Best Presentation of Structural Equation Modeling


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Best Presentation of Structural Equation Modeling

  2. 2. Introduction Literature Review Methodology Findings Conclusion 2
  3. 3. INTRODUCTION 3 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
  4. 4. 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. 4
  5. 5. SIGNIFICANT OF STUDY 5 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
  6. 6. 6 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.
  7. 7. Moderator-Mediator 7 •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
  8. 8. 8 Summary why benefits, barrier and challenges become mediator? AUTHOR/ YEARS STATEMENTS VARIABLE Dingle, 2001 Goverments 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, 2001 Describe 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
  10. 10. 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 10
  11. 11. 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 11
  12. 12. 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. 12
  13. 13. Fitness Indexes Name of Category Name of Index Index Full Name Level of Acceptance Literature Absolute Fit GFI Goodness-of-fit Index GFI > 0.90 Joreskog and Sorbom (1986) AGFI Adjusted Goodness-of-fit test AGFI > 0.90 Joreskog and Sorbom (1986) SRMR Standardized root mean square residual SRMR < 0.08 Bentler (1995) RMSEA Root 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 NFI Normed Fit Index NFI > 0.90 Bentler & Bonett (1980) TLI Tucker Lewis Index TLI > 0.95 Tucker and Lewis (1973) RNI Relative noncentrality Index Rni > 0.90 McDonald & Marsh (1990) CFI Comparative Fit Index CFI > 0.95 Bentler (1989,1990) IFI Incremental 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. 13
  14. 14. Mediating Effect • Mediation 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. 14
  15. 15. 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 occursFrone, 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)
  16. 16. DATAANALYSIS 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 16
  17. 17. Reliability Statistics Cronbach's Alpha N of Items .919 53 17
  18. 18. Motivation 18
  19. 19. Construct Validity 19
  20. 20. Summary for convergent validity Cronbach Alpha CR AVE Benefits 0.923 0.898 0.503 Motivation 0.941 0.941 0.519 Challenges 0.849 0.844 0.477 Barrier 0.761 0.758 0.452 Goverment_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 20
  21. 21. Multigroup Mediating Effect 21
  22. 22. 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 22
  23. 23. 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 23
  24. 24. Multigroup Moderator-Mediator Result 24
  25. 25. 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 25
  26. 26. 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 26
  27. 27. Standardized Estimates Result 27
  28. 28. 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.
  29. 29. 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. 29