scholarly journals A Review of Structural Equation Model for Construction Delay Study

2018 ◽  
Vol 7 (4.35) ◽  
pp. 299 ◽  
Author(s):  
Mohd Zakwan Ramli ◽  
Marlinda Abdul Malek ◽  
Mohamad Zaki Muda ◽  
Zulkhairi Abd Talib ◽  
Nor Syahirah Azman ◽  
...  
Keyword(s):  
Latent Variable ◽  
Structural Equation ◽  
First Generation ◽  
Complex Analysis ◽  
Structural Model ◽  
Hidden Variables ◽  
Analysis Tool ◽  
Error Measurement ◽  
Analysis Model ◽  
Construction Delay

Structural Equation Modelling (SEM) has been widely used in science social area compared to construction engineering and management field especially in area of delay construction. SEM is a second generation multivariate analysis that has an advance features compare to first generations of analysis tools. First generation techniques suffer with some assumptions such as error measurement is neglected, only observed variable allowed, only for simple model and other limitations. In construction delay study, comprehensive and complex analysis which involves hidden variables need to be considered to get precise results. Therefore, the main objective of this paper is to review the importance of applying SEM for construction delay study. Various papers which were taken from construction delay and construction management studies has been reviewed to observe the suitability of SEM for construction delay study. Outcome of this review reveals that SEM can include latent variable in the analysis model and consider of error measurement as integral part of the model as well as simultaneously analyse theory and measurement in a structural model while it is unobtainable for first generation techniques.  This review proves that SEM can be an appropriate analysis tool for construction delay study.

Modelling Museum Visitors’ Perception and Experience of Authenticity: Examining Heterogeneity with a Finite Mixture Model

2021 ◽  
Vol 56 ◽  
pp. 0-0
Author(s):  
Claudia Bauer-Krösbacher ◽  
Josef Mazanec
Keyword(s):  
Latent Variable ◽  
Structural Equation ◽  
Latent Class ◽  
Structural Model ◽  
Unobserved Heterogeneity ◽  
Model Fitting ◽  
Equation Model ◽  
Finite Mixture ◽  
Museum Visitors ◽  
Specific Strength

Purpose. In this study, the authors explore the role of museum visitors’ perceptions and experiences of authenticity. They introduce several variants of authenticity experience and analyse how they are intertwined and feed visitor satisfaction. Method. The authors apply a multi-step model fitting and validation procedure including inferred causation methods and finite mixture modelling to verify whether the visitors’ perceptions of authenticity are subject to unobserved heterogeneity. They elaborate an Authenticity Model that demonstrates out-of-sample validity and generalisability by being exposed to new data for another cultural attraction in another city. Then, they address the heterogeneity hypothesis and evaluate it for the case study with the larger sample. Findings. In both application cases, the Sisi museum in Vienna and the Guinness Storehouse in Dublin, the empirical results support the assumed cause-effect sequence, translating high quality information display—from traditional and multimedia sources—into Perceived Authenticity and its experiential consequences such as Depth and Satisfaction. Accounting for unobserved heterogeneity detects three latent classes with segment-specific strength of relationships within the structural model. Research and conclusions limitations. The combined latent-class, structural-equation model needs validation with another sample that would have to be larger than the available Guinness database. Future studies will have to complement the purely data-driven search for heterogeneity with theory-guided reasoning about potential causes of diversity in the strength of the structural relationships. Practical implications. Cultural heritage sites are among the attractions most typical of city tourism. History tends to materialise in the artefacts accumulated by the population among the urban agglomerations, and museums are the natural places for preserving exhibits of cultural value. Authenticity must be considered an important quality assessment criterion for many visitors, whereby, the distinction between object authenticity and existential authenticity is crucial. Originality. In addition to making substantive contributions to authenticity theory, the authors also extend previous research in terms of methodological effort. Authenticity research, so far, has neither exploited inferred causation methods nor combined latent variable modelling with detecting unobserved heterogeneity. Type of paper: Research article.

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>

MODELING OF SEA SURFACE TEMPERATURE BASED ON PARTIAL LEAST SQUARE - STRUCTURAL EQUATION

2021 ◽  
Vol 14 (2) ◽  
pp. 170-182
Author(s):  
Miftahuddin Miftahuddin ◽  
Retno Wahyuni Putri ◽  
Ichsan Setiawan ◽  
Rina Suryani Oktari
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Latent Variables ◽  
Latent Variable ◽  
Structural Equation ◽  
Structural Model ◽  
Partial Least Square ◽  
Least Square ◽  
Sea Surface ◽  
Equation Modeling

Variability of Sea Surface Temperature (SST) is one of the climatic features that influence global and regional climate dynamics. Missing data (gaps) in the SST dataset are worth investigating since they may statistically alter the value of the SST change. The partial least square-structural equation modeling (PLS-SEM) approach is used in this work to estimate the causality relationships between exogenous and endogenous latent variables. The findings of this study, which are significant indicators that have a loading factor value > 0.7 are as follows: i) sea surface temperature (oC) as a measure of the latent variable changes in SST, ii) wind speed (m/s) and relative humidity (%) as a measure of the latent variable of weather, and iii) air temperature (oC), long-wave solar radiation (w/m2) as a measure of climate latent variables. The size of the Rsquare value is influenced by the number of gaps. The results of the boostrapping show that the latent variables of weather and climate have a significant effect on changes in SST which are indicated by the value of tstatistics > ttabel. The structural model obtained Changes in SST (η) = -0.330 weather + 0.793 climate + ζ. The model shows that the weather has a negative coefficient, which means that the better the weather conditions, the lower the SST changes. Climate has a positive coefficient, which means that the better the climate, the SST changes will also increase. Rising sea surface temperatures caused by an increase in climate can lead to global warming, impacting El-Nino and La-Nina events.

scholarly journals ANALISIS KEKAMBUHAN ORANG DENGAN SKIZOFRENIA MENGGUNAKAN METODE PARTIAL LEAST SQUARE STRUCTURAL EQUATION MODEL

2019 ◽  
Vol 8 (3) ◽  
pp. 222
Author(s):  
IRA INDRIYANTI ◽  
G.K. GANDHIADI ◽  
MADE SUSILAWATI
Keyword(s):  
Family Support ◽  
Structural Equation Model ◽  
Latent Variables ◽  
Latent Variable ◽  
Structural Equation ◽  
Structural Model ◽  
Medication Compliance ◽  
Equation Model ◽  
Partial Least Square ◽  
Least Square

Schizophrenia is a psychotic disorder characterized by major disorders in the mind and emotions. People with schizophrenia (ODS) can experience recurrence if they do not receive proper care. The latent variable used in this study was ODS reccurence. One method that can determine the relationship between latent variables and latent variables with the indicator is the partial least square structural equation model (PLS-SEM). This study was conducted to see how the structural model of ODS recurrence data and to know the factors that most influence ODS recurrence. The results of this study concluded that the resulting model was good enough with a large R-square value of 0.8577, but not all variables used in this study had a significant effect on ODS recurrence. ODS recurrence is significantly influenced by family support and community social support variables. While medication compliance and physician control regularity will not have a significant effect without family support. The worse treatment of families and communities around ODS recurrence will occur more often.

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.

scholarly journals Parameter Estimation of Structural Equation Modeling Using Bayesian Approach

CAUCHY ◽  
2016 ◽  
Vol 4 (2) ◽  
pp. 86
Author(s):  
Dewi Kurnia Sari ◽  
Ni Wayan Surya Wardhani ◽  
Suci Astutik
Keyword(s):  
Job Satisfaction ◽  
Organizational Commitment ◽  
Leadership Style ◽  
Bayesian Approach ◽  
Latent Variable ◽  
Structural Equation ◽  
Structural Model ◽  
Employee Performance ◽  
Equation Modeling ◽  
Significant Difference

Leadership is a process of influencing, directing or giving an example of employees in order to achieve the objectives of the organization and is a key element in the effectiveness of the organization. In addition to the style of leadership, the success of an organization or company in achieving its objectives can also be influenced by the commitment of the organization. Where organizational commitment is a commitment created by each individual for the betterment of the organization. The purpose of this research is to obtain a model of leadership style and organizational commitment to job satisfaction and employee performance, and determine the factors that influence job satisfaction and employee performance using SEM with Bayesian approach. This research was conducted at Statistics FNI employees in Malang, with 15 people. The result of this study showed that the measurement model, all significant indicators measure each latent variable. Meanwhile in the structural model, it was concluded there are a significant difference between the variables of Leadership Style and Organizational Commitment toward Job Satisfaction directly as well as a significant difference between Job Satisfaction on Employee Performance. As for the influence of Leadership Style and variable Organizational Commitment on Employee Performance directly declared insignificant.

scholarly journals Communication Skills, Problem-Solving Ability, Understanding of Patients’ Conditions, and Nurse’s Perception of Professionalism among Clinical Nurses: A Structural Equation Model Analysis

2020 ◽  
Vol 17 (13) ◽  
pp. 4896
Author(s):  
Ae Young Kim ◽  
In Ok Sim
Keyword(s):  
Problem Solving ◽  
Communication Skills ◽  
Structural Equation ◽  
Structural Model ◽  
Measurement Model ◽  
Model Analysis ◽  
Mediating Effect ◽  
Job Skills ◽  
Analysis Model ◽  
Clinical Nurses

This study was intended to confirm the structural relationship between clinical nurse communication skills, problem-solving ability, understanding of patients’ conditions, and nurse’s perception of professionalism. Due to changes in the healthcare environment, it is becoming difficult to meet the needs of patients, and it is becoming very important to improve the ability to perform professional nursing jobs to meet expectations. In this study method, structural model analysis was applied to identify factors influencing the perception of professionalism in nurses. The subjects of this study were 171 nurses working at general hospitals in city of Se, Ga, and Geu. Data analysis included frequency analysis, identification factor analysis, reliability analysis, measurement model analysis, model fit, and intervention effects. In the results of the study, nurse’s perception of professionalism was influenced by factors of communication skills and understanding of the patient’s condition, but not by their ability to solve problems. Understanding of patient’s condition had a mediating effect on communication skills and nursing awareness. Communication skills and understanding of the patient’s condition greatly influenced the nurse’s perception of professionalism. To improve the professionalism of clinical nurses, nursing managers need to emphasize communication skills and understanding of the patient’s condition. The purpose of this study was to provide a rationale for developing a program to improve job skills by strengthening the awareness of professional positions of clinical nurses to develop nursing quality of community.

scholarly journals THE EFFECT OF MARKET ORIENTATION AND ENTERPRENEURAL ORIENTATION ON BUSINESS PERFORMANCE WITH MARKETING CAPABILITIES AS A MEDIATION VARIABLE (CASE IN PUBLIC BANK OF MAKASSAR CITY)

2020 ◽  
Vol 3 (1) ◽  
pp. 101-113
Author(s):  
Liliya Novita ◽  
Dian AS Parawansa ◽  
Jumidah Maming
Keyword(s):  
Market Orientation ◽  
Entrepreneurial Orientation ◽  
Structural Equation ◽  
Business Performance ◽  
Structural Model ◽  
Test Model ◽  
Marketing Capabilities ◽  
Analysis Model ◽  
Validity And Reliability ◽  
Combining Data

Business performance is an aspect that must be considered for the sustainability of a business. This type of research is quantitative research with SEM (Structural Equation Model) method that looks for relationships between variables. While the steps taken consisted of determining the structural model, followed by setting the measurement model, combining data and checking, pretest validity and reliability, determining the path analysis model, collecting models and analyzing with IBM-SPSS AMOS 23, testing the fit test model, if possible to re-checking the model, and then interpreting the results of data processing. The results showed that market orientation and entrepreneurial orientation had a positive and significant effect on marketing capabilities, marketing capabilities had a positive and significant effect on business performance, and, market orientation and entrepreneurial orientation does not have a positive and no significant effect on business performance.

scholarly journals PERSEPSI MASYARAKAT TERHADAP KEBIJAKAN TRANS KOETARADJA PADA KORIDOR PUSAT KOTA – MATA IE DAN PUSAT KOTA – AJUN – LHOKNGA MENGGUNAKAN INDIKATOR VARIABEL LATEN

2019 ◽  
Vol 2 (1) ◽  
pp. 58-67
Author(s):  
Muhammad Merfazi ◽  
Sugiarto Sugiarto ◽  
Renni Anggraini
Keyword(s):  
Latent Variable ◽  
Structural Equation ◽  
Structural Model ◽  
Stated Preference ◽  
Measurement Model ◽  
Equation Modeling ◽  
Regression Equations ◽  
Community Perception ◽  
City Center ◽  
The Impact

Community dependence on private transportation modes is a major factor in congestion in Banda Aceh. For this reason, the Aceh government implemented the operation policy of Trans Koetaradja to reduce the impact of congestion. Community perception is one of the important aspects in policy making, because by involving the community, decision makers will be able to capture the views, needs and expectations of the community. The purpose of this study is to examine public perceptions of the Trans Koetaradja policy as a case study at 2 (two) corridors, City Center - Mata Ie and City Center - Ajun - Lhoknga. The data collection method used was Stated Preference (SP) with a total of 220 respondents by stratified random sampling. The SP questionnaire contains information about socio-economic, travel behavior, and respondents' perceptions. The result of perception showed that all psychological questions had a good value above 2.5 (average) which was 2.81 (70.20%) from the reference of the 1-4 Likert scale with the indicator with the highest level of acceptance of 3.32 that was a private vehicle needed in everyday life. Data processing and analysis used the Multiple Indicators Multiple Causes (MIMIC) model which was one of the branches of Structural Equation Modeling (SEM), the regression parameters calibrated using Lisrel 9.3 software to produce a measurement model that was the perception of "personal mode dependence (t-value, 5.13)", whereas in the structural model produced five multiple regression equations with the most significant factor affecting each latent variable was "education".

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.

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