Conducting the analysis
Adapted from the University of Massachusetts, Amherst “Program-Based Review & Assessment Tools & Techniques for Program Improvement,” (April 2017) and from Marymount University Assessment Handbook (2015)
Analysis is a process that provides better understanding of data and allows inferences to be made concerning assessment findings. It summarizes the data, enhances the value of information gathered by identifying patterns within it, and provides direction for decisions regarding program improvement. While data analysis can be relatively complex, for the purpose of assessment it is usually basic. Assessment’s focus on student achievement of learning outcomes typically requires the determination of counts and percentages. Together they show clearly the number of students involved in the activity and the rate of successful display of the outcome. All data, regardless of type can be analyzed using counts.
To begin your analysis, we recommend that you review the data visually. The data you would review includes things like the frequencies, (data results showing how frequently students scored on each aspect of the rubric, or question of the exam), and the data in the spreadsheet. If you collected qualitative data, you will want to skim through faculty members’ comments. Reviewing data has two benefits: It allows for the identification of outliers and possible mistakes, and it enables basic patterns or trends to emerge. For example, it may be clear that all students who took a particular class had difficulty with a particular outcome.
Summarize what you found.
For rubric or exam data, you will want to:
- Identify the total number of students participating in the assessment activity for each outcome measure.
- Identify the percentage of students who met or exceeded the performance standard for each outcome measure.
For descriptive faculty feedback, describe the patterns and frequencies of faculty feedback for each learning outcome.
The Role of Advanced Statistical Analysis
As a program’s assessment activity and data increase, more advanced analysis may be useful in understanding student learning. It is possible to:
- Study differences in performance to examine the effects of curricular change
- Conduct pre and post assessments to evaluate effect of specific learning experiences
- Compare program students to national performance on certification examinations (sometimes these are provided by the organizations providing certification
Implementing more advanced statistical analyses requires that you have a
Implementing more advanced statistical analyses requires that you have conducted previous assessment successfully within your program.