Social Sciences Research Methods Programme course timetable
Monday 8 March
11:00 |
Factor Analysis
In progress
This module introduces the statistical techniques of Exploratory and Confirmatory Factor Analyses. Exploratory Factor Analysis (EFA) is used to uncover the latent structure (dimensions) of a set of variables. It reduces the attribute space from a larger number of variables to a smaller number of factors. Confirmatory Factor Analysis (CFA) examines whether collected data correspond to a model of what the data are meant to measure. STATA will be introduced as a powerful tool to conduct confirmatory factor analysis. A brief introduction will be given to confirmatory factor analysis and structural equation modelling.
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14:00 |
Factor Analysis
In progress
This module introduces the statistical techniques of Exploratory and Confirmatory Factor Analyses. Exploratory Factor Analysis (EFA) is used to uncover the latent structure (dimensions) of a set of variables. It reduces the attribute space from a larger number of variables to a smaller number of factors. Confirmatory Factor Analysis (CFA) examines whether collected data correspond to a model of what the data are meant to measure. STATA will be introduced as a powerful tool to conduct confirmatory factor analysis. A brief introduction will be given to confirmatory factor analysis and structural equation modelling.
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Tuesday 9 March
09:00 |
Secondary Data Analysis
[Full]
Using secondary data (that is, data collected by someone else, usually a government agency or large research organisation) has a number of advantages in social science research: sample sizes are usually larger than can be achieved by primary data collection, samples are more nearly representative of the populations they are drawn from, and using secondary data for a research project often represents significant savings in time and money. This short course, taught by Dr Deborah Wiltshire of the UK Data Archive, will discuss the advantages and limitations of using secondary data for research in the social sciences, and will introduce students to the wide range of available secondary data sources. The course is based in a computer lab; students will learn how to search online for suitable secondary data by browsing the database of the UK Data Archive. |
10:00 |
Conversation and Discourse Analysis
In progress
NB. NOTES FOR INTERESTED STUDENTS
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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14:00 |
Microsoft Access: Database Design and Use
In progress
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:
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Wednesday 10 March
12:00 |
A Critical Analysis of Null Hypothesis Testing and its Alternatives (Including Bayesian Analysis)
[Full]
This course will provide a detailed critique of the methods and philosophy of the Null Hypothesis Significance Testing (NHST) approach to statistics which is currently dominant in social and biomedical science. We will briefly contrast NHST with alternatives, especially with Bayesian methods. We will use some computer code (Matlab and R) to demonstrate some issues. However, we will focus on the big picture rather on the implementation of specific procedures. |
Thursday 11 March
09:00 |
Meta Analysis
[Full]
NB. The SSRMP are currently in the process of trying to source software provision for this module. Booking are subject to our success in this endeavour. 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. |
Friday 12 March
09:00 |
Meta Analysis
[Full]
NB. The SSRMP are currently in the process of trying to source software provision for this module. Booking are subject to our success in this endeavour. 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. |
Monday 15 March
09:00 |
Event History Analysis
![]() This course offers an introduction to event history analysis, which is a tool used for analyzing the occurrence and timing of events. Typical examples are life course transitions such as the transition to parenthood and partnership formation processes, labour market processes such as job promotions, mortality, and transitions to and from sickness and disability. The researcher may be interested in examining how the rate of a particular event varies over time or with individual characteristics, social conditions, or other factors. Event History Analysis lets the researcher handle censoring and truncation, include time-varying independent variables, account for unobserved heterogeneity (frailty), and so on. The course will rely on Stata as the main computing tool, but users of other statistical software could still benefit from the course. The course is taught through both lectures and lab sessions. |
10:00 |
Evaluation Methods
[Full]
This course aims to provide students with a range of specific technical skills that will enable them to undertake impact evaluation of policy. Too often policy is implemented but not fully evaluated. Without evaluation we cannot then tell what the short or longer term impact of a particular policy has been. On this course, students will learn the skills needed to evaluate particular policies and will have the opportunity to do some hands on data manipulation. A particular feature of this course is that it provides these skills in a real world context of policy evaluation. It also focuses primarily not on experimental evaluation (Random Control Trials) but rather quasi-experimental methodologies that can be used where an experiment is not desirable or feasible. Topics:
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13:00 |
Evaluation Methods
[Full]
This course aims to provide students with a range of specific technical skills that will enable them to undertake impact evaluation of policy. Too often policy is implemented but not fully evaluated. Without evaluation we cannot then tell what the short or longer term impact of a particular policy has been. On this course, students will learn the skills needed to evaluate particular policies and will have the opportunity to do some hands on data manipulation. A particular feature of this course is that it provides these skills in a real world context of policy evaluation. It also focuses primarily not on experimental evaluation (Random Control Trials) but rather quasi-experimental methodologies that can be used where an experiment is not desirable or feasible. Topics:
|
14:00 |
Event History Analysis
![]() This course offers an introduction to event history analysis, which is a tool used for analyzing the occurrence and timing of events. Typical examples are life course transitions such as the transition to parenthood and partnership formation processes, labour market processes such as job promotions, mortality, and transitions to and from sickness and disability. The researcher may be interested in examining how the rate of a particular event varies over time or with individual characteristics, social conditions, or other factors. Event History Analysis lets the researcher handle censoring and truncation, include time-varying independent variables, account for unobserved heterogeneity (frailty), and so on. The course will rely on Stata as the main computing tool, but users of other statistical software could still benefit from the course. The course is taught through both lectures and lab sessions. |
Tuesday 16 March
10:00 |
Evaluation Methods
[Full]
This course aims to provide students with a range of specific technical skills that will enable them to undertake impact evaluation of policy. Too often policy is implemented but not fully evaluated. Without evaluation we cannot then tell what the short or longer term impact of a particular policy has been. On this course, students will learn the skills needed to evaluate particular policies and will have the opportunity to do some hands on data manipulation. A particular feature of this course is that it provides these skills in a real world context of policy evaluation. It also focuses primarily not on experimental evaluation (Random Control Trials) but rather quasi-experimental methodologies that can be used where an experiment is not desirable or feasible. Topics:
|
13:00 |
Evaluation Methods
[Full]
This course aims to provide students with a range of specific technical skills that will enable them to undertake impact evaluation of policy. Too often policy is implemented but not fully evaluated. Without evaluation we cannot then tell what the short or longer term impact of a particular policy has been. On this course, students will learn the skills needed to evaluate particular policies and will have the opportunity to do some hands on data manipulation. A particular feature of this course is that it provides these skills in a real world context of policy evaluation. It also focuses primarily not on experimental evaluation (Random Control Trials) but rather quasi-experimental methodologies that can be used where an experiment is not desirable or feasible. Topics:
|
Wednesday 17 March
12:00 |
A Critical Analysis of Null Hypothesis Testing and its Alternatives (Including Bayesian Analysis)
[Full]
This course will provide a detailed critique of the methods and philosophy of the Null Hypothesis Significance Testing (NHST) approach to statistics which is currently dominant in social and biomedical science. We will briefly contrast NHST with alternatives, especially with Bayesian methods. We will use some computer code (Matlab and R) to demonstrate some issues. However, we will focus on the big picture rather on the implementation of specific procedures. |