Videos and Voice-over Slide Presentations

Videos with slides

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What is it and why you should do it:

Why use multilevel modelling?

The sorts of analyses you can do and the results you can get:

Global variations in health and mortality
Modelling social and educational segregation
Multilevel Structures and Classifications

What is it and why you should do it:

Why use multilevel modelling? - Jon Rasbash

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Slides only: Why use multilevel modelling?- Jon Rasbash

Note:Jon Rasbash's video is now available to download. Instructions for use: Please unzip the file and double click 'Start Video'.

The sorts of analyses you can do and the results you can get

Global variations in health and mortality: evaluating the relative income hypothesis - Kelvyn Jones

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Slides only: Global variations in health and mortality: evaluating the relative income hypothesis - Kelvyn Jones

Modelling social and educational segregation - Harvey Goldstein

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Slides only: Modelling social and educational segregation - Harvey Goldstein

Multilevel Structures and Classifications

Watch video synched with PowerPoint presentation by Jon Rasbash by Jon Rasbash

Jon Rasbash presents an intordiuctory talk on multilevel structures and classifications. (For more about this subject see Jon's pictionary of basic structures and classifications that underlie multilevel models.)

Residuals - An Introduction

residuals image sound icon Watch slide presentation with voice-over and subtitles by Rebecca Pillinger

see also full written transcript of Residuals presentation (with some slides)

Residuals can be important if we want to rank our units after controlling for a set of covariates (for example when drawing up league tables of schools), or if we are interested in the effect of a particular level 2 unit (for example if we want to see what effect a particular school is having on its students' performance). In this presentation, we show how both level 1 and level 2 residuals are calculated for multilevel models (though in practice this calculation will usually be performed by the software), and explain why residuals in multilevel models are shrunk in towards the overall regression line.

Measuring Dependency

sound icon Watch slide presentation with voice-over and subtitles by Rebecca Pillinger

see also full written transcript of Measuring Dependency presentation (with some slides)

We use multilevel modelling when we have dependent data, i.e. there is similarity between observations from the same group (for example, heights of children from the same family). This presentation explains how to measure the dependency using the variance partitioning coefficient (VPC). We explore the interpretation of the VPC through example graphs. We also see that the VPC shows how much of the variance is due to each level of the model, and thus gives some insight into what extent the response is determined at each level.

Covariance and Correlation Matrices

Watch slide presentation with voice-over and subtitles by Rebecca Pillinger

see also full written transcript of Covariance and Correlation matrices presentation (with some slides)

We use multilevel modelling when we have dependent data, i.e. there is similarity between observations from the same group (for example, heights of children from the same family). An obvious question is: just how does the multilevel model take this dependency into account? In this presentation, we examine the structure of the model: we see what the correlation is between each pair of level 1 units in our dataset. This allows us to see how the relation between different observations from the same group is specified by the model. We contrast this to a single level model, for which we see there is no correlation between different observations from the same group.

Significance Testing

Watch Powerpoint presentation with spoken commentary and MLwiN demo by Kelvyn Jones

Tests for coefficients of individual variables: eyeballing standard errors, Wald tests, and calculating p-values using the tail areas screen in MLwiN. Tests for comparing models: the Likelihood Ratio Test and the Deviance Information Criterion.