Analyzing and Interpreting Data
Jonnerious Gilder, Darryl Hardamon, Tenisha Holman, Simona Lewis, Shawanna Nelson, and Christine Norris
QNT/351
July 22, 2015
Huei Holloman

Analyzing and Interpreting Data

Describe the instrument used for data collection.
Data instruments apply “to the levels of measurement of your independent variables and dependent variable” (Lind, Marchal, & Wathen, 2011, p. 5). Survey is a quantitative research approach, not a data instrument. However, “what a scale means and what we can do with it depends on what its numbers represent” (Davis Education, 2015, para. 1). Statistics can be classified into four types or levels, which are nominal, ordinal, interval, and ratio (Lind, Marchal, & Wathen, 2011). Nominal is the easiest of them all. Nevertheless, the ratio is the most complicated of the four type of levels of measurement.
Nominal
Nominal is hardly measurement. “It refers to quality more than quantity” (Davis Education, 2015, para. 2). A nominal level of measurement is a concerned with differentiating by name. For example, 1 equals male, 2 equals female. Even though, when are using the numbers 1 and 2, they do not symbolize quantity. The two category of 0 and 1 used for the smart cell phone is a nominal level of measurement. They are categories or classifications. “Nominal measurement is like using categorical levels of variables” (Davis Education, 2015, para. 2).
Ordinal
Ordinal means the order in measurement. “An ordinal scale indicates direction, in addition to providing nominal information” (Davis Education, 2015, para. 3). For example, “Low/Medium/High or Faster/Slower are examples of ordinal levels of measurement” (Davis Education, 2015, para. 3).
Interval
“Interval scales provide information about the order, and also possess equal intervals” (Davis Education, 2015, para. 4). Now, let’s look back at the earlier example, “if we knew that the distance between 1 and 2 was the same as that between 7 and 8 on our 10-point rating scale, then we would have an interval scale” (Davis Education, 2015, para. 4).
However, a classic example of an interval scale is the temperature, which can be either measured on a Fahrenheit or Celsius scale (Lind, Marchal, & Wathen, 2011). “A degree represents the same underlying amount of heat, regardless of where it occurs on the scale” (Davis Education, 2015, para. 4). Measured in Fahrenheit units, the difference between a temperature of 52 and 48 is the same as the difference between 80 and 76.
“Equal-interval scales of measurement can be devised for opinions and attitudes” (Davis Education, 2015, para. 4). However, it is vital that he or she must be able to comprehend and recognize “the different levels of measurement when using and interpreting scales” (Davis Education, 2015, para. 4).
Ratio
Also, to keeping “the qualities of nominal, ordinal, and interval scales, a ratio scale has an absolute zero that is a point where none of the quality being measured exists” (Davis Education, 2015, para. 5). Now, when he or she is using a ratio scale, it will allows assessments, “such as being twice as high, or one-half as much” (Davis Education, 2015, para. 5).
Furthermore, the reaction time, which referred to how long it takes to respond to a signal of some sort uses a ratio scale of measurement, which is time (Davis Education. 2015). Even though, a person’s reaction time is always greater than zero, we theorize a zero point in time, and can state that a response of 23 milliseconds is twice as fast as a response time of 47 milliseconds.
“The level of measurement for a particular variable is defined by the highest category that it achieves” (Davis Education, 2015, para. 5). For instance, classifying somebody as outgoing or timid nominal. Now, when he or she is labeling individuals as “1 equal shy, 2 equals neither timid nor outgoing, and 3 equal outgoing” (Davis Education, 2015, para. 5). Now, ladies and gentlemen, we have an ordinal level of measurement.

References
Davis Education. (2015). Levels of measurement. Retrieved from http://psychology,ucdavis.edu/faculty_sites/sommerb/sommerdemo/scaling/levels.htm
Lind, D. A., Marchal, W. G., & Wathen, S. A. (2011). Basic statistics for business and economics (7th ed.). New York, NY: McGraw-Hill/Irwin.