Chi-Squared Test

• The critical value in a Chi-squared test is used to determine whether the result is statistically significant or not.
• The Chi-squared test measures the difference between the observed and expected frequencies of the categorical variables being tested.
• Chi-squared is a test of difference used when we collect nominal data and have an independent groups design.
• A Chi-squared test looks at the number of observations made in each category and compares this with the number of observations which would be expected if there was no difference between the groups.
• The Chi-squared test is a non-parametric statistical test of difference or association that allows researchers to see if their results are significant.
• Contingency tables are grids in which Chi-squared data is organised and displayed.
• Chi-squared tests do not count individual participants as they record behaviour in categories, therefore do not have N values. Instead they use degrees of freedom.
• Degrees of freedom is calculated using the formula; df= (rows – 1) x (columns – 1).
• If the value for Chi-squared is higher than the critical value given in the table, then the result is significant.
• In a Chi-squared formula O is the observed values recorded and E is the expected values.
• Expected values in a Chi-squared test have a formula for calculation.