Final answer:
Levels of measurement in statistics refer to the way data is measured and classified, with four different levels: nominal, ordinal, interval, and ratio. Each level has its own characteristics and determines the type of statistical analysis that can be applied. Nominal and ordinal scales are used for qualitative data, while interval and ratio scales are used for quantitative data.
Step-by-step explanation:
The term "levels of measurement" refers to the way data is measured and classified. There are four levels of measurement: nominal, ordinal, interval, and ratio. Each level has its own characteristics and determines the type of statistical analysis that can be applied to the data.
Nominal scale is used for qualitative data that is not ordered or ranked, such as categories, colors, or names. An example of nominal scale data would be the colors of crayons in a box.
Ordinal scale is used for qualitative data that is ordered or ranked, such as athletic ability classifications (superior, average, above average) of high school soccer players. While the categories have an order, the scale does not provide information about the magnitude or intervals between categories.
Interval and ratio scales are used for quantitative data. Interval scale has equal intervals between values, but does not have a meaningful zero point. An example of interval scale data would be baking temperatures for various main dishes. Ratio scale, on the other hand, has a meaningful zero point and equal intervals between values. Examples of ratio scale data include measuring time or distance.