The concept of defining data is usually lost with other more “important” questions of how to use data to make decisions. But if you don’t truly know what data are, how do you use data? Data are, simply put, information. This information is commonly in its raw form. Raw data, or raw information, are information that hasn’t been processed. For example, raw data could be number of items correct on a test, or number of days absent during the school year. Sometimes raw data are enough to draw conclusions from, but many times, we manipulate data to draw better conclusions. Many that are skeptical of data, the decisions we draw from data, or statistics in general, may have caught the use of the word “manipulate,” drawing upon a negative connotation in which we twist the truth. I challenge you, as the reader, to move past this reaction and continue reading this blog series, if only to reserve judgment until after you have critically read and digested the information presented here.
Often when we talk about data (this is especially true of researchers and evaluators), we refer to two types of data: quantitative and qualitative. Quantitative data are numerical data, or data that are or can be represented by numbers. As educational professionals, you will be very familiar with quantitative data. Examples include test scores (which we all know there many), school ratings, and attendance. Qualitative data are data often in the form of written responses or interviews. Qualitative data are less often collected and/or available for use in schools. In future blog posts we will discuss more deeply why quantitative data are preferred over qualitative, but in general this has to do with time and cost, and with the types of conclusions you can draw from quantitative data (more on this later).
So why is it important to understand what data are? If you are unable to understand your data, how do you use it effectively? How do you have confidence in your decisions? Depending on your position at your school, you may be in charge of school personnel changes or programmatic changes, for example. If we are unable to define and describe a given set of data, understanding not only what the data are but also where they come from, we blindly make decisions that can have great impact on an individual’s livelihood and/or the opportunities students receive. In the next blog post, we will discuss where we can get data, and what to do if you need more.