Examples of quantitative variables include height and weight, while examples of qualitative variables include hair color, religion and gender. Quantitative variables are often represented in units of measurement, and qualitative variables are represented in non-numerical terms.
The difference between qualitative and quantitative variables lies in the way each are measured. Qualitative variables have no inherent order to them while quantitative variables are numbers that can be naturally ordered. Qualitative variables are often referred to as categorical variables because they can be placed into categories, such as male or female. According to OnlineStatBook, these variables can be ordinal, interval or ratio variables.Learn More
Normally distributed variables, such as the speed of different automobiles at one spot on the highway, form a bell curve with enough measurements. This bell curve means that most cars of the sample are going roughly the same speed, while the outliers represent faster and slower vehicles.Full Answer >
Statistical significance shows the mathematical probability that a relationship between two or more variables exists, while practical significance refers to relationships between variables with real-world applications, according to California State University, Long Beach. Two or more variables do not need statistical significance to have practical significance, and vice versa.Full Answer >
A standardized regression coefficient removes the original unit of measurement for variables in a regression equation. These coefficients are standardized and converted to a scale from 0 to 1. Since the values are standardized, a researcher can more easily compare the effect sizes of variables measured on different scales.Full Answer >
Dichotomous variables are variables that have two levels. A very common example of a dichotomous variable is gender, which has two outcomes and is reported as male or female.Full Answer >