scholarly journals The Impact of Scaling Methods on the Properties and Interpretation of Parameter Estimates in Structural Equation Models with Latent Variables

2019 ◽  
Author(s):  
Eric Klopp ◽  
Stefan Klößner
Keyword(s):  
Latent Variables ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Wald Test ◽  
Parameter Estimates ◽  
Population Parameters ◽  
Scaling Method ◽  
Estimated Parameters ◽  
Scaling Methods ◽  
The Impact

Latent variables in structural equation models do not have an observable scale. Hence researchers resort to scaling methods, such as fixed marker, effects coding, or fixed factor, to assign scales to the latent variables. The use of such procedures results in numerically different estimates, in spite of a single underlying population model. In this paper, we provide a framework which not only allows for a translation between estimates obtained under different scaling methods, but also helps to explore the relation between the underlying population parameters and their estimates, thus providing a basis for the interpretation of estimated parameters. Addition- ally, the framework proves useful for demonstrating that the choice of scaling method affects the power of the Wald test for testing parameters’ significance.

scholarly journals Online Study: Understanding the Laws of Self-Regulated Study

2021 ◽  
Author(s):  
Xin Rao ◽  
Li Luo ◽  
Qiaoli Su ◽  
Xingyue Wang
Keyword(s):  
Latent Variables ◽  
Teacher Support ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Medical Training ◽  
Internal Motivation ◽  
Self Regulated Learning ◽  
Online Study ◽  
Regulated Learning ◽  
The Impact

Abstract Background:The sudden COVID-19 outbreak has posed challenges to the normal development of continuing education for general practitioners. Consequently, an online medical training program for family doctors has emerged. Online study helps us better understand the laws of self-regulated learning because we can track the process and outcome of family physicians and compare it to that of face-to-face training programs. The study track the GPCC online program to reveal this principle.Results:By recording learners’ study behaviors and explore the law of learning progress and analyse the impact of latent variables on learning through structural equation models,the study find that the the impact of teacher support and supervision and of internal motivation on learning input and the influence of teacher support and supervision on internal motivation can be researched through online study.Conclusions:Online study helps us better understand the laws of self-regulated learning. It helps to better understand the impact of teacher support, supervision, and internal motivation on learning input , as well as the influence of teacher support and supervision on internal motivation. Examining online study can also help in making effective use of the self-education characteristics of internal motivation and cultivate the ability of independent thinking and learning self-discipline .

scholarly journals Optimizing Detection of True Within-Person Effects for Intensive Measurement Designs: A Comparison of Multilevel SEM and Unit-Weighted Scale Scores

2020 ◽  
Author(s):  
Jonathan Rush ◽  
Philippe Rast ◽  
Scott Michael Hofer
Keyword(s):  
Latent Variables ◽  
Latent Variable ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Simulated Data ◽  
Variable Model ◽  
Alternative Approach ◽  
Scale Scores ◽  
Weighted Model ◽  
The Impact

Intensive repeated measurement designs are frequently used to investigate within-person variation over relatively brief intervals of time. The majority of research utilizing these designs rely on unit-weighted scale scores, which assume that the constructs are measured without error. An alternative approach makes use of multilevel structural equation models (MSEM), which permit the specification of latent variables at both within-person and between-person levels. These models disaggregate measurement error from systematic variance, which should result in less biased within-person estimates and larger effect sizes. Differences in power, precision, and bias between multilevel unit-weighted and MSEM models were compared through a series of Monte Carlo simulations. Results based on simulated data revealed that precision was consistently poorer in the MSEM models than the unit-weighted models, particularly when reliability was low. However, the degree of bias was considerably greater in the unit-weighted model than the latent variable model. Although the unit-weighted model consistently underestimated the effect of a covariate, it generally had similar power relative to the MSEM model due to the greater precision. Considerations for scale development and the impact of within-person reliability are highlighted.

scholarly journals A Tutorial on Testing the Equality of Standardized Regression Coefficients in Structural Equation Models using Wald Tests with lavaan

2019 ◽  
Author(s):  
Eric Klopp
Keyword(s):  
Social Sciences ◽  
Latent Variables ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Wald Test ◽  
Regression Coefficients ◽  
Explanatory Variables ◽  
Wald Tests ◽  
Regression Parameters ◽  
Standardized Regression Coefficients

Comparing the effects of two or more explanatory variables on a dependent variable in structural equation models, with either manifest or latent variables, may be hampered by the arbitrary metrics which are common in social sciences and psychology. A possible way to compare the effects is the comparison of standardized regression coefficients by means of the Wald test. In this tutorial, we show how a typical textbook display of the Wald test can be used to derive a calculation for standardized regression coefficients. Moreover, we demonstrate how this can be implemented in R using the lavaan package. Additionally, we provide a convenience function that allows doing a Wald test by only setting up equality constraints. We also discuss theoretical aspects and implications when hypotheses about the equality of standardized regression parameters in structural equation models are tested.

scholarly journals Croon’s Bias-Corrected Factor Score Path Analysis for Small- to Moderate-Sample Multilevel Structural Equation Models

2019 ◽  
Vol 24 (1) ◽  
pp. 55-77 ◽  
Author(s):  
Benjamin Kelcey ◽  
Kyle Cox ◽  
Nianbo Dong
Keyword(s):  
Maximum Likelihood ◽  
Path Analysis ◽  
Latent Variables ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Factor Score ◽  
Likelihood Estimation ◽  
Bias Error ◽  
Parameter Estimates ◽  
Multilevel Structural Equation

Maximum likelihood estimation of multilevel structural equation model (MLSEM) parameters is a preferred approach to probe theories involving latent variables in multilevel settings. Although maximum likelihood has many desirable properties, a major limitation is that it often fails to converge and can incur significant bias when implemented in studies with a small to moderate multilevel sample (e.g., fewer than 100 organizations with 10 or less individuals/organization). To address similar limitations in single-level SEM, literature has developed Croon’s bias-corrected factor score path analysis estimator that converges more regularly than maximum likelihood and delivers less biased parameter estimates with small to moderate sample sizes. We derive extensions to this framework for MLSEMs and probe the degree to which the estimator retains these advantages with small to moderate multilevel samples. The estimator emerges as a useful alternative or complement to maximum likelihood because it often outperforms maximum likelihood in small to moderate multilevel samples in terms of convergence, bias, error variance, and power. The proposed estimator is implemented as a function in R using lavaan and is illustrated using a multilevel mediation example.

An International Marketing Application of Outlier Analysis for Structural Equations: A Methodological Note

1995 ◽  
Vol 3 (1) ◽  
pp. 45-62 ◽  
Author(s):  
Michael R. Mullen ◽  
George R. Milne ◽  
Patricia M. Doney
Keyword(s):  
Latent Variables ◽  
Structural Equation ◽  
Structural Equation Models ◽  
International Marketing ◽  
Marketing Research ◽  
Structural Equations ◽  
Equation Modeling ◽  
Parameter Estimates ◽  
Outlier Analysis ◽  
Multivariate Outliers

Structural equation modeling with latent variables is being used more frequently in international marketing research. However, the authors argue that it is hazardous to conduct cross-national marketing research without evaluating the potential influential effects of multivariate outliers, which are observations distinct from the majority of cases. Because the presence of outliers in the data can significantly bias a study's findings, this is an important issue in international research. To improve upon current practice, the authors recommend using a two-step approach for detecting and analyzing multivariate outliers in structural equation models. The first step is to detect outliers using three techniques: Bollen's a ii (1987), Mahalanobis Distance, and the Observed Covariance Ratio, a new technique developed by the authors. The second step is to determine whether outliers unduly influence study findings. This is accomplished by estimating statistical models with and without outliers and comparing results. The authors demonstrate the two-step approach using data from a previous international marketing study. Several outliers were found to influence model fit, R 2, and the size and direction of parameter estimates. The study highlights the importance of multivariate outlier analysis to international researchers.

A Cautionary Note on Incremental Fit Indices Reported by LISREL

Methodology ◽  
2005 ◽  
Vol 1 (2) ◽  
pp. 81-85 ◽  
Author(s):  
Stefan C. Schmukle ◽  
Jochen Hardt
Keyword(s):  
Factor Analysis ◽  
Maximum Likelihood ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Null Model ◽  
Likelihood Estimation ◽  
Parameter Estimates ◽  
Fit Indices ◽  
Cautionary Note ◽  
Confirmatory Factor

Abstract. Incremental fit indices (IFIs) are regularly used when assessing the fit of structural equation models. IFIs are based on the comparison of the fit of a target model with that of a null model. For maximum-likelihood estimation, IFIs are usually computed by using the χ2 statistics of the maximum-likelihood fitting function (ML-χ2). However, LISREL recently changed the computation of IFIs. Since version 8.52, IFIs reported by LISREL are based on the χ2 statistics of the reweighted least squares fitting function (RLS-χ2). Although both functions lead to the same maximum-likelihood parameter estimates, the two χ2 statistics reach different values. Because these differences are especially large for null models, IFIs are affected in particular. Consequently, RLS-χ2 based IFIs in combination with conventional cut-off values explored for ML-χ2 based IFIs may lead to a wrong acceptance of models. We demonstrate this point by a confirmatory factor analysis in a sample of 2449 subjects.

scholarly journals Assessing the Impact of Multi-Morbidity and Related Constructs on Patient Reported Safety in Primary Care: Generalized Structural Equation Modelling of Observational Data

2021 ◽  
Vol 10 (8) ◽  
pp. 1782
Author(s):  
Ignacio Ricci-Cabello ◽  
Aina María Yañez-Juan ◽  
Maria A. Fiol-deRoque ◽  
Alfonso Leiva ◽  
Joan Llobera Canaves ◽  
...  
Keyword(s):  
Primary Care ◽  
Patient Safety ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Cross Sectional ◽  
Morbidity Burden ◽  
Patient Reported ◽  
Patient Complexity ◽  
Complex Relationships ◽  
The Impact

We aimed to examine the complex relationships between patient safety processes and outcomes and multimorbidity using a comprehensive set of constructs: multimorbidity, polypharmacy, discordant comorbidity (diseases not sharing either pathogenesis nor management), morbidity burden and patient complexity. We used cross-sectional data from 4782 patients in 69 primary care centres in Spain. We constructed generalized structural equation models to examine the associations between multimorbidity constructs and patient-reported patient safety (PREOS-PC questionnaire). These associations were modelled through direct and indirect (mediated by increased interactions with healthcare) pathways. For women, a consistent association between higher levels of the multimorbidity constructs and lower levels of patient safety was observed via either pathway. The findings for men replicated these observations for polypharmacy, morbidity burden and patient complexity via indirect pathways. However, direct pathways showed unexpected associations between higher levels of multimorbidity and better safety. The consistent association between multimorbidity constructs and worse patient safety among women makes it advisable to target this group for the development of interventions, with particular attention to the role of comorbidity discordance. Further research, particularly qualitative research, is needed for clarifying the complex associations among men.

scholarly journals COVID-19 and Beyond: Employee Perceptions of the Efficiency of Teleworking and Its Cybersecurity Implications

2021 ◽  
Vol 13 (12) ◽  
pp. 6750
Author(s):  
Andreja Mihailović ◽  
Julija Cerović Smolović ◽  
Ivan Radević ◽  
Neli Rašović ◽  
Nikola Martinović
Keyword(s):  
Information Security ◽  
Latent Variables ◽  
Structural Equation ◽  
Relevant Literature ◽  
Cyber Attacks ◽  
Employee Perceptions ◽  
Organizational Efficiency ◽  
Digital Information ◽  
Starting Point ◽  
The Impact

The main idea of this research is to examine how teleworking has affected employee perceptions of organizational efficiency and cybersecurity before and during the COVID-19 pandemic. The research is based on an analytical and empirical approach. The starting point of the research is a critical and comprehensive analysis of the relevant literature regarding the efficiency of organizations due to teleworking, digital information security, and cyber risk management. The quantitative approach is based on designing a structural equation model (SEM) on a sample of 1101 respondents from the category of employees in Montenegro. Within the model, we examine simultaneously the impact of their perceptions on the risks of teleworking, changes in cyber-attacks during teleworking, organizations’ capacity to respond to cyber-attacks, key challenges in achieving an adequate response to cyber-attacks, as well as perceptions of key challenges related to cybersecurity. The empirical aspects of our study involve constructing latent variables that correspond to different elements of employee perception; namely, their perception of organizational efficiency and the extent to which the digital information security of their organizations has been threatened during teleworking during the pandemic.

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