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Data Analysis

This guide provides information about the process of evaluating, inspecting, modeling, and transforming data.

Introduction

Studies can use quantitative data, qualititative data, or both types of data. Each approach has advantages and disadvantages. Explore the resources in the box at the left for more information.

Of the available library databases, only ERIC (for education topics) and PsycINFO (for psychology topics) allow you to limit your results by the type of data a study uses. Hover over the database name below for information on how to do so.

Note: database limits are helpful but not perfect. Rely on your own judgment when determining if data match the type you are seeking.

Quantitative Data

Numerical data.

Quantitative variables can be continuous or discrete.

  • Continuous: the variable can, in theory, be any value within a certain range. Can be measured.
    • Examples: height, weight, blood pressure, cholesterol.
  • Discrete: the variable can only have certain values, usually whole numbers. Can be counted.
    • Examples: number of visits to doctor in last year, number of fractures, number of children.

Qualitative Data

Non-numerical data.

Qualitative variables can be nominal or ordinal.

  • Nominal: the variable does not have a specific order.
    • Examples: eye color, blood type, ethnicity.
  • Ordinal: the variable has a specific order.
    • Examples: stages of cancer, class letter grade, position in a race.