RSM5224 Chapter 3 Research Methodology Sample: Assessment 3, Sunway University (2026)
For information and reference only – not to be submitted as your own work. This model extract shows the structure, depth and referencing that RSM5224 Assessment 3 asks for, using a generic example topic. Your own Chapter 3 must follow your own Chapters 1 and 2, your own research questions and Sunway University’s academic integrity and AI-use rules.
Read the original task on the RSM5224 Assessment 3 question page. The section-by-section method is explained in our guide to writing a research methodology Chapter 3.
Example study used in this sample
Working title (illustrative): The influence of workload and supervisor support on work engagement among employees of private-sector professional services firms in the Klang Valley, Malaysia.
Research questions carried forward from Chapter 1 (illustrative):
- RQ1: Does quantitative workload have a significant relationship with work engagement?
- RQ2: Does perceived supervisor support have a significant relationship with work engagement?
Chapter 2 is assumed to have framed the study with the Job Demands-Resources (JD-R) model (Bakker & Demerouti, 2007), with H1 (workload, negative) and H2 (supervisor support, positive).
Chapter 3: Research Methodology
3.1 Research Paradigm
A research paradigm is the set of assumptions about reality, knowledge and values that guides how a study is carried out (Saunders et al., 2023). This study adopts positivism. The JD-R model in Chapter 2 makes testable predictions about relationships between measurable constructs, and RQ1 and RQ2 ask whether those relationships hold. That is a positivist question.
Ontologically, the study treats workload, supervisor support and work engagement as real features of working life that exist independently of the researcher and can be measured with established scales. Epistemologically, knowledge is gained by observing these constructs across many respondents and testing hypotheses statistically (Creswell & Creswell, 2023). Axiologically, the researcher aims to stay value-neutral. Validated instruments, standardised administration and anonymous responses limit the influence of the researcher’s own views on the data.
Table 3.1 compares the three paradigms considered.
| Dimension | Positivism (adopted) | Interpretivism | Pragmatism |
|---|---|---|---|
| Ontology | One external reality that can be measured | Reality is socially constructed and multiple | Reality is judged by what works in practice |
| Epistemology | Observable, measurable facts; generalisation from data | Understanding meanings from participants’ perspectives | Whatever knowledge best answers the research question |
| Axiology | Value-free, detached researcher | Value-bound; researcher is part of what is studied | Values guide the choice of question and method |
| Typical methods | Surveys, experiments, statistical analysis | Interviews, focus groups, thematic analysis | Mixed methods |
| Fit with RQ1–RQ2 | Strong: tests hypothesised relationships drawn from the JD-R model | Weak: suits exploring how employees experience engagement, not testing H1–H2 | Partial: useful if a qualitative phase were added, but not needed for the current questions |
Sources: adapted from Saunders et al. (2023), Creswell and Creswell (2023) and Kaushik and Walsh (2019).
Interpretivism was rejected because the study does not aim to build new theory from participants’ meanings. Pragmatism was also considered, since it allows mixed methods (Kaushik & Walsh, 2019). However, adding a qualitative strand would stretch the resources of a single Masters researcher without improving the answer to the two research questions.
3.2 Research Design
Following the positivist stance, the study uses a deductive approach: hypotheses drawn from theory are tested against data (Saunders et al., 2023). It is a mono-method quantitative study with an explanatory purpose, since it seeks to explain variation in work engagement through two predictors.
The strategy is a survey using a self-administered online questionnaire. A survey suits this study because it can collect standardised data from a large, dispersed group of employees quickly and cheaply, and the data can be analysed statistically (Bell et al., 2022). An experiment was rejected because workload and supervisor support cannot be manipulated ethically in real organisations. A single-organisation case study would limit generalisation across the sector.
The time horizon is cross-sectional: data are collected once over about four weeks (illustrative timeline). This matches the time available for a Masters proposal. The trade-off is that cross-sectional data show association, not cause, and this limitation will be stated in the final chapter.
| Research question | Data needed | Planned analysis |
|---|---|---|
| RQ1: workload and engagement | Quantitative Workload Inventory and UWES-9 scores | Path coefficient and significance by bootstrapping in PLS-SEM |
| RQ2: supervisor support and engagement | Supervisor support items and UWES-9 scores | Path coefficient, R² and f² effect size |
Data will be screened and described in IBM SPSS Statistics. Hypotheses will then be tested with partial least squares structural equation modelling (PLS-SEM), which tests the measurement model and the structural model together and suits prediction-oriented models with moderate sample sizes (Hair et al., 2022).
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3.3 Research Context
The study is set in private-sector professional services firms in the Klang Valley, including accounting, consulting, legal and IT services. Chapter 1 identified sustained workload pressure and uneven line-manager support as concerns in knowledge-intensive service work. Because these firms depend on employee effort and client service, disengagement has a direct business cost, which makes the problem relevant to managers as well as researchers.
The Klang Valley was chosen for three reasons. First, it holds a large concentration of Malaysia’s professional services employers, so the population is big enough to reach the target sample. Second, studying one region keeps conditions such as commuting patterns, labour-market competition and employer practices broadly comparable across respondents. Third, the researcher is based in the region and can reach professional networks there.
Feasibility has been considered carefully. Access does not depend on one organisation’s permission. Respondents will be reached through professional networks, alumni groups and LinkedIn, and individuals take part voluntarily. Where an HR manager agrees to circulate the link internally, written gatekeeper permission will be obtained first, and the firm will not be named in the report.
The context has limits. Findings may not transfer to manufacturing or to the public sector, where job design and supervision differ. These limits will be reported rather than hidden, in line with the transparency expected of a positivist study (Saunders et al., 2023).
3.4 Choice of Respondents
Population and unit of analysis. The unit of analysis is the individual employee. The target population is full-time employees of private-sector professional services firms in the Klang Valley.
Inclusion criteria. Respondents must (a) be aged 18 or over, (b) be employed full-time in a private-sector professional services firm in the Klang Valley, (c) have worked under their current supervisor for at least six months, so they can judge supervisor support, and (d) not hold a role in which they supervise the researcher, to avoid any pressure to take part.
Sampling strategy. There is no complete, accessible list of all employees in the sector, so probability sampling is not practical. The study uses purposive sampling with screening questions on the inclusion criteria, supported by snowball referral through professional networks. Non-probability sampling limits statistical generalisation, and this will be acknowledged (Memon et al., 2020).
Sample size determination. An a priori power analysis was run for linear multiple regression with two predictors (Faul et al., 2007). With a medium effect size (f² = 0.15), α = .05 and power of .80, the minimum sample is 68. At the stricter power of .95, it is 107.
| Method | Inputs | Result | Role in this study |
|---|---|---|---|
| G*Power, F-test, linear multiple regression | f² = 0.15; α = .05; power = .80; 2 predictors | 68 | Absolute minimum |
| G*Power, same test | f² = 0.15; α = .05; power = .95; 2 predictors | 107 | Minimum for hypothesis testing |
| Krejcie and Morgan (1970) table | Large population; 95% confidence; ±5% margin | 384 | Benchmark only; designed for estimating a proportion, not for testing relationships |
| Ratio rule (Hair et al., 2019) | About 10 responses per item × 18 items (item count partly illustrative) | 180 | Supports a stable measurement model |
The study therefore targets at least 200 usable responses, which satisfies the power analysis and the ratio rule. Assuming about one-third of returns are unusable or incomplete (illustrative assumption), around 300 invitations will be sent.
Recruitment. The survey link will be shared with a short invitation through LinkedIn, professional association groups and alumni networks, with two reminders a week apart.
Ethical considerations. Ethical approval will be sought from the University’s research ethics committee before any data collection. The first page of the survey will explain the purpose, voluntary participation, the right to withdraw before submitting, and the expected completion time. Consent is given by ticking an agreement box. No names, employers or contact details are collected. Data will be stored in a password-protected account, used only for this study, and handled in line with the Personal Data Protection Act 2010 (Act 709).
3.5 Research Instrument
Data will be collected with a structured, self-administered online questionnaire built in Google Forms or Microsoft Forms. It has four sections. Section A holds screening questions and demographics: age band, gender, tenure, firm size and job level. Section B measures workload, Section C measures supervisor support, and Section D measures work engagement. Putting the outcome measure last and in a separate section is a procedural step to reduce common method bias (Podsakoff et al., 2003).
| Construct | Source scale | Items | Response format |
|---|---|---|---|
| Quantitative workload | Quantitative Workload Inventory (Spector & Jex, 1998) | 5 | 5-point frequency scale, as in the original |
| Perceived supervisor support | Survey of Perceived Organizational Support items adapted to the supervisor, following Eisenberger et al. (2002) | 4 (illustrative; confirm against the source) | 7-point agreement scale |
| Work engagement | Utrecht Work Engagement Scale, UWES-9 (Schaufeli et al., 2006) | 9 | 7-point frequency scale (0 = never, 6 = always) |
Adapted, published scales were chosen over newly written items because they have tested reliability and validity across countries, which strengthens content validity and allows comparison with earlier studies. Wording will only be changed where needed for the Malaysian workplace, for example “supervisor” in place of “organisation”. The original response formats are kept so that the psychometric evidence still applies.
Pilot test. Before the main survey, two academics and one HR practitioner will review the instrument for content validity and clarity. The revised questionnaire will then be piloted with about 30 eligible respondents, within the range Hertzog (2008) discusses for early reliability estimation. Pilot responses will be excluded from the main data set.
Reliability. Internal consistency will be assessed with Cronbach’s alpha and composite reliability. Values of .70 or above are taken as acceptable (Hair et al., 2019; Nunnally & Bernstein, 1994).
Validity. Convergent validity will be assessed with outer loadings of .708 or above and average variance extracted (AVE) of .50 or above. Discriminant validity will be checked with the heterotrait-monotrait ratio (HTMT), following Hair et al. (2022). Common method bias will be assessed with a full collinearity test, as well as by the procedural remedies above.
Resources. The survey tools are free, SPSS is available through the university, and SmartPLS offers a student licence. The design is therefore realistic for one researcher within the subject timeline.
Word count of the Chapter 3 extract above: about 1,350 words (excluding tables). A full submission would be developed to 2,000 words ± 10%.
What if your RSM5224 study is qualitative?
Many RSM5224 proposals explore how managers or professionals experience a problem, rather than testing hypotheses. In that case, the same five sections still apply, but the choices change:
- 3.1: interpretivism, with a value-bound axiology that acknowledges your position as the researcher.
- 3.2: an inductive approach, with semi-structured interviews or a case study and thematic analysis.
- 3.4: purposive sampling of information-rich participants, with sample size justified by data saturation rather than G*Power.
- 3.5: an interview guide that is piloted, with trustworthiness (credibility, dependability, transferability, confirmability) in place of reliability and validity.
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Amirul Azam holds an MBA from the University of Malaya. His profile lists marketing, management, organizational behaviour and international business, with APA, MLA, Harvard and Chicago referencing, over a decade in managerial roles and six years of academic assistance, and 2,341 completed orders.
Need a Chapter 3 built around your own topic? Our research methods team can guide you on your paradigm, sampling and instrument, with APA 7 references you can check. Get expert help with RSM5224 Assessment 3.
References
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Bell, E., Bryman, A., & Harley, B. (2022). Business research methods (6th ed.). Oxford University Press.
Creswell, J. W., & Creswell, J. D. (2023). Research design: Qualitative, quantitative, and mixed methods approaches (6th ed.). SAGE.
Eisenberger, R., Stinglhamber, F., Vandenberghe, C., Sucharski, I. L., & Rhoades, L. (2002). Perceived supervisor support: Contributions to perceived organizational support and employee retention. Journal of Applied Psychology, 87(3), 565–573. https://doi.org/10.1037/0021-9010.87.3.565
Faul, F., Erdfelder, E., Lang, A.-G., & Buchner, A. (2007). G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39(2), 175–191. https://doi.org/10.3758/BF03193146
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage.
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). SAGE.
Hertzog, M. A. (2008). Considerations in determining sample size for pilot studies. Research in Nursing & Health, 31(2), 180–191. https://doi.org/10.1002/nur.20247
Kaushik, V., & Walsh, C. A. (2019). Pragmatism as a research paradigm and its implications for social work research. Social Sciences, 8(9), Article 255. https://doi.org/10.3390/socsci8090255
Krejcie, R. V., & Morgan, D. W. (1970). Determining sample size for research activities. Educational and Psychological Measurement, 30(3), 607–610. https://doi.org/10.1177/001316447003000308
Memon, M. A., Ting, H., Cheah, J.-H., Thurasamy, R., Chuah, F., & Cham, T. H. (2020). Sample size for survey research: Review and recommendations. Journal of Applied Structural Equation Modeling, 4(2), i–xx. https://doi.org/10.47263/jasem.4(2)01
Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). McGraw-Hill.
Personal Data Protection Act 2010 (Act 709) (Malaysia).
Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879
Saunders, M. N. K., Lewis, P., & Thornhill, A. (2023). Research methods for business students (9th ed.). Pearson.
Schaufeli, W. B., Bakker, A. B., & Salanova, M. (2006). The measurement of work engagement with a short questionnaire: A cross-national study. Educational and Psychological Measurement, 66(4), 701–716. https://doi.org/10.1177/0013164405282471
Spector, P. E., & Jex, S. M. (1998). Development of four self-report measures of job stressors and strain: Interpersonal Conflict at Work Scale, Organizational Constraints Scale, Quantitative Workload Inventory, and Physical Symptoms Inventory. Journal of Occupational Health Psychology, 3(4), 356–367. https://doi.org/10.1037/1076-8998.3.4.356
