Page 110 - Quantitative Data Analysis
P. 110
Quantitative Data Analysis
Simply Explained Using SPSS
Finance Sales Human Resources Technology
Satisfied 12(15.2) 38(45.6) 5(6.4) 8(7.2)
Dissatisfied 7(3.8) 19(11.4) 3(1.6) 1(1.8)
Total 19 57 8 9
Now we can use the observed and expected values to calculate chi
square.
The next step is to use the chi square table found at the beginning
of the lesson to find the p-value. Because our data has four
categories (the four departments in the company), our degree of
freedom is three. Following the row for a degree of freedom of
three, we want to find the nearest value to the chi square value of
11.6806. The nearest value is 11.345, which corresponds to a p-
value of 0.01. It is common for statisticians to use a p-value of 0.05
to determine if the hypothesis should be accepted or refused. Since
our p-value is less than 0.05, the hypothesis should be refused. In
other words, the data does not support the business manager's
prediction that approximately 80% of the employees are satisfied.
The Theory and Applications of Statistical Inferences 94
