Understanding your data

As we collect and organize our assessment data, we will have at our disposal many documents, tables and graphs that are packed full of numbers. We may be tempted to start adding, subtracting, multiplying and dividing these numbers at will as we proceed in answering assessment questions like the following:

  • Is Billy making growth with respect to his reading fluency skills?
  • Has Jane closed the gap in math problem solving skills compared to her peers?
  • Are our 4th grade students meeting state trajectories in reading?
  • How much growth is necessary for Jill to become proficient in reading next year?
  • Has the newly implemented instructional strategies in writing made a difference with our middle school students’ writing skills?

This temptation may be fostered by the experiences that we have with numbers in the past and our knowledge of mathematical computation. Despite this fact, please understand that when dealing with assessment data, not all numbers are created the same, and thus, not all mathematical operations are applicable to each and every number.

Whether our data are considered to be Nominal, Ordinal, Interval or Ratio, we must have a solid understanding of the benefits and limitations of the different types of data so that we can make more accurate and appropriate judgments about students and their performance in comparison to identified achievement targets. For a review of the four types of data and the “permitted” mathematical computations of each type, please read Four Types of Data.

Different types of scoring

Raw Scores (Ratio Data)

Age/Grade Equivalents (Ordinal Data)

Percent Correct (Ratio Data)

Percentile Ranks (Ordinal Data)

Standard Scores (Interval/Ratio Data)

RIT Score (Rasch Unit) (Ratio Data)

Normal Curve Equivalents (NCEs) (Ratio Data)

Z Scores & T Scores

Disaggregation of data

Disaggregated data is data that is separated into specific subgroups of students. Disaggregated data uncovers important information about patterns and trends that could be missed when just looking at a data set.

Data management tools

There are several different programs that can be used to assist in the data collection and analyses processes. Some of the more popular options available for professionals are:

Excel

SPSS (Statistical Package for the Social Sciences)

AIMSweb

Edinsight