Faculty of Health

This module is run within the School of Sociology and Criminology but is often chosen by Health students

Module Learning Outcomes/Objectives:

The student should be able to demonstrate:

• An understanding of key concepts and principles in advanced statistical analysis
• Critical understanding of the rationale and assumptions of advanced statistical procedures
• Judicious selection and interpretation of statistical procedures
• Competence in the use of SPSS for advanced statistical analyses

Module Code - PTY-40022

Module Dates

to be advised

Module Aims:

• To develop students’ conceptual understanding of issues in statistical theory and their application to advanced methods of quantitative data analysis
• To enable a critical understanding of the rationale and assumptions of advanced inferential statistics, including multivariable and multivariable procedures
• To provide a theoretical and practical understanding of the choice and application of a range of such procedures, and to foster critical judgment as to their appropriateness for specific datasets
• To develop of practical skills in using SPSS for the advanced analysis of quantitative data

The course is aimed at students interested in acquiring advanced skills in quantitative data analysis in social and health sciences.

Candidates should normally have a first or second class honours degree in a relevant professional or academic area (e.g. medicine, physiotherapy, sociology, psychology, politics) and should normally have studied CRI-40022 Quantitative data analysis 1 or CLM-40003 Statistics and epidemiology (or have acquired equivalent knowledge and understanding).

Module Content:

Revision of statistical concepts related to descriptive and inferential statistics: statistics and parameters, statistical inference, hypothesis testing, Type 1 and Type 2 error, confidence intervals; General linear model 1: analysis of variance (between- and within-subjects, including multivariable models), a priori and a posteriori contrasts, relationship to various experimental designs; General linear model 2: multiple linear regression, analysis of covariance; Generalized linear model: log-linear models and binary logistic regression; multivariate methods: exploratory factor analysis; Statistical power and sample size calculation; Reporting and presentation of results; Use of SPSS for above statistical analysis.

Teaching Format

Six days, over a five week period.

Assessment Type:

30% marks - A series of short essays focusing on key conceptual issues

70% marks - An open-book examination involving the analysis of data in SPSS