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Multiple Indicator Cluster Surveys Data Dissemination and Further Analysis Workshop. Data Quality and Sampling Error Tables in MICS Reports. Data quality tables.
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Multiple Indicator Cluster SurveysData Dissemination and Further Analysis Workshop Data Quality and Sampling Error Tables in MICS Reports MICS4 Data Dissemination and Further Analysis Workshop
Data quality tables • One of the MICS primary goals is to produce high quality, statistically sound and internationally comparable estimates of indicators. • The quality of MICS data is assured by several processes: • Recommended training and field work supervision • Double data entry, consistency checks, secondary editing • Field check tables generated on a regular bases with goal to indicate potential problems in the field, etc.
Data quality tables • After field work is completed 16 tables are produced for assessment of data quality. • Intended to check distributions, heaping, understatement or overstatement, sex ratios, eligibility and coverage, out-transference of eligible persons, the extent of missing information, outliers, sex ratios, quality of anthropometric measurements. • Useful for understanding quality issues, familiarity with issues in data sets, indicative of the quality of training and implementation.
Age distribution of eligible and interviewed women, example country, 2010
Completion rates for under-5 questionnaires by socio-economic characteristics of households, example country, 2010
Completeness of reporting, example country, year Under 5 questionnaire Women questionnaire
Completeness of information for anthropometric indicators, example country, year Example 1 Example 2
Selection of children age 2-14 years for the child discipline module, example country, 2010
Sampling Error Tables: Background The sample selected in a survey is one of the many samples that could have been selected (with same design and size). Sampling errors are measures of the variability between all possible samples, which can be estimated from survey results.
Sampling Error Tables: Background • Calculation of sampling errors is very important; • Provides information on the reliability of your results • Tells you the ranges within which your estimates most probably fall • Provides clues as to the sample sizes (and designs) to be selected in forthcoming surveys
Sampling Error Tables: Background MICS4 sample designs are complex designs, usually based on stratified, multi-stage, cluster samples. It is not possible to use straightforward formula for the calculation of sampling errors. Sophisticated approaches have to be used. Versions 13 and above of SPSS are used for this purpose. SPSS uses Taylor linearization method of variance estimation for survey estimates that are means or proportions. This approach is used by most other package programs: Wesvar, Sudaan, Systat, EpiInfo, SAS
Sampling Error Tables: Background In MICS4, the objective is to calculate sampling errors for a selection of variables, for the national sample, as well as selected sub-populations, such as urban and rural areas, and regions.
Standard error is the square root of the variance – a measure of the variability between all possible samples
Coefficient of variation (relative error) is the ratio of SE to the estimate
Design effect is the ratio between the SE using the current design and the SE that would result if a simple random sample was used. A DEFT value of 1.0 indicates that the sample is as efficient as a SRS
Upper and lower confidence limits are calculated as p +/- 2.SE Indicate the ranges within which the estimate would fall in 95 percent of all possible samples of identical design and size