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Social Sciences Research Methods Centre course timetable

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Mon 17 Dec – Mon 11 Feb 2019

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Tuesday 15 January 2019

14:00
Introduction to R (1 of 2) [Full] 14:00 - 18:00 University Information Services, Titan Teaching Room 1, New Museums Site

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'''

Wednesday 16 January 2019

14:00
Introduction to R (2 of 2) [Full] 14:00 - 18:00 University Information Services, Titan Teaching Room 1, New Museums Site

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'''

Monday 21 January 2019

09:00
Foundations in Applied Statistics (FiAS Intensive) (1 of 2) Not bookable 09:00 - 13:00 8 Mill Lane, Lecture Room 6

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
14:00
Foundations in Applied Statistics (FiAS Intensive) (2 of 2) Not bookable 14:00 - 18:00 University Information Services, Titan Teaching Room 1, New Museums Site

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

Tuesday 22 January 2019

14:00
Introduction to Stata (Lent) (1 of 2) [Full] 14:00 - 18:00 University Information Services, Titan Teaching Room 1, New Museums Site

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.

Causal Inference in the Social Sciences [Places] 14:00 - 16:00 8 Mill Lane, Lecture Room 6

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.

16:00
Conversation and Discourse Analysis (1 of 4) [Places] 16:00 - 17:30 8 Mill Lane, Lecture Room 1

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

Wednesday 23 January 2019

09:00
Basic Quantitative Analysis (BQA Intensive) (1 of 2) Not bookable 09:00 - 13:00 8 Mill Lane, Lecture Room 6

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.

14:00
Basic Quantitative Analysis (BQA Intensive) (2 of 2) Not bookable 14:00 - 18:00 University Information Services, Titan Teaching Room 1, New Museums Site

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.

Monday 28 January 2019

09:00
Doing Multivariate Analysis (DMA Intensive) (1 of 2) Not bookable 09:00 - 13:00 8 Mill Lane, Lecture Room 6

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.

14:00
Doing Multivariate Analysis (DMA Intensive) (2 of 2) Not bookable 14:00 - 18:00 University Information Services, Titan Teaching Room 1, New Museums Site

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.

Tuesday 29 January 2019

14:00
Introduction to Stata (Lent) (2 of 2) [Full] 14:00 - 18:00 University Information Services, Titan Teaching Room 1, New Museums Site

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.

16:00
Conversation and Discourse Analysis (2 of 4) [Places] 16:00 - 17:30 8 Mill Lane, Lecture Room 1

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

Wednesday 30 January 2019

09:00
Social Network Analysis (1 of 2) [Full] 09:00 - 13:00 8 Mill Lane, Lecture Room 1

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

14:00
Social Network Analysis (2 of 2) [Full] 14:00 - 18:00 University Information Services, Titan Teaching Room 1, New Museums Site

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

Monday 4 February 2019

13:00
Research Ethics (Lent) [Places] 13:00 - 16:00 8 Mill Lane, Lecture Room 7

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.

16:00
Ethics in Data Collection and Use [Places] 16:00 - 18:00 8 Mill Lane, Lecture Room 6

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.

Tuesday 5 February 2019

14:00
Further Topics in Multivariate Analysis (FTMA) (1 of 2) Not bookable 14:00 - 18:00 University Information Services, Titan Teaching Room 1, New Museums Site

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.

15:30
Ethnographic Methods (1 of 4) [Places] 15:30 - 17:00 8 Mill Lane, Lecture Room 4

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?

16:00
Conversation and Discourse Analysis (3 of 4) [Places] 16:00 - 17:30 8 Mill Lane, Lecture Room 1

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

Wednesday 6 February 2019

09:00
Digital Data Collection: Web scraping for the Humanities and Social Sciences (1 of 2) [Full] 09:00 - 13:00 8 Mill Lane, Lecture Room 1

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 course is made up of four tutorials, designed to build the tools needed to effectively collect different types of data. The uses of web scraping are diverse: in this course we will use the programming language R to first access data directly from newspapers, and secondly by accessing live data streams using APIs (YouTube, Facebook, Google Maps, Wikipedia). Collectively these sessions will give the skillsets necessary to use web scraping in students’ own research. Slides from last year’s sessions may be consulted here: http://fredheir.github.io/WebScraping

14:00
Digital Data Collection: Web scraping for the Humanities and Social Sciences (2 of 2) [Full] 14:00 - 18:00 University Information Services, Titan Teaching Room 1, New Museums Site

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 course is made up of four tutorials, designed to build the tools needed to effectively collect different types of data. The uses of web scraping are diverse: in this course we will use the programming language R to first access data directly from newspapers, and secondly by accessing live data streams using APIs (YouTube, Facebook, Google Maps, Wikipedia). Collectively these sessions will give the skillsets necessary to use web scraping in students’ own research. Slides from last year’s sessions may be consulted here: http://fredheir.github.io/WebScraping

Thursday 7 February 2019

14:00
Geographical Information Systems (GIS) Workshop (1 of 4) [Full] 14:00 - 17:00 Department of Geography, Downing Site - Top Lab

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.

Monday 11 February 2019

14:00
Power Analysis [Full] 14:00 - 16:00 University Information Services, Titan Teaching Room 1, New Museums Site

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
15:00
Survey Research and Design (1 of 3) [Full] 15:00 - 18:00 8 Mill Lane, Lecture Room 4

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.

16:00
Meta Analysis (1 of 4) [Full] 16:00 - 18:00 University Information Services, Titan Teaching Room 1, New Museums Site

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.