Lecture note on replicability with an example for MBA students

Lecture note on replicability with an example for MBA students

 

Highlight 4 main ideas on the topic of replicability in the context of doing an MBA dissertation project. Clarify your ideas a bit with a few examples.

Replicability in an MBA dissertation means designing and reporting your study so that another researcher could repeat it (or something very close to it) and check whether similar results are obtained. In practice, it’s about clarity, transparency, and methodological rigor rather than expecting someone to literally rerun your project next year.

Below are four main ideas, each with MBA‑relevant examples.

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1. Clear, transparent research procedures

Replicability starts with making your procedures fully explicit so another researcher can follow the same steps. In social science and management research, replication is defined as repeating a research procedure to verify the truth or robustness of the findings.link.springer+1

What this means for your dissertation

• Describe your sampling: inclusion/exclusion criteria, where you recruited participants, time frame.
• Specify your measures: scales used, items, response formats, any translation or adaptation.
• Document your procedures: how you distributed questionnaires, conducted interviews, handled non‑responses.
• Explain your analysis steps: data cleaning, tests run, model choices, significance thresholds.

Example (survey-based dissertation)
You study the impact of perceived service quality on customer loyalty in Hong Kong e-commerce:

• You state: “I surveyed customers of my online outlet and two partner stores between March–April 2026; inclusion criteria: at least one purchase in last 6 months; target sample: 300, final usable responses: 247.”
• You append the full questionnaire, note that you used the 22-item SERVQUAL scale and a 5-item behavioral loyalty scale, both on 7-point Likert scales.
• You detail that you tested reliability (Cronbach’s alpha), did factor analysis, then ran multiple regression with loyalty as the dependent variable and controlled for age, gender, and frequency of purchase.

Because each step is spelled out, another MBA student could replicate the survey with their own outlet customers and see if the relationships hold.

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2. Standardized, well-defined constructs and measures

Replication is easier when you use established constructs and measurement instruments, rather than idiosyncratic or vaguely defined variables.openeconomics.zbw

What this means for your dissertation

• Use validated scales from prior literature for key variables (e.g., job satisfaction, organizational commitment, perceived usefulness, purchase intention).
• Define each construct conceptually and operationally (what it means and how you measure it).
• Justify any modifications to existing scales (dropping items, changing wording, translating to Chinese).

Example (marketing/consumer behavior dissertation)
You examine how social media engagement influences purchase intention toward K-pop merchandise:

• You define “social media engagement” using a three-dimensional scale (consumption, contribution, creation) from a well-cited paper.
• You use an established “purchase intention” scale (e.g., three Likert items: likelihood to buy, willingness to recommend, intention to continue buying).
• You report reliability and validity evidence for both constructs.

A future researcher could take the same scales, apply them to a different K-pop fandom (e.g., another group) or another geography (e.g., Taiwan), and test whether the engagement–intention relationship replicates.

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3. Sufficient detail about data and analysis for robustness checks

Replicability is not only about repeating the study; it’s about checking the robustness of your findings under similar or slightly varied conditions. Another researcher might replicate your analysis with:link.springer+1

• Similar data from another context.
• Different, but appropriate, statistical methods.
• Slightly changed operationalizations.

What this means for your dissertation

• Report descriptive statistics, correlations, model specifications, and assumptions checks (e.g., normality, multicollinearity).
• Make clear what decisions you took: how you handled missing data, outliers, non-response bias.
• If feasible, archive or at least structure your data and analysis scripts so they could be shared (even if you don’t actually publish them).

Example (finance/investment dissertation)
You analyze the relationship between dividend yield and share price stability among Hong Kong REITs:

• You specify the time window (2016–2025), the list of REITs, data sources (HKEX, Bloomberg/Refinitiv, etc.), and formulas used for volatility and dividend yield.
• You explain that you tested both linear regression and panel data models, and why you chose the final specification.
• You report effect sizes and confidence intervals, not just p-values.

A future researcher could replicate the study using Singapore REITs or extend the time window and see if the “higher dividend yield → lower volatility” pattern holds, making your findings more generalizable and robust.

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4. Distinguishing context-specific insights from generalizable claims

Replication is central to deciding how far your conclusions can be generalized beyond your specific dissertation setting.link.springer+1

What this means for your dissertation

• Be explicit about boundary conditions: sector, country (Hong Kong), time period, sample type (e.g., MBA students, online shoppers).
• When you claim implications for “businesses” or “managers,” make clear whether these are:
• Likely generalizable (because constructs and relationships are theoretically universal), or
• Mainly context-specific to your sample and setting.
• Discuss how future replication studies in different contexts could confirm, refine, or challenge your findings.

Example (management/HRM dissertation)
You test whether flexible working arrangements improve job satisfaction among part-time workers in Hong Kong SMEs:

• You find a positive effect and conclude that offering flexible hours is associated with higher satisfaction.
• In your discussion, you note that your sample is limited to Hong Kong SME employees, and local labor laws and cultural attitudes may influence the effect.
• You suggest that replication in large corporations, other Asian cities, or full-time workers could test whether the relationship is stable or context-bound.

This shows you understand that your dissertation is one piece of evidence; its real scientific value grows when others can replicate or extend it.

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Quick recap in dissertation terms

1.    Transparency of methods – Write your methodology so clearly that another MBA student could repeat your procedures step-by-step.

2.    Use of established constructs and scales – Build on validated measures to make replication easier and your results comparable.

3.    Detailed data and analysis reporting – Provide enough information for others to rerun your analysis or test your findings with similar data.

4.    Awareness of generalizability and context – Frame your conclusions with their limits and suggest where replication would strengthen or challenge them.

 

** A collection of lecture notes on the subject of research methods for MBA students, 2026 June

A collection of blog notes on using chatgpt for research purpose.

Lecture note on control variable with an example for MBA students

Lecture note on control variable with an example for MBA students

In an MBA dissertation, a control variable is a factor you include to stop it from distorting the relationship you are actually studying. It helps you make a stronger case that your independent variable, not some outside factor, is linked to the outcome.onlinelibrary.wiley+1

4 main ideas

1.    It helps isolate the real effect.
If you are studying whether social media marketing affects sales, you may control for firm size, because bigger firms often sell more regardless of marketing. If you do not control for firm size, you might wrongly credit social media for sales that are actually driven by the company’s scale.onlinelibrary.wiley+1

2.    It reduces confounding bias.
A control variable is usually something that can also influence the dependent variable, even though it is not your main focus. For example, if your dissertation examines employee training and job performance, you might control for work experience, age, or tenure because these can affect performance too.onlinelibrary.wiley+1

3.    It is chosen based on theory, not convenience.
Good dissertation research does not just control for random demographics automatically; it explains why a variable matters in your model. For instance, in a study on customer satisfaction and repurchase intention, you might control for customer income or age only if theory suggests they could change purchase behavior.onlinelibrary.wiley+1

4.    It can be handled differently depending on the research design.
In experiments, you may hold a variable constant; in survey or secondary-data research, you usually include it in regression analysis as a statistical control. For example, if you study the impact of digital transformation on SME performance, you may statistically control for firm age, industry, and company size rather than trying to keep them identical across firms.sciencedirect+1

Simple dissertation examples

·        If your topic is marketing mix and sales performance, control for firm size and industry type.

·        If your topic is leadership style and employee satisfaction, control for age, tenure, and department.

·        If your topic is online service quality and customer loyalty, control for customer income and frequency of online shopping.

·        If your topic is ESG disclosure and firm value, control for leverage, profitability, and company age.

What is the difference between a control variable and a confounding variable?

A control variable is a factor you deliberately hold constant or include in your model so it does not distort the relationship you are studying. A confounding variable is a factor that influences both your independent variable and dependent variable, creating a misleading association if you do not account for it.scribbr+1

Key difference

·        Control variable: chosen by the researcher to reduce bias and keep the analysis cleaner.

·        Confounding variable: an outside factor that can bias the result if left unaddressed.

In other words, a control variable is part of your research design or analysis, while a confounder is a source of error or distortion you are trying to manage.scribbr+1

Simple example

If you study the effect of training on employee performance, then:

·        Training = independent variable.

·        Performance = dependent variable.

·        Work experience = control variable if you include it to make comparisons fair.

·        Work experience can also be a confounder if it affects both training participation and performance, and you fail to adjust for it.scribbr+1

MBA dissertation tip

In dissertation writing, many variables can be described as controls once you include them in your model, but they become especially important when they are potential confounders. So the practical goal is: identify variables that could distort your main relationship, then control for them in a justified way.

** A collection of lecture notes on the subject of research methods for MBA students, 2026 June

A collection of blog notes on using chatgpt for research purpose.

Lecture note on confounding variable with an example for MBA students

Lecture note on confounding variable with an example for MBA students

Highlight 4 main ideas on the topic of confounding variable in the context of doing an MBA dissertation project. Clarify your ideas a bit with a few examples.

Here are 4 main ideas about confounding variables in an MBA dissertation project, with business-focused examples.

1. A confounding variable distorts the real relationship

A confounding variable is a third factor that affects both your independent variable and dependent variable, so it can make a relationship look stronger, weaker, or even false. In dissertation terms, it can threaten your ability to claim that one business factor truly causes another. For example, if you study whether digital marketing spend increases sales, seasonality may also affect both, because firms often spend more on marketing during peak sales periods.online.stat.psu+1

2. It is common in MBA topics

Confounding is especially important in MBA research because many business variables move together in real life. For instance, if you examine whether employee training improves productivity, then manager quality or firm size may also influence productivity and training access. If you study whether customer satisfaction drives repurchase intention, then brand reputation or service quality may be hidden confounders.iwh.on+1

3. You should identify confounders before collecting data

A good dissertation design starts with asking: “What else could explain this relationship?” That means you should review literature, build a conceptual framework, and think carefully about variables such as age, income, tenure, education, firm size, or market conditions depending on your topic. For example, in a study on flexible work arrangements and employee performance, job role complexity could be a confounder because it affects both flexibility and performance.iwh.on+1

4. You can reduce confounding with design and statistics

Researchers typically control confounding through randomization, matching, stratified analysis, or multivariate regression. In an MBA dissertation, you may not always be able to randomize, so statistical controls are often the practical solution; for example, you can include age, income, and education when testing whether financial literacy predicts investment intention. The key point is that if you ignore confounders, your findings may be misleading even if your statistics look significant.iwh.on+1

** A collection of lecture notes on the subject of research methods for MBA students, 2026 June

A collection of blog notes on using chatgpt for research purpose.

Lecture note on quasi-experiment with an example for MBA students

Lecture note on quasi-experiment with an example for MBA students

As a part-time MBA student learning to do my 4-month dissertation project, I am interested in the research method of quasi-experiment. I feel that it is more feasible and less cumbersome for me to apply as compared with the conventional method of experiment. Please highlight 4 main ideas of the research method of quasi-experiment as well as 4 how-to advices. Try to illustrate your ideas and advices with regard to the MBA research theme of "to evaluate the business value of using influencer marketing to promote UK MBA degree programs in Hong Kong to gen y"

Yes — for a 4-month MBA dissertation, a quasi-experiment is often a practical choice because it lets you study cause-and-effect in a real business setting without full random assignment. For your topic, it fits well if you want to test whether influencer marketing changes application interest, brand attitude, or enquiry intention for UK MBA programs in Hong Kong. Quasi-experiments are specifically useful when randomization is not feasible, and they often use pretest-posttest, control-group, or time-series structures.pmc.ncbi.nlm.nih+2

4 main ideas

1.    No random assignment, but still comparison.
The key difference from a true experiment is that participants are not randomly assigned, but you still compare a treatment group and a comparison group. For your topic, one group could see influencer-led content about UK MBA programs, while another group sees standard university advertising.pmc.ncbi.nlm.nih+2

2.    Focus on change over time or between groups.
Quasi-experiments usually measure outcomes before and after an intervention, or compare groups exposed to different conditions. In your MBA theme, you could measure Gen Y respondents’ perceived program value, trust, or application intention before and after exposure to influencer marketing.pmc.ncbi.nlm.nih+1

3.    Works well in real-world settings.
This design is valuable when the field setting is messy, such as marketing campaigns, policy changes, or education promotion. That makes it suitable for evaluating a live or simulated influencer campaign for UK MBA recruitment in Hong Kong, where you cannot control every consumer exposure.pmc.ncbi.nlm.nih+1

4.    Causal inference is possible, but weaker than a true experiment.
Because groups are not randomized, you must think carefully about selection bias, confounding variables, and baseline differences. In your case, age, prior interest in overseas study, brand familiarity, and income may affect responses, so you need to control or match these factors as much as possible.pmc.ncbi.nlm.nih+2

4 how-to advices

1.    Use a simple pretest-posttest with comparison group.
This is usually the most feasible design for an MBA dissertation. Example: compare one Gen Y group exposed to influencer content about UK MBA programs and another group exposed to non-influencer content, then measure intention to apply before and after.pmc.ncbi.nlm.nih+1

2.    Choose one clear outcome variable.
Do not try to measure too many things. For your topic, pick one main dependent variable such as perceived business value of the MBA, brand credibility, or application intention, and use 1–2 secondary outcomes only if needed.pmc.ncbi.nlm.nih+1

3.    Match or control for key differences.
Since you cannot randomize perfectly, try to make the two groups as similar as possible on important variables such as gender, income, work experience, prior overseas study intention, and prior awareness of UK universities. This strengthens the credibility of your findings.dimewiki.worldbank+1

4.    Use a realistic influencer stimulus.
Your treatment should look like a believable campaign, such as short Instagram or LinkedIn posts from a business influencer discussing UK MBA benefits, career outcomes, and Hong Kong relevance. Then compare reactions against a standard university brochure or neutral post so the effect of influencer marketing is easier to isolate.pmc.ncbi.nlm.nih+1

Example for your topic

A workable dissertation design could be:

• Treatment group: Gen Y respondents in Hong Kong view an influencer-endorsed campaign for a UK MBA program.
• Control group: Similar respondents view a standard university promotional post.
• Pretest and posttest: Measure perceived value, trust, and application intention before and after exposure.
• Analysis: Compare change scores or use ANCOVA to adjust for baseline differences.

 

 

Please highlight 4 main ideas of ANCOVA in the context of our discussion.

In our context, ANCOVA has four main ideas:

4 main ideas of ANCOVA

1.    It compares groups after adjustment.
ANCOVA tests whether the influencer-marketing group and the non-influencer group differ on the outcome, but it does so after statistically adjusting for a baseline variable such as pretest intention or prior interest in UK MBA study.pmc.ncbi.nlm.nih+1

2.    It controls for starting differences.
This is especially useful in quasi-experiments where groups are not randomly assigned and may differ before the intervention starts. For your topic, one group may already be more interested in studying abroad, so ANCOVA helps reduce that imbalance.pmc.ncbi.nlm.nih+2

3.    It usually improves precision.
By using the pretest or another covariate, ANCOVA can reduce error variance and make it easier to detect the effect of the influencer campaign on outcomes such as perceived value or application intention.pmc.ncbi.nlm.nih+1

4.    It produces adjusted means, not just raw means.
The result is an “adjusted” comparison, which is useful for dissertation reporting because you can say whether influencer marketing has an effect after controlling for baseline attitudes or prior exposure. In your study, that might mean comparing the adjusted post-campaign intention scores between the two groups.theanalysisfactor+1

Example in your MBA topic

If your dependent variable is intention to apply for a UK MBA, and your covariate is pretest interest in overseas study, ANCOVA would ask: after accounting for initial interest, did the influencer-marketing group show a higher posttest intention than the control group? That is the kind of question ANCOVA is designed to answer.pmc.ncbi.nlm.nih+1

 

** A collection of lecture notes on the subject of research methods for MBA students, 2026 June

A collection of blog notes on using chatgpt for research purpose.