A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM)

329 Pages·2013·16.619 MB·English

by  Josephb F. Hair, G. Tomas M. Hult, Christian M. Ringle, Marko Sarstedt

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A PRMIERO N
PARTILAELA SSTQ UARES
STRUCTUREAQLU ATION
MODELNIG (PLS-SEM)
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A PRIM01ENIR
PA,RTILAIELA S,ST1Q1 UARES
STIRUCTILJIREAQLU ATION
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Tableo fC ontents
Preface xi
Aboutt heA uthors xv
Chapte1r:A n IntruocdtiotnoS tructural
EquatiMoond eling 1
Chapte2r:S pecifyitnhge P athM odela nd
CollectDiantga 32
Chapte3r:P athM odelE stimation 73
Chapte4r:A ssessiPnLgS -SEMR esultPsa rIt:
EvaluatoifoR ne flectMievaes uremeMnotd els 95
Chapte5r:A ssessiPnLgS -SEMR esultPsa rItI :
Evaluatoifot nh eF ormatiMveea suremeMnotd els 118
Chapte6r:A ssessiPnLgS -SEMR esultPsa rItl l:
Evaluatoifot nh eS tructuMroadle l 167
Chapte7r:A dvanceTdo picisnP LS-SEM 205
Chapte8r:M odelinHge terogeneDoautsa 243
References 282
AuthoIrn dex 290
SubjeIcntd ex 293
DetaileTda ble
ofC ontents
Preface xi
Aboutt heA uthors xv
Chapte1r:A n IntrocdtuiotnoS tructural
EquatiMoond eling 1
LearniOnugt come1s
ChaptPerre vi1ew
WhatI sS tructEuqruaalt iMoond elin2g?
ConsideraitniU osnisnS gt ructural
EquatiMoond eli4ng
TheV aria4te
Measureme5nt
MeasuremSecnatl 7es
Codin8g
DataD istribu9tions
StructEuqruaalt iMoond eliWnigt h
PartiLeaals Stq uarPeast Mho deli1n1g
PatMho delWsi thL ateVnatr iab1l1es
MeasuremTehneto ry1 3
StructTuhreaolr 1y3
PLS-SEaMn dC B-SEM1 4
DataC haracter1i8stics
ModelC haracter2i3stics
OrganizaotfiR oenm ainiCnhga pte2r4s
Summar2y6
RevieQwu estio2n8s
Critical TQhuiensktiino2gn8 s
KeyTerm2s8
SuggesRteeadd in3g1s
Chapte2r:S pecifytihnegP athM odel
andC ollectDiantga 32
LearniOnugt come3s2
ChaptPerre vi3e2w
Stag1e:S peciftyhiSent gr uctMuordaell3 3
Mediati3o5n
Moderati3o7n
Higher-OarnddeH ri erarchical
ComponeMnotd el3s9
Stag2e:S peciftyhieMn ega suremMeondte l4s0
ReflecatnidvF eo rmatMievaes uremMeondte l4s2
Single-MIetaesmu r4e6s
Stag3e:D ataC ollecatnidoE nx aminat4i8o n
MissiDnagt a5 1
Suspicious Respon5s2e Patterns
Outlie5r3s
DataD istribu5t4i on
CasSet udIyl lustrSapteicoinf:ty hiPenL gS -SEMMo del5 5
ApplicaotfiS otna g1e:S tructMuordaell
Specific5a6t ion
ApplicaotfiS otna g2e:M easurement Model
Specific5a7t ion
ApplicaotfiS otna g3e:D ataC ollecatnido n
Examinat5i8o n
PatMho deClr eatiUosni ntgh eS martPSLoSf twa6r1e
Summar6y6
RevieQwu estio6n9s
CritiTchianlk iQnuge stio6n9s
KeyTerm6s9
SuggesRteeadd in7g1s
Chapte3r:P athM odelE stimation 73
LearniOnugt come7s3
ChaptPerre vi7e3w
Stag4e:M odeEls timatainodtn h e
PLS-SEAMl gorit7h 4m
How thAel goriWtohrmk s7 4
Statistical 7P8r operties
AlgoritOhpmtiico annsd P arameter
SettitnogR su nt hAel gorit8h0m
Resul8t1s
CasSet udIyl lustrPaLtSiP oant:h
ModeEls timat(iSotna4 g)e8 3
ModeEls timat8i3o n
EstimatRieosnu l8t5s
Summar8y8
RevieQwu estio9n0s
CritiTchailn kiQnuge stio9n0 s
KeyTerms 91
SuggesRteeadd ings 94
Chapte4r:A ssessiPnLgS -SEM ResPualrtIts:
EvaluatoifoR ne flectMievaes uremeMnotd els 95
LearniOnugt come9sS
ChaptPerre vi9e wS
OvervioefwS tagSe:E valuatoifo n
Measurement M9o6d els
StagSea A: ssessRiensgu lotfRs e flective
MeasuremMeondte l1s0 0
InterCnoanls istReenlciya bi1l0i1t y
ConvergVeanlti d1i0t2y
DiscrimiVnaalnitd 1i0t4y
CasSet udIyl lustration-Reflective
MeasuremMeondte l1s0 7
Runnintgh eP LS-SEAMl gorit1h0m7
ReflecMteiavseu remMeondte Elv aluat1i0o9n
Summar1y1 3
RevieQwu estio1n1s4
CritiTchailn kiQnuge stio1n1s4
KeyTerms1 14
SuggesRteeadd in1g1s6
Chapte5r:A ssessing PLS-SEM PRaersItuI l:t s
Evaluatoifot nh eF ormative MeasuMroedmeelnst 118
LearniOnugt come1s1 8
ChaptPerre vi1e1w8
StagSeb A:s sessing oRfeF sourlmtast ive
MeasuremMeondte l1s1 9
Ste1p:A ssess Convergen1t2 1V alidity
Ste2p: A sseFsosr matMievaes uremMeondte lfso r
CollineIasrsiut1ey2s 3
Ste3p: A ssetshseS i gnifiacnadnR ceel evaonfct eh e
FormatIinvdei cat1o2r6s
BootstrapPprioncge du1r3e0
Conceapntd J ustific1a3t0i on
BootstCroanpf ideInnctee rv1a3l6s
CasSet udIyl lustration-EovfaF louramtaitoinv e
MeasuremMeondte l1s3 9
ExtendtihneSg i mpPlaet Mho del1 39
ReflectMievaes uremMeondte Elv aluat1i47o n
FormatMievaes uremMeondte Elv aluat1i5o0n
Summar1y6 1
RevieQwu estio1n6s2
CritiTchianlk iQnuge stio1n6s3
KeyT erm1s6 3
SuggesRteeadd in1g6s5
Chapte6r:A ssessiPnLgS -SEMR esults IPlalr:t
Evalautioonft heS tructuMroadle l 167
LearniOnugt come1s6 7
ChaptPerre vi1e6w7
Stag6e:A ssessPiLnSg- SESMt ructural
ModeRle sul1t68s
Ste1p: C ollineAasrsietsys m1e7n0t
Ste2p: S tructMuordaelPl a tCho efficie1n7t0 s
(R2
Ste3p: C oefficioefDn ett erminatiVoanl ue1)7 4
f 2
Ste4p: E ffecSti ze 177
Q2
Ste5p: B lindfoladnidPn rge dictive Rel1e7v8a nce
Heterogen1e8i4t y
Goodness-oIfn-dFe1ix8t 5
CasSet udIyl lustrHaotwi Aorne:P LS-SEM
StructMuordaelRl e sulRtesp orte1d8?5
Summar1y9 8
RevieQwu estio2n0s0
CritiTchianlk iQnuge stio2n0s0
KeyT erm2s0 0
SuggesRteeadd in2g0s3
Chapte7r:A dvanceTdo picisnP LS-SEM 205
Learning Out2c0o5m es
Chapter Pr2e0v5i ew
Importance-PerMfaotrrmiaAxnn cael ys2i0s6
Method20 6
CasSet udIyl lustr2a1t0i on
MediatAonra lys2i1s9
Method21 9
CasSet udIyl lustr2a2t6i on