Quantitative

Quantitative describes information expressed numerically and research methods that emphasize measurement and quantity. It contrasts with qualitative approaches, which focus on descriptions, meanings, and attributes that are not readily reduced to numbers. In many fields, quantitative methods seek to quantify phenomena, test theories, and generalize findings to larger populations.

Data are collected through instruments and procedures that generate numerical values, such as surveys with fixed response options, laboratory measurements, or analysis of existing records. Data can be discrete (counts, categories coded as numbers) or continuous (measurements such as time, mass, or temperature). Measurements rely on defined units and scales to enable comparison.

Analysis uses statistical tools to summarize data and draw inferences. Descriptive statistics describe central tendency and variability; inferential statistics assess relationships and significance, enabling hypothesis testing, estimation, and predictive modeling. Common techniques include regression, correlation, ANOVA, and data visualization.

Applications span the natural and social sciences, engineering, economics, and market research. Quantitative methods underpin experiments, surveys, epidemiological studies, and policy analysis, where numerical evidence supports conclusions and decision making.

Quantification is the process of assigning numerical values to variables, enabling measurement but also introducing concerns about validity, reliability, sampling bias, and measurement error. The term derives from the Latin quantus, meaning "how much."