Forests provide crucial habitats for nature and people, but also harbor organisms, such as ticks, that can act as vectors for pathogens. Consequently, understanding how forest management practices influence host-parasite-pathogen interactions is essential for promoting both forest biodiversity conservation and nature’s contributions to people. Therefore, this study investigates the complex relationships between forest structural complexity, body condition, and tick infestation probability in a common forest bird, the great tit (Parus major), across 19 forests in the Flemish Ardennes, Belgium. Using Structural Equation Modeling (SEM), we first integrated multiple phenotypic health proxies into a single overall condition index. Subsequently, we assessed how variations in forest structural complexity impact the condition of forest birds and their chances of contracting ticks. Our findings revealed that birds in a better physiological condition, primarily driven by lower levels of cellular stress (indicated by longer telomeres) are more likely to carry ticks. This may be due to ticks preferring healthier and more nutritious hosts and/or condition-linked differences in bird behavior (e.g. foraging), resulting in higher contact rates with ticks. While forest structural complexity did not significantly affect the birds' overall body condition, it was responsible for an increased tick infestation probability. Specifically, forests with higher structural complexity were associated with increased densities of questing nymphs, thereby elevating the risk of tick infestation in birds. Our study highlights the multifaceted role of forest structure in shaping host-parasite dynamics. These insights are valuable for developing forest management practices that balance the enhancement of ecological health with the mitigation of health risks posed by tick-borne diseases.

Code and data for Introduction to Path Analysis and Structural Equation Modeling workshop (Michel 2014, http://dx.doi.org/10.6084/m9.figshare.1243187)

The graph shows the number of articles published in the discipline of ^.

With the concept of “healthy lifestyle” deeply rooted in people’s minds, the sports service industry is thriving, which has resulted in intense competition. The sports service industry must improve its service quality to be competitive. Customer orientation is the key factor for enterprises to gain competitive advantage. With the in-depth understanding of internal marketing in the service industry. Managers have realized that treating employees as internal consumer is a good way to improve their satisfaction and gain customer orientation. However, what internal marketing strategies will have a positive effect on internal consumer satisfaction and customer orientation of private-owned sports center employees are still unclear. In this investigation, a total of 326 employees from the private-owned sports center were used to investigate the effects of internal marketing strategies on internal consumer satisfaction and customer orientation. All employees were asked to complete a questionnaire on 5-point scale. A path model was used to investigate the direct and indirect effects of hypothetical measurements on internal consumer satisfaction and customer orientation. The findings suggested that internal communication, administrative support, and educational training were important factors affecting internal consumer satisfaction and customer orientation. We concluded that the implementation of internal marketing strategies could improve internal consumer satisfaction and customer orientation, and higher levels of internal consumer satisfaction will encourage employees to have higher degrees of customer orientation. Therefore, the implementation of internal marketing strategy was beneficial to the development of private-owned sports centers.

Discover the booming Structural Equation Modeling (SEM) software market! This analysis reveals key trends, growth drivers, and leading companies like IBM SPSS Amos and LISREL, forecasting substantial expansion through 2033. Learn about market segmentation, regional insights, and the impact of cloud-based solutions.

The graph shows the changes in the impact factor of ^ and its corresponding percentile for the sake of comparison with the entire literature. Impact Factor is the most common scientometric index, which is defined by the number of citations of papers in two preceding years divided by the number of papers published in those years.

The global structural equation modeling software market is anticipated to experience substantial growth over the forecast period from 2025 to 2033, with a projected CAGR of XX%. The market was valued at XXX million in 2025 and is expected to reach a significant value by the end of the forecast period. The increasing adoption of structural equation modeling techniques in various research and analysis applications across diverse industries, including education, medical, psychological, and economic research, is a major driving factor behind the market's growth. Other factors contributing to market expansion include the growing demand for advanced analytical tools to handle complex data and the emergence of cloud-based solutions for enhanced accessibility and collaboration. Key market players in the structural equation modeling software industry include LISREL, IBM SPSS Amos, Mplus, SmartPLS, EQS, semopy (Python), and lavaan. The competitive landscape is characterized by a combination of established vendors and emerging players offering a range of solutions tailored to specific application areas and user needs. The market is witnessing continuous innovation and development of advanced features, such as improved user interfaces, enhanced data visualization capabilities, and integration with other analytical software tools. Strategic partnerships and collaborations among industry players are also prevalent, aiming to expand market reach and offer comprehensive solutions to customers.

Structural equation modeling fit indices.

The Structural Equation Modeling (SEM) software market is experiencing robust growth, driven by increasing adoption across diverse sectors like education, healthcare, and the social sciences. The market's expansion is fueled by the need for sophisticated statistical analysis to understand complex relationships between variables. Researchers and analysts increasingly rely on SEM to test theoretical models, assess causal relationships, and gain deeper insights from intricate datasets. While the specific market size for 2025 isn't provided, a reasonable estimate, considering the growth in data analytics and the increasing complexity of research questions, places the market value at approximately $500 million. A Compound Annual Growth Rate (CAGR) of 8% seems plausible, reflecting steady but not explosive growth within a niche but essential software market. This CAGR anticipates continued demand from academia, government agencies, and market research firms. The market is segmented by software type (commercial and open-source) and application (education, medical, psychological, economic, and other fields). Commercial software dominates the market currently, due to its advanced features and professional support, however the open-source segment shows strong potential for growth, particularly within academic settings and amongst researchers with limited budgets. The competitive landscape is relatively concentrated with established players like LISREL, IBM SPSS Amos, and Mplus offering comprehensive solutions. However, the emergence of Python-based packages like semopy and lavaan demonstrates an ongoing shift towards flexible and programmable SEM software, potentially increasing market competition and innovation in the years to come. Geographic distribution shows North America and Europe currently holding the largest market share, with Asia-Pacific emerging as a key growth region due to increasing research funding and investment in data science capabilities. The sustained growth of the SEM software market is expected to continue throughout the forecast period (2025-2033), largely driven by the rising adoption of advanced analytical techniques within research and businesses. Factors limiting market growth include the high cost of commercial software, the steep learning curve associated with SEM techniques, and the availability of alternative statistical methods. However, increased user-friendliness of software interfaces, alongside the growing availability of online training and resources, are expected to mitigate these restraints and expand the market's reach to a broader audience. Continued innovation in SEM software, focusing on improved usability and incorporation of advanced features such as handling of missing data and multilevel modeling, will contribute significantly to the market's future trajectory. The development of cloud-based solutions and seamless integration with other analytical tools will also drive future market growth.

Discover the booming Structural Equation Modeling (SEM) software market. Explore key trends, growth drivers, and leading companies shaping this $400 million+ market by 2033. Learn about regional market shares and software types (commercial & open-source) in our comprehensive analysis.

Structural equation modeling (SEM) provides an extensive toolbox to analyze the multivariate interrelations of directly observed variables and latent constructs. Multilevel SEM integrates mixed effects to examine the covariances between observed and latent variables across many levels of analysis. However, while it is necessary to consider model fit, traditional indices are largely insufficient to analyze model fit at each level of analysis. The present article reviews (a) the partially saturated model fit approach first suggested by Ryu and West and (b) an alternative model parameterization that removes the multilevel data structure. We next describe the implementation of an algorithm to compute partially saturated model fit for 2-level structural equation models in the open source SEM package, OpenMx, including verification in a simulation study. Finally, an example empirical application evaluates leading theories on the structure of affect from ecological momentary assessment data collected thrice daily for two weeks from 345 participants.

This zip file contains: - 3 .zip files = projects to be imported into SmartPLS 3

DLOQ-A model with 7 dimensions DLOQ-A model with second-order latent variable ECSI model (Tenenhaus et al., 2005) to exemplify direct, indirect and total effects, as well as importance-performance map and moderation with continuous variables. ECSI Model (Sanches, 2013) to exemplify MGA (multi-group analysis)

• 5 files (csv, txt) with data to run 7 examples in SmartPLS 3

Note: - DLOQ-A = new dataset (ours) - ECSI-Tenenhaus et al. [model for mediation and moderation] = available at: http://www.smartpls.com > Resources > SmartPLS Project Examples - ECSI-Sanches [dataset for MGA] = available in the software R > library(plspm) > data(satisfaction)

Discover the booming Structural Equation Modeling (SEM) software market! This in-depth analysis reveals a projected CAGR of 12%, driven by rising demand in research, healthcare, and economics. Learn about key players, market trends, and regional growth forecasts from 2025 to 2033. Explore the potential of SEM software for advanced statistical analysis.

Summary of hypothesis test results using Structural Equation Modeling (SEM) (n = 150).

Structural equation model indices.

Structural equation modeling (SEM) results.

A general latent variable modeling framework called n-Level Structural Equations Modeling (NL-SEM) for dependent data-structures is introduced. NL-SEM is applicable to a wide range of complex multilevel data-structures (e.g., cross-classified, switching membership, etc.). Reciprocal dyadic ratings obtained in round-robin design involve complex set of dependencies that cannot be modeled within Multilevel Modeling (MLM) or Structural Equations Modeling (SEM) frameworks. The Social Relations Model (SRM) for round robin data is used as an example to illustrate key aspects of the NL-SEM framework. NL-SEM introduces novel constructs such as ‘virtual levels’ that allows a natural specification of latent variable SRMs. An empirical application of an explanatory SRM for personality using xxM, a software package implementing NL-SEM is presented. Results show that person perceptions are an integral aspect of personality. Methodological implications of NL-SEM for the analyses of an emerging class of contextual- and relational-SEMs are discussed.

Environmental, Social and Governance (ESG) is closely related to the "dual carbon" objective and the concept of sustainable development. The impact of ESG performance on audit efficiency, especially on audit delays, is still an issue to be studied in depth. Drawing on stakeholder theory, sustainable development theory, shared value concept and corporate social responsibility theory, this study adopts regression analysis and structural equation modeling (SEM) to investigate the impact of ESG on audit efficiency based on the data of A-share listed companies in the period of 2015–2022, with a focus on audit delay. The results of regression analysis show that ESG performance has a significant effect on reducing audit delay, and audit delay is reduced by 0.007 on average for each unit increase in ESG performance. In structural equation modeling, the effect of ESG performance on audit delay is more significant, with an estimated value of -0.555 and a standard error of 0.097. In addition, the study shows that the corporate ESG performance on audit efficiency has a positive impact is more pronounced among firms with stronger ESG practices, especially among non-state-owned firms with lower institutional investor ownership and firms audited by "Big Four" firms. These results not only demonstrate the importance of ESG performance in improving audit efficiency, but also provide important guidance for corporate management and policy making. This study enriches the existing literature on corporate ESG performance and audit efficiency and provides new perspectives and directions for future research.

Structural equation model results.

The data is a part of of the Longitudinal Study of Australian Children that is used in the above titled paper. The dataset is included in R package to demonstrate application of R to conduct Exploratory Structural Equation Modeling. Valid: 2022-02-25