Types of Data

Understanding Types of Data

  • Get accustomed to the concept of types of data and their importance in statistical analysis.
  • Identify two broad types of data: quantitative and qualitative.
  • Recognise quantitative data as numerical data that provides measurable information.
  • Understand qualitative data as non-numerical data that provides descriptive information.

Quantitative Data

  • Get familiar with two categories of quantitative data: discrete and continuous.
  • Understand discrete data as numerical values that can be counted and have a finite number of possibilities.
  • Identify continuous data as numerical values that measure characteristics and have an infinite number of possibilities.

Qualitative Data

  • Understand two types of qualitative data: nominal and ordinal.
  • Identify nominal data as data that can be categorised but not ranked or ordered, like colours or types of fruit.
  • Understand ordinal data as data that can be categorised and ordered, like survey responses ranging from very dissatisfied to very satisfied.

Data Collection and Types of Data

  • Understand the relationship between data collection techniques and the types of data collected.
  • Acknowledge that surveys, questionnaires, and interviews may result in both quantitative and qualitative data.
  • Recognise that observational studies typically yield qualitative data, while controlled experiments often produce quantitative data.

Data Representation and Types of Data

  • Understand the close relationship between types of data and how they’re represented or visualised.
  • Recognise that quantitative data can be presented using graphs and charts, including bar graphs, histograms, line graphs, and scatter plots.
  • Observe that qualitative data is typically presented using pie charts, bar graphs, or frequency tables.

Data Interpretation and Types of Data

  • Understand the importance of correctly interpreting data, taking into account the type of data dealt with.
  • Learn that measures of central tendency and dispersion are typically used to interpret quantitative data.
  • Understand that qualitative data is typically interpreted using frequencies and percentages.
  • Be aware that ordinal data requires careful interpretation because although the data can be ranked, the intervals between categories may not be equal.