Institute Technologies is choosing new cost drivers for its accounting system. One driver is labor hours; the other is a combination of machine hours for unit variable costs and number of setups for a pool of batch-level costs. Data for the past year follow. Budget Actual
Labor hours 200,000 200,000
Machine hours 360,000 450,000
Number of setups 3,000 3,300
Unit variable cost pool $1,600,000 $2,000,000
Batch-level cost pool $900,000 $990,000

Assume that both cost pools for Institute are combined into a single pool, and labor hours is the driver. The total flexible budget for the actual level of labor hours and the total variance for the combined pool are:

Reliability and validity reliability and validity are two important considerations that must be made with any type of data collection. reliability refers to the ability to consistently produce a given result. in the context of psychological research, this would mean that any instruments or tools used to collect data do so in consistent, reproducible ways. unfortunately, being consistent in measurement does not necessarily mean that you have measured something correctly. to illustrate this concept, consider a kitchen scale that would be used to measure the weight of cereal that you eat in the morning. if the scale is not properly calibrated, it may consistently under- or overestimate the amount of cereal that’s being measured. while the scale is highly reliable in producing consistent results (e.g., the same amount of cereal poured onto the scale produces the same reading each time), those results are incorrect. this is where validity comes into play. validity refers to the extent to which a given instrument or tool accurately measures what it’s supposed to measure. while any valid measure is by necessity reliable, the reverse is not necessarily true. researchers strive to use instruments that are both highly reliable and valid.