The level of measurement for income depends on the scale or properties of the data collected. There are four levels of measurement: nominal, ordinal, interval, and ratio. Each level represents a different degree of precision and mathematical properties. In the case of income, the level of measurement is typically ratio. Let's explore each level of measurement and discuss why income fits within the ratio level:
Nominal Level: This level involves categorizing data into distinct groups with no inherent order or numerical significance. Examples include gender, race, or nationality. Income does not fit within the nominal level because it represents a quantitative measure rather than a categorical grouping.
Ordinal Level: Data at the ordinal level have a meaningful order or ranking, but the differences between values are not necessarily consistent or measurable. For example, ranking satisfaction levels on a scale of "very unsatisfied" to "very satisfied." Income does not fit within the ordinal level because it represents an exact quantity that can be measured and compared numerically.
Interval Level: At this level, data have a meaningful order, and the differences between values are consistent and measurable. However, there is no true zero point. Temperature measured in Celsius or Fahrenheit is an example of data at the interval level. While income does have a meaningful order (e.g., $20,000 is less than $30,000), it does have a true zero point, making it inappropriate for the interval level.
Ratio Level: This level has all the properties of the interval level, but it also has a true zero point, meaning that zero represents the absence of the attribute being measured. Data at the ratio level allow for meaningful ratios and comparisons. Examples include height, weight, and income. Income fits within the ratio level because it meets all the criteria: it has a meaningful order, consistent differences between values, and a true zero point (i.e., zero income represents the absence of income).
In summary, the income of loan applicants would be considered at the ratio level of measurement. This level provides the most precise and informative data, allowing for meaningful comparisons, ratios, and mathematical operations. When analyzing income data, researchers can utilize various statistical techniques suited to ratio-level data, such as calculating means, medians, standard deviations, and conducting ratio-based comparisons.
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