Description

Research Methodology

Sep 2026 Examination

 

Q1 A private bank is preparing a customer satisfaction survey for its digital banking platform after receiving mixed feedback on app usability, transaction delays, and support responsiveness. The operations team is under pressure to launch the survey immediately because leadership wants quick insights before the next product review. However, past surveys have failed because customers misunderstood rating scales and skipped key questions. The research analyst recommends a pretest with a small but representative sample of users before full deployment. Senior managers are unsure whether this extra step is worth the time. They need to understand how pretesting can improve flow, clarity, timing, and response options in a structured questionnaire. Applying the concept of pretesting structured questions, what steps should the bank take to pilot the questionnaire, identify design weaknesses, and refine the final instrument before company-wide administration? (10 Marks)

Ans 1.

Introduction

Pretesting a structured questionnaire before full deployment is a critical step in survey research, since it identifies design flaws that would otherwise compromise data quality across an entire company wide administration. For the bank’s digital banking satisfaction survey, where past attempts failed due to confusing rating scales and skipped questions, a proper pretest can reveal exactly these problems on a small scale before they affect the full customer base, giving management confidence that the eventual data collected will genuinely reflect

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Q2 A beverage company is preparing to launch a new line of healthy energy drinks for young professionals in major metro cities. Management wants to assess market readiness, consumer expectations, and price sensitivity before the launch, but it also wants to understand whether attitudes and buying intentions change after repeated exposure to advertising and trial experiences. The finance team is pushing for a one-time survey to reduce costs and accelerate decision-making. However, the brand manager believes consumer perceptions may evolve over time, especially in a highly competitive category. The research team must determine the most suitable time horizon for the study. Evaluate whether a cross-sectional or longitudinal time horizon would be more effective for this study. Justify your recommendation by assessing the trade-offs involving depth, cost, speed, and ability to support stronger conclusions, and explain the managerial consequences of choosing the less suitable option? (10 Marks)

Ans 2.

Introduction

Choosing between a cross-sectional and a longitudinal time horizon is a fundamental research design decision that shapes the depth, cost, and strength of conclusions a study can support. For the beverage company launching healthy energy drinks, this choice directly affects whether the research can capture how consumer attitudes evolve with repeated exposure to advertising and trial, a concern the brand manager has specifically raised, and the finance team’s push for a cheaper one time survey makes this trade-off especially important to resolve

Q3(A) A national supermarket chain is facing inconsistent customer experience across metro outlets. Sales reports show long checkout abandonment, uneven movement across aisles, and poor conversion in premium product sections. The research director believes that customer surveys are failing because shoppers give socially desirable answers or skip forms altogether. She wants a field study that captures actual behavior such as queue reactions, navigation patterns, and responses to staff interaction in real time. However, the legal team is concerned about privacy, and store managers want a method that can be replicated across branches. You are hired to develop an observation-based research design that can produce reliable and ethically defensible insights. Design an observation framework for the chain that combines at least two observation dimensions and one major observation approach to generate valid, actionable findings while balancing authenticity, measurability, and ethics. What specific procedures, tools, and safeguards would you build into the study? (5 Marks)

Ans 3A.

Introduction

The supermarket chain’s inconsistent customer experience, combined with unreliable survey responses due to social desirability bias, calls for an observation based research design. Combining multiple observation dimensions with a suitable observation approach can capture authentic shopper behavior while respecting privacy and ensuring replicability across

 

Q3(B) Ritika, a public health researcher, has returned from rural Madhya Pradesh with dozens of interview transcripts collected from mothers, health workers, and village elders about barriers to healthcare access. The narratives reveal issues such as long travel distances, cultural taboos, mistrust of modern medicine, and gaps in outreach systems. However, the material is unstructured, emotionally rich, and highly diverse. Her university research supervisor wants her to move beyond simple description and build a rigorous qualitative analysis process that can produce meaningful insights for policy and practice. She must now organize, interpret, and validate these narratives without losing their depth and contextual significance. Design a qualitative data analysis framework for Ritika’s study that integrates data reduction, data display, and conclusion drawing, while also ensuring credibility, dependability, and confirmability. How would you structure coding, theme development, validation, and interpretation so that the villagers’ narratives are transformed into actionable and trustworthy research findings? (5 Marks)

Ans 3B.

Introduction

Ritika’s rich but unstructured interview transcripts require a rigorous qualitative analysis framework that organises the material into meaningful themes while preserving depth and ensuring the findings are credible enough to inform healthcare policy and practice in rural Madhya Pradesh.

Concept and Application

Data Reduction

Data reduction involves systematically coding the transcripts, identifying recurring concepts