Data analysis in academic research is a systematic process of applying statistical techniques to describe, recap, illustrate, and evaluate data. In simple words, the data analysis resembles the process of considering past experiences and future outcomes before making any decision. In academic research, researchers collect data and examine it by using various methods and tools in order to observe patterns in it. Unveiling the hidden patterns in the raw data is the most sought-after method of extracting information that either supports or opposes your thesis claim. Knowing different methods of data analysis in academic research is mandatory for students who are planning to write a dissertation (a passcode to get a research degree). This article is all about discussing both qualitative and quantitative data analysis methods.
- Data analysis methods:
Data analysis in academic research is the process of transforming and modelling data to find useful information to make academic research-related decisions. It aims to explore the relationships between identified variables. There are several techniques and tools that help researchers to achieve this aim. The core principle of selecting the most suitable method of data analysis is to review the type of data available for academic research. The tools and techniques that you can use for analysing data in the contextual form cannot analyse the statistical data. Thus, based on the availability of data, the data analysis methods are broadly divided into qualitative and quantitative methods. Top writers from a UK essay writing service have shared below a brief description of both types of data analysis in academic research.
- Qualitative data analysis:
Qualitative data is usually gathered to study the nature of the phenomenon. This type of data explains why something happened. It aims to explain a process in the light of experiences and opinions of experts. There are a number of qualitative data analysis methods that help researchers to solve scientific queries. There are some of the most common types of data analysis methods (remember, you cannot use these analysis methods to analyse the digital and numerical data)
- Content Analysis: Content analysis is one of the most straightforward and common methods of data analysis. It aims to review the full text to evaluate trends and quality of information within the content.
- Narrative Analysis: It is a dual-layer interpretation. In its first part, researchers collect information from participants in the form of a story or narrative. In the second part, they conduct their own analysis to extract logical consequences out of a story described by participants.
- Discourse Analysis:
This type of qualitative data analysis in academic research is extremely important to extract useful information from the recorded as well as written responses. It is simply a process of analysing language in the social context.
- Thematic Analysis:
It is the process of observing patterns of meaning in the data set. It does not consider only the superficial meaning of words, themes and concepts. Rather than making any concrete assertions, this method of analysis simply suggests themes and identifies specific codes to aid in interpretation.
- Grounded Theory:
It is another powerful qualitative data analysis method in academics. It aims to create a theory using first-hand data. It includes a series of tests and revisions to propose and prove a theory.
- Quantitative data analysis:
Quantitative data analysis is the process of analysing the numerical, digital or statistical form of information. To analyse the statistical data, you must need a statistical technique or tool. Statistically, quantitative data analysis in academic research is divided into two types: descriptive statistics and inferential statistics.
- Descriptive statistics:
Descriptive statistics summarise a data set that is either representative of a specific population or the entire population. It is the process of giving meaning to nonsensical numbers. It allows researchers to more manageably handle the large data set. It can be measured in three main ways:
- Distribution: It is the summary of the frequency of different variables. Percentage and measure of frequency is the most prominent method of measuring distribution.
- Central tendency: It is the process of estimating the centre of given values. Mean, median and mode are statistical methods to find the central tendency in research.
- Dispersion: The spread of values around the central tendency is known as dispersion. Range and standard deviation are some methods that help researchers in measuring dispersion for a given data set.
- Inferential statistics:
Descriptive data analysis in academic research aims to see trends in data, while inferential statistics aims to extract logical conclusions. Inferential statistics can be performed in two ways, such as:
- Hypothesis testing:
It is the form of inferential statistics that collects data from a sample in order to answer the research problem under study. This method of data analysis in academic research starts by making a tentative statement or hypothesis. The hypothesis can be of any type: simple hypothesis, null hypothesis, directional hypothesis, complex hypothesis, non-directional hypothesis, and associative or causal hypothesis. In all cases, the researcher’s duty is to prove a scientific claim by either approving or disproving a hypothesis.
- Estimating parameters:
Estimating parameters is a statistical method of data analysis in academic research that aims to collect data from a target population to estimate the pre-defined parameters of the distribution. The pre-defined parameters help researchers in exploring all essential characteristics of a population under study, such as age, gender, race, sex, education, and many more.
Finally, we can conclude that data analysis in academic research helps the researcher in forming logical scientific stories for making informed decisions. No matter what type of data analysis method (quantitative or qualitative) you choose for academic research, it must extract the information that best explains trends necessary to support or oppose a claim or to solve a scientific problem. If you still want guidance about which method of data analysis can help you in achieving your aims, you must write clear aims and objectives, research questions and problem statement of your research. All these parts of a dissertation are the best indicator to highlight the right method of data analysis in academic research.