QUESTION
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Week 5: Analyzing Parametric Statistics
As a practice scholar, you are searching for evidence to translate into practice. In your review of evidence, you locate a quasi-experimental research study as possible evidence to support a practice change. You notice that the study aims to make a prediction that relates to correlation between study variables. The study sample size is large and normally distributed. Reflect upon this scenario to address the following.
In your appraisal of the evidence, you note that an independent variable is not present and that a Spearman's ranked correlation is used to analyze data. Is this the correct level of correlational analysis? Explain your rationale.
Are association and correlational analysis equivalent in determining relationships between variables?
Do these findings impact your decision about whether to use this evidence to inform practice change? Why or why not?
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| Subject | Statistics | Pages | 3 | Style | APA |
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Answer
Analysing Parametric Statistics
The Use of Spearman’s Ranked Correlation
In the chosen research study, the aim is to make a prediction that will relate to the correlation between study variables. The sample size used is large, and is normally distributed. With this knowledge, it is clear that the use of Spearman’s ranked correlation to analyse data was not needed. The best option would be to use the Pearson’s correlation since the sample is normally distributed. Therefore, the study represents a parametric version of correlation.
The spearman’s ranked correlation represents the nonparametric version which is usually used when the assumptions of the Pearson correlation are markedly violated (Astivia & Zumbo, 2017). However, with normally distributed data, it is clear that the only factor that is needed is the analysis of the strength and direction of the linear relationship between two variables. The Spearman’s version features analysing the strength and direction of the monotonic relationship. The current study does not feature monotonic relationships since a linear relationship is expected from the normally distributed data.
Therefore, Pearson’s correlation is the best analysis even if there is no independent variable. The two available variables can be plotted on the x and y axis to check whether or not they will lead to the formation of a straight line. If this is achieved, then the negative slope or positive slope will inform on the kind of relationship that exists. Also, the straight line shows a strong association between the study variables.
Association and Correlational Analysis
The relationship between variables can be analysed through association and correlational analysis. The association will be important in determining the presence of a relationship, while the correlational analysis will determine the kind of relationship that exists (Munandar, Sumiati & Rosalina, 2020). The relationship can either be negatively or positively correlated. That means that as one variable increases, the other decreases. Or as one variable increases, the other one increases as well. Determining the kind of relationship is important since it enables the recommendations to be used to inform practice. There are instances where associations may not exist. This will mean that the data does not have any relationship or impacts on each other. If there is no association, then the correlation cannot be calculated as it will not lead to any useful conclusions. However, when an association is noted, the correlation can be calculated to check the strength of the existing relationship as well as the impact on variables.
Using Evidence to Inform Practice Change
Considering the analysis conducted, the research practice will still be used to inform practice change. That is because of the Spearman’s rank correlation will still measure a relationship between the variables, which is what the aim of the study was (Headrick, 2016). Therefore, even though irrelevant steps may have been included in the analysis, the findings will still be effective in showing the presence of a relationship, as well as the impacts of this relationship on the variables. The findings will not be inconclusive or false, therefore the outcome will be expected to follow a similar trend if at all the Pearson’s correlation had been used. If the findings were inconclusive, then the study would not have been used since there would be no evidence to back up the change requests. Fortunately, in this case the evidence to back up the decisions to be made are existent.
References
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Astivia, O., & Zumbo, B. (2017). Population models and simulation methods: The case of the Spearman rank correlation. British Journal of Mathematical and Statistical Psychology, 70(3), 347-367. https://doi.org/10.1111/bmsp.12085 Headrick, T. (2016). A Note on the Relationship between the Pearson Product-Moment and the Spearman Rank-Based Coefficients of Correlation. Open Journal of Statistics, 06(06), 1025-1027. https://doi.org/10.4236/ojs.2016.66082 Munandar, T., Sumiati, S., & Rosalina, V. (2020). Pattern of symptom correlation on type of heart disease using approach of pearson correlation coefficient. IOP Conference Series: Materials Science and Engineering, 830, 022086. https://doi.org/10.1088/1757-899x/830/2/022086 |