Work sampling is a statistical technique used to estimate the proportion of time spent on different activities by workers or machines. It relies on taking a large number of random observations over a period to get a representative picture of time allocation. This avoids the need for continuous observation, making it a more efficient method than continuous time studies for certain applications.
How Work Sampling Works
The method involves randomly observing the subject (worker or machine) at pre-determined intervals. Each observation records the activity being performed at that precise moment. After a sufficient number of observations, the percentage of observations showing each activity provides an estimate of the proportion of time spent on that activity.
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Random Sampling: The key to accuracy is the randomness of the observations. This minimizes bias and ensures a representative sample. Systematic sampling (e.g., observing every 15 minutes) can introduce bias and should be avoided.
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Large Number of Observations: The accuracy of the estimate increases with the number of observations. Statistical analysis helps determine the required sample size for a given level of precision.
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Activities: The activities being observed should be clearly defined and mutually exclusive. Examples include:
- Producing goods or services
- Doing paperwork
- Waiting for instructions
- Machine downtime
- Idle time
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Data Analysis: After collecting the data, the percentage of observations for each activity is calculated. This percentage serves as an estimate of the proportion of time spent on that activity. Statistical methods can then be used to calculate confidence intervals, indicating the level of uncertainty in the estimate.
Advantages of Work Sampling
- Cost-effective: It's less expensive than continuous time studies as it requires less observer time.
- Less disruptive: Brief observations are less likely to disrupt the normal workflow.
- Suitable for diverse activities: Can be used to study a wide range of activities, including those that are infrequent or difficult to observe continuously.
- Can be used for both workers and machines: Applies equally to human and machine activity analysis.
Example
Imagine a study analyzing the time spent by a machine operator on different tasks. Over 100 random observations, the operator was found to be:
- Operating the machine: 60 times
- Performing maintenance: 15 times
- Idle: 25 times
This suggests the operator spends approximately 60% of their time operating the machine, 15% on maintenance, and 25% idle.
The work sampling method, as described, involves estimating the proportions of time spent by people and machines on activities based on a large number of observations. These activities can include producing a service or product, doing paperwork, waiting for instructions, waiting for maintenance, or being idle.
