Research Methodology Essentials: Design, Quantitative & Qualitative Research, Sampling Methods

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Which research design focuses on understanding human behavior, experiences, and emotions in detail?

Qualitative research

What is the main goal of correlational studies?

Exploring the relationship between variables

Which sampling method divides the population into homogeneous groups before selecting participants?

Stratified sampling

What is the primary data collection technique used in ethnography?


Which method involves developing explanatory theories that emerge from the collected data itself?

Grounded theory

In survey research, what type of data is typically gathered?

Numerical data

Which aspect of research methodology involves numerical data and statistical analysis?

Quantitative research

What is the primary focus of research design?

Defining research goals

Which method involves manipulating variables to observe their effects on another variable?


What is the main goal of selecting appropriate sampling methods in research?

Minimizing bias

Which type of research relies on structured measurements or observations using instruments like scales and questionnaires?

Survey research

What aspect of research methodology involves identifying the target population for a study?

Research design

Study Notes

Research Methodology: Understanding Design, Quantitative Research, Qualitative Research, Sampling Methods, and Data Analysis

Research methodology refers to the techniques used by researchers to collect, analyze, and interpret data to answer questions related to their studies' objectives. It encompasses several aspects of research, including designing a study, conducting quantitative and qualitative research, selecting appropriate sampling methods, and analyzing collected data. This article will explore these concepts further.

Research Design

Research design is the plan that outlines how a study will be conducted from start to finish. It includes defining the research goals, selecting appropriate methods, identifying the target population, setting data collection procedures, and determining data analysis techniques. The design should be flexible enough to accommodate any unexpected issues that may arise during the research process.

Quantitative Research

Quantitative research involves numerical data and statistical analysis to test hypotheses or answer specific questions. It relies on structured measurements or observations, often utilizing instruments like scales, questionnaires, and surveys. Examples include experiments, correlational studies, and survey research.


Experimental research designs involve manipulating one or more variables and observing the effects on another variable. They may consist of randomised trials, pre-post tests, post-test-only controls, and panel-data regression models.

Correlational Studies

Correlational studies investigate the relationship between variables, such as correlation coefficients or regression analysis. They aim to determine whether two variables are related and, if so, what the strength of their relationship is.

Survey Research

Survey research involves gathering information via questionnaires or interviews. Surveys can be administered face-to-face, online, by phone, or in writing. The data gathered is typically numerical and can be analyzed using statistical methods.

Qualitative Research

Qualitative research focuses on understanding human behavior, experiences, and emotions in detail. It often involves non-numerical, subjective data collected through observation, interviews, focus groups, and content analysis. Examples of qualitative research designs include case studies, ethnography, and grounded theory.

Case Studies

Case studies delve deeply into a particular instance or situation, usually involving detailed observations, interviews, and document analysis. They can provide rich descriptions of a phenomenon and offer insights into its causes and effects.


Ethnographic research involves immersing oneself in a cultural group to study their customs, beliefs, and practices. Data collection techniques include participant observation, interviews, and document analysis. Ethnographies can provide nuanced insights into complex phenomena.

Grounded Theory

Grounded theory is a method for developing explanatory theories that emerge from data itself. Researchers collect and analyze data iteratively, constantly refining theoretical concepts to fit the findings. This approach promotes flexibility in concept development and enables researchers to discover unexpected patterns in the data.

Sampling Methods

Sampling methods involve selecting a representative segment of a population to participate in a study. Common sampling strategies include probability sampling, stratified sampling, cluster sampling, snowball sampling, and self-selection.

Probability Sampling

Probability sampling assigns every member in the population a chance to be selected for the study. Techniques include simple random sampling, systematic sampling, and multistage sampling. This method ensures that the sample accurately reflects the characteristics of the population.

Stratified Sampling

Stratified sampling divides the population into homogeneous groups (strata) and selects a sample from each stratum. This method ensures that the sample accurately represents the population's different groups and their characteristics.

Cluster Sampling

Cluster sampling divides the population into clusters (groups) and selects a sample from these clusters. This method is useful when the population is too large to study directly, and when it is cost-effective to study larger clusters.

Snowball Sampling

Snowball sampling begins with a few initial participants who refer others to participate in the study. This method is useful when studying hard-to-reach populations, such as drug users or prisoners.


Self-selection occurs when individuals choose themselves to participate in a study. This method is useful when the population is geographically dispersed and self-selects itself through online platforms.

Data Analysis Methods

Data analysis methods aim to identify patterns, relationships, and trends in collected data. Common techniques include descriptive statistics, inferential statistics, qualitative data analysis, and mixed methods.

Descriptive Statistics

Descriptive statistics summarize and describe quantitative data using measures such as mean, median, mode, range, standard deviation, and percentiles. They provide basic information about the distribution of the data.

Inferential Statistics

Inferential statistics make inferences about a population's characteristics based on a sample. Techniques include hypothesis testing, confidence intervals, and regression analysis. These methods enable researchers to draw conclusions beyond their immediate dataset.

Qualitative Data Analysis

Qualitative data analysis involves interpreting non-numerical data to understand human experiences, behaviors, and contexts. Techniques include content analysis, coding categories, themes, codes, and triangulation.

Mixed Methods

Mixed methods combine quantitative and qualitative data collection, analysis, and interpretation techniques. They enable researchers to explore both numerical and subjective aspects of their research questions.

In conclusion, understanding research methodology involves familiarizing oneself with various designs, techniques, and strategies for conducting research, collecting data, analyzing findings, and drawing conclusions. By considering the subtopics of research design, quantitative and qualitative research, sampling methods, and data analysis, researchers can choose the tools appropriate for their specific study and achieve valid and reliable results.

Explore the fundamentals of research methodology including research design, quantitative and qualitative research methods, sampling techniques, and data analysis approaches. Learn about key concepts like experimental studies, case studies, probability sampling, and descriptive statistics.

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