Data discrepancy: a key challenge to monitoring MDGs in Africa

Posted in conference, workshops on February 16th, 2010

Data discrepancy is a situation whereby different data collection methods about the same indicator for the same period of time produce statistically different data points. By statistically different, it is meant that although the sampling errors are indicated for the different data points, the data still remains different. It worth noting that differences in the data points are expected for as long as different methodologies are employed in data planning and collection, even for the same indicator. However, it becomes a challenge if the differences are significantly different at the same level of significance. One may require to be availed with all the metadata of the data in question such as the survey design, sample size, population size and number of indicators so as to analyse and come up solutions for the discrepancies in order to inform the indicator monitoring process. Failure to have a metadata analysis system at country level poses a bigger challenge in combating data discrepancies in Africa at country levels.

On the contrary, having multiple sources of data can be healthy for the measurement and monitoring of MDGs at country level. Validation is a basic tool for ensuring data quality and yet there is no better way of data validation than either a repeated survey or use of a similar or multiple data sources. A repeated survey seems a more costly venture than having a compromise amongst multiple data sources measuring the same indicator. Basically, sample surveys are designed to meet specific objectives, but it is also true that certain sample surveys may have the same specific objectives; hence the likelihood to yield more reliable data when used for validation purposes to improve data quality for the timely monitoring of MDG indicators.

Data discrepancy therefore poses a more fundamental challenge to researchers in the field of statistics to develop more reliable methodologies for detection of data discrepancies and align them accordingly. Data discrepancy management systems (DDMS) can then be proposed and developed to handle this eminent challenge to monitoring of MDGs in Africa as we tend towards the year 2015.

Kindly give your opinion below…

3 Responses to “Data discrepancy: a key challenge to monitoring MDGs in Africa”

  1. francis bbosa Says:
    March 3rd, 2010 at 12:22 pm

    This is a very excellent observation but however i would not expect the differences to be significantly different at the same level of significance ceteris paribus .i.e.if other parameters considered are the same.If such a case exists then someone would have to have a look at the exact(physical)data collected and the design used to collect it.

  2. francis bbosa Says:
    March 3rd, 2010 at 12:27 pm

    This is a very excellent observation but however i would not expect the differences in the level of sidnificance to be significantly different at the same level of significance ceteris paribus .i.e.if other parameters considered are the same.If such a case exists then someone would have to have a look at the exact(physical)data collected and the design used to collect it.

  3. francis bbosa Says:
    March 3rd, 2010 at 12:32 pm

    Excellent article but i dont think its possible to have different results at the same level of significance ceteris paribus.i.e.if other factors in the analysis are constant.

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