Statistical Analysis
Starting dates and places
Description
Course Code: RMSPAE_11
Masters Level Module
Aims of module
- To develop a practical understanding of the methods of modelling relationships between variables
- To understand which tests of significance are appropriate and how to carry them out
- To learn about Analysis of Variance, Covariance and Correlation
- To understand the principles and assumptions of regression-based analysis methods such as Linear, Logistic and Multinomial Logistic Regression
- To study the impact of violating analysis assumptions, their diagnosis and solutions0- To understand and practice the use of non-regression based analysis methods such as Principal Component Analysis and Factor Analysis
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Course Code: RMSPAE_11
Masters Level Module
Aims of module
- To develop a practical understanding of the methods of modelling relationships between variables
- To understand which tests of significance are appropriate and how to carry them out
- To learn about Analysis of Variance, Covariance and Correlation
- To understand the principles and assumptions of regression-based analysis methods such as Linear, Logistic and Multinomial Logistic Regression
- To study the impact of violating analysis assumptions, their diagnosis and solutions0- To understand and practice the use of non-regression based analysis methods such as Principal Component Analysis and Factor Analysis
After successful completion of this module students will be able to:
- Select methods of data analysis appropriate to their data and research questions
- Carry out a range of analyses using SPSS: t-test, chi-square, analysis of variance, correlation, linear, logistic & multinomial logistic regression, principal component analysis and factor analysis
- Diagnose whether the assumptions have been adhered to in the analyses covered, discuss the potential impact on the results and propose solutions to any problems that may have arisen
- Critically evaluate the data analysis methods proposed or undertaken in a given study
- Ensure interpretation of data analysis findings is done correctly and be able to defend the interpretation.
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