Data Analysis Techniques in Research
An investigation into a particular topic or gap is referred to as research, which is a general phrase. Research comprises a directed inquiry into a certain subject to enhance or develop already existing understanding in the area. Research is an organised and systematic inquiry into a specific objective to accomplish a specific goal. Research is systematic because the process is decomposed into distinct phases that result in inferences. It is organised because there is a deliberate framework or procedure followed to arrive at an insight. Research must generate or reaffirm an insight to be complete whether the insight is favourable to the hypothesis or not.
Academic research and business research are the two broad categories into which research can be skeletally divided; while this division is insufficient to encompass all of the different specialised research groups, it provides a general grasp of the majority of the groupings. Research that aims to close knowledge gaps or verify current information is referred to as academic research. Academic research is defined as “a systematic investigation into a problem or situation, where the intention is to identify facts and/or opinions that will assist in solving the problem or dealing with the situation” (PhD Assistance, 2019).
Business research is basically an inquiry into a business problem that is usually specific to how the problem directly or indirectly affects an organization, which is usually the main researcher. Business research is the procedure a firm uses to increase its earnings and organisational performance by gathering and analysing pertinent information about its operations. Business research is the procedure a firm does to increase its earnings and organisational performance by gathering and analysing pertinent information about its operations.
Data is generally referred to as raw unprocessed information. In this sense, that information is insights into the specific area the data is related to. To gain insights into any research data is a central tool, and to interpret data into useful insights analysis of the data is required. Analysing data to discover is referred to as Data analysis. Data analysis is defined, as the process of “systematically applying statistical and/or logical techniques to describe and illustrate, condense and recap, and evaluate data“ (Responsible Conduct of Research, 2005). According to a contemporary definition, data analysis is the act of looking over, cleaning up, modelling, and changing data in order to find usable information, draw conclusions, and support decision-making (Brown, 2014).
There are two main sources of data that researchers can leverage which are primary data and secondary data. Basically, primary data are data that are generated by the researcher or study team through experiments, interviews, and surveys. The information from this source is typically specific to the knowledge that the study needs in order to achieve its objective. Secondary data is information derived from prior research or publicly available information. The researcher does not generate them; they are rather cleaned and modelled to suit the research. Secondary data can be obtained from published datasets, including books, web scraping, social media, and others.
To understand the various techniques of data analysis in research we have to discuss the categories of data as this determines the techniques applied to a particular data. The categories are:
· Quantitative data:
This data is associated with a scale measure and is represented by numerical figures. The data is amenable to the majority of statistical manipulation and may be represented by ordinal, interval, or ratio scales. Typically, questionnaires from the Outcomes Measurement Systems (OMS) are used to collect it or published datasets or journals for secondary sources.
· Qualitative data:
Texts or words that represent experience, opinions, emotions, or descriptors are commonly used to represent this data. This data is primarily subjective because it relates to an individual’s perspective on a specific subject. They are typically difficult to analyse in research; sometimes they are scale scores that are converted to quantitative data to facilitate analysis. Interviews, text mining, web scraping, focus groups, and open-ended survey questions are common sources.
· Categorical data:
Data is displayed in groupings. A categorical data point can only belong to one group. When someone responds to a survey, they may provide categorical data such as their level of education, marital status, dietary preferences, or religion.
The method used to analyse a set of data depends on the kind of data that will be analysed. Among the methods used in data analysis in research are:
· Descriptive statistics:
In a summary that explains the data sample and its measurements, descriptive statistics describe, illustrate, and summarise the fundamental characteristics of a dataset found in a specific study. It aids in better data comprehension for analysts. The available data sample is represented by descriptive statistics, which exclude hypotheses, judgments, probabilities, and conclusions. It is usually applied to quantitative data
· Inferential Statistics:
In order to compare your sample data to other samples or to earlier research, it makes use of statistical models. The majority of research employs statistical models known as the Generalized Linear model, which includes Student’s t-tests, ANOVA (Analysis of Variance), regression analysis, and various other models that produce straight-line (“linear”) probabilities and results. In most cases, it is used to analyse quantitative data.
· Content analysis:
It is a technique for examining information that has been recorded in text, images, and occasionally physical objects. When and where to use this type of analysis are determined by the research questions.
· Narrative analysis
It involves using field texts like stories, interviews, letters, conversations, photos, journals, autobiographies, field notes, etc. as units to analyse in order to validate the reasons behind the research question.
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