Alleviating estimation problems in small sample structural equation modeling—A comparison of constrained maximum likelihood, Bayesian estimation, and fixed reliability approaches.

2021 ◽  
Author(s):  
Esther Ulitzsch ◽  
Oliver Lüdtke ◽  
Alexander Robitzsch
Keyword(s):  
Structural Equation Modeling ◽  
Maximum Likelihood ◽  
Bayesian Estimation ◽  
Structural Equation ◽  
Small Sample ◽  
Equation Modeling ◽  
Estimation Problems ◽  
Constrained Maximum Likelihood

Bayesian Structural Equation Modeling in Sport and Exercise Psychology

2015 ◽  
Vol 37 (4) ◽  
pp. 410-420 ◽  
Author(s):  
Andreas Stenling ◽  
Andreas Ivarsson ◽  
Urban Johnson ◽  
Magnus Lindwall
Keyword(s):  
Structural Equation Modeling ◽  
Maximum Likelihood ◽  
Bayesian Estimation ◽  
Bayesian Approach ◽  
Structural Equation ◽  
Equation Modeling ◽  
Exercise Psychology ◽  
Informative Priors ◽  
Sport And Exercise ◽  
Bayesian Structural Equation Modeling

Bayesian statistics is on the rise in mainstream psychology, but applications in sport and exercise psychology research are scarce. In this article, the foundations of Bayesian analysis are introduced, and we will illustrate how to apply Bayesian structural equation modeling in a sport and exercise psychology setting. More specifically, we contrasted a confirmatory factor analysis on the Sport Motivation Scale II estimated with the most commonly used estimator, maximum likelihood, and a Bayesian approach with weakly informative priors for cross-loadings and correlated residuals. The results indicated that the model with Bayesian estimation and weakly informative priors provided a good fit to the data, whereas the model estimated with a maximum likelihood estimator did not produce a well-fitting model. The reasons for this discrepancy between maximum likelihood and Bayesian estimation are discussed as well as potential advantages and caveats with the Bayesian approach.

Challenges in Nonlinear Structural Equation Modeling

Methodology ◽  
2007 ◽  
Vol 3 (3) ◽  
pp. 100-114 ◽  
Author(s):  
Polina Dimitruk ◽  
Karin Schermelleh-Engel ◽  
Augustin Kelava ◽  
Helfried Moosbrugger
Keyword(s):  
Structural Equation Modeling ◽  
Maximum Likelihood ◽  
Structural Equation ◽  
Nonlinear Effects ◽  
Structural Equations ◽  
Estimation Methods ◽  
Equation Modeling ◽  
Parameter Estimates ◽  
Nonlinear Structural ◽  
Nonlinear Structural Equation

Abstract. Challenges in evaluating nonlinear effects in multiple regression analyses include reliability, validity, multicollinearity, and dichotomization of continuous variables. While reliability and validity issues are solved by employing nonlinear structural equation modeling, multicollinearity remains a problem which may even be aggravated when using latent variable approaches. Further challenges of nonlinear latent analyses comprise the distribution of latent product terms, a problem especially relevant for approaches using maximum likelihood estimation methods based on multivariate normally distributed variables, and unbiased estimates of nonlinear effects under multicollinearity. The only methods that explicitly take the nonnormality of nonlinear latent models into account are latent moderated structural equations (LMS) and quasi-maximum likelihood (QML). In a small simulation study both methods yielded unbiased parameter estimates and correct estimates of standard errors for inferential statistics. The advantages and limitations of nonlinear structural equation modeling are discussed.

scholarly journals Estimasi Parameter Structural Equation Modeling Terhadap Kepuasan Pelanggan Layanan Telekomunikasi Menggunakan Metode Maximum Likelihood

2020 ◽  
Vol 1 (1) ◽  
pp. 48
Author(s):  
Dwicahyo Ramadhan Priyatna ◽  
Raupong Raupong ◽  
La Podje Talangko
Keyword(s):  
Structural Equation Modeling ◽  
Maximum Likelihood ◽  
Customer Satisfaction ◽  
Natural Science ◽  
Latent Variables ◽  
Structural Equation ◽  
Sampling Technique ◽  
Likelihood Method ◽  
Equation Modeling ◽  
Students Satisfaction

Structural Equation Modeling is a statistical technique that is able to analyze the pattern of simultan linear relationships between indicator variables and latent variables. In this study using structural equation modeling to analyze the relationship between perceived quality, perceived value, perceived bestscore, and customer satisfaction. The purpose of this study is to obtain the result parameter model estimation of structural equation modeling using maximum likelihood method and to obtain the level of students satisfaction from faculty of Mathematics and Natural Science Hasanuddin University toward Tri operator. Data collected by distributing questionnaire. Collecting sample in this study using Proporsional Random Sampling technique. To measure the level of students satisfaction from faculty of Mathematics and Natural Science Hasanuddin University toward Tri operator, the model chosen is the model used to measure Indonesian Customer Satisfaction Indeks. From the result of this study obtained in the amount of 92,04% with very satisfied criteria level of students satisfaction from faculty of Mathematics and Natural Science Hasanuddin University toward Tri operator with very satisfied criteria.

scholarly journals The Effect of Marketing Mix upon The Consumer’s Decision Making to Buy a Product at PT. Griya Pagelaran Bogor,

2018 ◽  
Vol 3 (1) ◽  
pp. 59
Author(s):  
Sumardjono Jono ◽  
Heni Ardila
Keyword(s):  
Decision Making ◽  
Structural Equation Modeling ◽  
Maximum Likelihood ◽  
Analytical Method ◽  
Structural Equation ◽  
Price Level ◽  
Marketing Mix ◽  
Equation Modeling ◽  
Significance Level ◽  
Level Of Significance

The purpose of this study is to determine and prove whether the variablesof the marketing mix significantly has influenced the consumer’s decision making tobuy the product at PT. Griya Pagelaran Bogor. The population of this study are thenumber of unknown sampling determination using Maximum Likelihood estimationmethod by taking samples of consumers who their needs has met with theresearcher requirement as many as 150 respondents. The analytical method hasused is Structural Equation Modeling (SEM) using AMOS 21 program. The result ofthe research shows that 1) Product Variables have a significance level of 0.05 whichis 1,965 > 1,96 and value (p) probability 0,49 ≤ 0.05. Then Ha is accepted andsignificant effect. 2) Variable Price level of significance 0.05 is 2.023 > 1.96 and hasa probability of 0.43 which is below 0.05. And the value (p) probability ≤ 0.05 then Hais accepted and significant effect. 3) Place Variables significance level of 0.05 is2.251 > 1.96 and has a probability of 0.24 which is below 0.05. And the value (p)probability ≤ 0.05 then Ha is accepted and significant effect. 4) Promotion Variables0.05 level of significance is 3.435 > 1.96 and has a probability in accordance with therecommended. And the value (p) probability ≤ 0.05 then Ha accepted and significanteffect.

Sign in / Sign up

Export Citation Format

Share Document