Quantitative data in the context of "Consumer research"

Marketing research is the systematic gathering, recording, and analysis of qualitative and quantitative data about issues relating to marketing products and services. The goal is to identify and assess how changing elements of the marketing mix impacts customer behavior.

This involves employing a data-driven marketing approach to specify the data required to address these issues, then designing the method for collecting information and implementing the data collection process. After analyzing the collected data, these results and findings, including their implications, are forwarded to those empowered to act on them.

In statistics, a categorical variable (also called qualitative variable) is a variable that can take on one of a limited, and usually fixed, number of possible values, assigning each individual or other unit of observation to a particular group or nominal category on the basis of some qualitative property. In computer science and some branches of mathematics, categorical variables are referred to as enumerations or enumerated types. Commonly (though not in this article), each of the possible values of a categorical variable is referred to as a level. The probability distribution associated with a random categorical variable is called a categorical distribution.

Categorical data is the statistical data type consisting of categorical variables or of data that has been converted into that form, for example as grouped data. More specifically, categorical data may derive from observations made of qualitative data that are summarised as counts or cross tabulations, or from observations of quantitative data grouped within given intervals. Often, purely categorical data are summarised in the form of a contingency table. However, particularly when considering data analysis, it is common to use the term "categorical data" to apply to data sets that, while containing some categorical variables, may also contain non-categorical variables. Ordinal variables have a meaningful ordering, while nominal variables have no meaningful ordering.

Economic history is the study of history using methodological tools from economics or with a special attention to economic phenomena. Research is conducted using a combination of historical methods, statistical methods and the application of economic theory to historical situations and institutions. The field can encompass a wide variety of topics, including equality, finance, technology, labour, and business. It emphasizes historicizing the economy itself, analyzing it as a dynamic entity and attempting to provide insights into the way it is structured and conceived.

Using both quantitative data and qualitative sources, economic historians emphasize understanding the historical context in which major economic events take place. They often focus on the institutional dynamics of systems of production, labor, and capital, as well as the economy's impact on society, culture, and language. Scholars of the discipline may approach their analysis from the perspective of different schools of economic thought, such as mainstream economics, Austrian economics, Marxian economics, the Chicago school of economics, and Keynesian economics.

Experimental data in science and engineering is data produced by a measurement, test method, experimental design or quasi-experimental design. In clinical research any data produced are the result of a clinical trial. Experimental data may be qualitative or quantitative, each being appropriate for different investigations.

Generally speaking, qualitative data are considered more descriptive and can be subjective in comparison to having a continuous measurement scale that produces numbers. Whereas quantitative data are gathered in a manner that is normally experimentally repeatable, qualitative information is usually more closely related to phenomenal meaning and is, therefore, subject to interpretation by individual observers.

Data quality refers to the state of qualitative or quantitative pieces of information. There are many definitions of data quality, but data is generally considered high quality if it is "fit for [its] intended uses in operations, decision making and planning". Data is deemed of high quality if it correctly represents the real-world construct to which it refers. Apart from these definitions, as the number of data sources increases, the question of internal data consistency becomes significant, regardless of fitness for use for any particular external purpose.

People's views on data quality can often be in disagreement, even when discussing the same set of data used for the same purpose. When this is the case, businesses may adopt recognised international standards for data quality (See #International Standards for Data Quality below). Data governance can also be used to form agreed upon definitions and standards, including international standards, for data quality. In such cases, data cleansing, including standardization, may be required in order to ensure data quality.

A test method is a method for a test in science or engineering, such as a physical test, chemical test, or statistical test. It is a specified procedure that produces a test result. To ensure accurate and relevant results, a test method should be "explicit, unambiguous, and experimentally feasible.", as well as effective and reproducible.

A test is an observation or experiment that determines one or more characteristics of a given sample, product, process, or service, with the purpose of comparing the test result to expected or desired results. The results can be qualitative (yes/no), quantitative (a measured value), or categorical and can be derived from personal observation or the output of a precision measuring instrument.