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Classifying and Understanding Data. 8/23/11. Classifying Data. Data is not very useful without context. Answering the “Five W’s” (Who, What, When, Where, Why) and sometimes How If a lot of data is present a spreadsheet or a data table is often used

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Classifying data
Classifying Data

  • Data is not very useful without context.

    • Answering the “Five W’s” (Who, What, When, Where, Why) and sometimes How

    • If a lot of data is present a spreadsheet or a data table is often used

    • The rows of a data table correspond to individual cases.

    • The columns in a data table are called variables


The who vocab
The “Who” Vocab

  • A case is the most general term for the “who.”

    • If you answer a survey, you are a respondent.

    • If you are being experimented upon, you are either a subject or participant.

    • Plants, animals and inanimate objects involved in an experiment are often called experimental units.


The what vocab
The “What” Vocab

  • The characteristics recorded are called variables.

    • What has been measured?

    • Are there units?

  • Variables are split into two kinds, categorical and quantitative.

    • Categorical variables are also called qualitative.


The why
The “Why”

  • The questions we ask about the variables are the “why” of our analysis.

  • We often cannot determine if a variable is categorical or quantitative without asking ourselves “why.”

  • Consider surveys:

    1 = Disagree; 2 = Somewhat Disagree; 3 = Neither Agree nor Disagree; 4 = Somewhat Agree; 5 = Agree

    Would results from a survey like this be considered categorical or quantitative?


  • Quantitative vs categorical
    Quantitative vs. Categorical

    • The weight in grams of the bacteria growth on a sample of Petri dishes.

      • Quantitative

    • The class designations (Freshman…Senior) in Mr. Martin’s Algebra I class period 7.

      • Categorical

    • The numbers of the starting offensive line for Judson High’s football team.

      • Categorical


    Classifying data1
    Classifying Data

    • There are four different data scales: Nominal, Ordinal, Interval and Ratio

    • Nominal refers to names or designations. The variable “University” from the previous data table is a nominal variable.

    • Ordinal variables can be sorted. Consider race results. “Place Finished” would be an ordinal variable. It is ordinal because the difference between 1st and 2nd place is not necessarily the same as the difference between 3rd and 4th or any other consecutive places.


    Classifying data2
    Classifying Data

    • There are four different data scales: Nominal, Ordinal, Interval and Ratio

    • Variables measured on an interval data scale preserve order, but the difference between values is also preserved. Consider temperature 5° C and 10° C. The difference between those values is the same as between 12° C and 17° C. The intervals are preserved, so we can say that the data are on the interval scale.


    Classifying data3
    Classifying Data

    • There are four different data scales: Nominal, Ordinal, Interval and Ratio

    • Data measured on the ratio scale preserve intervals but they also have meaningful ratios.

    • Consider: Is 2° C twice as hot as 1° C? No.

    • Data that are measured on ratio scale include: Height, Weight, Volume, Percent Correct, etc.


    I dentify the data scale for the variables on the data table as nominal ordinal interval or ratio
    Identify the data scale for the variables on the data table as Nominal, Ordinal, Interval or Ratio