Types of Data: Ratio Specific intervals between consecutive numbers True zero value

Interval level: Specific intervals between consecutive values Zero is just a number in the string

Ordinal level: numbers establish rank order distance between numbers is not any specific interval e.g., 1st, 2nd, 3rd...

Nominal level: Numbers only categorize data. Numbers have no mathematical value. e.g., 1 = male, 2 = female

Chi Square Goodness of fit for nominal level data identifies if a sample of people conform to the categories as expected.

Where: Chi Square Frequency expected Frequency observed

Frequency expected: The amount of subjects that you would expect to find in each category based on known information.

Frequency observed: The amount of subjects you actually find to be in each category in the present data.

Let’s assume that: h.s. Some college MA Ph.D college 20% 25% 30% 15% 10%

First: How many people do we expect to find in each category, based on these percentages? Assume that the sample that you test has n=50.

The percentage needs to be written as a fraction or decimal.

h.s. Some college MA Ph.D college 20% 25% 30% 15% 10%

h.s. Some college MA Ph.D college 20% 25% 30% 15% 10%

h.s. Some college MA Ph.D college 20% 25% 30% 15% 10%

h.s. Some college MA Ph.D college 20% 25% 30% 15% 10%

h.s. Some college MA Ph.D college 20% 25% 30% 15% 10%

h.s. Some college MA Ph.D college 20% 25% 30% 15% 10%

h.s. Some college MA Ph.D college 20% 25% 30% 15% 10%

h.s. Some college MA Ph.D college

h.s. Some college MA Ph.D college

Some college: 1) 2) 3) 4)

h.s. Some college MA Ph.D college

1) 2) 3) 4) College:

h.s. Some college MA Ph.D college

M.A. 1) 2) 3) 4)

h.s. Some college MA Ph.D college

Ph.D. 1) 2) 3) 4)

Critical Value: Chi Square table df = K-1 where k is # of groups df=4 crit value = 9.488

17.49 > 9.48 Therefore there is a significant difference between the expected and observed frequencies.