Create Categorical Outcome Variables and Run Frequency Tables : 

STUNTING, WASTING, and UNDERWEIGHT


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Using the Eastern Kenya data set, first run descriptives for HAZ, WHZ, and WAZ to verify that they have been properly cleaned.

1.  Open keast4j.sav

2.  Under the Statistics, Summarize,Descriptives.

3.  Select haz (haz std. deviations), waz (waz std. deviations), and whz (whz std. deviations).

4.  Under Options, you can select other statistics to be generated (e.g. Mean, Minimum, Maximum, Std. Deviation) and click on Continue.

6.  Click OK.

wpe12.jpg (13580 bytes)

INTERPRETATION:

From the output, it is clear that the data has been cleaned and therefore is ready for categorization into new outcome variables.  The minimum and maximum are within +5 and -5 standard deviations of the mean for WAZ, WHZ, and HAZ and the mean and median values are not drastically different to be wary of severe polarity of the data to one extreme or the other.


If you were to need to produce CATEGORICAL Anthropometric Data, the following exercise would be used used.  These variables already exist in the Eastern Kenya Data set, but to produce categorical anthropometric variables for other data sets, follow these steps:  

1.  Open the data set of interest

2.  Under the Transform menu, select Recode - Into Different Variables.

3.  Select haz - output is called stunt (click change)

4Select waz - output is called under (click change)

5.  Select whz - output is called waste (click change)

6.  Click on Old and New Values

7.  Under Old values click on Range, Lowest through [blank] and type -2.01. Its New Value is '1'. Click on Add.

8.  Under Old values, click on Range, [blank] through highest, and type -2.00. Its New Value is '0'. Click on Add.

9.  Click on Continue.

10.  Click on OK.

 

Now you have new variables called stunt, under, and waste. You will have to make a few modifications to them before running the frequencies. They will be the last three columns in the data set.

 

Make these modifications to the new variable stunt:

1.  Go to stunt and double click on the variable name (in the gray area - stunt).

2.  Click on Type and change Width to 4 and 0 decimal places.

3.  Click on Labels. Type Stunted in the variable label.

4.  For value, type '1' and value label is '< -2.00 haz' - click on Add.

5.  Next, type '0' in value and call it '>= -2.00 haz' and click Add.

6.  Click on Column Format and change the width to '5'.

7.  Click OK.

 

Go through the same steps to create the same categories for under (waz) and waste (whz).


Now produce frequencies for each of the CATEGORICAL anthropometric data:

1.  Open keast4j.sav

2.  Click on Statistics, Summarize, Frequencies.

3.  In the Frequencies box, select the three variables for stunting- haprev, wasting- whprev, and underweight- waprev.   Place each variable into the variable box one by one using the arrow key.

4.  Be sure the check mark  remains in the Display frequency tables box.

5.  Press OK.

 

For STUNTING:

wpe5.jpg (10485 bytes)

For Wasting: 

wpe6.jpg (9452 bytes)

For Underweight:

wpe7.jpg (11965 bytes)

INTERPRETATION:

In the Eastern Region of Kenya, about 34% of the children under 5 are stunted (<-2 SD height-for-age), approximately 7% of the children are wasted (<-2 SD weight-for-height), and almost 30% of the children are underweight (<-2 SD weight-for-age). All of these are above the expected frequencies of low z-scores in a normal population.

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