Data analysis is a practice that aims to make effective decisions after getting in-depth information related to any problem. The process of data analysis includes data collection, modelling and analysis. There are different methods of data analysis based on the type of research. The selection of a particular type of data analysis method is made based on the purpose of investigation. For example, the method of data analysis used for qualitative research is different from quantitative research. The methods used for quantitative research do not work well in qualitative research. Furthermore, the procedure of data analysis also has different steps that assist in finding the best results. As per its importance, this article aims to discuss different methods and procedure of data analysis in detail.
What are Untold Methods of Data Analysis?
Having the right information about data analysis methods makes it easy to get the best direction to find a solution of a particular problem. One of the best things about data analysis methods is report generation. It helps in creating an analytical report for better understanding. Following are some methods of data analysis:
- Cluster Analysis: this method of data analysis works for grouping based on similar data.
- Cohort Analysis: this method works with the record of data and its comparison with information collected in different phases.
- Regression Analysis: the change in one variable is observed based on the fluctuation in the second variable. In simple words, the identification of the relationship between different variables is made with the help of regression analysis.
- Conjoint Analysis: When the study aims to get insights into different facets, the conjoint analysis method is the best choice.
What are the steps of the Procedure of Data Analysis?
The procedure of data analysis consists of 5 different steps. Each step aims to perform a significant task that leads to a defined conclusion. The proper understanding of each step is necessary because skipping any stage and moving to the next one is impossible. The following are 5 steps for the procedure of data analysis:
Let’s discuss each step in detail. Moreover, there are different samples available on the websites offering graduate dissertation writing service, which can help in the proper use of methods and procedure of data analysis.
Many fresh researchers start the procedure of data analysis with a collection of data. This is not the right approach, but it increases the risk factor related to several aspects of data analysis. That is why the data collection should not be very direct. First of all, it is necessary to identify the main problem. In this step of identification, the best approach is to design a question that can provide a single direction to the procedure of data analysis. It is the question which is supposed to be answered at the time of data collection. Without working on this step, it is not good to jump on data collection. With the help of an example, the step of identification can be understood in a better way. Suppose there is a need for data analysis for business related to phone technology. Here, the question can be in the following forms:
- What are the expectations of potential customers related to business?
- Are customers satisfied with the delivery procedure of the selected item?
The second step in the procedure of data analysis is related to data collection. The major concern in data collection is the source. The source of data collection can be in more than one forms. It includes internal sources as well as external.
First of all, the selection of research type is important. Specify if the research is qualitative or quantitative. Both information sources (internal and external) can be used in qualitative and quantitative research. The difference is in the way of information collection. In a qualitative case, the collection is in the form of observation and interview. On the other hand, the quantitative case supports questionnaires, surveys and polls.
In the procedure of data analysis, data cleaning is done to make it ready for analysis. At the time of data collection, it is normal to get bundles of raw data from which much data is not used. Also, there can be different formats of information collected from different sources. In order to sort out data and its formats, there must be a cleaning of data. With bundles of raw data, the analysis becomes very complicated. Also, it does not remain feasible to get the desired results. At the time of data cleaning, it should be clear that data do not contain any error. Also, there should not be duplicate data in a file. Lastly, remove the blank spaces to make data easy to understand.
This is the main step in the procedure of data analysis. In this step, the use of some techniques works well for the extraction of valuable information. By using the right technique of analysis, the identification of data trends becomes productive. Based on the aim of investigation, the analysis technique is selected. Identify the aim and finalise if statistical, regression or neural networks can work well for analysis. The use of software is also common for data analysis without human errors.
The last and significant step in the procedure of data analysis is interpretation. The analysis of data gives some results. Based on the obtained results, there should be a proper solution for a defined problem.
In this step, we need to take into account two aspects:
- First of all, we should consider the relevance of our findings and conclusions. If they are not relevant to the topic, then our work will be useless. To make them relevant, we must find out what our findings mean and how they may influence on the real life situation.
- Secondly, we must ensure that our results are significant enough to prove or disprove an existing hypothesis or theory. This means that if you have proved your hypothesis right then it could be considered as a general rule applicable in any other case or situation.
Being a student of a graduate school, you have the responsibility to map out all the coursework for the completion of your graduate studies. The other important part of a graduate coursework that might appear overwhelming is the data analysis and field work. Undergoing and completing this process successfully could assuage your school to award you a degree; on the contrary, not doing this part might lead to failing in that series of tests and also losing academic opportunities.