File Name: generalized latent variable modeling multilevel longitudinal and structural equation models .zip
Table of contents. Please choose whether or not you want other users to be able to see on your profile that this library is a favorite of yours. Finding libraries that hold this item Most statisticians apply some form of latent variable modeling in their research, and this book presents the latest developments in the field in a clear and engaging way. The final application chapters deal with a broad collection of interesting applications to areas, such as meta-analyses, disease mapping, confirmatory factor analysis, and case-control studies.
Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. DOI: Chapter 2 describes five additional examples that provide the foundation for more detailed explanations in later chapters. The chapter also includes some of properties of sensitivity analysis and expands on the various settings in which sensitivity analysis is applicable. Chapter 3 presents the examples in greater detail by showing some practical problems in which sensitivity analysis has demonstrated usefulness. Save to Library.
This paper proposes a new model of measuring a latent variable, stock market manipulation. The model bears close resemblance with the literature on economic well-being. It interprets the manipulation of a stock as a latent variable, in the form of a multiple indicators and multiple causes MIMIC model. This approach exploits systematic relations between various indicators of manipulation and between manipulation and multiple causes, which allows it to identify the determinants of manipulation and an index of manipulation simultaneously. The main reason of stock market manipulation comes from the fact that information availability is not universally equal.
A unifying framework for generalized multilevel structural equation modeling is introduced. The models in the framework, called generalized linear latent and mixed models GLLAMM , combine features of generalized linear mixed models GLMM and structural equation models SEM and consist of a response model and a structural model for the latent variables. The response model generalizes GLMMs to incorporate factor structures in addition to random intercepts and coefficients. As in GLMMs, the data can have an arbitrary number of levels and can be highly unbalanced with different numbers of lower-level units in the higher-level units and missing data. A wide range of response processes can be modeled including ordered and unordered categorical responses, counts, and responses of mixed types. The structural model is similar to the structural part of a SEM except that it may include latent and observed variables varying at different levels. For example, unit-level latent variables factors or random coefficients can be regressed on cluster-level latent variables.
A unifying framework for generalized multilevel structural equation modeling is introduced. The models in the framework, called generalized linear latent and mixed models GLLAMM , combine features of generalized linear mixed models GLMM and structural equation models SEM and consist of a response model and a structural model for the latent variables. The response model generalizes GLMMs to incorporate factor structures in addition to random intercepts and coefficients.
Sexual selection is an intense evolutionary force, which operates through competition for the access to breeding resources. There are many cases where male copulatory success is highly asymmetric, and few males are able to sire most females. The literature reports contrasting results. This variability may reflect actual differences among studied populations, but it may also be generated by methodological differences and statistical shortcomings in data analysis. A review of the statistical methods used so far in lek studies, shows a prevalence of Linear Models LM and Generalized Linear Models GLM which may be affected by problems in inferring cause-effect relationships; multi-collinearity among explanatory variables and erroneous handling of non-normal and non-continuous distributions of the response variable.
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Generalized Latent Variable Modeling: Multilevel, Longitudinal, and Structural Equation Models Request Full-text Paper PDF For instance, the mixed effects structural equations model (MESE; Junker, Schofield, & Taylor.Marta L. 28.12.2020 at 04:18
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