Introduction and Brief History of Structural Equation Modeling for Health and Medical Research

2021 ◽  
pp. 3-16
Author(s):  
Douglas D. Gunzler ◽  
Adam T. Perzynski ◽  
Adam C. Carle
Keyword(s):  
Structural Equation Modeling ◽  
Medical Research ◽  
Structural Equation ◽  
Equation Modeling ◽  
History Of

scholarly journals A 'Weight of Evidence' approach to evaluating structural equation models

One Ecosystem ◽  
2020 ◽  
Vol 5 ◽  
Author(s):  
James Grace
Keyword(s):  
Structural Equation Modeling ◽  
Model Selection ◽  
Model Evaluation ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Weight Of Evidence ◽  
Equation Modeling ◽  
Future Studies ◽  
Current State ◽  
History Of

It is possible that model selection has been the most researched and most discussed topic in the history of both statistics and structural equation modeling (SEM). The reason for this is because selecting one model for interpretive use from amongst many possible models is both essential and difficult. The published protocols and advice for model evaluation and selection in SEM studies are complex and difficult to integrate with current approaches used in biology. Opposition to the use of p-values and decision thresholds has been voiced by the statistics community, yet certain phases of model evaluation have been historically tied to reliance on p-values. In this paper, I outline an approach to model evaluation, comparison and selection based on a weight-of-evidence paradigm. The details and proposed sequence of steps are illustrated using a real-world example. At the end of the paper, I briefly discuss the current state of knowledge and a possible direction for future studies.

scholarly journals The Construction of Patient Loyalty Model Using Bayesian Structural Equation Modeling Approach

CAUCHY ◽  
2018 ◽  
Vol 5 (2) ◽  
pp. 73
Author(s):  
Astari Rahmadita ◽  
Ferra Yanuar ◽  
Dodi Devianto
Keyword(s):  
Patient Satisfaction ◽  
Structural Equation Modeling ◽  
Structural Equation ◽  
Equation Modeling ◽  
Consistency Test ◽  
Equation Modelling ◽  
Patient Loyalty ◽  
History Of ◽  
Bayesian Structural Equation Modeling

<pre>The information on the health status of an individual is often gathered based on a health survey. Patient assessment on the quality of hospital services is important as a reference in improving the service so that it can increase a patient satisfaction and patient loyalty. The concepts of health service are often involve multivariate factors with multidimensional sructure of corresponding factors. One of the methods that can be used to model such these variables is SEM (Structural Equation Modeling). Structural Equation Modelling (SEM) is a multivariate method that incorporates ideas from regression, path-analysis and factor analysis. A Bayesian approach to SEM may enable models that reflect hypotheses based on complex theory. Bayesian SEM is used to construct the model for describing the patient loyalty at <em>Puskesmas</em> in Padang City. The convergence test with the history of trace plot, density plot and the model consistency test were performed with three different prior types. Based on Bayesian SEM approach, it is found that the quality of service and patient satisfaction significantly related to the patient loyalty.</pre>

scholarly journals Investigation of the Psycho-Social Factors Affecting the Adoption of a Healthy Nutrition Style in Women with a History of Gestational Diabetes Using Structural Equation Modeling

2020 ◽  
Keyword(s):  
Structural Equation Modeling ◽  
Gestational Diabetes ◽  
Social Factors ◽  
Self Efficacy ◽  
Structural Equation ◽  
Educational Interventions ◽  
Equation Modeling ◽  
Mazandaran Province ◽  
Healthy Nutrition ◽  
History Of

Background and aims: Women with a history of gestational diabetes mellitus are the main high-risk group for type 2 diabetes; however, a healthy nutrition style can reduce the risk of developing diabetes in this group. The present study aimed to determine the psycho-social factors that affect the adoption of a healthy nutrition style in women with a history of gestational diabetes. Materials and Methods: This cross-sectional study was conducted based on the Health Action Process Approach (HAPA) in the west of Mazandaran province, Iran in 2019. A total of 165 women with a history of gestational diabetes in a recent pregnancy were selected using the convenience sampling method. The required data were collected using a demographic characteristic form, a healthy nutrition style questionnaire, and a researcher-made questionnaire based on HAPA model constructs. Moreover, structural equation modeling was used for data analysis. Results: Based on the results, the data were fit to the model (Tucker–Lewis index=0.924, comparative fit index=0.928, root mean square error of approximation=0.045, χ2/degrees of freedom=1.332). The model constructs predicted 23% and 51% of intention variance and nutrition style variance, respectively. Action self-efficacy and risk perception were the most important predictors of intention. In addition, planning and recovery self-efficacy significantly predicted a healthy nutrition style. Conclusion: As the first step, using the HAPA for the prediction of the nutrition style of women with a history of gestational diabetes was confirmed. Therefore, this model can be used to design educational interventions to prevent diabetes.

Social Capital and Value Creation: A Replication of ‘The Role of Intrafirm Networks’ by Wenpin Tsai and Sumantra Ghoshal

2013 ◽  
Vol 2 (2) ◽  
pp. 106 ◽  
Author(s):  
Monica Lester
Keyword(s):  
Social Capital ◽  
Structural Equation Modeling ◽  
Value Creation ◽  
Structural Equation ◽  
Theoretical Work ◽  
Equation Modeling ◽  
Business Research ◽  
History Of ◽  
The One

Social sciences researchers commend the scientists in the field of natural science for their history of replication and reproduction of scientific research. Such advocates for replication warn that business research is frequently built on a foundation that is ever evolving and necessitates the replicating of theoretical work. Following this logic, this paper is a replication of the celebrated 1998 article by Tsai and Ghoshal, Social capital and value creation: The role of intrafirm networks. Replication was conducted utilizing Structural Equation Modeling. The data was collected by the original researchers through a survey administered by mail. The survey comprised questions rated using a Likert scale. Findings mostly support Tsai and Ghoshal’s results with the exception of the relationships among constructs measuring trustworthiness, resource combination and sharing, and product innovation. Utilizing the before-mentioned constructs and the same analysis as Tsai and Ghoshal--structural equation modeling (SEM); the replicated model presented in this paper shows a non-recursive relationship versus Tsai and Ghoshal’s recursive model. All in all, we contend that the replicated model presented in this paper agrees with current literature and is a more comprehensive model than the one offered by Tsai and Ghoshal.

Assessing the Relationships Between Past Victimization, Perceived Risk of Future Victimization, and Controversial Criminal Justice Policies Using Structural Equation Modeling

2021 ◽  
pp. 088740342110182
Author(s):  
Matthew J. Dolliver ◽  
Jennifer L. Kenney ◽  
Lesley Williams Reid
Keyword(s):  
Structural Equation Modeling ◽  
Criminal Justice ◽  
Latent Variables ◽  
Perceived Risk ◽  
Structural Equation ◽  
Strong Relationship ◽  
Equation Modeling ◽  
Future Research ◽  
History Of ◽  
Three Strikes

Several decades of research show a strong relationship between past victimization and perceived risk of future victimization. Yet, few studies have explored the potential connection to individuals’ support for criminal justice policies. The purpose of this study is to better understand the relationships between past victimization, perception of risk for future victimization, and support for several criminal justice policies (e.g., stand your ground, open carry, three strikes, and the death penalty). Through structural equation modeling, the researchers examined relationships between these latent variables. Having both a history of victimization and a belief in the risk of future victimization increased one’s support for punitive and self-protective policies. Implications for future research and potential policies and services for victims/survivors are discussed.

Testing the Assumption of Uncorrelated Errors for Short Scales by Means of Structural Equation Modeling

2014 ◽  
Vol 35 (4) ◽  
pp. 201-211 ◽  
Author(s):  
André Beauducel ◽  
Anja Leue
Keyword(s):  
Structural Equation Modeling ◽  
Negative Correlation ◽  
Structural Equation ◽  
Error Score ◽  
Classical Test Theory ◽  
Test Theory ◽  
Equation Modeling ◽  
Eysenck Personality Questionnaire ◽  
Personality Questionnaire ◽  
True Score

It is shown that a minimal assumption should be added to the assumptions of Classical Test Theory (CTT) in order to have positive inter-item correlations, which are regarded as a basis for the aggregation of items. Moreover, it is shown that the assumption of zero correlations between the error score estimates is substantially violated in the population of individuals when the number of items is small. Instead, a negative correlation between error score estimates occurs. The reason for the negative correlation is that the error score estimates for different items of a scale are based on insufficient true score estimates when the number of items is small. A test of the assumption of uncorrelated error score estimates by means of structural equation modeling (SEM) is proposed that takes this effect into account. The SEM-based procedure is demonstrated by means of empirical examples based on the Edinburgh Handedness Inventory and the Eysenck Personality Questionnaire-Revised.

The Measurement Invariance of the Short Dark Triad

2020 ◽  
Vol 41 (4) ◽  
pp. 207-218
Author(s):  
Mihaela Grigoraș ◽  
Andreea Butucescu ◽  
Amalia Miulescu ◽  
Cristian Opariuc-Dan ◽  
Dragoș Iliescu
Keyword(s):  
Structural Equation Modeling ◽  
Item Response ◽  
Structural Equation ◽  
Dark Triad ◽  
Theory And Practice ◽  
Equation Modeling ◽  
Scale Level ◽  
High Stake ◽  
Differential Functioning ◽  
Item Functioning

Abstract. Given the fact that most of the dark personality measures are developed based on data collected in low-stake settings, the present study addresses the appropriateness of their use in high-stake contexts. Specifically, we examined item- and scale-level differential functioning of the Short Dark Triad (SD3; Paulhus & Jones, 2011 ) measure across testing contexts. The Short Dark Triad was administered to applicant ( N = 457) and non-applicant ( N = 592) samples. Item- and scale-level invariances were tested using an Item Response Theory (IRT)-based approach and a Structural Equation Modeling (SEM) approach, respectively. Results show that more than half of the SD3 items were flagged for Differential Item Functioning (DIF), and Exploratory Structural Equation Modeling (ESEM) results supported configural, but not metric invariance. Implications for theory and practice are discussed.

Childhood Intelligence Predicts Adult Trait Openness

2016 ◽  
Vol 37 (2) ◽  
pp. 105-111 ◽  
Author(s):  
Adrian Furnham ◽  
Helen Cheng
Keyword(s):  
Structural Equation Modeling ◽  
Structural Equation ◽  
Social Background ◽  
Study Data ◽  
Equation Modeling ◽  
Direct Effects ◽  
Data Set ◽  
National Child Development Study ◽  
Nationally Representative ◽  
The Uk

Abstract. This study used a longitudinal data set of 5,672 adults followed for 50 years to determine the factors that influence adult trait Openness-to-Experience. In a large, nationally representative sample in the UK (the National Child Development Study), data were collected at birth, in childhood (age 11), adolescence (age 16), and adulthood (ages 33, 42, and 50) to examine the effects of family social background, childhood intelligence, school motivation during adolescence, education, and occupation on the personality trait Openness assessed at age 50 years. Structural equation modeling showed that parental social status, childhood intelligence, school motivation, education, and occupation all had modest, but direct, effects on trait Openness, among which childhood intelligence was the strongest predictor. Gender was not significantly associated with trait Openness. Limitations and implications of the study are discussed.

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