Thematische Bibliographien / Structural equation modelling / Zeitschriftenartikel

Zeitschriftenartikel zum Thema „Structural equation modelling“

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Autor: Grafiati
Veröffentlicht am 4. Juni 2021
Zuletzt aktualisiert am 25. Mai 2024

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1

Tenenhaus, Michel. „Component-based Structural Equation Modelling“. Total Quality Management & Business Excellence 19, Nr. 7-8 (August 2008): 871–86. http://dx.doi.org/10.1080/14783360802159543.

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2

Kelloway, E. Kevin. „Structural equation modelling in perspective“. Journal of Organizational Behavior 16, Nr. 3 (Mai 1995): 215–24. http://dx.doi.org/10.1002/job.4030160304.

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3

Chandrasekhar, S., und Chabi Sinha. „Modelling the Brand Equity Using Structural Equation Modelling“. Management Dynamics 8, Nr. 2 (26.04.2022): 59–70. http://dx.doi.org/10.57198/2583-4932.1179.

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4

Dangi, H. K., Ashmeet Kaur und Juhi Jham. „Structural Equation Modelling: A Powerful Antibiotic“. Journal of Business Thought 10 (04.03.2019): 19–23. http://dx.doi.org/10.18311/jbt/2019/23452.

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5

Mitra, Pradip Kumar. „Structural Equation Modelling: A small review“. Journal of Development Research 9, Nr. 3 (Juli 2016): 34. http://dx.doi.org/10.54366/jdr.9.3.2016.34-38.

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6

Ziegel, Eric R., und R. Mueller. „Basic Principles of Structural Equation Modelling“. Technometrics 39, Nr. 3 (August 1997): 343. http://dx.doi.org/10.2307/1271158.

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7

MacFarlane, Don. „Structural equation modelling in developmental psychiatry“. British Journal of Psychiatry 193, Nr. 6 (Dezember 2008): 509. http://dx.doi.org/10.1192/bjp.193.6.509.

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8

Smith, Philip T., Frank McKenna, Claire Pattison und Andrea Waylen. „Structural equation modelling of human judgement“. Thinking & Reasoning 7, Nr. 1 (Februar 2001): 51–68. http://dx.doi.org/10.1080/13546780042000037.

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9

Barrett, Paul. „Structural equation modelling: Adjudging model fit“. Personality and Individual Differences 42, Nr. 5 (Mai 2007): 815–24. http://dx.doi.org/10.1016/j.paid.2006.09.018.

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10

Testa, Mark R. „Structural Equation Modelling in Hospitality Research“. Tourism and Hospitality Research 2, Nr. 1 (April 2000): 67–74. http://dx.doi.org/10.1177/146735840000200110.

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Structural equation modelling (SEM) is a statistical procedure gaining increased attention in psychology, marketing and business research. As few studies in hospitality research utilise this cutting-edge method of modelling and testing linear relationships, this paper seeks to rationalise the implementation of its use. A conceptual and non-technical overview of the process is discussed, identifying the positive and negative aspects of the procedure. In addition, examples of SEM in practice in hospitality research are examined.
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11

Oertzen, Timo. „Power equivalence in structural equation modelling“. British Journal of Mathematical and Statistical Psychology 63, Nr. 2 (Mai 2010): 257–72. http://dx.doi.org/10.1348/000711009x441021.

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12

Zur, RM, S. Aballea und S. Sherman. „A Validation of Piecewise Structural Equation Modelling (SEM) Compared to Traditional Structural Equation Modelling: A Simulation Study“. Value in Health 21 (Mai 2018): S223—S224. http://dx.doi.org/10.1016/j.jval.2018.04.1512.

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13

Heimler, Ronald, Stuart Rosenberg und Elsa‐Sofia Morote. „Predicting career advancement with structural equation modelling“. Education + Training 54, Nr. 2/3 (13.04.2012): 85–94. http://dx.doi.org/10.1108/00400911211210215.

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14

Vinodh, S., und Dino Joy. „Structural Equation Modelling of lean manufacturing practices“. International Journal of Production Research 50, Nr. 6 (15.03.2012): 1598–607. http://dx.doi.org/10.1080/00207543.2011.560203.

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15

Oke, Ayodeji Emmanuel, und Deji Rufus Ogunsemi. „Structural equation modelling of construction bond administration“. Journal of Financial Management of Property and Construction 21, Nr. 3 (07.11.2016): 192–211. http://dx.doi.org/10.1108/jfmpc-02-2016-0008.

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Purpose The purpose of this paper is to examine construction bonds, which is a risk management tool for ensuring that projects are delivered successfully, with emphasis on the influence of stakeholders, project characteristics and bonding decision factors. Design/methodology/approach Using case study of completed public building projects, questionnaires were administered on stakeholders that participated in the projects to solicit information on identified issues. Structural equation modelling (SEM) was used to examine relationship among identified factors, and various model selection and validation tests were carried out to arrive at the best-fit model. Findings The final model revealed that type of bond, stakeholders’ influence, project characteristics, risks of bonded projects and bonding decision factors have significant effect on success of bonded construction projects. Of the stakeholders, guarantors and contractors have greater influence on effective administration of bonds. Research limitations/implications The study was limited to Lagos and Ondo states, Nigeria. Data were gathered from clients of public projects, banks and insurance companies (guarantors), as well as contracting, quantity surveying and architectural firms registered with the state governments. This is because of the fact that only such firms can be engaged on projects emanating from the governments. Practical implications To enhance project success, there is a need to adopt construction bond for both public and private projects as against the current practice where it is only mandated for public projects. Originality/value Using SEM, this research examined administration of construction bonds with a view to ascertaining their effects on projects success, thereby providing relevant empirical information for stakeholders for effective administration of construction bond.
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16

Turner, Lindsay W., Yvette Reisinger und Stephen F. Witt. „Tourism Demand Analysis Using Structural Equation Modelling“. Tourism Economics 4, Nr. 4 (Dezember 1998): 301–23. http://dx.doi.org/10.1177/135481669800400401.

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17

Finch, W. Holmes. „Structural equation modelling trees for invariance assessment“. International Journal of Quantitative Research in Education 4, Nr. 1/2 (2017): 72. http://dx.doi.org/10.1504/ijqre.2017.086508.

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18

Holmes Finch, W. „Structural equation modelling trees for invariance assessment“. International Journal of Quantitative Research in Education 4, Nr. 1/2 (2017): 72. http://dx.doi.org/10.1504/ijqre.2017.10007537.

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19

Jin, Shaobo, und Sebastian Ankargren. „Frequentist Model Averaging in Structural Equation Modelling“. Psychometrika 84, Nr. 1 (04.06.2018): 84–104. http://dx.doi.org/10.1007/s11336-018-9624-y.

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20

Dr. P. C. Sekar, Dr P. C. Sekar, und S. Dinesh S. Dinesh. „An Application of Structural Equation Modelling for Measuring Brand Attributes of Packed Milk“. Indian Journal of Applied Research 3, Nr. 4 (01.10.2011): 251–53. http://dx.doi.org/10.15373/2249555x/apr2013/85.

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21

Hidayat, Rachmat, und Patricia Wulandari. „Data Analysis Procedures with Structural Equation Modelling (SEM): Narrative Literature Review“. Open Access Indonesia Journal of Social Sciences 5, Nr. 6 (23.12.2022): 859–65. http://dx.doi.org/10.37275/oaijss.v5i6.142.

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The relationship between variables in structural equation modeling (SEM) forms a structural model. This structural model can be explained through structural equations, such as in regression analysis. This structural equation describes the prediction of the latent (exogenous) independent variable on the latent (endogenous) dependent variable. Researchers who use analysis with structural equation models need to know whether the model built with empirical data has a unique value or not so that the model can be estimated. If the model does not have a unique value, then the model cannot be identified (unidentified). The cause of a model is categorized as unidentified because the information contained in empirical data is not sufficient to produce a unique solution in calculating model estimation parameters. This literature review aims to describe the process of data analysis using SEM.
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22

Santor, Darcy. „Reviews of Using LISREL for structural equation modelling: A researcher’s guide and Principles and practice of structural equation modelling.“ Canadian Psychology/Psychologie canadienne 40, Nr. 4 (1999): 381–83. http://dx.doi.org/10.1037/h0092500.

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23

Tétard-Jones, Catherine, Angharad M. R. Gatehouse, Julia Cooper, Carlo Leifert und Steven Rushton. „Modelling Pathways to Rubisco Degradation: A Structural Equation Network Modelling Approach“. PLoS ONE 9, Nr. 2 (03.02.2014): e87597. http://dx.doi.org/10.1371/journal.pone.0087597.

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24

Aigbavboa, Clinton Ohis. „Structural Equation Modelling in Sustainable Human Settlement Research“. IABSE Symposium Report 105, Nr. 6 (23.09.2015): 1–9. http://dx.doi.org/10.2749/222137815818359401.

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25

Motawa, Ibrahim, und Michael G. Oladokun. „Structural equation modelling of energy consumption in buildings“. International Journal of Energy Sector Management 9, Nr. 4 (02.11.2015): 435–50. http://dx.doi.org/10.1108/ijesm-11-2014-0004.

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26

Austin, James T., und Lee M. Wolfle. „Annotated bibliography of structural equation modelling: Technical work“. British Journal of Mathematical and Statistical Psychology 44, Nr. 1 (Mai 1991): 93–152. http://dx.doi.org/10.1111/j.2044-8317.1991.tb00952.x.

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27

Angelini, M. E., G. B. M. Heuvelink und B. Kempen. „Multivariate mapping of soil with structural equation modelling“. European Journal of Soil Science 68, Nr. 5 (26.07.2017): 575–91. http://dx.doi.org/10.1111/ejss.12446.

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28

Mu, Di. „Structural Equation Modelling for Influencing Virtual Community Networks“. International Journal of Web Based Communities 16, Nr. 3 (2020): 1. http://dx.doi.org/10.1504/ijwbc.2020.10029914.

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29

Mu, Di. „Structural equation modelling for influencing virtual community networks“. International Journal of Web Based Communities 16, Nr. 3 (2020): 249. http://dx.doi.org/10.1504/ijwbc.2020.108631.

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30

Hackl, Peter, und Anders H. Westlund. „On structural equation modelling for customer satisfaction measurement“. Total Quality Management 11, Nr. 4-6 (Juli 2000): 820–25. http://dx.doi.org/10.1080/09544120050008264.

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31

Martínez‐López, Francisco J., Juan C. Gázquez‐Abad und Carlos M. P. Sousa. „Structural equation modelling in marketing and business research“. European Journal of Marketing 47, Nr. 1/2 (08.02.2013): 115–52. http://dx.doi.org/10.1108/03090561311285484.

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32

Lew, Yoke-Lian, Salihudin Hassim, Ratnasamy Muniandy und Law Teik Hua. „Structural equation modelling for subcontracting practice: Malaysia chapter“. Engineering, Construction and Architectural Management 25, Nr. 7 (20.08.2018): 835–60. http://dx.doi.org/10.1108/ecam-04-2017-0073.

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Purpose Most of the previous studies conducted on the subject of subcontractors often focussed on a single phase of subcontracting practice; either on registration, selection of subcontractors or on monitoring of subcontractors without much integration to other different phases involved. Thus, on the basis of that reason, the purpose of this paper is to link the gap between different phases of subcontracting practice. This study also attempts to explore the relationships between the key criteria used by general contractors in selecting subcontractors before job awarding (CSSC) and the key criteria used for monitoring subcontractors during construction work (CMSC); which will then include an investigation of the effects these criteria have on project performance (PP). Design/methodology/approach The data obtained from a total of 162 G7 contractors in Malaysia were analysed. The interrelationships between the criteria and the effects of these criteria on PP were investigated simultaneously by employing a single model based on structural equation modelling (SEM) method. Findings The final model has discovered four major criteria that are often considered during the selection of subcontractors namely, “communication”, “relationship”, “general obligation” and “resource management” of a subcontractor. Meanwhile, the major criteria that are referred to in the monitoring of subcontractors are “workmanship”, “awareness of environment, health and safety” and “communication and relationship”. The four CSSC were found to be interrelated among themselves and affected CMSC during the construction stage. The research also revealed that the criteria used in monitoring subcontractor do affect the performance of a project. Thus, based on the result of this investigation, the monitoring of subcontractor is essential in discerning the performance level of a project. Originality/value An established SEM improves a subcontracting practice by creating platforms for CSSC, CMSC and PP to influence each other. With the gap between selection and monitoring subcontractors are filled, then the prediction of the subcontractors’ performance can be made possible.
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33

Debbi, Hichem, Aimad Debbi und Mustapha Bourahla. „Debugging of probabilistic systems using structural equation modelling“. International Journal of Critical Computer-Based Systems 6, Nr. 4 (2016): 250. http://dx.doi.org/10.1504/ijccbs.2016.081805.

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34

Papadopoulos, Savas. „Inference for structural equation modelling on dependent populations“. International Journal of Computational Economics and Econometrics 2, Nr. 2 (2011): 123. http://dx.doi.org/10.1504/ijcee.2011.043252.

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35

Yuan, Ke-Hai, Wai Chan und Peter M. Bentler. „Robust transformation with applications to structural equation modelling“. British Journal of Mathematical and Statistical Psychology 53, Nr. 1 (Mai 2000): 31–50. http://dx.doi.org/10.1348/000711000159169.

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36

Hidayat, Rachmat, und Patricia Wulandari. „Structural Equation Modelling (SEM) in Research: Narrative Literature Review“. Open Access Indonesia Journal of Social Sciences 5, Nr. 6 (21.12.2022): 852–58. http://dx.doi.org/10.37275/oaijss.v5i6.141.

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The structural equation modelling (SEM) method has stronger predicting power than path analysis and multiple regression because SEM is able to analyze at the deepest level the variables or constructs studied. This literature review aimed to describe the use of structural equation modelling in research. In general, SEM can be used to analyze research models that have several independent (exogenous) and dependent (endogenous) variables, as well as moderating or intervening variables. SEM provides several benefits and advantages for researchers, including building research models with many variables, examining variables or constructs that cannot be observed or cannot be measured directly (unobserved), testing measurement errors (measurement errors) for observed variables or constructs ( observed) and confirmatory factor analysis. Broadly speaking, SEM methods can be classified into two types, namely covariance-based structural equation modelling (CB-SEM) and variance or component-based SEM (VB-SEM), which includes partial least squares (PLS) and generalized structured component analysis (GSCA). This literature review aimed to describe the use of structural equation modelling in research.
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37

Ramesh, Vani. „Influence of Organizational Citizenship Behaviour on Mauritian Hospitality Industry employee job-satisfaction: Structural Equation Modelling“. International Journal of Psychosocial Rehabilitation 24, Nr. 4 (28.02.2020): 4994–5005. http://dx.doi.org/10.37200/ijpr/v24i4/pr201599.

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38

NALBANTOGLU YILMAZ, Funda, und Hicran CETIN GUNDUZ. „Career Indecision and Career Anxiety in High School Students: An Investigation through Structural Equation Modelling“. Eurasian Journal of Educational Research 18, Nr. 78 (29.11.2018): 1–20. http://dx.doi.org/10.14689/ejer.2018.78.2.

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39

TAŞ, İbrahim. „Association between depression, anxiety, stress, social support, resilience and internet addiction: a structural equation modelling“. Malaysian Online Journal of Educational Technology 7, Nr. 3 (01.07.2019): 1–10. http://dx.doi.org/10.17220/mojet.2019.03.001.

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40

Y. Mehlsen, Mimi. „INTRODUCTION TO STRUCTURAL EQUATION MODELLING USING SPSS AND AMOS“. Psyke & Logos 30, Nr. 2 (31.12.2009): 5. http://dx.doi.org/10.7146/pl.v30i2.8768.

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41

Hasan, Rashedul, Niaz Mohammad und Mohammad Faridul Alam. „Tax Compliance in Bangladesh – A Structural Equation Modelling Approach“. American Journal of Trade and Policy 4, Nr. 3 (31.12.2017): 87–94. http://dx.doi.org/10.18034/ajtp.v4i3.422.

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Tax compliance behavior has been researched at different times in various parts of the world using various theories. This research has focused on the psychological aspects of tax compliance behavior by applying the theory of planned behavior (TPB) proposed by Ajzen. 300 taxpayers were studied using a questionnaire based on Likert scale, and the results were analyzed using structural equation modelling. Results found from SEM approach indicate that subjective norm has a significant influence on tax compliance behavior while intention mediates it and moderated by religion. Attitude and perceived behavioral control were found to have a significant influence on intention. Individual taxpayers are selected from the capital, and thus findings derived from the sample might not be generalizable for all individual taxpayers in Bangladesh. The results of this study might provide some insight to the tax non-compliance problem in Bangladesh and help authorities to design strategies to motivate taxpayers to comply with existing fiscal policies. Tax compliance issues have been explored from various perspectives in Bangladesh. Lack of significant influence on subjective norm toward taxpayer intention indicates that social and peer is yet to make a substantial contribution to improving tax compliance. This study has introduced structural equation modelling technique to explore the moderating influence of religion on individual taxpayers in the context of Bangladesh.
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42

Stafford, Mai, Amanda Sacker, Anne Ellaway, Steven Cummins, Dick Wiggins und Sally Macintyre. „Neighbourhood Effects on Health: A Structural Equation Modelling Approach“. Schmollers Jahrbuch 128, Nr. 1 (März 2008): 109–20. http://dx.doi.org/10.3790/schm.128.1.109.

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43

Garg, Ramesh Kumar, und Manoj Panwar. „Online learning: a stakeholders analysis using structural equation modelling“. International Journal of Technology Enhanced Learning 1, Nr. 1 (2021): 1. http://dx.doi.org/10.1504/ijtel.2021.10038440.

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44

Das, Promila. „Dimensions of women empowerment: A structural equation modelling approach“. Journal of Statistics and Management Systems 25, Nr. 5 (04.07.2022): 1059–72. http://dx.doi.org/10.1080/09720510.2022.2040858.

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45

Dandagi, Shivaprasad, Umesh Bhushi, Virupaxi Bagodi und Deepankar Sinha. „Strategic management of technical university: structural equation modelling approach“. Journal of Modelling in Management 11, Nr. 1 (08.02.2016): 75–90. http://dx.doi.org/10.1108/jm2-05-2014-0034.

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Purpose – The purpose of this paper is to examine the constructs and establish causal relationship between factors for strategically governing a technical university in Indian context. Further, the paper carries out a systemic study to emphasize on the need for these universities to design strategies that are endurable and sustainable. Design/methodology/approach – A structured questionnaire survey was carried out (207 responses). Factor analysis was carried out to bring out the latent variables representing the attributes, and later, the causality between these variables was established using structural equation modelling (SEM). These relationships between the factors helped in developing a robust system dynamic model for strategic management of technical universities. Findings – The peak points on the contours for varying strategic orientation revealed the adaptability and the time required for attaining that level of adaptability. The contour plots also revealed the limiting values in each case. Finally, it is concluded that university adaptability increases with increasing strategic orientation. The analysis also revealed that the process by which the technical universities formulate their strategies is an important determinant of various factors. Originality/value – Universities looking to implement strategic management-related methodologies for the improved management focusing on developing effective methods for developing strategy can be expected to yield better performance, rather than concentrating on the technologies and supporting infrastructures.
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46

Rose, Sarah A., und Barry Markman. „Monte Carlo Simulations For Structural Equation Modelling (Revolution R)“. Journal of Modern Applied Statistical Methods 15, Nr. 2 (01.11.2016): 737–42. http://dx.doi.org/10.22237/jmasm/1478004060.

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47

Curtis, A. M., J. E. Cavanaugh, S. M. Levy, J. VanBuren, T. A. Marshall und J. J. Warren. „Examining caries aetiology in adolescence with structural equation modelling“. Community Dentistry and Oral Epidemiology 46, Nr. 3 (21.12.2017): 258–64. http://dx.doi.org/10.1111/cdoe.12359.

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48

Yuan, Ke-Hai, und Peter M. Bentler. „Normal theory based test statistics in structural equation modelling“. British Journal of Mathematical and Statistical Psychology 51, Nr. 2 (November 1998): 289–309. http://dx.doi.org/10.1111/j.2044-8317.1998.tb00682.x.

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49

Nunkoo, Robin, und Haywantee Ramkissoon. „Structural equation modelling and regression analysis in tourism research“. Current Issues in Tourism 15, Nr. 8 (November 2012): 777–802. http://dx.doi.org/10.1080/13683500.2011.641947.

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50

Lukman, H. S., A. Setiani und N. Muhassanah. „Structural Equation Modelling of Teaching Quality on Students’ Satisfaction“. Journal of Physics: Conference Series 1657 (Oktober 2020): 012083. http://dx.doi.org/10.1088/1742-6596/1657/1/012083.

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