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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.

1 other event...

Date Availability
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 Finished

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.

NOTE: Strike action is taking place between 25 November and 4 December 2019. If the strike goes ahead, this module will be affected. Please see the 'latest news' section on the home page of the SSRMP website for more information.

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

NOTE: Strike action is taking place between 25 November and 4 December 2019. If the strike goes ahead, this module will be affected. Please see the 'latest news' section on the home page of the SSRMP website for more information.

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 [Standby]

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 [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.

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