2022/23 Taught Postgraduate Module Catalogue
MEDR5320M Capturing and Handling Data in Research
15 creditsClass Size: 45
Module manager: Rebecca Walwyn & Sarah Brown
Email: firstname.lastname@example.org, email@example.com
Taught: Semester 1 (Sep to Jan), Semester 2 (Jan to Jun) View Timetable
Year running 2022/23
Pre-requisite qualificationsAs per students parent programme
|MEDR5310M||Getting started in health research|
This module is mutually exclusive with
|MEDR5325M||Capturing and Handling Data in Research (ICATCH)|
Module replacesMEDR5100M: Capturing data for researchMEDR5110M: Handling data in research
This module is not approved as an Elective
Module summaryThis module will include topics on: ethics of research, sampling from populations, collecting data with questionnaires, measuring the repeatability and validity of scales and tests, describing data including prevalence, incidence, and standardization, recording and managing data, descriptive statistics and types of data, describing data in tables, charts and figures, and critical appraisal of quantitative and qualitative published research.
ObjectivesThis module is an introduction to the collection of health research data. It will include topics on: social inclusion in research; sampling from populations; types of data; collecting data through questionnaires; how scales and tests are used to collect data; and how data are collected and described using various fractions such as rates, ratios, risks and odds recording quantitative and qualitative data in suitable formats; using computers in the analysis of data; the importance of the statistics that summarise quantitative data; and an introduction to the analysis of quantitative and qualitative data. Critical appraisal of published research will underpin theory.
Once a student has successfully completed the module and its assignments they should be able to:
- select subjects for a research study sample - including the taking of random samples from study populations
- sample populations in a way that takes account of social inclusion and diversity
- determine whether clinical tests and research measures are repeatable and valid - including the calculation of sensitivity, specificity and predictive value;
- describe, explain and understand standardization, prevalence, incidence and other routinely collected data;
- calculate from research findings: basic rates, ratios, risks and odds
- understand the differences between types of data;
- appraise critically descriptions of samples, scales, tests and measures in the healthcare literature.
- collect and record data in a form suitable for its efficient analysis;
- use flexibly a range of methods to enter data into a computer application that is designed for the analysis of research data;
- understand the importance of differences between types of data;
- carry out data analysis using a computer application;
- describe data using text, tables, charts and figures;
- calculate and understand descriptive statistics - especially summaries and distributions;
- appraise critically descriptions of data, their distribution, and summary measures of average and spread.
This module provides students with a critical awareness of research planning and methods and develops their research skills.
It will include topics on: social inclusion in research; sampling from populations; types of data; how scales and tests are used to collect data; how data are collected and described using various fractions such as rates, ratios, risks and odds; and critical appraisal of published research. This module provides students with a critical awareness of research planning and methods and develops their research skills. It will include topics on: recording quantitative and qualitative data in suitable formats; using computers in the analysis of data; the importance of the statistics that summarise quantitative data; an introduction to the analysis of quantitative and qualitative data; and critical appraisal of published research.
The teaching style for this module will be active and participative. Where the module is taught entirely online we will replicate ‘group activities’ and students will be asked to complete online tasks and activities that mirror the pre-Covid19 teaching style., Students will be introduced to: social inclusion in research, sampling from populations, how questionnaires function in the collection of research data, and types of data. A problem-based learning method will be used to lead students to knowledge and understanding of: how to select representative samples of subjects; how to understand and calculate population fractions, rates, and standardized rates and ratios; and how scales and tests perform in research and clinical practice - their repeatability and validity (sensitivity, specificity and predictive value). Students will appraise critically how research data are introduced, described and dealt with in published research.
Students will be introduced to: data distributions, central measures and spread. A problem-based learning method will be used to lead students to knowledge and understanding of: how to display qualitative data; and how to undertake simple quantitative data analysis and display findings in tables, charts and figures. A quiz format with accompanying mini-lecture will be used to reinforce learning on basic statistics and introduce one new statistical concept. In a practical computer based class, students will work through a workbook introducing a statistical software package (e.g. Minitab) for the analysis of data (or if taught online will have access to the computer software remotely)
|Delivery type||Number||Length hours||Student hours|
|Problem Based Learning||2||3.00||6.00|
|Independent online learning hours||5.00|
|Private study hours||126.00|
|Total Contact hours||19.00|
|Total hours (100hr per 10 credits)||150.00|
Private studyIndependent online learning will mainly follow on from the formal classes and will make use of a portfolio of materials placed on the VLE. Students will also be expected to work in their own time, researching taught and online course work, building up their knowledge using the guidance provided by formal taught and online components of the module.
Opportunities for Formative FeedbackStudents will complete in their own time an online test (e.g. MCQ/EMQ) with automated feedback that will be marked as a formative assessment.
Methods of assessment
|Assessment type||Notes||% of formal assessment|
|In-course MCQ||Formative Quiz||0.00|
|Problem Sheet||Individual completion of workbook begun in class activity||40.00|
|Total percentage (Assessment Coursework)||100.00|
The dates on which coursework is set and submitted varies each time the module is delivered. Clear guidance will be given to students at the start of the module. Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated
Reading listThe reading list is available from the Library website
Last updated: 29/04/2022 15:42:19
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