Sunday, 2 August 2026

Lecture note on statistical analysis methods: for MBA students

 Lecture note on statistical analysis methods: for MBA students

 

Highlight 4 main ideas of the following statistics analysis methods in the context of doing MBA dissertation projects and provide 2 application examples (that utilize questionnaire survey data) on each of these methods: 1. correlation analysis 2. Anova (single factor) 3. Chi-square test

Below is an MBA-dissertation-friendly guide to the three methods, focusing on what each method is for, how to think about it, and how to use questionnaire survey data in practice. I’ve kept the examples close to common dissertation topics such as consumer behavior, employee attitudes, and service quality.utwente+2

1) Correlation analysis

Correlation analysis examines whether two quantitative variables move together, and in what direction. It is useful when your dissertation asks whether higher levels of one survey score are associated with higher or lower levels of another score.utwente

4 main ideas

  • It measures the strength of association between two variables.
  • It shows the direction of the relationship: positive, negative, or near zero.
  • It does not prove causation, only co-movement.
  • It works best when both variables are measured on a scale such as a Likert composite score, total score, or index created from questionnaire items.utwente

2 questionnaire-based application examples

  • Customer satisfaction and repurchase intention. Use survey items to create a satisfaction score and a repurchase intention score, then test whether more satisfied respondents also report stronger intention to buy again.
  • Work engagement and job performance self-rating. Use questionnaire scales for engagement and self-rated performance, then test whether respondents with higher engagement scores also report higher performance scores.

2) One-way ANOVA

One-way ANOVA tests whether the mean of a quantitative variable differs across three or more groups defined by a single categorical factor. In MBA dissertations, it is commonly used when you want to compare average survey scores across segments such as age groups, income groups, job levels, or customer types.utwente+1

4 main ideas

  • It compares group means rather than individual responses.
  • It uses one categorical independent variable with two or more levels.
  • It is appropriate when the dependent variable is quantitative.
  • If the overall test is significant, you usually follow up with post-hoc comparisons to see which groups differ.utwente+1

2 questionnaire-based application examples

  • Service quality perceptions across age groups. Survey respondents rate service quality on a 1–5 scale, then compare mean ratings across age groups such as under 30, 30–45, and over 45.
  • Purchase intention across income groups. Use a Likert-based purchase intention score and test whether the average intention differs among low-, middle-, and high-income respondents.

3) Chi-square test

The chi-square test is used with categorical data to examine whether two variables are related or whether observed responses differ from expected patterns. For questionnaire surveys, it is especially useful when responses are recorded as categories such as yes/no, male/female, brand choice, or grouped levels like satisfied/neutral/dissatisfied.libraryguides.mcgill+2

4 main ideas

  • It works with categorical variables, not scale means.
  • It compares observed frequencies with expected frequencies.
  • It can test independence between two survey variables.
  • It is common for cross-tabulation analysis in business research questionnaires.libraryguides.mcgill+2

2 questionnaire-based application examples

  • Preferred payment method by age group. Survey respondents choose payment method categories such as cash, card, or mobile wallet, and you test whether preference is associated with age group.
  • Brand awareness by education level. Respondents indicate whether they know a brand or not, and you test whether awareness is independent of education level.

Quick way to choose

  • Use correlation when both variables are numerical scores and you want association.
  • Use one-way ANOVA when one categorical factor explains differences in a numerical survey score.
  • Use chi-square when both variables are categorical or grouped into categories.


Friday, 31 July 2026

Lecture note on pragmatism and how to employ it in MBA dissertation projects

Lecture note on pragmatism and how to employ it in MBA dissertation projects

 

I am a part-time MBA student, learning the subject of Research Methods. My understanding is that the research philosophy of pragmatism is highly relevant for doing BA dissertation projects due to the pragmatic and managerial orientation of the MBA program design spirit. Please highlight 3 main ideas of pragmatism and suggest how to employ pragmatism-oriented dissertation investigation tasks with three how-to advices so that the dissertation report is able to create practical (and managerial oriented) values.

You’re right that pragmatism aligns very well with MBA‑style, managerial dissertations. In essence, it lets you focus on “what works” to solve a real business problem, using whatever methods are most useful, and judging your research by its practical consequences rather than abstract philosophical purity.[1][2][3]

Below are three core ideas of pragmatism, followed by three “how‑to” pieces of advice for designing a pragmatist, value‑creating MBA dissertation.

Three main ideas of pragmatism

1. Focus on practical outcomes and consequences

Pragmatism evaluates knowledge by its usefulness: ideas, theories, and findings matter if they help you act effectively and improve a real situation. Truth is understood in terms of “warranted assertions” – claims whose value is demonstrated through their practical consequences in the world.[4][1]

For an MBA dissertation, this means:

·       Start from a concrete managerial problem (e.g., low customer retention, poor employee engagement, inefficient logistics).

·       Judge your research design and findings by whether they help managers make better decisions, design interventions, or change policies.

2. “What works” and methodological flexibility

Pragmatism is methodologically pluralist: you choose methods because they help answer your question, not because they fit a rigid paradigm. Pragmatist researchers often combine quantitative and qualitative data, and move between deductive (hypothesis‑testing) and inductive (exploratory) reasoning as needed.[2][3][5]

Key implications:

·       You can legitimately use mixed methods – surveys, interviews, company data, case studies, experiments – in one coherent design, as long as each part clearly serves the research question.

·       You can draw on both positivist-style measurable relationships and interpretivist-style rich explanations of experience, treating them as complementary lenses on the same managerial issue.[6][2]

3. Inquiry as action and experience‑based learning

Pragmatism treats research as an action‑oriented inquiry process: you identify a problematic situation, develop possible lines of action, evaluate them by anticipated and observed consequences, and refine your understanding through experience.[5][7][8]

This leads to:

·       Emphasis on context and lived experience of stakeholders (managers, employees, customers) as a source of knowledge.

·       Viewing your dissertation as part of an ongoing cycle of problem recognition, intervention design, and organisational learning, rather than a purely theoretical exercise.

Three pragmatism‑oriented “how‑to” advices for your dissertation

1. Design the project around a clearly defined managerial problem

Aim: Ensure that every part of the dissertation is anchored in a practical issue and leads to actionable insights.

How‑to:

·       Start and end in practice. Frame your topic explicitly as a problematic situation in a specific organisation or sector (e.g., “How can Company X improve post‑purchase customer engagement to increase repeat purchases?”). Define the current symptoms, stakeholders affected, and business impact.[7][9]

·       Formulate pragmatist research questions. Write RQs that ask “What works, for whom, and under what conditions?” rather than only “Does X statistically affect Y?”. For instance:

o   RQ1 (quantitative): “What is the relationship between loyalty program participation and repeat purchase frequency among customers of Company X?”

o   RQ2 (qualitative): “How do different customer segments experience and interpret the loyalty program and its perceived value?”

·       Specify intended managerial outputs upfront. In your introduction and methodology chapters, state clearly what decisions your findings are meant to inform (e.g., redesign of loyalty tiers, staff training priorities, segmentation strategy). This aligns your whole project with pragmatist emphasis on consequences and actionable knowledge.[9][1]

This makes it easy, in the conclusion, to translate findings into concrete recommendations, KPIs, and implementation steps, which examiners in an MBA will view as practical value.

2. Use mixed methods strategically to answer “what works”

Aim: Combine quantitative “pattern‑finding” and qualitative “meaning‑making” methods to build robust, context‑sensitive guidance for managers.

How‑to:

·       Start from the research question, not the paradigm. For each sub‑question, ask: “What evidence will most convincingly help a manager decide what to do?” If the answer involves measuring effects, use quantitative methods; if it involves understanding why stakeholders behave or feel a certain way, use qualitative methods.[2][5]

·       Design complementary data streams. For example, in an MBA dissertation on employee engagement:

o   Quantitative: An online survey measuring engagement scores, job characteristics, and performance metrics.

o   Qualitative: Semi‑structured interviews or focus groups exploring how employees interpret engagement initiatives, leadership behaviour, and organisational culture.
The survey shows which factors correlate with engagement; the interviews explain how and why those factors matter in your specific context.
[1][6]

·       Integrate findings around managerial decisions. In the discussion chapter, don’t treat survey and interview findings as separate silos. Use a pragmatist “integration” logic:

o   Use quantitative results to identify priority levers (e.g., supervisor support and autonomy strongly predict engagement).

o   Use qualitative insights to design feasible interventions (e.g., micro‑practices managers can adopt, how autonomy can be increased without hurting compliance, how employees perceive fairness).
Make explicit how each integrated insight translates into a decision rule, policy change, process redesign, or management practice.
[9][1][2]

This is exactly the kind of “what works” mixed‑methods reasoning that pragmatism is known for, and it naturally produces managerial value.

3. Build a clear “impact pathway” from findings to change in practice

Aim: Ensure your dissertation doesn’t stop at “interesting findings”, but demonstrates how those findings can generate real organisational improvements.

How‑to:

·       Map the problem–evidence–action chain. In your discussion or implications chapter, use a simple structure for each key finding:

a.     Problem statement (e.g., low repeat purchase rate among a specific customer segment).

b.    Evidence summary (e.g., data show clear link between perceived value of after‑sales service and repeat purchase; interviews reveal customers feel ignored post‑purchase).

c.     Proposed actionable intervention (e.g., design a segmented after‑sales follow‑up protocol, with specific contact frequency and content, integrated into CRM).

d.    Expected consequences and KPIs (e.g., target % increase in repeat purchases; customer satisfaction scores).[7][9]

·       Co‑design or validate with practitioners where possible. If feasible, involve managers or stakeholders in reviewing your proposed interventions (through expert interviews, workshops, or feedback sessions). This is very much in line with pragmatism’s emphasis on inquiry embedded in practice and co‑creation with practitioners.[5][9]

·       Reflect on implementation constraints and learning cycles. Pragmatism emphasises that knowledge is fallible and always open to refinement based on experience. In your conclusion, explicitly discuss:[4][9]

a.     What organisational constraints might limit implementation (budget, culture, technology).

b.    How managers could pilot your recommendations, monitor outcomes, and iteratively adjust (e.g., A/B testing, phased roll‑out, feedback loops).
By showing this “learning cycle”, you position your dissertation as a starting point for ongoing organisational inquiry, not a static blueprint.

Putting it together in your MBA dissertation

If you frame your methodology chapter explicitly in pragmatist terms, you might emphasise:

·       Ontology/Epistemology (briefly): You treat reality as experienced in practice and knowable through its observed and felt consequences; you value both numerical patterns and subjective experiences as legitimate evidence for managerial decisions.[2][5]

·       Axiology: Your research is explicitly value‑oriented, aiming to improve organisational outcomes and stakeholder experiences, not just to advance theory.[3][9]

·       Methodological stance: You adopt a flexible, mixed‑methods design driven by the research question and the need to produce actionable, context‑specific insights.

With this framing, your dissertation will clearly embody the “spirit” of an MBA: solving real managerial problems, using whatever methods work best, and translating evidence into practical recommendations that your examiners can see as genuine, pragmatic value.



** a collection of lecture notes on research methods