Beware of faulty data analysis

Data can be spun in multiple ways. Date can be easily misconstrued or manipulated. This is why it's imperative that there be a holistic, inclusive look at data--data analysis is rarely good if it comes from a single source, particularly a source distanced from those who are included in the data.

As I think of recent data reports, I have the following thoughts and perspectives:

• I am fortunate to have had the chance to analyze the data first hand prior to the school year so I have a good take on what the data says about the work I do. I culled a number of important points from the data that I will use to improve the teaching/learning program I lead this year.
• I am aware of the potential for others to misconstrue the data, and will be ready to counter misrepresentation of the data.
• I am also aware that I see the data through one lens and will be open minded to looking at the data through other lenses to cull  more important information.
• There were some shortcomings in our overall data collection and assessments, and I'll be working with the team to remedy that situation.

Last year, the team used the data well to improve our teaching/learning programs. This year, I've culled some good areas for improvement too. I really like what good data analysis can do for teaching/learning programs if used well, and that's what we intend to do this year.