Reflection on Learning week 7
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WEEK5.docxWEEK 5
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?
When you opt to use spearman correlation in analysing a particular analysis, one part will be about checking to ensure “that the data that you want to analyse can actually be analysed by using the Spearman correlation”. This is so because it will only be corrected for you to use spearman correlation if the variables concerned pass “three assumptions that are required for the Spearman correlation so that you get a valid result. Practically, checking for these three assumptions will add a little bit more time to the analysis that you are doing” (Astivia & Zumbo, 2017). This expects you to check or rather click a few buttons in the SPSS statistics when you carrying out your analysis together with taking a little bit more time to think about but really not a hard task.
The three assumptions are as follows:
Assumptions number one is that the two variables need to be measured on a ratio scale, interval or ordinal scale. The instances of the ordinal scales should include the Likert scales like the 7-point scale such as "strongly agree through to strongly disagree), amongst other ways of ranking categories (e.g., a 3-point scale explaining how much a customer liked a product, ranging from Not very much, to It is OK, to Yes, a lot)”. The instances of the ratio or interval variables include the revision time, mostly measured in hours, the intelligence quotient, which is usually measured through the IQ scores, the performance of exam which is usually measured on a scale of 0 to 100 or even weight which is measured through kilograms.
The two variables need to be a paired observation. “For example, you could be interested in the relationship between the amount of exercise and the daily cigarette consumption done every week” (Astivia & Zumbo, 2017). An observation that is shared will reflect the score on every variable for one participant, like "the daily cigarette consumption of Participant 1 and the amount of exercise performed each week by Participant 1). If there are about 30 participants in the study, this simply means that there will be 30 paired observations”.
Assumption number three is that there is a “monotonic relationship between the two variables. This relationship exists only and only if the variables increase in value together or even as one variable value goes up, the other variable value goes down. While there are various approaches to check whether a monotonic relationship exists between your two factors, we propose making a scatterplot utilizing SPSS Statistics, where you can plot one variable against the other, and afterward outwardly assess the scatterplot to check for monotonicity”.
From the above assumptions, then it means for Spearman correlation to exist, then the variables must be dependent.
Are association and correlation analysis equivalent in determining relationships between variables?
Correlation usually explores the association between two or more variables and then make inference about the relationship strength. The two terms, association, and correlation can be used interchangeably. The association, technically, the association would refer to the relationship between two variables whereas the correlation is mostly used to any relationship between the two variables (Gogtay & Thatte,2017).
Do these findings impact your decision about whether to use this evidence to inform practice change?
For the two terms, a scatter plot can be used which then shows the association between two variables. They can also use covariance, which is used in measuring how much variables would change together. It can be used for a matrix that measures the covariance between several pairs of variables. So really, the two terms can be used interchangeably for the same purpose.
References
Astivia, O. L. O., & Zumbo, B. D. (2017). Population models and simulation methods: The case of the Spearman rank correlation. British Journal of Mathematical and Statistical Psychology, 70(3), 347-367. Retrieved from https://bpspsychub.onlinelibrary.wiley.com/doi/abs/10.1111/bmsp.12085 (Links to an external site.)
Gogtay, N. J., & Thatte, U. M. (2017). Principles of correlation analysis. Journal of the Association of Physicians of India, 65(3), 78-81. Retrieved from https://www.kem.edu/wp-
