Using Scatterplots on WINSS - Teacher Qualifications

WINSS Data Analysis



The WINSS Data Analysis section offers scatterplots to help you

  • compare the qualifications of teachers (e.g. Wisconsin license status, experience, education, etc.) in your district/school to other districts/schools in the state, your CESA, or your county, and
  • examine possible associations between teacher qualifications and other variables (e.g. school poverty level, district or school size, district spending, % students of color).

View live scatterplots: elementary schools, middle / junior high schools, high schools, combined elementary secondary schools, school districts and non-district charters.

Getting Started

To access the scatterplots, click on What are the qualifications of teachers? Next click on "Change your school or district" (circled link in FIGURE 1), select your school or district, then click on link titled "Scatterplot" (circled link in FIGURE 2).

FIGURE 1. "Change School or District" Link.
 FIGURE 2. "Scatterplot" Link.
FIGURE 3. Sample Scatterplot.
 FIGURE 4. Data Table Below Scatterplot.

Note the graphing options above the scatterplot in FIGURE 3. By default, "Subject Taught: Summary - All Subjects" and "Teacher Variable: % Full Wisconsin License" are selected for the Y-axis and "Relate To: %Economically Disadvantaged" is selected for the X-axis. Districts/schools with high teacher qualifications and high-poverty can be found in the upper right section of the scatterplot.

Each dot or symbol on the scatterplot represents one district/school. An enlarged red symbol is used to identify your district/school. You can view the names of those districts/schools in the table below the scatterplot.

Customizing Your Scatterplot

To change graph options, click on any link above the scatterplot. Options are shown in FIGURE 5.

FIGURE 5. Graph Options
  • Y-axis: Select teacher data of interest from the "Subject Taught" and "Teacher Variable" rows.
  • X-axis: Select a second variable from the "Relate To" row.
  • Location: Identify the geographic location of interest.

Note that all Y-axis data (the teacher data) are for the school year indicated in scatterplot title. All X-axis data are for that same school year unless these data are not yet available; if not yet available, then the most current X-axis data available on WINSS are used.

Viewing Data Details

To view the data for all districts or schools in the scatterplot, scroll to the table below the scatterplot. See FIGURE 4 for a sample data table that includes district and school names and other data in the scatterplot.

Users can download CSV files containing all the information displayed on the scatterplots plus codes and counts that might be useful in combining data across years, topics, categories or groups. Click on Download raw data from this page (below the scatterplot table) to request a csv file. See circled link in FIGURE 6. FIGURE 3. Download Data Link.

Comments about Scatterplots:

  • Scatterplots are considered by many to be one of the best tools for studying the association between two variables especially when you have a lot of data. A scatterplot suggests the strength (strong or weak), shape (linear, curvilinear), and direction (positive, negative) of the relationship between two variables.
  • The more the points in the scatterplot tend to cluster around a line, the stronger the relationship between the variables. The line may be straight or curved. If the line runs from lower left to upper right, then the plot suggests a positive relationship. If the line tends to reun from upper left to lower right, then the plot suggests a negative relationship.
  • Scatterplots may suggest an association between two variables because they are both associated with a third variable. A strong association does not mean there is a cause-effect relationship but may suggest possible explanations for low student performance OR important further questions to consider.
  • Plots of available Wisconsin teacher quality data vs. certain other variables believed to be associated with teacher quality are meant to be discussion starters. In study after study, teacher quality is found to be positively correlated with student outcomes. Wisconsin's goal is to have a qualified teacher in every classroom (especially in schools identified for improvement, SAGE, and P-5 schools). School improvement teams may find these plots useful when working to identify possible explanations for recruitment or retention issues and ideas for improving teacher qualifications to address student needs.

Cautions about the Data:

  • Three or four variables cannot sum up the qualifications of a teacher. What makes a teacher effective is difficult to assess and probably depends a lot on many factors including but not limited to the depth and relevance of initial preparation and on-going professional development, experience, staff and school climate/environment, and personal commitment.
  • WINSS data about teacher qualifications are based on an audit of data submitted by Wisconsin School Districts on the Fall Staff Report and data on DPI's educator license database. Licenses issued by DPI after the date of the audit or inaccurate reporting of assignments by districts could result in some teachers erroneously listed as unlicensed or not ESEA qualified.
  • Check "Total # of FTE teachers" before reaching conclusions about the significance of high or low percents. Note that "Total # of FTE teachers" is included in the table below each graph. Percents may be misleading if the total number of FTE teachers is small. One teacher may mean the difference between a high percent or a low percent. For example, if the "Total # of FTE teachers" is two and if one teacher has an "emergency license", then 50% have an "emergency license." This percent might be cause for more concern if the "Total # of FTE teachers" were 200 and 100 of these teachers have "emergency licenses."
  • Demographic data were provided by school districts based on standardized definitions and are unaudited.
  • Per member spending patterns vary across grades. This fact will affect interpretations of district-level spending differences when comparing elementary only or high school only districts to K-12 districts.
  • Current education cost per member is affected by changes in both cost and membership (FTE resident enrollment). A relatively small actual change in membership, particularly in a smaller district, can result in a significant cost per member change.


See also:

Questions to Consider When Using Teacher Data
Understanding Data About Teacher Qualifications
Fall Staff Collection
Staff and Salary Data
Educator Licensing
Search Wisconsin's Online Educator License Data Base
National Board for Professional Teaching Standards
WINSS Download Options


We welcome your questions, comments, and suggestions about WINSS.