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Data: Finding, Interpreting, and Managing Data: Home

Finding, interpreting, and managing data

Creator

This guide was created by Erin Richter-Weikum

Types of Data

Qualitative Data: Non-numerical data. Examples: eye color, socioeconomic status

Quantitative Data: Numerical data. Examples: height, shoe size

Continuous Data: Continuous data can have an infinite number of values and therefore 0 is not meaningful. Examples: weight, height

Discrete Data: Discrete data has finite values and a meaningful 0. Example: number of people living in a household

Time-Series: Studying the same variable over time; the instrument is the same but different people will be used.  

Longitudinal: Typically are surveys that are taken over time with the same people, but not always the same survey or instrument.

Related Guides

Introduction

What is the difference between statistics and data?  Sometimes the two words are used interchangeably when they are two different things.

  • Data is the raw information from which statistics are created.  
  • Statistics provide an interpretation and summary of the data.  

This guide is organized by the order in which data resources are collected and used.

  • Find Data: Find a data set to use.
  • Collect Data: For those who want to collect their own data, learn where and how to collect data including methods of data collection and what software to use.
  • Manage Data: Ensure your data is stored and managed properly.
  • Analyze Data and Statistics: This is primarily done through statistical analysis software. Includes information on the different software.
  • Visualize Data: Visually represent the data to share with others.
  • Data Science: This is a broad term for working with different types of data. Different data types include big data and data mining.

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