Module Details
| Module Code: |
SOCI8008 |
| Title: |
Social Data Analysis
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Long Title:
|
Social Data Analysis
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| NFQ Level: |
Advanced |
| Valid From: |
Semester 1 - 2019/20 ( September 2019 ) |
| Field of Study: |
3120 - Sociology
|
| Module Description: |
This module aims to teach and assist students to organise raw research data and analyze it using both quantitative and qualitative data analysis techniques. It is mostly beneficial to the writing process of a research dissertation.
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| Learning Outcomes |
| On successful completion of this module the learner will be able to: |
| # |
Learning Outcome Description |
| LO1 |
Input data and conduct basic analysis to produce descriptive statistics. |
| LO2 |
Create and test hypotheses using inferential statistics. |
| LO3 |
Reduce qualitative data: coding, thematic analysis, clusturing and partitioning. |
| LO4 |
Understand and present the strength and limitations of research conclusions and recommendations in the Social Sciences. |
| LO5 |
Use data analysis software packages in Social Studies. |
| Dependencies |
Module Recommendations
This is prior learning (or a practical skill) that is strongly recommended before enrolment in this module. You may enrol in this module if you have not acquired the recommended learning but you will have considerable difficulty in passing (i.e. achieving the learning outcomes of) the module. While the prior learning is expressed as named MTU module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
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Incompatible Modules
These are modules which have learning outcomes that are too similar to the learning outcomes of this module. You may not earn additional credit for the same learning and therefore you may not enrol in this module if you have successfully completed any modules in the incompatible list.
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| No incompatible modules listed |
Co-requisite Modules
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| No Co-requisite modules listed |
Requirements
This is prior learning (or a practical skill) that is mandatory before enrolment in this module is allowed. You may not enrol on this module if you have not acquired the learning specified in this section.
|
| No requirements listed |
| Indicative Content |
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Transfer and input of Data
The transfer and input of data collected through research instrument in preparation for analysis.
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Hypothesis Testing
Evolution of research question to hypothesis formulation and testing where and when appropriate.
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Transforming Data
Reduce raw data to manageable data amenable to analysis. Coding methods and thematic analysis
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Use of Computer in Social Data Analysis
Proficient use of computer software in data analysis.
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Analysis and Interpretation
Link analysis to Social Research theoretical frameworks, interpretation,conclusions and recommendations.
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Module Content & Assessment
|
| Assessment Breakdown | % |
|---|
| Coursework | 100.00% |
Assessments
| No End of Module Formal Examination |
| Reassessment Requirement |
Coursework Only
This module is reassessed solely on the basis of re-submitted coursework. There is no repeat written examination.
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The University reserves the right to alter the nature and timings of assessment
Module Workload
| Workload: Full Time |
| Workload Type |
Contact Type |
Workload Description |
Frequency |
Average Weekly Learner Workload |
Hours |
| Lecture |
Contact |
Data analysis guided by research questions and research design |
Every Week |
2.00 |
2 |
| Lab |
Contact |
Data Inputting, Analysis and Interpretation |
Every Week |
1.00 |
1 |
| Independent & Directed Learning (Non-contact) |
Non Contact |
Collection of Data, linking with thesis and interpretation |
Every Week |
4.00 |
4 |
| Total Hours |
7.00 |
| Total Weekly Learner Workload |
7.00 |
| Total Weekly Contact Hours |
3.00 |
| Workload: Part Time |
| Workload Type |
Contact Type |
Workload Description |
Frequency |
Average Weekly Learner Workload |
Hours |
| Lecture |
Contact |
Data analysis guided by research questions and research design |
Every Week |
2.00 |
2 |
| Lab |
Contact |
Data Inputting, Analysis and Interpretation |
Every Week |
1.00 |
1 |
| Independent & Directed Learning (Non-contact) |
Non Contact |
Collection of Data, linking with thesis and interpretation |
Every Week |
4.00 |
4 |
| Total Hours |
7.00 |
| Total Weekly Learner Workload |
7.00 |
| Total Weekly Contact Hours |
3.00 |
|
Module Resources
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| Recommended Book Resources |
|---|
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Alan Bryman. (2016), Social Research Methods, 5th. Oxford University Press, Oxford, p.840, [ISBN: 9780199689453].
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Bergin, T. (2018), An Introduction to Data Analysis: Quantitative, Qualitative and Mixed Methods, SAGE, London.
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Andy Field,. (2018), Discovering Statistics Using IBM SPSS Statistics, SAGE, London, [ISBN: 9781526419521].
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Allen, P; Bennett' K; Heritage, B.. (2019), SPSS Statistics: A practical Guide ., 4th. Cengage, Victoria, Melbourne.
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Harding, J.. (2019), Qualitative Data Analysis, 2nd. SAGE, London.
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Pallant, Julie. (2016), SPSS Survival Manual, 6th. McGraw Hill, New York, [ISBN: 9780335261543].
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GreeneJudith and D'Oliveira Manuela. (1982), Learning to use statistical tests in psychology, Open University Press, Milton Keynes, [ISBN: 0335101771].
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Matthew B. Miles, A. Michael Huberman. (1994), Qualitative data analysis, Sage Publications, Thousand Oaks, [ISBN: 0803955405].
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Aldrich, James O. & Rodriguez, Hilda M. ,. (2013), Building SPSS Graphs to Understand Data, SAGE, London, [ISBN: 978145221684].
| | Supplementary Book Resources |
|---|
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Richards Lyn. (2015), Handling Qualitative Data, 3rd. SAGE, London.
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Silverman, David. (2015), Interpreting qualitative data, 5th. SAGE, London, [ISBN: 9781446295434].
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Denzin and Lincoln. (1998), Collecting and Interpreting Qualitative Materials, Sage, London.
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Wagner, William E.. (2016), Using IBM (R) SPSS (R) Statistics for Research Methods and Social Science Statistics, SAGE, Thousand Oaks, CA, [ISBN: 9781506331720].
| | This module does not have any article/paper resources |
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| This module does not have any other resources |
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