Reporting Chi Square Test of Independence in APA
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This document provides guidance on reporting the results of a chi-square test of independence in APA style. It presents an example problem investigating the relationship between heart disease and gender. It then shows the general template for how to report a chi-square test, including reporting the chi-square value, degrees of freedom, and statistical significance. The template example finds a significant relationship between heart disease and gender, with men more likely to have heart disease than women.
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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