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Structural Equation Modeling (SEM) is a statistical approach to testing hypothesis about the relationships among observed and latent variables. The use of SEM in research has increased in psychology, sociology, and economics in recent years. In particular educational researchers try to obtain the complete image of the process of education through the measurement of personality differences, learning environment, motivation levels and host of other variables that affect the teaching and learning process. With the use of survey instruments and interviews with students, teachers and other stakeholders as a lens, educators can assess and gain valuable information about the social ecology of the classrooms that could help in improving the instructional approach, classroom management and the learning organizations. A considerable number of research have been conducted to identify the factors and interactions between students characteristics, personal preferences, affective traits, study skills, and various other factors that could help in better educational performance. In recent years, educational researchers use Structural Equation Modeling (SEM) as a statistical technique to explore the complex and dynamic nature of interactions in educational research and practice. SEM is becoming a powerful analytical tool and making methodological advances in multivariate analysis. This book presents the collective works on concepts, methodologies and applications of SEM in educational research and practice. The anthology of current research described in this book will be a valuable resource for the next generation educational practitioners.

We conduct a meta-analytic review that yields important insights about the existing research on transformational leadership and creativity. Additionally, we propose and test an integrated model using meta-analytic structural equation modeling (MASEM) and full information MASEM (FIMASEM) techniques to better understand the intervening mechanism through which transformational leadership acts on creativity. The results of the meta-analysis of 127 studies show that most of the bivariate relationships among transformational leadership, employee creativity, and pre-identified mediators are significant; further, geographic base of studies significantly moderates some of the relationships. The MASEM results indicate that several mediators intervene in the relationship between transformational leadership and creativity. Although the total effect of transformational leadership on creativity is positive, its direct effect is negative when mediators are included. Additionally, there are significant relationships among the mediators that can be theoretically supported, but have not been investigated in prior transformational leadership and creativity studies. On the basis of these findings, we provide conclusions and directions for future studies.

Research Summary A firm's strategic orientation has long been of interest in management and strategy research. In particular, entrepreneurial, market, and learning orientations have received thorough theoretical and empirical research attention. In this meta‐analysis, we compare the direct and combined performance effects of these orientations, explore their interrelatedness, and provide a theoretical foundation for complementarity between the three. Building on prior empirical findings from 210 samples and using structural equation modeling and seemingly unrelated regression techniques, we extend the knowledge base on strategic orientations. Our results provide evidence for interrelatedness and complementarity among strategic orientations, indicating that superior firm performance emerges from its capability to align entrepreneurial, market, and learning orientations. Managerial Summary Managers might be tempted to divide rather than combine their attention on various aspects of strategy, such as entrepreneurial, market, and learning orientations. Similarly, organizational culture might inhibit or promote collaboration between distinct organizational functions. We synthesize a vast body of research on firm‐level strategy making and reveal that while each strategic orientation is beneficial on its own, together, the three strategic orientations create synergies that surpass the effects of individual strategic orientations. Therefore, to achieve superior performance, firms need to align their strategy making efforts to (a) monitoring changes in customer needs and competitor moves, (b) engaging in creative processes, and (c) assimilating the extensive knowledge gained from these activities.

A reference guide for applications of SEM using Mplus Structural Equation Modeling: Applications Using Mplus is intended as both a teaching resource and a reference guide. Written in non-mathematical terms, this book focuses on the conceptual and practical aspects of Structural Equation Modeling (SEM). Basic concepts and examples of various SEM models are demonstrated along with recently developed advanced methods, such as mixture modeling and model-based power analysis and sample size estimate for SEM. The statistical modeling program, Mplus, is also featured and provides researchers with a flexible tool to analyze their data with an easy-to-use interface and graphical displays of data and analysis results. Key features: Presents a useful reference guide for applications of SEM whilst systematically demonstrating various advanced SEM models, such as multi-group and mixture models using Mplus. Discusses and demonstrates various SEM models using both cross-sectional and longitudinal data with both continuous and categorical outcomes. Provides step-by-step instructions of model specification and estimation, as well as detail interpretation of Mplus results. Explores different methods for sample size estimate and statistical power analysis for SEM. By following the examples provided in this book, readers will be able to build their own SEM models using Mplus. Teachers, graduate students, and researchers in social sciences and health studies will also benefit from this book.

[This book] reviews the basic concepts and applications of SEM using Mplus Version 6. ... The first two chapters introduce the fundamental concepts of SEM and important basics of the Mplus program. The remaining chapters focus on SEM applications and include a variety of SEM models presented within the context of three sections: Single-group analyses, Multiple-group analyses, and other important topics, the latter of which includes the multitrait-multimethod, latent growth curve, and multilevel models. (DIPF/Orig.).

Partial least squares structural equation modeling (PLS-SEM) has become more popular across many disciplines including health care. However, articles in health care often fail to discuss the choice of PLS-SEM and robustness testing is not undertaken. This article presents the steps to be followed in a thorough PLS-SEM analysis, and includes a conceptual comparison of PLS-SEM with the more traditional covariance-based structural equation modeling (CB-SEM) to enable health care researchers and policy makers make appropriate choices. PLS-SEM allows for critical exploratory research to lay the groundwork for follow-up studies using methods with stricter assumptions. The PLS-SEM analysis is illustrated in the context of residential aged care networks combining low-level and high-level care. Based on the illustrative setting, low-level care does not make a significant contribution to the overall quality of care in residential aged care networks. The article provides key references from outside the health care literature that are often overlooked by health care articles. Choosing between PLS-SEM and CB-SEM should be based on data characteristics, sample size, the types and numbers of latent constructs modelled, and the nature of the underlying theory (exploratory versus advanced). PLS-SEM can become an indispensable tool for managers, policy makers and regulators in the health care sector.

This study investigates 3 unexplored issues regarding Second Language (L2) Motivational Self System theory. It further validates the theory using multiple structural equation modeling (SEM) along with a procedure comparing the strength of corresponding paths. Japanese university freshmen (N = 2,631) responded to a questionnaire and took the TOEFL-ITP test. Results showed the following: (a) Stronger Ideal and Ought-to L2 self visions led to intended effort, accounting in turn for higher levels of objectively measured proficiency. (b) Two types of learner beliefs reflecting L2 learning experience-Communication Orientation (the tendency to value extensive use of language) and Grammar-Translation Orientation (the tendency to value learning grammar explicitly)-influenced the 2 future selves differently. The former affected Ideal self more than Ought-to self, while the reverse was true of the latter. (c) Women's greater tendency to value communication activities influenced their stronger vision of Ideal L2 self. A stronger link between Grammar-Translation Orientation and Ought-to self in male students than in female students was found. Finally, the etic approach using SEM allowed for a comparison of studies conducted in different sociocultural contexts, showing stronger motivating power for Ought-to self in Japan along with gender differences, a finding with context-specific explanations. (Verlag).

Presents a useful guide for applications of SEM whilst systematically demonstrating various SEM models using M plus Focusing on the conceptual and practical aspects of Structural Equation Modeling (SEM), this book demonstrates basic concepts and examples of various SEM models, along with updates on many advanced methods, including confirmatory factor analysis (CFA) with categorical items, bifactor model, Bayesian CFA model, item response theory (IRT) model, graded response model (GRM), multiple imputation (MI) of missing values, plausible values of latent variables, moderated mediation model, Bayesian SEM, latent growth modeling (LGM) with individually varying times of observations, dynamic structural equation modeling (DSEM), residual dynamic structural equation modeling (RDSEM), testing measurement invariance of instrument with categorical variables, longitudinal latent class analysis (LLCA), latent transition analysis (LTA), growth mixture modeling (GMM) with covariates and distal outcome, manual implementation of the BCH method and the three-step method for mixture modeling, Monte Carlo simulation power analysis for various SEM models, and estimate sample size for latent class analysis (LCA) model. The statistical modeling program Mplus Version 8.2 is featured with all models updated. It provides researchers with a flexible tool that allows them to analyze data with an easy-to-use interface and graphical displays of data and analysis results. Intended as both a teaching resource and a reference guide, and written in non-mathematical terms, Structural Equation Modeling: Applications Using Mplus, 2nd edition provides step-by-step instructions of model specification, estimation, evaluation, and modification. Chapters cover: Confirmatory Factor Analysis (CFA); Structural Equation Models (SEM); SEM for Longitudinal Data; Multi-Group Models; Mixture Models; and Power Analysis and Sample Size Estimate for SEM. * Presents a useful reference guide for applications of SEM while systematically demonstrating various advanced SEM models * Discusses and demonstrates various SEM models using both cross-sectional and longitudinal data with both continuous and categorical outcomes * Provides step-by-step instructions of model specification and estimation, as well as detailed interpretation of M plus results using real data sets * Introduces different methods for sample size estimate and statistical power analysis for SEM Structural Equation Modeling is an excellent book for researchers and graduate students of SEM who want to understand the theory and learn how to build their own SEM models using M plus.

Despite the importance of demonstrating and evaluating how structural equation modeling (SEM), exploratory structural equation modeling (ESEM), and Bayesian structural equation modeling (BSEM) work simultaneously, research comparing these analytic techniques is limited with few studies conducted to systematically compare them to each other using correlated-factor, hierarchical, and bifactor models of personality. In this study, we evaluate the performance of SEM, ESEM, and BSEM across correlated-factor, hierarchical, and bifactor structures and multiple estimation techniques (maximum likelihood, robust weighted least squares, and Bayesian estimation) to test the internal structure of personality. Results across correlated-factor, hierarchical, and bifactor models highlighted the importance of controlling for scale coarseness and allowing small off-target loadings when using maximum likelihood (ML) and robust weighted least squares estimation (WLSMV) and including informative priors (IP) when using Bayesian estimation. In general, Bayesian-IP and WLSMV ESEM models provided noticeably best model fits. This study is expected to serve as a guide for professionals and applied researchers, identify the most appropriate ways to represent the structure of personality, and provide templates for future research into personality and other multidimensional representations of psychological constructs. We provide Mplus code for conducting the demonstrated analyses in the online supplement.