Cambridge Research Methods course timetable

This module is part of the Social Science Research Methods Centre training programme which is a shared platform for providing research students with a broad range of quantitative and qualitative research methods skills that are relevant across the social sciences.

This module will introduce students to the process of reproducing published work. Replicating other scholars’ work is an essential tool to get familiar with methods, learn to select suitable models, and get a chance to publish early during their PhD. This replication module will therefore provide students with a deeper understanding of statistical modelling and professionalism in their field. With the right amount of value added, a replication study is publishable after the module.

This module is part of the Social Science Research Methods Centre training programme which is a shared platform for providing research students with a broad range of quantitative and qualitative research methods skills that are relevant across the social sciences.

Often researchers deal with outcomes that come in the form of ‘yes’ or ‘no’ responses to questions, or where respondents only have two options to choose from. Similarly, there are occasions where researchers are using unordered (e.g. red, yellow, green) or ordered categories (low, medium, high) as response variables. This module will teach you about how to analyse these different types of data. This will include (a) interpreting outputs, (b) conducting diagnostic tests, (c) calculating effect sizes and (d) making predictions.

This module is part of the Social Science Research Methods Centre training programme which is a shared platform for providing research students with a broad range of quantitative and qualitative research methods skills that are relevant across the social sciences.

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.

This module is part of the Social Science Research Methods Centre training programme which is a shared platform for providing research students with a broad range of quantitative and qualitative research methods skills that are relevant across the social sciences.

Students in the social sciences using historical methods, need to know they are entering alien territory. There are rules and customs governing the use of such sources but also freedoms associated with their use.

History is not a science though it shares some elements of method with science. It is not the arts either, though it also shares elements with the arts. It lies along a no-man's land somewhere between.

This module is part of the Social Science Research Methods Centre training programme which is a shared platform for providing research students with a broad range of quantitative and qualitative research methods skills that are relevant across the social sciences.

Structural Equation Modelling (SEM), also known as analysis of covariance structures, is a powerful statistical methodology that takes confirmatory approach to the analysis of various datasets usually collected in the Social Sciences. SEM simultaneously uses techniques from multiple regression, path analysis, and factor analyses. Based on these techniques researchers can test theories/hypotheses for their work. In the course, we will use the statistical package AMOS to generate and test models. This course will offer a basic introduction to SEM, but participants will be able further develop their knowledge in SEM from this foundation.

This module is part of the Social Science Research Methods Centre training programme which is a shared platform for providing research students with a broad range of quantitative and qualitative research methods skills that are relevant across the social sciences.

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, moving average, exponential smoothing and decomposition. The study of applied work is emphasized in this non-specialist module.

This module is part of the Social Science Research Methods Centre training programme which is a shared platform for providing research students with a broad range of quantitative and qualitative research methods skills that are relevant across the social sciences.

Students are introduced to meta-analysis, a powerful statistical technique allowing researchers to synthesize 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.

This module is part of the Social Science Research Methods Centre training programme which is a shared platform for providing research students with a broad range of quantitative and qualitative research methods skills that are relevant across the social sciences.

This module provides students with an introduction to panel data analysis (PDA). Panel data are gathered by taking repeated observations of a series of research units (eg. individuals) as they move through time. PDA therefore allows researchers to answer questions that cannot be addressed with cross-sectional data. The course begins by introducing students to key concepts in longitudinal research. Next, students are taught how to manipulate and prepare panel datasets using Stata. The final two sessions provide an overview of statistical modelling techniques for use with panel data. Throughout the course, emphasis is placed on giving students hands-on experience of working with real-world data using Stata.

This module is part of the Social Science Research Methods Centre training programme which is a shared platform for providing research students with a broad range of quantitative and qualitative research methods skills that are relevant across the social sciences.

This course will introduce two interactive methods that enable a deeper understanding of the views of research participants.

• Part 1 (lectures 1 and 2) Q method: The aim of this course is to provide students with the sufficient knowledge and resources to implement a Q method study independently. Q method is a structured and systematic methodology to research subjectivity, understood as how people think and communicate their points of view about a subject. The method is useful for a broad range of research questions that require an understanding of the different perspectives about a topic in order to aid decision-making and management.

• Part 2 (lectures 3 and 4) action research and researching in partnership with participants: Action research involves researching upon one’s own professional practice whilst researching with participants can take place outside one’s own professional context but involves sharing the processes of analysis and writing with participants. These sessions may be particularly useful for those who are aiming to use academic skills in a workplace context in the future. However, they can also other forms of research design.

This module is part of the Social Science Research Methods Centre training programme which is a shared platform for providing research students with a broad range of quantitative and qualitative research methods skills that are relevant across the social sciences.

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 course is made up of four tutorials which explore how to scrape different types of data. The uses of web scraping are diverse: in this course we will use the programming language R to explore how to access data from newspapers, YouTube, Wikipedia, and Twitter. Collectively these sessions will give the skillsets necessary to use web scraping in students’ own research.

This module is part of the Social Science Research Methods Centre training programme which is a shared platform for providing research students with a broad range of quantitative and qualitative research methods skills that are relevant across the social sciences.

This foundational course introduces students to basic theoretical concepts that underlie research on social networks and provides methodological and analytical insights through the use of the social network software UCINET. Given the growing popularity of the social network perspective across diverse subject areas, this module is designed for students interested in both micro directions, emphasizing cognitive and personality perspectives, and macro directions, emphasizing very large network configuration and evolution.

Topics covered include leading ideas in social network theory and research, such as network structure, interpersonal relations and the utility of network connections in terms of social capital [session 1]; key methods to run social network analysis through the use of UCINET [session 2]; the use of social network methods to conduct either micro or macro-level research in the field of social sciences [session 3]; hot frontiers in social network research, such as cognition, actors’ characteristics in terms of personality and motivation, network dynamics, networks and genetics [session 4].

This module is part of the Social Science Research Methods Centre training programme which is a shared platform for providing research students with a broad range of quantitative and qualitative research methods skills that are relevant across the social sciences.

This introductory course to Atlas.Ti CAQDAS will provide a general background to the process of qualitative analysis, situating the use of CAQDAS with reference to differing positions regarding its importance, together with a ‘hands on’ introduction to the software. The practical part of each session will familiarise students with the main functionalities of the software through a set of exercises developed with data collected through a mix of qualitative methods. Students will additionally be asked to apply the software to their own research data and to keep a reflective journal on this.

This module is part of the Social Science Research Methods Centre training programme which is a shared platform for providing research students with a broad range of quantitative and qualitative research methods skills that are relevant across the social sciences.

This module provides students with an introduction to panel data analysis (PDA). Panel data are gathered by taking repeated observations of a series of research units (eg. individuals) as they move through time. PDA therefore allows researchers to answer questions that cannot be addressed with cross-sectional data. The course begins by introducing students to key concepts in longitudinal research. Next, students are taught how to manipulate and prepare panel datasets using Stata. The final two sessions provide an overview of statistical modelling techniques for use with panel data. Throughout the course, emphasis is placed on giving students hands-on experience of working with real-world data using Stata.

This module is part of the Social Science Research Methods Centre training programme which is a shared platform for providing research students with a broad range of quantitative and qualitative research methods skills that are relevant across the social sciences.

This course will introduce two interactive methods that enable a deeper understanding of the views of research participants.

• Part 1 (lectures 1 and 2) Q method: The aim of this course is to provide students with the sufficient knowledge and resources to implement a Q method study independently. Q method is a structured and systematic methodology to research subjectivity, understood as how people think and communicate their points of view about a subject. The method is useful for a broad range of research questions that require an understanding of the different perspectives about a topic in order to aid decision-making and management.

• Part 2 (lectures 3 and 4) action research and researching in partnership with participants: Action research involves researching upon one’s own professional practice whilst researching with participants can take place outside one’s own professional context but involves sharing the processes of analysis and writing with participants. These sessions may be particularly useful for those who are aiming to use academic skills in a workplace context in the future. However, they can also other forms of research design.

This module is part of the Social Science Research Methods Centre training programme which is a shared platform for providing research students with a broad range of quantitative and qualitative research methods skills that are relevant across the social sciences.

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 course is made up of four tutorials which explore how to scrape different types of data. The uses of web scraping are diverse: in this course we will use the programming language R to explore how to access data from newspapers, YouTube, Wikipedia, and Twitter. Collectively these sessions will give the skillsets necessary to use web scraping in students’ own research.

This module is part of the Social Science Research Methods Centre training programme which is a shared platform for providing research students with a broad range of quantitative and qualitative research methods skills that are relevant across the social sciences.

Students are introduced to multilevel modelling techniques (a.k.a. hierarchical linear modelling). MLM allows one to analyse how contexts influence outcomes ie do schools/neighbourhoods influence behaviour.

STATA will be used during this module - there will be no overlap with other SPSS modules. No prior knowledge of STATA will be assumed.

This module is part of the Social Science Research Methods Centre training programme which is a shared platform for providing research students with a broad range of quantitative and qualitative research methods skills that are relevant across the social sciences.

This introductory course to Atlas.Ti CAQDAS will provide a general background to the process of qualitative analysis, situating the use of CAQDAS with reference to differing positions regarding its importance, together with a ‘hands on’ introduction to the software. The practical part of each session will familiarise students with the main functionalities of the software through a set of exercises developed with data collected through a mix of qualitative methods. Students will additionally be asked to apply the software to their own research data and to keep a reflective journal on this.

This module is part of the Social Science Research Methods Centre training programme which is a shared platform for providing research students with a broad range of quantitative and qualitative research methods skills that are relevant across the social sciences.

Students are introduced to meta-analysis, a powerful statistical technique allowing researchers to synthesize 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.

This module is part of the Social Science Research Methods Centre training programme which is a shared platform for providing research students with a broad range of quantitative and qualitative research methods skills that are relevant across the social sciences.

This module provides students with an introduction to panel data analysis (PDA). Panel data are gathered by taking repeated observations of a series of research units (eg. individuals) as they move through time. PDA therefore allows researchers to answer questions that cannot be addressed with cross-sectional data. The course begins by introducing students to key concepts in longitudinal research. Next, students are taught how to manipulate and prepare panel datasets using Stata. The final two sessions provide an overview of statistical modelling techniques for use with panel data. Throughout the course, emphasis is placed on giving students hands-on experience of working with real-world data using Stata.