Reporting Chi Square Test of Independence in APA

1. Reporting a Chi-Square
Test of Independence
in APA

2. Reporting a Chi-Square
Test of Independence
in APA
Note – that the reporting format shown in this
learning module is for APA. For other formats
consult specific format guides.
It is also recommended to consult the latest
APA manual to compare what is described in
this learning module with the most updated
formats for APA

3. Reporting a Chi-Square
Test of Independence
in APA
Note – that the reporting format shown in this
learning module is for APA. For other formats
consult specific format guides.
It is also recommended to consult the latest
APA manual to compare what is described in
this learning module with the most updated
formats for APA

4. • In this short tutorial you will see a problem that can
be investigated using a Chi-Square Test of
Independence.

5. • In this short tutorial you will see a problem that can
be investigated using a Chi-Square Test of
Independence.
• You will then see how the results of the analysis can
be reported using APA style.

6. Problem:

7. Problem:
We analyzed whether heart disease (no=1 and yes =2) and
gender (male = 1 and female = 2) are independent of one
another.
• Here is one general template for reporting a
Chi-Square Test of Independence:
A Chi-square test of independence was calculated
comparing the frequency of heart disease in men and
women. A significant interaction was found (2 (1) =
23.80, p < .05). Men were more likely to get heart
dease (68% than women (40%)

8. Problem:
We analyzed whether heart disease (no=1 and yes =2) and
gender (male = 1 and female = 2) are independent of one
another.
• Here is one general template for reporting a
Chi-Square Test of Independence:
A Chi-square test of independence was calculated
comparing the frequency of heart disease in men and
women. A significant interaction was found (2 (1) =
23.80, p < .05). Men were more likely to get heart
dease (68% than women (40%)

9. Problem:
We analyzed whether heart disease (no=1 and yes =2) and
gender (male = 1 and female = 2) are independent of one
another.
• Here is one general template for reporting a
Chi-Square Test of Independence:
A Chi-square test of independence was calculated
comparing the frequency of heart disease in men and
women. A significant interaction was found (2 (1) =
23.80, p < .05). Men were more likely to get heart
dease (68%) than women (40%)

10. Problem:
We analyzed whether heart disease (no=1 and yes =2) and
gender (male = 1 and female = 2) are independent of one
another.
• Here is one general template for reporting a
Chi-Square Test of Independence:
A Chi-square test of independence was calculated
comparing the frequency of heart disease in men and
women. A significant interaction was found (2 (1) =
23.80, p < .05). Men were more likely to get heart
dease (68%) than women (40%)

11. Problem:
We analyzed whether heart disease (no=1 and yes =2) and
gender (male = 1 and female = 2) are independent of one
another.
• Here is one general template for reporting a
Chi-Square Test of Independence:
A Chi-square test of independence was calculated
comparing the frequency of heart disease in men and
women. A significant interaction was found (2 (1) =
23.80, p < .05). Men were more likely to get heart
dease (68%) than women (40%)
Chi-
Square

12. Problem:
We analyzed whether heart disease (no=1 and yes =2) and
gender (male = 1 and female = 2) are independent of one
another.
• Here is one general template for reporting a
Chi-Square Test of Independence:
A Chi-square test of independence was calculated
comparing the frequency of heart disease in men and
women. A significant interaction was found (2 (1) =
23.80, p < .05). Men were more likely to get heart
dease (68%) than women (40%)
Degrees of
Freedom

13. Problem:
We analyzed whether heart disease (no=1 and yes =2) and
gender (male = 1 and female = 2) are independent of one
another.
• Here is one general template for reporting a
Chi-Square Test of Independence:
A Chi-square test of independence was calculated
comparing the frequency of heart disease in men and
women. A significant interaction was found (2 (1) =
23.80, p < .05). Men were more likely to get heart
dease (68%) than women (40%)
Statistical
Significance