Measurement Scales or Levels

Measurement Scales or Levels

Measurement Scales?

Measurement

• Assignment of numbers to Characteristics (variables) of objects, persons or events
• Measurement is the foundation of any scientific investigation

Scales: 

• Scaling is the process of measuring.

Measurement Scales:

Measurement scales are used to categorize and/or quantify variables.

A measurement scale is used to qualify or quantify data variables in statistics. It determines the kind of techniques to be used for statistical analysis.

Nominal Scale:

• It classifies data into mutually exclusive (overlapping), exhausting categories in which no order or ranking can be imposed on the data.
• Assign responses to different categories, having no numerical difference.
• It also called the categorical variable scale, is defined as a scale used for labelling variables into distinct classifications and doesn’t involve a quantitative value or order. 
• It just assign the label to the data by category-wise i.e. Male, Female

Examples:

• Gender: 1=Male, 2=Female (It doesn't matter that what is the number of male or female). 
• What about symptoms of depression from a psychiatric assessment? None=0, Mild=1, Moderate=2, Serve=3
• Where do you live?                1- Suburbs         2- City            3- Town

Nominal scale is often used in research surveys and questionnaires where only variable labels hold significance.

For instance:

• A customer survey asking “Which brand of smartphones do you prefer?” Options: “Apple”- 1, “Samsung”-2, “Oppo”-3, "Infinix"-4.
• Why do you like the above chosen brand for best? Options: “OS”- 1, “Battery”-2, “Design”-3, "Weight"-4.

Ordinal Scale

• It classifies the data into categories that can be ranked; however, precise differences between the rank do not exist.
• Set of categories that are ordered from least to most.
• In ordinal scales, numbers represent rank order and indicate the order of quality or quantity, but they do not provide an amount of quantity or degree of quality.

Example:

• The number 1 means that the person (or object or event) is better than the person labeled 2; person 2 is better than person 3, and so forth. 
• How are you feeling today?  a. Very Unhappy, b. Unhappy, c. OK, d. Happy, e. Very Happy
• How satisfied are you with our service? a. Very Unsatisfied, b. Unsatisfied, c. Neutral, d. Satisfied, e. Very satisfied
• Position in a Race, 1st, 2nd, 3rd, 4th and so on...
• Govt jobs Grade BPS-1, BPS-2, BPS-14 etc
• Military Rank
• Grades
• Satisfaction
• Happiness

Interval Scale

• It ranks data and precise differences between units of measure do exist; however, there is no meaningful zero.
• It is defined as a numerical scale where the order of the variables is known as well as the difference between these variables.
• Interval scales are numeric scales in which we know both the order and the exact differences between the values.

Examples:

• Temperature is on the Interval Scale: a difference of 10 degrees between 90 and 100 means the same as 10 degrees between 150 and 160.

• Time of each day in the meaning of a 12-hour clock.
  Grade levels in a school (1st grader, 2nd grader, and etc.)
• Voltage e.g. 110 and 120 volts (AC); 220 and 240 volts (AC) and etc.
• Dates (1015, 1442, 1726, etc.)
• Measuring an income as a range, like $0-$999; $1000-$1999; $2000-$2900, and etc. This is a classic example of turning a non-interval, ordered variable scale into an interval scale to support statistical analysis.
• Age is also a variable that is measurable on an interval scale, like 1, 2, 3, 4, 5 years and etc.
• IQ test (intelligence scale).
• Temperature, in degrees Fahrenheit or Celsius (but not Kelvin).

Ratio Scale

• It possesses all the characteristics of the interval measurement, and there exists a true zero.
• Ratio scale is a type of variable measurement scale which is quantitative in nature. Ratio scale allows any researcher to compare the intervals or differences.

Examples:

• Weight of a packet of candy.
• Height of a boy,
• Age.
• Sales Figures.
• Ruler measurements.
• Income earned in a week.
• Years of education.
• Number of children

Introduction to Statistics