Skip to main content
Statistics LibreTexts

2: Working with Data

  • Page ID
  • Learning Objectives

    Having read this chapter, you should be able to:

    • Distinguish between different types of variables (quantitative/qualitative, binary/integer/real, discrete/continuous) and give examples of each of these kinds of variables
    • Distinguish between the concepts of reliability and validity and apply each concept to a particular dataset

    • 2.1: What Are Data?
      The first important point about data is that data are - meaning that the word “data” is plural (though some people disagree with me on this). You might also wonder how to pronounce “data” – I say “day-tah” but I know many people who say “dah-tah” and I have been able to remain friends with them in spite of this. Now if I heard them say “the data is” then that would be bigger issue…
    • 2.2: Discrete Versus Continuous Measurements
      A discrete measurement is one that takes one of a set of particular values. These could be qualitative values (for example, different breeds of dogs) or numerical values (for example, how many friends one has on Facebook). A continuous measurement is one that is defined in terms of a real number. It could fall anywhere in a particular range of values, though usually our measurement tools will limit the precision with which we can measure.
    • 2.3: Suggested Readings
    • 2.4: Appendix
    • 2.5: What Makes a Good Measurement?
      It is usually impossible to measure a construct without some amount of error. In the example above, you might know the answer but you might mis-read the question and get it wrong. In other cases there is error intrinsic to the thing being measured, such as when we measure how long it takes a person to respond on a simple reaction time test, which will vary from trial to trial for many reasons. We generally want our measurement error to be as low as possible.

    • Was this article helpful?