ANALISIS STRUCTURAL EQUATION MODELING (SEM) DENGAN MULTIPLE GROUP MENGGUNAKAN R

Structural Equation Model (SEM) is a statistical technique with simultaneous processing involves measurement errors, indicator variables, and latent variables. SEM is used to test hypotheses that state the relationships between latent variables when latent variables have been assessed through each of the indicator variables. Multiple Group SEM is a basic model analysis that uses more than one sample. This analysis aims to determine whether the components or models of measurement and structural models are invariant for the two sample groups. In this study, the data generated by some requirements. First, the data generated with sample size n = 250. The first generated data is homogeneous data where the measurement model is the same as the structural model in group 1 and group 2, while the second data is non-homogeneous data where the measurement model and the structural model in group 1 and group 2 is not the same. The data was analyzed using the help of the lavaan package available in R to obtain SEM estimation results and Goodness of Fit Model from some data that was formed. From the results of the merger of the two groups, it shows that the invariant of the two models with the largest df (63) which is Fit Mean model states the simplest model. However, the smallest df (48) with Fit.configural model states the most complex model. Keywords: SEM, Multiple Group, R Program

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Structural Equation Modeling in Special Education Research

Remedial and Special Education ◽

10.1177/074193259501600307 ◽

1995 ◽

Vol 16 (3) ◽

pp. 178-183 ◽

Cited By ~ 7

Author(s):

Alan D. Moore

Keyword(s):

Special Education ◽

Education Research ◽

Structural Equation Modeling ◽

Experimental Research ◽

Latent Variables ◽

Structural Equation ◽

Structural Model ◽

Multivariate Data ◽

Measurement Model ◽

Equation Modeling

Structural equation modeling is a method for analysis of multivariate data from both nonexperimental and experimental research. the method combines a structural model linking latent variables and a measurement model linking observed variables with latent variables. its use in special education research has been limited to date, but the approach offers promise as a method useful in theory-based research. a nontechnical introduction to the method and cautions concerning the limits of its use are presented.

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Understanding consumer motivation to share IoT products data

Journal of Indian Business Research ◽

10.1108/jibr-09-2019-0268 ◽

2020 ◽

Vol 12 (1) ◽

pp. 5-22

Author(s):

Sushant Bhatnagar ◽

Rajeev Kumra

Keyword(s):

Latent Variables ◽

Structural Equation ◽

Structural Model ◽

Measurement Model ◽

Human Interaction ◽

Third Party ◽

Equation Modeling ◽

Product Reviews ◽

Moral Obligations ◽

Content Type

Purpose Almost every study undertaken by academicians or practitioners on the Internet of Things (IoT) has mainly highlighted the privacy concerns and information security issues with the IoT products. On the contrary, this paper aims to explore the motivators that could encourage customers of an IoT product to share their IoT product’s data with a third-party aggregator system to facilitate computer-generated product reviews which are defined as electronic Word of Thing (eWOT) in this paper. Design/methodology/approach An experiment was conducted with customized e-commerce prototypes of eWOT. Structural equation modeling analysis was conducted to test the measurement model by using confirmatory factor analysis and thereafter a structural model to test the relationships amongst the latent variables. Findings This paper found that five consumer motivators (personal innovativeness, enjoyment of helping, anticipated extrinsic rewards, moral obligations and venting negative feelings) contribute to eWOT intention. Practical implications This research advances the understanding of human interaction with computer-generated product reviews and opens up avenues for future studies in online consumer behavior in the IoT context. Originality/value This paper presents motivators for eWOT intention to share IoT product data. This is done through a novel concept of an experimental IoT-based prototype, namely, eWOT. These eWOT reviews can be generated from the IoT products data by applying analytics and using natural language generation. To the best of the authors’ knowledge, no other study has been conducted on this subject.

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Application of Structural Equation Modeling to Health Outcomes Research

Evaluation & the Health Professions ◽

10.1177/0163278705278277 ◽

2005 ◽

Vol 28 (3) ◽

pp. 295-309 ◽

Cited By ~ 51

Author(s):

Ron D. Hays ◽

Dennis Revicki ◽

Karin S. Coyne

Keyword(s):

Health Outcome ◽

Structural Equation Modeling ◽

Health Outcomes ◽

Outcomes Research ◽

Latent Variables ◽

Structural Equation ◽

Structural Model ◽

Measurement Model ◽

Equation Modeling ◽

Health Outcomes Research

This article provides an overview of the basic underlying principles of structural equation modeling (SEM). SEM models have two basic elements: a measurement model and a structural model. The measurement model describes the associations between the indicators (observed measures) of the latent variables, whereas the structural model delineates the direct and indirect substantive effects among latent variables and between measured and latent variables. The application of SEM to health outcomes research is illustrated using two examples: (a) assessing the equivalence of the SF-36 and patient evaluations of care for English- and Spanish-language respondents and (b)evaluating a theoretical model of health in myocardial infarction patients. The results of SEM studies can contribute to better understanding of the validity of health outcome measures and of relationships between physiologic, clinical, and health outcome variables.

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PLS-based SEM Algorithms: The Good Neighbor Assumption, Collinearity, and Nonlinearity

Information Management and Business Review ◽

10.22610/imbr.v7i2.1146 ◽

2015 ◽

Vol 7 (2) ◽

pp. 113-130 ◽

Cited By ~ 13

Author(s):

Ned Kock

Keyword(s):

Structural Equation Modeling ◽

Latent Variables ◽

Latent Variable ◽

Structural Equation ◽

Structural Model ◽

Measurement Model ◽

Equation Modeling ◽

Systems Research ◽

Inner Model ◽

Good Neighbor

The partial least squares (PLS) method has been extensively used in information systems research, particularly in the context of PLS-based structural equation modeling (SEM). Nevertheless, our understanding of PLS algorithms and their properties is still progressing. With the goal of improving that understanding, we provide a discussion on the treatment of reflective and formative latent variables in the context of three main algorithms used in PLS-based SEM analyses â€“PLS regression, PLS Mode A, and PLS Mode B. Two illustrative examples based on actual data are presented. It is shown that the â€œgood neighborâ€ assumption underlying modes A and B has several consequences, including the following: the inner model influences the outer model in a way that increases inner model coefficients of association and collinearity levels in tandem, and makes measurement model analysis tests dependent on structural model links; instances of Simpsonâ€™s paradox tend to occur with Mode B at the latent variable level; and nonlinearity is improperly captured. In spite of these mostly detrimental outcomes, it is argued that modes A and B may have important and yet unexplored roles to play in PLS-based structural equation modeling analyses.

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Development of a System Measurement Model of the Brazilian Hospital Accreditation System

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph15112520 ◽

2018 ◽

Vol 15 (11) ◽

pp. 2520 ◽

Cited By ~ 2

Author(s):

João Corrêa ◽

João Turrioni ◽

Carlos Mello ◽

Ana Santos ◽

Carlos da Silva ◽

...

Keyword(s):

Structural Equation Modeling ◽

Structural Equation ◽

Structural Model ◽

Measurement Model ◽

Equation Modeling ◽

Scientific Software ◽

Health Organizations ◽

Hospital Accreditation ◽

Management Quality ◽

Culture Process

The purpose of this study is to develop and validate a measurement model that evaluates the Brazilian hospital accreditation methodology (ONA), based on a multivariate model using structural equation modeling (SEM). The information used to develop the model was obtained from a questionnaire sent to all organizations accredited by the ONA methodology. A model was built based on the data obtained and tested through a structural equation modeling (SEM) technique using the LISREL® software (Scientific Software International, Inc., Skokie, IL, USA). Four different tests were performed: Initial, calibrated, simulated, and cross-validation models. By analyzing and validating the proposed measurement model, it can be verified that the selected factors satisfy the required criteria for the development of a structural model. The results show that leadership action is one of the most important factors in the process of health services accredited by ONA. Although, leadership, staff management, quality management, organizational culture, process orientation, and safety are strongly linked to the development of health organizations, and directly influence the accreditation process.

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The effect of clustering on competitiveness improvement in Hebron

Journal of Manufacturing Technology Management ◽

10.1108/jmtm-12-2016-0181 ◽

2017 ◽

Vol 28 (5) ◽

pp. 631-654 ◽

Cited By ~ 3

Author(s):

Ibrahim M. Awad ◽

Alaa A. Amro

Keyword(s):

Structural Equation ◽

Structural Model ◽

Model Building ◽

Measurement Model ◽

Background Information ◽

Equation Modeling ◽

Future Research ◽

Research Model ◽

Content Type ◽

The Impact

Purpose The purpose of this paper is to map the cluster in the leather and shoes sector for improving the competitiveness of the firms. Toward this end, the study is organized to examine the impact of clustering on competitiveness improvement. The influence of competitive elements and performance (Porter’s diamond) and balanced score card was utilized. Design/methodology/approach A random sample of 131 respondents was chosen during the period from May 2016 to July 2016. A structural equation modeling (SEM) analysis was applied to investigate the research model. This approach was chosen because of its ability to test casual relationships between constructs with multiple measurement items. Researchers proposed a two-stage model-building process for applying SEM. The measurement model was first examined for instrument validation, followed by an analysis of the structural model for testing associations hypothesized by the research model. Findings The main findings show that there is a unidirectional causal relationship between improvements of performance and achieve competitiveness and also reveal that the Palestinian shoes and leather cluster sector is vital and strong, and conclude that clustering can achieve competitiveness for small- and medium-sized enterprises. Research limitations/implications Future research can examine the relationship between clustering and innovation. The effect of clustering using other clustering models other than Porter’s model is advised to be used for future research. Practical implications The relationships among clustering and competitiveness may provide a practical clue to both, policymakers and researchers on how cluster enhances economic firms such as a skilled workforce, research, development capacity, and infrastructure. This is likely to create assets such as trust, synergy, collaboration and cooperation for improved competitiveness. Originality/value The findings of this study provide background information that can simultaneously be used to analyze relationships among factors of innovation, customer’s satisfaction, internal business and financial performance. This study also identified several essential factors in successful firms, and discussed the implications of these factors for developing organizational strategies to encourage and foster competitiveness.

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Identifying the Impact of Strategic Alliances on the Performance of SMEs (Case Study: The Industry of Automotive Parts Manufacturers in Iran)

International Business Research ◽

10.5539/ibr.v10n6p227 ◽

2017 ◽

Vol 10 (6) ◽

pp. 227 ◽

Cited By ~ 2

Author(s):

Kambiez Talebi ◽

Jahangir Yadollahi Farsi ◽

Hamideh Miriasl

Keyword(s):

Strategic Alliances ◽

Structural Equation ◽

Structural Model ◽

Reliability And Validity ◽

Measurement Model ◽

Equation Modeling ◽

Innovative Capabilities ◽

Automotive Parts ◽

Technical Features ◽

The Impact

This study has investigated the effects of strategic alliances on the performance of small and medium sized enterprises (SMEs) of the industry of automotive parts manufacturers. Questionnaires have been distributed among 400 senior managers of SMEs of the industry of auto parts manufacturers based on stratified random sampling. The data has been analyzed using structural equation modeling software and PLS2 software in two segments of measurement model and structural model. In the first segment, technical features of the questionnaire were tested in terms of reliability and validity. Moreover, in the second segment, t-test was used to test research hypotheses. The results show that there is a significant and positive relationship between the dimensions of strategic alliances, including new opportunities, entrepreneurial and innovative capabilities, social capital, and internationalization of business, and competitive advantage with the performance of SMEs.

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Prediksi Minat Perilaku Menggunakan Jasa Pajak Elektronik: Analisis Faktor Konfirmatori

InFestasi ◽

10.21107/infestasi.v13i2.3511 ◽

2018 ◽

Vol 13 (2) ◽

pp. 321

Author(s):

Jullie J. Sondakh

Keyword(s):

Factor Analysis ◽

Structural Equation Modeling ◽

Confirmatory Factor Analysis ◽

Behavioral Intention ◽

Latent Variable ◽

Structural Equation ◽

Structural Model ◽

Measurement Model ◽

Equation Modeling ◽

Confirmatory Factor

The purpose of this research is to predict the tax payer behavioral intention of using the e-SPT through the application of Technology Acceptance Model (TAM).This research used survey method to collect primary data from the population of tax payer in the city of Manado and Bitung with 156 respondents while using judgement sampling method.The data analysis is using Structural Equation Modeling (SEM) that consists of two steps; the measurement model and structural model. The focus of this research is on the first step of SEM modeling, which is the measurement model by using the Confirmatory Factor Analysis (CFA). The purpose of this analysis is to test the validity and reliability from the indicator of the construct or latent variable researched, thus, we will obtain the fit construct or latent variable before proceeding to the next step of SEM which is the structural model.Based on the confirmatory factor analysis (CFA), we obtained the validity test result, convergent validity, and reliability test result, construct reliability and variance extracted, from the indicator of construct or latent variable which are perceived usefulness, perceived ease of use, attitude towards e-SPT, and behavioral intention to use e-SPT. The reliabilty and validity test result showed that there is no indicator from all the tested latent variable to be excluded for the next step of Structural Equation Modeling (SEM) which is the structural model. Tujuan penelitian ini adalah melakukan prediksi minat perilaku wajib pajak menggunakan e-SPT melalui penerapanTechnology Acceptance Model (TAM). Penelitian ini menggunakan metode survei untuk mengumpulkan data primer dari populasi yaitu wajib pajak di Kota Manado dan Bitung dengan jumlah sampel sebanyak 156 responden serta penentuan sampel berdasarkan metode judgment sampling. Teknik analisis data menggunakan pemodelan Structural Equation Modeling (SEM) yang terdiri dari dua tahapan yaitu model pengukuran (measurement model) dan model struktural (structural model). Fokus penelitian ini adalah pada pemodelan SEM tahap pertama yaitu model pengukuran (measurement model) melalui analisis faktor konfirmatori (Confirmatory Factor Analysis - CFA). Analisis ini bertujuan untuk menguji validitas dan reliabilitas dari indikator-indikator pembentuk konstruk atau variabel laten yang diteliti sehingga diperoleh konstruk atau variabel laten yang fit sebelum lanjut ke tahap pemodelan SEM berikutnya yaitu model struktural. Berdasarkan analisis faktor konfirmatori (Confirmatory Factor Analysis - CFA) diperoleh hasil uji validitas yaitu signifikansi factor loading (convergent validity) dan reliabilitas (construct reliability dan variance extracted) dari indikator pembentuk konstruk atau variabel laten kegunaan persepsian (Perceived Usefulness), kemudahan penggunaan persepsian (Perceived ease of use), sikap terhadap penggunaan e-SPT (Attitude towards e-SPT) dan minat perilaku menggunakan e-SPT (Behavioral intention to use e-SPT). Hasil uji validitas dan reliabilitas ini menunjukan bahwa tidak ada indikator dari variabel kegunaan persepsian (Perceived Usefulness), kemudahan penggunaan persepsian (Perceived ease of use), sikap terhadap penggunaan e-SPT (Attitude towards e-SPT) dan minat perilaku menggunakan e-SPT (Behavioral intention to use e-SPT) yang di hilangkan pada analisis selanjutnya yaitu pemodelan Structural Equation Modeling (SEM) tahap kedua sehingga dapat dilakukan estimasi model persamaan struktural (structural model).

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Welfare and Wealth in Iran: Rural and Urban Dichotomies

10.21203/rs.2.15362/v1 ◽

2019 ◽

Author(s):

Sediqe Shafiei ◽

Shahram Yazdani ◽

A Hamid Zafarmand ◽

Mohammad-Pooyan Jadidfard ◽

sareh Shakerian

Keyword(s):

Latent Variables ◽

Structural Equation ◽

Structural Model ◽

Well Being ◽

Equation Modeling ◽

Comprehensive Model ◽

Contributing Factors ◽

Rural And Urban ◽

Effective Policy ◽

Relationship Of

Abstract Objective : Increasing social welfare and reducing poverty are to ensure the well-being of all classes of a society. Cities and villages are distinguished by cultural and economic disparities. The purpose of this study was to develop and present a comprehensive model on welfare and wealth components and their relationship with each other , as well as determining the contributing factors and variables affecting them by presenting a comprehensive model. Results : The Structural Equation Modeling ( SEM ) method was used to analyze the data and investigate the causal relationship of latent variables. Observed variables and latent variables of the model were analyzed and tested by using AMOS and SPSS (version 21) statistical methods, in two exploratory and confirmatory steps. Wealth and welfare were identified as two separate subjects in the conceptual model and in the final structural model for rural households. Unlike, in the urban community, they were recognized as a single category in the final structural model. The results of this study can provide the clear hints for effective policy making to break the cycle of deprivation and poverty in Iranian rural and urban population.

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Interactions Between Intensive Care and Palliative Care Are Influenced by Training, Professionals’ Perceptions and Institutional Barriers

Journal of Palliative Care ◽

10.1177/0825859720951361 ◽

2020 ◽

pp. 082585972095136

Author(s):

María Camila Calle ◽

Sara Lucia Pareja ◽

María Margarita Villa ◽

Juan Pablo Román-Calderón ◽

Mariantonia Lemos ◽

...

Keyword(s):

Palliative Care ◽

Intensive Care ◽

Latent Variables ◽

Structural Equation ◽

Ad Hoc ◽

Structural Model ◽

Measurement Model ◽

Perceived Barriers ◽

Validity And Reliability ◽

Institutional Barriers

Background: There is growing interest in the use of a Palliative care approach in Intensive care. However, it tends to remain inconsistent, infrequent or non-existent, as does its acceptance by intensive care physicians. This study sought to explore the perceptions, level of knowledge, perceived barriers, and practices of physicians regarding palliative care practices (PC) in Intensive Care Units (ICU). Methods: Descriptive-correlational study. Participating physicians working in ICU in Colombia (n = 101) completed an ad hoc questionnaire that included subscales of perceptions, knowledge, perceived barriers, and PC practices in ICU. A Structural Equation Model (PLS-SEM) was used to examine the reciprocal relationships between the measured variables and those that could predict interaction practices between the 2 specialties. Results: First, results from the measurement model to examine the validity and reliability of the latent variables found (PC training, favorable perceptions about PC, institutional barriers, and ICU-PC interaction practices) and their indicators were obtained. Second, the structural model found that, a greater number of hours of PC training, a favorable perception of PC and a lower perception of institutional barriers are related to greater interaction between PC and ICU, particularly when emotional or family problems are detected. Conclusions: PC-ICU interactions are influenced by training, a positive perception of PC and less perceived institutional barriers. An integrated ICU-PC model that strengthens the PC training of those who work in ICU and provides clearer guidelines for interaction practices, may help overcome perceived barriers and improve the perception of the potential impact of PC.

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