Qualitative and Quantitative Research Methods: A Comprehensive Overview
Quantitative vs. Qualitative Research
| Quantitative | Qualitative | |
|---|---|---|
| Purpose | Measure, test hypotheses, count, and analyze numerically | Understand experiences, meanings, and perceptions; non-numerical |
| Data | Numbers, statistics, surveys, experiments | Words, images, interviews, observations |
| External Validity | High | Low (context-specific) |
| Internal Validity | High (controlled studies) | Can be high (case studies, in-depth analysis) |
| Reliability | High (consistent results) | Lower (subjective interpretation) |
| Examples | Surveys, experiments, polls | Interviews, focus groups, case studies |
Inductive vs. Deductive Research
| Inductive | Deductive | |
|---|---|---|
| Approach | Starts with data, builds theory | Starts with theory, tests with data |
| Process | Open-ended, exploratory | Hypothesis-driven, structured |
| Example | Observing people, then forming a theory about behavior | Testing a hypothesis on a specific behavior or phenomenon |
Sampling Methods in Qualitative Research
- Purposive Sampling: Select participants who have specific knowledge or experience.
- Snowball Sampling: Initial participant refers others.
- Convenience Sampling: Participants chosen for ease of access.
Pros: In-depth insights, relevant data.
Cons: Non-generalizable, biased samples.
Data Collection Methods
Individual Interviews: One-on-one, in-depth, personal.
- Pros: Detailed, focused.
- Cons: Time-consuming, limited number of participants.
Focus Groups: Group discussion on a topic.
- Pros: Diverse viewpoints, interactive.
- Cons: Group dynamics can influence answers.
Observation/Fieldwork: Researcher observes participants in a natural setting.
- Pros: Real-world data, natural behavior.
- Cons: Observer bias, ethical concerns.
Rapport Building
- Purpose: Build trust and comfort with participants.
- When Important: Early in data collection to ensure openness.
- How: Be friendly, non-judgmental, empathetic.
Role Concept in Observation/Fieldwork
Complete Observer: Passive observer, no interaction.
- Challenge: Limited understanding.
Participant Observer: Engaged but still observes.
- Challenge: Maintaining objectivity.
Complete Participant: Fully immersed, researcher takes part.
- Challenge: Ethical issues, bias.
Going Native
- Definition: Researcher becomes too involved in the group, losing objectivity.
- Occurs in: Fieldwork, participant observation.
Qualitative vs. Quantitative Data
| Type of Data | Qualitative | Quantitative |
|---|---|---|
| Nature | Descriptive, thematic, non-numeric | Numerical, statistical |
| Examples | Interview transcripts, field notes, images | Survey data, test scores, counts |
| Analysis | Thematic analysis, coding | Statistical analysis (mean, median, regression) |
Unobtrusive Research
- Content Analysis: Analyzing texts or media for patterns (e.g., counting mentions of a word in articles).
- Latent Coding: Interpreting underlying meanings (e.g., hidden biases).
- Manifest Coding: Counting explicit content (e.g., number of mentions of a brand).
Quantitative Data Analysis
- Univariate Analysis: Analyzing one variable (e.g., average age).
- Bivariate Analysis: Analyzing the relationship between two variables (e.g., income and education).
- Multivariate Analysis: Examining multiple variables at once (e.g., income, education, and health outcomes).
- Central Tendency: Measures the center of data (mean, median, mode).
- Dispersion: Measures spread of data (range, variance, standard deviation).
- Outliers: Data points significantly different from others. They can skew results.
Data Visualization
- Pie Chart: Shows parts of a whole (categorical data).
- Bar Graph: Compares quantities across categories.
- Histogram: Shows frequency distribution for continuous data.
- Line Chart: Tracks data over time.
Additional Key Concepts
- Reliability: Consistency of measurements (same results if repeated).
- Validity: Accuracy of measurement (does it measure what it’s supposed to?).
- Sampling: The process of selecting participants. In qualitative research, it is often purposive or snowball sampling; in quantitative research, random sampling is preferred for generalizability.
Key Statistics
- Mean: Average of the data.
- Median: Middle value.
- Mode: Most frequent value.
- Range: Difference between the highest and lowest values.
- Variance: Measures how much values differ from the mean.
- Standard Deviation: Square root of the variance; indicates how spread out the data is.
Graphs and Charts
- Bar Graph: Compares quantities across categories.
- Pie Chart: Shows parts of a whole (useful for showing percentages).
- Histogram: Shows the frequency of continuous data in intervals.
- Line Graph: Shows trends over time.