Cambridge Research Methods course timetable
Wednesday 11 March 2020
09:30 |
Multilevel Modelling
Finished
In this module, students will be introduced to multilevel modelling, also known as hierarchical linear modelling. MLM allows the user to analyse how outcomes are influenced by factors acting at multiple levels. So, for example, we might conceptualise children's educational process as being influenced by individual or family-level factors, as well as by factors operating at the level of the school or the neighbourhood. Similarly, outcomes for prisoners might be influenced by individual and/or family-level characteristics, as well as by the characteristics of the prison in which they are detained.
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12:00 |
A Critical Analysis of Null Hypothesis Testing and its Alternatives (Including Bayesian Analysis)
Finished
This course will provide a detailed critique of the methods and philosophy of the Null Hypothesis Significance Testing (NHST) approach to statistics which is currently dominant in social and biomedical science. We will briefly contrast NHST with alternatives, especially with Bayesian methods. We will use some computer code (Matlab and R) to demonstrate some issues. However, we will focus on the big picture rather on the implementation of specific procedures. |
14:00 |
Multilevel Modelling
Finished
In this module, students will be introduced to multilevel modelling, also known as hierarchical linear modelling. MLM allows the user to analyse how outcomes are influenced by factors acting at multiple levels. So, for example, we might conceptualise children's educational process as being influenced by individual or family-level factors, as well as by factors operating at the level of the school or the neighbourhood. Similarly, outcomes for prisoners might be influenced by individual and/or family-level characteristics, as well as by the characteristics of the prison in which they are detained.
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Monday 16 March 2020
10:00 |
Evaluation Methods
CANCELLED
This course aims to provide students with a range of specific technical skills that will enable them to undertake impact evaluation of policy. Too often policy is implemented but not fully evaluated. Without evaluation we cannot then tell what the short or longer term impact of a particular policy has been. On this course, students will learn the skills needed to evaluate particular policies and will have the opportunity to do some hands on data manipulation. A particular feature of this course is that it provides these skills in a real world context of policy evaluation. It also focuses primarily not on experimental evaluation (Random Control Trials) but rather quasi-experimental methodologies that can be used where an experiment is not desirable or feasible. Topics:
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13:45 |
Evaluation Methods
CANCELLED
This course aims to provide students with a range of specific technical skills that will enable them to undertake impact evaluation of policy. Too often policy is implemented but not fully evaluated. Without evaluation we cannot then tell what the short or longer term impact of a particular policy has been. On this course, students will learn the skills needed to evaluate particular policies and will have the opportunity to do some hands on data manipulation. A particular feature of this course is that it provides these skills in a real world context of policy evaluation. It also focuses primarily not on experimental evaluation (Random Control Trials) but rather quasi-experimental methodologies that can be used where an experiment is not desirable or feasible. Topics:
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Tuesday 17 March 2020
10:00 |
Evaluation Methods
CANCELLED
This course aims to provide students with a range of specific technical skills that will enable them to undertake impact evaluation of policy. Too often policy is implemented but not fully evaluated. Without evaluation we cannot then tell what the short or longer term impact of a particular policy has been. On this course, students will learn the skills needed to evaluate particular policies and will have the opportunity to do some hands on data manipulation. A particular feature of this course is that it provides these skills in a real world context of policy evaluation. It also focuses primarily not on experimental evaluation (Random Control Trials) but rather quasi-experimental methodologies that can be used where an experiment is not desirable or feasible. Topics:
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13:30 |
Evaluation Methods
CANCELLED
This course aims to provide students with a range of specific technical skills that will enable them to undertake impact evaluation of policy. Too often policy is implemented but not fully evaluated. Without evaluation we cannot then tell what the short or longer term impact of a particular policy has been. On this course, students will learn the skills needed to evaluate particular policies and will have the opportunity to do some hands on data manipulation. A particular feature of this course is that it provides these skills in a real world context of policy evaluation. It also focuses primarily not on experimental evaluation (Random Control Trials) but rather quasi-experimental methodologies that can be used where an experiment is not desirable or feasible. Topics:
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Wednesday 18 March 2020
11:00 |
Factor Analysis
Finished
This module introduces the statistical techniques of Exploratory and Confirmatory Factor Analyses. Exploratory Factor Analysis (EFA) is used to uncover the latent structure (dimensions) of a set of variables. It reduces the attribute space from a larger number of variables to a smaller number of factors. Confirmatory Factor Analysis (CFA) examines whether collected data correspond to a model of what the data are meant to measure. STATA will be introduced as a powerful tool to conduct confirmatory factor analysis. A brief introduction will be given to confirmatory factor analysis and structural equation modelling.
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14:00 |
Factor Analysis
Finished
This module introduces the statistical techniques of Exploratory and Confirmatory Factor Analyses. Exploratory Factor Analysis (EFA) is used to uncover the latent structure (dimensions) of a set of variables. It reduces the attribute space from a larger number of variables to a smaller number of factors. Confirmatory Factor Analysis (CFA) examines whether collected data correspond to a model of what the data are meant to measure. STATA will be introduced as a powerful tool to conduct confirmatory factor analysis. A brief introduction will be given to confirmatory factor analysis and structural equation modelling.
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Wednesday 8 April 2020
09:00 |
Introduction to Python
![]() This module introduces the use of Python, a free programming language originally developed for statistical data analysis. 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. |
16:00 |
Introduction to Python
![]() This module introduces the use of Python, a free programming language originally developed for statistical data analysis. 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. |
Monday 12 October 2020
10:00 |
This module is shared with Psychology. Students from the Department of Psychology MUST book places on this course via the Department; any bookings made by Psychology students via the SSRMP portal will be cancelled. The course focuses on practical hands-on variable handling and programming implementation using rather than on theory. This course is intended for those who have never programmed before, including those who only call/run Matlab scripts but are not familiar with how code works and how matrices are handled in Matlab. (Note that calling a couple of scripts is not 'real' programming.) MATLAB (C) is a powerful scientific programming environment optimal for data analysis and engineering solutions. More information on the programme and its uses can be found here More information on the course can be found here |
14:00 |
This module is shared with Psychology. Students from the Department of Psychology MUST book places on this course via the Department; any bookings made by Psychology students via the SSRMP portal will be cancelled. The course focuses on practical hands-on variable handling and programming implementation using rather than on theory. This course is intended for those who have never programmed before, including those who only call/run Matlab scripts but are not familiar with how code works and how matrices are handled in Matlab. (Note that calling a couple of scripts is not 'real' programming.) MATLAB (C) is a powerful scientific programming environment optimal for data analysis and engineering solutions. More information on the programme and its uses can be found here More information on the course can be found here |
Introduction to Empirical Research
Finished
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:
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Tuesday 13 October 2020
10:00 |
This module is shared with Psychology. Students from the Department of Psychology MUST book places on this course via the Department; any bookings made by Psychology students via the SSRMP portal will be cancelled. The course focuses on practical hands-on variable handling and programming implementation using rather than on theory. This course is intended for those who have never programmed before, including those who only call/run Matlab scripts but are not familiar with how code works and how matrices are handled in Matlab. (Note that calling a couple of scripts is not 'real' programming.) MATLAB (C) is a powerful scientific programming environment optimal for data analysis and engineering solutions. More information on the programme and its uses can be found here More information on the course can be found here |
14:00 |
This module is shared with Psychology. Students from the Department of Psychology MUST book places on this course via the Department; any bookings made by Psychology students via the SSRMP portal will be cancelled. The course focuses on practical hands-on variable handling and programming implementation using rather than on theory. This course is intended for those who have never programmed before, including those who only call/run Matlab scripts but are not familiar with how code works and how matrices are handled in Matlab. (Note that calling a couple of scripts is not 'real' programming.) MATLAB (C) is a powerful scientific programming environment optimal for data analysis and engineering solutions. More information on the programme and its uses can be found here More information on the course can be found here |
Monday 19 October 2020
15:00 |
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. |
Tuesday 20 October 2020
12:30 |
With such a large variety of qualitative research methods to choose from, creating a research design can be confusing and difficult without a sufficiently informed overview. This module aims to provide an overview by introducing qualitative data collection and analysis methods commonly used in social science research. The module provides a foundation for other SSRMP qualitative methods modules such as ethnography, discourse analysis, interviews, or diary research. Knowing what is ‘out there’ will help a researcher purposefully select further modules to study on, provide readings to deepen knowledge on specific methods, and will facilitate a more informed research design that contributes to successful empirical research. NB. This module has video content that needs watching prior to the advertised start date. Please register on the module's Moodle page by 12th October, 2020 |
Monday 26 October 2020
10:00 |
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:
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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:
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11:30 |
Reading and Understanding Statistics
Finished
This module is for students who don’t plan to use quantitative methods in their own research, but who need to be able to read and understand published research using quantitative methods. You will learn how to interpret graphs, frequency tables and multivariate regression results, and to ask intelligent questions about sampling, methods and statistical inference. The module is aimed at complete beginners, with no prior knowledge of statistics or quantitative methods. |
14:00 |
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:
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16:00 |
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:
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Tuesday 27 October 2020
12:30 |
With such a large variety of qualitative research methods to choose from, creating a research design can be confusing and difficult without a sufficiently informed overview. This module aims to provide an overview by introducing qualitative data collection and analysis methods commonly used in social science research. The module provides a foundation for other SSRMP qualitative methods modules such as ethnography, discourse analysis, interviews, or diary research. Knowing what is ‘out there’ will help a researcher purposefully select further modules to study on, provide readings to deepen knowledge on specific methods, and will facilitate a more informed research design that contributes to successful empirical research. NB. This module has video content that needs watching prior to the advertised start date. Please register on the module's Moodle page by 12th October, 2020 |
Wednesday 28 October 2020
10:00 |
he course offers an introduction to critical approaches to discourse analysis with a focus on linking theory with method. The topic will be approached from a broadly Foucauldian angle, considering discourse: “as groups of signs signifying elements referring to contents of representations, but as practices that systematically form the objects of which they speak.” The emphasis of the two lectures will be less upon what is known as ‘conversation analysis’ or ‘content analysis’ and more on text and speech as social practices that create reality rather than reflect it. In the first session, we will discuss the theoretical ideas behind discourse analysis – focusing especially on the Foucauldian approach. In the second lecture, we will not only dive into methodological discussions but also apply the method in class by analysing a number of texts with support of a qualitative text analysis software. Session 1: The origins of critical discourse analysis (the Frankfurt school, Foucault, post-structuralism, feminism); how theoretical backgrounds shape research design |
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:
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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:
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14:00 |
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:
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16:00 |
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:
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