Mean Scores and ANOVA Tables


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Try the following exercise:

1. Open keast4j.sav

2. Click on Statistics, Compare Means, Means

3. Select the variables of interest that are bivariate in nature (or categorized) such as dlowedn (education level), lowlit (low/not low literacy level), dbadro (good/ bad roofing), watsourc (good / bad water source) and notoliet (no/yes toilet access). Place these variables in the Independent variable box

4. Select the variable waz and place it in the Dependent variable box

5. Click on Options and select the options for Mean, Std. deviation, Number of Cases and for Statistics for the first layer click on the box for ANOVA table

6. Click on Continue and OK.

The mean scores and ANOVA tables with P-values look like this in SPSS output. These can be sorted through and the interesting ones can be presented in a table for easy reading. Look at the output below, and a section will follow to show an interesting method for presentation. (Use the P = 0.05 to approximate a significant finding).

 

ROOFING (SES)

wpe15.jpg (7428 bytes)

wpe16.jpg (12474 bytes)

 

WATER SOURCE

wpe17.jpg (7812 bytes)

wpe18.jpg (12242 bytes)

 

EDUCATION

wpe19.jpg (8434 bytes)

wpe1A.jpg (10826 bytes)

 

TOILET (SANITATION)

wpe1B.jpg (8812 bytes)

wpe1C.jpg (10922 bytes)

The results for all of the variables come out with the expected associations, and the differences for roofing, education and sanitation are all significant (p-value < 0.05).  The one non-significant association is that for water source, mostly influenced by the small cell size for the group of districts that have safe water (n=33).  The size of the difference is still large, and would be the primary interest here (-0.98 SD WAZ vs. -1.30 SD WAZ).

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