TYPES OF DATA Module One Lesson Two

• This presentation is based on material and graphs from OpenStax and is copyrighted by OpenStax and Georgia Highlands College.

• Data classification in Biology and Botany (context for organizing information) is used to group organisms by taxonomic ranks (kingdom, phylum, class, order, family, genus, species) and by plant characteristics.

• Two main methods to classify data:

  • Type classification
  • Level of Measurement classification

• RECALL from Lesson One: Data is the set of actual responses or values you get when asking about a particular variable.

CLASSIFYING DATA BY TYPE

• QUALITATIVE DATA (also called categorical data)

  • Involves labels or descriptions of traits using words or phrases.
  • Could be numbers, but not typically used in calculations.
  • Sometimes referred to as “quality” data.
  • EXAMPLES:
  • Eye Color
  • ID numbers (GHC ID, SS#, license plate) – note: numbers used as identifiers, not for arithmetic
  • Gender
  • Ethnicity
  • Political Affiliation
  • Hometown
  • Yes/No or Agree/Disagree

• QUANTITATIVE DATA

  • Counts or measurements; numbers with which calculations can be performed.
  • “Quantity” data.
  • EXAMPLES:
  • Height
  • Weight
  • Age
  • Pulse Rate
  • Amount of rain
  • Exam scores
  • Temperature

• Quantitative data can be further divided into two groups:

  • Discrete Data
  • Continuous Data

DISCRETE DATA vs CONTINUOUS DATA

• Discrete Data

  • Result of counting; involves whole units; no fractions or decimals.
  • “Number of …”
  • EXAMPLES:
  • Number of children
  • Number of books on a shelf
  • Number of credit cards
  • Number of eggs a hen lays
  • Number of calls received in a day

• Continuous Data

  • Result of measuring; could involve fractions and/or decimals.
  • Can assume an infinite number of values between any two specific numbers (continuum).
  • EXAMPLES:
  • Height
  • Weight
  • Temperature
  • Distance traveled

Data Visualization: quick categorization

• Data can be classified as Quantitative or Qualitative.
• Quantitative data can be further classified as Discrete or Continuous.

CLASSIFYING DATA BY LEVEL OF MEASUREMENT

• There are four levels of measurement:
  • Nominal
  • Ordinal
  • Interval
  • Ratio
• The higher the level of measurement, the more mathematical calculations are possible and the more statistical analysis options are available.

NOMINAL DATA

• Involves Qualitative Data only.
• Classifies data into mutually exclusive (nonoverlapping) exhaustive groups.
• Has no sense of order or ranking.
• Cannot be used in mathematical calculations (it’s words!).
• EXAMPLES:
  • True/False
  • Political Affiliation
  • Zip Codes

ORDINAL DATA

• Involves Qualitative Data only.
• Classifies data into mutually exclusive exhaustive groups.
• Has RANKS or sense of order, but exact differences do not exist—the ranks are subjective or open to interpretation by the researcher.
• Cannot be used in mathematical calculations (no add/subtract).
• EXAMPLES:
  • Letter grades (A, B, C, D, F)
  • Survey responses (Above average, average, below average, poor)
  • Judging contest (1st, 2nd, 3rd)

INTERVAL DATA

• Involves Quantitative Data.
• Has ranks or sense of order; the exact differences between the values exist and are meaningful; ranks are objective.
• Can be added or subtracted.
• Do not have a TRUE zero; zero is a placeholder and does not mean “absence of something.”
• EXAMPLES:
  • Temperature (0° does not mean no heat)
  • Calendar dates
  • IQ scores (0 does not mean no intelligence or no brain)

RATIO DATA

• Involves Quantitative Data.
• Has ranks or sense of order; the exact differences between the values exist and are meaningful; ranks are objective.
• Can be added, subtracted, multiplied, and divided.
• Has a TRUE zero; zero implies “absence of something” or nothing.
• EXAMPLES:
  • Height
  • Number of phone calls in a day
  • Salary
  • Exam score

ADDITIONAL NOTES ON MEASUREMENT LEVELS

• ORDINAL, INTERVAL, RATIO, NOMINAL appear in a sequence showing increasing capacity for mathematical manipulation.
• Qualitative data remains non-numeric in most practical calculations, while Quantitative data allows arithmetic operations (with restrictions depending on level).
• The choice of level affects what analyses you can perform (e.g., mean, median, mode, regression, percentages, etc.).

EXAMPLES CLASSIFICATION EXERCISES

• Classify each variable (data) by TYPE and LEVEL OF MEASUREMENT:

  • Number of books students carry in their backpacks → Quantitative; Discrete → Ratio
  • Weights of the backpacks with books in them → Quantitative; Continuous → Ratio
  • Colors of backpacks students carry → Qualitative → Nominal (neither discrete nor continuous)
  • Prices of your favorite pair of jeans → Quantitative; Discrete → Ratio
  • Sizes of T-shirts → Qualitative → Ordinal (neither discrete nor continuous)
  • Number of t-shirts you own → Quantitative; Discrete → Ratio
  • Bank account PIN numbers → Qualitative → Nominal (neither discrete nor continuous)
  • Jersey numbers on baseball players → Qualitative → Nominal (neither discrete nor continuous)
  • Temperature at the local baseball game → Quantitative; Continuous → Interval

• Connections to real-world use:

  • Properly classifying data informs the selection of statistical methods and the interpretation of results.
  • Distinguishing qualitative vs. quantitative guides whether arithmetic means are meaningful.
  • Understanding the level of measurement helps in data collection design and ensures valid conclusions.

Note: All items and examples are taken from the provided transcript (OpenStax-based content) and are intended to reflect the module’s emphasis on TYPE vs LEVEL OF MEASUREMENT classifications and practical categorization examples.