NR 439 Discussion Data Analysis and Results
NR 439 Discussion Data Analysis and Results
Data analysis is very important – it crunches all of the numbers or themes and tells you what you have at the end. Unfortunately (for me, anyway), if it is a quantitative study, you are left with a bunch of statistics.
Mercifully for all of us, we are not asking you to perform a statistical analysis on any data – you just have to tell us what you’ve learned about these statistics, and why data analysis is so important.
Remember, you have to discuss three types of analysis: descriptive analysis (statistics), inferential analysis (statistics), and qualitative analysis of data.
Question 1. Share what you learned about descriptive 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 in nursing.
Descriptive data: Numbers in a data set that are collected to represent research variables. Houser (2018). Descriptive date uses simple mathematical /calculations. Most are straightforward and can be done using a calculator. These techniques provide essential information in a research study. Researchers who created descriptive reports must have the correct statistical technique for the data that has been collected. The data must be presented so readers easily comprehend and don’t misunderstand. This also applies to nurses using statistical data in the clinical setting.
Inferential data: Statistical test to determine if results found in a sample are representative of a larger population. Houser (2018). It’s the differences that occurs between samples and populations, between groups or over time, because of changes. These changes are seen as risk factors in control studies. An event interference is used to determine if the outcome was affected by the change. It’s a generalization about a population which is based on sampling.
Qualitative analysis: Focuses on an understanding of the means end of an experience. From the individual perspective. Houser (2018). It focuses on verbal descriptions and the observation of behaviors to analyze for the conclusions. These methods are most appropriate for obtaining the meaning of the patient’s experience. Thus understanding what is the best therapeutic intervention. What I found interesting was these methods can work together best for me in the clinical setting. The researcher utilizes the clinical data collected that will best get the expected outcome.
Question 2. Compare Clinical significance and statistical significance, include which one is more meaningful to you when considering application of findings in nursing practice.
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Clinical involves collecting data which involves people looking at understanding the disease, studying pattern, cause and how the disease effects the specific groups.
Statistical significance: Is a mathematical tool which determines whether the outcome of an experiment is the result of a relationship between specific factors or the result of chance. It claims that results from data by testing or experimentation does not occur randomly but is likely to be from a specific cause.
Clinical significance is more important to me. I can obtain the effectiveness of an intervention from the patient. This supports my nursing practice and validates the interventions chosen for the specific problems.
Houser, J. (2018). Nursing research: Reading, using, and creating evidence (4th edition). Jones & Bartlett
Nice job with your post this week. It was easy to read and understand. I see you described clinical significance and statistical significance but did not go into depth with the comparison. I do like your choice of clinical significance in the last paragraph. Clinical significance is so important in all that we do as nurses. The numbers can say what they want, but each patient is different, and seeing a good clinical outcome is what we as nurses want. The difference between Clinical and statistical significanceis that clinical significance is assigned to an outcome where the course of treatment had a positive and quantifiable effect on the patient (Houser, 2018). Whereas Statistical significance is when an event is unlikely to have occurred by chance and is calculated out. In extremely broad terms, Basically, Clinical Significance will verify the extent of the thing that is happening and Statistical Significance means it’s likely that something is happening or it is calculated to happen a certain way. Again, nice job, and thanks for sharing what you learned.
Houser, J. (2018). Nursing research: Reading, using, and creating evidence. (4th ed.). Burlington, MA: Jones and Bartlett Learning.
Descriptive analysis is a type of quantitative analysis. It uses simple mathematical calculations to summarize descriptive data and to provide the essential information in published research reports (Houser, 2018). Descriptive analysis gives basic information about variables in a dataset, delivers quantitative insight through numerical or graphical representation, and highlight the potential relationships between variables (Houser,2018). Descriptive analysis helps data visualization. It presents data in a meaningful and understanding way. I have learned that a patient’s vital sign is considered descriptive data.
Inferential analysis is a type of quantitative analysis used to determine if a specific result of the research from a sample size, can be expected to apply in a large target population (Houser, 2018). Inferential analysis refers to extending quantitative research findings as evidence basis into practice. The fundamental difference of inferential analysis from descriptive analysis is, inferential analysis allows nurses to make prediction of the strong evidence-based findings from quantitative research, relates them to the larger patient population (Houser, 2018).
Qualitative analysis is a process of data reduction. It is a process of reducing a large volume of rich characteristic information into meaningful units, so that they can be descripted, interpreted, and reported in an understandable way (Houser, 2018). Qualitative analysis involves with organizing and summarizing the enormous quantity of non-numerical data that have no standard methods. Through the process of thoughtfully review, qualitative analysis puts the intangible and inexact information into a manageable format, draw out the significance from the data collection (Houser, 2018).
According to week 6 online lesson, statistical significance “presents when the differences between two groups in the study sample are unlikely to have happened by error or change” (Chamberlain, 2018). Statistical significance indicates the reliability of the study result, but it does not assure that the results are clinically relevant (Ranganathan, 2015).
Clinical significance refers to the positive impact of the statistical significance on clinical practice. Clinical significance involves the evaluation of the effectiveness of strong research findings as evidence basis on clinical setting. Clinical significance facilitates the understanding and interpretation of the result, and helps clinical decision making (Ranganathan, 2015). Clinical significance is more meaningful to me because it complies with the principle of evidence-based practice. Clinical significance enables nurses to transfer of research knowledge into evidence-based practice.
Houser, J. (2018). Nursing research: Reading, using, and creating evidence (4th ed.). Jones & Bartlett.
Chamberlain College of Nursing (CCN). (2018). NR-439 Week 6 Lesson: Findings: Analysis and Results [Online lesson]. Downers Grove, IL: DeVry Education G
Ranganathan, P., Pramesh, C. S., & Buyse, M. (2015). Common pitfalls in statistical analysis: Clinical versus statistical significance. Perspect Clin Res.Links to an external site. 2015 Jul-Sep; 6(3): 169–170. doi: 10.4103/2229-3485.159943Links to an external site.https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4504060/Links to an external site.
According to Houser (2018), Descriptive Data Analysis is done to provide an overall summary of data for published research reports. The type of data analysis helps the investigator make decisions in regards to the strength and significance of the research as proof for implementation within the target population. One thing that I have learned about the descriptive data analysis is that information is summarized in tables or in graphs to give a visual representation of the sample being analyzed (Houser, 2018). Frequency tables, bar graphs, and bell curves (standard deviation) are the main types of visual representations for describing the distribution of data (Houser, 2018). This is important because it makes for easier interpretation of the results, especially for those that are visual learners.
Inferential analysis seeks to determine if the results for a study are indicative for the larger population (Houser, 2018). This is particularly interesting, especially in studies like clinical trials for medication. Clinical trials for medication seek to determine if a new medication does what it says it will do before it is rolled out to the public for mass consumption. The analysis from these types of studies is necessary for discovering credible findings for nursing because, as nurses, we need to know everything we can about a medication, including its side effects, before giving it to a patient.
Qualitative analysis is highly different from both descriptive and inferential analysis. As Houser (2018) explains, “there is no single standard for the analytic process,” (p. 419). This is particularly interesting because, essentially, in qualitative analysis, there is no structure. The research starts with a basic idea and then results develop organically. Because of this type of “free nature,” new results are constantly compared to each other (Constant Comparison) and new policies build upon previous policies. An example of this type of data analysis that Houser (2018) gave was the verbal abuse suffered by the staff in the emergency room. The results of the study lead quickly lead to “zero tolerance” policies, not only at that particular hospital, but also nationwide. “Zero tolerance” policies were (and continue to be) constantly evaluated and then re-evaluated to further include other staff members to get involved as needed in a given situation. Also, nurse managers developed coping strategies for abuse received and/or minimize the exposure to any aggression (Houser, 2018).
In regards to statistical significance and clinical significance, statistical significance depends heavily on the reliability of the study results, whereas, clinical significance focuses on the effect of current practices (Ranganathan et al, 2015). These effects drive treatment decisions. A study can be statistically significant (i.e. – we know that Lasix is a diuretic). However, if that diuretic is not benefitting the patient, then the statistical significance is irrelevant. Therefore, clinical significance would be more meaningful to me when considering application of findings to my nursing practice.
Houser, J. (2018). Nursing research: Reading, using, and creating evidence (4th ed.). Jones & Bartlett.
Ranganathan, P., Pramesh, C.S., Buyse, M. (2015). Common pitfalls in the statistical analysis: Clinical versus statistical significance. Perspectives in Clinical Research. 6(3). https://www.doi.org/10.4103/2229-3485.159943Links to an external site.
First, I would like to thank you for posting your discussion on data analysis. I agree with your post that descriptive data analysis is one of the most powerful tools in the hands of a researcher in an attempt to describe the data he/she has. The implication is that the researcher is able to make an informed decision concerning the significance of the particular research when it comes to its implementation to a specific group. The advantage of using descriptive data is that every data is summarized in a table, and every column can further be represented using visual representations such as histograms, pie charts, and bar graphs, among others. Such visual representations are sometimes easy to understand (LoBiondo-Wood, & Haber, 2017). While descriptive statistics deals with summarizing the major features of a particular dataset, it is important to note that inferential statistics majorly focus on generalizing a bigger population while working with a smaller representation of that population. One thing I learned about inferential statistics is that since it majorly deals with predictions, the results are normally in the form of probability.
I am also impressed by what you stated as having learned regarding the qualitative analysis. While descriptive and inferential statistics mainly deal with quantitative data, qualitative analysis, like the name suggests, is majorly about analyzing qualitative data, usually from interview transcripts. The major difference between qualitative and quantitative analysis is that quantitative analysis is data-driven, and the researcher can rarely have controls (LoBiondo-Wood, & Haber, 2017). On the other hand, in qualitative analysis, the investigator’s personal knowledge, integrative skills, and analytic skills play a great role. The implication is that an investigative and creative mindset is needed for an effective qualitative data analysis.
LoBiondo-Wood, G., & Haber, J. (2017). Nursing research-e-book: methods and critical appraisal for evidence-based practice. Elsevier Health Sciences.
Descriptive analysis serves a purpose to describe and understand the characteristics of the sample. According to the lesson, “Descriptive analysis of data is helpful in determining the characteristics of subjects and variables” (Houser, 2018). Examples of descriptive analysis is using a mean, median, and mode when determining the average grade of an assignment for a class, as chamberlain uses. Another example would be using range, standard deviation and variance when determining the variability of data. Inferential analysis is also known as quantitative analysis. This weeks lesson describes this type of analysis is “based on the assumption that chance or sampling error/random errors is the only explanation; however, researchers want to establish that chance/error is not the reason” (Nieswiadomy & Bailey, 2018, p. 250). This means that inferential analysis determines the strength and applicability of the findings and is an organized method using a mathematical and systematic approach. Qualitative analysis is also referred to as the results. According to the lesson, the nursing profession favors this type of analysis as it provides insight towards the patient preference and clinical experiences. When conducting my own research on qualitative analysis, I found that “Qualitative Data Analysis is outlined as the method of consistently looking and composing the interview records, observation notes, or completely different non-textual materials that the investigator accumulates to increase the understanding of an event.” (2021). I also learned that there are 5 different types of qualitative analysis. They consist of content analysis, narrative analysis, discourse analysis, framework analysis, and grounded theory. I found qualitative analysis the most interesting because there were so many subcategories to this 1 type of analysis.
I believe data analysis is necessary for discovering credible findings for nursing because of how much of a High Reliability Organization we are with pharmaceutical, physiological, and medical research. it is necessary for valid and truthful research to be conducted for such high stakes. When people’s lives are at stake, we cannot simply just base our practice of off nonreliable evidence, or undetailed statistics. We all aim to do no harm to our patients, and data analysis in research provides us with the best evidence based care for the safety, health, and well being of our patients.
Content analysis is either descriptive or interpretative, or basically asking yourself what is the data and what was meant by the data. Narrative analysis are stories, or transcribe experiences. The goal is to explicate stories in numerous contexts and experiences. Discourse analysis was the hardest for me to understand because it serves as an umbrella term for numerous meanings. The research explains that discourse analysis is a “general term for variety of approaches to analyze written, vocal, or language use or any vital philosophical theory event” and works well for political studies (2021). Framework analysis has 5 different phases to it. These are, familiarization which is just reading the information, then identifying a thematic framework, then coding the data, creating charts, then mapping out and interpreting the data, looking for patterns, ideas, and explanations. Finally, grounded theory is the gathering and analysis of the data. Ideas are technically “grounded” in the data., which means that the analysis and theories that develop concur when you have collected the information (2021).
Clinical significance is comparable to statistical significance because the statistics are based off the clinical experience. In different terms, statistical significance means something is going to happen, but the clinical significance shows on how broad of a spectrum something will occur. I believe that clinical significance is more meaningful to me when considering application of findings to my nursing practice, because a statistic just solely mean that we know that something is probably going to occur and an effect is taking place, whereas clinical significance is a critical tool for decision makers when dealing with high stakes research such as healthcare research because it seeks to understand the size and scope of an effect. It dives deeper into the research which makes it more trustworthy and reliable than just statistical significance.
Houser, J. (2018). Nursing research: Reading, using, & creating evidence (4th ed.). Jones and Bartlett.
Nieswiadomy, R., & Bailey, C. (2018). Foundations of nursing research (7th ed.). Pearson.
What is qualitative data Analysis: Types of qualitative analysis. (2021, March 02). Retrieved April 07, 2021, from https://www.educba.com/what-is-qualitative-data-analysis/Links to an external site.