Structural Equation Modeling for Health and Medicine

One of the major challenges for epidemiologists is to understand causal relationships between risk factors and health outcomes by analysing data from observational studies. Epidemiologists know too well that correlations, such as those in regression analysis, rarely mean causation, and it would be very desirable if there is a methodology for observational studies, analogous to randomization in experimental studies, which can discover causes and effects amongst variables or at least confirm or refute the proposed causal relationships. Randomized controlled trials (RCTs) are certainly the ‘gold standard’ of establishing causes and effects, but quite often it is either unethical or unfeasible to conduct RCTs to test causal relations in epidemiological research. Epidemiologists need a methodology, which is sort of a combination of the directed acyclic graphs (DAGs) for conceptual construction of causal models and regression analysis for testing those models. It is therefore surprising that structural equation modelling (SEM) has not been so frequently used in epidemiology as in the social sciences, given that both epidemiologists and social scientists want to delineate causes and effects from observational data. The difference between DAGs and path diagrams in SEM is almost trivial to epidemiologists: the path between two variables can only have one direction in DAGs. 1,2 An individual path in SEM is tested in the same way the regression coefficient is in regression analysis, and model fit indices provided by SEM software packages help the analysts to assess the adequacy of the proposed causal model compared with the observed associations in the sample data. 3,4 Why then is SEM still under-utilized in epidemiology? This is a question posed by a commentary 5 in this journal a few years ago. The answers cited included unfamiliar terminology (SEM theory is formulated in Greek), restriction in the assumptions of variables (outcome variables need to be continuous), difficulties in testing interaction and non-linear relationship (it can be quite tedious to set up SEM models to do these), and equivalent models (two different causal models imply the same correlation structure and consequently, it is impossible to tell which is better). 6 Recent advances in SEM theory and software development has resolved some of these issues: new estimation methods do not require the strict assumption of multivariate normality, 7 and the outcome variables can be binary, ordinal or counts. 7–9 The basic rationale behind SEM is rather simple: multiple linear equations are used to specify causal relationship between variables some of which are manifest variables (i.e. observed and collected by the researchers), whilst others are latent variables (i.e. derived from the observed variables by specifying their relations using equations), such as those in factor analysis. The multiple equations in each causal model entail a certain structure of correlational relationships between observed variables which is usually given as a correlation (or covariance) matrix . The estimation procedure is to minimize the difference between and the observed correlation/covariance matrix S formulated by a likelihood function. The 2 -test is then used to evaluate the difference between these two matrices by taking into account the number of the estimated parameters in the proposed model. When the 2 -value is large (i.e. the difference between the two matrices is large) relative to the model’s degree of freedom, the proposed model is rejected, i.e. something in the causal relationships specified by the proposed model is not quite right and require a second thought. When the 2 -value is small, we fail to reject the model or tentatively accept the model as adequate. Because of the possibility of equivalent models, our model may still be wrong in terms of the causal relations amongst variables but happen to entail the same correlation structure (the same S) of the ‘true’ model. So SEM seems to be an endeavour to search for the truth by approximation, and in this aspect, doing SEM is quite similar to the process of ‘Conjectures and Refutations’ as described by the great philosopher

This article is an attempt to scrutinize the applicability of the widely used statistical technique of Structural Equation Modelling (SEM). SEM is a comprehensive technique to test the model adequacy. SEM is considered as an important advancement in social science research as it combines measurement with substantive theories. It has been observed that many studies pay attention to statistical mechanisation of SEM rather than the assumptions on which it is based. The history of SEM can be traced to Regression Analysis, Path Analysis and Confirmatory Factor Analysis. SEM is popularly applied because of its use in estimating multiple dependence relationships. It is able to measure the unobserved variables, define the model representing the set of relationships and also corrects the measurement error. The technique is commonly applied in disciplines including sociology, psychology and other fields of behavioural science. The availability of various user-friendly software programmes like LISREL, AMOS, EQS, Mx, Mplus and PISTE is an added advantage. However, one should be careful while using SEM for causal inferences. In comparison to other common standard statistical techniques, SEM is based on several assumptions. The technique requires a priori knowledge of all the parameters to be estimated and a substantial amount of data pertaining to covariances, variances and path coefficients. It also requires relationships to be specified in the model. The model inherently assumes temporal precedence and is heavily dependent on researcher’s judgements about exogeneity and directionality. Normality is yet another important assumption of SEM. The mismatch between data characteristics and assumptions imperils inference and accuracy. Like antibiotics are a boon to mankind yet one needs to judiciously use them. Similarly, SEM is a powerful technique however, researchers are suggested to apply cautiously.

The Role of Providers in Mental Health Services Offered to American-Indian Youths

In this article, we illustrate ways in which generalizability theory (G-theory) can be used with continuous latent response variables (CLRVs) to address problems of scale coarseness resulting from categorization errors caused by representing ranges of continuous variables by discrete data points and transformation errors caused by unequal interval widths between those data points. The mechanism to address these problems is applying structural equation modeling (SEM) as a tool in deriving variance components needed to estimate indices of score consistency and validity. Illustrations include quantification of multiple sources of measurement error, use of non-nested and nested designs, derivation of indices of consistency for norm- and criterion-referenced interpretation of scores, estimation of effects when changing measurement procedures and designs, and disattenuation of correlation coefficients for measurement error. These illustrations underscore the effectiveness of G-theory with continuous latent response variables in providing stable indices of reliability and validity that are reasonably independent of the number of original scale points used, unevenness of scale intervals, and average degree of item skewness. We discuss general distinctions in reliability estimation within G-theory, SEM, and classical test theory; make specific recommendations for using G-theory on raw score and CLRV metrics; and provide computer code in an online supplement for doing all key analyses demonstrated in the article using R and Mplus. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Modeling and measuring attributes influencing DevOps implementation in an enterprise using structural equation modeling

Structural Equation Modeling (SEM) is one of the most versatile confirmatory modeling tools for university lecturers, researchers and postgraduate students, and in the areas of studies including social, medical, clinical and health sciences research. We describe in this article a new statistical analysis skill programme known as 'SEM Workshop' which was first time ever conducted in our institution. The importance of SEM analysis is becoming more apparent in the medical and health sciences research. SEM is an important analysis particularly for its use in confirmatory factor analysis (e.g. validation of questionnaire) and path analysis (e.g. infectious disease modelling) (1). This workshop was developed to expose the lecturers and postgraduate students to methods of SEM analysis, to develop essential skills in SEM analysis and to foster discussions among the lecturers with regard to potential study areas using SEM.

目的：隨著休閒及健康概念的興起，自行車已逐漸被推廣為全民運動及一種流行的活動。本研究旨在探討臺北市自行車參與者之休閒態度、休閒效益及幸福感之相關實證研究。方法：本研究以臺北市自行車參與者為研究對象，使用便利抽樣法，共計發出450份問卷，回收有效問卷397份，有效回收率為92.5%。資料分析方法採用經描述性統計、信度分析、驗證性因素分析等，並運用結構方程模式進行各項潛在變項關係之分析。結果：自行車參與者休閒態度顯著正向影響休閒效益與幸福感；而自行車參與者休閒效益顯著正向影響幸福感。結論：本研究主要的貢獻在提出臺北市自行車參與者之休閒態度、休閒效益及幸福感模式，並依據本研究結論提出理論意涵與後續研究做出建議。

Structural Equation Modeling (SEM) is a statistical technique used to build and test the statistical models are usually in the form of causal models. SEM is a combination from factor analysis, path analysis, and regression. This method is a statistical approach that serves to test hypotheses about the relationship between observed variables and latent variables. In this paper, SEM is applied to determine the motivation of the millennial generation for the general election 2019 in Indonesia. Data was obtained by distributing questionnaires online according to procedures which were then analyzed using SEM. Millennial’s motivation is seen from the knowledge of the millennial generation on voting rights commitments in the 2019 general election in Indonesia. Based on the result, millennial generation is committed to using voting rights in the 2019 general election. All indicator variables from this study are significant to the millennial generation’s commitment to use their voting rights

In this research, the objective is to create a performance index for movie theater services. In order to create the index, firstly the conceptual model for movie theater services was created. Secondly, physical evidence, social benefit, customer satisfaction and ambiance are determined as latent variables and verified with Confirmatory Factor Analysis (CFA). The relationships among latent variables are determined using the Structural Equation Model (SEM). Then Entertainment Performance Index is developed and calculated by using weights and scores of latent variables for movie theater services. Entertainment Performance Index is used for determining the level of performance and for proposing suggestions for decreasing the level of service quality gaps in movie theater services sector. In addition, satisfaction levels for different customer groups are compared according to the frequency of benefiting from revenue management applications. The ambiance dimension, which is about feeling yourself in the script and feeling the emotions more intensely in the movie theater atmosphere, was developed in this study.

Some authors affirm that privacy depends on the opportunities offered by the physical environment; the circumstances that favor privacy; and the personal and expectational needs and experiences of the individual. In an attempt to identify the influence of the physical environment of hospital rooms on the perception of privacy, a survey was applied to patients in a public hospital. The sample was made up by 88 female cancer patients aged 19 to 70 years. Three types of rooms were considered: private, semiprivate, and public. A “Hospital Room Privacy Scale” (HRPS) developed especially for this study and made up by 11 questions with ten answer options, ranging fully agree to strongly disagree, was applied. Confirmatory factor analysis was used to validate three factors: 1. Privacy, 2. Intimacy, and 3. Environmental facility; a model of structural equations with suitable fit levels (p=0.08, CFI=0.93, RMSEA=0.05) was obtained, identifying direct and indirect values for the latent variables (privacy, intimacy, environmental facility, and impact of the environment on patient health status and mood), attributive variables (age, education) and the direct variable (density). The results are discussed in accordance with the theoretical principles of privacy and environmental impact on highly sensitive patients.

Background and Aim: This research examined the factors affecting graduate students' behavioral intentions toward using a mobile learning platform. The study focused on several latent variables, including system quality (SQ), service quality (SVQ), information quality (IQ), perceived usefulness (PU), satisfaction (SA), attitude (ATT), and behavioral intention (BI). The goal of the study was to assess the degree to which each of these variables impacts the utilization of the mobile learning platform. Materials and Methods: This research collected survey responses from 500 graduate students at a public university in Hunan, China, regarding their perspectives on using mobile learning platforms. Structural equation modeling (SEM) and confirmatory factor analysis (CFA) were employed to analyze the data. Results: The data analysis results indicated that all hypothesized paths were statistically significant, demonstrating strong direct relationships between the variables in the model. Information quality, in particular, had the most substantial impact on perceived usefulness. These findings highlight the critical role of these factors in enhancing the effective use of mobile learning platforms in higher education, leading to increased student satisfaction and behavioral intentions. The study's strengths include a large sample size and the application of advanced statistical methods like SEM and CFA, offering a thorough evaluation of the model's validity. However, the study is limited by its focus on a single university, which may not capture the diverse experiences of students at other institutions. Moreover, while the study emphasizes key factors influencing mobile learning, it does not explore potential barriers or challenges students might encounter, such as technological issues or varying levels of digital literacy. Future research could address these aspects to gain a more comprehensive understanding of the effectiveness of mobile learning platforms.

Taking Yuhuang Shannan Fund Town as a typical example, this paper established a financial innovation characteristic town social benefit evaluation system through on-site investigation and quantitative analysis. It can be summarized into five major aspects: social and livelihood development, socioeconomic development, ecological environment, infrastructure construction and related system construction. Then we constructed a structural equation model (SEM) for the evaluation of social benefits of towns, and made the assumption that the impact of the five latent variables on the total variable of social benefits is positive. Through the first-order confirmatory factor analysis and the second-order confirmatory factor analysis of the structural equation, it is concluded that the five latent variables have a positively significant impact on the social benefits and have strong internal consistency. According to the degree of influence, effective suggestions are given from private equity and industrial foundation, which provide reference and practical guidance of the construction of financial innovative towns in the future.

This was presented in August 2016 to the EARLI SIG 1 Assessment and Evaluation Summer School in Munich. It provides an introduction to CFA and SEM techniques. It presumes understanding of exploratory factor analysis and multivariate statistical techniques. It extends basic CFA into SEM and multigroup invariance testing. It illustrates procedures and problems that can occur in testing models (e.g., negative error variance, not positive definite covariance matrices) and suggests solutions to overcome these issues. All illustrations are given using AMOS software.

Structural equation modeling (SEM) is becoming the central and arguably most popular analytical tool in social sciences. Many classical and modern statistical techniques such as regression analysis, path analysis, confirmatory factor analysis, and models with both measurement and structural components have been shown to fall under the umbrella of SEM. The flexibility of SEM makes it applicable to many research designs, including experimental and non-experimental data, cross-sectional and longitudinal data, and multiple-group and multilevel problems.Recently, there have been exciting advancements in SEM, from fundamental issues like alternative estimation methods that are robust to often violated assumptions to the expansions of SEM to incorporate complex multilevel and cross-classified data that are common in the social sciences. This Research Topic aims to bring in a collection of SEM papers which not only tackle technical estimation issues, but also examine and demonstrate the applications of SEM to more complex settings. The proposed topics include (but are not limited to): applying robust estimation method, testing interaction effect, examining measurement invariance, and specifying and evaluating models applied to more complex data including meta-analytic data, multilevel and longitudinal data.

This study was aimed to explore the application of Structural Equation Modeling (SEM) in the researches relevant with the field of athletic conducted from 2004 to 2010. Through the analysis and conclusion based on the latent growth curve model (LGM), it was hoped to better comprehend all these papers from 2004 to 2010 with SEM application. It was shown that a total of 192 SEM-applied athletic papers were produced during this certain period of time. There were only six SEM papers in 2004. Within these six years, it was increased to 50 papers a year in 2010. This was a tremendous growth in the quantities of papers SEM-applied in athletic fields, averagely increasing 0.3 paper year by year. SEM-applied papers had exceeding reliability and validity compared to those with conventional statistical methods. Moreover, the estimation of latent variables was the other strength of SEM. Many reference books related to SEM were continuously published. With a rapid increase in the theories underlying SEM, it was proved that SEM is a sophistically-developed statistical methodology. The findings and suggestions of this research can be referenced by other athletic related researches in research design and the application of SEM.