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Understanding Negative Growth
Welcome guest blogger Michael Lo Cascio. Michael is the Data and Assessment Specialist at Valley View Community School District 365U in Illinois.
As we begin to analyze the results of our latest NWEA testing many will notice a phenomenon known as negative growth, an occurrence most commonly seen in winter testing, where the RIT scores for some students appear to have fallen from previous levels in the year. This unexpected fall in a student’s score can lead to confusion, misinterpretation, and even frustration when it comes to understanding student data.
If we were to plot the fall, winter, and spring RIT scores for all of our students, most results would create a line graph similar to the one presented in figure 1. In this example, positive RIT growth is shown between each testing administration.
In these instances, any movement upward on the RIT scale is considered evidence of student growth and an indicator of effective instruction.
(NWEA does not consider winter assessments to be an official growth point due to the limited number of instructional weeks, but winter assessments are a useful barometer of student progress and should be viewed in this light.)
In examining data throughout the year we may come across occasions when students produce seemingly random scores or potentially inaccurate test data. This is most commonly seen in winter due to the limited number of instructional weeks but can occur in spring as well. If we were to plot the fall, winter, and spring RIT scores for these students, we may see graphs that show significant drops at some point in the year.
Why these falling outcomes occur and how to deal with them are important discussions we need to undertake. Not only do we need to know that our assessment data is reliable, but even more importantly, we need to know how to use this data to inform or adjust our instruction accordingly.
While we cannot always ascertain why an anomaly occurred, it is necessary that we gather information around the event in order to inform an appropriate response. How much time did the student take to complete the test? How much negative growth was actually observed? Have living conditions changed at home? Is the student suffering from poor attitude, anxiety, or motivation issues? Have there been changes in the student’s attendance or in classroom instruction? Is the data consistent with other classroom data? These questions are provided as a resource at the end of this article and are encouraged to be used when examining students’ scores. Gathering information around these questions can help inform our response, which generally would involve a combination of the following two actions:
1. Determining the need to retest the student (critical if the drop is more than 10 RIT)
2. Determining the need to adjust instruction (utilizing DesCartes)
PUTTING STUDENT GROWTH IN CONTEXT
There can be many reasons for a drop in a student’s score, but one important factor that must be considered is something known as standard error of measurement. This statistical term is used to indicate the error in a reported RIT score, providing a possible range of expected scores for the student generally known as a RIT Range.
Typically, this approximates to about plus or minus three RIT on any given test, so a RIT score of 200 would likely yield a RIT Range of 197-203. Let’s look at the considerations of this a bit further.
Spotlight: Standard Error of Measurement and RIT Ranges
Consider a student with the following test data:
Fall RIT Score: 210 +/- 3 RIT Fall RIT Range: 207-213
Winter RIT Score: 203 +/- 3 RIT Winter RIT Range: 200-206
How much negative growth is seen for this student between fall and winter testing?
Is it 1, 7, or 13 RIT? The truth is that the amount of negative growth could be any of these amounts. The RIT Ranges imply that the spread of accurate scores could have fallen from an extreme high of 213 in the fall to an extreme low of 200 in the winter. They also could have minimally fallen from 207 in the fall to 206 in the winter