**Chi
Square Analysis**

Chi Square according to SPSS 8.0 :

Tests the hypothesis that the row and column variables are independent, without indicating
strength or direction of the relationship. Pearson chi-square, likelihood-ratio
chi-square, and linear-by-linear association chi-square are displayed. For 2x2 tables,
Fisher's exact test is computed when a table that does not result from missing rows or
columns in a larger table has a cell with an expected frequency of less than 5. Yates'
corrected chi-square is computed for all other 2x2 tables.

To run a Chi Square Analysis:

1. Open **keast4j.sav**

2. Click on *Statistics, Summarize, Crosstabs* and enter **roof
**in the Row and **wazprev** in the Column.

3. Click on the button marked *Statistics* and click on the box for
**chi-square** then *Continue.*

4. Click on the button marked *Cells* and choose the box for **row**
*Percentages* and click *Continue*.

5. Click on *OK*.

The output will look like the following:

What is indicated in the results of the cross-tab is that there is a greater chance of finding a malnourished child among those with Grass /Thatch houses (38%) then out of those with Corrugated Iron houses (26%). As long as all of the cells have at least a count of 5, then use the Pearson Chi-Square for the significance level, which is 0.001 in this case. If any cell is less than 5, then use Fisher's Exact Test for the significance value. The value of 0.001 tells us that the difference in malnutrition levels seen between those with good versus poor roof is likely not by chance (>1/ 1000). Roofing (an estimate of SES) has gained evidence towards having a true association with nutritional status.