Data types and measurement

Data types and measurement

• Identify three activity or productivity metrics from your workplace. They can be your own responsibilities or those of your whole department.

In my previous employment, my department was responsible for monitoring the following productivity metrics:

1. Units produced per hour,
2. Number of product defects, and
3. Causes of the defects.

• Classify the data you would need to collect to measure your results. For each metric, state whether the data would be qualitative or quantitative. If quantitative, state whether the data would be discreet or continuous.

The number of units produced per hour was recorded as a numeric value. This makes it an example of a quantitative data. Since the units are finite and countable, it is an example of a discrete continuous data.

The number of products that were produced with defects was recorded as a numeric value. It is an example of quantitative data. The data could only assume whole-numbers, therefore, making it an example of a discrete quantitative data.

The causes of the failures or defects were recorded as alpha-numeric codes. This data relied on observation and assessment and was therefore non-numeric. It is an example of a qualitative data.

• Specify the Level of Measurement of the data collected.

Since metric 1, the units produced per hour can be used to calculate both accurate differences and ratio between values. It can have a value of zero as well. This makes it a Ratio level of measurement.

Since metric 2, the number of product defects, allows us to calculate both differences between values and ratio of values, it is a Ratio level of measurement.

Metric 3, the causes of the defects, is non-numeric and contains descriptive words.It has no sense of ranking, therefore, it is a Nominal level of measurement.

• Pick one of the metrics you identified in step one and discuss how you did, or could, measure the data.

For the second metric, the number of defective products was recorded at the end of each production line. Each Production line manager was responsible for recording this information and submitting the information to the General production manager at the end of the batch production.

• Identify any potential sources of error in the measurement process. Classify each source of error as either random or systematic.

Some of the potential sources of errors I could identify were:

1. When recording the number of units produced, some people may have recorded the wrong figures. Others may have guessed the value, because, maybe they were too tired to physically count the products. Since the errors can be quantified this is a random error in the measurement process
2. Some people may have failed to observe a defective product and failed to record it. This arises from an error in judgment by the person responsible, in terms of the extent of the defect. Since this resulting error does not consistently skew the results of certain values. It is a random error in the measurement process.
3. Another source of error I identified was peer pressure. This occurred when the information was being recorded. It arose when the perceptions and judgment of a person were changed by the opinions of his colleagues. For example, some workers would not want to be seen as producing defective products hence they would alter the figures.This is an example of a random error

• Identify one way to reduce or eliminate the measurement errors.

The company could come up ways to capture the number of units produced and the defective products without requiring us to manually report it. For example, a system can be put in place to automatically record the number of units produced and the number of defective products. This will help in reducing the errors that arise when recording these metrics.