Data Analysis and Results (ANSWERED)

QUESTION

Purpose

This week’s graded topics relate to the following Course Outcomes (COs).

CO 2: Apply research principles to the interpretation of the content of published research studies. (POs 4 & 8)
CO 5: Recognize the role of research findings in evidence-based practice. (POs 7 & 8)

Due Date

During the assigned week (Sunday the start of the assigned week through Sunday the end of the assigned week):
- Posts in the discussion at least two times, and
- Posts in the discussion on two different days

Directions

Discussions are designed to promote dialogue between faculty and students, and students and their peers. In discussions students:
- Demonstrate understanding of concepts for the week
- Integrate outside scholarly sources when required
- Engage in meaningful dialogue with classmates and/or instructor
- Express opinions clearly and logically, in a professional manner
Use the rubric on this page as you compose your answers.
Best Practices include:
- Participation early in the week is encouraged to stimulate meaningful discussion among classmates and instructor.
- Enter the discussion often during the week to read and learn from posts.
- Select different classmates for your reply each week.

Discussion Questions

Data analysis is key for discovering credible findings from implementing nursing studies. Discussion and conclusions can be made about the meaning of the findings from the data analysis.

Share what you learned about descriptive analysis (statistics), inferential analysis (statistics), and qualitative analysis of data; include something that you learned that was interesting to you and your thoughts on why data analysis is necessary for discovering credible findings for nursing.
Compare clinical significance and statistical significance; include which one is more meaningful to you when considering application of findings to nursing practice.

ANSWER

Data Analysis & Results

Data Analysis

Data analysis is essential in research because it allows researchers to interpret and make sense of their study’s findings. I learned that quantitative research has two types of statistics: descriptive and inferential analysis. Descriptive statistics describe and summarize the basic features of a dataset. Averages, means, and percentages are examples of descriptive statistics (Kaliyadan & Kulkarni, 2019). Researchers typically use graphs such as bar and pie charts, frequency tabulation and distributions, histograms, line graphs, etc., to present these data summaries.

On the other hand, inferential statistics provide the means to draw conclusions about a given population or sample based on probability theory. T-tests and analysis of variance (ANOVA) are the two primary sets of inferential analysis (Guetterman, 2019).

Polit and Beck (2020) reveal that, albeit easy to understand, qualitative analyses are more complex than quantitative analyses. The process involves organizing narrations, observation notes, interview transcripts, etc., coding & categorizing, identifying common themes in the data, and drawing meaning from the evidence to understand a phenomenon. Unlike quantitative research, qualitative analyses heavily rely on researchers’ inductive skills and integrity.

Clinical Significance versus Statistical Significance

Statistical significance suggests that the differences observed in a sample also exist in the general population. It verifies that the observed effect did result from chance or normal variation. In contrast, clinical significance indicates that the observed differences between treatment modalities are clinically important and might change clinical practice if the difference is observed. Simply put, statistical significance verifies the presence of an effect, while clinical significance means that the researchers found a clinically meaningful difference that might change clinical practice if implemented (Sharma, 2021).

Both concepts are important to practitioners. A study with statistical significance may not be clinically relevant, while that which is clinically relevant may not have statistical significance. Hence, practitioners must consider both in decision-making to optimize the benefits of EBP.

References

Guetterman, T. C. (2019). Basics of Statistics for Primary Care Research. Family Medicine and Community Health, 7(2), e000067. https://doi.org/10.1136/fmch-2018-000067

Kaliyadan, F., & Kulkarni, V. (2019). Types of variables, descriptive statistics, and sample size. Indian Dermatology Online Journal, 10(1), 82–86. https://doi.org/10.4103/idoj.IDOJ_468_18

Sharma, H. (2021). Statistical significance or clinical significance? A researcher’s dilemma for appropriate interpretation of research results. Saudi Journal of Anaesthesia, 15(4), 431. https://doi.org/10.4103/sja.sja_158_21

Polit, D., & Beck, C. (2020). Essentials of nursing research: Appraising evidence for nursing practice. Lippincott Williams & Wilkins.