Challenges in Research & Scientific Methods: Ethics, Bias, Validity, Bias, and Real-World Hurdles

Key Challenges in Research and Scientific Methods

Research is not just about collecting data; it’s about overcoming obstacles to ensure accuracy, fairness, and reliability. This unit dissects the common challenges—ethical, methodological, and practical—that researchers encounter, especially in business contexts. We’ll see how bias, validity threats, ethical violations, and real-world constraints (like time, budget, or cultural differences) can derail even the best-designed studies. We’ll also explore how companies like NEPSE, Daraz, and NTC face these challenges daily—and how you can anticipate them in your own research.

1. Types of Challenges in Research

Research challenges can be broadly classified into three categories:

mindmap
  root((Challenges in Research))
    Methodological Challenges
      Bias
        Sampling Bias
        Response Bias
        Measurement Bias
      Validity Threats
        Internal Validity
        External Validity
      Reliability
    Ethical Challenges
      Informed Consent
      Confidentiality
      Plagiarism & Fabrication
    Practical Challenges
      Time & Budget
      Data Access
      Cultural & Political Barriers

Why this matters: A study on customer satisfaction at ABC Bank (like in past exam cases) might fail if researchers ignore sampling bias (e.g., only interviewing urban customers) or ethical lapses (e.g., not disclosing the study’s purpose).

2. Methodological Challenges

A. Bias in Research

Bias distorts research findings, making them unreliable or misleading. Common types:

Worked Example: A study on Ncell’s customer loyalty in Nepal found high satisfaction rates. But if the survey was conducted only during promotions, the results would be artificially inflated due to response bias (customers were more likely to respond positively when offered discounts).

B. Validity and Reliability

• Validity: Does the study measure what it claims to measure?

  • Internal Validity: Are the results causally linked to the independent variable? Example: A study claiming Khalti’s payment speed improves customer trust must rule out other factors (e.g., website crashes, competitor promotions).
  • External Validity: Can results be generalized beyond the sample? Example: A NTC customer satisfaction survey in Kathmandu may not apply to rural users with poor internet access.

• Reliability: Would the study yield the same results if repeated?

  • Problem: If a bank loan approval study uses inconsistent interviewers, results may vary.

Visual: Threats to Validity

Real-World Tie:

• NEPSE’s stock manipulation scandals often arise from invalid research (e.g., analysts ignoring external validity by assuming all investors behave the same).
• eSewa’s data breaches highlight ethical lapses in handling confidentiality (a key validity threat).

3. Ethical Challenges in Business Research

Ethics ensure trust, transparency, and respect for participants. Major issues:

A. Informed Consent

• Definition: Participants must fully understand the study’s purpose, risks, and their rights.
• Violation Example:
  • A Nabil Bank study on loan defaults might hide its true purpose (e.g., “market research” instead of “loan risk analysis”), violating informed consent.

B. Confidentiality and Privacy

• Definition: Data must be protected from misuse.
• Violation Example:
  • Pathao’s surge pricing data was leaked, exposing driver earnings—a confidentiality breach.

C. Plagiarism and Fabrication

• Plagiarism: Copying others’ work without credit.
  • Example: A Daraz employee submitting a customer behavior report that directly copies a competitor’s study.
• Fabrication: Making up data.
  • Example: A bank researcher inventing customer feedback to justify a new policy.

Visual: Ethical Research Checklist

mindmap
  root((Ethical Research Checklist))
    Informed Consent
      Purpose Explained
      Risk Disclosure
      Withdrawal Option
    Confidentiality
      Data Anonymization
      Secure Storage
      Limited Access
    Honesty
      No Fabrication
      Proper Citation
      Conflict Disclosure

Real-World Case: NEPSE’s Ethical Failures

• In 2022, NEPSE analysts faced scrutiny for downplaying risks in IPOs (Initial Public Offerings) to boost investor confidence, violating transparency (a core ethical principle).

4. Practical Challenges

A. Time and Budget Constraints

• Problem: Limited funds or deadlines force compromises (e.g., smaller sample size, quicker (but less accurate) methods).
• Example: A Khalti payment study might use online surveys only, missing offline user feedback.

B. Access to Data/Samples

• Problem: Some groups are hard to reach (e.g., rural Ncell users, Daraz sellers in remote areas).
• Solution: Use multi-stage sampling or remote interviews.

C. Cultural and Political Barriers

• Problem: In Nepal, hierarchical cultures may make employees unwilling to speak freely in surveys.
• Example: A Nabil Bank study on employee satisfaction might get biased responses if subordinates fear retaliation.

Visual: Practical Challenges in Nepal

flowchart TD
    A["Time/Budget"] -->|"Small Sample"| B["Less Reliable Results"]
    A -->|"Rushed Analysis"| C["Poor Quality Data"]
    D["Access Issues"] -->|"Remote Areas"| E["Ncell Users Ignored"]
    D -->|"Sensitive Topics"| F["Political Censorship"]
    G["Cultural Barriers"] -->|"Hierarchy"| H["Untruthful Responses"]

5. Overcoming Challenges: Best Practices

6. Real-World Examples: Where These Challenges Appear

A. NEPSE and Stock Market Manipulation

• Challenge: Validity threats (e.g., analysts ignoring external factors like global oil prices).
• Real Case: In 2023, NEPSE’s IPO valuations were criticized for overestimating demand, likely due to confirmation bias (analysts favoring positive outcomes).

B. Daraz’s Logistics and Customer Feedback

• Challenge: Sampling bias (online reviews skew positive; offline complaints are ignored).
• Solution: Daraz now uses mixed methods (online surveys + in-person feedback from rural sellers).

C. eSewa’s Data Privacy Scandals

• Challenge: Ethical breach (data leaks due to poor confidentiality measures).
• Impact: Lost customer trust, regulatory fines.

In the Real World

1. NEPSE (Nepal Stock Exchange)

   • Idea Used: Validity and reliability in financial forecasting
   • How: NEPSE analysts must ensure their stock price predictions are free from bias (e.g., not influenced by political connections) and reliable (consistently accurate over time). When NEPSE’s IPO valuations were questioned in 2023, it was partly due to methodological flaws—analysts failed to account for external economic shocks (like global supply chain disruptions), leading to external validity issues.

2. Khalti (Digital Payment Platform)

   • Idea Used: Response bias in customer satisfaction surveys
   • How: Khalti’s app ratings often show artificially high satisfaction because users who had negative experiences (e.g., failed transactions) are less likely to leave reviews. To combat this, Khalti now uses follow-up surveys (e.g., “Why did you rate us 3/5?”) to reduce response bias and get deeper insights.

3. Pathao (Ride-Hailing App)

   • Idea Used: Sampling bias in driver behavior studies
   • How: If Pathao only surveys drivers in Kathmandu, their findings on traffic congestion won’t apply to Pokhara or Biratnagar, where road conditions differ. Pathao now uses geotagged data to adjust for regional variations, improving external validity.

Exam Tip: How to Score Full Marks

1. Understand the "Why" Behind Challenges

   • Examiners love real-world applications. For example:
     • If asked about sampling bias, tie it to NEPSE’s regional investor study.
     • If asked about ethical issues, mention eSewa’s data breach case.

2. Use the Right Terminology

   • Internal validity ≠ external validity (don’t confuse them!).
   • Response bias ≠ sampling bias (different causes, different fixes).

3. Compare and Contrast

   • The exam often asks to compare quantitative vs. qualitative challenges.
     • Example Answer:

       Quantitative research faces sampling bias (e.g., NEPSE’s urban investor study), while qualitative research may suffer from interviewer bias (e.g., Pathao drivers giving socially desirable answers).

4. Apply to Case Studies

   • For the ABC Bank case, identify:
     • Sampling bias: Only Kathmandu customers surveyed.
     • Ethical issue: Were customers truly informed about the study’s purpose?
     • Validity threat: Did the study control for external factors (e.g., recent bank promotions)?

5. Show, Don’t Just Tell

   • Draw a flowchart of how bias affects research (like the one above).
   • Create a table comparing ethical violations in NEPSE vs. eSewa.
   • Use a real example (e.g., Daraz’s mixed-methods approach) to explain overcoming sampling bias.

Final Note: This unit is not just theory—it’s about spotting flaws in real studies. Next time you see a news article about a bank’s loan policy or a NEPSE stock crash, ask: “What research challenges could have led to this?” That’s how you ace the exam and think like a researcher.