Self-Study: Correlation
Throughout the course, there will be a self-study Discussion pertaining to an important concept or topic covered within the assigned week. These Discussions are designed to give you the opportunity to collaborate with your peers and faculty, test your knowledge, ask questions, practice research analysis, and assist your peers.
You are not required to post to this forum; however, you are encouraged to post, review the posts of others, as well as answer questions and/or provide clarity and collaboration with your peers. Discussions will be graded as either Complete or Incomplete.
Resources
To prepare:
· Read and view the Learning Resources.
Use this Discussion to collaborate with your peers and faculty as an open office hours/ Q&A forum.
Post answers to the following:
· What is the relationship between correlation and causality?
· What is the role of previous research and theory with regard to causality?
· How does a scatterplot help to visualize a correlation?
· Identify and interpret the correlation used in Turgut and Yıldız (2023) and in Hazel (2023).
· Laerd Statistics
·
Pearson Correlation in SPSSLinks to an external site.
· Salkind, N., & Frey, B. (2019). Statistics for people who (think they) hate statistics (7th ed.). SAGE Publications.
· Chapter 6, “Computing Correlation Coefficients: Ice Cream and Crime” (pp. 119–144)
· Chapter 16, “Testing Relationships Using the Correlation Coefficient: Cousins or Just Good Friends” (pp. 303–312)
· Vigen, T. (n.d.).
Spurious correlationLinks to an external site.
.
·
Document:
Correlation, Scatterplot Exercise (Excel)
Download Correlation, Scatterplot Exercise (Excel)
·
Document:
Correlation Matrix, Data Output (Word document)
Required Resources for Topic: Correlation
· Turgut, M., & Yıldız, H. (2023).
Investigation of grief and posttraumatic growth related to patient loss in pediatric intensive care nurses: A cross-sectional studyLinks to an external site..
BMC Palliative Care, 22(1), 195.
· Document: Hazel, T. (2023, November).
Executive summary: Quality improvement initiative pediatric vaccine appointment compliance after text messaging reminders: A retrospective program evaluation of a local quality improvement initiative
Download Executive summary: Quality improvement initiative pediatric vaccine appointment compliance after text messaging reminders: A retrospective program evaluation of a local quality improvement initiative. Walden University.
· RESPOND TO THIS DIS. POST
Reply from Patience
Self-Study: Correlation
Correlation vs Causality?
Correlation describes the closeness of the relationship between two variables and just how much one of them moves in relation to the other. Though the correlation is significant, it does not mean that one variable is dependent on the other or that one causes the other to occur (Zaniletti et al., 2023). The possibility of a spurious relationship also occurs when two variables are correlated by other factors that affect both of them. Therefore, the approach of coordinating one event with another commits the ‘fallacy of confusion between correlation and causation’.
What is the role of previous research and theory with regard to causality?
Original studies and theoretical perspectives are a useful frame of reference to existent findings and observe the existing connections. In this instance, previous research findings help to provide some background knowledge that may aid in hypothesis generation and data analysis. The theories provide guidelines concerning what an observed statistic may express and whether this may point out a causal link or may point to other variables ignored in the analysis (Luft et al., 2022). This framework of interpretation aids in paying detailed attention within the analysis to disentangle the confounding factors from the actual structures that can be used to potentially change the system and avoid being led by the correlation coefficients.
How does a scatterplot help to visualize a correlation?
A form of data visualization, it involves breaking down data points into both its axes so the viewer is able to comprehend the distribution and possible correlation with visuals. Such plotting facilitates identification of linear patterns, grouping and outliers that might be concealed by simply analyzing means and/or variances (Zaniletti et al., 2023). Scatterplots are useful to investigate the strength, direction and form of the correlation in order to then decide what further analysis could be done or if there are outliers that need further exploration.
Identify and interpret the correlation used in Turgut and (Luft et al., 2022) and in (Zaniletti et al., 2023).
In both researches, the authors used correlation analysis to assess the relationship between variables that met their criterion in their samples. They found moderate to high levels of significance that encouraged (Zaniletti et al., 2023), to explore causal pathways in subsequent studies. The interpretations of these relations also underscore the advantages of using statistical techniques coupled with theory in order to confirm these relationships. In general, both investigations demonstrate the possibilities of machine learning based correlation analysis as an entry point for more in-depth analysis (Luft et al., 2022). They add more value in a way that that correlation analysis, when combined with theory and methodological best practices, can suggest patterns that are otherwise not discernible and hence provide a foundation for future investigations that are more attuned to exploring causality.
References
Luft, J. A., Jeong, S., Idsardi, R., & Gardner, G. (2022). Literature reviews, Theoretical frameworks, and Conceptual frameworks: an Introduction for New Biology Education Researchers.
CBE—Life Sciences Education,
21(3).
to an external site.
Zaniletti, I., Larson, D. R., Lewallen, D. G., Berry, D. J., & Maradit Kremers, H. (2023). How to Distinguish Correlation From Causation in Orthopaedic Research.
The Journal of Arthroplasty,
38(4), 634–637.
to an external site.
