OeNB Financial Literacy Evaluation Series: Quantitative data analysis in financial literacy evaluation research – visualization and statistical inference

Authors: Theresa Lorenz, Maximilian Zieser, Sofia Anyfantaki
Year: 2025

To evaluate financial literacy programs, researchers often work with quantitative data. To derive meaningful 
insights from raw data requires data analysis. In this article, we introduce two types of quantitative data analysis: descriptive statistics and statistical inference. Data visualization is a powerful way of descriptive data analysis but is frequently overlooked in evaluation handbooks. This article therefore emphasizes effective methods of data visualization. We show different types of visualizations that are effective for plotting time series, proportions, relationships and distributions. Additionally, we provide general guidelines to ensure an accurate representation of data and facilitate the effective interpretation of the intended message. For impact evaluations, however, descriptive statistics alone may be insufficient. Methods of statistical inference enable us to gain insights into populations from sample data and quantify the uncertainty of these insights. We thus provide an introduction to statistical inference by focusing on the principles behind hypothesis and significance testing.

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