The document outlines the fundamentals of evaluation, including objectives, logic models, evaluation designs, and data analysis approaches. It emphasizes the importance of assessing program design, implementation, outcomes, and cost-effectiveness through formative and summative evaluations. Key questions guide evaluators in understanding the purpose, methodology, and impact of their evaluations.

1. FUNDAMENTALS
OF
EVALUATION

2. LEARNING OBJECTIVES
• Evaluation fundamentals
• Developing a logic model
• Understanding evaluation design
• Data analysis approach

3. WHAT IS EVALUATION?
collecting, analyzing, and using information to answer
questions about projects, policies
and programs, particularly about their effectiveness
and efficiency.

4. EVALUATION FRAMEWORK
• Assessment of the need for the
program
Need assessment
• Assessment of program design
and logic/theory
Selection of the program
design and logic model
• Assessment of how the program
is being implemented
Implementation plan
• Assessment of the program's
outcome or impact
Impact measurement
• Assessment of the program's
cost and efficiency
Cost and efficiency
measurement
Source - Rossi, Lipsey and Freeman (2004) model

5. KEY QUESTIONS TO CONSIDER
• What am I going to evaluate?
• What is the purpose of this evaluation?
• Who will use this evaluation? How will they use it?
• What questions is this evaluation seeking to answer?
• What information do I need to answer the questions?
• When is the evaluation needed? What resources do I need?
• How will I collect the data I need?
• How will data be analyzed?
• What is my implementation timeline?

6. PURPOSES OF EVALUATION
Formative
evaluation
Summative
evaluation

7. TYPES OF EVALUATION
Process evaluation
Outcome evaluation
Economic evaluation

8. PROCESS EVALUATION
Key questions to be considered
Process evaluation • How is the program being
• How appropriate are the processes
compared with quality standards?
• Is the program being implemented
correctly?
• Are participants being reached as
intended? How satisfied are program
clients?
• For which clients?
• What has been done in an innovative
way?

9. OUTCOME EVALUATION (OR IMPACT EVALUATION)
Key questions to be considered
Outcome/Impact evaluation • How well did the program work?
• Did the program produce the intended
outcomes in the short, medium and long
term?
• For whom, in what ways and in what
circumstances?
• What unintended outcomes (positive
negative) were produced?
• To what extent can changes be
to the program?
• What were the particular features of the
program and context that made a
difference?
• What was the influence of other factors?

10. ECONOMIC EVALUATION
Key questions to be considered
Economic evaluation (cost-
effectiveness analysis and
cost-benefit analysis)
• What is the most cost-effective option?
• Has the intervention been cost-effective
(compared to alternatives)?
• Is the program the best use of
• What has been the ratio of costs to
benefits?

11. EVALUATION – LOGIC MODEL
A program theory or logic model explains how the
activities of an intervention are understood to contribute
to a chain of results (short-term outputs, medium-term
outcomes) that produce ultimate intended or actual
impacts.
- it is a road map
- it describes the assumptions or hypotheses
- It is a tool that identifies the links in a chain of
reasoning about 'what causes what

12. COMPONENTS OF A LOGIC MODEL
Source - https://www.health.nsw.gov.au/research/Publications/developing-program-logic.pdf

13. EXAMPLE – LOGIC MODEL
Source - https://www.health.nsw.gov.au/research/Publications/developing-program-logic.pdf

14. METHODOLOGY,
DESIGN, DATA
ANALYSIS

15. PURPOSE OF COMBINING DATA
• Enriching: using qualitative work to identify issues or obtain
information on variables not obtained by quantitative surveys.
• Examining: generating hypotheses from qualitative work to be
tested through the quantitative approach.
• Explaining: using qualitative data to understand unanticipated
results from quantitative data.
• Triangulation (Confirming/reinforcing; Rejecting): verifying or
rejecting results from quantitative data using qualitative data (or
vice versa)

16. METHODOLOGY
Quantitative
Qualitative

17. DESIGNING THE EVALUATION
METHODOLOGY
Evaluation
design
Experimental
Quasi-
experimental
Non-
experimental

18. DATA ANALYSIS
Quantitative studies result in data that provides quantifiable,
objective, and easy to interpret results. The data can typically be
summarized in a way that allows for generalizations that can be
applied to the greater population and the results can be
reproduced.

19. DATA ANALYSIS APPROACH
Descriptive Inferential

20. COMMONLY USED DESCRIPTIVE
STATISTICS:
Frequencies – a count of the number of times a particular score or value
is found in the data set
Percentages – used to express a set of scores or values as a percentage
of the whole
Mean – numerical average of the scores or values for a particular
variable
Median – the numerical midpoint of the scores or values that is at the
center of the distribution of the scores
Mode – the most common score or value for a particular variable
Minimum and maximum values (range) – the highest and lowest values
or scores for any variable

21. REFERENCES:
• https://www.betterevaluation.org/en
• https://www.lga.sa.gov.au/page.aspx?u=2297
• https://www.health.nsw.gov.au/research/Publications/developing-program-logic.pdf
• https://cirt.gcu.edu/research/developmentresources/research_ready/quantresearch/analyze_data