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Computer based tasks requiring qualitative and quantitative data management, manipulation and interpretation.

Computer based tasks requiring qualitative and quantitative data management, manipulation and interpretation.
This assignment is intended to assess the following learning outcomes:
•    Identify discipline relevant research databases
•    Differentiate between qualitative and quantitative data collection and analysis methods
•    Organise and describe qualitative and quantitative health data
•    Use databases to find and manage datasets
This assessment is intended to test students’ understanding of the basic principles of data analysis and data interpretation. Students will be asked to perform several analytical tasks and provide a written interpretation of the analysis performed. The written interpretation of the data will require students to draw on supporting theoretical, methodological and research evidence to support the ideas presented. Students’ submissions will include: 1) evidence of data manipulation; 2) data management; 3) evidence of data coding; 4) written interpretation of the data.
Details of the task
1)    In the ‘Assessment information’ section on the HS4103 Moodle page, you will find two datasets:
a)    A quantitative dataset: a spreadsheet of data sourced from the Global Health Observatory.
b)    A qualitative dataset: a focus group transcript sourced from the UK data service.
2)    The aim of this assessment is for you to demonstrate your ability to manage and manipulate this data, present it professionally and provide a written interpretation of these data.
3)    The two datasets are not related; therefore this assessment should be presented in two distinct sections (quantitative and qualitative).
4)    You are expected to include the following contents:
Quantitative section    Qualitative section
•    A very brief introduction to the dataset.
•    Description of data management and analysis techniques applied to the data (e.g. measures of central tendency and dispersion).
•    Presentation of selected data in tables, graphs and/or figures.
•    Description and interpretation of data in graphs, tables and/or figures.
•    References to research methods literature to justify the analysis techniques you have applied.
•    Brief discussion of the relevance of the data to policy, practice and future research.
•    References to the wider literature to support your interpretation of the data.    •    A very brief introduction to the dataset.
•    Description of data management and analysis techniques applied to the data (e.g. coding and theming processes).
•    Presentation of selected data in tables.
•    Description and interpretation of data in tables.
•    References to research methods literature to justify the analysis techniques you have applied.
•    Brief discussion of the relevance of the data to policy, practice and future research.
•    References to the wider literature to support your interpretation of the data.
5)    Information presented in tables or graphs/figures does not count towards the total word count.
6)    There are no strict requirements as to how you should present your management and interpretation of these data.  You should aspire to meet professional standards, such as the presentation style used in peer-reviewed academic journals.
7)    You are not expected to use all of the data in both assessments.  You can choose certain parts of the dataset to manage analyse and interpret.  This selection can be based on your own interests.
General guidance for both components:
•    You will need to demonstrate your skills, knowledge and understanding using a variety of methods.
•    You should provide references to literature where appropriate.  For example, it is appropriate to reference to literature on relevant research methods.  It is also appropriate to provide references to other literature such as research studies and other data that may be used to compare your findings.
•    The references should include academic books and textbooks; peer reviewed publications from the academic journals; and reports by national and international organisations;
•    Where internet sources are used, these should reliable and evidence-based sources and correctly referenced  with the website address and date (NOTE: weblinks alone are not acceptable and Wikipedia (and other sites as such) ARE NOT appropriate references);
•    All work must be word processed (except written exams) and written in your own words;
•    The word limit must be observed with a 10% discretion; the word count must be indicated on the last page of the essay;
•    The text must be divided using headings or sections and must include an introduction and conclusion;
•    All pages must be numbered;
•    The Title page must include; the title and the number of the module; the name of the module leader; your student number; the assignment title and the topic you selected.
•    All referencing should be in line with ‘cite them right’.  See the ‘citation’ link on MOODLE for further details of how to reference correctly.
•    Work must be proofread for grammar, spelling and formatting mistakes before handing in;
•    You should follow all additional given guidelines and marking criteria;
•    You should check your essay via Turnitin in prior to hand in; be sure that you leave ample time to make any necessary changes and note that Turnitin can take up to 24 hours to provide an ‘Originality Report’;
•    An electronic Word version of your essay should be submitted online to the electronic plagiarism detection system Turnitin via MOODLE.  If you fail to submit your electronic copy to the plagiarism detection system, your work will be considered late and penalised. If your plagiarism percentage is too high or there are several stretches of poorly referenced or copied text; you will be able to make amendments and resubmit your essay providing this is within the deadline period.  You will have an option to exclude the references and quotations and thus, reduce the percentage of plagiarism in your essay. Please, make sure you know how to do it well before the submission (see the ‘How To Use Turnitin’ document on Moodle).

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