Collection and Analysis of Data

How do we know what data to collect? Remember, data are nothing more than information, such as standardized test scores, rubric scores for writing, student attendance and grades, ratings or rubric scores for performances, observations, anecdotal records, student work, etc. The kind of information we need depends on the questions we are investigating

For example, consider this research question: "What happens to the quality of student writing when students use Inspiration for prewriting?" What evidence would we need in order to answer the question? We might collect samples of student writing with and without using Inspiration, students' reflection, and teachers' anecdotal notes.

Once we have collected data, how do we analyze it to answer our question? Remember that we create meaning from data by analysis and interpretation. Data are objective but interpretation of data is subjective. Individual schemas and perspectives influence the meaning that we derive from data. So, it can be helpful to use specific criteria when analyzing data. Triangulation of data (looking at 3 data sources for gaps, overlaps, patterns, and trends) is very common in teacher research (action research or inquiry).

Let's use the research question and data collection example above to consider data analysis strategies:

To analyze the student writing samples, we might use the 6+1 Writing Traits rubric developed by the Northwest Regional Education Lab (NWREL). The validity and reliability of this rubric is based on extensive research, and it is teacher and student friendly. First, we would score the student writing samples for the quality of ideas, organization, voice, word choice, sentence fluency, conventions, and presentation. Then, we could compare these diagnostic scores on writing with and without Inspiration for prewriting and look for patterns or trends in the quality of student writing.

To analyze students' reflections and teachers' anecdotal records, we could begin by reading them quickly looking for common threads or themes and making notes of them in the margin. Then, using different colored highlighters for the different themes, we would reread highlighting threads or themes using the appropriate color. See details on this activity.

Formulating a plan for the collection and analysis of data is an essential component of Developing Individual Plans. Individuals and groups create meaning by organizing, analyzing, and interpreting the data. Data are objective but interpretation of data is subjective. Individual schemas and perspectives influence the meaning that we derive from data. We make sense of data by analyzing and interpreting it through dialogue with colleagues and individual reflection.

Included are data collection and analysis activities that have been successfully used in the pilot sites. There are also links to additional activities and links that are potentially powerful but have not been used at the three pilot sites.

Sample Portfolio Entry with Reflection