scholarly journals Lagrange Multiplier Test for Spatial Autoregressive Model with Latent Variables

Symmetry ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 1375
Author(s):  
Anik Anekawati ◽  
Bambang Widjanarko Otok ◽  
Purhadi Purhadi ◽  
Sutikno Sutikno
Keyword(s):  
Least Squares ◽  
Lagrange Multiplier ◽  
Latent Variables ◽  
Autoregressive Model ◽  
Structural Equation ◽  
Weighted Least Squares ◽  
Equation Modeling ◽  
Spatial Autoregressive Model ◽  
Lm Test ◽  
Spatial Autoregressive

The focus of this research is to develop a Lagrange multiplier (LM) test of spatial dependence for the spatial autoregressive model (SAR) with latent variables (LVs). It was arranged by the standard SAR, where the independent variables were replaced by factor scores of the exogenous latent variables from a measurement model (in structural equation modeling) as well as their dependent variables. As a result, an error distribution of the SAR-LVs should have a different distribution from the standard SAR. Therefore, this LM test for the SAR-LVs is based on the new distribution. The estimation of the latent variables used a weighted least squares (WLS) method. The estimation of the SAR-LVs parameter used a two-stage least squares (2SLS) method. The SAR-LVs model was applied to the model with a positive and negative spatial autoregressive coefficient to illustrate how it was interpreted.

scholarly journals PERBANDINGAN ANTARA UNWEIGHTED LEAST SQUARES (ULS) DAN PARTIAL LEAST SQUARES (PLS) DALAM PEMODELAN PERSAMAAN STRUKTURAL (STUDI KASUS MODEL ANALISIS PRESTASI BELAJAR MAHASISWA TAHUN PERTAMA PROGRAM STUDI S1 MATEMATIKA FMIPA-INSTUTUT PERTANIAN BOGOR)

2017 ◽  
Vol 2 (1) ◽  
pp. 21
Author(s):  
Muhammad Amin Paris
Keyword(s):  
Least Squares ◽  
Student Learning ◽  
Partial Least Squares ◽  
Structural Equation ◽  
Weighted Least Squares ◽  
Generalized Least Squares ◽  
Primary Data ◽  
Equation Modeling ◽  
Multivariate Techniques ◽  
Mathematics Students

Structural Equation Modeling (SEM) is one of multivariate techniques  that can estimates a series of interrelated dependence relationships from a number of endogenous and exogenous variables, as well as latent (unobserved) variables simultaneously. Estimation of Parameter methods that is often applied in SEM are Maximum Likelihood (ML), Weighted Least Squares (WLS), Unweighted Least Squares (ULS), Generalized Least Squares (GLS) and Partial Least Squares (PLS). This research aims to compare ULS method and PLS method in estimating parameter model of achievement of student learning in first year undergraduate Mathematics students, FMIPA, Bogor  Agricultural University ( IPB). This research use secondary and primary data which amounts to 112. The result of this research indicates that ULS method is more accurate than PLS methods. The analysis done with ULS method shows that motivation, capability and environmental had an effect to achievement of student learning.

A Comparison of Accuracy Based on Data Imputation between Unconstrained Structural Equation Modeling and Weighted Least Squares Regression

2011 ◽  
Vol 130-134 ◽  
pp. 730-733
Author(s):  
Narong Phothi ◽  
Somchai Prakancharoen
Keyword(s):  
Structural Equation Modeling ◽  
Least Squares ◽  
Structural Equation ◽  
Weighted Least Squares ◽  
Equation Modeling ◽  
Data Imputation ◽  
Least Squares Regression ◽  
Waveform Generator ◽  
Data Set ◽  
Testing Group

This research proposed a comparison of accuracy based on data imputation between unconstrained structural equation modeling (Uncon-SEM) and weighted least squares (WLS) regression. This model is developed by University of California, Irvine (UCI) and measured using the mean magnitude of relative error (MMRE). Experimental data set is created using the waveform generator that contained 21 indicators (1,200 samples) and divided into two groups (1,000 for training and 200 for testing groups). In fact, training group was analyzed by three main factors (F1, F2, and F3) for creating the models. The result of the experiment show MMRE of Uncon-SEM method based on the testing group is 34.29% (accuracy is 65.71%). In contrast, WLS method produces MMRE for testing group is 55.54% (accuracy is 44.46%). So, Uncon-SEM is high accuracy and MMRE than WLS method that is 21.25%.

scholarly journals PEMANTAUAN PERSAMAAN MODEL STRUKTURAL DALAM DATA ORDINAL

2009 ◽  
Vol 8 (1) ◽  
pp. 21
Author(s):  
B. SUHARJO ◽  
LA MBAU ◽  
N. K. K. ARDANA
Keyword(s):  
Least Squares ◽  
Structural Equation ◽  
Weighted Least Squares ◽  
Generalized Least Squares ◽  
The Other ◽  
Estimation Methods ◽  
Equation Modeling ◽  
Multivariate Techniques ◽  
Exogenous Variables ◽  
Design Data

Structural equation modeling (SEM) is one of multivariate techniques  that can estimates a series of interrelated dependence relationships from a number of endogenous and exogenous variables, as well as latent (unobserved) variables simultaneously. To estimates their parameters, SEM based on structure covariance matrix, there are severals methods can be used as estimation methods, namely maximum likelihood (ML), weighted least squares (WLS), generalized least squares (GLS) and unweighted least squares (ULS). The purpose of this paper are to learn these methods in estimating SEM parameters and to compare their consistency, accuracy and sensitivity based on sample size and multinormality assumption of observed variables.  Using a fully crossed design, data were generated for 2 conditions of normality  and 5 different sample sizes. The result showed that when data are normally distributed, ML and GLS more consistent and accurate then the  other methods

scholarly journals A closer look at fixed effects regression in SEM

2019 ◽  
Author(s):  
Henrik Kenneth Andersen
Keyword(s):  
Least Squares ◽  
Latent Variables ◽  
Latent Variable ◽  
Fixed Effects ◽  
Structural Equation ◽  
Panel Data Analysis ◽  
Ordinary Least Squares ◽  
Equation Modeling ◽  
Real World Data ◽  
Fixed Effects Regression

This article provides an in-depth look at the method of fixed-effects regression in the structural equation modeling (SEM) framework. It is meant for those who are less familiar with SEM but interested in panel data analysis as well as those familiar with SEM but new to fixed-effects regression. It demonstrates the decomposition of observed variables into within- and between-unit variance components using latent variables and gives an intuitive least squares-based explanation of latent variable estimation. The estimation of the substantive effect coefficients is shown analytically. The procedure is demonstrated on simulated as well as real-world data using the German Family Panel Survey (pairfam). The example analyses show the SEM results are identical to the conventional methods of pooled ordinary least squares on demeaned data. The supplementary materials provide the model code for use in replication and further study.

scholarly journals Learning Mathematics of Financial Operations during the COVID-19 Era: An Assessment with Partial Least Squares Structural Equation Modeling

Mathematics ◽  
2021 ◽  
Vol 9 (17) ◽  
pp. 2120
Author(s):  
María del Carmen Valls Martínez ◽  
Pedro Antonio Martín-Cervantes ◽  
Ana María Sánchez Pérez ◽  
María del Carmen Martínez Victoria
Keyword(s):  
Structural Equation Modeling ◽  
Least Squares ◽  
Partial Least Squares ◽  
Latent Variables ◽  
Structural Equation ◽  
Equation Modeling ◽  
Valuation Model ◽  
Video Tutorials ◽  
Effective Performance ◽  
Teaching Learning

The COVID-19 pandemic has affected all walks of life, including education. Universities have been forced to teach in a blended or online environment, which has led professors to adapt their traditional teaching–learning methodologies. The professors of Mathematics of Financial Operations at the University of Almeria (Spain) have created video tutorials so that students can autonomously prepare the theoretical part of the subject, leaving the face-to-face classes for practical exercises. This article aims to analyze the effectiveness of video tutorials and the autonomy finally achieved by students in their learning. For this purpose, a questionnaire was carried out in which, through 21 questions, the constructs Autonomy, Effectiveness, Depth, Format, Challenge, and Use were assessed. Based on these six latent variables, the proposed model using the Partial Least Squares Structural Equation Modeling (PLS-SEM) methodology revealed that students considered the Format and Depth of the video tutorials crucial for genuinely effective performance learning and promoting their autonomy. On the other hand, the variables Challenge and Use were poorly rated. This article presents an original valuation model, which has the virtue of achieving a prediction of 78.6% and, in addition, has high predictive power.

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