Data collection for your thesis

You need the data to answer your research question. You collect it through the research design you have developed. 

This setup describes, among other things, whether you are conducting qualitative or quantitative research, what your sample will look like, and how you will carry out the research step by step.

Once that is clear, you will conduct research in order to collect the data needed to answer your research question. Two important factors here are reliability and validity. It is crucial that you reliably collect your data (so that you would get the same results in a subsequent study) and that you measure what you wanted to measure (validity). 

The method of data collection is often laid down in advance in your research plan. This way, your thesis supervisor can take a look at it before you start your research. 

You tailor the method you use for data collection to your research question. This collection method must fit with: 

● the aim of your research; 

● the type of data (qualitative or quantitative); 

● the research method you use; 

● the procedure you want to use to analyze the data. 

Qualitative vs. quantitative data 

The choice between qualitative or quantitative data is especially important. With quantitative data, you can express the results in numbers, such as percentages or graphs. You then analyze the data with statistical analysis. 

Do you use qualitative data? Then, your information can be expressed using non-numerical information, such as words, images, gestures, etc. To analyze this type of data, you often categorize it or interpret.

Usually, quantitative data is used when you want to test a hypothesis or to collect a lot of data. Qualitative data is more suitable to collect if you want to do an in-depth analysis of one specific situation or understand opinions or experiences in more detail. Sometimes you also see a combination of quantitative and qualitative research. This is called a mixed methods design. 

Data collection methods for quantitative research include: 

● Experiment: you manipulate a variable to investigate the effect of that manipulation on  a specific sample. 

● Closed question survey: you explore the opinions or experiences of a group of people by asking them questions through a questionnaire. You limit your test subjects to several answer options. 

● Literature search: this is only quantitative if you are going to chart the findings from the research numerically (for example, the number of times a certain word occurs in a text). 

Are you collecting qualitative data for your research? Then the following methods are suitable: 

● Interviews or focus groups: you question one or more people about a certain subject to gain more insight into their opinions or experiences. 

● Surveys with open questions: you ask the target group questions about their opinion, experiences, etc. and leave them free space to answer how they see fit.

● Observations: you observe your target group in a natural context without influencing it yourself. This is also known as 'passive observation'. 

● Ethnography: when you observe your target group, you become part of this group and you write down observations you make when you immerse yourself in the group. This is also known as 'active observation'. 

● Archive research: you retrieve information from old documents to map events, patterns or other information. 

Variables that are not directly measurable must first be operationalized. This is not always necessary: ​​some variables are directly measurable. For example, you can measure age by asking the people in your sample how old they are. But for many variables, such a direct measurement is not possible. You must first make the variables concrete, and therefore measurable. 

In surveys, for example, operationalization means that you determine questions that measure the extent to which a participant - for example - is self-confident. 

Another example of operationalization could look very different. Perhaps you let a rater determine the extent to which the behaviour of observed subjects shows self-confidence. You then make the variable measurable by assigning the assessors a number from 1 to 10. Each assessor uses a different assessment method.

Even after you have collected your data, you are not quite finished with it.. The next step is data analysis. This means that you will translate the data you collected into concrete results. You bring all data together to discover patterns. With quantitative data, you often do this based on a statistical test. For example, you look at the extent to which the differences found between the two groups are significant. Or, you count the frequencies to see how many people are for or against a certain statement. 

With a qualitative data set, you often start with interpreting and categorizing the data. Your data analysis will then be less straightforward. It is also possible that you decide to change the precise form of analysis after you have collected all the data. Qualitative research methods are more flexible than quantitative methods in this respect.

Writing your thesis is an intensive project. There is a lot involved: making a plan, reading literature, collecting data, data analysis, writing a conclusion… Would you like to have your thesis reviewed by a professional after all that hard work? The AthenaCheck editors are happy to help you. Let us check your thesis for language errors, structure and/or common thread.