part a introduction to sem and amos

Dr. Mohd Sobhi IshakDepartment of Multimedia Technology

School of Multimedia Technology and Communication

College of Arts and Sciences

Universiti Utara Malaysia, Kedah, Malaysia

[email protected]

012-2015528

Dr. Mohd Sobhi Ishak ([email protected], 012-2015528) Workshop SEM ke-2, Pusat Islam UUM» » 12-13 Januari 2013

Pemodelan Persamaan Struktur(Structural Equation Modelling)

SEM adalah satu teknik statistik multivariat yang

komprehensif yang menggabungkan beberapa analisis

multivariat (iaitu factor analysis dan multiple

regression) untuk menentukan satu siri pelbagai

hubungan secara serentak.

It is particularly useful in testing theories that

contain multiple equations involving dependence

relationships.

(Hair et al., 2010)


Research Goals

the goal is theory testing,

theory confirmation

comparison of alternative theories.


Philosophical Foundations

Research that applies SEM usually follows a

positivist epistemological belief:o Assumes an objective, physical, and social world that

exists independently of humans.

o Nature of this world can relatively easily apprehended,

characterized and measured.

o Researcher plays a passive, neutral role and does not

intervene in the phenomenon of interest.


Nature of Structural Equation Models

Purpose of many research projects is to analyze causal

relationships between variables.

SEM is a statistical technique for testing and estimating

those causal relationships using quantitative data and

qualitative causal assumptions.

SEM are considered second generational multivariate

analysis techniques.o Researcher can simultaneously consider relationships

among multiple independent and dependent constructs.

SEM also supports latent variables (LVs).o Hypothetical constructs invented by a scientist for the

purpose of understanding a research area (Bentler

1980).


Nature of Structural Equation Models

Second generational multivariate SEM techniques

permit answering a set of interrelated research

questions in a single, systematic, and comprehensive

analysis (Gefen 2000).

Latent Variables (LVs) are unobservable and cannot be

directly measured, so researchers use observable and

empirically measurable indicator variables (sometimes

called Manifest Variables, or MVs), to estimate the LVs

in the model.

Thus, relationships can be assessed among

unobservable, theoretical constructs like intentions,

perceptions, and satisfaction which straddle many

disciplines.


Mengapa perlu belajar SEM?

1. Supaya faham bila baca artikel yang ditulis guna

analisis SEM.

2. Supaya boleh membina kerangka konseptual

yang menyeluruh / kompleks (atau model) yang

menepati tahap PhD.

3. Supaya dapat menghasilkan artikel jurnal yang

berimpak tinggi seperti dilihat dalam jurnal

masakini.


Statistic U/B/Ma IVb DVc I/Nd Model

Mode U 1: N N X

Median U 1: O N X

Mean U 1: I/R N X

Range & interquartile range U 1: O N X

Standard deviation &

variance (sd2)

U 1: I/R N X

Standard error (SE) &

confidence interval (CI)

U 1: I/R I X

Common Statistical Technique

a U=Univariate, B=Bivariate, M=Multivariateb IV=Independent Variable (s), number and assumed level of measurement (N=nominal, O=Ordinal,

I/R= Interval/Ratioc DV=Dependent Variable (s), number and assumed level of measurement (N=nominal, O=Ordinal,

I/R= Interval/Ratiod Inferential or Nonparametric


Common Statistical TechniqueStatistic U/B/Ma IVb DVc I/Nd Model

Chi-square B 1: N 1: N N X Y

t-test B 1: N(2 group) 1: I/R I X Y

F-test B 1: N(3+group) 1: I/R I X Y

Spearman rank-

order coefficient

(rho)

B 1: O 1:O N X Y

Pearson correlation B 1: I/R 1: I/R I X Y

Bivariate

regression

B 1: I/R 1: I/R I X Y






Multiple-factor ANOVA M 2+: N 1: I/R I X1

X2

X3

X4

Y

Multivariate ANOVA

(MANOVA)

M 2+: N 2+: I/R I X1

X2

X3

X4

Y1

Y2

Y3

Y4






Discriminant Analysis M 2+: I/R 1:N I

Y

Factor Analysis M 2+:I/R None

(factors

emerge)

N/I

X1

X2

X3

X4

X1

X2

X3

X4

F1

F2






Multiple Regression M 2+: I/R 2+: I/R I

Y

Logistic Regression M 2+:I/R 1: N(2-

cat.)

N/I

X1

X2

X3

X4

X1

X2

X3

X4

Y






Canonical Correlation M 2+: I/R 2+: I/R I

Cluster Analysis M 2+: I/R None

(clusters

emerge)

IX1

X2

X3

X4

C1

C2

X1

X2

X3

X4

Y1

Y2

Y3









Multidimensional Scaling M 2+: I/R None (dims.

are extracted)

I

Y

Structural Equation

Modeling

M 2+: I/R 1+: I/R N/I

X1

X2

X3

X4

X1

X2

Y1

Y3 Y4

Y2


SEM encompasses model analysis techniques such as:

•Covariance structure analysis

•Latent variable analysis

•Confirmatory factor analysis

•Path analysis

•Multiple regression and

•Linear structural relation analysis

• It is the mother of all model analysis techniques.


Framework for testing (Kline,2010)

Modelling Strategy (Hair et al, 2010)

Strictly confirmatory/Confirmatory Modelling

Test a single model theory: reject or fail to reject

Alternative model/Competing Model

Test several alternative or competing model which are supported by theories. Choose the best fit

Model generating/Model Development

Test a single model theory. However may modify and re-estimate the model. Most commonly used framework

Framework for testing (Kline,2010)

Modelling Strategy (Hair et al, 2010)


Strictly confirmatory/Confirmatory ModellingTest a single model theory: reject or fail to reject

Technology Acceptance Model (TAM) (Sumber: Davis, et al., 1989)

Tanggapan

Kebergunaan

(PU)

Keinginan

Bertingkah

laku (BI)

Penggunaa

n Sistem

Sebenar

Tanggapan

Mudah

Diguna

(PEOU)


Alternative model/Competing ModelTest several alternative or competing model which are supported by theories. Choose the best fit

Generated model Re-specified/Competing Model


Model generating/Model DevelopmentTest a single model theory. However may modify and re-estimate the model. Most commonly used framework

Model Development


6 Proses

Pelaksanaan SEM


Six-Stage Process for SEM (Hair et al., 2010)

Defining Individual Construct

What item are to be used as measured variables?

Develop and specify the Measurement Model

Make measured variables with constructs

Draw a path diagram for the measurement model

Designing a Study to Produce Empirical Results

Access the adequacy of the sample size

Select the estimation method and missing data approach



Assessing Measurement Model Validity

Assess line GOF and construct validity of measurement model

Measurement model valid? YesNo

Refine measure and design a new study

Proceed to test structural model with stages 5&6



Specify Structural Model

Convert measurement model to structural model

Assess Structural Model Validity

Assess the GOF and significance, direction, and size of structural parameter estimates

Structural model valid? YesNo

Refine measure and design a new study

Draw substantive conclusions and

recommendations


SEM Specific-Software• Commercial packages

▫ AMOS in IBM SPSS

▫ EQS

▫ Stata

SEM (official software)

GLLAMM (user-contributed commands)

▫ LISREL

▫ Mplus

▫ SEPATH in STATISTICA(Electronic Statistics Textbook)

▫ SAS (software)procedures

CALIS

TCALIS

▫ WarpPLS

• Opensource packages in R: ▫ lavaan

▫ OpenMx (home page)

▫ sem2

• Other Free packages ▫ Ωnyx

http://en.wikipedia.org/wiki/Stata

http://www.stata.com/stata12/structural-equation-modeling/

http://www.gllamm.org/

http://www.statmodel.com/

http://en.wikipedia.org/wiki/STATISTICA

http://www.statsoft.com/textbook/structural-equation-modeling/

http://www.statsoft.com/textbook/structural-equation-modeling/

http://en.wikipedia.org/wiki/SAS_(software)

http://warppls.com/

http://lavaan.ugent.be/

http://en.wikipedia.org/wiki/OpenMx

http://openmx.psyc.virginia.edu/

http://socserv.mcmaster.ca/jfox/

http://onyx.brandmaier.de/

http://onyx.brandmaier.de/


What is AMOS?

• Analysis of Moment Structures

Moment Structures

Mean CovariancesVariances


1

2

3

4

5

6

7

8

1. Exogenous

2. Endogenous

3. Latent = unobserved

4. Manifest = observed

5. Covariance

6. Causal effect

7. Measurement error

8. Residual


1. Draw latent, manifest, &

measurement error

3. Draw Residual

5.Display variable & drag to

manifest. Label latent

variable

4. Data File

7. Tick Analysis Properties

8. Calculate Estimates

9. View Output

2. Draw Path

6. Write GOF

\format

CMIN: \cmin

DF: \df

CMIN/DF: \cmindf

P-VALUE: \p

CFI: \cfi

PNFI: \pnfi

RMSEA: \rmsea

part a introduction to sem and amos

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