Here's how you can navigate questions about economic research methodologies and data collection techniques.

If you want to get general results regarding a significant issue on a regional scale or about a large group of individuals, use quantitative methods. They will assist you with conducting rigorous statistical and economic analysis, as well as determining significant and clear relationships between variables of interest. If you just want to describe a small population or gain a better understanding of a particular topic, qualitative methods can help. They help to break the back of a new issue by asking experts or local communities what they think about it without having any preconceived ideas. Combining both of these strategies (a mixed method approach) can produce powerful and robust results by exploiting each method's primary advantages.

plaining your approach. Here's how you can handle such questions effectively: Be Prepared: Before discussing your research, ensure that you thoroughly understand the methodologies and data collection techniques you employed. Familiarize yourself with the strengths, limitations, and implications of each method used in your study. Start with Basics: Begin your explanation by providing a brief overview of the research methodologies and data collection techniques used in your study. Define any technical terms or concepts to ensure clarity for your audience. Highlight Rationale: Explain the rationale behind your choice of research methodologies and data collection techniques.

Defining terms and relationships between terms is essential to the presentation of economic research. This is because it sets a standard definition for the ideas being explored, and ensures that the audience fully understands what to expect.

There are multiple forms of economic study, but they are generally interconnected. For example, before conducting a survey, or any empirical study or test, you should be clear about your understanding of economic theory and your main hypotheses. You should also be familiar with the economic literature so that you do not forget anything. At the survey design stage, you must consider all of the issues that may build while analyzing the data (biases, odd results, and others), as well as how to react appropriately.

The best way to go about research is to try and find a blend of data that can be gathered. The more costly, time consuming aspects of the data can be gleaned from a combination of secondary source sand existing data, whereas some quantitative understanding can be easily gathered from primary research. Of course, the feasibility of primary qualitative research would depend upon the research scope selected, and could need replacement with smaller / easier to gather primary datasets

The issue is that primary data can be quite costly to gather, yet they are critical to comprehend local issues. In instances with few formal databases, there is no choice but to collect your own original data. One alternative is to target data from similar contexts, although this may produce incorrect results if key local factors are ignored. Initiatives such as open data are gaining popularity and should be encouraged to tackle these issues, but only provided data protection rules are observed, especially when dealing with sensitive information.

You can use mixed-methods in research, combining quantitative and qualitative data collection and analysis techniques to provide a comprehensive understanding of economic issues. But you can also use mixed-methods in evaluation, combining quantitative criteria like cost-benefit analysis and statistical modeling, with qualitative approaches, such as interviews or focus groups. By integrating multiple perspectives you will have a more holistic assessment of reality and the effectiveness of interventions.

Start simple. If you cannot code, try NoCode platforms. There are many statistics software programs that now offer simple data analytics with little or no lines of code. If you know a little coding, you can use generative AI to write your code. Simply ask what you want to do and tailor the response to your data. Think about this: even coders utilize search engines when they are stuck. Generative AI simply made everything easier. However, they do not prevent you from learning the fundamental statistics and econometric skills required for correctly interpreting your results!