This cluster of evaluation tasks involves collecting or retrieving data and analyzing it to answer evaluation questions about what has happened – activities, outcomes and impacts – and also important contextual information.
Sampling is the process of selecting units (e.g., people, organizations, time periods) from a population of interest, so that inferences can be drawn about the population. There are three clusters of sampling options: probability, purposive and accidental.
Are any existing measures or indicators appropriate, or should new measures or indicators be developed for use in describing implementation or results? Can these be developed in advance or will they need to emerge during the evaluation?
There are five clusters of options for collecting and/or retrieving data: information from individuals; information from groups; observation; physical measurements; and existing records and data.
Managing data during an evaluation involves options for storing and organizing data, cleaning datasets using standardized procedures, documenting changes to how data is organized and retrieving data.
Collecting both quantitative data (numbers) and qualitative data (text, images) is important for most evaluations although they differ in terms of whether the data are equally important in the evaluation. Plan ahead how these wil be combined. We have grouped options into three groups which relate to: the sequence of when the qualitative and quantitative data are collected, when qualitative and quantitative data are combined, and the purpose for combining.
There are many different ways of analyzing data. Here they are grouped into three clusters of options: numeric analysis, mapping and textual analysis.
Data visualization iis a particular type of analysis involving graphical analysis. Data visualization serves two purposes: to bring clarity during analysis and to communicate.