Weeks 7 to 11

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Weeks 7 to 11

11
Interactions: Categorical*Continuous
In the next three lectures we will discuss interactions between different types of variables. We will begin by discussing categorical by continuous variable interactions via a worked example.
David Hume Tower, Lecture Theatre C
30/10/2018 - 1:10pm to 2:00pm
12
Interactions: Continuous*Continuous
In this lecture we will extend the previous discussion to continuous by continuous variable interactions, and discuss in more detail different types of interaction, and methods to probe identified interactions in order to interpret them more completely.
Lecture Theatre 2, Appleton Tower
31/10/2018 - 11:10am to 12:00pm
13
Interactions: Categorical*Categorical
In the final of three lectures, we will discuss categorical by categorical variable interactions. In the first instance we will focus on dummy coded variables, before comparing this to effects coded variables. This will lead us into a discussion of the relation between ANOVA and regression in the next lecture.
David Hume Tower, Lecture Theatre C
06/11/2018 - 1:10pm to 2:00pm
14
Just one model: ANOVA and regression
In this lecture, we will explicitly pull together elements of the previous lectures to demonstrate the equivalence of ANOVA and regression models, broadly referred to as general linear models. We will advocate the use of the linear model framework - as opposed to ANOVA - due to the increased flexibility of the approach.
Lecture Theatre 2, Appleton Tower
07/11/2018 - 11:10am to 12:00pm
15
Repeated measures ANOVA
In this lecture we will discussed repeated measure ANOVA and the associated experimental designs.
David Hume Tower, Lecture Theatre C
13/11/2018 - 1:10pm to 2:00pm
16
Repeated measures ANOVA & linear mixed models
Here we will continue to discuss repeated measures ANOVA, and integrate this with the general linear model through a conceptual introduction to mixed models.
Lecture Theatre 2, Appleton Tower
14/11/2018 - 11:10am to 12:00pm
17
Practical Issues in Data Analysis
Out in the big bad world of practical data analysis, lots of issues can occur that make analysis more difficult and interpretation more complex. In the next two lectures, we will introduce and explain a number of the most common complexities along with common approaches to dealing with them. These will include topics of causality vs. prediction, confounding, suppression, Simpson's paradox, range restriction and missing data. We will also discuss extensions to the linear model that has been described in this course that handle different types of data.
David Hume Tower, Lecture Theatre C
20/11/2018 - 1:10pm to 2:00pm
18
Practical Issues in Data Analysis
Out in the big bad world of practical data analysis, lots of issues can occur that make analysis more difficult and interpretation more complex. In the next two lectures, we will introduce and explain a number of the most common complexities along with common approaches to dealing with them. These will include topics of causality vs. prediction, confounding, suppression, Simpson's paradox, range restriction and missing data. We will also discuss extensions to the linear model that has been described in this course that handle different types of data.
Lecture Theatre 2, Appleton Tower
21/11/2018 - 11:10am to 12:00pm
19
Power Analysis
In RMS1, we introduced the idea of power alongside hypothesis testing. We will come back to power here, and discuss the calculation of power via calculations and monte carlo simulation.
David Hume Tower, Lecture Theatre C
27/11/2018 - 1:10pm to 2:00pm
20
Course Q&A: Revision
In the final lecture of the course, we will have an open Q&A session covering all of the content of the course and preparation for the exam.
Lecture Theatre 2, Appleton Tower
28/11/2018 - 11:10am to 12:00pm