Quick guide prepared by the WSU Office of Assessment for Curricular Effectiveness | Last updated 12-15-20 Page 2 of 6
Before You Begin: Purpose, Context, Audience
It can be daunting to analyze qualitative data for the first time or in a new context, as there is no “one size
fits all” approach, but there are some ways to make it more approachable. It’s best to start thinking about
your data analysis plan when you are
first identifying your assessment questions and determining how you will collect the needed information, as it is important to match the analysis strategy to the type of
information that you have and the kinds of assessment questions that you are trying to answer. In other
words, decisions about how to analyze assessment data are guided by what assessment questions are
asked, needs of the audience/ stakeholders, as well as the data available and how they were collected. As
previously mentioned, qualitative data can be particularly useful for exploring “why” and “how” questions
Typically, assessment data are intended for discussion and use by program faculty, who are familiar with
the discipline, curriculum, and other sources of related, complementary data. When carefully analyzed and
interpreted in the context that they were collected, assessment data can offer useful insight into curricular
coherence and effectiveness. Data can be misleading, or worse, when they are taken out of context or used
for purposes other than originally intended and agreed upon.
As a result, you will want to understand the purpose and scope of the project, the assessment questions
that guided the project, the context, and the audience for the results before any type of analysis occurs.
You should be familiar with the basic data collection processes, including how the data were collected, who
participated, and any known limitations of the data, as this can help you make an informed decision about
what the data can
reasonably reveal. Ot
her factors to consider may
include: H
ow was the rand
om
sampling/sample size determined? What was the response rate? Has this measure been pilot tested and
refined? As a good practice, a short written description of the data collection processes, number of
participants, and a copy of any instrument used (i.e. survey or focus group questions) should accompany
the data analysis file, data summary, and/or final report.
Examining and Organizing Textual Data
Qualitative data analysis involves the identification, examination, and interpretation of patterns and
themes in textual data, and determines how these patterns and themes help answer the questions at hand.
There are many different ways to conduct qualitative analysis that vary in fluidity and adherence to set
structure. Qualitative data analysis is often an ongoing, fluid, and cyclical process that is highly dependent
on the evaluator and the context of the project. It is also likely to change and adapt as the data emerge.
Therefore, always keep in mind the kinds of assessment questions that you are trying to answer throughout
the analysis process, as it can be easy to become overwhelmed by the vast quantity of data and distracted
by all of the details.
Getting to know your data
Understanding your data set and what questions you want to answer are important first steps in qualitative
data analysis. In some cases, the qualitative data may focus on a particular area of interest, while other
times the area of interest may be interwoven with unrelated textual information. To better understand
your data, read and re-read the text. Make notes and jot down overall impressions. These impressions can
inform the direction of analysis and contribute to more effective analysis.
For open-ended survey questions, focus group notes, interviews, etc., don’t assume that answers will
necessarily follow the questions. Occasionally participants or respondents will provide answers to questions
asked earlier, or to questions that that have not been asked yet. In these cases, it may be appropriate to
move the answer to the appropriate question.