HBC203 ECA Quiet Quitting jamovi Report Sample (SUSS July 2026) Singapore

For information and reference only – not to be submitted as your own work. Every number in this extract is an illustrative placeholder, not a result from the real ECA dataset. Use the structure to see how an HBC203 report is organised, then run your own jamovi analysis and report your own values, following SUSS academic integrity and AI-declaration rules.

This model extract shows how a student could structure the SUSS HBC203 Statistics and Data Analysis for the Social and Behavioural Sciences End-of-Course Assessment (July 2026 semester) report on quiet quitting among Singapore employees. It covers the introduction, three research questions that each use a different course method, the APA reporting pattern, and the conclusion.

What this HBC203 ECA report has to do

The scenario is a multinational with about 1,200 employees in Singapore that ran anonymous surveys on quiet quitting in 2024 and again in 2025. The combined file holds one block of 2025 responses (pride, effort, five ranked workplace factors and an intention-to-stay score) and one block of 2024 responses (a three-category happiness item and an intention-to-stay score).

The report is 100 marks and all parts are compulsory. The brief sets a 2,000-word target, and the count excludes references, appendices and the jamovi output. Anything beyond the target is not graded, so the closing section is the part most at risk if you overrun.

Model extract: Introduction

Quiet quitting describes employees who keep their jobs but limit effort to the minimum their role requires. Formica and Sfodera (2022) treat it, alongside the Great Resignation, as a shift that organisations now have to manage rather than a passing label. Gallup (2026) reports that only 20% of employees worldwide were engaged in 2025, down from 21% in 2024, so a firm with 1,200 staff in Singapore has good reason to look at its own survey data.

Three questions can be answered from the file, and each needs a different method. RQ1 compares intention to stay across the two survey years. RQ2 asks whether pride in the organisation is linked to intention to stay in 2025. RQ3 asks whether intention to stay differs across the three happiness categories in 2024.

Subsetting and recoding decisions

Each question must use variables from a single block, because the 2024 and 2025 rows hold different columns. The file is anonymous, so respondents cannot be matched across years. That rules out a dependent-samples test for RQ1, and the report should say so in one sentence.

1. For RQ1, stack the two intention scores under a year label (2024 or 2025) so that year becomes a two-group variable.
2. For RQ2, filter to the 2025 rows only and keep pride and the 2025 intention score as continuous measures.
3. For RQ3, filter to the 2024 rows and set the happiness item as a nominal variable with three categories, not a number.

If you are unsure how to justify a recode or a filter in your own file, our statistics assignment help team can check your plan against the marking table before you start writing.

RQ1: Did intention to stay differ between 2024 and 2025?

Short literature review

Engagement research gives a reason to expect movement over time. Schaufeli et al. (2002) define engagement as vigour, dedication and absorption, the opposite end of the scale from the withdrawal that quiet quitting implies. Gallup (2026) shows a small global fall in engagement between 2024 and 2025, and Formica and Sfodera (2022) argue that post-pandemic expectations of work have changed.

Prediction

Because these sources point towards declining engagement, the report predicts that mean intention to stay will be lower in 2025 than in 2024.

Method and justification

An independent-samples t-test fits because the outcome is a continuous 0 to 100 score, there are two groups, and the groups contain different people. In jamovi, use Analyses, T-Tests, Independent Samples T-Test, add the stacked intention score as the dependent variable and year as the grouping variable. Tick Cohen’s d and descriptives, and include the mean difference with its confidence interval.

APA results (illustrative numbers)

Template: Intention to stay was significantly lower in 2025 (M = 62.4, SD = 21.3, n = 386) than in 2024 (M = 68.3, SD = 19.8, n = 486), t(870) = −4.23, p < .001, mean difference = −5.9, 95% CI [−8.6, −3.2], Cohen’s d = 0.29.

Replace every figure with the values in your own jamovi table, and paste a legible screenshot of the output beside the paragraph.

Discussion

If your own result looks like the pattern above, say that the prediction was supported and that the effect is small by Cohen’s (1992) conventions, which treat d near 0.2 as small. Link this back to Gallup (2026): a small decline in survey scores is consistent with a small global fall in engagement, but it does not show why individuals changed. A limitation is the repeated cross-section design, which cannot separate real change from different people answering. A practical fix is to recommend that the firm adds an anonymous respondent code in future waves so a dependent t-test becomes possible.

RQ2: Is pride in the organisation linked to intention to stay in 2025?

Short literature review

Harter et al. (2002) found a positive link between employee satisfaction, engagement and business outcomes in a large meta-analysis. Tett and Meyer (1993) reported that satisfaction and commitment are negatively related to turnover intention. Pride is an attachment to the employer, so the same direction is expected.

Prediction

Higher pride ratings will go with higher intention to stay, with a positive correlation of at least moderate size.

Method and justification

Pearson correlation fits two continuous variables, and simple linear regression adds a slope you can interpret. Treating a five-point rating as continuous is a judgement call, so state it and justify it in a sentence. In jamovi, use Regression, Correlation Matrix for r, then Regression, Linear Regression with intention as the dependent variable and pride as the covariate.

APA results (illustrative numbers)

Template: Pride and intention to stay were positively correlated, r(384) = .42, p < .001. Pride significantly predicted intention to stay, b = 9.94, t(384) = 9.07, p < .001, and explained 17.6% of the variance, R² = .18.

Discussion

A real result of this kind would support the prediction. Interpret the slope in plain language: each one-point rise in pride goes with roughly ten more points on the 0 to 100 intention scale (illustrative). Then compare with Tett and Meyer (1993) and note that 82% of the variance remains unexplained. The main limitation is that correlation is not causation, and pride and intention were both self-reported at the same moment. Adding other predictors would need methods beyond this course, so recommend them as future work instead of running them.

RQ3: Does intention to stay differ by happiness category in 2024?

Short literature review

Kahn (1990) argued that people engage more fully when work feels meaningful and safe, which suggests a link between how people feel at work and how long they plan to stay. Tett and Meyer (1993) support a graded relationship between job attitudes and turnover intention.

Prediction

Mean intention to stay will rise steadily from the least happy to the most happy category.

Method and justification

A one-way between-subjects ANOVA compares three independent groups on one continuous outcome. Running three separate t-tests would inflate the Type I error rate, so the ANOVA comes first and Tukey post hoc tests follow. In jamovi, use ANOVA, One-Way ANOVA, then tick Tukey and the effect size option for eta squared.

APA results (illustrative numbers)

Template: Intention to stay differed significantly across happiness categories, F(2, 483) = 21.89, p < .001, η² = .08. Tukey tests showed that the lowest category (M = 58.1, SD = 22.0) scored below the middle category (M = 67.9, SD = 19.8), p < .001, and the middle category scored below the highest (M = 74.6, SD = 18.4), p = .003.

Discussion

If your values show an ordered pattern, the prediction is supported. An eta squared of about .08 is a medium effect by common benchmarks, so happiness accounts for a meaningful but minority share of the variation. Because the ANOVA is not directional, rely on the Tukey table to say which pairs differ. A limitation is that the happiness item has only three categories, which loses detail. A more sensitive measure in future surveys would let the firm use correlation instead.

Model extract: Conclusion

Taken together, the three analyses give the organisation a layered picture: intention to stay moved between years, it is tied to pride in 2025, and it differs by happiness in 2024. The report should end with one practical recommendation, such as tracking pride and happiness in every wave, and one honest caution that all three designs are correlational and self-reported.

Word count statement and AI declaration

Write the count as a plain sentence, for example: “The word count of this report is 1,987 words, excluding references, appendices and jamovi output.” Count your own final text in your word processor, because the example figure here is not a real count. If the brief or your course requires an AI declaration, state exactly which tools you used and for what, and keep the supporting records.

Common mark-losing slips in this ECA

• Choosing a repeated-measures test for the 2024 and 2025 scores when the rows are not matched.
• Pasting jamovi screenshots that are too small to read, or cropped so the test name is missing.
• Reporting p = .000 instead of p < .001.
• Using a method outside the course, such as multiple regression, for no marks.
• Making all three questions the same type, so the brief’s requirement for different methods is not met.

For the wider approach to the ECA series, see our SUSS ECA help hub, and for an earlier version of this module see the general HBC203 assignment sample.

To choose your own set of questions and tests, read our guide to HBC203 ECA research questions.

If you want a specialist to review your research questions or your jamovi screenshots before you submit, see our SUSS assignment help page.

References

Cohen, J. (1992). A power primer. Psychological Bulletin, 112(1), 155–159.

Formica, S., & Sfodera, F. (2022). The Great Resignation and Quiet Quitting paradigm shifts: An overview of current situation and future research directions. Journal of Hospitality Marketing & Management, 31(8), 899–907. https://doi.org/10.1080/19368623.2022.2136601

Gallup. (2026). State of the global workplace: 2026 report. https://www.gallup.com/workplace/697904/state-of-the-global-workplace-global-data.aspx

Harter, J. K., Schmidt, F. L., & Hayes, T. L. (2002). Business-unit-level relationship between employee satisfaction, employee engagement, and business outcomes: A meta-analysis. Journal of Applied Psychology, 87(2), 268–279. https://doi.org/10.1037/0021-9010.87.2.268

Kahn, W. A. (1990). Psychological conditions of personal engagement and disengagement at work. Academy of Management Journal, 33(4), 692–724. https://doi.org/10.2307/256287

Schaufeli, W. B., Salanova, M., González-Romá, V., & Bakker, A. B. (2002). The measurement of engagement and burnout: A two sample confirmatory factor analytic approach. Journal of Happiness Studies, 3(1), 71–92. https://doi.org/10.1023/A:1015630930326

Tett, R. P., & Meyer, J. P. (1993). Job satisfaction, organizational commitment, turnover intention, and turnover: Path analyses based on meta-analytic findings. Personnel Psychology, 46(2), 259–293. https://doi.org/10.1111/j.1744-6570.1993.tb00874.x