Multilevel (hierarchical) modeling is a generalization of linear and generalized linear modeling in which regression coefficients are themselves given a model, whose parameters are also estimated from data. We illustrate the strengths and limitations of multilevel modeling through an example of the prediction of home radon levels in U.S. counties. The multilevel model is highly effective for predictions at both levels of the model, but could easily be misinterpreted for causal inference
In social research work, the structure of the data are often hierarchical. Hierarchical linear model...
Multilevel modelling is often used in the social sciences for analyzing data that has a hiearchial s...
Multilevel modeling is an increasingly popular technique for analyzing hierarchial data. We consider...
Whenever research is concerned with the analysis of relationships between lowerlevel units (e.g., in...
Organizations are hierarchical in nature. Individuals are subject to various group influences; and t...
textDue to the inherently hierarchical nature of many natural phenomena, data collected rests in ne...
Many phenomena in marketing involve multiple levels of theory and analysis. Adopting a multilevel le...
A major issue in educational research involves taking into consideration the multilevel nature of th...
Multilevel statistical models are characterized by analyses undertaken simultaneously at different l...
Analyzing multilevel data: An empirical comparison of parameter estimates of hierarchical linear mod...
Multilevel modeling is an increasingly popular technique for analyzing hierarchical data. This artic...
Multilevel analysis (or multilevel model), also known by names such as hierarchical linear model (HL...
A useful way to conceptualize ecological processes operating at different spatial scales is through ...
Abstract Purpose This paper aims to discuss multilevel modeling for longitudinal data, clarifying ...
Multilevel modeling is a flexible approach for the analysis of nested data structures, such as those...
In social research work, the structure of the data are often hierarchical. Hierarchical linear model...
Multilevel modelling is often used in the social sciences for analyzing data that has a hiearchial s...
Multilevel modeling is an increasingly popular technique for analyzing hierarchial data. We consider...
Whenever research is concerned with the analysis of relationships between lowerlevel units (e.g., in...
Organizations are hierarchical in nature. Individuals are subject to various group influences; and t...
textDue to the inherently hierarchical nature of many natural phenomena, data collected rests in ne...
Many phenomena in marketing involve multiple levels of theory and analysis. Adopting a multilevel le...
A major issue in educational research involves taking into consideration the multilevel nature of th...
Multilevel statistical models are characterized by analyses undertaken simultaneously at different l...
Analyzing multilevel data: An empirical comparison of parameter estimates of hierarchical linear mod...
Multilevel modeling is an increasingly popular technique for analyzing hierarchical data. This artic...
Multilevel analysis (or multilevel model), also known by names such as hierarchical linear model (HL...
A useful way to conceptualize ecological processes operating at different spatial scales is through ...
Abstract Purpose This paper aims to discuss multilevel modeling for longitudinal data, clarifying ...
Multilevel modeling is a flexible approach for the analysis of nested data structures, such as those...
In social research work, the structure of the data are often hierarchical. Hierarchical linear model...
Multilevel modelling is often used in the social sciences for analyzing data that has a hiearchial s...
Multilevel modeling is an increasingly popular technique for analyzing hierarchial data. We consider...