scholarly journals 10. Using Instrumental Variable Tests to Evaluate Model Specification in Latent Variable Structural Equation Models

2009 ◽  
Vol 39 (1) ◽  
pp. 327-355 ◽  
Author(s):  
James B. Kirby ◽  
Kenneth A. Bollen
Keyword(s):  
Latent Variables ◽  
Latent Variable ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Model Specification ◽  
Equation Modeling ◽  
Monte Carlo Experiment ◽  
Full Information ◽  
Limited Information ◽  
Finite Sample

Structural equation modeling (SEM) with latent variables is a powerful tool for social and behavioral scientists, combining many of the strengths of psychometrics and econometrics into a single framework. The most common estimator for SEM is the full-information maximum likelihood (ML) estimator, but there is continuing interest in limited information estimators because of their distributional robustness and their greater resistance to structural specification errors. However, the literature discussing model fit for limited information estimators for latent variable models is sparse compared with that for full-information estimators. We address this shortcoming by providing several specification tests basedon the 2SLS estimator for latent variable structural equation models developed by Bollen (1996). We explain how these tests can be used not only to identify a misspecified model but to help diagnose the source of misspecification within a model. We present and discuss results from a Monte Carlo experiment designed to evaluate the finite sample properties of these tests. Our findings suggest that the 2SLS tests successfully identify most misspecified models, even those with modest misspecification, and that they provide researchers with information that can help diagnose the source of misspecification.

A Tutorial in Bayesian Mediation Analysis With Latent Variables

Methodology ◽  
2019 ◽  
Vol 15 (4) ◽  
pp. 137-146 ◽  
Author(s):  
Milica Miočević
Keyword(s):  
Mediation Analysis ◽  
Latent Variables ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Estimation Method ◽  
Model Specification ◽  
Equation Modeling ◽  
Ml Estimation ◽  
Definition Of ◽  
Bayesian Mediation

Abstract. Maximum Likelihood (ML) estimation is a common estimation method in Structural Equation Modeling (SEM), and parameters in such analyses are interpreted using frequentist terms and definition of probability. It is also possible, and sometimes more advantageous ( Lee & Song, 2004 ; Rindskopf, 2012 ), to fit structural equation models in the Bayesian framework ( Kaplan & Depaoli, 2012 ; Levy & Choi, 2013 ; Scheines, Hoijtink, & Boomsma, 1999 ). Bayesian mediation analysis has been described for manifest variable models ( Enders, Fairchild, & MacKinnon, 2013 ; Yuan & MacKinnon, 2009 ). This tutorial outlines considerations in the analysis and interpretation of results for the single mediator model with latent variables. The reader is guided through model specification, estimation, and the interpretations of results obtained using two kinds of diffuse priors and one set of informative priors. Recommendations are made for applied researchers and annotated syntax is provided in R2OpenBUGS and Mplus. The target audience for this article are researchers wanting to learn how to fit the single mediator model as a Bayesian SEM.

scholarly journals Bayesian multilevel structural equation modeling: An investigation into robust prior distributions.

2020 ◽  
Author(s):  
Sara van Erp ◽  
William Browne
Keyword(s):  
Random Effects ◽  
Latent Variables ◽  
Latent Variable ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Equation Modeling ◽  
Prior Distributions ◽  
Inverse Gamma ◽  
Variance Parameters ◽  
Multilevel Structural Equation

Bayesian estimation of multilevel structural equation models (MLSEMs) offers advantages in terms of sample size requirements and computational feasibility, but does require careful specification of the prior distribution especially for the random effects variance parameters. The traditional “non-informative” conjugate choice of an inverse- Gamma prior with small hyperparameters has been shown time and again to be problematic. In this paper, we investigate alternative, more robust prior distributions. In contrast to multilevel models without latent variables, MLSEMs have multiple random effects variance parameters, both for the multilevel structure and for the latent variable structure. It is therefore even more important to construct reasonable priors for these parameters. We find that, although the robust priors outperform the traditional inverse-Gamma prior, their hyperparameters do require careful consideration.

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 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 Products of Variables in Structural Equation Models: Multiplicative Reticular Action Models

2019 ◽  
Author(s):  
Steven M. Boker ◽  
Timo von Oertzen ◽  
Andreas Markus Brandmaier
Keyword(s):  
Latent Variables ◽  
Latent Variable ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Equation Model ◽  
Random Coefficients ◽  
Action Model ◽  
Action Models ◽  
Special Case ◽  
General Method

A general method is introduced in which variables that are products of other variables in the context of a structural equation model (SEM) can be decomposed into the sources of variance due to the multiplicands. The result is a new category of SEM which we call a Multiplicative Reticular Action Model (XRAM). XRAM can include interactions between latent variables, multilevel random coefficients, latent variable moderators, and novel constructs such as factors of paths and twin genetic decomposition of multilevel random coefficients. The method relies on an assumption that all variance sources in a model can be decomposed into linear combinations of independent normal standardized variables. Although the distribution of a variable that is an outcome of multiplication between other variables is not normal, the assumption is that it can be decomposed into sources that are normal if one takes into account the non-normality induced by the multiplication. The method is applied to an example to show how in a special case it is equivalent to known unbiased and efficient estimators in the statistical literature. Two simulations are presented that demonstrate the precision of the approximation and implement the method to estimate parameters in a multilevel autoregressive framework.

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 On the Interpretation of Parameters in Multivariate Multilevel Models Across Different Combinations of Model Specification and Estimation

2019 ◽  
Vol 2 (3) ◽  
pp. 288-311 ◽  
Author(s):  
Lesa Hoffman
Keyword(s):  
Latent Variables ◽  
Structural Equation ◽  
Multilevel Models ◽  
Structural Equation Models ◽  
Simulated Data ◽  
Recent Change ◽  
Model Specification ◽  
Predictor Variables ◽  
Random Slopes ◽  
Multivariate Multilevel Models

The increasing availability of software with which to estimate multivariate multilevel models (also called multilevel structural equation models) makes it easier than ever before to leverage these powerful techniques to answer research questions at multiple levels of analysis simultaneously. However, interpretation can be tricky given that different choices for centering model predictors can lead to different versions of what appear to be the same parameters; this is especially the case when the predictors are latent variables created through model-estimated variance components. A further complication is a recent change to Mplus (Version 8.1), a popular software program for estimating multivariate multilevel models, in which the selection of Bayesian estimation instead of maximum likelihood results in different lower-level predictors when random slopes are requested. This article provides a detailed explication of how the parameters of multilevel models differ as a function of the analyst’s decisions regarding centering and the form of lower-level predictors (i.e., observed or latent), the method of estimation, and the variant of program syntax used. After explaining how different methods of centering lower-level observed predictor variables result in different higher-level effects within univariate multilevel models, this article uses simulated data to demonstrate how these same concepts apply in specifying multivariate multilevel models with latent lower-level predictor variables. Complete data, input, and output files for all of the example models have been made available online to further aid readers in accurately translating these central tenets of multivariate multilevel modeling into practice.

Sign in / Sign up

Export Citation Format

Share Document