Unit 2: Research design

Research Design

A research design is the overall plan or structure that a researcher uses to conduct a study.

It provides a blueprint for:

• What data to collect
• How to collect it
• How to analyze it

Think of it like an architect’s plan before building a house — it ensures that the research is organized, systematic, and focused.

📌 Example: If you want to study the effect of training on employee performance, your research design will help answer:

• What kind of data is needed? (Survey, performance reports)
• Who are the participants? (Employees)
• How will you collect data? (Questionnaire)
• How will you analyze it? (Statistical tools)

Features of a Good Research Design

A good research design must have certain qualities to ensure accurate and reliable results.

Uses of a Good Research Design

A well-prepared research design helps in the following ways:

Conclusion

A good research design:

• Clarifies the research problem
• Ensures data quality
• Saves time and resources
• Produces reliable and useful results

“Failing to plan is planning to fail.” – In research, design is the plan.

Qualitative and Quantitative Research Approaches

Qualitative Research Approach

Qualitative research focuses on understanding human behavior, experiences, and emotions through non-numerical data like words, images, or observations.

It answers "why" or "how" a phenomenon occurs.

Methods:

• Interviews
• Focus groups
• Case studies
• Observations

Example: Studying customer satisfaction by interviewing 10 customers about their experience with a new product.

Quantitative Research Approach

Quantitative research deals with numbers and statistics. It focuses on measuring and analyzing data using mathematical models.

It answers "how much", "how many", or "what is the relationship" between variables.

Methods:

• Surveys with numerical questions
• Experiments
• Statistical analysis

Example: Studying customer satisfaction by collecting responses from 500 customers on a rating scale of 1 to 5.

📊 Comparison Table: Qualitative vs Quantitative Research

Pros and Cons of Each Approach

📌 Qualitative Research

✔️ Pros:

• Provides deep insights into human behavior
• Flexible and open-ended
• Good for exploring new ideas

❌ Cons:

• Difficult to measure
• Results can't be generalized to large populations
• Time-consuming and subjective

📌 Quantitative Research

✔️ Pros:

• Results can be measured and compared
• Easier to analyze using statistics
• Can study large populations

❌ Cons:

• May miss the “why” behind behaviors
• Less flexible
• May oversimplify complex issues

✅ Conclusion:

• Both approaches are valuable and often used together in mixed-method research for better results.
• Use qualitative when you want to understand behavior.
• Use quantitative when you want to measure it.

Exploratory Research Design

Exploratory Research Design is used when the problem is not clearly defined.

• It aims to gain insights, explore ideas, and understand trends without giving final answers or conclusions.
• It is helpful in the early stages of research when the researcher wants to explore the problem before moving to more structured research.

🎯 Purpose:

• To identify research problems
• To formulate research questions or hypotheses
• To gain deeper understanding of a subject

🔠 Types of Exploratory Research (Qualitative Techniques)

Exploratory research often uses qualitative methods to explore underlying reasons, opinions, and motivations.

1. Projective Techniques

These are indirect methods where respondents project their thoughts or feelings onto ambiguous stimuli.

Common Techniques:

Word Association: Respondents say the first word that comes to mind when given a stimulus word. Example: "When I say ‘coffee,’ what’s the first word you think of?"

Sentence Completion: Respondents complete incomplete sentences. Example: "People who drink Coke are usually ______."

Picture Interpretation: Respondents interpret ambiguous images. Example: Showing a picture of a family at a dinner table and asking what’s happening.

Purpose: To uncover hidden emotions, attitudes, and perceptions.

2. Depth Interview

A one-on-one personal interview where the interviewer explores the respondent’s thoughts, feelings, and beliefs in detail.

Features:

• Unstructured
• Conducted by trained interviewers
• Lasts 30 minutes to 1 hour

Example: Interviewing a customer who switched mobile brands to explore the deeper reason behind the decision.

Purpose: To gain rich, qualitative insights into individual experiences.

3. Experience Survey

Informal interviews with experts or knowledgeable individuals to gain background information and insights.

Example: Talking to industry professionals, managers, or long-time employees to understand industry challenges.

Purpose: To gather practical views and insider perspectives that can help define a research problem.

4. Focus Groups

A discussion-based method where 6–10 participants share their views on a topic, led by a moderator.

Features:

• Semi-structured
• Duration: 1 to 2 hours
• Participants are selected based on relevance to the topic

Example: A group of teenagers discussing a new soft drink’s taste and packaging.

Purpose: To explore attitudes, preferences, and reactions in a group setting.

5. Observation

Involves watching and recording people’s behavior or actions in natural settings without asking questions.

Types:

Natural Observation: Watching customers in a store without interference

Controlled Observation: Conducting a planned experiment in a lab setting

Example: Observing how customers navigate a retail store to find products.

Purpose: To collect real-time data and understand natural behavior patterns.

📌 Summary Table: Qualitative Techniques in Exploratory Research

Conclusion:

Exploratory research helps to:

• Understand unknown problems
• Build hypotheses
• Design structured research for future studies

These techniques are not meant to provide final answers but to explore possibilities and guide deeper research.

Descriptive Research Design

Descriptive research is used to describe characteristics, behaviors, or trends of a population or phenomenon.

It answers the questions like:

• What is happening?
• Who is involved?
• When and where is it happening?

It does not explain causes but gives a clear picture of a situation.

Example: A company wants to know the buying habits of its customers. A descriptive survey can provide detailed data on age, income, preferences, etc.

Types of Descriptive Research

Uses of Descriptive Research Design

Concept of Cross-sectional and Longitudinal Research

Cross-Sectional Research

Definition: Data is collected at a single point in time.

Purpose: Compare different groups or variables at the same time & Fast and cost-effective.

Example: A survey conducted once to study the social media usage of teenagers in 2025.

Longitudinal Research

Definition: Data is collected from the same subjects over a long period.

Purpose: Track changes and trends over time.

Time-consuming but more accurate in identifying patterns.

Example: Studying the health habits of a group of people over 10 years.

📊 Comparison: Cross-Sectional vs Longitudinal

Conclusion:

Descriptive research helps provide a clear picture of a situation.

It is essential for:

• Market research
• Customer analysis
• Social science studies
• Cross-sectional is for current snapshots, while longitudinal tracks changes over time.

Experimental Design

Experimental design is a research method used to establish cause-and-effect relationships by manipulating one or more variables and observing the outcome. It answers the question: Does X cause Y?

Concept of Cause and Causal Relationships

Cause: A factor (variable) that directly produces a change in another variable.

Causal Relationship: When a change in one variable (the cause) leads to a change in another variable (the effect).

Example: A company increases advertising (cause), and sales increase (effect).

This suggests a causal relationship.

Variables in Experimental Research

a. Independent Variable (IV)

The variable that is manipulated or controlled by the researcher. It is considered the cause.

Example: Amount of fertilizer given to plants.

b. Dependent Variable (DV)

The variable that is measured in the experiment. It is considered the effect.

Example: Plant growth (as affected by fertilizer).

c. Concomitant Variable

Also called control variables, these are variables that are measured along with the independent variable because they might influence the outcome.

Example: In a study on exercise (IV) and weight loss (DV), diet can be a concomitant variable.

d. Extraneous Variable

Variables other than the IV that may affect the DV.

These variables must be controlled to avoid bias.

Example: If you're testing the effect of light on plant growth, soil quality is an extraneous variable.

📌 Difference between Concomitant & Extraneous Variables

Treatment and Control Group

🔹 Treatment Group

The group that receives the experimental condition (independent variable).

Used to test the effect of the treatment.

Example: A group that receives a new drug.

🔹 Control Group

The group that does not receive the treatment.

Used for comparison to see the real effect of the treatment.

Example: A group that receives a placebo instead of the drug.

🧪 Example of an Experimental Design in Business

Objective: To study the impact of training on employee productivity.

If the treatment group shows higher productivity than the control group, a causal relationship is established.

✅ Conclusion

• Experimental design is crucial for testing hypotheses and understanding cause-effect relationships in research.
• Controlling variables and using treatment & control groups ensures the results are reliable and valid.