Module Code: |
H9RTM |
Long Title
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Research Methods
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Title
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Research Methods
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Module Level: |
LEVEL 9 |
EQF Level: |
7 |
EHEA Level: |
Second Cycle |
Module Coordinator: |
ARLENE EGAN |
Module Author: |
ARLENE EGAN |
Departments: |
School of Computing
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Specifications of the qualifications and experience required of staff |
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Learning Outcomes |
On successful completion of this module the learner will be able to: |
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Learning Outcome Description |
LO1 |
Critically investigate quantitative data and research methods as applied in the computing technology field. |
LO2 |
Demonstrate comprehensive knowledge of how to solve problems of quantitative analysis that focus on core statistical concepts (which include; stating hypotheses, sampling, distribution, significance) |
LO3 |
Critically evaluate experimental research and literature published in the field of computing technology |
LO4 |
Demonstrate competence and skills in the use of statistical tools and the ability to critically assess outputs and levels of significance. |
LO5 |
Employ enhanced project management and collaboration skills. |
Dependencies |
Module Recommendations
This is prior learning (or a practical skill) that is required before enrolment on this module. While the prior learning is expressed as named NCI 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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No recommendations listed |
Co-requisite Modules
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No Co-requisite modules listed |
Module Content & Assessment
Indicative Content |
Introduction (5%)
• Characteristics of data and data types
• Revision of research cycles
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Hypothesis testing, power and effect size (15%)
• Probability
• Hypothesis testing
• Confidence intervals
• P values/significance level
• Type 1 and Type 2 errors
• Effect size
• Influencing the power of a test
• Calculating power
• Sample size
• Assumptions of quantification
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Data collection and Quantitative tests (25%)
• Quantitative data collection; surveys, observations; log data
• T-tests; rationale, assumptions and applications
• Correlations;
• One-way repeated measures
• Regression analysis
• Non-parametric analysis: Chi-square, Mann Whitney U test, Wilcoxen signed ranks test, Friedman’s test, Kruskal-Wallis
• Non parametric correlations
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Practical application (15%)
• Calculating descriptive statistics
• Calculating probabilities, standard error and confidence intervals
• Using tools to carry out statistical tests (e.g., t-tests, ANOVAs, regression)
• Reporting interpretations and assumptions of those tests
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Project Management (20%)
Project definition and phases
• Activities, milestones (code development) and deliverables (chapters/sections of documents)
• Public code repositories, change management, and versioning
• Project supervisor engagement and communication
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Communication and collaboration (20%)
• Oral, team and electronic group communication
• Visualisation techniques and application (e.g., trees, graphs, networks)
• Employing tools such as project management software and other virtual learning facilities and tools (e.g. WBS, gantt charts, process and progress plans)
• Peer-to-peer problem-based learning and evaluation
• Visual Information Processing
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Assessment Breakdown | % |
Coursework | 100.00% |
AssessmentsFull Time
Coursework |
Assessment Type: |
Assignment |
% of total: |
60 |
Assessment Date: |
n/a |
Outcome addressed: |
1,2,3,4 |
Non-Marked: |
No |
Assessment Description: In-class test on statistical concepts, techniques and procedures. |
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Assessment Type: |
Project |
% of total: |
40 |
Assessment Date: |
n/a |
Outcome addressed: |
1,2,3,4,5 |
Non-Marked: |
No |
Assessment Description: Team project to analyse the data and to draw conclusions on a case-study. |
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No End of Module Assessment |
Reassessment Requirement |
Repeat examination
Reassessment of this module will consist of a repeat examination. It is possible that there will also be a requirement to be reassessed in a coursework element.
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NCIRL reserves the right to alter the nature and timings of assessment
Module Workload
Module Target Workload Hours 0 Hours |
Workload: Full Time |
Workload Type |
Workload Description |
Hours |
Frequency |
Average Weekly Learner Workload |
Lecture |
per week |
1 |
Every Week |
1.00 |
Tutorial |
per week |
1 |
Every Week |
1.00 |
Independent Learning Time |
No Description |
8.5 |
Once per semester |
0.71 |
Total Weekly Contact Hours |
2.00 |
Workload: Part Time |
Workload Type |
Workload Description |
Hours |
Frequency |
Average Weekly Learner Workload |
Lecture |
per week |
1 |
Every Week |
1.00 |
Tutorial |
per week |
1 |
Every Week |
1.00 |
Independent Learning Time |
No Description |
8.5 |
Once per semester |
0.71 |
Total Weekly Contact Hours |
2.00 |
Module Resources
Recommended Book Resources |
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Giudici, P., Ingrassia, S., & Vichi, M.. (2013), Statistical Models for Data Analysis, Springer, London.
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Marder, M.P.. (2011), Research Methods for Science, Cambridge University Press.
| Supplementary Book Resources |
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Demir, F., Karakaya, M., & Tosun, H. (2012), Research Methods in Usability and Interaction design: Evaluations and case studies., Lambert Academic Press.
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Muata, K., Bryson, O., & Ngwenyama, O.. (2014), Advances in Research for information Systems Research: Data mining, data envelopment, value focused thinking, Springer, London.
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George, D. And Mallery, P.. (2011), SPSS for Windows Step by Step: A Simple Study Guide, 10th. Allyn & Bacon, UK.
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Goodman, E., Kuniavsky, M., & Moed A.. (2012), A Practitioners Guide to User Research:, MA: Elsevier.
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Grbich, C.. (2013), Qualitative Data Analysis: An Introduction, Sage, London.
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Vittingoff, E., Glidden, D.V., Shiboshi, S.C. & McCulloch, C.E.. (2012), Regression Methods in Biostatistcis: Linear, logistic, survival and repeated measures models, Springer, London.
| This module does not have any article/paper resources |
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Other Resources |
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[website], The Research Methods Knowledge Base,
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[website], Research Randomizer,
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[website], Glossary of Statistical Terms,
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[website], HyperStat Online,
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[website], InfoVis:Wiki, the Information
Visualization community platform,
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[journal], IEEE Transactions on Visualization and
Computer Graphics,
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[website], Information Visualization - Sage,
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[website], Interaction Design Foundation – Data
Visualization for Human Perception,
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