Data Sources, Collection, and Use

Types of Data Sources

Data sources are the resources used to obtain data for M&E activities. There are several levels from which data can come, including client, program, service environment, population, and geographic levels. Regardless of level, data are commonly divided into two general categories: routine and non-routine.

Routine data sources provide data that are collected on a continuous basis, such as information that clinics collect on the patients utilizing their services. Although these data are collected continuously, processing them and reporting on them usually occur only periodically, for instance, aggregated monthly and reported quarterly.

• Data collection from routine sources is useful because it can provide information on a timely basis. For instance, it can be used effectively to detect and correct problems in service delivery.
  
  
• However, it can be difficult to obtain accurate estimates of catchment areas or target populations through this method, and the quality of the data may be poor because of inaccurate record keeping or incomplete reporting.

Non-routine data sources provide data that are collected on a periodic basis, usually annually or less frequently.

• Using non-routine data avoids the problem of incorrectly estimating the target population when calculating coverage indicators. Another advantage is that both those using and those not using health facilities are included in the data.
  
  
• Non-routine data have two main limitations: collecting them is often expensive, and this collection is done on an irregular basis. In order to make informed program decisions, program managers usually need to receive data at more frequent intervals than non-routine data can accommodate.

Different Sources, Same Indicator

Data from different sources can be used to calculate the same indicator, although changes to the metric may be necessary. This illustration depicts one way that routine and non-routine data can be used together to provide for an effective M&E system.

For example, when calculating the coverage rate for the first dose of a Diphtheria-Tetanus-Pertussis (DTP) vaccine:

If population-based survey data are used, the definition could be proportion of children age 12-23 months who were immunized with the first dose of DTP vaccine before age 12 months.

• Numerator: Number of children age 12-23 months who were immunized with the first dose of DTP vaccine before age 12 months
  
  
• Denominator: Total number of children age 12-23 months surveyed
  

If a routine data source is used, such as clinic records, the definition could be proportion of infants 0-11 months of age in a specified calendar year who were immunized with the first dose of DTP vaccine in that calendar year.

• Numerator: Number immunized by age 12 months with the first dose of DTP vaccine in a given year
  
  
• Denominator: Total number of surviving infants less than 12 months of age in the same year

Data Collection

The M&E plan should include a data collection plan that summarizes information about the data sources needed to monitor and/or evaluate the program.

The plan should include information for each data source such as:

• The timing and frequency of collection
  
  
• The person/agency responsible for the collection
  
  
• The information needed for the indicators
  
  
• Any additional information that will be obtained from the source

Data Quality

Throughout the data collection process it is essential that data quality be monitored and maintained. Data quality is important to consider when determining the usefulness of various data sources; the data collected are most useful when they are of the highest quality.

It is important to use the highest quality data that are obtainable, but this often requires a trade off with what it is feasible to obtain. The highest quality data are usually obtained through the triangulation of data from several sources. It is also important to remember that behavioral and motivational factors on the part of the people collecting and analysing the data can also affect its quality.

Some types of errors or biases common in data collection include:

• Sampling bias: occurs when the sample taken to represent population values is not a representative sample
  
  
• Non-sampling error: all other kinds of mismeasurement, such as courtesy bias, incomplete records, or non-response rates
  
  
• Subjective measurement: occurs when the data are influenced by the measurer

Here are some data quality issues to consider:

• Coverage: Will the data cover all of the elements of interest?
  
  
• Completeness: Is there a complete set of data for each element of interest?
  
  
• Accuracy: Have the instruments been tested to ensure validity and reliability of the data?
  
  
• Frequency: Are the data collected as frequently as needed?
  
  
• Reporting Schedule: Do the available data reflect the time periods of interest?
  
  
• Accessibility: Are the data needed collectable/retrievable?
  
  
• Power: Is the sample size big enough to provide a stable estimate or detect change?

Data Use

The term data refers to raw, unprocessed information while information, or strategic information, usually refers to processed data or data presented in some sort of context.

Collecting data is only meaningful and worthwhile if it is subsequently used for evidence-based decision-making. To be useful, information must be based on quality data, and it also must be communicated effectively to policy makers and other interested stakeholders.

M&E data need to be manageable and timely, reliable, specific to the activities in question, and the results need to be well understood.

The key to effective data use involves linking the data to the decisions that need to be made and to those making these decisions.

The decision-maker needs to be aware of relevant information in order to make informed decisions. For example, if sales data from a program to provide insecticide-treated bednets show that the program is successfully increasing bednet distribution, the decision-maker may decide to maintain the program as is. Alternatively, the data may prompt the implementation of a new distribution system and could spur additional research to test the effectiveness of this new strategy compared to the existing one.

When decision-makers understand the kinds of information that can be used to inform decisions and improve results, they are more likely to seek out and use this information.

Source: www.globalhealthlearning.com

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