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.