Data were processed in clusters, with each cluster being processed as a complete unit through each stage of data processing. Each cluster goes through the following steps:
1) Questionnaire reception
2) Office editing and coding
3) Data entry
4) Structure and completeness checking
5) Verification entry
6) Comparison of verification data
7) Back up of raw data
8) Secondary editing
9) Edited data back up
After all clusters are processed, all data is concatenated together and then the following steps are completed for all data files:
10) Export to SPSS in 4 files (hh - household, hl - household members, wm - women, ch - children under 5)
11) Recoding of variables needed for analysis
12) Adding of sample weights
13) Calculation of wealth quintiles and merging into data
14) Structural checking of SPSS files
15) Data quality tabulations
16) Production of analysis tabulations
Details of each of these steps can be found in the data processing documentation, data editing guidelines, data processing programs in CSPro and SPSS, and tabulation guidelines in the MICS manual http://www.childinfo.org/mics/mics3/manual.php
Data entry was conducted by 8 data entry operators in tow shifts, supervised by 1 data entry supervisors, using a total of 9 computers (8 data entry computers plus one supervisor's computer). All data entry was conducted at the NSO using manual data entry. For data entry, CSPro version 2.6.007 was used with a highly structured data entry program, using system controlled approach that controlled entry of each variable. All range checks and skips were controlled by the program and operators could not override these. A limited set of consistency checks were also included in the data entry program. In addition, the calculation of anthropometric Z-scores was also included in the data entry programs for use during analysis. Open-ended responses ("Other" answers) were not entered or coded, except in rare circumstances where the response matched an existing code in the questionnaire.
Structure and completeness checking ensured that all questionnaires for the cluster had been entered, were structurally sound, and that women's and children's questionnaires existed for each eligible woman and child.
100% verification of all variables was performed using independent verification, i.e. double entry of data, with separate comparison of data followed by modification of one or both datasets to correct keying errors by original operators who first keyed the files.
After completion of all processing in CSPro, all individual cluster files were backed up before concatenating data together using the CSPro file concatenate utility.
For tabulation and analysis SPSS versions 13.0 and 14.0 were used. Version 13.0 was originally used for all tabulation programs, except for child mortality. Later version 14.0 was used for child mortality, data quality tabulations and other analysis activities.
After transferring all files to SPSS, certain variables were recoded for use as background characteristics in the tabulation of the data, including grouping age, education, and geographic areas as needed for analysis. In the process of recoding ages and dates some random imputation of dates (within calculated constraints) was performed to handle missing or "don't know" ages or dates.
Additionally, a wealth (asset) index of household members was calculated using principal components analysis, based on household assets, and both the score and quintiles were included in the datasets for use in tabulations. Conventionally, household economic status is being is defined by the data of household income and expenditure. This conventional method of data collection is time consuming (each household member is asked numerous questions by each of income sources). Besides such a method can result in incompleteness of data (interviewee may be unaware of income and expenditures of other members) and be challenged by irregularity of household economic activities and difficulties of capturing the higher incomes. Therefore, the current survey has estimated the indicator “wealth Index” to measure the household wealth which can be captured by a few and simple questions. For this purpose, it is quite possible to use the questions asked to measure other indicators (drinking water, sanitation facilities, housing type, access to electricity). One advantage of this index is to lessen the data effect of seasonal and temporary income sources as the index concentrates on assets or capitals accumulated over the longer period. (Rutstein & Johnson, 2004). The survey results were estimated by five equally weighted groups of wealth index. This includes the indicators of household type, condition, drinking water, sanitation facility, access to electricity, household consumerables (communications and transportation means, household electrical appliances). Using these indicators, each household was then weighted by the number of household members, and the household population was divided into five groups of equal size, from the poorest quintile to the richest quintile, based on the wealth scores of households they were living in. Total households were put in five groups with the following categories: poorest (I), second (II), middle (III), fourth (IV), richest (V).
The survey data has been disaggregated by national average, regions, urban and rural areas with household location and estimated by women education level and five wealth groups of household with equal weighting.
Regions: Western, Khangai, Central, Eastern and Ulaanbaatar
Location: Capital city, Aimag center, Soum center, Countryside
Urban, rural areas: Capital city and aimag centers are counted for urban areas and soum centers and the countryside makes up the category of rural areas.
Wealth index quintiles: Poorest (I), Second (II), Middle (III), Fourth (IV), Richest (V)