Structural Equation Modeling in Special Education Research

1995 ◽  
Vol 16 (3) ◽  
pp. 178-183 ◽  
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.

Application of Structural Equation Modeling to Health Outcomes Research

2005 ◽  
Vol 28 (3) ◽  
pp. 295-309 ◽  
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.

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

2015 ◽  
Vol 7 (2) ◽  
pp. 113-130 ◽  
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 –PLS regression, PLS Mode A, and PLS Mode B. Two illustrative examples based on actual data are presented. It is shown that the “good neighbor” 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 Simpson’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.

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

2018 ◽  
Vol 15 (11) ◽  
pp. 2520 ◽  
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.

scholarly journals Prediksi Minat Perilaku Menggunakan Jasa Pajak Elektronik: Analisis Faktor Konfirmatori

InFestasi ◽  
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

<p class="Ventura-Abstract">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).</p><p class="Ventura-Abstract">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.</p><p class="Ventura-Abstract"> </p><p class="Ventura-Abstract">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).</p>

Case study of a structural model to explore the effects of knowledge sharing on intellectual capital

2016 ◽  
Vol 46 (3) ◽  
pp. 338-352 ◽  
Author(s):  
Peyman Akhavan ◽  
Farnoosh Khosravian
Keyword(s):  
Structural Equation Modeling ◽  
Knowledge Sharing ◽  
Intellectual Capital ◽  
Structural Equation ◽  
Structural Model ◽  
Measurement Model ◽  
Equation Modeling ◽  
Extensive Literature ◽  
Content Type

Purpose It is commonly known that intellectual capital (IC) plays a remarkable role in organizations, especially in colleges and academic centers. The purpose of this study is to investigate the effects of knowledge sharing (KS) on IC. Design/methodology/approach Based on the extensive literature review, a questionnaire was designed. The questions were composed of two parts; KS questions and IC questions. In total, 352 students completed questionnaires in the Shahinshahr branch of Payam-e-Noor University. Structural equation modeling was used to develop the measurement model. Findings The findings showed that KS has a significant positive correlation with IC and its dimensions. The structural equation modeling confirmed the research model and showed a good match with it. Originality/value Given that this study aimed to examine KS and IC, it implies that with optimized knowledge management in universities, providing the infrastructures of KS and strengthening students’ motivational factors, KS capacities can be enhanced and IC of universities would be strengthened.

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

2019 ◽  
Vol 19 (2) ◽  
pp. 85
Author(s):  
Holipah Holipah ◽  
I Made Tirta ◽  
Dian Anggraeni
Keyword(s):  
Latent Variables ◽  
Structural Equation ◽  
Structural Model ◽  
Measurement Model ◽  
Equation Modeling ◽  
Multiple Group ◽  
Indicator Variables ◽  
Group 2 ◽  
Homogeneous Data ◽  
Group 1

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

One Core Task of Interpretability in Machine Learning — Expansion of Structural Equation Modeling

2019 ◽  
Vol 34 (01) ◽  
pp. 2051001
Author(s):  
Nina Fei ◽  
Youlong Yang ◽  
Xuying Bai
Keyword(s):  
Structural Equation Modeling ◽  
Latent Variable ◽  
Structural Equation ◽  
Structural Model ◽  
Measurement Model ◽  
Linear Measurement ◽  
Equation Modeling ◽  
Child Node ◽  
Causal Order ◽  
Parent Node

Structural equation modeling (SEM) is a system of two kinds of equations: a linear latent structural model (SM) and a linear measurement model (MM). The latent structure model is a causal model from the latent parent node to the latent child node. Meanwhile, MM’s link is from latent variable parent node to observed variable child node. However, researchers should determine the initial causal order between variables based on experience when applying SEM. The main reason is that SEM does not fully construct causal models between observed variables (OVs) from big data. When the artificial causal order is contrary to the fact, the causal inference from SEM is doubtful, and the implicit causal information between the OVs cannot be extracted and utilized. This study first objectively identifies the causal order of variables using the DirectLiNGAM method widely accepted in recent years. Then traditional SEM is converted to expanded SEM (ESEM) consisting of SM, MM and observation model (OM). Finally, through model testing and debugging, ESEM with good fit with data is obtained.

Variance-Based Structural Equation Modeling

2012 ◽  
pp. 193-221 ◽  
Author(s):  
José L. Roldán ◽  
Manuel J. Sánchez-Franco
Keyword(s):  
Structural Equation Modeling ◽  
Information Systems ◽  
Structural Equation ◽  
Structural Model ◽  
Measurement Model ◽  
Statistical Technique ◽  
Equation Modeling ◽  
Systems Research ◽  
Information Systems Research ◽  
Systems Models

Partial Least Squares (PLS) is an efficient statistical technique that is highly suited for Information Systems research. In this chapter, the authors propose both the theory underlying PLS and a discussion of the key differences between covariance-based SEM and variance-based SEM, i.e., PLS. In particular, authors: (a) provide an analysis of the origin, development, and features of PLS, and (b) discuss analysis problems as diverse as the nature of epistemic relationships and sample size requirements. In this regard, the authors present basic guidelines for the applying of PLS as well as an explanation of the different steps implied for the assessment of the measurement model and the structural model. Finally, the authors present two examples of Information Systems models in which they have put previous recommendations into effect.

Human Resource Practices as Predictors of Organizational Performance: A Structural Equation Modeling Approach

2018 ◽  
Vol 21 (4) ◽  
pp. 1087-1112 ◽  
Author(s):  
Subash Chandra Pattnaik ◽  
Rashmita Sahoo
Keyword(s):  
Structural Equation Modeling ◽  
Organizational Performance ◽  
Human Resource ◽  
Structural Equation ◽  
Structural Model ◽  
Developing Economies ◽  
Measurement Model ◽  
Equation Modeling ◽  
Hr Practices ◽  
The Relationship

The purpose of this research article is to examine the relationship between human resource (HR) practices and organizational performance. Though this research theme has been extensively researched in Western countries, very limited research has been done in the prevailing HRM scenario obtained in developing economies, especially in India. Hence, this article attempts to study the relationship and fill this research gap. A two-stage structural equation modeling (SEM) has been used to study this relationship. In the first stage, the proposed measurement model is validated through confirmatory factor analysis (CFA) and, in the second, the structural model examines the hypothesized relationship between the HR practices and organizational performance. It is found that the two are positively related. The findings of this study strengthen existing literature in the area and have important implications for managers engaged in HR functions.

Understanding consumer motivation to share IoT products data

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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