1. Data analysis: Data analysis involves examining and interpreting data to uncover patterns, trends, and insights. This can involve using statistical techniques, machine learning algorithms, or other methods to analyze large amounts of data and draw conclusions from it. 2. Literature review: A literature review involves searching, summarizing, and analyzing existing research studies and publications on a specific topic. This helps researchers understand the current state of knowledge in their field and identify gaps or areas for further research. 3. Experimentation: Experimentation involves designing and conducting controlled experiments to test hypotheses and gather data. This is often used in scientific research to establish causal relationships between variables and validate theoretical concepts. 4. Survey research: Survey research involves collecting data from a sample of individuals through structured questionnaires or interviews. This method is often used in social science research to gather information on people's attitudes, behaviors, and preferences. 5. Modeling and simulation: Modeling and simulation involve creating mathematical or computational models to simulate real-world processes or systems. This can be used to predict outcomes, test hypotheses, and explore different scenarios in a virtual environment. 6. Fieldwork: Fieldwork involves conducting research directly in the natural environment or setting where the phenomenon of interest occurs. This can involve observations, interviews, data collection, and other methods to gather firsthand information and insights. 7. Data visualization: Data visualization involves presenting data in graphical or visual formats to communicate key findings and insights effectively. This can help researchers and stakeholders understand complex data and trends more easily and make informed decisions based on the information.