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Social Sciences Research Methods Programme course timetable

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Fri 23 Nov 2018 – Wed 30 Jan

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Monday 26 November 2018

10:00
Doing Multivariate Analysis (DMA-1) (3 of 4) Finished 10:00 - 12:00 8 Mill Lane, Lecture Room 4

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-1) (4 of 4) Finished 14:00 - 16:00 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.

NVivo (2 of 2) Finished 14:00 - 18:00 University Information Services, Titan Teaching Room 2, New Museums Site

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.

16:00
Merging and Linking Data Sets Finished 16:00 - 18:00 University Information Services, Titan Teaching Room 1, New Museums Site

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.

Tuesday 27 November 2018

14:00
Microsoft Access: Database Design and Use (2 of 2) Finished 14:00 - 18:00 University Information Services, Titan Teaching Room 1, New Museums Site

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.

Wednesday 28 November 2018

10:00
Doing Multivariate Analysis (DMA-2) (3 of 4) Finished 10:00 - 12:00 8 Mill Lane, Lecture Room 4

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.

Doing Multivariate Analysis (DMA-3) (3 of 4) Finished 10:00 - 12:00 8 Mill Lane, Lecture Room 4

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.

13:00
Working with Archives (3 of 3) Finished 13:00 - 15:00 Pembroke Street Lecture Theatre - Division of Biological Anthropology

This unit is an introduction to archival research methods for postgraduates. Our goal is to develop an understanding of the key values and practices of both archival preservation and interpretation. Knowing the values and practices at the interface between evidence and argumentation will allow us to formulate a better awareness of the logics, accounts, and justifications of the methods researchers employ to do their work. Participants will develop a familiarity with the main considerations and techniques used in archival research as well as the different archival resources available to undertake independent research projects.

14:00
Doing Multivariate Analysis (DMA-2) (4 of 4) Finished 14:00 - 16:00 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.

16:00
Doing Multivariate Analysis (DMA-3) (4 of 4) Finished 16:00 - 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 15 January 2019

14:00
Introduction to R (1 of 2) Finished 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) Finished 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) Finished 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) Finished 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) Finished 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 Finished 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) Finished 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) Finished 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) Finished 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) Finished 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) Finished 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) Finished 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) Finished 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) Finished 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) Finished 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