Cambridge Research Methods course timetable

Using secondary data (that is, data collected by someone else, usually a government agency or large research organisation) has a number of advantages in social science research: sample sizes are usually larger than can be achieved by primary data collection, samples are more nearly representative of the populations they are drawn from, and using secondary data for a research project often represents significant savings in time and money. This short course, taught by Dr Deborah Wiltshire of the UK Data Archive, will discuss the advantages and limitations of using secondary data for research in the social sciences, and will introduce students to the wide range of available secondary data sources. The course is based in a computer lab; students will learn how to search online for suitable secondary data by browsing the database of the UK Data Archive.

The challenge of causal inference is ubiquitous in social science. Nearly every research project fundamentally is about causes and effects. This course will introduce graduate students to core issues about causal inference in quantitative social research, focusing especially on how one can move from demonstrating correlation to causation. The first lecture will define key concepts of correlates, risk factors, causes, mediators and moderators. The second lecture will discuss quasi-experimental research designs (studies without random assignment), and issues of “validity” in drawing causal conclusions. The third and fourth sessions will be lectures and practicals introducing two key analytic methods (propensity score matching and fixed effects regression models) that can be used to help identify causes. The course will focus on studies in which individual people are the basic unit of analyses, particularly longitudinal studies which follow the same people over multiple waves of assessment.

Topics:

• Key concepts, from correlates to causes
• Overview of quasi-experimental methods
• Propensity Score Matching

Note: this module was originally advertised as also covering fixed-effects regression models. Fixed-effects models have now been dropped from the content; students wishing to learn about them should attend the SSRMC module on panel data methods https://www.training.cam.ac.uk/jsss/event/2141519

The challenge of causal inference is ubiquitous in social science. Nearly every research project fundamentally is about causes and effects. This course will introduce graduate students to core issues about causal inference in quantitative social research, focusing especially on how one can move from demonstrating correlation to causation. The first lecture will define key concepts of correlates, risk factors, causes, mediators and moderators. The second lecture will discuss quasi-experimental research designs (studies without random assignment), and issues of “validity” in drawing causal conclusions. The third and fourth sessions will be lectures and practicals introducing two key analytic methods (propensity score matching and fixed effects regression models) that can be used to help identify causes. The course will focus on studies in which individual people are the basic unit of analyses, particularly longitudinal studies which follow the same people over multiple waves of assessment.

Topics:

• Key concepts, from correlates to causes
• Overview of quasi-experimental methods
• Propensity Score Matching

Note: this module was originally advertised as also covering fixed-effects regression models. Fixed-effects models have now been dropped from the content; students wishing to learn about them should attend the SSRMC module on panel data methods https://www.training.cam.ac.uk/jsss/event/2141519

This module provides an applied introduction to panel data analysis (PDA). Panel data are gathered by taking repeated observations from a series of research units (eg. individuals, firms) as they move through time. This course focuses primarily on panel data with a large number of research units tracked for a relatively small number of time points.

The module begins by introducing key concepts, benefits and pitfalls of PDA. Students are then taught how to manipulate and describe panel data in Stata. The latter part of the module introduces random and fixed effects panel models for continuous and dichotomous outcomes. The course is taught through a mixture of lectures and practical sessions designed to give students hands-on experience of working with real-world data from the British Household Panel Survey.

• Introduction to PDA: Concepts and uses
• Manipulating and describing panel data
• An overview of random effects, fixed effects and ‘hybrid’ panel models
• Panel models for dichotomous outcomes

This module provides an applied introduction to panel data analysis (PDA). Panel data are gathered by taking repeated observations from a series of research units (eg. individuals, firms) as they move through time. This course focuses primarily on panel data with a large number of research units tracked for a relatively small number of time points.

The module begins by introducing key concepts, benefits and pitfalls of PDA. Students are then taught how to manipulate and describe panel data in Stata. The latter part of the module introduces random and fixed effects panel models for continuous and dichotomous outcomes. The course is taught through a mixture of lectures and practical sessions designed to give students hands-on experience of working with real-world data from the British Household Panel Survey.

• Introduction to PDA: Concepts and uses
• Manipulating and describing panel data
• An overview of random effects, fixed effects and ‘hybrid’ panel models
• Panel models for dichotomous outcomes

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:

• Regression-based techniques
• Evaluation framework and concepts
• The limitations of regression based approaches and RCTs
• Before/After, Difference in Difference (DID) methods
• Computer exercise on difference in difference methods
• Instrumental variables techniques
• Regression discontinuity design.

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:

• Regression-based techniques
• Evaluation framework and concepts
• The limitations of regression based approaches and RCTs
• Before/After, Difference in Difference (DID) methods
• Computer exercise on difference in difference methods
• Instrumental variables techniques
• Regression discontinuity design.

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:

• Regression-based techniques
• Evaluation framework and concepts
• The limitations of regression based approaches and RCTs
• Before/After, Difference in Difference (DID) methods
• Computer exercise on difference in difference methods
• Instrumental variables techniques
• Regression discontinuity design.

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:

• Regression-based techniques
• Evaluation framework and concepts
• The limitations of regression based approaches and RCTs
• Before/After, Difference in Difference (DID) methods
• Computer exercise on difference in difference methods
• Instrumental variables techniques
• Regression discontinuity design.

Standard statistical techniques in the social sciences are good at uncovering relationships between variables, but less good at establishing whether these relationships are causal. If A and B are correlated, does that mean A "causes" B? That B "causes" A? Or could both A and B be driven by a third factor C?

Randomised controlled trials are a type of study often considered to be the gold standard in uncovering this kind of causality. Many students and early-career researchers avoid RCTs, assuming they are complex and expensive to run. However, that need not be the case. This module will explain the theory of RCTs, how they are implemented, and will encourage participants to think about how they might design an RCT in their own field of work.

Standard statistical techniques in the social sciences are good at uncovering relationships between variables, but less good at establishing whether these relationships are causal. If A and B are correlated, does that mean A "causes" B? That B "causes" A? Or could both A and B be driven by a third factor C?

Randomised controlled trials are a type of study often considered to be the gold standard in uncovering this kind of causality. Many students and early-career researchers avoid RCTs, assuming they are complex and expensive to run. However, that need not be the case. This module will explain the theory of RCTs, how they are implemented, and will encourage participants to think about how they might design an RCT in their own field of work.

This course will introduce students to the approach called "Exploratory Data Analysis" (EDA) where the aim is to extract useful information from data, with an enquiring, open and sceptical mind. It is, in many ways, an antidote to many advanced modelling approaches, where researchers lose touch with the richness of their data. Seeing interesting patterns in the data is the goal of EDA, rather than testing for statistical significance. The course will also consider the recent critiques of conventional "significance testing" approaches that have led some journals to ban significance tests.

Students who take this course will hopefully get more out of their data, achieve a more balanced overview of data analysis in the social sciences.

• To understand that the emphasis on statistical significance testing has obscured the goals of analysing data for many social scientists.
• To discuss other ways in which the significance testing paradigm has perverted scientific research, such as through the replication crisis and fraud.
• To understand the role of graphics in EDA

Please note - due to the change of lecturer, the description and some of the materials/reading for this module may change.

Ethics is becoming an increasingly important issue for all researchers and the aim of this session is to demonstrate the practical value of thinking seriously and systematically about what constitutes ethical conduct in social science research. The session will involve some small-group work.

This event details how the SSRMC works, more about the modules we offer, and everything you need to know about making a booking.

NB. ALL STUDENTS WISHING TO TAKE SSRMC COURSES THIS YEAR ARE EXPECTED TO ATTEND THIS INDUCTION SESSION

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 SSRMC 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: https://www.mathworks.com/products/matlab.html

More information on the course can be found, here: http://www.psychol.cam.ac.uk/grads/grads/pg-prog/programming#section-0

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 SSRMC 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: https://www.mathworks.com/products/matlab.html

More information on the course can be found, here: http://www.psychol.cam.ac.uk/grads/grads/pg-prog/programming#section-0

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 SSRMC 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: https://www.mathworks.com/products/matlab.html

More information on the course can be found, here: http://www.psychol.cam.ac.uk/grads/grads/pg-prog/programming#section-0

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 SSRMC 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: https://www.mathworks.com/products/matlab.html

More information on the course can be found, here: http://www.psychol.cam.ac.uk/grads/grads/pg-prog/programming#section-0

This module is for anyone considering studying on an SSRMC 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 SSRMC 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 SSRMC and how they might be appropriate to your needs

This module is for anyone considering studying on an SSRMC 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 SSRMC 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 SSRMC and how they might be appropriate to your needs

An introduction to the design, validation and implementation of tests and questionnaires in social science research, using both Classical Test Theory (CTT) and modern psychometric methods such as Item Response Theory (IRT). This course aims to enable students to: be able to construct and validate a test or questionnaire; understand the strengths, weaknesses and limitations of existing tests and questionnaires; appreciate the impact and potential of modern psychometric methods in the internet age.

Week 1: Introduction to psychometrics
a. Psychometrics, ancient and modern. Classical Test Theory
b. How to design and build your own psychometric test

Week 2: Testing in the online environment
a. Testing via the internet. How to, plus do’s and don’ts
b. Putting your test online

Week 3: Modern Psychometrics
a. Item Response Theory (IRT) models and their assumptions
b. Advanced assessment using computer adaptive testing

Week 4: Implementing adaptive tests online
a. How to automatically generate ability items
b. Practical

These four sessions will introduce students to comparative historical research methods, emphasizing their qualitative dimensions. In the first session, we will analyze some contemporary classics within this genre. In the second and third sessions, we will review and distinguish among a variety of intellectual justifications for this genre as a methodology. In the final session, we will focus on a "state of the art" defence of qualitative and comparative-historical research, both in theory and practice.

This course will introduce students to the general philosophical debates concerning scientific methodology, assessing their ramifications for the conduct of qualitative social research. It will enable students to critically evaluate major programmes in the philosophy of sciences, considering whether there are important analytic differences between the social and natural sciences; and whether qualitative methods themselves comprise a unified approach to the study of social reality.