Semester 2: Weeks 1 to 11
Semester 2: Weeks 1 to 11
2018/2019 Semester 2 Lectures
- Monday from 16:10 to 17:00
David Hume Tower LTs, Lecture Theatre B - Tuesday from 14:10 to 15:00
50 George Square, Lecture Theatre G.03
Date | Lecture Title | Description | |
21 | TBC | Chi-square tests, the final pieces | In this lecture we will recap chi-square tests of independence and discuss residuals, effect size, continuity corrections and what to do when data are dependent. |
22 | TBC | One sample & Paired t-test | In lectures 22 to 24 we discuss methods for testing the difference in means, collectively named t-test. In this lecture we consider comparing a single sample mean to a known value. |
23 | TBC | Independent sample t-test | In lectures 21 to 24 we discuss methods for testing the difference in means, collectively named t-test. In this lecture we consider testing the difference in means across independent groups. |
24 | TBC | Mann-whitney and Wilcoxon tests | Sometimes data do not meet the assumptions required for certain types of tests. When our t-test assumptions are not met, but we still wish to think about comparing averages across groups, we can use Mann-Whitney and Wilcoxon tests. |
25 | TBC | Covariance and Correlation | In lectures 25 and 26, we consider simple measures of association between two continuous variables. In this lecture we introduce the broad concept and discuss covariance. |
26 | TBC | Spearman Rank Order Correlation | In lectures 25 and 26, we consider simple measures of association between two continuous variables. In this lecture we introduce the correlation coefficient. |
27 | TBC | Introduction: Linear Models | For the remainder of the course (lectures 27-40) we will be discussing linear models (regression). In this lecture, we begin by introducing the core ideas and the types of questions we can approach. |
28 | TBC | Simple linear models: continuous predictor | Here we discuss a simple linear model with a single continuous predictor and show the links with the correlation coefficient. |
29 | TBC | Simple linear models: binary predictor | Here we discuss a simple linear model with a single binary predictor and show the links with the t-test. |
30 | TBC | Linear Model Assumptions | In this lecture we will talk about the assumptions of linear models (regression) |
31 | TBC | Experimental Data and one-way ANOVA | In lectures 31-34 we will be discussing the use of ANOVA for studying the differences in means across multiple levels of a single grouping variable, commonly called one-way ANOVA. |
32 | TBC | One-way ANOVA: Example and calculations | In lectures 31-34 we will be discussing the use of ANOVA for studying the differences in means across multiple levels of a single grouping variable, commonly called one-way ANOVA. |
33 | TBC | One-way ANOVA: Contrasts and model evaluation | In lectures 31-34 we will be discussing the use of ANOVA for studying the differences in means across multiple levels of a single grouping variable, commonly called one-way ANOVA. |
34 | TBC | One-way ANOVA: Assumptions and checks | In lectures 31-34 we will be discussing the use of ANOVA for studying the differences in means across multiple levels of a single grouping variable, commonly called one-way ANOVA. |
35 | TBC | Factorial ANOVA: Introduction 2x2 | In lectures 35 to 39, we will be discussion extensions to the one-way ANOVA where participants are grouped based on multiple categorical variables, commonly called factorial ANOVA. In doing so, we will introduce a critical concept in statistics in psychological science, namely, the interaction. |
36 | TBC | Factorial ANOVA: Interactions 2x2 | In lectures 35 to 39, we will be discussion extensions to the one-way ANOVA where participants are grouped based on multiple categorical variables, commonly called factorial ANOVA. In doing so, we will introduce a critical concept in statistics in psychological science, namely, the interaction. |
37 | TBC | Factorial ANOVA: Assumptions and checks | In lectures 35 to 39, we will be discussion extensions to the one-way ANOVA where participants are grouped based on multiple categorical variables, commonly called factorial ANOVA. In doing so, we will introduce a critical concept in statistics in psychological science, namely, the interaction. |
38 | TBC | Extending factorial ANOVA and interactions | In lectures 35 to 39, we will be discussion extensions to the one-way ANOVA where participants are grouped based on multiple categorical variables, commonly called factorial ANOVA. In doing so, we will introduce a critical concept in statistics in psychological science, namely, the interaction. |
39 | TBC | Extending factorial ANOVA and interactions | In lectures 35 to 39, we will be discussion extensions to the one-way ANOVA where participants are grouped based on multiple categorical variables, commonly called factorial ANOVA. In doing so, we will introduce a critical concept in statistics in psychological science, namely, the interaction. |
40 | TBC | And breathe..... | Final lecture of the year. This is simply an open Q&A about the course material. A specific exam revision lecture will be scheduled after the end of the course. |