Question

Chapter 18

Chi-square – Test of Independence

Certain editors at Sage like to think they’re a bit of a whiz at soccer. To see how well they play compared with Sussex lecturers and postgraduates, we invited various employees of Sage to join in our soccer matches. Every player was allowed only to play in one match. Over many matches, we counted the number of players that scored goals. After running a chi-square test of independence, determine whether or not the variables are independent of each other.

Use the following tables for questions 18.1 – 18.4.

Chi-Square Tests
	Value	df	Asymp. Sig. (2-sided)	Exact Sig. (2-sided)	Exact Sig. (1-sided)	Point Probability
Pearson Chi-Square	9.587a	1	.003	.002	.000
Continuity Correctionb	2.725	1	.099
Likelihood Ratio	3.834	1	.050	.075	.047
Fisher's Exact Test				.075	.047
Linear-by-Linear Association	3.587c	1	.058	.075	.047	.034
N of Valid Cases	77
a. 0 cells (0.0%) have expected count less than 5. The minimum expected count is 8.73.
b. Computed only for a 2x2 table
c. The standardized statistic is -1.894.

18.1. For this study, has the expected frequencies assumption been met? How do you know?

18.2. Will we reject or not reject our null hypothesis that the variables are independent? How do you know? Report the result in APA format.

18.3. For this study, what would it mean if we could not reject the null hypothesis?

18.4. For this study, what would it mean if we could reject the null hypothesis?

Answer 1