Skip to Main Content

Finding Data and Statistics

Discover reliable sources of data and statistics.

What is data?

Data are the raw building blocks of information. They are observations or measurements about the world around us, captured in various formats. Data can be numbers, text, images, or even sounds. Think of data as the raw ingredients in a recipe: they have potential, but need to be processed and analyzed to create something meaningful.

Examples of Data

  • Survey responses
  • Experiment results
  • Weather measurements
  • Economic indicators
  • Social media posts
  • Historical records

Key points about data

Data helps us answer questions, make informed decisions, and understand the world around us. Whether you're a student, a researcher, or just someone interested in learning more, knowing how to find and use data is an essential skill.

Data vs. Statistics: Data are the unprocessed sets of collected values, while statistics are the results of analyzing and summarizing data. Statistics help us understand patterns and trends within data.

Quantitative vs. Qualitative: Data can be quantitative (numerical, like scientific instrument measurements) or qualitative (descriptive, like interview transcripts). Both types are valuable for research. Mixed-methods studies can yield both quantitative and qualitative data.

Microdata vs. Aggregated Data: Microdata refers to individual observations (like responses from a single person), while aggregated data combines information from multiple sources for a broader picture.

Types of Studies: Data can be collected in different ways. Cross-sectional data is collected at a single point in time. Time Series are data collected on the same variables over multiple points in time. Longitudinal data are collected on the same individuals or groups over an extended period.

Data Formats: Data comes in many formats, such as spreadsheets (.csv, .xls), plain text (.txt), or specialized formats used in specific software.

Documentation is Key: "Codebooks" and "data dictionaries" explain how data is organized and what the values mean (e.g., '1' = male, '2' = female). This makes data usable and understandable.