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Journal articles on the topic 'Structural model equation'

Author: Grafiati

Published: 4 June 2021

Last updated: 10 February 2022

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1

Lee, Sik-Yum, and Xin-Yuan Song. "Bayesian structural equation model." Wiley Interdisciplinary Reviews: Computational Statistics 6, no. 4 (June 16, 2014): 276–87. http://dx.doi.org/10.1002/wics.1311.

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2

Brandmaier, Andreas M., Timo von Oertzen, John J. McArdle, and Ulman Lindenberger. "Structural equation model trees." Psychological Methods 18, no. 1 (2013): 71–86. http://dx.doi.org/10.1037/a0030001.

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3

J, Kannan, Venkatrama Raju D, and Kamaraj S. "Air Travel Passengers’ Expectation – An Analysis of Structural Equation Model." Journal of Advanced Research in Dynamical and Control Systems 11, no. 0009-SPECIAL ISSUE (September 25, 2019): 1402–8. http://dx.doi.org/10.5373/jardcs/v11/20192756.

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4

Yuan, Ke-Hai, and Wai Chan. "Fitting structural equation models using estimating equations: A model segregation approach." British Journal of Mathematical and Statistical Psychology 55, no. 1 (May 2002): 41–62. http://dx.doi.org/10.1348/000711002159699.

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5

Prasertratana, Supphakant, Wirote Sanratana, and Kanok-Orn Somprach. "Distributed Leadership: Structural Equation Model." Procedia - Social and Behavioral Sciences 116 (February 2014): 1610–14. http://dx.doi.org/10.1016/j.sbspro.2014.01.443.

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6

Raykov, Tenko, and Spiridon Penev. "On Structural Equation Model Equivalence." Multivariate Behavioral Research 34, no. 2 (April 1999): 199–244. http://dx.doi.org/10.1207/s15327906mb340204.

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7

Çolak, Tuğba Seda. "Somatic Expression of Psychological Problems (Somatization): Examination with Structural Equation Model." International Journal of Psychology and Educational Studies 1, no. 1 (September 1, 2014): 8–14. http://dx.doi.org/10.17220/ijpes.2014.01.003.

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8

Rappaport, Lance M., Ananda B. Amstadter, and Michael C. Neale. "Model Fit Estimation for Multilevel Structural Equation Models." Structural Equation Modeling: A Multidisciplinary Journal 27, no. 2 (July 2, 2019): 318–29. http://dx.doi.org/10.1080/10705511.2019.1620109.

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9

Fino, Emanuele, Sergio Melogno, Paolo Iliceto, Sara D'Aliesio, Maria Pinto, and Ugo Sabatello. "Executive functions, impulsivity, and inhibitory control in adolescents: A structural equation model." Advances in Cognitive Psychology 10, no. 2 (May 15, 2014): 32–38. http://dx.doi.org/10.5709/acp-0154-5.

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10

Shina, Arya Fendha Ibnu. "ESTIMASI PARAMETER PADA SISTEM MODEL PERSAMAAN SIMULTAN DATA PANEL DINAMIS DENGAN METODE 2 SLS GMM-AB." MEDIA STATISTIKA 11, no. 2 (December 30, 2018): 79–91. http://dx.doi.org/10.14710/medstat.11.2.79-91.

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Single equation models ignore interdependencies or two-way relationships between response variables. The simultaneous equation model accommodates this two-way relationship form. Two Stage Least Square Generalized Methods of Moment Arellano and Bond (2 SLS GMM-AB) is used to estimate the parameters in the simultaneous system model of dynamic panel data if each structural equation is exactly identified or over identified. In the simultaneous equation system model with dynamic panel data, each structural equation and reduced form is a dynamic panel data regression equation. Estimation of structural equations and reduced form using Ordinary Least Square (OLS) resulted biased and inconsistent estimators. Arellano and Bond GMM method (GMM AB) estimator produces unbiased, consistent, and efficient estimators.The purpose of this paper is to explain the steps of 2 SLS GMM-AB method to estimate parameter in simultaneous equation model with dynamic panel data. Keywords:2 SLS GMM-AB, Arellano and Bond estimator, Dynamic Panel Data, Simultaneous Equations

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11

Barrett, Paul. "Structural equation modelling: Adjudging model fit." Personality and Individual Differences 42, no. 5 (May 2007): 815–24. http://dx.doi.org/10.1016/j.paid.2006.09.018.

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12

Maydeu-Olivares, Alberto, and Dexin Shi. "Effect Sizes of Model Misfit in Structural Equation Models." Methodology 13, Supplement 1 (June 1, 2017): 23–30. http://dx.doi.org/10.1027/1614-2241/a000129.

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Abstract. Residual correlations and covariances provide effect sizes of the misfit of covariance structure models. In a simulation study, we found that accurate confidence intervals (CIs) for standardized residual covariances are obtained even in small samples (N = 100), regardless of model size, degree of model misspecification, and data distribution. Standardized residual covariances also provide information about the source of misfit in poorly fitting models. From this viewpoint, they may be considered an alternative to modification indices. We compared the empirical Type I errors and power rates of standardized residual covariances and modification indices and found that both procedures provide nearly identical rates across the simulation conditions investigated. Residual correlations and standardized residual covariances provide very similar results.

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13

Pan, Frank, and Yueh-Cheng E. Pan. "Applying structural equation model in HBM theory." International Journal of Research in Business and Social Science (2147- 4478) 9, no. 5 (September 20, 2020): 357–66. http://dx.doi.org/10.20525/ijrbs.v9i5.861.

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Diabetic Mellitus (DM) is a widespread chronic disease around the world, among which type 2 DM (T2DM) shared the majority. It caused multiple severe complications and consequently a high mortality rate. DM complications overwhelm jeopardize the patient’s quality of life. In response to the threats of such complications, some nutraceuticals were developed, including the highly praised bitter melon peptide (BMP). This study aims to apply the structural equation model in HBM. A theoretical model had been developed on the health belief model (HBM), and to test with the structural equation model (SEM) technique to examine the fitness of the theory and the data gathered in this research. A structural questionnaire was developed and used to collect 292 valid responses from DM patients. The SEM results indicated the fitness of the theory and the data were acceptable. Perceived susceptibility of DM complications and perceived benefits of nutraceutical were major predictors of intake behavior, and the association of perceived benefits and behavior was mediated by the patient’s self-efficacy on nutraceutical intake behavior.

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14

Lee, Jung-Min, and Cheol-Yong Park. "A structural equation model for career maturity." Journal of the Korean Data and Information Science Society 23, no. 2 (March 31, 2012): 333–42. http://dx.doi.org/10.7465/jkdi.2012.23.2.333.

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15

Ballon, Paola. "A Structural Equation Model of Female Empowerment." Journal of Development Studies 54, no. 8 (December 20, 2017): 1303–20. http://dx.doi.org/10.1080/00220388.2017.1414189.

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16

Lai, Keke, Samuel B. Green, Roy Levy, Ray E. Reichenberg, Yuning Xu, Marilyn S. Thompson, Nedim Yel, Natalie D. Eggum-Wilkens, Katie L. Kunze, and Masumi Iida. "Assessing Model Similarity in Structural Equation Modeling." Structural Equation Modeling: A Multidisciplinary Journal 23, no. 4 (April 8, 2016): 491–506. http://dx.doi.org/10.1080/10705511.2016.1154464.

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17

Daskin, Mustafa. "Testing a structural equation model of polychronicity." International Journal of Contemporary Hospitality Management 31, no. 7 (July 8, 2019): 2788–807. http://dx.doi.org/10.1108/ijchm-05-2018-0349.

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Purpose This paper aims to explore the role of ethical climate as an antecedent of polychronicity, then in turn the effect of polychronicity on frontline employees’ service innovative behaviours (SIBE) and error strain and the moderating role of organization mission fulfilment in these relationships in a hotel job context in Penang, Malaysia. Design/methodology/approach In the current study, the data were collected from 312 frontline employees in the survey premises. A partial least square approach of the structural equation modelling technique (PLS-SEM) was used to test the associations among study variables. Findings The study findings reveal that ethical climate had positive impact on polychronicity. Significantly, polychronicity made positive influence on SIBEs and negative influence on error strain. Organization mission fulfilment was found to boost the influence of ethical climate on polychronicity. Finally, while organization mission fulfilment was found to boost the positive influence of polychronicity on SIBEs, on the other hand, buffers the negative impact of polychronicity on error strain. Practical implications The present study procures implications for practitioners in terms of establishing ethical climate in work environment and maximizing the frontline employees’ SIBEs while minimizing error strain. In addition, this research procures valuable implications to apply efficient managerial tools and improve job results in the lodging industry context of Peninsular Malaysia. Originality/value This study by investigating the untested associations such as the influence of polychronicity on frontline employees’ SIBEs and error strain contributes to the related literature.

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18

Chinda, Thanwadee, and Sherif Mohamed. "Structural equation model of construction safety culture." Engineering, Construction and Architectural Management 15, no. 2 (February 29, 2008): 114–31. http://dx.doi.org/10.1108/09699980810852655.

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19

Mainul Islam, M. D., and Olusegun O. Faniran. "Structural equation model of project planning effectiveness." Construction Management and Economics 23, no. 2 (February 2005): 215–23. http://dx.doi.org/10.1080/0144619042000301384.

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20

Jin, Shaobo, and Sebastian Ankargren. "Frequentist Model Averaging in Structural Equation Modelling." Psychometrika 84, no. 1 (June 4, 2018): 84–104. http://dx.doi.org/10.1007/s11336-018-9624-y.

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21

Song, Xin-Yuan, Ye-Mao Xia, Jun-Hao Pan, and Sik-Yum Lee. "Model Comparison of Bayesian Semiparametric and Parametric Structural Equation Models." Structural Equation Modeling: A Multidisciplinary Journal 18, no. 1 (January 13, 2011): 55–72. http://dx.doi.org/10.1080/10705511.2011.532720.

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22

Lindstrøm, Jonas Christoffer, and Fredrik A. Dahl. "Model Selection with Lasso in Multi-group Structural Equation Models." Structural Equation Modeling: A Multidisciplinary Journal 27, no. 1 (August 14, 2019): 33–42. http://dx.doi.org/10.1080/10705511.2019.1638262.

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23

Congdon, Peter. "Structural equation models for area health outcomes with model selection." Journal of Applied Statistics 38, no. 4 (April 2011): 745–67. http://dx.doi.org/10.1080/02664760903563692.

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24

Maydeu-Olivares, Alberto. "Assessing the Size of Model Misfit in Structural Equation Models." Psychometrika 82, no. 3 (February 7, 2017): 533–58. http://dx.doi.org/10.1007/s11336-016-9552-7.

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25

Lee, Sik-Yum, and Xin-Yuan Song. "Model comparison of nonlinear structural equation models with fixed covariates." Psychometrika 68, no. 1 (March 2003): 27–47. http://dx.doi.org/10.1007/bf02296651.

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26

Wei, Zhi Rong. "Application of Structural Equation Model and AMOS Software." Applied Mechanics and Materials 687-691 (November 2014): 1577–79. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.1577.

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The Amos 17 is the structural equation model (Structural Equation Modeling, SEM), which is one of the important software analysis. With the application of SEM technology in depth, analysis of multi group structural equation model (Multiple-Group Analysis) has gradually attracted researcher’s attention.. Stability of deep test theoretical model by examining the effect of its regulating variable, and the method is not only suitable for fitting degree test theoretical models in the regulation of variables of different level, but also for the fitting problem to test the tracking data and theoretical model. Through a case study of their own, using Amos 17 software as the basis introduces the basic principle of multi group structural equation model analysis technique and its use method in the study of Applied Linguistics to domestic applied linguistics intends to use the method which will provide examples of reference.

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27

Cong, Danyang. "Research on Community Distribution Model Based on Structural Equation Model." IOP Conference Series: Materials Science and Engineering 688 (December 6, 2019): 033010. http://dx.doi.org/10.1088/1757-899x/688/3/033010.

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28

Bae, Yun Kyung. "Analysis on Comparison of Highway Accident Severity between Weekday and Weekend using Structural Equation Model." Journal of the Korean Society of Civil Engineers 33, no. 6 (2013): 2483. http://dx.doi.org/10.12652/ksce.2013.33.6.2483.

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29

Cavaliere, Alessia, Elena Siletti, and Alessandro Banterle. "Nutrition information, Mediterranean diet, and weight: A structural equation approach." Agricultural Economics (Zemědělská ekonomika) 66, No. 1 (January 27, 2020): 10–18. http://dx.doi.org/10.17221/25/2019-agricecon.

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This paper studies the relationships between adherence to the Mediterranean diet, food-related information, and possible effects they could jointly exert on weight status. The empirical analysis was conducted via a consumer survey using face-to-face interviews, and a structural equation model was implemented to the data analysis. This model choice was led by its ability to simultaneously evaluate multiple constructs. The results outline the central role played by adherence to the Mediterranean diet and food-related information, both in terms of nutritional knowledge and expert recommendations, which seem to be the key drivers affecting healthy weight. Moreover, food label use increases the nutritional knowledge of consumers, which in turn favours a healthy diet.

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30

Shan, Wei, Hengwen Liu, Yichao Pan, and Hengqing Tong. "Evaluation of Social Risk Using Structural Equation Model." iBusiness 02, no. 02 (2010): 188–92. http://dx.doi.org/10.4236/ib.2010.22023.

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31

Tong, Qiaoling, Xuecheng Zou, Chuanmei Wang, and Hengqing Tong. "A definite linear algorithm for structural equation model." Mathematical and Computer Modelling 52, no. 5-6 (September 2010): 744–51. http://dx.doi.org/10.1016/j.mcm.2010.05.002.

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32

Di Tommaso, Maria Laura. "Children capabilities: A structural equation model for India." Journal of Socio-Economics 36, no. 3 (June 2007): 436–50. http://dx.doi.org/10.1016/j.socec.2006.12.006.

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33

Congdon, Peter. "A spatial structural equation model for health outcomes." Journal of Statistical Planning and Inference 138, no. 7 (July 2008): 2090–105. http://dx.doi.org/10.1016/j.jspi.2007.09.001.

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34

Wong, Peter Shek Pui, and Sai On Cheung. "Structural Equation Model of Trust and Partnering Success." Journal of Management in Engineering 21, no. 2 (April 2005): 70–80. http://dx.doi.org/10.1061/(asce)0742-597x(2005)21:2(70).

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35

Duarte, Paulo O., Helena B. Alves, and Mário B. Raposo. "Understanding university image: a structural equation model approach." International Review on Public and Nonprofit Marketing 7, no. 1 (December 15, 2009): 21–36. http://dx.doi.org/10.1007/s12208-009-0042-9.

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36

Chen, Yunfeng, Hazar Dib, Robert F. Cox, Mark Shaurette, and Mihaela Vorvoreanu. "Structural Equation Model of Building Information Modeling Maturity." Journal of Construction Engineering and Management 142, no. 9 (September 2016): 04016032. http://dx.doi.org/10.1061/(asce)co.1943-7862.0001147.

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37

Zhang, Yue-Zhen, and Jun-Hong Luo. "Structural Equation Model of Population Sexual Health Informatics." Journal of Medical Imaging and Health Informatics 8, no. 2 (February 1, 2018): 351–58. http://dx.doi.org/10.1166/jmihi.2018.2295.

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38

Zhang, Yue-Zhen, and Jun-Hong Luo. "Structural Equation Model of Population Sexual Health Informatics." Journal of Medical Imaging and Health Informatics 9, no. 3 (March 1, 2019): 456–63. http://dx.doi.org/10.1166/jmihi.2019.2315.

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39

Graham, James M. "The General Linear Model as Structural Equation Modeling." Journal of Educational and Behavioral Statistics 33, no. 4 (December 2008): 485–506. http://dx.doi.org/10.3102/1076998607306151.

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40

Carrasco, Josep-Llu�s, and Llu�s Jover. "Assessing individual bioequivalence using the structural equation model." Statistics in Medicine 22, no. 6 (2003): 901–12. http://dx.doi.org/10.1002/sim.1452.

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41

Brandmaier, Andreas M., John J. Prindle, John J. McArdle, and Ulman Lindenberger. "Theory-guided exploration with structural equation model forests." Psychological Methods 21, no. 4 (2016): 566–82. http://dx.doi.org/10.1037/met0000090.

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42

Kelava, Augustin, and Holger Brandt. "A Nonlinear Dynamic Latent Class Structural Equation Model." Structural Equation Modeling: A Multidisciplinary Journal 26, no. 4 (January 24, 2019): 509–28. http://dx.doi.org/10.1080/10705511.2018.1555692.

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43

Kohn, Jonathan W., Michael A. McGinnis, and Ali Kara. "A structural equation model assessment of logistics strategy." International Journal of Logistics Management 22, no. 3 (November 2011): 284–305. http://dx.doi.org/10.1108/09574091111181336.

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44

Molenaar, Keith, Simon Washington, and James Diekmann. "Structural Equation Model of Construction Contract Dispute Potential." Journal of Construction Engineering and Management 126, no. 4 (July 2000): 268–77. http://dx.doi.org/10.1061/(asce)0733-9364(2000)126:4(268).

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45

Newsom, Jason T. "A Multilevel Structural Equation Model for Dyadic Data." Structural Equation Modeling: A Multidisciplinary Journal 9, no. 3 (July 2002): 431–47. http://dx.doi.org/10.1207/s15328007sem0903_7.

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46

Jedidi, Kamel, Harsharanjeet S. Jagpal, and Wayne S. DeSarbo. "STEMM: A General Finite Mixture Structural Equation Model." Journal of Classification 14, no. 1 (January 1, 1997): 23–50. http://dx.doi.org/10.1007/s003579900002.

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47

Georgiou, Stelios N., and Constantinos Christou. "Family parameters of achievement: A structural equation model." European Journal of Psychology of Education 15, no. 3 (September 2000): 297–311. http://dx.doi.org/10.1007/bf03173181.

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48

Igbaria, M. "End-user computing effectiveness: A structural equation model." Omega 18, no. 6 (1990): 637–52. http://dx.doi.org/10.1016/0305-0483(90)90055-e.

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49

Owolabi, H. O., J. K. Ayandele, and D. D. Olaoye. "A SYSTEMATIC REVIEW OF STRUCTURAL EQUATION MODEL (SEM)." Open Journal of Educational Development (ISSN: 2734-2050) 1, no. 2 (November 23, 2020): 27–39. http://dx.doi.org/10.52417/ojed.v1i2.163.

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Structural Equation Model (SEM) is a multivariate statistical technique that has been explored to test relationships between variables. The use of SEM to analyze relationship between variables is premised on the weak assumption of path analysis, regression analysis and so on; that variables are measured without error. This review thus sheds light on the meaning of SEM, its assumptions, steps and some of the terms used in SEM. The importance of item parcelling to SEM and its methods were briefly examined. It also dealt on the stages involved in SEM, similarities and differences between SEM and conventional statistical methods, software packages that can be used for SEM. This article employed systematic literature review method because it critically synthesized research studies and findings on structural equation modeling (SEM). It could be concluded that SEM is useful in analyzing a set of relationships between variables using diagrams. SEM can also be useful in minimizing measurement errors and in enhancing reliability of constructs. Based on this, it is recommended that SEM should be employed to test relationship between variables since it can explore complex relationships among variables such as direct, indirect, spurious, hierarchical and non-hierarchical.

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50

Pinto Silva, Thales Augusto Barbosa, Hilbeth Parente Azikri de Deus, and Cezar Otaviano Ribeiro Negrão. "A Numerical Approach on New Constitutive Model for Thixotropic Substances." Applied Mechanics and Materials 751 (April 2015): 95–101. http://dx.doi.org/10.4028/www.scientific.net/amm.751.95.

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The thixotropic substances can be found in different industrial sectors, such as chemical, biomedical, manufacturing and oil. These substances show a rheological time-dependent behavior, dependent of their structural level. Generally, a constitutive model for the thixotropic substances is composed by a pair of coupled equations: the constitutive equation (based on viscoelastic models) and the rate equation (that describes the structural evolution). In many works presented in the specialized literature, the shear modulus and viscosity dependencies with the structural nature are not formally considered in the dynamical principles from that the constitutive equation is originated. In the present work, a new, thermodynamically consistent, constitutive model for thixotropic substances, where such dependences are considered, is presented and some rheological tests are analyzed in a numerical simulation point of view (code developed in MATLAB). The constitutive model is based on Jeffreys’ model and the coagulation theory of Smoluchowsky.

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