Qualitative Research vs Quantitative Research

Apr 23, 2025 Lecture 11 13 min read

Research Methods

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Research Methodology and Its Types

Introduction:
Research methodology refers to the systematic framework used to conduct research and analyze data. It provides the blueprint for how research is conducted, ensuring reliability, validity, and accuracy in results. At its core, research methodology is divided into two primary types, each offering unique approaches to data collection and analysis.

Types of Research Methods:

Quantitative Research Method
This method focuses on numerical data, statistical analysis, and objective measurements. It is commonly used to test hypotheses, measure variables, and establish patterns or relationships through structured tools like surveys, experiments, and computational models.
Qualitative Research Method
This method is centered on understanding meanings, experiences, and concepts through non-numerical data. It involves methods such as interviews, observations, and content analysis, aiming to explore complex phenomena in depth and context.

Background:
Initially, computer science predominantly relied on quantitative research methods, emphasizing measurable outcomes and data-driven conclusions. In contrast, the social sciences began with qualitative approaches, aiming to explore human behavior, culture, and social interactions in depth. Over time, both disciplines have evolved to embrace a combination of these methods, recognizing the value of mixed-method approaches to gain more comprehensive insights.

Types of Data in Quantitative and Qualitative Research

1. Quantitative Research Method

Quantitative research deals with numerical data that can be measured and analyzed statistically. The data is often structured and collected through instruments like surveys, tests, and sensors.

Common Types of Quantitative Data:

Discrete Data: Countable values (e.g., number of students in a class, number of software bugs).
Continuous Data: Measurable quantities that can take any value within a range (e.g., time taken to complete a task, temperature, memory usage).
Categorical Data (when coded numerically): E.g., Yes = 1, No = 0.
Ordinal Data: Data with a set order but no fixed interval (e.g., satisfaction ratings: very satisfied, satisfied, neutral).

Examples of Quantitative Data:

Exam scores
System performance metrics
Frequency of user interactions
Duration of tasks
Survey results with fixed-response options

2. Qualitative Research Method

Qualitative research deals with non-numerical data and focuses on understanding concepts, opinions, and experiences. The data is often unstructured or semi-structured.

Common Types of Qualitative Data:

Textual Data: Transcripts of interviews, open-ended survey responses, emails, etc.
Audio/Video Data: Recordings of discussions, usability tests, or observations.
Observational Notes: Field notes taken during direct or participant observation.
Visual Data: Images, screenshots, or diagrams analyzed for meaning or themes.

Examples of Qualitative Data:

Interview transcripts with developers discussing coding practices
Observations of user behavior during a usability test
Open-ended responses in surveys
User feedback and reviews
Case study reports

Summary Table:

Aspect	Quantitative Research	Qualitative Research
Data Type	Numerical, structured	Textual, visual, audio, unstructured
Examples	Surveys, metrics, logs, performance scores	Interviews, observations, open-ended feedback
Analysis	Statistical, computational	Thematic, content, discourse analysis
Goal	Measure, test hypotheses, generalize findings	Explore, understand, gain in-depth insights

Exploring Key Qualitative Research Techniques

Common Qualitative Research Methods

1. Interviews

Description: One-on-one conversations between the researcher and the participant to gather detailed insights.
Types: Structured, semi-structured, or unstructured.
Use Case: Exploring personal experiences, opinions, and motivations.
Example: Interviewing software developers about their debugging process.

2. Focus Groups

Description: Guided group discussions led by a moderator to explore participants’ thoughts on a specific topic.
Ideal Size: Typically 6–10 participants.
Use Case: Exploring group dynamics, shared experiences, or reactions to a product or concept.
Example: Gathering feedback from a group of users on a new app feature.

3. Ethnography

Description: An in-depth study of people and cultures through direct observation and participation.
Method: Long-term fieldwork where researchers immerse themselves in the setting.
Use Case: Understanding behaviors and social interactions in real-life environments.
Example: Observing how a development team collaborates in a workplace setting.

4. Case Studies

Description: Detailed examination of a single subject (person, group, event, or organization) over time.
Use Case: Investigating complex issues in-depth and in context.
Example: Analyzing the development lifecycle of a successful open-source software project.

5. Observations

Description: Recording behaviors or events in their natural setting, either as a passive observer or active participant.
Types: Participant and non-participant observation.
Use Case: Capturing authentic actions and interactions without reliance on self-reporting.
Example: Observing how users navigate a website without giving them specific instructions.

6. Document or Content Analysis

Description: Systematic analysis of text, images, or media to interpret patterns or meanings.
Sources: Articles, reports, emails, social media posts, etc.
Use Case: Exploring communication patterns or media representation.
Example: Analyzing forum discussions around a new programming language.

Evaluating the Quality of Quantitative Research

Quantitative research may involve more data, but that doesn’t necessarily make it better—just different. To determine whether quantitative research is “good” or reliable, researchers use several key criteria.

Key Criteria to Judge Good Quantitative Research

Validity
- Definition: Does the research actually measure what it claims to measure?
- Types:
  - Internal Validity: Accuracy of results within the study.
  - External Validity: Can the results be generalized to other settings?
- Example: A study measuring user satisfaction must use a valid scale that actually reflects satisfaction, not just usage frequency.
Reliability
- Definition: Are the results consistent and reproducible over time?
- Example: A performance test on a computer algorithm should yield similar results under the same conditions, every time.
Objectivity
- Definition: Are the results free from researcher bias?
- Example: Statistical analysis should not be influenced by personal interpretation; tools like SPSS or Python libraries help ensure objectivity.
Sample Size and Representativeness
- Definition: Is the sample large and diverse enough to represent the entire population?
- Example: A survey about student learning habits should include a wide range of students across programs and levels—not just one class.
Statistical Significance
- Definition: Are the results due to actual effects and not just random chance?
- Example: A p-value less than 0.05 often indicates that the results are statistically significant.
Clear Research Design
- Definition: Is the study structured in a logical, clear, and replicable way?
- Example: A well-defined hypothesis, controlled variables, and a step-by-step methodology.

Examples of Good vs. Poor Quantitative Research

Aspect	Good Research Example	Poor Research Example
Sample Size	Surveying 1,000 users across different age groups and regions	Surveying only 20 users from the same college
Validity	Using a peer-reviewed scale to measure stress in IT workers	Creating your own unverified checklist to measure stress
Reliability	Running multiple trials of a sorting algorithm and averaging results	Running the algorithm once and reporting the result
Objectivity	Using automated tools for data collection and analysis	Manually selecting data that supports a hypothesis
Statistical Significance	Reporting p-values, confidence intervals, and effect sizes	Just stating “results showed improvement” without data or calculations

Conclusion

While qualitative research often involves fewer participants and more open-ended data, quantitative research requires rigorous design, analysis, and interpretation to ensure quality. A “good” quantitative study is not about how much data you have, but about how well you handle it.

Key Points:

Data needs to be in a numerical form to see the results.
In some cases, data can not be numerical, so you need to convert the data into numerical data.
There are some ML that can work without numerical data.

How Much Data is Enough for Valid Research Findings?

Great question! Knowing how much data is “good enough” in research—especially in quantitative research—depends on the concept of statistical power and representativeness of your sample. In simple terms, the amount of data should be large enough to confidently detect patterns or differences in your study and ensure that the results are not due to chance.

How to Know If You Have Enough Data?

You can determine if you have enough data by considering:

Sample Size Calculations (based on population size, confidence level, and margin of error),
Effect Size (how big the expected difference is),
Consistency in results (adding more data doesn’t change your results significantly),
And whether patterns start to repeat without new insights.

The goal is to have a sample that is both large enough to make your findings statistically meaningful and diverse enough to represent the larger population.

✅ Example:

Imagine you’re conducting a survey to measure how satisfied university students are with online learning. If you only survey 20 students from one department, your data is too limited and cannot represent the whole university. However, if you gather responses from 300+ students across different departments, years, and learning environments, you’ll likely have enough data to identify general satisfaction trends and make meaningful conclusions.

So, while there’s no one-size-fits-all number, enough data means the point where your results become reliable, consistent, and representative of the larger group you’re studying.

Another Point (Professor Point):

One more thing to consider is that when you’re determining how much data is enough, it’s important to check other research papers. If your research is closely related or intertwined with another study, you should compare sample sizes. If the sample size in your research is somewhat smaller, it’s usually acceptable, but if the sample size is significantly smaller, it may need to be increased.

For example, if the related research has a sample size of 100 and you’re using only 30, that’s an issue. However, if you’re using a sample size of around 90, that’s generally acceptable.

Sampling Method

📌 What is Sampling?

Sampling is the process of selecting a subset of individuals, items, or data from a larger population to represent the whole. Since studying an entire population is often impractical or impossible, sampling allows researchers to draw conclusions, identify patterns, and test hypotheses efficiently.

🎯 Why is Sampling Important?

Cost-effective: Studying a sample is much cheaper than studying an entire population.
Time-saving: Data collection and analysis are faster.
Feasibility: Large populations may not be accessible or measurable.
Accuracy (when done right): A good sample can provide highly accurate insights about the population.

🧪 Types of Sampling Methods

Sampling methods are mainly divided into two categories:

1. Probability Sampling (Randomized)

Each member of the population has a known, non-zero chance of being selected.

a. Simple Random Sampling

Every individual has an equal chance of being chosen.
Selection is entirely by chance (e.g., using a random number generator).

✅ Example: Randomly selecting 50 students from a university database.

b. Systematic Sampling

Every k-th member is selected from a list after a random start.

✅ Example: Selecting every 10th visitor to a website.

c. Stratified Sampling

The population is divided into subgroups (strata) based on characteristics like gender, age, or region.
Samples are randomly taken from each stratum proportionally.

✅ Example: If 60% of your population are men and 40% women, you sample accordingly.

d. Cluster Sampling

The population is divided into clusters (usually geographically).
Entire clusters are randomly selected for study.

✅ Example: Randomly selecting 5 schools and surveying all students in them.

2. Non-Probability Sampling (Non-Random)

Not every individual has a known or equal chance of being selected. Often used when randomization is difficult.

a. Convenience Sampling

Samples are taken from a group that is easy to access.

✅ Example: Asking friends and classmates to fill out a survey.

b. Purposive (Judgmental) Sampling

The researcher selects individuals intentionally based on characteristics relevant to the study.

✅ Example: Choosing experienced developers to study debugging habits.

c. Snowball Sampling

Existing participants recruit future subjects among their acquaintances.

✅ Example: Finding niche community members by referral, like open-source maintainers.

d. Quota Sampling

The population is divided into groups, and the researcher selects a fixed number from each group non-randomly.

✅ Example: Interviewing 10 male and 10 female employees, selected by availability.

⚖️ Probability vs Non-Probability Sampling

Aspect	Probability Sampling	Non-Probability Sampling
Randomness	Random	Non-random
Bias Risk	Low	Higher
Generalizability	High	Limited
Time & Cost	Often more time-consuming	Faster and cheaper
Use Case	Large-scale surveys, experiments	Pilot studies, qualitative research

Systematic and Stratified Sampling Explained with Examples

Systematic Sampling involves selecting participants at regular intervals from a larger population. For example, if a university wants to survey every 10th student from a list of 1,000 students, it would start at a random number (say 5) and then select every 10th student (5, 15, 25, 35, and so on). This method is efficient and easy to implement, especially when dealing with a well-organized population list.

Stratified Sampling, on the other hand, divides the population into distinct subgroups or “strata” based on specific characteristics like age, gender, or academic program. Then, samples are randomly selected from each stratum to ensure proportional representation. For instance, in a study on student stress levels, if a university has 60% undergraduates and 40% postgraduates, researchers might randomly select 60 undergrads and 40 postgrads out of a sample of 100 to accurately reflect the population structure. This approach ensures that all key groups are fairly represented in the study.

📌 How to Choose a Sampling Method?

Ask yourself:

What is the goal of my research?
Is a representative sample important for generalization?
How much time and budget do I have?
Is my population accessible?

✅ Use Probability Sampling when:

You need to generalize findings to the entire population.
You want statistical rigor.
You have a clear, complete population list.

✅ Use Non-Probability Sampling when:

You’re doing exploratory or qualitative research.
The population is hard to access or undefined.
You’re working under time or resource constraints.

🎯 Key Differences in Sample Size Between Qualitative and Quantitative Research

Feature	Quantitative Research	Qualitative Research
Purpose	Measure and quantify relationships or variables	Explore experiences, perspectives, or meanings
Sample Size	Larger (often 100s to 1000s)	Smaller (often 5–30 participants)
Selection Criteria	Random or representative sampling	Purposive or theoretical sampling
Type of Data	Numerical (e.g., scores, percentages)	Textual (e.g., interview transcripts, observations)
Analysis Method	Statistical analysis (e.g., SPSS, R)	Thematic or content analysis
Generalizability	High (to larger population)	Limited (deep understanding of specific context)

📊 Quantitative Research: Sample Size Considerations

A large sample is required for statistical power and validity.
Often calculated using formulas or software based on:
- Population size
- Margin of error
- Confidence level (e.g., 95%)
- Expected response distribution

🔢 Example: A survey on student anxiety might require 300+ responses to make generalizations.

📚 Qualitative Research: Sample Size Considerations

The goal is depth, not breadth.
Sample size is often determined by:
- Saturation: When no new themes are emerging from the data.
- Research design (e.g., case study, ethnography, grounded theory).
- Richness of individual data (one detailed interview might yield a lot of insight).

🔍 Typical Range:

Interviews: 5–20 participants
Focus groups: 2–5 groups (6–8 people each)
Case studies: 1–10 cases

Sample Size Differences in Qualitative vs. Quantitative Research (with Examples)

In research, sample size differs greatly between qualitative and quantitative methods due to their distinct purposes. Quantitative research seeks to test hypotheses or measure variables statistically, requiring large, randomly selected samples—for example, a study analyzing exam stress levels in 500 university students to generalize findings across campuses. In contrast, qualitative research aims to explore in-depth experiences or perceptions, using smaller, purposefully chosen samples—like conducting detailed interviews with just 10 students to understand how they emotionally cope with exam stress. While the quantitative study provides broad trends and generalizations, the qualitative one offers rich, nuanced insights. The sample size in qualitative research continues until data saturation (no new themes emerge), while in quantitative research, it’s determined by statistical formulas to ensure accuracy and generalizability.