Whether you equate success with work-life balance or how many pitches a baseball player throws in a game, metrics are key to assessing and improving your business. However, not all metrics are created equal.
Measures play an important role in math education as pupils learn how to compare lengths, weights and volume/capacity. Understanding the different types of measures will help you teach math effectively.
Measures of Effectiveness
There are a number of different ways that effectiveness can be measured. In business, this can include using key performance indicators (KPIs) or conducting customer satisfaction surveys. These metrics can help businesses determine how well they are performing and where there is room for improvement.
Other measures of effectiveness can be more subjective, like ratings on traits such as cooperation, critical judgment and communication. These can be difficult to evaluate and may vary widely from one person to another, but they can also be useful if they are used consistently and in conjunction with other assessments.
Regardless of the method chosen to measure effectiveness, it is important to ensure that it is comprehensive and gives an accurate picture of the entire situation. For example, scoring a support or call centre employee purely on the basis of their number of calls fielded ignores whether they are achieving their goal of providing quality customer service and answering questions satisfactorily.
Measures of Performance
Often, these measures are easy to decipher and provide management with a quick, cost-effective appraisal tool. They may include simple skills tests such as a yes/no questionnaire or the 9-box grid method.
However, to be most useful, performance measurement must be based on activities over which managers have control or influence and consistent in application. If a manager’s evaluation is based on income, for example, it should be measured in the same way each time, unless the measure is found to be inappropriate for its intended purpose (e.g., comparing against budgeted results or established standards).
The best performance measures have face validity and represent an important step toward the translation of the highest quality evidence into clinical practice. Ideally, they should also improve value for patients and purchasers of health care. This is a critical factor that will drive the effectiveness of any performance measurement system. Ultimately, this is the most effective way to make a meaningful difference in outcomes and community health.
Measures of Suitability
The ability of a person to behave in a way that is compatible with the requirements of the job. For example, a technical expert, manager, office administrator or customer representative may require different behaviors from that of a salesperson. Suitability is much more difficult to assess than eligibility, because behavioral factors are often less apparent and are often interrelated. Also, many jobs have unique behaviors that are hard to quantify or measure, and applicants have a strong incentive to conceal information that could harm their employment opportunity.
Financial suitability means deeply understanding a client including his or her goals, circumstances and personality — and tailoring a financial plan, strategy or product that ‘fits’ them. It’s also what FINRA requires broker-dealers to do in complying with regulations like Regulation Best Interest and Rule 2111 when recommending investments to retail investors.
Measures of Accuracy
Accuracy and precision are important for many professionals working with data, such as scientists. They rely on accurate measurements to help them establish standards that others can use as reference points.
In the world of measurement, accuracy refers to how close a measured value is to its true or accepted value. For example, if you weigh a substance in lab five times and get an average weight of 3.2 kg, your measurement is accurately close to the actual or known value.
Precision, on the other hand, refers to how closely the results of multiple measurements are to each other. For example, if you take 10 measurements of the same object and they all fall within the same range, your data is highly precise. It is possible to be both accurate and precise, but it is more common to be one or the other. Think of a dartboard: The data points on the left depict accurate data that are close together, while those on the right show precise data that are not close together.