SOC280 Lecture Notes - Lecture 12: Chi-Squared Distribution, Only Time, Normal Distribution
9 Mar 2014
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Chi-square tests tell you whether differences across categorical variables are significant or not. Compares the frequencies you actually find by the frequencies you would expect to get by chance. Assumptions: only time we don"t need to worry about normality and homogeneity of variance, not based on normal curve which is why. Expected cell counts are sufficient (no more than 20% of cells should have expected counts less than 5; none should have expected counts less than 1). If any are less than 5, that"s a problem. Makes the estimates unreliable if 20% is less than 5. If you don"t have enough cases with results you can trust its prone to random fluxuation and we run into issues. B: expected cell count = (row total/n) x (column total/n) x number of cases, = (a/b) + (c/b) x b, to check expected cell counts: Enter dependent variable into row and independent variable into column .
