In any research work, you have to collect and analyse data. This procedure comes under the category of research method. You have to select a research method as per the requirements. The research method is an important section of the research design. For research, two methods are available. These two methods are referred to as qualitative, and quantitative research methods. Now it is up to the researcher to identify which type is most suitable for the research.
To make it easy, you have to go through two key decisions. In the first one, you have to identify how to collect data for the research. And the second decision is about the data type. You have to understand which type of data is best to answer the research’s problem statement. These decisions are based on the form of words or numbers. You also have to see if you want to collect original data or work on the existing one.
You have to identify if your research is related to the experimental, or theoretical study as well. This is all about the aspect of data collection. Here the second step is related to the analysis of data. Now the researcher has to decide on an analysis type. If you have selected quantitative data, then the analysis type would be statistical. In this type of analysis, you have to derive a relationship and interlink the variables. But if you select qualitative data for the research, the analysis would be thematic. Here you have to design the data and signify different meanings from varying aspects (Mohajan, 2020).
In every research, the researcher has to design a research question. This research question is also referred to as a problem statement. The research aims to address that problem statement and answer it through the results and findings. This problem statement signifies how to collect data for the research.
Qualitative Vs. Quantitative
There can be a case where your research question asks about the schemes or plans, and is not related to numeric things. In this aspect, qualitative data is suitable for the research. In qualitative data, the advantage is that you can develop new ideas. Also, a qualitative data method is used when you have a small sample size for your research. But you also have to understand that this type of data cannot be utilised for statistical analysis. (Allan, 2020).
On the other hand, you have to use quantitative data when the sample size is large. You can also use it for developing a mechanical approach in the research. You can also generate unique ideas from it. But you have to make sure that you are well trained for handling large data and its statistical analysis. You can go for qualitative, or quantitative data. You can use a mix of both data types as well. If you are confused, getting dissertation proposal help can also be beneficial.
Primary Vs. Secondary Data
When your research is based on answering a particular question, you have to collect related data yourself. Here you have to conduct different surveys and experiments. All this comes under the category of primary data. If you go for primary data, you have command of your sample and measurements. But it is not economical at the student level, and also requires plenty of time. You have to train yourself for primary data collection as it is not simple to observe and manage either.
On the other hand, secondary data is based on literature. You take the existing data and work on it. When you have to explore things, the secondary data type is the best. Here you do not have to spend a long time on data collection. It is cost-effective as well. But you have to deal with the extra processing of data, and some researchers find this difficult.
Descriptive Vs. Experimental Data
In descriptive data, you have to achieve objectives by following the validity of research as a sampling method. You do not have to highlight the relationships between different variables. Here you have to describe the sets of data and their specifications. You may go for the centre of the data set, or take a broad perspective of the data. In descriptive data, you can explain the objective of the research without putting any influence. Also, it is easy to gather such data on a large scale. But you cannot go for the development of relationships between variables.
Whereas for experimental data, you must highlight the different relationships. Having a large sample for your experimental research data will also help you. This is related to the highlighting of different variables. It also helps in establishing relationships between them. But if you are not well trained, it may affect the subject. You also have to deal with the cost extension because you have spent more on collecting data. This is because such data types are collected on a large scale. You have to be an expert here because you need to have independent variables. These need to be measured against the dependent variables as well. Experimental data is best when you have to deal with something related to causes and effects.
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After data collection, the second step in managing research is to analyse the data. Now the analysis of the data is based on the information gathered. It means that analysis is different for qualitative, and quantitative data. You can also analyse your data for both qualitative and quantitative as well. This situation is applicable when you have a mix of data.
Now let’s discuss both of the types one by one. In the case of qualitative data, you go for surveys and interview you. Then you have to analyse it by creating meanings out of it. Here you have to develop an idea out of the responses. In this aspect, the researcher has command of the results. They are based on the observation and judgement of the researcher. It is also related to how he interprets things. Whereas quantitative data is based on the interrelation of things and the development of results.
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Allan, G., 2020. Qualitative research. In Handbook for research students in the social sciences (pp. 177-189). Routledge.
Mohajan, H.K., 2020. Quantitative Research: A Successful Investigation in Natural and Social Sciences. Journal of Economic Development, Environment and People, 9(4), pp.50-79.