Quantitative and Qualitative Data Types

Whenever conducting research, there are various methods that the researcher can employ depending on the type of research. For our purposes, I will focus on qualitative and quantitative approaches and the types of data under each.

Quantitative Methods and Data Types

Quantitative data represent data that can be measured or quantified. In statistics, quantitative data are aspects of a phenomenon or context that can be numerically measured or ascribed value(s). For example, the number of products sold, how many females are in a population, or how many degrees is the given room temperature. From the examples above, the first two examples represent an aspect of a context, while the third example represents a dimension of a phenomenon (temperature). Quantitative data are usually structured, which means that it follows specific rules and definitions. There are various ways for generating quantitative data, including surveys, tests, metrics, and experiments.

• ⮚ Discrete Data

  This type of data can only take defined or categorical values, or data that can be counted are often represented using graphs and charts. They are called "discrete" data because the values cannot be subdivided. For example, there can only be "whole numbers" of children, say 2, 3, or 10 kids, but we cannot have 1.5 children.

• ⮚Continuous Data

  Unlike discrete data, continuous data can take a value including decimal places. For instance, the temperature can be 12.5 degrees or a height of 7.2 meters. They are said to be continuous because the data are subject to change over time.

Qualitative Methods and Data Types

Unlike quantitative data that follow a defined structure, qualitative data are largely unstructured or semi-structured. This type of data is not considered statistical since the data under investigation does not necessarily require measuring. Qualitative data offers insights about some aspect of a context or phenomenon, including labels, attributes, and properties. For instance, the color of a table, the taste of the cuisine, or the tastes and preferences of customers. There are various ways of generating quantitative data, including text and documents, observations, focus groups, and audio/visual recordings.

• ⮚Nominal Data

  Nominal data cannot be quantified or ordered. They are used to categorize or label variables, for instance, gender, color, race, and country. Using gender as an example, one cannot quantify this label; you cannot say half a woman or a dog is either a Pitbull or not.

• ⮚Ordinal Data

  Unlike nominal data, ordinal data is ordered and usually highlights some meaning to the data. for instance, you can be asked to rate your experience using a product and given the options: “not satisfied,” “satisfied,” and “very satisfied.” In this example, although there are no numerical measurements or counts, there is meaning to the option chosen about “ the experience of using the product.”

• ⮚Binomial Data

  Binomial data or distribution tells us the probability outcomes of either “ success” or “ failure” given several trials. For instance, one may be asked what the probability of getting six “ heads” in a coin toss given ten trials; or finding the probability that a random sample of a population contains ten males. Binomial data determines an event occurring over the other (often binary) when the system is taken through multiple trials.

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