In research, what is the difference between methods vs methodology?

Research methods are the steps you follow when conducting research. A methods section should describe the type of research you’re conducting, sampling techniques, data collection methods, and data analysis.

Research methodology instead focuses on the theory behind your research methods and why you chose them to address your research question.

Though people sometimes use the terms method and methodology interchangeably, they are not the same. Methods describe how you conduct your research, and methodology describes why you chose these methods.

Related article: Data Collection | Definition, Methods & Examples

Data collection is the organized process of gathering observations, measurements, or other information. Whether the research is being carried out for business, government, or academic purposes, collecting data helps researchers obtain first-hand information and develop meaningful insights related to a specific research question or problem. While the goals and techniques used in different fields may […]

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Research: Other interesting questions

What is proportionate stratified sampling?: Proportionate sampling in stratified sampling is a technique where the sample size from each stratum is proportional to the size of that stratum in the overall population.

This ensures that each stratum is represented in the sample in the same proportion as it is in the population, representing the population’s overall structure and diversity in the sample.

For example, the population you’re investigating consists of approximately 60% women, 30% men, and 10% people with a different gender identity. With proportionate sampling, your sample would have a similar distribution instead of equal parts.
What is the difference between construct validity and internal validity?: Construct validity refers to the extent to which a study measures the underlying concept or construct that it is supposed to measure.

Internal validity refers to the extent to which observed changes in the dependent variable are caused by the manipulation of the independent variable rather than other factors, such as extraneous variables or research biases.

Construct validity vs. internal validity example
You’re studying the effect of exercise on happiness levels.

Construct validity would ask whether your measures of exercise and happiness levels accurately reflect the underlying concepts of physical activity and emotional state.

Internal validity would ask whether your study’s results are due to the exercise itself, or if some other factor (e.g., changes in diet or stress levels) might be causing changes in happiness levels.
What is a research design?: The research design is the backbone of your research project. It includes research objectives, the types of sources you will consult (i.e., primary vs secondary), data collection methods, and data analysis techniques.

A thorough and well-executed research design can facilitate your research and act as a guide throughout both the research process and the thesis or dissertation writing process.
How do I decide which level of measurement to use?: Some variables have fixed levels. For example, gender and ethnicity are always nominal level data because they cannot be ranked.

However, for other variables, you can choose the level of measurement. For example, income is a variable that can be recorded on an ordinal or a ratio scale:

At an ordinal level, you could create 5 income groupings and code the incomes that fall within them from 1–5.

At a ratio level, you would record exact numbers for income.

If you have a choice, the ratio level is always preferable because you can analyze data in more ways. The higher the level of measurement, the more precise your data is.
Is ordinal data qualitative or quantitative?: Ordinal data is usually considered qualitative in nature. The data can be numerical, but the differences between categories are not equal or meaningful. This means you can’t use them to calculate measures of central tendency (e.g., mean) or variability (e.g., standard deviation).

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