Cambridge Research Methods course timetable

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Wed 17 Nov 2021 – Tue 30 Nov 2021

Wednesday 17 November 2021

10:00	Basic Quantitative Analysis (BQA-3) (3 of 4) Finished 10:00 - 12:30 SSRMP pre-recorded lecture(s) on Moodle Building upon the univariate techniques introduced in the Foundations in Applied Statistics (FiAS) module, these sessions aim to provide students with a thorough understanding of statistical methods designed to test associations between two variables (bivariate statistics). Students will learn about the assumptions underlying each test, and will receive practical instruction on how to generate and interpret bivariate results using Stata. It introduces students to four of the most commonly used statistical tests in the social sciences: correlation, chi-square tests, t-tests, and analysis of variance (ANOVA). 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 apply these techniques to analyse real data using the statistical package, Stata. You will learn the following techniques: Cross-tabulations Scatterplots Covariance and correlation Nonparametric methods Two-sample t-tests ANOVA As well as viewing the pre-recorded mini lectures via Moodle and attending the live lab sessions, students are expected to do a few hours of independent study each week.
	Basic Quantitative Analysis (BQA-4) (3 of 4) Finished 10:00 - 12:30 SSRMP pre-recorded lecture(s) on Moodle Building upon the univariate techniques introduced in the Foundations in Applied Statistics (FiAS) module, these sessions aim to provide students with a thorough understanding of statistical methods designed to test associations between two variables (bivariate statistics). Students will learn about the assumptions underlying each test, and will receive practical instruction on how to generate and interpret bivariate results using Stata. It introduces students to four of the most commonly used statistical tests in the social sciences: correlation, chi-square tests, t-tests, and analysis of variance (ANOVA). 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 apply these techniques to analyse real data using the statistical package, Stata. You will learn the following techniques: Cross-tabulations Scatterplots Covariance and correlation Nonparametric methods Two-sample t-tests ANOVA As well as viewing the pre-recorded mini lectures via Moodle and attending the live lab sessions, students are expected to do a few hours of independent study each week.
14:00	Basic Quantitative Analysis (BQA-3) (4 of 4) Finished 14:00 - 16:00 SSRMP Zoom Building upon the univariate techniques introduced in the Foundations in Applied Statistics (FiAS) module, these sessions aim to provide students with a thorough understanding of statistical methods designed to test associations between two variables (bivariate statistics). Students will learn about the assumptions underlying each test, and will receive practical instruction on how to generate and interpret bivariate results using Stata. It introduces students to four of the most commonly used statistical tests in the social sciences: correlation, chi-square tests, t-tests, and analysis of variance (ANOVA). 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 apply these techniques to analyse real data using the statistical package, Stata. You will learn the following techniques: Cross-tabulations Scatterplots Covariance and correlation Nonparametric methods Two-sample t-tests ANOVA As well as viewing the pre-recorded mini lectures via Moodle and attending the live lab sessions, students are expected to do a few hours of independent study each week.
16:00	Basic Quantitative Analysis (BQA-4) (4 of 4) Finished 16:00 - 18:00 SSRMP Zoom Building upon the univariate techniques introduced in the Foundations in Applied Statistics (FiAS) module, these sessions aim to provide students with a thorough understanding of statistical methods designed to test associations between two variables (bivariate statistics). Students will learn about the assumptions underlying each test, and will receive practical instruction on how to generate and interpret bivariate results using Stata. It introduces students to four of the most commonly used statistical tests in the social sciences: correlation, chi-square tests, t-tests, and analysis of variance (ANOVA). 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 apply these techniques to analyse real data using the statistical package, Stata. You will learn the following techniques: Cross-tabulations Scatterplots Covariance and correlation Nonparametric methods Two-sample t-tests ANOVA As well as viewing the pre-recorded mini lectures via Moodle and attending the live lab sessions, students are expected to do a few hours of independent study each week.

Thursday 18 November 2021

09:00

Historical Sociological Methods

(4 of 4) Finished 09:00 - 10:00 SSRMP Zoom

The aim of this course is to introduce students to comparative historical research methods and encourage them to engage with practical exercises, to distinguish between different approaches in comparative historical research methods in social sciences.

Through the reading and seminars students will learn how to distinguish between different texts, theorists and approaches and learn how to apply these approaches to their own research and writing.

Comparative historical sociology studies major social transformations over periods of time and across different states, societies, and regions.

16:00

Geographical Information Systems (GIS) Workshop (3 of 4) Finished 16:00 - 17:00 SSRMP Zoom

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.

Monday 22 November 2021

10:00

Doing Multivariate Analysis (DMA-1) (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-1) (2 of 4) Finished 14:00 - 16:00 SSRMP Zoom

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 23 November 2021

09:00	Introduction to Python (1 of 2) Finished 09:00 - 12:00 SSRMP Zoom This module introduces the use of Python, a free programming language originally developed for statistical data analysis. Students will learn: Ways of reading data into Python How to manipulate data in major data types How to draw basic graphs and figures with Python 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.
10:30	Doing Qualitative Interviews (3 of 3) Finished 10:30 - 11:00 SSRMP Zoom 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
13:00	Introduction to Python (2 of 2) Finished 13:00 - 16:00 SSRMP Zoom This module introduces the use of Python, a free programming language originally developed for statistical data analysis. Students will learn: Ways of reading data into Python How to manipulate data in major data types How to draw basic graphs and figures with Python 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.
14:00	Survey Research and Design (1 of 6) Finished 14:00 - 15:30 SSRMP pre-recorded lecture(s) on Moodle The module aims to provide students with an introduction to and overview of survey methods and its uses and limitations. It will introduce students both to some of the main theoretical issues involved in survey research (such as survey sampling, non-response and question wording) and to practicalities of the design and analysis of surveys. The module consists of six 1.5 hour sessions, alternating between prerecorded lectures and practical exercises.
15:30	Survey Research and Design (2 of 6) Finished 15:30 - 17:00 SSRMP Zoom The module aims to provide students with an introduction to and overview of survey methods and its uses and limitations. It will introduce students both to some of the main theoretical issues involved in survey research (such as survey sampling, non-response and question wording) and to practicalities of the design and analysis of surveys. The module consists of six 1.5 hour sessions, alternating between prerecorded lectures and practical exercises.
17:30	Open Source Investigation for Academics (6 of 8) Finished 17:30 - 18:30 SSRMP Zoom Open Source Investigation for Academics is methodology course run by Cambridge’s Digital Verification Corps, in partnership with Cambridge’s Centre of Governance and Human Rights, Social Sciences Research Methods Programme and Cambridge Digital Humanities, as well as with the Citizen Evidence Lab at Amnesty International. NB. Places on this module are extremely limited, so please only make a booking if you are able to attend all of the sessions.

Wednesday 24 November 2021

10:00	Doing Multivariate Analysis (DMA-3) (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.
	Doing Multivariate Analysis (DMA-2) (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-2) (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.
16:00	Doing Multivariate Analysis (DMA-3) (2 of 4) Finished 16:00 - 18:00 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.