General Information
The ability to find, read, interpret and learn from data has become critical in determining the future of all human endeavours. This subject introduces you to the principles and applications of research methodology, equipping you with lifelong skills to design, execute and present research of integrity in your current studies and future professions. You will explore research purposes, methods and tools for data selection, interpretation and analysis, and learn how to present findings and visualise data in ways that address the needs of diverse educational and industry contexts.
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Details
Academic unit: Faculty of Society & Design Subject code: HUMR71-100 Subject title: Research Methods and Data Visualisation Subject level: Postgraduate Semester/Year: January 2025 Credit points: 10.000 -
Delivery & attendance
Timetable: https://bond.edu.au/timetable Delivery mode: Standard Workload items: - Tutorial: x12 (Total hours: 12) - Weekly Tutorial
- Forum: x12 (Total hours: 24) - Weekly Forum
- Personal Study Hours: x12 (Total hours: 84) - Recommended Study Hours
Attendance and learning activities: As successful completion of this subject is heavily dependent on participation during all scheduled sessions, attendance will be monitored. Most sessions build on the content of the previous one. It is difficult for a student to recover the information if a session is missed. In addition to synchronous sessions, students should plan to spend a minimum of 84 hours undertaking preparation/out of class work/personal study for this subject. This is intended as a general guide only for workload planning and more time may be required depending on different factors such as the familiarity of the content. -
Resources
Prescribed resources: Books
- Babbie, E (2021). The Practice of Social Research. 15th ed, Boston Cengage
iLearn@Bond & Email: iLearn@Bond is the Learning Management System at Bond University and is used to provide access to subject materials, class recordings and detailed subject information regarding the subject curriculum, assessment, and timing. Both iLearn and the Student Email facility are used to provide important subject notifications.
Additionally, official correspondence from the University will be forwarded to students’ Bond email account and must be monitored by the student.
To access these services, log on to the Student Portal from the Bond University website as www.bond.edu.au
| Academic unit: | Faculty of Society & Design |
|---|---|
| Subject code: | HUMR71-100 |
| Subject title: | Research Methods and Data Visualisation |
| Subject level: | Postgraduate |
| Semester/Year: | January 2025 |
| Credit points: | 10.000 |
| Timetable: | https://bond.edu.au/timetable |
|---|---|
| Delivery mode: | Standard |
| Workload items: |
|
| Attendance and learning activities: | As successful completion of this subject is heavily dependent on participation during all scheduled sessions, attendance will be monitored. Most sessions build on the content of the previous one. It is difficult for a student to recover the information if a session is missed. In addition to synchronous sessions, students should plan to spend a minimum of 84 hours undertaking preparation/out of class work/personal study for this subject. This is intended as a general guide only for workload planning and more time may be required depending on different factors such as the familiarity of the content. |
| Prescribed resources: | Books
|
|---|---|
| iLearn@Bond & Email: | iLearn@Bond is the Learning Management System at Bond University and is used to provide access to subject materials, class recordings and detailed subject information regarding the subject curriculum, assessment, and timing. Both iLearn and the Student Email facility are used to provide important subject notifications. Additionally, official correspondence from the University will be forwarded to students’ Bond email account and must be monitored by the student. To access these services, log on to the Student Portal from the Bond University website as www.bond.edu.au |
Enrolment requirements
| Requisites: |
Nil |
|---|---|
| Assumed knowledge: |
Assumed knowledge is the minimum level of knowledge of a subject area that students are assumed to have acquired through previous study. It is the responsibility of students to ensure they meet the assumed knowledge expectations of the subject. Students who do not possess this prior knowledge are strongly recommended against enrolling and do so at their own risk. No concessions will be made for students’ lack of prior knowledge.
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| Restrictions: |
Nil |
Assurance of learning
Assurance of Learning means that universities take responsibility for creating, monitoring and updating curriculum, teaching and assessment so that students graduate with the knowledge, skills and attributes they need for employability and/or further study.
At Bond University, we carefully develop subject and program outcomes to ensure that student learning in each subject contributes to the whole student experience. Students are encouraged to carefully read and consider subject and program outcomes as combined elements.
Program Learning Outcomes (PLOs)
Program Learning Outcomes provide a broad and measurable set of standards that incorporate a range of knowledge and skills that will be achieved on completion of the program. If you are undertaking this subject as part of a degree program, you should refer to the relevant degree program outcomes and graduate attributes as they relate to this subject.
Subject Learning Outcomes (SLOs)
On successful completion of this subject the learner will be able to:
- Identify, synthesise, interpret and communicate complex issues in research methodology and data visualisation.
- Independently and in teams, demonstrate expert cognitive skills to solve complex problems in research methodology and data visualisation.
- Apply gained knowledge and skills in research methodology and data visualisation to new contexts, demonstrating expert judgement, adaptability and responsibility as a practitioner or learner.
Generative Artificial Intelligence in Assessment
The University acknowledges that Generative Artificial Intelligence (Gen-AI) tools are an important facet of contemporary life. Their use in assessment is considered in line with students’ development of the skills and knowledge which demonstrate learning outcomes and underpin study and career success. Instructions on the use of Gen-AI are given for each assessment task; it is your responsibility to adhere to these instructions.
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Assessment details
Type Task % Timing* Outcomes assessed Literature Review Literature Review of the agreed research project 30.00% Week 6 1,2,3 Research Report Research report on the approved topic (2500 words) 60.00% Week 11 1,2,3 Student Engagement Students engagement during learning activities (synchronous or asynchronous), where students ask and answer questions, or brainstorm with fellow students. 10.00% Ongoing 1,2,3 - * Assessment timing is indicative of the week that the assessment is due or begins (where conducted over multiple weeks), and is based on the standard University academic calendar
- C = Students must reach a level of competency to successfully complete this assessment.
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Assessment criteria
Assessment criteria
High Distinction 85-100 Outstanding or exemplary performance in the following areas: interpretative ability; intellectual initiative in response to questions; mastery of the skills required by the subject, general levels of knowledge and analytic ability or clear thinking. Distinction 75-84 Usually awarded to students whose performance goes well beyond the minimum requirements set for tasks required in assessment, and who perform well in most of the above areas. Credit 65-74 Usually awarded to students whose performance is considered to go beyond the minimum requirements for work set for assessment. Assessable work is typically characterised by a strong performance in some of the capacities listed above. Pass 50-64 Usually awarded to students whose performance meets the requirements set for work provided for assessment. Fail 0-49 Usually awarded to students whose performance is not considered to meet the minimum requirements set for particular tasks. The fail grade may be a result of insufficient preparation, of inattention to assignment guidelines or lack of academic ability. A frequent cause of failure is lack of attention to subject or assignment guidelines. Quality assurance
For the purposes of quality assurance, Bond University conducts an evaluation process to measure and document student assessment as evidence of the extent to which program and subject learning outcomes are achieved. Some examples of student work will be retained for potential research and quality auditing purposes only. Any student work used will be treated confidentially and no student grades will be affected.
| Type | Task | % | Timing* | Outcomes assessed |
|---|---|---|---|---|
| Literature Review | Literature Review of the agreed research project | 30.00% | Week 6 | 1,2,3 |
| Research Report | Research report on the approved topic (2500 words) | 60.00% | Week 11 | 1,2,3 |
| Student Engagement | Students engagement during learning activities (synchronous or asynchronous), where students ask and answer questions, or brainstorm with fellow students. | 10.00% | Ongoing | 1,2,3 |
- * Assessment timing is indicative of the week that the assessment is due or begins (where conducted over multiple weeks), and is based on the standard University academic calendar
- C = Students must reach a level of competency to successfully complete this assessment.
Assessment criteria
| High Distinction | 85-100 | Outstanding or exemplary performance in the following areas: interpretative ability; intellectual initiative in response to questions; mastery of the skills required by the subject, general levels of knowledge and analytic ability or clear thinking. |
|---|---|---|
| Distinction | 75-84 | Usually awarded to students whose performance goes well beyond the minimum requirements set for tasks required in assessment, and who perform well in most of the above areas. |
| Credit | 65-74 | Usually awarded to students whose performance is considered to go beyond the minimum requirements for work set for assessment. Assessable work is typically characterised by a strong performance in some of the capacities listed above. |
| Pass | 50-64 | Usually awarded to students whose performance meets the requirements set for work provided for assessment. |
| Fail | 0-49 | Usually awarded to students whose performance is not considered to meet the minimum requirements set for particular tasks. The fail grade may be a result of insufficient preparation, of inattention to assignment guidelines or lack of academic ability. A frequent cause of failure is lack of attention to subject or assignment guidelines. |
Quality assurance
For the purposes of quality assurance, Bond University conducts an evaluation process to measure and document student assessment as evidence of the extent to which program and subject learning outcomes are achieved. Some examples of student work will be retained for potential research and quality auditing purposes only. Any student work used will be treated confidentially and no student grades will be affected.
Study Information
Submission procedures
Students must check the iLearn@Bond subject site for detailed assessment information and submission procedures.
Policy on late submission and extensions
A student who has not established a basis for an extension in compliance with University and Faculty policy either by 1) not applying before the assessment due date or 2) by having an application rejected due to failure to show a justifiable cause for an extension, will receive a penalty on assessment submitted after its due date. The penalty will be 10% of marks awarded to that assessment for every day late, with the first day counted after the required submission time has passed. No assessment will be accepted for consideration seven calendar days after the due date. Where a student has been granted an extension, the late penalty starts from the new due date and time set out in the extension.
Academic Integrity
Bond University‘s Student Code of Conduct Policy , Student Charter, Academic Integrity Policy and our Graduate Attributes guide expectations regarding student behaviour, their rights and responsibilities. Information on these topics can be found on our Academic Integrity webpage recognising that academic integrity involves demonstrating the principles of integrity (honesty, fairness, trust, professionalism, courage, responsibility, and respect) in words and actions across all aspects of academic endeavour.
Staff are required to report suspected misconduct. This includes all types of plagiarism, cheating, collusion, fabrication or falsification of data/content or other misconduct relating to assessment such as the falsification of medical certificates for assessment extensions. The longer term personal, social and financial consequences of misconduct can be severe, so please ask for help if you are unsure.
If your work is subject to an inquiry, you will be given an opportunity to respond and appropriate support will be provided. Academic work under inquiry will not be marked until the process has concluded. Penalties for misconduct include a warning, reduced grade, a requirement to repeat the assessment, suspension or expulsion from the University.
Feedback on assessment
Feedback on assessment will be provided to students according to the requirements of the Assessment Procedure Schedule A - Assessment Communication Procedure.
Whilst in most cases feedback should be provided within two weeks of the assessment submission due date, the Procedure should be checked if the assessment is linked to others or if the subject is a non-standard (e.g., intensive) subject.
Accessibility and Inclusion Support
Support is available to students where a physical, mental or neurological condition exists that would impact the student’s capacity to complete studies, exams or assessment tasks. For effective support, special requirement needs should be arranged with the University in advance of or at the start of each semester, or, for acute conditions, as soon as practicable after the condition arises. Reasonable adjustments are not guaranteed where applications are submitted late in the semester (for example, when lodged just prior to critical assessment and examination dates).
As outlined in the Accessibility and Inclusion Policy, to qualify for support, students must meet certain criteria. Students are also required to meet with the Accessibility and Inclusion Advisor who will ensure that reasonable adjustments are afforded to qualifying students.
For more information and to apply online, visit BondAbility.
Additional subject information
Subject curriculum
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Introduction to Research
The Nature and Process of Social Research; Research integrity and ethics.
SLOs included
- Identify, synthesise, interpret and communicate complex issues in research methodology and data visualisation.
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Research Design and Planning
Different research design strategies and planning your research project.
SLOs included
- Identify, synthesise, interpret and communicate complex issues in research methodology and data visualisation.
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Literature Review and Referencing Style
The key strategies to write a literature review.
SLOs included
- Identify, synthesise, interpret and communicate complex issues in research methodology and data visualisation.
- Independently and in teams, demonstrate expert cognitive skills to solve complex problems in research methodology and data visualisation.
- Apply gained knowledge and skills in research methodology and data visualisation to new contexts, demonstrating expert judgement, adaptability and responsibility as a practitioner or learner.
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Sampling and Data Collection
Overview of sampling methodology, and introduction to the notions of reliability and validity.
SLOs included
- Identify, synthesise, interpret and communicate complex issues in research methodology and data visualisation.
- Independently and in teams, demonstrate expert cognitive skills to solve complex problems in research methodology and data visualisation.
- Apply gained knowledge and skills in research methodology and data visualisation to new contexts, demonstrating expert judgement, adaptability and responsibility as a practitioner or learner.
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Quantitative Approaches
Overview of quantitative data collection.
SLOs included
- Identify, synthesise, interpret and communicate complex issues in research methodology and data visualisation.
- Independently and in teams, demonstrate expert cognitive skills to solve complex problems in research methodology and data visualisation.
- Apply gained knowledge and skills in research methodology and data visualisation to new contexts, demonstrating expert judgement, adaptability and responsibility as a practitioner or learner.
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Quantitative Data Analysis
Introduction to quantitative analysis software.
SLOs included
- Identify, synthesise, interpret and communicate complex issues in research methodology and data visualisation.
- Independently and in teams, demonstrate expert cognitive skills to solve complex problems in research methodology and data visualisation.
- Apply gained knowledge and skills in research methodology and data visualisation to new contexts, demonstrating expert judgement, adaptability and responsibility as a practitioner or learner.
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Qualitative Approaches
Overview of qualitative data collection.
SLOs included
- Identify, synthesise, interpret and communicate complex issues in research methodology and data visualisation.
- Independently and in teams, demonstrate expert cognitive skills to solve complex problems in research methodology and data visualisation.
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Other Research Methods
Overview of other research methods.
SLOs included
- Identify, synthesise, interpret and communicate complex issues in research methodology and data visualisation.
- Independently and in teams, demonstrate expert cognitive skills to solve complex problems in research methodology and data visualisation.
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Data Visualisation Tools and Technique
Introduction to data visualisation tools and techniques.
SLOs included
- Identify, synthesise, interpret and communicate complex issues in research methodology and data visualisation.
- Independently and in teams, demonstrate expert cognitive skills to solve complex problems in research methodology and data visualisation.
- Apply gained knowledge and skills in research methodology and data visualisation to new contexts, demonstrating expert judgement, adaptability and responsibility as a practitioner or learner.
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Research Reports: Overview
Discussion on student reports.
SLOs included
- Identify, synthesise, interpret and communicate complex issues in research methodology and data visualisation.
- Independently and in teams, demonstrate expert cognitive skills to solve complex problems in research methodology and data visualisation.
- Apply gained knowledge and skills in research methodology and data visualisation to new contexts, demonstrating expert judgement, adaptability and responsibility as a practitioner or learner.