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Types of Data

There are two types of data: quantitative and qualitative. Depending on your goals, one type may be better suited to meet your needs. The type of data you collect will influence the plan and approach you take.

Quantitative data is numerical and can be counted, quantified, and mathematically analyzed.

For example: The average number of students served by your college access program each month.

Quantitative data:

  • Answers the questions “what,” “how many,” or “who”
  • Draws correlations between factors
  • Is best used in statistical methods to generalize to population (but requires random sample)
  • Is easy to present in tables and charts
  • Requires carefully designed metrics

Quantitative data collection methods include:

  • Program records
  • Contact tracking
  • Data-matching with other organizations
  • Pre-and post-tests
  • Surveys

Qualitative data is used to describe meaning and is generally non-numerical.

For example: Student narratives about why they participate in your program each month.

Qualitative data:

  • Answers the questions “how” and “why”
  • Gains in-depth insight into experience, behavior, or beliefs
  • Represents the “voice” of the individual or group
  • Does not generalize to the population
  • Is time-consuming to collect and analyze
  • May lead to answers for questions you didn’t think to ask

Quantitative data collection methods include:

  • Document analysis
  • Observations
  • Journals
  • Interviews
  • Focus groups

 

Although quantitative and qualitative data are often presented as mutually exclusive alternatives, using a mixed method approach (collecting both quantitative and qualitative data) can ultimately provide the most comprehensive set of data for your evaluation.

  Quantitative + Qualitative = A reliable and valid picture of your results

  • Quantitative data is highly reliable.
    • Reliability is the extent to which a procedure yields the same results on repeated trials (Carmines & Zeller, 1979).
  • Qualitative data has greater validity.
    • Validity is the extent to which an indicator measures or reflects what it intends to measure (Carmines & Zeller, 1979)

You can learn more about how to analyze quantitative and qualitative data in the Analyze section and the Collect Data section of the Evaluation Toolkit.