Best Presentation of Structural Equation Modeling
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MODELLING THE MULTIGROUP MODERATOR-MEDIATOR ON MOTIVATION AMONG YOUTH IN HIGHER EDUCATION INSTITUTION TOWARDS
VOLUNTEERISM PROGRAM
NAME:WAN MOHAMAD ASYRAF BIN WAN
AFTHANORHANMATRIC NUMBER:
GSK1478MAIN SUPERVISOR:
ASSOCIATE PROFESOR DR. SABRI AHMAD
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Introduction Literature Review
Methodology Findings
Conclusion
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INTRODUCTION
Problem Statement
To what extent the role of goverment support as a source
variable creates barrier, benefits, and challenges to the
motivation
Ibrahim mamat, 2012 find out the level of involvement in
volunteerism program is low
Bollen, 1989 explore moderator-mediator can explain both effect at the
same time.
Carol Hardy-Fanter, 1993 found that males and
females took on different roles when volunteering.
To address the comparison between male and female in
volunteering activity
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OBJECTIVE RESEARCH
To compare the group effect for moderator variable.
To differentiate the type of moderating effect through the structural model.
To determine the gender as moderator variable on the path interest.
To identify the type of mediating effect through the structural model.
To develop the best structural (path) model through the model estimation, model fit, and model modification verification on motivation towards volunteerism program.
To validate the independent (exogenous) and dependent (endogenous) variables through measurement model.
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SIGNIFICANT OF STUDY
Significant of Study
The study on interrelation between goverment
support, benefits, barrier, challenges, and motivation in an integrated framework
by using Structural Equation Modeling (SEM)
is a good interest for researchers.
The undergraduates and postgraduates involvement towards
the volunteerism program is the focus in this study since it may
bring tremendous benefits to the universities in the future besides to provide optimum exposure to the
community.
This study claims itself to be among the first to explore the gender role
on the relationship between goverment support, barrier, benefits,
challenges and motivation.
The comparison between male and female can be conducted to investigate
which group is more pronounce in volunteerism
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LITERATURE REVIEW
Mediating Effect
•Mediation effect can be called as an intervening effects.• A mediator is a predictor link in the relationships between two other variables. •Normally, a mediator variable can become an exogenous and endogenous variable at same time. •According to Zainudin Awang (2010) the mediation have three types of mediator: 1. Full mediation, 2. Partial mediation 3. Non-mediation.
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Moderator-Mediator
•Moderation is quite different with mediation. •This method is employed to examine the strength influences of relationships between the endogenous and exogenous variables. •Moderation variable can be categorical and continuous variables.•In this case, the gender role become as moderator in this model to examine whether the gender influences of these relationship between exogenous and endogenous constructs.•According to Zainudin Awang (2012) the moderation have three types of moderator: 1. Full moderation 2. Partial moderation 3. Non-moderation
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Summary why benefits, barrier and challenges become mediator?
AUTHOR/ YEARS STATEMENTS VARIABLE
Dingle, 2001Goverments may contribute by supporting such infrastructure. Further, if goverments is better informed about the people who volunteeer, it is likely to become more aware of how policy legislation it introduces can affect, both directly and indirectly , people giving of their time
Benefits
Dingle, 2001Describe three factors that challenges volunteering which can be indirectly among people to involve the volunteerism program . These are : globalization, relations with the state, and the relation with the market
Challenges
Marlene wilson, 1976 and Eva Schindler- Rainman, 1987
Explores the barrier is the early mainstream( i.e not about supported volunteering specifically) volunteer program management literature contains encouraging messages about broadening the base of volunteering. In generals, this factor can be main research problem of people from getting involve in volunteerism program due to the scenario that they will faced. . Hence, the number whose involve in these activity will become decrease
Barrier
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THEREOTICAL FRAMEWORK
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METHODOLOGY
Respodent age’s must be between 15 to 40 years old.
The study applied the stratified sampling technique whereby in Terengganu only
Four higher education institution are selected randomly among the university available in Kuala Terengganu
All students in the selected university are taken as respondents in the study
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THE PROCEDURE FOR DATA ANALYSIS
STRUCTURAL EQUATION MODELLING (SEM)
•Commonly used for confirmatory factor analysis for unidimensionality procedure.
Measurement Model
•Assembled for the whole of measurement model with causal effect and correlation.
Structural Model
5 types of model required:
Model Identification
Model Specification
Model Evaluation
Model Modification
Model Estimation
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Construct Validity
Convergent Validity AVE AVE > 0.50
The validity is achieved when all items in a measurement model are
statiscally significant.
Construct Validity
GFI
CFI
RMSEA
Chisq/Df
GFI > 0.90
CFI > 0.90
RMSEA < 0.08
Chisq/Df < 5.0
This validity is achieved when the fitness indexes achieve the
following requirements
Discriminant Validity
Square Root of AVE and correlation of latent
constructs
All the correlation between these construct
should below 0.85
This validity is achieved when the measurement model is free from
redundant items.
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Fitness IndexesName of Category
Name of Index Index Full Name Level of Acceptance
Literature
Absolute Fit
GFIGoodness-of-fit Index
GFI > 0.90 Joreskog and Sorbom (1986)
AGFIAdjusted Goodness-of-fit test
AGFI > 0.90 Joreskog and Sorbom (1986)
SRMRStandardized root mean square residual
SRMR < 0.08 Bentler (1995)
RMSEARoot mean Square Error Approximation
RMSEA < 0.06 Steiger & Lind (1980)
Comment Higher values of GFI and AGFI as well as lower value of SRMR and RMSEA indicate better model data fit.
Incremental Fit
NFINormed Fit Index NFI > 0.90 Bentler & Bonett
(1980)
TLITucker Lewis Index TLI > 0.95 Tucker and Lewis
(1973)
RNIRelative noncentrality Index
Rni > 0.90 McDonald & Marsh (1990)
CFIComparative Fit Index
CFI > 0.95 Bentler (1989,1990)
IFIIncremental Fit Index
IFI > 0.90 Bollen (1989)
Comment Higher values of incremental fit indices indicate larger improvement over the baseline model in fit.
Parsiminous Fit Chisquare/Df
Chisquare/ degree of Freedom
Chisq/Df < 5.0 Marsh and Hancover (1985)
Comment Very sensitive to the sample size.
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Mediating Effect•Media
tion analysis or intervening effect permits examination process, allowing the researcher to examine by what means X exerts its effect on Y. Although systems of equations linking X to Y through multiple mediators are possibly to specify
MacKinnon,2000
Partially mediated model was proposed based on Baron and Kenny’s (1986) three required conditions is required for mediation effects:
• The independent variable must affect the mediating variable. In this instance, the goverment support predictor must affect the barrier, challenges, and benefits.
• The independent variable must affect the dependent variable. In this model, goverment support constructs must have effect on the outcome variable (i.e., motivation)
• The mediator must have effect on the dependent variable. In this case, the barrier, benefits, and challenges must affect motivation.
Moderator and Mediator
•Combination of moderator and mediator in simultaneously.
Moderated mediation
•Moderated mediation model attempt to explain both how and when a given effect occurs
Frone, 1999
•asserted that moderated mediation “happens if the mediating process that is responsible for producing the effect of the treatment on the outcome depends on the value of a moderator variable.
Muller et al. (2005)
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DATA ANALYSIS
To validate the independent (exogenous) and dependent
(endogenous) variables through measurement model.
To develop the best structural (path) model through the model
estimation, model fit, and model modification verification
on motivation towards volunteerism program.
To identify the type of mediating effect through the
structural model.
To determine the gender as moderator variable on the path
interest.
To differentiate the type of moderating effect through the
structural model.
To compare the group effect for moderator variable.
Reliability Normality
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Reliability Statistics
Cronbach's Alpha N of Items
.919 53
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Motivation
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Construct Validity
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Summary for convergent validity
Cronbach Alpha CR AVEBenefits 0.923 0.898 0.503
Motivation 0.941 0.941 0.519Challenges 0.849 0.844 0.477
Barrier 0.761 0.758 0.452Goverment_Support 0.835 0.838 0.467
Discriminant validity
Benefits Motivation Challenges Barrier Goverment_Support
0.709
0.690 0.721
0.219 0.229 0.691
0.287 0.297 0.390 0.672
0.451 0.449 0.277 0.261 0.683
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Multigroup Mediating Effect
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Findings For Mediating Effect
Estimate P Hypothesis
Barrier <--- Goverment_Support .353 *** Supported
Challenges <--- Goverment_Support .413 *** Supported
Benefits <--- Goverment_Support .536 *** Supported
Motivation <--- Goverment_Support .127 .027 Supported
Motivation <--- Barrier .090 .029 Supported
Motivation <--- Challenges .016 .645 Not Supported
Motivation <--- Benefits .812 *** Supported
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Type Mediator
Mediating Variable P-value Mediating Variable P-Value Type
Barrier <---Goverment_
Support*** Motivation <--- Barrier .029 Partial
Challenges <---Goverment_
Support*** Motivation <--- Challenge .645 Full
Benefits <---Goverment_
Support*** Motivation <--- Benefits *** Partial
Constant Motivation <---Goverment_
Support.027
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Multigroup Moderator-Mediator
Result
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Findings for Moderator-mediator
Male Female
Estimate P Estimate P z-score
Barrier <--- Goverment_Support 0.29 0.011 0.343 0.000 -0.174
Challenges <--- Goverment_Support 0.462 0.000 0.36 0.004 -1.192
Benefits <--- Goverment_Support 0.665 0.000 0.264 0.000 -2.933***
Motivation <--- Goverment_Support 0.177 0.057 0.132 0.058 -0.2
Motivation <--- Barrier 0.095 0.099 0.03 0.56 -0.59
Motivation <--- Challenges 0.021 0.696 0.008 0.822 -0.543
Motivation <--- Benefits 0.695 0.000 0.892 0.000 0.715
Notes: *** p-value < 0.01; ** p-value < 0.05; * p-value < 0.10
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Type Moderator
Constructs Male Female Type Moderation
Barrier <- Goverment_Support
0.011 0.000 Partially
Challenges <- Goverment_Support
0.000 0.004 Partially
Benefits <- Goverment_Support
0.000 0.000 Partially
Motivation <- Goverment_Support
0.057 0.058 Non
Motivation <- Barrier 0.099 0.56 Non
Motivation <- Challenges 0.696 0.822 Non
Motivation <- Benefits 0.000 0.000 Partially
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Standardized Estimates
Result
Comparing Group
Constructs Male P-value Female P-value
Barrier <--- Goverment_Support .265 0.011 .282 0.000
Challenges <--- Goverment_Support .347 0.000 .215 0.004
Benefits <--- Goverment_Support .573 0.000 .289 0.000
Motivation <--- Goverment_Support .108 0.057 .111 0.058
Motivation <--- Barrier .073 0.099 .031 0.56
Motivation <--- Challenges .050 0.696 .011 0.822
Motivation <--- Benefits .726 0.000 .687 0.000
Four significant path which is goverment support on barrier, challenges, and benefits while the benefits on motivation, one can conclude that the gender moderates the relationship between these variables
The effect of male group for government support on benefits and challenge, and benefits on motivation is more pronounced compare to female group.
The effect of female group for government support on barrier is more pronounced compare to male group only.
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Discussion and Conclusion
Conclusion
The study indicate the goverment support is statistical significant
different influences on benefits, challenges, barrier
and motivation.
Benefits is the most contribute on motivation
compare to other variables.
The male group is more contribute to involve in volunteerism program
than female group.
The theory to apply moderator-mediator in this study is supported.
Goverment support has evidence to support the
moderating effect of gender on the relationship
between benefits of volunteering.