Social Sciences Research Methods Programme course timetable

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'''

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'''

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

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

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

(i) set out some basic barriers to causal inference in the social sciences and why this matters;
(ii) describe the counterfactual framework that underpins much of the discussion of causal inference;
(iii) talk through the intuition of several research designs that can help researchers make stronger claims for causal relationships.

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.

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

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.

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.

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.

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.

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.

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

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

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

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

This 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.

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: Ethnographies in Confinement
The practice of ethnography varies greatly depending on its setting. This session will consider the value, practice, epistemology and ethics of ethnographic research conducted in organisations, particularly those, such as prisons and psychiatric institutions, which confine people. How can we ensure access, and what are the political and ethical ramifications of doing so? How can we ethically conduct research in an institution in which people are held against their will? What are the epistemological issues when ‘free’ researchers conduct research in spaces of confinement?

Session 3: Ethnographies of Freedom
Building on the previous week’s session, this session this session will consider how the practice of ethnography differs when it is conducted in more permeable institutions. There are many advantages to conducting research where the setting is less boundaried – access is less complex, and consent can feel harder to gauge – but other issues are raised. What is the role of the ethnographer in something that looks like everyday life? What does it mean to leave the field? What is the difference between ‘research’ and ‘friendship’? And what actually is the site of study?

Session 4: 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?

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

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.

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.