To solve this problem, we need to calculate the expected frequency for each cell in the contingency table. These values are calculated under the assumption that the two variables (type of attributions emphasized and level of psychology training) are independent. For each cell, the expected frequency is determined by the formula:

$\text{Expected Frequency} = \frac{\text{Row Total} \times \text{Column Total}}{\text{Grand Total}}$

Step-by-Step Solution

1. Identify the Totals:

   • Row Totals:
     • No psych training: 60
     • Psych degree, but not a psychologist: 55
     • Psychologist: 35
   • Column Totals:
     • Mostly situational: 30
     • Mixed: 50
     • Mostly dispositional: 70
   • Grand Total: 150

2. Calculate Expected Frequencies: Using the formula for each cell:

   • No psych training, Mostly situational: $\frac{60 \times 30}{150} = 12$

   • No psych training, Mixed: $\frac{60 \times 50}{150} = 20$

   • No psych training, Mostly dispositional: $\frac{60 \times 70}{150} = 28$

   • Psych degree, but not a psychologist, Mostly situational: $\frac{55 \times 30}{150} = 11$

   • Psych degree, but not a psychologist, Mixed: $\frac{55 \times 50}{150} = 18.33$

   • Psych degree, but not a psychologist, Mostly dispositional: $\frac{55 \times 70}{150} = 25.67$

   • Psychologist, Mostly situational: $\frac{35 \times 30}{150} = 7$

   • Psychologist, Mixed: $\frac{35 \times 50}{150} = 11.67$

   • Psychologist, Mostly dispositional: $\frac{35 \times 70}{150} = 16.33$

Final Expected Frequencies Table

Let me know if you'd like additional explanations or have further questions!

Related Questions

1. What is the fundamental attribution error, and how is it commonly studied?
2. How do we calculate expected values in a contingency table?
3. Why is it essential to check the independence assumption in such studies?
4. How would you interpret any significant difference between observed and expected frequencies in this context?
5. What statistical test is commonly used to analyze differences in contingency tables?

Tip: In contingency tables, if the sample size is too small, results might not be reliable for tests like chi-square, so it's important to ensure adequate sample sizes for each expected frequency.