| Module Title |
Research Methods 2 |
| Module Code |
EDU1164 (ITS: ED9002A) |
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Faculty |
DCU Institute of Education |
School |
Policy & Professional Practice |
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NFQ level |
10 |
Credit Rating |
20 |
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Description
This module situates quantitative research concepts, methods and tools in the context of practitioner research in Education.
It is taught by a combination of on-line sessions using Loop, related reading, intensive programmes, and laboratory sessions. These inputs focus two tasks which structure the module:
• A review of existing quantitative research of relevance to a potential research project leading to the formulation of a research question.
• Craft a workable project plan for a potential quantitative methods research project.
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Learning Outcomes
1. Identify paradigms of research inquiry consistent with quantitative research
2. Demonstrate advanced proficiency in advanced literature searching/reviewing skills and an ability to critique such literature in terms of theoretical positions and quantitative research evidence
3. Articulate and justify a quantitative research question drawing on the literature and personal professional practice
4. Manage a quantitative research project
5. Identify and address the contextual, ethical and quality issues in the planning and carrying out of a quantitative research project
6. Select and critique appropriate quantitative techniques to address research questions and problems
7. Select / modify /design, pilot and use appropriate quantitative research instruments
8. Critically reflect upon, select and use appropriate analyses for quantitative research data
9. Use SPSS (or equivalent) for data entry, manipulation, analysis and decision making in an educational context
10. Write and submit a research plan
11. Critically reflect on the contribution of quantitative methods to the process of practitioner research
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| Workload | Full time hours per semester | | Type | Hours | Description |
|---|
| Lecture | 26 | Formal input by the module coordinator to student group in lecture/seminar format. | | Laboratory | 20 | Supported sessions using SPSS | | Online activity | 44 | Participant engagement in synchronous and/or asynchronous online discussion fora hosted on the DCU LOOP system | | Assignment Completion | 200 | Development and presentation of research paper and small scale pilot project | | Independent Study | 210 | Independent pursuit of relevant issues and ideas raised during the module. Participants will be expected to engage in reading and critical review of literature relevant to the module. Participants will also be expected to engage with SPSS in supported and unsupported settings. |
| Total Workload: 500 |
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| Section Breakdown | | CRN | 11637 | Part of Term | Semester 1 & 2 | | Coursework | 100% | Examination Weight | 0% | | Grade Scale | PASS/FAIL | Pass Both Elements | N | | Resit Category | RC1 | Best Mark | N | | Module Co-ordinator | Martin Brown | Module Teacher | Alan Gorman, Therese Farrell |
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| Assessment Breakdown |
| Type | Description | % of total | Assessment Date |
|---|
| Report(s) | Literature Review and formulation of a research question (2,500 words) | 60% | n/a | | Assignment | Students are required to craft a workable Quantitative Methods Research plan for their research that maps the anticipated actions and decisions that will have to be made (or have already been made) at critical junctures on their research journey.
The research plan will include the following items:
1. Title
2. Introduction
3. Problem Statement
4. Purpose
5. Hypotheses
6. Definition of key terms
7. Review of Literature
8. Methodology
9. Research design
10. Population and sampling plan
11. Data collection procedures
12. Data analysis
13. Concluding statement (Significance and limitations)
14. References, Appendices
(2,500 words) | 40% | n/a |
| Reassessment Requirement Type |
Resit arrangements are explained by the following categories;
RC1: A resit is available for both* components of the module.
RC2: No resit is available for a 100% coursework module.
RC3: No resit is available for the coursework component where there is a coursework and summative examination element.
* ‘Both’ is used in the context of the module having a coursework/summative examination split; where the module is 100% coursework, there will also be a resit of the assessment
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Pre-requisite |
None
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Co-requisite |
None |
| Compatibles |
None |
| Incompatibles |
None |
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All module information is indicative and subject to change. For further information,students are advised to refer to the University's Marks and Standards and Programme Specific Regulations at: http://www.dcu.ie/registry/examinations/index.shtml
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Indicative Content and Learning Activities
Indicative Content
1. Identify, critique and interpret existing quantitative research in a potential research area - advanced literature searching including systematic reviews, meta-analysis
2. Quantitative data analysis: concepts and methods of relevance to practitioner research in education
3. Research Ethics: concepts, requirements and tools
4. Framing and operationalising a quantitative research question
5. Development of hypotheses (Null, research..., assumptions, predictions)
6. Study Variables: Independent, dependent and control; implications for data analysis
7. Develop (and critique existing) quantitative instruments (surveys, tests, scales, checklists)
8. Survey design, platforms and administration
9. Sampling (Probability and non-probability...)
10. Use SPSS (or equivalent) for data entry, preparation and analysis (Coding, entering, cleaning manually)
11. Use SPSS (or equivalent) for data analysis using parametric and non-parametric techniques (Selecting appropriate tests, describing trends, comparing groups, relating variables, correlations)
12. Interpreting and reporting results (significance levels, one/two tailed, effect sizes, magnitude)
13. Validity, reliability, generalisability, threats, error
14. Development of a small scale quantitative research plan
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Indicative Reading List
Books:
- • Bernhardt, V. L. (2013). Data analysis for continuous school improvement. London: Routledge: 0,
- Cox, J. and Cox, K.B., 2008. Your opinion, please! How to build the best questionnaires in the field of education. Corwin Press.: 0,
- Datnow, A. and Park, V., 2014. Data-driven leadership. CA: John Wiley & Sons: 0,
- Floyd, J. and Fowler, J., 2014. Survey Research Methods. Applied Social Research Methods. London: Sage: 0,
- • Hammersley, M. 2007, Educational Research and Evidence-based Practice. London: Sage: 0,
- Hancock, G.R., Mueller, R.O. and Stapleton, L.M. 2010. The reviewer’s guide to quantitative methods in the social sciences. London: Routledge.: 0,
- Hoy, W.K. and Adams, C.M., 2015. Quantitative research in education: A primer. London: Sage: 0,
- Mujis,D., 2011, Doing Quantitative Research in Education with SPSS, 2nd Ed. London Sage Publications: 0,
- Rubin, A. 2012, Statistics for evidence-based practice and evaluation. MA: Cengage Learning: 0,
Articles:
- • Campbell, C., & Levin, B. (2009). Using data to support educational improvement. Educational Assessment, Accountability, and Evaluation, 21(1), 47–65: 0, 521511
- 0: 521512, 1
- 521513: 1, Farrell, C. C. (2015). Designing school systems to encourage data use and instructional improvement: A comparison of school districts and charter management organizations. Educational Administration Q, 0,
- 1: Schildkamp, K., Poortman, C., Luyten, H. and Ebbeler, J., 2017. Factors promoting and hindering data-based decision making in schools. School effectiveness and school improvement, 28(2), pp.242-258., 0,
- Schildkamp, K., & Kuiper, W. (2010). Data informed curriculum reform: Which data, what purposes, and promoting and hindering factors. Teaching and Teacher Education.: 0, 521516
- 0: 521517, 1
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Other Resources
None |
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