Bayesian Structural Equation Modeling with lavaan: Applications in the Social Sciences
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Bayesian Structural Equation Modeling (BSEM) is a powerful statistical technique that allows researchers to test complex causal models with observed and latent variables.
BSEM is based on Bayesian inference, which provides a more flexible and informative approach to model estimation than traditional frequentist methods.
BSEM can be used to estimate a wide range of models, including path analysis, confirmatory factor analysis, and structural equation models.
BSEM is particularly well-suited for models with missing data, measurement error, and non-normal data.
BSEM can be used to test a variety of hypotheses, including the effects of interventions, the relationships between variables, and the validity of measurement instruments.
BSEM is a valuable tool for researchers in a variety of fields, including psychology, education, business, and public health.
This book provides a comprehensive introduction to BSEM, including the theoretical foundations, the estimation process, and the interpretation of results.
The book is written in a clear and concise style, and it is suitable for readers with a basic understanding of statistics.
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