrier
490	0		_aMethodology in the social sciences
504			_aIncludes bibliographical references and index.
520			_a"Lauded for its easy-to-understand, conversational discussion of the fundamentals of mediation, moderation, and conditional process analysis, this book has been fully revised with 50% new content, including sections on working with multicategorical antecedent variables, the use of PROCESS version 3 for SPSS and SAS for model estimation, and annotated PROCESS v3 outputs. Using the principles of ordinary least squares regression, Andrew F. Hayes carefully explains procedures for testing hypotheses about the conditions under and the mechanisms by which causal effects operate, as well as the moderation of such mechanisms. Hayes shows how to estimate and interpret direct, indirect, and conditional effects; probe and visualize interactions; test questions about moderated mediation; and report different types of analyses. Data for all the examples are available on the companion website ([ital]www.afhayes.com[/ital]), along with links to download PROCESS"-- _cProvided by publisher.
650		0	_aSocial sciences _xStatistical methods. _932231
650		0	_aMediation (Statistics) _932232
650		0	_aRegression analysis. _932233
906			_a7 _bcbc _corignew _d1 _eecip _f20 _gy-gencatlg
942			_2ddc _cBK
999			_c5877 _d5877