Social Sciences Research Methods Programme course timetable
Monday 20 November 2017
| 09:00 |
Researching Organisations
Finished
This course provides an introduction to some of the methodological issues involved in researching organisations. Drawing on examples of studies carried out in a wide range of different types of organisation, the aim will be to explore practical strategies to overcome some of problems that are typically encountered in undertaking such studies. Topics covered include:
|
| 10:00 |
Doing Multivariate Analysis (DMA-1)
Finished
This module will introduce you to the theory and practice of multivariate analysis, covering Ordinary Least Squares (OLS) and logistic regressions. You will learn how to read published results critically, to do simple multivariate modelling yourself, and to interpret and write about your results intelligently. Half of the module is based in the lecture theatre, and covers the theory behind multivariate regression; the other half is lab-based, in which students will work through practical exercises using statistical software. To get the most out of the course, you should also expect to spend some time between sessions having fun by building your own statistical models. |
| 14:00 |
Doing Multivariate Analysis (DMA-1)
Finished
This module will introduce you to the theory and practice of multivariate analysis, covering Ordinary Least Squares (OLS) and logistic regressions. You will learn how to read published results critically, to do simple multivariate modelling yourself, and to interpret and write about your results intelligently. Half of the module is based in the lecture theatre, and covers the theory behind multivariate regression; the other half is lab-based, in which students will work through practical exercises using statistical software. To get the most out of the course, you should also expect to spend some time between sessions having fun by building your own statistical models. |
| 16:00 |
Power Analysis
Finished
This two-hour short course will introduce students to the concept of power analysis (also known as power calculations), providing an easy and intuitive rationale behind the technique, as well as hands-on practice in how to perform power analysis in Stata. Power analysis is an important skill for anyone doing statistical research; it is particularly useful when writing a grant proposal, and is sometimes required by funders. It involves calculating the number of observations required to undertake a given statistical analysis. If a sample is too small, significant associations may not be detectable, even though they may be present in the population from which the sample is drawn. Power analysis is useful when:
|
Tuesday 21 November 2017
| 14:00 |
These two sessions will provide a basic introduction to the management and analysis of relational databases, using Microsoft Access and a set of historical datasets. The workshops will introduce participants to the following:
|
Wednesday 22 November 2017
| 10:00 |
Doing Multivariate Analysis (DMA-3)
Finished
This module will introduce you to the theory and practice of multivariate analysis, covering Ordinary Least Squares (OLS) and logistic regressions. You will learn how to read published results critically, to do simple multivariate modelling yourself, and to interpret and write about your results intelligently. Half of the module is based in the lecture theatre, and covers the theory behind multivariate regression; the other half is lab-based, in which students will work through practical exercises using statistical software. To get the most out of the course, you should also expect to spend some time between sessions having fun by building your own statistical models. |
|
Doing Multivariate Analysis (DMA-2)
Finished
This module will introduce you to the theory and practice of multivariate analysis, covering Ordinary Least Squares (OLS) and logistic regressions. You will learn how to read published results critically, to do simple multivariate modelling yourself, and to interpret and write about your results intelligently. Half of the module is based in the lecture theatre, and covers the theory behind multivariate regression; the other half is lab-based, in which students will work through practical exercises using statistical software. To get the most out of the course, you should also expect to spend some time between sessions having fun by building your own statistical models. |
|
| 14:00 |
Doing Multivariate Analysis (DMA-2)
Finished
This module will introduce you to the theory and practice of multivariate analysis, covering Ordinary Least Squares (OLS) and logistic regressions. You will learn how to read published results critically, to do simple multivariate modelling yourself, and to interpret and write about your results intelligently. Half of the module is based in the lecture theatre, and covers the theory behind multivariate regression; the other half is lab-based, in which students will work through practical exercises using statistical software. To get the most out of the course, you should also expect to spend some time between sessions having fun by building your own statistical models. |
| 16:00 |
Doing Multivariate Analysis (DMA-3)
Finished
This module will introduce you to the theory and practice of multivariate analysis, covering Ordinary Least Squares (OLS) and logistic regressions. You will learn how to read published results critically, to do simple multivariate modelling yourself, and to interpret and write about your results intelligently. Half of the module is based in the lecture theatre, and covers the theory behind multivariate regression; the other half is lab-based, in which students will work through practical exercises using statistical software. To get the most out of the course, you should also expect to spend some time between sessions having fun by building your own statistical models. |
Monday 27 November 2017
| 10:00 |
Doing Multivariate Analysis (DMA-1)
Finished
This module will introduce you to the theory and practice of multivariate analysis, covering Ordinary Least Squares (OLS) and logistic regressions. You will learn how to read published results critically, to do simple multivariate modelling yourself, and to interpret and write about your results intelligently. Half of the module is based in the lecture theatre, and covers the theory behind multivariate regression; the other half is lab-based, in which students will work through practical exercises using statistical software. To get the most out of the course, you should also expect to spend some time between sessions having fun by building your own statistical models. |
| 14:00 |
Doing Multivariate Analysis (DMA-1)
Finished
This module will introduce you to the theory and practice of multivariate analysis, covering Ordinary Least Squares (OLS) and logistic regressions. You will learn how to read published results critically, to do simple multivariate modelling yourself, and to interpret and write about your results intelligently. Half of the module is based in the lecture theatre, and covers the theory behind multivariate regression; the other half is lab-based, in which students will work through practical exercises using statistical software. To get the most out of the course, you should also expect to spend some time between sessions having fun by building your own statistical models. |
| 16:00 |
Workshop: Using Your Own Data
POSTPONED
All the SSRMC's statistics courses are hands-on: you'll learn how to analyse real data, using state-of-the-art statistical analysis packages. But sometimes things aren't so straightforward when it comes to using your own data: the data may not be in Stata format; it may be a funny "shape"; there may be no variable or value labels; or it may be very dirty. If you have completed your basic stats training and need a helping hand getting started with your own data, this workshop will help you to:
The workshop, based in a computer lab, is entirely devoted to helping students get started with their own data - there is no lecture component. You will need to bring your own data along. |
Tuesday 28 November 2017
| 14:00 |
These two sessions will provide a basic introduction to the management and analysis of relational databases, using Microsoft Access and a set of historical datasets. The workshops will introduce participants to the following:
|
Wednesday 29 November 2017
| 10:00 |
Doing Multivariate Analysis (DMA-3)
Finished
This module will introduce you to the theory and practice of multivariate analysis, covering Ordinary Least Squares (OLS) and logistic regressions. You will learn how to read published results critically, to do simple multivariate modelling yourself, and to interpret and write about your results intelligently. Half of the module is based in the lecture theatre, and covers the theory behind multivariate regression; the other half is lab-based, in which students will work through practical exercises using statistical software. To get the most out of the course, you should also expect to spend some time between sessions having fun by building your own statistical models. |
|
Doing Multivariate Analysis (DMA-2)
Finished
This module will introduce you to the theory and practice of multivariate analysis, covering Ordinary Least Squares (OLS) and logistic regressions. You will learn how to read published results critically, to do simple multivariate modelling yourself, and to interpret and write about your results intelligently. Half of the module is based in the lecture theatre, and covers the theory behind multivariate regression; the other half is lab-based, in which students will work through practical exercises using statistical software. To get the most out of the course, you should also expect to spend some time between sessions having fun by building your own statistical models. |
|
| 14:00 |
Doing Multivariate Analysis (DMA-2)
Finished
This module will introduce you to the theory and practice of multivariate analysis, covering Ordinary Least Squares (OLS) and logistic regressions. You will learn how to read published results critically, to do simple multivariate modelling yourself, and to interpret and write about your results intelligently. Half of the module is based in the lecture theatre, and covers the theory behind multivariate regression; the other half is lab-based, in which students will work through practical exercises using statistical software. To get the most out of the course, you should also expect to spend some time between sessions having fun by building your own statistical models. |
| 16:00 |
Doing Multivariate Analysis (DMA-3)
Finished
This module will introduce you to the theory and practice of multivariate analysis, covering Ordinary Least Squares (OLS) and logistic regressions. You will learn how to read published results critically, to do simple multivariate modelling yourself, and to interpret and write about your results intelligently. Half of the module is based in the lecture theatre, and covers the theory behind multivariate regression; the other half is lab-based, in which students will work through practical exercises using statistical software. To get the most out of the course, you should also expect to spend some time between sessions having fun by building your own statistical models. |
Tuesday 16 January 2018
| 14:00 |
Introduction to R (Lent)
Finished
This module introduces the use of R, a free programming language originally developed for statistical data analysis. In this course, we will use R through R Studio, a user-friendly interface. Students will learn:
This module is suitable for students who have no prior experience in programming, but participants will be assumed to have a good working knowledge of basic statistical techniques using another software package (for example Stata or SPSS). |
|
Experimental Methods
Finished
This course will constitute a practical introduction to experimental method and design suitable for students from any discipline who have had limited experience of empirical methods but who wish to be able to read and understand the experimental literature or to undertake their own experimental studies. The course includes:
At the end of the module, students will be equipped with the fundamental knowledge required to design and evaluate an experiment. |
Wednesday 17 January 2018
| 09:00 |
This is an introductory course for students who have little or no prior training in statistics. The module is divided between lectures, in which you'll learn the relevant theory, and hands-on practical sessions, in which you will learn how to analyze real data using the statistical package Stata. You will learn:
|
| 14:00 |
This is an introductory course for students who have little or no prior training in statistics. The module is divided between lectures, in which you'll learn the relevant theory, and hands-on practical sessions, in which you will learn how to analyze real data using the statistical package Stata. You will learn:
|
|
Experimental Methods
Finished
This course will constitute a practical introduction to experimental method and design suitable for students from any discipline who have had limited experience of empirical methods but who wish to be able to read and understand the experimental literature or to undertake their own experimental studies. The course includes:
At the end of the module, students will be equipped with the fundamental knowledge required to design and evaluate an experiment. |
Monday 22 January 2018
| 09:00 |
This module follows on from Foundations in Applied statistics, and will teach you the basics of common bivariate techniques (that is, techniques that examine the associations between two variables). The module is divided between lectures, in which you'll learn the relevant theory, and hands-on practical sessions, in which you will learn how to apply these techniques to the analysis of real data. Techniques to be covered include:
For best results, students should expect to do a few hours of private study and spend a little extra time in the computer labs, in addition to coming to class. |
| 14:00 |
This module follows on from Foundations in Applied statistics, and will teach you the basics of common bivariate techniques (that is, techniques that examine the associations between two variables). The module is divided between lectures, in which you'll learn the relevant theory, and hands-on practical sessions, in which you will learn how to apply these techniques to the analysis of real data. Techniques to be covered include:
For best results, students should expect to do a few hours of private study and spend a little extra time in the computer labs, in addition to coming to class. |
Tuesday 23 January 2018
| 14:00 |
Doing Qualitative Interviews
Finished
Face-to-face interviews are used to collect a wide range of information in the social sciences. They are appropriate for the gathering of information on individual and institutional patterns of behaviour; complex histories or processes; identities and cultural meanings; routines that are not written down; and life-history events. Face-to-face interviews thus comprise an appropriate method to generate information on individual behaviour, the reasons for certain patterns of acting and talking, and the type of connection people have with each other.
The first session provides an overview of interviewing as a social research method, then focuses on the processes of organising and conducting qualitative interviews. The second session explores the ethics and practical constraints of interviews as a research method, particularly relevant when attempting to engage with marginalised or stigmatised communities. The third session focuses on organisation and analysis after interviews, including interpretation through coding and close reading. This session involves practical examples from qualitative analysis software. The final session provides an opportunity for a hands-on session, to which students should bring their interview material (at whatever stage of the process: whether writing interview questions, coding or analysing data) in order to receive advice and support in taking the interview material/data to the next stage of the research process. Topics: 1. Conducting qualitative interviews 2. Ethics and practical constraints 3. Practical session: interpretation and analysis |
|
Introduction to R (Lent)
Finished
This module introduces the use of R, a free programming language originally developed for statistical data analysis. In this course, we will use R through R Studio, a user-friendly interface. Students will learn:
This module is suitable for students who have no prior experience in programming, but participants will be assumed to have a good working knowledge of basic statistical techniques using another software package (for example Stata or SPSS). |
|
| 16:00 |
Conversation and Discourse Analysis
Finished
The module will introduce students to the study of language use as a distinctive type of social practice. Attention will be focused primarily on the methodological and analytic principles of conversation analysis. (CA). However, it will explore the debates between CA and Critical Discourse Analysis (CDA), as a means of addressing the relationship between the study of language use and the study of other aspects of social life. It will also consider the roots of conversation analysis in the research initiatives of ethnomethodology, and the analysis of ordinary and institutional talk. It will finally consider the interface between CA and CDA. Topics:
|