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SOC202H1 (76)


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Scott Schieman

1 Chi-Square test March 13, 2012 Announcements • Tutorial Q and A cancelled because of lack of attendance. • Students can go meet one-on-one instead Theme: How work gets to us Exhaustion • How often during the past month have you felt used up at the end of the day? • Context: face-to-face interviews. How can we turn this interval variable to into a continuous variable? • Change the categories into days. Example: how many days did you feel exhausted? How to turn interval variable into a nominal variable. Example: How often do you feel exhausted? • Categories: Low, High Hypothesis: As level of work stress increase, there will be higher level of exhaustion. Null: Work stress has no impact to do with exhaustion. Chi-square: test of independence Work stress and exhaustion can be independent. SOC202H1S Quantitative Analysis in Soc. Science. March 13 2012. Scott Schieman. SP. TG. 2 Work Stress How often do you find your work stressful? Never Hardly ever Sometimes Often Always The categories of hardly ever and always are taken to High Low Chi-square test: example 1 • The relationship between tow nominal/categorical variables • Cross-tabulation table of the frequency of join occurrences; observed frequencies; marginal totals and appropriate percentages Independent variable: work stress. Need percentage of independent variable. What percentage of low work stress has high exhaustion? In order not confuse yourself, say the percentage of that column. • 75% of people with high work stress report high levels of exhaustion compared to 27% of those with low work stress report high levels of exhaustion. • The variables are not independent. If independent the levels of exhaustion would be the same regardless of the amount of work stress. Need to obtain expected frequencies: these are the values of the joint cell frequencies if null hypothesis is true. SOC202H1S Quantitative Analysis in Soc. Science. March 13 2012. Scott Schieman. SP. TG. 3 • The expected values if the null is true • Observed subtracted expected to see the difference. According to the chi-square chart • The number 236.8 is the expect number of people in high stress and high exhaustion category if null is true. • The observed number in the survey is 417 in the high stress and high exhaustion category. • Better case against null hypothesis is a larger difference between observe and expected. Possible test question: The df in the chi square will be blotted out and student must fill in the df. Then the numbers in the equation will change, student should be able to deductively state the new df. Key points • Build a vas against the null hypothesis: The larger the differences between expected and observed frequencies, the better the case against the null hypothesis.
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