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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.

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.

Atlas.ti new Wed 5 Feb 2020   14:00 [Places]

These two sessions will provide a basic introduction to the management and analysis of qualitative data using Atlas.ti. The sessions will introduce participants to the following:

  • consideration of the advantages and limitations of using qualitative analysis software
  • setting-up a research project in Atlas.ti
  • the use of Atlas.ti's menus and tool bars
  • importing and organising data
  • starting data analysis using Atlas.ti’s coding tools
  • exploring data using query and visualization tools

Please note: Atlas.ti for Mac will not be covered.

Basic Quantitative Analysis (BQA Intensive) Wed 29 Jan 2020   09:00   [More dates...] Not bookable

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.

4 other events...

Date Availability
Mon 11 Nov 2019 10:00 Not bookable
Mon 11 Nov 2019 10:00 Not bookable
Wed 13 Nov 2019 10:00 Not bookable
Wed 13 Nov 2019 10:00 Not bookable
Causal Inference in the Social Sciences Wed 4 Mar 2020   14:00 [Places]

The challenge of causal inference is ubiquitous in social science. Nearly every research project fundamentally is about causes and effects.

This introductory session will:

  • 1. Introduce three main approaches to elucidate causal relationship: structural equation models, causal directed acyclic graphs, and the counterfactual/potential outcome framework;
  • 2. Explain the common challenges in empirical research;
  • 3. Talk through some principles and intuition of several research designs that can help researchers make stronger claims for causality.

The emphasis is on setting out applications of each approach, along with pros and cons, so that participants understand when a particular design may be more or less suitable to a research problem.

Comparative Historical Methods Tue 15 Oct 2019   16:00 Finished

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.

Conversation and Discourse Analysis Tue 21 Jan 2020   16:00 [Places]

The module will introduce students to the study of language use as a distinctive type of social practice. Attention will be focused primarily on the methodological and analytic principles of conversation analysis. (CA). However, it will explore the debates between CA and Critical Discourse Analysis (CDA), as a means of addressing the relationship between the study of language use and the study of other aspects of social life. It will also consider the roots of conversation analysis in the research initiatives of ethnomethodology, and the analysis of ordinary and institutional talk. It will finally consider the interface between CA and CDA.

Topics:

  • Session 1: The Roots of Conversation Analysis
  • Session 2: Ordinary Talk
  • Session 3: Institutional Talk
  • Session 4: Conversation Analysis and Critical Discourse Analysis
Critical Approaches to Discourse Analysis Wed 30 Oct 2019   13:30 Finished

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

Diary Methodology Mon 28 Oct 2019   14:00 In progress

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.

The internet is a great resource for humanities and social science data, but most information is apparently chaotic. In this course we will explore how to programmatically access information stored online, typically in html, to create neat, tabulated data ready for analysis. The uses of web scraping are diverse: previous versions of this course used the the programming language R to access data directly from newspapers, and by accessing live data streams using APIs (YouTube, Facebook, Google Maps, Wikipedia). The one-day course is structured as follows: in the morning, we will consider general principles of webscraping, illustrated through examples. This session is designed to create a toolkit needed to effectively collect different types of online data. Then in the afternoon the session will take a workshop format, where students may chose to begin applying web scraping to their their own research, or work through a structured set of exercises. If there are any particular data sources you are interested in accessing, do email me at dt444@cam.ac.uk, as I may be able to integrate an example directly relevant to your research into the session.

Different from past years, this course will be taught using Python, Jupyter Notebooks and the BeautifulSoup library. The course will not assume any prior knowledge of Python, but students are encouraged to learn a bit of the tools before the course. Any introductory MOOC course on Python (such as edx or Cursera) will provide an excellent introduction.

Doing Multivariate Analysis (DMA-1) Mon 25 Nov 2019   10:00   [More dates...] Not bookable

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.

3 other events...

Date Availability
Wed 27 Nov 2019 10:00 Not bookable
Wed 27 Nov 2019 10:00 Not bookable
Fri 14 Feb 2020 09:00 Not bookable
Doing Qualitative Interviews Mon 11 Nov 2019   14:00 In progress

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

Ethics in Data Collection and Use Mon 27 Jan 2020   13:00 [Places]

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.

Ethnographic Methods Tue 4 Feb 2020   15:30 [Places]

This module is an introduction to ethnographic fieldwork and analysis and is intended for students in fields other than anthropology. It provides an introduction to contemporary debates in ethnography, and an outline of how selected methods may be used in ethnographic study.

The ethnographic method was originally developed in the field of social anthropology, but has grown in popularity across several disciplines, including sociology, geography, criminology, education and organization studies.

Ethnographic research is a largely qualitative method, based upon participant observation among small samples of people for extended periods. A community of research participants might be defined on the basis of ethnicity, geography, language, social class, or on the basis of membership of a group or organization. An ethnographer aims to engage closely with the culture and experiences of their research participants, to produce a holistic analysis of their fieldsite.


Session 1: The Ethnographic Method
What is ethnography? Can ethnographic research and writing be objective? How does one conduct ethnographic research responsibly and ethically?

Session 2: Photography and Audio Recording in Ethnographic Work
What kinds of audiovisual equipment, and practices of photography and sound recording, can be used to support an ethnographer’s research process? What kinds of the epistemological, theoretical, social, and ethical considerations tend to arise around possible use of these technologies in anthropological fieldwork and analysis?

Session 3: Relationships in the Field
Ethnographic methodology and participant observation often involve researchers’ positioning in existing networks of social relations. This session is meant to help attendees manage interpersonal relationships with research participants from academic, political, and ethical perspectives. We will discuss when and why relationships in ethnographic fieldwork can be a reason for concern. We will reflect on the social distinctions that emerge when doing fieldwork with other people and their effects on researchers’ decision-making process. Finally, we will think through different fieldwork strategies when working with others, and how they impact the production of ethnographic knowledge.

Session 4: Defining the Fieldsite
This session is meant to equip attendees with the practical skill of how to determine, or work with, the limits of the fieldsite. Drawing on reflections on the challenges of working across sprawling geographical fields, as well as more enclosed geographical sites, we will discuss strategies for either strategically bounding the seemingly infinite fieldsite, or letting the boundaries of an already limited one work for you. We will also discuss how this methodological decision might impact the theoretical insights that emerge from a period of fieldwork, as well as how it impacts the interview process, methods of participant observation, and strategies for developing relationships with gatekeepers and interlocutors

PLEASE NOTE: Update on additional teaching - we have now scheduled the two additional sessions on 18 and 25 February. Further information on their content will follow.

Evaluation Methods Mon 16 Mar 2020   10:00 [Places]

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.
Event History Analysis new Mon 2 Mar 2020   09:00 [Places]

This course offers an introduction to event history analysis, which is a tool used for analyzing the occurrence and timing of events. Typical examples are life course transitions such as the transition to parenthood and partnership formation processes, labour market processes such as job promotions, mortality, and transitions to and from sickness and disability. The researcher may be interested in examining how the rate of a particular event varies over time or with individual characteristics, social conditions, or other factors. Event History Analysis lets the researcher handle censoring and truncation, include time-varying independent variables, account for unobserved heterogeneity (frailty), and so on. The course will rely on Stata as the main computing tool, but users of other statistical software could still benefit from the course. The course is taught through both lectures and lab sessions.

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
Factor Analysis Mon 2 Mar 2020   11:00 [Full]

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.

  • Session 1: Exploratory Factor Analysis Introduction
  • Session 2: Factor Analysis Applications
  • Session 3: CFA and Path Analysis with STATA
  • Session 4: Introduction to SEM and programming
Foundations in Applied Statistics (FiAS Intensive) Mon 27 Jan 2020   09:00 Not bookable

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
Further Topics in Multivariate Analysis (FTMA) 1 Tue 11 Feb 2020   14:00   [More dates...] Not bookable

This module is an extension of the three previous modules in the Basic Statistics stream, and introduces more complex and nuanced aspects of the theory and practice of mutivariate analysis. Students will learn the theory behind the methods covered, how to implement them in practice, how to interpret their results, and how to write intelligently about their findings. Half of the module is based in the lecture theatre; the other half is lab-based, in which students will work through practical exercises using the statistical software Stata.

Topics covered include:

  • Interaction effects in regression models: how to estimate these and how to interpret them
  • Marginal effects from interacted models
  • Ordered and categorical discrete dependent variable models (ordered and multinomial logit and probit)

To get the most out of the course, you should also expect to spend some time between sessions building your own statistical models.

1 other event...

Date Availability
Tue 11 Feb 2020 14:00 Not bookable
Geographical Information Systems (GIS) Workshop Thu 7 Feb 2019   14:00 Finished

This module is shared with Geography. Students from the Department of Geography MUST book places on this course via the Department; any bookings made by Geography students via the SSRMC portal will be cancelled.

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.

Introduction to Empirical Research Mon 14 Oct 2019   14:00 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:

  • 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
Introduction to Python new Mon 27 Apr 2020   09:00 [Full]

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.

Introduction to R Tue 21 Jan 2020   14:00 [Full]

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'''

Introduction to Stata Tue 28 Jan 2020   14:00   [More dates...] [Places]

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.

1 other event...

Date Availability
Tue 12 Nov 2019 14:00 In progress
Issues in Measurement: Validity and Reliability Mon 3 Feb 2020   14:00 [Places]

This short two-hour course will provide an introduction to measurement issues in the social sciences. We design questions (or "survey instruments") to gain information on the concepts we are researching. Two prime considerations in whether an instrument is effective are validity (does our instrument actually measure what we want it to measure?) and reliability (does our instrument give consistent results across a range of different situations?) Considerations of validity and reliability are important across many areas of social science, including the measurement of personality and mental health; attitudes; ability tests; substance use disorders; and cultural differences and similarities between various groups. The course will discuss the importance, concepts, and types of validity and reliability. We will also briefly look at some statistical techniques for validity and reliability checks: Cronbach’s Alpha, Kappa coefficient, and Factor Analysis.

Merging and Linking Data Sets Mon 2 Dec 2019   16:00 [Places]

Merging and linking data sets are a process that researchers often encounter. In most cohort studies and longitudinal data sets, data on the same respondents who were interviewed at various times may be stored in different files. Or, data on different respondents but were interviewed at the same time, such as mothers and their children, may also be stored in various files. In either case, we may want to merge/link the files together before performing further analyses. This course will discuss two different ways of combining data files: merge (one-to-one merging and one-to-many merging) and append, and will demonstrate how to use ‘merge’ and ‘append’ commands in Stata.

Meta Analysis Mon 9 Mar 2020   09:00 [Full]

In this module students will be introduced to meta-analysis, a powerful statistical technique allowing researchers to synthesize the available evidence for a given research question using standardized (comparable) effect sizes across studies. The sessions teach students how to compute treatment effects, how to compute effect sizes based on correlational studies, how to address questions such as what is the association of bullying victimization with depression? The module will be useful for students who seek to draw statistical conclusions in a standardized manner from literature reviews they are conducting.

Microsoft Access: Database Design and Use Tue 26 Nov 2019   14:00 [Places]

These two sessions will provide a basic introduction to the management and analysis of relational databases, using Microsoft Access and a set of historical datasets. The workshops will introduce participants to the following:

  • The use of Access’s menus and tool bars
  • Viewing and browsing data tables
  • Creating quick forms formulating queries
  • Developing queries using Boolean operators
  • Performing simple statistical operations
  • Linking tables and working with linked tables
  • Querying multiple tables
  • Data transformation.
Mixed Methods Wed 17 Oct 2018   14:00 Finished

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.

Multilevel Modelling Wed 11 Mar 2020   09:30 [Full]

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.

  • Introduction to Stata/MLM theory
  • Applications I - Random intercept models
  • Applications II - Random slope models
  • Applications III - Revision session/growth models
NVivo Mon 19 Nov 2018   14:00 Finished

These two sessions will provide a basic introduction to the management and analysis of qualitative data using NVivo 12 for Windows*. The sessions will introduce participants to the following:

  • consideration of the advantages and limitations of using qualitative analysis software such as NVivo 12
  • setting-up a research project in NVivo
  • the use of NVivo’s menus and tool bars
  • importing and organising data
  • starting data analysis using NVivo’s coding tools
  • exploring data using query and visualization tools

Please note: NVivo for Mac will not be covered.

Panel Data Analysis (Intensive) Wed 26 Feb 2020   09:00 [Full]

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 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.

Power Analysis Mon 11 Feb 2019   14:00 Finished

This two-hour short course will introduce students to the concept of power analysis (also known as power calculations), type I and II errors, and how to do power analysis for T test, correlation and analysis of variance. Students should not expect to learn complex power analysis for structural equation modeling, multilevel modeling (the SSRMC offers individual courses on both) in this introductory course (Stata currently does not have commands for these analyses). This course aims to provide an easy and intuitive rationale behind the technique, as well as hands-on practice in how to perform power analysis in Stata.

Power analysis is an important skill for anyone doing statistical research; it is particularly useful when writing a grant proposal, and is sometimes required by funders. It involves calculating the number of observations required to undertake a given statistical analysis. If a sample is too small, significant associations may not be detectable, even though they may be present in the population from which the sample is drawn. Power analysis is useful when:

  • You plan to collect data for research, and want to calculate how many subjects are needed
  • You need to plan how much time and/or money to allow for a research project
  • Your face budget constraints in your research, and need to establish whether the research is feasible
  • You are writing a grant proposal which asks for a power calculation
Practical introduction to MATLAB Programming Thu 10 Oct 2019   10:00 Finished

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

Propensity Score Matching Wed 19 Feb 2020   09:00 [Places]

Propensity score matching (PSM) is a technique that simulates an experimental study in an observational data set in order to estimate a causal effect. In an experimental study, subjects are randomly allocated to “treatment” and “control” groups; if the randomisation is done correctly, there should be no differences in the background characteristics of the treated and non-treated groups, so any differences in the outcome between the two groups may be attributed to a causal effect of the treatment. An observational survey, by contrast, will contain some people who have been subject to the “treatment” and some people who have not, but they will not have not been randomly allocated to those groups. The characteristics of people in the treatment and control groups may differ, so differences in the outcome cannot be attributed to the treatment. PSM attempts to mimic the experimental situation trial by creating two groups from the sample, whose background characteristics are virtually identical. People in the treatment group are “matched” with similar people in the control group. The difference between the treatment and control groups in this case should may therefore more plausibly be attributed to the treatment itself. PSM is widely applied in many disciplines, including sociology, criminology, economics, politics, and epidemiology. The module covers the basic theory of PSM, the steps in the implementation (e.g. variable choice for matching and types of matching algorithms), and assessment of matching quality. We will also work through practical exercises using Stata, in which students will learn how to apply the technique to the analysis of real data and how to interpret the results.

Psychometrics Tue 15 Oct 2019   14:00 Finished

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

Public Policy Analysis Mon 24 Feb 2020   14:00 [Places]

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.

Qualitative Interviews with Vulnerable Groups new Tue 11 Feb 2020   09:30 [Full]

Qualitative Interviews with Vulnerable Groups

Qualitative interviews are often used in the social sciences to learn more about the world and can be particularly appropriate for people we might class as vulnerable. The course will try to achieve two things. First, it will have a strong practical arc, guiding students through the complete process of designing and delivering interviews and what to do with the data when you have it. It is particularly important, therefore, that students come to the course prepared with a research question in mind (it does not have to be your actual dissertation topic). Second, we will repeatedly think carefully about the challenges of interviewing with populations that are deemed vulnerable (especially prisoners, women in the criminal justice system, and people living with trauma). We will explore how, in all stages of the research cycle, questions of ethics and the importance of understanding ‘whole people’ remain pertinent.

In the first session we will think about how to frame a study and research question, and how to design an interview schedule that allows you to access your question sensibly and creatively! We will also think about the challenges of interviewing those with trauma, in particular, as a case study.

In the second session we will think through the challenges of actually undertaking interviews in the field. Many hints and tip will be shared, and students will be encouraged to undertake a short mock interview.

In the third session we explore various ways in which to approach a mass of interview data and different approaches towards analysis.

In the final session, we burrow down into analysis and talk about how to write up your research.

In both of the final sessions students will be asked to engage with real interview transcripts that have been anonymised.

Reading List

The following list of reading materials provide useful context for the course, but is not mandatory:

Van der Kolk, B. A. (2015). The body keeps the score: Brain, mind, and body in the healing of trauma. Penguin Books.

Levine, P. A. (2010). In an unspoken voice: How the body releases trauma and restores goodness. North Atlantic Books.

Noaks, L. and Wincup, E. (2004) Criminological Research: Understanding qualitative methods, London: Sage Publications. (An excellent overview of qualitative research methods, with useful examples.)

Brinkmann, S. and Kvale, S. (2015) InterViews: Learning the craft of qualitative research interviewing, 3rd ed., Los Angeles: Sage.

Maruna, S. (2001) Making Good: How ex-convicts reform and rebuild their lives, Washington, DC: American Psychological Association.

Becker, H.S. (1998) Tricks of the Trade: How to think about your research while you're doing it, Chicago, IL: University of Chicago Press.

Spradley, J. (1996) The Ethnographic Interview, London: Harcourt Brace Jovanovich. Or see the (1979) edition, New York: Holt, Rinehart & Winston.

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.

Reading and Understanding Statistics Mon 28 Oct 2019   16:00 In progress

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.

Research Ethics Mon 20 Jan 2020   15:00 [Places]

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.

Researching Organisations Fri 22 Nov 2019   11:00 [Places]

This course provides an introduction to some of the methodological issues involved in researching organisations. Drawing on examples of studies carried out in a wide range of different types of organisation, the aim will be to explore practical strategies to overcome some of problems that are typically encountered in undertaking such studies.

Secondary Data Analysis Tue 10 Mar 2020   14:00 [Full]

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.

Social Network Analysis Wed 30 Jan 2019   09:00 Finished

This introductory course is for graduate students who have no prior training in social network analysis (SNA). In the morning, we overview SNA concepts and analyse key articles in the literature. In the afternoon, students learn to handle relational databases and code for SNA research using R.

Link to a key paper in the SNA literature: https://www.jstor.org/stable/2781822?Search=yes&resultItemClick=true&searchText=robust&searchText=action&searchText=padgett&searchUri=%2Faction%2FdoBasicSearch%3FQuery%3Drobust%2Baction%2Bpadgett&refreqid=search%3Ac4254643dc4499f2a9c8608f9e871d96&seq=1#page_scan_tab_contents

Structural Equation Modelling (Intensive) Wed 27 Feb 2019   09:00 Finished

This intensive one-day course on structural equation modelling will provide an introduction to SEM using the statistical software Stata. The aim of the course is to introduce structural equation modelling as an analytical framework and to familiarize participants with the applications of the technique in the social sciences.

The application of the structural equation modelling framework to a variety of social science research questions will be illustrated through examples of published papers. The examples used are drawn from very recent papers, as well as publications from the early days of the technique; some use path analysis using cross-national data, others confirmatory factor analysis, and other still full structural models, to test particular hypotheses. Some example papers may be found below, though they should not be treated as the gold standard, rather as an illustration of the variety of approaches and reporting techniques within SEM.

  • Duff, A., Boyle, E., Dunleavy, K., & Ferguson, J. (2004). The relationship between personality, approach to learning and academic performance. Personality and individual differences, 36(8), 1907-1920.
  • Garnier, M., & Hout, M. (1976). Inequality of educational opportunity in France and the United States. Social Science Research, 5(3), 225-246.
  • Helm, F., Müller-Kalthoff, H., Mukowski, R., & Möller, J. (2018). Teacher judgment accuracy regarding students' self-concepts: Affected by social and dimensional comparisons?. Learning and Instruction, 55, 1-12.
  • Parker, P. D., Jerrim, J., Schoon, I., & Marsh, H. W. (2016). A multination study of socioeconomic inequality in expectations for progression to higher education: The role of between-school tracking and ability stratification. American Educational Research Journal, 53(1), 6-32.

Students will engage in a critique of such examples, with the aim of gaining a better understanding of the SEM framework, as well as its application to real-life data. To further facilitate this application focus, the theoretical introduction will be accompanied by practical examples based on real, publicly-available data.

Survey Research and Design Mon 17 Feb 2020   15:00 [Full]

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 three three-hour sessions, split between lectures and practical exercises.

At the start of the module, the theoretical aspects of designing surveys will feature more, and topics covered include: the background to and history of survey research (with examples mostly drawn from political polling); an overview of the issues involved in analysing data from surveys conducted by others and some practical advice on how to evaluate such data; issues of sampling, non-response and different ways of doing surveys; issues related to questionnaire design (question wording, answer options, etc.) and ethical considerations. These lectures are relevant for all students taking the module, irrespective of whether they will conduct surveys themselves or are 'passive' users of survey results.

As the module progresses the practical aspects of designing surveys will feature more, particularly issues directly related to questionnaires (and less on issues of sampling), such as the wording of questions, the order of questions, and the use of different answer options. Most of the exercises will be provided by the instructors, but there will also be opportunities for students to bring in examples of surveys they would like to develop for their own research (and participants in the sessions may be asked to answer each other's surveys as a pilot test). We encourage all students registered for the module to attend the more practical sessions, but it will be of most direct relevance to those who are using, or plan to use, surveys in their research.

Time Series Analysis (Intensive) Wed 19 Feb 2020   09:00 [Full]

This module introduces the time series techniques relevant to forecasting in social science research and computer implementation of the methods. Background in basic statistical theory and regression methods is assumed. Topics covered include time series regression, Vector Error Correction and Vector Autoregressive Models, Time-varying Volatility, and ARCH models. The study of applied work is emphasized in this non-specialist module. Topics include:

  • Introduction to Time Series: Time series and cross-sectional data; Components of a time series, Forecasting methods overview; Measuring forecasting accuracy, Choosing a forecasting technique
  • Time Series Regression; Modelling linear and nonlinear trend; Detecting autocorrelation; Modelling seasonal variation by using dummy variables
  • Stationarity; Unit Root test; Cointegration
  • Vector Error Correlation and Vector Autoregressive models; Impulse responses and variance decompositions
  • Time-varying volatility and ARCH models; GARCH models
Weighting and Imputation Mon 24 Feb 2020   13:00 [Places]

In order for the findings of statistical analysis to be generalisable, the sample on which the analysis is based should be representative of the population from which it is drawn. But it is well known that some groups are under-represented in social science surveys: they may be harder to contact in the first place, less likely to agree to participate in the survey, or less likely to answer particular questions even if they do agree to participate.

This short module will introduce students to the techniques used by survey statisticians to overcome these problems. Weighting is used to deal with the problem of certain groups being under-represented in the sample; imputation is used to deal with missing answers to individual questions. Students will learn how and why weighting and imputation work, and will be taken through practical lab-based exercises which will teach them how to work with secondary data containing weights or imputed values.

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