Dr. Mohd Sobhi IshakDepartment of Multimedia Technology
School of Multimedia Technology and Communication
College of Arts and Sciences
Universiti Utara Malaysia, Kedah, Malaysia
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)
Dr. Mohd Sobhi Ishak ([email protected], 012-2015528) Workshop SEM ke-2, Pusat Islam UUM» » 12-13 Januari 2013
Research Goals
the goal is theory testing,
theory confirmation
comparison of alternative theories.
Dr. Mohd Sobhi Ishak ([email protected], 012-2015528) Workshop SEM ke-2, Pusat Islam UUM» » 12-13 Januari 2013
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.
Dr. Mohd Sobhi Ishak ([email protected], 012-2015528) Workshop SEM ke-2, Pusat Islam UUM» » 12-13 Januari 2013
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).
Dr. Mohd Sobhi Ishak ([email protected], 012-2015528) Workshop SEM ke-2, Pusat Islam UUM» » 12-13 Januari 2013
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.
Dr. Mohd Sobhi Ishak ([email protected], 012-2015528) Workshop SEM ke-2, Pusat Islam UUM» » 12-13 Januari 2013
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.
Dr. Mohd Sobhi Ishak ([email protected], 012-2015528) Workshop SEM ke-2, Pusat Islam UUM» » 12-13 Januari 2013
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
Dr. Mohd Sobhi Ishak ([email protected], 012-2015528) Workshop SEM ke-2, Pusat Islam UUM» » 12-13 Januari 2013
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
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
Dr. Mohd Sobhi Ishak ([email protected], 012-2015528) Workshop SEM ke-2, Pusat Islam UUM» » 12-13 Januari 2013
Common Statistical TechniqueStatistic U/B/Ma IVb DVc I/Nd Model
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
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
Dr. Mohd Sobhi Ishak ([email protected], 012-2015528) Workshop SEM ke-2, Pusat Islam UUM» » 12-13 Januari 2013
Common Statistical TechniqueStatistic U/B/Ma IVb DVc I/Nd Model
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
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
Dr. Mohd Sobhi Ishak ([email protected], 012-2015528) Workshop SEM ke-2, Pusat Islam UUM» » 12-13 Januari 2013
Common Statistical TechniqueStatistic U/B/Ma IVb DVc I/Nd Model
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
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
Dr. Mohd Sobhi Ishak ([email protected], 012-2015528) Workshop SEM ke-2, Pusat Islam UUM» » 12-13 Januari 2013
Common Statistical TechniqueStatistic U/B/Ma IVb DVc I/Nd Model
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
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
Dr. Mohd Sobhi Ishak ([email protected], 012-2015528) Workshop SEM ke-2, Pusat Islam UUM» » 12-13 Januari 2013
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
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
Dr. Mohd Sobhi Ishak ([email protected], 012-2015528) Workshop SEM ke-2, Pusat Islam UUM» » 12-13 Januari 2013
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.
Dr. Mohd Sobhi Ishak ([email protected], 012-2015528) Workshop SEM ke-2, Pusat Islam UUM» » 12-13 Januari 2013
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)
Dr. Mohd Sobhi Ishak ([email protected], 012-2015528) Workshop SEM ke-2, Pusat Islam UUM» » 12-13 Januari 2013
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)
Dr. Mohd Sobhi Ishak ([email protected], 012-2015528) Workshop SEM ke-2, Pusat Islam UUM» » 12-13 Januari 2013
Alternative model/Competing ModelTest several alternative or competing model which are supported by theories. Choose the best fit
Generated model Re-specified/Competing Model
Dr. Mohd Sobhi Ishak ([email protected], 012-2015528) Workshop SEM ke-2, Pusat Islam UUM» » 12-13 Januari 2013
Model generating/Model DevelopmentTest a single model theory. However may modify and re-estimate the model. Most commonly used framework
Model Development
Dr. Mohd Sobhi Ishak ([email protected], 012-2015528) Workshop SEM ke-2, Pusat Islam UUM» » 12-13 Januari 2013
6 Proses
Pelaksanaan SEM
Dr. Mohd Sobhi Ishak ([email protected], 012-2015528) Workshop SEM ke-2, Pusat Islam UUM» » 12-13 Januari 2013
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
Dr. Mohd Sobhi Ishak ([email protected], 012-2015528) Workshop SEM ke-2, Pusat Islam UUM» » 12-13 Januari 2013
Six-Stage Process for SEM (Hair et al., 2010)
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
Dr. Mohd Sobhi Ishak ([email protected], 012-2015528) Workshop SEM ke-2, Pusat Islam UUM» » 12-13 Januari 2013
Six-Stage Process for SEM (Hair et al., 2010)
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
Dr. Mohd Sobhi Ishak ([email protected], 012-2015528) Workshop SEM ke-2, Pusat Islam UUM» » 12-13 Januari 2013
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
Dr. Mohd Sobhi Ishak ([email protected], 012-2015528) Workshop SEM ke-2, Pusat Islam UUM» » 12-13 Januari 2013
What is AMOS?
• Analysis of Moment Structures
Moment Structures
Mean CovariancesVariances
Dr. Mohd Sobhi Ishak ([email protected], 012-2015528) Workshop SEM ke-2, Pusat Islam UUM» » 12-13 Januari 2013
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
Dr. Mohd Sobhi Ishak ([email protected], 012-2015528) Workshop SEM ke-2, Pusat Islam UUM» » 12-13 Januari 2013
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
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