The most technical phase of a research is to start working on the data analysis and interpretation part. This is because data interpretation format is an act of acquiring relevant information from a big pool of raw data. Hence, deciding which one is more relevant and how the information is supporting or opposing the research goals is the most critical part, especially for the newbies. To keep things simple and understandable for beginners, this article will discuss the data interpretation format in terms of the difference between data analysis and interpretation, its methods and its steps for developing a better understanding about it.
Data Analysis And Interpretation
A common misunderstanding about the data analysis and interpretation is that they both are same things. In fact, these two steps are necessary to convert the raw information into a meaningful story. They both have their own importance in the life cycle of data sciences. Data analysis is a step that aims to bring order, meaning and structure to the data collected beforehand. This process unveils hidden patterns or regularities by using several data analysis tools or techniques. The most common ways to analyse data include observation, transformation and rearrangement of raw data. In case of any issue, you can get dissertation literature review help to overcome all analysis issue.
Further, data analysis is a methodical approach to use statistical techniques in order to evaluate, analyse or organise data. Statistical as well as non-statistical analysis methods are really helpful in drawing conclusions and the decision-making process. The data analysis steps help researchers to get logical answers to the research questions or to test the hypotheses, if available. Likewise, exploratory data analysis is another approach to discover the possible relationships between the variables. On the whole, data analysis is the scientific process to rearrange, summarise and organise data to sum up the data interpretation format in a research.
Moving ahead, after data analysis, the next step in the research process is to interpret the data. This step aims to develop an understanding of the data in the processed or analysed form. It assists us in drawing a logical conclusion and knowing the significance between the relationships of variables. Hence, instead of considering data interpretation and analysis the same thing, we should arrange them in a step-wise manner to complete the research process. In the research process, the data analysis step is always followed by the data interpretation step.
What Are Data Interpretation Methods?
As explained above, data interpretation is a process by which we extract information from analysed or processed data. The data analysis usually presents data in a different form of bar graphs, pie charts, flow diagrams and tabular form. Hence, to deal with such a variety of information, the data interpretation aims to know what exactly they are showing. Data interpretation techniques are ways for analysts to understand the data collected, analysed and presented.
When data is collected in its raw form, it can be difficult for a layman to understand it, so experts have to describe it so that others can also appreciate it. For this, there are two primary methods of data interpretations, namely qualitative and quantitative data interpretations. The visualisation of data analysed by the qualitative data analysis methods is the subject of qualitative data interpretation. Likewise, the visualisation of data analysed by the quantitative data analysis methods is the subject of quantitative data interpretation format. As a big picture, in both type of interpretation, the aim is to get a meaningful story to make sense to others.
How Do You Analyse And Interpret Data In Research?
Whenever you want to interpret the data, you must have to analyse it first. Based on the nature of data, the data analysis is of two types: qualitative analysis, which organises and summarises the textual information, and quantitative analysis, which organises or summarise the statistical data by means of statistical methods.
The qualitative analysis and interpretation deals with two types of data namely nominal and ordinal data. Though the method of analysis and interpretation of both types is the same, still, the ordinal data is easy to interpret than the nominal data. For analysis and interpretation of ordinal data, coding is not necessary, but the nominal data interpretation process cannot be completed without proper coding.
The quantitative data is a bit easier to analyse or interpret because of the availability of dozen of statistical analysis tools. By using R, Statistical Package for Social Sciences (SPSS), N-Vivo, Excel, and others as analysis tools, you can get a graphical or tabular form of data that are easy to interpret. The standard deviation, mean, error bars, and statistical differences are a few important approaches that help a researcher to draw a conclusion out of the raw information.
Steps In The Interpretation Of Data
In all types of data interpretation format, the shortest and easiest way to get through the interpretation process is by following three steps:
The first step in the data interpretation format is to examine each component of raw data to form a final thought. In the analysis step, you can see errors, inconsistencies, possible relationships, or patterns among the data.
Step two is an act of converting the findings into a contextual form. The main things to hit at this point are:
- To tell the readers about your findings.
- To tell what story the data is telling to unveil the hidden facts.
The last but not the least step in the data interpretation format is to present it. For this, you need to select, arrange, categorise, and evidence the main ideas systematically. The most common way to present data is to explain whether there is any statistical difference between the variables or not. Plus, for statistical data, the data can also be presented in terms of standard deviation or error bars. No matter what type of interpretation technique you are using, at this step, you just need to focus on whether the graphics or text is easy to read for the readers or not.
As a final note, data analysis helps in organising, summarising and categorising raw information. At the same time, data interpretation transforms the data into a story so that the audience can make sense of it. Importantly, they both complete the goals of research in step-wise manners.