scholarly journals HAMBATAN PENYALURAN DANA BANTUAN OPERASIONAL SEKOLAH (STUDI KASUS DI PROPINSI SUMATERA UTARA)

2017 ◽  
Vol 14 (4) ◽  
pp. 539
Author(s):  
Yuris Danilwan
Keyword(s):  
Junior High School ◽  
Indirect Effect ◽  
Latent Variables ◽  
Latent Variable ◽  
Structural Equation ◽  
Equation Modeling ◽  
Outcome Variable ◽  
Direct Influence ◽  
Input Process ◽  
Input Variables

In general, the study aims to see how far the effective use of policy implementation disbursements School Operational Assistance (BOS), which has been implemented so far in order to free tuition. The research involves several elements like: PIU Office Level II, Personnel at Bank dealer, School Committee, Principal and Students who are in districts north Sumatra province. This research also involves a number of factors thought to be determinants of implementation effectiveness of the School Operational Assistance (BOS) in the field. Statistical methods used are modeling Structural Equation Modeling (SEM). This research was carried out in Medan. Location of the study Elementary and Junior High School.  Respondents totaled 554 respondents. The results showed that: a). All factors considered valid or have a significant influence on the formation of each latent variable, namely: latent variable input, process, output and outcome. b). The amount of the direct influence of input variables to process variables of 0.83. While the contribution of 68.89%. c). There is a direct influence of input variables on output variables. The amount of the direct influence of input variables on output variables of 0.21. While the direct contribution given by the input variables on output variables of 4.41%. d). There are no direct influence on the input variables Outcome variables. The amount of indirect effect through the Input variable Output variable to the outcome variable that is equal to 0.162. While the contributions made by variable input through output variables Outcome variables at 2.61%. e). There is an indirect effect through the variable Input Variable Process and proceed through a variable output to outcome variables. The amount of indirect effect through the variable process input variables and proceed through a variable output to outcome variables of 0.50. Contributions made 25.49%. f). The influence of each factor formed on the latent variables of input, process and output of the factors increasing the value of education and National Final Test (UAN).

scholarly journals HAMBATAN PENYALURAN DANA BANTUAN OPERASIONAL SEKOLAH (STUDI KASUS DI PROPINSI SUMATERA UTARA)

2018 ◽  
Vol 14 (4) ◽  
pp. 539-554
Author(s):  
Yuris Danilwan
Keyword(s):  
Junior High School ◽  
Indirect Effect ◽  
Latent Variables ◽  
Latent Variable ◽  
Structural Equation ◽  
Equation Modeling ◽  
Outcome Variable ◽  
Direct Influence ◽  
Input Process ◽  
Input Variables

In general, the study aims to see how far the effective use of policy implementation disbursements School Operational Assistance (BOS), which has been implemented so far in order to free tuition. The research involves several elements like: PIU Office Level II, Personnel at Bank dealer, School Committee, Principal and Students who are in districts north Sumatra province. This research also involves a number of factors thought to be determinants of implementation effectiveness of the School Operational Assistance (BOS) in the field. Statistical methods used are modeling Structural Equation Modeling (SEM). This research was carried out in Medan. Location of the study Elementary and Junior High School. Respondents totaled 554 respondents. The results showed that: a). All factors considered valid or have a significant influence on the formation of each latent variable, namely: latent variable input, process, output and outcome. b). The amount of the direct influence of input variables to process variables of 0.83. While the contribution of 68.89%. c). There is a direct influence of input variables on output variables. The amount of the direct influence of input variables on output variables of 0.21. While the direct contribution given by the input variables on output variables of 4.41%. d). There are no direct influence on the input variables Outcome variables. The amount of indirect effect through the Input variable Output variable to the outcome variable that is equal to 0.162. While the contributions made by variable input through output variables Outcome variables at 2.61%. e). There is an indirect effect through the variable Input Variable Process and proceed through a variable output to outcome variables. The amount of indirect effect through the variable process input variables and proceed through a variable output to outcome variables of 0.50. Contributions made 25.49%. f). The influence of each factor formed on the latent variables of input, process and output of the factors increasing the value of education and National Final Test (UAN).

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

Forests ◽  
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.

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

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.

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

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.

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

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.

New developments in latent variable panel analyses of longitudinal data

2007 ◽  
Vol 31 (4) ◽  
pp. 357-365 ◽  
Author(s):  
Todd D. Little ◽  
Kristopher J. Preacher ◽  
James P. Selig ◽  
Noel A. Card
Keyword(s):  
Structural Equation Modeling ◽  
Longitudinal Data ◽  
Latent Variables ◽  
Latent Variable ◽  
Structural Equation ◽  
Null Model ◽  
Factorial Invariance ◽  
Equation Modeling ◽  
New Developments ◽  
Mediated Effects

We review fundamental issues in one traditional structural equation modeling (SEM) approach to analyzing longitudinal data — cross-lagged panel designs. We then discuss a number of new developments in SEM that are applicable to analyzing panel designs. These issues include setting appropriate scales for latent variables, specifying an appropriate null model, evaluating factorial invariance in an appropriate manner, and examining both direct and indirect (mediated), effects in ways better suited for panel designs. We supplement each topic with discussion intended to enhance conceptual and statistical understanding.

scholarly journals PEMODELAN STRUKTURAL DARI EMOSI POSITIF SEBAGAI PEMEDIASI TERHADAP PEMBELIAN TIDAK TERENCANA

2015 ◽  
Vol 4 (3) ◽  
pp. 104
Author(s):  
NI WAYAN ARNI YANITA ◽  
KETUT JAYANEGARA ◽  
I PUTU EKA N. KENCANA
Keyword(s):  
Structural Equation Modeling ◽  
Indirect Effect ◽  
Latent Variables ◽  
Structural Equation ◽  
Positive Emotions ◽  
Goodness Of Fit ◽  
Situational Factors ◽  
Equation Modeling ◽  
Impulse Buying ◽  
Mediation Models

Latent variables are variables that can not be observed directly. Latent variables can be observed with constituent indicators. One of the methods used to analyze the latent variables are Structural Equation Modeling (SEM). This research raised the case of impulse buying to be applied to the SEM method. Impulse buying influenced by the characteristics of the hypermarket, situational factors, the characteristics of the product, promotion and positive emotions. The purpose of this research was to determine the effect dari positive emotions as mediation to impulse buying.The results obtained indicate that positive emotions directly affect the impulse buying of 0.302, promotion directly affects the positive emotions of 0.367, and the promotion of indirect effect to impulse buying of 0.111. So positive emotions can mediate to impulse buying of 0.020. Goodness of fit mediation models not good with value 0.39.

scholarly journals ANALISIS KEPUASAN DAN LOYALITAS PELANGGAN DALAM PEMESANAN TIKET PESAWAT SECARA ONLINE MENGGUNAKAN PENDEKATAN PARTIAL LEAST SQUARE (PLS)

2018 ◽  
Vol 7 (4) ◽  
pp. 361-372
Author(s):  
Trisnawati Gusnawita Berutu ◽  
Abdul Hoyyi ◽  
Sugito Sugito
Keyword(s):  
Latent Variables ◽  
Latent Variable ◽  
Structural Equation ◽  
Service Providers ◽  
Web Design ◽  
Partial Least Square ◽  
Least Square ◽  
Equation Modeling ◽  
Internet Service ◽  
Access Security

Technology advances are bring rapid changes, thus bringing the world to the information society. From this technological progress thus e-commerce emerged, as a means to meet the needs of goods and services through internet access (online). This is what the airlines utilized by cooperating with various internet service providers (online), to provide convenience and comfort of airplane passengers in buying tickets without having to come directly to the place and through intermediaries. To provide the best service, need to know what factors that influence customer satisfaction in ordering airline tickets online. Appropriate modeling for this problem using structural equation modeling, with Partial Least Square (PLS) approach. The PLS approach is chosen because it is not based on several assumptions, one of these is the normal multivariate assumption. In this research, the exogenous latent variables used are performance, access, security, sensation, information, and web design, while the endogenous latent variables are satisfaction and loyalty. Based on the results of the analysis it can be concluded that the latent variables of access, security, sensation, information, and web design are able to explain the latent satisfaction variable of 70.32% while the satisfaction latent variable is able to explain the latent variable of loyalty by 36.02%. 

scholarly journals Pengaruh Metakognisi dan Motivasi terhadap Kemampuan Pemecahan Masalah Matematika Melalui Kreativitas Siswa Kelas VIII SMP Negeri di Kecamatan Kindang Kabupaten Bulukumba

2018 ◽  
Vol 4 (2) ◽  
pp. 129-140
Author(s):  
Muhammad Ihsan
Keyword(s):  
Problem Solving ◽  
Significant Influence ◽  
Junior High School ◽  
Structural Equation ◽  
Learning Motivation ◽  
Equation Modeling ◽  
Direct Influence ◽  
Mathematics Problem Solving ◽  
Indirect Influence ◽  
Mathematics Problem

Several factors that influence students ability to solve mathematics problem are metacognition, learning motivation, and learning creativity on mathematics. Therefore, this research aimed to identify the influence level of metacognition and learning motivation toward mathematics problem solving ability through learning creativity of students. The population was students at grade VIII of public junior high school in Kindang sub district of Bulukumba. Technique of sampling was by equal size random sampling. The data were collected through questionnaire and test, and then analyzed by using descriptive statistics and SEM (Structural Equation Modeling) analysis. The result revealed that (1) there is a positive significant influence of metacognition toward learning creativity; (2) there is also a positive significant influence of learning motivation toward learning creativity; (3) the direct influence of metacognition toward mathematics problem solving ability is positive and significant; (4) the direct influence of learning motivation toward mathematics problem solving ability is positive significant; (5) there is a positive significant influence of learning creativity toward mathematics problem solving ability; (6) the indirect influence of metacognition toward mathematics problem solving ability through learning creativity is positive significant; and (7) the indirect influence of learning motivation toward mathematics problem solving ability through learning creativity is positive and significant.

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.

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