SSRMC Training Programme 2018-19

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.

This is an introductory course for students whose research involves collecting, storing or analysing data using networked digital devices. Unless your research data is only collected using pen and paper or tape recorders and is written up on a manual typewriter, this course will be relevant to you. If you are planning to collect data online through either public or private communications, or you intend to share or publish data collected by other means it will be essential.

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 a lecture component and some small-group work.

Aims:
To allow students to distinguish between values, moral and ethical issues, encourage students to think about problems and dilemmas in conducting research, help students to gain an overview of ethical relationships, enable students to know when to ask for help, and prepare students in terms of defence of possible criticisms of their own research.

Topics:

• What do we mean by ethics?
• National and international policy frameworks
• Ethics and risk
• Ethics across disciplinary boundaries
• Dealing with ethical dilemmas
• The processes of applying for ethics approval within the University of Cambridge

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.

Neither quantitative nor qualitative data analysis has all the answers in social science research: qualitative research has depth and nuance but is not generalisable beyond the sample on which it is based, while quantitative research is generalisable but may lack depth.

A mixed methods approach, which uses evidence from both qualitative and quantitative approaches to shed light on a single research question, has the potential to gain the advantages of both approaches. However, genuine mixed methods work is not always easy. This short course will introduce students to the rationale behind the use of mixed methods approaches, and how to design mixed methods projects for best results.

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.

This is an introductory course for students who have little or no prior training in statistics. 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 analyze 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
• 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
• The basics of formal hypothesis testing
• Why statistical testing works
• Statistical methods used to test simple hypotheses
• How to use Stata

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.

This four-part workshop series provides an introduction to using solicited diaries as a research tool. The main goal of the course is to add diary methodology to students’ research toolboxes. It is a flexible and versatile tool that has been used by researchers in many fields, including public health, nursing, psychology, media studies, education, and sociology. The workshop is suitable for anybody interested in learning more about the method and/or using diaries in their research.

The course covers the use of qualitative and quantitative types of diaries, both as a self-standing tool and as a part of mixed-method research designs. The lectures and workshops aim to provide theoretical and practical foundations, as well as first-hand experience with solicited diaries as a research tool. The course also provides unique insights into diary data analysis and its challenges.

The course is equally driven by lectures and student participation/practicums. The initial workshop (Week 1) provides a solid theoretical introduction to the diary methodology, including the history of the method, qualitative and quantitative variants, modes of delivery, and use of technology. The follow-up workshops sequentially advance this knowledge base through practical exercises and discussions (Weeks 2 & 4), as well as a specialist lecture (Week 3) on data management, participant communication, ethics and data analysis.

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.

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 is an introductory course for students who have little or no prior training in statistics. 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 analyze 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
• 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
• The basics of formal hypothesis testing
• Why statistical testing works
• Statistical methods used to test simple hypotheses
• How to use Stata

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.

The focus of these two sessions will be the linking of theory to method, paying particular attention to the relationship between language or other forms of representation or communication and the broader social milieu with special attention to power relations. The topic will be approached from a broadly Foucauldian angle: Foucault writes that discourse “consists of not—of no longer—treating discourses 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 these two lectures will be less upon what is known as ‘conversation analysis’ or ‘content analysis’ and more on methods based on post-positivist methods and critical theory which emphasize how language and other social practices create reality rather than reflect it, and thus methods of interpreting discourse are themselves not ideologically or politically neutral practices.

Session 1: The origins of critical discourse analysis (the Frankfurt school, Foucault, post-structuralism, feminism); how theoretical backgrounds shape research design
Session 2: 'Doing' discourse analysis: analysing methods and approaches

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 is an introductory course for students who have little or no prior training in statistics. 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 analyze 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
• 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
• The basics of formal hypothesis testing
• Why statistical testing works
• Statistical methods used to test simple hypotheses
• How to use Stata

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.