Descriptive analysis is one of the most necessary and first steps in data analysis used for quantitative research. Descriptive analysis uses statistical processes describing, measuring, and summarising collected data. Week 6 lesson lists ways researchers can measure the data: “measure to summarize, a measure of central tendency, a measure of variability, a measure of position and relation, and summarizing using graphical presentation.” (Chamberlain, 2023). Inferential analysis numerically measures the accuracy of results and makes predictions about an entire population. This type leaves room for the uncertainty of the sample on the population or sampling error. (Houser, 2023). Qualitative analysis is a technique measuring non numerical data such as people’s experiences/perceptions to understand their beliefs better, points of view, and reality. This technique can be very challenging and time consuming. It allows the researcher to take part in the study. The three main qualitative analysis methods are template, editing, and immersion. This type of analysis reveals a deeper meaning. (Houser, 2023). Interestingly, this form of analysis searches for deeper meaning, so the researcher must take extra steps to ensure the results are trustworthy and reliable. Another interesting fact is that Houser explains that the “purpose of summarizing descriptive data for research differs from the purpose of summarizing data for the readers.”
Data analysis is important to nursing practice. As nurses, we use it daily for quality improvements, such as vitals, diseases, issues, and length of stay. Analysis helps conclusions drive change in how we practice, evidence-based practice, and to make a safer work environment for nurses. Houser, p.414, states, “Qualitative conclusions enhance the evidence for a holistic view of nursing practice.”
Statistical significance shows the reliability and validity of study results. Clinical significance reveals the impact on nursing practice and how interventions/treatments/methods affect patients and outcomes. Both are important. Studies may be statistically significant but not necessarily clinically relevant. So clinical significance is of high importance for practice. (Anaesth, 2021).
References:
Chamberlain college of nursing, (2023). Week 6 lesson: Findings: Analysis and results.
Houser, J., (2023). Nursing research: Reading, using and creating evidence. (5th ed.). Jones and Bartlett.
Anaesth, S., (2021). Statistical significance or clinical significance? A researcher’s dilemma for appropriate interpretation of research results. Saudi Journal of Anaesthesia. 15(4): 431–434 Retrieved from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8477766/#:~:text=The%20main%20difference%20between%20statistical,of%20the%20results%20or%20not Links to an external site. .
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