Q1: Focus Groups vs.
In-Depth Interviews
Advantages of Focus Groups
Encourages interaction and discussion among participants.
Provides diverse perspectives in a short time.
Helps generate new ideas through group dynamics.
Cost-effective compared to multiple in-depth interviews.
Disadvantages of Focus Groups
Risk of dominant participants influencing others.
Less depth in individual responses.
Groupthink may lead to biased findings.
Not suitable for sensitive topics.
Advantages of In-Depth Interviews
Allows for deeper exploration of individual experiences.
Reduces influence from external opinions.
More suitable for complex or sensitive topics.
Flexible and adaptable to respondent feedback.
Disadvantages of In-Depth Interviews
Time-consuming and expensive.
Limited to individual perspectives.
Requires skilled interviewers to maintain objectivity.
Research Problems Suited for Each Technique
Focus Groups: Testing new product ideas, understanding group decision-making,
assessing advertisements.
In-Depth Interviews: Exploring customer motivations, investigating sensitive issues
(e.g., health concerns), understanding expert opinions.
Q2: Research Type Selection
a) Descriptive Research – It aims to identify characteristics of the target market through
demographic analysis.
b) Causal Research – Establishing a relationship between advertising and sales requires
causal research, often involving experiments or statistical analysis.
c) Exploratory Research – Investigating consumer reactions to a new detergent idea
requires exploratory research, such as focus groups or pilot studies.
d) Descriptive Research – Estimating sales potential involves descriptive research using past
trends, market surveys, and statistical analysis.
Q3: Sampling Design & Probability Sampling
Sampling design refers to the framework used to select participants from a population.
Probability sampling is preferred because:
It ensures every unit has a known chance of selection.
It minimizes selection bias.
It allows for generalization of results.
Q4: Research Design & Types
Research design is the overall plan for conducting a study.
Exploratory Research
Used for problem identification.
Methods: Literature reviews, expert interviews.
Example: Investigating why a product is failing.
Conclusive Research
Used for decision-making.
Methods: Surveys, experiments.
Example: Measuring customer satisfaction levels.
Q5: Probability Sampling Techniques
1. Simple Random Sampling – Each unit has an equal chance.
2. Stratified Sampling – Population divided into subgroups, then sampled randomly.
3. Cluster Sampling – Population divided into clusters, and entire clusters are sampled.
4. Systematic Sampling – Every nth unit is chosen.
5. Multi-Stage Sampling – Combines multiple techniques.
Usage varies based on research goals, population size, and resources.
Q6: Elements of Research Design
1. Problem Definition – Clear research objectives.
2. Data Collection Methods – Surveys, experiments, etc.
3. Sampling Plan – Who to study and how.
4. Data Analysis – Statistical methods.
5. Time & Budget Constraints – Resource allocation.
Example: A company studying customer satisfaction might use a survey-based research
design.
Q7: Methods to Determine Sample Size
1. Cochran’s Formula – For large populations.
2. Slovin’s Formula – For estimating sample size with margin of error.
3. Power Analysis – Used in experimental research.
4. Rule of Thumb – Based on past studies.
Example: A market survey using Slovin’s formula to determine required respondents.
Q8: Importance of Sample Selection
Proper sampling ensures:
Representativeness – Reflects the population.
Accuracy – Reduces bias.
Cost-effectiveness – Uses optimal resources.
Factors to consider:
Population size.
Sampling technique.
Budget constraints.
Research objectives.
Q9: Sampling Process & Techniques
Steps in Sampling:
1. Define population.
2. Choose sampling frame.
3. Select sampling technique.
4. Determine sample size.
5. Collect data.
Types of Sampling:
Probability Sampling: Random, stratified, cluster.
Non-Probability Sampling: Convenience, judgmental, quota.
Q10: Short Explanations
a) Sampling Frame vs. Sampling Unit – Frame is the list from which units (participants) are
drawn. b) Causal Relationship Conditions – Temporal precedence, correlation, and
elimination of extraneous variables. c) Stratified vs. Cluster Sampling – Stratified samples
within subgroups, while cluster sampling selects entire groups. d) Sampling vs. Non-
Sampling Errors – Sampling errors are due to sample selection; non-sampling errors arise
from data collection issues. e) Point Estimate vs. Interval Estimate – Point estimate gives a
single value; interval estimate provides a range.
