All-provider course timetable

Much of the session comprises a case study based on a medium sized University department. Delegates will examine University regulations and procedures and decide the actions for various scenarios and correct anomalies for selected sources of funds.

This module introduces students to four of the most commonly used statistical tests in the social scinces: Correlations, Chi-square tests, T-tests, and one-way ANOVAs.

This module introduces students to four of the most commonly used statistical tests in the social scinces: Correlations, Chi-square tests, T-tests, and one-way ANOVAs.

This module is part of the Social Science Research Methods Course programme which is a shared platform for providing research students with a broad range of quantitative and qualitative research

Introduction to statistical techniques of Exploratory and Confirmation Factor Analyss. EFA is used to uncover the latent structure of a set of variables. CFA examines whether collected date correspond to a model of what the data are meant to measure. AMOS will be introduced as a powerful tool to conduct confirmatory factor analysis.

This module is part of the Social Science Research Methods Course programme which is a shared platform for providing research students with a broad range of quantitative and qualitative research

An introduction to the wide range of resources available at the MML Library and the UL, both in print and online.

Your lab notebook is one of the most important and precious objects you, as a scientist, will ever have. This session explore how keeping an exemplary laboratory notebook is crucial to good scientific practice in lab research. The course will consist of a short talk, a chance to assess some examples of good and bad practice, with plenty of time for questions and discussion. You might like to bring along your own lab notebook for feedback. (Please note that issues relating to protection of Intellectual Property Rights will not be covered in this session)

This module introduces students to four of the most commonly used statistical tests in the social scinces: Correlations, Chi-square tests, T-tests, and one-way ANOVAs.

This module introduces students to four of the most commonly used statistical tests in the social scinces: Correlations, Chi-square tests, T-tests, and one-way ANOVAs.

This module is part of the Social Science Research Methods Course programme which is a shared platform for providing research students with a broad range of quantitative and qualitative research

Introduction to statistical techniques of Exploratory and Confirmation Factor Analyss. EFA is used to uncover the latent structure of a set of variables. CFA examines whether collected date correspond to a model of what the data are meant to measure. AMOS will be introduced as a powerful tool to conduct confirmatory factor analysis.

Managing Safely is ideally suited to managers, research supervisors, administrators with safety responsibilities and Departmental Safety Officers across all sectors of the University. It leads to a nationally recognised and accredited training certificate. Please contact the course organiser, Will Hudson (wjh29@admin.cam.ac.uk) for further details before booking on the course.

It is an interactive course that includes state-of-the-art animation in the PowerPoint presentation, work books, DVDs, board games and quizzes, assessments, and a risk assessment project.

This module is part of the Social Science Research Methods Course 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 introduces time series techniques relevant to forecasting in social science research and computer implementation of the methods.

Module introduces students to one of the most fundamental statistical techniques, namely regression analysis. Students learn about assumptions underlying regression models, how to run regression analysis using SPSS and how to access and solve possible problems with a regression model.