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Multiple Indicator Cluster Surveys Data Processing Workshop

Multiple Indicator Cluster Surveys Data Processing Workshop. Adding Sample Weights, Wealth Index, and GPS Data. Secondary Data Processing Flow. Export Data from CSPro. Import Data into SPSS. Recode Variables. Add Sample Weights, Wealth Index, and GPS Data. Run Tables.

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Multiple Indicator Cluster Surveys Data Processing Workshop

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  1. Multiple Indicator Cluster SurveysData 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. 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. 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 26.6667 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 20000 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. 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.spsdefine 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 MICS4 Data Processing Workshop

  22. 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.xlsspreadsheet • 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. 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. Wealth Index MICS4 Data Processing Workshop

  27. 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. 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. 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. 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). MICS4 Data Processing Workshop

  32. 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. GPS MICS4 Data Processing Workshop

  35. 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. The GPS Form MICS4 Data Processing Workshop

  37. 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. 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

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