Joint Schools' Social Sciences course timetable
Wednesday 15 February 2012
| 14:00 |
Module 9: Meta Analysis
Finished
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
Finished
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 |
|
|
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
Finished
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
Finished
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. |
|
| 16:00 |
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 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 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
Finished
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
Finished
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. |
|
| 16:00 |
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 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 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
Finished
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
Finished
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. |
|
| 16:00 |
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 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 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
Finished
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
Finished
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. |
|
| 16:00 |
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 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. |