DATA VISUALIZATION

One of the constant features of M&E work is the representation of data. M&E personnel find themselves in positions where they need to or have to represent data to different audiences such as donors, local level stakeholders, and organizational hierarchy among others. Not planning how to represent or visualize data means that M&E personnel find themselves in a situation where the method they choose is inappropriate for the audience, or generally becomes misleading.

DATA QUALITY

Most organizations/ projects are faced with the data quality dilemma. Analysis of project data may leave the relevant personnel with reservations regarding the authenticity of the data, the enumerators or even the project impacts. M&E and other management staff may even contemplate the possibility of re-doing the process for the purposes of validation. Here, we take a look at what data quality is, data quality dimensions, causes of poor data and; eventually, ways of improving data quality.

DIFFERENCE BETWEEN INPUTS, ACTIVITIES, OUTPUTs, OUTCOMES AND IMPACT

Some of the most confused terminologies within the Research, Monitoring and Evaluation field are the words, “input”, “activities”, “outputs”,” outcomes” and “impact”. Within the R, M&E practice, it is important to distinguish between these terms. Understanding these terminologies not only ensures that appropriate indicators are identified, but also that they are effectively measured. Here, we will use specific project cases to demonstrate the difference between these terms.

FINAL REPORT FORMAT

An end of project report should follow the guidelines outlined below. It should include a title page, a list of abbreviations, acknowledgements, an executive summary, a table of contents, an introduction, a methodology section, a results section; conclusions, lessons learnt and recommendations section and; an annex section. The contents for each section are outlined below.