It seems you've uploaded an image that provides instructions related to obtaining Pearson correlation coefficients using SPSS. Here’s a summary based on what the image describes:

What is covered:

1. Objective: Learn how to obtain Pearson correlation coefficients using SPSS software.
2. Context: You're working with a demographic data set (presumably provided in the SPSS format). The goal is to analyze relationships between various variables in the data, calculating Pearson correlations and interpreting statistical significance (p-values).
3. SPSS Use:
   • SPSS will calculate the correlation matrix, but some exclusions may apply (e.g., certain variables are inappropriate for correlation).
   • Some variables may not make sense to correlate due to categorical nature or other reasons (e.g., gender or ID numbers).
   • Specific relationships are highlighted, like correlations between income and other variables, as well as between the number of children and age.

Missing Information:

• The blanks in the image likely ask for variable names or specific values that you would input or obtain from SPSS after running the analysis. These may include which variables should be excluded from the correlation matrix or specific values from the output (e.g., the correlation coefficient $r$ and significance $p$).

Would you like a step-by-step guide on how to carry out these Pearson correlations in SPSS, or help filling out the specific information in the image once you have your SPSS output?

Let me know if you'd like to explore this further, and here are some questions you might consider next:

1. What variables are included in the data set, and how do they relate to each other?
2. Why might some variables not make sense for Pearson correlation?
3. How do you interpret a statistically significant Pearson correlation (e.g., $p < .05$)?
4. What are the steps to perform the two-tailed test in SPSS?
5. How does the significance level influence the hypothesis test result?

Tip:

When interpreting Pearson correlation coefficients, remember that correlation does not imply causation—strong correlations might indicate relationships but don't necessarily suggest that one variable causes the other.