000			03627cam a2200481Mi 4500
001			9780429170362
003			FlBoTFG
005			20220509193020.0
006			m o d
007			cr |||||||||||
008			201020s2020 flua fo 000 0 eng d
040			_aOCoLC-P _beng _erda _epn _cOCoLC-P
020			_a1482227827
020			_a9780429170362 _q(electronic bk.)
020			_a042917036X _q(electronic bk.)
020			_a9781482227826 _q(electronic bk. : PDF)
020			_a9780429543555 _q(electronic bk. : Mobipocket)
020			_a0429543557 _q(electronic bk. : Mobipocket)
020			_a9780429528859 _q(electronic bk. : EPUB)
020			_a042952885X _q(electronic bk. : EPUB)
024	7		_a10.1201/9780429170362 _2doi
035			_a(OCoLC)1240715173
035			_a(OCoLC-P)1240715173
050		4	_aQA275
072		7	_aMAT _x029000 _2bisacsh
072		7	_aJMB _2bicssc
082	0	4	_a511.42 _223
100	1		_aMehmetoglu, Mehmet, _eauthor.
245	1	0	_aStructural equation modelling with partial least squares using stata and R _cMehmet Mehmetoglu, Sergio Venturini.
264		1	_aBoca Raton : _bChapman & Hall/CRC, _c2020.
300			_a1 online resource _billustrations (black and white)
336			_atext _2rdacontent
336			_astill image _2rdacontent
337			_acomputer _2rdamedia
338			_aonline resource _2rdacarrier
520			_aPartial least squares structural equation modelling (PLS-SEM) is becoming a popular statistical framework in many fields and disciplines of the social sciences. The main reason for this popularity is that PLS-SEM can be used to estimate models including latent variables, observed variables, or a combination of these. The popularity of PLS-SEM is predicted to increase even more as a result of the development of new and more robust estimation approaches, such as consistent PLS-SEM. The traditional and modern estimation methods for PLS-SEM are now readily facilitated by both open-source and commercial software packages. This book presents PLS-SEM as a useful practical statistical toolbox that can be used for estimating many different types of research models. In so doing, the authors provide the necessary technical prerequisites and theoretical treatment of various aspects of PLS-SEM prior to practical applications. What makes the book unique is the fact that it thoroughly explains and extensively uses comprehensive Stata (plssem) and R (cSEM and plspm) packages for carrying out PLS-SEM analysis. The book aims to help the reader understand the mechanics behind PLS-SEM as well as performing it for publication purposes. Features: Intuitive and technical explanations of PLS-SEM methods Complete explanations of Stata and R packages Lots of example applications of the methodology Detailed interpretation of software output Reporting of a PLS-SEM study Github repository for supplementary book material The book is primarily aimed at researchers and graduate students from statistics, social science, psychology, and other disciplines. Technical details have been moved from the main body of the text into appendices, but it would be useful if the reader has a solid background in linear regression analysis.
588			_aOCLC-licensed vendor bibliographic record.
650		0	_aLeast squares.
650		0	_aStructural equation modeling.
650		7	_aMATHEMATICS / Probability & Statistics / General _2bisacsh
700	1		_aVenturini, Sergio, _eauthor.
856	4	0	_3Taylor & Francis _uhttps://www.taylorfrancis.com/books/9780429170362
856	4	2	_3OCLC metadata license agreement _uhttp://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf
999			_c128275 _d128275