Social Sciences Research Methods Programme course timetable

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

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.

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.

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.

This module is shared with Geography. Students from the Department of Geography MUST book places on this course via the Department; any bookings made by Geography students via the SSRMC portal will be cancelled.

This workshop series aims to provide introductory training on Geographical Information Systems. Material covered includes the construction of geodatabases from a range of data sources, geovisualisation and mapping from geodatasets, raster-based modeling and presentation of maps and charts and other geodata outputs. Each session will start with an introductory lecture followed by practical exercises using GIS software.

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.

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.

Merging and linking data sets are a process that researchers often encounter. In most cohort studies and longitudinal data sets, data on the same respondents who were interviewed at various times may be stored in different files. Or, data on different respondents but were interviewed at the same time, such as mothers and their children, may also be stored in various files. In either case, we may want to merge/link the files together before performing further analyses. This course will discuss two different ways of combining data files: merge (one-to-one merging and one-to-many merging) and append, and will demonstrate how to use ‘merge’ and ‘append’ commands in Stata.

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.

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.

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.

This is an introductory course for students who have little or no prior training in statistics.

The module is divided between pre-recorded mini-lectures, in which you'll learn the relevant theory, and hands-on live practical sessions in Zoom, in which you will learn how to analyse real data using the statistical package, Stata.

You will learn:

• The key features of quantitative analysis, and how it differs from other types of empirical analysis
• The basics of formal hypothesis testing
• Basic concepts: what is a variable? what is the distribution of a variable? and how can we best represent a distribution graphically?
• Features of statistical distributions: measures of central tendency and dispersion
• The normal distribution
• Why statistical testing works
• Statistical methods used to test simple hypotheses
• How to use Stata

This is an introductory course for students who have little or no prior training in statistics.

The module is divided between pre-recorded mini-lectures, in which you'll learn the relevant theory, and hands-on live practical sessions in Zoom, in which you will learn how to analyse real data using the statistical package, Stata.

You will learn:

• The key features of quantitative analysis, and how it differs from other types of empirical analysis
• The basics of formal hypothesis testing
• Basic concepts: what is a variable? what is the distribution of a variable? and how can we best represent a distribution graphically?
• Features of statistical distributions: measures of central tendency and dispersion
• The normal distribution
• Why statistical testing works
• Statistical methods used to test simple hypotheses
• How to use Stata

This is an introductory course for students who have little or no prior training in statistics.

The module is divided between pre-recorded mini-lectures, in which you'll learn the relevant theory, and hands-on live practical sessions in Zoom, in which you will learn how to analyse real data using the statistical package, Stata.

You will learn:

• The key features of quantitative analysis, and how it differs from other types of empirical analysis
• The basics of formal hypothesis testing
• Basic concepts: what is a variable? what is the distribution of a variable? and how can we best represent a distribution graphically?
• Features of statistical distributions: measures of central tendency and dispersion
• The normal distribution
• Why statistical testing works
• Statistical methods used to test simple hypotheses
• How to use Stata

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:

• Ways of reading data into R
• How to manipulate data in major data types
• How to draw basic graphs and figures with R
• How to summarise data using descriptive statistics
• How to perform basic inferential statistics

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.

For an online example of how R can be used: https://www.ssc.wisc.edu/sscc/pubs/RFR/RFR_Introduction.html'''

Ethics is becoming an increasingly important issue for all researchers, particularly in the covid-19 era. The aim of this session is twofold: (I) to demonstrate the practical value of thinking seriously and systematically about what constitutes ethical conduct in social science research; (II) to discuss the new valences of research in the pandemic era and develop new practices to tackle the insecurity it has created.

This three-hour session will be delivered via Zoom, and involve mini-lectures, small group work, and group discussions.

This is an introductory course for students who have little or no prior training in statistics.

The module is divided between pre-recorded mini-lectures, in which you'll learn the relevant theory, and hands-on live practical sessions in Zoom, in which you will learn how to analyse real data using the statistical package, Stata.

You will learn:

• The key features of quantitative analysis, and how it differs from other types of empirical analysis
• The basics of formal hypothesis testing
• Basic concepts: what is a variable? what is the distribution of a variable? and how can we best represent a distribution graphically?
• Features of statistical distributions: measures of central tendency and dispersion
• The normal distribution
• Why statistical testing works
• Statistical methods used to test simple hypotheses
• How to use Stata

This is an introductory course for students who have little or no prior training in statistics.

The module is divided between pre-recorded mini-lectures, in which you'll learn the relevant theory, and hands-on live practical sessions in Zoom, in which you will learn how to analyse real data using the statistical package, Stata.

You will learn:

• The key features of quantitative analysis, and how it differs from other types of empirical analysis
• The basics of formal hypothesis testing
• Basic concepts: what is a variable? what is the distribution of a variable? and how can we best represent a distribution graphically?
• Features of statistical distributions: measures of central tendency and dispersion
• The normal distribution
• Why statistical testing works
• Statistical methods used to test simple hypotheses
• How to use Stata

This is an introductory course for students who have little or no prior training in statistics.

The module is divided between pre-recorded mini-lectures, in which you'll learn the relevant theory, and hands-on live practical sessions in Zoom, in which you will learn how to analyse real data using the statistical package, Stata.

You will learn:

• The key features of quantitative analysis, and how it differs from other types of empirical analysis
• The basics of formal hypothesis testing
• Basic concepts: what is a variable? what is the distribution of a variable? and how can we best represent a distribution graphically?
• Features of statistical distributions: measures of central tendency and dispersion
• The normal distribution
• Why statistical testing works
• Statistical methods used to test simple hypotheses
• How to use Stata