No Baseline Data Was Collected? It is Not Game Over

No Baseline Data Was Collected? It is Not Game Over

Updated: Jun 3

In the last blog post it was discussed that ideally, baseline data should be collected prior to the implementation of a project or programme. However, this does always happen. Sometimes a project is running for several years without even as much as a thought for the collection of baseline data. There are a myriad of reasons why this occurs.

Common reasons why baseline data are not collected

· A man-made/ natural disaster or civil unrest may not have made it possible to determine baseline conditions before implementation begins (Church and Rogers, 2006)

· There may have been a lack of awareness on the value of baseline data for monitoring purposes. Some persons truly believe that baseline data is only useful and relevant for an evaluation. As such, the need for baseline information only enters their consciousness when the project is half way to completion or drawing to a close (when either a mid-term or end evaluation is looming).

· In some cases there is an appreciation of baseline data for monitoring purposes, however the financial resources may be limited. As such, the baseline study may be scrapped from the budget in favour of funding project activities.

· Likewise, the administrative procedures involved in the conducting of a baseline study may be too onerous. For example, drafting Terms of Reference for the baseline study, recruiting and training M&E staff, commissioning consultants etc. may create long delays before baseline data can be collected (Bamberger, 2010).

However, the non-existence of baseline data does not have to spell the doom and gloom.

It can be rectified in two ways; reconstruction of baseline data and/or the use of an evaluation method that does not rely on the prior collection of baseline data. Let’s look at each option in greater detail.

Reconstruction of Baseline Data

In the event that a baseline study was not conducted prior or near the implementation of a project, it may be possible to approximate the preexisting conditions through the use of secondary data. For example, there may be national studies and reports on the literacy levels of children in the given area that pre dates the project start date. Other examples include census, survey data from government agencies, academic papers etc. The project data collected years ago when implementation just started can be another source of secondary data. Likewise data from the needs assessment, feasibility study and other internal project records such as registration forms, on-going monitoring reports, meeting minutes etc. (IFRC, 2013).

Additionally, surveys and key informant interviews may be employed to obtain information relevant to the prevailing conditions prior to the implementation of the project. Based on the nature of the intervention, the questions may be related to knowledge, attitude, behavior, awareness, access to services, participation and engagement, quality of service etc. before the project started. For example, in a project which sought to improve the physical infrastructure within a community, persons may be asked ‘Could you please describe the state of the roads in your community 3 years ago?’

However, keep in mind that the respondents who are asked to recall historical information may be susceptible to ‘selected memory’ and bias (IFRC, 2013). As such, it is wise to triangulate the data received from the respondents with other data sources. For example, a report from the Ministry from Land and Environment from 3 years ago that would have information on the state on the roads during that period.

Use of certain monitoring and evaluation methods

A second way of dealing with not having a baseline study or baseline data is the use of monitoring and evaluating methods for that don’t rely on this type of data. As a matter of fact, these methods don’t even rely on having predetermined objectives or indicators. These methods include Most Significant Change and Outcomes Harvesting. There are sometimes referred to as ‘complexity-aware’ methods (USAID, 2016). You can read more on these methods in my other blog articles.

If there is a way for a snowman to ‘chill’ in the sun, ways exist for a project to be monitored and evaluated without a previous baseline study

I hope you found this blog post helpful. Please feel free to share any other tips or useful information on baselines in the Comments section below.

Reference materials:

Bamberger (2010). Reconstructing Baseline Data for Impact Evaluation and Results Management

Church and Rogers (2006). Designing for results: Integrating Monitoring and Evaluation in Conflict Transformation Programs

IFRC (2013), Baseline Basics


#baselinestudy #baseline #complexityawareevaluation #MostSignificantChangeMSC #OutcomesHarvesting

A member of the following Professional Associations and Charity organisation

  • Facebook Clean Grey
  • Twitter Clean Grey
  • Google+ Clean Grey
  • LinkedIn Clean Grey

Dutch Association for Evaluators


Business Registration Number (KvK):69029334  |  The Hague, The Netherlands  |  © 2020 Ann-Murray Brown Consultancy