Social Sciences Research Methods Programme course timetable

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 course will constitute a practical introduction to experimental method and design suitable for students from any discipline who have had limited experience of empirical methods but who wish to be able to read and understand the experimental literature or to undertake their own experimental studies. The course includes:

• A theoretical introduction to the concepts and practices involved in experimental research in the human sciences, including ethical considerations;
• An introduction to experimental design and to appropriate analytic techniques;
• A practical component that can be undertaken away from the laboratory; and
• An introduction to issues involved in writing up results.

At the end of the module, students will be equipped with the fundamental knowledge required to design and evaluate an experiment.

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 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 spreadsheet data into R
• The notion of data type
• How to manipulate data in major data types
• How to draw basic graphs and figures with ggplot2
• How to summarise data using descriptive statistics
• How to perform basic inferential statistics (e.g. the t-test).

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 using another software package (for example Stata or SPSS).

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 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.

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 having fun by building your own statistical models.

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 having fun by building your own statistical models.

This workshop will provide support for students who are working on their own projects, and who need a little extra help with their data analysis. Bring your data along to this session (bearing in mind considerations of data security) and our demonstrators will do their best to help you with:

• Getting your data into shape
• Writing and documenting syntax files
• De-bugging syntax that doesn't work
• Understanding your output
• Your next steps, including choosing appropriate analytical techniques

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 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 having fun by building your own statistical models.

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 having fun by building your own statistical models.

This short two-hour course will provide an introduction to measurement issues in the social sciences. We design questions (or "survey instruments") to gain information on the concepts we are researching. Two prime considerations in whether an instrument is effective are validity (does our instrument actually measure what we want it to measure?) and reliability (does our instrument give consistent results across a range of different situations?)

Considerations of validity and reliability are important across many areas of social science, including the measurement of personality and mental health; attitudes; ability tests; political behaviour; cultural differences and similarities between various groups; and consumer behaviour.

The course will discuss what we mean by validity and reliability, the different ways we can think about the concepts, and different ways we can assess the quality of instruments using these criteria. We will also look at some statistical techniques for reliability and validity checks: Cronbach’s Alpha, Kappa coefficient, and Factor Analysis.

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 four two-hour sessions, each of which has two parts.

The first hour of each session will consist of a lecture. The four lectures cover: 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. Students who have attended these lectures will be able to evaluate research that uses surveys, in particular to understand issues concerning sample selection, response bias and data analysis; to appreciate and understand basic principles of questionnaire design; and to trace appropriate sources of data and appropriate exemplars of good survey practice.

The second hour of each session will focus more on the practical aspects of designing surveys and will feature some practical exercises. The focus will primarily be on 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 (and we may provide opportunities to field successful exercises as part of YouGov surveys), 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 these second parts of the sessions, but it will be of most direct relevant to who are using, or plan to use, surveys in their research. (It should also be noted that all students attending the second hour of the sessions are expected to participate and engage with the exercises.)

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