The document discusses key concepts in data analysis including descriptive analyses that describe a sample or population but do not determine causality. It also discusses setting targets, measuring coverage and retention, and using the appropriate graphs or tables to visually display and interpret data while noting service data can track progress but not show causality. The document is divided into three modules that cover data analysis concepts, basic analyses including targets, coverage and retention, and data presentation, interpretation and using graphs or charts effectively to add meaning.

1. Review of key themes

2.  Purpose of analysis is to provide answers to
programmatic questions
 Descriptive analyses describe the sample/target
population
 Descriptive analyses do not define causality – tell
you what, not why
 All data analysis does not require computers and
software
Module 1: Data analysis key concepts

3. Module 2: Basic analyses
 Target Setting – A specified level of performance
for a measure (indicator) at a predetermined
point in time. Both overall and annual targets are
set
 Coverage – Extent to which a program reaches
its intended target population, institution, or
geographic area
 Retention – The extent to which the range of
services are being delivered as initially intended

4. Module 3: Data presentation &
interpretation
 Use the right graph for the right data
 Tables – can display a large amount of data
 Graphs/charts – visual, easier to detect patterns
 Label the components of your graphic
 Interpreting data adds meaning by making
connections and comparisons to program
 Service data are good at tracking progress &
identifying concerns – do not show causality

5. MEASURE Evaluation is funded by the United States
Agency for International Development (USAID) through
Cooperative Agreement GHA-A-00-08-00003-00. The
views expressed in this publication do not necessarily
reflect the views of USAID or the United States
government.
Visit us online at http://www.cpc.unc.edu/measure.