HBC203 Statistics and Data Analysis ECA July 2026: Quiet Quitting Survey Analysis in jamovi, Singapore

The July 2026 HBC203 ECA asks students to study quiet quitting in a Singapore multinational with around 1,200 employees. Using survey data collected in 2024 and 2025, students form three research questions, answer each one with a different statistical test taught in the course, and interpret the jamovi results in APA format.

Description of the Study

Quiet quitting, where employees limit their effort to the bare minimum their jobs require, has become more visible in recent years and is closely linked to poor employee engagement. To understand the issue, a multinational corporation in Singapore ran an anonymous annual staff survey in 2024 and 2025. Each employee received a unique link by work email, each survey stayed open for one month, and weekly reminders were sent. The responses are provided in the HBC203 ECA Dataset.csv file.

Dataset Variables

• ID: respondent ID (IDs starting with 24 are from the 2024 survey and IDs starting with 25 from the 2025 survey; employees who answered both have two IDs).
• Future2024 / Future2025: likelihood (%) that the respondent will still be working in the organisation within the next 12 months.
• Happy (2024 only): whether the respondent is happy working in the organisation (1 = No, 2 = Maybe/Sometimes, 3 = Yes).
• Proud (2025 only): whether the respondent is proud to work in the organisation (1 = No, 2 = Maybe/Sometimes, 3 = Yes).
• Effort (2025 only): whether the respondent gives his or her best at work (1 = Seldom or sometimes, 2 = Most of the time, 3 = Always).
• Career, Flexiwork, Coworkers, Activities, Remuneration (2025 only): ranking of satisfaction with career progression, flexible work, co-worker relationships, social activities and remuneration (1 = most satisfied, 5 = least satisfied; no two factors share a rank).

Report Structure

1. Introduction

• Briefly explain why it is important to address the growing concern about quiet quitting in the study’s context.
• Identify THREE research questions to answer with the dataset, using only existing variables or variables created from them.
• You may change the scale of measurement of a variable or use a subset of respondents, but you must justify doing so.
• Use a different analysis method for each research question, and only methods taught in HBC203.

2. For Each Research Question

1. Write a short literature review explaining why the research question matters, with at least 2 academic references cited in APA 7.
2. State your prediction and explain it using findings from existing research.
3. Conduct the analysis in jamovi: justify the most appropriate method (assumption testing is not required), provide clear screenshots of all jamovi output used (copied tables are not accepted), describe and screenshot any new variables created, and report the results fully in APA format.
4. Discuss the results: whether they support the prediction, how they compare with the literature and why, their practical or theoretical implications, the key limitations of the study, and how you would address those limitations and why.

3. Conclusion

Summarise the key findings and implications from the three research questions.

4. Word Count and 5. References

State the word count from the Introduction to the Conclusion (including headings, citations and tables, excluding jamovi output) and list all cited references in APA 7 format.

Grading Criteria

• Quality of the introduction and literature review
• Quality of analysis: descriptive and inferential statistics, correct and justified tests, necessary follow-up analyses, correct interpretation
• Quality of the discussion and conclusion, including limitations and future directions
• Structure, organisation and flow of the report
• Adherence to APA formatting

Up to 5 marks each may be deducted for incorrect citations, non-academic sources and poor language. AI-generated content must be cited according to SUSS guidelines.