Mo&Ro 1.Sem-plsi (Dr.etty Elika)
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Transcript of Mo&Ro 1.Sem-plsi (Dr.etty Elika)
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7/31/2019 Mo&Ro 1.Sem-plsi (Dr.etty Elika)
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STRUCTURAL EQUATION
MODELING
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What are Structural Equation
Models? Systems of linear equations that
describe a network of relations among
variables.Structural, not simply predictive relations
Implied systems of nonlinear equations
that describe patterns of variances andcovariances among variables.
Output of software systems such as
LISREL, EQS, AMOS, and MPlus.
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Comparison with Multiple Regression
Multiple Regression Causal ModelingX1
X2
X3
X4
X5
Y
Q: How well do predictors
predict (explain variances) in
Y? What are independent
effects when effects of other
variables are controlled?
X1
X3 X4
X2 X5
Y
Q: How well do predictors
relate with regard to ultimate
prediction of Y?
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Why are SEM methods useful?
Hoyles (1994) review tells us that SEM
can address
Questions about causal process
Questions about causal process when
variables are not well measured
SEM methods share most of thestrengths of multiple regression
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Example of a Structural Model
X3 = aX1 + bX2 + U1
X4 = cX1 + dX2 + eX3 + U2
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Steps of Structural Equation
Modeling
STEP 1: SPECIFICATIONStatement of the theoretical model either
as a set of equations or as a diagram.
STEP 2: IDENTIFICATIONThe model can in theory and in practice
be estimated with observed data
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Steps of Structural Equation
Modeling
STEP 3: ESTIMATIONThe model's parameters are statisticallyestimated from data. Multiple regression is onesuch estimation method, but typically more
complicated estimated methods are used. STEP 4: MODEL FIT
The estimated model parameters are used topredict the correlations or covariances between
measured variables and the predictedcorrelations or covariances are compared to theobserved correlations or covariances
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Measuring Model Fit
Fit refers to the ability of a model to
reproduce the data. It should be noted that a
good-fitting model is not necessarily a validmodel. There are now literally hundreds of
measures of fit. Bollen and Long (Testing
structural equation models. Newbury Park,CA: Sage, 1993) explains these indexes and
others.
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Measuring Model Fit
Chi Square: 2For models with about 75 to 200 cases, this is
a reasonable measure of fit. But for modelswith more cases, the chi square is almostalways statistically significant. Chi squareis also affected by the size of thecorrelations in the model: the larger the
correlations, the poorer the fit. For thesereasons alternative measures of fit havebeen developed.
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Measuring Model Fit
Root Mean Square Error ofApproximation (RMSEA)
Good models have an RMSEA of .05 or less.Models whose RMSEA is .10 or more have
poor fit. Goodness of Fit Index
Nilai rentangan antara 0 (poor fit) 1(perfect fit)
etc
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Penggunaan: Behavioral
Information System Technology Acceptance Model (TAM)
Davis, 1989
Theory of Planned Behavior , Ajzen 1991
Unified Theory of Acceptance and Use of
Technology (UTAUT) Venkatesh, et al ,
2003
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Technology Acceptance Model
Reaksi dan persepsi pengguna TeknologiInformasi (TI) akan mempengaruhi sikapnyadalam penerimaan terhadap teknologi tersebut.Salah satu faktor yang dapat mempengaruhinyaadalah persepsi pengguna terhadap kemanfaatandan kemudahan penggunaan TI sebagai suatutindakan yang beralasan dalam konteks penggunateknologi, sehingga alasan seseorang dalam
melihat manfaat dan kemudahan penggunaan TImenjadikan tindakan atau perilaku orang tersebutsebagai tolok ukur dalam penerimaan sebuahteknologi.
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An example of a structural equation
model:
EksternalVariable
PerceivedUsefulnes
s (PU)
PerceivedEase of
Use(PEOU)
AttitudeToward
using (A)
BehavioralIntention
(BI)
ActualUse
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Perceived of Usefulness
Saya akan belajar menggunakan media pengajarankelas maya (virtual class)
Belajar menggunakan kelas maya tidak mudahbagi saya
Tidak mudah bagi saya menjadi terampil dalammemanfaatkan kelas maya sebagai media
pengajaran Interaksi yang saya gunakan pada kelas maya
dapat dengan mudah dimengerti
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PG1
PG2
PG3
PG4
pguna
pmudah
sikap
minat
psungguh
PM1
PM2
PM3
PM4
ST1
ST2
ST3
ST4
ST5
MT1
MT2
MT3
MT4
PTS1
PTS2
PTS3
Chi-Square=129.33, df=158, P-value=0.95392, RMSA=0.000
0.52
0.610.63
0.48
0.46
0.39
0.57
0.63
0.44
0.43
0.61
0.60
0.59
0.58
0.61
0.62
0.16
0.47
0.33
0.43
0.43
-0.09
0.45
-1.041.53
0.86
0.55
0.96
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Persepsi
Penggunaan
PersepsiKemudahanPenggunaan
Sikapmenggunakan
Teknologi
Minat terhadapTeknologi
Penggunaanteknologi
Sesungguhnya
Pengalaman
Gender
Persepsi
Penggunaan
Persepsi
Kemudahan
Penggunaan
Sikap
menggunakan
Teknologi
Minat
terhadap
Teknologi
Penggunaan
teknologi
Sesungguhnya
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Major problems with SEM are
that Models are often (usually?)
misspecified:
Linearity assumption is often madeuncritically
Measurement error distorts analysis
Important variables may be missing Communicating results is challenging