Quantitative Data

Quantitative data refers to information that can be measured and expressed numerically. It includes variables that are countable or measurable, such as age, height, weight, temperature, test scores, income, and distance. Quantitative data can be analyzed using statistical methods and often visualized through graphs, histograms, or charts.

Data collected from the world around us can be categorized as either qualitative or quantitative. Qualitative data describes characteristics based on observation, such as appearance, texture, color, smell, and taste. While numerical labels or scales can sometimes be assigned to qualitative data, these values cannot be used in mathematical operations like quantitative data. Qualitative data is also known as categorical data because it involves categorizing observations using either a nominal or ordinal scale.

Quantitative data includes any information that can be quantified, counted, or measured and is expressed as numerical values. These values can be analyzed mathematically. Quantitative data can be either discrete or continuous. Discrete data consists of distinct, separate values with clear gaps between them. Examples of discrete data include the number of people visiting a state fair or the number of fish caught in a day. Continuous data refers to data that can take on any value within a given range on a continuous sequence. Examples of continuous data include the weight of alligators in a sample or the height of elephants in a sample. 

Interval Data

This kind of data can be measured and ordered with the nearest items but have no meaningful zero. The term interval means space in between, which means that interval data is similar to ratio data, but the zero-point might be subjective.  Interval data can be negative, though ratio data can’t.  The following descriptive statistics can be calculated for interval data: central point (mean, median, mode), range (minimum, maximum), and spread (percentiles, interquartile range, and standard deviation). 

Interval data is continuous because it can represent measurements on a scale with equal intervals, where values can be divided infinitely. For example, temperature measured in degrees Celsius or Fahrenheit is interval data because it can take on any value, including fractions, within a given range. Interval data can also be discrete if it involves values that are countable and have specific, set intervals. For instance, the year of birth is interval data with discrete values since years are counted in whole numbers (e.g., 1990, 1991).  

Ratio Data 

This kind of data is measured and ordered with equidistant items and a meaningful zero and can never be negative like interval data. The best example of ratio data is the measurement of heights. It could be measured in centimeters, inches, meters, or feet and it would make no sense to have a negative height.  The descriptive statistics which you can calculate for ratio data are the same as interval data which are central point (mean, median, mode), range (minimum, maximum), and spread (percentiles, interquartile range, and standard deviation).  Because ratio data can take on an infinite number of values within a range and can be measured with great precision, it is classified as continuous. Examples include height, weight, and time.