Training Programme Evaluation Training Course

Participants can map how a learning intervention is expected to produce measurable results.

• Logic models connect inputs, activities, outputs, outcomes and intended impact
• Assumptions and external factors recorded beside each causal pathway
• Theory of change diagrams test how learning contributes to performance
• Outcome chains separate proximal behaviour change from organisational results
• Indicators specified for each result, including direction and timing
• Contribution claims calibrated against alternative explanations and contextual influences

Participants can select an evaluation model and design suited to a programme's decision needs.

• Kirkpatrick’s four levels used without treating reaction as effectiveness
• LTEM levels distinguish participation, knowledge, proficiency, transfer and effects
• Model selection matched to programme risk, maturity and decision stakes
• Evaluation matrices align questions, indicators, sources, methods and owners
• Mixed-method designs combine numerical measures with explanatory qualitative evidence
• Success criteria set before results are known or interpreted

Participants can specify measurable outcomes, indicators, baselines and targets for training evaluation.

• SMART outcome statements specify observable performance, conditions and acceptable standards
• Baseline measures establish starting points before participant learning begins
• Targets distinguish minimum acceptable change from aspirational performance gains
• Leading indicators signal transfer before downstream business results appear
• Indicator dictionaries define formulas, units, sources, frequencies and limitations
• Data quality checks address completeness, accuracy, timeliness and consistency

Participants can identify whether training is the likely cause of a performance gap.

• Performance analysis separates skill deficits from process, resource and incentive barriers
• Job task analysis identifies critical behaviours and observable performance standards
• Competency maps translate role expectations into measurable learning outcomes
• Training needs data triangulated through interviews, records, observation and surveys
• Root-cause analysis uses five whys and fishbone diagrams
• Evaluation priorities based on materiality, feasibility and stakeholder demand

Participants can create valid evaluation instruments for learning, behaviour and performance evidence.

• Survey items map directly to indicators and specified evaluation questions
• Likert scales balanced with labelled anchors and planned interpretation rules
• Knowledge tests use test blueprints, item difficulty and discrimination indices
• Behaviour observation checklists define evidence, rating anchors and sampling intervals
• Interview guides sequence opening, probing and closing questions neutrally
• Pilot testing identifies ambiguity, response burden and administration failures

Participants can select samples and protect participants when collecting evaluation data.

• Sampling frames define eligible populations, exclusions and unit of analysis
• Probability and purposive sampling selected for inference or insight
• Sample-size decisions balance precision, power, resources and response rates
• Nonresponse bias assessed through respondent profiles and follow-up patterns
• Consent scripts explain voluntary participation, data use and withdrawal rights
• Data protection controls apply minimisation, access permissions and retention schedules

Participants can choose practical designs that strengthen claims about training effects.

• Pretest-posttest designs measure change while recognising testing effects
• Comparison groups strengthen claims where random assignment is impractical
• Randomised controlled trials address selection bias through allocation procedures
• Difference-in-differences estimates programme effects using parallel trend assumptions
• Interrupted time series tracks outcomes before and after implementation
• Threats to validity include history, maturation, attrition and regression

Participants can manage timely, consistent and traceable evaluation data collection.

• Collection protocols specify timing, channels, responsibilities and escalation routes
• Learning management system records linked with attendance and assessment data
• Manager follow-up captures workplace application after defined transfer intervals
• Observation sampling plans reduce assessor burden while preserving evidence quality
• Data cleaning logs document missing values, duplicates and coding decisions
• Fieldwork monitoring tracks response rates and corrective actions daily

Participants can analyse numerical training data and communicate the meaning of results.

• Descriptive statistics summarise distributions, central tendency and variation
• Cross-tabulations compare outcomes by cohort, role, location and tenure
• Confidence intervals communicate uncertainty around estimated programme effects
• Paired comparisons test within-participant change between baseline and follow-up
• Effect sizes distinguish meaningful improvement from large-sample significance
• Correlation analysis avoids claims of causation without suitable design

Participants can turn interviews, observations and open comments into credible findings.

• Qualitative coding converts interview and observation evidence into themes
• Codebooks define labels, inclusion rules, exclusions and illustrative examples
• Thematic analysis identifies recurring mechanisms, barriers and enabling conditions
• Inter-rater agreement checks consistency across multiple qualitative coders
• Triangulation compares findings from surveys, records, interviews and observations
• Negative-case analysis tests whether conclusions fit contradictory evidence

Participants can measure whether learners apply new capability at work over time.

• Transfer measures assess use frequency, quality, independence and persistence
• Manager observation forms distinguish opportunity, capability and motivation barriers
• Workplace evidence portfolios document application against agreed performance criteria
• Transfer climate analysis examines supervisor support, cues and consequences
• Performance support usage tracked alongside formal training participation
• Action-plan follow-up links individual commitments to operational measures

Participants can calculate training costs, monetised benefits and return on investment.

• Cost categories include design, delivery, participant time, travel and technology
• Fully loaded cost calculations allocate shared services and overhead transparently
• Benefit estimates use avoided costs, productivity gains and error reduction
• Phillips ROI Method isolates monetary benefits before calculating return
• ROI calculations distinguish benefit-cost ratio, net benefits and payback period
• Sensitivity analysis tests assumptions behind high-value benefit estimates

Participants can state what training contributed without making unsupported causal claims.

• Attribution strategies combine control groups, trend analysis and expert estimates
• Participant estimates of training contribution separated from observed performance data
• Manager validation challenges unsupported claims about learning-driven results
• Contribution analysis documents plausible mechanisms, evidence and rival explanations
• Confidence ratings communicate evidence strength for each reported conclusion
• Benefits claims bounded by stated assumptions, limitations and counterfactual evidence

Participants can present evaluation findings in forms suited to different decision audiences.

• Evaluation dashboards prioritise decision metrics over data volume
• Run charts display outcome movement against baselines and targets
• Disaggregation reveals equity gaps by role, department or participant group
• Executive reports state findings, confidence, implications and recommended actions
• Technical appendices preserve methods, instruments, calculations and data limitations
• Data visualisation choices prevent misleading scales, truncated axes and clutter

Participants can turn evaluation evidence into owned and measurable programme improvements.

• Findings-to-action workshops convert evidence into design, support and policy changes
• Recommendation registers assign owners, due dates, resources and success measures
• Evaluation cycles schedule formative checks, summative reviews and longitudinal follow-up
• Decision logs record accepted recommendations, rationale and implementation status
• Adaptive learning design responds to evidence without changing outcome definitions
• ISO 10015 guidance links competence development, monitoring and improvement

Participants can plan and govern an evaluation project from initiation through reporting.

• Evaluation charters define purpose, scope, governance, budget and reporting audiences
• Work breakdown structures sequence design, collection, analysis and reporting tasks
• Risk registers address access delays, low response and data quality
• Resource plans assign evaluator effort, system access and procurement needs
• Timeline dependencies coordinate baseline, delivery, follow-up and decision meetings
• Quality assurance reviews test alignment across questions, methods and conclusions

Participants can commission, assess and govern evaluation work completed by training suppliers.

• Supplier evaluation clauses specify evidence, data ownership and reporting requirements
• Request-for-proposal scoring weights methodology, evaluator capability and cost transparency
• Service-level measures define response times, data delivery and correction procedures
• Independent evaluation roles protect credibility where providers collect evidence
• Data-sharing agreements set permitted uses, security controls and deletion dates
• Vendor reports assessed against agreed indicators, methods and evidence standards

Participants can develop and justify an evaluation design for a current workplace programme.

• Programme briefing converts sponsor concerns into testable evaluation questions
• Evaluation design choices justified against budget, access and decision deadlines
• Draft instruments undergo peer review for alignment and respondent burden
• Analysis plans specify comparisons, coding rules and interpretation thresholds
• Reporting prototypes match audiences with appropriate evidence and detail
• Implementation plans define how findings will alter future programme decisions

Participants can build, review and present a complete evaluation package ready for sponsor approval.

• Complete evaluation plans integrate logic model, questions, methods and governance
• Evidence matrices link indicators to sources, collection timing and owners
• Cost-and-benefit models document assumptions, calculations and sensitivity scenarios
• Peer review tests validity, feasibility, ethics and decision usefulness
• Executive presentations defend conclusions, limitations and recommended actions
• Revision priorities produce an evaluation package ready for sponsor approval