Running head: PARAMETRIC STATISTICS 1
PARAMETRIC STATISTICS 2
Analyzing Parametric Statistics
1. When looking at the evidence, one notes that there is an independent variable and that a spearman’s ranked correlation is used in the analysis of the data. The spearman’s correlation can be referred to as the nonparametric version of the Pearson correlation. The correlation coefficient by Spearman’s measures the direction and the strength of the association between two variables which are ranked. For the use of this test, one requires two variables which are either ratio, interval, or ordinal. The spearman correlation is used when the assumptions of the Pearson correlation have been violated. The spearman’s correlation determines the direction and the strength of the monotonic relationship between these two variables as opposed to the strength and direction of the linear relationship (Schober et al., 2018). The person correlation is the one which looks at the linear relationship between the two variables. The monotonic relationship refers to the relationship when either as the value of one increases the other variable is increasing or as the value of one of the variable is increasing, the other variable is decreasing (Bakdash and Marusich, 2017). The spearman correlation is used in measuring the direction and strength of monotonic association between these two variables. The monotonicity is considered to be less restrictive compared to that of the linear relationship. When one normally picks the measure of association, it is after looking at the pattern of the data which is observed. When one looks at the scatterplot and it shows a relationship which looks monotonic, then one would use the spearman’s correlation (Akoglu, 2018). If there is a liner relationship, then one uses a person’s correlation as it shows the direction and strength of the linear relationship. The correct level of correlation analysis which should have been used in this case is Pearson’s correlation analysis. The data that has been presented has a normal distribution and has therefore met all assumptions of the Pearson’s correlation. There is manipulation of the independent variable and the Pearson’s is the better level.
2. Correlational analysis and association are sometimes used interchangeably but they have slightly different meanings when it comes to the technicality. Association refers to the presence of any relationship between two variables but the correlation is used to refer where there is existence of a linear relationship between the variables. The terms are often used interchangeably when it comes to various texts despite the differences. The correlation analysis explores the association which is there between two or more variables. The correlational analysis makes an inference about the strength of the relationship between the variables (Akoglu, 2018). There are several differences which can be shown when it comes to association. A scatter plot, for example, shows the association between two variables. The scatter plot matrix indicates the pairwise scatter plots for the various variables. When it comes to the majority of books, correlated and associated all mean to the same thing. The technical meaning which is there in correlation is that there is strength of the association as measured by the correlation coefficient. Correlation as it is used is mainly a technical term while the association is not. The association simply means that there is a relationship presence (Schober et al., 2018). The association will mean that certain values of one variable tends to co-occur with certain values of the other variable. There is no dependent or independent variable when it comes to the correlation. It’s a descriptive statistic which is bivariate. When it comes to the technical side, one can note that there are several measures of association but only some of them can be referred to as correlations.
3. After the findings I have a different outlook on the decision on whether to use the evidence to inform practice change. A quasi-experimental research study involves the manipulation of an independent variable so as to see how the dependent variable will react. In this case the study sample size is large and the data is normally distributed. I believe that this study makes an error and should have instead used the Pearson’s correlation coefficient to show the relationship. The Pearson’s correlation coefficient fits the purpose and there is no need to use the spearman’s ranked correlation.
Schober, P., Boer, C., & Schwarte, L. A. (2018). Correlation coefficients: appropriate use and interpretation. Anesthesia & Analgesia, 126(5), 1763-1768.
Akoglu, H. (2018). User’s guide to correlation coefficients. Turkish journal of emergency medicine, 18(3), 91-93.
Bakdash, J. Z., & Marusich, L. R. (2017). Repeated measures correlation. Frontiers in psychology, 8, 456.
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