Research method refers to a systematic and organized procedure in which a researcher explores a specific research topic or problem, collects and analyzes data, and draws conclusions. Research methods can be divided into quantitative research and qualitative research. Research methods vary by academic field, and in each field, various methodologies and tools tailored to its characteristics are developed and used.

Original Korean article: Research Method Introduction to R Statistics: Understanding research design and analysis methods at a glance

Research Method R Statistics learning begins with understanding the research design before the analysis technique. The statistical method you use will depend on what questions you ask, what data you collect, and how you interpret the results. This article explains the differences between quantitative and qualitative research, cross-sectional and longitudinal research, and correlational and experimental research by linking them to the R statistical learning flow.

Ⅰ. Quantitative Research

Ⅰ – 1. Types and Features:

• Quantitative research is a research method that analyzes and interprets phenomena through numerical data.
• Purpose: The purpose is to verify hypotheses, clearly identify relationships between variables, and build a prediction model through generalization.
• Data collection method: Data is collected from a large sample through methods such as surveys, experiments, and observations.
• Analysis method: Data analysis is performed using statistical techniques and mathematical models.

Ⅰ – 2. Example of use:

• A study that analyzes the results of standardized tests administered nationally to assess the academic performance of students.
• Marketing research that examines the relationship between changes in market share of a specific product and consumer satisfaction.
• In the medical field, research that analyzes clinical trial data to verify the effectiveness of a specific drug.

Ⅱ. Qualitative Research

Ⅱ – 1. Types and characteristics:

• Qualitative research is a research method that seeks to deeply understand human behavior, experience, and social phenomena through non-numerical data.
• Purpose: Focus on deep understanding of complex phenomena or contexts and creation of new theories.
• Data collection method: Data are collected from a small sample through interviews, participant observation, and document analysis.
• Analysis method: Classify and interpret by topic, and derive results through narrative or case study methods.

Ⅱ – 2. Example of use:

• Medical sociology research that explores patients’ treatment experiences and emotions through in-depth interviews.
• Anthropological research that investigates through participant observation how culture and traditions are maintained and changed within a specific community.
• Focus group interview study to explore organizational culture and job satisfaction of employees within a company.

Ⅲ. Longitudinal Study

Ⅲ – 1. Types and Features:

• Longitudinal research is a research method that tracks changes over time by repeatedly examining the same group over a long period of time.
• Purpose: To identify patterns of change or development over time and to clearly identify the relationship between cause and effect.
• Data collection point: Collect data repeatedly at multiple points in time to track trends and changes.
• Advantages and limitations: It is possible to understand the individual change process in detail, but it is time consuming and expensive.

Ⅲ – 2. Example of use:

• Growth and development research that periodically examines the growth and development process of children from infancy to adolescence.
• Human resource management research that tracks and analyzes career development and job satisfaction changes in specific occupational groups over a long period of time.
• Medical research that evaluates long-term health outcomes in patients with chronic diseases by tracking treatment effectiveness and lifestyle changes.

Ⅳ. Correlation Study

Ⅳ – 1. Types and characteristics:

• Correlational research is a research method that determines the relationship between two variables.
• Purpose: To determine the relationship between variables and how changes in one variable affect other variables.
• Interpretation of results: Measure the strength and direction of the relationship between two variables through the correlation coefficient. The correlation coefficient has values ​​from -1 to +1, with +1 meaning a completely positive correlation and -1 meaning a completely negative correlation.
• Causality: Correlational studies do not prove causality; they simply show whether variables change together.

Ⅳ – 2. Example of use:

• A study examining the relationship between students' study time and grades.
• A study analyzing the relationship between smoking amount and lung cancer incidence.
• A study exploring the relationship between income level and happiness index.

Ⅴ. Cross-sectional Study

Ⅴ – 1. Types and Features:

• Cross-sectional research is a research method that collects data by investigating a group with one or more characteristics at a specific point in time.
• Purpose: To determine the status or distribution among various variables at a specific point in time.
• Data collection point: Since data is collected at a single point in time, temporal changes or trends are not reflected.
• Ease of comparison: Easy to compare various groups (e.g. age group, gender, etc.).

V – 2. Example of use:

• A study that examines the health status and lifestyle habits of a population of a specific age.
• A study that analyzes the differences between the education and income levels of residents of various regions within a country.
• A study that simultaneously surveys multiple populations to determine the prevalence of a specific disease.

Ⅵ. Behavioral Experimentation

Ⅵ – 1. Types and Features:

• Behavioral experimentation is a research method that attempts to understand psychology or human behavior patterns by inducing and observing the behavioral responses of subjects in an experimental environment.
• Purpose: To measure the behavioral responses of humans or animals under specific stimuli or conditions and to verify theories or make new discoveries based on this.
• Data collection method: Depending on the experimental design, various stimuli or tasks are provided to experimental participants in a controlled environment and their responses are recorded.
• Analysis method: Experiment results are statistically analyzed and used to verify hypotheses or derive theories.

Ⅵ – 2. Example of use:

• A marketing experiment to determine the impact of a specific advertising message on consumers' purchase intentions.
• A psychological experiment to assess the effects of stress on work performance.
• In the field of neuroscience, experiments are conducted to measure brain activity and record behavioral responses using various technologies such as electromagnetic waves to understand the relationship between brain activity and behavior.

Good article to read together

• 1. What is research? [R Statistics]
• 2. Variables and Measurements [R Statistics]
• 3. Measurement error [R statistics]
• 4. Validity, reliability [R statistics]
• Importance and usage of pipe operator %>%

Key Checklist

• Is the research purpose closer to exploration, explanation, or verification?
• Do you need quantitative or qualitative data?
• Which design is better: cross-sectional data or longitudinal data?
• Are you distinguishing between correlation and causation?

Good R statistics articles to read together

• What is research: Summary of research concepts for introduction to R statistics
• Variables and Measurement R Statistics: Understanding independent variables, dependent variables and measurement levels
• Measurement Error R Statistics: Easily Understand Random Error and Systematic Error
• Validity/Reliability R Statistics: Criteria for judging a good measurement tool

FAQ

Why do I need to learn research methods before R statistics?

R is a tool for performing analysis, and research methods are the framework for deciding what to analyze and why. A clear study design can help you choose appropriate statistical techniques and R functions.

What is the difference between quantitative and qualitative research?

Quantitative research analyzes numerically measurable data to identify patterns or relationships. Qualitative research, such as interviews, observations, and documents, focuses on deeply interpreting meaning and context.

When do you distinguish between correlational research and experimental research?

A correlational study is appropriate to determine the relationship between two variables, and an experimental study is needed to see whether a specific treatment affects the results. To make causal claims, study designs must be more rigorous.

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FAQ

What is this article about?

This article is an English translation and global-reader adaptation of the Korean post “Research Method Introduction to R Statistics: Understanding research design and analysis methods at a glance.” It preserves the original article’s main explanation, examples, and practical context.

Why is it translated into English?

The English version helps global readers access Thinknote articles through English search keywords while keeping the Korean source available as the original reference.

Where can I read the original Korean version?

You can read the original Korean article here: https://www.thinknote.co.kr/research-methods-r-statistics/