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JSSS Programme 2010-11

Programme of events provided by Joint Schools' Social Sciences
(Wed 6 Oct 2010 - Tue 15 Mar 2011)

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Tue 15 Feb 2011 – Mon 14 Mar 2011

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Tuesday 15 February 2011

14:00
Module 3: Bivariate Association for Judge Students (4 of 4) Finished 14:00 - 16:00 Judge Business School, Computer Room

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.

Module 9: Meta Analysis (4 of 4) Finished 14:00 - 16:00 Titan Teaching Room 2

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.

Module 11: Multilevel Modelling (1 of 4) Finished 14:00 - 16:00 Titan Teaching Room 1, New Museums Site

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

16:00
Module 8: Factor Analysis and SEM (4 of 4) Finished 16:00 - 18:00 Titan Teaching Room 2

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.

Monday 21 February 2011

14:00
Module 4: Linear Regression (Series 2) (1 of 4) Finished 14:00 - 16:00 Titan Teaching Room 2

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.

Module 10:Time Series Analysis (1 of 4) Finished 14:00 - 16:00 Phoenix Teaching Room

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.

16:00
Module 4: Linear Regression (Series 1) (1 of 4) Finished 16:00 - 18:00 Titan Teaching Room 1, New Museums Site

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.

Module 4: Linear Regression (Series 3) (1 of 4) Finished 16:00 - 18:00 Titan Teaching Room 2

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.

Tuesday 22 February 2011

14:00
Module 4: Linear Regression for Judge students (1 of 4) Finished 14:00 - 16:00 Judge Business School, Computer Room

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.

Module 11: Multilevel Modelling (2 of 4) Finished 14:00 - 16:00 Titan Teaching Room 2

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

Monday 28 February 2011

14:00
Module 4: Linear Regression (Series 2) (2 of 4) Finished 14:00 - 16:00 Titan Teaching Room 2

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.

Module 10:Time Series Analysis (2 of 4) Finished 14:00 - 16:00 Phoenix Teaching Room

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.

16:00
Module 4: Linear Regression (Series 1) (2 of 4) Finished 16:00 - 18:00 Titan Teaching Room 1, New Museums Site

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.

Module 4: Linear Regression (Series 3) (2 of 4) Finished 16:00 - 18:00 Titan Teaching Room 2

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.

Tuesday 1 March 2011

14:00
Module 4: Linear Regression for Judge students (2 of 4) Finished 14:00 - 16:00 Judge Business School, Computer Room

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.

Module 11: Multilevel Modelling (3 of 4) Finished 14:00 - 16:00 Titan Teaching Room 2

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

Monday 7 March 2011

14:00
Module 4: Linear Regression (Series 2) (3 of 4) Finished 14:00 - 16:00 Titan Teaching Room 2

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.

Module 10:Time Series Analysis (3 of 4) Finished 14:00 - 16:00 Phoenix Teaching Room

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.

16:00
Module 4: Linear Regression (Series 1) (3 of 4) Finished 16:00 - 18:00 Titan Teaching Room 1, New Museums Site

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.

Module 4: Linear Regression (Series 3) (3 of 4) Finished 16:00 - 18:00 Titan Teaching Room 2

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.

Tuesday 8 March 2011

14:00
Module 4: Linear Regression for Judge students (3 of 4) Finished 14:00 - 16:00 Judge Business School, Computer Room

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.

Module 11: Multilevel Modelling (4 of 4) Finished 14:00 - 16:00 Titan Teaching Room 2

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

Monday 14 March 2011

14:00
Module 4: Linear Regression (Series 2) (4 of 4) Finished 14:00 - 16:00 Titan Teaching Room 2

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.

Module 10:Time Series Analysis (4 of 4) Finished 14:00 - 16:00 Phoenix Teaching Room

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.

16:00
Module 4: Linear Regression (Series 1) (4 of 4) Finished 16:00 - 18:00 Titan Teaching Room 1, New Museums Site

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.

Module 4: Linear Regression (Series 3) (4 of 4) Finished 16:00 - 18:00 Titan Teaching Room 2

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.

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