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Lecture 12

Lecture 12 – Chi-square - summary page2.docx

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SOC 280
Owen Gallupe

1 Lecture 12 – Chi-square – summary page - ie male/female white/block/asian -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 o Not based on normal curve which is why -independence of cases -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. Example: Sex by contact with courts. Male Female Total Finish HS N expected N expected A Did not Finish HS N expected N Expected Total C B To get sell count: • 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: -“Analyse”“Descriptive Statistics””Crosstabs” -enter dependent variable into “Row” and independent variable into “Column” -under “Cells”, check “Expected” To get Chi-Square value need to calculate degrees of freedom as well • Degrees of freedom = (# of rows-1)x(# of columns -1) • Spss does this for
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