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Program Evaluation
Program Evaluation
Program evaluation
Methodological techniques of the social sciences
social policy
public welfare administration.
Evaluation
Formative – help form the program
Ongoing assessment to improve implementation
Outcome – after the fact
Needs Assessment
Program Theory Assessment
Process Evaluation
Outcome Evaluation
Efficiency Assessment
Needs assessment
Who needs the program?
How great is the need?
What might work to meet the need?
What resources are available?
“Evaluability” assessment
Is an evaluation feasible?
How stakeholders can shape its usefulness.
Structured Conceptualization
Define the program or technology.
Define the target population.
Define possible outcomes
Process Evaluation
Investigates the process of delivery and alternatives.
Summative – summarize the effects
Implementation evaluation
Monitors the fidelity of delivery
Outcome Evaluations
Demonstrable effects on defined targets.
Impact evaluation
Net effects intended and unintended on program as a whole
Cost-effectiveness / Cost benefit.
Examines efficiency by standardizing outcomes in dollar costs and values.
Secondary analysis
Examine existing data to address new questions or use different methods.
Meta analysis
Integrates outcome with other studies to get summary judgment.
Meta-analysis
Analysis of analyses
Summarize a body of work
Replication is good but can lead to inconsistent results
Useful for
1)clarifying inconsistencies
2) program evaluation
3) review work
4) broadly framed questions
replications treatment control diff
Exp 1 22 19 3
Exp 2 20 18 2
Exp 3 23 17 6
Exp 4 15 16 -1
• Sampling
• Error in measurement
• Systematic error
• 3 in 4 studies show..
• Or Mean difference = 2.5
• (average out experimental errors….)
replications treatment control diff
Exp 1 (n=10)
22 19 3
Exp 2
(n = 10)
20 18 2
Exp 3
(n= 15)
23 17 6
Exp 4
(n = 1000)
15 16 -1
replications treatment control diff
Exp 1 22 19 3 p<0.05
Exp 2 20 18 2 p<0.05
Exp 3 23 17 6 p<0.05
Exp 4 15 16 -1 p<0.001
• Pooled data 35 people in 1000 show….
• Can overpower data
• Statistics based on large N tend to be more reliable – but only if the study is valid
• Meta-analysis tends to decrease random and systematic errors
• What if studies are not replications but variations on a theme…
• Exp 1 uses a scale from 1-5• Exp 2 uses scale from 1-100
treatment control difference
Exp 1 500 400 100
Exp 2 24 22 2
Average difference =51 ???
• Average difference =51???????????
treatment control difference Effect size d
Exp 1 500 400 100 0.5
Exp 2 24 22 2 0.67
Average d = 0.58
What is summarized?
1) count studies for and againstdoes not give magnitude and has low power
2) combine significance levels
3) combine effect sizes(effect gives the magnitude of the relationship between 2 variables)
Advantage -a) increase sample size and powerb) increase internal validity-
soundness of conclusions about relationshipc) increase external validity –
generalizability to other places people etcd) shows effect even if small if it is consistent
• Synthesis is a better estimate of effect size
• If effect is real and consistent it will be detected
• BUT Limited by the original studies
Steps in meta-analysis
1)Formulate the question
2) Collect previous studies
3) Evaluate and code
4) Analyze and interpret
5) Presentation
Data Sources
Study Selection
Data Abstraction
Statistical Analysis
Data Sources
1. Computer searches
2. Cross-referencing
3. Hand-searching
4. Expert(s) to review list
Study Selection
1. Study designs2. Subjects3. Publication types4. Languages5. Interventions6. Time Frame
• Need to establish criteria for inclusion
• Eg if reading program for schools then maybe it is only effective for younger children . …
• Determine cut-off of age acceptable.
• Or separate analyses for two groups
• Or use it as a moderating factor
Data Abstraction
1. Number of items coded2. Inter-coder bias3. Items coded
Coding…
Are all studies the same?
One has N=10 another has N= 1000….
Different DV scales 1-5 vs 500 point scale
How flawed is ok??? Do we include a study if we think it has a confound?
Publication bias…
Statistical Analysis
1. Choice of metric
2. Choice of model/ heterogeneity
3. Publication bias
4. Study quality
5. Moderator analysis
Choice of Metric Original Standardized mean difference
(Mean/Standard Deviation)
Choice of Model/ Heterogeneity Fixed Effects – current group of studies
explained Random Effects – assumes that this is
a random group from all possible
Publication Bias Graphical methods Quantitative methods
Study Qualitya. Difficult to assessb. Interpret with cautionc. Numerous scales and checklists
available
Moderator Analysisa. Categorical Analysisb. Regression Analysis
Allows for explanation of effects
• Meta analysis compared to review
• Objective or subjective???
The Contingent Smile: A Meta-Analysis of Sex Differences in Smiling
M LaFranceM A. HechtE Levy Paluck
Psychological Bulletin.2003, Vol. 129, No. 2, 305–334
Based on 20 published studies, the effect size (d) she reported was a moderate 0.63. In a follow-up report, J. A. Hall and Halberstadt (1986) added seven new cases and reported a somewhat lower weighted effect size of 0.42.
We included in our meta-analysis unpublished studies such as conference papers and theses, as well as previously unanalyzed data that were not includedin their prior meta-analysis.
Second, we explored the influence of several moderators derived from work in other areas of sex difference research
The third goal for the present meta-analysis was to conduct amore fine-grained analysis of several moderators previously consideredby J. A. Hall and Halberstadt (1986)
Method
• Retrieval of Studies• We searched the empirical literature for studies
that documented a quantitative relationship between sex and smiling, even if that relationship was not the central one of the investigation.
• Along with published articles, unpublished materials such as conference papers, theses, dissertations, and other unpublished papers were included. This was done to counter the publication bias toward positive results
