Commentary: Is structural equation modelling a step forward for epidemiologists?

This paper integrates concepts from neurobiology, marketing and musicology to propose a causal model of music appreciation and exploratory listening, using directed acyclic graphs (DAGs) and structural equation models (SEMs). The key concepts are music appreciation (measured on a scale from 1 to 10), evaluations, experiences and the qualities of musical features, which the listeners explore and describe from a first-person perspective. The qualities are understood in terms of a satisfaction or dissatisfaction rating of operational features. The development of the causal model is based on a reiterative methodology involving surveys. Applying the causal model to a large survey of 800 listeners reveals that listeners adopt a slightly different causal pathway for their appreciation of liked versus disliked music. When listeners dislike music, the source of their dissatisfaction is more consistently attributed to the perceived or missed musical qualities rather than to their personal experiences. The iterative methodology and causal modeling offer a foundation for further investigation and refinement in various listening contexts.

Structural equation modeling (SEM) is a very general and flexible multivariate technique that allows relationships among variables to be examined. The roots of SEM are in the social sciences. In writing this textbook, the authors look to make SEM accessible to a wider audience of researchers across many disciplines, addressing issues unique to health and medicine. SEM is often used in practice to model and test hypothesized causal relationships among observed and latent (unobserved) variables, including in analysis across time and groups. It can be viewed as the merging of a conceptual model, path diagram, confirmatory factor analysis, and path analysis. In this textbook the authors also discuss techniques, such as mixture modeling, that expand the capacity of SEM using a combination of both continuous and categorical latent variables. Features: Basic, intermediate, and advanced SEM topics Detailed applications, particularly relevant for health and medical scientists Topics and examples that are pertinent to both new and experienced SEM researchers Substantive issues in health and medicine in the context of SEM Both methodological and applied examples Numerous figures and diagrams to illustrate the examples As SEM experts situated among clinicians and multidisciplinary researchers in medical settings, the authors provide a broad, current, on the ground understanding of the issues faced by clinical and health services researchers and decision scientists. This book gives health and medical researchers the tools to apply SEM approaches to study complex relationships between clinical measurements, individual and community-level characteristics, and patient-reported scales.

Correlation and Regression Analysis

It is common practice for psychologists to specify models with latent variables to represent concepts that are difficult to directly measure. Each latent variable needs a scale, and the most popular method of scaling as well as the default in most structural equation modeling (SEM) software uses a scaling or reference indicator. Much of the time, the choice of which indicator to use for this purpose receives little attention, and many analysts use the first indicator without considering whether there are better choices. When all indicators of the latent variable have essentially the same properties, then the choice matters less. But when this is not true, we could benefit from scaling indicator guidelines. Our article first demonstrates why latent variables need a scale. We then propose a set of criteria and accompanying diagnostic tools that can assist researchers in making informed decisions about scaling indicators. The criteria for a good scaling indicator include high face validity, high correlation with the latent variable, factor complexity of one, no correlated errors, no direct effects with other indicators, a minimal number of significant overidentification equation tests and modification indices, and invariance across groups and time. We demonstrate these criteria and diagnostics using two empirical examples and provide guidance on navigating conflicting results among criteria. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Mediation and conditional process analyses have become popular approaches for examining the mechanisms by which effects operate and the factors that influence them. To estimate mediation models, researchers often augment their structural equation modeling (SEM) analyses with additional regression analyses using the PROCESS macro. This duality is surprising considering that research has long acknowledged the limitations of regression analyses when estimating models with latent variables. In this article, we argue that much of the confusion regarding SEM’s efficacy for mediation analyses results from a singular focus on factor-based methods, and there is no need for a tandem use of SEM and PROCESS. Specifically, we highlight that composite-based SEM methods overcome the limitations of both regression and factor-based SEM analyses when estimating even highly complex mediation models. We further conclude that composite-based SEM methods such as partial least squares (PLS-SEM) are the preferred and superior approach when estimating mediation and conditional process models, and that the PROCESS approach is not needed when mediation is examined with PLS-SEM.

Directed acyclic graphs (DAGs) play a large role in the modern approach to causal inference. DAGs describe the relationship between measurements taken at various discrete times including the effect of interventions. The causal mechanisms, on the other hand, would naturally be assumed to be a continuous process operating over time in a cause–effect fashion. How does such immediate causation, that is causation occurring over very short time intervals, relate to DAGs constructed from discrete observations? We introduce a time-continuous model and simulate discrete observations in order to judge the relationship between the DAG and the immediate causal model. We find that there is no clear relationship; indeed the Bayesian network described by the DAG may not relate to the causal model. Typically, discrete observations of a process will obscure the conditional dependencies that are represented in the underlying mechanistic model of the process. It is therefore doubtful whether DAGs are always suited to describe causal relationships unless time is explicitly considered in the model. We relate the issues to mechanistic modeling by using the concept of local (in)dependence. An example using data from the Swiss HIV Cohort Study is presented.

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The purpose of this study is to analyze the causal relationshipk in the perspective of Total Quality Management, among the education quality factors, which were suggested in the previous researches. Lee had tried to analyze the relationship among education factors, but they did not estimate the education factor using latent variable concept, which is very reasonable and efficient to represent the constructed concepts. So this study attempts to analyze the causal relationship among education quality factors, represented as latent variables used in structural equation modeling (SEM), and compared with each other. In this study, education quality factors were measured by several measures, constructed as several latent variables, and then processed with AMOS, the most efficient statistical package in the SEM area. In order to analyze the causal relationship among the education quality factors constructed as latent variables, this study designed the structural equation model with suggested factors and established several research hypotheses. This study discovered a prominent causality among the education quality factors, such as education leadership, student scholastic performance and satisfaction of education quality, which is similar to that of previous research. This outcome is really a unique Korean syndrome manifest within our educational career orientation.

The article is based on the theory of industry cluster’s location selection and the authors discuss the formation mechanism of convention and convention and exhibition industry cluster by using the structural equation model. In addition, the authors analyze different elements, namely the regional factors which affect convention and exhibition industry cluster’s location selecting, through making reference to Weber's theory of industrial location and neoteric and modern analysis methods of location theory. Moreover, the authors use structural equation model to discuss the causal relations between regional factor, supporting industry factor, infrastructure factor and level of convention and exhibition industry cluster. In the article, the latent variables, manifest variables and their causal relations in the structural equation model are determined. The authors aim to provide references for development route and district layout of convention and exhibition industry cluster.

This study aims to analyze the effect of digital marketing on school performance, the influence of innovation on school performance and the influence of digital marketing on school performance. The analytical method used in this research is structural equation modeling (SEM) to determine the causal relationship between latent variables contained in structural equations. The analysis tool used is with the help of Smart PLS. The method used in this research is quantitative methods, data collection methods by distributing questionnaires to the teacher. The results of the questionnaire returned were 150 respondents. Based on the results of data analysis, it is concluded that innovation has a significant effect on teacher performance, digital marketing has no significant effect on teacher performance, digital marketing has a significant effect on innovation.

This study uses the structural equation model in Nepali stock market, chooses investor sentiment, monetary and macroeconomic factors as latent variables, and selects a few observed variables which can explain the latent variables to study the influence on stock prices. Based on existing empirical research conclusion, influence path diagram is designed and gets its path coefficient and causal path diagram using maximum likelihood estimation. The statistical significance of the results indicated that the causal relationships of Nepal’s stock market as follows can be accepted; firstly, investor sentiment, macroeconomic indicators, and monetary factors have certain influence on stock prices, the investor sentiment has a positive correlation. Secondly, the investor sentiment has the major impact on stock prices; when investor sentiment is more stable and optimistic, stock prices will relatively be better. This research also provides certain reference to investors for rational investment decisions.

This study explored the relationships of senior high school EFL students' motivation, strategy use, and English achievement. The first two attempts of the study were to investigate how well the senior high school EFL students' motivation and strategy use can predict students' English achievement respectively. The third attempt was to explore the causal relationship among senior high school EFL students' motivation, strategy use, and English achievement.Totally 132 senior high EFL school boy students took part in the study. Two questionnaires-Motivation Questionnaire (Chen, 2008; Lai, 2008) and Strategy Inventory for Language Learning (SILL, version 7.0)-were used for data collecting.With SPSS for Windows Version 17.0, data analysis, including item analysis, exploratory factor analysis (EFA), reliability analysis, and validity analysis, was used. Furthermore, Structural equation modeling (SEM) was adopted through second-order confirmatory factor analysis (CFA) in LISREL 8.8 to confirm the causal relationships among senior high school EFL students' motivation, strategy use, and English achievement.The causal relationships between motivation and English achievement and between strategy use and English achievement were both proved. Motivation could account for 39% of the total variance toward English achievement while strategy use could account for 19% of the total variance toward English achievement. Intrinsic Motivation and Utilitarian Purpose took up the most variance toward English achievement, whereas Metacognitive Strategies was the only individual factor contributing to the total variance toward English achievement. The relationship among senior high school EFL students' motivation, strategy use, and English achievement was a full mediating model with a reciprocal relationship between strategy use and English achievement.

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.

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Elements of Structural Equation Models (SEMs) blends theoretical foundations with practical applications, serving as both a learning tool and a lasting reference. Synthesizing material from diverse sources, including the author's own contributions, it provides a rigorous yet accessible guide for graduate students, faculty, and researchers across social, behavioral, health, and data sciences. The book covers essential SEM concepts – model assumptions, identification, estimation, and diagnostics – while also addressing advanced topics often overlooked, such as Bayesian SEMs, model-implied instrumental variables, and categorical variables. Readers will gain insights into missing data, longitudinal models, and comparisons with Directed Acyclic Graphs (DAGs). By presenting complex technical content in a clear, structured way, this authoritative resource deepens readers' understanding of SEMs, making it an indispensable guide for both newcomers and experts seeking a definitive treatment of the field.