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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… - PowerPoint PPT Presentation
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Hypothesis Testing Part 2: Hypothesis Testing Part 2: Categorical variablesCategorical variables
Intermediate Food Security Analysis TrainingRome, July 2010Rome, July 2010
Topics to be covered in this presentation
Pearson’s chi square
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
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.
Assumptions of the chi-square test
Two assumptions:1. For the test to be meaningful it is
imperative that each unit contributes to only one cell of the contingency table.
2. The expected frequencies should be greater than 5 in each cell (or the test may fail to detect a genuine effect)
Chi Square example… If we do it by spss, we get the same answer
Gender of child * WAZPREV Crosstabulation
2086 587 2673
2144.6 528.4 2673.0
78.0% 22.0% 100.0%
48.6% 55.5% 50.0%
2204 470 2674
2145.4 528.6 2674.0
82.4% 17.6% 100.0%
51.4% 44.5% 50.0%
4290 1057 5347
4290.0 1057.0 5347.0
80.2% 19.8% 100.0%
100.0% 100.0% 100.0%
Count
Expected Count
% within Gender of child
% within WAZPREV
Count
Expected Count
% within Gender of child
% within WAZPREV
Count
Expected Count
% within Gender of child
% within WAZPREV
Male
Female
Gender ofchild
Total
.00 1.00
WAZPREV
Total
Chi-Square Tests
16.196b 1 .000
15.921 1 .000
16.223 1 .000
.000 .000
16.193 1 .000
5347
Pearson Chi-Square
Continuity Correction a
Likelihood Ratio
Fisher's Exact Test
Linear-by-Linear Association
N of Valid Cases
Value dfAsymp. Sig.
(2-sided)Exact Sig.(2-sided)
Exact Sig.(1-sided)
Computed only for a 2x2 tablea.
0 cells (.0%) have expected count less than 5. The minimum expected count is 528.40.b.
To calculate chi-squares in SPSSIn SPSS, chi-square tests are run using the
following steps:
1. Click on “Analyze” drop down menu2. Click on “Descriptive Statistics”3. Click on “Crosstabs…”4. Move the variables into proper boxes5. Click on “Statistics…”6. Check box beside “Chi-square”7. Click “Continue”8. Click “OK”
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.
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.
SolutionsSolutions
1. Transform the category you want to analyze into a bivariate variable ( ex. Traders yes/no - 0/1) 1. Re-run the chi- square with two categories (easier to
interpret)2. Use the bivariate variable as a continuous variable – run
anova or t-test
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