QUESTION
Instructions: Unit 6: Identifying and Interpreting Descriptive Statistics
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Instructions: Unit 6: Identifying and Interpreting Descriptive Statistics Descriptive statistics can be very helpful in many areas of research and EBP. As you look at the different variables measured, ask yourself why these variables are relevant... what do they contribute from a clinical perspective? In short... why does it matter that we measure these variables at all? This week our objectives are: -Differentiate descriptive from inferential statistics that answers clinical questions about the evidence. -Interpret research results using the four rules of looking at data. -Value the impact that misrepresentation of research results can have on evidence and, in turn, on nursing practice. This last weekly objective is critically important to us as nurses as we implement EBP into our practice. A hallmark example of the ripple effect that misrepresentation of evidence can have, or unethical research practice is that of the former Dr. Wakefield's research study that suggested a link between MMR and Autism. This was written in the 1990's and it still has an influence on our public health today. This demonstrates the vital importance of critical appraisal of research before using it for EBP! This is the survey question to be used : 5. What is your gender? |
| Subject | Nursing | Pages | 3 | Style | APA |
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Answer
Identifying and Interpreting Descriptive Statistics
In nursing, the statistical methods used in quantitative research are essential for data description and analysis. Descriptive and inferential statistics are necessary statistical analysis methods used. Descriptive statistics allow a researcher to summarize data in a way that the readers can interpret and learn about the subject's features (Grove, 2016). Descriptive statistics include a measure of variability like standard deviation, measures of central tendency like mean and correlational techniques like the scattered plots. On the other hand, inferential statistics allow researchers to test a hypothesis and make assumptions and draw a conclusion from the data collected.
When analyzing and interpreting research results, the fundamental rule is to plot the data (Grove, 2019). Plotting data implies the graphical technique for data representation that mostly appears in the form of a graph. Plotting data helps one identify the relationship between a variable and draw conclusions. Hence, when looking at data, the first three rules to consider are: correlation versus causation; assumptions and avoid pie charts. In this case, the survey question to be used is "what is your gender? (male 7.5%; female 92.3%). Looking at the data would raise questions like is the data disaggregated by sex? To what extent are the assumptions made? And so on. The data demonstrate a correlation between the variables of male and female. Hence, the findings would state that I am female by 92.3%. Howepersver, it important to notice the 0.2% missing in the configuration.
Misrepresentation of research results may result in, which may lead to the rise of doubts and critiques on a study. Hence, misrepresentation of research results helps in understanding how an investigation is misused, thus improving on its use (Meeks et al., 2018). In nursing, misrepresentation of research results fosters better understanding and improvement of concepts and theories.
References
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Grove, S. K., & Cipher, D. J. (2016). Statistics for Nursing Research-E-Book: A Workbook for Evidence-Based Practice. Elsevier Health Sciences.
Grove, S. K., Gray, J. R., & Faan, P. R. (2019).Understanding Nursing Research: First South Asia Edition, E-Book: Building an Evidence-Based Practice. Elsevier India.
Meeks, B. D., Kiskaddon, E. M., Burton Jr, M. G., Froehle, A. W., Crosby, L. A., & Laughlin, R. T. (2018). Update on Misrepresentation of Research Publications Among Orthopaedic Surgery Residency Applicants. JBJS, 100(18), e121.