Questions tagged [multilevel-analysis]
Statistical analysis of datasets comprising several levels of hierarchy (e.g., students nested in classes nested in schools or hierarchical forecasting). For questions about mixed models use [mixed-model] tag. For nested random effects, use [nested-data].
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Multilevel Modeling in Linear Mixed Models versus Generalized Linear Mixed Models
I am analyzing a data set that includes several discrete and continuous outcome variables (DV). For the continuous DVs I intend to use Linear Mixed Models (LMM) processed in SPSS. For the discrete ...
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Multilevel Model Residuals Scatterplot Assumptions
I am conducting multilevel modelling (MLM) in SPSS (mixed modeling) to analyze cross-sectional repeated measures data. One of my dependent variables is a survey question scaled 1 to 10, which ...
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There are statistical models for nonindependent/multilevel data (e.g., Mixed Models). Is there an approach for multilevel data in Machine Learning?
There are many cases of multilevel/nested/hierarchical data: people in schools, schools in counties, trials within a person, web sessions or mobile sessions within a person, etc.
In traditional ...
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What is the mean of random effects?
Say, I have a mixed model: y = x + y + x:y + (1 + x * y | participant)
What would be the mean of the random effects? Do they fluctuate around 0 or the fixed effects?...
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Lack of within-cluster variability
I am working on patients' data. I want to do multilevel logistic regression. The cluster is hospital, exposure variable is treatment (A, B, C), and independent variables include sex, age and others. I ...
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What is the difference between hierarchical modeling and setting a (fixed) prior on a parameter?
I was reading through Chapter 11 of Data Analysis using Regression & Multilevel Models, and was confused by a slight variation of a simple hierarchical model posed in the text.
Lets say I have a ...
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Longitudinal analysis of peer effect
I am working with hierarchical data (2 repeated observations on children nested within households), obtained from a RCT with 2 treatment groups. The primary goal of my analysis is to see whether the ...
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The problem of double adjustment of p-values in multilevel models with categorical predictors
I have a set of around 30 multilevel models which have the same predictor variables (and the same cluster variable), but different outcome variables. Since there are so many models, I want to perform ...
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How can I regress group level outcomes on individual characteristics?
I am studying the effect of player characteristics on their team win rate (< 1). Because players are grouped in teams of 10, they share the same win rate. The goal is to predict team win rate from ...
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Mixed models - Intuition of correlated discrete random effects
I'm looking into this source and I'm trying to understand what does it mean to have "positive correlation between the random effects when the variables aren't continuous"
In the link there ...
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Centering Variables in Multilevel Models with Longitudinal Data
Enders and Tofighi 2007 provide a discussion on the various ways users can center variables in multilevel models and when each situation is appropriate. While they largely focus their commentary on ...
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What is the difference between a) multilevel modelling and b) adding a categorical IV to a multiple regression?
The examples of multilevel modelling I have seen are equivalent to treating the group as an extra categorical IV in a multiple regression. For example, if children are grouped into N classes, you ...
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Which Variables Should Be Centered in a Multilevel Model?
When only one variable in a multilevel model is of interest (all of the other variables are treated as nuisance parameters), and we wish to estimate between- and within-effects, should we only center ...
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Why do top-down approaches produce biased coherent forecasts?
The context is forecasting hierarchical time series. Section 10.4 of "Forecasting: Principles and Practice" (2nd edition) by Hyndman & Atahnasopoulos states:
One disadvantage of all top-...
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Moderation in linear mixed model
I ran a Linear Mixed Model in R with 2 centered predictors and a Group variable.
fit1a <- lmer(DV ~ Predictor1*Group + Predictor2*Group + (1|...), data)
One of ...
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