Kvantitativa

Kvantitativa refers to a family of research methods and data paradigms that rely on numerical measurement and statistical analysis. In Swedish-language scholarship, the term denotes quantitative approaches used to describe, explain, and predict phenomena by analyzing numerical data. Quantitative research aims to quantify variables, test hypotheses, and generalize findings to larger populations with representative samples.

Key features include predefined variables, standardized instruments, structured data collection, objectivity, and replicability. Data are typically discrete or continuous and analyzed with statistical methods. Common data sources include surveys, tests, administrative records, and experiments. Variables are measured on nominal, ordinal, interval, or ratio scales, with attention to reliability and validity.

Designs frequently used are cross-sectional studies, longitudinal cohorts, experiments, and quasi-experiments. Analysts apply descriptive statistics and inferential techniques such as regression, t-tests, chi-square tests, and ANOVA, often reporting effect sizes and confidence intervals.

Applications span the social sciences, health sciences, economics, education, and natural sciences. Strengths include the capacity to handle large samples, detect patterns, test causal hypotheses under controlled conditions, and generalize results. Limitations involve potential neglect of context, measurement error, and oversimplification of complex phenomena.

Kvantitativa is commonly contrasted with kvalitativa research, though mixed-methods studies combining both approaches are widespread. The field has roots in positivist philosophy and the development of statistical methods.