Four common levels of data measurement follow. •Nominal Level. The lowest level of data measurement is the nominal level. Numbers representing nominal level data (the word level often is omitted) can be used only to classify or categorize. Employee identification numbers are an example of nominal data. The numbers are used only to differentiate employees and not to make a value statement about them. Many demographic questions in surveys result in data that are nominal because the questions are used for classification only.
Some other types of variables that often produce nominal-level data are sex, religion, ethnicity, geographic location, and place of birth. Social Security numbers, telephone numbers, employee ID numbers, and ZIP code numbers are further examples of nominal data. Statistical techniques that are appropriate for analyzing nominal data are limited. However, some of the more widely used statistics, such as the chi-square statistic, can be applied to nominal data, often producing useful information. Ordinal-level data measurement is higher than the nominal level. In addition to the nominal level capabilities, ordinal-level measurement can be used to rank or order objects. •Interval-level data measurement is the next to the highest level of data in which the distances between consecutive numbers have meaning and the data are always numerical. The distances represented by the differences between consecutive numbers are equal; that is, interval data have equal intervals.
The Essay on Data Collection 2
... with employee morale and productivity issues. Level of measurement for each of the variables The questions collected information at a, ordinal, ratio, and nominal levels. ... It should be noted, that various levels of measurement require diversified method of measuring statistically data. Question number four asked how many times the ...
An example of interval measurement is Fahrenheit temperature. With Fahrenheit temperature numbers, the temperatures can be ranked, and the amounts of heat between consecutive readings, such as 200, 210, and 220, are the same. In addition, with interval-level data, the zero point is a matter of convention or convenience and not a natural or fixed zero point. Zero is just another point on the scale and does not mean the absence of the phenomenon. For example, zero degrees Fahrenheit are not the lowest possible temperature.
Some other examples of interval level data are the percentage change in employment, the percentage return on a stock, and the dollar change in stock price. •Ratio-level data measurement is the highest level of data measurement. ratio data have the same properties as interval data, but ratio data have an absolute zero, and the ratio of two numbers is meaningful. The notion of absolute zero means that zero is fixed, and the zero value in the data represents the absence of the characteristic being studied.
The value of zero cannot be arbitrarily assigned because it represents a fixed point. This definition enables the statistician to create ratios with the data. Examples of ratio data are height, weight, time, volume, and Kelvin temperature. With ratio data, a researcher can state that 180 pounds of weight is twice as much as 90 pounds or, in other words, make a ratio of 180:90. Many of the data gathered by machines in industry are ratio data. Reference link : http://classof1. com/homework-help/statistics-homework-help
