A Municipal Audit Fee Model Using Structural Equation Modeling

The purpose of this project is to model municipal audit fees using an audit economics framework and then analyze this conceptual framework empirically using structural equation modeling, because structural equation models are excellent for examining complex and interdependent environments.  The sample is large cities using 1996 data.  The theoretical model uses five constructs to explain audit fees:  (1) client size, (2) complexity of client operations, (3) financial risks including demographic characteristics, (4) auditing factors, and (5) governance structure.  The final model includes six variables directly related to audit fee plus five mediating variables.  The results demonstrate that SEM modeling can explain audit fees and provides more information on how the highly correlated independent variables are interrelated in the context of explaining audit fee levels.

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A 'Weight of Evidence' approach to evaluating structural equation models

One Ecosystem ◽

10.3897/oneeco.5.e50452 ◽

2020 ◽

Vol 5 ◽

Cited By ~ 1

Author(s):

James Grace

Keyword(s):

Structural Equation Modeling ◽

Model Selection ◽

Model Evaluation ◽

Structural Equation ◽

Structural Equation Models ◽

Weight Of Evidence ◽

Equation Modeling ◽

Future Studies ◽

Current State ◽

History Of

It is possible that model selection has been the most researched and most discussed topic in the history of both statistics and structural equation modeling (SEM). The reason for this is because selecting one model for interpretive use from amongst many possible models is both essential and difficult. The published protocols and advice for model evaluation and selection in SEM studies are complex and difficult to integrate with current approaches used in biology. Opposition to the use of p-values and decision thresholds has been voiced by the statistics community, yet certain phases of model evaluation have been historically tied to reliance on p-values. In this paper, I outline an approach to model evaluation, comparison and selection based on a weight-of-evidence paradigm. The details and proposed sequence of steps are illustrated using a real-world example. At the end of the paper, I briefly discuss the current state of knowledge and a possible direction for future studies.

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Structural equation models - PLS in engineering sciences: a brief guide for researchers through a case applied to the industry

Athenea ◽

10.47460/athenea.v2i4.17 ◽

2021 ◽

Vol 2 (4) ◽

pp. 5-18

Author(s):

Juan Enrique Villalva A.

Keyword(s):

Structural Equation Modeling ◽

Least Squares ◽

Partial Least Squares ◽

Structural Equation ◽

Structural Equation Models ◽

Structural Equations ◽

Partial Least Square ◽

Equation Modeling ◽

Engineering Sciences ◽

The Impact

Modeling using structural equations, is a second generation statistical data analysis technique, it has been positioned as the methodological options most used by researchers in various fields of science. The best known method is the covariance-based approach, but it presents some limitations for its application in certain cases. Another alternative method is based on the variance structure, through the analysis of partial least squares, which is an appropriate option when the research involves the use of latent variables (for example, composite indicators) prepared by the researcher, and where it is necessary to explain and predict complex models. This article presents a brief summary of the structural equation modeling technique, with an example on the relationship of constructs, sustainability and competitiveness in iron mining, and is intended to be a brief guide for future researchers in the engineering sciences. Keywords: Competitiveness, Structural equations, Iron mining, Sustainability. References [1]J. Hair, G. Hult, C. Ringle and M. Sarstedt. A Primer on Partial Least Square Structural Equation Modeling (PLS-SEM). California: United States. Sage, 2017. [2]H. Wold. Model Construction and Evaluation when Theoretical Knowledge Is Scarce: An Example of the Use of Partial Least Squares. Genève. Faculté des Sciences Économiques et Sociales, Université de Genève. 1979. [3]J. Henseler, G. Hubona & P. Ray. “Using PLS path modeling new technology research: updated guidelines”. Industrial Management & Data Systems, 116(1), 2-20. 2016. [4]G. Cepeda and Roldán J. “Aplicando en la Práctica la Técnica PLS en la Administración de Empresas”. Congreso de la ACEDE, Murcia, España, 2004. [5]D. Garson. Partial Least Squares. Regresión and Structural Equation Models. USA. Statistical Associates Publishing: 2016. [6]D. Barclay, C. Higgins & R. Thompson. “The Partial Least Squares (PLS) Approach to Causal Modeling: Personal Computer Adoption and Use as an Illustration”. Technology Studies. Special Issue on Research Methodology. (2:2), pp. 285-309. 1995. [7]J. Medina, N. Pedraza & M. Guerrero. “Modelado de Ecuaciones Estructurales. Un Enfoque de Partial Least Square Aplicado en las Ciencias Sociales y Administrativas”. XIV Congreso Internacional de la Academia de Ciencias Administrativas A.C. (ACACIA). EGADE – ITESM. Monterrey, México, 2010. [8]J. Medina & J. Chaparro. “The Impact of the Human Element in the Information Systems Quality for Decision Making and User Satisfaction”. Journal of Computer Information Systems. (48:2), pp. 44-52. 2008. [9]D. Leidner, S. Carlsson, J. Elam & M. Corrales. “Mexican and Swedish Managers’ Perceptions of the Impact of EIS on Organizational Intelligence, Decisión Making, and Structure”. Decision Science. (30:3), pp. 633-658. 1999.[10]W. Chin. “The partial least squares approach for structural equation modeling”. Chapter Ten, pp. 295-336 in Modern methods for business research. Edited by Macoulides, G. A., New Jersey: Lawrence Erlbaum Associates, 1998. [11]M. Höck & C. Ringle M. “Strategic networks in the software industry: An empirical analysis of the value continuum”. IFSAM VIIIth World Congress, Berlin 2006. [12]J. Henseler, Ch. Ringle & M. Sarstedt. Handbook of partial least squares: Concepts, methods and applications in marketing and related fields. Berlin: Springer, 2012. [13]S. Daskalakis & J. Mantas. “Evaluating the impact of a service-oriented framework for healthcare interoperability”. Studies in Health Technology and Informatics. pp. 285-290. 2008. [14]C. Fornell & D. Larcker: “Evaluating Structural Equation Models with Unobservable Variables and Measurement Error”, Journal of Marketing Research, vol. 18, pp. 39-50. Februay 1981. [15]C. Fornell. A Second Generation of Multivariate Analysis: An Overview. Vol. 1. New York, U.S.A. Praeger Publishers: 1982. [16]R. Falk and N. Miller. A Primer for Soft Modeling. Ohio: The University of Akron. 1992. [17]M. Martínez. Aplicación de la técnica PLS-SEM en la gestión del conocimiento: un enfoque técnico práctico. Revista Iberoamericana para Investigación y el Desarrollo Educativo. Vol. 8, Núm. 16. 2018. [18]S. Geisser. “A predictive approach to the random effects model”. Biometrika, Vol. 61(1), pp. 101-107. 1974. [19]J. Cohen. Statistical power analysis for the behavioral sciences. Mahwah, NJ: Lawrence Erlbaum, 1988. [20]GRI (2013). G4 Sustainability Reporting Guidelines. Global Reporting Initiative. Available: www.globalreporting.org

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Analyzing factorial survey data with structural equation models

Sociological Methods & Research ◽

10.1177/00491241211043139 ◽

2021 ◽

pp. 004912412110431

Author(s):

Bert Weijters ◽

Eldad Davidov ◽

Hans Baumgartner

Keyword(s):

Structural Equation Modeling ◽

Survey Data ◽

Structural Equation ◽

Multilevel Models ◽

Structural Equation Models ◽

Social Science Research ◽

Science Research ◽

Equation Modeling ◽

Factorial Survey ◽

Modeling Framework

In factorial survey designs, respondents evaluate multiple short descriptions of social objects (vignettes) that experimentally vary different levels of attributes of interest. Analytical methods (including individual-level regression analysis and multilevel models) estimate the weights (or utilities) assigned to the levels of the different attributes by participants to arrive at an overall response to the vignettes. In the current paper, we explain how data from factorial surveys can be analyzed in a structural equation modeling framework using an approach called structural equation modeling for within-subject experiments. We review the use of factorial surveys in social science research, discuss typically used methods to analyze factorial survey data, introduce the structural equation modeling for within-subject experiments approach, and present an empirical illustration of the proposed method. We conclude by describing several extensions, providing some practical recommendations, and discussing potential limitations.

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Structural Equation Modeling (SEM)-Based Meta-Analysis

10.31234/osf.io/93nfr ◽

2021 ◽

Author(s):

Mike W.-L. Cheung

Keyword(s):

Social Sciences ◽

Structural Equation Modeling ◽

Structural Equation ◽

Structural Equation Models ◽

Meta Analysis ◽

Equation Model ◽

Equation Modeling ◽

Future Directions ◽

Software Packages ◽

Meta Analyses

Structural equation modeling (SEM) and meta-analysis are two popular techniques in the behavioral, medical, and social sciences. They have their own research communities, terminologies, models, software packages, and even journals. This chapter introduces SEM-based meta-analysis, an approach to conduct meta-analyses using the SEM framework. By conceptualizing studies in a meta-analysis as subjects in a structural equation model, univariate, multivariate, and three-level meta-analyses can be fitted as structural equation models using definition variables. We will review fixed-, random-, and mixed-effects models using the SEM framework. Examples will be used to illustrate the procedures using the metaSEM and OpenMx packages in R. This chapter closes with a discussion of some future directions for research.

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Meta-Analytic Structural Equation Modeling

Oxford Research Encyclopedia of Business and Management ◽

10.1093/acrefore/9780190224851.013.225 ◽

2021 ◽

Author(s):

Mike W.-L. Cheung

Keyword(s):

Structural Equation Modeling ◽

Correlation Matrix ◽

Structural Equation ◽

Structural Equation Models ◽

Meta Analysis ◽

Equation Modeling ◽

Average Correlation ◽

Correlation Matrices ◽

Factor Analytic Model ◽

Special Cases

Meta-analysis and structural equation modeling (SEM) are two popular statistical models in the social, behavioral, and management sciences. Meta-analysis summarizes research findings to provide an estimate of the average effect and its heterogeneity. When there is moderate to high heterogeneity, moderators such as study characteristics may be used to explain the heterogeneity in the data. On the other hand, SEM includes several special cases, including the general linear model, path model, and confirmatory factor analytic model. SEM allows researchers to test hypothetical models with empirical data. Meta-analytic structural equation modeling (MASEM) is a statistical approach combining the advantages of both meta-analysis and SEM for fitting structural equation models on a pool of correlation matrices. There are usually two stages in the analyses. In the first stage of analysis, a pool of correlation matrices is combined to form an average correlation matrix. In the second stage of analysis, proposed structural equation models are tested against the average correlation matrix. MASEM enables researchers to synthesize researching findings using SEM as the research tool in primary studies. There are several popular approaches to conduct MASEM, including the univariate-r, generalized least squares, two-stage SEM (TSSEM), and one-stage MASEM (OSMASEM). MASEM helps to answer the following key research questions: (a) Are the correlation matrices homogeneous? (b) Do the proposed models fit the data? (c) Are there moderators that can be used to explain the heterogeneity of the correlation matrices? The MASEM framework has also been expanded to analyze large datasets or big data with or without the raw data.

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A Comparative Structural Equation Modeling Investigation of the Relationships among Teaching, Cognitive and Social Presence

Online Learning ◽

10.24059/olj.v20i3.654 ◽

2016 ◽

Vol 20 (3) ◽

Cited By ~ 8

Author(s):

Kadir Kozan

Keyword(s):

Structural Equation Modeling ◽

Social Presence ◽

Structural Equation ◽

Structural Equation Models ◽

Cognitive Presence ◽

Teaching Presence ◽

Equation Modeling ◽

Learning Contexts ◽

Indirect Relationship

The present study investigated the relationships among teaching, cognitive, and social presence through several structural equation models to see which model would better fit the data. To this end, the present study employed and compared several different structural equation models because different models could fit the data equally well. Among the models compared, the results indicated that the model with cognitive presence as a full mediator and the model with social presence as a partial mediator could achieve an equally satisfactory data fit. This conclusion may depend on the level of the presences: The present results indicated a statistically higher level of teaching presence than cognitive and social presence as well as a statistically higher level of cognitive presence compared to social presence. The results further suggested that teaching presence could either have a direct or indirect relationship with cognitive presence thereby increasing it without or with social presence as a mediator between teaching and cognitive presence. The results further suggested that teaching presence efforts spent on increasing cognitive presence can function directly, which may also promote social presence, and indirectly through social presence. Further research comparing different possible structural equation models of the relationships among the presences in different learning contexts is warranted.

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Power analysis for parameter estimation in structural equation modeling: A discussion and tutorial

10.31234/osf.io/pj67b ◽

2020 ◽

Author(s):

Yilin Andre Wang ◽

Mijke Rhemtulla

Keyword(s):

Structural Equation Modeling ◽

Sample Size ◽

Latent Variables ◽

Power Analysis ◽

Structural Equation ◽

Structural Equation Models ◽

Regression Coefficient ◽

Equation Modeling ◽

Shiny App ◽

Target Effect

Despite the widespread and rising popularity of structural equation modeling (SEM) in psychology, there is still much confusion surrounding how to choose an appropriate sample size for SEM. Currently available guidance primarily consists of sample size rules of thumb that are not backed up by research, and power analyses for detecting model misfit. Missing from most current practices is power analysis to detect a target effect (e.g., a regression coefficient between latent variables). In this paper we (a) distinguish power to detect model misspecification from power to detect a target effect, (b) report the results of a simulation study on power to detect a target regression coefficient in a 3-predictor latent regression model, and (c) introduce a Shiny app, pwrSEM, for user-friendly power analysis for detecting target effects in structural equation models.

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Measure of Customer Satisfaction in the Residential Electricity Distribution Service Using Structural Equation Modeling

10.20944/preprints202112.0294.v1 ◽

2021 ◽

Author(s):

Agenor S. Santos Neto ◽

Márcio R. C. Reis ◽

A. Paulo Coimbra ◽

Júlio C. V. Soares ◽

Wesley P. Calixto

Keyword(s):

Structural Equation Modeling ◽

Customer Satisfaction ◽

Continuous Improvement ◽

Structural Equation ◽

Structural Equation Models ◽

Electric Energy ◽

Equation Modeling ◽

Electricity Distribution ◽

Residential Electricity ◽

Quality Value

The main objective of this study is to apply structural equation modeling with partial least squares and based on covariance to assess the satisfaction of residential electricity consumers. The methodology used compares the results of both structural equation models to indicate the model that best fits the problem of measuring the satisfaction of residential consumers of electricity concessionaires and licensees. The sample used in the survey contained questionnaire responses from 86.175 individuals considering the period from 2014 to 2018. The constructs evaluated were satisfaction, quality, value, loyalty, and trust. Confidence interval analysis shows that all weights are significant, demonstrating the importance of all the indicators that represent the constructs. The trust, quality, and value constructs can explain 74.4% of the variability of the satisfaction construct, so the explanatory capacity of this relationship is considered substantial. Finally, the evaluation of the performance of the service provided by the electric energy concessionaires/licensees, measured by customer satisfaction, allows for the continuous improvement of services and meeting, even if minimally, the expectations of its consumers.

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PLS FAC-SEM: an illustrated step-by-step guideline to obtain a unique insight in factorial data

Industrial Management & Data Systems ◽

10.1108/imds-07-2015-0318 ◽

2016 ◽

Vol 116 (9) ◽

pp. 1922-1945 ◽

Cited By ~ 7

Author(s):

Sandra Streukens ◽

Sara Leroi-Werelds

Keyword(s):

Structural Equation Modeling ◽

Customer Value ◽

Structural Equation ◽

Structural Equation Models ◽

Group Analysis ◽

Predictive Ability ◽

Building Blocks ◽

Equation Modeling ◽

Specific Level ◽

Content Type

Purpose The purpose of this paper is to provide an illustrated step-by-step guideline of the partial least squares factorial structural equation modeling (PLS FAC-SEM) approach. This approach allows researchers to assess whether and how model relationships vary as a function of an underlying factorial design, both in terms of the design factors in isolation (i.e. main effects) as well as their joint impact (i.e. interaction effects). Design/methodology/approach After an introduction of its building blocks as well as a comparison with related methods (i.e. n-way analysis of variance (ANOVA) and multi-group analysis (MGA)), a step-by-step guideline of the PLS FAC-SEM approach is presented. Each of the steps involved in the PLS FAC-SEM approach is illustrated using data from a customer value study. Findings On a methodological level, the key result of this research is the presentation of a generally applicable step-by-step guideline of the PLS FAC-SEM approach. On a context-specific level, the findings demonstrate how the predictive ability of several key customer value measurement methods depends on the type of offering (feel-think), the level of customer involvement (low-high), and their interaction (feel-think offerings×low-high involvement). Originality/value This is a first attempt to apply the factorial structural equation models (FAC-SEM) approach in a PLS-SEM context. Consistent with the general differences between PLS-SEM and covariance-based structural equation modeling (CB-SEM), the FAC-SEM approach, which was originally developed for CB-SEM, therefore becomes available for a larger amount of and different types of research situations.

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The Ethical Use of Fit Indices in Structural Equation Modeling: Recommendations for Psychologists

Frontiers in Psychology ◽

10.3389/fpsyg.2021.783226 ◽

2021 ◽

Vol 12 ◽

Author(s):

Bryant M. Stone

Keyword(s):

Structural Equation Modeling ◽

Structural Equation ◽

Structural Equation Models ◽

Ethical Dilemmas ◽

Current Paper ◽

Equation Modeling ◽

Selective Reporting ◽

Fit Indices ◽

Research Practices ◽

Questionable Research Practices

Fit indices provide helpful information for researchers to assess the fit of their structural equation models to their data. However, like many statistics and methods, researchers can misuse fit indices, which suggest the potential for questionable research practices that might arise during the analytic and interpretative processes. In the current paper, the author highlights two critical ethical dilemmas regarding the use of fit indices, which are (1) the selective reporting of fit indices and (2) using fit indices to justify poorly-fitting models. The author highlights the dilemmas and provides potential solutions for researchers and journals to follow to reduce these questionable research practices.

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