Correlation.
In experimental psychology, there are two main tests, correlation and causation. Correlation is a test used to check for a significant relationship between two or more variables. A correlation coefficient shows the strength, direction, and nature of the relationship between the variables. The coefficient ranges from one to a negative one, zero showing no correlation, one showing the strongest positive relationship, and the negative one showing the strongest negative relationship. The closer a correlation matrix is to zero, the weaker it is, but the closer it is to plus or minus one, the stronger it becomes (Cherry, 2021).
- What is the strongest correlation in the matrix? (Provide the correlation value and the names of variables)
The strongest correlation is -0.316, a significant relationship between SF12 Physical Health Component Score and the number of doctor visits, past 12 months.
- What is the weakest correlation in the matrix? (Provide the correlation value and the names of variables)
On the other hand, the weakest correlation is -0.078, a significant relationship between Body mass index and SF12: Mental Health Component Score.
- How many original correlations are present on the matrix?
There are six original correlations present, the correlation between Body mass index and SF12: Mental Health Component Score, (-0.078), SF12 Physical Health Component Score and the number of doctor visits, past 12 months(-0.316), Body mass index and the number of doctor visits, past 12 (.131), SF12: Mental Health Component Score and the number of doctor visits, past 12 months (-0.133), SF12: Physical Health Component Score and Body Mass Index (-0.134), and SF12: Physical Health Component Score and SF12: mental Health Component Score(-0.168).
****If you include the correlation between the variables, it becomes ten original correlations.
- What does the entry of 1.00 indicate on the diagonal of the matrix?
One indicates a perfect linear relationship between variables, and in this case, it is the relationship between the variable and itself (Zach, 2020).
- Indicate the strength and direction of the relationship between body mass index (BMI) and physical health component subscale.
The relationship between body mass index (BMI) and physical health component subscale is negative and very weak since their correlation coefficient -.134, is a negative number below -0.3 (Moore et al., 2022).
- Which variable is most strongly correlated with BMI? What is the correlational coefficient? What is the sample size for this relationship?
The variable that is most strongly related to BMI is the physical health component subscale, with a correlation coefficient of -.134 and a sample size of 866 participants.
- What is the mean and standard deviation for BMI and doctor visits?
The mean score of doctor’s visits is 6.80, and a standard deviation of 12.720. On the other hand, the mean score of BMI is 29.2226, with a standard deviation of 7.37893.
- What is the mean and standard deviation for weight and BMI?
The mean score of weight is 171.4624 pounds with a standard deviation of 45.44083. On the other hand, the mean score of BMI is 29.2226, with a standard deviation of 7.37893.
- Describe the strength and direction of the relationship between weight and BMI.
Weight and BMI, have a strong positive significant relationship. Which means that when a participant’s weight increases, so does their BMI. Because, their relationship is strong, the p-value becomes very small; the chances of there being no relationship is very small (Frost, 2021).
- Describe the scatterplot. What information does it provide to a researcher?
Looking at the scatter plot, we can see that most or almost all the points are close to each other, which shows the relationship between the variables is strong. The points also seem to form a diagonal line (between the x and y-axis) that forms an equation equal to y = a + bx or y=x, which shows that the relationship between the two variables is linear. Since the diagonal line is between the x and y-axis, the relationship between the variables is positive; increasing one variable makes the other variable increase.
References
Moore, D., Notz, W. I., & Fligner, M. A. (2022). The Basic Practice of Statistics, 6th Edition (6th edition). W. H. Freeman.
Zach (2020, September 15). How to Read a Correlation Matrix. Statology. https://www.statology.org/how-to-read-a-correlation-matrix/
Cherry, K. (2021, April 14). The Role of Correlations in Psychology Research. Verywell Mind. https://www.verywellmind.com/what-is-correlation-2794986
Frost, J. (2021, August 27). Interpreting Correlation Coefficients. Statistics By Jim. https://statisticsbyjim.com/basics/correlations/
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