product and brand management
Types of Variables
We can distinguish between two types of variables according to the level of measurement:
1. Continuous or Quantitative Variables.
2. Discrete or Qualitative Variables.
A quantitative variable is one in which the variates differ in magnitude, e.g. income, age, GNP, etc. A qualitative variable is one in which the variates differ in kind rather than in magnitude, e.g. marital status, gender, nationality, etc.
Continuous or Quantitative Variables
Continuous variables can be classified into three categories:
Interval - scale Variables:
Interval scale data has order and equal intervals. Interval scale variables are measured on a linear scale, and can take on positive or negative values. It is assumed that the intervals keep the same importance throughout the scale. They allow us not only to rank order the items that are measured but also to quantify and compare the magnitudes of differences between them. We can say that the temperature of 40°C is higher than 30°C, and an increase from 20°C to 40°C is twice as much as the increase from 30°C to 40°C. Counts are interval scale measurements, such as counts of publications or citations, years of education, etc.
Continuous Ordinal Variables
They occur when the measurements are continuous, but one is not certain whether they are on a linear scale, the only trustworthy information being the rank order of the observations. For example, if a scale is transformed by an exponential, logarithmic or any other nonlinear monotonic transformation, it loses its interval - scale property. Here, it would be expedient to replace the observations by their ranks.
Ratio - scale Variables
These are continuous positive measurements on a nonlinear scale. A typical example is the growth of bacterial population (say, with a growth function AeBt.). In this model, equal time intervals multiply the population by the same ratio. (Hence, the name ratio - scale).
Ratio data are also interval data, but they are not measured
We can distinguish between two types of variables according to the level of measurement:
1. Continuous or Quantitative Variables.
2. Discrete or Qualitative Variables.
A quantitative variable is one in which the variates differ in magnitude, e.g. income, age, GNP, etc. A qualitative variable is one in which the variates differ in kind rather than in magnitude, e.g. marital status, gender, nationality, etc.
Continuous or Quantitative Variables
Continuous variables can be classified into three categories:
Interval - scale Variables:
Interval scale data has order and equal intervals. Interval scale variables are measured on a linear scale, and can take on positive or negative values. It is assumed that the intervals keep the same importance throughout the scale. They allow us not only to rank order the items that are measured but also to quantify and compare the magnitudes of differences between them. We can say that the temperature of 40°C is higher than 30°C, and an increase from 20°C to 40°C is twice as much as the increase from 30°C to 40°C. Counts are interval scale measurements, such as counts of publications or citations, years of education, etc.
Continuous Ordinal Variables
They occur when the measurements are continuous, but one is not certain whether they are on a linear scale, the only trustworthy information being the rank order of the observations. For example, if a scale is transformed by an exponential, logarithmic or any other nonlinear monotonic transformation, it loses its interval - scale property. Here, it would be expedient to replace the observations by their ranks.
Ratio - scale Variables
These are continuous positive measurements on a nonlinear scale. A typical example is the growth of bacterial population (say, with a growth function AeBt.). In this model, equal time intervals multiply the population by the same ratio. (Hence, the name ratio - scale).
Ratio data are also interval data, but they are not measured