The Role of Data Analysis in Health and Welfare Research
In health and welfare research, data analysis is the process by which researchers reduce data to a story and interpret it to make conclusions. Each form of data has the unique property of describing objects after they have been assigned a meaning (Braun & Clarke, 2021). These meanings must be organized, processed, and presented in a particular context to be helpful (Smith et al., 2020).
Applications of Data Science in Healthcare
Medical Imaging
Medical imaging is one of the most prevalent applications of Data Science in health care. Its techniques include X-ray processing, magnetic resonance imaging (MRI), ultrasounds, and CT scans. These non-invasive testing techniques of human internal organs enable the detection of anomalies and the diagnosis of illnesses.
Genomics
Genomics is a subfield of molecular genetics that studies the genomes and genes of living organisms. With the birth of the Human Genome endeavor, an international endeavor to research nucleotide sequences, data science, and big data began to be actively employed to optimize these investigations.
Data Types
Qualitative Data
Data can take numerous formats; these are the most common. Qualitative information includes words and descriptions. Although this data is visible, it is subjective and more difficult to analyze in the study, particularly for comparison (Kleinheksel et al., 2020).
Example: Qualitative data is defined as anything that describes flavor, sensation, texture, or opinion. This information is typically gathered through focus groups, in-person qualitative interviews, or open-ended survey questions.
Quantitative Data
Quantitative Data is any data expressed numerically, which is also known as quantitative data. This data can be classified, categorized, grouped, measured, computed, or rated. It includes age, rank, cost, length, weight, and points. The researcher might present such data graphically, as charts, or statistically analyze it. In surveys, Outcomes Measurement Systems (OMS) questions are a valuable source of numerical data collection.
Categorical Data
Categorical data is data that is displayed in groupings. A categorical data item, on the other hand, cannot belong to more than one group. An individual responding to a survey and giving their lifestyle, marital status, smoking, or drinking habits is an example of categorical data. The chi-square test is the usual approach for analyzing such data.
Strategies for Organizing, Reducing, and Interpreting Research Data
The data analysis method assists in breaking down vast amounts of data into smaller, more manageable parts. The first thing that happens in the data analysis process is the organization of the data obtained from the population (O’Connor & Joffe, 2020). When generalization and categorization are combined, they form the second known strategy for data reduction. It aids in discovering patterns and themes in data, enabling simple identification and linkage.
The third and last method is data analysis, which is done both top-down and bottom-up by researchers. Researchers rely primarily on data to communicate a story or solve issues (Brnich et al., 2020). Depending on the sort of data being investigated, the researchers’ objective and vision for the audience drive them to look for patterns to create the story they wish to convey. When analyzing data, researchers should stay open and open-minded regarding unexpected patterns, manifestations, and outcomes. Data analysis can often reveal unexpected yet intriguing stories not anticipated when the study began.
References
Braun, V., & Clarke, V. (2021). Can I use TA? Should I use TA? Should I not use TA? Comparing reflexive thematic analysis and other patternâbased qualitative analytic approaches. Counselling and Psychotherapy Research, 21(1), 37-47. Web.
Brnich, S. E., Abou Tayoun, A. N., Couch, F. J., Cutting, G. R., Greenblatt, M. S., Heinen, C. D.,… & Berg, J. S. (2020). Recommendations for application of the functional evidence PS3/BS3 criterion using the ACMG/AMP sequence variant interpretation framework. Genome Medicine, 12(1), 1-12. Web.
Kleinheksel, A. J., Rockich-Winston, N., Tawfik, H., & Wyatt, T. R. (2020). Demystifying content analysis. American journal of pharmaceutical education, 84(1). Web.
OâConnor, C., & Joffe, H. (2020). Intercoder reliability in qualitative research: debates and practical guidelines. International journal of qualitative methods, 19, 1609406919899220. Web.
Smith, J. D., Li, D. H., & Rafferty, M. R. (2020). The implementation research logic model: a method for planning, executing, reporting, and synthesizing implementation projects. Implementation Science, 15, 1-12. Web.