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Program Evaluation: Methods, Types & Framework Guide

Program evaluation methods, types, and the CDC 5-step framework. Worked example from a 320-participant workforce training cohort. 10 FAQs, interactive lifecycle.

Updated
May 15, 2026
360 feedback training evaluation
Use Case
Program Evaluation: Methods, Types & Framework Guide
The arc of this guide

Five steps from program theory to next-cycle decision.

The CDC framework is the most widely used procedure for program evaluation. It works because it puts stakeholder questions before instrument design, and decisions before data collection. The rest of this page applies the five steps to a real workforce training cohort.

Step 01 Engage stakeholders Funder, board, staff, participants
Step 02 Describe the program Logic model · theory of change
Step 03 Focus the design Pick types & methods
Step 04 Gather evidence One record per participant
Step 05 Justify conclusions Decide what changes next cycle
CDC framework

Reads top-to-bottom: what is program evaluation · types · framework · a real cohort, six stages · four reports from one dataset · methods · 10 FAQs.

Definition

What is program evaluation?

Program evaluation is the systematic process of collecting and analyzing evidence to judge whether a program produced the outcomes it set out to produce, for whom, and at what cost. It answers three questions: did the program deliver what it said it would, did participants change as intended, and can the change be attributed to the program rather than to factors outside it. Strong evaluation is built into program design from day one rather than added at the end as a reporting task.

Quantitative axis

What changed, in numbers

  • Pre-program technical skills score (rubric, 1 to 5)
  • Attendance and module completion percentage
  • Post-program technical skills score (same rubric)
  • Credential pass rate at week fourteen
  • Job placement at three months, wage at nine months
  • Bound at collection by participant ID
Qualitative axis

Why it changed, in narrative

  • Open-ended response on why a participant joined the program
  • Mid-cycle reflection on what is working and what is not
  • Post-program narrative on what they would do differently
  • Employer feedback on credential recognition and skill match
  • Nine-month follow-up on career trajectory and barriers
  • Same record as the quantitative axis

The integration is structural, not procedural. Process, outcome, and impact evaluation share one record per participant. The structured program report and the live program dashboard are two views of the same evidence. The cycle that produced the answers is the cycle that produced the data.

Interactive lifecycle · cohort program

Click any stage. Watch one record evolve.

12 weeks, 24 participants, one persistent learner ID each. Open-ended responses captured alongside scaled metrics. Mid-cycle coaching interviews ingested as structured evidence. AI narrative summaries written for every participant.

Cohort pulse
IT Support Credential Prep · Spring 2026 · 24 participants · 12-week program with weekly mentor sessions
100%
Low Skills Pre
70%
High Skills Post
+1.2
Peer rating Δ
4
Risk flags resolved
Coordinator view
Enroll a new participant
Maria Thompson
m.thompson@example.org
IT Support Credential Prep · Spring 2026
12-week · hands-on labs + employer mentor
Self-referred
Sopact platform
Cohort table · 24 participants enrolled
IDNameCohortSourceStatus
P-1247Maria ThompsonSpring 2026Self-referredEnrolled
P-1246Priya SundaramSpring 2026Sponsor-fundedEnrolled
P-1245James LiuSpring 2026Sponsor-fundedEnrolled
P-1244Aisha KhanSpring 2026Self-referredEnrolled
P-1243Diego RamirezSpring 2026Sponsor-fundedEnrolled
+19...19 moreSpring 2026MixedEnrolled
Validation at intake24 enrolled, 2 records flagged. Duplicate email caught for P-1233 (existing in Fall 2025 cohort). Missing email for P-1252, surfaced for HR re-collection. Persistent ID assigned to all 24. Every Pre, Mid, Post, and audio file from here on will land on these rows automatically.
01 · EnrollAuto-validation catches duplicates and missing fields at intake. Data infrastructure in place before the first measurement, not bolted on after.
Participant view · pre-assessment
Maria answers 3 questions in week 1
Q1 · scale 0–100
Technical skills self-rating
48 / 100
Q2 · yes/no
Have you worked in an IT support role in the past 30 days?
No
Yes
Q3 · open-ended · the one that matters most
What worries you most about transitioning to an IT support role?
I'm a career changer with a lab tech background. I can troubleshoot a centrifuge but I freeze on a basic ticket system. I'm afraid of asking obvious questions in front of people who came up through computer science.
Sopact platform · AI on collection
Maria's record · open answer becomes structured data
AI
Extracted from Q3
P-1247 · Pre · Jan 13
Sentiment
Anxious · self-aware
Top fear
Asking obvious questions in front of senior colleagues
Readiness
Low
Themes
freeze responseover-rehearsalstatus anxiety
Predicted track
Cluster B · benefits most from low-stakes practice with peer pairs (weeks 2-4)
AI narrative summary · for the coach
Maria shows classic career-changer anxiety, with credentialism concern (fear of being seen as underqualified by colleagues from CS backgrounds) as the dominant theme. Her response pattern matches participants who benefit most from hands-on ticket triage in weeks 2-4. Recommend pairing with Priya S. (similar profile) for weekly hands-on ticket triage. Risk to flag: avoidance may persist past Mid if not surfaced in week 3 check-in.
Cohort sentiment quadrant · all 24 at Pre
N=24 · plotted from open-ended responses
ConfidentUncertainConfidentAnxiousExcitedCluster A · 7Cluster C · 4Cluster B · 11Cluster D · 2Maria
Top fears from 24 open-ended responses
AI clusters
46% Status anxiety
33% Freeze response
21% Visual aids
02 · PreThe open question is the unlock. Q1 says 48. Q2 says No. Q3 says why: Maria is in Cluster B, fearing technical credentialism, ready for week-2 ticket triage drills. The AI writes a coaching note specific to her from one sentence.
Mentor view · 45-min structured interview
Week 6 mentor session · Maria and Tom Anderson
TA
Mentor: Tom Anderson · Maria T. (P-1247)
Mid · interview · Feb 24, 2026 · 45 min · recorded with consent
Skills practiced her cycle
Maria resolved 12 help-desk tickets in 4 complex tickets ther cycle (target was 2). Two were standard hardware swaps, one was a multi-system network issue, one was an after-hours emergency. Self-rates the resolution quality 7/10.
Real situation faced
Maria handled an after-hours network outage call alone. Walked the caller through router restart, then escalated when DNS was the real issue. By the third slide her pacing settled and the points landed. Two colleagues asked questions, both got clear answers.
Skills in own words
"The ticket system clicked in week 5. I'm not memorizing scripts any more, I'm reading what the user is actually saying. The lab background helps once I stop apologizing for it. Networking still drains me by Thursday."
Concern flagged
Has not yet handled multi-system tickets end-to-end. Defaults to escalating before attempting cross-system root-cause. Recommendation: weeks 7-9 lab module with simulated multi-system outages.
Sopact platform · interview to structured data
AI processes 45 minutes into one record
AI
Mid interview extraction
P-1247 · 45 min audio + notes
Readiness
65  +17 vs Pre
Speaking events
4 instances · target 2 · 200% of target
Skills
Moderate · up from Low
Strengths
preparation disciplinestructure adoptionrecovery in delivery
Risk signal
multi-system response gap · flag for weeks 7-9 facilitation module
Maria skills profile · 6 competencies
PreMid
HardwareNetworkOSSecurityDiagnoseTickets
Cohort readiness shift · Pre to Mid
N=24 · 4 risk flags
17% Low
50% Moderate
33% High
Low 4Moderate 12High 8
03 · Mid · InterviewA 45-minute conversation produces richer evidence than any survey. AI extracts the score, the feedback count, the skills shift, the strength tags, and a new risk signal in one pass. The radar chart shows two competencies (Pushback, Presence) still under-developed.
Participant view · week 12
Final assessment plus 360 plus audio
Q1 · scale 0–100
Final technical skills
82 / 100
Q2 · employer-rated readiness from 6 cohort members
Employer-rated readiness score
7.8 / 10
3:08
"I closed the major network outage call alone last week. Hands shaking, voice steady. Sarah from the cohort told me afterward she could hear I was nervous but the fix held. I want to mentor the next cohort orientation."
Sopact platform · the full Pre to Post arc
Maria's longitudinal record
12-week readiness trajectory
—Maria- - cohort avg
10080604020W1W4W6 · MidW9W12486582
AI narrative · final coaching note
Maria completed the program with a +34 skills assessment lift (48 to 82), outperforming cohort average of +24. Her turning point was the after-hours network outage call in week 6, which broke the help-desk-only pattern surfaced at Pre. Employer-mentor readiness score rose from 6.2 to 7.8 over 12 weeks. Recommend: tier-1 to tier-2 promotion track and peer-mentor role for the Summer 2026 cohort.
Score ΔPre to Post
+34
82 vs 48
Placement readiness
+1.6
7.8 vs 6.2
Risk status
Cleared
multi-system gap resolved
04 · PostThe Pre baseline is what makes the Post reading mean something. From "I freeze on a basic ticket system" to closing a major outage alone. From 48 to 82. Employer-mentor readiness rose +1.6 points. The behavior change is what funders, CFOs, and program officers all want to see.
Program manager view
Four canonical reports, one dataset
Funder · board · staff · participants
English, Português, Español, French
Correlation · Impact · Multivariate · Cohort compare
Same 24 participants, same Pre + Mid + Post data. Four different report shapes for four different audiences. All reproducible at the click of a button.
Sopact platform · live preview
Impact snapshot · Spring cohort
+24
Avg skills rubric lift
+1.2
Placement readiness pts
88%
Completion rate
Click into Component 2 below to switch between the four reports: Correlation (skills vs placement readiness), Impact (cohort-wide deltas), Impact in Spanish, and Multivariate (what predicts high-skills completion).
05 · ReportsExec, CHRO, board, participants. Same dataset, four report shapes. Multilingual is one click, not a translation project.
Program manager view · AI agent
Ask Claude anything · three example prompts
Prompt 1 · risk flag
Which participants showed early-warning patterns at Mid?
Prompt 2 · external benchmark
Compare our cohort skills lift against industry benchmarks.
Prompt 3 · cross-system join
Join Sopact data with our ATS. Which graduates placed in roles using the credential?
Sopact + Claude · joined live
Sample answer · prompt 2 preview
Avg skills rubric lift · our cohort vs benchmarks
Our Spring cohort
+24
BLS sector P75
+16
Self-paced MOOC P50
+12
Claude's readYour cohort outperforms benchmarks by 8 to 12 points. Driver candidates from the multivariate analysis: 45-min Mid interviews (most credential prep uses a 15-min check-in), AI-assisted instructor narratives (cited in 19 of 24 exit reflections), and structured employer-mentor pairing in weeks 4-6. See Component 3 below for the full Claude playground with all three prompts.
06 · ActionData + a plain-English question. No SQL, no BI ticket. AI joins, charts, explains. Three prompts · run all three in Component 3 below.
Stage 1 of 6 · Enroll
Working session

Bring your program theory. Watch the evaluation assemble itself.

A 60-minute working session against your actual program. Bring the logic model, the intake form, or the outcome measure you wish was working. We walk through how a record-level evaluation gets built around what you already have.

See how Sopact Sense works →
Types

The four working types of program evaluation.

There are four working types of program evaluation: process, outcome, impact, and cost-effectiveness. Process asks whether the program was delivered as planned. Outcome asks whether participants changed as intended. Impact asks whether the change can be attributed to the program. Cost-effectiveness asks whether the change was worth the resources spent. A separate axis is formative versus summative. Most programs need at least process and outcome; mature programs run all four.

Type Question it answers When to run Who reads it Maria's cohort, in numbers
Process
formative
Was the program delivered as planned? During the cycle, every cycle Program staff, operations 88% attendance > 80% threshold
Outcome
summative
Did participants change as intended? End of cycle + 3 to 9 months follow-up Funder, board, program lead 71% credential pass · 64% placement
Impact
attribution
Can the change be attributed to the program? After at least one outcome cycle Funder, researcher, policy +18 pp placement vs matched comparison
Cost-effectiveness
economic
Was the change worth the resources? End of cycle, after outcome data Board, finance, expansion decisions $4.8K cost per placement

Formative versus summative · a different axis

The four working types above describe what question the evaluation answers. Formative versus summative describes when in the program cycle the evaluation runs. Both axes apply at once. Process evaluation can be formative (running mid-cycle to improve delivery) or summative (running at end-of-cycle to judge fidelity). Outcome evaluation is almost always summative.

The mistake is treating these as competing categories. They are perpendicular. A program running its third cohort of an IT credential might run formative process evaluation in weeks 1–6, summative outcome evaluation in weeks 14–28, impact evaluation against a matched comparison at month nine, and cost-effectiveness analysis once the wage data lands at month twelve. One participant record, four evaluation lenses.

Sibling concepts

Often confused with types

  • Methods Quantitative, qualitative, or mixed. See section 11 below.
  • Designs Pre-post, comparison group, time-series, single-case.
  • Frameworks CDC 5-step, Kirkpatrick, Logic Model. See section 11.
  • Reports The artifact that communicates the evaluation, not a type of evaluation.
Worked example

A real cohort, four data sources, one participant record.

The IT Support Credential Prep program enrolls 320 participants over three cohorts a year, 12 weeks per cohort, with placement tracked at three and nine months. Four data sources feed one participant record per cycle: intake form, attendance + module log, post-program skills assessment, and 3-month placement follow-up. Process, outcome, impact, and cost-effectiveness evaluations all draw from the same record. Maria from Spring 2026 is one row in the dataset; the next three sections show how that row gets read.

Program · Spring 2026 IT Support Credential Prep 320 participants · 3 cohorts/year · 12 weeks
Participants

Career changers, recent grads, displaced workers. Mix of urban and suburban, mean age 31.

What's collected

Pre + Mid + Post skills, attendance, module completion, credential outcome, placement, 9-month wage.

The questions asked

Who passes? Who places? What dosage drove placement? Is 12 weeks better than 16?

Four sources, one row per participant per cycle

Source 01 · Intake form Sopact Sense · week 0 320 rows · Spring 2026
participant_id name prior_background pre_skills_score stated_goal
P-1247Maria ThompsonLab tech, no IT48Help-desk role within 6 months
P-1248Priya SinghRetail, self-taught52Transition out of retail
P-1249Tom AndersonVeteran, signals61Federal contractor work
P-1250Devon WilliamsRecent grad, BS Bio39Career pivot to tech
P-1251Aisha KhanCustomer service44Higher wage, stable hours
… 315 more rows · one row per participant, persistent participant_id across all four sources
Source 02 · Attendance + module log LMS (Cornerstone) · weeks 1–12 3,840 events · joined on participant_id
participant_id module sessions_attended sessions_total completion_pct
P-1247M02 · Networking111292%
P-1247M03 · Operating systems101283%
P-1247M04 · Security1212100%
P-1247M05 · Troubleshoot lab1212100%
P-1247M06 · Mentor session111292%
… 3,835 more rows · 6 modules × 12 weeks × 320 participants, joined on participant_id
Source 03 · Post skills + credential Sopact Sense + CompTIA portal · weeks 12–14 282 rows · 88% completion rate
participant_id post_skills_score delta credential_result narrative_summary
P-124782+34PassTickets clicked week 5; outage call week 9
P-124878+26PassStrong networking, needs OS deeper
P-124985+24PassVeteran discipline carried; mentor candidate
P-125058+19RetestHardware gap; needs 2-week catch-up
P-125176+32PassCustomer service translated cleanly
… 277 more rows · post score, credential result, AI-summarized narrative per participant
Source 04 · 3-month placement ATS + employer survey · month 3 263 rows · 92% follow-up response
participant_id placed role starting_wage employer_feedback
P-1247YesTier-1 help desk$24.50/hr"Strong call etiquette, escalates well"
P-1248YesNetwork ops, Tier-1$26.00/hr"Networking-strong, ramping fast"
P-1249YesFederal contractor$31.00/hr"Security clearance plus credential, ideal"
P-1250In retestn/an/aCatch-up cohort, expected month 5
P-1251YesTier-1 help desk$22.75/hr"Customer service shows on every call"
… 258 more rows · placement status, role title, wage, employer feedback at month 3

Figures illustrative · refresh against live program data at evaluation time.

Four sources, one row key. Process evaluation reads the attendance + module log against intake. Outcome evaluation reads post + credential against pre. Impact evaluation reads placement against a matched comparison cohort. Cost-effectiveness divides total program cost by placements. The next two sections show what reports come out of this data, and how a program lead asks the next question in plain English.

Component 2 · Reports

Four reports. One dataset. One click each.

Same 320 participants. Same Pre, Mid, Post evidence. Different shape for different audience. Multilingual is a toggle, not a translation project.

Correlation report

Skills × placement-rated effectiveness

Spring 2026 IT Credential Prep cohort · N=320 · Pearson correlation analysis

Pearson r
0.74
Strong positive
P-value
<0.001
Highly significant
Sample size
24
complete records
Outliers
2
P-1244 · P-1232
The scatter
Self-rated skills (Post) vs employer-rated effectiveness
r = 0.74 · slope 0.041
10 8 6 4 2 20 40 60 80 100 Post skills (self-rated, 0-100) Placement readiness (1-10) Maria T. Aisha K. outlier
Headline Skills and employer-rated effectiveness move together. A 10-point lift in self-reported skills corresponds to a 0.4-point lift in peer ratings on average. The relationship is strong (r=0.74) and significant (p<0.001).
Why this matters Internal feeling tracks external behavior. Participants are not merely claiming to feel better; their direct reports and peers see the change. The two outliers (Aisha K., one other) felt confident but did not change peer perception, flagged for follow-up.
Generated May 15, 2026 · Author Tom Anderson, Program Director · Source Sopact Sense
SkillsPlacement readiness
Impact report · Q1 2026

IT Credential Prep Cohort · Spring 2026

Pre to Post movement · cohort distribution · benchmark comparison · for board and exec audiences

Avg skills lift
+24
52 → 76 of 100
Completion rate
88%
21 of 24 finished
Placement readiness
+1.2
6.4 → 7.6 of 10
Risk flags cleared
4 of 4
100% resolved by Post
Cohort distribution shift
Pre · W1
100% Low skills
N=320
Mid · W6
17%
50% Moderate
33% High
N=320
Post · W12
30%
70% High skills
N=282
Benchmarks · external comparison
Our Spring cohort
+24
Toastmasters P75
+18
Self-paced LMS P50
+11
Corporate L&D avg
+9
Bottom line for the board The cohort outperformed every external benchmark by 6 to 15 points. Driver candidates from the multivariate (Report 04): 45-minute Mid mentor interviews, structured peer pairing in weeks 2-4, and AI-assisted coaching narratives. Recommend: continue the model for Summer 2026 cohort with same mentor-to-participant ratio.
Generated May 15, 2026 · Author Tom Anderson, Program Director · Source Sopact Sense
For the boardEN
Relatório de impacto · 1º trimestre 2026

Coorte de Habilidades de Comunicação · Primavera 2026

Movimento Pré para Pós · distribuição da coorte · comparação com referências · para diretoria e executivos

Ganho médio de confiança
+24
52 → 76 de 100
Taxa de conclusão
88%
21 de 24 concluíram
Efetividade entre pares
+1,2
6,4 → 7,6 de 10
Sinais de risco
4 de 4
100% resolvidos até Pós
Mudança de distribuição da coorte
Pré · S1
100% Baixa confiança
N=320
Meio · S6
17%
50% Moderada
33% Alta
N=320
Pós · S12
30%
70% Alta confiança
N=282
Referências · comparação externa
Nossa coorte da Primavera
+24
Toastmasters P75
+18
LMS auto-guiado P50
+11
Média L&D corporativo
+9
Conclusão para a diretoria A coorte superou todas as referências externas em 6 a 15 pontos. Fatores explicativos do Relatório 04: entrevistas de mentoria de 45 minutos na Semana 6, pareamento estruturado nas semanas 2-4, e narrativas de coaching assistidas por IA. Recomendação: manter o modelo para coorte do Verão 2026 com mesma proporção mentor-participante.
Gerado em 15 de maio de 2026 · Autor Tom Anderson, Diretor de Programa · Fonte Sopact Sense
Para a diretoriaPT
Multivariate analysis

What predicts high-skills completion

Linear regression · 5 program variables predicting Pre-to-Post skills delta · N=320

R² · model fit
0.68
68% variance explained
F-statistic
7.83
p<0.001
Strongest predictor
β=.42
Mentor session minutes
Weakest predictor
β=.09
LMS module completion
Standardized coefficients · ranked
Mentor session minutesLive, structured, recorded with consent
β = 0.42
p<0.001 ★
Peer pair sessionsWeekly 30-min practice with assigned partner
β = 0.31
p<0.001 ★
Lab hours loggedHands-on troubleshooting, weekend labs, sims
β = 0.24
p<0.01 ★
AI narrative engagementTimes participant referenced their coaching note
β = 0.18
p<0.05
LMS module completionAsync self-paced content from Cornerstone LMS
β = 0.09
n.s.
The model says Human elements drive skills change. Mentor minutes, peer pairs, and real-world ticket triage together explain 90% of the variance the model captures. LMS module completion was not statistically significant after controlling for the others.
Implication for Summer 2026 If we cut anything, cut LMS modules first. Reallocating 2 hours per participant from async content to extra mentor minutes is projected to add 6 to 8 points of skills lift. Component 3 below joins these results with live LMS data to identify the specific modules to deprioritize.
Generated May 15, 2026 · Author Tom Anderson, Program Director · Methods OLS regression, standardized coefficients
For program designAnalytical
Component 3 · Actionable insight

Ask Sopact + Claude. Plain English. Cross-system data.

No SQL. No BI ticket. The AI agent joins Sopact data with your LMS and your internal feedback system. Click a prompt to watch the answer come back with the sources tagged.

Connected systems · live
Last sync 4 min ago · 3 of 3 systems healthy
Sopact Sense
PARTICIPANT DATA
Pre + Mid + Post assessments, AI narratives, 24 participants, persistent IDs
Cornerstone LMS
LEARNING ACTIVITY
12 modules, completion rates, time in platform, quiz scores, last activity dates
Greenhouse ATS
PLACEMENT + HIRING
Application stage, interview rounds, hire/no-hire decisions, salary bands
AI
Click any prompt above The AI agent will join data from Sopact + LMS + Feedback systems and stream the answer back with sources tagged.
Compare LMS engagement against post skills score. Show me where the engagement paradox lives.
AI
Claude · joining Sopact Sense + Cornerstone LMS
1.4s · 48 records joined on P-ID
Joining 320 Sopact records with 24 LMS records on participant ID...

The engagement paradox lives in two participants who completed everything in the LMS but barely moved on post skills score.

Plotting LMS module completion against post skills score for the Spring 2026 cohort surfaces a quadrant pattern. Most participants cluster around the diagonal: high LMS engagement tracks with high post skills score (top-right). But two outliers break the pattern in opposite directions.

LMS modules completed × post skills score · N=24
2 outliers flagged · joined on P-ID
100 60 20 0 6/12 12/12 LMS modules completed (Cornerstone) post skills score (Sopact) human elements worked the expected pattern need re-engagement engagement paradox Aisha K. Diego R.

Aisha K. (P-1244) completed all 12 LMS modules with a 95 average quiz score, the highest in the cohort. Her post skills score only rose +6 points (52 to 58), bottom quartile. Pattern matches participants who treat the LMS as a checklist exercise without internalizing the skill. Diego R. (P-1243) finished only 8 of 12 modules but his post skills score jumped +22 points, driven by 14 attended hands-on lab sessions and 9 weekend mentor pair-ups.

What this means: LMS completion is not the change driver. Two participants saturated on async content and still showed the smallest growth. Three under-engaged on LMS but grew most. The human elements of the program carry the lift.

Sources joined: Sopact Sense · 24 Post records Cornerstone LMS · 24 user records · 12 modules
Rank LMS modules by their correlation with skills lift. Which content actually moves the needle?
AI
Claude · ranking 6 LMS modules + 2 program elements
1.8s · Pearson r vs Pre-to-post skills score Δ
Correlating module completion with skills delta across 24 participants...

The human elements outrank every single LMS module. Mentor sessions correlate twice as strongly with skills lift as your best async module.

I correlated each program element with the Pre-to-post skills score delta across 24 participants. Higher r means the element more reliably predicts a participant's skills growth. Two non-LMS elements (mentor sessions, peer pairs) are ranked alongside the 6 Cornerstone LMS modules to show the comparison.

Pearson r · program element vs skills Δ · N=24
Spring 2026 cohort
Mentor session minutesSOPACT · live coaching
0.78
Peer-pair sessionsSOPACT · structured practice
0.67
Module 04 · Security fundamentalsLMS · 22 min video + role-play
0.61
Module 06 · Lab troubleshootLMS · 18 min video + reflection
0.42
Module 05 · Hardware deep-diveLMS · 14 min video + worksheet
0.34
Module 02 · NetworkingLMS · 16 min video + worksheet
0.18
Module 01 · OS fundamentalsLMS · 12 min video + quiz
0.12
Module 03 · Mobile + cloudLMS · 20 min video + assignment
0.09

What this means: The 22-minute video on handling pushback (Module 04) is the only async content with a meaningful signal. It is also the module that maps closest to the most-rehearsed real-world situation, which probably explains the correlation. The five other modules sit at or below r=0.42.

Action: for Summer 2026, recommend keeping Module 04, replacing Modules 01 and 03 with one extended mentor session, and tracking whether the freed time materially shifts the cohort's post skills score distribution.

Sources joined: Sopact Sense · 24 skills deltas Cornerstone LMS · per-module completion
Find graduates ready to mentor the next cohort. Cross-reference completion, recent LMS activity, and peer-mentor activity.
AI
Claude · joining Sopact + Cornerstone + Greenhouse
2.3s · 320 records joined across 3 systems
Filtering Sopact graduates with active LMS sessions and high Greenhouse ATS placement signals...

Five Spring 2026 graduates qualify as Summer 2026 mentors based on the three-system join.

Filter criteria applied across all three systems: Sopact · completed program with post skills score above 75. Cornerstone LMS · logged into platform in the past 14 days, suggesting continued investment. Greenhouse · gave at least 4 pieces of peer feedback in the past month, indicating they are comfortable being a source of feedback for others. Five of 21 graduates meet all three criteria.

Maria Thompson P-1247 · IT Support · Tier-1
Δ +34 skills 12/12 modules · last 6d ago 9 peer feedbacks this month
SOPACT 82/100LMS ACTIVELATTICE 9 GIVEN
Assign →
Priya Sundaram P-1248 · Network Ops · Tier-1
Δ +26 skills 12/12 modules · last 3d ago 7 peer feedbacks this month
SOPACT 78/100LMS ACTIVELATTICE 7 GIVEN
Assign →
James Liu P-1245 · Federal contractor · Tier-2
Δ +21 skills 11/12 modules · last 9d ago 6 peer feedbacks this month
SOPACT 76/100LMS ACTIVELATTICE 6 GIVEN
Assign →
Sarah Chen P-1242 · Help desk · Tier-1
Δ +22 skills 10/12 modules · last 12d ago 5 peer feedbacks this month
SOPACT 79/100LMS ACTIVELATTICE 5 GIVEN
Assign →
Diego Ramirez P-1243 · Cloud support · Tier-2
Δ +22 skills 8/12 modules · last 4d ago 4 peer feedbacks this month
SOPACT 71/100LMS ACTIVELATTICE 4 GIVEN
Assign →

Note on Diego: his SOPACT score is the lowest of the five at 71, but the lift was outsized (+22) and his Greenhouse placement signals suggest she learned through peer practice rather than module completion. Could be the strongest peer-style mentor for Cluster B participants in Summer 2026.

Sources joined: Sopact Sense · graduation status Cornerstone LMS · last 14d activity Greenhouse ATS · placement role title
Ask anything · join data from your connected systems click a prompt above to try
Methods

Program evaluation methods: quantitative, qualitative, mixed.

Methods follow questions, not the other way around. Quantitative methods (surveys, attendance tracking, comparison-group designs) answer how much and for how many. Qualitative methods (interviews, focus groups, document analysis) answer why and how. Mixed-methods designs combine both on one record so a number can be explained and a story can be counted. Programs that pick a method first and then look for a question to answer with it produce evaluations that read as activity reports.

Stage Quantitative Mixed Qualitative What it surfaces
Pre Skills rubric, demographics Rubric + open-ended "why join" Intake interview transcript Baseline + motivation
During Attendance, module completion Dosage + week-6 reflection Mid-cycle interview Fidelity + what's clicking
Post Post rubric, credential result Post rubric + exit reflection Exit interview, employer note Outcome + explanation
Follow-up Placement, wage, retention ATS join + employer survey 9-month narrative call Sustained outcome + barriers
Analysis Pre-post deltas, regression Number + theme on one row Thematic coding, narrative Joint causal story

★ Mixed-methods is the default for outcome evaluation when both number and explanation are required, which is most of the time.

Framework

The CDC framework for program evaluation, applied.

The CDC framework for program evaluation is a five-step procedure widely adopted across public health, education, workforce, and community programs. The steps are: engage stakeholders, describe the program, focus the evaluation design, gather credible evidence, and justify conclusions. The framework is paired with four standards every evaluation should meet: utility, feasibility, propriety, and accuracy. It is process-oriented, not method-prescriptive: it tells you which question to answer at each step, not which instrument to use.

01

Engage stakeholders

Funders, board, program staff, and participants each ask different questions. Surface them before the instrument is designed. A question the board did not ask will not get answered, regardless of how well the data was collected.

Maria's cohort

Funder asked: pass rate and placement. Board asked: cost per placement. Staff asked: which modules drive outcomes. Participants asked: will I be ready for the credential. All four answered from one dataset.

02

Describe the program

Write a logic model connecting inputs, activities, outputs, outcomes, and impact. The model becomes the theory the evaluation tests. Skip this step and the evaluation defaults to measuring whatever the survey vendor pre-built.

Maria's cohort

Logic model: 12-week curriculum + employer-mentor pairing produces credential-ready candidates who place in Tier-1 IT roles at competitive wages. Each piece becomes a testable claim. "Employer-mentor pairing" became a measurable variable.

03

Focus the evaluation design

Pick the types and methods that match the stakeholder questions. Process for delivery questions. Outcome for change questions. Impact for attribution questions. Cost-effectiveness for resource questions. Most programs need at least process and outcome.

Maria's cohort

Process (attendance + module completion) + Outcome (pre/post + credential + placement) + Impact (matched comparison cohort) + Cost-effectiveness ($/placement). All four types, one data backbone.

04

Gather credible evidence

Use stable participant IDs so changes can be traced person by person. Numbers and narratives on the same record. The big lift: a participant who attended 92% of modules and rated week 6 as the turning point is one row, not three files.

Maria's cohort

Intake form, LMS attendance, post-program skills, ATS placement. All four sources on the participant_id key. No reconciliation step at the end. Maria's row carries her rubric, her week-6 reflection, her module completion, her credential result, her placement.

05

Justify conclusions

Link findings to decisions about the next cycle. Name what is settled, what is provisional, and what is still in measurement. A conclusion that does not change next cycle's program design is a conclusion that did not need to be drawn.

Maria's cohort

Spring conclusion drove three Summer 2026 changes: expand mentor-pair sessions weeks 4–6 (highest correlation with placement), add a 2-week catch-up cohort for retest candidates, swap one async module for a hands-on lab. Next cycle is different because this one was measured.

FAQ

Program evaluation questions, answered.

Ten questions covering definitions, types, methods, the CDC framework, and the boundary between evaluation, monitoring, and program reporting. The answers below match the structured data on this page word for word.

What is program evaluation?

Program evaluation is the systematic process of collecting and analyzing evidence to judge whether a program produced the outcomes it set out to produce, for whom, and at what cost. It answers three questions: did we deliver what we said we would, did participants change as intended, and can we attribute that change to our program rather than to factors outside it. Strong evaluation is built into program design from day one rather than added at the end as a reporting task.

What are the four main types of program evaluation?

Process evaluation asks whether the program was delivered as planned. Outcome evaluation asks whether participants changed as intended. Impact evaluation asks whether the change can be attributed to the program rather than to outside factors. Cost-effectiveness asks whether the change was worth the resources spent. A separate distinction is formative versus summative: formative work runs during the program to improve it, summative work runs at completion to judge it. Most programs need at least process and outcome; mature programs run all four.

What are the steps of program evaluation?

The CDC five-step framework is the most widely used. Engage stakeholders to align on what the evaluation will answer. Describe the program using a logic model so the evaluation tests a real theory. Focus the evaluation design by selecting questions and methods. Gather credible evidence with consistent measures and unique participant tracking. Justify conclusions by linking findings to decisions about the next cycle. Some agencies expand this into six steps by separating evidence-gathering from analysis. The discipline matters more than the count.

What are the methods of program evaluation?

Quantitative methods include pre and post surveys, attendance and dosage tracking, comparison-group designs where ethically possible, and cost analysis tied to outcome data. Qualitative methods include open-ended survey items, semi-structured interviews, focus groups, and document analysis of program records. Mixed-methods designs combine both on the same participant record so a number can be explained and a story can be counted. The method follows the question; programs that pick a method first and then look for a question to answer with it produce evaluations that read as activity reports.

What is an example of program evaluation?

A workforce training program enrolls 320 participants over three cohorts. The evaluation captures baseline technical skills before week one, attendance and module completion during the program, post-program skills at week twelve, credential pass at week fourteen, employer feedback at month three, and wage progression at month nine. Outcome evaluation shows 71 percent passed the credential and 64 percent placed in roles using it. Process evaluation shows that participants who attended at least 80 percent of sessions accounted for 89 percent of placements. Each is one type of program evaluation, working off the same participant record.

What is the difference between monitoring and evaluation?

Monitoring is the routine tracking of inputs, activities, outputs, and short-term outcomes against plan. Evaluation is the periodic, structured judgment of whether the program produced the outcomes it set out to produce. Monitoring asks whether the program is running on time and on plan. Evaluation asks whether the program is working. The two pair naturally: monitoring data, captured continuously, becomes the evidence base that an evaluation interprets. Without monitoring, evaluation is forced to rely on memory and reconstruction.

What is the difference between outputs and outcomes?

Outputs are what the program produced: people served, sessions delivered, materials distributed. Outcomes are what changed for the people served: skills gained, jobs secured, behaviors adopted, conditions improved. Outputs prove the program ran. Outcomes prove it worked. The shortest test: if you can count it on the day a session ends, it is an output. If you have to follow up with participants to know whether anything changed, it is an outcome. Most programs report outputs and call them outcomes.

What is the CDC framework for program evaluation?

The CDC framework for program evaluation is a five-step procedure published by the US Centers for Disease Control and Prevention and widely adopted across public health, education, workforce, and community programs. The steps are: engage stakeholders, describe the program, focus the evaluation design, gather credible evidence, and justify conclusions. The framework is paired with four standards that any evaluation should meet: utility, feasibility, propriety, and accuracy. The framework is process-oriented, not method-prescriptive: it specifies the questions to answer at each step, not the instruments to use.

What is program evaluation in education?

In an education setting, program evaluation studies whether a curriculum, intervention, or after-school program produced the learning, behavioral, or wellbeing outcomes it was designed to produce. The evaluation captures baseline measures before the program, dosage and fidelity during, and outcome measures after, with follow-up to test whether changes hold. Education program evaluation typically pairs standardized test data with teacher and student narratives so a score change can be explained by what students experienced. The same five CDC steps apply.

How does program evaluation connect to program reports and dashboards?

A program evaluation is the analytical work of judging whether a program produced its intended outcomes. A program report is the artifact that communicates the findings: the structured document about who participated, what changed, what participants said, and what comes next. A program dashboard is the live view of the same evidence base: the always-on summary that funders, leadership, and staff consult while the program runs. Evaluation produces the judgment, the report packages it for an audience, the dashboard exposes the underlying data continuously. All three pull from the same participant record.

Go deeper

The full Stakeholder Intelligence guide

Program evaluation is one application of stakeholder intelligence. The engine pillar covers how the same record-level architecture powers grant management, training programs, education, and community health evaluations. One backbone, many use cases.

Read the engine pillar →
Pages that pair with program evaluation

The artifact, the live view, and the methods that feed each.

Program evaluation produces the judgment. The program report packages it for a specific audience. The program dashboard exposes the underlying data continuously. The other guides cover the frameworks and methods that feed each.

A working session on program evaluation

Make your data work for what matters most.

Bring your logic model, intake form, or the outcome measure you wish was working. We walk through how a record-level evaluation gets built around what you already have. No procurement decision needed. The session shows the integration step in 60 minutes.

What to bring
  • Format 60-minute video call. Working session against your program, not a generic platform tour.
  • Bring A logic model or program theory. An intake instrument. The outcome question you cannot answer today.
  • Leave with A concrete picture of how process, outcome, and impact evaluation share one participant record.