| Module Title |
Research Methods & Statistics 2 |
| Module Code |
PSY1032 (ITS: PSYC212) |
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Faculty |
Psychology |
School |
Science & Health |
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NFQ level |
8 |
Credit Rating |
5 |
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Description
This module builds on PSYC113 and PSYC114 in year 1 and introduces students to more advanced quantitative research methodologies, designs, and analytical techniques in psychology, including applied psychology and technology-focused research. It also introduces students to advanced regression techniques (multiple regression and moderation) and their specific incarnations in tests such as ANOVAs, their appropriate usage and how to compute them. Practical experience through laboratory experiments and computer-based exercises will continue.
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Learning Outcomes
1. Discuss quantitative research designs and methods, and assess their strengths and limitations
2. Understand the principles of regression, appropriately carry out such analyses and report statistical results in a manner consistent with APA recommendations.
3. Identify the importance of power and effect size when carrying out any analysis
4. Be proficient in identifying the limitations of specific methodologies and in understanding the relative merits of quantitative approaches to an identified research question
5. Think critically about research to identify the strengths and weaknesses of design, methods, analysis, data, and conclusion
6. Use advanced searching skills to effectively use library and online psychology resources
7. Gain an understanding of data structures and data in the real world
8. Communicate data through statistical, graphical, and verbal means.
9. Develop, analyze, and interpret complex research designs, incorporating modulating factors.
10. Gain first hand research experience by participating in psychological research projects within the school including those of staff, post-graduate students and undergraduate final year projects.
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| Workload | Full time hours per semester | | Type | Hours | Description |
|---|
| Lecture | 36 | Psychological skills are supported using learning activities analysing data sets | | Independent Study | 39 | Write up of practicals, becoming familiar with statistical concepts and research methods, identifying and critiquing key readings, report and tutorial preparation | | Online activity | 13 | Students will access digital resources relating to statistical reasoning, data entry and data visualisation | | Online activity | 37 | Students will complete topic-related loop quizzes and access directed online resources that describe the fundamental concepts of statistics and relate to the processes involved in statistical modeling. |
| Total Workload: 125 |
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| Section Breakdown | | CRN | 11475 | Part of Term | Semester 1 | | Coursework | 0% | Examination Weight | 0% | | Grade Scale | 40PASS | Pass Both Elements | Y | | Resit Category | RC1 | Best Mark | N | | Module Co-ordinator | Gerard Loughnane | Module Teacher | Louise Hopper |
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| Assessment Breakdown |
| Type | Description | % of total | Assessment Date |
|---|
| Loop Quiz | Completion of online six Loop quizzes relating to the content of the module. | 12% | n/a | | Loop Quiz | Completion of online four Loop quizzes relating to the content of the module, combined with a section covering the writeup of a statistical test. | 28% | n/a | | Report(s) | Write-up of a complete APA-style psychology report with revision opportunity worth 50% of overall module grade. | 50% | n/a | | Report(s) | Structured response letter to the initial report submission | 10% | 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 |
p, |
| 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
Advanced information literacy skills
Describing and exploring Data using advanced graphs
Multiple Regression
Moderated Regression
Special Cases of Regression (ANOVA, Binary Data)
Open Science, Statistical power, Effect Size
Experimental and Survey Design with complex Dependencies
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Indicative Reading List
Books:
- Rajiv S. Jhangiani: 2019, Research Methods in Psychology, https://kpu.pressbooks.pub/psychmethods4e, 1085976920
- Garrett Grolemund,Hadley Wickham: 2017, R for Data Science, https://r4ds.had.co.nz/, 1491910399
- American Psychological Association: 2018, Publication Manual of the American Psychological Association (7th ed.),
- Russell A. Poldrack: 2018, Statistical Thinking for the 21st century, https://statsthinking21.github.io/statsthinking21-core-site/,
- Thulin, M.: 2021, ). Modern Statistics with R.,
- Dr Peter Allen,Kellie Bennett,Dr Brody Heritage: 2018, SPSS Statistics: A Practical Guide with Student Resource Access 12 Months, 4th, Cengage AU, 352, 9780170421140
- Andy Field,Jeremy Miles,Zoë Field: 2012, Discovering Statistics Using R, SAGE Publications, 957, 9781446200469
- Cozby,Scott Bates: 2018, Methods in Behavioral Research, 13th Ed, Mc Graw Hill, 9781260084207
- Cohen, B.H.: 2013, Explaining psychological statistics, 4th, Wiley, 9781118436608
- Barbara G. Tabachnick,Linda S. Fidell,Jodie B. Ullman: 2019, Using Multivariate Statistics, Pearson, 9780134790541
Articles:
None |
Other Resources
None |
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Readings will be supplemented by items such as journal articles, readers in psychology, and material prepared for lectures and practical sessions.
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