Structural Equation Modeling with the Smartpls [in English] (original) (raw)
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STRUCTURAL EQUATION MODELING WITH THE SMARTPLS
The objective of this article is to present a didactic example of Structural Equation Modeling using the software SmartPLS 2.0 M3. The program mentioned uses the method of Partial Least Squares and seeks to address the following situations frequently observed in marketing research: Absence of symmetric distributions of variables measured by a theory still in its beginning phase or with little “consolidation”, formative models, and/or a limited amount of data. The growing use of SmartPLS has demonstrated its robustness and the applicability of the model in the areas that are being studied.
Partial Least Squares Structural Equation Modeling (PLS-SEM) Techniques Using SmartPLS
SmartPLS is one of the prominent software applications for Partial Least Squares Structural Equation Modeling (PLS-SEM). It was developed by Ringle, . The software has gained popularity since its launch in 2005 not only because it is freely available to academics and researchers, but also because it has a friendly user interface and advanced reporting features. Although an extensive number of journal articles have been published on the topic of PLS modeling, the amount of instructional materials available for this software is limited. This paper is written to address this knowledge gap and help beginners to understand how PLS-SEM can be used in marketing research.
Revista Brasileira de Marketing, 2014
A modelagem de equaes estruturais (Structural Equation Modeling -SEM) cada vez mais usada como um mtodo para a conceituao e desenvolvimento de aspectos tericos nas cincias sociais aplicadas, em particular na rea de marketing, pois mais e mais h a necessidade de avaliar vrios constructos e relaes latentes complexas. Tambm, constructos de segunda ordem podem ser modelados fornecendo uma melhor compreenso terica de relaes com boa parcimnia. Modelagens do tipo SEM so, em particular, bem adequadas para investigar as relaes complexas entre os vrios constructos. Os dois mtodos analticos SEM mais prevalentes so os baseados em covarincia SEM (CB-SEM) e os baseados em varincia SEM (PLS-SEM). Embora cada tcnica tenha suas vantagens e limitaes, neste artigo vamos nos concentrar no CB-SEM com o AMOS para ilustrar sua aplicao na anlise das relaes entre orientao para o cliente, a orientao para os funcionrios e desempenho da empresa. Tambm ser demonstrado como constructos de segunda ordem so teis p...
Modelagem de Equações Estruturais com Utilização do Smartpls
Revista Brasileira de Marketing, 2014
O objetivo deste artigo a apresentao de um exemplo de forma mais didtica de uso da modelaem de Equaes Estruturais com o software SmathPLS 2.0 M3. O referido programa usa o mtodo de Mnimos Quadrados Parciais e busca atender situaes muito frequentes na pesquisa de Marketing: Ausncia de distribuies simtricas das variveis mensuradas, teoria ainda em fase inicial ou com pouca cristalizao, modelos formativos e/ou quantidade menor de dados. O uso crescente do SmartPLS vem mostrado a robusteza e aplicabilidade do modelo nas pesquisas da rea.DOI: 10.5585/remark.v13i2.2717
(CHAPTER 1)REVIEW ON PARTIAL LEAST SQUARE STRUCTURAL EQUATION MODELING (PLS-SEM) USING SMARTPLS 2.0
Structural Equation Modeling (SEM) are complex models allowing us to study the real world complexity by taking into account a whole number of causal relationships among latent concept (i.e. the Latent variables, LVs), each measured by several observed indicators usually defined as Manifest Variables (Esposito Vinzi, 2014) Covariance-based/ Factor based Methods: 1. The aim is to reproduce the sample covariance matrix of the manifest variables by means of the model parameters: The implied covariance matrix of the manifest variable is a function of the model parameters It is a confirmatory approach aiming at the validating a model (theory building) The latent variables are equivalent to common factors (Theoretical and random variables) Variance based/ Composite based / Component based Method 2. The aim is to provide latent variable scores (proxy, composites, factor scores) that are the most correlated to each other as possible and the most representative of their own block of manifest variables It focuses on latent variable scores computation It focuses on explaining variances It is more an explanatory approach than a confirmatory one (operational model strategy) The latent variables are defined as components or weighted sums of the manifest variables (Fixed variables: linear composites, scores) This leads to different parameters to estimate for latent variables Factor means and variances in covariance based methods Weight in component based approaches 1.2 THE CONCEPT OF PLS-SEM AND HOW IT WORKS Today, Partial Least Square Structural Equation Modeling (PLS-SEM) has gaining interesting in statistical inferential to help the researchers construct the complex model CB-SEM (Covariance based SEM) -This method is aimed to reproduced the theoretical covariance matrix, without focusing on explained variance as implemented in AMOS package.
Jurnal Ilmiah Ilmu Administrasi Publik, 2019
The purpose of this study is to compare the results of quantitative research data analysis in the marketing field using Lisrel, Tetrad, GSCA, SPSS, SmartPLS, WarpPLS and Amos software for a large sample, in this study the number of samples was 500 respondents. This research method is quantitative and research data analysis uses the four types of software to obtain a comparison of the results of the analysis. The analysis in this study focuses on the analysis of hypothesis testing and regression analysis. The data from this study uses quantitative data derived from questionnaire data totaling 500 respondents with three research variables, namely the independent variable digital marketing, customer satisfaction, and the dependent variable customer loyalty. Based on the results of the analysis using Lisrel, GSCA, SPSS, SmartPLS, WarpPLS and Amos software for a large sample of 500 respondents, the results showed that there was no significant difference in the significance value of p-value and t-value. There is also no significant difference in the determination value, and the correlation value in the resulting structural equation also has no significant difference in results.
SmartPLS 3: especificação, estimação, avaliação e relato
Administração: Ensino e Pesquisa, 2019
A modelagem de equações estruturais com estimação por mínimos quadrados parciais (PLS-SEM) tem sido empregada nas mais variadas áreas de pesquisa, aumentando a quantidade de artigos publicados com o uso desse método de modo exponencial. Há vários motivos para que isso esteja ocorrendo, mas um deles é o fato do software SmartPLS ter facilitado o uso do PLS-SEM. Este artigo tem o objetivo de apresentar sete exemplos didáticos com conjuntos de dados reais e disponíveis àqueles que queiram aprender ou ensinar PLS-SEM, tratando de temas como: avaliação do modelo de mensuração, avaliação do modelo estrutural, multicolinearidade, variável latente de segunda ordem, mediação, moderação com variável numérica e categórica (MGA – multi-group analysis).
An assessment of the use of partial least squares structural equation modeling in marketing research
Journal of The Academy of Marketing Science, 2012
Most methodological fields undertake regular critical reflections to ensure rigorous research and publication practices, and, consequently, acceptance in their domain. Interestingly, relatively little attention has been paid to assessing the use of partial least squares structural equation modeling (PLS-SEM) in marketing research—despite its increasing popularity in recent years. To fill this gap, we conducted an extensive search in the 30 top ranked marketing journals that allowed us to identify 204 PLS-SEM applications published in a 30-year period (1981 to 2010). A critical analysis of these articles addresses, amongst others, the following key methodological issues: reasons for using PLS-SEM, data and model characteristics, outer and inner model evaluations, and reporting. We also give an overview of the interdependencies between researchers’ choices, identify potential problem areas, and discuss their implications. On the basis of our findings, we provide comprehensive guidelines to aid researchers in avoiding common pitfalls in PLS-SEM use. This study is important for researchers and practitioners, as PLS-SEM requires several critical choices that, if not made correctly, can lead to improper findings, interpretations, and conclusions.