Levels of Data Analysis

The list is ordered according to the level of difficulty.

  • Descriptive just describe the data, common for census type of data
  • Exploratory find relationships that were not clear beforehand, useful for defining future studies, remember correlation does not imply causation
  • Inferential use a small dataset to say something about a larger population, most common goal of statistical analysis
  • Predictive use data from some object to predict something(values) for another object, important to measure the right values and to use as much data as possible
  • Causal what happens to one variable when you force another variable to change, usually requires a randomized study, this is the gold standard of data analysis
  • Mechanistic understanding the exact changes in variables that lead to changes in other variables for individual objects, typically from engineering and physical sciences, data analysis can be used to infer the parameters if the equations are known

This list comes from information presented in the first week of the Coursera Data Analysis class.

3 thoughts on “Levels of Data Analysis”

  1. Hi, I remembered you were interested in collecting data of our own lives and finding out how we spent our time doing what. I ran into Nicholas Felton’s website today and found he’s been working on a project since 2005 to illustrate that point. And he’s been doing really well on making those infographics.

    Anyways, here’s the link to his annual reports if you are interested:
    http://feltron.com/

    Also, he’s developed an iphone app “daytum” to make users be able to collect personal data as well. I wanted so much to play with it but the android version hasn’t been put up yet. (sad face)

    P.S. I couldn’t find your original blog on this topic so I post it here. Hope you don’t mind.

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