What is ratio data example?

An excellent example of ratio data is the measurement of heights. Height could be measured in centimeters, meters, inches, or feet. It is not possible to have a negative height. When comparing to interval data, for example, the temperature can be – 10-degree Celsius, but height cannot be negative, as stated above.

Why is the ratio scale most powerful?

Among four levels of measurement, including nominal, ordinal, interval, and ratio scales, the ratio scale is the most precise. Because attributes in a ratio scale have equal distances and a true zero point, statements about the ratio of attributes can be made.

Is age nominal or ordinal in SPSS?

Age is frequently collected as ratio data, but can also be collected as ordinal data. This happens on surveys when they ask, “What age group do you fall in?” There, you wouldn’t have data on your respondent’s individual ages – you’d only know how many were between 18-24, 25-34, etc.

What is an example of a nominal scale?

Nominal scale is qualitative in nature, which means numbers are used here only to categorize or identify objects. For example, football fans will be really excited, as the football world cup is around the corner!

Is gender nominal or ordinal?

There are four basic levels: nominal, ordinal, interval, and ratio. A variable measured on a “nominal” scale is a variable that does not really have any evaluative distinction. One value is really not any greater than another. A good example of a nominal variable is sex (or gender).

What type of data is temperature?

An interval scale is one where there is order and the difference between two values is meaningful. Examples of interval variables include: temperature (Farenheit), temperature (Celcius), pH, SAT score (200-800), credit score (300-850).

How do you write Chapter 3 of research?

This chapter deals effectively with the research methods to be adopted in conducting the research, and it is organized under the following sub-headings:

  1. Research Design.
  2. Area of Study.
  3. The population of the Study.
  4. Sample and Sampling Techniques.
  5. Instruments for Data Collection.
  6. The validity of the Instrument.