Mean Outcome by Independent Variables
Mean WAZ by Roofing Type and Education
Mean AC prevalence by Latrine and Water Type

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Individually: Roofing

First, examine each independent variable in relation with the outcome to determine the associations.  At this point allowances for any further cleaning of the independent variable or determining if it should be further categorized (to eliminate cells under a count of 5 for further analysis) can be made.

1.  Open keast4j.sav

2.  Click on Statistics, Compare Means, and Means.

3.  In the Dependent list, enter the variables waz (waz SD) and waprev (prev waz <-2 SD) from the left hand variable list

4.  In the Independent list, enter the variable for roofing type, roof and level of education of the respondent, edn.

5.   Click on the Options box and check the ANOVA table box under the area labeled Statistics for the first layer.

6.  Click on Continue and OK

First, notice the numbers (N) in each cell in the table. The group with tiles as roofing is very small, insignificant for consideration. Although the group with tiled roofs is better off nutritionally indicative of the higher SES, the two individuals that make up this category will be deselected for further analysis here.

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To deselect the group with tiles:

1.  In the Data Editor, click on Utilities, Variables, and then select the variable named roof from the list. Once you click on roof, it will give all of the details of that variable including those that are coded as missing (99).

2.  Click on Go To and it will take the cursor to highlight this variable in the data set.

3.  Double click on the word roof at the top of the column in the dataset and a Define Variable box will appear. Then click on the box labeled Missing Values and in the second Discrete value box, add the number 31 (so now both 99 and 31 are missing discrete values).

4.  Click on Continue, OK.

5.  Now, rerun the exercise to calculate the mean above, step 1-5 (now without tiles for roofing).

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The mean z-score for the grass /thatch (lower income indicator) is much lower than that of the corrugated iron roof group (higher income indicator). The mean z-score for low SES is -1.46 and high SES is -1.20, which corresponds in the right hand column to a prevalence (<-2 SD WAZ) of 38% and 26%, respectively.  The ANOVA results show that this difference is significant, as the p= 0.01. 

The ANOVA presentation here has two areas of interest for practical application which are the 'F-STATISTIC' AND 'SIGNIFICANCE'.

Individually: Education

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Running the analysis table by educational attainment, the results are as shown here, also in the expected direction. There is a decreasing prevalence of malnutrition in each higher educational category, and an increasing mean weight for age z-score.  The largest difference in the results is seen between the group that did and the group that did not complete primary level education (-1.1 to -1.5 SD WAZ and 37% to 20% prevalence of underweight).  The ANOVA results support that the difference between malnutrition levels in different educational groups is significantly different (p=0.000). 

Together:  Roofing and Education

We are now looking for the combined effect of education within category of housing quality-- in other words removing the effect of housing, as far as we can by using the two categories. When running an analysis with two independent variables together, the results will be presented in layers for each category. To run the tables, either the Compare means function can be used like we have done for the one independent variable.  There is one problem with this option though, it will not give the ANOVA with both education and roofing in the model.  Instead, it is necessary to run the analysis using General Linear Model (GLM), which will give both the tabular presentation and the ANOVA table. 

1.  Open keast4j.sav

2.  Select Statistics, General Linear Model, GLM – General Factorial

3.  Select waz and move it to the Dependent Variable field.

4.  Select edn (educational attainment of respondent) and roof (good/ bad roof), and move them into the Fixed Factor(s): field.

5.  Click the Options… button.

6.  Click on Display, Descriptive Statistics and Observed Power.

7.  Click Continue.

8.  Click OK.

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Within each educational group, there is a pattern in the expected direction of mean z-scores for roofing type, where individuals with grass/ thatch have lower mean z-scores than those with iron roofs. Also, malnutrition seems to decrease within increasing categories of education (seen by totals). Both education and roof type have the association with nutrition status of the child that follows the expected direction, but it appears that education has the stronger association.

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In the ANOVA, an additional factor called an INTERACTION variable is included.  It is labeled ROOF*EDN in the 'Tests Between Subjects Effects' output. Interaction determines if there is an association between the two variables (ROOF and EDN) to a degree that would change the effect of education or roofing individually. In this case, the interaction of education and roofing is not significant at the 0.05 level. Since the interaction variable is not significant, it is good to run the same exercise without the interaction term and reinterpret the results (Interaction will be covered in far more detail on page 2- Interaction of chapter 4).  To do so:

1.  Open keast4j.sav

2.  Select Statistics, General Linear Model, GLM – General FactorialO.K.

3.  Select. waz and move it to the Dependent Variable field.

4.  Select edn (educational attainment) and roof (good/ bad roof), and move them into the Fixed Factor(s): field.

5.  Click the Model button and select the dot marked Custom, then  move edn and roof from the box called Factors and Covariates and enter them into the box labeled Model using the arrow button.

6.  Click on the down arrow under the Build terms box and select Main Effects.

7.  Click Continue.

8.  Click OK.


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From the ANOVA without the interaction term, it appears that education and nutritional status has a more significant result ( p= 0.000) than for roof and nutritional status (p=0.133).  When both education and roof are in the model, the association with roof is no longer significant above the effect of education.  This does not indicate that roofing (an estimate of SES) is not an important determinant of nutrition status, but the size of the effect is much smaller when education of the respondent is considered in the model.   The results show that the level of education is a strong determinant of the nutritional status of the child and roofing is not as strong once education is accounted for. The next  task is to present this data, return to the two-way page 1 to see this.

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