Week 6 Discussion: Correlations

A statistical correlation analysis often seeks to explore the relationships or connections
between variables. Nearly all correlation statistics start with a research question. Researchers
then use correlational analytic techniques like regression analysis to define these relationships or
correlations. According to Gray & Grove (2020), the strength of a statistical correlation depends
on the variables utilized and how best the researcher(s) interpret the results. This can signify
whether the relationship is statistically significant or not. This discussion will examine how to
interpret outcomes from a correlational analysis. Specifically, the exercise will explore the
correlation outcomes provided in week six’s “Correlations Exercise SPSS” output document,
interpreting the meaning of various variables provided. The summary below answers a set of
questions based on the correlations exercise SPSS output document.

Summary

What is the strongest correlation in the matrix? (Provide the correlation value and the
names of variables)
The strongest correlation is between the “number of doctor visits within past 12 months” and
“SF12: physical health component score, standardized.” The Pearson correlation value is -0.316.
What is the weakest correlation in the matrix? (Provide the correlation value and the
names of variables)
The weakest correlation is between “Body Mass Index” and “SF12: Mental Health Component
Score, Standardized.” The Pearson correlation value is -0.078.
How many ‘original correlations’ are present in the matrix?
Six

WEEK 6 DISCUSSION: CORRELATIONS 3
What does the entry of 1.00 indicate on the diagonal of the matrix?
Entry 1.00 shows a perfect linear correlation or relationship
Indicate the strengths and direction of the relationship between body mass index (BMI)
and physical health component subscale.
The relationship between BMI and the physical health component subscale is weak because its
Pearson correlation value (-.134) is closer to 0 than 1. The negative sign means that one variable
increases while the other drops. Therefore, the line representing the correlation naturally slopes
downward (Viera Jr., 2017).
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 correlated with BMI is the physical health component score or
subscale. The correlational coefficient is -0.134, and the sample size (N) of the relationship is
866.
What is the mean and standard deviation for BMI and doctor visits?
The mean and standard deviation for BMI is 29.2226 and 7.37893, respectively. The values for
doctor visits are 6.80 and 12.720, respectively.
What is the mean and standard deviation for weight and BMI?
The mean and standard deviations for weight are 171.4624 and 45.44083, respectively. On the
other hand, the mean and standard deviation for BMI is 29.2226 and 7.37893, respectively.
Describe the strength and direction of the relationship between weight and BMI.
The relationship between weight and BMI is very strong, as indicated by a Pearson absolute
coefficient value of 0.937. As mentioned above, the correlation coefficient ranges between +1
and -1. The larger the coefficient, the stronger the relationship (Walker & Almond, 2010). As for

WEEK 6 DISCUSSION: CORRELATIONS 4
the direction, the positive value means that the line slopes upwards because an increase in one
variable (weight) equally increases the other one (BMI).
Describe the scatterplot. What information does it provide to a researcher?
The scatter plot shows the relationship or correlation between weight pounds and body mass
index. The scatter plot also shows that the linear relationship is large and positive (r = 0.937).
The linear relationship is positive because the points fall close to each other in the line. The
relationship can be termed positive since the line slopes upward to the right (Walker & Almond,
2010). This implies that an increase in one variable (weight-pounds) causes a subsequent
increase in another variable (BMI).

WEEK 6 DISCUSSION: CORRELATIONS 5

References

Gray, J. R., & Grove, S. K. (2020). Burns and Grove’s the practice of nursing research:
Appraisal, synthesis, and generation of evidence (9 th ed.). Elsevier.
Viera Jr., E. T. (2017). Introduction to real-world statistics: With step-by-step SPSS instructions.
Taylor & Francis.
Walker, J., & Almond, P. (2010). Interpreting statistical findings: A guide for health
professionals and students: A guide for health professionals and students. McGraw-Hill
Education (UK).