Social Sciences Research Methods Programme course timetable

Neither quantitative nor qualitative data analysis has all the answers in social science research: qualitative research has depth and nuance but is not generalisable beyond the sample on which it is based, while quantitative research is generalisable but may lack depth.

A mixed methods approach, which uses evidence from both qualitative and quantitative approaches to shed light on a single research question, has the potential to gain the advantages of both approaches. However, genuine mixed methods work is not always easy. This short course will introduce students to the rationale behind the use of mixed methods approaches, and how to design mixed methods projects for best results.

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.

Aims:

• To introduce students to the qualitative dimension of comparative historical research methods
• To analyse some contemporary classics within this genre
• To review and distinguish among the variety of intellectual justifications for this genre as a methodology
• To focus on a 'state-of-the-art' defence of qualitative and comparative-historical research in theory and practice

Topics:

• Session 1: Classics
• Session 2: Justifications I
• Session 3: Justifications II
• Session 4: State of the Art

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

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 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

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 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

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 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
  

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 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
  

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 is for students who don’t plan to use quantitative methods in their own research, but who need to be able to read and understand published research using quantitative methods. You will learn how to interpret graphs, frequency tables and multivariate regression results, and to ask intelligent questions about sampling, methods and statistical inference. The module is aimed at complete beginners, with no prior knowledge of statistics or quantitative methods.

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.

Aims:

• To introduce students to the qualitative dimension of comparative historical research methods
• To analyse some contemporary classics within this genre
• To review and distinguish among the variety of intellectual justifications for this genre as a methodology
• To focus on a 'state-of-the-art' defence of qualitative and comparative-historical research in theory and practice

Topics:

• Session 1: Classics
• Session 2: Justifications I
• Session 3: Justifications II
• Session 4: State of the Art

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

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 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

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 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

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 course provides an introduction to some of the methodological issues involved in researching organisations. Drawing on examples of studies carried out in a wide range of different types of organisation, the aim will be to explore practical strategies to overcome some of problems that are typically encountered in undertaking such studies.

Topics covered include:

• Session 1: "Getting In" - types of organisations; the research process; gaining access
• Session 2: "Getting on" - interviews and observation as sources of data; ethics in principle and in practice
• Session 3: "Getting out" - reporting; fulfilling the bargain; maintaining contact

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 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.