NR 439 Discussion Data Results and Analysis
NR 439 Discussion Data Results and Analysis
NR 439 Discussion Data Results and Analysis
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.
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.
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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.
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.
The information you shared is very informative. I agree the statistical significance is very important because it leads to further research. The further research yields clinical significance. “The results of a study can be statistically significant but still be too small to be of any practical value” (LeFebvre, 2011, p. 1). In research related to healthcare, results with 5% or less probability are considered to be statistically significant. It takes greater results to have clinical significance. As you stated, “clinical significance is generally expected to reflect the extent to which an intervention can make a real difference in patients’ lives.” If statistical significance requires such a small degree of probability, why do we look at it? We look at it because if 5% of the population can benefit from the information gained in the study, we can help one person out of every twenty. By utilizing the information gained, we may also choose to do our own research and determine if the results we yield are different.
Reference:
LeFebvre, R., DC. (2011). P Values, Statistical Significance & Clinical Significance . Retrieved August 8, 2017, from https://www.uws.edu/wp-content/uploads/2013/10/Links to an external site.P_Values_Statistical_Sig_Clinical_Sig.pdf
Thanks for reading and responding to my post. To answer your question on which article from the week 5 RRL has clinical significance, I would like to differentiate between statistical significance and clinical significance. Statistical significance tells us if the findings of a research study are real, while clinical significance tells us if the results are important for practice (Houser, 2018). Both forms the basis of evidenced based practice in nursing. The second article making sense of a new technology in clinical practice has clinical significance because according to (Houser, 2018), clinical significance is generally expected to reflect the extent to which an intervention can make a real difference in a patient’s life. The study was carried out to determine if the CAN technology could be a useful tool in assisting health professionals and patients in clinical decision making.
Reference
Houser, J. (2015). Nursing research: Reading, using, and creating evidence (3rd ed.). Sudbury, MA: Jones & Bartlett.
According to our lesson this week the four basic rules for looking at data: #1 Understand the purpose of the study, #2 Identify the variables-dependent and independent, #3 Identify how the variables are measured, and #4 look at the measures of central tendency and the measures of variability for the study variables. I chose rule #3, Identify how variables are measured. Variables can be measured by the type, such as a Qualitative variable that is measured by nominal (marital status) or ordinal (pain rating) or Quantitative that may be measured by interval (patients temp) or ratio (pulse rate). Dependent and independent are other types of variables that can be measured. Measurements provide information about the variable. “Data must be analyzed using the correct statistical procedure for the level of measurement of a variable” (Houser,2018.p.318).
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 difference in patients’ lives” (Houser.,2018.p.356). Statistical significance tell us the findings are real but clinical significance is important for nursing practice. Statistical significance is based on a sample size, and clinical significance is more subjective and patient driven. An example would be the medication Ativan. Ativan is statistically proven to help reduce anxiety in a large sample group but clinically if given to an older person it may have the opposite effect. I think they are both important but I put more weight on clinical significance when applying evidence to my practice (Kim& Mallory.2014).
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.
Descriptive statistics provide concise summary of data. You can summarize data numerically or graphically. An example for my clinical issue (ICU Burnout) would be: Tracking down nurses in the past month that have left ICU nursing for other areas of nursing and summarize the data. I could use a histogram to visualize the number of years they spent as an ICU nurse, number of years they have been practicing, and other types of information.
Inferential statistics use a random sample of data taken from a population to describe and make inferences about the population. This type of statistic is used when it is not possible to study a population as a whole. I could use this type of statistics and choose a few nurses to represent ICU nurses from all different types of ICU practices such as Neuro, Cardiac, Neonate, Medical, Trauma and Surgical ICU nurses to get samples from each practice.
Houser, J. (2018). Nursing research: Reading, using, and creating evidence (4th ed.). Sudbury, MA: Jones & Bartlett.
Kim,M., & Mallory,C. (2014). Statistics for evidence based practice in nursing. Burlington,MA: Jones & Bartlett Learning
The Quantitative Balance and Gait Measurement study had a ghastly amount of charts, statistics and measurements that took up literally half of the study. (3/6 printed pages minus the reference page) The data was hard to understand and the authors used histograms, columns, tables and some crazy octagon pie chart. I personally could care less for all of the P(ANOVA), and all of the statistical information that was overloaded in this particular study. I found it humorous in the end when I read the first sentence in the conclusion,” This study reveals balance and gait problems in normal elderly, as well as patients with AD and FTD…”(Velayutham. et al.2017). The reports could of used just 1 chart format and cut the statistical information down considerably.
This study made me think of something funny I read, There are too many scientific studies, says scientific studies (Matyszczyk, C., March,2017).
References
Matyszczyk, C.,(2017). There are too many scientific studies, says scientific study. Retrieved from https://www.cnet.com/news/there-are-too-many-scientific-studies-says-scientific-study/
Your post was very informative. I liked how you used the example of Ativan when comparing statistical significance to clinical significance. It shows how both of these are important when taking research into consideration. All research results should be taken into consideration when looking at changing practice to ensure patient safety and quality care. Research data that is only analyzed with statistical probability may not give enough information to make clinical decisions. Statistical differences only address whether to accept or reject a hypothesis. This data does not provide information on the actual effect of treatment (Page, 2014). This is why it is important to understand and interpret clinical significance in addition to statistical significance.
Reference
Page, P. (2014, October). Beyond Statistical Significance: Clinical Interpretation of Rehabilitation Research Literature. Retrieved August, 10, 2017, from https://www.ncbi.nlm.nih.gov/pic/articles/PMC4197528/
One of the four basic rules for understanding results in a research study is “understanding the purpose of the study”. This basic rule for understanding results in a research study is what the study is built on. This allows the researcher to determine the clinical problem, look for evidence based resources that can be used collectively as an intervention to improve clinical practices and provide quality care to patients. Understanding the purpose of the study tells a person why a study is being done. CNN (Week 6 lesson) gives an example of the purpose of a research. This example shows information about what needs to be answered in a study or helps a person determine the purpose of the study.
Clinical significance is the research outcome that brings about a clinical improvement based on the result(s) of the study. Statistical significance is based on the numerical results of the study. The hypothesis and the null hypothesis are compared and reliability can be determined base on the statistical outcome. My understanding is that the clinical significance can be based on a statistical significance in a quantitive study. However, Houser(2018), states “no single statistical significance can identify a result’s clinical significance”. Statistical significance with a high confidence level ensures the result of the study is reliable for clinical practice.
An example of clinical significance is an EBP that resulted from a study showing deep breathing, turning and positioning prevent post operative pneumonia and atelectasis. Practicing this on a unit resulted in no complication of atelectasis.
An example of statistical significance is collecting data that shows percentages of hypothesis or null hypothesis. The surgical patients were the subjects in a quantitive research study. A group showed a percentage of no complications with turning and deep breathing post operatively, and another group showed postoperative complications, with no turning and deep breathing after surgery.
CNN Lesson (week 6).Reading Research Literature – Results. Understanding the Language of Results. Retrieved from https://chamberlain.instructure.com/courses/7426/pages/week-6-lesson?module_item_id=567652
Houser, J. (2018). Nursing research: Reading, using and creating evidence (4th ed.). Sudbury, MA: Jones & Bartlett.