Mo&Ro 1.Sem-plsi (Dr.etty Elika)

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

Mo&Ro 1.Sem-plsi (Dr.etty Elika)

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