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 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 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.

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.

scholarly journals role of perceived satisfaction and the built environment on the frequency of cycle-commuting

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Tomás Echiburú ◽  
Ricardo Hurtubia ◽  
Juan Carlos Muñoz
Keyword(s):  
Built Environment ◽  
Travel Behavior ◽  
Latent Variable ◽  
Structural Equation ◽  
Subjective Experience ◽  
Central Business District ◽  
Variable Model ◽  
Perceived Satisfaction ◽  
New Findings ◽  
The Impact

Understanding how several street attributes influence the frequency of cycle commuting is relevant for policymaking in urban planning. However, to better understand the impact of the built environment on people's choices, we must understand the subjective experience of individuals while cycling. This study examines the relationship between perceived satisfaction and the attributes of the built environment along the route. Data was collected from a survey carried out within one district of Santiago’s central business district (N=2,545). It included socio-demographic information, origin-destination and route, travel behavior habits, and psychometric indicators. Two models were estimated. The first, a satisfaction latent variable model by mode, confirms previous findings in the literature, such as the correlation between cycling and a more enjoyable experience, while adding some new findings. For instance, satisfaction increases with distance and the number of trips per week. The second is a hybrid ordered logit model for cycle commuting frequency that includes satisfaction, through a structural equation, that shows this latent variable plays a significant role in travel behavior. The presence of buses along the route decreases cycling satisfaction and frequency, while the trip length and the availability of cycle paths has the opposite effect for male and female cyclists. These results allow us to understand the main factors that deliver satisfaction to cyclists and therefore induce frequent cycle commuting. Overall, our study provides evidence of the need for policymakers to focus their strategies so as to effectively promote cycling among different types of commuters.

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 A protocol for modelling generalised biological responses using latent variables in structural equation models

One Ecosystem ◽  
2021 ◽  
Vol 6 ◽  
Author(s):  
James Grace ◽  
Magdalena Steiner
Keyword(s):  
Latent Variables ◽  
General Property ◽  
Latent Variable ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Evaluation Process ◽  
Theoretical Development ◽  
Biological Responses ◽  
Internal Integration ◽  
Multiple Species

In this paper, we consider the problem of how to quantitatively characterise the degree to which a study object exhibits a generalised response. By generalised response, we mean a multivariate response where numerous individual properties change in concerted fashion due to some internal integration. In latent variable structural equation modelling (LVSEM), we would typically approach this situation using a latent variable to represent a general property of interest (e.g. performance) and multiple observed indicator variables that reflect the specific features associated with that general property. While ecologists have used LVSEM in a number of cases, there is substantial potential for its wider application. One obstacle is that LV models can be complex and easily over-specified, degrading their value as a means of generalisation. It can also be challenging to diagnose causes of misspecification and understand which model modifications are sensible. In this paper, we present a protocol, consisting of a series of questions, designed to guide the researchers through the evaluation process. These questions address: (1) theoretical development, (2) data requirements, (3) whether responses to perturbation are general, (4) unique reactions by individual measures and (5) how far generality can be extended. For this illustration, we reference a recent study considering the potential consequences of maintaining biodiversity as part of agricultural management on the overall quality of grapes used for wine-making. We extend our presentation to include the complexities that occur when there are multiple species with unique reactions.

scholarly journals Generalized ridge estimators adapted in structural equation models

2020 ◽  
Vol 43 ◽  
pp. e49929
Author(s):  
Gislene Araujo Pereira ◽  
Mariana Resende ◽  
Marcelo Ângelo Cirillo
Keyword(s):  
Latent Variables ◽  
Latent Variable ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Real Data ◽  
Alternative Methods ◽  
Estimation Methods ◽  
Specification Error ◽  
Maximum Likelihood Methods ◽  
Pls Method

Multicollinearity is detected via regression models, where independent variables are strongly correlated. Since they entail linear relations between observed or latent variables, the structural equation models (SEM) are subject to the multicollinearity effect, whose numerous consequences include the singularity between the inverse matrices used in estimation methods. Given to this behavior, it is natural to understand that the suitability of these estimators to structural equation models show the same features, either in the simulation results that validate the estimators in different multicollinearity degrees, or in their application to real data. Due to the multicollinearity overview arose from the fact that the matrices inversion is impracticable, the usage of numerical procedures demanded by the maximum likelihood methods leads to numerical singularity problems. An alternative could be the use of the Partial Least Squares (PLS) method, however, it is demanded that the observed variables are built by assuming a positive correlation with the latent variable. Thus, theoretically, it is expected that the load signals are positive, however, there are no restrictions to these signals in the algorithms used in the PLS method. This fact implies in corrective areas, such as the observed variables removal or new formulations of the theoretical model. In view of this problem, this paper aimed to propose adaptations of six generalized ridge estimators as alternative methods to estimate SEM parameters. The conclusion is that the evaluated estimators presented the same performance in terms of accuracy, precision while considering the scenarios represented by model without specification error and model with specification error, different levels of multicollinearity and sample sizes.

The Impact of Green Attributes From Suppliers on Supply Chain Performance

2019 ◽  
pp. 1216-1232
Author(s):  
Jose Roberto Mendoza Fong ◽  
Jorge Luis García-Alcaraz ◽  
Aidé Aracely Maldonado-Macías ◽  
Cuauhtémoc Sánchez Ramírez ◽  
Valeria Martínez Loya
Keyword(s):  
Latent Variables ◽  
Latent Variable ◽  
Structural Equation ◽  
Manufacturing Industry ◽  
Descriptive Analysis ◽  
Equation Model ◽  
Ciudad Juarez ◽  
Senior Managers ◽  
Positive Effects ◽  
The Impact

Nowadays, green supplier selection (GSS) is one of the most important activities for companies. Therefore, this research aims to demonstrate the relationship that exists between GSS and the marketing benefits of companies. The chapter proposes a structural equation model that integrates three latent variables. The two independent latent variables concern preproduction green attributes and process green attributes, and they are associated with a dependent latent variable: marketing indexes. Thus, three hypotheses are proposed to relate these latent variables. To validate such hypotheses, a survey is administered to 253 middle and senior managers from the manufacturing industry of Ciudad Juárez. Similarly, a descriptive analysis of the sample and the items is carried out. Results show direct and positive effects among the analyzed variables. However, the highest impact is caused by preproduction green attributes over production process green attributes.

A stronger latent‐variable methodology to actual–ideal discrepancy

2008 ◽  
Vol 22 (7) ◽  
pp. 629-654 ◽  
Author(s):  
L. Francesca Scalas ◽  
Herbert W. Marsh
Keyword(s):  
Latent Variable ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Simulated Data ◽  
Self Esteem ◽  
Self Concept ◽  
Multiple Indicators ◽  
Multiple Group ◽  
Gender And Age ◽  
Ideal Discrepancy

We introduce a latent actual–ideal discrepancy (LAID) approach based on structural equation models (SEMs) with multiple indicators and empirically weighted variables. In Study 1, we demonstrate with simulated data, the superiority of a weighted approach to discrepancy in comparison to a classic unweighted one. In Study 2, we evaluate the effects of actual and ideal appearance on physical self‐concept and self‐esteem. Actual appearance contributes positively to physical self‐concept and self‐esteem, whereas ideal appearance contributes negatively. In support of multidimensional perspective, actual‐ and ideal‐appearance effects on self‐esteem are substantially—but not completely—mediated by physical self‐concept. Whereas this pattern of results generalises across gender and age, multiple‐group invariance tests show that the effect of actual appearance on physical self‐concept is larger for women than for men. Copyright © 2008 John Wiley & Sons, Ltd.

scholarly journals A Note on Likelihood Ratio Tests for Models with Latent Variables

Psychometrika ◽  
2020 ◽  
Author(s):  
Yunxiao Chen ◽  
Irini Moustaki ◽  
Haoran Zhang
Keyword(s):  
General Theory ◽  
Likelihood Ratio ◽  
Latent Variables ◽  
Latent Variable ◽  
Structural Equation ◽  
Degrees Of Freedom ◽  
Structural Equation Models ◽  
Regularity Conditions ◽  
Ratio Test ◽  
Nested Models

AbstractThe likelihood ratio test (LRT) is widely used for comparing the relative fit of nested latent variable models. Following Wilks’ theorem, the LRT is conducted by comparing the LRT statistic with its asymptotic distribution under the restricted model, a $$\chi ^2$$ χ 2 distribution with degrees of freedom equal to the difference in the number of free parameters between the two nested models under comparison. For models with latent variables such as factor analysis, structural equation models and random effects models, however, it is often found that the $$\chi ^2$$ χ 2 approximation does not hold. In this note, we show how the regularity conditions of Wilks’ theorem may be violated using three examples of models with latent variables. In addition, a more general theory for LRT is given that provides the correct asymptotic theory for these LRTs. This general theory was first established in Chernoff (J R Stat Soc Ser B (Methodol) 45:404–413, 1954) and discussed in both van der Vaart (Asymptotic statistics, Cambridge, Cambridge University Press, 2000) and Drton (Ann Stat 37:979–1012, 2009), but it does not seem to have received enough attention. We illustrate this general theory with the three examples.

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