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Examples of Data Analysis Methods – An In-depth Overview for Freshers

Data Analysis Methods

Students mostly conduct data analysis while drafting undergraduate or graduate thesis for the first time, and writing short assignments is a quite familiar process for all. The skill set required for writing the data analysis chapter for a dissertation is entirely different from the skills required to write an introduction and review of the literature chapter. For writing an introduction and review of literature, a thorough reading to get the concept clarity for writing a point in your own words is more than enough.

However, the same cannot be applied to the data analysis chapter. Deciding on the right type of data, and selecting the most appropriate data analysis method. And also gathering information on tips or tricks to use the data analysis tools. Also, the software is an all-important point necessary to extract useful information from the collected data. This article will discuss different data analysis methods with examples. So, even fresher can easily use the most appropriate method of analysis for research.  

Data Analysis Methods

Data analysis is the process of analysing, cleaning, and organising data for the purpose of discovering insights for making informed decisions. The data analysis can be done in a variety of ways depending on which type of data a researcher collected in order to solve a problem under consideration. It is the goal of the research that decides whether qualitative or quantitative data will be more helpful for analysis in scientific research. In both cases, suitable data analysis tools are required to extract useful information out of the collected data. 

Before discussing different data analysis methods, it will be more beneficial to briefly describe why it is very important to analyse data. The reason for the importance of data analysis lies behind the fact that without analysis, extracting results, to sum up, research is near impossible. Actually, dissertation writing starts by raising logical questions. Also presenting a research problem that can highlight a serious condition in a social group or community. To find answers to research questions and explore the best-ever solution to a research problem. The collection of data is necessary.

Data may be collected in the form of statements, responses, experiences, and reading that can never suggest a final solution directly. To arrange all the scattered data or to observe a pattern in the collected data. The data can be processed by using a particular method named data analysis. After this analysis, the researcher can finish the dissertation by suggesting a logical solution. Thus, data analysis is not only important but an integral part of the dissertation writing process. There are some firms that provide the best dissertation help in this regard.

Different Data Analysis Methods With Examples:

Hopefully, now you know the purpose and usefulness of the data analysis process in the dissertation writing process. It is the right time to discuss different methods of data analysis.

Data Analysis Methods For Qualitative Data:

As the qualitative data includes experiences, statements, responses, and opinions. Thus, an entirely different set of analysis methods or tools can help researchers process the information. For example, qualitative content analysis, narrative analysis, grounded theory, and thematic analysis.

Qualitative Content Analysis:

Qualitative content analysis gives researchers an opportunity to deal with and interpret the subjectivity of the content before adding it to research to support or oppose a claim. It includes coding and theme construction to see the pattern in the collected data.

Narrative Analysis:

Narrative analysis helps researchers find how participants construct stories and share their personal experiences while giving responses.

Thematic Analysis:

It is the process of analysing the repeated patterns in a data set by constructing themes and selecting codes. 

Grounded Theory:

It is the qualitative data analysis method where the researcher develops new theories based on the collection and analysis of real-world data.  

For understanding these qualitative data analysis methods, the following example will be of great importance. Suppose a person collected data from sub-inspectors to find the number of incidences of street crimes in the UK via a questionnaire. Afterwards, what will be the most suitable qualitative method to analyse patterns in collected responses? Obviously, content analysis and thematic analysis can best serve the purpose as grounded theory will be useful. If the researcher aims to construct a new theory, and narrative is useful for research aims to extract information on the basis of narratives of sub-inspectors only. 

Data Analysis Methods For Quantitative Data:

Quantitative data includes facts, figures, reading, and digital data recorded using automatic tools, observations, and other scientific apparatus. Thus, to extract textual information out of the data collected in the form of digits. The statistical data analysis tools such as SPSS, Minitab, Matlab, or others are required. Furthermore, the statistical data analysis can be performed in two ways using descriptive statistics and inferential statistics. Usually, both of these statistical analysis methods are used one after the other.

Descriptive Statistical Analysis:

This is usually the first step in statistical data analysis. It is helpful in finding patterns in data by summarising it. For this summarisation, a few methods including mean, median, mode, standard deviation, frequency, and range are common. This type of analysis only gives absolute numbers and says nothing about the rationale of the number.  For example, if statistical data includes readings, such as 2, 4, 5, 6, 1, 0, 15, and 30, then calculating the mean will give you only a number of 36.75. The number does not say anything directly about the problem under study.

Inferential Statistical Analysis:

Even after performing descriptive analysis, the researcher must use an inferential statistical approach to extract the conclusion by testing a hypothesis or estimating parameters about a certain population. For example, if research aims to find the relationship between the average family income and annual scores of a child on the SAT, by using statistical tools, the researcher can test the hypothesis and in the light of the approval or rejection, present results. This data analysis method will be inferential statistical analysis.   

Bottom Lines:

After all, is said and done. We come to a point that data analysis is one of the most important components of the research or dissertation writing process. Weak or inaccurate data analysis methods produce invalid results that can hamper the authenticity of the whole research. Thus, a data analysis method must be chosen wisely. After reviewing all the pros and cons of a proposed method in a particular research context.

You might also like to read about how to write acknowledgement for dissertation.