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Results tagged with multilevel-analysis
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user 111259
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].
2
votes
Counting Biased Coins
There are a few solutions from a Fequentist perspective, but I'd like to offer a Bayesian perspective.
The Model
It would be useful to model the data generating process as a mixture of binomials; one …
- 39.6k
1
vote
Counting Biased Coins 2: Estimating Bias Imbalance
In your last question, I provided a hierarchical Bayesian mixture model. That model can be generalized quite easily (to my surprise) to accommodate your changes here. The extension is not intended t …
- 39.6k
1
vote
Accepted
Hierarchical / multilevel proportions test
Since the data are nested, the answer is technically no.
2, and a bit of 3. Straight forward depends on your experience, but here is one approach.
This sounds like exactly the right setup for a mix …
- 39.6k
0
votes
Hierarchical logistic regression package in R
OK, so:
I don't think this is hierarchical logistic regression. The word "hierarchical" is sometimes used to refer to random/mixed effects models (because parameters sit in a hierarchichy). This is …
- 39.6k
2
votes
Multilevel regression question: can you use level 2 variable as a predictor variable?
This sounds fine to me.
As written, the second model assumes that expected violence is different between private and non-private schools. Within private and non-private schools, violence is assumed t …
- 39.6k
4
votes
How to deal with treatment effect at state level but observations at school level?
I take it you're measuring at the school level and that
Schools are nested within states
States are either intervened upon or not
In what follows, I'll just assume the likelihoods are normal for s …
- 39.6k
2
votes
Should I incorporate sex as a control variable or use multi-level modelling?
My two cents: Adjust your estimate for sex by adding it into the model. Justification is below...
you should only control for confounding variables
This is not completely accurate.
If your goal is …
- 39.6k
7
votes
How to simulate random effects models?
Here is how I simulate random effects. I'll demonstrate for linear regression, but extending it to a different GLM should be straight forward.
Let's start with a random intercept model. The model is …
- 39.6k
1
vote
Accepted
A figure or narrative to compare multi-level models and fixed-effects ANOVAs
The fixed effect ANOVA is equivalent to a linear model in which the means of the groups are functions of indicator variables for group membership. Mathematically,the $j^{th}$ datum from group $i$ is
…
- 39.6k
0
votes
Can you use Multilevel Modeling (aka Hierarchical Linear Modeling) with Sequential Linear Mo...
Stepwise modelling is a fairly simple approach, so I don't see why it couldn't be done. Simply select your criterion for calling a model "better", and implement the stepwise procedure using a hierarc …
- 39.6k
8
votes
Accepted
Demonstrating complete-pooling, no-pooling, and partial-pooling regression in R
So I went ahead and generated some data to demonstrate that these work as expected.
library(tidyverse)
library(lme4)
if(!require(modelr)){
install.packages('modelr')
}
library(modelr)
pop_mean<-10 …
- 39.6k
1
vote
Getting a very big relative risk ratio value
A risk ratio looks like
$$ RR=\dfrac{Pr(Y\mid X=a)}{Pr(Y\mid X=b)} \>.$$
What happens when $Pr(Y\mid X=b) \rightarrow 0$? The answer is that the risk ratio approaches infinity.
What is very likely ha …
- 39.6k
5
votes
Accepted
Follow-up: Complete-pooling, no-pooling, and partial-pooling regression in R
Here are the no-pooling and partial pooling estimates for each school plotted against one another
Note that the partially pooled estimates are pooled towards the completely pooled estimates (in red). …
- 39.6k