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Analyzing/Interpreting Assessment Data

Assessment data can offer evidence of student learning in a program, reveal a program’s strengths and areas in need of improvement, and inform actions towards improving or enhancing the program. Analyzing the data gives meaning to the information collected and allows for departments to properly utilize and communicate the assessment results. This includes effectively organizing, synthesizing, interrelating, comparing, and presenting assessment data.

Tips and Considerations

  • Use both qualitative and quantitative methods to analyze and describe assessment results.
    • Quantitative data analysis is based numerical scores, statistics, and statistical hypothesis testing.
    • Qualitative data analysis is based on words and observations rather than numbers. Qualitative data can provide additional richness to quantitative data by providing answers to “how” and “why” questions about students’ learning experiences.
  • Compare data to provide greater meaning. Data can be compared to baseline data, previous assessment results, existing standards or criteria, or between different student populations.
  • Analyze data with an equity mindset. Are there gaps in learning outcomes achievement in certain student populations, especially historically underserved student populations?
  • Analyze data in the context of stated SLOs and benchmarks.
    • What does the assessment data say about achievement of student learning? Did students demonstrate an acceptable level of proficiency for the stated SLO? Did they meet established benchmarks?
    • Are there weaknesses in any particular skills?
    • Alternatively, are there areas where students excelled?
    • What does the assessment data say about students’ preparation for the next course in the program or next step in their career pathways?
  • Note and describe observations and trends. Include representative excerpts or samples of student work to accompany quantitative displays.
  • Consider your audience and vary your analysis according to the audience you intend to share the data with (colleagues, students, campus reports, etc.)

Examples of Quantitative Analysis

As with all data analysis, context is important. It is important to begin thinking about this in the assessment planning stages and to match your analysis to the types of assessment information you have and the kinds of assessment questions you are trying to answer.

  • Descriptive Statistics
    • Frequencies and percentage distributions
    • Measures of central tendency – mean, median, mode
    • Measures of spread – standard deviation and ranges
  • Hake gain – Used to analyze pre/post test scores or scores on individual questions from pre/post tests
  • Item Analysis – Used to analyze the validity of multiple-choice exam questions to determine if they should be included in an overall exam score and so that questions can potentially be better written for the next use of the exam

Washington State University’s Office of Assessment for Curricular Effectiveness has some excellent guides on:

Resources