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Hypothesis Testing Part 2: Categorical variables

Hypothesis Testing Part 2: Categorical variables. Intermediate Food Security Analysis Training Rome, July 2010. Topics to be covered in this presentation. Pearson’s chi square. Hypothesis testing for categorical variables…. We sometimes want to determine…

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Hypothesis Testing Part 2: Categorical variables

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  1. Hypothesis Testing Part 2: Categorical variables Intermediate Food Security Analysis Training Rome, July 2010

  2. Topics to be covered in this presentation Pearson’s chi square

  3. Hypothesis testing for categorical variables… We sometimes want to determine… Whether the proportion of people with some particular outcome differ by another variable Ex. Does the proportion of food insecure households differ in male and female headed households?? If we want to test whether there is a relationship between two categorical variables we should use Pearson’s chi square

  4. Pearson’s chi-square test • Pearson’s chi-squared test is an omnibus test that is used to test the hypothesis that the row and the column variables of a contingency table are independent • It’s a comparison of the frequencies you observe in certain categories to the frequency you might expect to get in those categories by chance.

  5. Assumptions of the chi-square test Two assumptions: • For the test to be meaningful it is imperative that each unit contributes to only one cell of the contingency table. • The expected frequencies should be greater than 5 in each cell (or the test may fail to detect a genuine effect)

  6. Chi Square example… • If we do it by spss, we get the same answer

  7. To calculate chi-squares in SPSS In SPSS, chi-square tests are run using the following steps: • Click on “Analyze” drop down menu • Click on “Descriptive Statistics” • Click on “Crosstabs…” • Move the variables into proper boxes • Click on “Statistics…” • Check box beside “Chi-square” • Click “Continue” • Click “OK”

  8. Reading the Chi-square test • Chi-Square (Crosstabs) Tests the hypothesis that the row and column variables are independent, without indicating strength or direction of the relationship.

  9. Alternatives • If you need to analyse the relationship between two categories and you want to test the significance of the differences (for example - traders and poor food consumption) chi-square is not the most appropriate test. Solutions • Transform the category you want to analyze into a bivariate variable ( ex. Traders yes/no - 0/1) • Re-run the chi- square with two categories (easier to interpret) • Use the bivariate variable as a continuous variable – run anova or t-test

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