MRES902 Quantitative Research Methods
| Assessment Overview | |
| Assessment | AT1 Practical Portfolio (20%) |
| Mark | 20 |
| Due Date | Week 4, 11.55 pm Sunday |
| Word limit | 1500 words |
| Submission format | PDF file (.pdf) or Word (.doc) only for the report |
| Submission method | Via the Turnitin Dropbox on eLearning |
| Marking Criteria | See rubric set out in the appendix |
| Other Requirements |
For the essay:
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| Assessment Details | |
| Description |
For this assessment, you will be required to respond to a structured set of questions based on the following case study. Tasks required you to critically evaluate quantitative research design, measuring variables and statistical reasoning.
Understanding Employee well-being and productivity in Hybrid Work Environments: An Australian Case Study
A service organisation in Australia has recently implemented a hybrid work model, allowing employees to work both on-site and remotely. While management firmly believes that the hybrid model can improve employee outcomes, such as well-being and productivity, there is insufficient internal evidence to support this claim. To inform future work policy decisions, this organisation has appointed an HR analytics consultant to conduct a quantitative research study.
In the initial stage of the study, employees from various departments at the organisation’s Sydney office will be surveyed. This survey will collect data on employees’ gender, age (in years), job level, employment status (full-time or part-time), number of remote workdays per week, perceived work-life balance, job satisfaction, and self-reported productivity levels. Productivity is measured using a standardised performance rating scale used internally by the organisation.
Moreover, the consultant also plans to conduct brief interviews with line managers to further understand perceived changes in employee performance and engagement since the implementation of hybrid work. To further support the findings, the consultant will also review secondary data sources, including the 2024 Australian Workplace Wellbeing Report, to identify national trends and the relationship between hybrid work and employee well-being.
In the second phase of this study, the consultant aims to investigate the association between the number of work hours per week and employee productivity. The consultant hypothesises that moderate levels of remote work are associated with higher employee productivity outcomes.
In the last phase of this study, the consultant plans to prepare a report summarising the statistical findings and providing evidence-based recommendations to senior managers regarding the future hybrid work arrangements. |
Questions:
Question 1: Research Design
1. Identify and justify the research design presented in this study.
2. Explain the limitations of this proposed research design, using some relevant academic literature.
Question 2: Sampling
1. Identify and explain the sampling method proposed by the consultant.
2. Discuss the limitations of this sampling approach.
3. Discuss how the consultant could further improve the sampling strategy to increase the validity. Use some relevant literature to support your claims.
Question 3: Variables and Measurements
1. Categorise the variables collected in this case study into nominal, ordinal, interval, and ratio scales.
2. Identify the independent and dependent variables presented in this case.
3. Identify and categorise the data collection methods used in the study.
Question 4: Control Variables and Statistical Reasoning
1. List out the proper control variables that should be included when examining the association between remote workdays and productivity.
2. Critically discuss why the inclusion of these control variables would enhance the validity and credibility of the findings of this study.
What if I Miss the Assessment?
- You must complete & submit AT1 by the due date and time.
- If you miss the assessment for the following reasons, you may apply for special consideration:
- acute illness or
- loss or bereavement or
- hardship/trauma or
- technological problems which could not be anticipated or avoided
- To apply for special consideration, fill out the following form and attach evidence to support your reason for seeking special consideration (within 5 days of the due date).
Special Consideration Application Form Link
- If your reason is invalid, if you do not provide evidence, or if your application is not made within 5 days of the due date your application will be rejected, and you will lose 20 marks for this course.
Can I Use Generative Artificial Intelligence for this Assessment?
You may use generative AI tools such as ChatGPT or Microsoft Co-Pilot ONLY to research and brainstorm ideas and approaches for completing your essay. Please make sure to properly acknowledge any use of generative AI using the CIM APA Referencing Guide.
Rubric for AT1 Practical Portfolio
| Criteria | Not Attempted (0) | Fail (1–34) | Marginal Fail (35-49) | Pass (50–64) | Credit (65–74) | Distinction (75–84) | High Distinction (85–100) |
| Identification and Justification of Quantitative Research Design (10%) | Not attempted/Not addressed | Fails to identify the research design; no justification provided. | Minimal identification: justification lacks clarity or relevance. | Limited identification: justification is vague or poorly supported. | Identifies the design but lacks depth in justification; basic examples provided. | Identifies the research design well with solid justification and some examples. | Clearly identify the research design with comprehensive justification, including relevant examples and insights. |
| Limitations and Suggestions for Improvement of Quantitative Research Design (15%) | Not attempted/Not addressed | Fails to address limitations or improvement suggestions. | Limited discussion of limitations; suggestions are weak or unclear. | Discusses limitations minimally; weak literature support and vague suggestions. | Identifies limitations but lacks depth or sufficient literature; basic improvement suggestions. | Explains limitations well with some supporting literature; reasonable suggestions provided. | Thoroughly explains limitations with strong supporting literature and offers insightful, practical suggestions for improvement. |
| Identification of Sampling Method (10%) | Not attempted/Not addressed | Fails to identify the sampling method; no explanation provided. | Minimal identification; lacks clarity. | Limited identification: explanation is vague or unclear. | Identifies the method but lacks depth in explanation. | Identifies the sampling method with a solid explanation. | Clearly identify the sampling method with detailed explanation and context. |
| Limitations and Suggestions for Improvement of Sampling Method (15%) | Not attempted/Not addressed | Fails to address limitations or improvement suggestions. | Limited discussion of limitations; suggestions are weak or unclear. | Discusses limitations minimally; weak literature support and vague suggestions. | Identifies limitations but lacks depth or sufficient literature; basic improvement suggestions. | Explains limitations well with some supporting literature; reasonable suggestions provided. | Thoroughly explains limitations with strong supporting literature and offers insightful, practical suggestions for improvement. |
| Comprehensive Analysis of Variables (15%) | Not attempted/Not addressed | Fails to correctly categorise most variables and identify any variables or methods. | Limited understanding with many inaccuracies in categorisation and variable identification; methods lack clarity. | Significant errors in categorisation, with incomplete identification of variables and minimal explanation of methods. | Some inaccuracies in categorisation or identification of variables, but methods are generally explained. | Mostly accurate categorisation with minor errors, identifies variables correctly with good explanations of methods. | Accurately categorises all variables (nominal, ordinal, interval, ratio) and clearly identifies dependent and independent variables, providing detailed explanations of each data collection method. |
| Control Variables and Justification (15%) | Not attempted/Not addressed | Fails to identify any control variables. | Fails to identify key control variables; rationale unclear. | Limited identification; vague rationale for importance. | Identifies control variables and provides some justification; mostly clear. | Identifies control variables with good justification; mostly clear. | Thoroughly identifies relevant control variables and convincingly justifies their importance with literature support. |
| Selection of Articles (10%) | Not attempted/Not addressed | Selected papers lack relevancy to the topic and are not current and from reputable sources. | Few of the papers are closely relevant to the topic and are current and from reputable sources. | Some of the papers are closely relevant to the topic and are current and from reputable sources. | Many of the papers are closely relevant to the topic and are current and from reputable sources. | Most of the papers are closely relevant to the topic and are current and from reputable sources. | All papers are closely relevant to the topic and are current and from reputable sources. |