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Relevant bibliographies by topics / Structural model equation

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Academic literature on the topic 'Structural model equation'

Author: Grafiati

Published: 4 June 2021

Last updated: 10 February 2022

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Journal articles on the topic "Structural model equation"

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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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Structural model equation"

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Morris, Nathan J. "Multivariate and Structural Equation Models for Family Data." Case Western Reserve University School of Graduate Studies / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=case1247004562.

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Preacher, Kristopher J. "The Role of Model Complexity in the Evaluation of Structural Equation Models." The Ohio State University, 2003. http://rave.ohiolink.edu/etdc/view?acc_num=osu1054130634.

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Cunningham, Shaylyn, and University of Lethbridge Faculty of Education. "Anxiety, depression and hopelessness in adolescents : a structural equation model." Thesis, Lethbridge, Alta. : University of Lethbridge, Faculty of Education, 2005, 2006. http://hdl.handle.net/10133/344.

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This study tested a structural model, examining the relationship between a latent variable termed demoralization and measured variables (anxiety, depression and hopelessness) in a community sample of Canadian youth. The combined sample consisted of data collected from four independent studies from 2001 to 2005. Nine hundred and seventy one (n=971) participants in each of the previous four studies were high school students (grades 10-12) from three geographic locations: Calgary, Saskatchewan and Lethbridge. Participants completed a battery of self-report questionnaires including the Beck Anxiety Inventory (BAI), Beck Depression Inventory-Revised (BDI-II), Beck Hopelessness Scale (BHS), and demographic survey. Structural equation modeling was used for statistical analysis. The analysis revealed that the final model, including depression, anxiety and hopelessness and one latent variable demoralization, fit the data (chi-square value, X2 (2) =7.24, p<. 001, goodness of fit indices (CFI=0.99, NFI=0.98) and standardized error (0.05). Overall, the findings suggest that close relationships exist among depression, anxiety, hopelessness and demoralization. In addition, the model was stable across demographic variables: sex, grade, and location. Further, the model explains the relationship between sub-clinical anxiety, depression and hopelessness. These findings contribute to a theoretical framework, which has implications with educational and clinical interventions. The present findings will help guide further preventative research in examining demoralization as a precursor to sub-clinical anxiety and depression.
xi, 127 leaves ; 29 cm.

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Jaffari, Fathima. "MODEL-FREE MEASUREMENT OF CASE INFLUENCE IN STRUCTURAL EQUATION MODELING." OpenSIUC, 2019. https://opensiuc.lib.siu.edu/dissertations/1689.

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This study introduces a new model-free case influence measure (DOCR) to the SEM field and evaluates its performance compared to that of Mahalanobis Distance (MD) and Generalized Cook’s Distance (gCD) when the sample size, proportion of target cases to non-target cases, and type of model used to generate the data are manipulated. The findings suggest the DOCR measure generally performed better than MD and gCD in identifying the target cases across all simulated conditions. However, the performance of the DOCR measure under small sample size was not satisfactory, and it raised a red flag about the sensitivity of this measure to small sample sizes. Therefore, researchers and practitioners should only use the DOCR measure with sufficiently large sample sizes, but not larger than 600.

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Moxley-Paquette, Elizabeth Ann. "Testing a Structural Equation Model of Language-based Cognitive Fitness." ScholarWorks, 2014. https://scholarworks.waldenu.edu/dissertations/1545.

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The normative development of language is often taken for granted, yet problems with language development can result in stress for the individual and family. A challenge with these language development problems lies within the contemporary education system, which assumes that children have appropriate skills when they begin school. The purpose of the study was to test a theoretical model of language readiness known as language-based cognitive fitness, which includes measures associated with structural concepts of language involving receptive language, expressive language, spontaneous narrative speech, and writing fluency. The sample included children from a private school who received an extensive battery of tests at admission and annually thereafter. Scores from a variety of cognitive measures were used in a structural equation modeling framework to test the model. Results demonstrated language-based cognitive fitness to be an interplay of verbal reasoning abilities, visual synthesis, and active analysis broadly representing receptive language, expressive language, spontaneous narrative expression, and writing fluency. Verbal reasoning, visual synthesis, and active analysis explained 91% of the variance in achievement. Implications for positive social change include an improved understanding for those who work with children's language development, specifically of the language structures responsible for language deficits and how these relate to overall cognitive fitness; interventions can be provided to help children more quickly make up language deficits.

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Yoo, Keunyoung. "Probabilistic SEM : an augmentation to classical Structural equation modelling." Diss., University of Pretoria, 2018. http://hdl.handle.net/2263/66521.

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Structural equation modelling (SEM) is carried out with the aim of testing hypotheses on the model of the researcher in a quantitative way, using the sampled data. Although SEM has developed in many aspects over the past few decades, there are still numerous advances which can make SEM an even more powerful technique. We propose representing the nal theoretical SEM by a Bayesian Network (BN), which we would like to call a Probabilistic Structural Equation Model (PSEM). With the PSEM, we can take things a step further and conduct inference by explicitly entering evidence into the network and performing di erent types of inferences. Because the direction of the inference is not an issue, various scenarios can be simulated using the BN. The augmentation of SEM with BN provides signi cant contributions to the eld. Firstly, structural learning can mine data for additional causal information which is not necessarily clear when hypothesising causality from theory. Secondly, the inference ability of the BN provides not only insight as mentioned before, but acts as an interactive tool as the `what-if' analysis is dynamic.
Mini Dissertation (MCom)--University of Pretoria, 2018.
Statistics
MCom
Unrestricted

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Aydin, Utkun. "A Structural Equation Modeling Study: The Metacognition-knowledge Model For Geometry." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/12608523/index.pdf.

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The purpose of this study is twofold: (1) to examine the effects of knowledge on cognition and regulation of cognition on declarative knowledge, conditional knowledge, and procedural knowledge in geometry and (2) to examine the interrelationships among declarative knowledge, conditional knowledge, and procedural knowledge in geometry. The reciprocal relationships between metacognitive and knowledge factors were modeled by using data from tenth grade secondary school students. Structural equation modeling was used to test the hypothesized relationships of two metacognitive factors (knowledge of cognition, regulation of cognition) and three knowledge factors (declarative knowledge, conditional knowledge, procedural knowledge). The observed variables representing the latent variables were determined by carrying out exploratory factor analysis and confirmatory factor analysis for the metacognitive awareness inventory and geometry knowledge test separately. Major findings revealed: (1) Declarative knowledge significantly and positively influences conditional and procedural knowledge
(2) Procedural knowledge has a signitificant and positive direct effect on conditional knowledge
(3) Declarative knowledge has a positive indirect effect on conditional knowledge
(4) Knowledge of cognition significantly and positively influences procedural knowledge
(5) Regulation of cognition has a significant but negative direct effect on procedural knowledge
(6) Knowledge of cognition has positive indirect effects on conditional and procedural knowledge
(7) Regulation of cognition has negative indirect effects on conditional and procedural knowledge
(8) Knowledge of cognition and regulation of cognition have non-significant direct effect on declarative and conditional knowledge. The results showed that knowledge of cognition has the strongest direct effect on procedural knowledge and the direct effect of declarative knowledge on conditional knowledge is stronger than on procedural knowledge. In view of the findings considerable suggestions is provided for teachers, instructional designers, and mathematics education researchers.

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Busko, Deborah Ann. "Causes and consequences of perfectionism and procrastination, a structural equation model." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape15/PQDD_0004/MQ31814.pdf.

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Chandio, Fida Hussain. "Studying acceptance of online banking information system : a structural equation model." Thesis, Brunel University, 2011. http://bura.brunel.ac.uk/handle/2438/6153.

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Acceptance of information technology (IT) has remained a topic of interest for a last few decades. Several theories, specifically over the conceptualisation of the technology acceptance model (TAM) have emerged and they have been applied in different contexts to investigate new insights into the acceptance behaviour at individual and organisational levels. However, despite TAM’s maturity and validity in different contexts, very little published literature strives to extend its capability to predict individuals’ acceptance behaviour about an online banking information system (OBIS). A possible rational for this gap may be conceptualisation of the TAM under which individuals’ acceptance behaviour can only be predicted with two beliefs: perceived ease of use (PEOU) and perceived usefulness (PU). The evidence in literature shows that PU and PEOU beliefs are not sufficient; hence, they may not explain individuals’ acceptance behaviour in emerging contexts, such as online banking information systems, especially in developing economies such as Pakistan. There is therefore a need for inclusion of any additional factor that can enhance prediction of acceptance of online banking information system by potential users. Extending research on the TAM, this study developed and tested a model of OBIS acceptance. The proposed model integrated key constructs from the information systems acceptance research stream into the theoretical frame of the TAM and other theories from social psychology, such as the theory of reasoned action (TRA), theory of planned behaviour (TPB) and the TAM2. According to the proposed conceptual model, OBIS acceptance was determined by eight main factors, which included perceived usefulness, perceived ease of use, trust, technological self-efficacy, response time, output quality, accessibility, and terminology clarity. In this model, PU was hypothesised to be affected by PEOU, trust, technological self-efficacy (TSE), output quality (OQ) and response time (RT). In addition, PEOU was hypothesised to be determined by three external factors: TSE, accessibility and terminology clarity (TC). The model was tested on a sample of 353 Internet banking users in Pakistan. Using structural equation modelling with Analysis of Moment Structures (AMOS) software, data analysis showed considerable support for the extended hypothesised model. The result indicated that, in order of importance, PU, PEOU and trust explained 45.7 % of the variance in the acceptance behaviour. The trust and the TSE predicted 28.1 % of the variance in the PU. However, the hypothesised relationships between the PU and the PEOU, OQ and RT were found to be not significant. While in the PEOU, 21.8 % of the variance was predicted by the TSE, accessibility and TC. TSE was found to be a more influential determinant of the PEOU than the PU. The consideration of factors that have a significant influence on the acceptance of OBIS, as identified in this research, is important for the managers in the banking sector, especially in developing countries, in order to increase wider acceptance and use of these systems, which provide benefits to both the customers and the service providers.

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Dolan, Amanda Avery. "Synthesizing Undergraduate College Student Persistence: A Meta-analytic Structural Equation Model." Kent State University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=kent1554756614807579.

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Books on the topic "Structural model equation"

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Ghoshal, Sumantra. A structural equation model of scanning behavior of managers. Cambridge, Mass: Massachusetts Institute of Technology, Alfred P. Sloan School of Management, 1985.

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Jonsson, Fan Yang. Non-linear structural equation models: Simulation studies of the Kenny-Judd model. Uppsala, Sweden: Uppsala University, 1997.

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Hui-xin, Ke. Software system for the analysis of linear structural equation model. Fukuoka, Japan: Kyushu University, Research Institute of Fundamental Inforamtion Science, 1988.

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Mishler, William. What are the political consequences of trust?: A Russian structural equation model. Glasgow: University of Strathclyde, Centre for the Study of Public Policy, 2003.

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Amit, Gupta. Effect of service climate on service quality: Test of a model using structural equation modeling. Bangalore: Indian Institute of Management, 2002.

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Westland, J. Christopher. Structural Equation Models. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-12508-0.

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Westland, J. Christopher. Structural Equation Models. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16507-3.

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van Montfort, Kees, Johan Oud, and Albert Satorra, eds. Recent Developments on Structural Equation Models. Dordrecht: Springer Netherlands, 2004. http://dx.doi.org/10.1007/978-1-4020-1958-6.

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1965-, Curran Patrick J., ed. Latent curve models: A structural equation perspective. Hoboken, NJ: John Wiley & Sons, 2005.

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McArdle, John J., and John R. Nesselroade. Longitudinal data analysis using structural equation models. Washington: American Psychological Association, 2014. http://dx.doi.org/10.1037/14440-000.

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Book chapters on the topic "Structural model equation"

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Lee, Sik-Yum, and Xin-Yuan Song. "Bayesian Model Comparison of Structural Equation Models." In Random Effect and Latent Variable Model Selection, 121–50. New York, NY: Springer New York, 2008. http://dx.doi.org/10.1007/978-0-387-76721-5_6.

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Wedel, Michel, and Wagner A. Kamakura. "Model-Based Segmentation Using Structural Equation Models." In International Series in Quantitative Marketing, 217–29. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-4651-1_13.

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Dasgupta, Ratan. "Unbounded Growth Model for Word Frequencies in Political Transition." In Growth Curve and Structural Equation Modeling, 209–21. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-17329-0_12.

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Dasgupta, Ratan. "Growth Model of Some Vernacular Word Usage During Political Transition." In Growth Curve and Structural Equation Modeling, 171–93. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-17329-0_10.

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McArdle, John J., and John R. Nesselroade. "Using the simplified reticular action model notation." In Longitudinal data analysis using structural equation models., 59–66. Washington: American Psychological Association, 2014. http://dx.doi.org/10.1037/14440-005.

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McArdle, John J., and John R. Nesselroade. "Applying a bivariate change model with multiple groups." In Longitudinal data analysis using structural equation models., 319–21. Washington: American Psychological Association, 2014. http://dx.doi.org/10.1037/14440-029.

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McArdle, John J., and John R. Nesselroade. "Repeated measures analysis of variance as a structural model." In Longitudinal data analysis using structural equation models., 151–58. Washington: American Psychological Association, 2014. http://dx.doi.org/10.1037/14440-013.

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McArdle, John J., and John R. Nesselroade. "The bivariate latent change score model for multiple occasions." In Longitudinal data analysis using structural equation models., 291–300. Washington: American Psychological Association, 2014. http://dx.doi.org/10.1037/14440-025.

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Kahyaoglu, Sezer Bozkuş, Bulent Balkan, and Ozlem Balkan. "Determinants of Ethics Auditing: Structural Equation Model Approach." In Accounting, Finance, Sustainability, Governance & Fraud: Theory and Application, 85–110. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-3203-6_6.

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Mostafa, Toka S., and Matthew J. Roorda. "A Structural Equation Model of Commercial Vehicle Ownership." In The Practice of Spatial Analysis, 203–18. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-89806-3_10.

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Conference papers on the topic "Structural model equation"

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Ishita, Emi, Yosuke Miyata, Shuichi Ueda, and Keiko Kurata. "A Structural Equation Model of Information Retrieval Skills." In CHIIR '17: Conference on Human Information Interaction and Retrieval. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3020165.3022142.

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Muangpan, T., M. Chaowarat, and J. Neamvonk. "Performance Model of Sustainable Supply Chain Management: The Structural Equation Model." In Annual International Conference on Sustainable Energy and Environmental Sciences. Global Science & Technology Forum (GSTF), 2015. http://dx.doi.org/10.5176/2251-189x_sees15.21.

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Hu, Shu-Jen, Sin-Ying Jou, and Yu-Hua Liu. "Structural Equation Model for Brand Image Measurement of Jeans." In 2009 Ninth International Conference on Hybrid Intelligent Systems. IEEE, 2009. http://dx.doi.org/10.1109/his.2009.25.

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Wyeld, Theodor, and Minoru Nakayama. "The Structural Equation Model Diagram as a Visualisation Tool." In 2019 23rd International Conference in Information Visualization – Part II. IEEE, 2019. http://dx.doi.org/10.1109/iv-2.2019.00024.

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Wenjing, Wu, and Juan Zhicai. "Activity-Travel Behavior Analysis Based on Structural Equation Model." In 2009 International Conference on Electronic Commerce and Business Intelligence, ECBI. IEEE, 2009. http://dx.doi.org/10.1109/ecbi.2009.85.

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Min, Li, and Guo Yubing. "To study medical service quality with structural-equation model." In 2011 International Conference on E-Business and E-Government (ICEE). IEEE, 2011. http://dx.doi.org/10.1109/icebeg.2011.5886761.

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Li, Lulu, and Xiangdong Cai. "Structural Equation Model Analysis of Urban Public Service Satisfaction." In 2nd International Conference on Humanities Science and Society Development (ICHSSD 2017). Paris, France: Atlantis Press, 2018. http://dx.doi.org/10.2991/ichssd-17.2018.13.

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Charbaji, Rima. "A Structural Equation Model of Perceived University Image in Qatar:." In Qatar University Annual Research Forum & Exhibition. Qatar University Press, 2020. http://dx.doi.org/10.29117/quarfe.2020.0255.

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The aim of this research is to examine the factors that influence student perceptions of the image of Qatar’s national university, Qatar University. The study builds on relevant scholarship on perceptions of organizational image and explores institutional image perceptions in the state of Qatar. Based on data collected from a sample of 1427 students in Qatar, this study uses factor analysis in order to identify the variables that influence students’ perceptions of Qatar University. The findings of this study indicate that the way students perceive Qatar University is determined by four main factors: Quality of education, comparability of educational standards, the facilities and extracurricular activities provided, and graduates’ employability. The paper concludes with recommendations for institutional policy and future research. This piece of research has been published in August 2020 at the scopus indexed International Journal of Management (IJM).

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Sirisawat, Pornwasil, and Tossapol Kiatcharoenpol. "Developing Reverse Logistics Practices Solutions Model Using Structural Equation Modeling." In 2018 3rd International Conference on Information Systems Engineering (ICISE). IEEE, 2018. http://dx.doi.org/10.1109/icise.2018.00030.

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Li Yan-wei and Yu Jian. "Study on airlines' customer satisfaction based on structural equation model." In 2011 International Conference on Management Science and Industrial Engineering (MSIE). IEEE, 2011. http://dx.doi.org/10.1109/msie.2011.5707444.

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Reports on the topic "Structural model equation"

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Beron, Kurt, Helen Tauchen, and Ann Dryden Witte. A Structural Equation Model for Tax Compliance and Auditing. Cambridge, MA: National Bureau of Economic Research, April 1988. http://dx.doi.org/10.3386/w2556.

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Wilcove, Gerry L., Donna G. Wolosin, and Michael J. Schwerin. Development of a New Quality of Life (QOL) Model Using Structural Equation Modeling. Fort Belvoir, VA: Defense Technical Information Center, September 2002. http://dx.doi.org/10.21236/ada405971.

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Sparks, Paul, Jesse Sherburn, William Heard, and Brett Williams. Penetration modeling of ultra‐high performance concrete using multiscale meshfree methods. Engineer Research and Development Center (U.S.), September 2021. http://dx.doi.org/10.21079/11681/41963.

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Terminal ballistics of concrete is of extreme importance to the military and civil communities. Over the past few decades, ultra‐high performance concrete (UHPC) has been developed for various applications in the design of protective structures because UHPC has an enhanced ballistic resistance over conventional strength concrete. Developing predictive numerical models of UHPC subjected to penetration is critical in understanding the material's enhanced performance. This study employs the advanced fundamental concrete (AFC) model, and it runs inside the reproducing kernel particle method (RKPM)‐based code known as the nonlinear meshfree analysis program (NMAP). NMAP is advantageous for modeling impact and penetration problems that exhibit extreme deformation and material fragmentation. A comprehensive experimental study was conducted to characterize the UHPC. The investigation consisted of fracture toughness testing, the utilization of nondestructive microcomputed tomography analysis, and projectile penetration shots on the UHPC targets. To improve the accuracy of the model, a new scaled damage evolution law (SDEL) is employed within the microcrack informed damage model. During the homogenized macroscopic calculation, the corresponding microscopic cell needs to be dimensionally equivalent to the mesh dimension when the partial differential equation becomes ill posed and strain softening ensues. Results of numerical investigations will be compared with results of penetration experiments.

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Haile, Phil, and Steven Berry. Identification of nonparametric simultaneous equations models with a residual index structure. Institute for Fiscal Studies, August 2015. http://dx.doi.org/10.1920/wp.cem.2015.4815.

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Full Equations (FEQ) model for the solution of the full, dynamic equations of motion for one-dimensional unsteady flow in open channels and through control structures. US Geological Survey, 1997. http://dx.doi.org/10.3133/wri964240.

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Full equations utilities (FEQUTL) model for the approximation of hydraulic characteristics of open channels and control structures during unsteady flow. US Geological Survey, 1997. http://dx.doi.org/10.3133/wri974037.

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