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Social Sciences Research Methods Programme course timetable

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Fri 4 Dec 2020 – Mon 1 Feb

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[ No events on Fri 4 Dec 2020 ]

Monday 18 January

10:00
Foundations in Applied Statistics (FiAS-6) (1 of 4) Finished 10:00 - 12:00 SSRMP pre-recorded lecture online

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
Foundations in Applied Statistics (FiAS-5) (1 of 4) Finished 10:00 - 12:00 SSRMP pre-recorded lecture online

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
14:00
Foundations in Applied Statistics (FiAS-5) (2 of 4) Finished 14:00 - 16:00 SSRMP Zoom

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
16:00
Foundations in Applied Statistics (FiAS-6) (2 of 4) Finished 16:00 - 18:00 SSRMP Zoom

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

Tuesday 19 January

14:00
Introduction to R (1 of 2) Finished 14:00 - 18:00 Taught Online

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'''

15:00
Research Ethics in the Social Sciences (1 of 2) Finished 15:00 - 17:00 Taught Online

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.

Wednesday 20 January

10:00
Foundations in Applied Statistics (FiAS-6) (3 of 4) Finished 10:00 - 12:00 SSRMP pre-recorded lecture online

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
Foundations in Applied Statistics (FiAS-5) (3 of 4) Finished 10:00 - 12:00 SSRMP pre-recorded lecture online

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
14:00
Introduction to R (2 of 2) Finished 14:00 - 18:00 Taught Online

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'''

Foundations in Applied Statistics (FiAS-5) (4 of 4) Finished 14:00 - 16:00 SSRMP Zoom

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
16:00
Foundations in Applied Statistics (FiAS-6) (4 of 4) Finished 16:00 - 18:00 SSRMP Zoom

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

Thursday 21 January

14:00
Introduction to Empirical Research Finished 14:00 - 16:00 Taught Online

This module is for anyone considering studying on an SSRMP module but not sure which one/s to choose. It provides an overview of the research process and issues in research design. Through reflection on a broad overview of empirical research, the module aims to encourage students to consider where they may wish to develop their research skills and knowledge. The module will signpost the different modules, both quantitative and qualitative, offered by SSRMP and encourage students to consider what modules might be appropriate for their research and career development.

You will learn:

  • The research process and the different stages it might consist of
  • Issues related to research design
  • To consider what data you will need to address your research aims
  • To consider the best methods to collect and analyse your data
  • What modules are offered by SSRMP and how they might be appropriate to your needs

Monday 25 January

10:00
Basic Quantitative Analysis (BQA-5) (1 of 4) Finished 10:00 - 12:00 SSRMP pre-recorded lecture online

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:

  • Cross-tabulations
  • Scatterplots
  • Covariance and correlation
  • Nonparametric methods
  • Two-sample t-tests
  • ANOVA
  • Ordinary Least Squares (OLS)

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.

Basic Quantitative Analysis (BQA-6) (1 of 4) Finished 10:00 - 12:00 SSRMP pre-recorded lecture online

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:

  • Cross-tabulations
  • Scatterplots
  • Covariance and correlation
  • Nonparametric methods
  • Two-sample t-tests
  • ANOVA
  • Ordinary Least Squares (OLS)

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
Basic Quantitative Analysis (BQA-5) (2 of 4) Finished 14:00 - 16:00 SSRMP Zoom

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:

  • Cross-tabulations
  • Scatterplots
  • Covariance and correlation
  • Nonparametric methods
  • Two-sample t-tests
  • ANOVA
  • Ordinary Least Squares (OLS)

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.

16:00
Basic Quantitative Analysis (BQA-6) (2 of 4) Finished 16:00 - 18:00 SSRMP Zoom

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:

  • Cross-tabulations
  • Scatterplots
  • Covariance and correlation
  • Nonparametric methods
  • Two-sample t-tests
  • ANOVA
  • Ordinary Least Squares (OLS)

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 26 January

14:00
Introduction to Stata (1 of 2) Finished 14:00 - 18:00 SSRMP Zoom

The course will provide students with an introduction to the popular and powerful statistics package Stata. Stata is commonly used by analysts in both the social and natural sciences, and is the statistics package used most widely by the SSRMC. You will learn:

  • How to open and manage a dataset in Stata
  • How to recode variables
  • How to select a sample for analysis
  • The commands needed to perform simple statistical analyses in Stata
  • Where to find additional resources to help you as you progress with Stata

The course is intended for students who already have a working knowledge of statistics - it's designed primarily as a ""second language"" course for students who are already familiar with another package, perhaps R or SPSS. Students who don't already have a working knowledge of applied statistics should look at courses in our Basic Statistics Stream.

15:00
Research Ethics in the Social Sciences (2 of 2) Finished 15:00 - 17:00 Taught Online

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.

Wednesday 27 January

10:00
Basic Quantitative Analysis (BQA-5) (3 of 4) Finished 10:00 - 12:00 SSRMP pre-recorded lecture online

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:

  • Cross-tabulations
  • Scatterplots
  • Covariance and correlation
  • Nonparametric methods
  • Two-sample t-tests
  • ANOVA
  • Ordinary Least Squares (OLS)

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.

Basic Quantitative Analysis (BQA-6) (3 of 4) Finished 10:00 - 12:00 SSRMP pre-recorded lecture online

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:

  • Cross-tabulations
  • Scatterplots
  • Covariance and correlation
  • Nonparametric methods
  • Two-sample t-tests
  • ANOVA
  • Ordinary Least Squares (OLS)

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
Basic Quantitative Analysis (BQA-5) (4 of 4) Finished 14:00 - 16:00 SSRMP Zoom

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:

  • Cross-tabulations
  • Scatterplots
  • Covariance and correlation
  • Nonparametric methods
  • Two-sample t-tests
  • ANOVA
  • Ordinary Least Squares (OLS)

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.

16:00
Basic Quantitative Analysis (BQA-6) (4 of 4) Finished 16:00 - 18:00 SSRMP Zoom

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:

  • Cross-tabulations
  • Scatterplots
  • Covariance and correlation
  • Nonparametric methods
  • Two-sample t-tests
  • ANOVA
  • Ordinary Least Squares (OLS)

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.

Friday 29 January

10:00
Doing Multivariate Analysis (DMA 4) (1 of 4) Finished 10:00 - 12:00 SSRMP Zoom

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 4) (2 of 4) Finished 14:00 - 16:00 SSRMP Zoom

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 1 February

14:00
Public Policy Analysis (1 of 3) Finished 14:00 - 16:00 SSRMP Zoom

The analysis of policy depends on many disciplines and techniques and so is difficult for many researchers to access. This module provides a mixed perspective on policy analysis, taking both an academic and a practitioner perspective. This is because the same tools and techniques can be used in academic research on policy options and change as those used in practice in a policy environment. This course is provided as three 2 hour sessions delivered as a mix of lectures and seminars. No direct analysis work will be done in the sessions themselves, but some sample data and questions will be provided for students who wish to take the material into practice.

Feminist Research Practice new (1 of 4) POSTPONED 14:00 - 15:15 SSRMP Zoom

This series of workshops are aimed at students interested in interdisciplinary and feminist research practice. The course revolves around a simple query: what makes research feminist? It is the starting point to engage with classic and more contemporary writings on feminist knowledge production to answer some of the following questions: what are the ‘proper’ objects of feminist research? Who can do feminist research? Why do we do feminist research, and what is its relevance? Who do we cite in our research? We will have in-class discussions and hands-on assignments that will allow students to practice some of the main debates we will read about.