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Continuing Professional Development - MSc Programme Modules (London-based)

STATISTICS FOR EPIDEMIOLOGY & POPULATION HEALTH (2021)

ORGANISERS: Dr George Ploubidis, Dr Phil Edwards, Dr Emily Webb

TIMETABLE SLOT: Term 1, Weeks 2-6; 8-12, Tues (p.m.) & Fri (a.m.)

DATES: 03 October 2011 to 16 December 2011 


AIM
To introduce the basic statistical methods used in medical and public health research, and to help students develop the skills needed to apply them using the STATA statistical software.


LEARNING OUTCOMES
By the end of this teaching module, students should be able to:

  1. describe and apply statistical methods in epidemiology and population health, and in their own disciplines;
  2. demonstrate skills in handling data on a computer and otherwise, and in deriving and presenting quantitative results using appropriate tables, figures and summaries;
  3. explain the nature of sampling variation and the role of statistical methods in quantifying it, and be able to calculate confidence limits and evaluate hypotheses;
  4. identify the key features of the normal and binomial distributions;
  5. identify the key features of methods appropriate for sampling surveys;
  6. select appropriate statistical methods for the analysis of simple data sets and apply them on computer using STATA statistical software;
  7. accurately interpret and assess the output from statistical analyses carried out on a computer in relation to research and other questions being asked;
  8. present and discuss the findings from statistical analyses in a clear, concise and logical manner.


CONSTITUENCY
This is a core module for the MSc courses in: Epidemiology, Demography & Health, Public Health in Developing Countries, Reproductive & Sexual Health Research, Public Health Nutrition, and Veterinary Epidemiology. It does not assume any previous knowledge of statistics and is a pre-requisite for the more advanced statistical courses taught in Term 2.


CONCEPTUAL OUTLINE
1.     Basic methods of presenting data
2.     Sampling variation, estimation and hypothesis testing
3.     Regression analysis
4.     Survey sampling
Interpretation of data analysis will be a central theme throughout the module.


TEACHING STRATEGY
Teaching will be carried out in a mixture of lectures and practical sessions. Practicals will involve 'pen & paper' exercises working in small groups, or computer exercises mainly working in pairs. The emphasis will be on making appropriate tabulations and graphical displays of data and appreciating their meaning, selecting and applying appropriate methods for statistical inference, and correctly interpreting the results. All methods will be illustrated using data from medical studies in developed and developing countries.


LEARNING TIME
The module is made up of 150 Notional Learning Hours – 70 hours contact time, 10 hours directed self-study, 35 hours self-directed learning, and 35 hours assessment, review and revision.


ASSESSMENT
Formal assessment will be by written examination in June. Informal assessment will comprise one multiple choice test and one data analysis assignment (neither of which will count towards students' final MSc degree grades).

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