Introduction To Structural Equation Modeling Issues And Practical Considerations
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Structural Equation Models
Author: Georgian Federation of Psychologists Academic Team
language: en
Publisher: Prof. Dr. Bilal Semih Bozdemir
Release Date:
Structural Equation Modeling has emerged as a powerful statistical technique for analyzing complex relationships in social science research. It allows researchers to simultaneously examine the relationships between multiple latent variables, while also considering the impact of measurement error. (Lei & Wu, 2007) (Lee et al., 2018) Structural Equation Modeling provides a framework for testing hypotheses about the underlying mechanisms that drive various social phenomena, making it a valuable tool for researchers in fields such as psychology, sociology, and education. One of the key benefits of Structural Equation Modeling is its ability to handle latent variables, which are unobserved constructs that cannot be directly measured, but can be inferred from observable indicators. This is particularly useful in the social sciences, where many of the variables of interest are not directly observable, such as intelligence, attitudes, or personality traits. By incorporating these latent variables into the model, researchers can gain a more comprehensive understanding of the relationships between different constructs and how they interact. Structural Equation Modeling typically involves two main components: the measurement model and the structural model. The measurement model specifies the relationships between the latent variables and their observed indicators, while the structural model examines the relationships between the latent variables themselves. One of the key steps in conducting a Structural Equation Modeling analysis is the specification of the model. This involves the researcher defining the relationships between the variables based on their theoretical understanding of the phenomenon being studied. Once the model has been specified, the researcher can then estimate the parameters of the model using a variety of statistical techniques, such as maximum likelihood or robust weighted least squares. A comprehensive understanding of Structural Equation Modeling requires a solid grounding in statistical theory and an understanding of the various assumptions and limitations of the technique.
Parceling in Structural Equation Modeling
Author: Todd D. Little
language: en
Publisher: Cambridge University Press
Release Date: 2022-07-28
Parceling is pre-modeling strategy to create fewer and more reliable indicators of constructs for use with latent variable models. Parceling is particularly useful for developmental scientists because longitudinal models can become quite complex and even intractable when measurement models of items are fit. In this Element the authors provide a detailed account of the advantages of using parcels, their potential pitfalls, as well as the techniques for creating them for conducting latent variable structural equation modeling (SEM) in the context of the developmental sciences. They finish with a review of the recent use of parcels in developmental journals. Although they focus on developmental applications of parceling, parceling is also highly applicable to any discipline that uses latent variable SEM.
The Routledge Companion to Behavioural Accounting Research
Behavioural research is well established in the social sciences, and has flourished in the field of accounting in recent decades. This far-reaching and reliable collection provides a definitive resource on current knowledge in this new approach, as well as providing a guide to the development and implementation of a Behavioural Accounting Research project. The Routledge Companion to Behavioural Accounting Research covers a full range of theoretical, methodological and statistical approaches relied upon by behavioural accounting researchers, giving the reader a good grounding in both theoretical perspectives and practical applications. The perspectives cover a range of countries and contexts, bringing in seminal chapters by an international selection of behavioural accounting scholars, including Robert Libby and William R. Kinney, Jr. This book is a vital introduction for Ph.D. students as well as a valuable resource for established behavioural accounting researchers.