Entities such as individuals, teams, or organizations can vary systematically from one another. Researchers typically model such data using multilevel models, assuming that the random effects are uncorrelated with the regressors. Violating this testable assumption, which is often ignored, creates an endogeneity problem thus preventing causal interpretations. Focusing on two-level models, we explain how researchers can avoid this problem by including cluster means of the Level 1 explanatory variables as controls; we explain this point conceptually and with a large scale simulation. We further show why the common practice of centering the predictor variables is mostly unnecessary. Moreover, to examine the state of the science, we reviewed 204...
This article challenges Fixed Effects (FE) modelling as the ‘default’ for time-series-cross-sectiona...
Context effects, where a characteristic of an upper-level unit or cluster (e.g., a country) affects ...
This paper examined the amount bias in standard errors for fixed effects when the random part of a m...
Entities such as individuals, teams, or organizations can vary systematically from one another. Rese...
Entities such as individuals, teams, or organizations can vary systematically from one another. Rese...
Entities such as individuals, teams, or organizations can vary systematically from one another. Res...
Many surveys of respondents from multiple countries or subnational regions have now been fielded on ...
Many surveys of respondents from multiple countries or subnational regions have now been fielded on ...
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/72623/1/j.0039-0402.2003.00254.x.pd
Random effects models (that is, regressions with varying intercepts that are modeled with error) are...
In educational psychology, observational units are oftentimes nested within superordinate groups. Re...
"Multilevel or mixed effects models are commonly applied to hierarchical data; for example,nsee Gold...
This paper assesses the options available to researchers analysing multilevel (including longitudina...
This article discusses estimation of multilevel/hierarchical linear models that include cluster-leve...
Summary. Multilevel or mixed effects models are commonly applied to hierarchical data. The level 2 r...
This article challenges Fixed Effects (FE) modelling as the ‘default’ for time-series-cross-sectiona...
Context effects, where a characteristic of an upper-level unit or cluster (e.g., a country) affects ...
This paper examined the amount bias in standard errors for fixed effects when the random part of a m...
Entities such as individuals, teams, or organizations can vary systematically from one another. Rese...
Entities such as individuals, teams, or organizations can vary systematically from one another. Rese...
Entities such as individuals, teams, or organizations can vary systematically from one another. Res...
Many surveys of respondents from multiple countries or subnational regions have now been fielded on ...
Many surveys of respondents from multiple countries or subnational regions have now been fielded on ...
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/72623/1/j.0039-0402.2003.00254.x.pd
Random effects models (that is, regressions with varying intercepts that are modeled with error) are...
In educational psychology, observational units are oftentimes nested within superordinate groups. Re...
"Multilevel or mixed effects models are commonly applied to hierarchical data; for example,nsee Gold...
This paper assesses the options available to researchers analysing multilevel (including longitudina...
This article discusses estimation of multilevel/hierarchical linear models that include cluster-leve...
Summary. Multilevel or mixed effects models are commonly applied to hierarchical data. The level 2 r...
This article challenges Fixed Effects (FE) modelling as the ‘default’ for time-series-cross-sectiona...
Context effects, where a characteristic of an upper-level unit or cluster (e.g., a country) affects ...
This paper examined the amount bias in standard errors for fixed effects when the random part of a m...