There is a consensus drawn by shamoo and Resnik (2003), data analysis is a process or systematically application of statistical tools used by researchers to derive insights over the years. It helps in reducing voluminous datasets into smaller segments whose mass structuring brought new ideas and interpreting the results. Nowadays, researchers rely on the data to provide immense value to their research and explore information through processes known as Data Mining. Sometimes it is needed to be unbiased while recognizing the pattern, expression and results. Data analysis entails unforeseen information that might not a researcher come across at the initiation of research, therefore when a researcher begins data mining processes, the journey of exploratory research starts simultaneously.
Amid these things it has been figured out data analysis in qualitative research precede with statistical tools, getting information from words, description and event could be complicated. Herein this step the scrutiny based technique or text analysis methods used by researchers to identify a pattern(QuestionPro, 2015). There are so many uncover aspects of data analysis that you might not know will be discussed in this blog illustrating the significance of data analysis in academic research.
Data Analysis In Academic Research
Every sort of data has unique features describing issues of specific topic effectively. These data can be of different types-
Qualitative data- Those data that can be observed and recorded and non-numerical in nature is known as qualitative data. As said by essay deutsch it allows respondents to express his/her views fully in a contextual manner. It offers dynamic approaches; generate valuable conversation over a particular subject. This is specifically used for conducting marketing and consumer research, for instance, if a new brand is accepting by consumers or not. To know the answer, a researcher use qualitative approach to gather information about consumer experiences and their feedbacks.
Quantitative Data– Those data that can be expressed in numbers, figures and tables are known as quantitative data. Some examples of quantitative data are height, class interval, length of a wall, number of peoples, percentage of marks etc. Numerically the quantitative data collected from polls questionnaire and surveyor by manipulating pre-existing data using computational skills.
Categorical Data- Those data sets that can be represented in the groups fall under the categorical data structure. It can’t belong to more than one group. A chi-square test is highly preferred by several assignment writers to calculate categorical data sets.
Data Analysis Types And Tools
There are five major types of data analysis used by researcher to interpret data out of which text analysis is predominant.
Text Analysis– Also refers as Data Mining is a method used to discover the pattern among voluminous data sets using data mining tools. This is best known for transforming raw data into business information
Statistical Analysis– By using past data collection, statistical data analysis is performed. It is applicable to analyze summarized numerical data and frequency of categorical data. You can also find the same data via selecting a different sample
Diagnostic Analysis- This is a form of advanced data analytics used to determine why the particular event has happened. It is further classified using different techniques such as data mining, data drilling, data discovery and its correlations.
Predictive Analysis– To identify likelihood futuristic outcomes based on past data, predictive analysis is performed using machine learning techniques. The goal of data analysis is beyond describing why it is happened to providing the best assessments of what will happen in future (Hart, 2019).
Prescriptive Analysis– It helps the business to define the course of action using machine learning. It uses data to determine the near outcomes using a variety of tools. Most of the driven data within the business have to go through the prescriptive analysis.
Significance Of Data Analysis In Academic Research
- Structuring Of Finding From Different Resources
As per assignment help experts, unintentionally scientific misconduct is likely to have resulted in poor research construction and its follow up. Ideally, investigators should have a basic understanding of the rationale behind the analysis over others. Also, researchers performing data analysis should be aware of challenges of reliability and validity so that the structuring of finding taken place efficiently.
As the figure depicts a massive volume of data generated proposing the emergence of data analysis using Meta-learning tool, perhaps this supports the signposting of descriptive research done in the literature. Henceforth this can be said the increasing use of the data analysis in research robust the challenges of acquiring mining. But this also produces satisfactory research in terms of extracting findings proposing better results.
2. Segmenting Macro Problems Into Micro Components
The integrity of data analysis is so useful in segregating the macro research problem into a minor one. Since the data collection could be influenced by environment and context, the researcher has to careful while structuring the details of a particular event.
3- To Acquire Insights Out Of Voluminous Data Sets
Many essay writer believes data analysis provides insights among varied database at times enhancing the significance of the research. This can allow the researcher to supervise the single course of biostatics and can help in forming an informed decision.
4. To Avoid Biasness With The Help Of Statistical Treatment
No doubt, any biases during researches may lead to inappropriate results either reflecting true effects or a false one. A common practice of data analysis could be understood from this- As said by Altman, 2001 biasness can occur while recruiting the participants or demonstrating the statistical power, so it is better to envisage meta-learning tools that can predict better outcomes without inferring false results(Northern Illinois University Pres (2005).
5.At Last Understand The Nature Of Variables Used
To establish a conventional norm of natures of variables, data analysis often used to understand the acceptability of variables. However, every effort must be taken to reduce or eliminate the major challenges of data integrity. In case, you are stuck with research paper writing on data analysis, then you can also take instant essay help from SourceEssay.
Conclusion
The article proposing tries to provide a holistic view of the significance of data analysis in academic research. It has covered several dimensions that entail the part of data analysis incorporated to understand the pattern like text analysis, diagnostics analysis, statistical predictive analysis, prescriptive analysis. This also discussed how meta-learning tools are being utilized to segregate the macro problem of research into micro one. It has also highlighted how researchers use data analysis norms to understand the nature of variables also to determine the power of statistics. Thus it can be said, either it is qualitative research or quantitative data analysis has successfully applied to infer the findings and interpretation results.
Reference
Shamoo, A.E., Resnik, B.R. (2003). Responsible Conduct of Research. Oxford University Press.
Sivarajah, U., Kamal, M. M., Irani, Z., & Weerakkody, V. (2017). Critical analysis of Big Data challenges and analytical methods. Journal of Business Research, 70, 263-286.
Hart, K. (2019). Critical analysis of Big Data challenges and analytical methods. Journal of Child Behaviour, 1(1), 28-35.
QuestionPro (2019) Data analysis in research: Why data, types of data, data analysis in qualitative and quantitative research, Retrieved from- https://www.questionpro.com/blog/data-analysis-in-research/, Data Accessed on 12 August, 2020
Northern Illinois University Pres (2005). Data Analysis, https://ori.hhs.gov/education/products/n_illinois_u/datamanagement/datopic.html#, Data Accessed on, 12 August, 2020