Postgraduate taught 

Global Mental Health MSc/PgDip/PgCert

Introduction to Statistical Methods MED5477

  • Academic Session: 2021-22
  • School: Health and Wellbeing
  • Credits: 20
  • Level: Level 5 (SCQF level 11)
  • Typically Offered: Semester 1
  • Available to Visiting Students: Yes
  • Available to Erasmus Students: No
  • Taught Wholly by Distance Learning: Yes

Short Description

This course assumes no prior knowledge of statistics. It covers graphical and numerical methods of displaying and summarising data along with the use and interpretation of confidence intervals, significance tests (t tests, chi-square tests, etc.), correlation and linear regression. Students get hands on experience of using appropriate statistical software to carry out these analyses.

Timetable

This 10 week online course comprises 10 weekly lectures, each taking the form of two or three short lectures totalling around 90 minutes duration, and nine related weekly academic exercises e.g. discussion forums, computing based practical exercises. Exercises will provide opportunity to apply statistical tests and each will require around four notional hours of student effort and each week an academic staff member will convene an online tutorial related to these.

Excluded Courses

MED5029 Introduction to Statistical Methods

Co-requisites

None

Assessment

One 1,500 word essay will address a series of data analysis tasks and this will comprise 25% of the assessment. The practical skills assessment will be a compilation of completed statistical analyses of 2,000 words arising from weekly exercises and this will comprise 75% of the assessment.

Course Aims

1. To introduce fundamental concepts in biostatistics, especially uncertainty, variation, estimation and comparison.

2. To examine statistical issues in study design.

3. To introduce the most commonly used methods of analysis of data.

4. To give students a framework for critically reading published papers.

5. To give students experience of carrying out standard statistical analysis of small data sets using a computer.

Intended Learning Outcomes of Course

On successful completion of this course, students will be able to:

Differentiate between population and sample, population parameters and sample statistics and recognise the importance of sampling variability.

Understand the importance of randomisation, control groups, placebos, single and double blind in study design

 

Distinguish between, and critically apply, appropriate diagrammatic methods (line plots, histograms, boxplots, scatterplots) and summary statistics such as the mean, median, standard deviation, quartiles, proportions, percentages in data analysis

 

Critically interpret the results of a significance test and a confidence interval and P value.

 

Distinguish the circumstances in which to use: 1, 2 and paired sample t-tests, one way analysis of variance, chi square tests, Fisher's exact test, relative risk, odds ratio and the corresponding confidence intervals and critically appraise their appropriateness within the context of the research question

 

Understand the difference between correlation and linear regression and critically discuss the design conditions as when to apply the most appropriate technique

 

Interpret and critically appraise the output from a multiple linear regression model

 

Criticise the statistical content of simple published papers and reports

 

Apply an appropriate statistical analysis computer package to carry out analyses of data.

 

Calculate required study sample size and critically appraise the main factors affecting this

 

Identify the problems of variation in measurement and critically appraise the methods of controlling and measuring this variation

 

Critically appraise the concept of crude death (or incidence) rates and the rationale for the use of standardised rates based on the direct and indirect methods

 

Interpret and critically appraise the output from a univariate and multivariable logistic regression model

Minimum Requirement for Award of Credits

Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.