Digitizing a Work Sampling Study
Digitise sampling plans and data capture without confusing automation with freedom from errors or bias.
WORKSAMP · LEARNING RESOURCES
Study design
Observation plans, categories, routes and reliable data capture.
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Statistical foundations
Sample size, precision, confidence intervals and the limits of inference.
Explore guidesStudy design
Observation plans, categories, routes and reliable data capture.
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Proportions, time, capacity and improvement decisions grounded in data.
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Examples and limits in manufacturing, maintenance and services.
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Tools, organization and context for putting a study into practice.
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Showing 1–12 of 13
Study Validity with Missing Observations
Analyse missing observations, coverage and possible nonresponse bias before interpreting results.
The Instant Observation Concept (Snap Reading)
Record a state at a defined instant and distinguish that observation from continuous duration measurement.
Designing Observation Routes
Design safe routes and an observation plan that documents selection, coverage and dependence between records.
The 'Other' Category: Risks and Management
Define and review an Other category without confusing classification uncertainty with sampling error.
Observation and the Hawthorne Effect: Sampling Limits
Distinguish selection bias, classification error and reactivity when observing work activities.
Multi-Subject Studies: Observing Complete Teams
Observe whole teams while distinguishing visits, recorded participants and statistical units of analysis.
The MECE Principle in Methods Engineering
Define mutually exclusive, collectively exhaustive categories and check their consistent use by observers.
VA/NVA Taxonomy: Classifying Activities
Classify value-adding work, support and losses without automatically equating activity with productivity.
Root Cause Analysis with Hierarchical Taxonomy
Organise causes and activities into a useful taxonomy and understand the additional checks required for causal analysis.
Hawthorne Effect: Reducing Observer Reactivity
Understand observer reactivity and reduce it without claiming that randomisation or a large sample eliminates it.
SCADA System Audit Using Observations
Compare SCADA states with observations while accounting for time alignment and each system’s definitions.
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