HUBUNGAN AKREDITASI DAN UJIAN NASIONAL PADA SEKOLAH NEGERI DENGAN GENERALIZED STRUCTURED COMPONENT ANALYSIS

There are several views and tendencies that distinguish between schools and madrasas in several aspects, one of them is the curriculum. Madrasah as islamic educational institution contains more religious lessons compared to public schools. As a result, madrasah are considered less able to provide good result in educational achievement. Overall, the education system which is based on National Education Standards (SNP) is used for assessing the educational accreditation. SNP is the minimum criterion of education system in Indonesia can be evaluated from the National Examination (UN). As latent variable, SNP is measured through 124 items as variable indicators. One of methods which is used to measure the relationship among latent variables, and latent variables with their indicator variables is structural equation modeling (SEM). A component-based SEM is called Generalized Structured Component Analysis (GSCA). GSCA analysis based on measurement model, there were 9 indicators were not significant, in which 1 indicator of standard of education and staff (SPT), 5 indicators on standard of infrastructure (SSP), and 3 indicators on standard of cost (SB). Evaluation of the structural model, it was found that the path coefficient of standard of content (SI) to UN was not significant and standard of competency (SKL) given the biggest direct effect to UN. The overall goodness of fit model showed that the total variance that can be explained of all indicators and latent variables in evaluating model of accreditation and national examinations was 63.9%. The difference in the percentage of accreditation status between schools and madrasas shows different UN results. In the 2017-2018 period, MTsN had a higher percentage of accredited schools, in line with that the average MTsN UN obtained was better than that of SMP in all types of subjects.

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PENERAPAN METODE GENERALIZED STRUCTURED COMPONENT ANALYSIS PADA KEPUASAN KONSUMEN (Studi Kasus: Pasien Klinik Q)

Jurnal Gaussian ◽

10.14710/j.gauss.v9i4.29416 ◽

2020 ◽

Vol 9 (4) ◽

pp. 454-463

Author(s):

Farisiyah Fitriani ◽

Agus Rusgiyono ◽

Tatik Widiharih

Keyword(s):

Structural Equation Modeling ◽

Latent Variables ◽

Random Sampling ◽

Structural Equation ◽

Goodness Of Fit ◽

Component Analysis ◽

Least Square ◽

Equation Modeling ◽

Generalized Structured Component Analysis ◽

The Impact

Customer satisfaction is used by a company to evaluate products or services whether it is sufficient with customer’s expectations. Satisfaction is influenced by factors that cannot be measured directly are called latent variables and can be measured through indicators used to measure satisfaction with Structural Equation Modeling (SEM). Generalized Structured Component Analysis (GSCA) method is part of a SEM based on a variant that does not require the assumption of a multivariate normal distribution and has a measure overall goodness of fit. The parameters used are factor loading, coefficients parameter, and weight of indicators and estimated with alternating least square. The type of data used primary data from the results of the questionnaire with stratified proportional random sampling and number of samples 286. This research using indicators as measurable variables as many 32 indicators and 8 latent variable. Considering to the evaluation of the structural model, it is found there are 5 variables that influence satisfaction, they are prices, quality yield, cleanliness, doctor's services, and employee services with a large influence of 77.18% and the impact of satisfaction on loyalty is 48.63 %. For the overall goodness of fit measure, it is known that the FIT value is 63.75% and the adjusted FIT (AFIT) value is 63.47%. The goodness of fit (GFI) produced the value in the amount of 96.43%, indicating that the general model has the good level of compatibility.Keywords: Generalized Structured Component Analysis, Structural Equation Modeling, Overall goodness of fit, Alternating Least Square, Stratified Proportional Random Sampling

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Cutoff criteria for overall model fit indexes in generalized structured component analysis

Journal of Marketing Analytics ◽

10.1057/s41270-020-00089-1 ◽

2020 ◽

Vol 8 (4) ◽

pp. 189-202

Author(s):

Gyeongcheol Cho ◽

Heungsun Hwang ◽

Marko Sarstedt ◽

Christian M. Ringle

Keyword(s):

Structural Equation Modeling ◽

Structural Equation ◽

Goodness Of Fit ◽

Component Analysis ◽

Model Fit ◽

Equation Modeling ◽

Generalized Structured Component Analysis ◽

Root Mean Square Residual ◽

Fit Indexes ◽

Cutoff Criteria

AbstractGeneralized structured component analysis (GSCA) is a technically well-established approach to component-based structural equation modeling that allows for specifying and examining the relationships between observed variables and components thereof. GSCA provides overall fit indexes for model evaluation, including the goodness-of-fit index (GFI) and the standardized root mean square residual (SRMR). While these indexes have a solid standing in factor-based structural equation modeling, nothing is known about their performance in GSCA. Addressing this limitation, we present a simulation study’s results, which confirm that both GFI and SRMR indexes distinguish effectively between correct and misspecified models. Based on our findings, we propose rules-of-thumb cutoff criteria for each index in different sample sizes, which researchers could use to assess model fit in practice.

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Agents Affecting the Productivity of Pine Plantations on the Loess Plateau in China: A Study Based on Structural Equation Modeling

Forests ◽

10.3390/f11121328 ◽

2020 ◽

Vol 11 (12) ◽

pp. 1328

Author(s):

Xuan Zhao ◽

Yanjie Li ◽

Hao Song ◽

Yuhuan Jia ◽

Jianjun Liu

Keyword(s):

Indirect Effect ◽

Latent Variables ◽

Latent Variable ◽

Structural Equation ◽

Equation Modeling ◽

Path Coefficient ◽

Site Conditions ◽

Population Stability ◽

Forest Density ◽

Weight Value

Stability and productivity are important indicators used to measure the state of forest ecosystems. Artificial forests populations with reasonable structures and strong stability are critical for ecosystem productivity. Previous studies have focused on individual factors, while the mechanisms of how multiple factors affect population productivity remain unknown. We used 57 plots in a Chinese pine (Pinus tabuliformis) plantation to investigate 23 stand factors and analyzed the relationships among site factors, population structure, population stability, and population productivity using partial least square-structural equation modeling (PLS-SEM). The results showed that the population productivity of the plantation was directly affected by the population stability latent variable but indirectly affected by the site conditions latent variables (indirect effect path coefficient = 0.249) and forest structure (indirect effect path coefficient = 0.222). However, the site conditions latent variable was the main factor directly affecting the population stability latent variables; the total effect was 0.511 (direct effect path coefficient = 0.307, indirect effect path coefficient = 0.204), and the influence of forest structure on population stability was lower than that of the site conditions latent variable (direct effect path coefficient = 0.454). The factor with the greatest weight among the site conditions latent variable was slope (0.747), indicating that slope contributes the most to latent variables related to forest population stability. Among all variables affecting the forest stability latent variables, forest density had the highest weight value (0.803), and the weight value of forest mortality was lower than that of forest density. The weights of the latent variables associated with population structure from high to low were canopy density, the uniform angle index, and the spatial competition index, indicating that competition for space had the lowest influence on the population stability latent variables. The results provide new insights and ideas for quantifying relationships among different driving factors and a basis for scientific and rational plantation management.

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Presenting of Indifference Management Model of Education System in Ardabil Province Using Structural Equation Modeling

International Education Studies ◽

10.5539/ies.v9n11p90 ◽

2016 ◽

Vol 9 (11) ◽

pp. 90

Author(s):

Elham Abolfazli ◽

Reza Yousefi Saidabadi ◽

Vahid Fallah

Keyword(s):

Organizational Climate ◽

Education System ◽

Latent Variables ◽

Structural Equation ◽

Goodness Of Fit ◽

Structural Model ◽

Simple Random Sampling ◽

Equation Modeling ◽

Great Ability ◽

And Performance

<p class="apa">The purpose of the present study is to investigate indifference management structural model in education system of Ardabil Province. The research method was integration study using Alli modeling. Statistical society of research was 420 assistant professors of educational science, managers and deputies of Ardabil’ second period of high schools that 383 individuals were selected by simple random sampling. The data collection tool was researcher-made questionnaire. Face and content validity of the questionnaire was confirmed by experts and its Cronbach’s alpha coefficient was obtained as of 0/80. In order to investigate hypothesizes of research, obtained data were transferred to LISREL software to fit Alli model of structural equation and then were analyzed. The obtained findings of the statistical analysis showed that behavior and performance of manager indirectly has a meaningful impact on indifference management variable through employee’ empowerment variables, job satisfaction of employee, organization’s culture, organizational climate and employee’ perception of the organization. Goodness of fit index (GFI) 0/92 and root mean square of residuals latent variables model was RMSEA=0/045. Therefore, the model has a good fit and has a great ability to measure main variables of research.</p>

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Structural Equation Modeling for the Effect of Main Factors on Abortion Issue

Journal of Raparin University ◽

10.26750/vol(7).no(1).paper1 ◽

2019 ◽

Vol 7 (1) ◽

pp. 1-13

Author(s):

Aras Jalal Mhamad ◽

Renas Abubaker Ahmed

Keyword(s):

Structural Equation Modeling ◽

Latent Variables ◽

Latent Variable ◽

Structural Equation ◽

Close Contact ◽

Maternity Hospital ◽

Equation Modeling ◽

Research Model ◽

Statistical Tools ◽

Main Factors

Based on medical exchange and medical information processing theories with statistical tools, our study proposes and tests a research model that investigates main factors behind abortion issue. Data were collected from the survey of Maternity hospital in Sulaimani, Kurdistan-Iraq. Structural Equation Modelling (SEM) is a powerful technique as it estimates the causal relationship between more than one dependent variable and many independent variables, which is ability to incorporate quantitative and qualitative data, and it shows how all latent variables are related to each other. The dependent latent variable in SEM which have one-way arrows pointing to them is called endogenous variable while others are exogenous variables. The structural equation modeling results reveal is underlying mechanism through which statistical tools, as relationship between factors; previous disease information, food and drug information, patient address, mother’s information, abortion information, which are caused abortion problem. Simply stated, the empirical data support the study hypothesis and the research model we have proposed is viable. The data of the study were obtained from a survey of Maternity hospital in Sulaimani, Kurdistan-Iraq, which is in close contact with patients for long periods, and it is number one area for pregnant women to obtain information about the abortion issue. The results shows arrangement about factors effectiveness as mentioned at section five of the study. This gives the conclusion that abortion problem must be more concern than the other pregnancy problem.

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A Behavioral Model of Drivers’ Mean Speed Influenced by Weather Conditions, Road Geometry, and Driver Characteristics Using a Driving Simulator Study

Advances in Civil Engineering ◽

10.1155/2021/5542905 ◽

2021 ◽

Vol 2021 ◽

pp. 1-18

Author(s):

Mehdi Zolali ◽

Babak Mirbaha ◽

Maziyar Layegh ◽

Hamid Reza Behnood

Keyword(s):

Latent Variables ◽

Latent Variable ◽

Structural Equation ◽

Driving Simulator ◽

Weather Conditions ◽

Geometric Design ◽

Equation Modeling ◽

Novice Drivers ◽

Sight Distance ◽

The Mean

Driving above the speed limit is one of the factors that significantly affect safety. Many studies examined the factors affecting the speed of vehicles in the simulated environment. The present study aimed to analyze drivers’ characteristics, time and weather conditions, and geometric features’ effect on mean speed in simulated conditions simultaneously. In this regard, the simulator experiment data of 70 drivers were collected in a two-lane rural highway at six different times, and weather scenarios and their socioeconomic characteristics were collected by a questionnaire. Structural equation modeling (SEM) was used to capture the complex relationships among related variables. Eleven variables were grouped into four latent variables in the structural model. Latent variables including “Novice Drivers,” “Experienced Drivers,” “Sight Distance,” and “Geometric Design” were defined and found significant on their mean speed. The results showed that “Novice Drivers” have a positive correlation with the mean speed. Meanwhile, “Experienced Drivers,” who drive 12% slower than the novice group, negatively affect the mean speed with a standard regression weight of −0.08. This relation means that young and novice drivers are more inclined to choose higher speeds. Among variables, the latent variable “Sight Distance” has the most significant effect on the mean speed. This model shows that foggy weather conditions strongly affect the speed selection behavior and reduce the mean speed by 40%. Nighttime also reduces mean speed due to poor visibility conditions. Furthermore, “Geometric design” as the latent variable indicates the presence of curves on the simulated road, and it can be concluded that the existence of a curve on the road encourages drivers to slow down, even young drivers. It is noteworthy that the parts of the simulated road with a horizontal curve act as a speed reduction tool for drivers.

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Multigroup Moderation Test in Generalized Structured Component Analysis

CAUCHY ◽

10.18860/ca.v4i2.3491 ◽

2016 ◽

Vol 4 (2) ◽

pp. 81

Author(s):

Angga Dwi Mulyanto ◽

Solimun Solimun ◽

Ni Wayan Surya Wardhani ◽

Suharno Suharno

Keyword(s):

Least Squares ◽

Standard Error ◽

Complex Analysis ◽

Component Analysis ◽

T Test ◽

Free Software ◽

Path Coefficient ◽

Generalized Structured Component Analysis ◽

Sample Size Estimate ◽

Long Time

Generalized Structured Component Analysis (GSCA) is an alternative method in structural modeling using alternating least squares. GSCA can be used for the complex analysis including multigroup. GSCA can be run with a free software called GeSCA, but in GeSCA there is no multigroup moderation test to compare the effect between groups. In this research we propose to use the T test in PLS for testing moderation Multigroup on GSCA. T test only requires sample size, estimate path coefficient, and standard error of each group that are already available on the output of GeSCA and the formula is simple so the user does not need a long time for analysis.

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FAKTOR-FAKTOR PENENTU PERILAKU GURU SMP MENGGUNAKAN TEKNOLOGI DALAM PEMBELAJARAN ONLINE: STUDI KASUS DI PROVINSI RIAU

Jurnal Pendidikan ◽

10.33830/jp.v22i2.1934.2021 ◽

2021 ◽

Vol 22 (2) ◽

pp. 123-133

Author(s):

Defrizal Hamka ◽

Neng Sholihat

Keyword(s):

Online Learning ◽

Latent Variables ◽

Latent Variable ◽

Structural Equation ◽

Partial Least Square ◽

Least Square ◽

Influential Factor ◽

Equation Modeling ◽

Purposive Sampling ◽

Facility Conditions

The purpose of this research is to investigate factors that influence the intent of behavior using technology in online learning. The study uses structural equation modeling using a partial least square approach to test the hypotheses. Respondents selected using purposive sampling, and the questionnaires were distributed through online surveys and received a response of 96 respondents. Results show that latent variables, performance expectations, business expectations, and facility conditions have a positive and significant relationship with the intent of individual behaviour in the use of technology in online learning. The latent variable "condition facility" is the most influential factor. This research provides an important overview and understanding for policymakers in designing frameworks to pay attention to facility conditions. Further research is suggested in the future covering samples from various provinces in Indonesia. This study adds to the literature primarily on factors affecting behavioral intent to use technology in online learning. Tujuan dari penelitian ini adalah untuk menganalisis faktor-faktor yang mempengaruhi niat perilaku guru menggunakan teknologi dalam pembelajaran online. Penelitian ini menggunakan pemodelan persamaan struktural dengan menggunakan pendekatan partial least square untuk menguji hipotesis. Berdasarkan purposive sampling, kuesioner disebarkan melalui survei online dan mendapat tanggapan dari 96 responden. Hasil penelitian menunjukkan bahwa variabel laten, ekspektasi kinerja, ekspektasi usaha, dan kondisi fasilitas memiliki hubungan positif dan signifikan dengan niat perilaku individu dalam penggunaan teknologi dalam pembelajaran online. Variabel laten “fasilitas kondisi” merupakan faktor yang paling berpengaruh. Penelitian ini memberikan gambaran dan pemahaman penting bagi pembuat kebijakan dalam merancang kerangka kerja untuk memperhatikan kondisi fasilitas. Penelitian lebih lanjut disarankan di masa depan mencakup sampel dari berbagai provinsi di Indonesia. Studi ini menambah literatur terutama pada faktor-faktor yang mempengaruhi niat perilaku untuk menggunakan teknologi dalam pembelajaran online.

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