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WK7DIS.DATA.docx Self-Study: Correlation  Throughout the course, there will be a self-study Discussion pertaining to an important concept or topic covered wit

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WK7DIS.DATA.docx 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. https://statistics.laerd.com/premium/spss/pc/pearson-correlation-in-spss.php · 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. . https://www.tylervigen.com/spurious-correlations  · 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. https://bmcpalliatcare.biomedcentral.com/articles/10.1186/s12904-023-01316-z · 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. · · Elizabeth · · · Correlation  refers to a statistical measure that expresses the extent to which two variables are linearly related. It is quantified by the correlation coefficient, which ranges from -1 to 1. A value close to 1 indicates a strong positive linear relationship, a value close to -1 indicates a strong negative linear relationship, and a value near 0 suggests no linear relationship (Hung et al., 2017). Correlation does not imply causation, just because two variables are correlated Correlation does not imply causation, just because two variables are correlated does not mean that one causes the other. The observed correlation may be caused or explained by lurking variables or coincidences. Additionally, spurious correlation can occur due to two variables showing correlation purely by chance or due to a third variable affecting both (Niedz, 2024). Causality , on the contrary, refers to a relationship where one variable is responsible for causing the change in another, in other words, it implies a cause-and-effect relationship (Hung et al., 2017). Role of Previous Research and Theory Regarding Causality The role of previous research and theory regarding causality includes, · Supporting Evidence: Previous research helps identify existing correlations and supports or refutes potential causal relationships. · Developing Hypotheses: Theories guide the formulation of hypotheses about causal mechanisms, explaining why one variable might affect another. · Designing Experiments: Based on prior findings, researchers can design experiments to test causal relationships while controlling for other variables. · Evaluating Plausibility: Theoretical frameworks provide a basis for assessing whether a causal relationship between variables is plausible and aligns with existing scientific knowledge. · Distinguishing Causation from Correlation: A thorough review of literature helps differentiate genuine causal relationships from spurious correlations (Hung et al., 2017). How a Scatterplot Helps Visualize Correlation A scatterplot is a graphical representation of the relationship between two quantitative variables and consists of a collection of points, each representing an observation's values on the two variables being plotted, that is one on the x-axis and the other on the y-axis. Scatterplots are intuitive and easily interpreted by most people, making them a valuable tool for initial exploratory data analysis (Moore et al., 2013).  A scatterplot helps us visualize correlation through, · Visual Pattern Recognition: By plotting data points, you can immediately observe patterns or trends indicating correlation. The plot shows whether the relationship is positive, negative, or nonexistent. For example, If the data points tend to rise together, you will observe an upward slope from left to right. This indicates a positive correlation, meaning that as one variable increases, the other tends to increase as well. If the data tends to decrease together, you will see a downward slope from left to right, indicating a · together, you will see a downward slope from left to right, indicating a negative correlation, where one variable increases as the other decreases. And if the data points are scattered without any identifiable pattern or slope, the variables likely have no correlation (Moore et al., 2013). · Strength and Direction: Scatterplots allow us to gauge the strength (tightness of points around a line) and direction (upward or downward slope) of the relationship. · Outliers Identification: Scatterplots easily reveal outliers that may affect correlation and causation in the data. Outliers are data points that fall significantly outside the general pattern of the data, and they can influence the perception of the relationship between variables. · Linearity Check: You can visually assess if the relationship looks linear or if a different model might be more suitable. That is, if the points form a pattern that's not straight but perhaps quadratic or exponential, it indicates a non-linear correlation. · Although, a scatterplot can visually suggest correlation and help interpret it, establishing causality requires deeper analysis, and consideration of existing theoretical frameworks and previous research (Moore et al., 2013). · Identify and interpret the correlation used in Turgut and Yıldız (2023) and in Hazel (2023). · In the Turgut and Yıldız (2023) study, a (R= 0.0144 and a p value of 0.041) implied that when grief is elevated so is the posttraumatic growth. This suggests that approximately 1.44% of the variance in the dependent variable can be explained by the independent variable. This indicates a very weak relationship. Since the P-value (0.041) is less than 0.05, the result is statistically significant. Therefore, despite the weak relationship, it is unlikely to be due to random chance (Turgut & Yıldız, 2023). · In the Hazel (2023) study however, a (r=0.710 and a p value of 0.004) showed a statistically significant relationship between the passage of time between 2021 to 2022 on competed appointments. This indicates a strong positive linear relationship between the two variables. As one variable increases, the other tends to increase as well. The result statistical significance is shown by the P-value (0.004) which is well below 0.05. Therefore, showing a very low probability that this strong correlation is due to chance (Hazel, 2023). In summary, Turgut and Yıldız (2023) study shows a very weak but statistically significant relationship while Hazel (2023) study demonstrates a strong and statistically significant positive correlation. These interpretations help us understand the strength and significance of the relationships in both studies. · References · 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. Walden University · Hung, M., Bounsanga, J., Voss, M. W. (2017). Interpretation of correlations in clinical research. Postgrad Med. 2017 Nov;129(8):902-906.  https://doi.org/10.1080/00325481.2017.1383820 Links to an external site. · Moore, D. S., Notz, W. I., Flinger, M. A. (2013). The basic practice of statistics (6th ed.). New York, NY: W. H. Freeman and Company. · Niedz, B. (2024). Correlation [Video]. Walden University Canvas.  https://waldenu.insrtucture.com Links to an external site. · Turgut, M., & Yıldız, H. (2023). Investigation of grief and posttraumatic growth related to patient loss in pediatric intensive care nurses: A cross-sectional Turgut, M., & Yıldız, H. (2023). Investigation of grief and posttraumatic growth related to patient loss in pediatric intensive care nurses: A cross-sectional study. BMC Palliative Care, 22(1), 195.  https://doi.org/10.1186/s12904-023-01276-y TopLinks to an external site.  of Form Order this assignment or a similar task.