Statistical Analysis

Level

Statistical Analysis

Institute of Education
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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
Intended Learning Outcomes

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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
Intended Learning Outcomes
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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