Social Sciences Research Methods Programme course timetable
Monday 28 January 2019
09:00 |
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 |
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)
Finished
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:
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
Finished
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:
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Wednesday 30 January 2019
09:00 |
Social Network Analysis
Finished
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
Finished
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)
Finished
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. |
Tuesday 5 February 2019
14:00 |
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:
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
Finished
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
Session 2: Ethnographies in Confinement Session 3: Ethnographies of Freedom Session 4: Photography and Audio Recording in Ethnographic Work |
16:00 |
Conversation and Discourse Analysis
Finished
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:
|
Wednesday 6 February 2019
09:00 |
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. |
14:00 |
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. |
Thursday 7 February 2019
14:00 |
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
Finished
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:
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15:00 |
Survey Research and Design
Finished
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
Finished
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. |
Tuesday 12 February 2019
14:00 |
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:
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
Finished
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
Session 2: Ethnographies in Confinement Session 3: Ethnographies of Freedom Session 4: Photography and Audio Recording in Ethnographic Work |
Wednesday 13 February 2019
09:00 |
Time Series Analysis (Intensive)
Finished
This module introduces the time series techniques relevant to forecasting in social science research and computer implementation of the methods. Background in basic statistical theory and regression methods is assumed. Topics covered include time series regression, Vector Error Correction and Vector Autoregressive Models, Time-varying Volatility, and ARCH models. The study of applied work is emphasized in this non-specialist module. Topics include:
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Propensity Score Matching
Finished
Propensity score matching (PSM) is a technique that simulates an experimental study in an observational data set in order to estimate a causal effect. In an experimental study, subjects are randomly allocated to “treatment” and “control” groups; if the randomisation is done correctly, there should be no differences in the background characteristics of the treated and non-treated groups, so any differences in the outcome between the two groups may be attributed to a causal effect of the treatment. An observational survey, by contrast, will contain some people who have been subject to the “treatment” and some people who have not, but they will not have not been randomly allocated to those groups. The characteristics of people in the treatment and control groups may differ, so differences in the outcome cannot be attributed to the treatment. PSM attempts to mimic the experimental situation trial by creating two groups from the sample, whose background characteristics are virtually identical. People in the treatment group are “matched” with similar people in the control group. The difference between the treatment and control groups in this case should may therefore more plausibly be attributed to the treatment itself. PSM is widely applied in many disciplines, including sociology, criminology, economics, politics, and epidemiology. The module covers the basic theory of PSM, the steps in the implementation (e.g. variable choice for matching and types of matching algorithms), and assessment of matching quality. We will also work through practical exercises using Stata, in which students will learn how to apply the technique to the analysis of real data and how to interpret the results. |
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14:00 |
Time Series Analysis (Intensive)
Finished
This module introduces the time series techniques relevant to forecasting in social science research and computer implementation of the methods. Background in basic statistical theory and regression methods is assumed. Topics covered include time series regression, Vector Error Correction and Vector Autoregressive Models, Time-varying Volatility, and ARCH models. The study of applied work is emphasized in this non-specialist module. Topics include:
|
Propensity Score Matching
Finished
Propensity score matching (PSM) is a technique that simulates an experimental study in an observational data set in order to estimate a causal effect. In an experimental study, subjects are randomly allocated to “treatment” and “control” groups; if the randomisation is done correctly, there should be no differences in the background characteristics of the treated and non-treated groups, so any differences in the outcome between the two groups may be attributed to a causal effect of the treatment. An observational survey, by contrast, will contain some people who have been subject to the “treatment” and some people who have not, but they will not have not been randomly allocated to those groups. The characteristics of people in the treatment and control groups may differ, so differences in the outcome cannot be attributed to the treatment. PSM attempts to mimic the experimental situation trial by creating two groups from the sample, whose background characteristics are virtually identical. People in the treatment group are “matched” with similar people in the control group. The difference between the treatment and control groups in this case should may therefore more plausibly be attributed to the treatment itself. PSM is widely applied in many disciplines, including sociology, criminology, economics, politics, and epidemiology. The module covers the basic theory of PSM, the steps in the implementation (e.g. variable choice for matching and types of matching algorithms), and assessment of matching quality. We will also work through practical exercises using Stata, in which students will learn how to apply the technique to the analysis of real data and how to interpret the results. |
Thursday 14 February 2019
14:00 |
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 18 February 2019
14:00 |
Weighting and Imputation
Finished
In order for the findings of statistical analysis to be generalisable, the sample on which the analysis is based should be representative of the population from which it is drawn. But it is well known that some groups are under-represented in social science surveys: they may be harder to contact in the first place, less likely to agree to participate in the survey, or less likely to answer particular questions even if they do agree to participate. This short module will introduce students to the techniques used by survey statisticians to overcome these problems. Weighting is used to deal with the problem of certain groups being under-represented in the sample; imputation is used to deal with missing answers to individual questions. Students will learn how and why weighting and imputation work, and will be taken through practical lab-based exercises which will teach them how to work with secondary data containing weights or imputed values. |
15:00 |
Survey Research and Design
Finished
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
Finished
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. |