NR 439 Week 6: Data Results and Analysis
NR 439 Week 6: Data Results and Analysis
Class this week we are going to review the data collected. Our discussion will review the following course outcomes.
CO2: Apply research principles to the interpretation of the content of published research studies. (PO: 4, 8)
CO4: Evaluate published nursing research for credibility and clinical significance related to evidence-based practice. (PO: 4, 8)
After the data are collected, it is time to analyze the results!
Discuss one of the four basic rules for understanding results in a research study.
Compare clinical significance and statistical significance. Which one is more meaningful when considering applying evidence to your practice?
Compare descriptive statistics and inferential statistics in research. Please give an example of each type that could be collected in a study that would be done on your nursing clinical issue you identified in previous weeks.
According to this week’s lesson, the four basic rules for understanding results in a research study are understand the purpose of the study, identify the variables—dependent and independent, identify how the variables are measured, and look at the measures of central tendency and the measures of variability for the study variables. I chose to explore the rule: identify the variables-dependent and independent. A dependent variable is something that depends on other factors. An independent variable is a variable that stands alone and isn’t changed by the other variables you are trying to measure. A dependent factor can be changed by what happens with the independent factor but a dependent factor can never change an independent factor. A simple example would be: Insulin causes a drop in blood sugar. Insulin is the independent factor and blood sugar is the dependent factor. There is no way for blood sugar to cause a drop in insulin.
“Statistical significance tells us the findings are real; clinical significance tells us if the results are important for practice” (Houser, 2018, p. 356). Both statistical significance and clinical significance relate to quantitative data. Statistical significance could mean that in 0.5% of the population x, y, and z occurred. The probability of it happening could be chance because it is such a small percentage of the population. Clinical significance shows to what degree the new intervention is needed to make a difference in a client’s life. Clinical significance is thought to be much more meaningful but without the initial statistical significance, further studies would not have been done to prove a clinical significance. In reference to practice, clinical significance is more important when applying evidence to my practice. When utilizing clinical significance, there is evidence-based support of your actions.
“The goal of statistical inference is to estimate likely true or large-sample effects based on random samples from the collective(s) of interest” (Wilkinson & Winter, 2014, p. 492). In a study, the variances between groups are measured quantitatively and examined using inferential statistics. Inferential statistics utilize numbers to determine the probability that random error plays a role in the outcome. It also suggests that independent variables have an effect on the results. Descriptive statistics are usually related to the mean, minimum, maximum, standard deviation, and median of results. These studies are not usually utilized for change in evidence-based practice but are more likely to be used to measure current practice. An example of inferential statistics would be if I questioned all of the Emergency Department nurses at my facility about the effects of education on compassion fatigue. The results would infer that the results would be the same in another location but I only used a small population. For the descriptive statistics, I would use a table, graph, or chart in addition to the statistical data to summarize my study.
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References:
Houser, J. (2018). Nursing research: Reading, using, and creating evidence (4th ed.). Sudbury, MA: Jones & Bartlett.
Wilkinson, M., & Winter, E.M. (2014). Clinical and practical importance vs statistical significance: Limitations of conventional statistical inference. International Journal of Therapy & Rehabilitation, 21(10), 488-495.
For one independent variable, there may be more than one dependent variable. On the contrary, for more than one dependent variable, there is always one independent variable. The value of independent variable is changeable, while we cannot change the value of dependent variable. The independent variable is controllable, while we cannot control the value of dependent variable (Petter, DeLone, & McLean, 2013). Dependent variable depends upon independent variable, as when independent variable will change, there must be a change in the value of dependent variable. On the other hand, there is no impact of dependent variable upon independent variable. The value of independent variable is that which is manipulated in an experiment, while dependent variable is that value, which is observed by the researcher in an experiment
Petter, S., DeLone, W., & McLean, E. R. (2013). Information systems success: The quest for the independent variables. Journal of Management Information Systems, 29(4), 7-62.
Discuss one of the four basic rules for understanding results in a research study.
According to CCN, 2017-week 6 lesson, the four basic rules for understanding results in a research study are: Understanding the purpose of the study, identify the variables- dependent and independent, identify how the variables are measured, and look at the measures of central tendency and the measures of variability for the study variables. My discussion will be focused on rule # 2 Identify the variables-dependent and independent. Business dictionary defines variable as a characteristic, number, or quantity that increases or decreases over time, or takes different values in different situations. There are two basic types of variables (1) Independent variable that can take different values and can cause corresponding changes in other variables, and (2) Dependent variable are those that can take different values only in response to an independent variable (businessdictionary.com). An example of a variable is patient’s vital signs. We can measure a patient’s vital signs, but they can increase or decrease.
Compare clinical significance and statistical significance. Which one is more meaningful when considering applying evidence to your practice?
Clinical significance is generally expected to reflect the extent to which an intervention can make a real difference in patients’ lives. Statistical significance is the comparison of differences to standard error and the calculation of the probability of error that gives inferential analysis its strength. Nevertheless, statistical significance is just one of the important measures that determine whether research is truly applicable to practice (Houser, 2018). Statistical significance is a requirement for using evidence in practice: If results are due to error, then their application is irrelevant. At the same time, statistical significance tells the nurse little about whether the results will have a real impact in patient care. (Houser, 2018).
Compare descriptive statistics and inferential statistics in research. Please give an example of each type that could be collected in a study that would be done on your nursing clinical issue you identified in previous week.
Descriptive statistics use numbers narratively, in tables, or in graphic displays to organize and describe the characteristics of a sample (Houser, 2018, p291). It uses data to provide descriptions of the population, either through numerical calculations or graphs or tables. Descriptive statistics are the characteristics that are given to the sample of a research study. Descriptive statistics tell us, who was in the study and what did the study show us about the hypothesis (CCN, 2018). An example of descriptive statistics is my research question: Will follow-up telephone call and visit by home health nurse 3 to 7 days post discharge help reduce the rate of hospital readmission for patients 65 years and above with CHF. The descriptive study for my research will be patients 65 years old and above with CHF.
Inferential statistics can help to make a general statement about the sample population and compare them with other populations. (Houser, 2018). It makes inferences and predictions about a population based on a sample of data taken from the population in question. Inferential statistics help answer the question. How strong is the evidence from the study? “An example of inferential statistics will be all patients 65 years and above with CHF will not experience hospital readmission if they receive follow-up telephone calls and visit by home health nurse 3 to 7 days post discharge.
References
Houser, J. (2018). Nursing research: Reading, using, and creating evidence (4th ed.). Sudbury, MA: Jones & Bartlett.
http://www.businessdictionary.com/definition/variable.htmlLinks to an external site.
https://chamberlain.instructure.com/courses/7426/pages/week-6-lesson?module_item_id=567652