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Data: Finding, Interpreting, and Managing Data: Home

Finding, interpreting, and managing data


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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Best Practices

  • When looking for data, clearly define what data you need. Ask yourself questions about the type of data you are looking for.
  • Before conducting research with human subjects, you must receive approval from your school's Institutional Review Board (IRB) to ensure information is kept confident and/or anonymous.
  • Before analyzing data, determine the level of measurement associated with the data - nominal, ordinal, interval, or ration.
  • When visualizing data, be sure axes and keys are labeled; colors are logical, distinguishable, accessible, and necessary; keep in mind your intended audience; and be aware of conscious and preconscious judgments.

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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.

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