HOW TO ASSESS
How to analyse data
How to Assess
Once you have data you need to be able to make sense of it. Being ‘evaluative’ means assessing performance, value, effectiveness and quality.
It is the process you use to make judgements, to be able to come to conclusions about how ‘good’ something is.
One way to do this is to develop a simple rubric. Evaluative rubrics are a way to define quality and value by:
- Identifying what aspects are important to assess.
- Identifying criteria to make an assessment.
- Using these criteria to make a judgement about how good, excellent (or poor) each aspect is.
Rubrics have been used for some time in the education field and in that context are generally thought of as a scoring guide to evaluate the quality of a student’s work.
In this picture below from the Kinnect Group (provided by Kate McKegg), rubrics clarify how you will make sense of your data to make judgements about worth and value.
| Level of performance | Description |
|---|---|
| Excellent | Performance is clearly very strong or exemplary. Any gaps or weaknesses are not significant and are managed effectively. |
| Very good | Performance is generally strong. No significant gaps or weaknesses, and less significant gaps or weaknesses are mostly managed effectively. |
| Good | Performance is reasonable. A few gaps or weaknesses, but none that are considered serious. |
| Adequate | Performance is inconsistent. Some gaps or weaknesses. Meets minimum requirements/expectations. |
| Poor | Performance is unacceptably weak. Does not meet minimum requirements/expectations. |
| Insufficient evidence | Evidence is unavailable or of insufficient quality to judge performance. |
In essence, a rubric can show the basis upon which you will make assessments or judgements of value, impact and worth.
FURTHER RESOURCES
Rubrics for Non-Profits
Article on Evaluative Rubrics by Kinnect Group
NZQA CORE Approach
Group analysis of data
- An engaging and simple way to involve decision makers in analysing results from New Zealand evaluator Bob Williams. It gets people to look at four things when analysing data as a group activity: generalisations and exceptions, contradictions, surprises and puzzles.
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