1. Multiple Indicator Cluster Surveys Data Processing Workshop
Adding Sample Weights,
Wealth Index, and
GPS Data
MICS4 Data Processing Workshop

2. Secondary Data Processing Flow
Export Data from CSPro
Import Data into SPSS
Recode Variables
Add Sample Weights, Wealth Index, and GPS Data
Run Tables
MICS4 Data Processing Workshop

3. MICS4 Data Processing Workshop
Adding Sample Weights
MICS4 Data Processing Workshop

4. Sampling
In most MICS surveys, if not all, samples are not self-weighting
Household samples are selected with different probabilities of selection from each domain of interest
Examples: Regions, area (urban-rural), combination of these (typical in MICS), or other domains

5. Sampling: Example Popstan
Example: the probability of selecting a household for MICS interviews was not equal across all of Popstan
The country has two regions: North and West (which are equal size)
In North region
500 households were selected and interviewed per 10,000
In West Region
250 households were selected and interviewed per 10,000
Which means that overall
750 households were selected and interviewed from 20,000
MICS4 Data Processing Workshop

6. MICS4 Data Processing Workshop
Sample Weights
Sample weights are used to adjust the sample to produce accurate estimates for the whole country
Sample weights are the inverse of the probabilities of selection
For example, the weights for North and West region
North region 10,000/500 = 20
West region 10,000/250 = 40
In North region, each household selected represents 20 households in that region – same figure is 40 in West
MICS4 Data Processing Workshop

7. Sample Weights
Overall, every household selected in Popstan represents households (20000/750)
In other words, relative to a proportional selection (should be 375 households selected from each region), more households have been selected from North, less have been selected from West

8. Sample Weights
This has to be “compensated” by using sample weights during analysis to re-calibrate the sample to the national level

9. Sample weights Weights should always be used when tabulating
Sample weights will have two components
The initial probability of selection
Non-response: We have to take into account what proportion of households (women, under-5s) we have successfully interviewed
In Popstan North region, if the sample was initially selected with a probability of 500 households per 10,000, but we then were able to successfully interview 450, the final sample weight should be calculated based on 450, not on 500

10. Why sample weights
25 percent of households in North use improved water sources
75 percent of households in West use improved water sources
If the sample was selected proportionally (375 households from each region), then our survey estimate would be
((375 * 0.25) + (375 * 0.75)) / 750 = 0.50

11. Why sample weights
If we do not weight, then our national estimate will be
((500 * 0.25) + (250 * 0.75)) / 750 = 0.417
Because, we have over-sampled a region where use of improved water sources is less
We need to calculate sample weights to “correct” this situation

12. -----------------------------------------------
Why sample weights
If we assigned a weight of 20 to each household in North, and 40 to each household in West, this would do the trick
(500 * 20 * 0.25) + (250 * 40 * 0.75)
(500 * 20) + (250 * 40)
= 0.50

13. Why sample weights
This is fine, but SPSS tables would show households as the denominator
We do not want this
So, we normalize the weights
We calibrate (normalize) them so that the average of the weights in the data set is equal to 1

14. -------------------------------------------------- = -----
Why sample weights
The normalized weight for the North region is calculated as (10000/500)/(20000/750) = 0.75
And for the West region, (10000/250)/(20000/750)
= 1.5
When we calculate the national use of improved water sources by using normalized weights,
(500 * 0.75 * 0.25) + (250 * 1.5 * 0.75) 375 =
(500 * 0.75) + (250 * 1.5) 750

15. Sample weights
Based on the design of the sample, there are two (common) approaches to calculating weights:
Each cluster has a unique sample weight (weights.xls)
Each stratum has a unique sample weight (weights_alt.xls)
We have templates for both. You will need to work with your sampling expert to see which one you will use

16. Sample Weights Objects
weights.xls
spreadsheet that calculates weights
weights_table.sps
SPSS program that provides input data for spreadsheet
weights.sps
SPSS program that defines structure of spreadsheet’s output
weights_merge.sps
SPSS program that merges weights onto the MICS data files
MICS4 Data Processing Workshop

17. Steps in Adding Weights
1. Update weights.xls to have one row per strata or cluster depending on sample design
2. Add sampling information to weights.xls
3. Adapt strata definitions in weights_table.sps
4. Execute weights_table.sps program
5. Copy resulting table’s contents into “Calculations” sheet of weights.xls
6. Save “Output” sheet of weights.xls as weights.xls in directory c:\mics4\weights
7. Execute weights_merge.sps program
MICS4 Data Processing Workshop

18. Step 1: Updating weights.xls
Spreadsheet has one row per cluster
Adjust the number of rows in “Calculations” to reflect the number of clusters in your survey
do so by copying and pasting internal rows
Check that the totals cells have the correct ranges
Adjust the number of rows in “Output”
Check that data in “Output” is correct
MICS4 Data Processing Workshop

19. Step 2: Adding Sampling Info
Open weights.xls
Complete columns C and D – probabilities of selection of households in a cluster, and of clusters in a stratum or
Complete the “stratum sampling fraction” column
MICS4 Data Processing Workshop

20. MICS4 Data Processing Workshop
Step 3: Defining Strata
Your survey has sampling strata. Examples:
all combinations of area (HH6) and region (HH7)
region
Lines 3-10 of weights_table.sps define the standard survey’s strata
Update these statements to reflect the definition of strata in your country
MICS4 Data Processing Workshop

21. Step 4: Executing weights_table.sps
Open weights_table.sps in SPSS
Select Run--->all
Check output for error messages
Examine output table
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22. MICS4 Data Processing Workshop
Step 5: Copying Output
Double-click inside the table to open it
Select the household results
Paste them in the “Calculations” sheet of weights.xls
Repeat for the women and children results
Save weights.xls
MICS4 Data Processing Workshop

23. Step 6: Saving the Output Sheet
Click on the “output” tab in the weights.xls spreadsheet
Select File ---> Save As
Navigate to the directory c:\mics4\spss
Save under name weights.xls
Click the save button
MICS4 Data Processing Workshop

24. Step 7: Merging Weights into SPSS
Open weights_merge.sps in SPSS
Select Run ---> all
Check output for error messages
Open each data file—HH, HL, TN, WM, BH, and CH — and check that weights were correctly added
MICS4 Data Processing Workshop

25. MICS4 Data Processing Workshop
weights_merge.sps
Source Files: c:\mics4\spss\weights.sav Destination Files: HH.sav, HL.sav, TN.sav, WM.sav, BH.sav, FG.sav, CH.sav, MN.sav Match By: HH1 Variables Added: xxWeight where xx is HH, WM, CH, MN, TN, BH, FG file
MICS4 Data Processing Workshop

26. MICS4 Data Processing Workshop
Wealth Index
MICS4 Data Processing Workshop

27. MICS4 Data Processing Workshop
The Wealth Index
The MICS wealth index is an attempt to measure the socio-economic status of households
The analysis section of this process will be done at the 3rd workshop
The goal today is to discuss the programs and how they work
MICS4 Data Processing Workshop

28. The Wealth Index But briefly
The wealth index is a method to divide households into 5 groups of equal size (quintiles) in terms of “wealth” – from poorest to richest
“Wealth” is constructed by using information on household characteristics (crowding), amenities (water and sanitation), household assets (durable goods) owned by households
Useful in the absence of information on income and expenditures

29. MICS4 Data Processing Workshop
Wealth Index Programs
The program related to the wealth index is:
wealth.sps—This program calculates the wealth index and merges the wealth index values to the SPSS data files
MICS4 Data Processing Workshop

30. MICS4 Data Processing Workshop
wealth.sps
Calculates a wealth index using factor analysis
Inputs:
dichotomous variables related to household/ individual assets
Outputs:
wscore - a wealth index score for each household
windex5 - a wealth quintile for each household
MICS4 Data Processing Workshop

31. MICS4 Data Processing Workshop
A Recoding Example
Code below creates variable with value 1 if household owns a car, value 0 otherwise
Recode hc9f (1=1) (9=9) (else=0) into car.
variable label car 'Household member owns: car/truck'.
value label car 0 'No' 1 'Yes'.
Missing values car (9).
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32. MICS4 Data Processing Workshop
The Rest of the Program
The factor statement
creates wealth index score
The compute statement
generates household member weights
The rank statement
creates wealth quintiles
The save outfile statement
saves wealth variables in wealth.sav file
MICS4 Data Processing Workshop

33. The rest of the program
Calls each file (hh.sav, hl.sav, wm.sav, ch.sav, tn.sav, bh.sav, fg.sav, mn.sav) at a time, and based on HH1 and HH2, adds wealth index variables (windex5 and wscore).
Saves data files with wealth variables.

34. MICS4 Data Processing Workshop
GPS
MICS4 Data Processing Workshop

35. MICS4 Data Processing Workshop
GPS Readings
Some countries will take GPS readings during their MICS survey
These readings allow researchers to merge diverse data sets using a cluster’s location
Data sets that can be linked to the MICS data
Climate data
Agricultural data
MICS4 Data Processing Workshop

36. MICS4 Data Processing Workshop
The GPS Form
MICS4 Data Processing Workshop

37. MICS4 Data Processing Workshop
GPS Programs
GPS.dcf
CSPro dictionary
GPSEntry.ent
CSPro data entry application
GPS.sps
SPSS version of GPS.dcf
GPS_merge.SPS
reads in GPS data and merges it onto SPSS data files
MICS4 Data Processing Workshop

38. MICS4 Data Processing Workshop
gps_merge.sps
Source Files: c:\mics4\spss\gps.dat Destination Files: HH.sav, HL.sav, TN.sav, WM.sav, BH.sav, CH.sav, MN.sav Match By: HH1 Variables Added: all variables on GPS form
MICS4 Data Processing Workshop