Suppressor variables and multilevel mixture modelling
نویسنده
چکیده
A major issue in educational research involves taking into consideration the multilevel nature of the data. Since the late 1980s, attempts have been made to model social science data that conform to a nested structure. Among other models, two-level structural equation modelling or two-level path modelling and hierarchical linear modelling are two of the techniques that are commonly employed in analysing multilevel data. Despite their advantages, the two-level path models do not include the estimation of cross-level interaction effects and hierarchical linear models are not designed to take into consideration the indirect effects. In addition, hierarchical linear models might also suffer from multicollinearity that exists among the predictor variables. This paper seeks to investigate other possible models, namely the use of latent constructs, indirect paths, random slopes and random intercepts in a hierarchical model.
منابع مشابه
Multilevel Mixture Kalman Filter
The mixture Kalman filter is a general sequential Monte Carlo technique for conditional linear dynamic systems. It generates samples of some indicator variables recursively based on sequential importance sampling (SIS) and integrates out the linear and Gaussian state variables conditioned on these indicators. Due to the marginalization process, the complexity of the mixture Kalman filter is qui...
متن کاملStudents Reading Motivation: A Multilevel Mixture Factor Analysis
Latent variable modeling is a commonly used data analysis tool in social sciences and other applied fields. The most popular latent variable models are factor analysis (FA) and latent class analysis (LCA). FA assumes that there is one or more continuous latent variables – called factors – determining the responses on a set of observed variables, while LCA assumes that there is an underlying cat...
متن کاملMultilevel Mixture Factor Models.
Factor analysis is a statistical method for describing the associations among sets of observed variables in terms of a small number of underlying continuous latent variables. Various authors have proposed multilevel extensions of the factor model for the analysis of data sets with a hierarchical structure. These Multilevel Factor Models (MFMs) have in common that-as in multilevel regression ana...
متن کاملMixture Modeling: A Useful Analytical Approach for Drug Use Studies
The analytic methods often used in drug use studies, such as ANOVA, multiple regression, logistic regression, multilevel models, and structural equation modeling (SEM) including path analysis, factor analysis, and latent growth curve model, are variable-centered approaches. Those approaches assume that the study sample arises from a homogeneous population; and focus on relations among variables...
متن کاملAn Instrumental Variable Consistent Estimation Procedure to Overcome the Problem of Endogenous Variables in Multilevel Models
It is far from unusual for a multilevel model to contain a regressor that can be regarded as an endogenous variable. The term endogeneity as opposed to exogeneity is a familiar term in econometrics. Often it manifests itself by explanatory variable being subject to the same influences as the response variable. It is thus not exogenous in the model being fitted. More particularly it may mean tha...
متن کامل