Body size as a latent variable in a structural equation model: thermal acclimation and energetics of the leaf-eared mouse

A Monte Carlo simulation was performed for estimating and testing hypotheses of three-way interaction effect in latent variable regression models. A considerable amount of research has been done on estimation of simple interaction and quadratic effect in nonlinear structural equation. The present study extended to three-way continuous latent interaction in structural equation model. The latent moderated structural equation (LMS) approach was used to estimate the parameters of the three-way interaction in structural equation model and investigate the properties of the method under different conditions though simulations. The approach showed least bias, standard error,and root mean square error as indicator reliability and sample size increased. The power to detect interaction effect and type I error control were also manipulated showing that power increased as interaction effect size, sample size and latent covariance increased.

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Assessment of Body Size by Structural Equation Model Using Anthropometric Traits of Fishermen Community: A Methodological Approach

International Journal of Human Anatomy ◽

10.14302/issn.2577-2279.ijha-20-3591 ◽

2020 ◽

Vol 2 (3) ◽

pp. 10-22

Author(s):

Baidyanath Pal

Keyword(s):

Body Size ◽

Structural Equation Model ◽

Latent Variables ◽

Structural Equation ◽

Methodological Approach ◽

Equation Model ◽

Size Category ◽

Model Estimate ◽

Composite Body ◽

Body Sizes

Objectives Aim of the study was to develop a ‘composite body size score’ (CBSS) using anthropometric traits to estimate body size and to assess the nutritional status of each study individual on the basis of CBSS. Materials and Methods Data on seventeen anthropometric traits were collected from 710 individuals (Male, Female) from fishermen community inhabiting coastal villages of West Bengal, India. For estimating body sizes, Structural Equation Model (SEM) was constructed with Path Analysis (PA). Later, second order Confirmatory Factor Analysis (CFA) was applied on SEM to determine CBSS. It was hypothesized in the models that CBSS is composed with three sets of latent variables viz., linear, circular and skinfold, constructed from anthropometric traits. Applying new derived optimal cut off points of CBSS was used to determine lean, normal and robust body sizes. Individuals with negative values of CBSS were categorised as lean body size,. Positive values of CBSS were categorised into two categories- normal and robust body size. Results On the basis of CBSS, result showed that 50.6%, 48.8% and 0.6% of the individuals were categorised under lean, normal and robust body size respectively. Females showed relatively higher percent of lean body size i.e. under nutrition (73.8%) compared to males (26.2%). Conclusion The hypothesized model estimate more accurate composite body size score, based on anthropometric traits. All the traits are highly significant on the model. The lean body size category can be use in predicting ‘Undernutrition’.

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How and how much does expert error matter? Implications for quantitative peace research

Journal of Peace Research ◽

10.1177/0022343320959121 ◽

2020 ◽

Vol 57 (6) ◽

pp. 692-700 ◽

Cited By ~ 1

Author(s):

Kyle L Marquardt

Keyword(s):

Measurement Error ◽

Multiple Imputation ◽

Structural Equation Model ◽

Latent Variable ◽

Structural Equation ◽

Equation Model ◽

Latent Variable Model ◽

Point Estimate ◽

Variable Model ◽

Peace Research

Expert-coded datasets provide scholars with otherwise unavailable data on important concepts. However, expert coders vary in their reliability and scale perception, potentially resulting in substantial measurement error. These concerns are acute in expert coding of key concepts for peace research. Here I examine (1) the implications of these concerns for applied statistical analyses, and (2) the degree to which different modeling strategies ameliorate them. Specifically, I simulate expert-coded country-year data with different forms of error and then regress civil conflict onset on these data, using five different modeling strategies. Three of these strategies involve regressing conflict onset on point estimate aggregations of the simulated data: the mean and median over expert codings, and the posterior median from a latent variable model. The remaining two strategies incorporate measurement error from the latent variable model into the regression process by using multiple imputation and a structural equation model. Analyses indicate that expert-coded data are relatively robust: across simulations, almost all modeling strategies yield regression results roughly in line with the assumed true relationship between the expert-coded concept and outcome. However, the introduction of measurement error to expert-coded data generally results in attenuation of the estimated relationship between the concept and conflict onset. The level of attenuation varies across modeling strategies: a structural equation model is the most consistently robust estimation technique, while the median over expert codings and multiple imputation are the least robust.

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Using multiple biomarkers and determinants to obtain a better measurement of oxidative stress: a latent variable structural equation model approach

Biomarkers ◽

10.1080/1354750x.2017.1306752 ◽

2017 ◽

Vol 22 (6) ◽

pp. 517-524 ◽

Cited By ~ 7

Author(s):

Ronald C. Eldridge ◽

W. Dana Flanders ◽

Roberd M. Bostick ◽

Veronika Fedirko ◽

Myron Gross ◽

...

Keyword(s):

Oxidative Stress ◽

Structural Equation Model ◽

Latent Variable ◽

Structural Equation ◽

Equation Model ◽

Multiple Biomarkers ◽

Model Approach

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ANALISIS KEKAMBUHAN ORANG DENGAN SKIZOFRENIA MENGGUNAKAN METODE PARTIAL LEAST SQUARE STRUCTURAL EQUATION MODEL

E-Jurnal Matematika ◽

10.24843/mtk.2019.v08.i03.p257 ◽

2019 ◽

Vol 8 (3) ◽

pp. 222

Author(s):

IRA INDRIYANTI ◽

G.K. GANDHIADI ◽

MADE SUSILAWATI

Keyword(s):

Family Support ◽

Structural Equation Model ◽

Latent Variables ◽

Latent Variable ◽

Structural Equation ◽

Structural Model ◽

Medication Compliance ◽

Equation Model ◽

Partial Least Square ◽

Least Square

Schizophrenia is a psychotic disorder characterized by major disorders in the mind and emotions. People with schizophrenia (ODS) can experience recurrence if they do not receive proper care. The latent variable used in this study was ODS reccurence. One method that can determine the relationship between latent variables and latent variables with the indicator is the partial least square structural equation model (PLS-SEM). This study was conducted to see how the structural model of ODS recurrence data and to know the factors that most influence ODS recurrence. The results of this study concluded that the resulting model was good enough with a large R-square value of 0.8577, but not all variables used in this study had a significant effect on ODS recurrence. ODS recurrence is significantly influenced by family support and community social support variables. While medication compliance and physician control regularity will not have a significant effect without family support. The worse treatment of families and communities around ODS recurrence will occur more often.

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Empirical Social-Ecological System Analysis: From Theoretical Framework to Latent Variable Structural Equation Model

Environmental Management ◽

10.1007/s00267-008-9172-9 ◽

2008 ◽

Vol 42 (6) ◽

pp. 1077-1090 ◽

Cited By ~ 14

Author(s):

Stanley Tanyi Asah

Keyword(s):

Structural Equation Model ◽

Latent Variable ◽

Structural Equation ◽

System Analysis ◽

Theoretical Framework ◽

Equation Model ◽

Ecological System ◽

Social Ecological

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How motivations and satisfaction influence wine tourists’ loyalty? An analysis of the Italian case

International Journal of Culture Tourism and Hospitality Research ◽

10.1108/ijcthr-04-2018-0054 ◽

2019 ◽

Vol 13 (1) ◽

pp. 55-69 ◽

Cited By ~ 5

Author(s):

Maria Giovanna Brandano ◽

Linda Osti ◽

Manuela Pulina

Keyword(s):

Structural Equation Model ◽

Latent Variable ◽

Structural Equation ◽

Design Methodology ◽

Equation Model ◽

Content Type ◽

Behavioral Loyalty ◽

Italian Case ◽

The Empirical Analysis ◽

Satisfaction Relationship

Purpose The purpose of this paper is to assess the “motivation-satisfaction-loyalty” framework. Through a structural equation model (SEM), it is possible to disentangle attitudinal and behavioral loyalty as a multifaceted latent variable. Design/methodology/approach The empirical analysis is based on data collected in wineries located in two important wine destinations: Trentino and South Tyrol (Italy). Notably, the motivation–satisfaction relationship is confirmed, and the SEM has also assessed the importance of winery services in affecting loyalty, expressed in terms of “visit other cellars,” “repeat a wine vacation” and “recommend wine routes.” Findings Destination managers should consider the wine-related “relaxation” as the main push motivation, while the interactions experience are important pull motivations to drive wine tourists’ satisfaction. Nevertheless, the findings reveal that more proactive policies are needed to enhance local wines loyalty. Originality/value The novelty of this study is to explore loyalty. In this respect, a multifaceted latent variable is expressed as follows: “buy local wines,” “visit other cellars,” “repeat a wine vacation” as behavioral attitudinal stated loyalty and “recommend wine routes” as attitudinal stated loyalty.

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