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Jsss Programme 2011-12

Programme of events provided by Joint Schools' Social Sciences
(Thu 6 Oct 2011 - Wed 14 Mar 2012)

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Wed 15 Feb 2012 – Tue 13 Mar 2012

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Wednesday 15 February 2012

14:00
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

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.

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.

Tuesday 21 February 2012

14:00
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.

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.

16:00
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.

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.

Wednesday 22 February 2012

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

Tuesday 28 February 2012

14:00
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.

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.

16:00
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.

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.

Wednesday 29 February 2012

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

Tuesday 6 March 2012

14:00
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.

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.

16:00
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.

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.

Wednesday 7 March 2012

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

Tuesday 13 March 2012

14:00
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.

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.

16:00
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.

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.

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