How to Build Research Methods for Your Dissertation

Research methods are specific procedures for collecting and analyzing data. Therefore, your research methods form the most critical part of your research design. You must make two crucial decisions during your method planning.


How to Build Research Methods for Your Dissertation

Research methods

Research methods are specific procedures for collecting and analyzing data. Your research methods form the most critical part of your research design. You must make two crucial decisions during your method planning. The first one involves how you gather data. The data may be qualitative or quantitative, primary or secondary, and descriptive or experimental. Qualitative or quantitative data imply whether your data involve numbers or words. Primary versus secondary data suggest if you will gather the data or use the data already collected by someone else. Descriptive data involve taking measurements as it is, while experimental data include experimenting. The second one consists in deciding how you will analyze the data. You may resort to statistical methods to explore the relationships between the variables for quantitative data. In contrast, you can use methods, including thematic analysis, to infer models and implications in the data for qualitative ones.

Data collection methods

Quantitative data or qualitative data

You collect data to address your research question. The aims of your research determine what type of data you will gather. Assume you wish to answer questions about ideas, experiences, or meanings. In that case, you must collect qualitative data. However, you need to gather quantitative data to build a systematic understanding of a topic or if your study is about hypothesis testing. Qualitative data are flexible and can involve small samples.

Nonetheless, statistical analysis is improbable, and research standardization is challenging. Quite to the contrary, quantitative data have a broader scope and can generate reproducible knowledge. On the negative side, they demand statistical training for data analysis and require much larger samples.

You may also choose a mixed method. In that case, you employ both qualitative and quantitative research methods.

Primary data or secondary data

Primary data include original information you gather to address your research question using surveys, observations, or experiments. Conversely, secondary data are information already collected by other researchers, such as previous scientific studies and governmental statistics.

Suppose you wish to explore a novel research question. In that case, what you need is to amass primary data. However, secondary data may require synthesizing extant knowledge, analyzing historical trends, or specifying models on a large scale.

Primary data can help you answer specific research questions, and you can employ sampling and measurement methods. However, data collection may be time-consuming and costly. Moreover, you need training before data collection.

You can readily access secondary data. They can include information from broader areas and timescales. Nonetheless, you almost have no idea how data have been gathered, and you may need to modify them to fit your research purposes.

Descriptive or experimental data

Suppose you gather data about your research without intervening. In that case, you will have descriptive data. Remember that your sampling method will determine the validity of your study. Conversely, in experimental research, you are more active and take the initiative to intervene systematically in a process and meter its outcome. Your experimental design will be the determiner of your research validity.

When experimenting, you control the levels of your independent variable (x). This control will provide you with the precise measurement of your independent variable (y). Please notice that you should be able to control any confounding variable. If you can ensure them all, you can address the questions about cause and effect.

With descriptive data, you can describe your research subject without affecting it and collect big data. However, no control exists for confounding variables, and you cannot study cause-and-effect relationships. On the contrary, experimental data allow you to have more control over confounding variables, and you can build a cause-and-effect relationship. On the negative side, you might unexpectedly affect your research subject and need more extraordinary expertise and resources to gather the data.

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Research methods for dissertations and theses

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Research methods for dissertations and theses

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