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Session: 2022/23
Last modified: 04/07/2022 11:34:03
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Title of Module: Data Analysis for the Social Sciences |
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Code: SOCY09055 |
SCQF Level: 9
(Scottish Credit and Qualifications Framework) |
Credit Points: 20 |
ECTS: 10
(European Credit Transfer Scheme) |
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| School: | School of Education & Social Sciences |
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| Module Co-ordinator: | Nick
Jenkins |
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| Summary of Module |
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This practical module equips students with work-ready skills in quantitative data analysis (analysing numerical information) and qualitative data analysis (analysing non-numerical information).
Part 1 focuses on developing knowledge and skills in data analysis - such as how to ‘standardise’ numerical information and look for patterns in distributions, as well as how to condense, display and draw inferences from qualitative information.
Part 2 enables students to specialise in either quantitative data analysis or qualitative analysis by conducting a secondary data analysis project under the direction of the teaching team.
During Part 1 and Part 2, students learn how to use standard office software (e.g. Microsoft Word & Excel) to help them analyse data. Towards the end of the course, staff demonstrate the use of specialist analysis software (e.g. SPSS, R, NVivo, HyperReserch) and discuss how students may wish to consider using these during their 4th year dissertation projects.
On successful completion of the module, students will have the skills and the confidence necessary to conduct robust data analysis, which will enhance students’ employment prospects and help prepare students for their honours year dissertation projects. Students planning to collect real world data for their dissertation projects are therefore ** strongly encouraged** to take this module.
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| Module Delivery Method |
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| Face-To-Face | Blended | Fully Online | HybridC | HybridO | Work-based Learning |
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Face-To-Face
Term used to describe the traditional classroom environment where the students and the lecturer meet synchronously in the same room for the whole provision.
Blended
A mode of delivery of a module or a programme that involves online and face-to-face delivery of learning, teaching and assessment activities, student support and feedback. A programme may be considered “blended” if it includes a combination of face-to-face, online and blended modules. If an online programme has any compulsory face-to-face and campus elements it must be described as blended with clearly articulated delivery information to manage student expectations
Fully Online
Instruction that is solely delivered by web-based or internet-based technologies. This term is used to describe the previously used terms distance learning and e learning.
HybridC
Online with mandatory face-to-face learning on Campus
HybridO
Online with optional face-to-face learning on Campus
Work-based Learning
Learning activities where the main location for the learning experience is in the workplace.
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| Term(s) for Module Delivery |
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(Provided viable student numbers permit).
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| Term 1 | | Term 2 | | Term 3 | |
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| Learning Outcomes: (maximum of 5 statements) |
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On successful completion of this module the student will be able to:
L1.
Demonstrate critical knowledge, skills and understanding in quantitative data analysis.
L2.
Demonstrate critical knowledge, skills and understanding in qualitative data analysis.
L3.
Conduct a secondary data analysis project using either quantitative data or qualitative data, under the supervision of the teaching team.
L4.
Demonstrate critical understanding of the strengths and the limitations of one’s own data analysis work. |
| Employability Skills and Personal Development Planning (PDP) Skills |
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| SCQF Headings |
During completion of this module, there will be an opportunity to achieve
core skills in:
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| Knowledge and Understanding (K and U) |
SCQF Level 9.
Demonstrate critical knowledge and understanding of quantitative data analysis and qualitative data analysis, including the role of data analysis in advancing social theory. |
| Practice: Applied Knowledge and Understanding |
SCQF Level 9.
Application of critical knowledge and understanding in order to conduct a discrete piece of secondary data analysis. |
| Generic Cognitive skills |
SCQF Level 9.
Critical presentation and evaluation of the results of secondary data analysis that address a pre-defined research question. |
| Communication, ICT and Numeracy Skills |
SCQF Level 9.
Introductory knowledge of contemporary computer assisted data analysis software (CADAS), including their affordances, strengths and limitations.
Use of non-specialist ICT (e.g. Word, Excel) to manage, organise and analyse data.
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| Autonomy, Accountability and Working with others |
SCQF Level 9.
To complete a secondary data analysis project to an agreed deadline under the supervision of the teaching team. |
| Pre-requisites: |
Before undertaking this module the student should have
undertaken the following:
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Module Code:
| Module Title:
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| Other: | Foundations of Quantitative Research and Foundations of Qualitative Research (Or equivalent) |
| Co-requisites | Module Code:
| Module Title:
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* Indicates that module descriptor is not published.
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| Learning and Teaching |
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Learning Activities
During completion of this module, the learning activities undertaken to
achieve the module learning outcomes are stated below:
| Student Learning Hours
(Normally totalling 200 hours):
(Note: Learning hours include both contact hours and hours spent on other learning activities) |
| Lecture/Core Content Delivery | 12 |
| Tutorial/Synchronous Support Activity | 22 |
| Asynchronous Class Activity | 10 |
| Independent Study | 156 |
| 200
Hours Total
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**Indicative Resources: (eg. Core text, journals, internet
access)
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The following materials form essential underpinning for the module content
and ultimately for the learning outcomes:
Salkind N. and Frey B. (2019). Statistics for People Who (Think They) Hate Statistics. 7th Edition. Thousand Oaks: Sage.
Salkind N. (2015). Excel Statistics: A Quick Guide. 3rd Edition. Thousand Oaks: Sage.
Miles M, Huberman A, Saldana J. (2019). Qualitative Data Analysis: A Methods Sourcebook. 4th Edition. Thousand Oaks: Sage.
Saldana J. (2015). The Coding Manual for Qualitative Researchers. 3rd Edition. Thousand Oaks: Sage.
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(**N.B. Although reading lists should include current publications,
students are advised (particularly for material marked with an asterisk*) to
wait until the start of session for confirmation of the most up-to-date
material)
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| Engagement Requirements |
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In line with the Academic Engagement Procedure, Students are defined as academically engaged if they are regularly engaged with timetabled teaching sessions, course-related learning resources including those in the Library and on the relevant learning platform, and complete assessments and submit these on time. Please refer to the Academic Engagement Procedure at the following link: Academic engagement procedure
Where a module has Professional, Statutory or Regulatory Body requirements these will be listed here:
All fulltime students (part-time and distant learning students should check with their programme leader for any queries) are required to attend all scheduled classes and participate with all delivered elements of the module as part of their engagement with their programme of study. Consideration will be given to students who have protection under the appropriate equality law. Please refer to UWS Regulations, Chapter 1, 1.64 – 1.67, available at the following link: http://www.uws.ac.uk/current-students/rights-and-regulations/regulatory-framework/
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Supplemental Information
| Programme Board | Social Sciences |
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| Assessment Results (Pass/Fail) |
No
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| Subject Panel | UG Social Sciences- Sociology and Social Policy |
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| Moderator | Diarmuid McDonnell |
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| External Examiner | A Tresidder |
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| Accreditation Details | |
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| Version Number | 1.01 |
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| Assessment: (also refer to Assessment Outcomes Grids below) |
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| Assessment 1 Class Test (30%) Students will complete a timed test that will assess their understandings of key techniques in quantitative data analysis and qualitative data analysis. |
| Assessment 2 Secondary Data Analysis Project (70%) Students will conduct a discrete piece of EITHER quantitative data analysis OR qualitative data analysis under the supervision of the teaching team. |
(N.B. (i) Assessment Outcomes Grids for the module
(one for each component) can be found below which clearly demonstrate how the learning outcomes of the module
will be assessed.
(ii) An indicative schedule listing approximate times
within the academic calendar when assessment is likely to feature will be
provided within the Student Handbook.)
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Assessment Outcome Grids (Footnote A.)
Footnotes
A. Referred to within Assessment Section above
B. Identified in the Learning Outcome Section above
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Note(s):
- More than one assessment method can be used to assess individual learning outcomes.
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Schools are responsible for determining student contact hours. Please refer to University Policy on contact hours (extract contained within section 10 of the Module Descriptor guidance note).
This will normally be variable across Schools, dependent on Programmes &/or Professional requirements.
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| Equality and Diversity |
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Aligned with the overall commitment to equality and diversity stated in the Programme Specifications, the module supports equality of opportunity for students from all backgrounds and with different learning needs. Using Moodle, learning materials will be presented electronically in formats that allow flexible access and manipulation of content. The module complies with University regulations and guidance on inclusive learning and teaching practice. Specialist assistive equipment, support provision and adjustment to assessment practice will be made in accordance with UWS policy and regulations. The University’s Equality, Diversity and Human Rights Policy can be accessed at the following link: http://www.uws.ac.uk/equality/
Our partners are fully committed to the principles and practice of inclusiveness and our modules are designed to be accessible to all. Where this module is delivered overseas, local equivalent support for students and appropriate legislation applies
UWS Equality and Diversity Policy |
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(N.B. Every effort
will be made by the University to accommodate any equality and diversity issues
brought to the attention of the School)
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