Multiple indicator cluster surveys data dissemination and further analysis workshop
Download
1 / 34

Multiple Indicator Cluster Surveys Data Dissemination and Further Analysis Workshop - PowerPoint PPT Presentation


  • 74 Views
  • Uploaded on

Multiple Indicator Cluster Surveys Data Dissemination and Further Analysis Workshop. Data Quality from MICS4. Looking at data quality – Why?. Confidence in survey results Identify limitations in results Inform dissemination and policy formulation All surveys are subject to errors.

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about ' Multiple Indicator Cluster Surveys Data Dissemination and Further Analysis Workshop' - loc


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
Multiple indicator cluster surveys data dissemination and further analysis workshop

Multiple Indicator Cluster SurveysData Dissemination and Further Analysis Workshop

Data Quality from MICS4

MICS4 Data Dissemination and Further Analysis Workshop


Looking at data quality why
Looking at data quality – Why?

  • Confidence in survey results

  • Identify limitations in results

    • Inform dissemination and policy formulation

  • All surveys are subject to errors


Data quality
Data quality

  • Two types of errors in surveys

    • Sampling errors

    • Non-sampling errors: All other types of errors, due to any stage of the survey process other than the sample design, including

      • Management decisions

      • Data processing

      • Fieldwork performance, etc

  • All survey stages are interconnected and play roles in non-sampling errors


Data quality1
Data quality

  • Sampling errors can be estimated before data collection, and measured after data collection

  • More difficult to control and/or identify non-sampling errors


Data quality2
Data quality

  • MICS incorporates several features to minimize non-sampling errors: A series of recommendations for quality assurance, including

    • Roles and responsibilities of fieldwork teams

    • Easy-to-use data processing programs

    • Training length and content

    • Editing and supervision guidelines

    • Survey tools

  • Failure to comply with principles behind these recommendations leads to problems in data quality


Data quality3
Data quality

  • Survey tools to monitor and improve quality, assess quality, identify non-sampling errors

    • Field check tables to quantitatively identify non-sampling errors during data collection and to improve quality

      • Possible with simultaneous data entry, when data collection is not too rapid

    • Data quality tables to be produced at the time of final report


Data quality4
Data quality

  • Results from data quality tables used to

    • Identify departures from expected patterns

    • Identify departures from recommended procedures

    • Check internal consistency

    • Completeness

    • Produce indicators of performance


Data quality analyses
Data quality analyses

  • Various surveys from different regions used to

    • compile

    • aggregate

    • graphically illustrate

      results in data quality tables


Age distribution by sex dq1
Age distribution by sex (DQ1)

  • Age heaping

  • Out-transference

  • Omission

  • Extent of missing cases

  • Sex ratios


Low percentages in household lists with missing data on age

Evidence of out-transference and/or heaping for under-5s

Out-transference from age 15 – for women

Large heaping on age 50 – possible out-transference but also digit preference for males and females









Completion rates by age women and under 5s dq2 dq3 dq4 dq5
Completion rates by age - women and under-5s (DQ2, DQ3, DQ4, DQ5)

  • Fieldwork performance – re-visits, good planning

  • Completion rates need to be high, but also uniform by age and background characteristics

  • Low completion rates in younger women, for better-off groups, for less accessible or challenging areas are likely to bias results


Severe heaping on age 5 or more probably, out-transference (C3, C4, C8, C9)

Completion rates are high for all ages


High ratios of women age 50-54 to 45-49 (C3, C4, C8, C9)

Completion rates generally high in all age groups, but very low for young women in one country (C8)

Completion rates by quintiles not alarmingly different


Completeness of reporting various dq6
Completeness of reporting – various (DQ6) (C3, C4, C8, C9)

  • Missing information is problematic in surveys

  • Rule of thumb: Keep missing/don’t know/other cases to less than 10 percent – larger percentages may lead to biased results

  • No tolerance to missing information on some key variables, such as age, date of last birth


Good results for salt testing (C3, C4, C8, C9)

On other key variables, C2 and C5 look very problematic

Poor performance on dates has an impact on (almost) all other indicators in the survey – from eligibility to calculation of indicators


Completeness of anthropometric data dq7
Completeness of anthropometric data (DQ7) (C3, C4, C8, C9)

  • Assessing data quality of anthropometric indicators is relatively easy

  • Many tools have been developed for this purpose

  • Expected patterns, recommended procedures, completeness

  • Completeness of anthropometric data influenced by

    • Birth date reporting

    • Children not weighed, measured

    • Bad quality measurements


Large percentages of children excluded from analysis in two surveys (C2, C3)

Both incomplete date of birth and poor quality measurements can be responsible

Low percentages of children not weighed


Completeness of anthropometric data by age may be a concern, if large differences exist – may lead to biases in the anthropometric indicators

Usually U or J shaped distributions – large ranges in completion by age in some surveys (e.g. C2)


Heaping in anthropometric data dq8
Heaping in anthropometric data (DQ8) if large differences exist – may lead to biases in the anthropometric indicators

  • “Digit preference” – failure to record decimal points, or round…

  • Or even worse, truncate

  • Is known to have significant impact on results

  • Systematic truncation of measurement results can lead to biases of up to 5-10 percent in anthropometric indicators

  • May be due to insufficient training, use of non-recommended equipment



Digit preference in weight and height measurements
Digit preference in weight and height measurements if large differences exist – may lead to biases in the anthropometric indicators


Observation of documents dq10 dq12
Observation of documents (DQ10-DQ12) if large differences exist – may lead to biases in the anthropometric indicators

  • Objective is to see the maximum percentage of specified documents, and copy information from documents onto questionnaires

  • Better quality when majority of information is coming from documents



Respondent for under 5 questionnaire dq13
Respondent for under-5 questionnaire (DQ13) documents seen

  • Respondent for under-5 questionnaire (DQ13)

  • Random selection for child discipline module (DQ14)

  • Sex ratios among children ever born and living (DQ16)


Mothers are found and interviewed documents seen

Correct selection of children for child discipline module

Sex ratios among children ever born are expected to be around 1.05, within the range 1.02 to 1.07 – see C2, C8

Expected pattern: higher sex ratios among children deceased – C8?




ad